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diff --git a/session.json b/session.json new file mode 100644 index 0000000..51027fb --- /dev/null +++ b/session.json @@ -0,0 +1,21599 @@ +{ + "info": { + "id": "ses_0dcaec232ffe29bNbkePnXKhbd", + "slug": "quick-pixel", + "projectID": "aa1b31e405e16cea5cbaf62138a622f9b54b7b23", + "directory": "/home/magnus-bk/repos/material-hunters", + "path": "home/magnus-bk/repos/material-hunters", + "title": "Update AGENTS.md with repo-specific OpenCode guidance", + "agent": "build", + "model": { + "id": "gpt-5.3-codex", + "providerID": "openai", + "variant": "default" + }, + "version": "1.17.13", + "summary": { + "additions": 0, + "deletions": 0, + "files": 0 + }, + "cost": 6.0151798, + "tokens": { + "input": 674244, + "output": 101978, + "reasoning": 23938, + "cache": { + "read": 17556736, + "write": 0 + } + }, + "time": { + "created": 1783003954637, + "updated": 1783231437483 + } + }, + "messages": [ + { + "info": { + "role": "user", + "time": { + "created": 1783003954700 + }, + "agent": "build", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f23513e0c001Boad2cUQUTOW6x", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "Create or update `AGENTS.md` for this repository.\n\nThe goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: \"Would an agent likely miss this without help?\" If not, leave it out.\n\nUser-provided focus or constraints (honor these):\n\n\n## How to investigate\n\nRead the highest-value sources first:\n- `README*`, root manifests, workspace config, lockfiles\n- build, test, lint, formatter, typecheck, and codegen config\n- CI workflows and pre-commit / task runner config\n- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)\n- repo-local OpenCode config such as `opencode.json`\n\nIf architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.\n\nPrefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.\n\n## What to extract\n\nLook for the highest-signal facts for an agent working in this repo:\n- exact developer commands, especially non-obvious ones\n- how to run a single test, a single package, or a focused verification step\n- required command order when it matters, such as `lint -> typecheck -> test`\n- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints\n- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow\n- repo-specific style or workflow conventions that differ from defaults\n- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites\n- important constraints from existing instruction files worth preserving\n\nGood `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.\n\n## Questions\n\nOnly ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.\n\nGood questions:\n- undocumented team conventions\n- branch / PR / release expectations\n- missing setup or test prerequisites that are known but not written down\n\nDo not ask about anything the repo already makes clear.\n\n## Writing rules\n\nInclude only high-signal, repo-specific guidance such as:\n- exact commands and shortcuts the agent would otherwise guess wrong\n- architecture notes that are not obvious from filenames\n- conventions that differ from language or framework defaults\n- setup requirements, environment quirks, and operational gotchas\n- references to existing instruction sources that matter\n\nExclude:\n- generic software advice\n- long tutorials or exhaustive file trees\n- obvious language conventions\n- speculative claims or anything you could not verify\n- content better stored in another file referenced via `opencode.json` `instructions`\n\nWhen in doubt, omit.\n\nPrefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.\n\nIf `AGENTS.md` already exists at `/`, improve it in place rather than rewriting blindly. 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The intended workflow is that a surveyor taps an object in an image, a segmentation model isolates it, and AI identifies both the object and its material composition to build a structured material inventory that supports circular construction. We'll start with a Google Colab proof of concept focused on validating the ML pipeline rather than building a production application. Before we start coding or writing, please interview me to identify the actual goal and the core decision this project is intended to drive. Once we define that, let's break the project into small, agile buckets. We will build one bucket at a time, and I want you to present a plan for each, followed by a checkpoint where I can review the output before we move on. Please also verify key decisions explicitly as we go to ensure we don't drift from the original intent.\n\n(End of file - total 1 lines)\n</content>", + "metadata": { + "preview": "I am building an ML-assisted surveying system for reconstruction and demolition projects that identifies construction objects and their material compositions from photographs. The intended workflow is that a surveyor taps an object in an image, a segmentation model isolates it, and AI identifies both the object and its material composition to build a structured material inventory that supports circular construction. We'll start with a Google Colab proof of concept focused on validating the ML pipeline rather than building a production application. Before we start coding or writing, please interview me to identify the actual goal and the core decision this project is intended to drive. Once we define that, let's break the project into small, agile buckets. 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The intended workflow is that a surveyor taps an object in an image, a segmentation model isolates it, and AI identifies both the object and its material composition to build a structured material inventory that supports circular construction. We'll start with a Google Colab proof of concept focused on validating the ML pipeline rather than building a production application. Before we start coding or writing, please interview me to identify the actual goal and the core decision this project is intended to drive. Once we define that, let's break the project into small, agile buckets. We will build one bucket at a time, and I want you to present a plan for each, followed by a checkpoint where I can review the output before we move on. Please also verify key decisions explicitly as we go to ensure we don't drift from the original intent.\n+# AGENTS\n+\n+- Repo state: only this file exists right now; there are no manifests, source files, tests, or CI configs to infer tooling from.\n+- Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n+- Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n+- Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n+- Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n+- Decision hygiene: explicitly restate/verify key decisions during execution to avoid drift from the agreed goal.\n+- Update this file once code/config lands; prefer executable repo sources over prose when they conflict.\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/AGENTS.md", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/AGENTS.MD\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/AGENTS.MD\n+++ /home/magnus-bk/repos/material-hunters/AGENTS.MD\n@@ -1,1 +1,9 @@\n-I am building an ML-assisted surveying system for reconstruction and demolition projects that identifies construction objects and their material compositions from photographs. The intended workflow is that a surveyor taps an object in an image, a segmentation model isolates it, and AI identifies both the object and its material composition to build a structured material inventory that supports circular construction. We'll start with a Google Colab proof of concept focused on validating the ML pipeline rather than building a production application. 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Please also verify key decisions explicitly as we go to ensure we don't drift from the original intent.\n+# AGENTS\n+\n+- Repo state: only this file exists right now; there are no manifests, source files, tests, or CI configs to infer tooling from.\n+- Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n+- Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n+- Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n+- Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n+- Decision hygiene: explicitly restate/verify key decisions during execution to avoid drift from the agreed goal.\n+- Update this file once code/config lands; prefer executable repo sources over prose when they conflict.\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/AGENTS.MD", + "relativePath": "home/magnus-bk/repos/material-hunters/AGENTS.md", + "type": "move", + "patch": "Index: /home/magnus-bk/repos/material-hunters/AGENTS.MD\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/AGENTS.MD\n+++ /home/magnus-bk/repos/material-hunters/AGENTS.MD\n@@ -1,1 +1,9 @@\n-I am building an ML-assisted surveying system for reconstruction and demolition projects that identifies construction objects and their material compositions from photographs. 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Please also verify key decisions explicitly as we go to ensure we don't drift from the original intent.\n+# AGENTS\n+\n+- Repo state: only this file exists right now; there are no manifests, source files, tests, or CI configs to infer tooling from.\n+- Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n+- Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n+- Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n+- Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n+- Decision hygiene: explicitly restate/verify key decisions during execution to avoid drift from the agreed goal.\n+- Update this file once code/config lands; prefer executable repo sources over prose when they conflict.\n", + "additions": 9, + "deletions": 1, + "movePath": "/home/magnus-bk/repos/material-hunters/AGENTS.md" + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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then edit any details before finalizing; the object should then be saved as a material passport (in a json file).", + "id": "prt_f235794a5001qp6HHgdGuCpQNE", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235794a4001sv3P2VTiM9tcxZ" + } + ] + }, + { + "info": { + "parentID": "msg_f235794a4001sv3P2VTiM9tcxZ", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0210595, + "tokens": { + "total": 9816, + "input": 6610, + "output": 568, + "reasoning": 78, + "cache": { + "write": 0, + "read": 2560 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783004370097, + "completed": 1783004383124 + }, + "finish": "stop", + "id": "msg_f235794b1002cNBj9mYFqAid8k", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f235797cc0013r2z1Lp90JSs5W", + "sessionID": 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+ } + }, + "id": "prt_f23579b51001us0huqpEm9SfJB", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235794b1002cNBj9mYFqAid8k" + }, + { + "type": "text", + "text": "Perfect — that’s a clear core decision. \nI’ll restate it as the working decision target so we can anchor all work:\n\n- User taps an object in an image.\n- `SAM2/SAM3` segments that object.\n- A second model (`DINOv3` or `SigCLIP`) classifies material.\n- User can review/edit details.\n- Final result is saved as a material passport JSON.\n\nHere’s a small-bucket execution plan with checkpoints.\n\n**Bucket 1: Define the contract (no model work yet)**\n- Output: one JSON schema for a “material passport” + one example JSON.\n- Decide required fields: `object_label`, `material_label`, `material_confidence`, `mask_ref`, `bbox`, `source_image`, `user_edits`, `timestamps`.\n- Checkpoint: you approve exact schema before we build anything else.\n\n**Bucket 2: Segmentation proof (tap -> mask)**\n- Implement Colab step that takes image + tap point and returns one mask (plus overlay preview).\n- Store segmentation artifacts (mask path or RLE + bbox + area).\n- Checkpoint: verify mask quality on 5–10 sample taps.\n\n**Bucket 3: Material classification on segmented crop**\n- Build classifier step from masked region using either `DINOv3` or `SigCLIP`.\n- Return top-k materials with confidence and keep raw logits/scores for debugging.\n- Checkpoint: compare model outputs on a small validation set and choose baseline model.\n\n**Bucket 4: Human-in-the-loop edit step**\n- Add editable fields in notebook UI (or simple form cell): object/material labels, confidence override reason, notes.\n- Track provenance: model output vs user-corrected output.\n- Checkpoint: ensure edit flow is fast and captures all needed audit info.\n\n**Bucket 5: Passport export**\n- Serialize final reviewed result to JSON using Bucket 1 schema.\n- Save one JSON per object and optionally aggregate per image/session.\n- Checkpoint: confirm exported JSON is stable and usable downstream.\n\n**Bucket 6: Decision test**\n- Run a focused acceptance test over a small curated set (e.g., 20–30 taps).\n- Measure: segmentation acceptability + material top-1/top-3 + edit rate/time.\n- Checkpoint: explicit go/no-go against your decision criteria.\n\nOne key choice to lock before implementation (recommended now): \n- **Pick one baseline classifier first** (`SigCLIP` recommended for faster POC iteration), then compare against `DINOv3` in Bucket 3 once the pipeline is working end-to-end.\n\nIf you want, next I can provide the exact success criteria template for Bucket 6 so we can make the go/no-go objective.", + "time": { + "start": 1783004373641, + "end": 1783004383073 + }, + "metadata": { + "openai": { + "itemId": "msg_09404155a185708e016a467cd552d8819dade9c125682d547d", + "phase": "final_answer" + } + }, + "id": "prt_f2357a289001rKJGHtqYk3YTQZ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": 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+ } + }, + "id": "prt_f23599b5d001V0NqRIZz6P6RHk", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2359964a002RKdPVkAwrJc1Gm" + }, + { + "type": "text", + "text": "Great — using `SigCLIP` first is the right POC baseline.\n\nHere’s a practical **success criteria set** for your go/no-go decision.\n\n**Decision Statement**\n- “Tap -> segment -> classify -> human edit -> JSON passport is reliable and efficient enough for pilot workflows.”\n\n**Dataset For Decision Test**\n- `N = 30` object taps across varied materials/scenes.\n- Each sample has a human-verified target object + material label.\n- Same dataset used for both `SigCLIP` and `DINOv3` comparison.\n\n**Primary Success Criteria (Go/No-Go)**\n- `Segmentation acceptance rate >= 85%` \n (accepted = user says mask is good enough without redraw)\n- `Material top-1 accuracy >= 70%` before user edits\n- `Material top-3 accuracy >= 90%` before user edits\n- `Manual edit rate <= 35%` \n (percent of samples where user changes model material label)\n- `Median end-to-end time <= 12s/object` \n (tap to saved passport, including normal review)\n- `Passport schema validity = 100%` \n (all exported JSONs pass schema checks, no missing required fields)\n\n**Secondary Quality Gates (Should Pass)**\n- `Failure recovery = 100%` \n (if segmentation/classification fails, user can still complete record manually)\n- `Auditability = 100%` \n (store model prediction, confidence, user final label, and edit flag)\n- `Reproducibility >= 95%` \n (re-running same input gives same/near-same output)\n\n**SigCLIP vs DINOv3 Comparison Rule**\n- Start with `SigCLIP` as baseline.\n- Promote `DINOv3` only if it achieves **either**:\n 1) `+5 percentage points` top-1 accuracy, **or**\n 2) `-10 percentage points` edit rate, \n **without** adding more than `+20%` median runtime.\n- If gains are smaller than above, keep `SigCLIP` for POC simplicity.\n\nIf you want, next I can give you a one-page evaluation sheet format (exact columns + formulas) so you can run this in Colab with minimal setup.", + "time": { + "start": 1783004505716, + "end": 1783004512042 + }, + "metadata": { + "openai": { + "itemId": "msg_0e5717505d401cd6016a467d59b0a4819e8167bd6c964ecbb7", + "phase": "final_answer" + } + }, + "id": "prt_f2359a6740016MiwEhlfIPqCUh", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2359964a002RKdPVkAwrJc1Gm" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 10403, + "input": 1059, + "output": 485, + "reasoning": 155, + "cache": { + "write": 0, + "read": 8704 + } + }, + "cost": 0.01233645, + "id": "prt_f2359bf3d001pY2j8FFo9R1n7o", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2359964a002RKdPVkAwrJc1Gm" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783004532486 + }, + "agent": "build", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": 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+ } + }, + "id": "prt_f235a4281001adm3ByDFP6hmAd", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235a3e7e002zK9YcEZUVoGMO3" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_Cditua3HPwqOYYPy26dtE2ZG", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Add File: /home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md\n+# POC Evaluation Sheet (SigCLIP Baseline)\n+\n+Use `poc_eval_sheet_template.csv` as the single source for decision testing.\n+\n+## Test setup\n+\n+- Target size: `N = 30` taps across varied scenes/materials.\n+- Baseline model first: `SigCLIP` in `model_name`.\n+- One row = one tapped object attempt.\n+- Keep `status` as `ok` or `fail` for every row.\n+\n+## Required columns\n+\n+- Identity: `sample_id`, `image_id`, `image_path`\n+- Interaction: `tap_x`, `tap_y`\n+- Ground truth: `ground_truth_object`, `ground_truth_material`\n+- Model output: `pred_object`, `pred_material_top1`, `pred_material_top3`, `pred_material_conf_top1`, `model_name`\n+- Segmentation quality: `segmentation_accepted` (`1`/`0`)\n+- Human correction: `user_edited_material` (`1`/`0`), `user_final_material`, `edit_reason`\n+- Runtime: `end_to_end_time_sec`, `segmentation_time_sec`, `classification_time_sec`\n+- Reliability: `status`, `error_type`\n+- Artifacts: `mask_area_px`, `bbox_xywh`, `passport_json_path`\n+\n+## Decision metrics and thresholds\n+\n+- Segmentation acceptance rate: `>= 85%`\n+- Material top-1 accuracy: `>= 70%`\n+- Material top-3 hit rate: `>= 90%`\n+- Manual edit rate: `<= 35%`\n+- Median end-to-end time: `<= 12 sec`\n+- Passport schema validity: `100%` (all `status = ok`, all JSON files valid)\n+\n+## Google Sheets formulas\n+\n+Assume headers are in row `1` and data rows are `2:1000`.\n+\n+- Segmentation acceptance rate \n+`=AVERAGE(N2:N1000)`\n+\n+- Top-1 accuracy (add helper col `Y`: top1_correct) \n+`Y2: =IF(H2=\"\",,IF(H2=G2,1,0))` then fill down \n+Metric: `=AVERAGE(Y2:Y1000)`\n+\n+- Top-3 hit rate (add helper col `Z`: top3_hit; top-3 delimiter is `|`) \n+`Z2: =IF(I2=\"\",,IF(ISNUMBER(SEARCH(G2,I2)),1,0))` then fill down \n+Metric: `=AVERAGE(Z2:Z1000)`\n+\n+- Manual edit rate \n+`=AVERAGE(O2:O1000)`\n+\n+- Median end-to-end time \n+`=MEDIAN(Q2:Q1000)`\n+\n+- Failure rate (`status != ok`) \n+`=COUNTIF(T2:T1000,\"<>ok\")/COUNTA(T2:T1000)`\n+\n+## Colab/Pandas metric cell\n+\n+```python\n+import pandas as pd\n+\n+df = pd.read_csv(\"/content/poc_eval_sheet_template.csv\")\n+df = df[df[\"status\"].notna()]\n+\n+df[\"top1_correct\"] = (df[\"pred_material_top1\"] == df[\"ground_truth_material\"]).astype(int)\n+df[\"top3_hit\"] = df.apply(\n+ lambda r: int(str(r[\"ground_truth_material\"]) in str(r[\"pred_material_top3\"]).split(\"|\")),\n+ axis=1,\n+)\n+\n+metrics = {\n+ \"n_samples\": len(df),\n+ \"segmentation_acceptance_rate\": df[\"segmentation_accepted\"].mean(),\n+ \"top1_accuracy\": df[\"top1_correct\"].mean(),\n+ \"top3_hit_rate\": df[\"top3_hit\"].mean(),\n+ \"manual_edit_rate\": df[\"user_edited_material\"].mean(),\n+ \"median_e2e_time_sec\": df[\"end_to_end_time_sec\"].median(),\n+ \"failure_rate\": (df[\"status\"] != \"ok\").mean(),\n+}\n+\n+thresholds = {\n+ \"segmentation_acceptance_rate\": 0.85,\n+ \"top1_accuracy\": 0.70,\n+ \"top3_hit_rate\": 0.90,\n+ \"manual_edit_rate_max\": 0.35,\n+ \"median_e2e_time_sec_max\": 12.0,\n+}\n+\n+print(metrics)\n+print(\"go_no_go:\", (\n+ metrics[\"segmentation_acceptance_rate\"] >= thresholds[\"segmentation_acceptance_rate\"]\n+ and metrics[\"top1_accuracy\"] >= thresholds[\"top1_accuracy\"]\n+ and metrics[\"top3_hit_rate\"] >= thresholds[\"top3_hit_rate\"]\n+ and metrics[\"manual_edit_rate\"] <= thresholds[\"manual_edit_rate_max\"]\n+ and metrics[\"median_e2e_time_sec\"] <= thresholds[\"median_e2e_time_sec_max\"]\n+ and metrics[\"failure_rate\"] == 0\n+))\n+```\n+\n+## SigCLIP vs DINOv3 promotion rule\n+\n+Promote `DINOv3` only if, on the same dataset, it delivers either:\n+\n+- `+5` percentage points top-1 accuracy, or\n+- `-10` percentage points manual edit rate,\n+\n+while keeping median end-to-end time increase within `+20%`.\n*** End Patch" + }, + "output": "Success. Updated the following files:\nA home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md\n@@ -0,0 +1,107 @@\n+# POC Evaluation Sheet (SigCLIP Baseline)\n+\n+Use `poc_eval_sheet_template.csv` as the single source for decision testing.\n+\n+## Test setup\n+\n+- Target size: `N = 30` taps across varied scenes/materials.\n+- Baseline model first: `SigCLIP` in `model_name`.\n+- One row = one tapped object attempt.\n+- Keep `status` as `ok` or `fail` for every row.\n+\n+## Required columns\n+\n+- Identity: `sample_id`, `image_id`, `image_path`\n+- Interaction: `tap_x`, `tap_y`\n+- Ground truth: `ground_truth_object`, `ground_truth_material`\n+- Model output: `pred_object`, `pred_material_top1`, `pred_material_top3`, `pred_material_conf_top1`, `model_name`\n+- Segmentation quality: `segmentation_accepted` (`1`/`0`)\n+- Human correction: `user_edited_material` (`1`/`0`), `user_final_material`, `edit_reason`\n+- Runtime: `end_to_end_time_sec`, `segmentation_time_sec`, `classification_time_sec`\n+- Reliability: `status`, `error_type`\n+- Artifacts: `mask_area_px`, `bbox_xywh`, `passport_json_path`\n+\n+## Decision metrics and thresholds\n+\n+- Segmentation acceptance rate: `>= 85%`\n+- Material top-1 accuracy: `>= 70%`\n+- Material top-3 hit rate: `>= 90%`\n+- Manual edit rate: `<= 35%`\n+- Median end-to-end time: `<= 12 sec`\n+- Passport schema validity: `100%` (all `status = ok`, all JSON files valid)\n+\n+## Google Sheets formulas\n+\n+Assume headers are in row `1` and data rows are `2:1000`.\n+\n+- Segmentation acceptance rate \n+`=AVERAGE(N2:N1000)`\n+\n+- Top-1 accuracy (add helper col `Y`: top1_correct) \n+`Y2: =IF(H2=\"\",,IF(H2=G2,1,0))` then fill down \n+Metric: `=AVERAGE(Y2:Y1000)`\n+\n+- Top-3 hit rate (add helper col `Z`: top3_hit; top-3 delimiter is `|`) \n+`Z2: =IF(I2=\"\",,IF(ISNUMBER(SEARCH(G2,I2)),1,0))` then fill down \n+Metric: `=AVERAGE(Z2:Z1000)`\n+\n+- Manual edit rate \n+`=AVERAGE(O2:O1000)`\n+\n+- Median end-to-end time \n+`=MEDIAN(Q2:Q1000)`\n+\n+- Failure rate (`status != ok`) \n+`=COUNTIF(T2:T1000,\"<>ok\")/COUNTA(T2:T1000)`\n+\n+## Colab/Pandas metric cell\n+\n+```python\n+import pandas as pd\n+\n+df = pd.read_csv(\"/content/poc_eval_sheet_template.csv\")\n+df = df[df[\"status\"].notna()]\n+\n+df[\"top1_correct\"] = (df[\"pred_material_top1\"] == df[\"ground_truth_material\"]).astype(int)\n+df[\"top3_hit\"] = df.apply(\n+ lambda r: int(str(r[\"ground_truth_material\"]) in str(r[\"pred_material_top3\"]).split(\"|\")),\n+ axis=1,\n+)\n+\n+metrics = {\n+ \"n_samples\": len(df),\n+ \"segmentation_acceptance_rate\": df[\"segmentation_accepted\"].mean(),\n+ \"top1_accuracy\": df[\"top1_correct\"].mean(),\n+ \"top3_hit_rate\": df[\"top3_hit\"].mean(),\n+ \"manual_edit_rate\": df[\"user_edited_material\"].mean(),\n+ \"median_e2e_time_sec\": df[\"end_to_end_time_sec\"].median(),\n+ \"failure_rate\": (df[\"status\"] != \"ok\").mean(),\n+}\n+\n+thresholds = {\n+ \"segmentation_acceptance_rate\": 0.85,\n+ \"top1_accuracy\": 0.70,\n+ \"top3_hit_rate\": 0.90,\n+ \"manual_edit_rate_max\": 0.35,\n+ \"median_e2e_time_sec_max\": 12.0,\n+}\n+\n+print(metrics)\n+print(\"go_no_go:\", (\n+ metrics[\"segmentation_acceptance_rate\"] >= thresholds[\"segmentation_acceptance_rate\"]\n+ and metrics[\"top1_accuracy\"] >= thresholds[\"top1_accuracy\"]\n+ and metrics[\"top3_hit_rate\"] >= thresholds[\"top3_hit_rate\"]\n+ and metrics[\"manual_edit_rate\"] <= thresholds[\"manual_edit_rate_max\"]\n+ and metrics[\"median_e2e_time_sec\"] <= thresholds[\"median_e2e_time_sec_max\"]\n+ and metrics[\"failure_rate\"] == 0\n+))\n+```\n+\n+## SigCLIP vs DINOv3 promotion rule\n+\n+Promote `DINOv3` only if, on the same dataset, it delivers either:\n+\n+- `+5` percentage points top-1 accuracy, or\n+- `-10` percentage points manual edit rate,\n+\n+while keeping median end-to-end time increase within `+20%`.\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md", + "type": "add", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md\n@@ -0,0 +1,107 @@\n+# POC Evaluation Sheet (SigCLIP Baseline)\n+\n+Use `poc_eval_sheet_template.csv` as the single source for decision testing.\n+\n+## Test setup\n+\n+- Target size: `N = 30` taps across varied scenes/materials.\n+- Baseline model first: `SigCLIP` in `model_name`.\n+- One row = one tapped object attempt.\n+- Keep `status` as `ok` or `fail` for every row.\n+\n+## Required columns\n+\n+- Identity: `sample_id`, `image_id`, `image_path`\n+- Interaction: `tap_x`, `tap_y`\n+- Ground truth: `ground_truth_object`, `ground_truth_material`\n+- Model output: `pred_object`, `pred_material_top1`, `pred_material_top3`, `pred_material_conf_top1`, `model_name`\n+- Segmentation quality: `segmentation_accepted` (`1`/`0`)\n+- Human correction: `user_edited_material` (`1`/`0`), `user_final_material`, `edit_reason`\n+- Runtime: `end_to_end_time_sec`, `segmentation_time_sec`, `classification_time_sec`\n+- Reliability: `status`, `error_type`\n+- Artifacts: `mask_area_px`, `bbox_xywh`, `passport_json_path`\n+\n+## Decision metrics and thresholds\n+\n+- Segmentation acceptance rate: `>= 85%`\n+- Material top-1 accuracy: `>= 70%`\n+- Material top-3 hit rate: `>= 90%`\n+- Manual edit rate: `<= 35%`\n+- Median end-to-end time: `<= 12 sec`\n+- Passport schema validity: `100%` (all `status = ok`, all JSON files valid)\n+\n+## Google Sheets formulas\n+\n+Assume headers are in row `1` and data rows are `2:1000`.\n+\n+- Segmentation acceptance rate \n+`=AVERAGE(N2:N1000)`\n+\n+- Top-1 accuracy (add helper col `Y`: top1_correct) \n+`Y2: =IF(H2=\"\",,IF(H2=G2,1,0))` then fill down \n+Metric: `=AVERAGE(Y2:Y1000)`\n+\n+- Top-3 hit rate (add helper col `Z`: top3_hit; top-3 delimiter is `|`) \n+`Z2: =IF(I2=\"\",,IF(ISNUMBER(SEARCH(G2,I2)),1,0))` then fill down \n+Metric: `=AVERAGE(Z2:Z1000)`\n+\n+- Manual edit rate \n+`=AVERAGE(O2:O1000)`\n+\n+- Median end-to-end time \n+`=MEDIAN(Q2:Q1000)`\n+\n+- Failure rate (`status != ok`) \n+`=COUNTIF(T2:T1000,\"<>ok\")/COUNTA(T2:T1000)`\n+\n+## Colab/Pandas metric cell\n+\n+```python\n+import pandas as pd\n+\n+df = pd.read_csv(\"/content/poc_eval_sheet_template.csv\")\n+df = df[df[\"status\"].notna()]\n+\n+df[\"top1_correct\"] = (df[\"pred_material_top1\"] == df[\"ground_truth_material\"]).astype(int)\n+df[\"top3_hit\"] = df.apply(\n+ lambda r: int(str(r[\"ground_truth_material\"]) in str(r[\"pred_material_top3\"]).split(\"|\")),\n+ axis=1,\n+)\n+\n+metrics = {\n+ \"n_samples\": len(df),\n+ \"segmentation_acceptance_rate\": df[\"segmentation_accepted\"].mean(),\n+ \"top1_accuracy\": df[\"top1_correct\"].mean(),\n+ \"top3_hit_rate\": df[\"top3_hit\"].mean(),\n+ \"manual_edit_rate\": df[\"user_edited_material\"].mean(),\n+ \"median_e2e_time_sec\": df[\"end_to_end_time_sec\"].median(),\n+ \"failure_rate\": (df[\"status\"] != \"ok\").mean(),\n+}\n+\n+thresholds = {\n+ \"segmentation_acceptance_rate\": 0.85,\n+ \"top1_accuracy\": 0.70,\n+ \"top3_hit_rate\": 0.90,\n+ \"manual_edit_rate_max\": 0.35,\n+ \"median_e2e_time_sec_max\": 12.0,\n+}\n+\n+print(metrics)\n+print(\"go_no_go:\", (\n+ metrics[\"segmentation_acceptance_rate\"] >= thresholds[\"segmentation_acceptance_rate\"]\n+ and metrics[\"top1_accuracy\"] >= thresholds[\"top1_accuracy\"]\n+ and metrics[\"top3_hit_rate\"] >= thresholds[\"top3_hit_rate\"]\n+ and metrics[\"manual_edit_rate\"] <= thresholds[\"manual_edit_rate_max\"]\n+ and metrics[\"median_e2e_time_sec\"] <= thresholds[\"median_e2e_time_sec_max\"]\n+ and metrics[\"failure_rate\"] == 0\n+))\n+```\n+\n+## SigCLIP vs DINOv3 promotion rule\n+\n+Promote `DINOv3` only if, on the same dataset, it delivers either:\n+\n+- `+5` percentage points top-1 accuracy, or\n+- `-10` percentage points manual edit rate,\n+\n+while keeping median end-to-end time increase within `+20%`.\n", + "additions": 107, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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there are no manifests, source files, tests, or CI configs to infer tooling from.\n4: - Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n5: - Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n6: - Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n7: - Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n8: - Decision hygiene: explicitly restate/verify key decisions during execution to avoid drift from the agreed goal.\n9: - Update this file once code/config lands; prefer executable repo sources over prose when they conflict.\n\n(End of file - total 9 lines)\n</content>", + "metadata": { + "preview": "# AGENTS\n\n- Repo state: only this file exists right now; there are no manifests, source files, tests, or CI configs to infer tooling from.\n- Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n- Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n- Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n- Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n- Decision hygiene: explicitly restate/verify key decisions during execution to avoid drift from the agreed goal.\n- Update this file once code/config lands; prefer executable repo sources over prose when they conflict.", + "truncated": false, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/AGENTS.md", + "text": "# AGENTS\n\n- Repo state: only this file exists right now; there are no manifests, source files, tests, or CI configs to infer tooling from.\n- Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n- Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n- Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n- Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n- Decision hygiene: explicitly restate/verify key decisions during execution to avoid drift from the agreed goal.\n- Update this file once code/config lands; prefer executable repo sources over prose when they conflict.", + "lineStart": 1, + "lineEnd": 9, + "totalLines": 9, + "truncated": false + } + }, + "title": "home/magnus-bk/repos/material-hunters/AGENTS.md", + "time": { + "start": 1783004563215, + "end": 1783004563226 + } + }, + "metadata": { + "openai": { + "itemId": "fc_039e1f3d34d8b164016a467d92edc08192821c7495a9b9483c" + } + }, + "id": "prt_f235a86ef001IwGiOSakSeRol3", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235a7cb8002iGZZXS7uyZd4Ad" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 12402, + "input": 1552, + "output": 35, + "reasoning": 63, + "cache": { + "write": 0, + "read": 10752 + } + }, + "cost": 0.0059696, + "id": "prt_f235a871d001Answ2puLtobEFO", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235a7cb8002iGZZXS7uyZd4Ad" + } + ] + }, + { + "info": { + "parentID": "msg_f235a0f06001bZ4c4ZGdPo7uVS", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0049084, + "tokens": { + "total": 12807, + "input": 368, + "output": 132, + "reasoning": 19, + "cache": { + "write": 0, + "read": 12288 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783004563240, + "completed": 1783004565914 + }, + "finish": "tool-calls", + "id": "msg_f235a8728002FTyqD49Y7TRjPm", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f235a8a4d001aSsOSsuIsvgkUm", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235a8728002FTyqD49Y7TRjPm" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783004564258, + "end": 1783004565159 + }, + "metadata": { + "openai": { + "itemId": "rs_0c2dccda7aed3d13016a467d9440a4819eadacea8df893882a", + "reasoningEncryptedContent": "gAAAAABqRn2Vr_VOm0nLTIbdLoxuUj8QH_Q05Uj1hZCzHczat2z7v9Xa0eBEgC60uzkFq-vDg_aVuE4efxGbbdlpMeLjyignp-mHY92K0adwKODovyh8-ftwKg_IpJC9o8-AHCQ4eOoFYAJOCwmPdBxV9moK2xsjN9qRgJp4CmZe990rOZd1gBH3u5t2ZhVqI3dViiPlHFgth-FDQNgI579R6JA1uhPtzQWWsqUeIci10wIks0SdanwcpDjoNpTrAimEAnJT1vKp6jRKAbtAmpgA8h9OfZ8H3onP5U2WvlI2F9mUIJb6DS7ZSLg_zBBpa1Wc1LL7mYysp8bFyMuvAihpekxgdn6n4MvaJ41W2zWE8qHo2PXq-uv6sszEE_b2Hn29jKVOBabitB5yrkEVlfdbRPCytKJ6jOUsfpMFNF6Mc5tRb4CIkNnN0d5NYiiTNV5dtM8-thkww60Uj_P__KGwp1_L3fTTKhwlOd87z1D-bysfMnJ7kaTXWzHFbpL7O0zUbFzx_N0_whpBeFIsYupjWWfOSIa7Uiw09naL3ALs32qs5iWH0utHmC-oHEkI6fNvYet71XOXATBYrxY4umFAu1-1X5-dGaSviQbznUZnwm9_fZHmNel-N2lweujczYDVmA1QWfqbF983ns1zilF_WHm52nBHiadNEULQG2oUrwh_Ij6KFaq8_vOtRuiieUBBMxG-m9blM-vPbzMQiJGJvhfiZAVurTFCo-4a7MxQRawsukuoU4k52CRHEENKwHs46sN5D-TZdctcRqCNB4rjSz0rrBKXg0_ldopDNj1ZUXHdO2QzkpRK2wZVWpLRld4niJpyPc2aMXsOUX_D7e6cMrnrHWR5E5bADJtpU3GUkbyq_AkQo7vaw5fNfRl0O0BLPFW9KPyLCRTfuOOacTNV8tY258AwwQ5k8yemKwUHwOCXxCxktC2hF6ulJfyV0499XQqVPDpqmRTH6BxRRUEqDkubbX2A4YkKQskWzZZFLBDCl-ZGSgW9ZCS_Pr1twoRgVF70yLN-" + } + }, + "id": "prt_f235a8b22001H24fy6sVEWMxvu", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235a8728002FTyqD49Y7TRjPm" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_quxY4DDJPHZF8IGV7qVloD3t", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/AGENTS.md\n@@\n-- Repo state: only this file exists right now; there are no manifests, source files, tests, or CI configs to infer tooling from.\n+- Repo state: currently docs/templates only (`AGENTS.md`, `poc_eval_sheet_guide.md`, `poc_eval_sheet_template.csv`); there are still no manifests, source code, tests, or CI configs to infer tooling from.\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/AGENTS.md", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/AGENTS.md\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/AGENTS.md\n+++ /home/magnus-bk/repos/material-hunters/AGENTS.md\n@@ -1,7 +1,7 @@\n # AGENTS\n \n-- Repo state: only this file exists right now; there are no manifests, source files, tests, or CI configs to infer tooling from.\n+- Repo state: currently docs/templates only (`AGENTS.md`, `poc_eval_sheet_guide.md`, `poc_eval_sheet_template.csv`); there are still no manifests, source code, tests, or CI configs to infer tooling from.\n - Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n - Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n - Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n - Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/AGENTS.md", + "relativePath": "home/magnus-bk/repos/material-hunters/AGENTS.md", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/AGENTS.md\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/AGENTS.md\n+++ /home/magnus-bk/repos/material-hunters/AGENTS.md\n@@ -1,7 +1,7 @@\n # AGENTS\n \n-- Repo state: only this file exists right now; there are no manifests, source files, tests, or CI configs to infer tooling from.\n+- Repo state: currently docs/templates only (`AGENTS.md`, `poc_eval_sheet_guide.md`, `poc_eval_sheet_template.csv`); there are still no manifests, source code, tests, or CI configs to infer tooling from.\n - Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n - Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n - Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n - Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n", + "additions": 1, + "deletions": 1 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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+ } + }, + "id": "prt_f235a97b4001j7cOfgIKWLfvjc", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235a91a50029Oy2eLy8p9sRw4" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_hTYuGUpnRHLp3lTixO6vyVoP", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md" + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md</path>\n<type>file</type>\n<content>\n1: # POC Evaluation Sheet (SigCLIP Baseline)\n2: \n3: Use `poc_eval_sheet_template.csv` as the single source for decision testing.\n4: \n5: ## Test setup\n6: \n7: - Target size: `N = 30` taps across varied scenes/materials.\n8: - Baseline model first: `SigCLIP` in `model_name`.\n9: - One row = one tapped object attempt.\n10: - Keep `status` as `ok` or `fail` for every row.\n11: \n12: ## Required columns\n13: \n14: - Identity: `sample_id`, `image_id`, `image_path`\n15: - Interaction: `tap_x`, `tap_y`\n16: - Ground truth: `ground_truth_object`, `ground_truth_material`\n17: - Model output: `pred_object`, `pred_material_top1`, `pred_material_top3`, `pred_material_conf_top1`, `model_name`\n18: - Segmentation quality: `segmentation_accepted` (`1`/`0`)\n19: - Human correction: `user_edited_material` (`1`/`0`), `user_final_material`, `edit_reason`\n20: - Runtime: `end_to_end_time_sec`, `segmentation_time_sec`, `classification_time_sec`\n21: - Reliability: `status`, `error_type`\n22: - Artifacts: `mask_area_px`, `bbox_xywh`, `passport_json_path`\n23: \n24: ## Decision metrics and thresholds\n25: \n26: - Segmentation acceptance rate: `>= 85%`\n27: - Material top-1 accuracy: `>= 70%`\n28: - Material top-3 hit rate: `>= 90%`\n29: - Manual edit rate: `<= 35%`\n30: - Median end-to-end time: `<= 12 sec`\n31: - Passport schema validity: `100%` (all `status = ok`, all JSON files valid)\n32: \n33: ## Google Sheets formulas\n34: \n35: Assume headers are in row `1` and data rows are `2:1000`.\n36: \n37: - Segmentation acceptance rate \n38: `=AVERAGE(N2:N1000)`\n39: \n40: - Top-1 accuracy (add helper col `Y`: top1_correct) \n41: `Y2: =IF(H2=\"\",,IF(H2=G2,1,0))` then fill down \n42: Metric: `=AVERAGE(Y2:Y1000)`\n43: \n44: - Top-3 hit rate (add helper col `Z`: top3_hit; top-3 delimiter is `|`) \n45: `Z2: =IF(I2=\"\",,IF(ISNUMBER(SEARCH(G2,I2)),1,0))` then fill down \n46: Metric: `=AVERAGE(Z2:Z1000)`\n47: \n48: - Manual edit rate \n49: `=AVERAGE(O2:O1000)`\n50: \n51: - Median end-to-end time \n52: `=MEDIAN(Q2:Q1000)`\n53: \n54: - Failure rate (`status != ok`) \n55: `=COUNTIF(T2:T1000,\"<>ok\")/COUNTA(T2:T1000)`\n56: \n57: ## Colab/Pandas metric cell\n58: \n59: ```python\n60: import pandas as pd\n61: \n62: df = pd.read_csv(\"/content/poc_eval_sheet_template.csv\")\n63: df = df[df[\"status\"].notna()]\n64: \n65: df[\"top1_correct\"] = (df[\"pred_material_top1\"] == df[\"ground_truth_material\"]).astype(int)\n66: df[\"top3_hit\"] = df.apply(\n67: lambda r: int(str(r[\"ground_truth_material\"]) in str(r[\"pred_material_top3\"]).split(\"|\")),\n68: axis=1,\n69: )\n70: \n71: metrics = {\n72: \"n_samples\": len(df),\n73: \"segmentation_acceptance_rate\": df[\"segmentation_accepted\"].mean(),\n74: \"top1_accuracy\": df[\"top1_correct\"].mean(),\n75: \"top3_hit_rate\": df[\"top3_hit\"].mean(),\n76: \"manual_edit_rate\": df[\"user_edited_material\"].mean(),\n77: \"median_e2e_time_sec\": df[\"end_to_end_time_sec\"].median(),\n78: \"failure_rate\": (df[\"status\"] != \"ok\").mean(),\n79: }\n80: \n81: thresholds = {\n82: \"segmentation_acceptance_rate\": 0.85,\n83: \"top1_accuracy\": 0.70,\n84: \"top3_hit_rate\": 0.90,\n85: \"manual_edit_rate_max\": 0.35,\n86: \"median_e2e_time_sec_max\": 12.0,\n87: }\n88: \n89: print(metrics)\n90: print(\"go_no_go:\", (\n91: metrics[\"segmentation_acceptance_rate\"] >= thresholds[\"segmentation_acceptance_rate\"]\n92: and metrics[\"top1_accuracy\"] >= thresholds[\"top1_accuracy\"]\n93: and metrics[\"top3_hit_rate\"] >= thresholds[\"top3_hit_rate\"]\n94: and metrics[\"manual_edit_rate\"] <= thresholds[\"manual_edit_rate_max\"]\n95: and metrics[\"median_e2e_time_sec\"] <= thresholds[\"median_e2e_time_sec_max\"]\n96: and metrics[\"failure_rate\"] == 0\n97: ))\n98: ```\n99: \n100: ## SigCLIP vs DINOv3 promotion rule\n101: \n102: Promote `DINOv3` only if, on the same dataset, it delivers either:\n103: \n104: - `+5` percentage points top-1 accuracy, or\n105: - `-10` percentage points manual edit rate,\n106: \n107: while keeping median end-to-end time increase within `+20%`.\n\n(End of file - total 107 lines)\n</content>", + "metadata": { + "preview": "# POC Evaluation Sheet (SigCLIP Baseline)\n\nUse `poc_eval_sheet_template.csv` as the single source for decision testing.\n\n## Test setup\n\n- Target size: `N = 30` taps across varied scenes/materials.\n- Baseline model first: `SigCLIP` in `model_name`.\n- One row = one tapped object attempt.\n- Keep `status` as `ok` or `fail` for every row.\n\n## Required columns\n\n- Identity: `sample_id`, `image_id`, `image_path`\n- Interaction: `tap_x`, `tap_y`\n- Ground truth: `ground_truth_object`, `ground_truth_material`\n- Model output: `pred_object`, `pred_material_top1`, `pred_material_top3`, `pred_material_conf_top1`, `model_name`\n- Segmentation quality: `segmentation_accepted` (`1`/`0`)\n- Human correction: `user_edited_material` (`1`/`0`), `user_final_material`, `edit_reason`\n- Runtime: `end_to_end_time_sec`, `segmentation_time_sec`, `classification_time_sec`", + "truncated": false, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md", + "text": "# POC Evaluation Sheet (SigCLIP Baseline)\n\nUse `poc_eval_sheet_template.csv` as the single source for decision testing.\n\n## Test setup\n\n- Target size: `N = 30` taps across varied scenes/materials.\n- Baseline model first: `SigCLIP` in `model_name`.\n- One row = one tapped object attempt.\n- Keep `status` as `ok` or `fail` for every row.\n\n## Required columns\n\n- Identity: `sample_id`, `image_id`, `image_path`\n- Interaction: `tap_x`, `tap_y`\n- Ground truth: `ground_truth_object`, `ground_truth_material`\n- Model output: `pred_object`, `pred_material_top1`, `pred_material_top3`, `pred_material_conf_top1`, `model_name`\n- Segmentation quality: `segmentation_accepted` (`1`/`0`)\n- Human correction: `user_edited_material` (`1`/`0`), `user_final_material`, `edit_reason`\n- Runtime: `end_to_end_time_sec`, `segmentation_time_sec`, `classification_time_sec`\n- Reliability: `status`, `error_type`\n- Artifacts: `mask_area_px`, `bbox_xywh`, `passport_json_path`\n\n## Decision metrics and thresholds\n\n- Segmentation acceptance rate: `>= 85%`\n- Material top-1 accuracy: `>= 70%`\n- Material top-3 hit rate: `>= 90%`\n- Manual edit rate: `<= 35%`\n- Median end-to-end time: `<= 12 sec`\n- Passport schema validity: `100%` (all `status = ok`, all JSON files valid)\n\n## Google Sheets formulas\n\nAssume headers are in row `1` and data rows are `2:1000`.\n\n- Segmentation acceptance rate \n`=AVERAGE(N2:N1000)`\n\n- Top-1 accuracy (add helper col `Y`: top1_correct) \n`Y2: =IF(H2=\"\",,IF(H2=G2,1,0))` then fill down \nMetric: `=AVERAGE(Y2:Y1000)`\n\n- Top-3 hit rate (add helper col `Z`: top3_hit; top-3 delimiter is `|`) \n`Z2: =IF(I2=\"\",,IF(ISNUMBER(SEARCH(G2,I2)),1,0))` then fill down \nMetric: `=AVERAGE(Z2:Z1000)`\n\n- Manual edit rate \n`=AVERAGE(O2:O1000)`\n\n- Median end-to-end time \n`=MEDIAN(Q2:Q1000)`\n\n- Failure rate (`status != ok`) \n`=COUNTIF(T2:T1000,\"<>ok\")/COUNTA(T2:T1000)`\n\n## Colab/Pandas metric cell\n\n```python\nimport pandas as pd\n\ndf = pd.read_csv(\"/content/poc_eval_sheet_template.csv\")\ndf = df[df[\"status\"].notna()]\n\ndf[\"top1_correct\"] = (df[\"pred_material_top1\"] == df[\"ground_truth_material\"]).astype(int)\ndf[\"top3_hit\"] = df.apply(\n lambda r: int(str(r[\"ground_truth_material\"]) in str(r[\"pred_material_top3\"]).split(\"|\")),\n axis=1,\n)\n\nmetrics = {\n \"n_samples\": len(df),\n \"segmentation_acceptance_rate\": df[\"segmentation_accepted\"].mean(),\n \"top1_accuracy\": df[\"top1_correct\"].mean(),\n \"top3_hit_rate\": df[\"top3_hit\"].mean(),\n \"manual_edit_rate\": df[\"user_edited_material\"].mean(),\n \"median_e2e_time_sec\": df[\"end_to_end_time_sec\"].median(),\n \"failure_rate\": (df[\"status\"] != \"ok\").mean(),\n}\n\nthresholds = {\n \"segmentation_acceptance_rate\": 0.85,\n \"top1_accuracy\": 0.70,\n \"top3_hit_rate\": 0.90,\n \"manual_edit_rate_max\": 0.35,\n \"median_e2e_time_sec_max\": 12.0,\n}\n\nprint(metrics)\nprint(\"go_no_go:\", (\n metrics[\"segmentation_acceptance_rate\"] >= thresholds[\"segmentation_acceptance_rate\"]\n and metrics[\"top1_accuracy\"] >= thresholds[\"top1_accuracy\"]\n and metrics[\"top3_hit_rate\"] >= thresholds[\"top3_hit_rate\"]\n and metrics[\"manual_edit_rate\"] <= thresholds[\"manual_edit_rate_max\"]\n and metrics[\"median_e2e_time_sec\"] <= thresholds[\"median_e2e_time_sec_max\"]\n and metrics[\"failure_rate\"] == 0\n))\n```\n\n## SigCLIP vs DINOv3 promotion rule\n\nPromote `DINOv3` only if, on the same dataset, it delivers either:\n\n- `+5` percentage points top-1 accuracy, or\n- `-10` percentage points manual edit rate,\n\nwhile keeping median end-to-end time increase within `+20%`.", + "lineStart": 1, + "lineEnd": 107, + "totalLines": 107, + "truncated": false + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md", + "time": { + "start": 1783004568726, + "end": 1783004568765 + } + }, + "metadata": { + "openai": { + "itemId": "fc_00d563deab43cc0d016a467d98835c819caca8e320701fbb15" + } + }, + "id": "prt_f235a9c8a001xtuyTGzo56Ydr7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235a91a50029Oy2eLy8p9sRw4" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_pdEFZYINpK4R1GCfTs2RdGOP", + "state": { + "status": "completed", + 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"sample_id,image_id,image_path,tap_x,tap_y,ground_truth_object,ground_truth_material,pred_object,pred_material_top1,pred_material_top3,pred_material_conf_top1,model_name,segmentation_accepted,user_edited_material,user_final_material,edit_reason,end_to_end_time_sec,segmentation_time_sec,classification_time_sec,status,error_type,mask_area_px,bbox_xywh,passport_json_path\nS001,IMG001,/content/images/img001.jpg,524,312,window,glass,window,glass,\"glass|aluminum|plastic\",0.83,SigCLIP,1,0,glass,,10.4,4.2,2.7,ok,,14522,\"487,221,156,204\",/content/passports/S001.json", + "truncated": false, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_sheet_template.csv", + "text": "sample_id,image_id,image_path,tap_x,tap_y,ground_truth_object,ground_truth_material,pred_object,pred_material_top1,pred_material_top3,pred_material_conf_top1,model_name,segmentation_accepted,user_edited_material,user_final_material,edit_reason,end_to_end_time_sec,segmentation_time_sec,classification_time_sec,status,error_type,mask_area_px,bbox_xywh,passport_json_path\nS001,IMG001,/content/images/img001.jpg,524,312,window,glass,window,glass,\"glass|aluminum|plastic\",0.83,SigCLIP,1,0,glass,,10.4,4.2,2.7,ok,,14522,\"487,221,156,204\",/content/passports/S001.json", + "lineStart": 1, + "lineEnd": 2, + "totalLines": 2, + "truncated": false + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_sheet_template.csv", + "time": { + "start": 1783004568737, + "end": 1783004568777 + } + }, + "metadata": { + "openai": { + "itemId": "fc_00d563deab43cc0d016a467d988378819cb1502b024a33a961" + } + }, + "id": "prt_f235a9c9b001oSq8BgJlz9F5Gw", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", 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material inventory for circular construction.\n7: - Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n8: - Decision hygiene: explicitly restate/verify key decisions during execution to avoid drift from the agreed goal.\n9: - Update this file once code/config lands; prefer executable repo sources over prose when they conflict.\n\n(End of file - total 9 lines)\n</content>", + "metadata": { + "preview": "# AGENTS\n\n- Repo state: currently docs/templates only (`AGENTS.md`, `poc_eval_sheet_guide.md`, `poc_eval_sheet_template.csv`); there are still no manifests, source code, tests, or CI configs to infer tooling from.\n- Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n- Current phase: Google Colab proof of concept focused on validating the ML pipeline (not 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Generate a minimal Colab notebook scaffold (`.ipynb`) with cells wired to this CSV + metrics.\n2. Add a JSON Schema file for the material passport and a validator script.\n3. 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\"status\": \"pending\",\n \"priority\": \"high\"\n },\n {\n \"content\": \"Update AGENTS.md repo-state note to include new notebook artifact\",\n \"status\": \"pending\",\n \"priority\": \"medium\"\n }\n]", + "metadata": { + "todos": [ + { + "content": "Create Colab notebook scaffold for POC evaluation workflow", + "status": "in_progress", + "priority": "high" + }, + { + "content": "Add metric computation and go/no-go cells aligned with existing guide thresholds", + "status": "pending", + "priority": "high" + }, + { + "content": "Update AGENTS.md repo-state note to include new notebook artifact", + "status": "pending", + "priority": "medium" + } + ], + "truncated": false + }, + "title": "3 todos", + "time": { + "start": 1783004661300, + "end": 1783004661310 + } + }, + "metadata": { + "openai": { + "itemId": "fc_07b1e420f042157c016a467df4909481a0a509627a38f7469d" + } + }, + "id": "prt_f235c047f0016jQI0KtBTuC6cK", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": 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"ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f235c096e001quOh7uQcq7XtnO", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235c0656002onVjh7M4Vv57cA" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_ClUmWo4MgMqk3rGZRj7KcCqY", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Add File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"# Material Hunters POC Evaluation (SigCLIP Baseline)\\n\",\n+ \"\\n\",\n+ \"This notebook tracks the decision test for the tap -> segment -> classify -> edit -> passport flow.\\n\",\n+ \"\\n\",\n+ \"Use this flow:\\n\",\n+ \"1. Fill the CSV rows while testing (`status` must be `ok` or `fail`).\\n\",\n+ \"2. Run the metrics cell to get go/no-go status.\\n\",\n+ \"3. Repeat with `model_name = DINOv3` for comparison.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"import pandas as pd\\n\",\n+ \"from pathlib import Path\\n\",\n+ \"\\n\",\n+ \"THRESHOLDS = {\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n+ \" \\\"top1_accuracy\\\": 0.70,\\n\",\n+ \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n+ \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n+ \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n+ \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# If you uploaded only the template, create a working copy once.\\n\",\n+ \"if not CSV_PATH.exists() and TEMPLATE_PATH.exists():\\n\",\n+ \" df_template = pd.read_csv(TEMPLATE_PATH)\\n\",\n+ \" df_template.to_csv(CSV_PATH, index=False)\\n\",\n+ \" print(f\\\"Created working CSV from template: {CSV_PATH}\\\")\\n\",\n+ \"\\n\",\n+ \"if not CSV_PATH.exists():\\n\",\n+ \" raise FileNotFoundError(\\n\",\n+ \" f\\\"Missing {CSV_PATH}. Upload poc_eval_sheet_template.csv and set CSV_PATH.\\\"\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \"df = pd.read_csv(CSV_PATH)\\n\",\n+ \"print(f\\\"Loaded {len(df)} rows from {CSV_PATH}\\\")\\n\",\n+ \"df.head(3)\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n+ \" df = df_in.copy()\\n\",\n+ \" df = df[df[\\\"status\\\"].notna()]\\n\",\n+ \"\\n\",\n+ \" if df.empty:\\n\",\n+ \" raise ValueError(\\\"No rows with non-empty status. Fill status with ok/fail for tested samples.\\\")\\n\",\n+ \"\\n\",\n+ \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n+ \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n+ \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n+ \" axis=1,\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" metrics = {\\n\",\n+ \" \\\"n_samples\\\": int(len(df)),\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n+ \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n+ \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n+ \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n+ \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n+ \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" go_no_go = (\\n\",\n+ \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n+ \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n+ \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n+ \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n+ \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n+ \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"result = compute_metrics(df)\\n\",\n+ \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n+ \"print(\\\"Metrics:\\\")\\n\",\n+ \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n+ \" print(f\\\"- {k}: {v}\\\")\\n\",\n+ \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"## Compare SigCLIP vs DINOv3\\n\",\n+ \"\\n\",\n+ \"Use the same dataset and set `model_name` per row. Promotion rule for DINOv3:\\n\",\n+ \"- +5 points top-1 accuracy OR\\n\",\n+ \"- -10 points manual edit rate\\n\",\n+ \"- while median end-to-end time increase is <= +20%.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n+ \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n+ \"\\n\",\n+ \"sigclip_df = model_slice(df, \\\"SigCLIP\\\")\\n\",\n+ \"dinov3_df = model_slice(df, \\\"DINOv3\\\")\\n\",\n+ \"\\n\",\n+ \"if sigclip_df.empty:\\n\",\n+ \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"SigCLIP:\\\")\\n\",\n+ \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if dinov3_df.empty:\\n\",\n+ \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"DINOv3:\\\")\\n\",\n+ \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\",\n+ \"\\n\",\n+ \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n+ \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n+ \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n+ \"\\n\",\n+ \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n+ \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n+ \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n+ \"\\n\",\n+ \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n+ \"\\n\",\n+ \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n+ \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n+ \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n+ \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ ]\n+ }\n+ ],\n+ \"metadata\": {\n+ \"kernelspec\": {\n+ \"display_name\": \"Python 3\",\n+ \"language\": \"python\",\n+ \"name\": \"python3\"\n+ },\n+ \"language_info\": {\n+ \"name\": \"python\",\n+ \"version\": \"3\"\n+ }\n+ },\n+ \"nbformat\": 4,\n+ \"nbformat_minor\": 5\n+}\n*** End Patch" + }, + "output": "Success. Updated the following files:\nA home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -0,0 +1,181 @@\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"# Material Hunters POC Evaluation (SigCLIP Baseline)\\n\",\n+ \"\\n\",\n+ \"This notebook tracks the decision test for the tap -> segment -> classify -> edit -> passport flow.\\n\",\n+ \"\\n\",\n+ \"Use this flow:\\n\",\n+ \"1. Fill the CSV rows while testing (`status` must be `ok` or `fail`).\\n\",\n+ \"2. Run the metrics cell to get go/no-go status.\\n\",\n+ \"3. Repeat with `model_name = DINOv3` for comparison.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"import pandas as pd\\n\",\n+ \"from pathlib import Path\\n\",\n+ \"\\n\",\n+ \"THRESHOLDS = {\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n+ \" \\\"top1_accuracy\\\": 0.70,\\n\",\n+ \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n+ \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n+ \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n+ \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# If you uploaded only the template, create a working copy once.\\n\",\n+ \"if not CSV_PATH.exists() and TEMPLATE_PATH.exists():\\n\",\n+ \" df_template = pd.read_csv(TEMPLATE_PATH)\\n\",\n+ \" df_template.to_csv(CSV_PATH, index=False)\\n\",\n+ \" print(f\\\"Created working CSV from template: {CSV_PATH}\\\")\\n\",\n+ \"\\n\",\n+ \"if not CSV_PATH.exists():\\n\",\n+ \" raise FileNotFoundError(\\n\",\n+ \" f\\\"Missing {CSV_PATH}. Upload poc_eval_sheet_template.csv and set CSV_PATH.\\\"\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \"df = pd.read_csv(CSV_PATH)\\n\",\n+ \"print(f\\\"Loaded {len(df)} rows from {CSV_PATH}\\\")\\n\",\n+ \"df.head(3)\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n+ \" df = df_in.copy()\\n\",\n+ \" df = df[df[\\\"status\\\"].notna()]\\n\",\n+ \"\\n\",\n+ \" if df.empty:\\n\",\n+ \" raise ValueError(\\\"No rows with non-empty status. Fill status with ok/fail for tested samples.\\\")\\n\",\n+ \"\\n\",\n+ \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n+ \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n+ \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n+ \" axis=1,\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" metrics = {\\n\",\n+ \" \\\"n_samples\\\": int(len(df)),\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n+ \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n+ \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n+ \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n+ \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n+ \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" go_no_go = (\\n\",\n+ \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n+ \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n+ \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n+ \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n+ \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n+ \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"result = compute_metrics(df)\\n\",\n+ \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n+ \"print(\\\"Metrics:\\\")\\n\",\n+ \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n+ \" print(f\\\"- {k}: {v}\\\")\\n\",\n+ \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"## Compare SigCLIP vs DINOv3\\n\",\n+ \"\\n\",\n+ \"Use the same dataset and set `model_name` per row. Promotion rule for DINOv3:\\n\",\n+ \"- +5 points top-1 accuracy OR\\n\",\n+ \"- -10 points manual edit rate\\n\",\n+ \"- while median end-to-end time increase is <= +20%.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n+ \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n+ \"\\n\",\n+ \"sigclip_df = model_slice(df, \\\"SigCLIP\\\")\\n\",\n+ \"dinov3_df = model_slice(df, \\\"DINOv3\\\")\\n\",\n+ \"\\n\",\n+ \"if sigclip_df.empty:\\n\",\n+ \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"SigCLIP:\\\")\\n\",\n+ \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if dinov3_df.empty:\\n\",\n+ \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"DINOv3:\\\")\\n\",\n+ \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\",\n+ \"\\n\",\n+ \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n+ \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n+ \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n+ \"\\n\",\n+ \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n+ \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n+ \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n+ \"\\n\",\n+ \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n+ \"\\n\",\n+ \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n+ \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n+ \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n+ \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ ]\n+ }\n+ ],\n+ \"metadata\": {\n+ \"kernelspec\": {\n+ \"display_name\": \"Python 3\",\n+ \"language\": \"python\",\n+ \"name\": \"python3\"\n+ },\n+ \"language_info\": {\n+ \"name\": \"python\",\n+ \"version\": \"3\"\n+ }\n+ },\n+ \"nbformat\": 4,\n+ \"nbformat_minor\": 5\n+}\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "add", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -0,0 +1,181 @@\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"# Material Hunters POC Evaluation (SigCLIP Baseline)\\n\",\n+ \"\\n\",\n+ \"This notebook tracks the decision test for the tap -> segment -> classify -> edit -> passport flow.\\n\",\n+ \"\\n\",\n+ \"Use this flow:\\n\",\n+ \"1. Fill the CSV rows while testing (`status` must be `ok` or `fail`).\\n\",\n+ \"2. Run the metrics cell to get go/no-go status.\\n\",\n+ \"3. Repeat with `model_name = DINOv3` for comparison.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"import pandas as pd\\n\",\n+ \"from pathlib import Path\\n\",\n+ \"\\n\",\n+ \"THRESHOLDS = {\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n+ \" \\\"top1_accuracy\\\": 0.70,\\n\",\n+ \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n+ \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n+ \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n+ \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# If you uploaded only the template, create a working copy once.\\n\",\n+ \"if not CSV_PATH.exists() and TEMPLATE_PATH.exists():\\n\",\n+ \" df_template = pd.read_csv(TEMPLATE_PATH)\\n\",\n+ \" df_template.to_csv(CSV_PATH, index=False)\\n\",\n+ \" print(f\\\"Created working CSV from template: {CSV_PATH}\\\")\\n\",\n+ \"\\n\",\n+ \"if not CSV_PATH.exists():\\n\",\n+ \" raise FileNotFoundError(\\n\",\n+ \" f\\\"Missing {CSV_PATH}. Upload poc_eval_sheet_template.csv and set CSV_PATH.\\\"\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \"df = pd.read_csv(CSV_PATH)\\n\",\n+ \"print(f\\\"Loaded {len(df)} rows from {CSV_PATH}\\\")\\n\",\n+ \"df.head(3)\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n+ \" df = df_in.copy()\\n\",\n+ \" df = df[df[\\\"status\\\"].notna()]\\n\",\n+ \"\\n\",\n+ \" if df.empty:\\n\",\n+ \" raise ValueError(\\\"No rows with non-empty status. Fill status with ok/fail for tested samples.\\\")\\n\",\n+ \"\\n\",\n+ \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n+ \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n+ \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n+ \" axis=1,\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" metrics = {\\n\",\n+ \" \\\"n_samples\\\": int(len(df)),\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n+ \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n+ \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n+ \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n+ \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n+ \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" go_no_go = (\\n\",\n+ \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n+ \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n+ \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n+ \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n+ \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n+ \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"result = compute_metrics(df)\\n\",\n+ \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n+ \"print(\\\"Metrics:\\\")\\n\",\n+ \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n+ \" print(f\\\"- {k}: {v}\\\")\\n\",\n+ \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"## Compare SigCLIP vs DINOv3\\n\",\n+ \"\\n\",\n+ \"Use the same dataset and set `model_name` per row. Promotion rule for DINOv3:\\n\",\n+ \"- +5 points top-1 accuracy OR\\n\",\n+ \"- -10 points manual edit rate\\n\",\n+ \"- while median end-to-end time increase is <= +20%.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n+ \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n+ \"\\n\",\n+ \"sigclip_df = model_slice(df, \\\"SigCLIP\\\")\\n\",\n+ \"dinov3_df = model_slice(df, \\\"DINOv3\\\")\\n\",\n+ \"\\n\",\n+ \"if sigclip_df.empty:\\n\",\n+ \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"SigCLIP:\\\")\\n\",\n+ \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if dinov3_df.empty:\\n\",\n+ \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"DINOv3:\\\")\\n\",\n+ \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\",\n+ \"\\n\",\n+ \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n+ \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n+ \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n+ \"\\n\",\n+ \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n+ \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n+ \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n+ \"\\n\",\n+ \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n+ \"\\n\",\n+ \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n+ \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n+ \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n+ \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ ]\n+ }\n+ ],\n+ \"metadata\": {\n+ \"kernelspec\": {\n+ \"display_name\": \"Python 3\",\n+ \"language\": \"python\",\n+ \"name\": \"python3\"\n+ },\n+ \"language_info\": {\n+ \"name\": \"python\",\n+ \"version\": \"3\"\n+ }\n+ },\n+ \"nbformat\": 4,\n+ \"nbformat_minor\": 5\n+}\n", + "additions": 181, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/AGENTS.md", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/AGENTS.md\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/AGENTS.md\n+++ /home/magnus-bk/repos/material-hunters/AGENTS.md\n@@ -1,7 +1,7 @@\n # AGENTS\n \n-- Repo state: currently docs/templates only (`AGENTS.md`, `poc_eval_sheet_guide.md`, `poc_eval_sheet_template.csv`); there are still no manifests, source code, tests, or CI configs to infer tooling from.\n+- Repo state: currently docs/templates/notebook only (`AGENTS.md`, `poc_eval_sheet_guide.md`, `poc_eval_sheet_template.csv`, `poc_eval_notebook.ipynb`); there are still no manifests, source code packages, tests, or CI configs to infer tooling from.\n - Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n - Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n - Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n - Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/AGENTS.md", + "relativePath": "home/magnus-bk/repos/material-hunters/AGENTS.md", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/AGENTS.md\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/AGENTS.md\n+++ /home/magnus-bk/repos/material-hunters/AGENTS.md\n@@ -1,7 +1,7 @@\n # AGENTS\n \n-- Repo state: currently docs/templates only (`AGENTS.md`, `poc_eval_sheet_guide.md`, `poc_eval_sheet_template.csv`); there are still no manifests, source code, tests, or CI configs to infer tooling from.\n+- Repo state: currently docs/templates/notebook only (`AGENTS.md`, `poc_eval_sheet_guide.md`, `poc_eval_sheet_template.csv`, `poc_eval_notebook.ipynb`); there are still no manifests, source code packages, tests, or CI configs to infer tooling from.\n - Project intent: ML-assisted surveying for reconstruction/demolition photos, identifying objects and their material composition.\n - Current phase: Google Colab proof of concept focused on validating the ML pipeline (not production app architecture).\n - Expected pipeline concept: surveyor taps object -> segmentation isolates target -> model predicts object + material composition -> structured material inventory for circular construction.\n - Collaboration workflow: start by confirming the real goal and core decision to drive, then work in small buckets; for each bucket, provide a plan and pause for checkpoint review before continuing.\n", + "additions": 1, + "deletions": 1 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Start logging real rows (SigCLIP first), then we can add a small helper cell to append rows interactively in-notebook if you want.", + "time": { + "start": 1783004710028, + "end": 1783004711272 + }, + "metadata": { + "openai": { + "itemId": "msg_02dcb9e818ba4c4f016a467e25dabc81a3987ee98a686602e4", + "phase": "final_answer" + } + }, + "id": "prt_f235cc48c0017QyV4PwUALjIB4", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235cae8c002H4RLAq3DMrDIY7" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 19510, + "input": 287, + "output": 248, + "reasoning": 31, + "cache": { + "write": 0, + "read": 18944 + } + }, + "cost": 0.00772345, + "id": "prt_f235cc971001HWLBvITTDsdYyk", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f235cae8c002H4RLAq3DMrDIY7" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783005238885 + }, + "agent": "build", + "model": { + "providerID": "openai", + "modelID": 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s9xUiuWOxu6GhgienpOw5n62sg1_2OiaABU64nHcve55644g2u4mBR6kjbZi2cTGv1vaMreEWws3TDu6bGitjnlEdHgvmhApTydIivBvmuDuvSHmf7zl5pvih0YoOjjuWalbx7oo9rJGgqb0B2Vu-ibLWmmeTXxthUepb3wy05_uRNGbBDTBNJR3b-2JNfZVBHhZh4xheUDIVzhVHIoWnlOPcOcWf4ODbaNFwCbstKLOoaqKjFdqwGPPs-YYBSbXGKBidxDT-msUwmzl_KpltqK7maPxYz8Kje2rFbWmfp0iIoULQqE8nFDC9b_tCuM_D02sXBSbw6k89LoFiVTjz_aoD2-vNkyBw9PORyz75rDXuuri8EtBdtOJ7DHFbzTtrcOfvwGvRfnk-tV4e4NXUdLVPp05a46btfcPM9pmyxmY0uwIT2AMwdPkUr4jQSXGMIWuayvFESJfM8DLGOdhBsX_K3C8CA_1tAhGwHR0MS0tVs82JvPN9TbWEL0C1rSKwn2QL3p3Fd7wCojRii40WwAxx5PD7t6DI6YfRZKWtTy_ZBLnzjl-jfC7V6wGNqJpfZLLOMumYCTt2iTaCXbdnjkcnqpNFf3m0KxVZVEbxn41FWbDFdVahilHNqhrR2WOcLskZGLCjA91IM37AC3q-a2UbRagmHPAa0Bb3UZ-0hv9QPZjTZf8O-C0j_AmQ_x4DNAM8k12EiftQhqbHoeiQOiSVSpY1mU_9AaiP7Wb9UrODrYWvtthOM_BMy_Q-Hq9pzqwFF_kDQqsLM_VzyQJRNZx31TpRsmxYihnOHJBWvzf8H9dAS3YOuzf0T2FS2XLvISTITSnBruzkj_tU-FAA0hWmuM15oygsArZqjaMOcJGBhQLluSKuNRiAQa-8H9SI_3wHHDgIGxmA8ZpGSm_WXNE91GzJC0eIXuObCspiKeLsCm3kCD-3TMkjhKCYyOCeUiuFHwKV9oP0Xx9Eokof3GUUPuWIjiMr-lS92HeMs6pqx6-t5A8gTjqj6fmltvj3OANRmr5ufkcZfC6L7UYPuVNGxOdUUgygshUijl0NM9HKexhqKkGoCdh5gIHXhDZwh65WxrEeDfafTU35wYtBAOz3HpekvDHJwN0a1p_VBDwIzQcdQX7BgmKygKaeqht32ALaRunaQxF7OgSQCRb8qip8EQPRIyV6jpIw6SFThavJ4NHm2FJdrclTvuPDLdOgDzoAnw5Ac9NU1jMX9oRwdiHSPptg8lXRUeDeGGSXw56tsNbyjAi5sJxqSXdRXeqB_2-rJwFcW_A5mG6gRht8FnbrCxQfG9ynmgLd7Y22NYTxuxFjel7HCCpiZa5TrOWJcTxw25ec-mhOVxcsl0i2J1GMoBLEDnORQiUH50I02FTt14ZN9o54-Yx4gOdhvCF6bPeHKYiwIosyZ3caUVpNJ_d-3aXlW6puAmYVaioKcxylMMcd-qhm0_duYPewVO1mFYTZfyNY7DNbUU2jfRnTlg6NwUyI8Kwj-F0z7rt2qRKaB1Oa9YRS-bk5hu9-VUq_8HCQUgms4JfitdkiTXlITsA-vBbz7Wi3k7MSiNdznAiCB8spfZcYA36erXAfE2lTo0GnwJbDRb55vDS2GeYwz9BoKwrliFqArd0A0uOl9Rlg19HfigEAGKjzUce5CuCyGlbtvBP3rCAU48R1PIn6icllfp1M2G1dgcRJqT5aku1Uak4gQ30wKH63Q-N1vOQ2RiPtPNEFK7e7eCrM4s3jGRiuRr7VuNUCQOmEDS5yRgNxxMcGoWcv5oD_R3W8z35nQGaVSGt_ZZYz0MMW9kZHnwVNLSfn-REn33Sfb7LWaXJ159YuCGzjMQGZN2QF-7PcUodLzBv4OzjLut4dqynv0Je7FAJW5tWbePC6hXhrrSZIFcxCJajhvOzdO1WOLYLhYHver5ga4mEmc_WPZ_Z2aqHDHishkRWuCGykUobF6w_Eaj1JXo1t-1rHCZVD9hyeiQ2bLJnNndLzUYnFdugk2b3udVBV70nrAIbwHrgEp1ZpRaGKPvJEXgip8tcV4DpWFjMUuGF5DAUzWlgb2FzAonoaaIuyotC8lqZup3q1RtE_mOVm-ugdIQv_5qDIa5g1lXheeRERJH1AcKqWHHnTzaqniQmOLjN3Z6YFXScgLwkbBpqOf7cC4M2ouIY_zFrJ4dgSpeIc7L_-yAnpLPmsYRAkakVJiVV0XH6gMnhcrt5ffGHAV-XqQIsT6G2XluzcuCmkqzZOHRMVbm1TInBcwh0qGdV4b0Vr2GZ3igwSLHZ2Z818T4cH6Ifgmi1Ja2yon9660OLJl8GS-Yp_tEfZptc1ieHX49T-BsISwO5VWe6APMPda7y1awcoaQV2GozewyJRkwxf3hdf2zQ-ekjm62k6h6orPK0RhEigIDwR5FPE9rVyiIntkBjSS-4MdeF6jyxNLDpuuG9pYq54Rn-cUgv3VvnldINWwwVt88C0lD3CVj-59z2Io13UkwlgCK5chUdsu4Q_wcYoA-LA3XAPh_2uQyHTICPMZfFtuNMtt7T5RNjLESCACeh2uj4lhA2zDKzQ-7_9a7Q6bYPCYRiGGcVCXxGUDRAhNmVnnG8gAmEQ0mRvIyiE6AHNmlMGv8hjSi9ehsf7XQJRCHcxOOw5IDfolJbonJ4pzN8_o-qCnc79S2bP8bAeuL5uqBUpCEU8g32PCnCBJ5mYZkVhZpPqDrWtokN78dzOkrA6_x5VrP8VDRSTfU57AMaSgp25N7bH62fiZfXEFXM4DA8LgM2jRnLTj5w9SqAsFIhkHZW3oLn1rR22HB1BnWAtWq7_lDCy0gNq4Xw8sUQOec_4XIFSZ31a1JJB8DXRdd5o1wXa8nKzEVm9vQUncZB3yykfMyiFf2W3EcG1IwoGgm9fbJK8bXB3MCQJVtaZgj-1NRr1-NSBfIaDYRvvG26DeuO3XjhxxFCnHxQDjpGWhuQFRyHcA3MX88QhtApSbGzG7zzf7nDqPVwgAsmV6UO-Tl4STn5tnzdhZzENxq2e-tHIeFInrdGt4_lwXgTqaJxkQws71Q3l2rJzBGR21LUVjbhPkiSGXaAa2ReDRV9n2RPaxHVuumSiRSRUaXmgsH2SK_C2G4cR8ikvYAwfre6Us_kKc4lXsoANtfzO22oLSI3x4i8t3LRDg70i2mynglYCN9SGlF_bqT4QO8vqUOr5DTxqL7LLDBj9KtZQZXyDTsbLyiLuOGD6YmMXSw-ZmYo2CfLrqhbIGzbh0LduWLot2FDePT28SOSvfVaCuRD7MMgvGwp2BhsNgrVXD88f9ZtT7AMeF0Ehz5AOdxiDixX9s5AXnN1O6wmgNufv3prnIpIjMpPkX0JQ-aU_Iplh257qehcKtl_shJtVEuliiqMVdDCHUhIljJFGug_x-ZJbCMKCjij7vyEOj60Lk85LTDnTXZsbe3BgZYlFEhrUGbFygQXh_masRO1GDKmpp6qeK1ikKy17MRj79Ivn2yOzWP8YmOnHxadPy2vXS0wRTnWLCSs5S4PqP9tBzt6AFBbztRjxSoPLg_zVBra2628ksFcr0E2fL5Cc-2g1Nu16Y8O_Vnjca94Wo58uHHwKGfNDHcHNTqrZi0qdjSG169uGkc2xMc-SK1nPMxsxFrqRQD4vQNRwtHuGzsofPehKEOuM-5hK2l2jq4Gc60sZdrRZyrF-VN-cH0New-ISDHI2-M9UiMEYE3RKKdtEBt7o8X0j8YPpKUG045oORT7pp3BUdcKjeZYic_sYIhoVqWNoHfnKhqGnHXq9h6bZUCNJW_VJMN-VNR_iUIWowPwfqVbW-i4ZN8KU2zoXC02e2bCH6l2LvopRmdbMslKecTMVvhl3bSz2tC05PG7ExmXh6EsKrAYuLhC13FZAtZhpLs-qF-ZZnXwmIj2OIb5b3EyNxdi1pljD9o3yaCixujVi1BxoUVrDsISCPbMsZvenYNqkwERmjVFNZVAypS8KRKVS5emFWr67HEacDr9_0G42S-DWjYSlPj04kzxS-hq0USZjXBLxJwp2ktzqOZFJzo-QgP-b3jGWtqyAYkVVsN5dPAF49BoCO3UZqk2v4dQ8gkJ-68xVDZjdc5gXdKgeutOmjjwz8cAn02eBkpwo_kN9gJAdAw-8GzqtlKXpn7Kdlb4ABMvn0VFa83FZhPwyDF-MbxKuZoyLaOon1WFmVkpqQKWPXUvawtY2zXIwZHoC8JqypuiH7fBUIF5lxQ0Mqem1WZP0jDRVpH6Mqp_c5U3Y2suiu4ZVYokfDEXfTd200uyNDEjOMkcuYan0wzhkyngtCPVvmL8w3j0SIyRc3PyXmbK1lIJH4xTOpUilJXyFRKc7asi7KKfWtIaCCEDQFYdSYL0C8Wj06QrCVlQjNIKp65AVEjEtM3Cu9EkYz5gThYHd9yJcyD1wySglNgJ0Q_qufj32FzhB24AaSMDk7GV1vXxqee8c8ZbtO8veiW2q6XXv9nuncky2d6GG2XCcXkdBV-fK1iAZdNL0DbVLVsMffZKDNWffVInB01eJ7gEsINLmDSVpAby7MOkC3zOexiVCoR-exBGjQRwAkHY-IgRB2IleCkUDXZt1W6AXYQ-fyC8mA5Tf6cGn5NpOrdTd8lNGmZFcMxfIwOIclbo-bGlHQy-pkHzQ3rmB2CLirqEB-ZDiiHtCeaaZUoGkOzvzBx4QFR03NAkr-Rb30ozULpRzVlkBQDSmgHKwylNfbUT30EsqNhAEhg6x8Qxdn38XIZFtIzU0BF0E-y6BmxIYlJDILHk41mdiD6XFH9jRoWPh8Jp1Ke943cH1qjcqe_TsDm_qLDtnDaLmpSn_8MbV3wjA250NwqDAUBTNi-ADRqK7QBWT_wkTWTMXwNSWo4RKwowySBUwYo4UMzTWra7VgSCmbmWgwsbBGUIfsPkRjz774QX8L_uZ515iJw6OpCJ79uJ2rtnZZ2Q-8iNIwOZiIEOqo9QO3YpkAUvyob6UJUMGaxSV5jSI-LwKmyiNJmRiLPs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Updated the following files:\nD home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1,181 +0,0 @@\n-{\n- \"cells\": [\n- {\n- \"cell_type\": \"markdown\",\n- \"metadata\": {},\n- \"source\": [\n- \"# Material Hunters POC Evaluation (SigCLIP Baseline)\\n\",\n- \"\\n\",\n- \"This notebook tracks the decision test for the tap -> segment -> classify -> edit -> passport flow.\\n\",\n- \"\\n\",\n- \"Use this flow:\\n\",\n- \"1. Fill the CSV rows while testing (`status` must be `ok` or `fail`).\\n\",\n- \"2. Run the metrics cell to get go/no-go status.\\n\",\n- \"3. Repeat with `model_name = DINOv3` for comparison.\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"import pandas as pd\\n\",\n- \"from pathlib import Path\\n\",\n- \"\\n\",\n- \"THRESHOLDS = {\\n\",\n- \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n- \" \\\"top1_accuracy\\\": 0.70,\\n\",\n- \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n- \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n- \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n- \"}\\n\",\n- \"\\n\",\n- \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n- \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"# If you uploaded only the template, create a working copy once.\\n\",\n- \"if not CSV_PATH.exists() and TEMPLATE_PATH.exists():\\n\",\n- \" df_template = pd.read_csv(TEMPLATE_PATH)\\n\",\n- \" df_template.to_csv(CSV_PATH, index=False)\\n\",\n- \" print(f\\\"Created working CSV from template: {CSV_PATH}\\\")\\n\",\n- \"\\n\",\n- \"if not CSV_PATH.exists():\\n\",\n- \" raise FileNotFoundError(\\n\",\n- \" f\\\"Missing {CSV_PATH}. Upload poc_eval_sheet_template.csv and set CSV_PATH.\\\"\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \"df = pd.read_csv(CSV_PATH)\\n\",\n- \"print(f\\\"Loaded {len(df)} rows from {CSV_PATH}\\\")\\n\",\n- \"df.head(3)\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n- \" df = df_in.copy()\\n\",\n- \" df = df[df[\\\"status\\\"].notna()]\\n\",\n- \"\\n\",\n- \" if df.empty:\\n\",\n- \" raise ValueError(\\\"No rows with non-empty status. Fill status with ok/fail for tested samples.\\\")\\n\",\n- \"\\n\",\n- \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n- \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n- \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n- \" axis=1,\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \" metrics = {\\n\",\n- \" \\\"n_samples\\\": int(len(df)),\\n\",\n- \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n- \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n- \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n- \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n- \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n- \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n- \" }\\n\",\n- \"\\n\",\n- \" go_no_go = (\\n\",\n- \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n- \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n- \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n- \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n- \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n- \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"result = compute_metrics(df)\\n\",\n- \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n- \"print(\\\"Metrics:\\\")\\n\",\n- \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n- \" print(f\\\"- {k}: {v}\\\")\\n\",\n- \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n- ]\n- },\n- {\n- \"cell_type\": \"markdown\",\n- \"metadata\": {},\n- \"source\": [\n- \"## Compare SigCLIP vs DINOv3\\n\",\n- \"\\n\",\n- \"Use the same dataset and set `model_name` per row. Promotion rule for DINOv3:\\n\",\n- \"- +5 points top-1 accuracy OR\\n\",\n- \"- -10 points manual edit rate\\n\",\n- \"- while median end-to-end time increase is <= +20%.\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n- \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n- \"\\n\",\n- \"sigclip_df = model_slice(df, \\\"SigCLIP\\\")\\n\",\n- \"dinov3_df = model_slice(df, \\\"DINOv3\\\")\\n\",\n- \"\\n\",\n- \"if sigclip_df.empty:\\n\",\n- \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n- \"else:\\n\",\n- \" print(\\\"SigCLIP:\\\")\\n\",\n- \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n- \"\\n\",\n- \"if dinov3_df.empty:\\n\",\n- \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n- \"else:\\n\",\n- \" print(\\\"DINOv3:\\\")\\n\",\n- \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\",\n- \"\\n\",\n- \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n- \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n- \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n- \"\\n\",\n- \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n- \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n- \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n- \"\\n\",\n- \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n- \"\\n\",\n- \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n- \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n- \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n- \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n- ]\n- }\n- ],\n- \"metadata\": {\n- \"kernelspec\": {\n- \"display_name\": \"Python 3\",\n- \"language\": \"python\",\n- \"name\": \"python3\"\n- },\n- \"language_info\": {\n- \"name\": \"python\",\n- \"version\": \"3\"\n- }\n- },\n- \"nbformat\": 4,\n- \"nbformat_minor\": 5\n-}\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "delete", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1,181 +0,0 @@\n-{\n- \"cells\": [\n- {\n- \"cell_type\": \"markdown\",\n- \"metadata\": {},\n- \"source\": [\n- \"# Material Hunters POC Evaluation (SigCLIP Baseline)\\n\",\n- \"\\n\",\n- \"This notebook tracks the decision test for the tap -> segment -> classify -> edit -> passport flow.\\n\",\n- \"\\n\",\n- \"Use this flow:\\n\",\n- \"1. Fill the CSV rows while testing (`status` must be `ok` or `fail`).\\n\",\n- \"2. Run the metrics cell to get go/no-go status.\\n\",\n- \"3. Repeat with `model_name = DINOv3` for comparison.\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"import pandas as pd\\n\",\n- \"from pathlib import Path\\n\",\n- \"\\n\",\n- \"THRESHOLDS = {\\n\",\n- \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n- \" \\\"top1_accuracy\\\": 0.70,\\n\",\n- \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n- \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n- \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n- \"}\\n\",\n- \"\\n\",\n- \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n- \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"# If you uploaded only the template, create a working copy once.\\n\",\n- \"if not CSV_PATH.exists() and TEMPLATE_PATH.exists():\\n\",\n- \" df_template = pd.read_csv(TEMPLATE_PATH)\\n\",\n- \" df_template.to_csv(CSV_PATH, index=False)\\n\",\n- \" print(f\\\"Created working CSV from template: {CSV_PATH}\\\")\\n\",\n- \"\\n\",\n- \"if not CSV_PATH.exists():\\n\",\n- \" raise FileNotFoundError(\\n\",\n- \" f\\\"Missing {CSV_PATH}. Upload poc_eval_sheet_template.csv and set CSV_PATH.\\\"\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \"df = pd.read_csv(CSV_PATH)\\n\",\n- \"print(f\\\"Loaded {len(df)} rows from {CSV_PATH}\\\")\\n\",\n- \"df.head(3)\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n- \" df = df_in.copy()\\n\",\n- \" df = df[df[\\\"status\\\"].notna()]\\n\",\n- \"\\n\",\n- \" if df.empty:\\n\",\n- \" raise ValueError(\\\"No rows with non-empty status. Fill status with ok/fail for tested samples.\\\")\\n\",\n- \"\\n\",\n- \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n- \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n- \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n- \" axis=1,\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \" metrics = {\\n\",\n- \" \\\"n_samples\\\": int(len(df)),\\n\",\n- \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n- \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n- \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n- \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n- \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n- \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n- \" }\\n\",\n- \"\\n\",\n- \" go_no_go = (\\n\",\n- \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n- \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n- \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n- \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n- \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n- \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"result = compute_metrics(df)\\n\",\n- \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n- \"print(\\\"Metrics:\\\")\\n\",\n- \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n- \" print(f\\\"- {k}: {v}\\\")\\n\",\n- \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n- ]\n- },\n- {\n- \"cell_type\": \"markdown\",\n- \"metadata\": {},\n- \"source\": [\n- \"## Compare SigCLIP vs DINOv3\\n\",\n- \"\\n\",\n- \"Use the same dataset and set `model_name` per row. Promotion rule for DINOv3:\\n\",\n- \"- +5 points top-1 accuracy OR\\n\",\n- \"- -10 points manual edit rate\\n\",\n- \"- while median end-to-end time increase is <= +20%.\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n- \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n- \"\\n\",\n- \"sigclip_df = model_slice(df, \\\"SigCLIP\\\")\\n\",\n- \"dinov3_df = model_slice(df, \\\"DINOv3\\\")\\n\",\n- \"\\n\",\n- \"if sigclip_df.empty:\\n\",\n- \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n- \"else:\\n\",\n- \" print(\\\"SigCLIP:\\\")\\n\",\n- \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n- \"\\n\",\n- \"if dinov3_df.empty:\\n\",\n- \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n- \"else:\\n\",\n- \" print(\\\"DINOv3:\\\")\\n\",\n- \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\",\n- \"\\n\",\n- \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n- \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n- \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n- \"\\n\",\n- \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n- \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n- \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n- \"\\n\",\n- \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n- \"\\n\",\n- \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n- \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n- \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n- \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n- ]\n- }\n- ],\n- \"metadata\": {\n- \"kernelspec\": {\n- \"display_name\": \"Python 3\",\n- \"language\": \"python\",\n- \"name\": \"python3\"\n- },\n- \"language_info\": {\n- \"name\": \"python\",\n- \"version\": \"3\"\n- }\n- },\n- \"nbformat\": 4,\n- \"nbformat_minor\": 5\n-}\n", + "additions": 0, + "deletions": 182 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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"prt_f23653f56001YOKKfiW4eYX3w3", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f23653925002NxNeeZN4M1IHsW" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_h93StCTl2Y1DFLQW6hRwgk1C", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Add File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"# Material Hunters POC Notebook\\n\",\n+ \"\\n\",\n+ \"This notebook runs a minimal end-to-end baseline:\\n\",\n+ \"- point-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+ \"- material classification with SigCLIP\\n\",\n+ \"- optional user edits\\n\",\n+ \"- passport JSON export\\n\",\n+ \"- evaluation row append + go/no-go metrics\\n\",\n+ \"\\n\",\n+ \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Run once in Colab if needed.\\n\",\n+ \"%pip -q install transformers accelerate pillow matplotlib pandas\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"from pathlib import Path\\n\",\n+ \"from datetime import datetime, timezone\\n\",\n+ \"import json\\n\",\n+ \"\\n\",\n+ \"import numpy as np\\n\",\n+ \"import pandas as pd\\n\",\n+ \"import torch\\n\",\n+ \"import matplotlib.pyplot as plt\\n\",\n+ \"from PIL import Image\\n\",\n+ \"\\n\",\n+ \"from transformers import (\\n\",\n+ \" SamModel,\\n\",\n+ \" SamProcessor,\\n\",\n+ \" AutoModel,\\n\",\n+ \" AutoProcessor,\\n\",\n+ \")\\n\",\n+ \"\\n\",\n+ \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n+ \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n+ \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n+ \"\\n\",\n+ \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n+ \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n+ \"PASSPORT_DIR = Path(\\\"/content/passports\\\")\\n\",\n+ \"PASSPORT_DIR.mkdir(parents=True, exist_ok=True)\\n\",\n+ \"\\n\",\n+ \"THRESHOLDS = {\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n+ \" \\\"top1_accuracy\\\": 0.70,\\n\",\n+ \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n+ \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n+ \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"MATERIAL_LABELS = [\\n\",\n+ \" \\\"concrete\\\",\\n\",\n+ \" \\\"brick\\\",\\n\",\n+ \" \\\"wood\\\",\\n\",\n+ \" \\\"steel\\\",\\n\",\n+ \" \\\"aluminum\\\",\\n\",\n+ \" \\\"glass\\\",\\n\",\n+ \" \\\"plastic\\\",\\n\",\n+ \" \\\"ceramic\\\",\\n\",\n+ \" \\\"gypsum\\\",\\n\",\n+ \" \\\"insulation wool\\\",\\n\",\n+ \"]\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def ensure_eval_csv(csv_path: Path, template_path: Path):\\n\",\n+ \" if csv_path.exists():\\n\",\n+ \" return\\n\",\n+ \" if not template_path.exists():\\n\",\n+ \" raise FileNotFoundError(\\n\",\n+ \" f\\\"Missing {template_path}. Upload poc_eval_sheet_template.csv first.\\\"\\n\",\n+ \" )\\n\",\n+ \" pd.read_csv(template_path).iloc[0:0].to_csv(csv_path, index=False)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def load_image(image_path: str) -> Image.Image:\\n\",\n+ \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n+ \" ys, xs = np.where(mask)\\n\",\n+ \" if len(xs) == 0 or len(ys) == 0:\\n\",\n+ \" return (0, 0, 0, 0)\\n\",\n+ \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n+ \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n+ \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n+ \" arr = np.array(image)\\n\",\n+ \" h, w = arr.shape[:2]\\n\",\n+ \" x, y, bw, bh = bbox_xywh\\n\",\n+ \" x0 = max(0, x - pad)\\n\",\n+ \" y0 = max(0, y - pad)\\n\",\n+ \" x1 = min(w, x + bw + pad)\\n\",\n+ \" y1 = min(h, y + bh + pad)\\n\",\n+ \"\\n\",\n+ \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n+ \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n+ \"\\n\",\n+ \" bg = np.full_like(crop, 255)\\n\",\n+ \" bg[crop_mask] = crop[crop_mask]\\n\",\n+ \" return Image.fromarray(bg)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n+ \" inputs = sam_processor(\\n\",\n+ \" image,\\n\",\n+ \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" )\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \"\\n\",\n+ \" with torch.no_grad():\\n\",\n+ \" outputs = sam_model(**inputs)\\n\",\n+ \"\\n\",\n+ \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n+ \" outputs.pred_masks.cpu(),\\n\",\n+ \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n+ \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n+ \" best_idx = int(np.argmax(scores))\\n\",\n+ \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n+ \" bbox = mask_bbox_xywh(mask)\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"mask\\\": mask,\\n\",\n+ \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n+ \" \\\"bbox_xywh\\\": bbox,\\n\",\n+ \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def classify_material_sigclip(image: Image.Image, labels, sigclip_model, sigclip_processor, top_k: int = 3):\\n\",\n+ \" inputs = sigclip_processor(\\n\",\n+ \" text=labels,\\n\",\n+ \" images=image,\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" padding=\\\"max_length\\\",\\n\",\n+ \" )\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \"\\n\",\n+ \" with torch.no_grad():\\n\",\n+ \" outputs = sigclip_model(**inputs)\\n\",\n+ \"\\n\",\n+ \" probs = torch.softmax(outputs.logits_per_image[0], dim=0)\\n\",\n+ \" top_k = min(top_k, len(labels))\\n\",\n+ \" vals, idxs = torch.topk(probs, k=top_k)\\n\",\n+ \"\\n\",\n+ \" top_labels = [labels[int(i)] for i in idxs]\\n\",\n+ \" top_scores = [float(v) for v in vals]\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n+ \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n+ \" \\\"pred_material_conf_top1\\\": top_scores[0],\\n\",\n+ \" \\\"topk_labels\\\": top_labels,\\n\",\n+ \" \\\"topk_scores\\\": top_scores,\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n+ \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n+ \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n+ \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n+ \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n+ \" return out_path\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n+ \" df_new = pd.DataFrame([row])\\n\",\n+ \" if not csv_path.exists():\\n\",\n+ \" df_new.to_csv(csv_path, index=False)\\n\",\n+ \" return\\n\",\n+ \" df_existing = pd.read_csv(csv_path)\\n\",\n+ \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n+ \" df_out.to_csv(csv_path, index=False)\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n+ \"print(\\\"Loading SAM model...\\\")\\n\",\n+ \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n+ \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n+ \"sam_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n+ \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n+ \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n+ \"sigclip_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Models ready.\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Set these values per sample.\\n\",\n+ \"sample_id = \\\"S002\\\"\\n\",\n+ \"image_id = \\\"IMG002\\\"\\n\",\n+ \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n+ \"tap_x = 300\\n\",\n+ \"tap_y = 220\\n\",\n+ \"ground_truth_object = \\\"window\\\"\\n\",\n+ \"ground_truth_material = \\\"glass\\\"\\n\",\n+ \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n+ \"\\n\",\n+ \"img = load_image(image_path)\\n\",\n+ \"plt.figure(figsize=(8, 6))\\n\",\n+ \"plt.imshow(img)\\n\",\n+ \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n+ \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n+ \"plt.axis(\\\"off\\\")\\n\",\n+ \"plt.show()\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"t0 = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"seg_start = pd.Timestamp.utcnow()\\n\",\n+ \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+ \"seg_end = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"crop = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"\\n\",\n+ \"clf_start = pd.Timestamp.utcnow()\\n\",\n+ \"clf = classify_material_sigclip(crop, MATERIAL_LABELS, sigclip_model, sigclip_processor, top_k=3)\\n\",\n+ \"clf_end = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"t1 = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n+ \"classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n+ \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n+ \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n+ \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n+ \"\\n\",\n+ \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n+ \"axes[0].imshow(img)\\n\",\n+ \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n+ \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+ \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n+ \"axes[0].axis(\\\"off\\\")\\n\",\n+ \"\\n\",\n+ \"axes[1].imshow(crop)\\n\",\n+ \"axes[1].set_title(\\\"Masked Crop\\\")\\n\",\n+ \"axes[1].axis(\\\"off\\\")\\n\",\n+ \"plt.tight_layout()\\n\",\n+ \"plt.show()\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Human review/edit step.\\n\",\n+ \"segmentation_accepted = 1 # 1 or 0\\n\",\n+ \"user_edited_material = 0 # 1 if user overrides prediction\\n\",\n+ \"user_final_material = clf[\\\"pred_material_top1\\\"]\\n\",\n+ \"edit_reason = \\\"\\\" # e.g. \\\"model confused glass vs plastic\\\"\\n\",\n+ \"status = \\\"ok\\\" # ok or fail\\n\",\n+ \"error_type = \\\"\\\"\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"passport = {\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap\\\": {\\\"x\\\": int(tap_x), \\\"y\\\": int(tap_y)},\\n\",\n+ \" \\\"object\\\": {\\n\",\n+ \" \\\"ground_truth\\\": ground_truth_object,\\n\",\n+ \" \\\"predicted\\\": pred_object,\\n\",\n+ \" },\\n\",\n+ \" \\\"material\\\": {\\n\",\n+ \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n+ \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n+ \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"user_final\\\": user_final_material,\\n\",\n+ \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n+ \" \\\"edit_reason\\\": edit_reason,\\n\",\n+ \" },\\n\",\n+ \" \\\"segmentation\\\": {\\n\",\n+ \" \\\"accepted\\\": bool(segmentation_accepted),\\n\",\n+ \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n+ \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n+ \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" },\\n\",\n+ \" \\\"status\\\": status,\\n\",\n+ \" \\\"error_type\\\": error_type,\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n+ \"print(f\\\"Saved passport: {passport_path}\\\")\\n\",\n+ \"\\n\",\n+ \"eval_row = {\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap_x\\\": tap_x,\\n\",\n+ \" \\\"tap_y\\\": tap_y,\\n\",\n+ \" \\\"ground_truth_object\\\": ground_truth_object,\\n\",\n+ \" \\\"ground_truth_material\\\": ground_truth_material,\\n\",\n+ \" \\\"pred_object\\\": pred_object,\\n\",\n+ \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n+ \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"segmentation_accepted\\\": segmentation_accepted,\\n\",\n+ \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n+ \" \\\"user_final_material\\\": user_final_material,\\n\",\n+ \" \\\"edit_reason\\\": edit_reason,\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" \\\"status\\\": status,\\n\",\n+ \" \\\"error_type\\\": error_type,\\n\",\n+ \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n+ \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n+ \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n+ \"}\\n\",\n+ \"append_eval_row(CSV_PATH, eval_row)\\n\",\n+ \"print(f\\\"Appended evaluation row to: {CSV_PATH}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n+ \" df = df_in.copy()\\n\",\n+ \" df = df[df[\\\"status\\\"].notna()]\\n\",\n+ \"\\n\",\n+ \" if df.empty:\\n\",\n+ \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n+ \"\\n\",\n+ \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n+ \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n+ \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n+ \" axis=1,\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" metrics = {\\n\",\n+ \" \\\"n_samples\\\": int(len(df)),\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n+ \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n+ \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n+ \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n+ \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n+ \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" go_no_go = (\\n\",\n+ \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n+ \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n+ \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n+ \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n+ \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n+ \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n+ \"result = compute_metrics(df_eval)\\n\",\n+ \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n+ \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n+ \" print(f\\\"- {k}: {v}\\\")\\n\",\n+ \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n+ \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n+ \"\\n\",\n+ \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n+ \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n+ \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n+ \"\\n\",\n+ \"if sigclip_df.empty:\\n\",\n+ \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"SigCLIP metrics:\\\")\\n\",\n+ \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if dinov3_df.empty:\\n\",\n+ \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"DINOv3 metrics:\\\")\\n\",\n+ \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n+ \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n+ \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n+ \"\\n\",\n+ \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n+ \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n+ \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n+ \"\\n\",\n+ \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n+ \"\\n\",\n+ \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n+ \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n+ \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n+ \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ ]\n+ }\n+ ],\n+ \"metadata\": {\n+ \"kernelspec\": {\n+ \"display_name\": \"Python 3\",\n+ \"language\": \"python\",\n+ \"name\": \"python3\"\n+ },\n+ \"language_info\": {\n+ \"name\": \"python\",\n+ \"version\": \"3\"\n+ }\n+ },\n+ \"nbformat\": 4,\n+ \"nbformat_minor\": 5\n+}\n*** End Patch" + }, + "output": "Success. Updated the following files:\nA home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -0,0 +1,487 @@\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"# Material Hunters POC Notebook\\n\",\n+ \"\\n\",\n+ \"This notebook runs a minimal end-to-end baseline:\\n\",\n+ \"- point-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+ \"- material classification with SigCLIP\\n\",\n+ \"- optional user edits\\n\",\n+ \"- passport JSON export\\n\",\n+ \"- evaluation row append + go/no-go metrics\\n\",\n+ \"\\n\",\n+ \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Run once in Colab if needed.\\n\",\n+ \"%pip -q install transformers accelerate pillow matplotlib pandas\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"from pathlib import Path\\n\",\n+ \"from datetime import datetime, timezone\\n\",\n+ \"import json\\n\",\n+ \"\\n\",\n+ \"import numpy as np\\n\",\n+ \"import pandas as pd\\n\",\n+ \"import torch\\n\",\n+ \"import matplotlib.pyplot as plt\\n\",\n+ \"from PIL import Image\\n\",\n+ \"\\n\",\n+ \"from transformers import (\\n\",\n+ \" SamModel,\\n\",\n+ \" SamProcessor,\\n\",\n+ \" AutoModel,\\n\",\n+ \" AutoProcessor,\\n\",\n+ \")\\n\",\n+ \"\\n\",\n+ \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n+ \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n+ \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n+ \"\\n\",\n+ \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n+ \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n+ \"PASSPORT_DIR = Path(\\\"/content/passports\\\")\\n\",\n+ \"PASSPORT_DIR.mkdir(parents=True, exist_ok=True)\\n\",\n+ \"\\n\",\n+ \"THRESHOLDS = {\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n+ \" \\\"top1_accuracy\\\": 0.70,\\n\",\n+ \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n+ \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n+ \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"MATERIAL_LABELS = [\\n\",\n+ \" \\\"concrete\\\",\\n\",\n+ \" \\\"brick\\\",\\n\",\n+ \" \\\"wood\\\",\\n\",\n+ \" \\\"steel\\\",\\n\",\n+ \" \\\"aluminum\\\",\\n\",\n+ \" \\\"glass\\\",\\n\",\n+ \" \\\"plastic\\\",\\n\",\n+ \" \\\"ceramic\\\",\\n\",\n+ \" \\\"gypsum\\\",\\n\",\n+ \" \\\"insulation wool\\\",\\n\",\n+ \"]\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def ensure_eval_csv(csv_path: Path, template_path: Path):\\n\",\n+ \" if csv_path.exists():\\n\",\n+ \" return\\n\",\n+ \" if not template_path.exists():\\n\",\n+ \" raise FileNotFoundError(\\n\",\n+ \" f\\\"Missing {template_path}. Upload poc_eval_sheet_template.csv first.\\\"\\n\",\n+ \" )\\n\",\n+ \" pd.read_csv(template_path).iloc[0:0].to_csv(csv_path, index=False)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def load_image(image_path: str) -> Image.Image:\\n\",\n+ \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n+ \" ys, xs = np.where(mask)\\n\",\n+ \" if len(xs) == 0 or len(ys) == 0:\\n\",\n+ \" return (0, 0, 0, 0)\\n\",\n+ \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n+ \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n+ \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n+ \" arr = np.array(image)\\n\",\n+ \" h, w = arr.shape[:2]\\n\",\n+ \" x, y, bw, bh = bbox_xywh\\n\",\n+ \" x0 = max(0, x - pad)\\n\",\n+ \" y0 = max(0, y - pad)\\n\",\n+ \" x1 = min(w, x + bw + pad)\\n\",\n+ \" y1 = min(h, y + bh + pad)\\n\",\n+ \"\\n\",\n+ \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n+ \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n+ \"\\n\",\n+ \" bg = np.full_like(crop, 255)\\n\",\n+ \" bg[crop_mask] = crop[crop_mask]\\n\",\n+ \" return Image.fromarray(bg)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n+ \" inputs = sam_processor(\\n\",\n+ \" image,\\n\",\n+ \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" )\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \"\\n\",\n+ \" with torch.no_grad():\\n\",\n+ \" outputs = sam_model(**inputs)\\n\",\n+ \"\\n\",\n+ \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n+ \" outputs.pred_masks.cpu(),\\n\",\n+ \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n+ \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n+ \" best_idx = int(np.argmax(scores))\\n\",\n+ \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n+ \" bbox = mask_bbox_xywh(mask)\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"mask\\\": mask,\\n\",\n+ \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n+ \" \\\"bbox_xywh\\\": bbox,\\n\",\n+ \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def classify_material_sigclip(image: Image.Image, labels, sigclip_model, sigclip_processor, top_k: int = 3):\\n\",\n+ \" inputs = sigclip_processor(\\n\",\n+ \" text=labels,\\n\",\n+ \" images=image,\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" padding=\\\"max_length\\\",\\n\",\n+ \" )\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \"\\n\",\n+ \" with torch.no_grad():\\n\",\n+ \" outputs = sigclip_model(**inputs)\\n\",\n+ \"\\n\",\n+ \" probs = torch.softmax(outputs.logits_per_image[0], dim=0)\\n\",\n+ \" top_k = min(top_k, len(labels))\\n\",\n+ \" vals, idxs = torch.topk(probs, k=top_k)\\n\",\n+ \"\\n\",\n+ \" top_labels = [labels[int(i)] for i in idxs]\\n\",\n+ \" top_scores = [float(v) for v in vals]\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n+ \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n+ \" \\\"pred_material_conf_top1\\\": top_scores[0],\\n\",\n+ \" \\\"topk_labels\\\": top_labels,\\n\",\n+ \" \\\"topk_scores\\\": top_scores,\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n+ \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n+ \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n+ \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n+ \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n+ \" return out_path\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n+ \" df_new = pd.DataFrame([row])\\n\",\n+ \" if not csv_path.exists():\\n\",\n+ \" df_new.to_csv(csv_path, index=False)\\n\",\n+ \" return\\n\",\n+ \" df_existing = pd.read_csv(csv_path)\\n\",\n+ \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n+ \" df_out.to_csv(csv_path, index=False)\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n+ \"print(\\\"Loading SAM model...\\\")\\n\",\n+ \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n+ \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n+ \"sam_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n+ \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n+ \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n+ \"sigclip_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Models ready.\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Set these values per sample.\\n\",\n+ \"sample_id = \\\"S002\\\"\\n\",\n+ \"image_id = \\\"IMG002\\\"\\n\",\n+ \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n+ \"tap_x = 300\\n\",\n+ \"tap_y = 220\\n\",\n+ \"ground_truth_object = \\\"window\\\"\\n\",\n+ \"ground_truth_material = \\\"glass\\\"\\n\",\n+ \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n+ \"\\n\",\n+ \"img = load_image(image_path)\\n\",\n+ \"plt.figure(figsize=(8, 6))\\n\",\n+ \"plt.imshow(img)\\n\",\n+ \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n+ \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n+ \"plt.axis(\\\"off\\\")\\n\",\n+ \"plt.show()\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"t0 = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"seg_start = pd.Timestamp.utcnow()\\n\",\n+ \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+ \"seg_end = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"crop = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"\\n\",\n+ \"clf_start = pd.Timestamp.utcnow()\\n\",\n+ \"clf = classify_material_sigclip(crop, MATERIAL_LABELS, sigclip_model, sigclip_processor, top_k=3)\\n\",\n+ \"clf_end = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"t1 = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n+ \"classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n+ \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n+ \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n+ \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n+ \"\\n\",\n+ \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n+ \"axes[0].imshow(img)\\n\",\n+ \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n+ \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+ \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n+ \"axes[0].axis(\\\"off\\\")\\n\",\n+ \"\\n\",\n+ \"axes[1].imshow(crop)\\n\",\n+ \"axes[1].set_title(\\\"Masked Crop\\\")\\n\",\n+ \"axes[1].axis(\\\"off\\\")\\n\",\n+ \"plt.tight_layout()\\n\",\n+ \"plt.show()\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Human review/edit step.\\n\",\n+ \"segmentation_accepted = 1 # 1 or 0\\n\",\n+ \"user_edited_material = 0 # 1 if user overrides prediction\\n\",\n+ \"user_final_material = clf[\\\"pred_material_top1\\\"]\\n\",\n+ \"edit_reason = \\\"\\\" # e.g. \\\"model confused glass vs plastic\\\"\\n\",\n+ \"status = \\\"ok\\\" # ok or fail\\n\",\n+ \"error_type = \\\"\\\"\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"passport = {\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap\\\": {\\\"x\\\": int(tap_x), \\\"y\\\": int(tap_y)},\\n\",\n+ \" \\\"object\\\": {\\n\",\n+ \" \\\"ground_truth\\\": ground_truth_object,\\n\",\n+ \" \\\"predicted\\\": pred_object,\\n\",\n+ \" },\\n\",\n+ \" \\\"material\\\": {\\n\",\n+ \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n+ \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n+ \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"user_final\\\": user_final_material,\\n\",\n+ \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n+ \" \\\"edit_reason\\\": edit_reason,\\n\",\n+ \" },\\n\",\n+ \" \\\"segmentation\\\": {\\n\",\n+ \" \\\"accepted\\\": bool(segmentation_accepted),\\n\",\n+ \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n+ \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n+ \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" },\\n\",\n+ \" \\\"status\\\": status,\\n\",\n+ \" \\\"error_type\\\": error_type,\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n+ \"print(f\\\"Saved passport: {passport_path}\\\")\\n\",\n+ \"\\n\",\n+ \"eval_row = {\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap_x\\\": tap_x,\\n\",\n+ \" \\\"tap_y\\\": tap_y,\\n\",\n+ \" \\\"ground_truth_object\\\": ground_truth_object,\\n\",\n+ \" \\\"ground_truth_material\\\": ground_truth_material,\\n\",\n+ \" \\\"pred_object\\\": pred_object,\\n\",\n+ \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n+ \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"segmentation_accepted\\\": segmentation_accepted,\\n\",\n+ \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n+ \" \\\"user_final_material\\\": user_final_material,\\n\",\n+ \" \\\"edit_reason\\\": edit_reason,\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" \\\"status\\\": status,\\n\",\n+ \" \\\"error_type\\\": error_type,\\n\",\n+ \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n+ \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n+ \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n+ \"}\\n\",\n+ \"append_eval_row(CSV_PATH, eval_row)\\n\",\n+ \"print(f\\\"Appended evaluation row to: {CSV_PATH}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n+ \" df = df_in.copy()\\n\",\n+ \" df = df[df[\\\"status\\\"].notna()]\\n\",\n+ \"\\n\",\n+ \" if df.empty:\\n\",\n+ \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n+ \"\\n\",\n+ \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n+ \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n+ \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n+ \" axis=1,\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" metrics = {\\n\",\n+ \" \\\"n_samples\\\": int(len(df)),\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n+ \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n+ \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n+ \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n+ \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n+ \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" go_no_go = (\\n\",\n+ \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n+ \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n+ \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n+ \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n+ \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n+ \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n+ \"result = compute_metrics(df_eval)\\n\",\n+ \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n+ \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n+ \" print(f\\\"- {k}: {v}\\\")\\n\",\n+ \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n+ \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n+ \"\\n\",\n+ \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n+ \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n+ \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n+ \"\\n\",\n+ \"if sigclip_df.empty:\\n\",\n+ \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"SigCLIP metrics:\\\")\\n\",\n+ \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if dinov3_df.empty:\\n\",\n+ \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"DINOv3 metrics:\\\")\\n\",\n+ \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n+ \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n+ \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n+ \"\\n\",\n+ \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n+ \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n+ \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n+ \"\\n\",\n+ \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n+ \"\\n\",\n+ \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n+ \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n+ \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n+ \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ ]\n+ }\n+ ],\n+ \"metadata\": {\n+ \"kernelspec\": {\n+ \"display_name\": \"Python 3\",\n+ \"language\": \"python\",\n+ \"name\": \"python3\"\n+ },\n+ \"language_info\": {\n+ \"name\": \"python\",\n+ \"version\": \"3\"\n+ }\n+ },\n+ \"nbformat\": 4,\n+ \"nbformat_minor\": 5\n+}\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "add", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -0,0 +1,487 @@\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"markdown\",\n+ \"metadata\": {},\n+ \"source\": [\n+ \"# Material Hunters POC Notebook\\n\",\n+ \"\\n\",\n+ \"This notebook runs a minimal end-to-end baseline:\\n\",\n+ \"- point-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+ \"- material classification with SigCLIP\\n\",\n+ \"- optional user edits\\n\",\n+ \"- passport JSON export\\n\",\n+ \"- evaluation row append + go/no-go metrics\\n\",\n+ \"\\n\",\n+ \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Run once in Colab if needed.\\n\",\n+ \"%pip -q install transformers accelerate pillow matplotlib pandas\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"from pathlib import Path\\n\",\n+ \"from datetime import datetime, timezone\\n\",\n+ \"import json\\n\",\n+ \"\\n\",\n+ \"import numpy as np\\n\",\n+ \"import pandas as pd\\n\",\n+ \"import torch\\n\",\n+ \"import matplotlib.pyplot as plt\\n\",\n+ \"from PIL import Image\\n\",\n+ \"\\n\",\n+ \"from transformers import (\\n\",\n+ \" SamModel,\\n\",\n+ \" SamProcessor,\\n\",\n+ \" AutoModel,\\n\",\n+ \" AutoProcessor,\\n\",\n+ \")\\n\",\n+ \"\\n\",\n+ \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n+ \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n+ \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n+ \"\\n\",\n+ \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n+ \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n+ \"PASSPORT_DIR = Path(\\\"/content/passports\\\")\\n\",\n+ \"PASSPORT_DIR.mkdir(parents=True, exist_ok=True)\\n\",\n+ \"\\n\",\n+ \"THRESHOLDS = {\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n+ \" \\\"top1_accuracy\\\": 0.70,\\n\",\n+ \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n+ \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n+ \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"MATERIAL_LABELS = [\\n\",\n+ \" \\\"concrete\\\",\\n\",\n+ \" \\\"brick\\\",\\n\",\n+ \" \\\"wood\\\",\\n\",\n+ \" \\\"steel\\\",\\n\",\n+ \" \\\"aluminum\\\",\\n\",\n+ \" \\\"glass\\\",\\n\",\n+ \" \\\"plastic\\\",\\n\",\n+ \" \\\"ceramic\\\",\\n\",\n+ \" \\\"gypsum\\\",\\n\",\n+ \" \\\"insulation wool\\\",\\n\",\n+ \"]\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def ensure_eval_csv(csv_path: Path, template_path: Path):\\n\",\n+ \" if csv_path.exists():\\n\",\n+ \" return\\n\",\n+ \" if not template_path.exists():\\n\",\n+ \" raise FileNotFoundError(\\n\",\n+ \" f\\\"Missing {template_path}. Upload poc_eval_sheet_template.csv first.\\\"\\n\",\n+ \" )\\n\",\n+ \" pd.read_csv(template_path).iloc[0:0].to_csv(csv_path, index=False)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def load_image(image_path: str) -> Image.Image:\\n\",\n+ \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n+ \" ys, xs = np.where(mask)\\n\",\n+ \" if len(xs) == 0 or len(ys) == 0:\\n\",\n+ \" return (0, 0, 0, 0)\\n\",\n+ \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n+ \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n+ \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n+ \" arr = np.array(image)\\n\",\n+ \" h, w = arr.shape[:2]\\n\",\n+ \" x, y, bw, bh = bbox_xywh\\n\",\n+ \" x0 = max(0, x - pad)\\n\",\n+ \" y0 = max(0, y - pad)\\n\",\n+ \" x1 = min(w, x + bw + pad)\\n\",\n+ \" y1 = min(h, y + bh + pad)\\n\",\n+ \"\\n\",\n+ \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n+ \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n+ \"\\n\",\n+ \" bg = np.full_like(crop, 255)\\n\",\n+ \" bg[crop_mask] = crop[crop_mask]\\n\",\n+ \" return Image.fromarray(bg)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n+ \" inputs = sam_processor(\\n\",\n+ \" image,\\n\",\n+ \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" )\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \"\\n\",\n+ \" with torch.no_grad():\\n\",\n+ \" outputs = sam_model(**inputs)\\n\",\n+ \"\\n\",\n+ \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n+ \" outputs.pred_masks.cpu(),\\n\",\n+ \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n+ \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n+ \" best_idx = int(np.argmax(scores))\\n\",\n+ \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n+ \" bbox = mask_bbox_xywh(mask)\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"mask\\\": mask,\\n\",\n+ \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n+ \" \\\"bbox_xywh\\\": bbox,\\n\",\n+ \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def classify_material_sigclip(image: Image.Image, labels, sigclip_model, sigclip_processor, top_k: int = 3):\\n\",\n+ \" inputs = sigclip_processor(\\n\",\n+ \" text=labels,\\n\",\n+ \" images=image,\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" padding=\\\"max_length\\\",\\n\",\n+ \" )\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \"\\n\",\n+ \" with torch.no_grad():\\n\",\n+ \" outputs = sigclip_model(**inputs)\\n\",\n+ \"\\n\",\n+ \" probs = torch.softmax(outputs.logits_per_image[0], dim=0)\\n\",\n+ \" top_k = min(top_k, len(labels))\\n\",\n+ \" vals, idxs = torch.topk(probs, k=top_k)\\n\",\n+ \"\\n\",\n+ \" top_labels = [labels[int(i)] for i in idxs]\\n\",\n+ \" top_scores = [float(v) for v in vals]\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n+ \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n+ \" \\\"pred_material_conf_top1\\\": top_scores[0],\\n\",\n+ \" \\\"topk_labels\\\": top_labels,\\n\",\n+ \" \\\"topk_scores\\\": top_scores,\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n+ \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n+ \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n+ \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n+ \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n+ \" return out_path\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n+ \" df_new = pd.DataFrame([row])\\n\",\n+ \" if not csv_path.exists():\\n\",\n+ \" df_new.to_csv(csv_path, index=False)\\n\",\n+ \" return\\n\",\n+ \" df_existing = pd.read_csv(csv_path)\\n\",\n+ \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n+ \" df_out.to_csv(csv_path, index=False)\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n+ \"print(\\\"Loading SAM model...\\\")\\n\",\n+ \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n+ \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n+ \"sam_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n+ \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n+ \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n+ \"sigclip_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Models ready.\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Set these values per sample.\\n\",\n+ \"sample_id = \\\"S002\\\"\\n\",\n+ \"image_id = \\\"IMG002\\\"\\n\",\n+ \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n+ \"tap_x = 300\\n\",\n+ \"tap_y = 220\\n\",\n+ \"ground_truth_object = \\\"window\\\"\\n\",\n+ \"ground_truth_material = \\\"glass\\\"\\n\",\n+ \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n+ \"\\n\",\n+ \"img = load_image(image_path)\\n\",\n+ \"plt.figure(figsize=(8, 6))\\n\",\n+ \"plt.imshow(img)\\n\",\n+ \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n+ \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n+ \"plt.axis(\\\"off\\\")\\n\",\n+ \"plt.show()\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"t0 = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"seg_start = pd.Timestamp.utcnow()\\n\",\n+ \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+ \"seg_end = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"crop = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"\\n\",\n+ \"clf_start = pd.Timestamp.utcnow()\\n\",\n+ \"clf = classify_material_sigclip(crop, MATERIAL_LABELS, sigclip_model, sigclip_processor, top_k=3)\\n\",\n+ \"clf_end = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"t1 = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \"segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n+ \"classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n+ \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n+ \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n+ \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n+ \"\\n\",\n+ \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n+ \"axes[0].imshow(img)\\n\",\n+ \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n+ \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+ \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n+ \"axes[0].axis(\\\"off\\\")\\n\",\n+ \"\\n\",\n+ \"axes[1].imshow(crop)\\n\",\n+ \"axes[1].set_title(\\\"Masked Crop\\\")\\n\",\n+ \"axes[1].axis(\\\"off\\\")\\n\",\n+ \"plt.tight_layout()\\n\",\n+ \"plt.show()\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# Human review/edit step.\\n\",\n+ \"segmentation_accepted = 1 # 1 or 0\\n\",\n+ \"user_edited_material = 0 # 1 if user overrides prediction\\n\",\n+ \"user_final_material = clf[\\\"pred_material_top1\\\"]\\n\",\n+ \"edit_reason = \\\"\\\" # e.g. \\\"model confused glass vs plastic\\\"\\n\",\n+ \"status = \\\"ok\\\" # ok or fail\\n\",\n+ \"error_type = \\\"\\\"\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"passport = {\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap\\\": {\\\"x\\\": int(tap_x), \\\"y\\\": int(tap_y)},\\n\",\n+ \" \\\"object\\\": {\\n\",\n+ \" \\\"ground_truth\\\": ground_truth_object,\\n\",\n+ \" \\\"predicted\\\": pred_object,\\n\",\n+ \" },\\n\",\n+ \" \\\"material\\\": {\\n\",\n+ \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n+ \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n+ \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"user_final\\\": user_final_material,\\n\",\n+ \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n+ \" \\\"edit_reason\\\": edit_reason,\\n\",\n+ \" },\\n\",\n+ \" \\\"segmentation\\\": {\\n\",\n+ \" \\\"accepted\\\": bool(segmentation_accepted),\\n\",\n+ \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n+ \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n+ \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" },\\n\",\n+ \" \\\"status\\\": status,\\n\",\n+ \" \\\"error_type\\\": error_type,\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n+ \"print(f\\\"Saved passport: {passport_path}\\\")\\n\",\n+ \"\\n\",\n+ \"eval_row = {\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap_x\\\": tap_x,\\n\",\n+ \" \\\"tap_y\\\": tap_y,\\n\",\n+ \" \\\"ground_truth_object\\\": ground_truth_object,\\n\",\n+ \" \\\"ground_truth_material\\\": ground_truth_material,\\n\",\n+ \" \\\"pred_object\\\": pred_object,\\n\",\n+ \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n+ \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"segmentation_accepted\\\": segmentation_accepted,\\n\",\n+ \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n+ \" \\\"user_final_material\\\": user_final_material,\\n\",\n+ \" \\\"edit_reason\\\": edit_reason,\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" \\\"status\\\": status,\\n\",\n+ \" \\\"error_type\\\": error_type,\\n\",\n+ \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n+ \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n+ \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n+ \"}\\n\",\n+ \"append_eval_row(CSV_PATH, eval_row)\\n\",\n+ \"print(f\\\"Appended evaluation row to: {CSV_PATH}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n+ \" df = df_in.copy()\\n\",\n+ \" df = df[df[\\\"status\\\"].notna()]\\n\",\n+ \"\\n\",\n+ \" if df.empty:\\n\",\n+ \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n+ \"\\n\",\n+ \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n+ \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n+ \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n+ \" axis=1,\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" metrics = {\\n\",\n+ \" \\\"n_samples\\\": int(len(df)),\\n\",\n+ \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n+ \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n+ \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n+ \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n+ \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n+ \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" go_no_go = (\\n\",\n+ \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n+ \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n+ \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n+ \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n+ \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n+ \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n+ \"result = compute_metrics(df_eval)\\n\",\n+ \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n+ \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n+ \" print(f\\\"- {k}: {v}\\\")\\n\",\n+ \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": null,\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n+ \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n+ \"\\n\",\n+ \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n+ \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n+ \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n+ \"\\n\",\n+ \"if sigclip_df.empty:\\n\",\n+ \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"SigCLIP metrics:\\\")\\n\",\n+ \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if dinov3_df.empty:\\n\",\n+ \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(\\\"DINOv3 metrics:\\\")\\n\",\n+ \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n+ \"\\n\",\n+ \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n+ \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n+ \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n+ \"\\n\",\n+ \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n+ \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n+ \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n+ \"\\n\",\n+ \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n+ \"\\n\",\n+ \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n+ \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n+ \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n+ \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ ]\n+ }\n+ ],\n+ \"metadata\": {\n+ \"kernelspec\": {\n+ \"display_name\": \"Python 3\",\n+ \"language\": \"python\",\n+ \"name\": 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I’ve now upgraded the notebook so it actually runs model inference, not just evaluation math.\n\n- Updated `poc_eval_notebook.ipynb:1` into an end-to-end baseline pipeline:\n - installs deps in Colab,\n - loads SAM (`facebook/sam-vit-base`) for point-prompt segmentation,\n - loads SigCLIP (`google/siglip-base-patch16-224`) for material classification,\n - visualizes mask + crop,\n - supports human edit fields,\n - exports passport JSON,\n - appends a row to the eval CSV,\n - computes go/no-go metrics.\n- It’s valid notebook JSON (verified parse locally).\n\nWhat you need to do in Colab now:\n1. Upload `poc_eval_sheet_template.csv` and at least one test image (e.g. `/content/images/example.jpg`).\n2. Run the notebook top-to-bottom.\n3. In the sample cell, set `image_path`, `tap_x`, `tap_y`, `ground_truth_*`, `sample_id`.\n4. 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+ } + }, + "id": "prt_f23739441001pZqpfxLxy28cTq", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f237389ea002U1lZC4ISrDRmf6" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_I0Lv2ubkEbYgMCiqFCxWTgeD", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb" + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n1: {\n2: \"cells\": [\n3: {\n4: \"cell_type\": \"markdown\",\n5: \"metadata\": {},\n6: \"source\": [\n7: \"# Material Hunters POC Notebook\\n\",\n8: \"\\n\",\n9: \"This notebook runs a minimal end-to-end baseline:\\n\",\n10: \"- point-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n11: \"- material classification with SigCLIP\\n\",\n12: \"- optional user edits\\n\",\n13: \"- passport JSON export\\n\",\n14: \"- evaluation row append + go/no-go metrics\\n\",\n15: \"\\n\",\n16: \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n17: ]\n18: },\n19: {\n20: \"cell_type\": \"code\",\n21: \"execution_count\": null,\n22: \"metadata\": {},\n23: \"outputs\": [],\n24: \"source\": [\n25: \"# Run once in Colab if needed.\\n\",\n26: \"%pip -q install transformers accelerate pillow matplotlib pandas\"\n27: ]\n28: },\n29: {\n30: \"cell_type\": \"code\",\n31: \"execution_count\": null,\n32: \"metadata\": {},\n33: \"outputs\": [],\n34: \"source\": [\n35: \"from pathlib import Path\\n\",\n36: \"from datetime import datetime, timezone\\n\",\n37: \"import json\\n\",\n38: \"\\n\",\n39: \"import numpy as np\\n\",\n40: \"import pandas as pd\\n\",\n41: \"import torch\\n\",\n42: \"import matplotlib.pyplot as plt\\n\",\n43: \"from PIL import Image\\n\",\n44: \"\\n\",\n45: \"from transformers import (\\n\",\n46: \" SamModel,\\n\",\n47: \" SamProcessor,\\n\",\n48: \" AutoModel,\\n\",\n49: \" AutoProcessor,\\n\",\n50: \")\\n\",\n51: \"\\n\",\n52: \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n53: \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n54: \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n55: \"\\n\",\n56: \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n57: \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n58: \"PASSPORT_DIR = Path(\\\"/content/passports\\\")\\n\",\n59: \"PASSPORT_DIR.mkdir(parents=True, exist_ok=True)\\n\",\n60: \"\\n\",\n61: \"THRESHOLDS = {\\n\",\n62: \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n63: \" \\\"top1_accuracy\\\": 0.70,\\n\",\n64: \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n65: \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n66: \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n67: \"}\\n\",\n68: \"\\n\",\n69: \"MATERIAL_LABELS = [\\n\",\n70: \" \\\"concrete\\\",\\n\",\n71: \" \\\"brick\\\",\\n\",\n72: \" \\\"wood\\\",\\n\",\n73: \" \\\"steel\\\",\\n\",\n74: \" \\\"aluminum\\\",\\n\",\n75: \" \\\"glass\\\",\\n\",\n76: \" \\\"plastic\\\",\\n\",\n77: \" \\\"ceramic\\\",\\n\",\n78: \" \\\"gypsum\\\",\\n\",\n79: \" \\\"insulation wool\\\",\\n\",\n80: \"]\"\n81: ]\n82: },\n83: {\n84: \"cell_type\": \"code\",\n85: \"execution_count\": null,\n86: \"metadata\": {},\n87: \"outputs\": [],\n88: \"source\": [\n89: \"def ensure_eval_csv(csv_path: Path, template_path: Path):\\n\",\n90: \" if csv_path.exists():\\n\",\n91: \" return\\n\",\n92: \" if not template_path.exists():\\n\",\n93: \" raise FileNotFoundError(\\n\",\n94: \" f\\\"Missing {template_path}. Upload poc_eval_sheet_template.csv first.\\\"\\n\",\n95: \" )\\n\",\n96: \" pd.read_csv(template_path).iloc[0:0].to_csv(csv_path, index=False)\\n\",\n97: \"\\n\",\n98: \"\\n\",\n99: \"def load_image(image_path: str) -> Image.Image:\\n\",\n100: \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n101: \"\\n\",\n102: \"\\n\",\n103: \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n104: \" ys, xs = np.where(mask)\\n\",\n105: \" if len(xs) == 0 or len(ys) == 0:\\n\",\n106: \" return (0, 0, 0, 0)\\n\",\n107: \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n108: \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n109: \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n110: \"\\n\",\n111: \"\\n\",\n112: \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n113: \" arr = np.array(image)\\n\",\n114: \" h, w = arr.shape[:2]\\n\",\n115: \" x, y, bw, bh = bbox_xywh\\n\",\n116: \" x0 = max(0, x - pad)\\n\",\n117: \" y0 = max(0, y - pad)\\n\",\n118: \" x1 = min(w, x + bw + pad)\\n\",\n119: \" y1 = min(h, y + bh + pad)\\n\",\n120: \"\\n\",\n121: \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n122: \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n123: \"\\n\",\n124: \" bg = np.full_like(crop, 255)\\n\",\n125: \" bg[crop_mask] = crop[crop_mask]\\n\",\n126: \" return Image.fromarray(bg)\\n\",\n127: \"\\n\",\n128: \"\\n\",\n129: \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n130: \" inputs = sam_processor(\\n\",\n131: \" image,\\n\",\n132: \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n133: \" return_tensors=\\\"pt\\\",\\n\",\n134: \" )\\n\",\n135: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n136: \"\\n\",\n137: \" with torch.no_grad():\\n\",\n138: \" outputs = sam_model(**inputs)\\n\",\n139: \"\\n\",\n140: \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n141: \" outputs.pred_masks.cpu(),\\n\",\n142: \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n143: \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n144: \" )\\n\",\n145: \"\\n\",\n146: \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n147: \" best_idx = int(np.argmax(scores))\\n\",\n148: \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n149: \" bbox = mask_bbox_xywh(mask)\\n\",\n150: \"\\n\",\n151: \" return {\\n\",\n152: \" \\\"mask\\\": mask,\\n\",\n153: \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n154: \" \\\"bbox_xywh\\\": bbox,\\n\",\n155: \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n156: \" }\\n\",\n157: \"\\n\",\n158: \"\\n\",\n159: \"def classify_material_sigclip(image: Image.Image, labels, sigclip_model, sigclip_processor, top_k: int = 3):\\n\",\n160: \" inputs = sigclip_processor(\\n\",\n161: \" text=labels,\\n\",\n162: \" images=image,\\n\",\n163: \" return_tensors=\\\"pt\\\",\\n\",\n164: \" padding=\\\"max_length\\\",\\n\",\n165: \" )\\n\",\n166: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n167: \"\\n\",\n168: \" with torch.no_grad():\\n\",\n169: \" outputs = sigclip_model(**inputs)\\n\",\n170: \"\\n\",\n171: \" probs = torch.softmax(outputs.logits_per_image[0], dim=0)\\n\",\n172: \" top_k = min(top_k, len(labels))\\n\",\n173: \" vals, idxs = torch.topk(probs, k=top_k)\\n\",\n174: \"\\n\",\n175: \" top_labels = [labels[int(i)] for i in idxs]\\n\",\n176: \" top_scores = [float(v) for v in vals]\\n\",\n177: \"\\n\",\n178: \" return {\\n\",\n179: \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n180: \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n181: \" \\\"pred_material_conf_top1\\\": top_scores[0],\\n\",\n182: \" \\\"topk_labels\\\": top_labels,\\n\",\n183: \" \\\"topk_scores\\\": top_scores,\\n\",\n184: \" }\\n\",\n185: \"\\n\",\n186: \"\\n\",\n187: \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n188: \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n189: \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n190: \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n191: \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n192: \" return out_path\\n\",\n193: \"\\n\",\n194: \"\\n\",\n195: \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n196: \" df_new = pd.DataFrame([row])\\n\",\n197: \" if not csv_path.exists():\\n\",\n198: \" df_new.to_csv(csv_path, index=False)\\n\",\n199: \" return\\n\",\n200: \" df_existing = pd.read_csv(csv_path)\\n\",\n201: \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n202: \" df_out.to_csv(csv_path, index=False)\"\n203: ]\n204: },\n205: {\n206: \"cell_type\": \"code\",\n207: \"execution_count\": null,\n208: \"metadata\": {},\n209: \"outputs\": [],\n210: \"source\": [\n211: \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n212: \"\\n\",\n213: \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n214: \"print(\\\"Loading SAM model...\\\")\\n\",\n215: \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n216: \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n217: \"sam_model.eval()\\n\",\n218: \"\\n\",\n219: \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n220: \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n221: \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n222: \"sigclip_model.eval()\\n\",\n223: \"\\n\",\n224: \"print(\\\"Models ready.\\\")\"\n225: ]\n226: },\n227: {\n228: \"cell_type\": \"code\",\n229: \"execution_count\": null,\n230: \"metadata\": {},\n231: \"outputs\": [],\n232: \"source\": [\n233: \"# Set these values per sample.\\n\",\n234: \"sample_id = \\\"S002\\\"\\n\",\n235: \"image_id = \\\"IMG002\\\"\\n\",\n236: \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n237: \"tap_x = 300\\n\",\n238: \"tap_y = 220\\n\",\n239: \"ground_truth_object = \\\"window\\\"\\n\",\n240: \"ground_truth_material = \\\"glass\\\"\\n\",\n241: \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n242: \"\\n\",\n243: \"img = load_image(image_path)\\n\",\n244: \"plt.figure(figsize=(8, 6))\\n\",\n245: \"plt.imshow(img)\\n\",\n246: \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n247: \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n248: \"plt.axis(\\\"off\\\")\\n\",\n249: \"plt.show()\"\n250: ]\n251: },\n252: {\n253: \"cell_type\": \"code\",\n254: \"execution_count\": null,\n255: \"metadata\": {},\n256: \"outputs\": [],\n257: \"source\": [\n258: \"t0 = pd.Timestamp.utcnow()\\n\",\n259: \"\\n\",\n260: \"seg_start = pd.Timestamp.utcnow()\\n\",\n261: \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n262: \"seg_end = pd.Timestamp.utcnow()\\n\",\n263: \"\\n\",\n264: \"crop = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n265: \"\\n\",\n266: \"clf_start = pd.Timestamp.utcnow()\\n\",\n267: \"clf = classify_material_sigclip(crop, MATERIAL_LABELS, sigclip_model, sigclip_processor, top_k=3)\\n\",\n268: \"clf_end = pd.Timestamp.utcnow()\\n\",\n269: \"\\n\",\n270: \"t1 = pd.Timestamp.utcnow()\\n\",\n271: \"\\n\",\n272: \"segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n273: \"classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n274: \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n275: \"\\n\",\n276: \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n277: \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n278: \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n279: \"\\n\",\n280: \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n281: \"axes[0].imshow(img)\\n\",\n282: \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n283: \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n284: \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n285: \"axes[0].axis(\\\"off\\\")\\n\",\n286: \"\\n\",\n287: \"axes[1].imshow(crop)\\n\",\n288: \"axes[1].set_title(\\\"Masked Crop\\\")\\n\",\n289: \"axes[1].axis(\\\"off\\\")\\n\",\n290: \"plt.tight_layout()\\n\",\n291: \"plt.show()\"\n292: ]\n293: },\n294: {\n295: \"cell_type\": \"code\",\n296: \"execution_count\": null,\n297: \"metadata\": {},\n298: \"outputs\": [],\n299: \"source\": [\n300: \"# Human review/edit step.\\n\",\n301: \"segmentation_accepted = 1 # 1 or 0\\n\",\n302: \"user_edited_material = 0 # 1 if user overrides prediction\\n\",\n303: \"user_final_material = clf[\\\"pred_material_top1\\\"]\\n\",\n304: \"edit_reason = \\\"\\\" # e.g. \\\"model confused glass vs plastic\\\"\\n\",\n305: \"status = \\\"ok\\\" # ok or fail\\n\",\n306: \"error_type = \\\"\\\"\"\n307: ]\n308: },\n309: {\n310: \"cell_type\": \"code\",\n311: \"execution_count\": null,\n312: \"metadata\": {},\n313: \"outputs\": [],\n314: \"source\": [\n315: \"passport = {\\n\",\n316: \" \\\"sample_id\\\": sample_id,\\n\",\n317: \" \\\"image_id\\\": image_id,\\n\",\n318: \" \\\"image_path\\\": image_path,\\n\",\n319: \" \\\"tap\\\": {\\\"x\\\": int(tap_x), \\\"y\\\": int(tap_y)},\\n\",\n320: \" \\\"object\\\": {\\n\",\n321: \" \\\"ground_truth\\\": ground_truth_object,\\n\",\n322: \" \\\"predicted\\\": pred_object,\\n\",\n323: \" },\\n\",\n324: \" \\\"material\\\": {\\n\",\n325: \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n326: \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n327: \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n328: \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n329: \" \\\"user_final\\\": user_final_material,\\n\",\n330: \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n331: \" \\\"edit_reason\\\": edit_reason,\\n\",\n332: \" },\\n\",\n333: \" \\\"segmentation\\\": {\\n\",\n334: \" \\\"accepted\\\": bool(segmentation_accepted),\\n\",\n335: \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n336: \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n337: \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n338: \" },\\n\",\n339: \" \\\"timing\\\": {\\n\",\n340: \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n341: \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n342: \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n343: \" },\\n\",\n344: \" \\\"status\\\": status,\\n\",\n345: \" \\\"error_type\\\": error_type,\\n\",\n346: \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n347: \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n348: \"}\\n\",\n349: \"\\n\",\n350: \"passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n351: \"print(f\\\"Saved passport: {passport_path}\\\")\\n\",\n352: \"\\n\",\n353: \"eval_row = {\\n\",\n354: \" \\\"sample_id\\\": sample_id,\\n\",\n355: \" \\\"image_id\\\": image_id,\\n\",\n356: \" \\\"image_path\\\": image_path,\\n\",\n357: \" \\\"tap_x\\\": tap_x,\\n\",\n358: \" \\\"tap_y\\\": tap_y,\\n\",\n359: \" \\\"ground_truth_object\\\": ground_truth_object,\\n\",\n360: \" \\\"ground_truth_material\\\": ground_truth_material,\\n\",\n361: \" \\\"pred_object\\\": pred_object,\\n\",\n362: \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n363: \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n364: \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n365: \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n366: \" \\\"segmentation_accepted\\\": segmentation_accepted,\\n\",\n367: \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n368: \" \\\"user_final_material\\\": user_final_material,\\n\",\n369: \" \\\"edit_reason\\\": edit_reason,\\n\",\n370: \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n371: \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n372: \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n373: \" \\\"status\\\": status,\\n\",\n374: \" \\\"error_type\\\": error_type,\\n\",\n375: \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n376: \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n377: \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n378: \"}\\n\",\n379: \"append_eval_row(CSV_PATH, eval_row)\\n\",\n380: \"print(f\\\"Appended evaluation row to: {CSV_PATH}\\\")\"\n381: ]\n382: },\n383: {\n384: \"cell_type\": \"code\",\n385: \"execution_count\": null,\n386: \"metadata\": {},\n387: \"outputs\": [],\n388: \"source\": [\n389: \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n390: \" df = df_in.copy()\\n\",\n391: \" df = df[df[\\\"status\\\"].notna()]\\n\",\n392: \"\\n\",\n393: \" if df.empty:\\n\",\n394: \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n395: \"\\n\",\n396: \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n397: \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n398: \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n399: \" axis=1,\\n\",\n400: \" )\\n\",\n401: \"\\n\",\n402: \" metrics = {\\n\",\n403: \" \\\"n_samples\\\": int(len(df)),\\n\",\n404: \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n405: \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n406: \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n407: \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n408: \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n409: \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n410: \" }\\n\",\n411: \"\\n\",\n412: \" go_no_go = (\\n\",\n413: \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n414: \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n415: \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n416: \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n417: \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n418: \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n419: \" )\\n\",\n420: \"\\n\",\n421: \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n422: \"\\n\",\n423: \"\\n\",\n424: \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n425: \"result = compute_metrics(df_eval)\\n\",\n426: \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n427: \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n428: \" print(f\\\"- {k}: {v}\\\")\\n\",\n429: \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n430: ]\n431: },\n432: {\n433: \"cell_type\": \"code\",\n434: \"execution_count\": null,\n435: \"metadata\": {},\n436: \"outputs\": [],\n437: \"source\": [\n438: \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n439: \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n440: \"\\n\",\n441: \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n442: \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n443: \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n444: \"\\n\",\n445: \"if sigclip_df.empty:\\n\",\n446: \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n447: \"else:\\n\",\n448: \" print(\\\"SigCLIP metrics:\\\")\\n\",\n449: \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n450: \"\\n\",\n451: \"if dinov3_df.empty:\\n\",\n452: \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n453: \"else:\\n\",\n454: \" print(\\\"DINOv3 metrics:\\\")\\n\",\n455: \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n456: \"\\n\",\n457: \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n458: \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n459: \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n460: \"\\n\",\n461: \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n462: \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n463: \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n464: \"\\n\",\n465: \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n466: \"\\n\",\n467: \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n468: \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n469: \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n470: \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n471: ]\n472: }\n473: ],\n474: \"metadata\": {\n475: \"kernelspec\": {\n476: \"display_name\": \"Python 3\",\n477: \"language\": \"python\",\n478: \"name\": \"python3\"\n479: },\n480: \"language_info\": {\n481: \"name\": \"python\",\n482: \"version\": \"3\"\n483: }\n484: },\n485: \"nbformat\": 4,\n486: \"nbformat_minor\": 5\n487: }\n\n(End of file - total 487 lines)\n</content>", + "metadata": { + "preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Material Hunters POC Notebook\\n\",\n \"\\n\",\n \"This notebook runs a minimal end-to-end baseline:\\n\",\n \"- point-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n \"- material classification with SigCLIP\\n\",\n \"- optional user edits\\n\",\n \"- passport JSON export\\n\",\n \"- evaluation row append + go/no-go metrics\\n\",\n \"\\n\",\n \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",", + "truncated": false, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Material Hunters POC Notebook\\n\",\n \"\\n\",\n \"This notebook runs a minimal end-to-end baseline:\\n\",\n \"- point-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n \"- material classification with SigCLIP\\n\",\n \"- optional user edits\\n\",\n \"- passport JSON export\\n\",\n \"- evaluation row append + go/no-go metrics\\n\",\n \"\\n\",\n \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run once in Colab if needed.\\n\",\n \"%pip -q install transformers accelerate pillow matplotlib pandas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n \"from datetime import datetime, timezone\\n\",\n \"import json\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n \"from transformers import (\\n\",\n \" SamModel,\\n\",\n \" SamProcessor,\\n\",\n \" AutoModel,\\n\",\n \" AutoProcessor,\\n\",\n \")\\n\",\n \"\\n\",\n \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n \"\\n\",\n \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n \"PASSPORT_DIR = Path(\\\"/content/passports\\\")\\n\",\n \"PASSPORT_DIR.mkdir(parents=True, exist_ok=True)\\n\",\n \"\\n\",\n \"THRESHOLDS = {\\n\",\n \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n \" \\\"top1_accuracy\\\": 0.70,\\n\",\n \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n \"}\\n\",\n \"\\n\",\n \"MATERIAL_LABELS = [\\n\",\n \" \\\"concrete\\\",\\n\",\n \" \\\"brick\\\",\\n\",\n \" \\\"wood\\\",\\n\",\n \" \\\"steel\\\",\\n\",\n \" \\\"aluminum\\\",\\n\",\n \" \\\"glass\\\",\\n\",\n \" \\\"plastic\\\",\\n\",\n \" \\\"ceramic\\\",\\n\",\n \" \\\"gypsum\\\",\\n\",\n \" \\\"insulation wool\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def ensure_eval_csv(csv_path: Path, template_path: Path):\\n\",\n \" if csv_path.exists():\\n\",\n \" return\\n\",\n \" if not template_path.exists():\\n\",\n \" raise FileNotFoundError(\\n\",\n \" f\\\"Missing {template_path}. Upload poc_eval_sheet_template.csv first.\\\"\\n\",\n \" )\\n\",\n \" pd.read_csv(template_path).iloc[0:0].to_csv(csv_path, index=False)\\n\",\n \"\\n\",\n \"\\n\",\n \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n \"\\n\",\n \"\\n\",\n \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n \" arr = np.array(image)\\n\",\n \" h, w = arr.shape[:2]\\n\",\n \" x, y, bw, bh = bbox_xywh\\n\",\n \" x0 = max(0, x - pad)\\n\",\n \" y0 = max(0, y - pad)\\n\",\n \" x1 = min(w, x + bw + pad)\\n\",\n \" y1 = min(h, y + bh + pad)\\n\",\n \"\\n\",\n \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n \"\\n\",\n \" bg = np.full_like(crop, 255)\\n\",\n \" bg[crop_mask] = crop[crop_mask]\\n\",\n \" return Image.fromarray(bg)\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sam_model(**inputs)\\n\",\n \"\\n\",\n \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n \" outputs.pred_masks.cpu(),\\n\",\n \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n \" )\\n\",\n \"\\n\",\n \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n \" best_idx = int(np.argmax(scores))\\n\",\n \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n \" bbox = mask_bbox_xywh(mask)\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def classify_material_sigclip(image: Image.Image, labels, sigclip_model, sigclip_processor, top_k: int = 3):\\n\",\n \" inputs = sigclip_processor(\\n\",\n \" text=labels,\\n\",\n \" images=image,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" padding=\\\"max_length\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sigclip_model(**inputs)\\n\",\n \"\\n\",\n \" probs = torch.softmax(outputs.logits_per_image[0], dim=0)\\n\",\n \" top_k = min(top_k, len(labels))\\n\",\n \" vals, idxs = torch.topk(probs, k=top_k)\\n\",\n \"\\n\",\n \" top_labels = [labels[int(i)] for i in idxs]\\n\",\n \" top_scores = [float(v) for v in vals]\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n \" \\\"pred_material_conf_top1\\\": top_scores[0],\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n \" \\\"topk_scores\\\": top_scores,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n \" return out_path\\n\",\n \"\\n\",\n \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n \" df_out.to_csv(csv_path, index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n \"print(\\\"Loading SAM model...\\\")\\n\",\n \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n \"sam_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set these values per sample.\\n\",\n \"sample_id = \\\"S002\\\"\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"tap_x = 300\\n\",\n \"tap_y = 220\\n\",\n \"ground_truth_object = \\\"window\\\"\\n\",\n \"ground_truth_material = \\\"glass\\\"\\n\",\n \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n \"plt.figure(figsize=(8, 6))\\n\",\n \"plt.imshow(img)\\n\",\n \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \"seg_start = pd.Timestamp.utcnow()\\n\",\n \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n \"seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \"crop = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \"\\n\",\n \"clf_start = pd.Timestamp.utcnow()\\n\",\n \"clf = classify_material_sigclip(crop, MATERIAL_LABELS, sigclip_model, sigclip_processor, top_k=3)\\n\",\n \"clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \"t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \"segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n \"classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n \"\\n\",\n \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \"axes[0].imshow(img)\\n\",\n \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n \"axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n \"axes[1].imshow(crop)\\n\",\n \"axes[1].set_title(\\\"Masked Crop\\\")\\n\",\n \"axes[1].axis(\\\"off\\\")\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Human review/edit step.\\n\",\n \"segmentation_accepted = 1 # 1 or 0\\n\",\n \"user_edited_material = 0 # 1 if user overrides prediction\\n\",\n \"user_final_material = clf[\\\"pred_material_top1\\\"]\\n\",\n \"edit_reason = \\\"\\\" # e.g. \\\"model confused glass vs plastic\\\"\\n\",\n \"status = \\\"ok\\\" # ok or fail\\n\",\n \"error_type = \\\"\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"passport = {\\n\",\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n \" \\\"tap\\\": {\\\"x\\\": int(tap_x), \\\"y\\\": int(tap_y)},\\n\",\n \" \\\"object\\\": {\\n\",\n \" \\\"ground_truth\\\": ground_truth_object,\\n\",\n \" \\\"predicted\\\": pred_object,\\n\",\n \" },\\n\",\n \" \\\"material\\\": {\\n\",\n \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n \" \\\"user_final\\\": user_final_material,\\n\",\n \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n \" \\\"edit_reason\\\": edit_reason,\\n\",\n \" },\\n\",\n \" \\\"segmentation\\\": {\\n\",\n \" \\\"accepted\\\": bool(segmentation_accepted),\\n\",\n \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n \" },\\n\",\n \" \\\"timing\\\": {\\n\",\n \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n \" },\\n\",\n \" \\\"status\\\": status,\\n\",\n \" \\\"error_type\\\": error_type,\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n \"}\\n\",\n \"\\n\",\n \"passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n \"print(f\\\"Saved passport: {passport_path}\\\")\\n\",\n \"\\n\",\n \"eval_row = {\\n\",\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n \" \\\"ground_truth_object\\\": ground_truth_object,\\n\",\n \" \\\"ground_truth_material\\\": ground_truth_material,\\n\",\n \" \\\"pred_object\\\": pred_object,\\n\",\n \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n \" \\\"segmentation_accepted\\\": segmentation_accepted,\\n\",\n \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n \" \\\"user_final_material\\\": user_final_material,\\n\",\n \" \\\"edit_reason\\\": edit_reason,\\n\",\n \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n \" \\\"status\\\": status,\\n\",\n \" \\\"error_type\\\": error_type,\\n\",\n \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n \"}\\n\",\n \"append_eval_row(CSV_PATH, eval_row)\\n\",\n \"print(f\\\"Appended evaluation row to: {CSV_PATH}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n \" df = df_in.copy()\\n\",\n \" df = df[df[\\\"status\\\"].notna()]\\n\",\n \"\\n\",\n \" if df.empty:\\n\",\n \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n \"\\n\",\n \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n \" axis=1,\\n\",\n \" )\\n\",\n \"\\n\",\n \" metrics = {\\n\",\n \" \\\"n_samples\\\": int(len(df)),\\n\",\n \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n \" }\\n\",\n \"\\n\",\n \" go_no_go = (\\n\",\n \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n \" )\\n\",\n \"\\n\",\n \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n \"\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"result = compute_metrics(df_eval)\\n\",\n \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n \" print(f\\\"- {k}: {v}\\\")\\n\",\n \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n \"\\n\",\n \"if sigclip_df.empty:\\n\",\n \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"SigCLIP metrics:\\\")\\n\",\n \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n \"\\n\",\n \"if dinov3_df.empty:\\n\",\n \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"DINOv3 metrics:\\\")\\n\",\n \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n \"\\n\",\n \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n \"\\n\",\n \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - 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\\\"steel\\\": [\\\"steel\\\", \\\"iron metal\\\"],\\n\",\n+ \" \\\"aluminum\\\": [\\\"aluminum\\\", \\\"aluminium\\\"],\\n\",\n+ \" \\\"glass\\\": [\\\"glass\\\", \\\"window glass\\\"],\\n\",\n+ \" \\\"plastic\\\": [\\\"plastic\\\", \\\"polymer\\\", \\\"pvc\\\"],\\n\",\n+ \" \\\"ceramic\\\": [\\\"ceramic\\\", \\\"tile\\\", \\\"porcelain\\\"],\\n\",\n+ \" \\\"gypsum\\\": [\\\"gypsum\\\", \\\"drywall\\\", \\\"plasterboard\\\"],\\n\",\n+ \" \\\"insulation wool\\\": [\\\"insulation wool\\\", \\\"mineral wool\\\", \\\"fiberglass insulation\\\"],\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"PROMPT_TEMPLATES = [\\n\",\n+ \" \\\"a close-up photo of {material}\\\",\\n\",\n+ \" \\\"a construction material made of {material}\\\",\\n\",\n+ \" \\\"the surface texture is {material}\\\",\\n\",\n+ \"]\\n\",\n+ \"\\n\",\n+ \"OBJECT_MATERIAL_PRIORS = {\\n\",\n+ \" \\\"window\\\": [\\\"glass\\\", \\\"aluminum\\\", \\\"wood\\\", \\\"plastic\\\", \\\"steel\\\"],\\n\",\n+ \" \\\"door\\\": [\\\"wood\\\", \\\"steel\\\", \\\"aluminum\\\", \\\"glass\\\", \\\"plastic\\\"],\\n\",\n+ \" \\\"wall\\\": [\\\"concrete\\\", \\\"brick\\\", \\\"gypsum\\\", \\\"wood\\\"],\\n\",\n+ \" \\\"floor\\\": [\\\"concrete\\\", \\\"wood\\\", \\\"ceramic\\\", \\\"plastic\\\"],\\n\",\n+ \" \\\"roof\\\": [\\\"steel\\\", \\\"wood\\\", \\\"ceramic\\\", \\\"insulation wool\\\"],\\n\",\n+ \" \\\"pipe\\\": [\\\"steel\\\", \\\"plastic\\\", \\\"aluminum\\\"],\\n\",\n+ \" \\\"beam\\\": [\\\"steel\\\", \\\"wood\\\", \\\"concrete\\\"],\\n\",\n+ \" \\\"column\\\": [\\\"concrete\\\", \\\"steel\\\", \\\"brick\\\", \\\"wood\\\"],\\n\",\n+ \"}\"\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -65,20 +65,37 @@\n \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n \"}\\n\",\n \"\\n\",\n- \"MATERIAL_LABELS = [\\n\",\n- \" \\\"concrete\\\",\\n\",\n- \" \\\"brick\\\",\\n\",\n- \" \\\"wood\\\",\\n\",\n- \" \\\"steel\\\",\\n\",\n- \" \\\"aluminum\\\",\\n\",\n- \" \\\"glass\\\",\\n\",\n- \" \\\"plastic\\\",\\n\",\n- \" \\\"ceramic\\\",\\n\",\n- \" \\\"gypsum\\\",\\n\",\n- \" \\\"insulation wool\\\",\\n\",\n- \"]\"\n+ \"MATERIAL_TAXONOMY = {\\n\",\n+ \" \\\"concrete\\\": [\\\"concrete\\\", \\\"cement\\\"],\\n\",\n+ \" \\\"brick\\\": [\\\"brick\\\", \\\"masonry brick\\\"],\\n\",\n+ \" \\\"wood\\\": [\\\"wood\\\", \\\"timber\\\", \\\"plywood\\\"],\\n\",\n+ \" \\\"steel\\\": [\\\"steel\\\", \\\"iron metal\\\"],\\n\",\n+ \" \\\"aluminum\\\": [\\\"aluminum\\\", \\\"aluminium\\\"],\\n\",\n+ \" \\\"glass\\\": [\\\"glass\\\", \\\"window glass\\\"],\\n\",\n+ \" \\\"plastic\\\": [\\\"plastic\\\", \\\"polymer\\\", \\\"pvc\\\"],\\n\",\n+ \" \\\"ceramic\\\": [\\\"ceramic\\\", \\\"tile\\\", \\\"porcelain\\\"],\\n\",\n+ \" \\\"gypsum\\\": [\\\"gypsum\\\", \\\"drywall\\\", \\\"plasterboard\\\"],\\n\",\n+ \" \\\"insulation wool\\\": [\\\"insulation wool\\\", \\\"mineral wool\\\", \\\"fiberglass insulation\\\"],\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"PROMPT_TEMPLATES = [\\n\",\n+ \" \\\"a close-up photo of {material}\\\",\\n\",\n+ \" \\\"a construction material made of {material}\\\",\\n\",\n+ \" \\\"the surface texture is {material}\\\",\\n\",\n+ \"]\\n\",\n+ \"\\n\",\n+ \"OBJECT_MATERIAL_PRIORS = {\\n\",\n+ \" \\\"window\\\": [\\\"glass\\\", \\\"aluminum\\\", \\\"wood\\\", \\\"plastic\\\", \\\"steel\\\"],\\n\",\n+ \" \\\"door\\\": [\\\"wood\\\", \\\"steel\\\", \\\"aluminum\\\", \\\"glass\\\", \\\"plastic\\\"],\\n\",\n+ \" \\\"wall\\\": [\\\"concrete\\\", \\\"brick\\\", \\\"gypsum\\\", \\\"wood\\\"],\\n\",\n+ \" \\\"floor\\\": [\\\"concrete\\\", \\\"wood\\\", \\\"ceramic\\\", \\\"plastic\\\"],\\n\",\n+ \" \\\"roof\\\": [\\\"steel\\\", \\\"wood\\\", \\\"ceramic\\\", \\\"insulation wool\\\"],\\n\",\n+ \" \\\"pipe\\\": [\\\"steel\\\", \\\"plastic\\\", \\\"aluminum\\\"],\\n\",\n+ \" \\\"beam\\\": [\\\"steel\\\", \\\"wood\\\", \\\"concrete\\\"],\\n\",\n+ \" \\\"column\\\": [\\\"concrete\\\", \\\"steel\\\", \\\"brick\\\", \\\"wood\\\"],\\n\",\n+ \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -65,20 +65,37 @@\n \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n \"}\\n\",\n \"\\n\",\n- \"MATERIAL_LABELS = [\\n\",\n- \" \\\"concrete\\\",\\n\",\n- \" \\\"brick\\\",\\n\",\n- \" \\\"wood\\\",\\n\",\n- \" \\\"steel\\\",\\n\",\n- \" \\\"aluminum\\\",\\n\",\n- \" \\\"glass\\\",\\n\",\n- \" \\\"plastic\\\",\\n\",\n- \" \\\"ceramic\\\",\\n\",\n- \" \\\"gypsum\\\",\\n\",\n- \" \\\"insulation wool\\\",\\n\",\n- \"]\"\n+ \"MATERIAL_TAXONOMY = {\\n\",\n+ \" \\\"concrete\\\": [\\\"concrete\\\", \\\"cement\\\"],\\n\",\n+ \" \\\"brick\\\": [\\\"brick\\\", \\\"masonry brick\\\"],\\n\",\n+ \" \\\"wood\\\": [\\\"wood\\\", \\\"timber\\\", \\\"plywood\\\"],\\n\",\n+ \" \\\"steel\\\": [\\\"steel\\\", \\\"iron metal\\\"],\\n\",\n+ \" \\\"aluminum\\\": [\\\"aluminum\\\", \\\"aluminium\\\"],\\n\",\n+ \" \\\"glass\\\": [\\\"glass\\\", \\\"window glass\\\"],\\n\",\n+ \" \\\"plastic\\\": [\\\"plastic\\\", \\\"polymer\\\", \\\"pvc\\\"],\\n\",\n+ \" \\\"ceramic\\\": [\\\"ceramic\\\", \\\"tile\\\", \\\"porcelain\\\"],\\n\",\n+ \" \\\"gypsum\\\": [\\\"gypsum\\\", \\\"drywall\\\", \\\"plasterboard\\\"],\\n\",\n+ \" \\\"insulation wool\\\": [\\\"insulation wool\\\", \\\"mineral wool\\\", \\\"fiberglass insulation\\\"],\\n\",\n+ \"}\\n\",\n+ \"\\n\",\n+ \"PROMPT_TEMPLATES = [\\n\",\n+ \" \\\"a close-up photo of {material}\\\",\\n\",\n+ \" \\\"a construction material made of {material}\\\",\\n\",\n+ \" \\\"the surface texture is {material}\\\",\\n\",\n+ \"]\\n\",\n+ \"\\n\",\n+ \"OBJECT_MATERIAL_PRIORS = {\\n\",\n+ \" \\\"window\\\": [\\\"glass\\\", \\\"aluminum\\\", \\\"wood\\\", \\\"plastic\\\", \\\"steel\\\"],\\n\",\n+ \" \\\"door\\\": [\\\"wood\\\", \\\"steel\\\", \\\"aluminum\\\", \\\"glass\\\", \\\"plastic\\\"],\\n\",\n+ \" \\\"wall\\\": [\\\"concrete\\\", \\\"brick\\\", \\\"gypsum\\\", \\\"wood\\\"],\\n\",\n+ \" \\\"floor\\\": [\\\"concrete\\\", \\\"wood\\\", \\\"ceramic\\\", \\\"plastic\\\"],\\n\",\n+ \" \\\"roof\\\": [\\\"steel\\\", \\\"wood\\\", \\\"ceramic\\\", \\\"insulation wool\\\"],\\n\",\n+ \" \\\"pipe\\\": [\\\"steel\\\", \\\"plastic\\\", \\\"aluminum\\\"],\\n\",\n+ \" \\\"beam\\\": [\\\"steel\\\", \\\"wood\\\", \\\"concrete\\\"],\\n\",\n+ \" \\\"column\\\": [\\\"concrete\\\", \\\"steel\\\", \\\"brick\\\", \\\"wood\\\"],\\n\",\n+ \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n", + "additions": 29, + "deletions": 12 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783006238420, + "end": 1783006238460 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0f8b503aa727946c016a4684143e8481a3819fa61483dc5886" + } + }, + "id": "prt_f2373ef35001vbaqm0Bnh4nU27", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2373b751002uRaP2XQDSbZBzs" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 35815, + "input": 6861, + "output": 941, + "reasoning": 621, + "cache": { + "write": 0, + "read": 27392 + } + }, + "cost": 0.03866835, + "id": "prt_f23741702001C3OtmC8AGqv90B", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2373b751002uRaP2XQDSbZBzs" + } + ] + }, + { + "info": { + "parentID": "msg_f237389d1001tdNvvrKWzhhrAS", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + 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+ } + }, + "id": "prt_f2374216d001TkURcjLwFmtWcV", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2374170f002toKJZ5LTqNJ0W5" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_ft2XrbRJUmUueOFHazYigZ8H", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n@@\n \" bg[crop_mask] = crop[crop_mask]\\n\",\n \" return Image.fromarray(bg)\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n+ \" arr = np.array(image)\\n\",\n+ \" h, w = arr.shape[:2]\\n\",\n+ \" x, y, bw, bh = bbox_xywh\\n\",\n+ \" x0 = max(0, x - pad)\\n\",\n+ \" y0 = max(0, y - pad)\\n\",\n+ \" x1 = min(w, x + bw + pad)\\n\",\n+ \" y1 = min(h, y + bh + pad)\\n\",\n+ \" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n+ \" prompts = []\\n\",\n+ \" prompt_materials = []\\n\",\n+ \" for material, aliases in material_taxonomy.items():\\n\",\n+ \" for alias in aliases:\\n\",\n+ \" for template in prompt_templates:\\n\",\n+ \" prompts.append(template.format(material=alias))\\n\",\n+ \" prompt_materials.append(material)\\n\",\n+ \" return prompts, prompt_materials\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n+ \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def resolve_candidate_materials(object_label: str | None):\\n\",\n+ \" if not object_label:\\n\",\n+ \" return ALL_CANONICAL_MATERIALS\\n\",\n+ \" key = object_label.strip().lower()\\n\",\n+ \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n+ \" if len(candidates) < 2:\\n\",\n+ \" return ALL_CANONICAL_MATERIALS\\n\",\n+ \" return candidates\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n@@\n- \"def classify_material_sigclip(image: Image.Image, labels, sigclip_model, sigclip_processor, top_k: int = 3):\\n\",\n- \" inputs = sigclip_processor(\\n\",\n- \" text=labels,\\n\",\n- \" images=image,\\n\",\n- \" return_tensors=\\\"pt\\\",\\n\",\n- \" padding=\\\"max_length\\\",\\n\",\n- \" )\\n\",\n- \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n- \"\\n\",\n- \" with torch.no_grad():\\n\",\n- \" outputs = sigclip_model(**inputs)\\n\",\n- \"\\n\",\n- \" probs = torch.softmax(outputs.logits_per_image[0], dim=0)\\n\",\n- \" top_k = min(top_k, len(labels))\\n\",\n- \" vals, idxs = torch.topk(probs, k=top_k)\\n\",\n- \"\\n\",\n- \" top_labels = [labels[int(i)] for i in idxs]\\n\",\n- \" top_scores = [float(v) for v in vals]\\n\",\n- \"\\n\",\n- \" return {\\n\",\n- \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n- \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n- \" \\\"pred_material_conf_top1\\\": top_scores[0],\\n\",\n- \" \\\"topk_labels\\\": top_labels,\\n\",\n- \" \\\"topk_scores\\\": top_scores,\\n\",\n- \" }\\n\",\n+ \"def classify_material_sigclip(\\n\",\n+ \" image_views: list,\\n\",\n+ \" object_label: str | None,\\n\",\n+ \" sigclip_model,\\n\",\n+ \" sigclip_processor,\\n\",\n+ \" top_k: int = 3,\\n\",\n+ \" view_weights: list | None = None,\\n\",\n+ \"):\\n\",\n+ \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n+ \"\\n\",\n+ \" selected_idx = [\\n\",\n+ \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n+ \" ]\\n\",\n+ \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n+ \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n+ \"\\n\",\n+ \" if not selected_prompts:\\n\",\n+ \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n+ \"\\n\",\n+ \" if view_weights is None:\\n\",\n+ \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n+ \" if len(view_weights) != len(image_views):\\n\",\n+ \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n+ \"\\n\",\n+ \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n+ \"\\n\",\n+ \" for image, weight in zip(image_views, view_weights):\\n\",\n+ \" inputs = sigclip_processor(\\n\",\n+ \" text=selected_prompts,\\n\",\n+ \" images=image,\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" padding=\\\"max_length\\\",\\n\",\n+ \" )\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \"\\n\",\n+ \" with torch.no_grad():\\n\",\n+ \" outputs = sigclip_model(**inputs)\\n\",\n+ \"\\n\",\n+ \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n+ \"\\n\",\n+ \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n+ \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n+ \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n+ \"\\n\",\n+ \" for m in candidate_materials:\\n\",\n+ \" agg_scores[m] += weight * per_material_max[m]\\n\",\n+ \"\\n\",\n+ \" total = sum(agg_scores.values())\\n\",\n+ \" if total > 0:\\n\",\n+ \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n+ \"\\n\",\n+ \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+ \" top_k = min(top_k, len(ranked))\\n\",\n+ \" top_items = ranked[:top_k]\\n\",\n+ \"\\n\",\n+ \" top_labels = [x[0] for x in top_items]\\n\",\n+ \" top_scores = [x[1] for x in top_items]\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n+ \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n+ \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n+ \" \\\"topk_labels\\\": top_labels,\\n\",\n+ \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n+ \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n+ \" }\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -142,8 +142,44 @@\n \" bg[crop_mask] = crop[crop_mask]\\n\",\n \" return Image.fromarray(bg)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n+\" arr = np.array(image)\\n\",\n+\" h, w = arr.shape[:2]\\n\",\n+\" x, y, bw, bh = bbox_xywh\\n\",\n+\" x0 = max(0, x - pad)\\n\",\n+\" y0 = max(0, y - pad)\\n\",\n+\" x1 = min(w, x + bw + pad)\\n\",\n+\" y1 = min(h, y + bh + pad)\\n\",\n+\" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n+\" prompts = []\\n\",\n+\" prompt_materials = []\\n\",\n+\" for material, aliases in material_taxonomy.items():\\n\",\n+\" for alias in aliases:\\n\",\n+\" for template in prompt_templates:\\n\",\n+\" prompts.append(template.format(material=alias))\\n\",\n+\" prompt_materials.append(material)\\n\",\n+\" return prompts, prompt_materials\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n+\"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def resolve_candidate_materials(object_label: str | None):\\n\",\n+\" if not object_label:\\n\",\n+\" return ALL_CANONICAL_MATERIALS\\n\",\n+\" key = object_label.strip().lower()\\n\",\n+\" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n+\" if len(candidates) < 2:\\n\",\n+\" return ALL_CANONICAL_MATERIALS\\n\",\n+\" return candidates\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n@@ -172,33 +208,73 @@\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n-\"def classify_material_sigclip(image: Image.Image, labels, sigclip_model, sigclip_processor, top_k: int = 3):\\n\",\n-\" inputs = sigclip_processor(\\n\",\n-\" text=labels,\\n\",\n-\" images=image,\\n\",\n-\" return_tensors=\\\"pt\\\",\\n\",\n-\" padding=\\\"max_length\\\",\\n\",\n-\" )\\n\",\n-\" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+\"def classify_material_sigclip(\\n\",\n+\" image_views: list,\\n\",\n+\" object_label: str | None,\\n\",\n+\" sigclip_model,\\n\",\n+\" sigclip_processor,\\n\",\n+\" top_k: int = 3,\\n\",\n+\" view_weights: list | None = None,\\n\",\n+\"):\\n\",\n+\" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n \"\\n\",\n-\" with torch.no_grad():\\n\",\n-\" outputs = sigclip_model(**inputs)\\n\",\n+\" selected_idx = [\\n\",\n+\" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n+\" ]\\n\",\n+\" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n+\" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n \"\\n\",\n-\" probs = torch.softmax(outputs.logits_per_image[0], dim=0)\\n\",\n-\" top_k = min(top_k, len(labels))\\n\",\n-\" vals, idxs = torch.topk(probs, k=top_k)\\n\",\n+\" if not selected_prompts:\\n\",\n+\" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n \"\\n\",\n-\" top_labels = [labels[int(i)] for i in idxs]\\n\",\n-\" top_scores = [float(v) for v in vals]\\n\",\n+\" if view_weights is None:\\n\",\n+\" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n+\" if len(view_weights) != len(image_views):\\n\",\n+\" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n \"\\n\",\n+\" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n+\"\\n\",\n+\" for image, weight in zip(image_views, view_weights):\\n\",\n+\" inputs = sigclip_processor(\\n\",\n+\" text=selected_prompts,\\n\",\n+\" images=image,\\n\",\n+\" return_tensors=\\\"pt\\\",\\n\",\n+\" padding=\\\"max_length\\\",\\n\",\n+\" )\\n\",\n+\" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+\"\\n\",\n+\" with torch.no_grad():\\n\",\n+\" outputs = sigclip_model(**inputs)\\n\",\n+\"\\n\",\n+\" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n+\"\\n\",\n+\" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n+\" for p, m in zip(probs, selected_prompt_materials):\\n\",\n+\" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n+\"\\n\",\n+\" for m in candidate_materials:\\n\",\n+\" agg_scores[m] += weight * per_material_max[m]\\n\",\n+\"\\n\",\n+\" total = sum(agg_scores.values())\\n\",\n+\" if total > 0:\\n\",\n+\" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n+\"\\n\",\n+\" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top_k = min(top_k, len(ranked))\\n\",\n+\" top_items = ranked[:top_k]\\n\",\n+\"\\n\",\n+\" top_labels = [x[0] for x in top_items]\\n\",\n+\" top_scores = [x[1] for x in top_items]\\n\",\n+\"\\n\",\n \" return {\\n\",\n \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n-\" \\\"pred_material_conf_top1\\\": top_scores[0],\\n\",\n+\" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n-\" \\\"topk_scores\\\": top_scores,\\n\",\n+\" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n+\" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -142,8 +142,44 @@\n \" bg[crop_mask] = crop[crop_mask]\\n\",\n \" return Image.fromarray(bg)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n+\" arr = np.array(image)\\n\",\n+\" h, w = arr.shape[:2]\\n\",\n+\" x, y, bw, bh = bbox_xywh\\n\",\n+\" x0 = max(0, x - pad)\\n\",\n+\" y0 = max(0, y - pad)\\n\",\n+\" x1 = min(w, x + bw + pad)\\n\",\n+\" y1 = min(h, y + bh + pad)\\n\",\n+\" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n+\" prompts = []\\n\",\n+\" prompt_materials = []\\n\",\n+\" for material, aliases in material_taxonomy.items():\\n\",\n+\" for alias in aliases:\\n\",\n+\" for template in prompt_templates:\\n\",\n+\" prompts.append(template.format(material=alias))\\n\",\n+\" prompt_materials.append(material)\\n\",\n+\" return prompts, prompt_materials\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n+\"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def resolve_candidate_materials(object_label: str | None):\\n\",\n+\" if not object_label:\\n\",\n+\" return ALL_CANONICAL_MATERIALS\\n\",\n+\" key = object_label.strip().lower()\\n\",\n+\" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n+\" if len(candidates) < 2:\\n\",\n+\" return ALL_CANONICAL_MATERIALS\\n\",\n+\" return candidates\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n@@ -172,33 +208,73 @@\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n-\"def classify_material_sigclip(image: Image.Image, labels, sigclip_model, sigclip_processor, top_k: int = 3):\\n\",\n-\" inputs = sigclip_processor(\\n\",\n-\" text=labels,\\n\",\n-\" images=image,\\n\",\n-\" return_tensors=\\\"pt\\\",\\n\",\n-\" padding=\\\"max_length\\\",\\n\",\n-\" )\\n\",\n-\" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+\"def classify_material_sigclip(\\n\",\n+\" image_views: list,\\n\",\n+\" object_label: str | None,\\n\",\n+\" sigclip_model,\\n\",\n+\" sigclip_processor,\\n\",\n+\" top_k: int = 3,\\n\",\n+\" view_weights: list | None = None,\\n\",\n+\"):\\n\",\n+\" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n \"\\n\",\n-\" with torch.no_grad():\\n\",\n-\" outputs = sigclip_model(**inputs)\\n\",\n+\" selected_idx = [\\n\",\n+\" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n+\" ]\\n\",\n+\" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n+\" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n \"\\n\",\n-\" probs = torch.softmax(outputs.logits_per_image[0], dim=0)\\n\",\n-\" top_k = min(top_k, len(labels))\\n\",\n-\" vals, idxs = torch.topk(probs, k=top_k)\\n\",\n+\" if not selected_prompts:\\n\",\n+\" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n \"\\n\",\n-\" top_labels = [labels[int(i)] for i in idxs]\\n\",\n-\" top_scores = [float(v) for v in vals]\\n\",\n+\" if view_weights is None:\\n\",\n+\" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n+\" if len(view_weights) != len(image_views):\\n\",\n+\" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n \"\\n\",\n+\" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n+\"\\n\",\n+\" for image, weight in zip(image_views, view_weights):\\n\",\n+\" inputs = sigclip_processor(\\n\",\n+\" text=selected_prompts,\\n\",\n+\" images=image,\\n\",\n+\" return_tensors=\\\"pt\\\",\\n\",\n+\" padding=\\\"max_length\\\",\\n\",\n+\" )\\n\",\n+\" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+\"\\n\",\n+\" with torch.no_grad():\\n\",\n+\" outputs = sigclip_model(**inputs)\\n\",\n+\"\\n\",\n+\" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n+\"\\n\",\n+\" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n+\" for p, m in zip(probs, selected_prompt_materials):\\n\",\n+\" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n+\"\\n\",\n+\" for m in candidate_materials:\\n\",\n+\" agg_scores[m] += weight * per_material_max[m]\\n\",\n+\"\\n\",\n+\" total = sum(agg_scores.values())\\n\",\n+\" if total > 0:\\n\",\n+\" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n+\"\\n\",\n+\" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top_k = min(top_k, len(ranked))\\n\",\n+\" top_items = ranked[:top_k]\\n\",\n+\"\\n\",\n+\" top_labels = [x[0] for x in top_items]\\n\",\n+\" top_scores = [x[1] for x in top_items]\\n\",\n+\"\\n\",\n \" return {\\n\",\n \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n-\" \\\"pred_material_conf_top1\\\": top_scores[0],\\n\",\n+\" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n-\" \\\"topk_scores\\\": top_scores,\\n\",\n+\" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n+\" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n", + "additions": 93, + "deletions": 17 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783006264618, + "end": 1783006264695 + } + }, + "metadata": { + "openai": { + "itemId": "fc_053d33315a475dcd016a468421dadc81a19b974899bdc6adcf" + } + }, + "id": "prt_f237425190013zNfYdDLaoRVWL", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2374170f002toKJZ5LTqNJ0W5" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 38120, + "input": 141, + "output": 2231, + "reasoning": 36, + "cache": { + "write": 0, + "read": 35712 + } + }, + "cost": 0.03823435, + "id": "prt_f23747d7c001rdeLPUepeqkk9c", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2374170f002toKJZ5LTqNJ0W5" + } + ] + }, + { + "info": { + "parentID": "msg_f237389d1001tdNvvrKWzhhrAS", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0148393, + "tokens": { + "total": 38725, + "input": 142, + "output": 528, + "reasoning": 39, + "cache": { + "write": 0, + "read": 38016 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783006264711, + "completed": 1783006274772 + }, + "finish": "tool-calls", + "id": "msg_f23747d87002fuaLQT3vSd8x14", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f237484b50015reAZa8XSbSbs6", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f23747d87002fuaLQT3vSd8x14" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783006267554, + "end": 1783006267987 + }, + "metadata": { + "openai": { + "itemId": "rs_0ac5107a2eb45009016a46843b30f0819ea1829f7b087a3878", + "reasoningEncryptedContent": "gAAAAABqRoQ7q6hANUF-x8SlLtW_alDy_GWyso5ZkXsn06ZHBrnNmMinr_XAtG3grxlNQzObeqDQvmDKxSX-bf0dqnbyKOx-gCzksGn8esZ5qMp5UejD9UCJz8ycPlQPheTdyAHn4eJnIMrGNcrpAOXSmM6bC9Jdn62wfJoUGY0CM0Cwlhs_iiMH55_Bqlz9tOOeZsU9T7r5OoKYxiZB2Q7w6I4keR9pLR8t1C5xJOtdXzQETw5Ar_bKy6Pps2rBXzdSkIkHnkdUf1aAB48u66Tx48opCiMgzx19Zn7bKZ9MsJyJ1md7ZzsMDVXNA3elXp00m4hJ3tKE4BO1zZsTIuGgOl4iNR-2zcwKJoH2wMRB5raUy3ydM12Z_TFpUIiOJfTWkwmGVtlSTzJ2yxo4YRSfzpXOYRfYONNHHntdFQAA3JLBwxqNpJVCtB8TfWDhFDS3wFbp0h3t3UZhcUA9Fdx0cL-hQ_c4nnEw4yFlYn22RZjuSWDNf86BKMPLrXOn7TnRc7ca1iT7MHZx9sMo-i6SY-usaD7-dNFAlJoPl7AsFxd2_JCZbZCyr-slYy4azJ9Wh_M0L9Q6zFsOmVCYNs4VoqIJpOgX4qPsJCDQHnxDmaZkcBV3HzM6fhORt12a3C9-BwPmxNWUo1kIxN5DtsHIAgcMQR9nVqiuVTJCiV2l6iskxZYfJcrs5osRRDwcmuIJgWIhUfRsnmjopTBs5fO6M8PejGcdl12Ew_o6Wq2-heUy1739Xe4taBVopFP1C_GuThDMPYjV0vnh1e1ijkyc75mQJAMdT4ppP3Ih_m3xYTFG2u8Ev5_-_M6ZWdcpP9F2FPs2ich9xFm08mJaNzrSH0wz6l4weD6jPbUr5C2HtjQ-XtrX5swTEhtB7wp3bWLke8nh5ExkATI2SaCy8visOo_5Exm6mOpO20kB2tDearqOXjk_LxdKWK-np9YiF_MPLUGwc7FC7KOKS30itE1kOL_ENZwENHrMaQvj69lfWWu9zKyAoXwJAaifn-z8H_JWtZzjW6QZUyDf_-Tp184O_xklxBHL2iD4JBqOspO7GIr9n5qwZZw43kVKqdVXThcRHH9WeU-g6Fwzj2IL7uwzAh6W4s40DZJ0xHzOc2RiXeJJeccGOFs=" + } + }, + "id": "prt_f237488a1001Ol14JBRN07FEwL", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f23747d87002fuaLQT3vSd8x14" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_zxD0MGGlk9gt8qcNMmdNxXRx", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"crop = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n@@\n- \"clf = classify_material_sigclip(crop, MATERIAL_LABELS, sigclip_model, sigclip_processor, top_k=3)\\n\",\n+ \"clf = classify_material_sigclip(\\n\",\n+ \" image_views=[crop_masked, crop_bbox, img],\\n\",\n+ \" object_label=pred_object,\\n\",\n+ \" sigclip_model=sigclip_model,\\n\",\n+ \" sigclip_processor=sigclip_processor,\\n\",\n+ \" top_k=3,\\n\",\n+ \" view_weights=[0.5, 0.35, 0.15],\\n\",\n+ \")\\n\",\n@@\n- \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n+ \"print(\\\"Candidate materials:\\\", clf[\\\"candidate_materials\\\"])\\n\",\n+ \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n+ \"print(\\\"SigCLIP top3 scores:\\\", [round(s, 3) for s in clf[\\\"topk_scores\\\"]])\\n\",\n@@\n- \"axes[1].imshow(crop)\\n\",\n- \"axes[1].set_title(\\\"Masked Crop\\\")\\n\",\n+ \"axes[1].imshow(crop_masked)\\n\",\n+ \"axes[1].set_title(\\\"Masked Crop (SigCLIP input)\\\")\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -353,12 +353,20 @@\n \"seg_start = pd.Timestamp.utcnow()\\n\",\n \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n \"seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"crop = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n \"\\n\",\n \"clf_start = pd.Timestamp.utcnow()\\n\",\n- \"clf = classify_material_sigclip(crop, MATERIAL_LABELS, sigclip_model, sigclip_processor, top_k=3)\\n\",\n+ \"clf = classify_material_sigclip(\\n\",\n+ \" image_views=[crop_masked, crop_bbox, img],\\n\",\n+ \" object_label=pred_object,\\n\",\n+ \" sigclip_model=sigclip_model,\\n\",\n+ \" sigclip_processor=sigclip_processor,\\n\",\n+ \" top_k=3,\\n\",\n+ \" view_weights=[0.5, 0.35, 0.15],\\n\",\n+ \")\\n\",\n \"clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \"t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n@@ -367,19 +375,21 @@\n \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n \"\\n\",\n \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n+ \"print(\\\"Candidate materials:\\\", clf[\\\"candidate_materials\\\"])\\n\",\n \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n+ \"print(\\\"SigCLIP top3 scores:\\\", [round(s, 3) for s in clf[\\\"topk_scores\\\"]])\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \"axes[0].imshow(img)\\n\",\n \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n \"axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n- \"axes[1].imshow(crop)\\n\",\n- \"axes[1].set_title(\\\"Masked Crop\\\")\\n\",\n+ \"axes[1].imshow(crop_masked)\\n\",\n+ \"axes[1].set_title(\\\"Masked Crop (SigCLIP input)\\\")\\n\",\n \"axes[1].axis(\\\"off\\\")\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -353,12 +353,20 @@\n \"seg_start = pd.Timestamp.utcnow()\\n\",\n \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n \"seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"crop = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \"crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n \"\\n\",\n \"clf_start = pd.Timestamp.utcnow()\\n\",\n- \"clf = classify_material_sigclip(crop, MATERIAL_LABELS, sigclip_model, sigclip_processor, top_k=3)\\n\",\n+ \"clf = classify_material_sigclip(\\n\",\n+ \" image_views=[crop_masked, crop_bbox, img],\\n\",\n+ \" object_label=pred_object,\\n\",\n+ \" sigclip_model=sigclip_model,\\n\",\n+ \" sigclip_processor=sigclip_processor,\\n\",\n+ \" top_k=3,\\n\",\n+ \" view_weights=[0.5, 0.35, 0.15],\\n\",\n+ \")\\n\",\n \"clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \"t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n@@ -367,19 +375,21 @@\n \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n \"\\n\",\n \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n+ \"print(\\\"Candidate materials:\\\", clf[\\\"candidate_materials\\\"])\\n\",\n \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n+ \"print(\\\"SigCLIP top3 scores:\\\", [round(s, 3) for s in clf[\\\"topk_scores\\\"]])\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \"axes[0].imshow(img)\\n\",\n \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n \"axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n- \"axes[1].imshow(crop)\\n\",\n- \"axes[1].set_title(\\\"Masked Crop\\\")\\n\",\n+ \"axes[1].imshow(crop_masked)\\n\",\n+ \"axes[1].set_title(\\\"Masked Crop (SigCLIP input)\\\")\\n\",\n \"axes[1].axis(\\\"off\\\")\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n", + "additions": 14, + "deletions": 4 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -428,8 +428,10 @@\n \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+\" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n+\" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n \" \\\"user_final\\\": user_final_material,\\n\",\n \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n \" \\\"edit_reason\\\": edit_reason,\\n\",\n \" },\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -428,8 +428,10 @@\n \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+\" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n+\" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n \" \\\"user_final\\\": user_final_material,\\n\",\n \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n \" \\\"edit_reason\\\": edit_reason,\\n\",\n \" },\\n\",\n", + "additions": 2, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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+ } + }, + "id": "prt_f239521f7001TaOCr6nDtBP1t5", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f23951bf9002nrX4CBWa0fVanr" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_7Cxh75JT6ruW2Ag6jFH1q3gm", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"from pathlib import Path\\n\",\n+ \"from pathlib import Path\\n\",\n+ \"import base64\\n\",\n+ \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n \"import json\\n\",\n@@\n \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def select_tap_point_colab(image: Image.Image):\\n\",\n+ \" try:\\n\",\n+ \" from google.colab.output import eval_js\\n\",\n+ \" from IPython.display import Javascript, display\\n\",\n+ \" except Exception as exc:\\n\",\n+ \" raise RuntimeError(\\\"Click-to-select requires Google Colab.\\\") from exc\\n\",\n+ \"\\n\",\n+ \" buf = io.BytesIO()\\n\",\n+ \" image.save(buf, format=\\\"PNG\\\")\\n\",\n+ \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+ \"\\n\",\n+ \" js = \\\"\\\"\\\"\\n\",\n+ \" async function materialHuntersPickPoint(dataUrl) {\\n\",\n+ \" const overlay = document.createElement('div');\\n\",\n+ \" overlay.style.position = 'fixed';\\n\",\n+ \" overlay.style.inset = '0';\\n\",\n+ \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+ \" overlay.style.zIndex = '9999';\\n\",\n+ \" overlay.style.display = 'flex';\\n\",\n+ \" overlay.style.flexDirection = 'column';\\n\",\n+ \" overlay.style.alignItems = 'center';\\n\",\n+ \" overlay.style.justifyContent = 'center';\\n\",\n+ \" overlay.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const label = document.createElement('div');\\n\",\n+ \" label.textContent = 'Click the target point (Esc to cancel)';\\n\",\n+ \" label.style.color = 'white';\\n\",\n+ \" label.style.font = '16px sans-serif';\\n\",\n+ \"\\n\",\n+ \" const img = document.createElement('img');\\n\",\n+ \" img.src = dataUrl;\\n\",\n+ \" img.style.maxWidth = '90vw';\\n\",\n+ \" img.style.maxHeight = '80vh';\\n\",\n+ \" img.style.cursor = 'crosshair';\\n\",\n+ \" img.style.border = '2px solid white';\\n\",\n+ \"\\n\",\n+ \" overlay.appendChild(label);\\n\",\n+ \" overlay.appendChild(img);\\n\",\n+ \" document.body.appendChild(overlay);\\n\",\n+ \"\\n\",\n+ \" return await new Promise((resolve) => {\\n\",\n+ \" const cleanup = () => {\\n\",\n+ \" document.removeEventListener('keydown', onKey);\\n\",\n+ \" overlay.remove();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const onKey = (e) => {\\n\",\n+ \" if (e.key === 'Escape') {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolve([-1, -1]);\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" document.addEventListener('keydown', onKey);\\n\",\n+ \"\\n\",\n+ \" img.onclick = (e) => {\\n\",\n+ \" const rect = img.getBoundingClientRect();\\n\",\n+ \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n+ \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n+ \" cleanup();\\n\",\n+ \" resolve([x, y]);\\n\",\n+ \" };\\n\",\n+ \" });\\n\",\n+ \" }\\n\",\n+ \" \\\"\\\"\\\"\\n\",\n+ \"\\n\",\n+ \" display(Javascript(js))\\n\",\n+ \" x, y = eval_js(f\\\"materialHuntersPickPoint({json.dumps(data_url)})\\\")\\n\",\n+ \" if x < 0 or y < 0:\\n\",\n+ \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n+ \" return int(x), int(y)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -32,8 +32,10 @@\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n+ \"import base64\\n\",\n+ \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n \"import json\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n@@ -116,8 +118,81 @@\n \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def select_tap_point_colab(image: Image.Image):\\n\",\n+ \" try:\\n\",\n+ \" from google.colab.output import eval_js\\n\",\n+ \" from IPython.display import Javascript, display\\n\",\n+ \" except Exception as exc:\\n\",\n+ \" raise RuntimeError(\\\"Click-to-select requires Google Colab.\\\") from exc\\n\",\n+ \"\\n\",\n+ \" buf = io.BytesIO()\\n\",\n+ \" image.save(buf, format=\\\"PNG\\\")\\n\",\n+ \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+ \"\\n\",\n+ \" js = \\\"\\\"\\\"\\n\",\n+ \" async function materialHuntersPickPoint(dataUrl) {\\n\",\n+ \" const overlay = document.createElement('div');\\n\",\n+ \" overlay.style.position = 'fixed';\\n\",\n+ \" overlay.style.inset = '0';\\n\",\n+ \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+ \" overlay.style.zIndex = '9999';\\n\",\n+ \" overlay.style.display = 'flex';\\n\",\n+ \" overlay.style.flexDirection = 'column';\\n\",\n+ \" overlay.style.alignItems = 'center';\\n\",\n+ \" overlay.style.justifyContent = 'center';\\n\",\n+ \" overlay.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const label = document.createElement('div');\\n\",\n+ \" label.textContent = 'Click the target point (Esc to cancel)';\\n\",\n+ \" label.style.color = 'white';\\n\",\n+ \" label.style.font = '16px sans-serif';\\n\",\n+ \"\\n\",\n+ \" const img = document.createElement('img');\\n\",\n+ \" img.src = dataUrl;\\n\",\n+ \" img.style.maxWidth = '90vw';\\n\",\n+ \" img.style.maxHeight = '80vh';\\n\",\n+ \" img.style.cursor = 'crosshair';\\n\",\n+ \" img.style.border = '2px solid white';\\n\",\n+ \"\\n\",\n+ \" overlay.appendChild(label);\\n\",\n+ \" overlay.appendChild(img);\\n\",\n+ \" document.body.appendChild(overlay);\\n\",\n+ \"\\n\",\n+ \" return await new Promise((resolve) => {\\n\",\n+ \" const cleanup = () => {\\n\",\n+ \" document.removeEventListener('keydown', onKey);\\n\",\n+ \" overlay.remove();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const onKey = (e) => {\\n\",\n+ \" if (e.key === 'Escape') {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolve([-1, -1]);\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" document.addEventListener('keydown', onKey);\\n\",\n+ \"\\n\",\n+ \" img.onclick = (e) => {\\n\",\n+ \" const rect = img.getBoundingClientRect();\\n\",\n+ \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n+ \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n+ \" cleanup();\\n\",\n+ \" resolve([x, y]);\\n\",\n+ \" };\\n\",\n+ \" });\\n\",\n+ \" }\\n\",\n+ \" \\\"\\\"\\\"\\n\",\n+ \"\\n\",\n+ \" display(Javascript(js))\\n\",\n+ \" x, y = eval_js(f\\\"materialHuntersPickPoint({json.dumps(data_url)})\\\")\\n\",\n+ \" if x < 0 or y < 0:\\n\",\n+ \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n+ \" return int(x), int(y)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -32,8 +32,10 @@\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n+ \"import base64\\n\",\n+ \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n \"import json\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n@@ -116,8 +118,81 @@\n \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def select_tap_point_colab(image: Image.Image):\\n\",\n+ \" try:\\n\",\n+ \" from google.colab.output import eval_js\\n\",\n+ \" from IPython.display import Javascript, display\\n\",\n+ \" except Exception as exc:\\n\",\n+ \" raise RuntimeError(\\\"Click-to-select requires Google Colab.\\\") from exc\\n\",\n+ \"\\n\",\n+ \" buf = io.BytesIO()\\n\",\n+ \" image.save(buf, format=\\\"PNG\\\")\\n\",\n+ \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+ \"\\n\",\n+ \" js = \\\"\\\"\\\"\\n\",\n+ \" async function materialHuntersPickPoint(dataUrl) {\\n\",\n+ \" const overlay = document.createElement('div');\\n\",\n+ \" overlay.style.position = 'fixed';\\n\",\n+ \" overlay.style.inset = '0';\\n\",\n+ \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+ \" overlay.style.zIndex = '9999';\\n\",\n+ \" overlay.style.display = 'flex';\\n\",\n+ \" overlay.style.flexDirection = 'column';\\n\",\n+ \" overlay.style.alignItems = 'center';\\n\",\n+ \" overlay.style.justifyContent = 'center';\\n\",\n+ \" overlay.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const label = document.createElement('div');\\n\",\n+ \" label.textContent = 'Click the target point (Esc to cancel)';\\n\",\n+ \" label.style.color = 'white';\\n\",\n+ \" label.style.font = '16px sans-serif';\\n\",\n+ \"\\n\",\n+ \" const img = document.createElement('img');\\n\",\n+ \" img.src = dataUrl;\\n\",\n+ \" img.style.maxWidth = '90vw';\\n\",\n+ \" img.style.maxHeight = '80vh';\\n\",\n+ \" img.style.cursor = 'crosshair';\\n\",\n+ \" img.style.border = '2px solid white';\\n\",\n+ \"\\n\",\n+ \" overlay.appendChild(label);\\n\",\n+ \" overlay.appendChild(img);\\n\",\n+ \" document.body.appendChild(overlay);\\n\",\n+ \"\\n\",\n+ \" return await new Promise((resolve) => {\\n\",\n+ \" const cleanup = () => {\\n\",\n+ \" document.removeEventListener('keydown', onKey);\\n\",\n+ \" overlay.remove();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const onKey = (e) => {\\n\",\n+ \" if (e.key === 'Escape') {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolve([-1, -1]);\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" document.addEventListener('keydown', onKey);\\n\",\n+ \"\\n\",\n+ \" img.onclick = (e) => {\\n\",\n+ \" const rect = img.getBoundingClientRect();\\n\",\n+ \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n+ \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n+ \" cleanup();\\n\",\n+ \" resolve([x, y]);\\n\",\n+ \" };\\n\",\n+ \" });\\n\",\n+ \" }\\n\",\n+ \" \\\"\\\"\\\"\\n\",\n+ \"\\n\",\n+ \" display(Javascript(js))\\n\",\n+ \" x, y = eval_js(f\\\"materialHuntersPickPoint({json.dumps(data_url)})\\\")\\n\",\n+ \" if x < 0 or y < 0:\\n\",\n+ \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n+ \" return int(x), int(y)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n", + "additions": 75, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -401,15 +401,22 @@\n \"# Set these values per sample.\\n\",\n \"sample_id = \\\"S002\\\"\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n-\"tap_x = 300\\n\",\n-\"tap_y = 220\\n\",\n+\"USE_CLICK_TO_SELECT = True\\n\",\n+\"MANUAL_TAP_X = 300\\n\",\n+\"MANUAL_TAP_Y = 220\\n\",\n \"ground_truth_object = \\\"window\\\"\\n\",\n \"ground_truth_material = \\\"glass\\\"\\n\",\n \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n+\"if USE_CLICK_TO_SELECT:\\n\",\n+\" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n+\"else:\\n\",\n+\" tap_x, tap_y = MANUAL_TAP_X, MANUAL_TAP_Y\\n\",\n+\"\\n\",\n+\"print(f\\\"Using tap point: ({tap_x}, {tap_y})\\\")\\n\",\n \"plt.figure(figsize=(8, 6))\\n\",\n \"plt.imshow(img)\\n\",\n \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -401,15 +401,22 @@\n \"# Set these values per sample.\\n\",\n \"sample_id = \\\"S002\\\"\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n-\"tap_x = 300\\n\",\n-\"tap_y = 220\\n\",\n+\"USE_CLICK_TO_SELECT = True\\n\",\n+\"MANUAL_TAP_X = 300\\n\",\n+\"MANUAL_TAP_Y = 220\\n\",\n \"ground_truth_object = \\\"window\\\"\\n\",\n \"ground_truth_material = \\\"glass\\\"\\n\",\n \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n+\"if USE_CLICK_TO_SELECT:\\n\",\n+\" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n+\"else:\\n\",\n+\" tap_x, tap_y = MANUAL_TAP_X, MANUAL_TAP_Y\\n\",\n+\"\\n\",\n+\"print(f\\\"Using tap point: ({tap_x}, {tap_y})\\\")\\n\",\n \"plt.figure(figsize=(8, 6))\\n\",\n \"plt.imshow(img)\\n\",\n \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n", + "additions": 9, + "deletions": 2 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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in a batch.", + "time": { + "start": 1783008431958, + "end": 1783008434604 + }, + "metadata": { + "openai": { + "itemId": "msg_0717c703536a95c2016a468caff14c819ca6c947d63e1afb6f", + "phase": "final_answer" + } + }, + "id": "prt_f23958f55001Udz4nnY1jmyy09", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f23958a23002fpnyXlK7chhiEK" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 42215, + "input": 263, + "output": 224, + "reasoning": 0, + "cache": { + "write": 0, + "read": 41728 + } + }, + "cost": 0.01089865, + "id": "prt_f239599ca001rGBzjFEbAH3zbK", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f23958a23002fpnyXlK7chhiEK" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783008577990 + }, + "agent": "build", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f2397c9c60016WJu917z6CV66U", + "sessionID": 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SHFSjy54vF1Sky3ZBC_9qrO4Zf5sFa5LhF-HJ2Oe9aJA7uH7KaGRjRCui6mvOB7nGM4919h-ZQHunTNHBSTCY6oTkrR2nJfS3nVquULL1oauqdRW949MJqgvsgAeH3GXfvASXkxgqvr2ZRFOuCIBWhpnFweuw0soAO_RNGKBTiiX1lnzUYVRrFDMuGqA8zleMMn4fg46HtpNREq9pJFQqMhkTv_NM2d9DHPEV50-eJjygH9c1LwiY2IThnDs5w3eUzmdy4d4NQ3KO291Zr2tHpB50vsgmjrVvpPMt6qUvAxJagn9skSFb_1vi5Jx013Qt1vtPOno28Q710Is5PcEqVKBoQSdDlyWREosIMIOEu9Echy1blyqqFmuUoBFe" + } + }, + "id": "prt_f2397d00d001LxysdQNfQoXLV4", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2397c9d9002spZvfY5H3hT6O5" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_NmMXl4NI73K7ukwCD44G4Qkd", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \" if x < 0 or y < 0:\\n\",\n \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n \" return int(x), int(y)\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n+ \" try:\\n\",\n+ \" from google.colab.output import eval_js\\n\",\n+ \" from IPython.display import Javascript, display\\n\",\n+ \" except Exception as exc:\\n\",\n+ \" raise RuntimeError(\\\"Multi-point selection requires Google Colab.\\\") from exc\\n\",\n+ \"\\n\",\n+ \" buf = io.BytesIO()\\n\",\n+ \" image.save(buf, format=\\\"PNG\\\")\\n\",\n+ \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+ \"\\n\",\n+ \" js = \\\"\\\"\\\"\\n\",\n+ \" async function materialHuntersPickPoints(dataUrl, expectedPoints) {\\n\",\n+ \" const overlay = document.createElement('div');\\n\",\n+ \" overlay.style.position = 'fixed';\\n\",\n+ \" overlay.style.inset = '0';\\n\",\n+ \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+ \" overlay.style.zIndex = '9999';\\n\",\n+ \" overlay.style.display = 'flex';\\n\",\n+ \" overlay.style.flexDirection = 'column';\\n\",\n+ \" overlay.style.alignItems = 'center';\\n\",\n+ \" overlay.style.justifyContent = 'center';\\n\",\n+ \" overlay.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const label = document.createElement('div');\\n\",\n+ \" label.style.color = 'white';\\n\",\n+ \" label.style.font = '16px sans-serif';\\n\",\n+ \" label.textContent = expectedPoints\\n\",\n+ \" ? `Click ${expectedPoints} points, then press Done (Esc cancels)`\\n\",\n+ \" : 'Click one or more points, then press Done (Esc cancels)';\\n\",\n+ \"\\n\",\n+ \" const container = document.createElement('div');\\n\",\n+ \" container.style.position = 'relative';\\n\",\n+ \"\\n\",\n+ \" const img = document.createElement('img');\\n\",\n+ \" img.src = dataUrl;\\n\",\n+ \" img.style.maxWidth = '90vw';\\n\",\n+ \" img.style.maxHeight = '75vh';\\n\",\n+ \" img.style.cursor = 'crosshair';\\n\",\n+ \" img.style.border = '2px solid white';\\n\",\n+ \" img.style.display = 'block';\\n\",\n+ \"\\n\",\n+ \" const controls = document.createElement('div');\\n\",\n+ \" controls.style.display = 'flex';\\n\",\n+ \" controls.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const doneBtn = document.createElement('button');\\n\",\n+ \" doneBtn.textContent = 'Done';\\n\",\n+ \" const undoBtn = document.createElement('button');\\n\",\n+ \" undoBtn.textContent = 'Undo';\\n\",\n+ \"\\n\",\n+ \" controls.appendChild(doneBtn);\\n\",\n+ \" controls.appendChild(undoBtn);\\n\",\n+ \"\\n\",\n+ \" container.appendChild(img);\\n\",\n+ \" overlay.appendChild(label);\\n\",\n+ \" overlay.appendChild(container);\\n\",\n+ \" overlay.appendChild(controls);\\n\",\n+ \" document.body.appendChild(overlay);\\n\",\n+ \"\\n\",\n+ \" const points = [];\\n\",\n+ \" const markers = [];\\n\",\n+ \"\\n\",\n+ \" const drawMarker = (xClient, yClient, n) => {\\n\",\n+ \" const m = document.createElement('div');\\n\",\n+ \" m.style.position = 'fixed';\\n\",\n+ \" m.style.left = `${xClient - 6}px`;\\n\",\n+ \" m.style.top = `${yClient - 6}px`;\\n\",\n+ \" m.style.width = '12px';\\n\",\n+ \" m.style.height = '12px';\\n\",\n+ \" m.style.borderRadius = '6px';\\n\",\n+ \" m.style.background = '#ff5252';\\n\",\n+ \" m.style.border = '1px solid white';\\n\",\n+ \" m.title = `Point ${n}`;\\n\",\n+ \" m.style.zIndex = '10000';\\n\",\n+ \" document.body.appendChild(m);\\n\",\n+ \" markers.push(m);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const cleanup = () => {\\n\",\n+ \" document.removeEventListener('keydown', onKey);\\n\",\n+ \" markers.forEach((m) => m.remove());\\n\",\n+ \" overlay.remove();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const updateLabel = () => {\\n\",\n+ \" const count = points.length;\\n\",\n+ \" if (expectedPoints) {\\n\",\n+ \" label.textContent = `Selected ${count}/${expectedPoints}. Click more points or press Done.`;\\n\",\n+ \" } else {\\n\",\n+ \" label.textContent = `Selected ${count} point(s). Click more points or press Done.`;\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" return await new Promise((resolve) => {\\n\",\n+ \" const onKey = (e) => {\\n\",\n+ \" if (e.key === 'Escape') {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolve([]);\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \" document.addEventListener('keydown', onKey);\\n\",\n+ \"\\n\",\n+ \" img.onclick = (e) => {\\n\",\n+ \" const rect = img.getBoundingClientRect();\\n\",\n+ \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n+ \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n+ \" points.push([x, y]);\\n\",\n+ \" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n+ \" updateLabel();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" undoBtn.onclick = () => {\\n\",\n+ \" if (points.length === 0) return;\\n\",\n+ \" points.pop();\\n\",\n+ \" const m = markers.pop();\\n\",\n+ \" if (m) m.remove();\\n\",\n+ \" updateLabel();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" doneBtn.onclick = () => {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolve(points);\\n\",\n+ \" };\\n\",\n+ \" });\\n\",\n+ \" }\\n\",\n+ \" \\\"\\\"\\\"\\n\",\n+ \"\\n\",\n+ \" display(Javascript(js))\\n\",\n+ \" pts = eval_js(\\n\",\n+ \" f\\\"materialHuntersPickPoints({json.dumps(data_url)}, {json.dumps(expected_points)})\\\"\\n\",\n+ \" )\\n\",\n+ \" if not pts:\\n\",\n+ \" raise RuntimeError(\\\"Point selection cancelled or empty.\\\")\\n\",\n+ \" return [(int(p[0]), int(p[1])) for p in pts]\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -191,8 +191,145 @@\n \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n \" return int(x), int(y)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n+\" try:\\n\",\n+\" from google.colab.output import eval_js\\n\",\n+\" from IPython.display import Javascript, display\\n\",\n+\" except Exception as exc:\\n\",\n+\" raise RuntimeError(\\\"Multi-point selection requires Google Colab.\\\") from exc\\n\",\n+\"\\n\",\n+\" buf = io.BytesIO()\\n\",\n+\" image.save(buf, format=\\\"PNG\\\")\\n\",\n+\" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+\"\\n\",\n+\" js = \\\"\\\"\\\"\\n\",\n+\" async function materialHuntersPickPoints(dataUrl, expectedPoints) {\\n\",\n+\" const overlay = document.createElement('div');\\n\",\n+\" overlay.style.position = 'fixed';\\n\",\n+\" overlay.style.inset = '0';\\n\",\n+\" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+\" overlay.style.zIndex = '9999';\\n\",\n+\" overlay.style.display = 'flex';\\n\",\n+\" overlay.style.flexDirection = 'column';\\n\",\n+\" overlay.style.alignItems = 'center';\\n\",\n+\" overlay.style.justifyContent = 'center';\\n\",\n+\" overlay.style.gap = '8px';\\n\",\n+\"\\n\",\n+\" const label = document.createElement('div');\\n\",\n+\" label.style.color = 'white';\\n\",\n+\" label.style.font = '16px sans-serif';\\n\",\n+\" label.textContent = expectedPoints\\n\",\n+\" ? `Click ${expectedPoints} points, then press Done (Esc cancels)`\\n\",\n+\" : 'Click one or more points, then press Done (Esc cancels)';\\n\",\n+\"\\n\",\n+\" const container = document.createElement('div');\\n\",\n+\" container.style.position = 'relative';\\n\",\n+\"\\n\",\n+\" const img = document.createElement('img');\\n\",\n+\" img.src = dataUrl;\\n\",\n+\" img.style.maxWidth = '90vw';\\n\",\n+\" img.style.maxHeight = '75vh';\\n\",\n+\" img.style.cursor = 'crosshair';\\n\",\n+\" img.style.border = '2px solid white';\\n\",\n+\" img.style.display = 'block';\\n\",\n+\"\\n\",\n+\" const controls = document.createElement('div');\\n\",\n+\" controls.style.display = 'flex';\\n\",\n+\" controls.style.gap = '8px';\\n\",\n+\"\\n\",\n+\" const doneBtn = document.createElement('button');\\n\",\n+\" doneBtn.textContent = 'Done';\\n\",\n+\" const undoBtn = document.createElement('button');\\n\",\n+\" undoBtn.textContent = 'Undo';\\n\",\n+\"\\n\",\n+\" controls.appendChild(doneBtn);\\n\",\n+\" controls.appendChild(undoBtn);\\n\",\n+\"\\n\",\n+\" container.appendChild(img);\\n\",\n+\" overlay.appendChild(label);\\n\",\n+\" overlay.appendChild(container);\\n\",\n+\" overlay.appendChild(controls);\\n\",\n+\" document.body.appendChild(overlay);\\n\",\n+\"\\n\",\n+\" const points = [];\\n\",\n+\" const markers = [];\\n\",\n+\"\\n\",\n+\" const drawMarker = (xClient, yClient, n) => {\\n\",\n+\" const m = document.createElement('div');\\n\",\n+\" m.style.position = 'fixed';\\n\",\n+\" m.style.left = `${xClient - 6}px`;\\n\",\n+\" m.style.top = `${yClient - 6}px`;\\n\",\n+\" m.style.width = '12px';\\n\",\n+\" m.style.height = '12px';\\n\",\n+\" m.style.borderRadius = '6px';\\n\",\n+\" m.style.background = '#ff5252';\\n\",\n+\" m.style.border = '1px solid white';\\n\",\n+\" m.title = `Point ${n}`;\\n\",\n+\" m.style.zIndex = '10000';\\n\",\n+\" document.body.appendChild(m);\\n\",\n+\" markers.push(m);\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const cleanup = () => {\\n\",\n+\" document.removeEventListener('keydown', onKey);\\n\",\n+\" markers.forEach((m) => m.remove());\\n\",\n+\" overlay.remove();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const updateLabel = () => {\\n\",\n+\" const count = points.length;\\n\",\n+\" if (expectedPoints) {\\n\",\n+\" label.textContent = `Selected ${count}/${expectedPoints}. Click more points or press Done.`;\\n\",\n+\" } else {\\n\",\n+\" label.textContent = `Selected ${count} point(s). Click more points or press Done.`;\\n\",\n+\" }\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" return await new Promise((resolve) => {\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolve([]);\\n\",\n+\" }\\n\",\n+\" };\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n+\"\\n\",\n+\" img.onclick = (e) => {\\n\",\n+\" const rect = img.getBoundingClientRect();\\n\",\n+\" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n+\" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n+\" points.push([x, y]);\\n\",\n+\" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n+\" updateLabel();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" undoBtn.onclick = () => {\\n\",\n+\" if (points.length === 0) return;\\n\",\n+\" points.pop();\\n\",\n+\" const m = markers.pop();\\n\",\n+\" if (m) m.remove();\\n\",\n+\" updateLabel();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" doneBtn.onclick = () => {\\n\",\n+\" cleanup();\\n\",\n+\" resolve(points);\\n\",\n+\" };\\n\",\n+\" });\\n\",\n+\" }\\n\",\n+\" \\\"\\\"\\\"\\n\",\n+\"\\n\",\n+\" display(Javascript(js))\\n\",\n+\" pts = eval_js(\\n\",\n+\" f\\\"materialHuntersPickPoints({json.dumps(data_url)}, {json.dumps(expected_points)})\\\"\\n\",\n+\" )\\n\",\n+\" if not pts:\\n\",\n+\" raise RuntimeError(\\\"Point selection cancelled or empty.\\\")\\n\",\n+\" return [(int(p[0]), int(p[1])) for p in pts]\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -191,8 +191,145 @@\n \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n \" return int(x), int(y)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n+\" try:\\n\",\n+\" from google.colab.output import eval_js\\n\",\n+\" from IPython.display import Javascript, display\\n\",\n+\" except Exception as exc:\\n\",\n+\" raise RuntimeError(\\\"Multi-point selection requires Google Colab.\\\") from exc\\n\",\n+\"\\n\",\n+\" buf = io.BytesIO()\\n\",\n+\" image.save(buf, format=\\\"PNG\\\")\\n\",\n+\" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+\"\\n\",\n+\" js = \\\"\\\"\\\"\\n\",\n+\" async function materialHuntersPickPoints(dataUrl, expectedPoints) {\\n\",\n+\" const overlay = document.createElement('div');\\n\",\n+\" overlay.style.position = 'fixed';\\n\",\n+\" overlay.style.inset = '0';\\n\",\n+\" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+\" overlay.style.zIndex = '9999';\\n\",\n+\" overlay.style.display = 'flex';\\n\",\n+\" overlay.style.flexDirection = 'column';\\n\",\n+\" overlay.style.alignItems = 'center';\\n\",\n+\" overlay.style.justifyContent = 'center';\\n\",\n+\" overlay.style.gap = '8px';\\n\",\n+\"\\n\",\n+\" const label = document.createElement('div');\\n\",\n+\" label.style.color = 'white';\\n\",\n+\" label.style.font = '16px sans-serif';\\n\",\n+\" label.textContent = expectedPoints\\n\",\n+\" ? `Click ${expectedPoints} points, then press Done (Esc cancels)`\\n\",\n+\" : 'Click one or more points, then press Done (Esc cancels)';\\n\",\n+\"\\n\",\n+\" const container = document.createElement('div');\\n\",\n+\" container.style.position = 'relative';\\n\",\n+\"\\n\",\n+\" const img = document.createElement('img');\\n\",\n+\" img.src = dataUrl;\\n\",\n+\" img.style.maxWidth = '90vw';\\n\",\n+\" img.style.maxHeight = '75vh';\\n\",\n+\" img.style.cursor = 'crosshair';\\n\",\n+\" img.style.border = '2px solid white';\\n\",\n+\" img.style.display = 'block';\\n\",\n+\"\\n\",\n+\" const controls = document.createElement('div');\\n\",\n+\" controls.style.display = 'flex';\\n\",\n+\" controls.style.gap = '8px';\\n\",\n+\"\\n\",\n+\" const doneBtn = document.createElement('button');\\n\",\n+\" doneBtn.textContent = 'Done';\\n\",\n+\" const undoBtn = document.createElement('button');\\n\",\n+\" undoBtn.textContent = 'Undo';\\n\",\n+\"\\n\",\n+\" controls.appendChild(doneBtn);\\n\",\n+\" controls.appendChild(undoBtn);\\n\",\n+\"\\n\",\n+\" container.appendChild(img);\\n\",\n+\" overlay.appendChild(label);\\n\",\n+\" overlay.appendChild(container);\\n\",\n+\" overlay.appendChild(controls);\\n\",\n+\" document.body.appendChild(overlay);\\n\",\n+\"\\n\",\n+\" const points = [];\\n\",\n+\" const markers = [];\\n\",\n+\"\\n\",\n+\" const drawMarker = (xClient, yClient, n) => {\\n\",\n+\" const m = document.createElement('div');\\n\",\n+\" m.style.position = 'fixed';\\n\",\n+\" m.style.left = `${xClient - 6}px`;\\n\",\n+\" m.style.top = `${yClient - 6}px`;\\n\",\n+\" m.style.width = '12px';\\n\",\n+\" m.style.height = '12px';\\n\",\n+\" m.style.borderRadius = '6px';\\n\",\n+\" m.style.background = '#ff5252';\\n\",\n+\" m.style.border = '1px solid white';\\n\",\n+\" m.title = `Point ${n}`;\\n\",\n+\" m.style.zIndex = '10000';\\n\",\n+\" document.body.appendChild(m);\\n\",\n+\" markers.push(m);\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const cleanup = () => {\\n\",\n+\" document.removeEventListener('keydown', onKey);\\n\",\n+\" markers.forEach((m) => m.remove());\\n\",\n+\" overlay.remove();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const updateLabel = () => {\\n\",\n+\" const count = points.length;\\n\",\n+\" if (expectedPoints) {\\n\",\n+\" label.textContent = `Selected ${count}/${expectedPoints}. Click more points or press Done.`;\\n\",\n+\" } else {\\n\",\n+\" label.textContent = `Selected ${count} point(s). Click more points or press Done.`;\\n\",\n+\" }\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" return await new Promise((resolve) => {\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolve([]);\\n\",\n+\" }\\n\",\n+\" };\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n+\"\\n\",\n+\" img.onclick = (e) => {\\n\",\n+\" const rect = img.getBoundingClientRect();\\n\",\n+\" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n+\" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n+\" points.push([x, y]);\\n\",\n+\" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n+\" updateLabel();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" undoBtn.onclick = () => {\\n\",\n+\" if (points.length === 0) return;\\n\",\n+\" points.pop();\\n\",\n+\" const m = markers.pop();\\n\",\n+\" if (m) m.remove();\\n\",\n+\" updateLabel();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" doneBtn.onclick = () => {\\n\",\n+\" cleanup();\\n\",\n+\" resolve(points);\\n\",\n+\" };\\n\",\n+\" });\\n\",\n+\" }\\n\",\n+\" \\\"\\\"\\\"\\n\",\n+\"\\n\",\n+\" display(Javascript(js))\\n\",\n+\" pts = eval_js(\\n\",\n+\" f\\\"materialHuntersPickPoints({json.dumps(data_url)}, {json.dumps(expected_points)})\\\"\\n\",\n+\" )\\n\",\n+\" if not pts:\\n\",\n+\" raise RuntimeError(\\\"Point selection cancelled or empty.\\\")\\n\",\n+\" return [(int(p[0]), int(p[1])) for p in pts]\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n", + "additions": 137, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Placeholder until object-classifier is added.\\n\",\n- \"\\n\",\n- \"img = load_image(image_path)\\n\",\n- \"if USE_CLICK_TO_SELECT:\\n\",\n- \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n- \"else:\\n\",\n- \" tap_x, tap_y = MANUAL_TAP_X, MANUAL_TAP_Y\\n\",\n- \"\\n\",\n- \"print(f\\\"Using tap point: ({tap_x}, {tap_y})\\\")\\n\",\n- \"plt.figure(figsize=(8, 6))\\n\",\n- \"plt.imshow(img)\\n\",\n- \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n- \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n- \"plt.axis(\\\"off\\\")\\n\",\n- \"plt.show()\"\n+ \"# Configure one image and one-or-many objects in it.\\n\",\n+ \"image_id = \\\"IMG002\\\"\\n\",\n+ \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n+ \"\\n\",\n+ \"USE_CLICK_TO_SELECT = True\\n\",\n+ \"MULTI_OBJECT_MODE = True\\n\",\n+ \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n+ \"\\n\",\n+ \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n+ \"OBJECT_SPECS = [\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n+ \" },\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n+ \" },\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n+ \" },\\n\",\n+ \"]\\n\",\n+ \"\\n\",\n+ \"img = load_image(image_path)\\n\",\n+ \"\\n\",\n+ \"if USE_CLICK_TO_SELECT:\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n+ \" if len(points) > len(OBJECT_SPECS):\\n\",\n+ \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match clicked points.\\\")\\n\",\n+ \" for i, (x, y) in enumerate(points):\\n\",\n+ \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n+ \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[: len(points)]\\n\",\n+ \" else:\\n\",\n+ \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n+ \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n+ \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n+ \"else:\\n\",\n+ \" for obj in OBJECT_SPECS:\\n\",\n+ \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n+ \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n+ \"\\n\",\n+ \"for obj in OBJECT_RECORDS:\\n\",\n+ \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n+ \"\\n\",\n+ \"plt.figure(figsize=(9, 7))\\n\",\n+ \"plt.imshow(img)\\n\",\n+ \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n+ \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n+ \" plt.scatter([x], [y], c=\\\"red\\\", s=60)\\n\",\n+ \" plt.text(x + 6, y + 6, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9)\\n\",\n+ \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n+ \"plt.axis(\\\"off\\\")\\n\",\n+ \"plt.show()\"\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -534,30 +534,72 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"# Set these values per sample.\\n\",\n- \"sample_id = \\\"S002\\\"\\n\",\n+ \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n+ \"\\n\",\n \"USE_CLICK_TO_SELECT = True\\n\",\n- \"MANUAL_TAP_X = 300\\n\",\n- \"MANUAL_TAP_Y = 220\\n\",\n- \"ground_truth_object = \\\"window\\\"\\n\",\n- \"ground_truth_material = \\\"glass\\\"\\n\",\n- \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n+ \"MULTI_OBJECT_MODE = True\\n\",\n+ \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n \"\\n\",\n+ \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n+ \"OBJECT_SPECS = [\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n+ \" },\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n+ \" },\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n+ \" },\\n\",\n+ \"]\\n\",\n+ \"\\n\",\n \"img = load_image(image_path)\\n\",\n+ \"\\n\",\n \"if USE_CLICK_TO_SELECT:\\n\",\n- \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n+ \" if len(points) > len(OBJECT_SPECS):\\n\",\n+ \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match clicked points.\\\")\\n\",\n+ \" for i, (x, y) in enumerate(points):\\n\",\n+ \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n+ \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[: len(points)]\\n\",\n+ \" else:\\n\",\n+ \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n+ \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n+ \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n- \" tap_x, tap_y = MANUAL_TAP_X, MANUAL_TAP_Y\\n\",\n+ \" for obj in OBJECT_SPECS:\\n\",\n+ \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n+ \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n- \"print(f\\\"Using tap point: ({tap_x}, {tap_y})\\\")\\n\",\n- \"plt.figure(figsize=(8, 6))\\n\",\n+ \"for obj in OBJECT_RECORDS:\\n\",\n+ \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n+ \"\\n\",\n+ \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n- \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n- \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n+ \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n+ \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n+ \" plt.scatter([x], [y], c=\\\"red\\\", s=60)\\n\",\n+ \" plt.text(x + 6, y + 6, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9)\\n\",\n+ \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -534,30 +534,72 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"# Set these values per sample.\\n\",\n- \"sample_id = \\\"S002\\\"\\n\",\n+ \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n+ \"\\n\",\n \"USE_CLICK_TO_SELECT = True\\n\",\n- \"MANUAL_TAP_X = 300\\n\",\n- \"MANUAL_TAP_Y = 220\\n\",\n- \"ground_truth_object = \\\"window\\\"\\n\",\n- \"ground_truth_material = \\\"glass\\\"\\n\",\n- \"pred_object = ground_truth_object # Placeholder until object-classifier is added.\\n\",\n+ \"MULTI_OBJECT_MODE = True\\n\",\n+ \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n \"\\n\",\n+ \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n+ \"OBJECT_SPECS = [\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n+ \" },\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n+ \" },\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n+ \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n+ \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n+ \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n+ \" },\\n\",\n+ \"]\\n\",\n+ \"\\n\",\n \"img = load_image(image_path)\\n\",\n+ \"\\n\",\n \"if USE_CLICK_TO_SELECT:\\n\",\n- \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n+ \" if len(points) > len(OBJECT_SPECS):\\n\",\n+ \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match clicked points.\\\")\\n\",\n+ \" for i, (x, y) in enumerate(points):\\n\",\n+ \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n+ \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[: len(points)]\\n\",\n+ \" else:\\n\",\n+ \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n+ \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n+ \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n- \" tap_x, tap_y = MANUAL_TAP_X, MANUAL_TAP_Y\\n\",\n+ \" for obj in OBJECT_SPECS:\\n\",\n+ \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n+ \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n- \"print(f\\\"Using tap point: ({tap_x}, {tap_y})\\\")\\n\",\n- \"plt.figure(figsize=(8, 6))\\n\",\n+ \"for obj in OBJECT_RECORDS:\\n\",\n+ \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n+ \"\\n\",\n+ \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n- \"plt.scatter([tap_x], [tap_y], c=\\\"red\\\", s=60)\\n\",\n- \"plt.title(f\\\"Tap preview: ({tap_x}, {tap_y})\\\")\\n\",\n+ \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n+ \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n+ \" plt.scatter([x], [y], c=\\\"red\\\", s=60)\\n\",\n+ \" plt.text(x + 6, y + 6, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9)\\n\",\n+ \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n", + "additions": 55, + "deletions": 13 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783008641400, + "end": 1783008641419 + } + }, + "metadata": { + "openai": { + "itemId": "fc_05c0990b750781f5016a468d6cd450819ca8b5e5d336e3c246" + } + }, + "id": "prt_f2398716d001kva47KJH7ZhD1A", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f239867730025e4kna6bCodgQ5" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 46420, + "input": 120, + "output": 1618, + "reasoning": 10, + "cache": { + "write": 0, + "read": 44672 + } + }, + "cost": 0.0308196, + "id": "prt_f2398c190001rlVkpH3yKseEFn", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f239867730025e4kna6bCodgQ5" + } + ] + }, + { + "info": { + "parentID": "msg_f2397c9c60016WJu917z6CV66U", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0373023, + "tokens": { + "total": 48528, + "input": 122, + "output": 2059, + "reasoning": 11, + "cache": { + "write": 0, + "read": 46336 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783008641439, + "completed": 1783008659227 + }, + "finish": "tool-calls", + "id": "msg_f2398c19f0026OkvI7lq90gKAv", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f2398c4e7001UVp9zK3dHWHds5", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2398c19f0026OkvI7lq90gKAv" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783008643123, + "end": 1783008653799 + }, + "metadata": { + "openai": { + "itemId": "rs_0d750aebfb19509c016a468d831c108191824f34a74f3458f3", + "reasoningEncryptedContent": "gAAAAABqRo2NpHUjokctk7UUJ6_g1IyfFvQ2noicUakavVQ_wrBQzakpwbyOKg5AERyZuRvmAPFkEg1v75gnKtYg2OTTyT0lqNZSeOzsKRnBB6uJo5y-i2QvdE7GxytsUT8b7lU7W0qvQGYtsSzYvrliy6-kaTBcHUAhYFINYw9ctC54YBmSQAgJPDPpk-kPm9qlBO0uwiCnIhSibBvgZIr5A9F-rJnSGqu5RAeNPG8hBeqPZrp1Pe2hdUaIpkS0R4bvF_ofu0-I_w906aJWMhV9Q3bpEjTX1b021NnrbrdU9w6715IsMvAN3m-MqDTLUsOgeEu9lU6KBiDb1LiFY-NeM1IYNoavr95rDs2bIIF5a55eg6iIIAL5L8gP-MjhDLUAJGTjMIWrxtj5Duv_iu57k5xxDCSq0aEHppAFuof6Wa5FloE3d_3NpvjdEp_akAsw9APYI0g_JWXQmXLsK3TjvzX-Oh_sAZt7BfQVLONt_GMsISwLFGEzWnp87vFsLyP3iNLDLF2CTn7MSpLnw8Y51Ulo8Dh6qdymbTpufW9gedE0ZRRt6_2HTp30TogPBGof8S3XAn6rQuZJoZuq3BzLX9Vc-PeSoEXpMTs0nba7kcTDTomJQD3vaxSMeIHrQWnKyCvgtkUmAzobLUN70sUJluE2-4_3gsiIRF6sfmSBTRdhCEwaDNMEk5oDmjKfWVnEgSBVrCNxxwramIq3t90R5sESgAVrCncVzn-JFMrSBsDEve7FakGrnyWBHg5gYJI0P38h_wDuOkXlLXqpf7J4DeDnNJIIC-Wi5Yd6WyBOedhqHCmG4nMqK4v1gLt6rGLbuPOKtZIu9NlN8El-RcKsrT-YnsxSoJdoD-_Tq072hc_8YRLn-odT5UFchbAIyyNg4AGzsH6_AXA6A62mFqlmfp36IAUKngrCJOnAksHYJjQ7N6Kl_2-LxKrRa1xkvBOD3GPa0VjNZfJLqXL5V6EgSGQ5M9IUeA==" + } + }, + "id": "prt_f2398c833001PL8JQyzZnZF34n", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2398c19f0026OkvI7lq90gKAv" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_y8Ls0DjKnHiT1pNxBmYY37Mw", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"t0 = pd.Timestamp.utcnow()\\n\",\n- \"\\n\",\n- \"seg_start = pd.Timestamp.utcnow()\\n\",\n- \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n- \"seg_end = pd.Timestamp.utcnow()\\n\",\n- \"\\n\",\n- \"crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n- \"crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n- \"\\n\",\n- \"clf_start = pd.Timestamp.utcnow()\\n\",\n- \"clf = classify_material_sigclip(\\n\",\n- \" image_views=[crop_masked, crop_bbox, img],\\n\",\n- \" object_label=pred_object,\\n\",\n- \" sigclip_model=sigclip_model,\\n\",\n- \" sigclip_processor=sigclip_processor,\\n\",\n- \" top_k=3,\\n\",\n- \" view_weights=[0.5, 0.35, 0.15],\\n\",\n- \")\\n\",\n- \"clf_end = pd.Timestamp.utcnow()\\n\",\n- \"\\n\",\n- \"t1 = pd.Timestamp.utcnow()\\n\",\n- \"\\n\",\n- \"segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n- \"classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n- \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n- \"\\n\",\n- \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n- \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n- \"print(\\\"Candidate materials:\\\", clf[\\\"candidate_materials\\\"])\\n\",\n- \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n- \"print(\\\"SigCLIP top3 scores:\\\", [round(s, 3) for s in clf[\\\"topk_scores\\\"]])\\n\",\n- \"\\n\",\n- \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n- \"axes[0].imshow(img)\\n\",\n- \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n- \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n- \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n- \"axes[0].axis(\\\"off\\\")\\n\",\n- \"\\n\",\n- \"axes[1].imshow(crop_masked)\\n\",\n- \"axes[1].set_title(\\\"Masked Crop (SigCLIP input)\\\")\\n\",\n- \"axes[1].axis(\\\"off\\\")\\n\",\n- \"plt.tight_layout()\\n\",\n- \"plt.show()\"\n+ \"RUN_ROWS = []\\n\",\n+ \"\\n\",\n+ \"for obj in OBJECT_RECORDS:\\n\",\n+ \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n+ \" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n+ \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n+ \"\\n\",\n+ \" t0 = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \" seg_start = pd.Timestamp.utcnow()\\n\",\n+ \" seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+ \" seg_end = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n+ \"\\n\",\n+ \" clf_start = pd.Timestamp.utcnow()\\n\",\n+ \" clf = classify_material_sigclip(\\n\",\n+ \" image_views=[crop_masked, crop_bbox, img],\\n\",\n+ \" object_label=pred_object,\\n\",\n+ \" sigclip_model=sigclip_model,\\n\",\n+ \" sigclip_processor=sigclip_processor,\\n\",\n+ \" top_k=3,\\n\",\n+ \" view_weights=[0.5, 0.35, 0.15],\\n\",\n+ \" )\\n\",\n+ \" clf_end = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \" t1 = pd.Timestamp.utcnow()\\n\",\n+ \"\\n\",\n+ \" segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n+ \" classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n+ \" end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n+ \"\\n\",\n+ \" RUN_ROWS.append({\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap_x\\\": tap_x,\\n\",\n+ \" \\\"tap_y\\\": tap_y,\\n\",\n+ \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n+ \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n+ \" \\\"pred_object\\\": pred_object,\\n\",\n+ \" \\\"seg\\\": seg,\\n\",\n+ \" \\\"clf\\\": clf,\\n\",\n+ \" \\\"crop_masked\\\": crop_masked,\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" },\\n\",\n+ \" })\\n\",\n+ \"\\n\",\n+ \" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n+ \"\\n\",\n+ \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n+ \" axes[0].imshow(img)\\n\",\n+ \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n+ \" axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+ \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n+ \" axes[0].axis(\\\"off\\\")\\n\",\n+ \"\\n\",\n+ \" axes[1].imshow(crop_masked)\\n\",\n+ \" axes[1].set_title(f\\\"Masked Crop ({sample_id})\\\")\\n\",\n+ \" axes[1].axis(\\\"off\\\")\\n\",\n+ \" plt.tight_layout()\\n\",\n+ \" plt.show()\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -608,52 +608,76 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"t0 = pd.Timestamp.utcnow()\\n\",\n+ \"RUN_ROWS = []\\n\",\n \"\\n\",\n- \"seg_start = pd.Timestamp.utcnow()\\n\",\n- \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n- \"seg_end = pd.Timestamp.utcnow()\\n\",\n+ \"for obj in OBJECT_RECORDS:\\n\",\n+ \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n+ \" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n+ \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n- \"crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n- \"crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n+ \" t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"clf_start = pd.Timestamp.utcnow()\\n\",\n- \"clf = classify_material_sigclip(\\n\",\n- \" image_views=[crop_masked, crop_bbox, img],\\n\",\n- \" object_label=pred_object,\\n\",\n- \" sigclip_model=sigclip_model,\\n\",\n- \" sigclip_processor=sigclip_processor,\\n\",\n- \" top_k=3,\\n\",\n- \" view_weights=[0.5, 0.35, 0.15],\\n\",\n- \")\\n\",\n- \"clf_end = pd.Timestamp.utcnow()\\n\",\n+ \" seg_start = pd.Timestamp.utcnow()\\n\",\n+ \" seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+ \" seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"t1 = pd.Timestamp.utcnow()\\n\",\n+ \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n \"\\n\",\n- \"segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n- \"classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n- \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n+ \" clf_start = pd.Timestamp.utcnow()\\n\",\n+ \" clf = classify_material_sigclip(\\n\",\n+ \" image_views=[crop_masked, crop_bbox, img],\\n\",\n+ \" object_label=pred_object,\\n\",\n+ \" sigclip_model=sigclip_model,\\n\",\n+ \" sigclip_processor=sigclip_processor,\\n\",\n+ \" top_k=3,\\n\",\n+ \" view_weights=[0.5, 0.35, 0.15],\\n\",\n+ \" )\\n\",\n+ \" clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n- \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n- \"print(\\\"Candidate materials:\\\", clf[\\\"candidate_materials\\\"])\\n\",\n- \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n- \"print(\\\"SigCLIP top3 scores:\\\", [round(s, 3) for s in clf[\\\"topk_scores\\\"]])\\n\",\n+ \" t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n- \"axes[0].imshow(img)\\n\",\n- \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n- \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n- \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n- \"axes[0].axis(\\\"off\\\")\\n\",\n+ \" segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n+ \" classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n+ \" end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n \"\\n\",\n- \"axes[1].imshow(crop_masked)\\n\",\n- \"axes[1].set_title(\\\"Masked Crop (SigCLIP input)\\\")\\n\",\n- \"axes[1].axis(\\\"off\\\")\\n\",\n- \"plt.tight_layout()\\n\",\n- \"plt.show()\"\n+ \" RUN_ROWS.append({\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap_x\\\": tap_x,\\n\",\n+ \" \\\"tap_y\\\": tap_y,\\n\",\n+ \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n+ \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n+ \" \\\"pred_object\\\": pred_object,\\n\",\n+ \" \\\"seg\\\": seg,\\n\",\n+ \" \\\"clf\\\": clf,\\n\",\n+ \" \\\"crop_masked\\\": crop_masked,\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" },\\n\",\n+ \" })\\n\",\n+ \"\\n\",\n+ \" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n+ \"\\n\",\n+ \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n+ \" axes[0].imshow(img)\\n\",\n+ \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n+ \" axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+ \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n+ \" axes[0].axis(\\\"off\\\")\\n\",\n+ \"\\n\",\n+ \" axes[1].imshow(crop_masked)\\n\",\n+ \" axes[1].set_title(f\\\"Masked Crop ({sample_id})\\\")\\n\",\n+ \" axes[1].axis(\\\"off\\\")\\n\",\n+ \" plt.tight_layout()\\n\",\n+ \" plt.show()\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -608,52 +608,76 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"t0 = pd.Timestamp.utcnow()\\n\",\n+ \"RUN_ROWS = []\\n\",\n \"\\n\",\n- \"seg_start = pd.Timestamp.utcnow()\\n\",\n- \"seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n- \"seg_end = pd.Timestamp.utcnow()\\n\",\n+ \"for obj in OBJECT_RECORDS:\\n\",\n+ \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n+ \" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n+ \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n- \"crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n- \"crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n+ \" t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"clf_start = pd.Timestamp.utcnow()\\n\",\n- \"clf = classify_material_sigclip(\\n\",\n- \" image_views=[crop_masked, crop_bbox, img],\\n\",\n- \" object_label=pred_object,\\n\",\n- \" sigclip_model=sigclip_model,\\n\",\n- \" sigclip_processor=sigclip_processor,\\n\",\n- \" top_k=3,\\n\",\n- \" view_weights=[0.5, 0.35, 0.15],\\n\",\n- \")\\n\",\n- \"clf_end = pd.Timestamp.utcnow()\\n\",\n+ \" seg_start = pd.Timestamp.utcnow()\\n\",\n+ \" seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+ \" seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"t1 = pd.Timestamp.utcnow()\\n\",\n+ \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n+ \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n \"\\n\",\n- \"segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n- \"classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n- \"end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n+ \" clf_start = pd.Timestamp.utcnow()\\n\",\n+ \" clf = classify_material_sigclip(\\n\",\n+ \" image_views=[crop_masked, crop_bbox, img],\\n\",\n+ \" object_label=pred_object,\\n\",\n+ \" sigclip_model=sigclip_model,\\n\",\n+ \" sigclip_processor=sigclip_processor,\\n\",\n+ \" top_k=3,\\n\",\n+ \" view_weights=[0.5, 0.35, 0.15],\\n\",\n+ \" )\\n\",\n+ \" clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"print(\\\"Segmentation bbox (xywh):\\\", seg[\\\"bbox_xywh\\\"])\\n\",\n- \"print(\\\"Mask area px:\\\", seg[\\\"mask_area_px\\\"])\\n\",\n- \"print(\\\"Candidate materials:\\\", clf[\\\"candidate_materials\\\"])\\n\",\n- \"print(\\\"SigCLIP top3:\\\", clf[\\\"pred_material_top3\\\"])\\n\",\n- \"print(\\\"SigCLIP top3 scores:\\\", [round(s, 3) for s in clf[\\\"topk_scores\\\"]])\\n\",\n+ \" t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n- \"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n- \"axes[0].imshow(img)\\n\",\n- \"axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n- \"axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n- \"axes[0].set_title(\\\"Segmentation Overlay\\\")\\n\",\n- \"axes[0].axis(\\\"off\\\")\\n\",\n+ \" segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n+ \" classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n+ \" end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n \"\\n\",\n- \"axes[1].imshow(crop_masked)\\n\",\n- \"axes[1].set_title(\\\"Masked Crop (SigCLIP input)\\\")\\n\",\n- \"axes[1].axis(\\\"off\\\")\\n\",\n- \"plt.tight_layout()\\n\",\n- \"plt.show()\"\n+ \" RUN_ROWS.append({\\n\",\n+ \" \\\"sample_id\\\": sample_id,\\n\",\n+ \" \\\"image_id\\\": image_id,\\n\",\n+ \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"tap_x\\\": tap_x,\\n\",\n+ \" \\\"tap_y\\\": tap_y,\\n\",\n+ \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n+ \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n+ \" \\\"pred_object\\\": pred_object,\\n\",\n+ \" \\\"seg\\\": seg,\\n\",\n+ \" \\\"clf\\\": clf,\\n\",\n+ \" \\\"crop_masked\\\": crop_masked,\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n+ \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n+ \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n+ \" },\\n\",\n+ \" })\\n\",\n+ \"\\n\",\n+ \" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n+ \"\\n\",\n+ \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n+ \" axes[0].imshow(img)\\n\",\n+ \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n+ \" axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+ \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n+ \" axes[0].axis(\\\"off\\\")\\n\",\n+ \"\\n\",\n+ \" axes[1].imshow(crop_masked)\\n\",\n+ \" axes[1].set_title(f\\\"Masked Crop ({sample_id})\\\")\\n\",\n+ \" axes[1].axis(\\\"off\\\")\\n\",\n+ \" plt.tight_layout()\\n\",\n+ \" plt.show()\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n", + "additions": 60, + "deletions": 36 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Set defaults and optional per-sample overrides.\\n\",\n+ \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n+ \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n+ \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n+ \"\\n\",\n+ \"# Example override format:\\n\",\n+ \"# USER_REVIEW_OVERRIDES = {\\n\",\n+ \"# \\\"S003\\\": {\\n\",\n+ \"# \\\"segmentation_accepted\\\": 0,\\n\",\n+ \"# \\\"user_edited_material\\\": 1,\\n\",\n+ \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n+ \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n+ \"# \\\"status\\\": \\\"ok\\\",\\n\",\n+ \"# \\\"error_type\\\": \\\"\\\",\\n\",\n+ \"# }\\n\",\n+ \"# }\\n\",\n+ \"USER_REVIEW_OVERRIDES = {}\\n\",\n+ \"\\n\",\n+ \"for run in RUN_ROWS:\\n\",\n+ \" sid = run[\\\"sample_id\\\"]\\n\",\n+ \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n+ \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n+ \"\\n\",\n+ \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n+ \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n+ \"\\n\",\n+ \" run[\\\"review\\\"] = {\\n\",\n+ \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n+ \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n+ \" \\\"user_final_material\\\": user_final_material,\\n\",\n+ \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n+ \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"pd.DataFrame([\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n+ \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n+ \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n+ \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n+ \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n+ \" }\\n\",\n+ \" for r in RUN_ROWS\\n\",\n+ \"])\"\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -684,15 +684,54 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"# Human review/edit step.\\n\",\n- \"segmentation_accepted = 1 # 1 or 0\\n\",\n- \"user_edited_material = 0 # 1 if user overrides prediction\\n\",\n- \"user_final_material = clf[\\\"pred_material_top1\\\"]\\n\",\n- \"edit_reason = \\\"\\\" # e.g. \\\"model confused glass vs plastic\\\"\\n\",\n- \"status = \\\"ok\\\" # ok or fail\\n\",\n- \"error_type = \\\"\\\"\"\n+ \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n+ \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n+ \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n+ \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n+ \"\\n\",\n+ \"# Example override format:\\n\",\n+ \"# USER_REVIEW_OVERRIDES = {\\n\",\n+ \"# \\\"S003\\\": {\\n\",\n+ \"# \\\"segmentation_accepted\\\": 0,\\n\",\n+ \"# \\\"user_edited_material\\\": 1,\\n\",\n+ \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n+ \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n+ \"# \\\"status\\\": \\\"ok\\\",\\n\",\n+ \"# \\\"error_type\\\": \\\"\\\",\\n\",\n+ \"# }\\n\",\n+ \"# }\\n\",\n+ \"USER_REVIEW_OVERRIDES = {}\\n\",\n+ \"\\n\",\n+ \"for run in RUN_ROWS:\\n\",\n+ \" sid = run[\\\"sample_id\\\"]\\n\",\n+ \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n+ \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n+ \"\\n\",\n+ \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n+ \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n+ \"\\n\",\n+ \" run[\\\"review\\\"] = {\\n\",\n+ \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n+ \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n+ \" \\\"user_final_material\\\": user_final_material,\\n\",\n+ \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n+ \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"pd.DataFrame([\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n+ \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n+ \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n+ \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n+ \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n+ \" }\\n\",\n+ \" for r in RUN_ROWS\\n\",\n+ \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -684,15 +684,54 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"# Human review/edit step.\\n\",\n- \"segmentation_accepted = 1 # 1 or 0\\n\",\n- \"user_edited_material = 0 # 1 if user overrides prediction\\n\",\n- \"user_final_material = clf[\\\"pred_material_top1\\\"]\\n\",\n- \"edit_reason = \\\"\\\" # e.g. \\\"model confused glass vs plastic\\\"\\n\",\n- \"status = \\\"ok\\\" # ok or fail\\n\",\n- \"error_type = \\\"\\\"\"\n+ \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n+ \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n+ \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n+ \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n+ \"\\n\",\n+ \"# Example override format:\\n\",\n+ \"# USER_REVIEW_OVERRIDES = {\\n\",\n+ \"# \\\"S003\\\": {\\n\",\n+ \"# \\\"segmentation_accepted\\\": 0,\\n\",\n+ \"# \\\"user_edited_material\\\": 1,\\n\",\n+ \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n+ \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n+ \"# \\\"status\\\": \\\"ok\\\",\\n\",\n+ \"# \\\"error_type\\\": \\\"\\\",\\n\",\n+ \"# }\\n\",\n+ \"# }\\n\",\n+ \"USER_REVIEW_OVERRIDES = {}\\n\",\n+ \"\\n\",\n+ \"for run in RUN_ROWS:\\n\",\n+ \" sid = run[\\\"sample_id\\\"]\\n\",\n+ \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n+ \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n+ \"\\n\",\n+ \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n+ \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n+ \"\\n\",\n+ \" run[\\\"review\\\"] = {\\n\",\n+ \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n+ \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n+ \" \\\"user_final_material\\\": user_final_material,\\n\",\n+ \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n+ \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"pd.DataFrame([\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n+ \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n+ \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n+ \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n+ \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n+ \" }\\n\",\n+ \" for r in RUN_ROWS\\n\",\n+ \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n", + "additions": 46, + "deletions": 7 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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} + }, + "id": "prt_f23993961001MF7NgryHyFXREf", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f239934e3002AyPhtqzJqPWMjs" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_Pz4QOr3h2MRQuNMR8fUqne0T", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"passport = {\\n\",\n- \" \\\"sample_id\\\": sample_id,\\n\",\n- \" \\\"image_id\\\": image_id,\\n\",\n- \" \\\"image_path\\\": image_path,\\n\",\n- \" \\\"tap\\\": {\\\"x\\\": int(tap_x), \\\"y\\\": int(tap_y)},\\n\",\n- \" \\\"object\\\": {\\n\",\n- \" \\\"ground_truth\\\": ground_truth_object,\\n\",\n- \" \\\"predicted\\\": pred_object,\\n\",\n- \" },\\n\",\n- \" \\\"material\\\": {\\n\",\n- \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n- \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n- \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n- \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n- \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n- \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n- \" \\\"user_final\\\": user_final_material,\\n\",\n- \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n- \" \\\"edit_reason\\\": edit_reason,\\n\",\n- \" },\\n\",\n- \" \\\"segmentation\\\": {\\n\",\n- \" \\\"accepted\\\": bool(segmentation_accepted),\\n\",\n- \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n- \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n- \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n- \" },\\n\",\n- \" \\\"timing\\\": {\\n\",\n- \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n- \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n- \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n- \" },\\n\",\n- \" \\\"status\\\": status,\\n\",\n- \" \\\"error_type\\\": error_type,\\n\",\n- \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n- \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n- \"}\\n\",\n- \"\\n\",\n- \"passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n- \"print(f\\\"Saved passport: {passport_path}\\\")\\n\",\n- \"\\n\",\n- \"eval_row = {\\n\",\n- \" \\\"sample_id\\\": sample_id,\\n\",\n- \" \\\"image_id\\\": image_id,\\n\",\n- \" \\\"image_path\\\": image_path,\\n\",\n- \" \\\"tap_x\\\": tap_x,\\n\",\n- \" \\\"tap_y\\\": tap_y,\\n\",\n- \" \\\"ground_truth_object\\\": ground_truth_object,\\n\",\n- \" \\\"ground_truth_material\\\": ground_truth_material,\\n\",\n- \" \\\"pred_object\\\": pred_object,\\n\",\n- \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n- \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n- \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n- \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n- \" \\\"segmentation_accepted\\\": segmentation_accepted,\\n\",\n- \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n- \" \\\"user_final_material\\\": user_final_material,\\n\",\n- \" \\\"edit_reason\\\": edit_reason,\\n\",\n- \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n- \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n- \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n- \" \\\"status\\\": status,\\n\",\n- \" \\\"error_type\\\": error_type,\\n\",\n- \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n- \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n- \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n- \"}\\n\",\n- \"append_eval_row(CSV_PATH, eval_row)\\n\",\n- \"print(f\\\"Appended evaluation row to: {CSV_PATH}\\\")\"\n+ \"for run in RUN_ROWS:\\n\",\n+ \" review = run.get(\\\"review\\\", {})\\n\",\n+ \" seg = run[\\\"seg\\\"]\\n\",\n+ \" clf = run[\\\"clf\\\"]\\n\",\n+ \" timing = run[\\\"timing\\\"]\\n\",\n+ \"\\n\",\n+ \" passport = {\\n\",\n+ \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n+ \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n+ \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n+ \" \\\"tap\\\": {\\\"x\\\": int(run[\\\"tap_x\\\"]), \\\"y\\\": int(run[\\\"tap_y\\\"])},\\n\",\n+ \" \\\"object\\\": {\\n\",\n+ \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n+ \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"material\\\": {\\n\",\n+ \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n+ \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n+ \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n+ \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n+ \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+ \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n+ \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" },\\n\",\n+ \" \\\"segmentation\\\": {\\n\",\n+ \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n+ \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n+ \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n+ \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n+ \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n+ \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n+ \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n+ \"\\n\",\n+ \" eval_row = {\\n\",\n+ \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n+ \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n+ \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n+ \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n+ \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n+ \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n+ \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n+ \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n+ \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n+ \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n+ \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n+ \" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+ \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n+ \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n+ \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n+ \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n+ \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n+ \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n+ \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n+ \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n+ \" }\\n\",\n+ \" append_eval_row(CSV_PATH, eval_row)\\n\",\n+ \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -738,76 +738,83 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"passport = {\\n\",\n- \" \\\"sample_id\\\": sample_id,\\n\",\n- \" \\\"image_id\\\": image_id,\\n\",\n- \" \\\"image_path\\\": image_path,\\n\",\n- \" \\\"tap\\\": {\\\"x\\\": int(tap_x), \\\"y\\\": int(tap_y)},\\n\",\n- \" \\\"object\\\": {\\n\",\n- \" \\\"ground_truth\\\": ground_truth_object,\\n\",\n- \" \\\"predicted\\\": pred_object,\\n\",\n- \" },\\n\",\n- \" \\\"material\\\": {\\n\",\n- \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n- \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n- \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n- \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n- \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n- \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n- \" \\\"user_final\\\": user_final_material,\\n\",\n- \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n- \" \\\"edit_reason\\\": edit_reason,\\n\",\n- \" },\\n\",\n- \" \\\"segmentation\\\": {\\n\",\n- \" \\\"accepted\\\": bool(segmentation_accepted),\\n\",\n- \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n- \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n- \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n- \" },\\n\",\n- \" \\\"timing\\\": {\\n\",\n- \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n- \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n- \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n- \" },\\n\",\n- \" \\\"status\\\": status,\\n\",\n- \" \\\"error_type\\\": error_type,\\n\",\n- \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n- \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n- \"}\\n\",\n+ \"for run in RUN_ROWS:\\n\",\n+ \" review = run.get(\\\"review\\\", {})\\n\",\n+ \" seg = run[\\\"seg\\\"]\\n\",\n+ \" clf = run[\\\"clf\\\"]\\n\",\n+ \" timing = run[\\\"timing\\\"]\\n\",\n \"\\n\",\n- \"passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n- \"print(f\\\"Saved passport: {passport_path}\\\")\\n\",\n+ \" passport = {\\n\",\n+ \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n+ \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n+ \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n+ \" \\\"tap\\\": {\\\"x\\\": int(run[\\\"tap_x\\\"]), \\\"y\\\": int(run[\\\"tap_y\\\"])},\\n\",\n+ \" \\\"object\\\": {\\n\",\n+ \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n+ \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"material\\\": {\\n\",\n+ \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n+ \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n+ \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n+ \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n+ \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+ \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n+ \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" },\\n\",\n+ \" \\\"segmentation\\\": {\\n\",\n+ \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n+ \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n+ \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n+ \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n+ \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n+ \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n+ \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n+ \" }\\n\",\n \"\\n\",\n- \"eval_row = {\\n\",\n- \" \\\"sample_id\\\": sample_id,\\n\",\n- \" \\\"image_id\\\": image_id,\\n\",\n- \" \\\"image_path\\\": image_path,\\n\",\n- \" \\\"tap_x\\\": tap_x,\\n\",\n- \" \\\"tap_y\\\": tap_y,\\n\",\n- \" \\\"ground_truth_object\\\": ground_truth_object,\\n\",\n- \" \\\"ground_truth_material\\\": ground_truth_material,\\n\",\n- \" \\\"pred_object\\\": pred_object,\\n\",\n- \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n- \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n- \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n- \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n- \" \\\"segmentation_accepted\\\": segmentation_accepted,\\n\",\n- \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n- \" \\\"user_final_material\\\": user_final_material,\\n\",\n- \" \\\"edit_reason\\\": edit_reason,\\n\",\n- \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n- \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n- \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n- \" \\\"status\\\": status,\\n\",\n- \" \\\"error_type\\\": error_type,\\n\",\n- \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n- \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n- \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n- \"}\\n\",\n- \"append_eval_row(CSV_PATH, eval_row)\\n\",\n- \"print(f\\\"Appended evaluation row to: {CSV_PATH}\\\")\"\n+ \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n+ \"\\n\",\n+ \" eval_row = {\\n\",\n+ \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n+ \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n+ \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n+ \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n+ \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n+ \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n+ \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n+ \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n+ \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n+ \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n+ \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n+ \" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+ \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n+ \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n+ \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n+ \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n+ \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n+ \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n+ \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n+ \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n+ \" }\\n\",\n+ \" append_eval_row(CSV_PATH, eval_row)\\n\",\n+ \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -738,76 +738,83 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"passport = {\\n\",\n- \" \\\"sample_id\\\": sample_id,\\n\",\n- \" \\\"image_id\\\": image_id,\\n\",\n- \" \\\"image_path\\\": image_path,\\n\",\n- \" \\\"tap\\\": {\\\"x\\\": int(tap_x), \\\"y\\\": int(tap_y)},\\n\",\n- \" \\\"object\\\": {\\n\",\n- \" \\\"ground_truth\\\": ground_truth_object,\\n\",\n- \" \\\"predicted\\\": pred_object,\\n\",\n- \" },\\n\",\n- \" \\\"material\\\": {\\n\",\n- \" \\\"ground_truth\\\": ground_truth_material,\\n\",\n- \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n- \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n- \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n- \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n- \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n- \" \\\"user_final\\\": user_final_material,\\n\",\n- \" \\\"user_edited\\\": bool(user_edited_material),\\n\",\n- \" \\\"edit_reason\\\": edit_reason,\\n\",\n- \" },\\n\",\n- \" \\\"segmentation\\\": {\\n\",\n- \" \\\"accepted\\\": bool(segmentation_accepted),\\n\",\n- \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n- \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n- \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n- \" },\\n\",\n- \" \\\"timing\\\": {\\n\",\n- \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n- \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n- \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n- \" },\\n\",\n- \" \\\"status\\\": status,\\n\",\n- \" \\\"error_type\\\": error_type,\\n\",\n- \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n- \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n- \"}\\n\",\n+ \"for run in RUN_ROWS:\\n\",\n+ \" review = run.get(\\\"review\\\", {})\\n\",\n+ \" seg = run[\\\"seg\\\"]\\n\",\n+ \" clf = run[\\\"clf\\\"]\\n\",\n+ \" timing = run[\\\"timing\\\"]\\n\",\n \"\\n\",\n- \"passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n- \"print(f\\\"Saved passport: {passport_path}\\\")\\n\",\n+ \" passport = {\\n\",\n+ \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n+ \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n+ \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n+ \" \\\"tap\\\": {\\\"x\\\": int(run[\\\"tap_x\\\"]), \\\"y\\\": int(run[\\\"tap_y\\\"])},\\n\",\n+ \" \\\"object\\\": {\\n\",\n+ \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n+ \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"material\\\": {\\n\",\n+ \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n+ \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n+ \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n+ \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n+ \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+ \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n+ \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" },\\n\",\n+ \" \\\"segmentation\\\": {\\n\",\n+ \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n+ \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n+ \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n+ \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"timing\\\": {\\n\",\n+ \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n+ \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n+ \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n+ \" },\\n\",\n+ \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n+ \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n+ \" }\\n\",\n \"\\n\",\n- \"eval_row = {\\n\",\n- \" \\\"sample_id\\\": sample_id,\\n\",\n- \" \\\"image_id\\\": image_id,\\n\",\n- \" \\\"image_path\\\": image_path,\\n\",\n- \" \\\"tap_x\\\": tap_x,\\n\",\n- \" \\\"tap_y\\\": tap_y,\\n\",\n- \" \\\"ground_truth_object\\\": ground_truth_object,\\n\",\n- \" \\\"ground_truth_material\\\": ground_truth_material,\\n\",\n- \" \\\"pred_object\\\": pred_object,\\n\",\n- \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n- \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n- \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n- \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n- \" \\\"segmentation_accepted\\\": segmentation_accepted,\\n\",\n- \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n- \" \\\"user_final_material\\\": user_final_material,\\n\",\n- \" \\\"edit_reason\\\": edit_reason,\\n\",\n- \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n- \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n- \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n- \" \\\"status\\\": status,\\n\",\n- \" \\\"error_type\\\": error_type,\\n\",\n- \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n- \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n- \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n- \"}\\n\",\n- \"append_eval_row(CSV_PATH, eval_row)\\n\",\n- \"print(f\\\"Appended evaluation row to: {CSV_PATH}\\\")\"\n+ \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n+ \"\\n\",\n+ \" eval_row = {\\n\",\n+ \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n+ \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n+ \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n+ \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n+ \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n+ \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n+ \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n+ \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n+ \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n+ \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n+ \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n+ \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n+ \" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+ \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n+ \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n+ \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n+ \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n+ \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n+ \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n+ \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n+ \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n+ \" }\\n\",\n+ \" append_eval_row(CSV_PATH, eval_row)\\n\",\n+ \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n", + "additions": 73, + "deletions": 66 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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you want to label.\n- Keep `USE_CLICK_TO_SELECT = True` and `MULTI_OBJECT_MODE = True`.\n- Click points in the same order as `OBJECT_SPECS`.\n- Run the remaining cells once; it will batch-process all objects from that image.\n\nIf you want next, I can add a tiny guard that auto-generates missing `sample_id`s so you don’t have to prefill all IDs manually.", + "time": { + "start": 1783008705041, + "end": 1783008708455 + }, + "metadata": { + "openai": { + "itemId": "msg_02f420a8569bcd89016a468dc106c4819fbde7296f261c9443", + "phase": "final_answer" + } + }, + "id": "prt_f2399ba11001N15yi8wDCB42Z4", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2399b49b002bIpt5SnKnorHTK" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 53504, + "input": 204, + "output": 308, + "reasoning": 0, + "cache": { + "write": 0, + "read": 52992 + } + }, + "cost": 0.0139426, + "id": "prt_f239a57d6001UyUfVv3UyAEb9V", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", 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afterward in a small review table (less upfront setup).", + "time": { + "start": 1783008865465, + "end": 1783008867940 + }, + "metadata": { + "openai": { + "itemId": "msg_04bfbdd21fb4c11d016a468e6172d081a3890ca881dcc8e0a7", + "phase": "final_answer" + } + }, + "id": "prt_f239c2cb9001U70af8AHbHjfBW", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f239c1f0d002bFqib2TKuJQZf8" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 52460, + "input": 52181, + "output": 205, + "reasoning": 74, + "cache": { + "write": 0, + "read": 0 + } + }, + "cost": 0.09522275, + "id": "prt_f239c368f001p0hwedUv5UTy06", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f239c1f0d002bFqib2TKuJQZf8" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783009490979 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": 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the 'tap' object functionality -- the resulting plot to tap is very small before selecting the object. what needs to be changed to make it larger?", + "id": "prt_f23ad46fe001ZYDoDiOw4v4HlP", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f23ad46f6001GnANwSvH37j4GY" + } + ] + }, + { + "info": { + "parentID": "msg_f23ad46f6001GnANwSvH37j4GY", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.03593625, + "tokens": { + "total": 53003, + "input": 11415, + "output": 228, + "reasoning": 400, + "cache": { + "write": 0, + "read": 40960 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783009986318, + "completed": 1783009998147 + }, + "finish": "tool-calls", + "id": "msg_f23ad470e0024qnnI0AQn7CqMg", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": 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The segmentation (currently using SAM1) does recognize boundaries quite well but it generally only selects subparts of objects; that is, given a sofa, it will select the pillow. is this better in SAM2/SAM3?", + "id": "prt_f27c2c2b1001JBbRsHse2Svd7C", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c2c2af001gVczDrsRsIeSTY" + } + ] + }, + { + "info": { + "parentID": "msg_f27c2c2af001gVczDrsRsIeSTY", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.101444, + "tokens": { + "total": 53558, + "input": 52928, + "output": 316, + "reasoning": 314, + "cache": { + "write": 0, + "read": 0 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783078503121, + "completed": 1783078521807 + }, + "finish": "stop", + "id": "msg_f27c2c2d1002diSvQyrx3b22iS", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": 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+ } + }, + "id": "prt_f27c2d320001suB1Vm0F1T5Ha4", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c2c2d1002diSvQyrx3b22iS" + }, + { + "type": "text", + "text": "Great observation — and you’re describing a classic **prompt ambiguity** issue, not just a SAM1 weakness.\n\nShort answer:\n- **SAM2 usually helps somewhat**, but it does **not reliably solve “part vs whole object”** from a single tap.\n- For a sofa tap, SAM can still return a cushion/armrest if that region is the strongest local object.\n- I’m not aware of an official, widely adopted **SAM3** baseline with guaranteed better whole-object point prompting than SAM2 yet; if you have a specific SAM3 repo/model in mind, we can evaluate that exact one.\n\nWhat improves this most in practice (more than swapping SAM1 -> SAM2 alone):\n- Use **box + point** prompts (box around sofa, tap inside) instead of point-only.\n- Use **multi-point prompting**: positive on sofa frame, negative on pillow.\n- Change mask selection heuristic: don’t always take highest score; prefer a mask that contains tap and matches expected object scale.\n- Optional object prior: if object is `sofa`, reject tiny masks unless no larger valid candidate exists.\n\nSo yes, trying SAM2 is worthwhile, but I’d treat it as an incremental gain. \nThe bigger win is prompt strategy + mask selection logic.\n\nIf you want, next I can implement a “whole-object mode” in your notebook:\n1) click one positive + optional negatives, \n2) choose among candidate masks with size-aware heuristic, \n3) preview top-3 masks and let you pick one quickly.", + "time": { + "start": 1783078514986, + "end": 1783078521739 + }, + "metadata": { + "openai": { + "itemId": "msg_09562006255a7f86016a479e729f70819f8319ce74aadc82f4", + "phase": "final_answer" + } + }, + "id": "prt_f27c2f12a001McnFuG4prx5sD5", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c2c2d1002diSvQyrx3b22iS" + }, + { + "reason": "stop", + "type": "step-finish", 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sample_id,image_id,image_path,tap_x,tap_y,ground_truth_object,ground_truth_material,pred_object,pred_material_top1,pred_material_top3,pred_material_conf_top1,model_name,segmentation_accepted,user_edited_material,user_final_material,edit_reason,end_to_end_time_sec,segmentation_time_sec,classification_time_sec,status,error_type,mask_area_px,bbox_xywh,passport_json_path\n\n\n/home/magnus-bk/repos/material-hunters/poc_eval_sheet_guide.md:\n Line 15: - Interaction: `tap_x`, `tap_y`\n\n\n/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb:\n Line 122: \"def select_tap_point_colab(image: Image.Image):\\n\",\n\n Line 196: \"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n\n Line 396: \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n\n Line 399: \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n\n Line 546: \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n\n Line 548: \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n\n Line 574: \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n\n Line 578: \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n\n Line 579: \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n\n Line 582: \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n\n Line 583: \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n\n Line 584: \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n\n Line 588: \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n\n Line 589: \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n\n Line 600: \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n\n Line 603: \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n\n Line 618: \" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n\n Line 624: \" seg = 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Upload poc_eval_sheet_template.csv first.\\\"\\n\",\n114: \" )\\n\",\n115: \" pd.read_csv(template_path).iloc[0:0].to_csv(csv_path, index=False)\\n\",\n116: \"\\n\",\n117: \"\\n\",\n118: \"def load_image(image_path: str) -> Image.Image:\\n\",\n119: \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n120: \"\\n\",\n121: \"\\n\",\n122: \"def select_tap_point_colab(image: Image.Image):\\n\",\n123: \" try:\\n\",\n124: \" from google.colab.output import eval_js\\n\",\n125: \" from IPython.display import Javascript, display\\n\",\n126: \" except Exception as exc:\\n\",\n127: \" raise RuntimeError(\\\"Click-to-select requires Google Colab.\\\") from exc\\n\",\n128: \"\\n\",\n129: \" buf = io.BytesIO()\\n\",\n130: \" image.save(buf, format=\\\"PNG\\\")\\n\",\n131: \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n132: \"\\n\",\n133: \" js = \\\"\\\"\\\"\\n\",\n134: \" async function materialHuntersPickPoint(dataUrl) {\\n\",\n135: \" const overlay = document.createElement('div');\\n\",\n136: \" overlay.style.position = 'fixed';\\n\",\n137: \" overlay.style.inset = '0';\\n\",\n138: \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n139: \" overlay.style.zIndex = '9999';\\n\",\n140: \" overlay.style.display = 'flex';\\n\",\n141: \" overlay.style.flexDirection = 'column';\\n\",\n142: \" overlay.style.alignItems = 'center';\\n\",\n143: \" overlay.style.justifyContent = 'center';\\n\",\n144: \" overlay.style.gap = '8px';\\n\",\n145: \"\\n\",\n146: \" const label = document.createElement('div');\\n\",\n147: \" label.textContent = 'Click the target point (Esc to cancel)';\\n\",\n148: \" label.style.color = 'white';\\n\",\n149: \" label.style.font = '16px sans-serif';\\n\",\n150: \"\\n\",\n151: \" const img = document.createElement('img');\\n\",\n152: \" img.src = dataUrl;\\n\",\n153: \" img.style.width = '95vw';\\n\",\n154: \" img.style.maxWidth = '1800px';\\n\",\n155: \" img.style.maxHeight = '90vh';\\n\",\n156: \" img.style.cursor = 'crosshair';\\n\",\n157: \" img.style.border = '2px solid white';\\n\",\n158: \"\\n\",\n159: \" overlay.appendChild(label);\\n\",\n160: \" overlay.appendChild(img);\\n\",\n161: \" document.body.appendChild(overlay);\\n\",\n162: \"\\n\",\n163: \" return await new Promise((resolve) => {\\n\",\n164: \" const cleanup = () => {\\n\",\n165: \" document.removeEventListener('keydown', onKey);\\n\",\n166: \" overlay.remove();\\n\",\n167: \" };\\n\",\n168: \"\\n\",\n169: \" const onKey = (e) => {\\n\",\n170: \" if (e.key === 'Escape') {\\n\",\n171: \" cleanup();\\n\",\n172: \" resolve([-1, -1]);\\n\",\n173: \" }\\n\",\n174: \" };\\n\",\n175: \"\\n\",\n176: \" document.addEventListener('keydown', onKey);\\n\",\n177: \"\\n\",\n178: \" img.onclick = (e) => {\\n\",\n179: \" const rect = img.getBoundingClientRect();\\n\",\n180: \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n181: \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n182: \" cleanup();\\n\",\n183: \" resolve([x, y]);\\n\",\n184: \" };\\n\",\n185: \" });\\n\",\n186: \" }\\n\",\n187: \" \\\"\\\"\\\"\\n\",\n188: \"\\n\",\n189: \" display(Javascript(js))\\n\",\n190: \" x, y = eval_js(f\\\"materialHuntersPickPoint({json.dumps(data_url)})\\\")\\n\",\n191: \" if x < 0 or y < 0:\\n\",\n192: \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n193: \" return int(x), int(y)\\n\",\n194: \"\\n\",\n195: \"\\n\",\n196: \"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n197: \" try:\\n\",\n198: \" from google.colab.output import eval_js\\n\",\n199: \" from IPython.display import Javascript, display\\n\",\n200: \" except Exception as exc:\\n\",\n201: \" raise RuntimeError(\\\"Multi-point selection requires Google Colab.\\\") from exc\\n\",\n202: \"\\n\",\n203: \" buf = io.BytesIO()\\n\",\n204: \" image.save(buf, format=\\\"PNG\\\")\\n\",\n205: \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n206: \"\\n\",\n207: \" js = \\\"\\\"\\\"\\n\",\n208: \" async function materialHuntersPickPoints(dataUrl, expectedPoints) {\\n\",\n209: \" const overlay = document.createElement('div');\\n\",\n210: \" overlay.style.position = 'fixed';\\n\",\n211: \" overlay.style.inset = '0';\\n\",\n212: \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n213: \" overlay.style.zIndex = '9999';\\n\",\n214: \" overlay.style.display = 'flex';\\n\",\n215: \" overlay.style.flexDirection = 'column';\\n\",\n216: \" overlay.style.alignItems = 'center';\\n\",\n217: \" overlay.style.justifyContent = 'center';\\n\",\n218: \" overlay.style.gap = '8px';\\n\",\n219: \"\\n\",\n220: \" const label = document.createElement('div');\\n\",\n221: \" label.style.color = 'white';\\n\",\n222: \" label.style.font = '16px sans-serif';\\n\",\n223: \" label.textContent = expectedPoints\\n\",\n224: \" ? `Click ${expectedPoints} points, then press Done (Esc cancels)`\\n\",\n225: \" : 'Click one or more points, then press Done (Esc cancels)';\\n\",\n226: \"\\n\",\n227: \" const container = document.createElement('div');\\n\",\n228: \" container.style.position = 'relative';\\n\",\n229: \"\\n\",\n230: \" const img = document.createElement('img');\\n\",\n231: \" img.src = dataUrl;\\n\",\n232: \" img.style.width = '95vw';\\n\",\n233: \" img.style.maxWidth = '1800px';\\n\",\n234: \" img.style.maxHeight = '90vh';\\n\",\n235: \" img.style.cursor = 'crosshair';\\n\",\n236: \" img.style.border = '2px solid white';\\n\",\n237: \" img.style.display = 'block';\\n\",\n238: \"\\n\",\n239: \" const controls = document.createElement('div');\\n\",\n240: \" controls.style.display = 'flex';\\n\",\n241: \" controls.style.gap = '8px';\\n\",\n242: \"\\n\",\n243: \" const doneBtn = document.createElement('button');\\n\",\n244: \" doneBtn.textContent = 'Done';\\n\",\n245: \" const undoBtn = document.createElement('button');\\n\",\n246: \" undoBtn.textContent = 'Undo';\\n\",\n247: \"\\n\",\n248: \" controls.appendChild(doneBtn);\\n\",\n249: \" controls.appendChild(undoBtn);\\n\",\n250: \"\\n\",\n251: \" container.appendChild(img);\\n\",\n252: \" overlay.appendChild(label);\\n\",\n253: \" overlay.appendChild(container);\\n\",\n254: \" overlay.appendChild(controls);\\n\",\n255: \" document.body.appendChild(overlay);\\n\",\n256: \"\\n\",\n257: \" const points = [];\\n\",\n258: \" const markers = [];\\n\",\n259: \"\\n\",\n260: \" const drawMarker = (xClient, yClient, n) => {\\n\",\n261: \" const m = document.createElement('div');\\n\",\n262: \" m.style.position = 'fixed';\\n\",\n263: \" m.style.left = `${xClient - 6}px`;\\n\",\n264: \" m.style.top = `${yClient - 6}px`;\\n\",\n265: \" m.style.width = '12px';\\n\",\n266: \" m.style.height = '12px';\\n\",\n267: \" m.style.borderRadius = '6px';\\n\",\n268: \" m.style.background = '#ff5252';\\n\",\n269: \" m.style.border = '1px solid white';\\n\",\n270: \" m.title = `Point ${n}`;\\n\",\n271: \" m.style.zIndex = '10000';\\n\",\n272: \" document.body.appendChild(m);\\n\",\n273: \" markers.push(m);\\n\",\n274: \" };\\n\",\n275: \"\\n\",\n276: \" const cleanup = () => {\\n\",\n277: \" document.removeEventListener('keydown', onKey);\\n\",\n278: \" markers.forEach((m) => m.remove());\\n\",\n279: \" overlay.remove();\\n\",\n280: \" };\\n\",\n281: \"\\n\",\n282: \" const updateLabel = () => {\\n\",\n283: \" const count = points.length;\\n\",\n284: \" if (expectedPoints) {\\n\",\n285: \" label.textContent = `Selected ${count}/${expectedPoints}. Click more points or press Done.`;\\n\",\n286: \" } else {\\n\",\n287: \" label.textContent = `Selected ${count} point(s). Click more points or press Done.`;\\n\",\n288: \" }\\n\",\n289: \" };\\n\",\n\n(Showing lines 100-289 of 925. Use offset=290 to continue.)\n</content>", + "metadata": { + "preview": " ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def ensure_eval_csv(csv_path: Path, template_path: Path):\\n\",\n \" if csv_path.exists():\\n\",\n \" return\\n\",\n \" if not template_path.exists():\\n\",\n \" raise FileNotFoundError(\\n\",\n \" f\\\"Missing {template_path}. Upload poc_eval_sheet_template.csv first.\\\"\\n\",\n \" )\\n\",\n \" pd.read_csv(template_path).iloc[0:0].to_csv(csv_path, index=False)\\n\",\n \"\\n\",\n \"\\n\",\n \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def ensure_eval_csv(csv_path: Path, template_path: Path):\\n\",\n \" if csv_path.exists():\\n\",\n \" return\\n\",\n \" if not template_path.exists():\\n\",\n \" raise FileNotFoundError(\\n\",\n \" f\\\"Missing {template_path}. Upload poc_eval_sheet_template.csv first.\\\"\\n\",\n \" )\\n\",\n \" pd.read_csv(template_path).iloc[0:0].to_csv(csv_path, index=False)\\n\",\n \"\\n\",\n \"\\n\",\n \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_tap_point_colab(image: Image.Image):\\n\",\n \" try:\\n\",\n \" from google.colab.output import eval_js\\n\",\n \" from IPython.display import Javascript, display\\n\",\n \" except Exception as exc:\\n\",\n \" raise RuntimeError(\\\"Click-to-select requires Google Colab.\\\") from exc\\n\",\n \"\\n\",\n \" buf = io.BytesIO()\\n\",\n \" image.save(buf, format=\\\"PNG\\\")\\n\",\n \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n \"\\n\",\n \" js = \\\"\\\"\\\"\\n\",\n \" async function materialHuntersPickPoint(dataUrl) {\\n\",\n \" const overlay = document.createElement('div');\\n\",\n \" overlay.style.position = 'fixed';\\n\",\n \" overlay.style.inset = '0';\\n\",\n \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n \" overlay.style.zIndex = '9999';\\n\",\n \" overlay.style.display = 'flex';\\n\",\n \" overlay.style.flexDirection = 'column';\\n\",\n \" overlay.style.alignItems = 'center';\\n\",\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n \" label.textContent = 'Click the target point (Esc to cancel)';\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n \" const img = document.createElement('img');\\n\",\n \" img.src = dataUrl;\\n\",\n \" img.style.width = '95vw';\\n\",\n \" img.style.maxWidth = '1800px';\\n\",\n \" img.style.maxHeight = '90vh';\\n\",\n \" img.style.cursor = 'crosshair';\\n\",\n \" img.style.border = '2px solid white';\\n\",\n \"\\n\",\n \" overlay.appendChild(label);\\n\",\n \" overlay.appendChild(img);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n \" return await new Promise((resolve) => {\\n\",\n \" const cleanup = () => {\\n\",\n \" document.removeEventListener('keydown', onKey);\\n\",\n \" overlay.remove();\\n\",\n \" };\\n\",\n \"\\n\",\n \" const onKey = (e) => {\\n\",\n \" if (e.key === 'Escape') {\\n\",\n \" cleanup();\\n\",\n \" resolve([-1, -1]);\\n\",\n \" }\\n\",\n \" };\\n\",\n \"\\n\",\n \" document.addEventListener('keydown', onKey);\\n\",\n \"\\n\",\n \" img.onclick = (e) => {\\n\",\n \" const rect = img.getBoundingClientRect();\\n\",\n \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n \" cleanup();\\n\",\n \" resolve([x, y]);\\n\",\n \" };\\n\",\n \" });\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" display(Javascript(js))\\n\",\n \" x, y = eval_js(f\\\"materialHuntersPickPoint({json.dumps(data_url)})\\\")\\n\",\n \" if x < 0 or y < 0:\\n\",\n \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n \" return int(x), int(y)\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n \" try:\\n\",\n \" from google.colab.output import eval_js\\n\",\n \" from IPython.display import Javascript, display\\n\",\n \" except Exception as exc:\\n\",\n \" raise RuntimeError(\\\"Multi-point selection requires Google Colab.\\\") from exc\\n\",\n \"\\n\",\n \" buf = io.BytesIO()\\n\",\n \" image.save(buf, format=\\\"PNG\\\")\\n\",\n \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n \"\\n\",\n \" js = \\\"\\\"\\\"\\n\",\n \" async function materialHuntersPickPoints(dataUrl, expectedPoints) {\\n\",\n \" const overlay = document.createElement('div');\\n\",\n \" overlay.style.position = 'fixed';\\n\",\n \" overlay.style.inset = '0';\\n\",\n \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n \" overlay.style.zIndex = '9999';\\n\",\n \" overlay.style.display = 'flex';\\n\",\n \" overlay.style.flexDirection = 'column';\\n\",\n \" overlay.style.alignItems = 'center';\\n\",\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \" label.textContent = expectedPoints\\n\",\n \" ? `Click ${expectedPoints} points, then press Done (Esc cancels)`\\n\",\n \" : 'Click one or more points, then press Done (Esc cancels)';\\n\",\n \"\\n\",\n \" const container = document.createElement('div');\\n\",\n \" container.style.position = 'relative';\\n\",\n \"\\n\",\n \" const img = document.createElement('img');\\n\",\n \" img.src = dataUrl;\\n\",\n \" img.style.width = '95vw';\\n\",\n \" img.style.maxWidth = '1800px';\\n\",\n \" img.style.maxHeight = '90vh';\\n\",\n \" img.style.cursor = 'crosshair';\\n\",\n \" img.style.border = '2px solid white';\\n\",\n \" img.style.display = 'block';\\n\",\n \"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n \"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n \" const undoBtn = document.createElement('button');\\n\",\n \" undoBtn.textContent = 'Undo';\\n\",\n \"\\n\",\n \" controls.appendChild(doneBtn);\\n\",\n \" controls.appendChild(undoBtn);\\n\",\n \"\\n\",\n \" container.appendChild(img);\\n\",\n \" overlay.appendChild(label);\\n\",\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n \" const points = [];\\n\",\n \" const markers = [];\\n\",\n \"\\n\",\n \" const drawMarker = (xClient, yClient, n) => {\\n\",\n \" const m = document.createElement('div');\\n\",\n \" m.style.position = 'fixed';\\n\",\n \" m.style.left = `${xClient - 6}px`;\\n\",\n \" m.style.top = `${yClient - 6}px`;\\n\",\n \" m.style.width = '12px';\\n\",\n \" m.style.height = '12px';\\n\",\n \" m.style.borderRadius = '6px';\\n\",\n \" m.style.background = '#ff5252';\\n\",\n \" m.style.border = '1px solid white';\\n\",\n \" m.title = `Point ${n}`;\\n\",\n \" m.style.zIndex = '10000';\\n\",\n \" document.body.appendChild(m);\\n\",\n \" markers.push(m);\\n\",\n \" };\\n\",\n \"\\n\",\n \" const cleanup = () => {\\n\",\n \" document.removeEventListener('keydown', onKey);\\n\",\n \" markers.forEach((m) => m.remove());\\n\",\n \" overlay.remove();\\n\",\n \" };\\n\",\n \"\\n\",\n \" const updateLabel = () => {\\n\",\n \" const count = points.length;\\n\",\n \" if (expectedPoints) {\\n\",\n \" label.textContent = `Selected ${count}/${expectedPoints}. Click more points or press Done.`;\\n\",\n \" } else {\\n\",\n \" label.textContent = `Selected ${count} point(s). Click more points or press Done.`;\\n\",\n \" }\\n\",\n \" };\\n\",", + "lineStart": 100, + "lineEnd": 289, + "totalLines": 925, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078615108, + "end": 1783078615153 + } + }, + "metadata": { + "openai": { + "itemId": "fc_007732157a50c48d016a479ed698b881a092278098312ba7cd" + } + }, + "id": "prt_f27c476b6001LhiVjjFl63JDYJ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c4688b0022svwy1ECBt2t3r" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 54965, + "input": 977, + "output": 47, + "reasoning": 53, + "cache": { + "write": 0, + "read": 53888 + } + }, + "cost": 0.01254015, + "id": "prt_f27c478760012yWtGmFuD2Tt20", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c4688b0022svwy1ECBt2t3r" + } + ] + }, + { + "info": { + "parentID": "msg_f27c43663001JCENYPiYbSmtTC", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.01557045, + "tokens": { + "total": 57776, + "input": 2931, + "output": 47, + "reasoning": 14, + "cache": { + "write": 0, + "read": 54784 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783078615184, + "completed": 1783078617388 + }, + "finish": "tool-calls", + "id": "msg_f27c47890002CDO1ZP2aitFfLr", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27c47d390015WDK14RBs8UK6e", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c47890002CDO1ZP2aitFfLr" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783078616387, + "end": 1783078616968 + }, + "metadata": { + "openai": { + "itemId": "rs_07a2a086b6f41471016a479ed83dec81a1866d268bf08f5f3b", + "reasoningEncryptedContent": "gAAAAABqR57YgIc98M_29Ew2odiLz74QeHh0gW5pZhe_z_kfjf21ZoycfgTKDt1S5fZVOznUe-atsoCyBi1T4NzRHxsHZkTpCrhUSEdiFGKVJkolL4WHH2Ws42U7Xvy90Oy9geUj1EYir06hAN1wXrEj5YBDczqnUOVOS0VXTTnQalueP7PMO8jNTzCD8PIKBVIIxCPSG8xHnMj3rYRHxGViYzMt4vBfq2WhmdCBY2pGIFl6r8Ejj6Y-EGDYiDvoALQ-v-ZQfqqG1u23Uwalbd9rVTreqOywkvyKfU5F4dvkapbrk_t4_hKBV0DziLNJH5rK_89-sL4FZHHRc3bEECtjhd2euimXNl5UvC6IL0VYIcCdjo7AUjDXBEjTsSupsARcY8eXU6ZZEajsqHcI2R8VnDGMj3bcWkxnn1_FTcZzi1cUSvWaXFHnwBNTFM1qWS_iNZ60jVVHDtoQ1vIJPDI6oSAw80k17vDzn2pBHNPaXZk4yKhJQC03JXhPsKikJpfkss08BCV7f80ApxbLH49HwczKXczhWxeK9qARkefBIFfp3XRE5xUcYa8TZ8jUgtv8xdpFNDWgClFNUeyMsA8xsMG9kyOIjJoK7iDGl9E3Q47YsEecAnMESBDtU9W8BSySVR_-QrQF1RwKZj3HRvJjLojKG9RPkgfQ1_yDcUYkSuQgGo-Wz4onlDmtst1l_olATggbxT1KQNYXjCZdAnQVuE46ObNuheWhY0ij09OGTuXqzfvmCaMcBfM4_pm6SK64mTr4PCgUMkHDa_LzYdhBQJw-kh3EaaDTaRY1MejFgwgZveiep2-SEEl_FZU2G8c7oqs-9w1TETr0tKb-buSrh3795vkE6YqZAK35kj73mlTHQGR_o_DhEVHLhbJUJmPZ7HGRTK7lHprUTvyKtN01VukMg5Psp-tBorj1ZLegrn2rDKKszjXGeTVcxGkQimdZm--24Yq_C1UP6b6kvrgObBlNYz0L7USKDg9jRnSZv26lbPyCWfNt26vuwASzmrtGzteh7iZ5" + } + }, + "id": "prt_f27c47d43001RFDSEyc19eJzBK", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c47890002CDO1ZP2aitFfLr" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_5QXCyQTRjOPr2n8sDFRMccG9", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 289, + "limit": 220 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n289: \" };\\n\",\n290: \"\\n\",\n291: \" return await new Promise((resolve) => {\\n\",\n292: \" const onKey = (e) => {\\n\",\n293: \" if (e.key === 'Escape') {\\n\",\n294: \" cleanup();\\n\",\n295: \" resolve([]);\\n\",\n296: \" }\\n\",\n297: \" };\\n\",\n298: \" document.addEventListener('keydown', onKey);\\n\",\n299: \"\\n\",\n300: \" img.onclick = (e) => {\\n\",\n301: \" const rect = img.getBoundingClientRect();\\n\",\n302: \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n303: \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n304: \" points.push([x, y]);\\n\",\n305: \" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n306: \" updateLabel();\\n\",\n307: \" };\\n\",\n308: \"\\n\",\n309: \" undoBtn.onclick = () => {\\n\",\n310: \" if (points.length === 0) return;\\n\",\n311: \" points.pop();\\n\",\n312: \" const m = markers.pop();\\n\",\n313: \" if (m) m.remove();\\n\",\n314: \" updateLabel();\\n\",\n315: \" };\\n\",\n316: \"\\n\",\n317: \" doneBtn.onclick = () => {\\n\",\n318: \" cleanup();\\n\",\n319: \" resolve(points);\\n\",\n320: \" };\\n\",\n321: \" });\\n\",\n322: \" }\\n\",\n323: \" \\\"\\\"\\\"\\n\",\n324: \"\\n\",\n325: \" display(Javascript(js))\\n\",\n326: \" pts = eval_js(\\n\",\n327: \" f\\\"materialHuntersPickPoints({json.dumps(data_url)}, {json.dumps(expected_points)})\\\"\\n\",\n328: \" )\\n\",\n329: \" if not pts:\\n\",\n330: \" raise RuntimeError(\\\"Point selection cancelled or empty.\\\")\\n\",\n331: \" return [(int(p[0]), int(p[1])) for p in pts]\\n\",\n332: \"\\n\",\n333: \"\\n\",\n334: \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n335: \" ys, xs = np.where(mask)\\n\",\n336: \" if len(xs) == 0 or len(ys) == 0:\\n\",\n337: \" return (0, 0, 0, 0)\\n\",\n338: \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n339: \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n340: \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n341: \"\\n\",\n342: \"\\n\",\n343: \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n344: \" arr = np.array(image)\\n\",\n345: \" h, w = arr.shape[:2]\\n\",\n346: \" x, y, bw, bh = bbox_xywh\\n\",\n347: \" x0 = max(0, x - pad)\\n\",\n348: \" y0 = max(0, y - pad)\\n\",\n349: \" x1 = min(w, x + bw + pad)\\n\",\n350: \" y1 = min(h, y + bh + pad)\\n\",\n351: \"\\n\",\n352: \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n353: \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n354: \"\\n\",\n355: \" bg = np.full_like(crop, 255)\\n\",\n356: \" bg[crop_mask] = crop[crop_mask]\\n\",\n357: \" return Image.fromarray(bg)\\n\",\n358: \"\\n\",\n359: \"\\n\",\n360: \"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n361: \" arr = np.array(image)\\n\",\n362: \" h, w = arr.shape[:2]\\n\",\n363: \" x, y, bw, bh = bbox_xywh\\n\",\n364: \" x0 = max(0, x - pad)\\n\",\n365: \" y0 = max(0, y - pad)\\n\",\n366: \" x1 = min(w, x + bw + pad)\\n\",\n367: \" y1 = min(h, y + bh + pad)\\n\",\n368: \" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n369: \"\\n\",\n370: \"\\n\",\n371: \"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n372: \" prompts = []\\n\",\n373: \" prompt_materials = []\\n\",\n374: \" for material, aliases in material_taxonomy.items():\\n\",\n375: \" for alias in aliases:\\n\",\n376: \" for template in prompt_templates:\\n\",\n377: \" prompts.append(template.format(material=alias))\\n\",\n378: \" prompt_materials.append(material)\\n\",\n379: \" return prompts, prompt_materials\\n\",\n380: \"\\n\",\n381: \"\\n\",\n382: \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n383: \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n384: \"\\n\",\n385: \"\\n\",\n386: \"def resolve_candidate_materials(object_label: str | None):\\n\",\n387: \" if not object_label:\\n\",\n388: \" return ALL_CANONICAL_MATERIALS\\n\",\n389: \" key = object_label.strip().lower()\\n\",\n390: \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n391: \" if len(candidates) < 2:\\n\",\n392: \" return ALL_CANONICAL_MATERIALS\\n\",\n393: \" return candidates\\n\",\n394: \"\\n\",\n395: \"\\n\",\n396: \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n397: \" inputs = sam_processor(\\n\",\n398: \" image,\\n\",\n399: \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n400: \" return_tensors=\\\"pt\\\",\\n\",\n401: \" )\\n\",\n402: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n403: \"\\n\",\n404: \" with torch.no_grad():\\n\",\n405: \" outputs = sam_model(**inputs)\\n\",\n406: \"\\n\",\n407: \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n408: \" outputs.pred_masks.cpu(),\\n\",\n409: \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n410: \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n411: \" )\\n\",\n412: \"\\n\",\n413: \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n414: \" best_idx = int(np.argmax(scores))\\n\",\n415: \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n416: \" bbox = mask_bbox_xywh(mask)\\n\",\n417: \"\\n\",\n418: \" return {\\n\",\n419: \" \\\"mask\\\": mask,\\n\",\n420: \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n421: \" \\\"bbox_xywh\\\": bbox,\\n\",\n422: \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n423: \" }\\n\",\n424: \"\\n\",\n425: \"\\n\",\n426: \"def classify_material_sigclip(\\n\",\n427: \" image_views: list,\\n\",\n428: \" object_label: str | None,\\n\",\n429: \" sigclip_model,\\n\",\n430: \" sigclip_processor,\\n\",\n431: \" top_k: int = 3,\\n\",\n432: \" view_weights: list | None = None,\\n\",\n433: \"):\\n\",\n434: \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n435: \"\\n\",\n436: \" selected_idx = [\\n\",\n437: \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n438: \" ]\\n\",\n439: \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n440: \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n441: \"\\n\",\n442: \" if not selected_prompts:\\n\",\n443: \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n444: \"\\n\",\n445: \" if view_weights is None:\\n\",\n446: \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n447: \" if len(view_weights) != len(image_views):\\n\",\n448: \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n449: \"\\n\",\n450: \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n451: \"\\n\",\n452: \" for image, weight in zip(image_views, view_weights):\\n\",\n453: \" inputs = sigclip_processor(\\n\",\n454: \" text=selected_prompts,\\n\",\n455: \" images=image,\\n\",\n456: \" return_tensors=\\\"pt\\\",\\n\",\n457: \" padding=\\\"max_length\\\",\\n\",\n458: \" )\\n\",\n459: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n460: \"\\n\",\n461: \" with torch.no_grad():\\n\",\n462: \" outputs = sigclip_model(**inputs)\\n\",\n463: \"\\n\",\n464: \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n465: \"\\n\",\n466: \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n467: \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n468: \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n469: \"\\n\",\n470: \" for m in candidate_materials:\\n\",\n471: \" agg_scores[m] += weight * per_material_max[m]\\n\",\n472: \"\\n\",\n473: \" total = sum(agg_scores.values())\\n\",\n474: \" if total > 0:\\n\",\n475: \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n476: \"\\n\",\n477: \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n478: \" top_k = min(top_k, len(ranked))\\n\",\n479: \" top_items = ranked[:top_k]\\n\",\n480: \"\\n\",\n481: \" top_labels = [x[0] for x in top_items]\\n\",\n482: \" top_scores = [x[1] for x in top_items]\\n\",\n483: \"\\n\",\n484: \" return {\\n\",\n485: \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n486: \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n487: \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n488: \" \\\"topk_labels\\\": top_labels,\\n\",\n489: \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n490: \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n491: \" }\\n\",\n492: \"\\n\",\n493: \"\\n\",\n494: \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n495: \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n496: \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n497: \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n498: \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n499: \" return out_path\\n\",\n500: \"\\n\",\n501: \"\\n\",\n502: \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n503: \" df_new = pd.DataFrame([row])\\n\",\n504: \" if not csv_path.exists():\\n\",\n505: \" df_new.to_csv(csv_path, index=False)\\n\",\n506: \" return\\n\",\n507: \" df_existing = pd.read_csv(csv_path)\\n\",\n508: \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n\n(Showing lines 289-508 of 925. Use offset=509 to continue.)\n</content>", + "metadata": { + "preview": " \" };\\n\",\n \"\\n\",\n \" return await new Promise((resolve) => {\\n\",\n \" const onKey = (e) => {\\n\",\n \" if (e.key === 'Escape') {\\n\",\n \" cleanup();\\n\",\n \" resolve([]);\\n\",\n \" }\\n\",\n \" };\\n\",\n \" document.addEventListener('keydown', onKey);\\n\",\n \"\\n\",\n \" img.onclick = (e) => {\\n\",\n \" const rect = img.getBoundingClientRect();\\n\",\n \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n \" points.push([x, y]);\\n\",\n \" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n \" updateLabel();\\n\",\n \" };\\n\",\n \"\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" };\\n\",\n \"\\n\",\n \" return await new Promise((resolve) => {\\n\",\n \" const onKey = (e) => {\\n\",\n \" if (e.key === 'Escape') {\\n\",\n \" cleanup();\\n\",\n \" resolve([]);\\n\",\n \" }\\n\",\n \" };\\n\",\n \" document.addEventListener('keydown', onKey);\\n\",\n \"\\n\",\n \" img.onclick = (e) => {\\n\",\n \" const rect = img.getBoundingClientRect();\\n\",\n \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n \" points.push([x, y]);\\n\",\n \" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n \" updateLabel();\\n\",\n \" };\\n\",\n \"\\n\",\n \" undoBtn.onclick = () => {\\n\",\n \" if (points.length === 0) return;\\n\",\n \" points.pop();\\n\",\n \" const m = markers.pop();\\n\",\n \" if (m) m.remove();\\n\",\n \" updateLabel();\\n\",\n \" };\\n\",\n \"\\n\",\n \" doneBtn.onclick = () => {\\n\",\n \" cleanup();\\n\",\n \" resolve(points);\\n\",\n \" };\\n\",\n \" });\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" display(Javascript(js))\\n\",\n \" pts = eval_js(\\n\",\n \" f\\\"materialHuntersPickPoints({json.dumps(data_url)}, {json.dumps(expected_points)})\\\"\\n\",\n \" )\\n\",\n \" if not pts:\\n\",\n \" raise RuntimeError(\\\"Point selection cancelled or empty.\\\")\\n\",\n \" return [(int(p[0]), int(p[1])) for p in pts]\\n\",\n \"\\n\",\n \"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n \"\\n\",\n \"\\n\",\n \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n \" arr = np.array(image)\\n\",\n \" h, w = arr.shape[:2]\\n\",\n \" x, y, bw, bh = bbox_xywh\\n\",\n \" x0 = max(0, x - pad)\\n\",\n \" y0 = max(0, y - pad)\\n\",\n \" x1 = min(w, x + bw + pad)\\n\",\n \" y1 = min(h, y + bh + pad)\\n\",\n \"\\n\",\n \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n \"\\n\",\n \" bg = np.full_like(crop, 255)\\n\",\n \" bg[crop_mask] = crop[crop_mask]\\n\",\n \" return Image.fromarray(bg)\\n\",\n \"\\n\",\n \"\\n\",\n \"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n \" arr = np.array(image)\\n\",\n \" h, w = arr.shape[:2]\\n\",\n \" x, y, bw, bh = bbox_xywh\\n\",\n \" x0 = max(0, x - pad)\\n\",\n \" y0 = max(0, y - pad)\\n\",\n \" x1 = min(w, x + bw + pad)\\n\",\n \" y1 = min(h, y + bh + pad)\\n\",\n \" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n \"\\n\",\n \"\\n\",\n \"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n \" prompts = []\\n\",\n \" prompt_materials = []\\n\",\n \" for material, aliases in material_taxonomy.items():\\n\",\n \" for alias in aliases:\\n\",\n \" for template in prompt_templates:\\n\",\n \" prompts.append(template.format(material=alias))\\n\",\n \" prompt_materials.append(material)\\n\",\n \" return prompts, prompt_materials\\n\",\n \"\\n\",\n \"\\n\",\n \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n \"\\n\",\n \"\\n\",\n \"def resolve_candidate_materials(object_label: str | None):\\n\",\n \" if not object_label:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" key = object_label.strip().lower()\\n\",\n \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n \" if len(candidates) < 2:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" return candidates\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sam_model(**inputs)\\n\",\n \"\\n\",\n \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n \" outputs.pred_masks.cpu(),\\n\",\n \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n \" )\\n\",\n \"\\n\",\n \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n \" best_idx = int(np.argmax(scores))\\n\",\n \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n \" bbox = mask_bbox_xywh(mask)\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def classify_material_sigclip(\\n\",\n \" image_views: list,\\n\",\n \" object_label: str | None,\\n\",\n \" sigclip_model,\\n\",\n \" sigclip_processor,\\n\",\n \" top_k: int = 3,\\n\",\n \" view_weights: list | None = None,\\n\",\n \"):\\n\",\n \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n \"\\n\",\n \" selected_idx = [\\n\",\n \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n \" ]\\n\",\n \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n \"\\n\",\n \" if not selected_prompts:\\n\",\n \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n \"\\n\",\n \" if view_weights is None:\\n\",\n \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n \" if len(view_weights) != len(image_views):\\n\",\n \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n \"\\n\",\n \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n \"\\n\",\n \" for image, weight in zip(image_views, view_weights):\\n\",\n \" inputs = sigclip_processor(\\n\",\n \" text=selected_prompts,\\n\",\n \" images=image,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" padding=\\\"max_length\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sigclip_model(**inputs)\\n\",\n \"\\n\",\n \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n \"\\n\",\n \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n \"\\n\",\n \" for m in candidate_materials:\\n\",\n \" agg_scores[m] += weight * per_material_max[m]\\n\",\n \"\\n\",\n \" total = sum(agg_scores.values())\\n\",\n \" if total > 0:\\n\",\n \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n \"\\n\",\n \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n \" top_k = min(top_k, len(ranked))\\n\",\n \" top_items = ranked[:top_k]\\n\",\n \"\\n\",\n \" top_labels = [x[0] for x in top_items]\\n\",\n \" top_scores = [x[1] for x in top_items]\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n \" return out_path\\n\",\n \"\\n\",\n \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",", + "lineStart": 289, + "lineEnd": 508, + "totalLines": 925, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078617316, + "end": 1783078617363 + } + }, + "metadata": { + "openai": { + "itemId": "fc_07a2a086b6f41471016a479ed8e3cc81a1ae71ae840e740e09" + } + }, + "id": "prt_f27c47f90001Hp7FCqNMtm2NFI", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c47890002CDO1ZP2aitFfLr" + }, + { + "reason": "tool-calls", + "type": "step-finish", + 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AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n529: \"sigclip_model.eval()\\n\",\n530: \"\\n\",\n531: \"print(\\\"Models ready.\\\")\"\n532: ]\n533: },\n534: {\n535: \"cell_type\": \"code\",\n536: \"execution_count\": null,\n537: \"metadata\": {},\n538: \"outputs\": [],\n539: \"source\": [\n540: \"# Configure one image and one-or-many objects in it.\\n\",\n541: \"image_id = \\\"IMG002\\\"\\n\",\n542: \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n543: \"\\n\",\n544: \"USE_CLICK_TO_SELECT = True\\n\",\n545: \"MULTI_OBJECT_MODE = True\\n\",\n546: \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n547: \"\\n\",\n548: \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n549: \"OBJECT_SPECS = [\\n\",\n550: \" {\\n\",\n551: \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n552: \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n553: \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n554: \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n555: \" },\\n\",\n556: \" {\\n\",\n557: \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n558: \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n559: \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n560: \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n561: \" },\\n\",\n562: \" {\\n\",\n563: \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n564: \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n565: \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n566: \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n567: \" },\\n\",\n568: \"]\\n\",\n569: \"\\n\",\n570: \"img = load_image(image_path)\\n\",\n571: \"\\n\",\n572: \"if USE_CLICK_TO_SELECT:\\n\",\n573: \" if MULTI_OBJECT_MODE:\\n\",\n574: \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n575: \" if len(points) > len(OBJECT_SPECS):\\n\",\n576: \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match clicked points.\\\")\\n\",\n577: \" for i, (x, y) in enumerate(points):\\n\",\n578: \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n579: \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n580: \" OBJECT_RECORDS = OBJECT_SPECS[: len(points)]\\n\",\n581: \" else:\\n\",\n582: \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n583: \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n584: \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n585: \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n586: \"else:\\n\",\n587: \" for obj in OBJECT_SPECS:\\n\",\n588: \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n589: \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n590: \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n591: \"\\n\",\n592: \"for obj in OBJECT_RECORDS:\\n\",\n593: \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n594: \"\\n\",\n595: \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n596: \"\\n\",\n597: \"plt.figure(figsize=(9, 7))\\n\",\n598: \"plt.imshow(img)\\n\",\n599: \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n600: \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n601: \" plt.scatter([x], [y], c=\\\"red\\\", s=60)\\n\",\n602: \" plt.text(x + 6, y + 6, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9)\\n\",\n603: \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n604: \"plt.axis(\\\"off\\\")\\n\",\n605: \"plt.show()\"\n606: ]\n607: },\n608: {\n609: \"cell_type\": \"code\",\n610: \"execution_count\": null,\n611: \"metadata\": {},\n612: \"outputs\": [],\n613: \"source\": [\n614: \"RUN_ROWS = []\\n\",\n615: \"\\n\",\n616: \"for obj in OBJECT_RECORDS:\\n\",\n617: \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n618: \" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n619: \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n620: \"\\n\",\n621: \" t0 = pd.Timestamp.utcnow()\\n\",\n622: \"\\n\",\n623: \" seg_start = pd.Timestamp.utcnow()\\n\",\n624: \" seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n625: \" seg_end = pd.Timestamp.utcnow()\\n\",\n626: \"\\n\",\n627: \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n628: \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n629: \"\\n\",\n630: \" clf_start = pd.Timestamp.utcnow()\\n\",\n631: \" clf = classify_material_sigclip(\\n\",\n632: \" image_views=[crop_masked, crop_bbox, img],\\n\",\n633: \" object_label=pred_object,\\n\",\n634: \" sigclip_model=sigclip_model,\\n\",\n635: \" sigclip_processor=sigclip_processor,\\n\",\n636: \" top_k=3,\\n\",\n637: \" view_weights=[0.5, 0.35, 0.15],\\n\",\n638: \" )\\n\",\n639: \" clf_end = pd.Timestamp.utcnow()\\n\",\n640: \"\\n\",\n641: \" t1 = pd.Timestamp.utcnow()\\n\",\n642: \"\\n\",\n643: \" segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n644: \" classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n645: \" end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n646: \"\\n\",\n647: \" RUN_ROWS.append({\\n\",\n648: \" \\\"sample_id\\\": sample_id,\\n\",\n649: \" \\\"image_id\\\": image_id,\\n\",\n650: \" \\\"image_path\\\": image_path,\\n\",\n651: \" \\\"tap_x\\\": tap_x,\\n\",\n652: \" \\\"tap_y\\\": tap_y,\\n\",\n653: \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n654: \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n655: \" \\\"pred_object\\\": pred_object,\\n\",\n656: \" \\\"seg\\\": seg,\\n\",\n657: \" \\\"clf\\\": clf,\\n\",\n658: \" \\\"crop_masked\\\": crop_masked,\\n\",\n659: \" \\\"timing\\\": {\\n\",\n660: \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n661: \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n662: \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n663: \" },\\n\",\n664: \" })\\n\",\n665: \"\\n\",\n666: \" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n667: \"\\n\",\n668: \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n669: \" axes[0].imshow(img)\\n\",\n670: \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n671: \" axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n672: \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n673: \" axes[0].axis(\\\"off\\\")\\n\",\n674: \"\\n\",\n675: \" axes[1].imshow(crop_masked)\\n\",\n676: \" axes[1].set_title(f\\\"Masked Crop ({sample_id})\\\")\\n\",\n677: \" axes[1].axis(\\\"off\\\")\\n\",\n678: \" plt.tight_layout()\\n\",\n679: \" plt.show()\\n\",\n680: \"\\n\",\n681: \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n682: ]\n683: },\n684: {\n685: \"cell_type\": \"code\",\n686: \"execution_count\": null,\n687: \"metadata\": {},\n688: \"outputs\": [],\n689: \"source\": [\n690: \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n691: \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n692: \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n693: \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n694: \"\\n\",\n695: \"# Example override format:\\n\",\n696: \"# USER_REVIEW_OVERRIDES = {\\n\",\n697: \"# \\\"S003\\\": {\\n\",\n698: \"# \\\"segmentation_accepted\\\": 0,\\n\",\n699: \"# \\\"user_edited_material\\\": 1,\\n\",\n700: \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n701: \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n702: \"# \\\"status\\\": \\\"ok\\\",\\n\",\n703: \"# \\\"error_type\\\": \\\"\\\",\\n\",\n704: \"# }\\n\",\n705: \"# }\\n\",\n706: \"USER_REVIEW_OVERRIDES = {}\\n\",\n707: \"\\n\",\n708: \"for run in RUN_ROWS:\\n\",\n709: \" sid = run[\\\"sample_id\\\"]\\n\",\n710: \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n711: \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n712: \"\\n\",\n713: \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n714: \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n715: \"\\n\",\n716: \" run[\\\"review\\\"] = {\\n\",\n717: \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n718: \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n719: \" \\\"user_final_material\\\": user_final_material,\\n\",\n720: \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n721: \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n722: \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n723: \" }\\n\",\n724: \"\\n\",\n725: \"pd.DataFrame([\\n\",\n726: \" {\\n\",\n727: \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n728: \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n729: \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n730: \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n731: \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n732: \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n733: \" }\\n\",\n734: \" for r in RUN_ROWS\\n\",\n735: \"])\"\n736: ]\n737: },\n738: {\n739: \"cell_type\": \"code\",\n740: \"execution_count\": null,\n741: \"metadata\": {},\n742: \"outputs\": [],\n743: \"source\": [\n744: \"for run in RUN_ROWS:\\n\",\n745: \" review = run.get(\\\"review\\\", {})\\n\",\n746: \" seg = run[\\\"seg\\\"]\\n\",\n747: \" clf = run[\\\"clf\\\"]\\n\",\n748: \" timing = run[\\\"timing\\\"]\\n\",\n749: \"\\n\",\n\n(Showing lines 520-749 of 925. Use offset=750 to continue.)\n</content>", + "metadata": { + "preview": " \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n \"print(\\\"Loading SAM model...\\\")\\n\",\n \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n \"sam_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n \"print(\\\"Loading SAM model...\\\")\\n\",\n \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n \"sam_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n \"USE_CLICK_TO_SELECT = True\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n \"\\n\",\n \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n \"OBJECT_SPECS = [\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n \" },\\n\",\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n \"\\n\",\n \"if USE_CLICK_TO_SELECT:\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n \" if len(points) > len(OBJECT_SPECS):\\n\",\n \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match clicked points.\\\")\\n\",\n \" for i, (x, y) in enumerate(points):\\n\",\n \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS[: len(points)]\\n\",\n \" else:\\n\",\n \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n \" plt.scatter([x], [y], c=\\\"red\\\", s=60)\\n\",\n \" plt.text(x + 6, y + 6, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9)\\n\",\n \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"RUN_ROWS = []\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n \" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \" t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" seg_start = pd.Timestamp.utcnow()\\n\",\n \" seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n \" seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n \"\\n\",\n \" clf_start = pd.Timestamp.utcnow()\\n\",\n \" clf = classify_material_sigclip(\\n\",\n \" image_views=[crop_masked, crop_bbox, img],\\n\",\n \" object_label=pred_object,\\n\",\n \" sigclip_model=sigclip_model,\\n\",\n \" sigclip_processor=sigclip_processor,\\n\",\n \" top_k=3,\\n\",\n \" view_weights=[0.5, 0.35, 0.15],\\n\",\n \" )\\n\",\n \" clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n \" classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n \" end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n \"\\n\",\n \" RUN_ROWS.append({\\n\",\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n \" \\\"pred_object\\\": pred_object,\\n\",\n \" \\\"seg\\\": seg,\\n\",\n \" \\\"clf\\\": clf,\\n\",\n \" \\\"crop_masked\\\": crop_masked,\\n\",\n \" \\\"timing\\\": {\\n\",\n \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n \" },\\n\",\n \" })\\n\",\n \"\\n\",\n \" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n \"\\n\",\n \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \" axes[0].imshow(img)\\n\",\n \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n \" axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n \" axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n \" axes[1].imshow(crop_masked)\\n\",\n \" axes[1].set_title(f\\\"Masked Crop ({sample_id})\\\")\\n\",\n \" axes[1].axis(\\\"off\\\")\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n \"\\n\",\n \"# Example override format:\\n\",\n \"# USER_REVIEW_OVERRIDES = {\\n\",\n \"# \\\"S003\\\": {\\n\",\n \"# \\\"segmentation_accepted\\\": 0,\\n\",\n \"# \\\"user_edited_material\\\": 1,\\n\",\n \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n \"# \\\"status\\\": \\\"ok\\\",\\n\",\n \"# \\\"error_type\\\": \\\"\\\",\\n\",\n \"# }\\n\",\n \"# }\\n\",\n \"USER_REVIEW_OVERRIDES = {}\\n\",\n \"\\n\",\n \"for run in RUN_ROWS:\\n\",\n \" sid = run[\\\"sample_id\\\"]\\n\",\n \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n \"\\n\",\n \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n \"\\n\",\n \" run[\\\"review\\\"] = {\\n\",\n \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n \" \\\"user_final_material\\\": user_final_material,\\n\",\n \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n \" }\\n\",\n \"\\n\",\n \"pd.DataFrame([\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n \" }\\n\",\n \" for r in RUN_ROWS\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for run in RUN_ROWS:\\n\",\n \" review = run.get(\\\"review\\\", {})\\n\",\n \" seg = run[\\\"seg\\\"]\\n\",\n \" clf = run[\\\"clf\\\"]\\n\",\n \" timing = run[\\\"timing\\\"]\\n\",\n \"\\n\",", + "lineStart": 520, + "lineEnd": 749, + "totalLines": 925, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078619607, + "end": 1783078619621 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0a5ab4a5ef2a37da016a479edb464c819eae4e01f074661a55" + } + }, + "id": "prt_f27c48929001pqscwaK7w4tDRZ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c48138002X5nk8Bm1O63tXA" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 61141, + "input": 3484, + "output": 47, + "reasoning": 10, + "cache": { + "write": 0, + "read": 57600 + } + }, + "cost": 0.016975, + "id": "prt_f27c489ec001JiYJEmoP4U0AP2", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c48138002X5nk8Bm1O63tXA" + } + ] + }, + { + "info": { + "parentID": "msg_f27c43663001JCENYPiYbSmtTC", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.01752765, + "tokens": { + "total": 64536, + "input": 3563, + "output": 45, + "reasoning": 0, + "cache": { + "write": 0, + "read": 60928 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783078619646, + "completed": 1783078621928 + }, + "finish": "tool-calls", + "id": "msg_f27c489fe001t6c2Hg8GUqoi7n", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27c48f27001QCMj7BtOScURvd", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c489fe001t6c2Hg8GUqoi7n" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_Mjk4TtwNg85blryeF1B1LD3C", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 744, + "limit": 170 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n744: \"for run in RUN_ROWS:\\n\",\n745: \" review = run.get(\\\"review\\\", {})\\n\",\n746: \" seg = run[\\\"seg\\\"]\\n\",\n747: \" clf = run[\\\"clf\\\"]\\n\",\n748: \" timing = run[\\\"timing\\\"]\\n\",\n749: \"\\n\",\n750: \" passport = {\\n\",\n751: \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n752: \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n753: \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n754: \" \\\"tap\\\": {\\\"x\\\": int(run[\\\"tap_x\\\"]), \\\"y\\\": int(run[\\\"tap_y\\\"])},\\n\",\n755: \" \\\"object\\\": {\\n\",\n756: \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n757: \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n758: \" },\\n\",\n759: \" \\\"material\\\": {\\n\",\n760: \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n761: \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n762: \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n763: \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n764: \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n765: \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n766: \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n767: \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n768: \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n769: \" },\\n\",\n770: \" \\\"segmentation\\\": {\\n\",\n771: \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n772: \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n773: \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n774: \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n775: \" },\\n\",\n776: \" \\\"timing\\\": {\\n\",\n777: \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n778: \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n779: \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n780: \" },\\n\",\n781: \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n782: \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n783: \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n784: \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n785: \" }\\n\",\n786: \"\\n\",\n787: \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n788: \"\\n\",\n789: \" eval_row = {\\n\",\n790: \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n791: \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n792: \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n793: \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n794: \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n795: \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n796: \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n797: \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n798: \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n799: \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n800: \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n801: \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n802: \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n803: \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n804: \" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n805: \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n806: \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n807: \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n808: \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n809: \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n810: \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n811: \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n812: \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n813: \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n814: \" }\\n\",\n815: \" append_eval_row(CSV_PATH, eval_row)\\n\",\n816: \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n817: \"\\n\",\n818: \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n819: ]\n820: },\n821: {\n822: \"cell_type\": \"code\",\n823: \"execution_count\": null,\n824: \"metadata\": {},\n825: \"outputs\": [],\n826: \"source\": [\n827: \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n828: \" df = df_in.copy()\\n\",\n829: \" df = df[df[\\\"status\\\"].notna()]\\n\",\n830: \"\\n\",\n831: \" if df.empty:\\n\",\n832: \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n833: \"\\n\",\n834: \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n835: \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n836: \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n837: \" axis=1,\\n\",\n838: \" )\\n\",\n839: \"\\n\",\n840: \" metrics = {\\n\",\n841: \" \\\"n_samples\\\": int(len(df)),\\n\",\n842: \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n843: \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n844: \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n845: \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n846: \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n847: \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n848: \" }\\n\",\n849: \"\\n\",\n850: \" go_no_go = (\\n\",\n851: \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n852: \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n853: \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n854: \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n855: \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n856: \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n857: \" )\\n\",\n858: \"\\n\",\n859: \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n860: \"\\n\",\n861: \"\\n\",\n862: \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n863: \"result = compute_metrics(df_eval)\\n\",\n864: \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n865: \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n866: \" print(f\\\"- {k}: {v}\\\")\\n\",\n867: \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n868: ]\n869: },\n870: {\n871: \"cell_type\": \"code\",\n872: \"execution_count\": null,\n873: \"metadata\": {},\n874: \"outputs\": [],\n875: \"source\": [\n876: \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n877: \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n878: \"\\n\",\n879: \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n880: \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n881: \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n882: \"\\n\",\n883: \"if sigclip_df.empty:\\n\",\n884: \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n885: \"else:\\n\",\n886: \" print(\\\"SigCLIP metrics:\\\")\\n\",\n887: \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n888: \"\\n\",\n889: \"if dinov3_df.empty:\\n\",\n890: \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n891: \"else:\\n\",\n892: \" print(\\\"DINOv3 metrics:\\\")\\n\",\n893: \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n894: \"\\n\",\n895: \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n896: \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n897: \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n898: \"\\n\",\n899: \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n900: \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n901: \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n902: \"\\n\",\n903: \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n904: \"\\n\",\n905: \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n906: \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n907: \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n908: \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n909: ]\n910: }\n911: ],\n912: \"metadata\": {\n913: \"kernelspec\": {\n\n(Showing lines 744-913 of 925. Use offset=914 to continue.)\n</content>", + "metadata": { + "preview": " \"for run in RUN_ROWS:\\n\",\n \" review = run.get(\\\"review\\\", {})\\n\",\n \" seg = run[\\\"seg\\\"]\\n\",\n \" clf = run[\\\"clf\\\"]\\n\",\n \" timing = run[\\\"timing\\\"]\\n\",\n \"\\n\",\n \" passport = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n \" \\\"tap\\\": {\\\"x\\\": int(run[\\\"tap_x\\\"]), \\\"y\\\": int(run[\\\"tap_y\\\"])},\\n\",\n \" \\\"object\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n \" },\\n\",\n \" \\\"material\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \"for run in RUN_ROWS:\\n\",\n \" review = run.get(\\\"review\\\", {})\\n\",\n \" seg = run[\\\"seg\\\"]\\n\",\n \" clf = run[\\\"clf\\\"]\\n\",\n \" timing = run[\\\"timing\\\"]\\n\",\n \"\\n\",\n \" passport = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n \" \\\"tap\\\": {\\\"x\\\": int(run[\\\"tap_x\\\"]), \\\"y\\\": int(run[\\\"tap_y\\\"])},\\n\",\n \" \\\"object\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n \" },\\n\",\n \" \\\"material\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" },\\n\",\n \" \\\"segmentation\\\": {\\n\",\n \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n \" },\\n\",\n \" \\\"timing\\\": {\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" },\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n \" }\\n\",\n \"\\n\",\n \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n \"\\n\",\n \" eval_row = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n \" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n \" }\\n\",\n \" append_eval_row(CSV_PATH, eval_row)\\n\",\n \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n \"\\n\",\n \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n \" df = df_in.copy()\\n\",\n \" df = df[df[\\\"status\\\"].notna()]\\n\",\n \"\\n\",\n \" if df.empty:\\n\",\n \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n \"\\n\",\n \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n \" axis=1,\\n\",\n \" )\\n\",\n \"\\n\",\n \" metrics = {\\n\",\n \" \\\"n_samples\\\": int(len(df)),\\n\",\n \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n \" }\\n\",\n \"\\n\",\n \" go_no_go = (\\n\",\n \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n \" )\\n\",\n \"\\n\",\n \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n \"\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"result = compute_metrics(df_eval)\\n\",\n \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n \" print(f\\\"- {k}: {v}\\\")\\n\",\n \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n \"\\n\",\n \"if sigclip_df.empty:\\n\",\n \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"SigCLIP metrics:\\\")\\n\",\n \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n \"\\n\",\n \"if dinov3_df.empty:\\n\",\n \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"DINOv3 metrics:\\\")\\n\",\n \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n \"\\n\",\n \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n \"\\n\",\n \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n \"\\n\",\n \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n \"\\n\",\n \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {", + "lineStart": 744, + "lineEnd": 913, + "totalLines": 925, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078621840, + "end": 1783078621897 + } + }, + "metadata": { + "openai": { + 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -41,8 +41,9 @@\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n+\"import matplotlib.patches as patches\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n \"from transformers import (\\n\",\n \" SamModel,\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -41,8 +41,9 @@\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n+\"import matplotlib.patches as patches\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n \"from transformers import (\\n\",\n \" SamModel,\\n\",\n", + "additions": 1, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -6,9 +6,9 @@\n \"source\": [\n \"# Material Hunters POC Notebook\\n\",\n \"\\n\",\n \"This notebook runs a minimal end-to-end baseline:\\n\",\n- \"- point-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+ \"- box-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n \"- material classification with SigCLIP\\n\",\n \"- optional user edits\\n\",\n \"- passport JSON export\\n\",\n \"- evaluation row append + go/no-go metrics\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -6,9 +6,9 @@\n \"source\": [\n \"# Material Hunters POC Notebook\\n\",\n \"\\n\",\n \"This notebook runs a minimal end-to-end baseline:\\n\",\n- \"- point-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+ \"- box-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n \"- material classification with SigCLIP\\n\",\n \"- optional user edits\\n\",\n \"- passport JSON export\\n\",\n \"- evaluation row append + go/no-go metrics\\n\",\n", + "additions": 1, + "deletions": 1 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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materialHuntersPickPoint(dataUrl) {\\n\",\n- \" const overlay = document.createElement('div');\\n\",\n- \" overlay.style.position = 'fixed';\\n\",\n- \" overlay.style.inset = '0';\\n\",\n- \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n- \" overlay.style.zIndex = '9999';\\n\",\n- \" overlay.style.display = 'flex';\\n\",\n- \" overlay.style.flexDirection = 'column';\\n\",\n- \" overlay.style.alignItems = 'center';\\n\",\n- \" overlay.style.justifyContent = 'center';\\n\",\n- \" overlay.style.gap = '8px';\\n\",\n- \"\\n\",\n- \" const label = document.createElement('div');\\n\",\n- \" label.textContent = 'Click the target point (Esc to cancel)';\\n\",\n- \" label.style.color = 'white';\\n\",\n- \" label.style.font = '16px sans-serif';\\n\",\n- \"\\n\",\n- \" const img = document.createElement('img');\\n\",\n- \" img.src = dataUrl;\\n\",\n- \" img.style.width = '95vw';\\n\",\n- \" img.style.maxWidth = '1800px';\\n\",\n- \" img.style.maxHeight = '90vh';\\n\",\n- \" img.style.cursor = 'crosshair';\\n\",\n- \" img.style.border = '2px solid white';\\n\",\n- \"\\n\",\n- \" overlay.appendChild(label);\\n\",\n- \" overlay.appendChild(img);\\n\",\n- \" document.body.appendChild(overlay);\\n\",\n- \"\\n\",\n- \" return await new Promise((resolve) => {\\n\",\n- \" const cleanup = () => {\\n\",\n- \" document.removeEventListener('keydown', onKey);\\n\",\n- \" overlay.remove();\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const onKey = (e) => {\\n\",\n- \" if (e.key === 'Escape') {\\n\",\n- \" cleanup();\\n\",\n- \" resolve([-1, -1]);\\n\",\n- \" }\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" document.addEventListener('keydown', onKey);\\n\",\n- \"\\n\",\n- \" img.onclick = (e) => {\\n\",\n- \" const rect = img.getBoundingClientRect();\\n\",\n- \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n- \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n- \" cleanup();\\n\",\n- \" resolve([x, y]);\\n\",\n- \" };\\n\",\n- \" });\\n\",\n- \" }\\n\",\n- \" \\\"\\\"\\\"\\n\",\n- \"\\n\",\n- \" display(Javascript(js))\\n\",\n- \" x, y = eval_js(f\\\"materialHuntersPickPoint({json.dumps(data_url)})\\\")\\n\",\n- \" if x < 0 or y < 0:\\n\",\n- \" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n- \" return int(x), int(y)\\n\",\n- \"\\n\",\n- \"\\n\",\n- \"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n- \" try:\\n\",\n- \" from google.colab.output import eval_js\\n\",\n- \" from IPython.display import Javascript, display\\n\",\n- \" except Exception as exc:\\n\",\n- \" raise RuntimeError(\\\"Multi-point selection requires Google Colab.\\\") from exc\\n\",\n- \"\\n\",\n- \" buf = io.BytesIO()\\n\",\n- \" image.save(buf, format=\\\"PNG\\\")\\n\",\n- \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n- \"\\n\",\n- \" js = \\\"\\\"\\\"\\n\",\n- \" async function materialHuntersPickPoints(dataUrl, expectedPoints) {\\n\",\n- \" const overlay = document.createElement('div');\\n\",\n- \" overlay.style.position = 'fixed';\\n\",\n- \" overlay.style.inset = '0';\\n\",\n- \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n- \" overlay.style.zIndex = '9999';\\n\",\n- \" overlay.style.display = 'flex';\\n\",\n- \" overlay.style.flexDirection = 'column';\\n\",\n- \" overlay.style.alignItems = 'center';\\n\",\n- \" overlay.style.justifyContent = 'center';\\n\",\n- \" overlay.style.gap = '8px';\\n\",\n- \"\\n\",\n- \" const label = document.createElement('div');\\n\",\n- \" label.style.color = 'white';\\n\",\n- \" label.style.font = '16px sans-serif';\\n\",\n- \" label.textContent = expectedPoints\\n\",\n- \" ? `Click ${expectedPoints} points, then press Done (Esc cancels)`\\n\",\n- \" : 'Click one or more points, then press Done (Esc cancels)';\\n\",\n- \"\\n\",\n- \" const container = document.createElement('div');\\n\",\n- \" container.style.position = 'relative';\\n\",\n- \"\\n\",\n- \" const img = document.createElement('img');\\n\",\n- \" img.src = dataUrl;\\n\",\n- \" img.style.width = '95vw';\\n\",\n- \" img.style.maxWidth = '1800px';\\n\",\n- \" img.style.maxHeight = '90vh';\\n\",\n- \" img.style.cursor = 'crosshair';\\n\",\n- \" img.style.border = '2px solid white';\\n\",\n- \" img.style.display = 'block';\\n\",\n- \"\\n\",\n- \" const controls = document.createElement('div');\\n\",\n- \" controls.style.display = 'flex';\\n\",\n- \" controls.style.gap = '8px';\\n\",\n- \"\\n\",\n- \" const doneBtn = document.createElement('button');\\n\",\n- \" doneBtn.textContent = 'Done';\\n\",\n- \" const undoBtn = document.createElement('button');\\n\",\n- \" undoBtn.textContent = 'Undo';\\n\",\n- \"\\n\",\n- \" controls.appendChild(doneBtn);\\n\",\n- \" controls.appendChild(undoBtn);\\n\",\n- \"\\n\",\n- \" container.appendChild(img);\\n\",\n- \" overlay.appendChild(label);\\n\",\n- \" overlay.appendChild(container);\\n\",\n- \" overlay.appendChild(controls);\\n\",\n- \" document.body.appendChild(overlay);\\n\",\n- \"\\n\",\n- \" const points = [];\\n\",\n- \" const markers = [];\\n\",\n- \"\\n\",\n- \" const drawMarker = (xClient, yClient, n) => {\\n\",\n- \" const m = document.createElement('div');\\n\",\n- \" m.style.position = 'fixed';\\n\",\n- \" m.style.left = `${xClient - 6}px`;\\n\",\n- \" m.style.top = `${yClient - 6}px`;\\n\",\n- \" m.style.width = '12px';\\n\",\n- \" m.style.height = '12px';\\n\",\n- \" m.style.borderRadius = '6px';\\n\",\n- \" m.style.background = '#ff5252';\\n\",\n- \" m.style.border = '1px solid white';\\n\",\n- \" m.title = `Point ${n}`;\\n\",\n- \" m.style.zIndex = '10000';\\n\",\n- \" document.body.appendChild(m);\\n\",\n- \" markers.push(m);\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const cleanup = () => {\\n\",\n- \" document.removeEventListener('keydown', onKey);\\n\",\n- \" markers.forEach((m) => m.remove());\\n\",\n- \" overlay.remove();\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const updateLabel = () => {\\n\",\n- \" const count = points.length;\\n\",\n- \" if (expectedPoints) {\\n\",\n- \" label.textContent = `Selected ${count}/${expectedPoints}. Click more points or press Done.`;\\n\",\n- \" } else {\\n\",\n- \" label.textContent = `Selected ${count} point(s). Click more points or press Done.`;\\n\",\n- \" }\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" return await new Promise((resolve) => {\\n\",\n- \" const onKey = (e) => {\\n\",\n- \" if (e.key === 'Escape') {\\n\",\n- \" cleanup();\\n\",\n- \" resolve([]);\\n\",\n- \" }\\n\",\n- \" };\\n\",\n- \" document.addEventListener('keydown', onKey);\\n\",\n- \"\\n\",\n- \" img.onclick = (e) => {\\n\",\n- \" const rect = img.getBoundingClientRect();\\n\",\n- \" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n- \" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n- \" points.push([x, y]);\\n\",\n- \" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n- \" updateLabel();\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" undoBtn.onclick = () => {\\n\",\n- \" if (points.length === 0) return;\\n\",\n- \" points.pop();\\n\",\n- \" const m = markers.pop();\\n\",\n- \" if (m) m.remove();\\n\",\n- \" updateLabel();\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" doneBtn.onclick = () => {\\n\",\n- \" cleanup();\\n\",\n- \" resolve(points);\\n\",\n- \" };\\n\",\n- \" });\\n\",\n- \" }\\n\",\n- \" \\\"\\\"\\\"\\n\",\n- \"\\n\",\n- \" display(Javascript(js))\\n\",\n- \" pts = eval_js(\\n\",\n- \" f\\\"materialHuntersPickPoints({json.dumps(data_url)}, {json.dumps(expected_points)})\\\"\\n\",\n- \" )\\n\",\n- \" if not pts:\\n\",\n- \" raise RuntimeError(\\\"Point selection cancelled or empty.\\\")\\n\",\n- \" return [(int(p[0]), int(p[1])) for p in pts]\\n\",\n- \"\\n\",\n- \"\\n\",\n+ \"def select_bbox_colab(image: Image.Image, prompt: str = \\\"Draw a box around the object\\\"):\\n\",\n+ \" try:\\n\",\n+ \" from google.colab.output import eval_js\\n\",\n+ \" from IPython.display import Javascript, display\\n\",\n+ \" except Exception as exc:\\n\",\n+ \" raise RuntimeError(\\\"Box selection requires Google Colab.\\\") from exc\\n\",\n+ \"\\n\",\n+ \" buf = io.BytesIO()\\n\",\n+ \" image.save(buf, format=\\\"PNG\\\")\\n\",\n+ \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+ \"\\n\",\n+ \" js = \\\"\\\"\\\"\\n\",\n+ \" async function materialHuntersPickBox(dataUrl, promptText) {\\n\",\n+ \" const overlay = document.createElement('div');\\n\",\n+ \" overlay.style.position = 'fixed';\\n\",\n+ \" overlay.style.inset = '0';\\n\",\n+ \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+ \" overlay.style.zIndex = '9999';\\n\",\n+ \" overlay.style.display = 'flex';\\n\",\n+ \" overlay.style.flexDirection = 'column';\\n\",\n+ \" overlay.style.alignItems = 'center';\\n\",\n+ \" overlay.style.justifyContent = 'center';\\n\",\n+ \" overlay.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const label = document.createElement('div');\\n\",\n+ \" label.textContent = `${promptText} (drag mouse, Done to confirm, Esc to cancel)`;\\n\",\n+ \" label.style.color = 'white';\\n\",\n+ \" label.style.font = '16px sans-serif';\\n\",\n+ \"\\n\",\n+ \" const container = document.createElement('div');\\n\",\n+ \" container.style.position = 'relative';\\n\",\n+ \"\\n\",\n+ \" const img = document.createElement('img');\\n\",\n+ \" img.src = dataUrl;\\n\",\n+ \" img.style.width = '95vw';\\n\",\n+ \" img.style.maxWidth = '1800px';\\n\",\n+ \" img.style.maxHeight = '90vh';\\n\",\n+ \" img.style.cursor = 'crosshair';\\n\",\n+ \" img.style.border = '2px solid white';\\n\",\n+ \" img.style.display = 'block';\\n\",\n+ \"\\n\",\n+ \" const box = document.createElement('div');\\n\",\n+ \" box.style.position = 'absolute';\\n\",\n+ \" box.style.border = '2px solid #00e5ff';\\n\",\n+ \" box.style.background = 'rgba(0,229,255,0.15)';\\n\",\n+ \" box.style.display = 'none';\\n\",\n+ \"\\n\",\n+ \" container.appendChild(img);\\n\",\n+ \" container.appendChild(box);\\n\",\n+ \"\\n\",\n+ \" const controls = document.createElement('div');\\n\",\n+ \" controls.style.display = 'flex';\\n\",\n+ \" controls.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const doneBtn = document.createElement('button');\\n\",\n+ \" doneBtn.textContent = 'Done';\\n\",\n+ \" const resetBtn = document.createElement('button');\\n\",\n+ \" resetBtn.textContent = 'Reset';\\n\",\n+ \"\\n\",\n+ \" controls.appendChild(doneBtn);\\n\",\n+ \" controls.appendChild(resetBtn);\\n\",\n+ \"\\n\",\n+ \" overlay.appendChild(label);\\n\",\n+ \" overlay.appendChild(container);\\n\",\n+ \" overlay.appendChild(controls);\\n\",\n+ \" document.body.appendChild(overlay);\\n\",\n+ \"\\n\",\n+ \" let dragging = false;\\n\",\n+ \" let startX = 0;\\n\",\n+ \" let startY = 0;\\n\",\n+ \" let latest = null;\\n\",\n+ \"\\n\",\n+ \" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n+ \"\\n\",\n+ \" const cleanup = () => {\\n\",\n+ \" document.removeEventListener('keydown', onKey);\\n\",\n+ \" overlay.remove();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const toNatural = (xCss, yCss, rect) => {\\n\",\n+ \" const nx = Math.round(xCss * (img.naturalWidth / rect.width));\\n\",\n+ \" const ny = Math.round(yCss * (img.naturalHeight / rect.height));\\n\",\n+ \" return [nx, ny];\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const onKey = (e) => {\\n\",\n+ \" if (e.key === 'Escape') {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolvePromise([-1, -1, -1, -1]);\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" document.addEventListener('keydown', onKey);\\n\",\n+ \"\\n\",\n+ \" img.onmousedown = (e) => {\\n\",\n+ \" const rect = img.getBoundingClientRect();\\n\",\n+ \" dragging = true;\\n\",\n+ \" startX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n+ \" startY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n+ \" box.style.display = 'block';\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" img.onmousemove = (e) => {\\n\",\n+ \" if (!dragging) return;\\n\",\n+ \" const rect = img.getBoundingClientRect();\\n\",\n+ \" const curX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n+ \" const curY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n+ \"\\n\",\n+ \" const left = Math.min(startX, curX);\\n\",\n+ \" const top = Math.min(startY, curY);\\n\",\n+ \" const width = Math.max(1, Math.abs(curX - startX));\\n\",\n+ \" const height = Math.max(1, Math.abs(curY - startY));\\n\",\n+ \"\\n\",\n+ \" box.style.left = `${left}px`;\\n\",\n+ \" box.style.top = `${top}px`;\\n\",\n+ \" box.style.width = `${width}px`;\\n\",\n+ \" box.style.height = `${height}px`;\\n\",\n+ \"\\n\",\n+ \" const [x0, y0] = toNatural(left, top, rect);\\n\",\n+ \" const [x1, y1] = toNatural(left + width, top + height, rect);\\n\",\n+ \" latest = [Math.min(x0, x1), Math.min(y0, y1), Math.max(x0, x1), Math.max(y0, y1)];\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" img.onmouseup = () => {\\n\",\n+ \" dragging = false;\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" img.onmouseleave = () => {\\n\",\n+ \" dragging = false;\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" resetBtn.onclick = () => {\\n\",\n+ \" latest = null;\\n\",\n+ \" box.style.display = 'none';\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" let resolvePromise;\\n\",\n+ \" const promise = new Promise((resolve) => {\\n\",\n+ \" resolvePromise = resolve;\\n\",\n+ \" });\\n\",\n+ \"\\n\",\n+ \" doneBtn.onclick = () => {\\n\",\n+ \" if (!latest) {\\n\",\n+ \" label.textContent = 'Draw a box first.';\\n\",\n+ \" return;\\n\",\n+ \" }\\n\",\n+ \" cleanup();\\n\",\n+ \" resolvePromise(latest);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" return await promise;\\n\",\n+ \" }\\n\",\n+ \" \\\"\\\"\\\"\\n\",\n+ \"\\n\",\n+ \" display(Javascript(js))\\n\",\n+ \" x0, y0, x1, y1 = eval_js(\\n\",\n+ \" f\\\"materialHuntersPickBox({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n+ \" )\\n\",\n+ \" if x0 < 0 or y0 < 0:\\n\",\n+ \" raise RuntimeError(\\\"Box selection cancelled.\\\")\\n\",\n+ \" if x1 <= x0 or y1 <= y0:\\n\",\n+ \" raise RuntimeError(\\\"Invalid box. Draw a box with non-zero area.\\\")\\n\",\n+ \" return (int(x0), int(y0), int(x1), int(y1))\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def select_multiple_bboxes_colab(image: Image.Image, expected_boxes: int):\\n\",\n+ \" if expected_boxes < 1:\\n\",\n+ \" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n+ \" bboxes = []\\n\",\n+ \" for i in range(expected_boxes):\\n\",\n+ \" bbox = select_bbox_colab(image, prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\")\\n\",\n+ \" bboxes.append(bbox)\\n\",\n+ \" return bboxes\\n\",\n+ \"\\n\",\n+ \"\\n\",\n@@\n- \"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n+ \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n+ \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n+ \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def run_sam_box_segmentation(image: Image.Image, prompt_bbox_xyxy, sam_model, sam_processor):\\n\",\n+ \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n- \" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n+ \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n@@\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n+ \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -119,21 +119,21 @@\n \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n-\"def select_tap_point_colab(image: Image.Image):\\n\",\n+\"def select_bbox_colab(image: Image.Image, prompt: str = \\\"Draw a box around the object\\\"):\\n\",\n \" try:\\n\",\n \" from google.colab.output import eval_js\\n\",\n \" from IPython.display import Javascript, display\\n\",\n \" except Exception as exc:\\n\",\n-\" raise RuntimeError(\\\"Click-to-select requires Google Colab.\\\") from exc\\n\",\n+\" raise RuntimeError(\\\"Box selection requires Google Colab.\\\") from exc\\n\",\n \"\\n\",\n \" buf = io.BytesIO()\\n\",\n \" image.save(buf, format=\\\"PNG\\\")\\n\",\n \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n \"\\n\",\n \" js = \\\"\\\"\\\"\\n\",\n-\" async function materialHuntersPickPoint(dataUrl) {\\n\",\n+\" async function materialHuntersPickBox(dataUrl, promptText) {\\n\",\n \" const overlay = document.createElement('div');\\n\",\n \" overlay.style.position = 'fixed';\\n\",\n \" overlay.style.inset = '0';\\n\",\n \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n@@ -144,88 +144,12 @@\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n-\" label.textContent = 'Click the target point (Esc to cancel)';\\n\",\n+\" label.textContent = `${promptText} (drag mouse, Done to confirm, Esc to cancel)`;\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n-\" const img = document.createElement('img');\\n\",\n-\" img.src = dataUrl;\\n\",\n-\" img.style.width = '95vw';\\n\",\n-\" img.style.maxWidth = '1800px';\\n\",\n-\" img.style.maxHeight = '90vh';\\n\",\n-\" img.style.cursor = 'crosshair';\\n\",\n-\" img.style.border = '2px solid white';\\n\",\n-\"\\n\",\n-\" overlay.appendChild(label);\\n\",\n-\" overlay.appendChild(img);\\n\",\n-\" document.body.appendChild(overlay);\\n\",\n-\"\\n\",\n-\" return await new Promise((resolve) => {\\n\",\n-\" const cleanup = () => {\\n\",\n-\" document.removeEventListener('keydown', onKey);\\n\",\n-\" overlay.remove();\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" const onKey = (e) => {\\n\",\n-\" if (e.key === 'Escape') {\\n\",\n-\" cleanup();\\n\",\n-\" resolve([-1, -1]);\\n\",\n-\" }\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" document.addEventListener('keydown', onKey);\\n\",\n-\"\\n\",\n-\" img.onclick = (e) => {\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n-\" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n-\" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n-\" cleanup();\\n\",\n-\" resolve([x, y]);\\n\",\n-\" };\\n\",\n-\" });\\n\",\n-\" }\\n\",\n-\" \\\"\\\"\\\"\\n\",\n-\"\\n\",\n-\" display(Javascript(js))\\n\",\n-\" x, y = eval_js(f\\\"materialHuntersPickPoint({json.dumps(data_url)})\\\")\\n\",\n-\" if x < 0 or y < 0:\\n\",\n-\" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n-\" return int(x), int(y)\\n\",\n-\"\\n\",\n-\"\\n\",\n-\"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n-\" try:\\n\",\n-\" from google.colab.output import eval_js\\n\",\n-\" from IPython.display import Javascript, display\\n\",\n-\" except Exception as exc:\\n\",\n-\" raise RuntimeError(\\\"Multi-point selection requires Google Colab.\\\") from exc\\n\",\n-\"\\n\",\n-\" buf = io.BytesIO()\\n\",\n-\" image.save(buf, format=\\\"PNG\\\")\\n\",\n-\" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n-\"\\n\",\n-\" js = \\\"\\\"\\\"\\n\",\n-\" async function materialHuntersPickPoints(dataUrl, expectedPoints) {\\n\",\n-\" const overlay = document.createElement('div');\\n\",\n-\" overlay.style.position = 'fixed';\\n\",\n-\" overlay.style.inset = '0';\\n\",\n-\" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n-\" overlay.style.zIndex = '9999';\\n\",\n-\" overlay.style.display = 'flex';\\n\",\n-\" overlay.style.flexDirection = 'column';\\n\",\n-\" overlay.style.alignItems = 'center';\\n\",\n-\" overlay.style.justifyContent = 'center';\\n\",\n-\" overlay.style.gap = '8px';\\n\",\n-\"\\n\",\n-\" const label = document.createElement('div');\\n\",\n-\" label.style.color = 'white';\\n\",\n-\" label.style.font = '16px sans-serif';\\n\",\n-\" label.textContent = expectedPoints\\n\",\n-\" ? `Click ${expectedPoints} points, then press Done (Esc cancels)`\\n\",\n-\" : 'Click one or more points, then press Done (Esc cancels)';\\n\",\n-\"\\n\",\n \" const container = document.createElement('div');\\n\",\n \" container.style.position = 'relative';\\n\",\n \"\\n\",\n \" const img = document.createElement('img');\\n\",\n@@ -236,103 +160,142 @@\n \" img.style.cursor = 'crosshair';\\n\",\n \" img.style.border = '2px solid white';\\n\",\n \" img.style.display = 'block';\\n\",\n \"\\n\",\n+\" const box = document.createElement('div');\\n\",\n+\" box.style.position = 'absolute';\\n\",\n+\" box.style.border = '2px solid #00e5ff';\\n\",\n+\" box.style.background = 'rgba(0,229,255,0.15)';\\n\",\n+\" box.style.display = 'none';\\n\",\n+\"\\n\",\n+\" container.appendChild(img);\\n\",\n+\" container.appendChild(box);\\n\",\n+\"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n \"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n-\" const undoBtn = document.createElement('button');\\n\",\n-\" undoBtn.textContent = 'Undo';\\n\",\n+\" const resetBtn = document.createElement('button');\\n\",\n+\" resetBtn.textContent = 'Reset';\\n\",\n \"\\n\",\n \" controls.appendChild(doneBtn);\\n\",\n-\" controls.appendChild(undoBtn);\\n\",\n+\" controls.appendChild(resetBtn);\\n\",\n \"\\n\",\n-\" container.appendChild(img);\\n\",\n \" overlay.appendChild(label);\\n\",\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n-\" const points = [];\\n\",\n-\" const markers = [];\\n\",\n+\" let dragging = false;\\n\",\n+\" let startX = 0;\\n\",\n+\" let startY = 0;\\n\",\n+\" let latest = null;\\n\",\n \"\\n\",\n-\" const drawMarker = (xClient, yClient, n) => {\\n\",\n-\" const m = document.createElement('div');\\n\",\n-\" m.style.position = 'fixed';\\n\",\n-\" m.style.left = `${xClient - 6}px`;\\n\",\n-\" m.style.top = `${yClient - 6}px`;\\n\",\n-\" m.style.width = '12px';\\n\",\n-\" m.style.height = '12px';\\n\",\n-\" m.style.borderRadius = '6px';\\n\",\n-\" m.style.background = '#ff5252';\\n\",\n-\" m.style.border = '1px solid white';\\n\",\n-\" m.title = `Point ${n}`;\\n\",\n-\" m.style.zIndex = '10000';\\n\",\n-\" document.body.appendChild(m);\\n\",\n-\" markers.push(m);\\n\",\n-\" };\\n\",\n+\" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n \"\\n\",\n \" const cleanup = () => {\\n\",\n \" document.removeEventListener('keydown', onKey);\\n\",\n-\" markers.forEach((m) => m.remove());\\n\",\n \" overlay.remove();\\n\",\n \" };\\n\",\n \"\\n\",\n-\" const updateLabel = () => {\\n\",\n-\" const count = points.length;\\n\",\n-\" if (expectedPoints) {\\n\",\n-\" label.textContent = `Selected ${count}/${expectedPoints}. Click more points or press Done.`;\\n\",\n-\" } else {\\n\",\n-\" label.textContent = `Selected ${count} point(s). Click more points or press Done.`;\\n\",\n+\" const toNatural = (xCss, yCss, rect) => {\\n\",\n+\" const nx = Math.round(xCss * (img.naturalWidth / rect.width));\\n\",\n+\" const ny = Math.round(yCss * (img.naturalHeight / rect.height));\\n\",\n+\" return [nx, ny];\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise([-1, -1, -1, -1]);\\n\",\n \" }\\n\",\n \" };\\n\",\n \"\\n\",\n-\" return await new Promise((resolve) => {\\n\",\n-\" const onKey = (e) => {\\n\",\n-\" if (e.key === 'Escape') {\\n\",\n-\" cleanup();\\n\",\n-\" resolve([]);\\n\",\n-\" }\\n\",\n-\" };\\n\",\n-\" document.addEventListener('keydown', onKey);\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n \"\\n\",\n-\" img.onclick = (e) => {\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n-\" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n-\" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n-\" points.push([x, y]);\\n\",\n-\" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n-\" updateLabel();\\n\",\n-\" };\\n\",\n+\" img.onmousedown = (e) => {\\n\",\n+\" const rect = img.getBoundingClientRect();\\n\",\n+\" dragging = true;\\n\",\n+\" startX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n+\" startY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n+\" box.style.display = 'block';\\n\",\n+\" };\\n\",\n \"\\n\",\n-\" undoBtn.onclick = () => {\\n\",\n-\" if (points.length === 0) return;\\n\",\n-\" points.pop();\\n\",\n-\" const m = markers.pop();\\n\",\n-\" if (m) m.remove();\\n\",\n-\" updateLabel();\\n\",\n-\" };\\n\",\n+\" img.onmousemove = (e) => {\\n\",\n+\" if (!dragging) return;\\n\",\n+\" const rect = img.getBoundingClientRect();\\n\",\n+\" const curX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n+\" const curY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n \"\\n\",\n-\" doneBtn.onclick = () => {\\n\",\n-\" cleanup();\\n\",\n-\" resolve(points);\\n\",\n-\" };\\n\",\n+\" const left = Math.min(startX, curX);\\n\",\n+\" const top = Math.min(startY, curY);\\n\",\n+\" const width = Math.max(1, Math.abs(curX - startX));\\n\",\n+\" const height = Math.max(1, Math.abs(curY - startY));\\n\",\n+\"\\n\",\n+\" box.style.left = `${left}px`;\\n\",\n+\" box.style.top = `${top}px`;\\n\",\n+\" box.style.width = `${width}px`;\\n\",\n+\" box.style.height = `${height}px`;\\n\",\n+\"\\n\",\n+\" const [x0, y0] = toNatural(left, top, rect);\\n\",\n+\" const [x1, y1] = toNatural(left + width, top + height, rect);\\n\",\n+\" latest = [Math.min(x0, x1), Math.min(y0, y1), Math.max(x0, x1), Math.max(y0, y1)];\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" img.onmouseup = () => {\\n\",\n+\" dragging = false;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" img.onmouseleave = () => {\\n\",\n+\" dragging = false;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" resetBtn.onclick = () => {\\n\",\n+\" latest = null;\\n\",\n+\" box.style.display = 'none';\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" let resolvePromise;\\n\",\n+\" const promise = new Promise((resolve) => {\\n\",\n+\" resolvePromise = resolve;\\n\",\n \" });\\n\",\n+\"\\n\",\n+\" doneBtn.onclick = () => {\\n\",\n+\" if (!latest) {\\n\",\n+\" label.textContent = 'Draw a box first.';\\n\",\n+\" return;\\n\",\n+\" }\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise(latest);\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" return await promise;\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" display(Javascript(js))\\n\",\n-\" pts = eval_js(\\n\",\n-\" f\\\"materialHuntersPickPoints({json.dumps(data_url)}, {json.dumps(expected_points)})\\\"\\n\",\n+\" x0, y0, x1, y1 = eval_js(\\n\",\n+\" f\\\"materialHuntersPickBox({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n \" )\\n\",\n-\" if not pts:\\n\",\n-\" raise RuntimeError(\\\"Point selection cancelled or empty.\\\")\\n\",\n-\" return [(int(p[0]), int(p[1])) for p in pts]\\n\",\n+\" if x0 < 0 or y0 < 0:\\n\",\n+\" raise RuntimeError(\\\"Box selection cancelled.\\\")\\n\",\n+\" if x1 <= x0 or y1 <= y0:\\n\",\n+\" raise RuntimeError(\\\"Invalid box. Draw a box with non-zero area.\\\")\\n\",\n+\" return (int(x0), int(y0), int(x1), int(y1))\\n\",\n \"\\n\",\n \"\\n\",\n+\"def select_multiple_bboxes_colab(image: Image.Image, expected_boxes: int):\\n\",\n+\" if expected_boxes < 1:\\n\",\n+\" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n+\" bboxes = []\\n\",\n+\" for i in range(expected_boxes):\\n\",\n+\" bbox = select_bbox_colab(image, prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\")\\n\",\n+\" bboxes.append(bbox)\\n\",\n+\" return bboxes\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n@@ -393,12 +356,18 @@\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" return candidates\\n\",\n \"\\n\",\n \"\\n\",\n-\"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n+\"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n+\" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n+\" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def run_sam_box_segmentation(image: Image.Image, prompt_bbox_xyxy, sam_model, sam_processor):\\n\",\n+\" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n-\" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n+\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n@@ -419,8 +388,9 @@\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -119,21 +119,21 @@\n \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n-\"def select_tap_point_colab(image: Image.Image):\\n\",\n+\"def select_bbox_colab(image: Image.Image, prompt: str = \\\"Draw a box around the object\\\"):\\n\",\n \" try:\\n\",\n \" from google.colab.output import eval_js\\n\",\n \" from IPython.display import Javascript, display\\n\",\n \" except Exception as exc:\\n\",\n-\" raise RuntimeError(\\\"Click-to-select requires Google Colab.\\\") from exc\\n\",\n+\" raise RuntimeError(\\\"Box selection requires Google Colab.\\\") from exc\\n\",\n \"\\n\",\n \" buf = io.BytesIO()\\n\",\n \" image.save(buf, format=\\\"PNG\\\")\\n\",\n \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n \"\\n\",\n \" js = \\\"\\\"\\\"\\n\",\n-\" async function materialHuntersPickPoint(dataUrl) {\\n\",\n+\" async function materialHuntersPickBox(dataUrl, promptText) {\\n\",\n \" const overlay = document.createElement('div');\\n\",\n \" overlay.style.position = 'fixed';\\n\",\n \" overlay.style.inset = '0';\\n\",\n \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n@@ -144,88 +144,12 @@\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n-\" label.textContent = 'Click the target point (Esc to cancel)';\\n\",\n+\" label.textContent = `${promptText} (drag mouse, Done to confirm, Esc to cancel)`;\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n-\" const img = document.createElement('img');\\n\",\n-\" img.src = dataUrl;\\n\",\n-\" img.style.width = '95vw';\\n\",\n-\" img.style.maxWidth = '1800px';\\n\",\n-\" img.style.maxHeight = '90vh';\\n\",\n-\" img.style.cursor = 'crosshair';\\n\",\n-\" img.style.border = '2px solid white';\\n\",\n-\"\\n\",\n-\" overlay.appendChild(label);\\n\",\n-\" overlay.appendChild(img);\\n\",\n-\" document.body.appendChild(overlay);\\n\",\n-\"\\n\",\n-\" return await new Promise((resolve) => {\\n\",\n-\" const cleanup = () => {\\n\",\n-\" document.removeEventListener('keydown', onKey);\\n\",\n-\" overlay.remove();\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" const onKey = (e) => {\\n\",\n-\" if (e.key === 'Escape') {\\n\",\n-\" cleanup();\\n\",\n-\" resolve([-1, -1]);\\n\",\n-\" }\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" document.addEventListener('keydown', onKey);\\n\",\n-\"\\n\",\n-\" img.onclick = (e) => {\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n-\" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n-\" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n-\" cleanup();\\n\",\n-\" resolve([x, y]);\\n\",\n-\" };\\n\",\n-\" });\\n\",\n-\" }\\n\",\n-\" \\\"\\\"\\\"\\n\",\n-\"\\n\",\n-\" display(Javascript(js))\\n\",\n-\" x, y = eval_js(f\\\"materialHuntersPickPoint({json.dumps(data_url)})\\\")\\n\",\n-\" if x < 0 or y < 0:\\n\",\n-\" raise RuntimeError(\\\"Point selection cancelled.\\\")\\n\",\n-\" return int(x), int(y)\\n\",\n-\"\\n\",\n-\"\\n\",\n-\"def select_multiple_tap_points_colab(image: Image.Image, expected_points: int | None = None):\\n\",\n-\" try:\\n\",\n-\" from google.colab.output import eval_js\\n\",\n-\" from IPython.display import Javascript, display\\n\",\n-\" except Exception as exc:\\n\",\n-\" raise RuntimeError(\\\"Multi-point selection requires Google Colab.\\\") from exc\\n\",\n-\"\\n\",\n-\" buf = io.BytesIO()\\n\",\n-\" image.save(buf, format=\\\"PNG\\\")\\n\",\n-\" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n-\"\\n\",\n-\" js = \\\"\\\"\\\"\\n\",\n-\" async function materialHuntersPickPoints(dataUrl, expectedPoints) {\\n\",\n-\" const overlay = document.createElement('div');\\n\",\n-\" overlay.style.position = 'fixed';\\n\",\n-\" overlay.style.inset = '0';\\n\",\n-\" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n-\" overlay.style.zIndex = '9999';\\n\",\n-\" overlay.style.display = 'flex';\\n\",\n-\" overlay.style.flexDirection = 'column';\\n\",\n-\" overlay.style.alignItems = 'center';\\n\",\n-\" overlay.style.justifyContent = 'center';\\n\",\n-\" overlay.style.gap = '8px';\\n\",\n-\"\\n\",\n-\" const label = document.createElement('div');\\n\",\n-\" label.style.color = 'white';\\n\",\n-\" label.style.font = '16px sans-serif';\\n\",\n-\" label.textContent = expectedPoints\\n\",\n-\" ? `Click ${expectedPoints} points, then press Done (Esc cancels)`\\n\",\n-\" : 'Click one or more points, then press Done (Esc cancels)';\\n\",\n-\"\\n\",\n \" const container = document.createElement('div');\\n\",\n \" container.style.position = 'relative';\\n\",\n \"\\n\",\n \" const img = document.createElement('img');\\n\",\n@@ -236,103 +160,142 @@\n \" img.style.cursor = 'crosshair';\\n\",\n \" img.style.border = '2px solid white';\\n\",\n \" img.style.display = 'block';\\n\",\n \"\\n\",\n+\" const box = document.createElement('div');\\n\",\n+\" box.style.position = 'absolute';\\n\",\n+\" box.style.border = '2px solid #00e5ff';\\n\",\n+\" box.style.background = 'rgba(0,229,255,0.15)';\\n\",\n+\" box.style.display = 'none';\\n\",\n+\"\\n\",\n+\" container.appendChild(img);\\n\",\n+\" container.appendChild(box);\\n\",\n+\"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n \"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n-\" const undoBtn = document.createElement('button');\\n\",\n-\" undoBtn.textContent = 'Undo';\\n\",\n+\" const resetBtn = document.createElement('button');\\n\",\n+\" resetBtn.textContent = 'Reset';\\n\",\n \"\\n\",\n \" controls.appendChild(doneBtn);\\n\",\n-\" controls.appendChild(undoBtn);\\n\",\n+\" controls.appendChild(resetBtn);\\n\",\n \"\\n\",\n-\" container.appendChild(img);\\n\",\n \" overlay.appendChild(label);\\n\",\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n-\" const points = [];\\n\",\n-\" const markers = [];\\n\",\n+\" let dragging = false;\\n\",\n+\" let startX = 0;\\n\",\n+\" let startY = 0;\\n\",\n+\" let latest = null;\\n\",\n \"\\n\",\n-\" const drawMarker = (xClient, yClient, n) => {\\n\",\n-\" const m = document.createElement('div');\\n\",\n-\" m.style.position = 'fixed';\\n\",\n-\" m.style.left = `${xClient - 6}px`;\\n\",\n-\" m.style.top = `${yClient - 6}px`;\\n\",\n-\" m.style.width = '12px';\\n\",\n-\" m.style.height = '12px';\\n\",\n-\" m.style.borderRadius = '6px';\\n\",\n-\" m.style.background = '#ff5252';\\n\",\n-\" m.style.border = '1px solid white';\\n\",\n-\" m.title = `Point ${n}`;\\n\",\n-\" m.style.zIndex = '10000';\\n\",\n-\" document.body.appendChild(m);\\n\",\n-\" markers.push(m);\\n\",\n-\" };\\n\",\n+\" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n \"\\n\",\n \" const cleanup = () => {\\n\",\n \" document.removeEventListener('keydown', onKey);\\n\",\n-\" markers.forEach((m) => m.remove());\\n\",\n \" overlay.remove();\\n\",\n \" };\\n\",\n \"\\n\",\n-\" const updateLabel = () => {\\n\",\n-\" const count = points.length;\\n\",\n-\" if (expectedPoints) {\\n\",\n-\" label.textContent = `Selected ${count}/${expectedPoints}. Click more points or press Done.`;\\n\",\n-\" } else {\\n\",\n-\" label.textContent = `Selected ${count} point(s). Click more points or press Done.`;\\n\",\n+\" const toNatural = (xCss, yCss, rect) => {\\n\",\n+\" const nx = Math.round(xCss * (img.naturalWidth / rect.width));\\n\",\n+\" const ny = Math.round(yCss * (img.naturalHeight / rect.height));\\n\",\n+\" return [nx, ny];\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise([-1, -1, -1, -1]);\\n\",\n \" }\\n\",\n \" };\\n\",\n \"\\n\",\n-\" return await new Promise((resolve) => {\\n\",\n-\" const onKey = (e) => {\\n\",\n-\" if (e.key === 'Escape') {\\n\",\n-\" cleanup();\\n\",\n-\" resolve([]);\\n\",\n-\" }\\n\",\n-\" };\\n\",\n-\" document.addEventListener('keydown', onKey);\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n \"\\n\",\n-\" img.onclick = (e) => {\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n-\" const x = Math.round((e.clientX - rect.left) * (img.naturalWidth / rect.width));\\n\",\n-\" const y = Math.round((e.clientY - rect.top) * (img.naturalHeight / rect.height));\\n\",\n-\" points.push([x, y]);\\n\",\n-\" drawMarker(e.clientX, e.clientY, points.length);\\n\",\n-\" updateLabel();\\n\",\n-\" };\\n\",\n+\" img.onmousedown = (e) => {\\n\",\n+\" const rect = img.getBoundingClientRect();\\n\",\n+\" dragging = true;\\n\",\n+\" startX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n+\" startY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n+\" box.style.display = 'block';\\n\",\n+\" };\\n\",\n \"\\n\",\n-\" undoBtn.onclick = () => {\\n\",\n-\" if (points.length === 0) return;\\n\",\n-\" points.pop();\\n\",\n-\" const m = markers.pop();\\n\",\n-\" if (m) m.remove();\\n\",\n-\" updateLabel();\\n\",\n-\" };\\n\",\n+\" img.onmousemove = (e) => {\\n\",\n+\" if (!dragging) return;\\n\",\n+\" const rect = img.getBoundingClientRect();\\n\",\n+\" const curX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n+\" const curY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n \"\\n\",\n-\" doneBtn.onclick = () => {\\n\",\n-\" cleanup();\\n\",\n-\" resolve(points);\\n\",\n-\" };\\n\",\n+\" const left = Math.min(startX, curX);\\n\",\n+\" const top = Math.min(startY, curY);\\n\",\n+\" const width = Math.max(1, Math.abs(curX - startX));\\n\",\n+\" const height = Math.max(1, Math.abs(curY - startY));\\n\",\n+\"\\n\",\n+\" box.style.left = `${left}px`;\\n\",\n+\" box.style.top = `${top}px`;\\n\",\n+\" box.style.width = `${width}px`;\\n\",\n+\" box.style.height = `${height}px`;\\n\",\n+\"\\n\",\n+\" const [x0, y0] = toNatural(left, top, rect);\\n\",\n+\" const [x1, y1] = toNatural(left + width, top + height, rect);\\n\",\n+\" latest = [Math.min(x0, x1), Math.min(y0, y1), Math.max(x0, x1), Math.max(y0, y1)];\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" img.onmouseup = () => {\\n\",\n+\" dragging = false;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" img.onmouseleave = () => {\\n\",\n+\" dragging = false;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" resetBtn.onclick = () => {\\n\",\n+\" latest = null;\\n\",\n+\" box.style.display = 'none';\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" let resolvePromise;\\n\",\n+\" const promise = new Promise((resolve) => {\\n\",\n+\" resolvePromise = resolve;\\n\",\n \" });\\n\",\n+\"\\n\",\n+\" doneBtn.onclick = () => {\\n\",\n+\" if (!latest) {\\n\",\n+\" label.textContent = 'Draw a box first.';\\n\",\n+\" return;\\n\",\n+\" }\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise(latest);\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" return await promise;\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" display(Javascript(js))\\n\",\n-\" pts = eval_js(\\n\",\n-\" f\\\"materialHuntersPickPoints({json.dumps(data_url)}, {json.dumps(expected_points)})\\\"\\n\",\n+\" x0, y0, x1, y1 = eval_js(\\n\",\n+\" f\\\"materialHuntersPickBox({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n \" )\\n\",\n-\" if not pts:\\n\",\n-\" raise RuntimeError(\\\"Point selection cancelled or empty.\\\")\\n\",\n-\" return [(int(p[0]), int(p[1])) for p in pts]\\n\",\n+\" if x0 < 0 or y0 < 0:\\n\",\n+\" raise RuntimeError(\\\"Box selection cancelled.\\\")\\n\",\n+\" if x1 <= x0 or y1 <= y0:\\n\",\n+\" raise RuntimeError(\\\"Invalid box. Draw a box with non-zero area.\\\")\\n\",\n+\" return (int(x0), int(y0), int(x1), int(y1))\\n\",\n \"\\n\",\n \"\\n\",\n+\"def select_multiple_bboxes_colab(image: Image.Image, expected_boxes: int):\\n\",\n+\" if expected_boxes < 1:\\n\",\n+\" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n+\" bboxes = []\\n\",\n+\" for i in range(expected_boxes):\\n\",\n+\" bbox = select_bbox_colab(image, prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\")\\n\",\n+\" bboxes.append(bbox)\\n\",\n+\" return bboxes\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n@@ -393,12 +356,18 @@\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" return candidates\\n\",\n \"\\n\",\n \"\\n\",\n-\"def run_sam_point_segmentation(image: Image.Image, tap_x: int, tap_y: int, sam_model, sam_processor):\\n\",\n+\"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n+\" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n+\" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def run_sam_box_segmentation(image: Image.Image, prompt_bbox_xyxy, sam_model, sam_processor):\\n\",\n+\" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n-\" input_points=[[[float(tap_x), float(tap_y)]]],\\n\",\n+\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n@@ -419,8 +388,9 @@\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n", + "additions": 112, + "deletions": 142 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078706346, + "end": 1783078706399 + } + }, + "metadata": { + "openai": { + "itemId": "fc_08adc39e6b486c48016a479ef9853c8191abc8b27ab1abf1a6" + } + }, + "id": "prt_f27c4fef8001HdUwI3cHkY8fhJ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c4f38c002Q0Cmp7Rt4xh7cf" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 75002, + "input": 118, + "output": 6260, + "reasoning": 16, + "cache": { + "write": 0, + "read": 68608 + } + }, + "cost": 0.1000769, + "id": "prt_f27c5dce8001EPPsqkiYrh6vmE", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c4f38c002Q0Cmp7Rt4xh7cf" + } + ] + }, + { + "info": { + "parentID": "msg_f27c43663001JCENYPiYbSmtTC", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.030982, + "tokens": { + "total": 76297, + "input": 160, + "output": 1242, + "reasoning": 15, + "cache": { + "write": 0, + "read": 74880 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783078706427, + "completed": 1783078720260 + }, + "finish": "tool-calls", + "id": "msg_f27c5dcfa002mMy0dJmxSa2Z4D", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27c5e199001JA4pPp4IoVTN7O", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c5dcfa002mMy0dJmxSa2Z4D" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783078707713, + "end": 1783078708216 + }, + "metadata": { + "openai": { + "itemId": "rs_05c738c3877546e6016a479f3381e8819e9d6a1ce1ed400da4", + "reasoningEncryptedContent": "gAAAAABqR58ziLiuW38NisUFZ-Vu4_7udTzUMNOSmIgxRNtAl6uA3VQDA5YvuICN38yprZW6eC9fMtJdE3WD2XtkkBULNlCjq_zxrCcRTtnP9JJugvGdajPd4UcptlWhmRYdBsO48ot79f6_cpAFsesbvpKFb4ZVFjioyEpf08OSAKeZ0rjYo1RSeq9dJR0rySGjNNGfy9KeiA3Inrn6eTRhg07U5I78JZquPhPbsOfc19qk_SfrhPvB3p3CirdPbm5D9N-q2cVZoxbXuES6dQnjzM2z5xAACxNFJt1l_11w0bNPCQ9dwElbTHYaxiO97QB7_G35w8HATs6rSwUxXevVApPOSytxM7IUTuK0D8qL4YKf2tS55yyR-OY6U5Zu4LMTeaEm5wuh48q37JZGFBCe2vXBcZhAO47pw03zrlcvynZLXGk7Bhuo6yAbQhN_4rA11yRPuTtag7OxPC4tAtKTLK3tsoyvA_AeF2W0KnidOt61JG8mJx_noWMnjK8CN45sscpdr27QuyBLUzM-pXPK1P-jmHSl_0YirZGgMgZudfqaNpDliz8IhSFsZQjKt3ivvhRPIEfVsWxs1_KDCwupmgAMFBEkw5IN8_DxHun9dpFwaedQ0K4RHRQy7-neXfEMv6eUgBLqDtSuFvoMak-OWk8fu8TlbKu-71VoNyKTQSb41GwIFKj1sXvXuMvIMAqt0etG5QQXDSlevHCnDwWIvlshktCF9jIO2FI4Qqf73bcvvb8s6b23EEC5Ez2eFnACr2aBxxM1mM2ViPUMcrAwQpFo_nNM16g_ov7LGETM_-zRYg8p597Ks7-L2QAVFUxewXRkYpbpnyyMK6sDUyDZvNANcwUuSmujWAHSqiLpIXPepKAxHScdYedaIogMGcKKuxwGBPKHLT1DwVDEvF4gbjttDe2kmAguUiu3uAcvYXqGbz-TYf80GnowD1jNrBeY1pxVhtScGegQovfZioMjON8cEpahTlKimUXIeQd10yd59mKepHw=" + } + }, + "id": "prt_f27c5e201001atQI0rN2siPhPL", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c5dcfa002mMy0dJmxSa2Z4D" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_D96EKA2nG2e2wB0T5X8zZNb2", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"USE_CLICK_TO_SELECT = True\\n\",\n+ \"USE_BOX_TO_SELECT = True\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n- \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n+ \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n \"\\n\",\n- \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n+ \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in box mode.\\n\",\n@@\n- \"if USE_CLICK_TO_SELECT:\\n\",\n+ \"if USE_BOX_TO_SELECT:\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n- \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n- \" if len(points) > len(OBJECT_SPECS):\\n\",\n- \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match clicked points.\\\")\\n\",\n- \" for i, (x, y) in enumerate(points):\\n\",\n- \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n- \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n- \" OBJECT_RECORDS = OBJECT_SPECS[: len(points)]\\n\",\n+ \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n+ \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n+ \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n+ \" for i, box in enumerate(boxes):\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n- \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n- \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n- \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n+ \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n- \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n- \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n+ \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n+ \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n@@\n- \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n- \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n- \" plt.scatter([x], [y], c=\\\"red\\\", s=60)\\n\",\n- \" plt.text(x + 6, y + 6, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9)\\n\",\n- \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n+ \"ax = plt.gca()\\n\",\n+ \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n+ \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n+ \" ax.add_patch(rect)\\n\",\n+ \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n+ \"plt.title(\\\"Prompt box preview (indexed)\\\")\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -511,13 +511,13 @@\n \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n- \"USE_CLICK_TO_SELECT = True\\n\",\n+ \"USE_BOX_TO_SELECT = True\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n- \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n+ \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n \"\\n\",\n- \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n+ \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in box mode.\\n\",\n \"OBJECT_SPECS = [\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n@@ -539,26 +539,24 @@\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n \"\\n\",\n- \"if USE_CLICK_TO_SELECT:\\n\",\n+ \"if USE_BOX_TO_SELECT:\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n- \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n- \" if len(points) > len(OBJECT_SPECS):\\n\",\n- \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match clicked points.\\\")\\n\",\n- \" for i, (x, y) in enumerate(points):\\n\",\n- \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n- \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n- \" OBJECT_RECORDS = OBJECT_SPECS[: len(points)]\\n\",\n+ \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n+ \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n+ \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n+ \" for i, box in enumerate(boxes):\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n- \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n- \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n- \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n+ \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n- \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n- \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n+ \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n+ \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n@@ -566,13 +564,15 @@\n \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n+ \"ax = plt.gca()\\n\",\n \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n- \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n- \" plt.scatter([x], [y], c=\\\"red\\\", s=60)\\n\",\n- \" plt.text(x + 6, y + 6, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9)\\n\",\n- \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n+ \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n+ \" ax.add_patch(rect)\\n\",\n+ \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n+ \"plt.title(\\\"Prompt box preview (indexed)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -511,13 +511,13 @@\n \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n- \"USE_CLICK_TO_SELECT = True\\n\",\n+ \"USE_BOX_TO_SELECT = True\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n- \"N_OBJECTS_TO_CLICK = 3 # used only when click+multi mode is enabled\\n\",\n+ \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n \"\\n\",\n- \"# Define object metadata in click order. tap_x/tap_y are optional when click mode is on.\\n\",\n+ \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in box mode.\\n\",\n \"OBJECT_SPECS = [\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n@@ -539,26 +539,24 @@\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n \"\\n\",\n- \"if USE_CLICK_TO_SELECT:\\n\",\n+ \"if USE_BOX_TO_SELECT:\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n- \" points = select_multiple_tap_points_colab(img, expected_points=N_OBJECTS_TO_CLICK)\\n\",\n- \" if len(points) > len(OBJECT_SPECS):\\n\",\n- \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match clicked points.\\\")\\n\",\n- \" for i, (x, y) in enumerate(points):\\n\",\n- \" OBJECT_SPECS[i][\\\"tap_x\\\"] = int(x)\\n\",\n- \" OBJECT_SPECS[i][\\\"tap_y\\\"] = int(y)\\n\",\n- \" OBJECT_RECORDS = OBJECT_SPECS[: len(points)]\\n\",\n+ \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n+ \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n+ \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n+ \" for i, box in enumerate(boxes):\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n- \" tap_x, tap_y = select_tap_point_colab(img)\\n\",\n- \" OBJECT_SPECS[0][\\\"tap_x\\\"] = int(tap_x)\\n\",\n- \" OBJECT_SPECS[0][\\\"tap_y\\\"] = int(tap_y)\\n\",\n+ \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n- \" if \\\"tap_x\\\" not in obj or \\\"tap_y\\\" not in obj:\\n\",\n- \" raise ValueError(\\\"Manual mode requires tap_x/tap_y for each object.\\\")\\n\",\n+ \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n+ \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n@@ -566,13 +564,15 @@\n \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n+ \"ax = plt.gca()\\n\",\n \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n- \" x, y = obj[\\\"tap_x\\\"], obj[\\\"tap_y\\\"]\\n\",\n- \" plt.scatter([x], [y], c=\\\"red\\\", s=60)\\n\",\n- \" plt.text(x + 6, y + 6, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9)\\n\",\n- \"plt.title(\\\"Tap preview (indexed)\\\")\\n\",\n+ \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n+ \" ax.add_patch(rect)\\n\",\n+ \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n+ \"plt.title(\\\"Prompt box preview (indexed)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n", + "additions": 20, + "deletions": 20 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078720214, + "end": 1783078720250 + } + }, + "metadata": { + "openai": { + "itemId": "fc_05c738c3877546e6016a479f33f9ec819ebf5239ba7ccb8efb" + } + }, + "id": "prt_f27c5e4010018bnNkCmLkwFO6s", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c5dcfa002mMy0dJmxSa2Z4D" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 76297, + "input": 160, + "output": 1242, + "reasoning": 15, + "cache": { + "write": 0, + "read": 74880 + } + }, + "cost": 0.030982, + "id": "prt_f27c612ff001B6twt3ym1Bmk1f", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c5dcfa002mMy0dJmxSa2Z4D" + } + ] + }, + { + "info": { + "parentID": "msg_f27c43663001JCENYPiYbSmtTC", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.02232825, + "tokens": { + "total": 76956, + "input": 175, + "output": 611, + "reasoning": 10, + "cache": { + "write": 0, + "read": 76160 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783078720269, + "completed": 1783078727636 + }, + "finish": "tool-calls", + "id": "msg_f27c6130d002FQ2EI8yJJtSg5j", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27c617a30017y4DTnxoSU4nUS", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c6130d002FQ2EI8yJJtSg5j" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783078721462, + "end": 1783078724192 + }, + "metadata": { + "openai": { + "itemId": "rs_047fbb93ff8689ed016a479f41592081a38072156891f69786", + "reasoningEncryptedContent": "gAAAAABqR59DSeiiHCmlsPMOoiWNq5ecBBOu3LTUY76xWlYE5GiUnhVHpgVYQZzIrAciR5BSCN4TvhMVPlimh-WT6YPwrzj5mwusKuWw_NcjAtRJXRRjRXFN2KuXFV5lZaVBSzof-96JXL7IaZSmTs-Dj4CR9YRP8e0Iw3smNsb0AaK5aFi5rFdWcM_eXXs8DCUCj_J63we3fH7D_ufRqenLi674Jv9IWn4jymIGFWNSmUa6c53C7U7mnR8U1_SSUCBU0zZJnkJsshSYVtDv3rGsK8IWD1KEbwLoR1illMxFKvR4bO7KNLdTcSB78Vdhz4xF5ciSnkQ8O8wqW67tmVY1tythk8ada6zrVqKRrNxsnM2RwkqkvtDOJVg_e1GUmr-trnfpZq9hL3qXq34ShxgmVUx5SDnHVYskY0p2A_FqUtseHfFXJuj-U9zIlqbowo48Pu0m_D0pXnO_nEyEUkai8ZnNdL49pzHusNk39X6bsy3X1ZxTVLI5lWvUyPcXpaI6zEbDmuV-Tv6-ZCjZ5dxvk_8oGAm6HUjB06yU_OBdr0Ap8JjeCYDyRQvNH7GCvtLrxeH0WOb8Rfza_Unf3sTLWW0cpRjSCYz2Pca7PqQ0s2v5uu0_9F56pnAtZz9AoZe7Beh-8ErL6kJ1_xXMUlC3d971-DN0mIK3kNfazuAtfiNmI40Diq3122-VDZwiS5h_d6ZeAehQNwiaDLQ7och2xkh8vm50BeKyXeAWjM91nNDwDukew4ImgCMqsa57fSPkKEYP6gtjb_-ObqnxknahsCqRZ_EPtVdVFO1-KMK3sYsNsOswvCvBCO5Iu8BxZ-HZT1lDQfEqvgClbJsu2-5dzYybnsXSuFxTIRQgCfDUZftAwngjhnCn5PLanFO36sXqwXDNYI9ydbam8ehkURIbAl8p_d1T08ILNCEpespyIbVtIkOrdQGXbzy2wpf4CNRUWNT4Ydr7HuW7-xf2P6CO8wNKt3fagg==" + } + }, + "id": "prt_f27c617b6001x5jzL0XBNNJxZh", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c6130d002FQ2EI8yJJtSg5j" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_sHjbrt9exQcxxpzCUjFqEiOQ", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"RUN_ROWS = []\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n- \" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n+ \" prompt_bbox_xyxy = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" tap_x, tap_y = bbox_xyxy_to_center_xy(prompt_bbox_xyxy)\\n\",\n \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n@@\n- \" seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+ \" seg = run_sam_box_segmentation(img, prompt_bbox_xyxy, sam_model, sam_processor)\\n\",\n@@\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n+ \" \\\"prompt_bbox_xyxy\\\": prompt_bbox_xyxy,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n@@\n- \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n- \" axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+ \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n+ \" px0, py0, px1, py1 = prompt_bbox_xyxy\\n\",\n+ \" prompt_rect = patches.Rectangle((px0, py0), px1 - px0, py1 - py0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n+ \" axes[0].add_patch(prompt_rect)\\n\",\n \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -585,15 +585,16 @@\n \"RUN_ROWS = []\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n-\" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n+\" prompt_bbox_xyxy = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+\" tap_x, tap_y = bbox_xyxy_to_center_xy(prompt_bbox_xyxy)\\n\",\n \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \" t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" seg_start = pd.Timestamp.utcnow()\\n\",\n-\" seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+\" seg = run_sam_box_segmentation(img, prompt_bbox_xyxy, sam_model, sam_processor)\\n\",\n \" seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n@@ -618,8 +619,9 @@\n \" RUN_ROWS.append({\\n\",\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": prompt_bbox_xyxy,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n@@ -638,9 +640,11 @@\n \"\\n\",\n \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \" axes[0].imshow(img)\\n\",\n \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n-\" axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+\" px0, py0, px1, py1 = prompt_bbox_xyxy\\n\",\n+\" prompt_rect = patches.Rectangle((px0, py0), px1 - px0, py1 - py0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n+\" axes[0].add_patch(prompt_rect)\\n\",\n \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n \" axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n \" axes[1].imshow(crop_masked)\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -585,15 +585,16 @@\n \"RUN_ROWS = []\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n-\" tap_x, tap_y = int(obj[\\\"tap_x\\\"]), int(obj[\\\"tap_y\\\"])\\n\",\n+\" prompt_bbox_xyxy = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+\" tap_x, tap_y = bbox_xyxy_to_center_xy(prompt_bbox_xyxy)\\n\",\n \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \" t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" seg_start = pd.Timestamp.utcnow()\\n\",\n-\" seg = run_sam_point_segmentation(img, tap_x, tap_y, sam_model, sam_processor)\\n\",\n+\" seg = run_sam_box_segmentation(img, prompt_bbox_xyxy, sam_model, sam_processor)\\n\",\n \" seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n@@ -618,8 +619,9 @@\n \" RUN_ROWS.append({\\n\",\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": prompt_bbox_xyxy,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n@@ -638,9 +640,11 @@\n \"\\n\",\n \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \" axes[0].imshow(img)\\n\",\n \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n-\" axes[0].scatter([tap_x], [tap_y], c=\\\"cyan\\\", s=60)\\n\",\n+\" px0, py0, px1, py1 = prompt_bbox_xyxy\\n\",\n+\" prompt_rect = patches.Rectangle((px0, py0), px1 - px0, py1 - py0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n+\" axes[0].add_patch(prompt_rect)\\n\",\n \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n \" axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n \" axes[1].imshow(crop_masked)\\n\",\n", + "additions": 7, + "deletions": 3 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -725,9 +725,12 @@\n \" passport = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n-\" \\\"tap\\\": {\\\"x\\\": int(run[\\\"tap_x\\\"]), \\\"y\\\": int(run[\\\"tap_y\\\"])},\\n\",\n+\" \\\"interaction\\\": {\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": [int(v) for v in run[\\\"prompt_bbox_xyxy\\\"]],\\n\",\n+\" \\\"bbox_center_xy\\\": [int(run[\\\"tap_x\\\"]), int(run[\\\"tap_y\\\"])],\\n\",\n+\" },\\n\",\n \" \\\"object\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n \" },\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -725,9 +725,12 @@\n \" passport = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n-\" \\\"tap\\\": {\\\"x\\\": int(run[\\\"tap_x\\\"]), \\\"y\\\": int(run[\\\"tap_y\\\"])},\\n\",\n+\" \\\"interaction\\\": {\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": [int(v) for v in run[\\\"prompt_bbox_xyxy\\\"]],\\n\",\n+\" \\\"bbox_center_xy\\\": [int(run[\\\"tap_x\\\"]), int(run[\\\"tap_y\\\"])],\\n\",\n+\" },\\n\",\n \" \\\"object\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n \" },\\n\",\n", + "additions": 4, + "deletions": 1 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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center so your existing eval sheet keeps working.\n\nWhat you need to do in Colab:\n1. Re-upload `poc_eval_notebook.ipynb`.\n2. Re-run from the cell that defines helper functions onward.\n3. In config cell, keep `USE_BOX_TO_SELECT = True`.\n4. Draw one box per object when prompted.\n\nIf you want, next I can add a tiny “accept/re-draw box” loop per object so you can quickly correct bad boxes before segmentation runs.", + "time": { + "start": 1783078747321, + "end": 1783078751097 + }, + "metadata": { + "openai": { + "itemId": "msg_0680de08c42bf5dc016a479f5b4118819f977cefd9fd398b9e", + "phase": "final_answer" + } + }, + "id": "prt_f27c67cb9001tCBYzUeIOVKo1v", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c6733a002aUIOyu2c6o0y9z" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 78389, + "input": 221, + "output": 310, + "reasoning": 34, + "cache": { + "write": 0, + "read": 77824 + } + }, + "cost": 0.01882195, + "id": "prt_f27c68b7e001QQJA0uo6FWBqtG", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c6733a002aUIOyu2c6o0y9z" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783078774632 + }, + "agent": "build", + "model": { + 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+ } + }, + "id": "prt_f27c6ee5b001QZnfp5LWQ6FhZx", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c6e786002eX4p9PpWIbUzbm" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_SoG4tZsPc5S5i0Td1dmPnpZk", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"def select_multiple_bboxes_colab(image: Image.Image, expected_boxes: int):\\n\",\n \" if expected_boxes < 1:\\n\",\n \" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n \" bboxes = []\\n\",\n \" for i in range(expected_boxes):\\n\",\n \" bbox = select_bbox_colab(image, prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\")\\n\",\n \" bboxes.append(bbox)\\n\",\n \" return bboxes\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def review_bbox_acceptance(image: Image.Image, bbox_xyxy, title: str = \\\"Review selected box\\\"):\\n\",\n+ \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n+ \" plt.figure(figsize=(8, 6))\\n\",\n+ \" ax = plt.gca()\\n\",\n+ \" ax.imshow(image)\\n\",\n+ \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n+ \" ax.add_patch(rect)\\n\",\n+ \" ax.set_title(title)\\n\",\n+ \" ax.axis(\\\"off\\\")\\n\",\n+ \" plt.show()\\n\",\n+ \"\\n\",\n+ \" answer = input(\\\"Accept this box? [y/n]: \\\" ).strip().lower()\\n\",\n+ \" return answer in {\\\"y\\\", \\\"yes\\\"}\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def select_bbox_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n+ \" for attempt in range(1, max_attempts + 1):\\n\",\n+ \" bbox = select_bbox_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n+ \" accepted = review_bbox_acceptance(image, bbox, title=f\\\"{prompt} - attempt {attempt}\\\")\\n\",\n+ \" if accepted:\\n\",\n+ \" return bbox\\n\",\n+ \" print(\\\"Re-draw requested.\\\")\\n\",\n+ \"\\n\",\n+ \" raise RuntimeError(\\\"Box not accepted within max attempts.\\\")\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def select_multiple_bboxes_with_redraw_colab(image: Image.Image, expected_boxes: int, max_attempts_per_box: int = 5):\\n\",\n+ \" if expected_boxes < 1:\\n\",\n+ \" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n+ \" bboxes = []\\n\",\n+ \" for i in range(expected_boxes):\\n\",\n+ \" bbox = select_bbox_with_redraw_colab(\\n\",\n+ \" image=image,\\n\",\n+ \" prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\",\\n\",\n+ \" max_attempts=max_attempts_per_box,\\n\",\n+ \" )\\n\",\n+ \" bboxes.append(bbox)\\n\",\n+ \" return bboxes\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -294,8 +294,48 @@\n \" bboxes.append(bbox)\\n\",\n \" return bboxes\\n\",\n \"\\n\",\n \"\\n\",\n+\"def review_bbox_acceptance(image: Image.Image, bbox_xyxy, title: str = \\\"Review selected box\\\"):\\n\",\n+\" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n+\" plt.figure(figsize=(8, 6))\\n\",\n+\" ax = plt.gca()\\n\",\n+\" ax.imshow(image)\\n\",\n+\" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n+\" ax.add_patch(rect)\\n\",\n+\" ax.set_title(title)\\n\",\n+\" ax.axis(\\\"off\\\")\\n\",\n+\" plt.show()\\n\",\n+\"\\n\",\n+\" answer = input(\\\"Accept this box? [y/n]: \\\" ).strip().lower()\\n\",\n+\" return answer in {\\\"y\\\", \\\"yes\\\"}\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def select_bbox_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n+\" for attempt in range(1, max_attempts + 1):\\n\",\n+\" bbox = select_bbox_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n+\" accepted = review_bbox_acceptance(image, bbox, title=f\\\"{prompt} - attempt {attempt}\\\")\\n\",\n+\" if accepted:\\n\",\n+\" return bbox\\n\",\n+\" print(\\\"Re-draw requested.\\\")\\n\",\n+\"\\n\",\n+\" raise RuntimeError(\\\"Box not accepted within max attempts.\\\")\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def select_multiple_bboxes_with_redraw_colab(image: Image.Image, expected_boxes: int, max_attempts_per_box: int = 5):\\n\",\n+\" if expected_boxes < 1:\\n\",\n+\" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n+\" bboxes = []\\n\",\n+\" for i in range(expected_boxes):\\n\",\n+\" bbox = select_bbox_with_redraw_colab(\\n\",\n+\" image=image,\\n\",\n+\" prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\",\\n\",\n+\" max_attempts=max_attempts_per_box,\\n\",\n+\" )\\n\",\n+\" bboxes.append(bbox)\\n\",\n+\" return bboxes\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -294,8 +294,48 @@\n \" bboxes.append(bbox)\\n\",\n \" return bboxes\\n\",\n \"\\n\",\n \"\\n\",\n+\"def review_bbox_acceptance(image: Image.Image, bbox_xyxy, title: str = \\\"Review selected box\\\"):\\n\",\n+\" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n+\" plt.figure(figsize=(8, 6))\\n\",\n+\" ax = plt.gca()\\n\",\n+\" ax.imshow(image)\\n\",\n+\" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n+\" ax.add_patch(rect)\\n\",\n+\" ax.set_title(title)\\n\",\n+\" ax.axis(\\\"off\\\")\\n\",\n+\" plt.show()\\n\",\n+\"\\n\",\n+\" answer = input(\\\"Accept this box? [y/n]: \\\" ).strip().lower()\\n\",\n+\" return answer in {\\\"y\\\", \\\"yes\\\"}\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def select_bbox_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n+\" for attempt in range(1, max_attempts + 1):\\n\",\n+\" bbox = select_bbox_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n+\" accepted = review_bbox_acceptance(image, bbox, title=f\\\"{prompt} - attempt {attempt}\\\")\\n\",\n+\" if accepted:\\n\",\n+\" return bbox\\n\",\n+\" print(\\\"Re-draw requested.\\\")\\n\",\n+\"\\n\",\n+\" raise RuntimeError(\\\"Box not accepted within max attempts.\\\")\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def select_multiple_bboxes_with_redraw_colab(image: Image.Image, expected_boxes: int, max_attempts_per_box: int = 5):\\n\",\n+\" if expected_boxes < 1:\\n\",\n+\" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n+\" bboxes = []\\n\",\n+\" for i in range(expected_boxes):\\n\",\n+\" bbox = select_bbox_with_redraw_colab(\\n\",\n+\" image=image,\\n\",\n+\" prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\",\\n\",\n+\" max_attempts=max_attempts_per_box,\\n\",\n+\" )\\n\",\n+\" bboxes.append(bbox)\\n\",\n+\" return bboxes\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n", + "additions": 40, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078788950, + "end": 1783078788966 + } + }, + "metadata": { + "openai": { + "itemId": "fc_06517ceea00b82dd016a479f7be5ac81a1bd136bc158f8776a" + } + }, + "id": "prt_f27c6fc3e0010BvtldPE71yurH", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c6e786002eX4p9PpWIbUzbm" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 77810, + "input": 23575, + "output": 924, + "reasoning": 191, + "cache": { + "write": 0, + "read": 53120 + } + }, + "cost": 0.06616225, + "id": "prt_f27c71f89001g3AvmlFXgOGG8Q", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c6e786002eX4p9PpWIbUzbm" + } + ] + }, + { + "info": { + "parentID": "msg_f27c6e768001eJtZIDYqTxhvrC", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0230188, + "tokens": { + "total": 78502, + "input": 152, + "output": 631, + "reasoning": 23, + "cache": { + "write": 0, + "read": 77696 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783078789026, + "completed": 1783078796812 + }, + "finish": "tool-calls", + "id": "msg_f27c71fa2002OoaZJW9BrKDKeb", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27c723a0001LYzb0KLzS7ykT1", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c71fa2002OoaZJW9BrKDKeb" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783078790186, + "end": 1783078791163 + }, + "metadata": { + "openai": { + "itemId": "rs_0c22a72ce02f25c4016a479f861f90819dbca4a561af6e0da9", + "reasoningEncryptedContent": "gAAAAABqR5-GPRidwpQy2XQRpHHJLJx3nP6PFjmrJ0R1x2VoBAinkHpjl9lF4caU6r58-e2ur_7_rjhhtre_oulvOw3eSw3hahFHF-MifjcMlmD_UUva8HxP_F1pD7_iK0rm-u28fgjJMBFsJ4S0oXfP3GPBaCbKUcv9_w5a4a0nPbWLjmfmdzNhcb2AUOJXeYQ7WbuB0K9pfLo8OiaZq2sYEw4s15Wvn7_rcYYJ-V3Nt2EC_ZDGdg8fBsR1ZZ3hjWowo87-GmeErqDIJPzF9bcsqm7ZVUk0bxmVPtMB4_qAADfF4SgdfIMLMv1uskAgW5Qw7Fwef_EGzTEaBZ-_NLCfhCDP7RfWN6kR7HE6ktaQpkAu_vA7BvedrvQihjVcEeLZS6ireOtK3WXWVfIPLXDo3cNEMCYanP98vgfIZKQkfnC3vmj6tSX1kuJ6lmkZMjbYaznhxPeI9ANpVshEPujf3LaAQJCIZv1b87RzMbKrp0sBRrkuUr5WoZfHWSO-9bZTCPAngAqZSlA_SBTQIobtNdPwImIhMtVe5PgnitTbMkJ-9mBwUq84g_s89NF7AhsvrsEBN1LpOqXw69Lxb-Uku5kTJ2eSOZV5Nx44nqwZdu3BFNK9VGsWtotaVVMl_jJ8VOYsV9wTWibGsqfAO5NSDcTqZhMdyJU4zuiEZ-G1vgZzQYLem2VgcGc0T5-m8-QeiCP6dsikDWko4eRkWRawvwrkE-W1YOtSMuJTMpcS1jPU1w3zutLPLQcozGlNIT2KUlt2QcoH2sBJbY0klKKFM2yOaJ0-RFPAr2nEzmrdCXL-qiHiTq6yP3dI7sVZvOPmgb-zk0Ul3WgjqYi9uBeUCR53zf-ENNn-d_9yMKAtSYxH4F34fumoH5ILIc1GAgqkWvCJUTr51ok_NJr1fbWFtUM8nKnDYpMzzuEMUlV2UZQbfyzSi0coSZ3sRad5dVdZPw9fDtetmvVHL5Nd86dtZ764YrJuDVD_VKeBRHXjG2-NFMXLxF6oAXyvoacglKO8Gx-cYwYGyFgwdCoqfRu-7I6AAkr1ImJw0n4idCiqaPEE07YLbys=" + } + }, + "id": "prt_f27c7242a0012Z32y8M2Is8hi1", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c71fa2002OoaZJW9BrKDKeb" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_9bF4GqrYVEU3tv2882JS6a7o", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"USE_BOX_TO_SELECT = True\\n\",\n+ \"USE_BOX_ACCEPT_REDRAW = True\\n\",\n+ \"MAX_BOX_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n@@\n \"if USE_BOX_TO_SELECT:\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n- \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n+ \" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+ \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" expected_boxes=N_OBJECTS_TO_BOX,\\n\",\n+ \" max_attempts_per_box=MAX_BOX_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n \" for i, box in enumerate(boxes):\\n\",\n \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n- \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n+ \" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+ \" box = select_bbox_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=\\\"Draw box around target object\\\",\\n\",\n+ \" max_attempts=MAX_BOX_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -552,8 +552,10 @@\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n \"USE_BOX_TO_SELECT = True\\n\",\n+\"USE_BOX_ACCEPT_REDRAW = True\\n\",\n+\"MAX_BOX_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n \"\\n\",\n \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in box mode.\\n\",\n@@ -581,16 +583,30 @@\n \"img = load_image(image_path)\\n\",\n \"\\n\",\n \"if USE_BOX_TO_SELECT:\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n-\" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n+\" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+\" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n+\" img,\\n\",\n+\" expected_boxes=N_OBJECTS_TO_BOX,\\n\",\n+\" max_attempts_per_box=MAX_BOX_ATTEMPTS,\\n\",\n+\" )\\n\",\n+\" else:\\n\",\n+\" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n \" for i, box in enumerate(boxes):\\n\",\n \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n-\" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n+\" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+\" box = select_bbox_with_redraw_colab(\\n\",\n+\" img,\\n\",\n+\" prompt=\\\"Draw box around target object\\\",\\n\",\n+\" max_attempts=MAX_BOX_ATTEMPTS,\\n\",\n+\" )\\n\",\n+\" else:\\n\",\n+\" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -552,8 +552,10 @@\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n \"USE_BOX_TO_SELECT = True\\n\",\n+\"USE_BOX_ACCEPT_REDRAW = True\\n\",\n+\"MAX_BOX_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n \"\\n\",\n \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in box mode.\\n\",\n@@ -581,16 +583,30 @@\n \"img = load_image(image_path)\\n\",\n \"\\n\",\n \"if USE_BOX_TO_SELECT:\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n-\" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n+\" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+\" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n+\" img,\\n\",\n+\" expected_boxes=N_OBJECTS_TO_BOX,\\n\",\n+\" max_attempts_per_box=MAX_BOX_ATTEMPTS,\\n\",\n+\" )\\n\",\n+\" else:\\n\",\n+\" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n \" for i, box in enumerate(boxes):\\n\",\n \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n-\" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n+\" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+\" box = select_bbox_with_redraw_colab(\\n\",\n+\" img,\\n\",\n+\" prompt=\\\"Draw box around target object\\\",\\n\",\n+\" max_attempts=MAX_BOX_ATTEMPTS,\\n\",\n+\" )\\n\",\n+\" else:\\n\",\n+\" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n", + "additions": 18, + "deletions": 2 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078796782, + "end": 1783078796802 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0c22a72ce02f25c4016a479f86f12c819d94a6f2895fc250f2" + } + }, + "id": "prt_f27c72802001Yr8Cxh6x86kPIj", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c71fa2002OoaZJW9BrKDKeb" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 78502, + "input": 152, + "output": 631, + "reasoning": 23, + "cache": { + "write": 0, + "read": 77696 + } + }, + "cost": 0.0230188, + "id": "prt_f27c73e06001Hi846kPfJuCJXc", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c71fa2002OoaZJW9BrKDKeb" + } + ] + }, + { + "info": { + "parentID": "msg_f27c6e768001eJtZIDYqTxhvrC", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0170198, + "tokens": { + "total": 78679, + "input": 844, + "output": 113, + "reasoning": 26, + "cache": { + "write": 0, + "read": 77696 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783078796823, + "completed": 1783078799847 + }, + "finish": "tool-calls", + "id": "msg_f27c73e17002kWPHqAqt0dAGQr", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27c741fc001R7MsHmxKHM4YoM", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c73e17002kWPHqAqt0dAGQr" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783078797979, + "end": 1783078798882 + }, + "metadata": { + "openai": { + "itemId": "rs_0fa63b5f7b54381d016a479f8de744819caba383b9b9cbbd58", + "reasoningEncryptedContent": "gAAAAABqR5-O1V8cyXGg8RXAIYIQYKyPwjvbhwCpno9mS--A2FTc7Dn-M9WSoF6HnwWmrSDWKlAHxR4FO3rIpo7NcYgjgnWzMHFmcAiQmqoMp5o89vJHTg2kW_lRLIqMaqQAOReG-f1C71US-TXH7_ipFbuN6s2dW_Z32XixRkYC3sjMaFs1hBRLsK6gqXkyQnSoMg0Q_g2lx0iOoiaWjD2xaVgLs-B1MzxxRKgi5ppEJn-719EuthOyMw2TBUjfhDfpw-giHfGB0tZ_l-ZvGDLvPvzuJLaqL-RcW5pbYJd7YMvBFtczB312G5IlpmRqTnk3LUhJNIVNuv-JGiUQjPcQC1hcNlMGKkr70RuRY3R8GQD7npjZNlDOW5y6gH2QIJfPcGWLlOofjnxPTQBBnLYyO73M7H5jAYzK4aduGa2S08YzRre06uN-6Fl-7znTPTkyi4vUnB8vtP3mUFE9hJaKbXTrl5uzS1JRWTTy4Hn310bnboMH2sTXJoxIbJCAIDMkZkonnL_qKO5SdfWptl-So0jBLgAXUWh1twUGOw3EmPt27ruAunK9FwTfUZnOp8dRWGPCmEqaKVFKt0HnH03C7Z6ohACMDuWhZvcrmC9JfMRhBk-Yi-AuPRZbh1LYlGWerKQIvVnw-46GE0hwW0z5GBtlXYrdu8saove4d5hebn0QFth-AH46qiMbhbH41bPPPSC-OiB08FYAZiKVB1x1RnCBAyIxfbAjWAWb-mGqET96H5omasOO4V8SYTeDPlTTy9vLerhy376-I3dlwBReq3etqTrN_w0qd5_G4nBLcWxEJQ5I7zPF4GokgIgLADCyZ55v0ejYP4SlTwnToMFCHrEu5JVWQLuF4X2QdjN2m4w3ECTxsbuYD6w3bfQNX68Or_8JQASm0RSKF0lX9yZRRwCiifiTzGKugPwqGl8Wdsar2Z-fcTuI638ouKirgqzvJX-GffwAAdpg9tEAu61rXMBvjJz9arnrBCTH9J7EFSC5bKWurD4JykxIKnQ8qeyaBqxeLlLFY0kr8NIRi59J6of944WdlvKIcpOmuzmalYSQgrBnZ8A00WKup7UU8NxT7_dUEmIC" + } + }, + "id": "prt_f27c7429b001vYPJuRI7wU6ImC", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c73e17002kWPHqAqt0dAGQr" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_MxjHs5xNfr4eJetngLJYEmTQ", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \" answer = input(\\\"Accept this box? 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n", + "additions": 0, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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+ } + }, + "id": "prt_f27c85f3e001z0fwiDEn20PP5d", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c85526002JMOFztOqK5tmVd" + }, + { + "type": "text", + "text": "Yes — absolutely possible, and it is a good idea for hard objects.\n\nA practical approach is to add a second interaction mode:\n\n- `box` mode (current): draw rectangle -> SAM refines mask.\n- `brush` mode (new): user paints foreground mask directly (mouse/pen) -> optional erase tool -> finalize mask.\n\nHigh-level implementation plan (no code changes yet):\n\n- Add `SELECTION_MODE = \"box\" | \"brush\"` in the setup cell.\n- Build a Colab JS canvas overlay for brush input:\n - adjustable brush size,\n - draw/erase toggle,\n - undo/clear,\n - Done/Cancel.\n- Return a binary mask to Python (`H x W`), then:\n - compute `mask_area_px`,\n - derive `bbox_xywh`,\n - generate masked crop for SigCLIP.\n- Keep downstream flow unchanged (review/edit, passport export, eval row append).\n- In exports, store interaction metadata like:\n - `\"interaction\": {\"mode\": \"brush\", ...}`\n - plus mask artifact path (PNG/RLE) for auditability.\n\nOne key product decision before implementing:\n- Should brush mode **replace SAM entirely** (manual mask is final), or should it be used as a **hint** and still run SAM refinement afterward?\n\nRecommended default for POC: manual mask is final (faster, fewer failure modes).", + "time": { + "start": 1783078875623, + "end": 1783078880187 + }, + "metadata": { + "openai": { + "itemId": "msg_010f7cb6294afd91016a479fdb8ca4819c81007f154bebbd55", + "phase": "final_answer" + } + }, + "id": "prt_f27c871e70014YWFRIz1WQ4sA2", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c85526002JMOFztOqK5tmVd" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 80146, + "input": 79659, + "output": 291, + "reasoning": 196, + "cache": { + "write": 0, + "read": 0 + } + }, + "cost": 0.14622125, + "id": 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+ } + }, + "id": "prt_f27c9b263001OaAgzNkV5RMkQy", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c9a2b4002hvT82cdTkvJ4H3" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_Px2sekHj0teYKyuHJxSMuGW4", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"def select_multiple_bboxes_with_redraw_colab(image: Image.Image, expected_boxes: int, max_attempts_per_box: int = 5):\\n\",\n@@\n \" bboxes.append(bbox)\\n\",\n \" return bboxes\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def select_brush_hint_colab(image: Image.Image, prompt: str = \\\"Brush over target object\\\"):\\n\",\n+ \" try:\\n\",\n+ \" from google.colab.output import eval_js\\n\",\n+ \" from IPython.display import Javascript, display\\n\",\n+ \" except Exception as exc:\\n\",\n+ \" raise RuntimeError(\\\"Brush selection requires Google Colab.\\\") from exc\\n\",\n+ \"\\n\",\n+ \" buf = io.BytesIO()\\n\",\n+ \" image.save(buf, format=\\\"PNG\\\")\\n\",\n+ \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+ \"\\n\",\n+ \" js = \\\"\\\"\\\"\\n\",\n+ \" async function materialHuntersBrushHint(dataUrl, promptText) {\\n\",\n+ \" const overlay = document.createElement('div');\\n\",\n+ \" overlay.style.position = 'fixed';\\n\",\n+ \" overlay.style.inset = '0';\\n\",\n+ \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+ \" overlay.style.zIndex = '9999';\\n\",\n+ \" overlay.style.display = 'flex';\\n\",\n+ \" overlay.style.flexDirection = 'column';\\n\",\n+ \" overlay.style.alignItems = 'center';\\n\",\n+ \" overlay.style.justifyContent = 'center';\\n\",\n+ \" overlay.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const label = document.createElement('div');\\n\",\n+ \" label.textContent = `${promptText} (paint mask, Done to confirm, Esc to cancel)`;\\n\",\n+ \" label.style.color = 'white';\\n\",\n+ \" label.style.font = '16px sans-serif';\\n\",\n+ \"\\n\",\n+ \" const container = document.createElement('div');\\n\",\n+ \" container.style.position = 'relative';\\n\",\n+ \"\\n\",\n+ \" const img = document.createElement('img');\\n\",\n+ \" img.src = dataUrl;\\n\",\n+ \" img.style.width = '95vw';\\n\",\n+ \" img.style.maxWidth = '1800px';\\n\",\n+ \" img.style.maxHeight = '90vh';\\n\",\n+ \" img.style.display = 'block';\\n\",\n+ \" img.style.userSelect = 'none';\\n\",\n+ \"\\n\",\n+ \" const canvas = document.createElement('canvas');\\n\",\n+ \" canvas.style.position = 'absolute';\\n\",\n+ \" canvas.style.left = '0';\\n\",\n+ \" canvas.style.top = '0';\\n\",\n+ \" canvas.style.cursor = 'crosshair';\\n\",\n+ \"\\n\",\n+ \" container.appendChild(img);\\n\",\n+ \" container.appendChild(canvas);\\n\",\n+ \"\\n\",\n+ \" const controls = document.createElement('div');\\n\",\n+ \" controls.style.display = 'flex';\\n\",\n+ \" controls.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const doneBtn = document.createElement('button');\\n\",\n+ \" doneBtn.textContent = 'Done';\\n\",\n+ \" const clearBtn = document.createElement('button');\\n\",\n+ \" clearBtn.textContent = 'Clear';\\n\",\n+ \" const brush = document.createElement('input');\\n\",\n+ \" brush.type = 'range';\\n\",\n+ \" brush.min = '4';\\n\",\n+ \" brush.max = '48';\\n\",\n+ \" brush.value = '18';\\n\",\n+ \"\\n\",\n+ \" controls.appendChild(doneBtn);\\n\",\n+ \" controls.appendChild(clearBtn);\\n\",\n+ \" controls.appendChild(brush);\\n\",\n+ \"\\n\",\n+ \" overlay.appendChild(label);\\n\",\n+ \" overlay.appendChild(container);\\n\",\n+ \" overlay.appendChild(controls);\\n\",\n+ \" document.body.appendChild(overlay);\\n\",\n+ \"\\n\",\n+ \" let drawing = false;\\n\",\n+ \" let hasInk = false;\\n\",\n+ \"\\n\",\n+ \" const resizeCanvas = () => {\\n\",\n+ \" const rect = img.getBoundingClientRect();\\n\",\n+ \" canvas.width = Math.max(1, Math.round(rect.width));\\n\",\n+ \" canvas.height = Math.max(1, Math.round(rect.height));\\n\",\n+ \" canvas.style.width = `${rect.width}px`;\\n\",\n+ \" canvas.style.height = `${rect.height}px`;\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const ctx = canvas.getContext('2d');\\n\",\n+ \" const setBrush = () => {\\n\",\n+ \" ctx.lineCap = 'round';\\n\",\n+ \" ctx.lineJoin = 'round';\\n\",\n+ \" ctx.strokeStyle = 'rgba(0,255,255,1)';\\n\",\n+ \" ctx.lineWidth = Number(brush.value);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const cleanup = () => {\\n\",\n+ \" document.removeEventListener('keydown', onKey);\\n\",\n+ \" window.removeEventListener('resize', resizeCanvas);\\n\",\n+ \" overlay.remove();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const onKey = (e) => {\\n\",\n+ \" if (e.key === 'Escape') {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const toCanvasXY = (e) => {\\n\",\n+ \" const rect = canvas.getBoundingClientRect();\\n\",\n+ \" const x = Math.max(0, Math.min(rect.width, e.clientX - rect.left));\\n\",\n+ \" const y = Math.max(0, Math.min(rect.height, e.clientY - rect.top));\\n\",\n+ \" return [x, y];\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const toNaturalXY = (x, y) => {\\n\",\n+ \" const scaleX = img.naturalWidth / canvas.width;\\n\",\n+ \" const scaleY = img.naturalHeight / canvas.height;\\n\",\n+ \" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" brush.oninput = setBrush;\\n\",\n+ \"\\n\",\n+ \" canvas.onmousedown = (e) => {\\n\",\n+ \" drawing = true;\\n\",\n+ \" const [x, y] = toCanvasXY(e);\\n\",\n+ \" setBrush();\\n\",\n+ \" ctx.beginPath();\\n\",\n+ \" ctx.moveTo(x, y);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" canvas.onmousemove = (e) => {\\n\",\n+ \" if (!drawing) return;\\n\",\n+ \" const [x, y] = toCanvasXY(e);\\n\",\n+ \" ctx.lineTo(x, y);\\n\",\n+ \" ctx.stroke();\\n\",\n+ \" hasInk = true;\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const stopDraw = () => { drawing = false; };\\n\",\n+ \" canvas.onmouseup = stopDraw;\\n\",\n+ \" canvas.onmouseleave = stopDraw;\\n\",\n+ \"\\n\",\n+ \" clearBtn.onclick = () => {\\n\",\n+ \" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n+ \" hasInk = false;\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" let resolvePromise;\\n\",\n+ \" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n+ \"\\n\",\n+ \" doneBtn.onclick = () => {\\n\",\n+ \" if (!hasInk) {\\n\",\n+ \" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n+ \" return;\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\\n\",\n+ \" let minX = canvas.width, minY = canvas.height, maxX = -1, maxY = -1;\\n\",\n+ \" const pts = [];\\n\",\n+ \" const stride = Math.max(3, Math.floor(Math.sqrt((canvas.width * canvas.height) / 1200)));\\n\",\n+ \"\\n\",\n+ \" for (let y = 0; y < canvas.height; y += 1) {\\n\",\n+ \" for (let x = 0; x < canvas.width; x += 1) {\\n\",\n+ \" const a = data[(y * canvas.width + x) * 4 + 3];\\n\",\n+ \" if (a > 8) {\\n\",\n+ \" if (x < minX) minX = x;\\n\",\n+ \" if (y < minY) minY = y;\\n\",\n+ \" if (x > maxX) maxX = x;\\n\",\n+ \" if (y > maxY) maxY = y;\\n\",\n+ \" if ((x % stride === 0) && (y % stride === 0)) pts.push([x, y]);\\n\",\n+ \" }\\n\",\n+ \" }\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" if (maxX < minX || maxY < minY) {\\n\",\n+ \" label.textContent = 'No valid brush mask found. Try again.';\\n\",\n+ \" return;\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" const pad = 4;\\n\",\n+ \" minX = Math.max(0, minX - pad);\\n\",\n+ \" minY = Math.max(0, minY - pad);\\n\",\n+ \" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n+ \" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n+ \"\\n\",\n+ \" const [nx0, ny0] = toNaturalXY(minX, minY);\\n\",\n+ \" const [nx1, ny1] = toNaturalXY(maxX, maxY);\\n\",\n+ \"\\n\",\n+ \" const sampled = [];\\n\",\n+ \" const limit = 16;\\n\",\n+ \" const step = Math.max(1, Math.floor(pts.length / limit));\\n\",\n+ \" for (let i = 0; i < pts.length && sampled.length < limit; i += step) {\\n\",\n+ \" sampled.push(toNaturalXY(pts[i][0], pts[i][1]));\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" cleanup();\\n\",\n+ \" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" img.onload = () => { resizeCanvas(); setBrush(); };\\n\",\n+ \" if (img.complete) { resizeCanvas(); setBrush(); }\\n\",\n+ \" window.addEventListener('resize', resizeCanvas);\\n\",\n+ \" document.addEventListener('keydown', onKey);\\n\",\n+ \"\\n\",\n+ \" return await promise;\\n\",\n+ \" }\\n\",\n+ \" \\\"\\\"\\\"\\n\",\n+ \"\\n\",\n+ \" display(Javascript(js))\\n\",\n+ \" result = eval_js(\\n\",\n+ \" f\\\"materialHuntersBrushHint({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n+ \" )\\n\",\n+ \" bbox = result.get(\\\"bbox\\\", [-1, -1, -1, -1])\\n\",\n+ \" points = result.get(\\\"points\\\", [])\\n\",\n+ \" if bbox[0] < 0 or bbox[1] < 0:\\n\",\n+ \" raise RuntimeError(\\\"Brush selection cancelled.\\\")\\n\",\n+ \" if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]:\\n\",\n+ \" raise RuntimeError(\\\"Invalid brush hint bbox.\\\")\\n\",\n+ \" points_xy = [(int(p[0]), int(p[1])) for p in points]\\n\",\n+ \" if not points_xy:\\n\",\n+ \" cx = (int(bbox[0]) + int(bbox[2])) // 2\\n\",\n+ \" cy = (int(bbox[1]) + int(bbox[3])) // 2\\n\",\n+ \" points_xy = [(cx, cy)]\\n\",\n+ \" return {\\n\",\n+ \" \\\"prompt_bbox_xyxy\\\": [int(v) for v in bbox],\\n\",\n+ \" \\\"prompt_points_xy\\\": points_xy,\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def select_brush_hint_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n+ \" for attempt in range(1, max_attempts + 1):\\n\",\n+ \" hint = select_brush_hint_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n+ \" accepted = review_bbox_acceptance(\\n\",\n+ \" image=image,\\n\",\n+ \" bbox_xyxy=hint[\\\"prompt_bbox_xyxy\\\"],\\n\",\n+ \" title=f\\\"{prompt} - attempt {attempt}\\\",\\n\",\n+ \" )\\n\",\n+ \" if accepted:\\n\",\n+ \" return hint\\n\",\n+ \" print(\\\"Re-draw requested.\\\")\\n\",\n+ \"\\n\",\n+ \" raise RuntimeError(\\\"Brush hint not accepted within max attempts.\\\")\\n\",\n+ \"\\n\",\n+ \"\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -334,8 +334,249 @@\n \" bboxes.append(bbox)\\n\",\n \" return bboxes\\n\",\n \"\\n\",\n \"\\n\",\n+\"def select_brush_hint_colab(image: Image.Image, prompt: str = \\\"Brush over target object\\\"):\\n\",\n+\" try:\\n\",\n+\" from google.colab.output import eval_js\\n\",\n+\" from IPython.display import Javascript, display\\n\",\n+\" except Exception as exc:\\n\",\n+\" raise RuntimeError(\\\"Brush selection requires Google Colab.\\\") from exc\\n\",\n+\"\\n\",\n+\" buf = io.BytesIO()\\n\",\n+\" image.save(buf, format=\\\"PNG\\\")\\n\",\n+\" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+\"\\n\",\n+\" js = \\\"\\\"\\\"\\n\",\n+\" async function materialHuntersBrushHint(dataUrl, promptText) {\\n\",\n+\" const overlay = document.createElement('div');\\n\",\n+\" overlay.style.position = 'fixed';\\n\",\n+\" overlay.style.inset = '0';\\n\",\n+\" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+\" overlay.style.zIndex = '9999';\\n\",\n+\" overlay.style.display = 'flex';\\n\",\n+\" overlay.style.flexDirection = 'column';\\n\",\n+\" overlay.style.alignItems = 'center';\\n\",\n+\" overlay.style.justifyContent = 'center';\\n\",\n+\" overlay.style.gap = '8px';\\n\",\n+\"\\n\",\n+\" const label = document.createElement('div');\\n\",\n+\" label.textContent = `${promptText} (paint mask, Done to confirm, Esc to cancel)`;\\n\",\n+\" label.style.color = 'white';\\n\",\n+\" label.style.font = '16px sans-serif';\\n\",\n+\"\\n\",\n+\" const container = document.createElement('div');\\n\",\n+\" container.style.position = 'relative';\\n\",\n+\"\\n\",\n+\" const img = document.createElement('img');\\n\",\n+\" img.src = dataUrl;\\n\",\n+\" img.style.width = '95vw';\\n\",\n+\" img.style.maxWidth = '1800px';\\n\",\n+\" img.style.maxHeight = '90vh';\\n\",\n+\" img.style.display = 'block';\\n\",\n+\" img.style.userSelect = 'none';\\n\",\n+\"\\n\",\n+\" const canvas = document.createElement('canvas');\\n\",\n+\" canvas.style.position = 'absolute';\\n\",\n+\" canvas.style.left = '0';\\n\",\n+\" canvas.style.top = '0';\\n\",\n+\" canvas.style.cursor = 'crosshair';\\n\",\n+\"\\n\",\n+\" container.appendChild(img);\\n\",\n+\" container.appendChild(canvas);\\n\",\n+\"\\n\",\n+\" const controls = document.createElement('div');\\n\",\n+\" controls.style.display = 'flex';\\n\",\n+\" controls.style.gap = '8px';\\n\",\n+\"\\n\",\n+\" const doneBtn = document.createElement('button');\\n\",\n+\" doneBtn.textContent = 'Done';\\n\",\n+\" const clearBtn = document.createElement('button');\\n\",\n+\" clearBtn.textContent = 'Clear';\\n\",\n+\" const brush = document.createElement('input');\\n\",\n+\" brush.type = 'range';\\n\",\n+\" brush.min = '4';\\n\",\n+\" brush.max = '48';\\n\",\n+\" brush.value = '18';\\n\",\n+\"\\n\",\n+\" controls.appendChild(doneBtn);\\n\",\n+\" controls.appendChild(clearBtn);\\n\",\n+\" controls.appendChild(brush);\\n\",\n+\"\\n\",\n+\" overlay.appendChild(label);\\n\",\n+\" overlay.appendChild(container);\\n\",\n+\" overlay.appendChild(controls);\\n\",\n+\" document.body.appendChild(overlay);\\n\",\n+\"\\n\",\n+\" let drawing = false;\\n\",\n+\" let hasInk = false;\\n\",\n+\"\\n\",\n+\" const resizeCanvas = () => {\\n\",\n+\" const rect = img.getBoundingClientRect();\\n\",\n+\" canvas.width = Math.max(1, Math.round(rect.width));\\n\",\n+\" canvas.height = Math.max(1, Math.round(rect.height));\\n\",\n+\" canvas.style.width = `${rect.width}px`;\\n\",\n+\" canvas.style.height = `${rect.height}px`;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const ctx = canvas.getContext('2d');\\n\",\n+\" const setBrush = () => {\\n\",\n+\" ctx.lineCap = 'round';\\n\",\n+\" ctx.lineJoin = 'round';\\n\",\n+\" ctx.strokeStyle = 'rgba(0,255,255,1)';\\n\",\n+\" ctx.lineWidth = Number(brush.value);\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const cleanup = () => {\\n\",\n+\" document.removeEventListener('keydown', onKey);\\n\",\n+\" window.removeEventListener('resize', resizeCanvas);\\n\",\n+\" overlay.remove();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n+\" }\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const toCanvasXY = (e) => {\\n\",\n+\" const rect = canvas.getBoundingClientRect();\\n\",\n+\" const x = Math.max(0, Math.min(rect.width, e.clientX - rect.left));\\n\",\n+\" const y = Math.max(0, Math.min(rect.height, e.clientY - rect.top));\\n\",\n+\" return [x, y];\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const toNaturalXY = (x, y) => {\\n\",\n+\" const scaleX = img.naturalWidth / canvas.width;\\n\",\n+\" const scaleY = img.naturalHeight / canvas.height;\\n\",\n+\" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" brush.oninput = setBrush;\\n\",\n+\"\\n\",\n+\" canvas.onmousedown = (e) => {\\n\",\n+\" drawing = true;\\n\",\n+\" const [x, y] = toCanvasXY(e);\\n\",\n+\" setBrush();\\n\",\n+\" ctx.beginPath();\\n\",\n+\" ctx.moveTo(x, y);\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" canvas.onmousemove = (e) => {\\n\",\n+\" if (!drawing) return;\\n\",\n+\" const [x, y] = toCanvasXY(e);\\n\",\n+\" ctx.lineTo(x, y);\\n\",\n+\" ctx.stroke();\\n\",\n+\" hasInk = true;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const stopDraw = () => { drawing = false; };\\n\",\n+\" canvas.onmouseup = stopDraw;\\n\",\n+\" canvas.onmouseleave = stopDraw;\\n\",\n+\"\\n\",\n+\" clearBtn.onclick = () => {\\n\",\n+\" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n+\" hasInk = false;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" let resolvePromise;\\n\",\n+\" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n+\"\\n\",\n+\" doneBtn.onclick = () => {\\n\",\n+\" if (!hasInk) {\\n\",\n+\" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n+\" return;\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\\n\",\n+\" let minX = canvas.width, minY = canvas.height, maxX = -1, maxY = -1;\\n\",\n+\" const pts = [];\\n\",\n+\" const stride = Math.max(3, Math.floor(Math.sqrt((canvas.width * canvas.height) / 1200)));\\n\",\n+\"\\n\",\n+\" for (let y = 0; y < canvas.height; y += 1) {\\n\",\n+\" for (let x = 0; x < canvas.width; x += 1) {\\n\",\n+\" const a = data[(y * canvas.width + x) * 4 + 3];\\n\",\n+\" if (a > 8) {\\n\",\n+\" if (x < minX) minX = x;\\n\",\n+\" if (y < minY) minY = y;\\n\",\n+\" if (x > maxX) maxX = x;\\n\",\n+\" if (y > maxY) maxY = y;\\n\",\n+\" if ((x % stride === 0) && (y % stride === 0)) pts.push([x, y]);\\n\",\n+\" }\\n\",\n+\" }\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" if (maxX < minX || maxY < minY) {\\n\",\n+\" label.textContent = 'No valid brush mask found. Try again.';\\n\",\n+\" return;\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" const pad = 4;\\n\",\n+\" minX = Math.max(0, minX - pad);\\n\",\n+\" minY = Math.max(0, minY - pad);\\n\",\n+\" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n+\" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n+\"\\n\",\n+\" const [nx0, ny0] = toNaturalXY(minX, minY);\\n\",\n+\" const [nx1, ny1] = toNaturalXY(maxX, maxY);\\n\",\n+\"\\n\",\n+\" const sampled = [];\\n\",\n+\" const limit = 16;\\n\",\n+\" const step = Math.max(1, Math.floor(pts.length / limit));\\n\",\n+\" for (let i = 0; i < pts.length && sampled.length < limit; i += step) {\\n\",\n+\" sampled.push(toNaturalXY(pts[i][0], pts[i][1]));\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" img.onload = () => { resizeCanvas(); setBrush(); };\\n\",\n+\" if (img.complete) { resizeCanvas(); setBrush(); }\\n\",\n+\" window.addEventListener('resize', resizeCanvas);\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n+\"\\n\",\n+\" return await promise;\\n\",\n+\" }\\n\",\n+\" \\\"\\\"\\\"\\n\",\n+\"\\n\",\n+\" display(Javascript(js))\\n\",\n+\" result = eval_js(\\n\",\n+\" f\\\"materialHuntersBrushHint({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n+\" )\\n\",\n+\" bbox = result.get(\\\"bbox\\\", [-1, -1, -1, -1])\\n\",\n+\" points = result.get(\\\"points\\\", [])\\n\",\n+\" if bbox[0] < 0 or bbox[1] < 0:\\n\",\n+\" raise RuntimeError(\\\"Brush selection cancelled.\\\")\\n\",\n+\" if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]:\\n\",\n+\" raise RuntimeError(\\\"Invalid brush hint bbox.\\\")\\n\",\n+\" points_xy = [(int(p[0]), int(p[1])) for p in points]\\n\",\n+\" if not points_xy:\\n\",\n+\" cx = (int(bbox[0]) + int(bbox[2])) // 2\\n\",\n+\" cy = (int(bbox[1]) + int(bbox[3])) // 2\\n\",\n+\" points_xy = [(cx, cy)]\\n\",\n+\" return {\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": [int(v) for v in bbox],\\n\",\n+\" \\\"prompt_points_xy\\\": points_xy,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def select_brush_hint_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n+\" for attempt in range(1, max_attempts + 1):\\n\",\n+\" hint = select_brush_hint_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n+\" accepted = review_bbox_acceptance(\\n\",\n+\" image=image,\\n\",\n+\" bbox_xyxy=hint[\\\"prompt_bbox_xyxy\\\"],\\n\",\n+\" title=f\\\"{prompt} - attempt {attempt}\\\",\\n\",\n+\" )\\n\",\n+\" if accepted:\\n\",\n+\" return hint\\n\",\n+\" print(\\\"Re-draw requested.\\\")\\n\",\n+\"\\n\",\n+\" raise RuntimeError(\\\"Brush hint not accepted within max attempts.\\\")\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -334,8 +334,249 @@\n \" bboxes.append(bbox)\\n\",\n \" return bboxes\\n\",\n \"\\n\",\n \"\\n\",\n+\"def select_brush_hint_colab(image: Image.Image, prompt: str = \\\"Brush over target object\\\"):\\n\",\n+\" try:\\n\",\n+\" from google.colab.output import eval_js\\n\",\n+\" from IPython.display import Javascript, display\\n\",\n+\" except Exception as exc:\\n\",\n+\" raise RuntimeError(\\\"Brush selection requires Google Colab.\\\") from exc\\n\",\n+\"\\n\",\n+\" buf = io.BytesIO()\\n\",\n+\" image.save(buf, format=\\\"PNG\\\")\\n\",\n+\" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n+\"\\n\",\n+\" js = \\\"\\\"\\\"\\n\",\n+\" async function materialHuntersBrushHint(dataUrl, promptText) {\\n\",\n+\" const overlay = document.createElement('div');\\n\",\n+\" overlay.style.position = 'fixed';\\n\",\n+\" overlay.style.inset = '0';\\n\",\n+\" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+\" overlay.style.zIndex = '9999';\\n\",\n+\" overlay.style.display = 'flex';\\n\",\n+\" overlay.style.flexDirection = 'column';\\n\",\n+\" overlay.style.alignItems = 'center';\\n\",\n+\" overlay.style.justifyContent = 'center';\\n\",\n+\" overlay.style.gap = '8px';\\n\",\n+\"\\n\",\n+\" const label = document.createElement('div');\\n\",\n+\" label.textContent = `${promptText} (paint mask, Done to confirm, Esc to cancel)`;\\n\",\n+\" label.style.color = 'white';\\n\",\n+\" label.style.font = '16px sans-serif';\\n\",\n+\"\\n\",\n+\" const container = document.createElement('div');\\n\",\n+\" container.style.position = 'relative';\\n\",\n+\"\\n\",\n+\" const img = document.createElement('img');\\n\",\n+\" img.src = dataUrl;\\n\",\n+\" img.style.width = '95vw';\\n\",\n+\" img.style.maxWidth = '1800px';\\n\",\n+\" img.style.maxHeight = '90vh';\\n\",\n+\" img.style.display = 'block';\\n\",\n+\" img.style.userSelect = 'none';\\n\",\n+\"\\n\",\n+\" const canvas = document.createElement('canvas');\\n\",\n+\" canvas.style.position = 'absolute';\\n\",\n+\" canvas.style.left = '0';\\n\",\n+\" canvas.style.top = '0';\\n\",\n+\" canvas.style.cursor = 'crosshair';\\n\",\n+\"\\n\",\n+\" container.appendChild(img);\\n\",\n+\" container.appendChild(canvas);\\n\",\n+\"\\n\",\n+\" const controls = document.createElement('div');\\n\",\n+\" controls.style.display = 'flex';\\n\",\n+\" controls.style.gap = '8px';\\n\",\n+\"\\n\",\n+\" const doneBtn = document.createElement('button');\\n\",\n+\" doneBtn.textContent = 'Done';\\n\",\n+\" const clearBtn = document.createElement('button');\\n\",\n+\" clearBtn.textContent = 'Clear';\\n\",\n+\" const brush = document.createElement('input');\\n\",\n+\" brush.type = 'range';\\n\",\n+\" brush.min = '4';\\n\",\n+\" brush.max = '48';\\n\",\n+\" brush.value = '18';\\n\",\n+\"\\n\",\n+\" controls.appendChild(doneBtn);\\n\",\n+\" controls.appendChild(clearBtn);\\n\",\n+\" controls.appendChild(brush);\\n\",\n+\"\\n\",\n+\" overlay.appendChild(label);\\n\",\n+\" overlay.appendChild(container);\\n\",\n+\" overlay.appendChild(controls);\\n\",\n+\" document.body.appendChild(overlay);\\n\",\n+\"\\n\",\n+\" let drawing = false;\\n\",\n+\" let hasInk = false;\\n\",\n+\"\\n\",\n+\" const resizeCanvas = () => {\\n\",\n+\" const rect = img.getBoundingClientRect();\\n\",\n+\" canvas.width = Math.max(1, Math.round(rect.width));\\n\",\n+\" canvas.height = Math.max(1, Math.round(rect.height));\\n\",\n+\" canvas.style.width = `${rect.width}px`;\\n\",\n+\" canvas.style.height = `${rect.height}px`;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const ctx = canvas.getContext('2d');\\n\",\n+\" const setBrush = () => {\\n\",\n+\" ctx.lineCap = 'round';\\n\",\n+\" ctx.lineJoin = 'round';\\n\",\n+\" ctx.strokeStyle = 'rgba(0,255,255,1)';\\n\",\n+\" ctx.lineWidth = Number(brush.value);\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const cleanup = () => {\\n\",\n+\" document.removeEventListener('keydown', onKey);\\n\",\n+\" window.removeEventListener('resize', resizeCanvas);\\n\",\n+\" overlay.remove();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n+\" }\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const toCanvasXY = (e) => {\\n\",\n+\" const rect = canvas.getBoundingClientRect();\\n\",\n+\" const x = Math.max(0, Math.min(rect.width, e.clientX - rect.left));\\n\",\n+\" const y = Math.max(0, Math.min(rect.height, e.clientY - rect.top));\\n\",\n+\" return [x, y];\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const toNaturalXY = (x, y) => {\\n\",\n+\" const scaleX = img.naturalWidth / canvas.width;\\n\",\n+\" const scaleY = img.naturalHeight / canvas.height;\\n\",\n+\" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" brush.oninput = setBrush;\\n\",\n+\"\\n\",\n+\" canvas.onmousedown = (e) => {\\n\",\n+\" drawing = true;\\n\",\n+\" const [x, y] = toCanvasXY(e);\\n\",\n+\" setBrush();\\n\",\n+\" ctx.beginPath();\\n\",\n+\" ctx.moveTo(x, y);\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" canvas.onmousemove = (e) => {\\n\",\n+\" if (!drawing) return;\\n\",\n+\" const [x, y] = toCanvasXY(e);\\n\",\n+\" ctx.lineTo(x, y);\\n\",\n+\" ctx.stroke();\\n\",\n+\" hasInk = true;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const stopDraw = () => { drawing = false; };\\n\",\n+\" canvas.onmouseup = stopDraw;\\n\",\n+\" canvas.onmouseleave = stopDraw;\\n\",\n+\"\\n\",\n+\" clearBtn.onclick = () => {\\n\",\n+\" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n+\" hasInk = false;\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" let resolvePromise;\\n\",\n+\" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n+\"\\n\",\n+\" doneBtn.onclick = () => {\\n\",\n+\" if (!hasInk) {\\n\",\n+\" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n+\" return;\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\\n\",\n+\" let minX = canvas.width, minY = canvas.height, maxX = -1, maxY = -1;\\n\",\n+\" const pts = [];\\n\",\n+\" const stride = Math.max(3, Math.floor(Math.sqrt((canvas.width * canvas.height) / 1200)));\\n\",\n+\"\\n\",\n+\" for (let y = 0; y < canvas.height; y += 1) {\\n\",\n+\" for (let x = 0; x < canvas.width; x += 1) {\\n\",\n+\" const a = data[(y * canvas.width + x) * 4 + 3];\\n\",\n+\" if (a > 8) {\\n\",\n+\" if (x < minX) minX = x;\\n\",\n+\" if (y < minY) minY = y;\\n\",\n+\" if (x > maxX) maxX = x;\\n\",\n+\" if (y > maxY) maxY = y;\\n\",\n+\" if ((x % stride === 0) && (y % stride === 0)) pts.push([x, y]);\\n\",\n+\" }\\n\",\n+\" }\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" if (maxX < minX || maxY < minY) {\\n\",\n+\" label.textContent = 'No valid brush mask found. Try again.';\\n\",\n+\" return;\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" const pad = 4;\\n\",\n+\" minX = Math.max(0, minX - pad);\\n\",\n+\" minY = Math.max(0, minY - pad);\\n\",\n+\" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n+\" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n+\"\\n\",\n+\" const [nx0, ny0] = toNaturalXY(minX, minY);\\n\",\n+\" const [nx1, ny1] = toNaturalXY(maxX, maxY);\\n\",\n+\"\\n\",\n+\" const sampled = [];\\n\",\n+\" const limit = 16;\\n\",\n+\" const step = Math.max(1, Math.floor(pts.length / limit));\\n\",\n+\" for (let i = 0; i < pts.length && sampled.length < limit; i += step) {\\n\",\n+\" sampled.push(toNaturalXY(pts[i][0], pts[i][1]));\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" img.onload = () => { resizeCanvas(); setBrush(); };\\n\",\n+\" if (img.complete) { resizeCanvas(); setBrush(); }\\n\",\n+\" window.addEventListener('resize', resizeCanvas);\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n+\"\\n\",\n+\" return await promise;\\n\",\n+\" }\\n\",\n+\" \\\"\\\"\\\"\\n\",\n+\"\\n\",\n+\" display(Javascript(js))\\n\",\n+\" result = eval_js(\\n\",\n+\" f\\\"materialHuntersBrushHint({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n+\" )\\n\",\n+\" bbox = result.get(\\\"bbox\\\", [-1, -1, -1, -1])\\n\",\n+\" points = result.get(\\\"points\\\", [])\\n\",\n+\" if bbox[0] < 0 or bbox[1] < 0:\\n\",\n+\" raise RuntimeError(\\\"Brush selection cancelled.\\\")\\n\",\n+\" if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]:\\n\",\n+\" raise RuntimeError(\\\"Invalid brush hint bbox.\\\")\\n\",\n+\" points_xy = [(int(p[0]), int(p[1])) for p in points]\\n\",\n+\" if not points_xy:\\n\",\n+\" cx = (int(bbox[0]) + int(bbox[2])) // 2\\n\",\n+\" cy = (int(bbox[1]) + int(bbox[3])) // 2\\n\",\n+\" points_xy = [(cx, cy)]\\n\",\n+\" return {\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": [int(v) for v in bbox],\\n\",\n+\" \\\"prompt_points_xy\\\": points_xy,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def select_brush_hint_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n+\" for attempt in range(1, max_attempts + 1):\\n\",\n+\" hint = select_brush_hint_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n+\" accepted = review_bbox_acceptance(\\n\",\n+\" image=image,\\n\",\n+\" bbox_xyxy=hint[\\\"prompt_bbox_xyxy\\\"],\\n\",\n+\" title=f\\\"{prompt} - attempt {attempt}\\\",\\n\",\n+\" )\\n\",\n+\" if accepted:\\n\",\n+\" return hint\\n\",\n+\" print(\\\"Re-draw requested.\\\")\\n\",\n+\"\\n\",\n+\" raise RuntimeError(\\\"Brush hint not accepted within max attempts.\\\")\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n", + "additions": 241, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783078994869, + "end": 1783078994938 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0b7dec403c3bfc67016a47a02eca64819fa1a8236db22db55f" + } + }, + "id": "prt_f27c9b71b001op3J7KxYRlQAeo", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c9a2b4002hvT82cdTkvJ4H3" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 84667, + "input": 203, + "output": 3918, + "reasoning": 34, + "cache": { + "write": 0, + "read": 80512 + } + }, + "cost": 0.06977285, + "id": "prt_f27ca4400001HkUuGHwvubCbib", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27c9a2b4002hvT82cdTkvJ4H3" + } + ] + }, + { + "info": { + "parentID": "msg_f27c963970016FwNEmvjBO6ZfR", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.02360015, + "tokens": { + "total": 85321, + "input": 97, + "output": 591, + "reasoning": 25, + "cache": { + "write": 0, + "read": 84608 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783078994960, + "completed": 1783079002363 + }, + "finish": "tool-calls", + "id": "msg_f27ca44100026w7EYSX8ViyUlA", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27ca4998001Co5VQSuhupR37Z", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27ca44100026w7EYSX8ViyUlA" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783078997902, + "end": 1783078998431 + }, + "metadata": { + "openai": { + "itemId": "rs_0a659bc101b791a9016a47a0558e00819e86a22e8ee4dbb0d0", + "reasoningEncryptedContent": "gAAAAABqR6BWvjKnizcDY-pDhqybGmq-0aHmAeBPVbMzE5MPTdR-SCuCmUXfchOwokS5DxjdBcBGn83oBViz25FQX9CjDlT--VG1uixe5agekAE-OUln_vCwQrzP_z348wVFRLeAzerA20mW1hL6wPyRXTZ7MoMaXg6k9MVtrsHrtsqxVA-pGzufNsf8rwdQhi3v3A23hJZTLqbxX2Y_ByPAYOpEuN27EFwjn7fxmh-K_1flfYywhnGDASiO8ys9yK8IoH6QpAQmXYr-yIvltxvkQcLHNcl7KKB4QIZ4DGaTCl1vhgtp2-yJGQBOmuWv1xzlfUn5KyibIeM6Wd1dAIKpsU7AvZPxIwPnlgy85uEOIjm4I3WmTQ9N2sMAYyc3ehbs9kYoqNVg13jTAKyc3C08EhYyrusQKr9rBmWRDzCZZPiv6USk9mfV0JTWTdbAKv-gpW_zrfFpcw3mi3Ov_KLB52hwFGOI5rTA74Su5uL1vFE6fux81ZBeeOGYn3I6nV3AdlGHH1ZdGCCH0I938Sd9iG_VpozofH_t62QOtzrUHQRz9mT-lBIVi6vqTyGDxrvKb1_FB2VbW0LAveT0o31JpSfeHIrXPYUmzkaE56LosCYFEAAD8ZcHaTmBebOTlhW7DXShdsNFejdAx0OUQM-pXmIyA_HwGCrfUcP9mAvpdDcM3q8NHMw1Bgrob6NZ6boLNk7MlEcG09B2wRz2Zjx2d2LbL0nxdJACeJmn6bSaQqof9vRo4rp1Oqy0LSOAcfLqrw8NpSJrTGCo52UyoFwBEJmYIT-asNW6Iuip6HUv02ZlKncvDHEr_YWDacYVFkze0IZZphs1ePrffReHtaJTl7FPdWx0K4XUjxOjpTc7jRoQMB0Izw3912Pa44E-JUOM8z6EBl45l9SqjfXBIXvV-hYClOSSBVKucxsm8tY4ySjU1pI3MT30I-frVAsPB82xk3HO0sQ85itFvI8oyeBaiL7uIxfng1iVZuy79Ww4Xs3J5Z6hlM9U3aGXz96Vfrt2BEe_TILa0rWOUeNTPhBkNKAu9-Vu2cBB-gUfFcZ1rTKVdeaQoSA=" + } + }, + "id": "prt_f27ca4f8e001IOXfoNnHioAIIU", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27ca44100026w7EYSX8ViyUlA" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_12cdKEJAICg8Q6vJUydzqhiL", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"def run_sam_box_segmentation(image: Image.Image, prompt_bbox_xyxy, sam_model, sam_processor):\\n\",\n+ \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n- \" inputs = sam_processor(\\n\",\n- \" image,\\n\",\n- \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n- \" return_tensors=\\\"pt\\\",\\n\",\n- \" )\\n\",\n+ \" if prompt_points_xy:\\n\",\n+ \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n+ \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n+ \" inputs = sam_processor(\\n\",\n+ \" image,\\n\",\n+ \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n+ \" input_points=pts,\\n\",\n+ \" input_labels=labels,\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" inputs = sam_processor(\\n\",\n+ \" image,\\n\",\n+ \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" )\\n\",\n@@\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n+ \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -642,15 +642,26 @@\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n-\"def run_sam_box_segmentation(image: Image.Image, prompt_bbox_xyxy, sam_model, sam_processor):\\n\",\n+\"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n-\" inputs = sam_processor(\\n\",\n-\" image,\\n\",\n-\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n-\" return_tensors=\\\"pt\\\",\\n\",\n-\" )\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n+\" labels = [[1 for _ in prompt_points_xy]]\\n\",\n+\" inputs = sam_processor(\\n\",\n+\" image,\\n\",\n+\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n+\" input_points=pts,\\n\",\n+\" input_labels=labels,\\n\",\n+\" return_tensors=\\\"pt\\\",\\n\",\n+\" )\\n\",\n+\" else:\\n\",\n+\" inputs = sam_processor(\\n\",\n+\" image,\\n\",\n+\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n+\" return_tensors=\\\"pt\\\",\\n\",\n+\" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sam_model(**inputs)\\n\",\n@@ -670,8 +681,9 @@\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n+\" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -642,15 +642,26 @@\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n-\"def run_sam_box_segmentation(image: Image.Image, prompt_bbox_xyxy, sam_model, sam_processor):\\n\",\n+\"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n-\" inputs = sam_processor(\\n\",\n-\" image,\\n\",\n-\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n-\" return_tensors=\\\"pt\\\",\\n\",\n-\" )\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n+\" labels = [[1 for _ in prompt_points_xy]]\\n\",\n+\" inputs = sam_processor(\\n\",\n+\" image,\\n\",\n+\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n+\" input_points=pts,\\n\",\n+\" input_labels=labels,\\n\",\n+\" return_tensors=\\\"pt\\\",\\n\",\n+\" )\\n\",\n+\" else:\\n\",\n+\" inputs = sam_processor(\\n\",\n+\" image,\\n\",\n+\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n+\" return_tensors=\\\"pt\\\",\\n\",\n+\" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sam_model(**inputs)\\n\",\n@@ -670,8 +681,9 @@\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n+\" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n", + "additions": 18, + "deletions": 6 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783079002324, + "end": 1783079002354 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0a659bc101b791a9016a47a0565544819e90c1b5f126e01258" + } + }, + "id": "prt_f27ca51ac001dsaV2EIWwHJ676", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27ca44100026w7EYSX8ViyUlA" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 85321, + "input": 97, + "output": 591, + "reasoning": 25, + "cache": { + "write": 0, + "read": 84608 + } + }, + "cost": 0.02360015, + "id": "prt_f27ca60f6001hiod33ooadjMiE", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27ca44100026w7EYSX8ViyUlA" + } + ] + }, + { + "info": { + "parentID": "msg_f27c963970016FwNEmvjBO6ZfR", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 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+ } + }, + "id": "prt_f27ca6681001kTzIkG7NzQbDS6", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27ca61060024chj4v8emgg05k" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_YZAvsX11Zv6xXS1up6lBM4zt", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"USE_BOX_TO_SELECT = True\\n\",\n- \"USE_BOX_ACCEPT_REDRAW = True\\n\",\n- \"MAX_BOX_ATTEMPTS = 5\\n\",\n+ \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n+ \"USE_ACCEPT_REDRAW = True\\n\",\n+ \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n- \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n+ \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n@@\n- \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in box mode.\\n\",\n+ \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n@@\n- \"if USE_BOX_TO_SELECT:\\n\",\n- \" if MULTI_OBJECT_MODE:\\n\",\n- \" if USE_BOX_ACCEPT_REDRAW:\\n\",\n- \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n- \" img,\\n\",\n- \" expected_boxes=N_OBJECTS_TO_BOX,\\n\",\n- \" max_attempts_per_box=MAX_BOX_ATTEMPTS,\\n\",\n- \" )\\n\",\n- \" else:\\n\",\n- \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n- \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n- \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n- \" for i, box in enumerate(boxes):\\n\",\n- \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n- \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n- \" else:\\n\",\n- \" if USE_BOX_ACCEPT_REDRAW:\\n\",\n- \" box = select_bbox_with_redraw_colab(\\n\",\n- \" img,\\n\",\n- \" prompt=\\\"Draw box around target object\\\",\\n\",\n- \" max_attempts=MAX_BOX_ATTEMPTS,\\n\",\n- \" )\\n\",\n- \" else:\\n\",\n- \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n- \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n- \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n+ \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n+ \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n+ \"\\n\",\n+ \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n+ \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n+ \"\\n\",\n+ \"if SELECTION_MODE == \\\"box\\\":\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" expected_boxes=N_OBJECTS_TO_SELECT,\\n\",\n+ \" max_attempts_per_box=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_SELECT)\\n\",\n+ \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n+ \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n+ \" for i, box in enumerate(boxes):\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_points_xy\\\"] = []\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n+ \" else:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" box = select_bbox_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=\\\"Draw box around target object\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = []\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n+ \"else:\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" if len(OBJECT_SPECS) < N_OBJECTS_TO_SELECT:\\n\",\n+ \" raise ValueError(\\\"Add enough OBJECT_SPECS entries for brush selection.\\\")\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[:N_OBJECTS_TO_SELECT]\\n\",\n+ \" for i, obj in enumerate(OBJECT_RECORDS):\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" hint = select_brush_hint_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" hint = select_brush_hint_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n+ \" )\\n\",\n+ \" obj[\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" obj[\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n+ \" else:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" hint = select_brush_hint_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=\\\"Brush over target object\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n+ \" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n@@\n- \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n+ \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id} (mode={SELECTION_MODE}).\\\")\\n\",\n@@\n- \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n+ \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n \" ax.add_patch(rect)\\n\",\n+ \" for p in obj.get(\\\"prompt_points_xy\\\", []):\\n\",\n+ \" ax.scatter([p[0]], [p[1]], c=\\\"cyan\\\", s=12)\\n\",\n \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n- \"plt.title(\\\"Prompt box preview (indexed)\\\")\\n\",\n+ \"plt.title(\\\"Prompt preview (bbox + optional brush points)\\\")\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -804,15 +804,15 @@\n \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n- \"USE_BOX_TO_SELECT = True\\n\",\n- \"USE_BOX_ACCEPT_REDRAW = True\\n\",\n- \"MAX_BOX_ATTEMPTS = 5\\n\",\n+ \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n+ \"USE_ACCEPT_REDRAW = True\\n\",\n+ \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n- \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n+ \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n \"\\n\",\n- \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in box mode.\\n\",\n+ \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n \"OBJECT_SPECS = [\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n@@ -834,54 +834,96 @@\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n \"\\n\",\n- \"if USE_BOX_TO_SELECT:\\n\",\n+ \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n+ \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n+ \"\\n\",\n+ \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n+ \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n+ \"\\n\",\n+ \"if SELECTION_MODE == \\\"box\\\":\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n- \" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n \" img,\\n\",\n- \" expected_boxes=N_OBJECTS_TO_BOX,\\n\",\n- \" max_attempts_per_box=MAX_BOX_ATTEMPTS,\\n\",\n+ \" expected_boxes=N_OBJECTS_TO_SELECT,\\n\",\n+ \" max_attempts_per_box=MAX_SELECTION_ATTEMPTS,\\n\",\n \" )\\n\",\n \" else:\\n\",\n- \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n+ \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_SELECT)\\n\",\n \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n \" for i, box in enumerate(boxes):\\n\",\n \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_points_xy\\\"] = []\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n- \" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n \" box = select_bbox_with_redraw_colab(\\n\",\n \" img,\\n\",\n \" prompt=\\\"Draw box around target object\\\",\\n\",\n- \" max_attempts=MAX_BOX_ATTEMPTS,\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = []\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" if len(OBJECT_SPECS) < N_OBJECTS_TO_SELECT:\\n\",\n+ \" raise ValueError(\\\"Add enough OBJECT_SPECS entries for brush selection.\\\")\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[:N_OBJECTS_TO_SELECT]\\n\",\n+ \" for i, obj in enumerate(OBJECT_RECORDS):\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" hint = select_brush_hint_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" hint = select_brush_hint_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n+ \" )\\n\",\n+ \" obj[\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" obj[\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n+ \" else:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" hint = select_brush_hint_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=\\\"Brush over target object\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n+ \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n- \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n+ \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n+ \" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n- \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n+ \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id} (mode={SELECTION_MODE}).\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n \"ax = plt.gca()\\n\",\n \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n \" ax.add_patch(rect)\\n\",\n+ \" for p in obj.get(\\\"prompt_points_xy\\\", []):\\n\",\n+ \" ax.scatter([p[0]], [p[1]], c=\\\"cyan\\\", s=12)\\n\",\n \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n- \"plt.title(\\\"Prompt box preview (indexed)\\\")\\n\",\n+ \"plt.title(\\\"Prompt preview (bbox + optional brush points)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -804,15 +804,15 @@\n \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n- \"USE_BOX_TO_SELECT = True\\n\",\n- \"USE_BOX_ACCEPT_REDRAW = True\\n\",\n- \"MAX_BOX_ATTEMPTS = 5\\n\",\n+ \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n+ \"USE_ACCEPT_REDRAW = True\\n\",\n+ \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n- \"N_OBJECTS_TO_BOX = 3 # used only when box+multi mode is enabled\\n\",\n+ \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n \"\\n\",\n- \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in box mode.\\n\",\n+ \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n \"OBJECT_SPECS = [\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n@@ -834,54 +834,96 @@\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n \"\\n\",\n- \"if USE_BOX_TO_SELECT:\\n\",\n+ \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n+ \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n+ \"\\n\",\n+ \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n+ \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n+ \"\\n\",\n+ \"if SELECTION_MODE == \\\"box\\\":\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n- \" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n \" img,\\n\",\n- \" expected_boxes=N_OBJECTS_TO_BOX,\\n\",\n- \" max_attempts_per_box=MAX_BOX_ATTEMPTS,\\n\",\n+ \" expected_boxes=N_OBJECTS_TO_SELECT,\\n\",\n+ \" max_attempts_per_box=MAX_SELECTION_ATTEMPTS,\\n\",\n \" )\\n\",\n \" else:\\n\",\n- \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_BOX)\\n\",\n+ \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_SELECT)\\n\",\n \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n \" for i, box in enumerate(boxes):\\n\",\n \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_points_xy\\\"] = []\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n- \" if USE_BOX_ACCEPT_REDRAW:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n \" box = select_bbox_with_redraw_colab(\\n\",\n \" img,\\n\",\n \" prompt=\\\"Draw box around target object\\\",\\n\",\n- \" max_attempts=MAX_BOX_ATTEMPTS,\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = []\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" if len(OBJECT_SPECS) < N_OBJECTS_TO_SELECT:\\n\",\n+ \" raise ValueError(\\\"Add enough OBJECT_SPECS entries for brush selection.\\\")\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[:N_OBJECTS_TO_SELECT]\\n\",\n+ \" for i, obj in enumerate(OBJECT_RECORDS):\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" hint = select_brush_hint_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" hint = select_brush_hint_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n+ \" )\\n\",\n+ \" obj[\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" obj[\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n+ \" else:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" hint = select_brush_hint_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=\\\"Brush over target object\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n+ \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n- \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n+ \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n+ \" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n- \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id}.\\\")\\n\",\n+ \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id} (mode={SELECTION_MODE}).\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n \"ax = plt.gca()\\n\",\n \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n \" ax.add_patch(rect)\\n\",\n+ \" for p in obj.get(\\\"prompt_points_xy\\\", []):\\n\",\n+ \" ax.scatter([p[0]], [p[1]], c=\\\"cyan\\\", s=12)\\n\",\n \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n- \"plt.title(\\\"Prompt box preview (indexed)\\\")\\n\",\n+ \"plt.title(\\\"Prompt preview (bbox + optional brush points)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n", + "additions": 57, + "deletions": 15 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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+ } + }, + "id": "prt_f27cac1ae001a3p4KsFq2Xpomf", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27cabb74002zGxY7h0I7cKGIm" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_HTU1Z4f7QKiT65NV02kpVZXR", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 700, + "limit": 220 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n700: \" selected_idx = [\\n\",\n701: \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n702: \" ]\\n\",\n703: \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n704: \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n705: \"\\n\",\n706: \" if not selected_prompts:\\n\",\n707: \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n708: \"\\n\",\n709: \" if view_weights is None:\\n\",\n710: \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n711: \" if len(view_weights) != len(image_views):\\n\",\n712: \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n713: \"\\n\",\n714: \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n715: \"\\n\",\n716: \" for image, weight in zip(image_views, view_weights):\\n\",\n717: \" inputs = sigclip_processor(\\n\",\n718: \" text=selected_prompts,\\n\",\n719: \" images=image,\\n\",\n720: \" return_tensors=\\\"pt\\\",\\n\",\n721: \" padding=\\\"max_length\\\",\\n\",\n722: \" )\\n\",\n723: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n724: \"\\n\",\n725: \" with torch.no_grad():\\n\",\n726: \" outputs = sigclip_model(**inputs)\\n\",\n727: \"\\n\",\n728: \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n729: \"\\n\",\n730: \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n731: \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n732: \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n733: \"\\n\",\n734: \" for m in candidate_materials:\\n\",\n735: \" agg_scores[m] += weight * per_material_max[m]\\n\",\n736: \"\\n\",\n737: \" total = sum(agg_scores.values())\\n\",\n738: \" if total > 0:\\n\",\n739: \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n740: \"\\n\",\n741: \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n742: \" top_k = min(top_k, len(ranked))\\n\",\n743: \" top_items = ranked[:top_k]\\n\",\n744: \"\\n\",\n745: \" top_labels = [x[0] for x in top_items]\\n\",\n746: \" top_scores = [x[1] for x in top_items]\\n\",\n747: \"\\n\",\n748: \" return {\\n\",\n749: \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n750: \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n751: \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n752: \" \\\"topk_labels\\\": top_labels,\\n\",\n753: \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n754: \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n755: \" }\\n\",\n756: \"\\n\",\n757: \"\\n\",\n758: \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n759: \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n760: \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n761: \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n762: \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n763: \" return out_path\\n\",\n764: \"\\n\",\n765: \"\\n\",\n766: \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n767: \" df_new = pd.DataFrame([row])\\n\",\n768: \" if not csv_path.exists():\\n\",\n769: \" df_new.to_csv(csv_path, index=False)\\n\",\n770: \" return\\n\",\n771: \" df_existing = pd.read_csv(csv_path)\\n\",\n772: \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n773: \" df_out.to_csv(csv_path, index=False)\"\n774: ]\n775: },\n776: {\n777: \"cell_type\": \"code\",\n778: \"execution_count\": null,\n779: \"metadata\": {},\n780: \"outputs\": [],\n781: \"source\": [\n782: \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n783: \"\\n\",\n784: \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n785: \"print(\\\"Loading SAM model...\\\")\\n\",\n786: \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n787: \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n788: \"sam_model.eval()\\n\",\n789: \"\\n\",\n790: \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n791: \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n792: \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n793: \"sigclip_model.eval()\\n\",\n794: \"\\n\",\n795: \"print(\\\"Models ready.\\\")\"\n796: ]\n797: },\n798: {\n799: \"cell_type\": \"code\",\n800: \"execution_count\": null,\n801: \"metadata\": {},\n802: \"outputs\": [],\n803: \"source\": [\n804: \"# Configure one image and one-or-many objects in it.\\n\",\n805: \"image_id = \\\"IMG002\\\"\\n\",\n806: \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n807: \"\\n\",\n808: \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n809: \"USE_ACCEPT_REDRAW = True\\n\",\n810: \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n811: \"MULTI_OBJECT_MODE = True\\n\",\n812: \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n813: \"\\n\",\n814: \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n815: \"OBJECT_SPECS = [\\n\",\n816: \" {\\n\",\n817: \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n818: \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n819: \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n820: \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n821: \" },\\n\",\n822: \" {\\n\",\n823: \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n824: \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n825: \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n826: \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n827: \" },\\n\",\n828: \" {\\n\",\n829: \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n830: \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n831: \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n832: \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n833: \" },\\n\",\n834: \"]\\n\",\n835: \"\\n\",\n836: \"img = load_image(image_path)\\n\",\n837: \"\\n\",\n838: \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n839: \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n840: \"\\n\",\n841: \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n842: \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n843: \"\\n\",\n844: \"if SELECTION_MODE == \\\"box\\\":\\n\",\n845: \" if MULTI_OBJECT_MODE:\\n\",\n846: \" if USE_ACCEPT_REDRAW:\\n\",\n847: \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n848: \" img,\\n\",\n849: \" expected_boxes=N_OBJECTS_TO_SELECT,\\n\",\n850: \" max_attempts_per_box=MAX_SELECTION_ATTEMPTS,\\n\",\n851: \" )\\n\",\n852: \" else:\\n\",\n853: \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_SELECT)\\n\",\n854: \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n855: \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n856: \" for i, box in enumerate(boxes):\\n\",\n857: \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n858: \" OBJECT_SPECS[i][\\\"prompt_points_xy\\\"] = []\\n\",\n859: \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n860: \" else:\\n\",\n861: \" if USE_ACCEPT_REDRAW:\\n\",\n862: \" box = select_bbox_with_redraw_colab(\\n\",\n863: \" img,\\n\",\n864: \" prompt=\\\"Draw box around target object\\\",\\n\",\n865: \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n866: \" )\\n\",\n867: \" else:\\n\",\n868: \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n869: \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n870: \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = []\\n\",\n871: \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n872: \"else:\\n\",\n873: \" if MULTI_OBJECT_MODE:\\n\",\n874: \" if len(OBJECT_SPECS) < N_OBJECTS_TO_SELECT:\\n\",\n875: \" raise ValueError(\\\"Add enough OBJECT_SPECS entries for brush selection.\\\")\\n\",\n876: \" OBJECT_RECORDS = OBJECT_SPECS[:N_OBJECTS_TO_SELECT]\\n\",\n877: \" for i, obj in enumerate(OBJECT_RECORDS):\\n\",\n878: \" if USE_ACCEPT_REDRAW:\\n\",\n879: \" hint = select_brush_hint_with_redraw_colab(\\n\",\n880: \" img,\\n\",\n881: \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n882: \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n883: \" )\\n\",\n884: \" else:\\n\",\n885: \" hint = select_brush_hint_colab(\\n\",\n886: \" img,\\n\",\n887: \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n888: \" )\\n\",\n889: \" obj[\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n890: \" obj[\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n891: \" else:\\n\",\n892: \" if USE_ACCEPT_REDRAW:\\n\",\n893: \" hint = select_brush_hint_with_redraw_colab(\\n\",\n894: \" img,\\n\",\n895: \" prompt=\\\"Brush over target object\\\",\\n\",\n896: \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n897: \" )\\n\",\n898: \" else:\\n\",\n899: \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n900: \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n901: \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n902: \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n903: \"else:\\n\",\n904: \" for obj in OBJECT_SPECS:\\n\",\n905: \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n906: \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n907: \" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n908: \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n909: \"\\n\",\n910: \"for obj in OBJECT_RECORDS:\\n\",\n911: \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n912: \"\\n\",\n913: \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id} (mode={SELECTION_MODE}).\\\")\\n\",\n914: \"\\n\",\n915: \"plt.figure(figsize=(9, 7))\\n\",\n916: \"plt.imshow(img)\\n\",\n917: \"ax = plt.gca()\\n\",\n918: \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n919: \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n\n(Showing lines 700-919 of 1254. Use offset=920 to continue.)\n</content>", + "metadata": { + "preview": " \" selected_idx = [\\n\",\n \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n \" ]\\n\",\n \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n \"\\n\",\n \" if not selected_prompts:\\n\",\n \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n \"\\n\",\n \" if view_weights is None:\\n\",\n \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n \" if len(view_weights) != len(image_views):\\n\",\n \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n \"\\n\",\n \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n \"\\n\",\n \" for image, weight in zip(image_views, view_weights):\\n\",\n \" inputs = sigclip_processor(\\n\",\n \" text=selected_prompts,\\n\",\n \" images=image,\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" selected_idx = [\\n\",\n \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n \" ]\\n\",\n \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n \"\\n\",\n \" if not selected_prompts:\\n\",\n \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n \"\\n\",\n \" if view_weights is None:\\n\",\n \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n \" if len(view_weights) != len(image_views):\\n\",\n \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n \"\\n\",\n \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n \"\\n\",\n \" for image, weight in zip(image_views, view_weights):\\n\",\n \" inputs = sigclip_processor(\\n\",\n \" text=selected_prompts,\\n\",\n \" images=image,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" padding=\\\"max_length\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sigclip_model(**inputs)\\n\",\n \"\\n\",\n \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n \"\\n\",\n \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n \"\\n\",\n \" for m in candidate_materials:\\n\",\n \" agg_scores[m] += weight * per_material_max[m]\\n\",\n \"\\n\",\n \" total = sum(agg_scores.values())\\n\",\n \" if total > 0:\\n\",\n \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n \"\\n\",\n \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n \" top_k = min(top_k, len(ranked))\\n\",\n \" top_items = ranked[:top_k]\\n\",\n \"\\n\",\n \" top_labels = [x[0] for x in top_items]\\n\",\n \" top_scores = [x[1] for x in top_items]\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n \" return out_path\\n\",\n \"\\n\",\n \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n \" df_out.to_csv(csv_path, index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n \"print(\\\"Loading SAM model...\\\")\\n\",\n \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n \"sam_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n \"USE_ACCEPT_REDRAW = True\\n\",\n \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n \"\\n\",\n \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n \"OBJECT_SPECS = [\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n \" },\\n\",\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n \"\\n\",\n \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n \"\\n\",\n \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n \"\\n\",\n \"if SELECTION_MODE == \\\"box\\\":\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n \" if USE_ACCEPT_REDRAW:\\n\",\n \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n \" img,\\n\",\n \" expected_boxes=N_OBJECTS_TO_SELECT,\\n\",\n \" max_attempts_per_box=MAX_SELECTION_ATTEMPTS,\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_SELECT)\\n\",\n \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n \" for i, box in enumerate(boxes):\\n\",\n \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_SPECS[i][\\\"prompt_points_xy\\\"] = []\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n \" else:\\n\",\n \" if USE_ACCEPT_REDRAW:\\n\",\n \" box = select_bbox_with_redraw_colab(\\n\",\n \" img,\\n\",\n \" prompt=\\\"Draw box around target object\\\",\\n\",\n \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = []\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n \" if len(OBJECT_SPECS) < N_OBJECTS_TO_SELECT:\\n\",\n \" raise ValueError(\\\"Add enough OBJECT_SPECS entries for brush selection.\\\")\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS[:N_OBJECTS_TO_SELECT]\\n\",\n \" for i, obj in enumerate(OBJECT_RECORDS):\\n\",\n \" if USE_ACCEPT_REDRAW:\\n\",\n \" hint = select_brush_hint_with_redraw_colab(\\n\",\n \" img,\\n\",\n \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" hint = select_brush_hint_colab(\\n\",\n \" img,\\n\",\n \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n \" )\\n\",\n \" obj[\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" obj[\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n \" else:\\n\",\n \" if USE_ACCEPT_REDRAW:\\n\",\n \" hint = select_brush_hint_with_redraw_colab(\\n\",\n \" img,\\n\",\n \" prompt=\\\"Brush over target object\\\",\\n\",\n \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n \"else:\\n\",\n \" for obj in OBJECT_SPECS:\\n\",\n \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n \" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id} (mode={SELECTION_MODE}).\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n \"ax = plt.gca()\\n\",\n \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",", + "lineStart": 700, + "lineEnd": 919, + "totalLines": 1254, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783079028309, + "end": 1783079028369 + } + }, + "metadata": { + "openai": { + "itemId": "fc_009243e00734ac8c016a47a073cdd0819d8c652e1d657f51e0" + } + }, + "id": "prt_f27cac49f001eqlFoZ7VbdBXCe", + "sessionID": 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+ } + }, + "id": "prt_f27cacebd001HwZsVKxJmPnuEf", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27cac6ed002iWbV93zJc5HAGa" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_MSFCiPlmzT7rdX4Llu1xS6iK", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"img = load_image(image_path)\\n\",\n- \"\\n\",\n- \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n- \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n- \"\\n\",\n- \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n- \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n- \"\\n\",\n- \"if SELECTION_MODE == \\\"box\\\":\\n\",\n- \" if MULTI_OBJECT_MODE:\\n\",\n- \" if USE_ACCEPT_REDRAW:\\n\",\n- \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n- \" img,\\n\",\n- \" expected_boxes=N_OBJECTS_TO_SELECT,\\n\",\n- \" max_attempts_per_box=MAX_SELECTION_ATTEMPTS,\\n\",\n- \" )\\n\",\n- \" else:\\n\",\n- \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_SELECT)\\n\",\n- \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n- \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n- \" for i, box in enumerate(boxes):\\n\",\n- \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n- \" OBJECT_SPECS[i][\\\"prompt_points_xy\\\"] = []\\n\",\n- \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n- \" else:\\n\",\n- \" if USE_ACCEPT_REDRAW:\\n\",\n- \" box = select_bbox_with_redraw_colab(\\n\",\n- \" img,\\n\",\n- \" prompt=\\\"Draw box around target object\\\",\\n\",\n- \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n- \" )\\n\",\n- \" else:\\n\",\n- \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n- \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n- \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = []\\n\",\n- \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n- \"else:\\n\",\n- \" if MULTI_OBJECT_MODE:\\n\",\n- \" if len(OBJECT_SPECS) < N_OBJECTS_TO_SELECT:\\n\",\n- \" raise ValueError(\\\"Add enough OBJECT_SPECS entries for brush selection.\\\")\\n\",\n- \" OBJECT_RECORDS = OBJECT_SPECS[:N_OBJECTS_TO_SELECT]\\n\",\n- \" for i, obj in enumerate(OBJECT_RECORDS):\\n\",\n- \" if USE_ACCEPT_REDRAW:\\n\",\n- \" hint = select_brush_hint_with_redraw_colab(\\n\",\n- \" img,\\n\",\n- \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n- \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n- \" )\\n\",\n- \" else:\\n\",\n- \" hint = select_brush_hint_colab(\\n\",\n- \" img,\\n\",\n- \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n- \" )\\n\",\n- \" obj[\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n- \" obj[\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n- \" else:\\n\",\n- \" if USE_ACCEPT_REDRAW:\\n\",\n- \" hint = select_brush_hint_with_redraw_colab(\\n\",\n- \" img,\\n\",\n- \" prompt=\\\"Brush over target object\\\",\\n\",\n- \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n- \" )\\n\",\n- \" else:\\n\",\n- \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n- \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n- \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n- \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n- \"else:\\n\",\n- \" for obj in OBJECT_SPECS:\\n\",\n- \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n- \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n- \" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n- \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n+ \"img = load_image(image_path)\\n\",\n+ \"MANUAL_SELECTION = False\\n\",\n+ \"\\n\",\n+ \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n+ \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n+ \"\\n\",\n+ \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n+ \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n+ \"\\n\",\n+ \"if MANUAL_SELECTION:\\n\",\n+ \" for obj in OBJECT_SPECS:\\n\",\n+ \" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n+ \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n+ \" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n+ \"elif SELECTION_MODE == \\\"box\\\":\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" expected_boxes=N_OBJECTS_TO_SELECT,\\n\",\n+ \" max_attempts_per_box=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" boxes = select_multiple_bboxes_colab(img, expected_boxes=N_OBJECTS_TO_SELECT)\\n\",\n+ \" if len(boxes) > len(OBJECT_SPECS):\\n\",\n+ \" raise ValueError(\\\"Add more OBJECT_SPECS entries to match drawn boxes.\\\")\\n\",\n+ \" for i, box in enumerate(boxes):\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_SPECS[i][\\\"prompt_points_xy\\\"] = []\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[: len(boxes)]\\n\",\n+ \" else:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" box = select_bbox_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=\\\"Draw box around target object\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" box = select_bbox_colab(img, prompt=\\\"Draw box around target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in box]\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = []\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n+ \"else:\\n\",\n+ \" if MULTI_OBJECT_MODE:\\n\",\n+ \" if len(OBJECT_SPECS) < N_OBJECTS_TO_SELECT:\\n\",\n+ \" raise ValueError(\\\"Add enough OBJECT_SPECS entries for brush selection.\\\")\\n\",\n+ \" OBJECT_RECORDS = OBJECT_SPECS[:N_OBJECTS_TO_SELECT]\\n\",\n+ \" for i, obj in enumerate(OBJECT_RECORDS):\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" hint = select_brush_hint_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" hint = select_brush_hint_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=f\\\"Brush object {i + 1}/{N_OBJECTS_TO_SELECT}\\\",\\n\",\n+ \" )\\n\",\n+ \" obj[\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" obj[\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n+ \" else:\\n\",\n+ \" if USE_ACCEPT_REDRAW:\\n\",\n+ \" hint = select_brush_hint_with_redraw_colab(\\n\",\n+ \" img,\\n\",\n+ \" prompt=\\\"Brush over target object\\\",\\n\",\n+ \" max_attempts=MAX_SELECTION_ATTEMPTS,\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n+ \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -833,16 +833,23 @@\n \" },\\n\",\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n+\"MANUAL_SELECTION = False\\n\",\n \"\\n\",\n \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n \"\\n\",\n \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n \"\\n\",\n-\"if SELECTION_MODE == \\\"box\\\":\\n\",\n+\"if MANUAL_SELECTION:\\n\",\n+\" for obj in OBJECT_SPECS:\\n\",\n+\" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n+\" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n+\" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n+\" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n+\"elif SELECTION_MODE == \\\"box\\\":\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n \" if USE_ACCEPT_REDRAW:\\n\",\n \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n \" img,\\n\",\n@@ -899,14 +906,8 @@\n \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n-\"else:\\n\",\n-\" for obj in OBJECT_SPECS:\\n\",\n-\" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n- \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n-\" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n-\" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -833,16 +833,23 @@\n \" },\\n\",\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",\n+\"MANUAL_SELECTION = False\\n\",\n \"\\n\",\n \"if SELECTION_MODE not in {\\\"box\\\", \\\"brush\\\"}:\\n\",\n \" raise ValueError(\\\"SELECTION_MODE must be 'box' or 'brush'\\\")\\n\",\n \"\\n\",\n \"if MULTI_OBJECT_MODE and N_OBJECTS_TO_SELECT < 1:\\n\",\n \" raise ValueError(\\\"N_OBJECTS_TO_SELECT must be >= 1 in multi-object mode\\\")\\n\",\n \"\\n\",\n-\"if SELECTION_MODE == \\\"box\\\":\\n\",\n+\"if MANUAL_SELECTION:\\n\",\n+\" for obj in OBJECT_SPECS:\\n\",\n+\" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n+\" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n+\" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n+\" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n+\"elif SELECTION_MODE == \\\"box\\\":\\n\",\n \" if MULTI_OBJECT_MODE:\\n\",\n \" if USE_ACCEPT_REDRAW:\\n\",\n \" boxes = select_multiple_bboxes_with_redraw_colab(\\n\",\n \" img,\\n\",\n@@ -899,14 +906,8 @@\n \" hint = select_brush_hint_colab(img, prompt=\\\"Brush over target object\\\")\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_bbox_xyxy\\\"] = [int(v) for v in hint[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" OBJECT_SPECS[0][\\\"prompt_points_xy\\\"] = [[int(x), int(y)] for x, y in hint[\\\"prompt_points_xy\\\"]]\\n\",\n \" OBJECT_RECORDS = [OBJECT_SPECS[0]]\\n\",\n-\"else:\\n\",\n-\" for obj in OBJECT_SPECS:\\n\",\n-\" if \\\"prompt_bbox_xyxy\\\" not in obj:\\n\",\n- \" raise ValueError(\\\"Manual mode requires prompt_bbox_xyxy for each object.\\\")\\n\",\n-\" obj.setdefault(\\\"prompt_points_xy\\\", [])\\n\",\n-\" OBJECT_RECORDS = OBJECT_SPECS if MULTI_OBJECT_MODE else [OBJECT_SPECS[0]]\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n", + "additions": 8, + "deletions": 7 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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+ } + }, + "id": "prt_f27cb462b001LpwcJtgX1uOphq", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27cb410a002Mmfm5aoWsrsnIi" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_OCd33XDFaw4CmVjUBvWUl3Mj", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"for obj in OBJECT_RECORDS:\\n\",\n \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n \" prompt_bbox_xyxy = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+ \" prompt_points_xy = [[int(p[0]), int(p[1])] for p in obj.get(\\\"prompt_points_xy\\\", [])]\\n\",\n \" tap_x, tap_y = bbox_xyxy_to_center_xy(prompt_bbox_xyxy)\\n\",\n \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n@@\n- \" seg = run_sam_box_segmentation(img, prompt_bbox_xyxy, sam_model, sam_processor)\\n\",\n+ \" seg = run_sam_guided_segmentation(\\n\",\n+ \" image=img,\\n\",\n+ \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n+ \" prompt_points_xy=prompt_points_xy,\\n\",\n+ \" sam_model=sam_model,\\n\",\n+ \" sam_processor=sam_processor,\\n\",\n+ \" )\\n\",\n@@\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": prompt_bbox_xyxy,\\n\",\n+ \" \\\"prompt_points_xy\\\": prompt_points_xy,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n@@\n \" px0, py0, px1, py1 = prompt_bbox_xyxy\\n\",\n \" prompt_rect = patches.Rectangle((px0, py0), px1 - px0, py1 - py0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n \" axes[0].add_patch(prompt_rect)\\n\",\n+ \" if prompt_points_xy:\\n\",\n+ \" xs = [p[0] for p in prompt_points_xy]\\n\",\n+ \" ys = [p[1] for p in prompt_points_xy]\\n\",\n+ \" axes[0].scatter(xs, ys, c=\\\"lime\\\", s=10)\\n\",\n \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -938,15 +938,22 @@\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n \" prompt_bbox_xyxy = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+\" prompt_points_xy = [[int(p[0]), int(p[1])] for p in obj.get(\\\"prompt_points_xy\\\", [])]\\n\",\n \" tap_x, tap_y = bbox_xyxy_to_center_xy(prompt_bbox_xyxy)\\n\",\n \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \" t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" seg_start = pd.Timestamp.utcnow()\\n\",\n-\" seg = run_sam_box_segmentation(img, prompt_bbox_xyxy, sam_model, sam_processor)\\n\",\n+\" seg = run_sam_guided_segmentation(\\n\",\n+\" image=img,\\n\",\n+\" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n+\" prompt_points_xy=prompt_points_xy,\\n\",\n+\" sam_model=sam_model,\\n\",\n+\" sam_processor=sam_processor,\\n\",\n+\" )\\n\",\n \" seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n@@ -972,8 +979,9 @@\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": prompt_bbox_xyxy,\\n\",\n+\" \\\"prompt_points_xy\\\": prompt_points_xy,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n@@ -995,8 +1003,12 @@\n \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n \" px0, py0, px1, py1 = prompt_bbox_xyxy\\n\",\n \" prompt_rect = patches.Rectangle((px0, py0), px1 - px0, py1 - py0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n \" axes[0].add_patch(prompt_rect)\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" xs = [p[0] for p in prompt_points_xy]\\n\",\n+\" ys = [p[1] for p in prompt_points_xy]\\n\",\n+\" axes[0].scatter(xs, ys, c=\\\"lime\\\", s=10)\\n\",\n \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n \" axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n \" axes[1].imshow(crop_masked)\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -938,15 +938,22 @@\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n \" prompt_bbox_xyxy = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n+\" prompt_points_xy = [[int(p[0]), int(p[1])] for p in obj.get(\\\"prompt_points_xy\\\", [])]\\n\",\n \" tap_x, tap_y = bbox_xyxy_to_center_xy(prompt_bbox_xyxy)\\n\",\n \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \" t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" seg_start = pd.Timestamp.utcnow()\\n\",\n-\" seg = run_sam_box_segmentation(img, prompt_bbox_xyxy, sam_model, sam_processor)\\n\",\n+\" seg = run_sam_guided_segmentation(\\n\",\n+\" image=img,\\n\",\n+\" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n+\" prompt_points_xy=prompt_points_xy,\\n\",\n+\" sam_model=sam_model,\\n\",\n+\" sam_processor=sam_processor,\\n\",\n+\" )\\n\",\n \" seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n@@ -972,8 +979,9 @@\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": prompt_bbox_xyxy,\\n\",\n+\" \\\"prompt_points_xy\\\": prompt_points_xy,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n@@ -995,8 +1003,12 @@\n \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n \" px0, py0, px1, py1 = prompt_bbox_xyxy\\n\",\n \" prompt_rect = patches.Rectangle((px0, py0), px1 - px0, py1 - py0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n \" axes[0].add_patch(prompt_rect)\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" xs = [p[0] for p in prompt_points_xy]\\n\",\n+\" ys = [p[1] for p in prompt_points_xy]\\n\",\n+\" axes[0].scatter(xs, ys, c=\\\"lime\\\", s=10)\\n\",\n \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n \" axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n \" axes[1].imshow(crop_masked)\\n\",\n", + "additions": 13, + "deletions": 1 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -7,8 +7,9 @@\n \"# Material Hunters POC Notebook\\n\",\n \"\\n\",\n \"This notebook runs a minimal end-to-end baseline:\\n\",\n \"- box-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+\"- optional brush-hint guidance (converted to SAM box + positive points)\\n\",\n \"- material classification with SigCLIP\\n\",\n \"- optional user edits\\n\",\n \"- passport JSON export\\n\",\n \"- evaluation row append + go/no-go metrics\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -7,8 +7,9 @@\n \"# Material Hunters POC Notebook\\n\",\n \"\\n\",\n \"This notebook runs a minimal end-to-end baseline:\\n\",\n \"- box-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+\"- optional brush-hint guidance (converted to SAM box + positive points)\\n\",\n \"- material classification with SigCLIP\\n\",\n \"- optional user edits\\n\",\n \"- passport JSON export\\n\",\n \"- evaluation row append + go/no-go metrics\\n\",\n", + "additions": 1, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Re-upload and run `poc_eval_notebook.ipynb`.\n2. In setup cell, set:\n - `SELECTION_MODE = \"brush\"`\n - `USE_ACCEPT_REDRAW = True` (recommended)\n3. Paint over each target object, accept/redraw as needed.\n4. Continue with inference/export cells as before.\n\nThis keeps your requirement: brush is a **hint** to SAM, not a final manual mask.", + "time": { + "start": 1783079148016, + "end": 1783079149972 + }, + "metadata": { + "openai": { + "itemId": "msg_0cb2813f649884a4016a47a0ebf53481a18188c18d09b0b016", + "phase": "final_answer" + } + }, + "id": "prt_f27cc99f0001p8xMWWQXo8TCfv", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27cc6f5e0027hmp4IkAYHkamI" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 97385, + "input": 276, + "output": 321, + "reasoning": 20, + "cache": { + "write": 0, + "read": 96768 + } + }, + "cost": 0.0221914, + "id": "prt_f27cca1a8001AZ7s7NQ10ueT86", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27cc6f5e0027hmp4IkAYHkamI" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783079590556 + }, + "agent": "build", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f27d35a9c001HbhLUY1EeOUQws", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "Something is wrong. I cannot draw the bounding box, it at most just select a tiny box without the ability to drag the corners around; furthermore, the brush tool 'draws' but the resulting mask seems to just be the whole image.", + "id": "prt_f27d35a9d001bWrEtKzod1vwA6", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d35a9c001HbhLUY1EeOUQws" + } + ] + }, + { + "info": { + "parentID": "msg_f27d35a9c001HbhLUY1EeOUQws", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.05185355, + "tokens": { + "total": 96784, + "input": 16525, + "output": 71, + "reasoning": 572, + "cache": { + "write": 0, + "read": 79616 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783079590599, + "completed": 1783079604103 + }, + "finish": "tool-calls", + "id": "msg_f27d35ac7002fDp46HYbTm9Rrs", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + 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+ } + }, + "id": "prt_f27d36530001oDjvUdnmI1Hl1C", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d35ac7002fDp46HYbTm9Rrs" + }, + { + "type": "tool", + "tool": "grep", + "callID": "call_cCK3JVk6lxylMFvjle127w1i", + "state": { + "status": "completed", + "input": { + "pattern": "def select_bbox_colab|def select_brush_hint_colab|async function materialHuntersPickBox|async function materialHuntersBrushHint", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb" + }, + "output": "Found 4 matches\n/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb:\n Line 124: \"def select_bbox_colab(image: Image.Image, prompt: str = \\\"Draw a box around the object\\\"):\\n\",\n\n Line 136: \" async function materialHuntersPickBox(dataUrl, promptText) {\\n\",\n\n Line 339: \"def select_brush_hint_colab(image: Image.Image, prompt: str = \\\"Brush over target object\\\"):\\n\",\n\n Line 351: \" async function materialHuntersBrushHint(dataUrl, promptText) {\\n\",\n", + "metadata": { + "matches": 4, + "truncated": false + }, + "title": "def select_bbox_colab|def select_brush_hint_colab|async function materialHuntersPickBox|async function materialHuntersBrushHint", + "time": { + "start": 1783079604050, + "end": 1783079604089 + } + }, + "metadata": { + "openai": { + "itemId": "fc_012d4895127038c3016a47a2b351cc81a3b692979d78fdd7be" + } + }, + "id": "prt_f27d38cb2001C9cs0SwA4j4KtJ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d35ac7002fDp46HYbTm9Rrs" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 96784, + "input": 16525, + "output": 71, + "reasoning": 572, + "cache": { + "write": 0, + "read": 79616 + } + }, + "cost": 0.05185355, + "id": "prt_f27d38f7d001Tl6mVf1kIbmsIl", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d35ac7002fDp46HYbTm9Rrs" + } + ] + }, + { + "info": { + "parentID": "msg_f27d35a9c001HbhLUY1EeOUQws", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.018053, + "tokens": { + "total": 96977, + "input": 292, + "output": 45, + "reasoning": 0, + "cache": { + "write": 0, + "read": 96640 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783079604116, + "completed": 1783079610514 + }, + "finish": "tool-calls", + "id": "msg_f27d38f940028j72mtefy2pYun", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27d396d9001Z6TxX3Rjhg9IVo", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d38f940028j72mtefy2pYun" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_Yw9g1mosiiXVR7GQ26xSsu5l", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 120, + "limit": 330 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n120: \"def load_image(image_path: str) -> Image.Image:\\n\",\n121: \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n122: \"\\n\",\n123: \"\\n\",\n124: \"def select_bbox_colab(image: Image.Image, prompt: str = \\\"Draw a box around the object\\\"):\\n\",\n125: \" try:\\n\",\n126: \" from google.colab.output import eval_js\\n\",\n127: \" from IPython.display import Javascript, display\\n\",\n128: \" except Exception as exc:\\n\",\n129: \" raise RuntimeError(\\\"Box selection requires Google Colab.\\\") from exc\\n\",\n130: \"\\n\",\n131: \" buf = io.BytesIO()\\n\",\n132: \" image.save(buf, format=\\\"PNG\\\")\\n\",\n133: \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n134: \"\\n\",\n135: \" js = \\\"\\\"\\\"\\n\",\n136: \" async function materialHuntersPickBox(dataUrl, promptText) {\\n\",\n137: \" const overlay = document.createElement('div');\\n\",\n138: \" overlay.style.position = 'fixed';\\n\",\n139: \" overlay.style.inset = '0';\\n\",\n140: \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n141: \" overlay.style.zIndex = '9999';\\n\",\n142: \" overlay.style.display = 'flex';\\n\",\n143: \" overlay.style.flexDirection = 'column';\\n\",\n144: \" overlay.style.alignItems = 'center';\\n\",\n145: \" overlay.style.justifyContent = 'center';\\n\",\n146: \" overlay.style.gap = '8px';\\n\",\n147: \"\\n\",\n148: \" const label = document.createElement('div');\\n\",\n149: \" label.textContent = `${promptText} (drag mouse, Done to confirm, Esc to cancel)`;\\n\",\n150: \" label.style.color = 'white';\\n\",\n151: \" label.style.font = '16px sans-serif';\\n\",\n152: \"\\n\",\n153: \" const container = document.createElement('div');\\n\",\n154: \" container.style.position = 'relative';\\n\",\n155: \"\\n\",\n156: \" const img = document.createElement('img');\\n\",\n157: \" img.src = dataUrl;\\n\",\n158: \" img.style.width = '95vw';\\n\",\n159: \" img.style.maxWidth = '1800px';\\n\",\n160: \" img.style.maxHeight = '90vh';\\n\",\n161: \" img.style.cursor = 'crosshair';\\n\",\n162: \" img.style.border = '2px solid white';\\n\",\n163: \" img.style.display = 'block';\\n\",\n164: \"\\n\",\n165: \" const box = document.createElement('div');\\n\",\n166: \" box.style.position = 'absolute';\\n\",\n167: \" box.style.border = '2px solid #00e5ff';\\n\",\n168: \" box.style.background = 'rgba(0,229,255,0.15)';\\n\",\n169: \" box.style.display = 'none';\\n\",\n170: \"\\n\",\n171: \" container.appendChild(img);\\n\",\n172: \" container.appendChild(box);\\n\",\n173: \"\\n\",\n174: \" const controls = document.createElement('div');\\n\",\n175: \" controls.style.display = 'flex';\\n\",\n176: \" controls.style.gap = '8px';\\n\",\n177: \"\\n\",\n178: \" const doneBtn = document.createElement('button');\\n\",\n179: \" doneBtn.textContent = 'Done';\\n\",\n180: \" const resetBtn = document.createElement('button');\\n\",\n181: \" resetBtn.textContent = 'Reset';\\n\",\n182: \"\\n\",\n183: \" controls.appendChild(doneBtn);\\n\",\n184: \" controls.appendChild(resetBtn);\\n\",\n185: \"\\n\",\n186: \" overlay.appendChild(label);\\n\",\n187: \" overlay.appendChild(container);\\n\",\n188: \" overlay.appendChild(controls);\\n\",\n189: \" document.body.appendChild(overlay);\\n\",\n190: \"\\n\",\n191: \" let dragging = false;\\n\",\n192: \" let startX = 0;\\n\",\n193: \" let startY = 0;\\n\",\n194: \" let latest = null;\\n\",\n195: \"\\n\",\n196: \" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n197: \"\\n\",\n198: \" const cleanup = () => {\\n\",\n199: \" document.removeEventListener('keydown', onKey);\\n\",\n200: \" overlay.remove();\\n\",\n201: \" };\\n\",\n202: \"\\n\",\n203: \" const toNatural = (xCss, yCss, rect) => {\\n\",\n204: \" const nx = Math.round(xCss * (img.naturalWidth / rect.width));\\n\",\n205: \" const ny = Math.round(yCss * (img.naturalHeight / rect.height));\\n\",\n206: \" return [nx, ny];\\n\",\n207: \" };\\n\",\n208: \"\\n\",\n209: \" const onKey = (e) => {\\n\",\n210: \" if (e.key === 'Escape') {\\n\",\n211: \" cleanup();\\n\",\n212: \" resolvePromise([-1, -1, -1, -1]);\\n\",\n213: \" }\\n\",\n214: \" };\\n\",\n215: \"\\n\",\n216: \" document.addEventListener('keydown', onKey);\\n\",\n217: \"\\n\",\n218: \" img.onmousedown = (e) => {\\n\",\n219: \" const rect = img.getBoundingClientRect();\\n\",\n220: \" dragging = true;\\n\",\n221: \" startX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n222: \" startY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n223: \" box.style.display = 'block';\\n\",\n224: \" };\\n\",\n225: \"\\n\",\n226: \" img.onmousemove = (e) => {\\n\",\n227: \" if (!dragging) return;\\n\",\n228: \" const rect = img.getBoundingClientRect();\\n\",\n229: \" const curX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n230: \" const curY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n231: \"\\n\",\n232: \" const left = Math.min(startX, curX);\\n\",\n233: \" const top = Math.min(startY, curY);\\n\",\n234: \" const width = Math.max(1, Math.abs(curX - startX));\\n\",\n235: \" const height = Math.max(1, Math.abs(curY - startY));\\n\",\n236: \"\\n\",\n237: \" box.style.left = `${left}px`;\\n\",\n238: \" box.style.top = `${top}px`;\\n\",\n239: \" box.style.width = `${width}px`;\\n\",\n240: \" box.style.height = `${height}px`;\\n\",\n241: \"\\n\",\n242: \" const [x0, y0] = toNatural(left, top, rect);\\n\",\n243: \" const [x1, y1] = toNatural(left + width, top + height, rect);\\n\",\n244: \" latest = [Math.min(x0, x1), Math.min(y0, y1), Math.max(x0, x1), Math.max(y0, y1)];\\n\",\n245: \" };\\n\",\n246: \"\\n\",\n247: \" img.onmouseup = () => {\\n\",\n248: \" dragging = false;\\n\",\n249: \" };\\n\",\n250: \"\\n\",\n251: \" img.onmouseleave = () => {\\n\",\n252: \" dragging = false;\\n\",\n253: \" };\\n\",\n254: \"\\n\",\n255: \" resetBtn.onclick = () => {\\n\",\n256: \" latest = null;\\n\",\n257: \" box.style.display = 'none';\\n\",\n258: \" };\\n\",\n259: \"\\n\",\n260: \" let resolvePromise;\\n\",\n261: \" const promise = new Promise((resolve) => {\\n\",\n262: \" resolvePromise = resolve;\\n\",\n263: \" });\\n\",\n264: \"\\n\",\n265: \" doneBtn.onclick = () => {\\n\",\n266: \" if (!latest) {\\n\",\n267: \" label.textContent = 'Draw a box first.';\\n\",\n268: \" return;\\n\",\n269: \" }\\n\",\n270: \" cleanup();\\n\",\n271: \" resolvePromise(latest);\\n\",\n272: \" };\\n\",\n273: \"\\n\",\n274: \" return await promise;\\n\",\n275: \" }\\n\",\n276: \" \\\"\\\"\\\"\\n\",\n277: \"\\n\",\n278: \" display(Javascript(js))\\n\",\n279: \" x0, y0, x1, y1 = eval_js(\\n\",\n280: \" f\\\"materialHuntersPickBox({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n281: \" )\\n\",\n282: \" if x0 < 0 or y0 < 0:\\n\",\n283: \" raise RuntimeError(\\\"Box selection cancelled.\\\")\\n\",\n284: \" if x1 <= x0 or y1 <= y0:\\n\",\n285: \" raise RuntimeError(\\\"Invalid box. Draw a box with non-zero area.\\\")\\n\",\n286: \" return (int(x0), int(y0), int(x1), int(y1))\\n\",\n287: \"\\n\",\n288: \"\\n\",\n289: \"def select_multiple_bboxes_colab(image: Image.Image, expected_boxes: int):\\n\",\n290: \" if expected_boxes < 1:\\n\",\n291: \" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n292: \" bboxes = []\\n\",\n293: \" for i in range(expected_boxes):\\n\",\n294: \" bbox = select_bbox_colab(image, prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\")\\n\",\n295: \" bboxes.append(bbox)\\n\",\n296: \" return bboxes\\n\",\n297: \"\\n\",\n298: \"\\n\",\n299: \"def review_bbox_acceptance(image: Image.Image, bbox_xyxy, title: str = \\\"Review selected box\\\"):\\n\",\n300: \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n301: \" plt.figure(figsize=(8, 6))\\n\",\n302: \" ax = plt.gca()\\n\",\n303: \" ax.imshow(image)\\n\",\n304: \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n305: \" ax.add_patch(rect)\\n\",\n306: \" ax.set_title(title)\\n\",\n307: \" ax.axis(\\\"off\\\")\\n\",\n308: \" plt.show()\\n\",\n309: \"\\n\",\n310: \" answer = input(\\\"Accept this box? [y/n]: \\\" ).strip().lower()\\n\",\n311: \" return answer in {\\\"y\\\", \\\"yes\\\"}\\n\",\n312: \"\\n\",\n313: \"\\n\",\n314: \"def select_bbox_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n315: \" for attempt in range(1, max_attempts + 1):\\n\",\n316: \" bbox = select_bbox_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n317: \" accepted = review_bbox_acceptance(image, bbox, title=f\\\"{prompt} - attempt {attempt}\\\")\\n\",\n318: \" if accepted:\\n\",\n319: \" return bbox\\n\",\n320: \" print(\\\"Re-draw requested.\\\")\\n\",\n321: \"\\n\",\n322: \" raise RuntimeError(\\\"Box not accepted within max attempts.\\\")\\n\",\n323: \"\\n\",\n324: \"\\n\",\n325: \"def select_multiple_bboxes_with_redraw_colab(image: Image.Image, expected_boxes: int, max_attempts_per_box: int = 5):\\n\",\n326: \" if expected_boxes < 1:\\n\",\n327: \" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n328: \" bboxes = []\\n\",\n329: \" for i in range(expected_boxes):\\n\",\n330: \" bbox = select_bbox_with_redraw_colab(\\n\",\n331: \" image=image,\\n\",\n332: \" prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\",\\n\",\n333: \" max_attempts=max_attempts_per_box,\\n\",\n334: \" )\\n\",\n335: \" bboxes.append(bbox)\\n\",\n336: \" return bboxes\\n\",\n337: \"\\n\",\n338: \"\\n\",\n339: \"def select_brush_hint_colab(image: Image.Image, prompt: str = \\\"Brush over target object\\\"):\\n\",\n340: \" try:\\n\",\n341: \" from google.colab.output import eval_js\\n\",\n342: \" from IPython.display import Javascript, display\\n\",\n343: \" except Exception as exc:\\n\",\n344: \" raise RuntimeError(\\\"Brush selection requires Google Colab.\\\") from exc\\n\",\n345: \"\\n\",\n346: \" buf = io.BytesIO()\\n\",\n347: \" image.save(buf, format=\\\"PNG\\\")\\n\",\n348: \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n349: \"\\n\",\n350: \" js = \\\"\\\"\\\"\\n\",\n351: \" async function materialHuntersBrushHint(dataUrl, promptText) {\\n\",\n352: \" const overlay = document.createElement('div');\\n\",\n353: \" overlay.style.position = 'fixed';\\n\",\n354: \" overlay.style.inset = '0';\\n\",\n355: \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n356: \" overlay.style.zIndex = '9999';\\n\",\n357: \" overlay.style.display = 'flex';\\n\",\n358: \" overlay.style.flexDirection = 'column';\\n\",\n359: \" overlay.style.alignItems = 'center';\\n\",\n360: \" overlay.style.justifyContent = 'center';\\n\",\n361: \" overlay.style.gap = '8px';\\n\",\n362: \"\\n\",\n363: \" const label = document.createElement('div');\\n\",\n364: \" label.textContent = `${promptText} (paint mask, Done to confirm, Esc to cancel)`;\\n\",\n365: \" label.style.color = 'white';\\n\",\n366: \" label.style.font = '16px sans-serif';\\n\",\n367: \"\\n\",\n368: \" const container = document.createElement('div');\\n\",\n369: \" container.style.position = 'relative';\\n\",\n370: \"\\n\",\n371: \" const img = document.createElement('img');\\n\",\n372: \" img.src = dataUrl;\\n\",\n373: \" img.style.width = '95vw';\\n\",\n374: \" img.style.maxWidth = '1800px';\\n\",\n375: \" img.style.maxHeight = '90vh';\\n\",\n376: \" img.style.display = 'block';\\n\",\n377: \" img.style.userSelect = 'none';\\n\",\n378: \"\\n\",\n379: \" const canvas = document.createElement('canvas');\\n\",\n380: \" canvas.style.position = 'absolute';\\n\",\n381: \" canvas.style.left = '0';\\n\",\n382: \" canvas.style.top = '0';\\n\",\n383: \" canvas.style.cursor = 'crosshair';\\n\",\n384: \"\\n\",\n385: \" container.appendChild(img);\\n\",\n386: \" container.appendChild(canvas);\\n\",\n387: \"\\n\",\n388: \" const controls = document.createElement('div');\\n\",\n389: \" controls.style.display = 'flex';\\n\",\n390: \" controls.style.gap = '8px';\\n\",\n391: \"\\n\",\n392: \" const doneBtn = document.createElement('button');\\n\",\n393: \" doneBtn.textContent = 'Done';\\n\",\n394: \" const clearBtn = document.createElement('button');\\n\",\n395: \" clearBtn.textContent = 'Clear';\\n\",\n396: \" const brush = document.createElement('input');\\n\",\n397: \" brush.type = 'range';\\n\",\n398: \" brush.min = '4';\\n\",\n399: \" brush.max = '48';\\n\",\n400: \" brush.value = '18';\\n\",\n401: \"\\n\",\n402: \" controls.appendChild(doneBtn);\\n\",\n403: \" controls.appendChild(clearBtn);\\n\",\n404: \" controls.appendChild(brush);\\n\",\n405: \"\\n\",\n406: \" overlay.appendChild(label);\\n\",\n407: \" overlay.appendChild(container);\\n\",\n408: \" overlay.appendChild(controls);\\n\",\n409: \" document.body.appendChild(overlay);\\n\",\n410: \"\\n\",\n411: \" let drawing = false;\\n\",\n412: \" let hasInk = false;\\n\",\n413: \"\\n\",\n414: \" const resizeCanvas = () => {\\n\",\n415: \" const rect = img.getBoundingClientRect();\\n\",\n416: \" canvas.width = Math.max(1, Math.round(rect.width));\\n\",\n417: \" canvas.height = Math.max(1, Math.round(rect.height));\\n\",\n418: \" canvas.style.width = `${rect.width}px`;\\n\",\n419: \" canvas.style.height = `${rect.height}px`;\\n\",\n420: \" };\\n\",\n421: \"\\n\",\n422: \" const ctx = canvas.getContext('2d');\\n\",\n423: \" const setBrush = () => {\\n\",\n424: \" ctx.lineCap = 'round';\\n\",\n425: \" ctx.lineJoin = 'round';\\n\",\n426: \" ctx.strokeStyle = 'rgba(0,255,255,1)';\\n\",\n427: \" ctx.lineWidth = Number(brush.value);\\n\",\n428: \" };\\n\",\n429: \"\\n\",\n430: \" const cleanup = () => {\\n\",\n431: \" document.removeEventListener('keydown', onKey);\\n\",\n432: \" window.removeEventListener('resize', resizeCanvas);\\n\",\n433: \" overlay.remove();\\n\",\n434: \" };\\n\",\n435: \"\\n\",\n436: \" const onKey = (e) => {\\n\",\n437: \" if (e.key === 'Escape') {\\n\",\n438: \" cleanup();\\n\",\n439: \" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n440: \" }\\n\",\n441: \" };\\n\",\n442: \"\\n\",\n443: \" const toCanvasXY = (e) => {\\n\",\n444: \" const rect = canvas.getBoundingClientRect();\\n\",\n445: \" const x = Math.max(0, Math.min(rect.width, e.clientX - rect.left));\\n\",\n446: \" const y = Math.max(0, Math.min(rect.height, e.clientY - rect.top));\\n\",\n447: \" return [x, y];\\n\",\n448: \" };\\n\",\n449: \"\\n\",\n\n(Showing lines 120-449 of 1270. Use offset=450 to continue.)\n</content>", + "metadata": { + "preview": " \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_bbox_colab(image: Image.Image, prompt: str = \\\"Draw a box around the object\\\"):\\n\",\n \" try:\\n\",\n \" from google.colab.output import eval_js\\n\",\n \" from IPython.display import Javascript, display\\n\",\n \" except Exception as exc:\\n\",\n \" raise RuntimeError(\\\"Box selection requires Google Colab.\\\") from exc\\n\",\n \"\\n\",\n \" buf = io.BytesIO()\\n\",\n \" image.save(buf, format=\\\"PNG\\\")\\n\",\n \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n \"\\n\",\n \" js = \\\"\\\"\\\"\\n\",\n \" async function materialHuntersPickBox(dataUrl, promptText) {\\n\",\n \" const overlay = document.createElement('div');\\n\",\n \" overlay.style.position = 'fixed';\\n\",\n \" overlay.style.inset = '0';\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \"def load_image(image_path: str) -> Image.Image:\\n\",\n \" return Image.open(image_path).convert(\\\"RGB\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_bbox_colab(image: Image.Image, prompt: str = \\\"Draw a box around the object\\\"):\\n\",\n \" try:\\n\",\n \" from google.colab.output import eval_js\\n\",\n \" from IPython.display import Javascript, display\\n\",\n \" except Exception as exc:\\n\",\n \" raise RuntimeError(\\\"Box selection requires Google Colab.\\\") from exc\\n\",\n \"\\n\",\n \" buf = io.BytesIO()\\n\",\n \" image.save(buf, format=\\\"PNG\\\")\\n\",\n \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n \"\\n\",\n \" js = \\\"\\\"\\\"\\n\",\n \" async function materialHuntersPickBox(dataUrl, promptText) {\\n\",\n \" const overlay = document.createElement('div');\\n\",\n \" overlay.style.position = 'fixed';\\n\",\n \" overlay.style.inset = '0';\\n\",\n \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n \" overlay.style.zIndex = '9999';\\n\",\n \" overlay.style.display = 'flex';\\n\",\n \" overlay.style.flexDirection = 'column';\\n\",\n \" overlay.style.alignItems = 'center';\\n\",\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n \" label.textContent = `${promptText} (drag mouse, Done to confirm, Esc to cancel)`;\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n \" const container = document.createElement('div');\\n\",\n \" container.style.position = 'relative';\\n\",\n \"\\n\",\n \" const img = document.createElement('img');\\n\",\n \" img.src = dataUrl;\\n\",\n \" img.style.width = '95vw';\\n\",\n \" img.style.maxWidth = '1800px';\\n\",\n \" img.style.maxHeight = '90vh';\\n\",\n \" img.style.cursor = 'crosshair';\\n\",\n \" img.style.border = '2px solid white';\\n\",\n \" img.style.display = 'block';\\n\",\n \"\\n\",\n \" const box = document.createElement('div');\\n\",\n \" box.style.position = 'absolute';\\n\",\n \" box.style.border = '2px solid #00e5ff';\\n\",\n \" box.style.background = 'rgba(0,229,255,0.15)';\\n\",\n \" box.style.display = 'none';\\n\",\n \"\\n\",\n \" container.appendChild(img);\\n\",\n \" container.appendChild(box);\\n\",\n \"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n \"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n \" const resetBtn = document.createElement('button');\\n\",\n \" resetBtn.textContent = 'Reset';\\n\",\n \"\\n\",\n \" controls.appendChild(doneBtn);\\n\",\n \" controls.appendChild(resetBtn);\\n\",\n \"\\n\",\n \" overlay.appendChild(label);\\n\",\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n \" let dragging = false;\\n\",\n \" let startX = 0;\\n\",\n \" let startY = 0;\\n\",\n \" let latest = null;\\n\",\n \"\\n\",\n \" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n \"\\n\",\n \" const cleanup = () => {\\n\",\n \" document.removeEventListener('keydown', onKey);\\n\",\n \" overlay.remove();\\n\",\n \" };\\n\",\n \"\\n\",\n \" const toNatural = (xCss, yCss, rect) => {\\n\",\n \" const nx = Math.round(xCss * (img.naturalWidth / rect.width));\\n\",\n \" const ny = Math.round(yCss * (img.naturalHeight / rect.height));\\n\",\n \" return [nx, ny];\\n\",\n \" };\\n\",\n \"\\n\",\n \" const onKey = (e) => {\\n\",\n \" if (e.key === 'Escape') {\\n\",\n \" cleanup();\\n\",\n \" resolvePromise([-1, -1, -1, -1]);\\n\",\n \" }\\n\",\n \" };\\n\",\n \"\\n\",\n \" document.addEventListener('keydown', onKey);\\n\",\n \"\\n\",\n \" img.onmousedown = (e) => {\\n\",\n \" const rect = img.getBoundingClientRect();\\n\",\n \" dragging = true;\\n\",\n \" startX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n \" startY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n \" box.style.display = 'block';\\n\",\n \" };\\n\",\n \"\\n\",\n \" img.onmousemove = (e) => {\\n\",\n \" if (!dragging) return;\\n\",\n \" const rect = img.getBoundingClientRect();\\n\",\n \" const curX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n \" const curY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n \"\\n\",\n \" const left = Math.min(startX, curX);\\n\",\n \" const top = Math.min(startY, curY);\\n\",\n \" const width = Math.max(1, Math.abs(curX - startX));\\n\",\n \" const height = Math.max(1, Math.abs(curY - startY));\\n\",\n \"\\n\",\n \" box.style.left = `${left}px`;\\n\",\n \" box.style.top = `${top}px`;\\n\",\n \" box.style.width = `${width}px`;\\n\",\n \" box.style.height = `${height}px`;\\n\",\n \"\\n\",\n \" const [x0, y0] = toNatural(left, top, rect);\\n\",\n \" const [x1, y1] = toNatural(left + width, top + height, rect);\\n\",\n \" latest = [Math.min(x0, x1), Math.min(y0, y1), Math.max(x0, x1), Math.max(y0, y1)];\\n\",\n \" };\\n\",\n \"\\n\",\n \" img.onmouseup = () => {\\n\",\n \" dragging = false;\\n\",\n \" };\\n\",\n \"\\n\",\n \" img.onmouseleave = () => {\\n\",\n \" dragging = false;\\n\",\n \" };\\n\",\n \"\\n\",\n \" resetBtn.onclick = () => {\\n\",\n \" latest = null;\\n\",\n \" box.style.display = 'none';\\n\",\n \" };\\n\",\n \"\\n\",\n \" let resolvePromise;\\n\",\n \" const promise = new Promise((resolve) => {\\n\",\n \" resolvePromise = resolve;\\n\",\n \" });\\n\",\n \"\\n\",\n \" doneBtn.onclick = () => {\\n\",\n \" if (!latest) {\\n\",\n \" label.textContent = 'Draw a box first.';\\n\",\n \" return;\\n\",\n \" }\\n\",\n \" cleanup();\\n\",\n \" resolvePromise(latest);\\n\",\n \" };\\n\",\n \"\\n\",\n \" return await promise;\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" display(Javascript(js))\\n\",\n \" x0, y0, x1, y1 = eval_js(\\n\",\n \" f\\\"materialHuntersPickBox({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n \" )\\n\",\n \" if x0 < 0 or y0 < 0:\\n\",\n \" raise RuntimeError(\\\"Box selection cancelled.\\\")\\n\",\n \" if x1 <= x0 or y1 <= y0:\\n\",\n \" raise RuntimeError(\\\"Invalid box. Draw a box with non-zero area.\\\")\\n\",\n \" return (int(x0), int(y0), int(x1), int(y1))\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_multiple_bboxes_colab(image: Image.Image, expected_boxes: int):\\n\",\n \" if expected_boxes < 1:\\n\",\n \" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n \" bboxes = []\\n\",\n \" for i in range(expected_boxes):\\n\",\n \" bbox = select_bbox_colab(image, prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\")\\n\",\n \" bboxes.append(bbox)\\n\",\n \" return bboxes\\n\",\n \"\\n\",\n \"\\n\",\n \"def review_bbox_acceptance(image: Image.Image, bbox_xyxy, title: str = \\\"Review selected box\\\"):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" plt.figure(figsize=(8, 6))\\n\",\n \" ax = plt.gca()\\n\",\n \" ax.imshow(image)\\n\",\n \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n \" ax.add_patch(rect)\\n\",\n \" ax.set_title(title)\\n\",\n \" ax.axis(\\\"off\\\")\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \" answer = input(\\\"Accept this box? [y/n]: \\\" ).strip().lower()\\n\",\n \" return answer in {\\\"y\\\", \\\"yes\\\"}\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_bbox_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n \" for attempt in range(1, max_attempts + 1):\\n\",\n \" bbox = select_bbox_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n \" accepted = review_bbox_acceptance(image, bbox, title=f\\\"{prompt} - attempt {attempt}\\\")\\n\",\n \" if accepted:\\n\",\n \" return bbox\\n\",\n \" print(\\\"Re-draw requested.\\\")\\n\",\n \"\\n\",\n \" raise RuntimeError(\\\"Box not accepted within max attempts.\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_multiple_bboxes_with_redraw_colab(image: Image.Image, expected_boxes: int, max_attempts_per_box: int = 5):\\n\",\n \" if expected_boxes < 1:\\n\",\n \" raise ValueError(\\\"expected_boxes must be >= 1\\\")\\n\",\n \" bboxes = []\\n\",\n \" for i in range(expected_boxes):\\n\",\n \" bbox = select_bbox_with_redraw_colab(\\n\",\n \" image=image,\\n\",\n \" prompt=f\\\"Draw box {i + 1}/{expected_boxes}\\\",\\n\",\n \" max_attempts=max_attempts_per_box,\\n\",\n \" )\\n\",\n \" bboxes.append(bbox)\\n\",\n \" return bboxes\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_brush_hint_colab(image: Image.Image, prompt: str = \\\"Brush over target object\\\"):\\n\",\n \" try:\\n\",\n \" from google.colab.output import eval_js\\n\",\n \" from IPython.display import Javascript, display\\n\",\n \" except Exception as exc:\\n\",\n \" raise RuntimeError(\\\"Brush selection requires Google Colab.\\\") from exc\\n\",\n \"\\n\",\n \" buf = io.BytesIO()\\n\",\n \" image.save(buf, format=\\\"PNG\\\")\\n\",\n \" data_url = \\\"data:image/png;base64,\\\" + base64.b64encode(buf.getvalue()).decode(\\\"utf-8\\\")\\n\",\n \"\\n\",\n \" js = \\\"\\\"\\\"\\n\",\n \" async function materialHuntersBrushHint(dataUrl, promptText) {\\n\",\n \" const overlay = document.createElement('div');\\n\",\n \" overlay.style.position = 'fixed';\\n\",\n \" overlay.style.inset = '0';\\n\",\n \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n \" overlay.style.zIndex = '9999';\\n\",\n \" overlay.style.display = 'flex';\\n\",\n \" overlay.style.flexDirection = 'column';\\n\",\n \" overlay.style.alignItems = 'center';\\n\",\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n \" label.textContent = `${promptText} (paint mask, Done to confirm, Esc to cancel)`;\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n \" const container = document.createElement('div');\\n\",\n \" container.style.position = 'relative';\\n\",\n \"\\n\",\n \" const img = document.createElement('img');\\n\",\n \" img.src = dataUrl;\\n\",\n \" img.style.width = '95vw';\\n\",\n \" img.style.maxWidth = '1800px';\\n\",\n \" img.style.maxHeight = '90vh';\\n\",\n \" img.style.display = 'block';\\n\",\n \" img.style.userSelect = 'none';\\n\",\n \"\\n\",\n \" const canvas = document.createElement('canvas');\\n\",\n \" canvas.style.position = 'absolute';\\n\",\n \" canvas.style.left = '0';\\n\",\n \" canvas.style.top = '0';\\n\",\n \" canvas.style.cursor = 'crosshair';\\n\",\n \"\\n\",\n \" container.appendChild(img);\\n\",\n \" container.appendChild(canvas);\\n\",\n \"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n \"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n \" const clearBtn = document.createElement('button');\\n\",\n \" clearBtn.textContent = 'Clear';\\n\",\n \" const brush = document.createElement('input');\\n\",\n \" brush.type = 'range';\\n\",\n \" brush.min = '4';\\n\",\n \" brush.max = '48';\\n\",\n \" brush.value = '18';\\n\",\n \"\\n\",\n \" controls.appendChild(doneBtn);\\n\",\n \" controls.appendChild(clearBtn);\\n\",\n \" controls.appendChild(brush);\\n\",\n \"\\n\",\n \" overlay.appendChild(label);\\n\",\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n \" let drawing = false;\\n\",\n \" let hasInk = false;\\n\",\n \"\\n\",\n \" const resizeCanvas = () => {\\n\",\n \" const rect = img.getBoundingClientRect();\\n\",\n \" canvas.width = Math.max(1, Math.round(rect.width));\\n\",\n \" canvas.height = Math.max(1, Math.round(rect.height));\\n\",\n \" canvas.style.width = `${rect.width}px`;\\n\",\n \" canvas.style.height = `${rect.height}px`;\\n\",\n \" };\\n\",\n \"\\n\",\n \" const ctx = canvas.getContext('2d');\\n\",\n \" const setBrush = () => {\\n\",\n \" ctx.lineCap = 'round';\\n\",\n \" ctx.lineJoin = 'round';\\n\",\n \" ctx.strokeStyle = 'rgba(0,255,255,1)';\\n\",\n \" ctx.lineWidth = Number(brush.value);\\n\",\n \" };\\n\",\n \"\\n\",\n \" const cleanup = () => {\\n\",\n \" document.removeEventListener('keydown', onKey);\\n\",\n \" window.removeEventListener('resize', resizeCanvas);\\n\",\n \" overlay.remove();\\n\",\n \" };\\n\",\n \"\\n\",\n \" const onKey = (e) => {\\n\",\n \" if (e.key === 'Escape') {\\n\",\n \" cleanup();\\n\",\n \" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n \" }\\n\",\n \" };\\n\",\n \"\\n\",\n \" const toCanvasXY = (e) => {\\n\",\n \" const rect = canvas.getBoundingClientRect();\\n\",\n \" const x = Math.max(0, Math.min(rect.width, e.clientX - rect.left));\\n\",\n \" const y = Math.max(0, Math.min(rect.height, e.clientY - rect.top));\\n\",\n \" return [x, y];\\n\",\n \" };\\n\",\n \"\\n\",", + "lineStart": 120, + "lineEnd": 449, + "totalLines": 1270, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783079610475, + "end": 1783079610495 + } + }, + "metadata": { + "openai": { + 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+ "time": { + "created": 1783079610528, + "completed": 1783079613600 + }, + "finish": "tool-calls", + "id": "msg_f27d3a8a0002hLLGHRQOaV7npK", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27d3ae99001H8EWJf5f456DRK", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d3a8a0002hLLGHRQOaV7npK" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_JDuQdwphkyLSIBQrBU1moSZe", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 450, + "limit": 220 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n450: \" const toNaturalXY = (x, y) => {\\n\",\n451: \" const scaleX = img.naturalWidth / canvas.width;\\n\",\n452: \" const scaleY = img.naturalHeight / canvas.height;\\n\",\n453: \" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n454: \" };\\n\",\n455: \"\\n\",\n456: \" brush.oninput = setBrush;\\n\",\n457: \"\\n\",\n458: \" canvas.onmousedown = (e) => {\\n\",\n459: \" drawing = true;\\n\",\n460: \" const [x, y] = toCanvasXY(e);\\n\",\n461: \" setBrush();\\n\",\n462: \" ctx.beginPath();\\n\",\n463: \" ctx.moveTo(x, y);\\n\",\n464: \" };\\n\",\n465: \"\\n\",\n466: \" canvas.onmousemove = (e) => {\\n\",\n467: \" if (!drawing) return;\\n\",\n468: \" const [x, y] = toCanvasXY(e);\\n\",\n469: \" ctx.lineTo(x, y);\\n\",\n470: \" ctx.stroke();\\n\",\n471: \" hasInk = true;\\n\",\n472: \" };\\n\",\n473: \"\\n\",\n474: \" const stopDraw = () => { drawing = false; };\\n\",\n475: \" canvas.onmouseup = stopDraw;\\n\",\n476: \" canvas.onmouseleave = stopDraw;\\n\",\n477: \"\\n\",\n478: \" clearBtn.onclick = () => {\\n\",\n479: \" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n480: \" hasInk = false;\\n\",\n481: \" };\\n\",\n482: \"\\n\",\n483: \" let resolvePromise;\\n\",\n484: \" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n485: \"\\n\",\n486: \" doneBtn.onclick = () => {\\n\",\n487: \" if (!hasInk) {\\n\",\n488: \" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n489: \" return;\\n\",\n490: \" }\\n\",\n491: \"\\n\",\n492: \" const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\\n\",\n493: \" let minX = canvas.width, minY = canvas.height, maxX = -1, maxY = -1;\\n\",\n494: \" const pts = [];\\n\",\n495: \" const stride = Math.max(3, Math.floor(Math.sqrt((canvas.width * canvas.height) / 1200)));\\n\",\n496: \"\\n\",\n497: \" for (let y = 0; y < canvas.height; y += 1) {\\n\",\n498: \" for (let x = 0; x < canvas.width; x += 1) {\\n\",\n499: \" const a = data[(y * canvas.width + x) * 4 + 3];\\n\",\n500: \" if (a > 8) {\\n\",\n501: \" if (x < minX) minX = x;\\n\",\n502: \" if (y < minY) minY = y;\\n\",\n503: \" if (x > maxX) maxX = x;\\n\",\n504: \" if (y > maxY) maxY = y;\\n\",\n505: \" if ((x % stride === 0) && (y % stride === 0)) pts.push([x, y]);\\n\",\n506: \" }\\n\",\n507: \" }\\n\",\n508: \" }\\n\",\n509: \"\\n\",\n510: \" if (maxX < minX || maxY < minY) {\\n\",\n511: \" label.textContent = 'No valid brush mask found. Try again.';\\n\",\n512: \" return;\\n\",\n513: \" }\\n\",\n514: \"\\n\",\n515: \" const pad = 4;\\n\",\n516: \" minX = Math.max(0, minX - pad);\\n\",\n517: \" minY = Math.max(0, minY - pad);\\n\",\n518: \" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n519: \" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n520: \"\\n\",\n521: \" const [nx0, ny0] = toNaturalXY(minX, minY);\\n\",\n522: \" const [nx1, ny1] = toNaturalXY(maxX, maxY);\\n\",\n523: \"\\n\",\n524: \" const sampled = [];\\n\",\n525: \" const limit = 16;\\n\",\n526: \" const step = Math.max(1, Math.floor(pts.length / limit));\\n\",\n527: \" for (let i = 0; i < pts.length && sampled.length < limit; i += step) {\\n\",\n528: \" sampled.push(toNaturalXY(pts[i][0], pts[i][1]));\\n\",\n529: \" }\\n\",\n530: \"\\n\",\n531: \" cleanup();\\n\",\n532: \" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n533: \" };\\n\",\n534: \"\\n\",\n535: \" img.onload = () => { resizeCanvas(); setBrush(); };\\n\",\n536: \" if (img.complete) { resizeCanvas(); setBrush(); }\\n\",\n537: \" window.addEventListener('resize', resizeCanvas);\\n\",\n538: \" document.addEventListener('keydown', onKey);\\n\",\n539: \"\\n\",\n540: \" return await promise;\\n\",\n541: \" }\\n\",\n542: \" \\\"\\\"\\\"\\n\",\n543: \"\\n\",\n544: \" display(Javascript(js))\\n\",\n545: \" result = eval_js(\\n\",\n546: \" f\\\"materialHuntersBrushHint({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n547: \" )\\n\",\n548: \" bbox = result.get(\\\"bbox\\\", [-1, -1, -1, -1])\\n\",\n549: \" points = result.get(\\\"points\\\", [])\\n\",\n550: \" if bbox[0] < 0 or bbox[1] < 0:\\n\",\n551: \" raise RuntimeError(\\\"Brush selection cancelled.\\\")\\n\",\n552: \" if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]:\\n\",\n553: \" raise RuntimeError(\\\"Invalid brush hint bbox.\\\")\\n\",\n554: \" points_xy = [(int(p[0]), int(p[1])) for p in points]\\n\",\n555: \" if not points_xy:\\n\",\n556: \" cx = (int(bbox[0]) + int(bbox[2])) // 2\\n\",\n557: \" cy = (int(bbox[1]) + int(bbox[3])) // 2\\n\",\n558: \" points_xy = [(cx, cy)]\\n\",\n559: \" return {\\n\",\n560: \" \\\"prompt_bbox_xyxy\\\": [int(v) for v in bbox],\\n\",\n561: \" \\\"prompt_points_xy\\\": points_xy,\\n\",\n562: \" }\\n\",\n563: \"\\n\",\n564: \"\\n\",\n565: \"def select_brush_hint_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n566: \" for attempt in range(1, max_attempts + 1):\\n\",\n567: \" hint = select_brush_hint_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n568: \" accepted = review_bbox_acceptance(\\n\",\n569: \" image=image,\\n\",\n570: \" bbox_xyxy=hint[\\\"prompt_bbox_xyxy\\\"],\\n\",\n571: \" title=f\\\"{prompt} - attempt {attempt}\\\",\\n\",\n572: \" )\\n\",\n573: \" if accepted:\\n\",\n574: \" return hint\\n\",\n575: \" print(\\\"Re-draw requested.\\\")\\n\",\n576: \"\\n\",\n577: \" raise RuntimeError(\\\"Brush hint not accepted within max attempts.\\\")\\n\",\n578: \"\\n\",\n579: \"\\n\",\n580: \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n581: \" ys, xs = np.where(mask)\\n\",\n582: \" if len(xs) == 0 or len(ys) == 0:\\n\",\n583: \" return (0, 0, 0, 0)\\n\",\n584: \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n585: \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n586: \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n587: \"\\n\",\n588: \"\\n\",\n589: \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n590: \" arr = np.array(image)\\n\",\n591: \" h, w = arr.shape[:2]\\n\",\n592: \" x, y, bw, bh = bbox_xywh\\n\",\n593: \" x0 = max(0, x - pad)\\n\",\n594: \" y0 = max(0, y - pad)\\n\",\n595: \" x1 = min(w, x + bw + pad)\\n\",\n596: \" y1 = min(h, y + bh + pad)\\n\",\n597: \"\\n\",\n598: \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n599: \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n600: \"\\n\",\n601: \" bg = np.full_like(crop, 255)\\n\",\n602: \" bg[crop_mask] = crop[crop_mask]\\n\",\n603: \" return Image.fromarray(bg)\\n\",\n604: \"\\n\",\n605: \"\\n\",\n606: \"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n607: \" arr = np.array(image)\\n\",\n608: \" h, w = arr.shape[:2]\\n\",\n609: \" x, y, bw, bh = bbox_xywh\\n\",\n610: \" x0 = max(0, x - pad)\\n\",\n611: \" y0 = max(0, y - pad)\\n\",\n612: \" x1 = min(w, x + bw + pad)\\n\",\n613: \" y1 = min(h, y + bh + pad)\\n\",\n614: \" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n615: \"\\n\",\n616: \"\\n\",\n617: \"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n618: \" prompts = []\\n\",\n619: \" prompt_materials = []\\n\",\n620: \" for material, aliases in material_taxonomy.items():\\n\",\n621: \" for alias in aliases:\\n\",\n622: \" for template in prompt_templates:\\n\",\n623: \" prompts.append(template.format(material=alias))\\n\",\n624: \" prompt_materials.append(material)\\n\",\n625: \" return prompts, prompt_materials\\n\",\n626: \"\\n\",\n627: \"\\n\",\n628: \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n629: \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n630: \"\\n\",\n631: \"\\n\",\n632: \"def resolve_candidate_materials(object_label: str | None):\\n\",\n633: \" if not object_label:\\n\",\n634: \" return ALL_CANONICAL_MATERIALS\\n\",\n635: \" key = object_label.strip().lower()\\n\",\n636: \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n637: \" if len(candidates) < 2:\\n\",\n638: \" return ALL_CANONICAL_MATERIALS\\n\",\n639: \" return candidates\\n\",\n640: \"\\n\",\n641: \"\\n\",\n642: \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n643: \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n644: \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n645: \"\\n\",\n646: \"\\n\",\n647: \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n648: \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n649: \" if prompt_points_xy:\\n\",\n650: \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n651: \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n652: \" inputs = sam_processor(\\n\",\n653: \" image,\\n\",\n654: \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n655: \" input_points=pts,\\n\",\n656: \" input_labels=labels,\\n\",\n657: \" return_tensors=\\\"pt\\\",\\n\",\n658: \" )\\n\",\n659: \" else:\\n\",\n660: \" inputs = sam_processor(\\n\",\n661: \" image,\\n\",\n662: \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n663: \" return_tensors=\\\"pt\\\",\\n\",\n664: \" )\\n\",\n665: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n666: \"\\n\",\n667: \" with torch.no_grad():\\n\",\n668: \" outputs = sam_model(**inputs)\\n\",\n669: \"\\n\",\n\n(Showing lines 450-669 of 1270. Use offset=670 to continue.)\n</content>", + "metadata": { + "preview": " \" const toNaturalXY = (x, y) => {\\n\",\n \" const scaleX = img.naturalWidth / canvas.width;\\n\",\n \" const scaleY = img.naturalHeight / canvas.height;\\n\",\n \" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n \" };\\n\",\n \"\\n\",\n \" brush.oninput = setBrush;\\n\",\n \"\\n\",\n \" canvas.onmousedown = (e) => {\\n\",\n \" drawing = true;\\n\",\n \" const [x, y] = toCanvasXY(e);\\n\",\n \" setBrush();\\n\",\n \" ctx.beginPath();\\n\",\n \" ctx.moveTo(x, y);\\n\",\n \" };\\n\",\n \"\\n\",\n \" canvas.onmousemove = (e) => {\\n\",\n \" if (!drawing) return;\\n\",\n \" const [x, y] = toCanvasXY(e);\\n\",\n \" ctx.lineTo(x, y);\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" const toNaturalXY = (x, y) => {\\n\",\n \" const scaleX = img.naturalWidth / canvas.width;\\n\",\n \" const scaleY = img.naturalHeight / canvas.height;\\n\",\n \" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n \" };\\n\",\n \"\\n\",\n \" brush.oninput = setBrush;\\n\",\n \"\\n\",\n \" canvas.onmousedown = (e) => {\\n\",\n \" drawing = true;\\n\",\n \" const [x, y] = toCanvasXY(e);\\n\",\n \" setBrush();\\n\",\n \" ctx.beginPath();\\n\",\n \" ctx.moveTo(x, y);\\n\",\n \" };\\n\",\n \"\\n\",\n \" canvas.onmousemove = (e) => {\\n\",\n \" if (!drawing) return;\\n\",\n \" const [x, y] = toCanvasXY(e);\\n\",\n \" ctx.lineTo(x, y);\\n\",\n \" ctx.stroke();\\n\",\n \" hasInk = true;\\n\",\n \" };\\n\",\n \"\\n\",\n \" const stopDraw = () => { drawing = false; };\\n\",\n \" canvas.onmouseup = stopDraw;\\n\",\n \" canvas.onmouseleave = stopDraw;\\n\",\n \"\\n\",\n \" clearBtn.onclick = () => {\\n\",\n \" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n \" hasInk = false;\\n\",\n \" };\\n\",\n \"\\n\",\n \" let resolvePromise;\\n\",\n \" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n \"\\n\",\n \" doneBtn.onclick = () => {\\n\",\n \" if (!hasInk) {\\n\",\n \" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\\n\",\n \" let minX = canvas.width, minY = canvas.height, maxX = -1, maxY = -1;\\n\",\n \" const pts = [];\\n\",\n \" const stride = Math.max(3, Math.floor(Math.sqrt((canvas.width * canvas.height) / 1200)));\\n\",\n \"\\n\",\n \" for (let y = 0; y < canvas.height; y += 1) {\\n\",\n \" for (let x = 0; x < canvas.width; x += 1) {\\n\",\n \" const a = data[(y * canvas.width + x) * 4 + 3];\\n\",\n \" if (a > 8) {\\n\",\n \" if (x < minX) minX = x;\\n\",\n \" if (y < minY) minY = y;\\n\",\n \" if (x > maxX) maxX = x;\\n\",\n \" if (y > maxY) maxY = y;\\n\",\n \" if ((x % stride === 0) && (y % stride === 0)) pts.push([x, y]);\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"\\n\",\n \" if (maxX < minX || maxY < minY) {\\n\",\n \" label.textContent = 'No valid brush mask found. Try again.';\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" const pad = 4;\\n\",\n \" minX = Math.max(0, minX - pad);\\n\",\n \" minY = Math.max(0, minY - pad);\\n\",\n \" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n \" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n \"\\n\",\n \" const [nx0, ny0] = toNaturalXY(minX, minY);\\n\",\n \" const [nx1, ny1] = toNaturalXY(maxX, maxY);\\n\",\n \"\\n\",\n \" const sampled = [];\\n\",\n \" const limit = 16;\\n\",\n \" const step = Math.max(1, Math.floor(pts.length / limit));\\n\",\n \" for (let i = 0; i < pts.length && sampled.length < limit; i += step) {\\n\",\n \" sampled.push(toNaturalXY(pts[i][0], pts[i][1]));\\n\",\n \" }\\n\",\n \"\\n\",\n \" cleanup();\\n\",\n \" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n \" };\\n\",\n \"\\n\",\n \" img.onload = () => { resizeCanvas(); setBrush(); };\\n\",\n \" if (img.complete) { resizeCanvas(); setBrush(); }\\n\",\n \" window.addEventListener('resize', resizeCanvas);\\n\",\n \" document.addEventListener('keydown', onKey);\\n\",\n \"\\n\",\n \" return await promise;\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" display(Javascript(js))\\n\",\n \" result = eval_js(\\n\",\n \" f\\\"materialHuntersBrushHint({json.dumps(data_url)}, {json.dumps(prompt)})\\\"\\n\",\n \" )\\n\",\n \" bbox = result.get(\\\"bbox\\\", [-1, -1, -1, -1])\\n\",\n \" points = result.get(\\\"points\\\", [])\\n\",\n \" if bbox[0] < 0 or bbox[1] < 0:\\n\",\n \" raise RuntimeError(\\\"Brush selection cancelled.\\\")\\n\",\n \" if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]:\\n\",\n \" raise RuntimeError(\\\"Invalid brush hint bbox.\\\")\\n\",\n \" points_xy = [(int(p[0]), int(p[1])) for p in points]\\n\",\n \" if not points_xy:\\n\",\n \" cx = (int(bbox[0]) + int(bbox[2])) // 2\\n\",\n \" cy = (int(bbox[1]) + int(bbox[3])) // 2\\n\",\n \" points_xy = [(cx, cy)]\\n\",\n \" return {\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(v) for v in bbox],\\n\",\n \" \\\"prompt_points_xy\\\": points_xy,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def select_brush_hint_with_redraw_colab(image: Image.Image, prompt: str, max_attempts: int = 5):\\n\",\n \" for attempt in range(1, max_attempts + 1):\\n\",\n \" hint = select_brush_hint_colab(image, prompt=f\\\"{prompt} (attempt {attempt}/{max_attempts})\\\")\\n\",\n \" accepted = review_bbox_acceptance(\\n\",\n \" image=image,\\n\",\n \" bbox_xyxy=hint[\\\"prompt_bbox_xyxy\\\"],\\n\",\n \" title=f\\\"{prompt} - attempt {attempt}\\\",\\n\",\n \" )\\n\",\n \" if accepted:\\n\",\n \" return hint\\n\",\n \" print(\\\"Re-draw requested.\\\")\\n\",\n \"\\n\",\n \" raise RuntimeError(\\\"Brush hint not accepted within max attempts.\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"def mask_bbox_xywh(mask: np.ndarray):\\n\",\n \" ys, xs = np.where(mask)\\n\",\n \" if len(xs) == 0 or len(ys) == 0:\\n\",\n \" return (0, 0, 0, 0)\\n\",\n \" x0, x1 = int(xs.min()), int(xs.max())\\n\",\n \" y0, y1 = int(ys.min()), int(ys.max())\\n\",\n \" return (x0, y0, x1 - x0 + 1, y1 - y0 + 1)\\n\",\n \"\\n\",\n \"\\n\",\n \"def crop_masked_rgb(image: Image.Image, mask: np.ndarray, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n \" arr = np.array(image)\\n\",\n \" h, w = arr.shape[:2]\\n\",\n \" x, y, bw, bh = bbox_xywh\\n\",\n \" x0 = max(0, x - pad)\\n\",\n \" y0 = max(0, y - pad)\\n\",\n \" x1 = min(w, x + bw + pad)\\n\",\n \" y1 = min(h, y + bh + pad)\\n\",\n \"\\n\",\n \" crop = arr[y0:y1, x0:x1].copy()\\n\",\n \" crop_mask = mask[y0:y1, x0:x1]\\n\",\n \"\\n\",\n \" bg = np.full_like(crop, 255)\\n\",\n \" bg[crop_mask] = crop[crop_mask]\\n\",\n \" return Image.fromarray(bg)\\n\",\n \"\\n\",\n \"\\n\",\n \"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n \" arr = np.array(image)\\n\",\n \" h, w = arr.shape[:2]\\n\",\n \" x, y, bw, bh = bbox_xywh\\n\",\n \" x0 = max(0, x - pad)\\n\",\n \" y0 = max(0, y - pad)\\n\",\n \" x1 = min(w, x + bw + pad)\\n\",\n \" y1 = min(h, y + bh + pad)\\n\",\n \" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n \"\\n\",\n \"\\n\",\n \"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n \" prompts = []\\n\",\n \" prompt_materials = []\\n\",\n \" for material, aliases in material_taxonomy.items():\\n\",\n \" for alias in aliases:\\n\",\n \" for template in prompt_templates:\\n\",\n \" prompts.append(template.format(material=alias))\\n\",\n \" prompt_materials.append(material)\\n\",\n \" return prompts, prompt_materials\\n\",\n \"\\n\",\n \"\\n\",\n \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n \"\\n\",\n \"\\n\",\n \"def resolve_candidate_materials(object_label: str | None):\\n\",\n \" if not object_label:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" key = object_label.strip().lower()\\n\",\n \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n \" if len(candidates) < 2:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" return candidates\\n\",\n \"\\n\",\n \"\\n\",\n \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n \" if prompt_points_xy:\\n\",\n \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" input_points=pts,\\n\",\n \" input_labels=labels,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" 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'8px';\\n\",\n- \"\\n\",\n- \" const label = document.createElement('div');\\n\",\n- \" label.textContent = `${promptText} (drag mouse, Done to confirm, Esc to cancel)`;\\n\",\n- \" label.style.color = 'white';\\n\",\n- \" label.style.font = '16px sans-serif';\\n\",\n- \"\\n\",\n- \" const container = document.createElement('div');\\n\",\n- \" container.style.position = 'relative';\\n\",\n- \"\\n\",\n- \" const img = document.createElement('img');\\n\",\n- \" img.src = dataUrl;\\n\",\n- \" img.style.width = '95vw';\\n\",\n- \" img.style.maxWidth = '1800px';\\n\",\n- \" img.style.maxHeight = '90vh';\\n\",\n- \" img.style.cursor = 'crosshair';\\n\",\n- \" img.style.border = '2px solid white';\\n\",\n- \" img.style.display = 'block';\\n\",\n- \"\\n\",\n- \" const box = document.createElement('div');\\n\",\n- \" box.style.position = 'absolute';\\n\",\n- \" box.style.border = '2px solid #00e5ff';\\n\",\n- \" box.style.background = 'rgba(0,229,255,0.15)';\\n\",\n- \" box.style.display = 'none';\\n\",\n- \"\\n\",\n- \" container.appendChild(img);\\n\",\n- \" container.appendChild(box);\\n\",\n- \"\\n\",\n- \" const controls = document.createElement('div');\\n\",\n- \" controls.style.display = 'flex';\\n\",\n- \" controls.style.gap = '8px';\\n\",\n- \"\\n\",\n- \" const doneBtn = document.createElement('button');\\n\",\n- \" doneBtn.textContent = 'Done';\\n\",\n- \" const resetBtn = document.createElement('button');\\n\",\n- \" resetBtn.textContent = 'Reset';\\n\",\n- \"\\n\",\n- \" controls.appendChild(doneBtn);\\n\",\n- \" controls.appendChild(resetBtn);\\n\",\n- \"\\n\",\n- \" overlay.appendChild(label);\\n\",\n- \" overlay.appendChild(container);\\n\",\n- \" overlay.appendChild(controls);\\n\",\n- \" document.body.appendChild(overlay);\\n\",\n- \"\\n\",\n- \" let dragging = false;\\n\",\n- \" let startX = 0;\\n\",\n- \" let startY = 0;\\n\",\n- \" let latest = null;\\n\",\n- \"\\n\",\n- \" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n- \"\\n\",\n- \" const cleanup = () => {\\n\",\n- \" document.removeEventListener('keydown', onKey);\\n\",\n- \" overlay.remove();\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const toNatural = (xCss, yCss, rect) => {\\n\",\n- \" const nx = Math.round(xCss * (img.naturalWidth / rect.width));\\n\",\n- \" const ny = Math.round(yCss * (img.naturalHeight / rect.height));\\n\",\n- \" return [nx, ny];\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const onKey = (e) => {\\n\",\n- \" if (e.key === 'Escape') {\\n\",\n- \" cleanup();\\n\",\n- \" resolvePromise([-1, -1, -1, -1]);\\n\",\n- \" }\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" document.addEventListener('keydown', onKey);\\n\",\n- \"\\n\",\n- \" img.onmousedown = (e) => {\\n\",\n- \" const rect = img.getBoundingClientRect();\\n\",\n- \" dragging = true;\\n\",\n- \" startX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n- \" startY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n- \" box.style.display = 'block';\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" img.onmousemove = (e) => {\\n\",\n- \" if (!dragging) return;\\n\",\n- \" const rect = img.getBoundingClientRect();\\n\",\n- \" const curX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n- \" const curY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n- \"\\n\",\n- \" const left = Math.min(startX, curX);\\n\",\n- \" const top = Math.min(startY, curY);\\n\",\n- \" const width = Math.max(1, Math.abs(curX - startX));\\n\",\n- \" const height = Math.max(1, Math.abs(curY - startY));\\n\",\n- \"\\n\",\n- \" box.style.left = `${left}px`;\\n\",\n- \" box.style.top = `${top}px`;\\n\",\n- \" box.style.width = `${width}px`;\\n\",\n- \" box.style.height = `${height}px`;\\n\",\n- \"\\n\",\n- \" const [x0, y0] = toNatural(left, top, rect);\\n\",\n- \" const [x1, y1] = toNatural(left + width, top + height, rect);\\n\",\n- \" latest = [Math.min(x0, x1), Math.min(y0, y1), Math.max(x0, x1), Math.max(y0, y1)];\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" img.onmouseup = () => {\\n\",\n- \" dragging = false;\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" img.onmouseleave = () => {\\n\",\n- \" dragging = false;\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" resetBtn.onclick = () => {\\n\",\n- \" latest = null;\\n\",\n- \" box.style.display = 'none';\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" let resolvePromise;\\n\",\n- \" const promise = new Promise((resolve) => {\\n\",\n- \" resolvePromise = resolve;\\n\",\n- \" });\\n\",\n- \"\\n\",\n- \" doneBtn.onclick = () => {\\n\",\n- \" if (!latest) {\\n\",\n- \" label.textContent = 'Draw a box first.';\\n\",\n- \" return;\\n\",\n- \" }\\n\",\n- \" cleanup();\\n\",\n- \" resolvePromise(latest);\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" return await promise;\\n\",\n- \" }\\n\",\n- \" \\\"\\\"\\\"\\n\",\n+ \" js = \\\"\\\"\\\"\\n\",\n+ \" async function materialHuntersPickBox(dataUrl, promptText) {\\n\",\n+ \" const overlay = document.createElement('div');\\n\",\n+ \" overlay.style.position = 'fixed';\\n\",\n+ \" overlay.style.inset = '0';\\n\",\n+ \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+ \" overlay.style.zIndex = '9999';\\n\",\n+ \" overlay.style.display = 'flex';\\n\",\n+ \" overlay.style.flexDirection = 'column';\\n\",\n+ \" overlay.style.alignItems = 'center';\\n\",\n+ \" overlay.style.justifyContent = 'center';\\n\",\n+ \" overlay.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const label = document.createElement('div');\\n\",\n+ \" label.textContent = `${promptText} (click-drag to draw, Done to confirm, Esc to cancel)`;\\n\",\n+ \" label.style.color = 'white';\\n\",\n+ \" label.style.font = '16px sans-serif';\\n\",\n+ \"\\n\",\n+ \" const container = document.createElement('div');\\n\",\n+ \" container.style.position = 'relative';\\n\",\n+ \"\\n\",\n+ \" const img = document.createElement('img');\\n\",\n+ \" img.src = dataUrl;\\n\",\n+ \" img.style.width = '95vw';\\n\",\n+ \" img.style.maxWidth = '1800px';\\n\",\n+ \" img.style.maxHeight = '90vh';\\n\",\n+ \" img.style.border = '2px solid white';\\n\",\n+ \" img.style.display = 'block';\\n\",\n+ \" img.style.userSelect = 'none';\\n\",\n+ \"\\n\",\n+ \" const canvas = document.createElement('canvas');\\n\",\n+ \" canvas.style.position = 'absolute';\\n\",\n+ \" canvas.style.left = '0';\\n\",\n+ \" canvas.style.top = '0';\\n\",\n+ \" canvas.style.cursor = 'crosshair';\\n\",\n+ \" canvas.style.touchAction = 'none';\\n\",\n+ \"\\n\",\n+ \" container.appendChild(img);\\n\",\n+ \" container.appendChild(canvas);\\n\",\n+ \"\\n\",\n+ \" const controls = document.createElement('div');\\n\",\n+ \" controls.style.display = 'flex';\\n\",\n+ \" controls.style.gap = '8px';\\n\",\n+ \" const doneBtn = document.createElement('button');\\n\",\n+ \" doneBtn.textContent = 'Done';\\n\",\n+ \" const resetBtn = document.createElement('button');\\n\",\n+ \" resetBtn.textContent = 'Reset';\\n\",\n+ \" controls.appendChild(doneBtn);\\n\",\n+ \" controls.appendChild(resetBtn);\\n\",\n+ \"\\n\",\n+ \" overlay.appendChild(label);\\n\",\n+ \" overlay.appendChild(container);\\n\",\n+ \" overlay.appendChild(controls);\\n\",\n+ \" document.body.appendChild(overlay);\\n\",\n+ \"\\n\",\n+ \" await new Promise((resolve) => {\\n\",\n+ \" if (img.complete && img.naturalWidth > 0) resolve();\\n\",\n+ \" else img.onload = () => resolve();\\n\",\n+ \" });\\n\",\n+ \"\\n\",\n+ \" const resizeCanvas = () => {\\n\",\n+ \" const r = img.getBoundingClientRect();\\n\",\n+ \" canvas.width = Math.max(1, Math.round(r.width));\\n\",\n+ \" canvas.height = Math.max(1, Math.round(r.height));\\n\",\n+ \" canvas.style.width = `${r.width}px`;\\n\",\n+ \" canvas.style.height = `${r.height}px`;\\n\",\n+ \" };\\n\",\n+ \" resizeCanvas();\\n\",\n+ \"\\n\",\n+ \" const ctx = canvas.getContext('2d');\\n\",\n+ \" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n+ \" const toCanvas = (e) => {\\n\",\n+ \" const r = canvas.getBoundingClientRect();\\n\",\n+ \" return [\\n\",\n+ \" clamp(e.clientX - r.left, 0, r.width - 1),\\n\",\n+ \" clamp(e.clientY - r.top, 0, r.height - 1),\\n\",\n+ \" ];\\n\",\n+ \" };\\n\",\n+ \" const toNatural = (x, y) => [\\n\",\n+ \" Math.round(x * (img.naturalWidth / canvas.width)),\\n\",\n+ \" Math.round(y * (img.naturalHeight / canvas.height)),\\n\",\n+ \" ];\\n\",\n+ \"\\n\",\n+ \" let dragging = false;\\n\",\n+ \" let sx = 0;\\n\",\n+ \" let sy = 0;\\n\",\n+ \" let latest = null;\\n\",\n+ \"\\n\",\n+ \" const redraw = () => {\\n\",\n+ \" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n+ \" if (!latest) return;\\n\",\n+ \" const [x0, y0, x1, y1] = latest;\\n\",\n+ \" ctx.fillStyle = 'rgba(0,229,255,0.18)';\\n\",\n+ \" ctx.strokeStyle = 'rgba(0,229,255,1)';\\n\",\n+ \" ctx.lineWidth = 2;\\n\",\n+ \" ctx.fillRect(x0, y0, x1 - x0, y1 - y0);\\n\",\n+ \" ctx.strokeRect(x0, y0, x1 - x0, y1 - y0);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" canvas.onpointerdown = (e) => {\\n\",\n+ \" canvas.setPointerCapture(e.pointerId);\\n\",\n+ \" dragging = true;\\n\",\n+ \" [sx, sy] = toCanvas(e);\\n\",\n+ \" latest = [sx, sy, sx + 1, sy + 1];\\n\",\n+ \" redraw();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" canvas.onpointermove = (e) => {\\n\",\n+ \" if (!dragging) return;\\n\",\n+ \" const [cx, cy] = toCanvas(e);\\n\",\n+ \" latest = [Math.min(sx, cx), Math.min(sy, cy), Math.max(sx, cx), Math.max(sy, cy)];\\n\",\n+ \" redraw();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const stopDrag = () => { dragging = false; };\\n\",\n+ \" canvas.onpointerup = stopDrag;\\n\",\n+ \" canvas.onpointercancel = stopDrag;\\n\",\n+ \"\\n\",\n+ \" resetBtn.onclick = () => { latest = null; redraw(); };\\n\",\n+ \"\\n\",\n+ \" let resolvePromise;\\n\",\n+ \" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n+ \"\\n\",\n+ \" const cleanup = () => {\\n\",\n+ \" document.removeEventListener('keydown', onKey);\\n\",\n+ \" window.removeEventListener('resize', resizeCanvas);\\n\",\n+ \" overlay.remove();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const onKey = (e) => {\\n\",\n+ \" if (e.key === 'Escape') {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolvePromise([-1, -1, -1, -1]);\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \" document.addEventListener('keydown', onKey);\\n\",\n+ \" window.addEventListener('resize', resizeCanvas);\\n\",\n+ \"\\n\",\n+ \" doneBtn.onclick = () => {\\n\",\n+ \" if (!latest) {\\n\",\n+ \" label.textContent = 'Draw a box first.';\\n\",\n+ \" return;\\n\",\n+ \" }\\n\",\n+ \" const [cx0, cy0, cx1, cy1] = latest;\\n\",\n+ \" if ((cx1 - cx0) < 3 || (cy1 - cy0) < 3) {\\n\",\n+ \" label.textContent = 'Box too small. Drag a larger box.';\\n\",\n+ \" return;\\n\",\n+ \" }\\n\",\n+ \" const [nx0, ny0] = toNatural(cx0, cy0);\\n\",\n+ \" const [nx1, ny1] = toNatural(cx1, cy1);\\n\",\n+ \" cleanup();\\n\",\n+ \" resolvePromise([Math.min(nx0,nx1), Math.min(ny0,ny1), Math.max(nx0,nx1), Math.max(ny0,ny1)]);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" return await promise;\\n\",\n+ \" }\\n\",\n+ \" \\\"\\\"\\\"\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -145,9 +145,9 @@\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n-\" label.textContent = `${promptText} (drag mouse, Done to confirm, Esc to cancel)`;\\n\",\n+\" label.textContent = `${promptText} (click-drag to draw, Done to confirm, Esc to cancel)`;\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n \" const container = document.createElement('div');\\n\",\n@@ -157,119 +157,134 @@\n \" img.src = dataUrl;\\n\",\n \" img.style.width = '95vw';\\n\",\n \" img.style.maxWidth = '1800px';\\n\",\n \" img.style.maxHeight = '90vh';\\n\",\n-\" img.style.cursor = 'crosshair';\\n\",\n \" img.style.border = '2px solid white';\\n\",\n \" img.style.display = 'block';\\n\",\n+\" img.style.userSelect = 'none';\\n\",\n \"\\n\",\n-\" const box = document.createElement('div');\\n\",\n-\" box.style.position = 'absolute';\\n\",\n-\" box.style.border = '2px solid #00e5ff';\\n\",\n-\" box.style.background = 'rgba(0,229,255,0.15)';\\n\",\n-\" box.style.display = 'none';\\n\",\n+\" const canvas = document.createElement('canvas');\\n\",\n+\" canvas.style.position = 'absolute';\\n\",\n+\" canvas.style.left = '0';\\n\",\n+\" canvas.style.top = '0';\\n\",\n+\" canvas.style.cursor = 'crosshair';\\n\",\n+\" canvas.style.touchAction = 'none';\\n\",\n \"\\n\",\n \" container.appendChild(img);\\n\",\n-\" container.appendChild(box);\\n\",\n+\" container.appendChild(canvas);\\n\",\n \"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n-\"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n \" const resetBtn = document.createElement('button');\\n\",\n \" resetBtn.textContent = 'Reset';\\n\",\n-\"\\n\",\n \" controls.appendChild(doneBtn);\\n\",\n \" controls.appendChild(resetBtn);\\n\",\n \"\\n\",\n \" overlay.appendChild(label);\\n\",\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n-\" let dragging = false;\\n\",\n-\" let startX = 0;\\n\",\n-\" let startY = 0;\\n\",\n-\" let latest = null;\\n\",\n+\" await new Promise((resolve) => {\\n\",\n+\" if (img.complete && img.naturalWidth > 0) resolve();\\n\",\n+\" else img.onload = () => resolve();\\n\",\n+\" });\\n\",\n \"\\n\",\n-\" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n-\"\\n\",\n-\" const cleanup = () => {\\n\",\n-\" document.removeEventListener('keydown', onKey);\\n\",\n-\" overlay.remove();\\n\",\n+\" const resizeCanvas = () => {\\n\",\n+\" const r = img.getBoundingClientRect();\\n\",\n+\" canvas.width = Math.max(1, Math.round(r.width));\\n\",\n+\" canvas.height = Math.max(1, Math.round(r.height));\\n\",\n+\" canvas.style.width = `${r.width}px`;\\n\",\n+\" canvas.style.height = `${r.height}px`;\\n\",\n \" };\\n\",\n+\" resizeCanvas();\\n\",\n \"\\n\",\n-\" const toNatural = (xCss, yCss, rect) => {\\n\",\n-\" const nx = Math.round(xCss * (img.naturalWidth / rect.width));\\n\",\n-\" const ny = Math.round(yCss * (img.naturalHeight / rect.height));\\n\",\n-\" return [nx, ny];\\n\",\n+\" const ctx = canvas.getContext('2d');\\n\",\n+\" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n+\" const toCanvas = (e) => {\\n\",\n+\" const r = canvas.getBoundingClientRect();\\n\",\n+\" return [\\n\",\n+\" clamp(e.clientX - r.left, 0, r.width - 1),\\n\",\n+\" clamp(e.clientY - r.top, 0, r.height - 1),\\n\",\n+\" ];\\n\",\n \" };\\n\",\n+\" const toNatural = (x, y) => [\\n\",\n+\" Math.round(x * (img.naturalWidth / canvas.width)),\\n\",\n+\" Math.round(y * (img.naturalHeight / canvas.height)),\\n\",\n+\" ];\\n\",\n \"\\n\",\n-\" const onKey = (e) => {\\n\",\n-\" if (e.key === 'Escape') {\\n\",\n-\" cleanup();\\n\",\n-\" resolvePromise([-1, -1, -1, -1]);\\n\",\n-\" }\\n\",\n+\" let dragging = false;\\n\",\n+\" let sx = 0;\\n\",\n+\" let sy = 0;\\n\",\n+\" let latest = null;\\n\",\n+\"\\n\",\n+\" const redraw = () => {\\n\",\n+\" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n+\" if (!latest) return;\\n\",\n+\" const [x0, y0, x1, y1] = latest;\\n\",\n+\" ctx.fillStyle = 'rgba(0,229,255,0.18)';\\n\",\n+\" ctx.strokeStyle = 'rgba(0,229,255,1)';\\n\",\n+\" ctx.lineWidth = 2;\\n\",\n+\" ctx.fillRect(x0, y0, x1 - x0, y1 - y0);\\n\",\n+\" ctx.strokeRect(x0, y0, x1 - x0, y1 - y0);\\n\",\n \" };\\n\",\n \"\\n\",\n-\" document.addEventListener('keydown', onKey);\\n\",\n-\"\\n\",\n-\" img.onmousedown = (e) => {\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n+\" canvas.onpointerdown = (e) => {\\n\",\n+\" canvas.setPointerCapture(e.pointerId);\\n\",\n \" dragging = true;\\n\",\n-\" startX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n-\" startY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n-\" box.style.display = 'block';\\n\",\n+\" [sx, sy] = toCanvas(e);\\n\",\n+\" latest = [sx, sy, sx + 1, sy + 1];\\n\",\n+\" redraw();\\n\",\n \" };\\n\",\n \"\\n\",\n-\" img.onmousemove = (e) => {\\n\",\n+\" canvas.onpointermove = (e) => {\\n\",\n \" if (!dragging) return;\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n-\" const curX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n-\" const curY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n+\" const [cx, cy] = toCanvas(e);\\n\",\n+\" latest = [Math.min(sx, cx), Math.min(sy, cy), Math.max(sx, cx), Math.max(sy, cy)];\\n\",\n+\" redraw();\\n\",\n+\" };\\n\",\n \"\\n\",\n-\" const left = Math.min(startX, curX);\\n\",\n-\" const top = Math.min(startY, curY);\\n\",\n-\" const width = Math.max(1, Math.abs(curX - startX));\\n\",\n-\" const height = Math.max(1, Math.abs(curY - startY));\\n\",\n+\" const stopDrag = () => { dragging = false; };\\n\",\n+\" canvas.onpointerup = stopDrag;\\n\",\n+\" canvas.onpointercancel = stopDrag;\\n\",\n \"\\n\",\n-\" box.style.left = `${left}px`;\\n\",\n-\" box.style.top = `${top}px`;\\n\",\n-\" box.style.width = `${width}px`;\\n\",\n-\" box.style.height = `${height}px`;\\n\",\n+\" resetBtn.onclick = () => { latest = null; redraw(); };\\n\",\n \"\\n\",\n-\" const [x0, y0] = toNatural(left, top, rect);\\n\",\n-\" const [x1, y1] = toNatural(left + width, top + height, rect);\\n\",\n-\" latest = [Math.min(x0, x1), Math.min(y0, y1), Math.max(x0, x1), Math.max(y0, y1)];\\n\",\n-\" };\\n\",\n+\" let resolvePromise;\\n\",\n+\" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n \"\\n\",\n-\" img.onmouseup = () => {\\n\",\n-\" dragging = false;\\n\",\n+\" const cleanup = () => {\\n\",\n+\" document.removeEventListener('keydown', onKey);\\n\",\n+\" window.removeEventListener('resize', resizeCanvas);\\n\",\n+\" overlay.remove();\\n\",\n \" };\\n\",\n \"\\n\",\n-\" img.onmouseleave = () => {\\n\",\n-\" dragging = false;\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise([-1, -1, -1, -1]);\\n\",\n+\" }\\n\",\n \" };\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n+\" window.addEventListener('resize', resizeCanvas);\\n\",\n \"\\n\",\n-\" resetBtn.onclick = () => {\\n\",\n-\" latest = null;\\n\",\n-\" box.style.display = 'none';\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" let resolvePromise;\\n\",\n-\" const promise = new Promise((resolve) => {\\n\",\n-\" resolvePromise = resolve;\\n\",\n-\" });\\n\",\n-\"\\n\",\n \" doneBtn.onclick = () => {\\n\",\n \" if (!latest) {\\n\",\n \" label.textContent = 'Draw a box first.';\\n\",\n \" return;\\n\",\n \" }\\n\",\n+\" const [cx0, cy0, cx1, cy1] = latest;\\n\",\n+\" if ((cx1 - cx0) < 3 || (cy1 - cy0) < 3) {\\n\",\n+\" label.textContent = 'Box too small. Drag a larger box.';\\n\",\n+\" return;\\n\",\n+\" }\\n\",\n+\" const [nx0, ny0] = toNatural(cx0, cy0);\\n\",\n+\" const [nx1, ny1] = toNatural(cx1, cy1);\\n\",\n \" cleanup();\\n\",\n-\" resolvePromise(latest);\\n\",\n+\" resolvePromise([Math.min(nx0,nx1), Math.min(ny0,ny1), Math.max(nx0,nx1), Math.max(ny0,ny1)]);\\n\",\n \" };\\n\",\n \"\\n\",\n \" return await promise;\\n\",\n \" }\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -145,9 +145,9 @@\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n-\" label.textContent = `${promptText} (drag mouse, Done to confirm, Esc to cancel)`;\\n\",\n+\" label.textContent = `${promptText} (click-drag to draw, Done to confirm, Esc to cancel)`;\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n \" const container = document.createElement('div');\\n\",\n@@ -157,119 +157,134 @@\n \" img.src = dataUrl;\\n\",\n \" img.style.width = '95vw';\\n\",\n \" img.style.maxWidth = '1800px';\\n\",\n \" img.style.maxHeight = '90vh';\\n\",\n-\" img.style.cursor = 'crosshair';\\n\",\n \" img.style.border = '2px solid white';\\n\",\n \" img.style.display = 'block';\\n\",\n+\" img.style.userSelect = 'none';\\n\",\n \"\\n\",\n-\" const box = document.createElement('div');\\n\",\n-\" box.style.position = 'absolute';\\n\",\n-\" box.style.border = '2px solid #00e5ff';\\n\",\n-\" box.style.background = 'rgba(0,229,255,0.15)';\\n\",\n-\" box.style.display = 'none';\\n\",\n+\" const canvas = document.createElement('canvas');\\n\",\n+\" canvas.style.position = 'absolute';\\n\",\n+\" canvas.style.left = '0';\\n\",\n+\" canvas.style.top = '0';\\n\",\n+\" canvas.style.cursor = 'crosshair';\\n\",\n+\" canvas.style.touchAction = 'none';\\n\",\n \"\\n\",\n \" container.appendChild(img);\\n\",\n-\" container.appendChild(box);\\n\",\n+\" container.appendChild(canvas);\\n\",\n \"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n-\"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n \" const resetBtn = document.createElement('button');\\n\",\n \" resetBtn.textContent = 'Reset';\\n\",\n-\"\\n\",\n \" controls.appendChild(doneBtn);\\n\",\n \" controls.appendChild(resetBtn);\\n\",\n \"\\n\",\n \" overlay.appendChild(label);\\n\",\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n-\" let dragging = false;\\n\",\n-\" let startX = 0;\\n\",\n-\" let startY = 0;\\n\",\n-\" let latest = null;\\n\",\n+\" await new Promise((resolve) => {\\n\",\n+\" if (img.complete && img.naturalWidth > 0) resolve();\\n\",\n+\" else img.onload = () => resolve();\\n\",\n+\" });\\n\",\n \"\\n\",\n-\" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n-\"\\n\",\n-\" const cleanup = () => {\\n\",\n-\" document.removeEventListener('keydown', onKey);\\n\",\n-\" overlay.remove();\\n\",\n+\" const resizeCanvas = () => {\\n\",\n+\" const r = img.getBoundingClientRect();\\n\",\n+\" canvas.width = Math.max(1, Math.round(r.width));\\n\",\n+\" canvas.height = Math.max(1, Math.round(r.height));\\n\",\n+\" canvas.style.width = `${r.width}px`;\\n\",\n+\" canvas.style.height = `${r.height}px`;\\n\",\n \" };\\n\",\n+\" resizeCanvas();\\n\",\n \"\\n\",\n-\" const toNatural = (xCss, yCss, rect) => {\\n\",\n-\" const nx = Math.round(xCss * (img.naturalWidth / rect.width));\\n\",\n-\" const ny = Math.round(yCss * (img.naturalHeight / rect.height));\\n\",\n-\" return [nx, ny];\\n\",\n+\" const ctx = canvas.getContext('2d');\\n\",\n+\" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n+\" const toCanvas = (e) => {\\n\",\n+\" const r = canvas.getBoundingClientRect();\\n\",\n+\" return [\\n\",\n+\" clamp(e.clientX - r.left, 0, r.width - 1),\\n\",\n+\" clamp(e.clientY - r.top, 0, r.height - 1),\\n\",\n+\" ];\\n\",\n \" };\\n\",\n+\" const toNatural = (x, y) => [\\n\",\n+\" Math.round(x * (img.naturalWidth / canvas.width)),\\n\",\n+\" Math.round(y * (img.naturalHeight / canvas.height)),\\n\",\n+\" ];\\n\",\n \"\\n\",\n-\" const onKey = (e) => {\\n\",\n-\" if (e.key === 'Escape') {\\n\",\n-\" cleanup();\\n\",\n-\" resolvePromise([-1, -1, -1, -1]);\\n\",\n-\" }\\n\",\n+\" let dragging = false;\\n\",\n+\" let sx = 0;\\n\",\n+\" let sy = 0;\\n\",\n+\" let latest = null;\\n\",\n+\"\\n\",\n+\" const redraw = () => {\\n\",\n+\" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n+\" if (!latest) return;\\n\",\n+\" const [x0, y0, x1, y1] = latest;\\n\",\n+\" ctx.fillStyle = 'rgba(0,229,255,0.18)';\\n\",\n+\" ctx.strokeStyle = 'rgba(0,229,255,1)';\\n\",\n+\" ctx.lineWidth = 2;\\n\",\n+\" ctx.fillRect(x0, y0, x1 - x0, y1 - y0);\\n\",\n+\" ctx.strokeRect(x0, y0, x1 - x0, y1 - y0);\\n\",\n \" };\\n\",\n \"\\n\",\n-\" document.addEventListener('keydown', onKey);\\n\",\n-\"\\n\",\n-\" img.onmousedown = (e) => {\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n+\" canvas.onpointerdown = (e) => {\\n\",\n+\" canvas.setPointerCapture(e.pointerId);\\n\",\n \" dragging = true;\\n\",\n-\" startX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n-\" startY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n-\" box.style.display = 'block';\\n\",\n+\" [sx, sy] = toCanvas(e);\\n\",\n+\" latest = [sx, sy, sx + 1, sy + 1];\\n\",\n+\" redraw();\\n\",\n \" };\\n\",\n \"\\n\",\n-\" img.onmousemove = (e) => {\\n\",\n+\" canvas.onpointermove = (e) => {\\n\",\n \" if (!dragging) return;\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n-\" const curX = clamp(e.clientX - rect.left, 0, rect.width);\\n\",\n-\" const curY = clamp(e.clientY - rect.top, 0, rect.height);\\n\",\n+\" const [cx, cy] = toCanvas(e);\\n\",\n+\" latest = [Math.min(sx, cx), Math.min(sy, cy), Math.max(sx, cx), Math.max(sy, cy)];\\n\",\n+\" redraw();\\n\",\n+\" };\\n\",\n \"\\n\",\n-\" const left = Math.min(startX, curX);\\n\",\n-\" const top = Math.min(startY, curY);\\n\",\n-\" const width = Math.max(1, Math.abs(curX - startX));\\n\",\n-\" const height = Math.max(1, Math.abs(curY - startY));\\n\",\n+\" const stopDrag = () => { dragging = false; };\\n\",\n+\" canvas.onpointerup = stopDrag;\\n\",\n+\" canvas.onpointercancel = stopDrag;\\n\",\n \"\\n\",\n-\" box.style.left = `${left}px`;\\n\",\n-\" box.style.top = `${top}px`;\\n\",\n-\" box.style.width = `${width}px`;\\n\",\n-\" box.style.height = `${height}px`;\\n\",\n+\" resetBtn.onclick = () => { latest = null; redraw(); };\\n\",\n \"\\n\",\n-\" const [x0, y0] = toNatural(left, top, rect);\\n\",\n-\" const [x1, y1] = toNatural(left + width, top + height, rect);\\n\",\n-\" latest = [Math.min(x0, x1), Math.min(y0, y1), Math.max(x0, x1), Math.max(y0, y1)];\\n\",\n-\" };\\n\",\n+\" let resolvePromise;\\n\",\n+\" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n \"\\n\",\n-\" img.onmouseup = () => {\\n\",\n-\" dragging = false;\\n\",\n+\" const cleanup = () => {\\n\",\n+\" document.removeEventListener('keydown', onKey);\\n\",\n+\" window.removeEventListener('resize', resizeCanvas);\\n\",\n+\" overlay.remove();\\n\",\n \" };\\n\",\n \"\\n\",\n-\" img.onmouseleave = () => {\\n\",\n-\" dragging = false;\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise([-1, -1, -1, -1]);\\n\",\n+\" }\\n\",\n \" };\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n+\" window.addEventListener('resize', resizeCanvas);\\n\",\n \"\\n\",\n-\" resetBtn.onclick = () => {\\n\",\n-\" latest = null;\\n\",\n-\" box.style.display = 'none';\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" let resolvePromise;\\n\",\n-\" const promise = new Promise((resolve) => {\\n\",\n-\" resolvePromise = resolve;\\n\",\n-\" });\\n\",\n-\"\\n\",\n \" doneBtn.onclick = () => {\\n\",\n \" if (!latest) {\\n\",\n \" label.textContent = 'Draw a box first.';\\n\",\n \" return;\\n\",\n \" }\\n\",\n+\" const [cx0, cy0, cx1, cy1] = latest;\\n\",\n+\" if ((cx1 - cx0) < 3 || (cy1 - cy0) < 3) {\\n\",\n+\" label.textContent = 'Box too small. Drag a larger box.';\\n\",\n+\" return;\\n\",\n+\" }\\n\",\n+\" const [nx0, ny0] = toNatural(cx0, cy0);\\n\",\n+\" const [nx1, ny1] = toNatural(cx1, cy1);\\n\",\n \" cleanup();\\n\",\n-\" resolvePromise(latest);\\n\",\n+\" resolvePromise([Math.min(nx0,nx1), Math.min(ny0,ny1), Math.max(nx0,nx1), Math.max(ny0,ny1)]);\\n\",\n \" };\\n\",\n \"\\n\",\n \" return await promise;\\n\",\n \" }\\n\",\n", + "additions": 81, + "deletions": 66 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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'8px';\\n\",\n- \"\\n\",\n- \" const label = document.createElement('div');\\n\",\n- \" label.textContent = `${promptText} (paint mask, Done to confirm, Esc to cancel)`;\\n\",\n- \" label.style.color = 'white';\\n\",\n- \" label.style.font = '16px sans-serif';\\n\",\n- \"\\n\",\n- \" const container = document.createElement('div');\\n\",\n- \" container.style.position = 'relative';\\n\",\n- \"\\n\",\n- \" const img = document.createElement('img');\\n\",\n- \" img.src = dataUrl;\\n\",\n- \" img.style.width = '95vw';\\n\",\n- \" img.style.maxWidth = '1800px';\\n\",\n- \" img.style.maxHeight = '90vh';\\n\",\n- \" img.style.display = 'block';\\n\",\n- \" img.style.userSelect = 'none';\\n\",\n- \"\\n\",\n- \" const canvas = document.createElement('canvas');\\n\",\n- \" canvas.style.position = 'absolute';\\n\",\n- \" canvas.style.left = '0';\\n\",\n- \" canvas.style.top = '0';\\n\",\n- \" canvas.style.cursor = 'crosshair';\\n\",\n- \"\\n\",\n- \" container.appendChild(img);\\n\",\n- \" container.appendChild(canvas);\\n\",\n- \"\\n\",\n- \" const controls = document.createElement('div');\\n\",\n- \" controls.style.display = 'flex';\\n\",\n- \" controls.style.gap = '8px';\\n\",\n- \"\\n\",\n- \" const doneBtn = document.createElement('button');\\n\",\n- \" doneBtn.textContent = 'Done';\\n\",\n- \" const clearBtn = document.createElement('button');\\n\",\n- \" clearBtn.textContent = 'Clear';\\n\",\n- \" const brush = document.createElement('input');\\n\",\n- \" brush.type = 'range';\\n\",\n- \" brush.min = '4';\\n\",\n- \" brush.max = '48';\\n\",\n- \" brush.value = '18';\\n\",\n- \"\\n\",\n- \" controls.appendChild(doneBtn);\\n\",\n- \" controls.appendChild(clearBtn);\\n\",\n- \" controls.appendChild(brush);\\n\",\n- \"\\n\",\n- \" overlay.appendChild(label);\\n\",\n- \" overlay.appendChild(container);\\n\",\n- \" overlay.appendChild(controls);\\n\",\n- \" document.body.appendChild(overlay);\\n\",\n- \"\\n\",\n- \" let drawing = false;\\n\",\n- \" let hasInk = false;\\n\",\n- \"\\n\",\n- \" const resizeCanvas = () => {\\n\",\n- \" const rect = img.getBoundingClientRect();\\n\",\n- \" canvas.width = Math.max(1, Math.round(rect.width));\\n\",\n- \" canvas.height = Math.max(1, Math.round(rect.height));\\n\",\n- \" canvas.style.width = `${rect.width}px`;\\n\",\n- \" canvas.style.height = `${rect.height}px`;\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const ctx = canvas.getContext('2d');\\n\",\n- \" const setBrush = () => {\\n\",\n- \" ctx.lineCap = 'round';\\n\",\n- \" ctx.lineJoin = 'round';\\n\",\n- \" ctx.strokeStyle = 'rgba(0,255,255,1)';\\n\",\n- \" ctx.lineWidth = Number(brush.value);\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const cleanup = () => {\\n\",\n- \" document.removeEventListener('keydown', onKey);\\n\",\n- \" window.removeEventListener('resize', resizeCanvas);\\n\",\n- \" overlay.remove();\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const onKey = (e) => {\\n\",\n- \" if (e.key === 'Escape') {\\n\",\n- \" cleanup();\\n\",\n- \" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n- \" }\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const toCanvasXY = (e) => {\\n\",\n- \" const rect = canvas.getBoundingClientRect();\\n\",\n- \" const x = Math.max(0, Math.min(rect.width, e.clientX - rect.left));\\n\",\n- \" const y = Math.max(0, Math.min(rect.height, e.clientY - rect.top));\\n\",\n- \" return [x, y];\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const toNaturalXY = (x, y) => {\\n\",\n- \" const scaleX = img.naturalWidth / canvas.width;\\n\",\n- \" const scaleY = img.naturalHeight / canvas.height;\\n\",\n- \" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" brush.oninput = setBrush;\\n\",\n- \"\\n\",\n- \" canvas.onmousedown = (e) => {\\n\",\n- \" drawing = true;\\n\",\n- \" const [x, y] = toCanvasXY(e);\\n\",\n- \" setBrush();\\n\",\n- \" ctx.beginPath();\\n\",\n- \" ctx.moveTo(x, y);\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" canvas.onmousemove = (e) => {\\n\",\n- \" if (!drawing) return;\\n\",\n- \" const [x, y] = toCanvasXY(e);\\n\",\n- \" ctx.lineTo(x, y);\\n\",\n- \" ctx.stroke();\\n\",\n- \" hasInk = true;\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" const stopDraw = () => { drawing = false; };\\n\",\n- \" canvas.onmouseup = stopDraw;\\n\",\n- \" canvas.onmouseleave = stopDraw;\\n\",\n- \"\\n\",\n- \" clearBtn.onclick = () => {\\n\",\n- \" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n- \" hasInk = false;\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" let resolvePromise;\\n\",\n- \" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n- \"\\n\",\n- \" doneBtn.onclick = () => {\\n\",\n- \" if (!hasInk) {\\n\",\n- \" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n- \" return;\\n\",\n- \" }\\n\",\n- \"\\n\",\n- \" const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\\n\",\n- \" let minX = canvas.width, minY = canvas.height, maxX = -1, maxY = -1;\\n\",\n- \" const pts = [];\\n\",\n- \" const stride = Math.max(3, Math.floor(Math.sqrt((canvas.width * canvas.height) / 1200)));\\n\",\n- \"\\n\",\n- \" for (let y = 0; y < canvas.height; y += 1) {\\n\",\n- \" for (let x = 0; x < canvas.width; x += 1) {\\n\",\n- \" const a = data[(y * canvas.width + x) * 4 + 3];\\n\",\n- \" if (a > 8) {\\n\",\n- \" if (x < minX) minX = x;\\n\",\n- \" if (y < minY) minY = y;\\n\",\n- \" if (x > maxX) maxX = x;\\n\",\n- \" if (y > maxY) maxY = y;\\n\",\n- \" if ((x % stride === 0) && (y % stride === 0)) pts.push([x, y]);\\n\",\n- \" }\\n\",\n- \" }\\n\",\n- \" }\\n\",\n- \"\\n\",\n- \" if (maxX < minX || maxY < minY) {\\n\",\n- \" label.textContent = 'No valid brush mask found. Try again.';\\n\",\n- \" return;\\n\",\n- \" }\\n\",\n- \"\\n\",\n- \" const pad = 4;\\n\",\n- \" minX = Math.max(0, minX - pad);\\n\",\n- \" minY = Math.max(0, minY - pad);\\n\",\n- \" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n- \" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n- \"\\n\",\n- \" const [nx0, ny0] = toNaturalXY(minX, minY);\\n\",\n- \" const [nx1, ny1] = toNaturalXY(maxX, maxY);\\n\",\n- \"\\n\",\n- \" const sampled = [];\\n\",\n- \" const limit = 16;\\n\",\n- \" const step = Math.max(1, Math.floor(pts.length / limit));\\n\",\n- \" for (let i = 0; i < pts.length && sampled.length < limit; i += step) {\\n\",\n- \" sampled.push(toNaturalXY(pts[i][0], pts[i][1]));\\n\",\n- \" }\\n\",\n- \"\\n\",\n- \" cleanup();\\n\",\n- \" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n- \" };\\n\",\n- \"\\n\",\n- \" img.onload = () => { resizeCanvas(); setBrush(); };\\n\",\n- \" if (img.complete) { resizeCanvas(); setBrush(); }\\n\",\n- \" window.addEventListener('resize', resizeCanvas);\\n\",\n- \" document.addEventListener('keydown', onKey);\\n\",\n- \"\\n\",\n- \" return await promise;\\n\",\n- \" }\\n\",\n- \" \\\"\\\"\\\"\\n\",\n+ \" js = \\\"\\\"\\\"\\n\",\n+ \" async function materialHuntersBrushHint(dataUrl, promptText) {\\n\",\n+ \" const overlay = document.createElement('div');\\n\",\n+ \" overlay.style.position = 'fixed';\\n\",\n+ \" overlay.style.inset = '0';\\n\",\n+ \" overlay.style.background = 'rgba(0,0,0,0.75)';\\n\",\n+ \" overlay.style.zIndex = '9999';\\n\",\n+ \" overlay.style.display = 'flex';\\n\",\n+ \" overlay.style.flexDirection = 'column';\\n\",\n+ \" overlay.style.alignItems = 'center';\\n\",\n+ \" overlay.style.justifyContent = 'center';\\n\",\n+ \" overlay.style.gap = '8px';\\n\",\n+ \"\\n\",\n+ \" const label = document.createElement('div');\\n\",\n+ \" label.textContent = `${promptText} (paint object area, Done to confirm, Esc to cancel)`;\\n\",\n+ \" label.style.color = 'white';\\n\",\n+ \" label.style.font = '16px sans-serif';\\n\",\n+ \"\\n\",\n+ \" const container = document.createElement('div');\\n\",\n+ \" container.style.position = 'relative';\\n\",\n+ \"\\n\",\n+ \" const img = document.createElement('img');\\n\",\n+ \" img.src = dataUrl;\\n\",\n+ \" img.style.width = '95vw';\\n\",\n+ \" img.style.maxWidth = '1800px';\\n\",\n+ \" img.style.maxHeight = '90vh';\\n\",\n+ \" img.style.display = 'block';\\n\",\n+ \" img.style.userSelect = 'none';\\n\",\n+ \" img.style.border = '2px solid white';\\n\",\n+ \"\\n\",\n+ \" const canvas = document.createElement('canvas');\\n\",\n+ \" canvas.style.position = 'absolute';\\n\",\n+ \" canvas.style.left = '0';\\n\",\n+ \" canvas.style.top = '0';\\n\",\n+ \" canvas.style.cursor = 'crosshair';\\n\",\n+ \" canvas.style.touchAction = 'none';\\n\",\n+ \"\\n\",\n+ \" container.appendChild(img);\\n\",\n+ \" container.appendChild(canvas);\\n\",\n+ \"\\n\",\n+ \" const controls = document.createElement('div');\\n\",\n+ \" controls.style.display = 'flex';\\n\",\n+ \" controls.style.gap = '8px';\\n\",\n+ \" const doneBtn = document.createElement('button');\\n\",\n+ \" doneBtn.textContent = 'Done';\\n\",\n+ \" const clearBtn = document.createElement('button');\\n\",\n+ \" clearBtn.textContent = 'Clear';\\n\",\n+ \" const brush = document.createElement('input');\\n\",\n+ \" brush.type = 'range';\\n\",\n+ \" brush.min = '4';\\n\",\n+ \" brush.max = '48';\\n\",\n+ \" brush.value = '18';\\n\",\n+ \"\\n\",\n+ \" controls.appendChild(doneBtn);\\n\",\n+ \" controls.appendChild(clearBtn);\\n\",\n+ \" controls.appendChild(brush);\\n\",\n+ \"\\n\",\n+ \" overlay.appendChild(label);\\n\",\n+ \" overlay.appendChild(container);\\n\",\n+ \" overlay.appendChild(controls);\\n\",\n+ \" document.body.appendChild(overlay);\\n\",\n+ \"\\n\",\n+ \" await new Promise((resolve) => {\\n\",\n+ \" if (img.complete && img.naturalWidth > 0) resolve();\\n\",\n+ \" else img.onload = () => resolve();\\n\",\n+ \" });\\n\",\n+ \"\\n\",\n+ \" const resizeCanvas = () => {\\n\",\n+ \" const r = img.getBoundingClientRect();\\n\",\n+ \" canvas.width = Math.max(1, Math.round(r.width));\\n\",\n+ \" canvas.height = Math.max(1, Math.round(r.height));\\n\",\n+ \" canvas.style.width = `${r.width}px`;\\n\",\n+ \" canvas.style.height = `${r.height}px`;\\n\",\n+ \" };\\n\",\n+ \" resizeCanvas();\\n\",\n+ \"\\n\",\n+ \" const ctx = canvas.getContext('2d');\\n\",\n+ \" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n+ \" const toCanvas = (e) => {\\n\",\n+ \" const r = canvas.getBoundingClientRect();\\n\",\n+ \" return [\\n\",\n+ \" clamp(e.clientX - r.left, 0, r.width - 1),\\n\",\n+ \" clamp(e.clientY - r.top, 0, r.height - 1),\\n\",\n+ \" ];\\n\",\n+ \" };\\n\",\n+ \" const toNatural = (x, y) => [\\n\",\n+ \" Math.round(x * (img.naturalWidth / canvas.width)),\\n\",\n+ \" Math.round(y * (img.naturalHeight / canvas.height)),\\n\",\n+ \" ];\\n\",\n+ \"\\n\",\n+ \" const drawn = [];\\n\",\n+ \" let drawing = false;\\n\",\n+ \" let lx = 0;\\n\",\n+ \" let ly = 0;\\n\",\n+ \"\\n\",\n+ \" const paintSegment = (x0, y0, x1, y1) => {\\n\",\n+ \" ctx.lineCap = 'round';\\n\",\n+ \" ctx.lineJoin = 'round';\\n\",\n+ \" ctx.strokeStyle = 'rgba(0,255,255,0.85)';\\n\",\n+ \" ctx.lineWidth = Number(brush.value);\\n\",\n+ \" ctx.beginPath();\\n\",\n+ \" ctx.moveTo(x0, y0);\\n\",\n+ \" ctx.lineTo(x1, y1);\\n\",\n+ \" ctx.stroke();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const maybeRecord = (x, y) => {\\n\",\n+ \" if (drawn.length === 0) { drawn.push([x, y]); return; }\\n\",\n+ \" const [px, py] = drawn[drawn.length - 1];\\n\",\n+ \" const dx = x - px;\\n\",\n+ \" const dy = y - py;\\n\",\n+ \" if ((dx*dx + dy*dy) >= 9) drawn.push([x, y]);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" canvas.onpointerdown = (e) => {\\n\",\n+ \" canvas.setPointerCapture(e.pointerId);\\n\",\n+ \" drawing = true;\\n\",\n+ \" [lx, ly] = toCanvas(e);\\n\",\n+ \" paintSegment(lx, ly, lx, ly);\\n\",\n+ \" maybeRecord(lx, ly);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" canvas.onpointermove = (e) => {\\n\",\n+ \" if (!drawing) return;\\n\",\n+ \" const [cx, cy] = toCanvas(e);\\n\",\n+ \" paintSegment(lx, ly, cx, cy);\\n\",\n+ \" lx = cx;\\n\",\n+ \" ly = cy;\\n\",\n+ \" maybeRecord(cx, cy);\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const stopDraw = () => { drawing = false; };\\n\",\n+ \" canvas.onpointerup = stopDraw;\\n\",\n+ \" canvas.onpointercancel = stopDraw;\\n\",\n+ \"\\n\",\n+ \" clearBtn.onclick = () => {\\n\",\n+ \" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n+ \" drawn.length = 0;\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" let resolvePromise;\\n\",\n+ \" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n+ \"\\n\",\n+ \" const cleanup = () => {\\n\",\n+ \" document.removeEventListener('keydown', onKey);\\n\",\n+ \" window.removeEventListener('resize', resizeCanvas);\\n\",\n+ \" overlay.remove();\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" const onKey = (e) => {\\n\",\n+ \" if (e.key === 'Escape') {\\n\",\n+ \" cleanup();\\n\",\n+ \" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n+ \" }\\n\",\n+ \" };\\n\",\n+ \" document.addEventListener('keydown', onKey);\\n\",\n+ \" window.addEventListener('resize', resizeCanvas);\\n\",\n+ \"\\n\",\n+ \" doneBtn.onclick = () => {\\n\",\n+ \" if (drawn.length < 1) {\\n\",\n+ \" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n+ \" return;\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity;\\n\",\n+ \" for (const [x, y] of drawn) {\\n\",\n+ \" if (x < minX) minX = x;\\n\",\n+ \" if (y < minY) minY = y;\\n\",\n+ \" if (x > maxX) maxX = x;\\n\",\n+ \" if (y > maxY) maxY = y;\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" const pad = Math.max(4, Math.round(Number(brush.value) * 0.8));\\n\",\n+ \" minX = Math.max(0, minX - pad);\\n\",\n+ \" minY = Math.max(0, minY - pad);\\n\",\n+ \" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n+ \" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n+ \"\\n\",\n+ \" const [nx0, ny0] = toNatural(minX, minY);\\n\",\n+ \" const [nx1, ny1] = toNatural(maxX, maxY);\\n\",\n+ \"\\n\",\n+ \" const sampled = [];\\n\",\n+ \" const limit = 16;\\n\",\n+ \" const step = Math.max(1, Math.floor(drawn.length / limit));\\n\",\n+ \" for (let i = 0; i < drawn.length && sampled.length < limit; i += step) {\\n\",\n+ \" sampled.push(toNatural(drawn[i][0], drawn[i][1]));\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" cleanup();\\n\",\n+ \" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n+ \" };\\n\",\n+ \"\\n\",\n+ \" return await promise;\\n\",\n+ \" }\\n\",\n+ \" \\\"\\\"\\\"\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -375,9 +375,9 @@\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n-\" label.textContent = `${promptText} (paint mask, Done to confirm, Esc to cancel)`;\\n\",\n+\" label.textContent = `${promptText} (paint object area, Done to confirm, Esc to cancel)`;\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n \" const container = document.createElement('div');\\n\",\n@@ -389,22 +389,23 @@\n \" img.style.maxWidth = '1800px';\\n\",\n \" img.style.maxHeight = '90vh';\\n\",\n \" img.style.display = 'block';\\n\",\n \" img.style.userSelect = 'none';\\n\",\n+\" img.style.border = '2px solid white';\\n\",\n \"\\n\",\n \" const canvas = document.createElement('canvas');\\n\",\n \" canvas.style.position = 'absolute';\\n\",\n \" canvas.style.left = '0';\\n\",\n \" canvas.style.top = '0';\\n\",\n \" canvas.style.cursor = 'crosshair';\\n\",\n+\" canvas.style.touchAction = 'none';\\n\",\n \"\\n\",\n \" container.appendChild(img);\\n\",\n \" container.appendChild(canvas);\\n\",\n \"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n-\"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n \" const clearBtn = document.createElement('button');\\n\",\n \" clearBtn.textContent = 'Clear';\\n\",\n@@ -422,137 +423,138 @@\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n-\" let drawing = false;\\n\",\n-\" let hasInk = false;\\n\",\n+\" await new Promise((resolve) => {\\n\",\n+\" if (img.complete && img.naturalWidth > 0) resolve();\\n\",\n+\" else img.onload = () => resolve();\\n\",\n+\" });\\n\",\n \"\\n\",\n \" const resizeCanvas = () => {\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n-\" canvas.width = Math.max(1, Math.round(rect.width));\\n\",\n-\" canvas.height = Math.max(1, Math.round(rect.height));\\n\",\n-\" canvas.style.width = `${rect.width}px`;\\n\",\n-\" canvas.style.height = `${rect.height}px`;\\n\",\n+\" const r = img.getBoundingClientRect();\\n\",\n+\" canvas.width = Math.max(1, Math.round(r.width));\\n\",\n+\" canvas.height = Math.max(1, Math.round(r.height));\\n\",\n+\" canvas.style.width = `${r.width}px`;\\n\",\n+\" canvas.style.height = `${r.height}px`;\\n\",\n \" };\\n\",\n+\" resizeCanvas();\\n\",\n \"\\n\",\n \" const ctx = canvas.getContext('2d');\\n\",\n-\" const setBrush = () => {\\n\",\n+\" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n+\" const toCanvas = (e) => {\\n\",\n+\" const r = canvas.getBoundingClientRect();\\n\",\n+\" return [\\n\",\n+\" clamp(e.clientX - r.left, 0, r.width - 1),\\n\",\n+\" clamp(e.clientY - r.top, 0, r.height - 1),\\n\",\n+\" ];\\n\",\n+\" };\\n\",\n+\" const toNatural = (x, y) => [\\n\",\n+\" Math.round(x * (img.naturalWidth / canvas.width)),\\n\",\n+\" Math.round(y * (img.naturalHeight / canvas.height)),\\n\",\n+\" ];\\n\",\n+\"\\n\",\n+\" const drawn = [];\\n\",\n+\" let drawing = false;\\n\",\n+\" let lx = 0;\\n\",\n+\" let ly = 0;\\n\",\n+\"\\n\",\n+\" const paintSegment = (x0, y0, x1, y1) => {\\n\",\n \" ctx.lineCap = 'round';\\n\",\n \" ctx.lineJoin = 'round';\\n\",\n-\" ctx.strokeStyle = 'rgba(0,255,255,1)';\\n\",\n+\" ctx.strokeStyle = 'rgba(0,255,255,0.85)';\\n\",\n \" ctx.lineWidth = Number(brush.value);\\n\",\n+\" ctx.beginPath();\\n\",\n+\" ctx.moveTo(x0, y0);\\n\",\n+\" ctx.lineTo(x1, y1);\\n\",\n+\" ctx.stroke();\\n\",\n \" };\\n\",\n \"\\n\",\n-\" const cleanup = () => {\\n\",\n-\" document.removeEventListener('keydown', onKey);\\n\",\n-\" window.removeEventListener('resize', resizeCanvas);\\n\",\n-\" overlay.remove();\\n\",\n+\" const maybeRecord = (x, y) => {\\n\",\n+\" if (drawn.length === 0) { drawn.push([x, y]); return; }\\n\",\n+\" const [px, py] = drawn[drawn.length - 1];\\n\",\n+\" const dx = x - px;\\n\",\n+\" const dy = y - py;\\n\",\n+\" if ((dx*dx + dy*dy) >= 9) drawn.push([x, y]);\\n\",\n \" };\\n\",\n \"\\n\",\n-\" const onKey = (e) => {\\n\",\n-\" if (e.key === 'Escape') {\\n\",\n-\" cleanup();\\n\",\n-\" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n-\" }\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" const toCanvasXY = (e) => {\\n\",\n-\" const rect = canvas.getBoundingClientRect();\\n\",\n-\" const x = Math.max(0, Math.min(rect.width, e.clientX - rect.left));\\n\",\n-\" const y = Math.max(0, Math.min(rect.height, e.clientY - rect.top));\\n\",\n-\" return [x, y];\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" const toNaturalXY = (x, y) => {\\n\",\n-\" const scaleX = img.naturalWidth / canvas.width;\\n\",\n-\" const scaleY = img.naturalHeight / canvas.height;\\n\",\n-\" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" brush.oninput = setBrush;\\n\",\n-\"\\n\",\n-\" canvas.onmousedown = (e) => {\\n\",\n+\" canvas.onpointerdown = (e) => {\\n\",\n+\" canvas.setPointerCapture(e.pointerId);\\n\",\n \" drawing = true;\\n\",\n-\" const [x, y] = toCanvasXY(e);\\n\",\n-\" setBrush();\\n\",\n-\" ctx.beginPath();\\n\",\n-\" ctx.moveTo(x, y);\\n\",\n+\" [lx, ly] = toCanvas(e);\\n\",\n+\" paintSegment(lx, ly, lx, ly);\\n\",\n+\" maybeRecord(lx, ly);\\n\",\n \" };\\n\",\n \"\\n\",\n-\" canvas.onmousemove = (e) => {\\n\",\n+\" canvas.onpointermove = (e) => {\\n\",\n \" if (!drawing) return;\\n\",\n-\" const [x, y] = toCanvasXY(e);\\n\",\n-\" ctx.lineTo(x, y);\\n\",\n-\" ctx.stroke();\\n\",\n-\" hasInk = true;\\n\",\n+\" const [cx, cy] = toCanvas(e);\\n\",\n+\" paintSegment(lx, ly, cx, cy);\\n\",\n+\" lx = cx;\\n\",\n+\" ly = cy;\\n\",\n+\" maybeRecord(cx, cy);\\n\",\n \" };\\n\",\n \"\\n\",\n \" const stopDraw = () => { drawing = false; };\\n\",\n-\" canvas.onmouseup = stopDraw;\\n\",\n-\" canvas.onmouseleave = stopDraw;\\n\",\n+\" canvas.onpointerup = stopDraw;\\n\",\n+\" canvas.onpointercancel = stopDraw;\\n\",\n \"\\n\",\n \" clearBtn.onclick = () => {\\n\",\n \" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n-\" hasInk = false;\\n\",\n+\" drawn.length = 0;\\n\",\n \" };\\n\",\n \"\\n\",\n \" let resolvePromise;\\n\",\n \" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n \"\\n\",\n+\" const cleanup = () => {\\n\",\n+\" document.removeEventListener('keydown', onKey);\\n\",\n+\" window.removeEventListener('resize', resizeCanvas);\\n\",\n+\" overlay.remove();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n+\" }\\n\",\n+\" };\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n+\" window.addEventListener('resize', resizeCanvas);\\n\",\n+\"\\n\",\n \" doneBtn.onclick = () => {\\n\",\n-\" if (!hasInk) {\\n\",\n+\" if (drawn.length < 1) {\\n\",\n \" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n-\" const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\\n\",\n-\" let minX = canvas.width, minY = canvas.height, maxX = -1, maxY = -1;\\n\",\n-\" const pts = [];\\n\",\n-\" const stride = Math.max(3, Math.floor(Math.sqrt((canvas.width * canvas.height) / 1200)));\\n\",\n-\"\\n\",\n-\" for (let y = 0; y < canvas.height; y += 1) {\\n\",\n-\" for (let x = 0; x < canvas.width; x += 1) {\\n\",\n-\" const a = data[(y * canvas.width + x) * 4 + 3];\\n\",\n-\" if (a > 8) {\\n\",\n-\" if (x < minX) minX = x;\\n\",\n-\" if (y < minY) minY = y;\\n\",\n-\" if (x > maxX) maxX = x;\\n\",\n-\" if (y > maxY) maxY = y;\\n\",\n-\" if ((x % stride === 0) && (y % stride === 0)) pts.push([x, y]);\\n\",\n-\" }\\n\",\n-\" }\\n\",\n+\" let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity;\\n\",\n+\" for (const [x, y] of drawn) {\\n\",\n+\" if (x < minX) minX = x;\\n\",\n+\" if (y < minY) minY = y;\\n\",\n+\" if (x > maxX) maxX = x;\\n\",\n+\" if (y > maxY) maxY = y;\\n\",\n \" }\\n\",\n \"\\n\",\n-\" if (maxX < minX || maxY < minY) {\\n\",\n-\" label.textContent = 'No valid brush mask found. Try again.';\\n\",\n-\" return;\\n\",\n-\" }\\n\",\n-\"\\n\",\n-\" const pad = 4;\\n\",\n+\" const pad = Math.max(4, Math.round(Number(brush.value) * 0.8));\\n\",\n \" minX = Math.max(0, minX - pad);\\n\",\n \" minY = Math.max(0, minY - pad);\\n\",\n \" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n \" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n \"\\n\",\n-\" const [nx0, ny0] = toNaturalXY(minX, minY);\\n\",\n-\" const [nx1, ny1] = toNaturalXY(maxX, maxY);\\n\",\n+\" const [nx0, ny0] = toNatural(minX, minY);\\n\",\n+\" const [nx1, ny1] = toNatural(maxX, maxY);\\n\",\n \"\\n\",\n \" const sampled = [];\\n\",\n \" const limit = 16;\\n\",\n-\" const step = Math.max(1, Math.floor(pts.length / limit));\\n\",\n-\" for (let i = 0; i < pts.length && sampled.length < limit; i += step) {\\n\",\n-\" sampled.push(toNaturalXY(pts[i][0], pts[i][1]));\\n\",\n+\" const step = Math.max(1, Math.floor(drawn.length / limit));\\n\",\n+\" for (let i = 0; i < drawn.length && sampled.length < limit; i += step) {\\n\",\n+\" sampled.push(toNatural(drawn[i][0], drawn[i][1]));\\n\",\n \" }\\n\",\n \"\\n\",\n \" cleanup();\\n\",\n \" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n \" };\\n\",\n \"\\n\",\n-\" img.onload = () => { resizeCanvas(); setBrush(); };\\n\",\n-\" if (img.complete) { resizeCanvas(); setBrush(); }\\n\",\n-\" window.addEventListener('resize', resizeCanvas);\\n\",\n-\" document.addEventListener('keydown', onKey);\\n\",\n-\"\\n\",\n \" return await promise;\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -375,9 +375,9 @@\n \" overlay.style.justifyContent = 'center';\\n\",\n \" overlay.style.gap = '8px';\\n\",\n \"\\n\",\n \" const label = document.createElement('div');\\n\",\n-\" label.textContent = `${promptText} (paint mask, Done to confirm, Esc to cancel)`;\\n\",\n+\" label.textContent = `${promptText} (paint object area, Done to confirm, Esc to cancel)`;\\n\",\n \" label.style.color = 'white';\\n\",\n \" label.style.font = '16px sans-serif';\\n\",\n \"\\n\",\n \" const container = document.createElement('div');\\n\",\n@@ -389,22 +389,23 @@\n \" img.style.maxWidth = '1800px';\\n\",\n \" img.style.maxHeight = '90vh';\\n\",\n \" img.style.display = 'block';\\n\",\n \" img.style.userSelect = 'none';\\n\",\n+\" img.style.border = '2px solid white';\\n\",\n \"\\n\",\n \" const canvas = document.createElement('canvas');\\n\",\n \" canvas.style.position = 'absolute';\\n\",\n \" canvas.style.left = '0';\\n\",\n \" canvas.style.top = '0';\\n\",\n \" canvas.style.cursor = 'crosshair';\\n\",\n+\" canvas.style.touchAction = 'none';\\n\",\n \"\\n\",\n \" container.appendChild(img);\\n\",\n \" container.appendChild(canvas);\\n\",\n \"\\n\",\n \" const controls = document.createElement('div');\\n\",\n \" controls.style.display = 'flex';\\n\",\n \" controls.style.gap = '8px';\\n\",\n-\"\\n\",\n \" const doneBtn = document.createElement('button');\\n\",\n \" doneBtn.textContent = 'Done';\\n\",\n \" const clearBtn = document.createElement('button');\\n\",\n \" clearBtn.textContent = 'Clear';\\n\",\n@@ -422,137 +423,138 @@\n \" overlay.appendChild(container);\\n\",\n \" overlay.appendChild(controls);\\n\",\n \" document.body.appendChild(overlay);\\n\",\n \"\\n\",\n-\" let drawing = false;\\n\",\n-\" let hasInk = false;\\n\",\n+\" await new Promise((resolve) => {\\n\",\n+\" if (img.complete && img.naturalWidth > 0) resolve();\\n\",\n+\" else img.onload = () => resolve();\\n\",\n+\" });\\n\",\n \"\\n\",\n \" const resizeCanvas = () => {\\n\",\n-\" const rect = img.getBoundingClientRect();\\n\",\n-\" canvas.width = Math.max(1, Math.round(rect.width));\\n\",\n-\" canvas.height = Math.max(1, Math.round(rect.height));\\n\",\n-\" canvas.style.width = `${rect.width}px`;\\n\",\n-\" canvas.style.height = `${rect.height}px`;\\n\",\n+\" const r = img.getBoundingClientRect();\\n\",\n+\" canvas.width = Math.max(1, Math.round(r.width));\\n\",\n+\" canvas.height = Math.max(1, Math.round(r.height));\\n\",\n+\" canvas.style.width = `${r.width}px`;\\n\",\n+\" canvas.style.height = `${r.height}px`;\\n\",\n \" };\\n\",\n+\" resizeCanvas();\\n\",\n \"\\n\",\n \" const ctx = canvas.getContext('2d');\\n\",\n-\" const setBrush = () => {\\n\",\n+\" const clamp = (v, lo, hi) => Math.max(lo, Math.min(hi, v));\\n\",\n+\" const toCanvas = (e) => {\\n\",\n+\" const r = canvas.getBoundingClientRect();\\n\",\n+\" return [\\n\",\n+\" clamp(e.clientX - r.left, 0, r.width - 1),\\n\",\n+\" clamp(e.clientY - r.top, 0, r.height - 1),\\n\",\n+\" ];\\n\",\n+\" };\\n\",\n+\" const toNatural = (x, y) => [\\n\",\n+\" Math.round(x * (img.naturalWidth / canvas.width)),\\n\",\n+\" Math.round(y * (img.naturalHeight / canvas.height)),\\n\",\n+\" ];\\n\",\n+\"\\n\",\n+\" const drawn = [];\\n\",\n+\" let drawing = false;\\n\",\n+\" let lx = 0;\\n\",\n+\" let ly = 0;\\n\",\n+\"\\n\",\n+\" const paintSegment = (x0, y0, x1, y1) => {\\n\",\n \" ctx.lineCap = 'round';\\n\",\n \" ctx.lineJoin = 'round';\\n\",\n-\" ctx.strokeStyle = 'rgba(0,255,255,1)';\\n\",\n+\" ctx.strokeStyle = 'rgba(0,255,255,0.85)';\\n\",\n \" ctx.lineWidth = Number(brush.value);\\n\",\n+\" ctx.beginPath();\\n\",\n+\" ctx.moveTo(x0, y0);\\n\",\n+\" ctx.lineTo(x1, y1);\\n\",\n+\" ctx.stroke();\\n\",\n \" };\\n\",\n \"\\n\",\n-\" const cleanup = () => {\\n\",\n-\" document.removeEventListener('keydown', onKey);\\n\",\n-\" window.removeEventListener('resize', resizeCanvas);\\n\",\n-\" overlay.remove();\\n\",\n+\" const maybeRecord = (x, y) => {\\n\",\n+\" if (drawn.length === 0) { drawn.push([x, y]); return; }\\n\",\n+\" const [px, py] = drawn[drawn.length - 1];\\n\",\n+\" const dx = x - px;\\n\",\n+\" const dy = y - py;\\n\",\n+\" if ((dx*dx + dy*dy) >= 9) drawn.push([x, y]);\\n\",\n \" };\\n\",\n \"\\n\",\n-\" const onKey = (e) => {\\n\",\n-\" if (e.key === 'Escape') {\\n\",\n-\" cleanup();\\n\",\n-\" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n-\" }\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" const toCanvasXY = (e) => {\\n\",\n-\" const rect = canvas.getBoundingClientRect();\\n\",\n-\" const x = Math.max(0, Math.min(rect.width, e.clientX - rect.left));\\n\",\n-\" const y = Math.max(0, Math.min(rect.height, e.clientY - rect.top));\\n\",\n-\" return [x, y];\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" const toNaturalXY = (x, y) => {\\n\",\n-\" const scaleX = img.naturalWidth / canvas.width;\\n\",\n-\" const scaleY = img.naturalHeight / canvas.height;\\n\",\n-\" return [Math.round(x * scaleX), Math.round(y * scaleY)];\\n\",\n-\" };\\n\",\n-\"\\n\",\n-\" brush.oninput = setBrush;\\n\",\n-\"\\n\",\n-\" canvas.onmousedown = (e) => {\\n\",\n+\" canvas.onpointerdown = (e) => {\\n\",\n+\" canvas.setPointerCapture(e.pointerId);\\n\",\n \" drawing = true;\\n\",\n-\" const [x, y] = toCanvasXY(e);\\n\",\n-\" setBrush();\\n\",\n-\" ctx.beginPath();\\n\",\n-\" ctx.moveTo(x, y);\\n\",\n+\" [lx, ly] = toCanvas(e);\\n\",\n+\" paintSegment(lx, ly, lx, ly);\\n\",\n+\" maybeRecord(lx, ly);\\n\",\n \" };\\n\",\n \"\\n\",\n-\" canvas.onmousemove = (e) => {\\n\",\n+\" canvas.onpointermove = (e) => {\\n\",\n \" if (!drawing) return;\\n\",\n-\" const [x, y] = toCanvasXY(e);\\n\",\n-\" ctx.lineTo(x, y);\\n\",\n-\" ctx.stroke();\\n\",\n-\" hasInk = true;\\n\",\n+\" const [cx, cy] = toCanvas(e);\\n\",\n+\" paintSegment(lx, ly, cx, cy);\\n\",\n+\" lx = cx;\\n\",\n+\" ly = cy;\\n\",\n+\" maybeRecord(cx, cy);\\n\",\n \" };\\n\",\n \"\\n\",\n \" const stopDraw = () => { drawing = false; };\\n\",\n-\" canvas.onmouseup = stopDraw;\\n\",\n-\" canvas.onmouseleave = stopDraw;\\n\",\n+\" canvas.onpointerup = stopDraw;\\n\",\n+\" canvas.onpointercancel = stopDraw;\\n\",\n \"\\n\",\n \" clearBtn.onclick = () => {\\n\",\n \" ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n-\" hasInk = false;\\n\",\n+\" drawn.length = 0;\\n\",\n \" };\\n\",\n \"\\n\",\n \" let resolvePromise;\\n\",\n \" const promise = new Promise((resolve) => { resolvePromise = resolve; });\\n\",\n \"\\n\",\n+\" const cleanup = () => {\\n\",\n+\" document.removeEventListener('keydown', onKey);\\n\",\n+\" window.removeEventListener('resize', resizeCanvas);\\n\",\n+\" overlay.remove();\\n\",\n+\" };\\n\",\n+\"\\n\",\n+\" const onKey = (e) => {\\n\",\n+\" if (e.key === 'Escape') {\\n\",\n+\" cleanup();\\n\",\n+\" resolvePromise({bbox:[-1,-1,-1,-1], points:[]});\\n\",\n+\" }\\n\",\n+\" };\\n\",\n+\" document.addEventListener('keydown', onKey);\\n\",\n+\" window.addEventListener('resize', resizeCanvas);\\n\",\n+\"\\n\",\n \" doneBtn.onclick = () => {\\n\",\n-\" if (!hasInk) {\\n\",\n+\" if (drawn.length < 1) {\\n\",\n \" label.textContent = 'Paint over the object before pressing Done.';\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n-\" const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\\n\",\n-\" let minX = canvas.width, minY = canvas.height, maxX = -1, maxY = -1;\\n\",\n-\" const pts = [];\\n\",\n-\" const stride = Math.max(3, Math.floor(Math.sqrt((canvas.width * canvas.height) / 1200)));\\n\",\n-\"\\n\",\n-\" for (let y = 0; y < canvas.height; y += 1) {\\n\",\n-\" for (let x = 0; x < canvas.width; x += 1) {\\n\",\n-\" const a = data[(y * canvas.width + x) * 4 + 3];\\n\",\n-\" if (a > 8) {\\n\",\n-\" if (x < minX) minX = x;\\n\",\n-\" if (y < minY) minY = y;\\n\",\n-\" if (x > maxX) maxX = x;\\n\",\n-\" if (y > maxY) maxY = y;\\n\",\n-\" if ((x % stride === 0) && (y % stride === 0)) pts.push([x, y]);\\n\",\n-\" }\\n\",\n-\" }\\n\",\n+\" let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity;\\n\",\n+\" for (const [x, y] of drawn) {\\n\",\n+\" if (x < minX) minX = x;\\n\",\n+\" if (y < minY) minY = y;\\n\",\n+\" if (x > maxX) maxX = x;\\n\",\n+\" if (y > maxY) maxY = y;\\n\",\n \" }\\n\",\n \"\\n\",\n-\" if (maxX < minX || maxY < minY) {\\n\",\n-\" label.textContent = 'No valid brush mask found. Try again.';\\n\",\n-\" return;\\n\",\n-\" }\\n\",\n-\"\\n\",\n-\" const pad = 4;\\n\",\n+\" const pad = Math.max(4, Math.round(Number(brush.value) * 0.8));\\n\",\n \" minX = Math.max(0, minX - pad);\\n\",\n \" minY = Math.max(0, minY - pad);\\n\",\n \" maxX = Math.min(canvas.width - 1, maxX + pad);\\n\",\n \" maxY = Math.min(canvas.height - 1, maxY + pad);\\n\",\n \"\\n\",\n-\" const [nx0, ny0] = toNaturalXY(minX, minY);\\n\",\n-\" const [nx1, ny1] = toNaturalXY(maxX, maxY);\\n\",\n+\" const [nx0, ny0] = toNatural(minX, minY);\\n\",\n+\" const [nx1, ny1] = toNatural(maxX, maxY);\\n\",\n \"\\n\",\n \" const sampled = [];\\n\",\n \" const limit = 16;\\n\",\n-\" const step = Math.max(1, Math.floor(pts.length / limit));\\n\",\n-\" for (let i = 0; i < pts.length && sampled.length < limit; i += step) {\\n\",\n-\" sampled.push(toNaturalXY(pts[i][0], pts[i][1]));\\n\",\n+\" const step = Math.max(1, Math.floor(drawn.length / limit));\\n\",\n+\" for (let i = 0; i < drawn.length && sampled.length < limit; i += step) {\\n\",\n+\" sampled.push(toNatural(drawn[i][0], drawn[i][1]));\\n\",\n \" }\\n\",\n \"\\n\",\n \" cleanup();\\n\",\n \" resolvePromise({bbox:[nx0, ny0, nx1, ny1], points: sampled});\\n\",\n \" };\\n\",\n \"\\n\",\n-\" img.onload = () => { resizeCanvas(); setBrush(); };\\n\",\n-\" if (img.complete) { resizeCanvas(); setBrush(); }\\n\",\n-\" window.addEventListener('resize', resizeCanvas);\\n\",\n-\" document.addEventListener('keydown', onKey);\\n\",\n-\"\\n\",\n \" return await promise;\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n", + "additions": 85, + "deletions": 83 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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<<'PY'\nimport json,ast\nnb=json.load(open('poc_eval_notebook.ipynb'))\nfor i,c in enumerate(nb['cells']):\n if c.get('cell_type')!='code':\n continue\n src=''.join(c.get('source',[]))\n if any(line.lstrip().startswith('%') for line in src.splitlines()):\n continue\n ast.parse(src)\nprint('ok',len(nb['cells']))\nPY", + "workdir": "/home/magnus-bk/repos/material-hunters" + }, + "output": "ok 11\n", + "metadata": { + "output": "ok 11\n", + "exit": 0, + "truncated": false + }, + "title": "python3 -m json.tool \"poc_eval_notebook.ipynb\" >/tmp/opencode/poc_eval_notebook.pretty.json && python3 - <<'PY'\nimport json,ast\nnb=json.load(open('poc_eval_notebook.ipynb'))\nfor i,c in enumerate(nb['cells']):\n if c.get('cell_type')!='code':\n continue\n src=''.join(c.get('source',[]))\n if any(line.lstrip().startswith('%') for line in src.splitlines()):\n continue\n ast.parse(src)\nprint('ok',len(nb['cells']))\nPY", + "time": { + "start": 1783079727539, + "end": 1783079727547 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0f526fd4b1ce4537016a47a32e1f8081a2b3ca13a5a1ffa4a1" + } + }, + "id": "prt_f27d56c620012q2M9FriCPicgn", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d55306002LpvMgwoKEhktEo" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 116785, + "input": 223, + "output": 175, + "reasoning": 35, + "cache": { + "write": 0, + "read": 116352 + } + }, + "cost": 0.02369185, + "id": "prt_f27d571be00121aKkwuazqT4A3", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d55306002LpvMgwoKEhktEo" + } + ] + }, + { + "info": { + "parentID": "msg_f27d35a9c001HbhLUY1EeOUQws", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0257964, + "tokens": { + "total": 117161, + "input": 192, + "output": 335, + "reasoning": 26, + "cache": { + "write": 0, + "read": 116608 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783079727569, + "completed": 1783079733949 + }, + "finish": "stop", + "id": "msg_f27d571d0002Lx2TiVXwXIO3qD", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27d5779f0011yyLnBnbK8IOiA", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d571d0002Lx2TiVXwXIO3qD" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783079729059, + "end": 1783079729741 + }, + "metadata": { + "openai": { + "itemId": "rs_01269556db67175f016a47a330ec348192ae932f0956885dd0", + "reasoningEncryptedContent": "gAAAAABqR6MxH9OqY6slKFCjoHP91G30T-yf2DpbYcaJ8KkiCBy5-UPSOJLfvr0pSkzHIBM0X5kug1cElRVFKZIhnmIwSwD--9e2fMRx1kBlv1eGIIYCWZr1NRD3c6cS4IDOGqbfTtp9BR58DSjtGda__FP8cPJjEgoAx7ZMJ4eqczWFz8RKuOQcfQ0988lmDM9ff_Qfta7iVGlmd_dwlD8du1DAAgFeNMQxHy7e6_-2wy8DlpV6xf_Hv2irfQ71XrrcGV3whFER0N11N6f4RdaGsUuTcuZ7QmfzRXI8c4_nZubnEdLOcLE41GGvgQrU0UWzXIHRNn5fLxa1WK2g6eFZmRVwY2eRJBFTI-x_3P4utpu769hygvG10GY5CS0rXbfIrLxh-xGXOhcif8PtYMnoCTmj0YtPAJM5s4IItxG_XZPeCWLqEqMu1mtD2zc5CiCbfbH_AuxSAjFAi6qBNJUyaZBv9nwRr45GCHoP9DNLBuB1bXYtBNLbF9MOUq41-koT3ivbgbrSDUqUKbBSw_tafN1PEHoK5gtQIYPJY5TFcw9Qyo6X3FR3ScX0AdbFqvAHl7ERIzKMpAFHgaYnf7SG5dVPe58D29W89-PU5TRXq85ej8XeJO34eMZCw0sW2Lun_t43QPmN2BUJjqQdizcgql9RMv-XDBGDdKRxF3UfMNEneO-kzgVGK3Zv0ReTgXyn7JEimkMODGxfc7ZhgndpEHrm26qWsSHXWRqQyQi9vHdbSoT0DYMUUhT0ieVhhtqXZMdiHh6NvcmPGIXcd45LLOaQQiFBeE-wVI4f1u2ptUuXJoCVHYl36CcDwVsvSMdtUMkBUz9eBFYylfJ5r0Is4X4xTp8GxsLKjqXkkPkaQo8ZztD4GnMKimBONNPtAEmGv9-DOdI59zbqmxyR5V2AM13bLtUt9HxwZzKYZZm5X645zwEQz4HhdjB0Jcs3NWByNY8bWMYt0QJxU3YA8n8Ecs1Qnra6XRZq3Hsn1t01HDipAk3S-XSNqBllUE47Srv8f3KKocxm4vK1kwkVHOocaQqv0gG7S5vgkRfIUOhpOujFa088bpinP3v-XorHuZ9yMKpsOFmq" + } + }, + "id": "prt_f27d577a3001uSsBhF4mnjGrDU", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d571d0002Lx2TiVXwXIO3qD" + }, + { + "type": "text", + "text": "You’re right — both issues were real, and I fixed the interaction code in `poc_eval_notebook.ipynb:124`.\n\nWhat I changed:\n\n- **Box tool fixed**\n - Rebuilt box selection to use a dedicated overlay canvas + pointer events (instead of image mouse events).\n - This now supports proper click-drag behavior and avoids the tiny 1–2 px box issue.\n - Added small-box guard (`\"Box too small. Drag a larger box.\"`).\n\n- **Brush tool fixed**\n - Reworked brush hint extraction to use recorded stroke points (not alpha scan of the whole canvas).\n - This prevents the “whole image becomes mask” behavior.\n - Brush now computes bbox from painted trajectory + padding and samples positive points for SAM guidance.\n\n- **Stability improvements**\n - Both tools now wait for image load/layout before enabling input.\n - Both use `pointer` events with better capture behavior on mouse/pen.\n - Kept `Esc` cancel + existing accept/re-draw flow.\n\nWhat to do in Colab now:\n\n1. Re-upload updated `poc_eval_notebook.ipynb`.\n2. Re-run from the function-definition cell onward (or just **Runtime -> Restart and run all**).\n3. Test:\n - `SELECTION_MODE = \"box\"` first,\n - then `SELECTION_MODE = \"brush\"`.\n\nIf it still misbehaves in your browser, I can add a fallback “click two corners” box mode (first click top-left, second click bottom-right), which is very robust in Colab frontends.", + "time": { + "start": 1783079729750, + "end": 1783079733930 + }, + "metadata": { + "openai": { + "itemId": "msg_01269556db67175f016a47a331ac3c8192bafb383aa1c8e0ed", + "phase": "final_answer" + } + }, + "id": "prt_f27d57a56001jmW6s2lyZn1is9", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d571d0002Lx2TiVXwXIO3qD" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 117161, + "input": 192, + "output": 335, + "reasoning": 26, + "cache": { + "write": 0, + "read": 116608 + } + }, + "cost": 0.0257964, + "id": "prt_f27d58ab7001ZQ3ffXKV91XETA", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27d571d0002Lx2TiVXwXIO3qD" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783080627546 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f27e32d5a001ShYztJqQBXJ0K2", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "The bounding box seems to work great. the brush also works now, but the segmentation (object mask) is not good (it often segments the wrong thing). Would it be a good idea to make the brush 'generate' a bounding box? or what is the approach now?", + "id": "prt_f27e32d63001IBbF5phFkHDoTr", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e32d5a001ShYztJqQBXJ0K2" + } + ] + }, + { + "info": { + "parentID": "msg_f27e32d5a001ShYztJqQBXJ0K2", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0928053, + "tokens": { + "total": 117164, + "input": 37918, + "output": 346, + "reasoning": 564, + "cache": { + "write": 0, + "read": 78336 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783080627586, + "completed": 1783080646397 + }, + "finish": "stop", + "id": "msg_f27e32d82002LpwqgQbUMInFU0", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27e336e0001ERTRuNiBvuiPwb", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + 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+ } + }, + "id": "prt_f27e33d3f001nKtahe405aTR2P", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e32d82002LpwqgQbUMInFU0" + }, + { + "type": "text", + "text": "Great signal — and yes, your instinct is right.\n\n`Brush -> bounding box` is a good idea, and in fact that is already what the current brush mode does. \nRight now the flow is effectively:\n\n- brush strokes -> inferred `prompt_bbox_xyxy` + sampled positive points\n- SAM runs with box + positive points\n- we pick the mask with best SAM internal score\n\nWhy it still fails:\n- SAM internal score is not the same as “matches user brush intent”\n- we currently give no negative constraints\n- brush points may include noisy edge points\n\nBest next approach (recommended):\n1. Keep brush as **hint**, not final mask.\n2. Build a simple brush hint mask from strokes.\n3. Ask SAM for multiple candidates.\n4. Select the candidate by **overlap with brush hint** (not only SAM score), with checks:\n - covers most brush points,\n - stays mostly inside prompt box,\n - reasonable area vs box.\n5. Add fallback: if no candidate passes threshold, either re-prompt or use brush mask as temporary fallback.\n\nSo: “make brush generate a box” is good, but the key improvement is **mask selection heuristic guided by brush overlap**.\n\nIf you want, I can implement this as the next small bucket:\n- add brush-overlap scoring + candidate selection,\n- keep existing UI unchanged,\n- print debug metrics per object (`sam_score`, `brush_iou`, selected mask reason).\n\nOne quick decision to lock first: \n- If no SAM candidate matches brush well, should we default to `(A) ask user to redraw` (recommended) or `(B) accept brush mask as final fallback`?", + "time": { + "start": 1783080641545, + "end": 1783080646367 + }, + "metadata": { + "openai": { + "itemId": "msg_01ba0d68bb0a9429016a47a6c17d44819cb4b96d9a5945baee", + "phase": "final_answer" + } + }, + "id": "prt_f27e36409001Gf26DJyVqT5ez0", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e32d82002LpwqgQbUMInFU0" + }, + { + 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+ } + }, + "id": "prt_f27e7b87e0014AlxBmgb4nvGoW", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e7b0ba002C6dYSKBsmX3brt" + }, + { + "type": "text", + "text": "Yes — this is the standard approach for interactive segmentation.\n\nFor this class of problem, teams usually do:\n- user hint input (box / points / scribbles),\n- generate several mask candidates,\n- rank by **agreement with user hints** (not just model confidence),\n- optionally request redraw if confidence/overlap is low.\n\nSo your “brush as hint” idea is exactly right; the key is the **selection logic** after SAM inference.\n\nOn SAM2/SAM3:\n- It can improve quality, but it usually does **not** remove this issue by itself.\n- The main failure mode (wrong object despite good edges) is often prompt ambiguity + candidate selection, not only model version.\n- Expect incremental gains from SAM2, but biggest gains still come from:\n - positive + negative hints,\n - overlap-based candidate ranking,\n - redraw/fallback rules.\n\nRecommended next eval setup:\n- Keep current UI,\n- implement brush-overlap candidate ranking,\n- then A/B compare `SAM1` vs `SAM2` on the same 20–30 objects with:\n - brush-overlap score,\n - segmentation acceptance rate,\n - redraw rate.\n\nIf you want, I can draft the exact scoring formula and acceptance thresholds before implementation.", + "time": { + "start": 1783080929294, + "end": 1783080932976 + }, + "metadata": { + "openai": { + "itemId": "msg_0103a8f1b8d9bf1d016a47a7e0f2e081a188c8c5855c3572e5", + "phase": "final_answer" + } + }, + "id": "prt_f27e7c80e001evNufUVX8OZLn8", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e7b0ba002C6dYSKBsmX3brt" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 117096, + "input": 1428, + "output": 260, + "reasoning": 208, + "cache": { + "write": 0, + "read": 115200 + } + }, + "cost": 0.029211, + "id": "prt_f27e7d6ac001LeD4s2bVpq5SaL", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e7b0ba002C6dYSKBsmX3brt" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783080979545 + }, + "agent": "build", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f27e88c59001WDHGKxybDZKGza", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "Alright, you can implement. But hold off on the A/B comparison for now. We will do that later.", + "id": "prt_f27e88c5d001Io8yewShkMhFNJ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e88c59001WDHGKxybDZKGza" + } + ] + }, + { + "info": { + "parentID": "msg_f27e88c59001WDHGKxybDZKGza", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.056392, + "tokens": { + "total": 116937, + "input": 20696, + "output": 96, + "reasoning": 145, + "cache": { + "write": 0, + "read": 96000 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783080979566, + "completed": 1783080988805 + }, + "finish": "tool-calls", + "id": "msg_f27e88c6e002RJrwlw7z014Qts", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27e892d9001omssydzUyC1SXg", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e88c6e002RJrwlw7z014Qts" + }, + { + "type": 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x0:x1])\\n\",\n632: \"\\n\",\n633: \"\\n\",\n634: \"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n635: \" prompts = []\\n\",\n636: \" prompt_materials = []\\n\",\n637: \" for material, aliases in material_taxonomy.items():\\n\",\n638: \" for alias in aliases:\\n\",\n639: \" for template in prompt_templates:\\n\",\n640: \" prompts.append(template.format(material=alias))\\n\",\n641: \" prompt_materials.append(material)\\n\",\n642: \" return prompts, prompt_materials\\n\",\n643: \"\\n\",\n644: \"\\n\",\n645: \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n646: \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n647: \"\\n\",\n648: \"\\n\",\n649: \"def resolve_candidate_materials(object_label: str | None):\\n\",\n650: \" if not object_label:\\n\",\n651: \" return ALL_CANONICAL_MATERIALS\\n\",\n652: \" key = object_label.strip().lower()\\n\",\n653: \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n654: \" if len(candidates) < 2:\\n\",\n655: \" return ALL_CANONICAL_MATERIALS\\n\",\n656: \" return candidates\\n\",\n657: \"\\n\",\n658: \"\\n\",\n659: \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n660: \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n661: \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n662: \"\\n\",\n663: \"\\n\",\n664: \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n665: \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n666: \" if prompt_points_xy:\\n\",\n667: \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n668: \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n669: \" inputs = sam_processor(\\n\",\n670: \" image,\\n\",\n671: \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n672: \" input_points=pts,\\n\",\n673: \" input_labels=labels,\\n\",\n674: \" return_tensors=\\\"pt\\\",\\n\",\n675: \" )\\n\",\n676: \" else:\\n\",\n677: \" inputs = sam_processor(\\n\",\n678: \" image,\\n\",\n679: \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n680: \" return_tensors=\\\"pt\\\",\\n\",\n681: \" )\\n\",\n682: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n683: \"\\n\",\n684: \" with torch.no_grad():\\n\",\n685: \" outputs = sam_model(**inputs)\\n\",\n686: \"\\n\",\n687: \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n688: \" outputs.pred_masks.cpu(),\\n\",\n689: \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n690: \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n691: \" )\\n\",\n692: \"\\n\",\n693: \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n694: \" best_idx = int(np.argmax(scores))\\n\",\n695: \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n696: \" bbox = mask_bbox_xywh(mask)\\n\",\n697: \"\\n\",\n698: \" return {\\n\",\n699: \" \\\"mask\\\": mask,\\n\",\n700: \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n701: \" \\\"bbox_xywh\\\": bbox,\\n\",\n702: \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n703: \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n704: \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n705: \" }\\n\",\n706: \"\\n\",\n707: \"\\n\",\n708: \"def classify_material_sigclip(\\n\",\n709: \" image_views: list,\\n\",\n710: \" object_label: str | None,\\n\",\n711: \" sigclip_model,\\n\",\n712: \" sigclip_processor,\\n\",\n713: \" top_k: int = 3,\\n\",\n714: \" view_weights: list | None = None,\\n\",\n715: \"):\\n\",\n716: \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n717: \"\\n\",\n718: \" selected_idx = [\\n\",\n719: \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n720: \" ]\\n\",\n721: \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n722: \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n723: \"\\n\",\n724: \" if not selected_prompts:\\n\",\n725: \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n726: \"\\n\",\n727: \" if view_weights is None:\\n\",\n728: \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n729: \" if len(view_weights) != len(image_views):\\n\",\n730: \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n731: \"\\n\",\n732: \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n733: \"\\n\",\n734: \" for image, weight in zip(image_views, view_weights):\\n\",\n735: \" inputs = sigclip_processor(\\n\",\n736: \" text=selected_prompts,\\n\",\n737: \" images=image,\\n\",\n738: \" return_tensors=\\\"pt\\\",\\n\",\n739: \" padding=\\\"max_length\\\",\\n\",\n740: \" )\\n\",\n741: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n742: \"\\n\",\n743: \" with torch.no_grad():\\n\",\n744: \" outputs = sigclip_model(**inputs)\\n\",\n745: \"\\n\",\n746: \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n747: \"\\n\",\n748: \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n749: \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n750: \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n751: \"\\n\",\n752: \" for m in candidate_materials:\\n\",\n753: \" agg_scores[m] += weight * per_material_max[m]\\n\",\n754: \"\\n\",\n755: \" total = sum(agg_scores.values())\\n\",\n756: \" if total > 0:\\n\",\n757: \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n758: \"\\n\",\n759: \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n760: \" top_k = min(top_k, len(ranked))\\n\",\n761: \" top_items = ranked[:top_k]\\n\",\n762: \"\\n\",\n763: \" top_labels = [x[0] for x in top_items]\\n\",\n764: \" top_scores = [x[1] for x in top_items]\\n\",\n765: \"\\n\",\n766: \" return {\\n\",\n767: \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n768: \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n769: \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n770: \" \\\"topk_labels\\\": top_labels,\\n\",\n771: \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n772: \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n773: \" }\\n\",\n774: \"\\n\",\n775: \"\\n\",\n776: \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n777: \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n778: \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n779: \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n780: \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n781: \" return out_path\\n\",\n782: \"\\n\",\n783: \"\\n\",\n784: \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n785: \" df_new = pd.DataFrame([row])\\n\",\n786: \" if not csv_path.exists():\\n\",\n787: \" df_new.to_csv(csv_path, index=False)\\n\",\n788: \" return\\n\",\n789: \" df_existing = pd.read_csv(csv_path)\\n\",\n790: \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n791: \" df_out.to_csv(csv_path, index=False)\"\n792: ]\n793: },\n794: {\n795: \"cell_type\": \"code\",\n796: \"execution_count\": null,\n797: \"metadata\": {},\n798: \"outputs\": [],\n799: \"source\": [\n\n(Showing lines 620-799 of 1287. Use offset=800 to continue.)\n</content>", + "metadata": { + "preview": " \" return Image.fromarray(bg)\\n\",\n \"\\n\",\n \"\\n\",\n \"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n \" arr = np.array(image)\\n\",\n \" h, w = arr.shape[:2]\\n\",\n \" x, y, bw, bh = bbox_xywh\\n\",\n \" x0 = max(0, x - pad)\\n\",\n \" y0 = max(0, y - pad)\\n\",\n \" x1 = min(w, x + bw + pad)\\n\",\n \" y1 = min(h, y + bh + pad)\\n\",\n \" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n \"\\n\",\n \"\\n\",\n \"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n \" prompts = []\\n\",\n \" prompt_materials = []\\n\",\n \" for material, aliases in material_taxonomy.items():\\n\",\n \" for alias in aliases:\\n\",\n \" for template in prompt_templates:\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" return Image.fromarray(bg)\\n\",\n \"\\n\",\n \"\\n\",\n \"def crop_bbox_rgb(image: Image.Image, bbox_xywh, pad: int = 8) -> Image.Image:\\n\",\n \" arr = np.array(image)\\n\",\n \" h, w = arr.shape[:2]\\n\",\n \" x, y, bw, bh = bbox_xywh\\n\",\n \" x0 = max(0, x - pad)\\n\",\n \" y0 = max(0, y - pad)\\n\",\n \" x1 = min(w, x + bw + pad)\\n\",\n \" y1 = min(h, y + bh + pad)\\n\",\n \" return Image.fromarray(arr[y0:y1, x0:x1])\\n\",\n \"\\n\",\n \"\\n\",\n \"def build_material_prompt_table(material_taxonomy: dict, prompt_templates: list):\\n\",\n \" prompts = []\\n\",\n \" prompt_materials = []\\n\",\n \" for material, aliases in material_taxonomy.items():\\n\",\n \" for alias in aliases:\\n\",\n \" for template in prompt_templates:\\n\",\n \" prompts.append(template.format(material=alias))\\n\",\n \" prompt_materials.append(material)\\n\",\n \" return prompts, prompt_materials\\n\",\n \"\\n\",\n \"\\n\",\n \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n \"\\n\",\n \"\\n\",\n \"def resolve_candidate_materials(object_label: str | None):\\n\",\n \" if not object_label:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" key = object_label.strip().lower()\\n\",\n \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n \" if len(candidates) < 2:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" return candidates\\n\",\n \"\\n\",\n \"\\n\",\n \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n \" if prompt_points_xy:\\n\",\n \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" input_points=pts,\\n\",\n \" input_labels=labels,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sam_model(**inputs)\\n\",\n \"\\n\",\n \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n \" outputs.pred_masks.cpu(),\\n\",\n \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n \" )\\n\",\n \"\\n\",\n \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n \" best_idx = int(np.argmax(scores))\\n\",\n \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n \" bbox = mask_bbox_xywh(mask)\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def classify_material_sigclip(\\n\",\n \" image_views: list,\\n\",\n \" object_label: str | None,\\n\",\n \" sigclip_model,\\n\",\n \" sigclip_processor,\\n\",\n \" top_k: int = 3,\\n\",\n \" view_weights: list | None = None,\\n\",\n \"):\\n\",\n \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n \"\\n\",\n \" selected_idx = [\\n\",\n \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n \" ]\\n\",\n \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n \"\\n\",\n \" if not selected_prompts:\\n\",\n \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n \"\\n\",\n \" if view_weights is None:\\n\",\n \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n \" if len(view_weights) != len(image_views):\\n\",\n \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n \"\\n\",\n \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n \"\\n\",\n \" for image, weight in zip(image_views, view_weights):\\n\",\n \" inputs = sigclip_processor(\\n\",\n \" text=selected_prompts,\\n\",\n \" images=image,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" padding=\\\"max_length\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sigclip_model(**inputs)\\n\",\n \"\\n\",\n \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n \"\\n\",\n \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n \"\\n\",\n \" for m in candidate_materials:\\n\",\n \" agg_scores[m] += weight * per_material_max[m]\\n\",\n \"\\n\",\n \" total = sum(agg_scores.values())\\n\",\n \" if total > 0:\\n\",\n \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n \"\\n\",\n \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n \" top_k = min(top_k, len(ranked))\\n\",\n \" top_items = ranked[:top_k]\\n\",\n \"\\n\",\n \" top_labels = [x[0] for x in top_items]\\n\",\n \" top_scores = [x[1] for x in top_items]\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n \" out_path.write_text(json.dumps(passport, indent=2), encoding=\\\"utf-8\\\")\\n\",\n \" return out_path\\n\",\n \"\\n\",\n \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n \" df_out.to_csv(csv_path, index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [", + "lineStart": 620, + "lineEnd": 799, + "totalLines": 1287, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783080992809, + "end": 1783080992842 + } + }, + "metadata": { + "openai": { + "itemId": "fc_00089fbc98176d6a016a47a8200bb081a0988c6532fc057b2f" + } + }, + "id": "prt_f27e8bd88001YCAXvRRJwHugk7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e8b093002H2py81vIrrxEJY" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 117135, + "input": 200, + "output": 47, + "reasoning": 24, + "cache": { + "write": 0, + "read": 116864 + } + }, + "cost": 0.0217952, + "id": "prt_f27e8c1ab001aj7ebR6t4w368V", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e8b093002H2py81vIrrxEJY" + } + ] + }, + { + "info": { + "parentID": "msg_f27e88c59001WDHGKxybDZKGza", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.02697485, + "tokens": { + "total": 119965, + "input": 2867, + "output": 47, + "reasoning": 59, + "cache": { + "write": 0, + "read": 116992 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783080993218, + "completed": 1783080999392 + }, + "finish": "tool-calls", + "id": "msg_f27e8c1c200294YnsO0qmyzQHW", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27e8cb230013ij0Mn5Y2ZHMG6", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e8c1c200294YnsO0qmyzQHW" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783080997779, + "end": 1783080997912 + }, + "metadata": { + "openai": { + "itemId": 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+ } + }, + "id": "prt_f27e8d393001LF3hs3CifpJtgd", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e8c1c200294YnsO0qmyzQHW" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_h8z7uHBPy6uwHmvCRs48ouGr", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 930, + "limit": 170 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n930: \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n931: \"\\n\",\n932: \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id} (mode={SELECTION_MODE}).\\\")\\n\",\n933: \"\\n\",\n934: \"plt.figure(figsize=(9, 7))\\n\",\n935: \"plt.imshow(img)\\n\",\n936: \"ax = plt.gca()\\n\",\n937: \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n938: \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n939: \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n940: \" ax.add_patch(rect)\\n\",\n941: \" for p in obj.get(\\\"prompt_points_xy\\\", []):\\n\",\n942: \" ax.scatter([p[0]], [p[1]], c=\\\"cyan\\\", s=12)\\n\",\n943: \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n944: \"plt.title(\\\"Prompt preview (bbox + optional brush points)\\\")\\n\",\n945: \"plt.axis(\\\"off\\\")\\n\",\n946: \"plt.show()\"\n947: ]\n948: },\n949: {\n950: \"cell_type\": \"code\",\n951: \"execution_count\": null,\n952: \"metadata\": {},\n953: \"outputs\": [],\n954: \"source\": [\n955: \"RUN_ROWS = []\\n\",\n956: \"\\n\",\n957: \"for obj in OBJECT_RECORDS:\\n\",\n958: \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n959: \" prompt_bbox_xyxy = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n960: \" prompt_points_xy = [[int(p[0]), int(p[1])] for p in obj.get(\\\"prompt_points_xy\\\", [])]\\n\",\n961: \" tap_x, tap_y = bbox_xyxy_to_center_xy(prompt_bbox_xyxy)\\n\",\n962: \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n963: \"\\n\",\n964: \" t0 = pd.Timestamp.utcnow()\\n\",\n965: \"\\n\",\n966: \" seg_start = pd.Timestamp.utcnow()\\n\",\n967: \" seg = run_sam_guided_segmentation(\\n\",\n968: \" image=img,\\n\",\n969: \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n970: \" prompt_points_xy=prompt_points_xy,\\n\",\n971: \" sam_model=sam_model,\\n\",\n972: \" sam_processor=sam_processor,\\n\",\n973: \" )\\n\",\n974: \" seg_end = pd.Timestamp.utcnow()\\n\",\n975: \"\\n\",\n976: \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n977: \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n978: \"\\n\",\n979: \" clf_start = pd.Timestamp.utcnow()\\n\",\n980: \" clf = classify_material_sigclip(\\n\",\n981: \" image_views=[crop_masked, crop_bbox, img],\\n\",\n982: \" object_label=pred_object,\\n\",\n983: \" sigclip_model=sigclip_model,\\n\",\n984: \" sigclip_processor=sigclip_processor,\\n\",\n985: \" top_k=3,\\n\",\n986: \" view_weights=[0.5, 0.35, 0.15],\\n\",\n987: \" )\\n\",\n988: \" clf_end = pd.Timestamp.utcnow()\\n\",\n989: \"\\n\",\n990: \" t1 = pd.Timestamp.utcnow()\\n\",\n991: \"\\n\",\n992: \" segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n993: \" classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n994: \" end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n995: \"\\n\",\n996: \" RUN_ROWS.append({\\n\",\n997: \" \\\"sample_id\\\": sample_id,\\n\",\n998: \" \\\"image_id\\\": image_id,\\n\",\n999: \" \\\"image_path\\\": image_path,\\n\",\n1000: \" \\\"prompt_bbox_xyxy\\\": prompt_bbox_xyxy,\\n\",\n1001: \" \\\"prompt_points_xy\\\": prompt_points_xy,\\n\",\n1002: \" \\\"tap_x\\\": tap_x,\\n\",\n1003: \" \\\"tap_y\\\": tap_y,\\n\",\n1004: \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n1005: \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n1006: \" \\\"pred_object\\\": pred_object,\\n\",\n1007: \" \\\"seg\\\": seg,\\n\",\n1008: \" \\\"clf\\\": clf,\\n\",\n1009: \" \\\"crop_masked\\\": crop_masked,\\n\",\n1010: \" \\\"timing\\\": {\\n\",\n1011: \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n1012: \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n1013: \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n1014: \" },\\n\",\n1015: \" })\\n\",\n1016: \"\\n\",\n1017: \" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n1018: \"\\n\",\n1019: \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n1020: \" axes[0].imshow(img)\\n\",\n1021: \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n1022: \" px0, py0, px1, py1 = prompt_bbox_xyxy\\n\",\n1023: \" prompt_rect = patches.Rectangle((px0, py0), px1 - px0, py1 - py0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n1024: \" axes[0].add_patch(prompt_rect)\\n\",\n1025: \" if prompt_points_xy:\\n\",\n1026: \" xs = [p[0] for p in prompt_points_xy]\\n\",\n1027: \" ys = [p[1] for p in prompt_points_xy]\\n\",\n1028: \" axes[0].scatter(xs, ys, c=\\\"lime\\\", s=10)\\n\",\n1029: \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n1030: \" axes[0].axis(\\\"off\\\")\\n\",\n1031: \"\\n\",\n1032: \" axes[1].imshow(crop_masked)\\n\",\n1033: \" axes[1].set_title(f\\\"Masked Crop ({sample_id})\\\")\\n\",\n1034: \" axes[1].axis(\\\"off\\\")\\n\",\n1035: \" plt.tight_layout()\\n\",\n1036: \" plt.show()\\n\",\n1037: \"\\n\",\n1038: \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n1039: ]\n1040: },\n1041: {\n1042: \"cell_type\": \"code\",\n1043: \"execution_count\": null,\n1044: \"metadata\": {},\n1045: \"outputs\": [],\n1046: \"source\": [\n1047: \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n1048: \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n1049: \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n1050: \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n1051: \"\\n\",\n1052: \"# Example override format:\\n\",\n1053: \"# USER_REVIEW_OVERRIDES = {\\n\",\n1054: \"# \\\"S003\\\": {\\n\",\n1055: \"# \\\"segmentation_accepted\\\": 0,\\n\",\n1056: \"# \\\"user_edited_material\\\": 1,\\n\",\n1057: \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n1058: \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n1059: \"# \\\"status\\\": \\\"ok\\\",\\n\",\n1060: \"# \\\"error_type\\\": \\\"\\\",\\n\",\n1061: \"# }\\n\",\n1062: \"# }\\n\",\n1063: \"USER_REVIEW_OVERRIDES = {}\\n\",\n1064: \"\\n\",\n1065: \"for run in RUN_ROWS:\\n\",\n1066: \" sid = run[\\\"sample_id\\\"]\\n\",\n1067: \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n1068: \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n1069: \"\\n\",\n1070: \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n1071: \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n1072: \"\\n\",\n1073: \" run[\\\"review\\\"] = {\\n\",\n1074: \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n1075: \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n1076: \" \\\"user_final_material\\\": user_final_material,\\n\",\n1077: \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n1078: \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n1079: \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n1080: \" }\\n\",\n1081: \"\\n\",\n1082: \"pd.DataFrame([\\n\",\n1083: \" {\\n\",\n1084: \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n1085: \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n1086: \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n1087: \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n1088: \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n1089: \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n1090: \" }\\n\",\n1091: \" for r in RUN_ROWS\\n\",\n1092: \"])\"\n1093: ]\n1094: },\n1095: {\n1096: \"cell_type\": \"code\",\n1097: \"execution_count\": null,\n1098: \"metadata\": {},\n1099: \"outputs\": [],\n\n(Showing lines 930-1099 of 1287. Use offset=1100 to continue.)\n</content>", + "metadata": { + "preview": " \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id} (mode={SELECTION_MODE}).\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n \"ax = plt.gca()\\n\",\n \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n \" ax.add_patch(rect)\\n\",\n \" for p in obj.get(\\\"prompt_points_xy\\\", []):\\n\",\n \" ax.scatter([p[0]], [p[1]], c=\\\"cyan\\\", s=12)\\n\",\n \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n \"plt.title(\\\"Prompt preview (bbox + optional brush points)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" obj.setdefault(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \"print(f\\\"Prepared {len(OBJECT_RECORDS)} object(s) for image {image_id} (mode={SELECTION_MODE}).\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 7))\\n\",\n \"plt.imshow(img)\\n\",\n \"ax = plt.gca()\\n\",\n \"for idx, obj in enumerate(OBJECT_RECORDS, start=1):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" rect = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=2, edgecolor=\\\"red\\\", facecolor=\\\"none\\\")\\n\",\n \" ax.add_patch(rect)\\n\",\n \" for p in obj.get(\\\"prompt_points_xy\\\", []):\\n\",\n \" ax.scatter([p[0]], [p[1]], c=\\\"cyan\\\", s=12)\\n\",\n \" ax.text(x0 + 4, y0 + 14, f\\\"{idx}:{obj['sample_id']}\\\", color=\\\"yellow\\\", fontsize=9, bbox={\\\"facecolor\\\": \\\"black\\\", \\\"alpha\\\": 0.4, \\\"pad\\\": 2})\\n\",\n \"plt.title(\\\"Prompt preview (bbox + optional brush points)\\\")\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"RUN_ROWS = []\\n\",\n \"\\n\",\n \"for obj in OBJECT_RECORDS:\\n\",\n \" sample_id = obj[\\\"sample_id\\\"]\\n\",\n \" prompt_bbox_xyxy = [int(v) for v in obj[\\\"prompt_bbox_xyxy\\\"]]\\n\",\n \" prompt_points_xy = [[int(p[0]), int(p[1])] for p in obj.get(\\\"prompt_points_xy\\\", [])]\\n\",\n \" tap_x, tap_y = bbox_xyxy_to_center_xy(prompt_bbox_xyxy)\\n\",\n \" pred_object = obj.get(\\\"pred_object\\\", obj.get(\\\"ground_truth_object\\\", \\\"\\\"))\\n\",\n \"\\n\",\n \" t0 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" seg_start = pd.Timestamp.utcnow()\\n\",\n \" seg = run_sam_guided_segmentation(\\n\",\n \" image=img,\\n\",\n \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n \" prompt_points_xy=prompt_points_xy,\\n\",\n \" sam_model=sam_model,\\n\",\n \" sam_processor=sam_processor,\\n\",\n \" )\\n\",\n \" seg_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n \"\\n\",\n \" clf_start = pd.Timestamp.utcnow()\\n\",\n \" clf = classify_material_sigclip(\\n\",\n \" image_views=[crop_masked, crop_bbox, img],\\n\",\n \" object_label=pred_object,\\n\",\n \" sigclip_model=sigclip_model,\\n\",\n \" sigclip_processor=sigclip_processor,\\n\",\n \" top_k=3,\\n\",\n \" view_weights=[0.5, 0.35, 0.15],\\n\",\n \" )\\n\",\n \" clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" segmentation_time_sec = (seg_end - seg_start).total_seconds()\\n\",\n \" classification_time_sec = (clf_end - clf_start).total_seconds()\\n\",\n \" end_to_end_time_sec = (t1 - t0).total_seconds()\\n\",\n \"\\n\",\n \" RUN_ROWS.append({\\n\",\n \" \\\"sample_id\\\": sample_id,\\n\",\n \" \\\"image_id\\\": image_id,\\n\",\n \" \\\"image_path\\\": image_path,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": prompt_bbox_xyxy,\\n\",\n \" \\\"prompt_points_xy\\\": prompt_points_xy,\\n\",\n \" \\\"tap_x\\\": tap_x,\\n\",\n \" \\\"tap_y\\\": tap_y,\\n\",\n \" \\\"ground_truth_object\\\": obj.get(\\\"ground_truth_object\\\", \\\"\\\"),\\n\",\n \" \\\"ground_truth_material\\\": obj.get(\\\"ground_truth_material\\\", \\\"\\\"),\\n\",\n \" \\\"pred_object\\\": pred_object,\\n\",\n \" \\\"seg\\\": seg,\\n\",\n \" \\\"clf\\\": clf,\\n\",\n \" \\\"crop_masked\\\": crop_masked,\\n\",\n \" \\\"timing\\\": {\\n\",\n \" \\\"segmentation_time_sec\\\": segmentation_time_sec,\\n\",\n \" \\\"classification_time_sec\\\": classification_time_sec,\\n\",\n \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n \" },\\n\",\n \" })\\n\",\n \"\\n\",\n \" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n \"\\n\",\n \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \" axes[0].imshow(img)\\n\",\n \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n \" px0, py0, px1, py1 = prompt_bbox_xyxy\\n\",\n \" prompt_rect = patches.Rectangle((px0, py0), px1 - px0, py1 - py0, linewidth=2, edgecolor=\\\"cyan\\\", facecolor=\\\"none\\\")\\n\",\n \" axes[0].add_patch(prompt_rect)\\n\",\n \" if prompt_points_xy:\\n\",\n \" xs = [p[0] for p in prompt_points_xy]\\n\",\n \" ys = [p[1] for p in prompt_points_xy]\\n\",\n \" axes[0].scatter(xs, ys, c=\\\"lime\\\", s=10)\\n\",\n \" axes[0].set_title(f\\\"Segmentation Overlay ({sample_id})\\\")\\n\",\n \" axes[0].axis(\\\"off\\\")\\n\",\n \"\\n\",\n \" axes[1].imshow(crop_masked)\\n\",\n \" axes[1].set_title(f\\\"Masked Crop ({sample_id})\\\")\\n\",\n \" axes[1].axis(\\\"off\\\")\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n \"\\n\",\n \"# Example override format:\\n\",\n \"# USER_REVIEW_OVERRIDES = {\\n\",\n \"# \\\"S003\\\": {\\n\",\n \"# \\\"segmentation_accepted\\\": 0,\\n\",\n \"# \\\"user_edited_material\\\": 1,\\n\",\n \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n \"# \\\"status\\\": \\\"ok\\\",\\n\",\n \"# \\\"error_type\\\": \\\"\\\",\\n\",\n \"# }\\n\",\n \"# }\\n\",\n \"USER_REVIEW_OVERRIDES = {}\\n\",\n \"\\n\",\n \"for run in RUN_ROWS:\\n\",\n \" sid = run[\\\"sample_id\\\"]\\n\",\n \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n \"\\n\",\n \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n \"\\n\",\n \" run[\\\"review\\\"] = {\\n\",\n \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n \" \\\"user_final_material\\\": user_final_material,\\n\",\n \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n \" }\\n\",\n \"\\n\",\n \"pd.DataFrame([\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n \" }\\n\",\n \" for r in RUN_ROWS\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],", + "lineStart": 930, + "lineEnd": 1099, + "totalLines": 1287, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783080998578, + "end": 1783080998599 + } + }, + "metadata": { + "openai": { + "itemId": "fc_033a46a102c6eb58016a47a825595c819c93c337e8aef5c323" + } + }, + "id": "prt_f27e8d41c001zDL2dkAyLKqLfN", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e8c1c200294YnsO0qmyzQHW" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 119965, + "input": 2867, + "output": 47, + "reasoning": 59, + "cache": { + "write": 0, + "read": 116992 + } + }, + "cost": 0.02697485, + "id": 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+ } + }, + "id": "prt_f27e8e9aa001PRwrHiaFtLP8M8", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27e8d9fa002Oi5qzwBZVI5JOZ" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_48D77oZ0Oe8fe6kQ2jYVSnKc", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n- \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n- \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n- \" if prompt_points_xy:\\n\",\n- \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n- \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n- \" inputs = sam_processor(\\n\",\n- \" image,\\n\",\n- \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n- \" input_points=pts,\\n\",\n- \" input_labels=labels,\\n\",\n- \" return_tensors=\\\"pt\\\",\\n\",\n- \" )\\n\",\n- \" else:\\n\",\n- \" inputs = sam_processor(\\n\",\n- \" image,\\n\",\n- \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n- \" return_tensors=\\\"pt\\\",\\n\",\n- \" )\\n\",\n- \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n- \"\\n\",\n- \" with torch.no_grad():\\n\",\n- \" outputs = sam_model(**inputs)\\n\",\n- \"\\n\",\n- \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n- \" outputs.pred_masks.cpu(),\\n\",\n- \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n- \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n- \" best_idx = int(np.argmax(scores))\\n\",\n- \" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n- \" bbox = mask_bbox_xywh(mask)\\n\",\n- \"\\n\",\n- \" return {\\n\",\n- \" \\\"mask\\\": mask,\\n\",\n- \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n- \" \\\"bbox_xywh\\\": bbox,\\n\",\n- \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n- \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n- \" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n- \" }\\n\",\n+ \"def build_brush_hint_mask(image_shape_hw, prompt_bbox_xyxy, prompt_points_xy, point_radius_px: int = 16):\\n\",\n+ \" h, w = image_shape_hw\\n\",\n+ \" hint = np.zeros((h, w), dtype=bool)\\n\",\n+ \"\\n\",\n+ \" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n+ \" x0 = max(0, min(w - 1, x0))\\n\",\n+ \" y0 = max(0, min(h - 1, y0))\\n\",\n+ \" x1 = max(0, min(w - 1, x1))\\n\",\n+ \" y1 = max(0, min(h - 1, y1))\\n\",\n+ \"\\n\",\n+ \" if x1 > x0 and y1 > y0:\\n\",\n+ \" hint[y0:y1, x0:x1] = True\\n\",\n+ \"\\n\",\n+ \" if not prompt_points_xy:\\n\",\n+ \" return hint\\n\",\n+ \"\\n\",\n+ \" yy, xx = np.ogrid[:h, :w]\\n\",\n+ \" for px, py in prompt_points_xy:\\n\",\n+ \" px = int(max(0, min(w - 1, px)))\\n\",\n+ \" py = int(max(0, min(h - 1, py)))\\n\",\n+ \" disk = (xx - px) ** 2 + (yy - py) ** 2 <= point_radius_px ** 2\\n\",\n+ \" hint |= disk\\n\",\n+ \"\\n\",\n+ \" return hint\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def score_sam_candidate(mask, sam_score, prompt_bbox_xyxy, prompt_points_xy, hint_mask):\\n\",\n+ \" mask = mask.astype(bool)\\n\",\n+ \" mask_area = int(mask.sum())\\n\",\n+ \"\\n\",\n+ \" if mask_area == 0:\\n\",\n+ \" return {\\n\",\n+ \" \\\"total\\\": -1.0,\\n\",\n+ \" \\\"sam_score\\\": float(sam_score),\\n\",\n+ \" \\\"point_coverage\\\": 0.0,\\n\",\n+ \" \\\"hint_iou\\\": 0.0,\\n\",\n+ \" \\\"inside_box_ratio\\\": 0.0,\\n\",\n+ \" \\\"mask_area\\\": 0,\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" h, w = mask.shape\\n\",\n+ \" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n+ \" x0 = max(0, min(w - 1, x0))\\n\",\n+ \" y0 = max(0, min(h - 1, y0))\\n\",\n+ \" x1 = max(0, min(w, x1))\\n\",\n+ \" y1 = max(0, min(h, y1))\\n\",\n+ \"\\n\",\n+ \" box_mask = np.zeros_like(mask)\\n\",\n+ \" if x1 > x0 and y1 > y0:\\n\",\n+ \" box_mask[y0:y1, x0:x1] = True\\n\",\n+ \"\\n\",\n+ \" inside_box_ratio = float((mask & box_mask).sum() / max(mask_area, 1))\\n\",\n+ \"\\n\",\n+ \" if prompt_points_xy:\\n\",\n+ \" hits = 0\\n\",\n+ \" for px, py in prompt_points_xy:\\n\",\n+ \" if 0 <= int(py) < h and 0 <= int(px) < w and mask[int(py), int(px)]:\\n\",\n+ \" hits += 1\\n\",\n+ \" point_coverage = float(hits / len(prompt_points_xy))\\n\",\n+ \" else:\\n\",\n+ \" point_coverage = 0.0\\n\",\n+ \"\\n\",\n+ \" if hint_mask is not None:\\n\",\n+ \" inter = float((mask & hint_mask).sum())\\n\",\n+ \" union = float((mask | hint_mask).sum())\\n\",\n+ \" hint_iou = inter / max(union, 1.0)\\n\",\n+ \" else:\\n\",\n+ \" hint_iou = 0.0\\n\",\n+ \"\\n\",\n+ \" if prompt_points_xy:\\n\",\n+ \" total = 0.50 * hint_iou + 0.30 * point_coverage + 0.10 * inside_box_ratio + 0.10 * float(sam_score)\\n\",\n+ \" else:\\n\",\n+ \" total = 0.80 * float(sam_score) + 0.20 * inside_box_ratio\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"total\\\": float(total),\\n\",\n+ \" \\\"sam_score\\\": float(sam_score),\\n\",\n+ \" \\\"point_coverage\\\": float(point_coverage),\\n\",\n+ \" \\\"hint_iou\\\": float(hint_iou),\\n\",\n+ \" \\\"inside_box_ratio\\\": float(inside_box_ratio),\\n\",\n+ \" \\\"mask_area\\\": int(mask_area),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n+ \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n+ \" if prompt_points_xy:\\n\",\n+ \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n+ \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n+ \" inputs = sam_processor(\\n\",\n+ \" image,\\n\",\n+ \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n+ \" input_points=pts,\\n\",\n+ \" input_labels=labels,\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" )\\n\",\n+ \" else:\\n\",\n+ \" inputs = sam_processor(\\n\",\n+ \" image,\\n\",\n+ \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n+ \" return_tensors=\\\"pt\\\",\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \"\\n\",\n+ \" with torch.no_grad():\\n\",\n+ \" outputs = sam_model(**inputs)\\n\",\n+ \"\\n\",\n+ \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n+ \" outputs.pred_masks.cpu(),\\n\",\n+ \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n+ \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n+ \" )\\n\",\n+ \"\\n\",\n+ \" sam_scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n+ \" candidate_masks = masks[0][0].numpy().astype(bool)\\n\",\n+ \"\\n\",\n+ \" img_arr = np.array(image)\\n\",\n+ \" hint_mask = None\\n\",\n+ \" if prompt_points_xy:\\n\",\n+ \" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n+ \"\\n\",\n+ \" candidate_debug = []\\n\",\n+ \" for idx in range(candidate_masks.shape[0]):\\n\",\n+ \" metrics = score_sam_candidate(\\n\",\n+ \" mask=candidate_masks[idx],\\n\",\n+ \" sam_score=float(sam_scores[idx]),\\n\",\n+ \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n+ \" prompt_points_xy=prompt_points_xy,\\n\",\n+ \" hint_mask=hint_mask,\\n\",\n+ \" )\\n\",\n+ \" metrics[\\\"candidate_idx\\\"] = int(idx)\\n\",\n+ \" candidate_debug.append(metrics)\\n\",\n+ \"\\n\",\n+ \" best = max(candidate_debug, key=lambda d: d[\\\"total\\\"])\\n\",\n+ \" best_idx = int(best[\\\"candidate_idx\\\"])\\n\",\n+ \" mask = candidate_masks[best_idx]\\n\",\n+ \" bbox = mask_bbox_xywh(mask)\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"mask\\\": mask,\\n\",\n+ \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n+ \" \\\"bbox_xywh\\\": bbox,\\n\",\n+ \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n+ \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n+ \" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n+ \" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n+ \" \\\"selection_metrics\\\": {\\n\",\n+ \" \\\"point_coverage\\\": float(best[\\\"point_coverage\\\"]),\\n\",\n+ \" \\\"hint_iou\\\": float(best[\\\"hint_iou\\\"]),\\n\",\n+ \" \\\"inside_box_ratio\\\": float(best[\\\"inside_box_ratio\\\"]),\\n\",\n+ \" },\\n\",\n+ \" \\\"sam_candidates\\\": candidate_debug,\\n\",\n+ \" }\\n\",\n \"\\n\",\n \"\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -660,8 +660,92 @@\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def build_brush_hint_mask(image_shape_hw, prompt_bbox_xyxy, prompt_points_xy, point_radius_px: int = 16):\\n\",\n+\" h, w = image_shape_hw\\n\",\n+\" hint = np.zeros((h, w), dtype=bool)\\n\",\n+\"\\n\",\n+\" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n+\" x0 = max(0, min(w - 1, x0))\\n\",\n+\" y0 = max(0, min(h - 1, y0))\\n\",\n+\" x1 = max(0, min(w - 1, x1))\\n\",\n+\" y1 = max(0, min(h - 1, y1))\\n\",\n+\"\\n\",\n+\" if x1 > x0 and y1 > y0:\\n\",\n+\" hint[y0:y1, x0:x1] = True\\n\",\n+\"\\n\",\n+\" if not prompt_points_xy:\\n\",\n+\" return hint\\n\",\n+\"\\n\",\n+\" yy, xx = np.ogrid[:h, :w]\\n\",\n+\" for px, py in prompt_points_xy:\\n\",\n+\" px = int(max(0, min(w - 1, px)))\\n\",\n+\" py = int(max(0, min(h - 1, py)))\\n\",\n+\" disk = (xx - px) ** 2 + (yy - py) ** 2 <= point_radius_px ** 2\\n\",\n+\" hint |= disk\\n\",\n+\"\\n\",\n+\" return hint\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def score_sam_candidate(mask, sam_score, prompt_bbox_xyxy, prompt_points_xy, hint_mask):\\n\",\n+\" mask = mask.astype(bool)\\n\",\n+\" mask_area = int(mask.sum())\\n\",\n+\"\\n\",\n+\" if mask_area == 0:\\n\",\n+\" return {\\n\",\n+\" \\\"total\\\": -1.0,\\n\",\n+\" \\\"sam_score\\\": float(sam_score),\\n\",\n+\" \\\"point_coverage\\\": 0.0,\\n\",\n+\" \\\"hint_iou\\\": 0.0,\\n\",\n+\" \\\"inside_box_ratio\\\": 0.0,\\n\",\n+\" \\\"mask_area\\\": 0,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" h, w = mask.shape\\n\",\n+\" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n+\" x0 = max(0, min(w - 1, x0))\\n\",\n+\" y0 = max(0, min(h - 1, y0))\\n\",\n+\" x1 = max(0, min(w, x1))\\n\",\n+\" y1 = max(0, min(h, y1))\\n\",\n+\"\\n\",\n+\" box_mask = np.zeros_like(mask)\\n\",\n+\" if x1 > x0 and y1 > y0:\\n\",\n+\" box_mask[y0:y1, x0:x1] = True\\n\",\n+\"\\n\",\n+\" inside_box_ratio = float((mask & box_mask).sum() / max(mask_area, 1))\\n\",\n+\"\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" hits = 0\\n\",\n+\" for px, py in prompt_points_xy:\\n\",\n+\" if 0 <= int(py) < h and 0 <= int(px) < w and mask[int(py), int(px)]:\\n\",\n+\" hits += 1\\n\",\n+\" point_coverage = float(hits / len(prompt_points_xy))\\n\",\n+\" else:\\n\",\n+\" point_coverage = 0.0\\n\",\n+\"\\n\",\n+\" if hint_mask is not None:\\n\",\n+\" inter = float((mask & hint_mask).sum())\\n\",\n+\" union = float((mask | hint_mask).sum())\\n\",\n+\" hint_iou = inter / max(union, 1.0)\\n\",\n+\" else:\\n\",\n+\" hint_iou = 0.0\\n\",\n+\"\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" total = 0.50 * hint_iou + 0.30 * point_coverage + 0.10 * inside_box_ratio + 0.10 * float(sam_score)\\n\",\n+\" else:\\n\",\n+\" total = 0.80 * float(sam_score) + 0.20 * inside_box_ratio\\n\",\n+\"\\n\",\n+\" return {\\n\",\n+\" \\\"total\\\": float(total),\\n\",\n+\" \\\"sam_score\\\": float(sam_score),\\n\",\n+\" \\\"point_coverage\\\": float(point_coverage),\\n\",\n+\" \\\"hint_iou\\\": float(hint_iou),\\n\",\n+\" \\\"inside_box_ratio\\\": float(inside_box_ratio),\\n\",\n+\" \\\"mask_area\\\": int(mask_area),\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n \" if prompt_points_xy:\\n\",\n \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n@@ -678,8 +762,9 @@\n \" image,\\n\",\n \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n+\"\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sam_model(**inputs)\\n\",\n@@ -689,20 +774,47 @@\n \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n \" )\\n\",\n \"\\n\",\n-\" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n-\" best_idx = int(np.argmax(scores))\\n\",\n-\" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n+\" sam_scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n+\" candidate_masks = masks[0][0].numpy().astype(bool)\\n\",\n+\"\\n\",\n+\" img_arr = np.array(image)\\n\",\n+\" hint_mask = None\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n+\"\\n\",\n+\" candidate_debug = []\\n\",\n+\" for idx in range(candidate_masks.shape[0]):\\n\",\n+\" metrics = score_sam_candidate(\\n\",\n+\" mask=candidate_masks[idx],\\n\",\n+\" sam_score=float(sam_scores[idx]),\\n\",\n+\" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n+\" prompt_points_xy=prompt_points_xy,\\n\",\n+\" hint_mask=hint_mask,\\n\",\n+\" )\\n\",\n+\" metrics[\\\"candidate_idx\\\"] = int(idx)\\n\",\n+\" candidate_debug.append(metrics)\\n\",\n+\"\\n\",\n+\" best = max(candidate_debug, key=lambda d: d[\\\"total\\\"])\\n\",\n+\" best_idx = int(best[\\\"candidate_idx\\\"])\\n\",\n+\" mask = candidate_masks[best_idx]\\n\",\n \" bbox = mask_bbox_xywh(mask)\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n-\" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n+\" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n+\" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n+\" \\\"selection_metrics\\\": {\\n\",\n+\" \\\"point_coverage\\\": float(best[\\\"point_coverage\\\"]),\\n\",\n+\" \\\"hint_iou\\\": float(best[\\\"hint_iou\\\"]),\\n\",\n+\" \\\"inside_box_ratio\\\": float(best[\\\"inside_box_ratio\\\"]),\\n\",\n+\" },\\n\",\n+\" \\\"sam_candidates\\\": candidate_debug,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def classify_material_sigclip(\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -660,8 +660,92 @@\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def build_brush_hint_mask(image_shape_hw, prompt_bbox_xyxy, prompt_points_xy, point_radius_px: int = 16):\\n\",\n+\" h, w = image_shape_hw\\n\",\n+\" hint = np.zeros((h, w), dtype=bool)\\n\",\n+\"\\n\",\n+\" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n+\" x0 = max(0, min(w - 1, x0))\\n\",\n+\" y0 = max(0, min(h - 1, y0))\\n\",\n+\" x1 = max(0, min(w - 1, x1))\\n\",\n+\" y1 = max(0, min(h - 1, y1))\\n\",\n+\"\\n\",\n+\" if x1 > x0 and y1 > y0:\\n\",\n+\" hint[y0:y1, x0:x1] = True\\n\",\n+\"\\n\",\n+\" if not prompt_points_xy:\\n\",\n+\" return hint\\n\",\n+\"\\n\",\n+\" yy, xx = np.ogrid[:h, :w]\\n\",\n+\" for px, py in prompt_points_xy:\\n\",\n+\" px = int(max(0, min(w - 1, px)))\\n\",\n+\" py = int(max(0, min(h - 1, py)))\\n\",\n+\" disk = (xx - px) ** 2 + (yy - py) ** 2 <= point_radius_px ** 2\\n\",\n+\" hint |= disk\\n\",\n+\"\\n\",\n+\" return hint\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def score_sam_candidate(mask, sam_score, prompt_bbox_xyxy, prompt_points_xy, hint_mask):\\n\",\n+\" mask = mask.astype(bool)\\n\",\n+\" mask_area = int(mask.sum())\\n\",\n+\"\\n\",\n+\" if mask_area == 0:\\n\",\n+\" return {\\n\",\n+\" \\\"total\\\": -1.0,\\n\",\n+\" \\\"sam_score\\\": float(sam_score),\\n\",\n+\" \\\"point_coverage\\\": 0.0,\\n\",\n+\" \\\"hint_iou\\\": 0.0,\\n\",\n+\" \\\"inside_box_ratio\\\": 0.0,\\n\",\n+\" \\\"mask_area\\\": 0,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" h, w = mask.shape\\n\",\n+\" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n+\" x0 = max(0, min(w - 1, x0))\\n\",\n+\" y0 = max(0, min(h - 1, y0))\\n\",\n+\" x1 = max(0, min(w, x1))\\n\",\n+\" y1 = max(0, min(h, y1))\\n\",\n+\"\\n\",\n+\" box_mask = np.zeros_like(mask)\\n\",\n+\" if x1 > x0 and y1 > y0:\\n\",\n+\" box_mask[y0:y1, x0:x1] = True\\n\",\n+\"\\n\",\n+\" inside_box_ratio = float((mask & box_mask).sum() / max(mask_area, 1))\\n\",\n+\"\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" hits = 0\\n\",\n+\" for px, py in prompt_points_xy:\\n\",\n+\" if 0 <= int(py) < h and 0 <= int(px) < w and mask[int(py), int(px)]:\\n\",\n+\" hits += 1\\n\",\n+\" point_coverage = float(hits / len(prompt_points_xy))\\n\",\n+\" else:\\n\",\n+\" point_coverage = 0.0\\n\",\n+\"\\n\",\n+\" if hint_mask is not None:\\n\",\n+\" inter = float((mask & hint_mask).sum())\\n\",\n+\" union = float((mask | hint_mask).sum())\\n\",\n+\" hint_iou = inter / max(union, 1.0)\\n\",\n+\" else:\\n\",\n+\" hint_iou = 0.0\\n\",\n+\"\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" total = 0.50 * hint_iou + 0.30 * point_coverage + 0.10 * inside_box_ratio + 0.10 * float(sam_score)\\n\",\n+\" else:\\n\",\n+\" total = 0.80 * float(sam_score) + 0.20 * inside_box_ratio\\n\",\n+\"\\n\",\n+\" return {\\n\",\n+\" \\\"total\\\": float(total),\\n\",\n+\" \\\"sam_score\\\": float(sam_score),\\n\",\n+\" \\\"point_coverage\\\": float(point_coverage),\\n\",\n+\" \\\"hint_iou\\\": float(hint_iou),\\n\",\n+\" \\\"inside_box_ratio\\\": float(inside_box_ratio),\\n\",\n+\" \\\"mask_area\\\": int(mask_area),\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n \" if prompt_points_xy:\\n\",\n \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n@@ -678,8 +762,9 @@\n \" image,\\n\",\n \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n+\"\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sam_model(**inputs)\\n\",\n@@ -689,20 +774,47 @@\n \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n \" )\\n\",\n \"\\n\",\n-\" scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n-\" best_idx = int(np.argmax(scores))\\n\",\n-\" mask = masks[0][0][best_idx].numpy().astype(bool)\\n\",\n+\" sam_scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n+\" candidate_masks = masks[0][0].numpy().astype(bool)\\n\",\n+\"\\n\",\n+\" img_arr = np.array(image)\\n\",\n+\" hint_mask = None\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n+\"\\n\",\n+\" candidate_debug = []\\n\",\n+\" for idx in range(candidate_masks.shape[0]):\\n\",\n+\" metrics = score_sam_candidate(\\n\",\n+\" mask=candidate_masks[idx],\\n\",\n+\" sam_score=float(sam_scores[idx]),\\n\",\n+\" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n+\" prompt_points_xy=prompt_points_xy,\\n\",\n+\" hint_mask=hint_mask,\\n\",\n+\" )\\n\",\n+\" metrics[\\\"candidate_idx\\\"] = int(idx)\\n\",\n+\" candidate_debug.append(metrics)\\n\",\n+\"\\n\",\n+\" best = max(candidate_debug, key=lambda d: d[\\\"total\\\"])\\n\",\n+\" best_idx = int(best[\\\"candidate_idx\\\"])\\n\",\n+\" mask = candidate_masks[best_idx]\\n\",\n \" bbox = mask_bbox_xywh(mask)\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n-\" \\\"sam_score\\\": float(scores[best_idx]),\\n\",\n+\" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n+\" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n+\" \\\"selection_metrics\\\": {\\n\",\n+\" \\\"point_coverage\\\": float(best[\\\"point_coverage\\\"]),\\n\",\n+\" \\\"hint_iou\\\": float(best[\\\"hint_iou\\\"]),\\n\",\n+\" \\\"inside_box_ratio\\\": float(best[\\\"inside_box_ratio\\\"]),\\n\",\n+\" },\\n\",\n+\" \\\"sam_candidates\\\": candidate_debug,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def classify_material_sigclip(\\n\",\n", + "additions": 116, + "deletions": 4 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1125,9 +1125,15 @@\n \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n \" },\\n\",\n \" })\\n\",\n \"\\n\",\n-\" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n+\" print(\\n\",\n+\" f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} \\\"\\n\",\n+\" f\\\"sam={seg['sam_score']:.3f} sel={seg['selection_score']:.3f} \\\"\\n\",\n+\" f\\\"hint_iou={seg['selection_metrics']['hint_iou']:.3f} \\\"\\n\",\n+\" f\\\"pt_cov={seg['selection_metrics']['point_coverage']:.3f} \\\"\\n\",\n+\" f\\\"top3={clf['pred_material_top3']}\\\"\\n\",\n+\" )\\n\",\n \"\\n\",\n \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \" axes[0].imshow(img)\\n\",\n \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1125,9 +1125,15 @@\n \" \\\"end_to_end_time_sec\\\": end_to_end_time_sec,\\n\",\n \" },\\n\",\n \" })\\n\",\n \"\\n\",\n-\" print(f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} top3={clf['pred_material_top3']}\\\")\\n\",\n+\" print(\\n\",\n+\" f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} \\\"\\n\",\n+\" f\\\"sam={seg['sam_score']:.3f} sel={seg['selection_score']:.3f} \\\"\\n\",\n+\" f\\\"hint_iou={seg['selection_metrics']['hint_iou']:.3f} \\\"\\n\",\n+\" f\\\"pt_cov={seg['selection_metrics']['point_coverage']:.3f} \\\"\\n\",\n+\" f\\\"top3={clf['pred_material_top3']}\\\"\\n\",\n+\" )\\n\",\n \"\\n\",\n \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \" axes[0].imshow(img)\\n\",\n \" axes[0].imshow(seg[\\\"mask\\\"], alpha=0.35, cmap=\\\"spring\\\")\\n\",\n", + "additions": 7, + "deletions": 1 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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\\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n1249: \" },\\n\",\n1250: \" \\\"segmentation\\\": {\\n\",\n1251: \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n1252: \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n1253: \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n1254: \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n1255: \" },\\n\",\n1256: \" \\\"timing\\\": {\\n\",\n1257: \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n1258: \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n1259: \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n1260: \" },\\n\",\n1261: \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n1262: \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n1263: \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n1264: \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n1265: \" }\\n\",\n1266: \"\\n\",\n1267: \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n1268: \"\\n\",\n1269: \" eval_row = {\\n\",\n1270: \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n1271: \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n1272: \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n1273: \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n1274: \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n1275: \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n1276: \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n1277: \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n1278: \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n1279: \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n1280: \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n1281: \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n1282: 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\\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n \" },\\n\",\n \" \\\"timing\\\": {\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" },\\n\",\n \" \\\"material\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" },\\n\",\n \" \\\"segmentation\\\": {\\n\",\n \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n \" },\\n\",\n \" \\\"timing\\\": {\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" },\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n \" \\\"created_at_utc\\\": 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -670,12 +670,11 @@\n \" y0 = max(0, min(h - 1, y0))\\n\",\n \" x1 = max(0, min(w - 1, x1))\\n\",\n \" y1 = max(0, min(h - 1, y1))\\n\",\n \"\\n\",\n-\" if x1 > x0 and y1 > y0:\\n\",\n-\" hint[y0:y1, x0:x1] = True\\n\",\n-\"\\n\",\n \" if not prompt_points_xy:\\n\",\n+\" if x1 > x0 and y1 > y0:\\n\",\n+\" hint[y0:y1, x0:x1] = True\\n\",\n \" return hint\\n\",\n \"\\n\",\n \" yy, xx = np.ogrid[:h, :w]\\n\",\n \" for px, py in prompt_points_xy:\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -670,12 +670,11 @@\n \" y0 = max(0, min(h - 1, y0))\\n\",\n \" x1 = max(0, min(w - 1, x1))\\n\",\n \" y1 = max(0, min(h - 1, y1))\\n\",\n \"\\n\",\n-\" if x1 > x0 and y1 > y0:\\n\",\n-\" hint[y0:y1, x0:x1] = True\\n\",\n-\"\\n\",\n \" if not prompt_points_xy:\\n\",\n+\" if x1 > x0 and y1 > y0:\\n\",\n+\" hint[y0:y1, x0:x1] = True\\n\",\n \" return hint\\n\",\n \"\\n\",\n \" yy, xx = np.ogrid[:h, :w]\\n\",\n \" for px, py in prompt_points_xy:\\n\",\n", + "additions": 2, + "deletions": 3 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Re-upload latest `poc_eval_notebook.ipynb`.\n2. Re-run from the function-definition cell onward (or restart + run all).\n3. Test `SELECTION_MODE = \"brush\"` first and compare whether selected masks align better with painted intent.\n\nIf you want next, I can add one optional guard: if `hint_iou` or `point_coverage` is too low, force a redraw before classification.", + "time": { + "start": 1783081124713, + "end": 1783081129587 + }, + "metadata": { + "openai": { + "itemId": "msg_0dd6fbdd0adabb47016a47a8a4aa54819fa1dcd3a07ad1e5d9", + "phase": "final_answer" + } + }, + "id": "prt_f27eac369001hsVRAiZojfzbXM", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27ea8b180022RPUBiX8qff2BP" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 130180, + "input": 216, + "output": 403, + "reasoning": 25, + "cache": { + "write": 0, + "read": 129536 + } + }, + "cost": 0.0290388, + "id": "prt_f27ead689001EizymARozRgzq6", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27ea8b180022RPUBiX8qff2BP" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783081383424 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f27eeb600001kIEGX2cpilEndu", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "I think this is good; thank you. 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Files and versions xet Community 5 Deploy Copy to bucket new Use this model Instructions to use facebook/sam2.1-hiera-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers How to use facebook/sam2.1-hiera-large with Transformers: # Use a pipeline as a high-level helper\nfrom transformers import pipeline\n\npipe = pipeline(\"mask-generation\", model=\"facebook/sam2.1-hiera-large\") # Load model directly\nfrom transformers import AutoProcessor, AutoModel\n\nprocessor = AutoProcessor.from_pretrained(\"facebook/sam2.1-hiera-large\")\nmodel = AutoModel.from_pretrained(\"facebook/sam2.1-hiera-large\") Notebooks Google Colab Kaggle Usage Usage with 🤗 Transformers Automatic Mask Generation with Pipeline Basic Image Segmentation Single Point ClickMultiple Points for RefinementBounding Box InputMultiple Objects SegmentationBatch Inference Batched ImagesBatched Objects per ImageBatched Images with Batched Objects and Multiple PointsBatched Bounding BoxesUsing Previous Masks as Input Video Segmentation and Tracking Basic Video TrackingMulti-Object Video TrackingRefining Video SegmentationStreaming Video Inference Video Batch Processing for Multiple ObjectsCitation Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the SAM 2 paper for more information.\nThe official code is publicly release in this repo.\n\n\t\n\t\t\n\t\n\t\n\t\tUsage\n\t\n\nFor image prediction:\nimport torch\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\n\npredictor = SAM2ImagePredictor.from_pretrained(\"facebook/sam2.1-hiera-large\")\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n predictor.set_image(<your_image>)\n masks, _, _ = predictor.predict(<input_prompts>)\n\nFor video prediction:\nimport torch\nfrom sam2.sam2_video_predictor import SAM2VideoPredictor\n\npredictor = SAM2VideoPredictor.from_pretrained(\"facebook/sam2.1-hiera-large\")\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n state = predictor.init_state(<your_video>)\n\n # add new prompts and instantly get the output on the same frame\n frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):\n\n # propagate the prompts to get masklets throughout the video\n for frame_idx, object_ids, masks in predictor.propagate_in_video(state):\n ...\n\nRefer to the demo notebooks for more details.\n\n\t\n\t\t\n\t\n\t\n\t\tUsage with 🤗 Transformers\n\t\n\n\n\t\n\t\t\n\t\n\t\n\t\tAutomatic Mask Generation with Pipeline\n\t\n\nSAM2 can be used for automatic mask generation to segment all objects in an image using the mask-generation pipeline:\n>>> from transformers import pipeline\n\n>>> generator = pipeline(\"mask-generation\", model=\"facebook/sam2.1-hiera-large\", device=0)\n>>> image_url = \"https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg\"\n>>> outputs = generator(image_url, points_per_batch=64)\n\n>>> len(outputs[\"masks\"]) # Number of masks generated\n39\n\n\n\t\n\t\t\n\t\n\t\n\t\tBasic Image Segmentation\n\t\n\n\n\t\n\t\t\n\t\n\t\n\t\tSingle Point Click\n\t\n\nYou can segment objects by providing a single point click on the object you want to segment:\n>>> from transformers import Sam2Processor, Sam2Model\n>>> import torch\n>>> from PIL import Image\n>>> import requests\n\n>>> device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n>>> model = Sam2Model.from_pretrained(\"facebook/sam2.1-hiera-large\").to(device)\n>>> processor = Sam2Processor.from_pretrained(\"facebook/sam2.1-hiera-large\")\n\n>>> image_url = \"https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg\"\n>>> raw_image = Image.open(requests.get(image_url, stream=True).raw).convert(\"RGB\")\n\n>>> input_points = [[[[500, 375]]]] # Single point click, 4 dimensions (image_dim, object_dim, point_per_object_dim, coordinates)\n>>> input_labels = [[[1]]] # 1 for positive click, 0 for negative click, 3 dimensions (image_dim, object_dim, point_label)\n\n>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs)\n\n>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs[\"original_sizes\"])[0]\n\n>>> # The model outputs multiple mask predictions ranked by quality score\n>>> print(f\"Generated {masks.shape[1]} masks with shape {masks.shape}\")\nGenerated 3 masks with shape torch.Size(1, 3, 1500, 2250)\n\n\n\t\n\t\t\n\t\n\t\n\t\tMultiple Points for Refinement\n\t\n\nYou can provide multiple points to refine the segmentation:\n>>> # Add both positive and negative points to refine the mask\n>>> input_points = [[[[500, 375], [1125, 625]]]] # Multiple points for refinement\n>>> input_labels = [[[1, 1]]] # Both positive clicks\n\n>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs)\n\n>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs[\"original_sizes\"])[0]\n\n\n\t\n\t\t\n\t\n\t\n\t\tBounding Box Input\n\t\n\nSAM2 also supports bounding box inputs for segmentation:\n>>> # Define bounding box as [x_min, y_min, x_max, y_max]\n>>> input_boxes = [[[75, 275, 1725, 850]]]\n\n>>> inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs)\n\n>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs[\"original_sizes\"])[0]\n\n\n\t\n\t\t\n\t\n\t\n\t\tMultiple Objects Segmentation\n\t\n\nYou can segment multiple objects simultaneously:\n>>> # Define points for two different objects\n>>> input_points = [[[[500, 375]], [[650, 750]]]] # Points for two objects in same image\n>>> input_labels = [[[1], [1]]] # Positive clicks for both objects\n\n>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs, multimask_output=False)\n\n>>> # Each object gets its own mask\n>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs[\"original_sizes\"])[0]\n>>> print(f\"Generated masks for {masks.shape[0]} objects\")\nGenerated masks for 2 objects\n\n\n\t\n\t\t\n\t\n\t\n\t\tBatch Inference\n\t\n\n\n\t\n\t\t\n\t\n\t\n\t\tBatched Images\n\t\n\nProcess multiple images simultaneously for improved efficiency:\n>>> from transformers import Sam2Processor, Sam2Model\n>>> import torch\n>>> from PIL import Image\n>>> import requests\n\n>>> device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n>>> model = Sam2Model.from_pretrained(\"facebook/sam2.1-hiera-large\").to(device)\n>>> processor = Sam2Processor.from_pretrained(\"facebook/sam2.1-hiera-large\")\n\n>>> # Load multiple images\n>>> image_urls = [\n... \"https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg\",\n... \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dog-sam.png\"\n... ]\n>>> raw_images = [Image.open(requests.get(url, stream=True).raw).convert(\"RGB\") for url in image_urls]\n\n>>> # Single point per image\n>>> input_points = [[[[500, 375]]], [[[770, 200]]]] # One point for each image\n>>> input_labels = [[[1]], [[1]]] # Positive clicks for both images\n\n>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs, multimask_output=False)\n\n>>> # Post-process masks for each image\n>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs[\"original_sizes\"])\n>>> print(f\"Processed {len(all_masks)} images, each with {all_masks[0].shape[0]} objects\")\nProcessed 2 images, each with 1 objects\n\n\n\t\n\t\t\n\t\n\t\n\t\tBatched Objects per Image\n\t\n\nSegment multiple objects within each image using batch inference:\n>>> # Multiple objects per image - different numbers of objects per image\n>>> input_points = [\n... [[[500, 375]], [[650, 750]]], # Truck image: 2 objects\n... [[[770, 200]]] # Dog image: 1 object\n... ]\n>>> input_labels = [\n... [[1], [1]], # Truck image: positive clicks for both objects\n... [[1]] # Dog image: positive click for the object\n... ]\n\n>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs, multimask_output=False)\n\n>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs[\"original_sizes\"])\n\n\n\t\n\t\t\n\t\n\t\n\t\tBatched Images with Batched Objects and Multiple Points\n\t\n\nHandle complex batch scenarios with multiple points per object:\n>>> # Add groceries image for more complex example\n>>> groceries_url = \"https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg\"\n>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert(\"RGB\")\n>>> raw_images = [raw_images[0], groceries_image] # Use truck and groceries images\n\n>>> # Complex batching: multiple images, multiple objects, multiple points per object\n>>> input_points = [\n... [[[500, 375]], [[650, 750]]], # Truck image: 2 objects with 1 point each\n... [[[400, 300]], [[630, 300], [550, 300]]] # Groceries image: obj1 has 1 point, obj2 has 2 points\n... ]\n>>> input_labels = [\n... [[1], [1]], # Truck image: positive clicks\n... [[1], [1, 1]] # Groceries image: positive clicks for refinement\n... ]\n\n>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs, multimask_output=False)\n\n>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs[\"original_sizes\"])\n\n\n\t\n\t\t\n\t\n\t\n\t\tBatched Bounding Boxes\n\t\n\nProcess multiple images with bounding box inputs:\n>>> # Multiple bounding boxes per image (using truck and groceries images)\n>>> input_boxes = [\n... [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]], # Truck image: 4 boxes\n... [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]] # Groceries image: 4 boxes\n... ]\n\n>>> # Update images for this example\n>>> raw_images = [raw_images[0], groceries_image] # truck and groceries\n\n>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs, multimask_output=False)\n\n>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs[\"original_sizes\"])\n>>> print(f\"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively\")\nProcessed 2 images with 4 and 4 boxes respectively\n\n\n\t\n\t\t\n\t\n\t\n\t\tUsing Previous Masks as Input\n\t\n\nSAM2 can use masks from previous predictions as input to refine segmentation:\n>>> # Get initial segmentation\n>>> input_points = [[[[500, 375]]]]\n>>> input_labels = [[[1]]]\n>>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors=\"pt\").to(device)\n\n>>> with torch.no_grad():\n... outputs = model(**inputs)\n\n>>> # Use the best mask as input for refinement\n>>> mask_input = outputs.pred_masks[:, :, torch.argmax(outputs.iou_scores.squeeze())]\n\n>>> # Add additional points with the mask input\n>>> new_input_points = [[[[500, 375], [450, 300]]]]\n>>> new_input_labels = [[[1, 1]]]\n>>> inputs = processor(\n... input_points=new_input_points,\n... input_labels=new_input_labels,\n... original_sizes=inputs[\"original_sizes\"],\n... return_tensors=\"pt\",\n... ).to(device)\n\n>>> with torch.no_grad():\n... refined_outputs = model(\n... **inputs,\n... input_masks=mask_input,\n... image_embeddings=outputs.image_embeddings,\n... multimask_output=False,\n... )\n\n\n\t\n\t\t\n\t\n\t\n\t\tVideo Segmentation and Tracking\n\t\n\nSAM2's key strength is its ability to track objects across video frames. Here's how to use it for video segmentation:\n\n\t\n\t\t\n\t\n\t\n\t\tBasic Video Tracking\n\t\n\n>>> from transformers import Sam2VideoModel, Sam2VideoProcessor\n>>> import torch\n\n>>> device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n>>> model = Sam2VideoModel.from_pretrained(\"facebook/sam2.1-hiera-large\").to(device, dtype=torch.bfloat16)\n>>> processor = Sam2VideoProcessor.from_pretrained(\"facebook/sam2.1-hiera-large\")\n\n>>> # Load video frames (example assumes you have a list of PIL Images)\n>>> # video_frames = [Image.open(f\"frame_{i:05d}.jpg\") for i in range(num_frames)]\n\n>>> # For this example, we'll use the video loading utility\n>>> from transformers.video_utils import load_video\n>>> video_url = \"https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/bedroom.mp4\"\n>>> video_frames, _ = load_video(video_url)\n\n>>> # Initialize video inference session\n>>> inference_session = processor.init_video_session(\n... video=video_frames,\n... inference_device=device,\n... torch_dtype=torch.bfloat16,\n... )\n\n>>> # Add click on first frame to select object\n>>> ann_frame_idx = 0\n>>> ann_obj_id = 1\n>>> points = [[[[210, 350]]]]\n>>> labels = [[[1]]]\n\n>>> processor.add_inputs_to_inference_session(\n... inference_session=inference_session,\n... frame_idx=ann_frame_idx,\n... obj_ids=ann_obj_id,\n... input_points=points,\n... input_labels=labels,\n... )\n\n>>> # Segment the object on the first frame\n>>> outputs = model(\n... inference_session=inference_session,\n... frame_idx=ann_frame_idx,\n... )\n>>> video_res_masks = processor.post_process_masks(\n... [outputs.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False\n... )[0]\n>>> print(f\"Segmentation shape: {video_res_masks.shape}\")\nSegmentation shape: torch.Size([1, 1, 480, 854])\n\n>>> # Propagate through the entire video\n>>> video_segments = {}\n>>> for sam2_video_output in model.propagate_in_video_iterator(inference_session):\n... video_res_masks = processor.post_process_masks(\n... [sam2_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False\n... )[0]\n... video_segments[sam2_video_output.frame_idx] = video_res_masks\n\n>>> print(f\"Tracked object through {len(video_segments)} frames\")\nTracked object through 180 frames\n\n\n\t\n\t\t\n\t\n\t\n\t\tMulti-Object Video Tracking\n\t\n\nTrack multiple objects simultaneously across video frames:\n>>> # Reset for new tracking session\n>>> inference_session.reset_inference_session()\n\n>>> # Add multiple objects on the first frame\n>>> ann_frame_idx = 0\n>>> obj_ids = [2, 3]\n>>> input_points = [[[[200, 300]], [[400, 150]]]] # Points for two objects (batched)\n>>> input_labels = [[[1], [1]]]\n\n>>> processor.add_inputs_to_inference_session(\n... inference_session=inference_session,\n... frame_idx=ann_frame_idx,\n... obj_ids=obj_ids,\n... input_points=input_points,\n... input_labels=input_labels,\n... )\n\n>>> # Get masks for both objects on first frame\n>>> outputs = model(\n... inference_session=inference_session,\n... frame_idx=ann_frame_idx,\n... )\n\n>>> # Propagate both objects through video\n>>> video_segments = {}\n>>> for sam2_video_output in model.propagate_in_video_iterator(inference_session):\n... video_res_masks = processor.post_process_masks(\n... [sam2_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False\n... )[0]\n... video_segments[sam2_video_output.frame_idx] = {\n... obj_id: video_res_masks[i]\n... for i, obj_id in enumerate(inference_session.obj_ids)\n... }\n\n>>> print(f\"Tracked {len(inference_session.obj_ids)} objects through {len(video_segments)} frames\")\nTracked 2 objects through 180 frames\n\n\n\t\n\t\t\n\t\n\t\n\t\tRefining Video Segmentation\n\t\n\nYou can add additional clicks on any frame to refine the tracking:\n>>> # Add refinement click on a later frame\n>>> refine_frame_idx = 50\n>>> ann_obj_id = 2 # Refining first object\n>>> points = [[[[220, 280]]]] # Additional point\n>>> labels = [[[1]]] # Positive click\n\n>>> processor.add_inputs_to_inference_session(\n... inference_session=inference_session,\n... frame_idx=refine_frame_idx,\n... obj_ids=ann_obj_id,\n... input_points=points,\n... input_labels=labels,\n... )\n\n>>> # Re-propagate with the additional information\n>>> video_segments = {}\n>>> for sam2_video_output in model.propagate_in_video_iterator(inference_session):\n... video_res_masks = processor.post_process_masks(\n... [sam2_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False\n... )[0]\n... video_segments[sam2_video_output.frame_idx] = video_res_masks\n\n\n\t\n\t\t\n\t\n\t\n\t\tStreaming Video Inference\n\t\n\nFor real-time applications, SAM2 supports processing video frames as they arrive:\n>>> # Initialize session for streaming\n>>> inference_session = processor.init_video_session(\n... inference_device=device,\n... torch_dtype=torch.bfloat16,\n... )\n\n>>> # Process frames one by one\n>>> for frame_idx, frame in enumerate(video_frames[:10]): # Process first 10 frames\n... inputs = processor(images=frame, device=device, return_tensors=\"pt\")\n...\n... if frame_idx == 0:\n... # Add point input on first frame\n... processor.add_inputs_to_inference_session(\n... inference_session=inference_session,\n... frame_idx=0,\n... obj_ids=1,\n... input_points=[[[[210, 350], [250, 220]]]],\n... input_labels=[[[1, 1]]],\n... original_size=inputs.original_sizes[0], # need to be provided when using streaming video inference\n... )\n...\n... # Process current frame\n... sam2_video_output = model(inference_session=inference_session, frame=inputs.pixel_values[0])\n...\n... video_res_masks = processor.post_process_masks(\n... [sam2_video_output.pred_masks], original_sizes=inputs.original_sizes, binarize=False\n... )[0]\n... print(f\"Frame {frame_idx}: mask shape {video_res_masks.shape}\")\n\n\n\t\n\t\t\n\t\n\t\n\t\tVideo Batch Processing for Multiple Objects\n\t\n\nTrack multiple objects simultaneously in video by adding them all at once:\n>>> # Initialize video session\n>>> inference_session = processor.init_video_session(\n... video=video_frames,\n... inference_device=device,\n... torch_dtype=torch.bfloat16,\n... )\n\n>>> # Add multiple objects on the first frame using batch processing\n>>> ann_frame_idx = 0\n>>> obj_ids = [2, 3] # Track two different objects\n>>> input_points = [\n... [[[200, 300], [230, 250], [275, 175]], [[400, 150]]]\n... ] # Object 2: 3 points (2 positive, 1 negative); Object 3: 1 point\n>>> input_labels = [\n... [[1, 1, 0], [1]]\n... ] # Object 2: positive, positive, negative; Object 3: positive\n\n>>> processor.add_inputs_to_inference_session(\n... inference_session=inference_session,\n... frame_idx=ann_frame_idx,\n... obj_ids=obj_ids,\n... input_points=input_points,\n... input_labels=input_labels,\n... )\n\n>>> # Get masks for all objects on the first frame\n>>> outputs = model(\n... inference_session=inference_session,\n... frame_idx=ann_frame_idx,\n... )\n>>> video_res_masks = processor.post_process_masks(\n... [outputs.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False\n... )[0]\n>>> print(f\"Generated masks for {video_res_masks.shape[0]} objects\")\nGenerated masks for 2 objects\n\n>>> # Propagate all objects through the video\n>>> video_segments = {}\n>>> for sam2_video_output in model.propagate_in_video_iterator(inference_session):\n... video_res_masks = processor.post_process_masks(\n... [sam2_video_output.pred_masks], original_sizes=[[inference_session.video_height, inference_session.video_width]], binarize=False\n... )[0]\n... video_segments[sam2_video_output.frame_idx] = {\n... obj_id: video_res_masks[i]\n... for i, obj_id in enumerate(inference_session.obj_ids)\n... }\n\n>>> print(f\"Tracked {len(inference_session.obj_ids)} objects through {len(video_segments)} frames\")\nTracked 2 objects through 180 frames\n\n\n\t\n\t\t\n\t\n\t\n\t\tCitation\n\t\n\nTo cite the paper, model, or software, please use the below:\n@article{ravi2024sam2,\n title={SAM 2: Segment Anything in Images and Videos},\n author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\\\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\\'a}r, Piotr and Feichtenhofer, Christoph},\n journal={arXiv preprint arXiv:2408.00714},\n url={https://arxiv.org/abs/2408.00714},\n year={2024}\n}\n\n Downloads last month 62,600 \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\t\t\n\t\t Safetensors Model size 0.2B params Tensor type F32 · Files info Inference Providers NEW Mask Generation This model isn't deployed by any Inference Provider. 🙋 1 Ask for provider support Model tree for facebook/sam2.1-hiera-large Finetunes 5 models Quantizations 1 model Spaces using facebook/sam2.1-hiera-large 98 facebook/EdgeTAM 🐌 kulkas2pintu/wan2-2-fp8da-aoti-preview-nsfw 🐌 ghjjhv/wan-i2v-kira 🚀 merve/EdgeTAM 👀 yonigozlan/Segment-Anything-2-video-tracking 🔥 Mar2Ding/SAM2Long-Demo ⚡ LPX55/Lightning-Painter-Multitool 🐨 cyun9286/Align3R + 93 Spaces + 90 Spaces Paper for facebook/sam2.1-hiera-large SAM 2: Segment Anything in Images and Videos Paper • 2408.00714 • Published Aug 1, 2024 • 123 System theme Company TOS Privacy About Careers Website Models Datasets Spaces Pricing Docs", + "metadata": { + "truncated": false + }, + "title": "https://huggingface.co/facebook/sam2.1-hiera-large (text/html; charset=utf-8)", + "time": { + "start": 1783081396090, + "end": 1783081396608 + } + }, + "metadata": { + "openai": { + "itemId": "fc_08928a7fea8c128d016a47a9b402b881a08eed8d7a5f16ed43" + } + }, + "id": "prt_f27eee76c0013jY3yXq1ph92qP", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27eed40a002weKuaZIZ3mlUx6" + }, + { + "type": "tool", + "tool": "webfetch", + "callID": "call_E92BLtNRl8hPxTMHDZ1FFNBG", + "state": { + "status": "completed", + "input": { + "url": "https://github.com/facebookresearch/sam2", + "format": "text" + }, + "output": "GitHub - facebookresearch/sam2: The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model. · GitHub\n\n\n\n \n \n \n \n\n \n \n\n\n \n\n\n \n\n\n \n \n\n \n \n\n \n\n\n\n\n\n\n\n \n\n \n\n\n\n\n \n\n \n\n \n\n \n\n \n\n \n\n \n \n \n\n \n \n \n\n\n\n\n \n\n\n\n \n\n\n \n \n \n \n\n \n\n \n\n \n\n \n\n \n \n\n\n\n \n\n \n \n\n\n \n\n \n\n \n \n\n \n \n \n\n\n\n\n\n \n\n \n\n \n \n \n \n\n \n Skip to content\n\n \n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n \n\n \n\n \n\n\n\n\n\n\n\n Navigation Menu\n\n \n Toggle navigation\n \n\n \n \n \n \n \n \n \n \n\n \n\n \n \n \n\n \n\n \n \n Sign in\n \n \n \n \n \n\nAppearance settings\n\n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n \n \n\n\n \n \n \n\n\n\n \n \n PlatformAI CODE CREATIONGitHub CopilotWrite better code with AIGitHub Copilot appDirect agents from issue to mergeMCP RegistryNewIntegrate external toolsDEVELOPER WORKFLOWSActionsAutomate any workflowCodespacesInstant dev environmentsIssuesPlan and track workCode ReviewManage code changesAPPLICATION SECURITYGitHub Advanced SecurityFind and fix vulnerabilitiesCode securitySecure your code as you buildSecret protectionStop leaks before they startEXPLOREWhy GitHubDocumentationBlogChangelogMarketplaceView all featuresSolutionsBY COMPANY SIZEEnterprisesSmall and medium teamsStartupsNonprofitsBY USE CASEApp ModernizationDevSecOpsDevOpsCI/CDView all use casesBY INDUSTRYHealthcareFinancial servicesManufacturingGovernmentView all industriesView all solutionsResourcesEXPLORE BY TOPICAISoftware DevelopmentDevOpsSecurityView all topicsEXPLORE BY TYPECustomer storiesEvents & webinarsEbooks & reportsBusiness insightsGitHub SkillsSUPPORT & SERVICESDocumentationCustomer supportCommunity forumTrust centerPartnersView all resourcesOpen SourceCOMMUNITYGitHub SponsorsFund open source developersPROGRAMSSecurity LabMaintainer CommunityAcceleratorGitHub StarsArchive ProgramREPOSITORIESTopicsTrendingCollectionsEnterpriseENTERPRISE SOLUTIONSEnterprise platformAI-powered developer platformAVAILABLE ADD-ONSGitHub Advanced SecurityEnterprise-grade security featuresCopilot for BusinessEnterprise-grade AI featuresPremium SupportEnterprise-grade 24/7 supportPricing\n\n\n\n\n \n \n\n\n\n \n \n \n \n \n\n \n Search or jump to...\n \n \n \n \n\n \n\n \n\n \n Search code, repositories, users, issues, pull requests...\n \n \n \n \n \n \n \n Search\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n Clear\n \n \n\n\n\n \n \n \n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n\n \n \n\n\n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n Search syntax tips \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n \n \n Provide feedback\n \n \n \n \n \n \n\n \n \n \n\n \n \n We read every piece of feedback, and take your input very seriously.\n \n \n Include my email address so I can be contacted\n\n \n Cancel\n\n Submit feedback\n\n\n\n\n \n \n\n \n \n \n \n \n Saved searches\n \n Use saved searches to filter your results more quickly\n \n \n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n\n \n Name\n \n \n \n \n \n\n \n Query\n \n \n\n \n To see all available qualifiers, see our documentation.\n \n \n\n \n \n \n\n\n \n Cancel\n\n Create saved search\n\n\n\n \n \n\n\n\n \n \n Sign in\n \n \n\n \n Sign up\n \n\n \n \n \n \n\nAppearance settings\n\n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n Resetting focus\n \n \n \n \n\n\n \n \n \n \n\n You signed in with another tab or window. 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We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing. We build a model-in-the-loop data engine, which improves model and data via user interaction, to collect our SA-V dataset, the largest video segmentation dataset to date. SAM 2 trained on our data provides strong performance across a wide range of tasks and visual domains.\n\nLatest updates\n12/11/2024 -- full model compilation for a major VOS speedup and a new SAM2VideoPredictor to better handle multi-object tracking\n\nWe now support torch.compile of the entire SAM 2 model on videos, which can be turned on by setting vos_optimized=True in build_sam2_video_predictor, leading to a major speedup for VOS inference.\nWe update the implementation of SAM2VideoPredictor to support independent per-object inference, allowing us to relax the assumption of prompting for multi-object tracking and adding new objects after tracking starts.\nSee RELEASE_NOTES.md for full details.\n\n09/30/2024 -- SAM 2.1 Developer Suite (new checkpoints, training code, web demo) is released\n\nA new suite of improved model checkpoints (denoted as SAM 2.1) are released. See Model Description for details.\n\nTo use the new SAM 2.1 checkpoints, you need the latest model code from this repo. If you have installed an earlier version of this repo, please first uninstall the previous version via pip uninstall SAM-2, pull the latest code from this repo (with git pull), and then reinstall the repo following Installation below.\n\n\nThe training (and fine-tuning) code has been released. See training/README.md on how to get started.\nThe frontend + backend code for the SAM 2 web demo has been released. See demo/README.md for details.\n\nInstallation\nSAM 2 needs to be installed first before use. The code requires python>=3.10, as well as torch>=2.5.1 and torchvision>=0.20.1. Please follow the instructions here to install both PyTorch and TorchVision dependencies. You can install SAM 2 on a GPU machine using:\ngit clone https://github.com/facebookresearch/sam2.git && cd sam2\n\npip install -e .\nIf you are installing on Windows, it's strongly recommended to use Windows Subsystem for Linux (WSL) with Ubuntu.\nTo use the SAM 2 predictor and run the example notebooks, jupyter and matplotlib are required and can be installed by:\npip install -e \".[notebooks]\"\nNote:\n\nIt's recommended to create a new Python environment via Anaconda for this installation and install PyTorch 2.5.1 (or higher) via pip following https://pytorch.org/. If you have a PyTorch version lower than 2.5.1 in your current environment, the installation command above will try to upgrade it to the latest PyTorch version using pip.\nThe step above requires compiling a custom CUDA kernel with the nvcc compiler. If it isn't already available on your machine, please install the CUDA toolkits with a version that matches your PyTorch CUDA version.\nIf you see a message like Failed to build the SAM 2 CUDA extension during installation, you can ignore it and still use SAM 2 (some post-processing functionality may be limited, but it doesn't affect the results in most cases).\n\nPlease see INSTALL.md for FAQs on potential issues and solutions.\nGetting Started\nDownload Checkpoints\nFirst, we need to download a model checkpoint. All the model checkpoints can be downloaded by running:\ncd checkpoints && \\\n./download_ckpts.sh && \\\ncd ..\nor individually from:\n\nsam2.1_hiera_tiny.pt\nsam2.1_hiera_small.pt\nsam2.1_hiera_base_plus.pt\nsam2.1_hiera_large.pt\n\n(note that these are the improved checkpoints denoted as SAM 2.1; see Model Description for details.)\nThen SAM 2 can be used in a few lines as follows for image and video prediction.\nImage prediction\nSAM 2 has all the capabilities of SAM on static images, and we provide image prediction APIs that closely resemble SAM for image use cases. The SAM2ImagePredictor class has an easy interface for image prompting.\nimport torch\nfrom sam2.build_sam import build_sam2\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\n\ncheckpoint = \"./checkpoints/sam2.1_hiera_large.pt\"\nmodel_cfg = \"configs/sam2.1/sam2.1_hiera_l.yaml\"\npredictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint))\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n predictor.set_image(<your_image>)\n masks, _, _ = predictor.predict(<input_prompts>)\nPlease refer to the examples in image_predictor_example.ipynb (also in Colab here) for static image use cases.\nSAM 2 also supports automatic mask generation on images just like SAM. Please see automatic_mask_generator_example.ipynb (also in Colab here) for automatic mask generation in images.\nVideo prediction\nFor promptable segmentation and tracking in videos, we provide a video predictor with APIs for example to add prompts and propagate masklets throughout a video. SAM 2 supports video inference on multiple objects and uses an inference state to keep track of the interactions in each video.\nimport torch\nfrom sam2.build_sam import build_sam2_video_predictor\n\ncheckpoint = \"./checkpoints/sam2.1_hiera_large.pt\"\nmodel_cfg = \"configs/sam2.1/sam2.1_hiera_l.yaml\"\npredictor = build_sam2_video_predictor(model_cfg, checkpoint)\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n state = predictor.init_state(<your_video>)\n\n # add new prompts and instantly get the output on the same frame\n frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):\n\n # propagate the prompts to get masklets throughout the video\n for frame_idx, object_ids, masks in predictor.propagate_in_video(state):\n ...\nPlease refer to the examples in video_predictor_example.ipynb (also in Colab here) for details on how to add click or box prompts, make refinements, and track multiple objects in videos.\nLoad from 🤗 Hugging Face\nAlternatively, models can also be loaded from Hugging Face (requires pip install huggingface_hub).\nFor image prediction:\nimport torch\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\n\npredictor = SAM2ImagePredictor.from_pretrained(\"facebook/sam2-hiera-large\")\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n predictor.set_image(<your_image>)\n masks, _, _ = predictor.predict(<input_prompts>)\nFor video prediction:\nimport torch\nfrom sam2.sam2_video_predictor import SAM2VideoPredictor\n\npredictor = SAM2VideoPredictor.from_pretrained(\"facebook/sam2-hiera-large\")\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n state = predictor.init_state(<your_video>)\n\n # add new prompts and instantly get the output on the same frame\n frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):\n\n # propagate the prompts to get masklets throughout the video\n for frame_idx, object_ids, masks in predictor.propagate_in_video(state):\n ...\nModel Description\nSAM 2.1 checkpoints\nThe table below shows the improved SAM 2.1 checkpoints released on September 29, 2024.\n\n\n\nModel\nSize (M)\nSpeed (FPS)\nSA-V test (J&F)\nMOSE val (J&F)\nLVOS v2 (J&F)\n\n\n\n\nsam2.1_hiera_tiny (config, checkpoint)\n38.9\n91.2\n76.5\n71.8\n77.3\n\n\nsam2.1_hiera_small (config, checkpoint)\n46\n84.8\n76.6\n73.5\n78.3\n\n\nsam2.1_hiera_base_plus (config, checkpoint)\n80.8\n64.1\n78.2\n73.7\n78.2\n\n\nsam2.1_hiera_large (config, checkpoint)\n224.4\n39.5\n79.5\n74.6\n80.6\n\n\n\nSAM 2 checkpoints\nThe previous SAM 2 checkpoints released on July 29, 2024 can be found as follows:\n\n\n\nModel\nSize (M)\nSpeed (FPS)\nSA-V test (J&F)\nMOSE val (J&F)\nLVOS v2 (J&F)\n\n\n\n\nsam2_hiera_tiny (config, checkpoint)\n38.9\n91.5\n75.0\n70.9\n75.3\n\n\nsam2_hiera_small (config, checkpoint)\n46\n85.6\n74.9\n71.5\n76.4\n\n\nsam2_hiera_base_plus (config, checkpoint)\n80.8\n64.8\n74.7\n72.8\n75.8\n\n\nsam2_hiera_large (config, checkpoint)\n224.4\n39.7\n76.0\n74.6\n79.8\n\n\n\nSpeed measured on an A100 with torch 2.5.1, cuda 12.4. See benchmark.py for an example on benchmarking (compiling all the model components). Compiling only the image encoder can be more flexible and also provide (a smaller) speed-up (set compile_image_encoder: True in the config).\nSegment Anything Video Dataset\nSee sav_dataset/README.md for details.\nTraining SAM 2\nYou can train or fine-tune SAM 2 on custom datasets of images, videos, or both. Please check the training README on how to get started.\nWeb demo for SAM 2\nWe have released the frontend + backend code for the SAM 2 web demo (a locally deployable version similar to https://sam2.metademolab.com/demo). Please see the web demo README for details.\nLicense\nThe SAM 2 model checkpoints, SAM 2 demo code (front-end and back-end), and SAM 2 training code are licensed under Apache 2.0, however the Inter Font and Noto Color Emoji used in the SAM 2 demo code are made available under the SIL Open Font License, version 1.1.\nContributing\nSee contributing and the code of conduct.\nContributors\nThe SAM 2 project was made possible with the help of many contributors (alphabetical):\nKaren Bergan, Daniel Bolya, Alex Bosenberg, Kai Brown, Vispi Cassod, Christopher Chedeau, Ida Cheng, Luc Dahlin, Shoubhik Debnath, Rene Martinez Doehner, Grant Gardner, Sahir Gomez, Rishi Godugu, Baishan Guo, Caleb Ho, Andrew Huang, Somya Jain, Bob Kamma, Amanda Kallet, Jake Kinney, Alexander Kirillov, Shiva Koduvayur, Devansh Kukreja, Robert Kuo, Aohan Lin, Parth Malani, Jitendra Malik, Mallika Malhotra, Miguel Martin, Alexander Miller, Sasha Mitts, William Ngan, George Orlin, Joelle Pineau, Kate Saenko, Rodrick Shepard, Azita Shokrpour, David Soofian, Jonathan Torres, Jenny Truong, Sagar Vaze, Meng Wang, Claudette Ward, Pengchuan Zhang.\nThird-party code: we use a GPU-based connected component algorithm adapted from cc_torch (with its license in LICENSE_cctorch) as an optional post-processing step for the mask predictions.\nCiting SAM 2\nIf you use SAM 2 or the SA-V dataset in your research, please use the following BibTeX entry.\n@article{ravi2024sam2,\n title={SAM 2: Segment Anything in Images and Videos},\n author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\\\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\\'a}r, Piotr and Feichtenhofer, Christoph},\n journal={arXiv preprint arXiv:2408.00714},\n url={https://arxiv.org/abs/2408.00714},\n year={2024}\n}\n\n\n\n \n \n \n About\n\n \n The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.\n \n\n\n Resources\n \n \n \n \n\n Readme\n \n\n \n License\n \n \n \n \n\n Apache-2.0, BSD-3-Clause licenses found\n \n \n \n \n \n \n\n \n Licenses found\n \n \n \n Apache-2.0\n LICENSE\n \n \n \n BSD-3-Clause\n LICENSE_cctorch\n \n \n\n\n\n Code of conduct\n \n \n \n \n\n Code of conduct\n \n \n\n\n Contributing\n \n \n \n \n\n Contributing\n \n \n\n Security policy\n \n \n \n \n\n Security policy\n \n \n\n \n \n\n \n \n \n \n \n\n Uh oh!\n\n There was an error while loading. 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Please reload this page.\n\n\n \n \n\n \n \n\n \n \n \n Languages\n\n \n \n \n\n\n \n \n \n \n\n Jupyter Notebook\n 97.9%\n \n \n \n \n \n \n\n Python\n 2.1%\n \n \n\n\n \n \n\n \n \n \n\n\n\n\n\n \n\n\n\n \n \n\n \n\n \n Footer\n\n \n\n\n \n \n \n \n \n\n\n \n © 2026 GitHub, Inc.\n \n \n\n \n Footer navigation\n\n \n\n\n \n Terms\n \n\n \n Privacy\n \n\n \n Security\n \n\n \n Status\n \n\n \n Community\n \n\n \n Docs\n \n\n \n Contact\n \n\n \n \n \n Manage cookies\n \n \n\n\n\n \n \n Do not share my personal information\n \n \n\n\n \n \n \n\n\n\n\n \n\n\n\n\n \n \n \n\n \n \n \n\n \n You can’t perform that action at this time.", + "metadata": { + "truncated": false + }, + "title": "https://github.com/facebookresearch/sam2 (text/html; charset=utf-8)", + "time": { + "start": 1783081396133, + "end": 1783081397254 + } + }, + "metadata": { + "openai": { + "itemId": "fc_08928a7fea8c128d016a47a9b402cc81a0a3ddfe1bac1cdbdf" + } + }, + "id": "prt_f27eee78c001qiu9Olxs39rquK", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27eed40a002weKuaZIZ3mlUx6" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 129894, + "input": 153, + "output": 80, + "reasoning": 125, + "cache": { + "write": 0, + "read": 129536 + } + }, + "cost": 0.02580655, + "id": "prt_f27eeec0c001Oopj2WL5F5dShq", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27eed40a002weKuaZIZ3mlUx6" + } + ] + }, + { + "info": { + "parentID": "msg_f27eeb600001kIEGX2cpilEndu", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0500906, + "tokens": { + "total": 140907, + "input": 10468, + "output": 450, + "reasoning": 197, + "cache": { + "write": 0, + "read": 129792 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783081397282, + "completed": 1783081408143 + }, + "finish": "stop", + "id": "msg_f27eeec22002xCKUzAnLuJjgt4", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27eef1af001XfEJZ5krUEHG4g", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27eeec22002xCKUzAnLuJjgt4" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783081399605, + "end": 1783081402132 + }, + "metadata": { + "openai": { + "itemId": "rs_0f4bcf8979a24608016a47a9b73bd48191aae6367070d4be74", + "reasoningEncryptedContent": "gAAAAABqR6m6cUN9s8vwHQaFgafXHfF54vlI7aOIHQNa4r6asqqq4xWtyoPuVF_hd4Xt7ELw7Xg8tywHgdIAJ2y0Q2ga98PNCV9tc_WoAe2aK0w-QfDU1IXXf-V6ZuW5E3xl_X37-jap9f6P51uxQ_5XeOQkxBeO9sjtJ5MB018H9gzATQ-v4xmVFLGCdIFDq4maq1g1klWpCqIMRSn_4G9mW6IxaXSKXDAiy9hF0MBrN4a7rDUvUICu9Mz5WvcsTU_TZI9RJmUII44QtCj7j3BS9QxCDqWpRAJ3VN09HplaUJaFpnba8lZSJCcVzJ1yxDPIvkSwUXEQ3pqcsbWuUgtb9Z4choL3K0S9yIEwgHBa5tBUzUUMz5ClVcx0UYAnLxZrAG79Qy1njDwav-AnDAYUd2u4XdWgwLF1tPHe6tOtpZruKhamas9Cb9PVyjM47mRzMBj7k1D0QpAEJfk01rkrmufFbSU7aRDd11vGbljKwCnMHG1vqsSjHKfsK9CCMDkcfOovdU1cCAOfzOfQ3tsM8MONPTYosqkiPc2NSXIP5pYGM8tHVxi5UaRvla5z_WYqtOWz69mFAfc_ooqNySvqsoVNKGbXTdgfZbMwglUVhs4-yl3OkeXzKpHPOPSFjEQVfXiINK5W7VACRziNbJ7kUsQsZpkEEb68x8J-vj2gNKQQCN8yWLDWEdfqVgMSNXrbis1UWDKKxirS7ttICKFtzMq6iEpF-OqDdsl8jl1-R6TQqpeW58X84Q1OegtVcVB619a7ZiIxtF_BW-J3bdao7UFMYP8eC-btf3KEJkO1ReBFp_qWL74fTwDflGZGbYAsh4xJRwzf19zeusR3ffuSfi-IDDrO-zSlKV3l5TvPvcmrMW5ahwdEoNBR-3QTXbn5AgS2qCYaIcERp-0dNnbzmWkjlJhlEwNpTZOnj2W85EIetjgShH6tZsCaH7hQT3PhtPhqSluI8ys0nuIjw0ycNuCXLlCgAANryuHn-oNy8otFlRHUagjluLGgu0mOjJV7eaFG7XQqD4aITYRuXn8eni0JDSgWi2i0sB_MrNzZhKK4fAuFbyXyvGznPLNrDEJp9EtH1THrU06RpCVsmP0_oRmUxZGpqpbXYMR5GbI1Uu1ttBP9TecraISXxCQVf6QhINubQBpiDC8G23CYEkDU7fRd0bAtQdQGsrMXhpQ3-CV6NYJz28ns58eHBbW9rbrQRWZcZJOcwdXfzPQ2ISvBy10NB9B15Yp9vvYhqjrCoNQ3QGcJgkyv7mad9ZkCttTbe4h4DGqizFp-QjDwHKR7Zs5Eg6YIR-V9W6STOWVfBy-vKo0dvwweAjyFhs47daNBvkOI2gDl-8U6DQYl8Yn74wRQI3Gu4wzDVf2eSausTfstDs7g2G3lzRhLc5Xv6y-pJs2IyF0ul1lU6ITtHjqoLWL4O_p6Kua-bdofQUZDBEQBw1a20KnVgtfFGO9t4wmXDT0nOXxYmwggL4vp1oonHP2Yf4SJTyrMLUZaoyd5FAwi3TJHhgH8G7C0cWQB7W-7vpdyjLTdF3lYUIvMCS8Fjgy6d6GL73t2K0fdjcHh4yEXcnh6F5yLA7dA4srPtOB8fOatHPYVNdE3djZAeonTOr5ysS9r9WN3i9EQH3s0NWx5Us4gKEPZBQ3KR8DafHOA_3Hwin-N-_G990K7F2toCbX9Sxb1zTm3rsu0NpFlvEV17SWZPPaNHpWwBLGzDjCny44G2m2DQqGo6aInMXCtiA7fyvIVeFKKc8fUPqx6PmfkoiRxMt70f1QAAhL2jOayazrzQZXKP5iyehGDeSzspU0Y8LTy-Mju2sDlYRNl_efWRELT1VvGzQnXQOO7SJi4ifwEb9FBMZ3_3LjsTD3HiSofC0OmXjUlQs_6skd9dJy_OzFzaRoDKtOr0cH3fGZZMeBmF1GAg7tbo3o2pF4_ezzf7T-mzZDMzFJpchxHlmy06KocAV11uw25eZ3g34Q6ess5aV0-FHSyyn4zptuJThC_mH2N_zNQi9qxik8PKh6-Se7WKgueIWSVUFR0P_x0OEfM4bhetxlMMIrwKBXC6ZLfvpQKQEFPBIFK4NCMohMkmguyFPgZWcVixBT7IwsQ2EI7rz7GaUa9cQb9-iqPBx64LdDYG0uMWStCXOf96pKohEN1BlBMUrJHCacsPmjEhA0201fF" + } + }, + "id": "prt_f27eef535001ILgqF1EU9T12Hd", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27eeec22002xCKUzAnLuJjgt4" + }, + { + "type": "text", + "text": "Great — we can move to this next. I did a quick check, and I can verify official/public support for **SAM 2.1** (Meta + Hugging Face), but I can’t confidently identify a canonical/public **“SAM 3.1”** artifact from that alone.\n\nSo the safest plan is:\n\n- **Step 1: Model-source lock (blocking)**\n - Confirm exact SAM3.1 source you want (`repo/checkpoint/model id`).\n - If you meant Meta’s latest public branch, we can use `facebook/sam2.1-hiera-*` immediately.\n\n- **Step 2: Segmentation backend swap in `poc_eval_notebook.ipynb`**\n - Replace SAM1 imports (`SamModel`, `SamProcessor`) with backend-agnostic loader.\n - Add `SEGMENTATION_BACKEND` config (e.g. `sam1`, `sam2.1`, `sam3.1`) and model-id/config fields.\n\n- **Step 3: Prompt path parity**\n - Keep your current interaction modes unchanged (box + brush hints).\n - Map prompt inputs to the SAM3.1 API (`input_boxes`, optional points/labels).\n - Preserve current hint-aware mask selection logic (candidate ranking by overlap/coverage).\n\n- **Step 4: Output compatibility**\n - Keep return contract stable (`mask`, `bbox_xywh`, `sam_score`, `selection_score`, etc.) so downstream SigCLIP/passport cells do not need rewrites.\n\n- **Step 5: Smoke validation (no A/B yet)**\n - Single image, box mode.\n - Single image, brush-hint mode.\n - Multi-object run.\n - Confirm CSV/passport outputs unchanged structurally.\n\nOne thing I need from you before implementation:\n- Please send the exact **SAM3.1 model reference** you want me to wire (GitHub repo + checkpoint name, or Hugging Face model id). \n - If you want, I’ll default to `facebook/sam2.1-hiera-large` as the immediate upgrade path while we wait for your SAM3.1 artifact.", + "time": { + "start": 1783081402136, + "end": 1783081408120 + }, + "metadata": { + "openai": { + "itemId": "msg_0f4bcf8979a24608016a47a9ba181c8191a93ce6c70de1bd1f", + "phase": "final_answer" + } + }, + "id": "prt_f27eeff18001vFRnvFBpVCqFLe", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": 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SAM 3.1 \n\t\n\t\t\n\t\n\t\n\t\tSAM 3.1\n\t\n\nSAM 3 (Segment Anything with Concepts) is a unified foundation model from Meta for promptable segmentation in images and videos. It detects, segments, and tracks objects using text or visual prompts such as points, boxes, and masks. SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short text phrase, handling over 50x more unique concepts than existing benchmarks. SAM 3.1 builds on this with Object Multiplex, a shared-memory approach for joint multi-object tracking that delivers ~7x faster inference at 128 objects on a single H100 GPU without sacrificing accuracy, along with improved VOS performance on 6 out of 7 benchmarks.\n\n \n\n\nThis repository hosts only the SAM 3.1 model checkpoints — there is no Hugging Face Transformers integration. For installation, code, usage examples, and full documentation, please visit the SAM 3 GitHub repository.\n Downloads last month 79,711 \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\t\t\n\t\t Inference Providers NEW Mask Generation This model isn't deployed by any Inference Provider. 🙋 8 Ask for provider support Model tree for facebook/sam3.1 Finetunes 4 models Quantizations 6 models Spaces using facebook/sam3.1 7 🎯 magboola/sam3-zerogpu 🤖 subirmansukhani/smolvla-inspect 🎯 sumitjh/sam-benchmark 🎯 Alirezakzt/Concept-Segmentation 🟦 Harshil07/SAM3-Multiclass-segmentation 🎯 qq520085583/sam3-webui 🔧 Darth-Hidious/abicor-assembly-gpu + 2 Spaces Collection including facebook/sam3.1 SAM3 Collection 6 items • Updated Mar 26 • 295 System theme Company TOS Privacy About Careers Website Models Datasets Spaces Pricing Docs", + "metadata": { + "truncated": false + }, + "title": "https://huggingface.co/facebook/sam3.1 (text/html; charset=utf-8)", + "time": { + "start": 1783081483219, + "end": 1783081483431 + } + }, + "metadata": { + "openai": { + "itemId": "fc_09138965d11e8267016a47aa0acc0081a3b70fbf695e9d8480" + } + }, + "id": "prt_f27f03a6f001g2Xeyza0fhePXZ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f02d68002nf0eY3EbxphZm6" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 140518, + "input": 11916, + "output": 35, + "reasoning": 55, + "cache": { + "write": 0, + "read": 128512 + } + }, + "cost": 0.0446026, + "id": "prt_f27f03cac001PWS4j18cRPxP4t", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f02d68002nf0eY3EbxphZm6" + } + ] + }, + { + "info": { + "parentID": "msg_f27f02d4e001SonzlLEYjWgR79", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0280098, + "tokens": { + "total": 141323, + "input": 756, + "output": 79, + "reasoning": 72, + "cache": { + "write": 0, + "read": 140416 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783081483464, + "completed": 1783081488828 + }, + "finish": "tool-calls", + "id": "msg_f27f03cc70021lUfE1C70Ru2AL", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27f04368001TV3dUEYbi550M1", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f03cc70021lUfE1C70Ru2AL" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783081485427, + "end": 1783081487782 + }, + "metadata": { + "openai": { + "itemId": "rs_0701772a2ab54c1f016a47aa0d578881a2ba10ab05c500afdc", + "reasoningEncryptedContent": "gAAAAABqR6oPGW143sPYeGpclr-y_ho9S1qkbQ5cdqQs7xSuKpkI4xUk5q196bnA15F-U93Mk1eGD2mFGBgBv9llyrRwLh1xS3CATRpcIxoFjk1OU3s30QhSmOabl1Io8nrtxaw0Nq_kVdXVO2jA8iSSZ6pjzVj5mdrowAMqdL_9aFk60sTesapvw3iJxQ7hLDbWREpfkq0Ue0ehvUTDHMN85PWSSJJwZ8vkJfQxhtVwKktKfr5-ANF6-29Vq3UprfFEqj1cjLELuGa4MrH-oO4ZAEaaGg6bTnXlI7pe0Sv7W8EPC9cuq3hXlZoP3dC25bi_ZLTwLFqtrorM_yTidyLUI1ppoE4rS2MUFLbHrfcvViUEWF6UPsLBHv5cHUB6cAJMJfmYkfW0FvZg1QUXwpKBKxEQrtHnbcqKhIyKH2TuoLJpHqKD8dG_s-01MYqBprYSkN4KgvQZF5b0o--1h2Uuk4NxNrqZOvb0_2_KNnPMM-LraNYQ1Z4_7UdrHmqQ5_2S3kjl96MChFQhjhJLl-N7zGRfyTQ3GSSHg_8n1rcoFxZ3lwntvyPKc97IYemzLzHciE_1J4vUeMpVF_dL-P3kwAFnIlw0SEz98TBMmGzjgMRmDS4-wUc1o9RLre_I-5wSJ-Bw97FyfxUw-aionLbZYYgk-Bh6pNHqEYVBFdJPdVEeTump1c8RESYugueHtT76urrxvJQ1ovb3Oo6mKMN-jg-NE8Dpum2snlTE2rF8AchYmXUfrmpSk59IbEDOTswuFpjkhhpiQ6prD7wBy62oRiiyrL_KM7KoKo0nrqzMKzN3Qy9V2NSAj0m1fhJwZC0G5f_VtCMMLq0Bn_Warxm_Vea51jjd7j6nZ8JTdDiGCW_Figb7BDBe9SZ-T66WSD0dh7yWnNJDeM5cmgy2JJWz58T_6Ha4JdCEV266ZVExtTM_kKsELxZZOZpKV06WOlrIXPl4FB9pqvAd1actZJSz2TTOB0Xi_oTPBKZoD1fMdYyPC3chUezVO-4e0Cwws-Bo2FyyBAk_WU-zKeNE4wp1QG3mHYWe-inSVsLEokjUs9CVHIbmGe6qeRcI1CcfqTgDmEB9B-sqiI9DLh9YYW24EapO0R5iBfQ2a5nebMmLxJJJ4Tca8Wn5rqtDs6L_Urxq47mNS6KvJ0eeusNPDmBYVIZ0yBe2lq9ZCeK0-wyZ-YJ2AF61iCjl-KxJXWvdcuzuDBYGwnnU60VJkT_umFYOqz0NO5EvBy7ejH7ErCgPoQHGVyMf2fGcWEHkcVUK7x8XDJaXQBdYc2iy7uGiYqLrXu89vnoYFYb09u0M-cFc_rp_rsUFx1Q8t7_zSSwuKfNFBJHPO2vHZZGJ43Nlk3s1UnML52_-4849rDYwUv7KMXfoTi4HbYQvl-3Fns4i-t67K7F8Knxr3kG5ok1BF87kPuWiodbwbA==" + } + }, + "id": "prt_f27f04473001LfykDF2Yje1fQW", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f03cc70021lUfE1C70Ru2AL" + }, + { + "type": "tool", + "tool": "webfetch", + "callID": "call_BsRXXPzLv5V7msabcBGPmMcX", + "state": { + "status": "completed", + "input": { + "url": "https://github.com/facebookresearch/sam3", + "format": "text" + }, + "output": "GitHub - 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It can detect, segment, and track objects using text or visual prompts such as points, boxes, and masks. Compared to its predecessor SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short text phrase or exemplars. Unlike prior work, SAM 3 can handle a vastly larger set of open-vocabulary prompts. It achieves 75-80% of human performance on our new SA-CO benchmark which contains 270K unique concepts, over 50 times more than existing benchmarks.\nThis breakthrough is driven by an innovative data engine that has automatically annotated over 4 million unique concepts, creating the largest high-quality open-vocabulary segmentation dataset to date. In addition, SAM 3 introduces a new model architecture featuring a presence token that improves discrimination between closely related text prompts (e.g., “a player in white” vs. “a player in red”), as well as a decoupled detector–tracker design that minimizes task interference and scales efficiently with data.\n\n \n \n\nLatest updates\n03/27/2026 -- SAM 3.1 Object Multiplex is released. It introduces a shared-memory approach for joint multi-object tracking that is significantly faster without sacrificing accuracy.\n\nA new suite of improved model checkpoints (denoted as SAM 3.1) are released on Hugging Face. See RELEASE_SAM3p1.md for full details.\n\nTo use the new SAM 3.1 checkpoints, you need the latest model code from this repo. If you have installed an earlier version of this repo, pull the latest code from this repo (with git pull), and then reinstall the repo following Installation below.\n\n\n\nInstallation\nPrerequisites\n\nPython 3.12 or higher\nPyTorch 2.7 or higher\nCUDA-compatible GPU with CUDA 12.6 or higher\n\n\nCreate a new Conda environment:\n\nconda create -n sam3 python=3.12\nconda deactivate\nconda activate sam3\n\nInstall PyTorch with CUDA support:\n\npip install torch==2.10.0 torchvision --index-url https://download.pytorch.org/whl/cu128\n\nClone the repository and install the package:\n\ngit clone https://github.com/facebookresearch/sam3.git\ncd sam3\npip install -e .\n\nInstall additional dependencies for example notebooks or development:\n\n# For running example notebooks\npip install -e \".[notebooks]\"\n\n# For development\npip install -e \".[train,dev]\"\n\nOptional dependencies for faster inference\n\npip install einops ninja && pip install flash-attn-3 --no-deps --index-url https://download.pytorch.org/whl/cu128\npip install git+https://github.com/ronghanghu/cc_torch.git\nGetting Started\n⚠️ Before using SAM 3, please request access to the checkpoints on the SAM 3\nHugging Face repo. Once accepted, you\nneed to be authenticated to download the checkpoints. You can do this by running\nthe following steps\n(e.g. hf auth login after generating an access token.)\nBasic Usage\nimport torch\n#################################### For Image ####################################\nfrom PIL import Image\nfrom sam3.model_builder import build_sam3_image_model\nfrom sam3.model.sam3_image_processor import Sam3Processor\n# Load the model\nmodel = build_sam3_image_model()\nprocessor = Sam3Processor(model)\n# Load an image\nimage = Image.open(\"<YOUR_IMAGE_PATH.jpg>\")\ninference_state = processor.set_image(image)\n# Prompt the model with text\noutput = processor.set_text_prompt(state=inference_state, prompt=\"<YOUR_TEXT_PROMPT>\")\n\n# Get the masks, bounding boxes, and scores\nmasks, boxes, scores = output[\"masks\"], output[\"boxes\"], output[\"scores\"]\n\n#################################### For Video ####################################\n\nfrom sam3.model_builder import build_sam3_video_predictor\n\nvideo_predictor = build_sam3_video_predictor()\nvideo_path = \"<YOUR_VIDEO_PATH>\" # a JPEG folder or an MP4 video file\n# Start a session\nresponse = video_predictor.handle_request(\n request=dict(\n type=\"start_session\",\n resource_path=video_path,\n )\n)\nresponse = video_predictor.handle_request(\n request=dict(\n type=\"add_prompt\",\n session_id=response[\"session_id\"],\n frame_index=0, # Arbitrary frame index\n text=\"<YOUR_TEXT_PROMPT>\",\n )\n)\noutput = response[\"outputs\"]\nExamples\nThe examples directory contains notebooks demonstrating how to use SAM3 with\nvarious types of prompts:\n\nsam3_image_predictor_example.ipynb\n: Demonstrates how to prompt SAM 3 with text and visual box prompts on images.\nsam3_video_predictor_example.ipynb\n: Demonstrates how to prompt SAM 3 with text prompts on videos, and doing\nfurther interactive refinements with points.\nsam3_image_batched_inference.ipynb\n: Demonstrates how to run batched inference with SAM 3 on images.\nsam3_agent.ipynb: Demonsterates the use of SAM\n3 Agent to segment complex text prompt on images.\nsaco_gold_silver_vis_example.ipynb\n: Shows a few examples from SA-Co image evaluation set.\nsaco_veval_vis_example.ipynb :\nShows a few examples from SA-Co video evaluation set.\n\nThere are additional notebooks in the examples directory that demonstrate how to\nuse SAM 3 for interactive instance segmentation in images and videos (SAM 1/2\ntasks), or as a tool for an MLLM, and how to run evaluations on the SA-Co\ndataset.\nTo run the Jupyter notebook examples:\n# Make sure you have the notebooks dependencies installed\npip install -e \".[notebooks]\"\n\n# Start Jupyter notebook\njupyter notebook examples/sam3_image_predictor_example.ipynb\nModel\nSAM 3 consists of a detector and a tracker that share a vision encoder. It has 848M parameters. The\ndetector is a DETR-based model conditioned on text, geometry, and image\nexemplars. The tracker inherits the SAM 2 transformer encoder-decoder\narchitecture, supporting video segmentation and interactive refinement.\nImage Results\n\n\n \n \n Model\n Instance Segmentation\n Box Detection\n \n \n LVIS\n SA-Co/Gold\n LVIS\n COCO\n SA-Co/Gold\n \n \n cgF1\n AP\n cgF1\n cgF1\n AP\n AP\n APo\n\n cgF1\n \n \n \n \n Human\n -\n -\n 72.8\n -\n -\n -\n -\n 74.0\n \n \n OWLv2*\n 29.3\n 43.4\n 24.6\n 30.2\n 45.5\n 46.1\n 23.9\n 24.5\n \n \n DINO-X\n -\n 38.5\n 21.3\n -\n 52.4\n 56.0\n -\n 22.5\n \n \n Gemini 2.5\n 13.4\n -\n 13.0\n 16.1\n -\n -\n -\n 14.4\n \n \n SAM 3\n 37.2\n 48.5\n 54.1\n 40.6\n 53.6\n 56.4\n 55.7\n 55.7\n \n \n\n* Partially trained on LVIS, APo refers to COCO-O accuracy\n\nVideo Results\n\n\n \n \n Model\n SA-V test\n YT-Temporal-1B test\n SmartGlasses test\n LVVIS test\n BURST test\n \n \n cgF1\n pHOTA\n cgF1\n pHOTA\n cgF1\n pHOTA\n mAP\n HOTA\n \n \n \n \n Human\n 53.1\n 70.5\n 71.2\n 78.4\n 58.5\n 72.3\n -\n -\n \n \n SAM 3\n 30.3\n 58.0\n 50.8\n 69.9\n 36.4\n 63.6\n 36.3\n 44.5\n \n \n\n\nSA-Co Dataset\nWe release 2 image benchmarks, SA-Co/Gold and\nSA-Co/Silver, and a video benchmark\nSA-Co/VEval. The datasets contain images (or videos) with annotated noun phrases. Each image/video and noun phrase pair is annotated with instance masks and unique IDs of each object matching the phrase. Phrases that have no matching objects (negative prompts) have no masks, shown in red font in the figure. See the linked READMEs for more details on how to download and run evaluations on the datasets.\n\nHuggingFace host: SA-Co/Gold, SA-Co/Silver and SA-Co/VEval\nRoboflow host: SA-Co/Gold, SA-Co/Silver and SA-Co/VEval\n\n\nDevelopment\nTo set up the development environment:\npip install -e \".[dev,train]\"\nTo format the code:\nufmt format .\nContributing\nSee contributing and the\ncode of conduct.\nLicense\nThis project is licensed under the SAM License - see the LICENSE file\nfor details.\nAcknowledgements\nWe would like to thank the following people for their contributions to the SAM 3 project: Alex He, Alexander Kirillov,\nAlyssa Newcomb, Ana Paula Kirschner Mofarrej, Andrea Madotto, Andrew Westbury, Ashley Gabriel, Azita Shokpour,\nBen Samples, Bernie Huang, Carleigh Wood, Ching-Feng Yeh, Christian Puhrsch, Claudette Ward, Daniel Bolya,\nDaniel Li, Facundo Figueroa, Fazila Vhora, George Orlin, Hanzi Mao, Helen Klein, Hu Xu, Ida Cheng, Jake Kinney,\nJiale Zhi, Jo Sampaio, Joel Schlosser, Justin Johnson, Kai Brown, Karen Bergan, Karla Martucci, Kenny Lehmann,\nMaddie Mintz, Mallika Malhotra, Matt Ward, Michelle Chan, Michelle Restrepo, Miranda Hartley, Muhammad Maaz,\nNisha Deo, Peter Park, Phillip Thomas, Raghu Nayani, Rene Martinez Doehner, Robbie Adkins, Ross Girshik, Sasha\nMitts, Shashank Jain, Spencer Whitehead, Ty Toledano, Valentin Gabeur, Vincent Cho, Vivian Lee, William Ngan,\nXuehai He, Yael Yungster, Ziqi Pang, Ziyi Dou, Zoe Quake.\nCiting SAM 3\nIf you use SAM 3 or the SA-Co dataset in your research, please use the following BibTeX entry.\n@misc{carion2025sam3segmentconcepts,\n title={SAM 3: Segment Anything with Concepts},\n author={Nicolas Carion and Laura Gustafson and Yuan-Ting Hu and Shoubhik Debnath and Ronghang Hu and Didac Suris and Chaitanya Ryali and Kalyan Vasudev Alwala and Haitham Khedr and Andrew Huang and Jie Lei and Tengyu Ma and Baishan Guo and Arpit Kalla and Markus Marks and Joseph Greer and Meng Wang and Peize Sun and Roman Rädle and Triantafyllos Afouras and Effrosyni Mavroudi and Katherine Xu and Tsung-Han Wu and Yu Zhou and Liliane Momeni and Rishi Hazra and Shuangrui Ding and Sagar Vaze and Francois Porcher and Feng Li and Siyuan Li and Aishwarya Kamath and Ho Kei Cheng and Piotr Dollár and Nikhila Ravi and Kate Saenko and Pengchuan Zhang and Christoph Feichtenhofer},\n year={2025},\n eprint={2511.16719},\n archivePrefix={arXiv},\n primaryClass={cs.CV},\n url={https://arxiv.org/abs/2511.16719},\n}\n\n\n\n \n \n \n About\n\n \n The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.\n \n \n \n \n\n \n ai.meta.com/sam3/\n \n \n\n\n Resources\n \n \n \n \n\n Readme\n \n\n \n License\n \n \n \n \n\n View license\n \n \n\n\n Code of conduct\n \n \n \n \n\n Code of conduct\n \n \n\n\n Contributing\n \n \n \n \n\n Contributing\n \n \n\n Security policy\n \n \n \n \n\n Security policy\n \n \n\n \n \n\n \n \n \n \n \n\n Uh oh!\n\n There was an error while loading. 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It can detect, segment, and track objects using text or visual prompts such as points, boxes, and masks. Compared to its predecessor SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short text phrase or exemplars. Unlike prior work, SAM 3 can handle a vastly larger set of open-vocabulary prompts. It achieves 75-80% of human performance on our new SA-CO benchmark which contains 270K unique concepts, over 50 times more than existing benchmarks.\nThis breakthrough is driven by an innovative data engine that has automatically annotated over 4 million unique concepts, creating the largest high-quality open-vocabulary segmentation dataset to date. In addition, SAM 3 introduces a new model architecture featuring a presence token that improves discrimination between closely related text prompts (e.g., “a player in white” vs. “a player in red”), as well as a decoupled detector–tracker design that minimizes task interference and scales efficiently with data.\n\n \n \n\nLatest updates\n03/27/2026 -- SAM 3.1 Object Multiplex is released. It introduces a shared-memory approach for joint multi-object tracking that is significantly faster without sacrificing accuracy.\n\nA new suite of improved model checkpoints (denoted as SAM 3.1) are released on Hugging Face. See RELEASE_SAM3p1.md for full details.\n\nTo use the new SAM 3.1 checkpoints, you need the latest model code from this repo. If you have installed an earlier version of this repo, pull the latest code from this repo (with git pull), and then reinstall the repo following Installation below.\n\n\n\nInstallation\nPrerequisites\n\nPython 3.12 or higher\nPyTorch 2.7 or higher\nCUDA-compatible GPU with CUDA 12.6 or higher\n\n\nCreate a new Conda environment:\n\nconda create -n sam3 python=3.12\nconda deactivate\nconda activate sam3\n\nInstall PyTorch with CUDA support:\n\npip install torch==2.10.0 torchvision --index-url https://download.pytorch.org/whl/cu128\n\nClone the repository and install the package:\n\ngit clone https://github.com/facebookresearch/sam3.git\ncd sam3\npip install -e .\n\nInstall additional dependencies for example notebooks or development:\n\n# For running example notebooks\npip install -e \".[notebooks]\"\n\n# For development\npip install -e \".[train,dev]\"\n\nOptional dependencies for faster inference\n\npip install einops ninja && pip install flash-attn-3 --no-deps --index-url https://download.pytorch.org/whl/cu128\npip install git+https://github.com/ronghanghu/cc_torch.git\nGetting Started\n⚠️ Before using SAM 3, please request access to the checkpoints on the SAM 3\nHugging Face repo. Once accepted, you\nneed to be authenticated to download the checkpoints. You can do this by running\nthe following steps\n(e.g. hf auth login after generating an access token.)\nBasic Usage\nimport torch\n#################################### For Image ####################################\nfrom PIL import Image\nfrom sam3.model_builder import build_sam3_image_model\nfrom sam3.model.sam3_image_processor import Sam3Processor\n# Load the model\nmodel = build_sam3_image_model()\nprocessor = Sam3Processor(model)\n# Load an image\nimage = Image.open(\"<YOUR_IMAGE_PATH.jpg>\")\ninference_state = processor.set_image(image)\n# Prompt the model with text\noutput = processor.set_text_prompt(state=inference_state, prompt=\"<YOUR_TEXT_PROMPT>\")\n\n# Get the masks, bounding boxes, and scores\nmasks, boxes, scores = output[\"masks\"], output[\"boxes\"], output[\"scores\"]\n\n#################################### For Video ####################################\n\nfrom sam3.model_builder import build_sam3_video_predictor\n\nvideo_predictor = build_sam3_video_predictor()\nvideo_path = \"<YOUR_VIDEO_PATH>\" # a JPEG folder or an MP4 video file\n# Start a session\nresponse = video_predictor.handle_request(\n request=dict(\n type=\"start_session\",\n resource_path=video_path,\n )\n)\nresponse = video_predictor.handle_request(\n request=dict(\n type=\"add_prompt\",\n session_id=response[\"session_id\"],\n frame_index=0, # Arbitrary frame index\n text=\"<YOUR_TEXT_PROMPT>\",\n )\n)\noutput = response[\"outputs\"]\nExamples\nThe examples directory contains notebooks demonstrating how to use SAM3 with\nvarious types of prompts:\n\nsam3_image_predictor_example.ipynb\n: Demonstrates how to prompt SAM 3 with text and visual box prompts on images.\nsam3_video_predictor_example.ipynb\n: Demonstrates how to prompt SAM 3 with text prompts on videos, and doing\nfurther interactive refinements with points.\nsam3_image_batched_inference.ipynb\n: Demonstrates how to run batched inference with SAM 3 on images.\nsam3_agent.ipynb: Demonsterates the use of SAM\n3 Agent to segment complex text prompt on images.\nsaco_gold_silver_vis_example.ipynb\n: Shows a few examples from SA-Co image evaluation set.\nsaco_veval_vis_example.ipynb :\nShows a few examples from SA-Co video evaluation set.\n\nThere are additional notebooks in the examples directory that demonstrate how to\nuse SAM 3 for interactive instance segmentation in images and videos (SAM 1/2\ntasks), or as a tool for an MLLM, and how to run evaluations on the SA-Co\ndataset.\nTo run the Jupyter notebook examples:\n# Make sure you have the notebooks dependencies installed\npip install -e \".[notebooks]\"\n\n# Start Jupyter notebook\njupyter notebook examples/sam3_image_predictor_example.ipynb\nModel\nSAM 3 consists of a detector and a tracker that share a vision encoder. It has 848M parameters. The\ndetector is a DETR-based model conditioned on text, geometry, and image\nexemplars. The tracker inherits the SAM 2 transformer encoder-decoder\narchitecture, supporting video segmentation and interactive refinement.\nImage Results\n\n\n \n \n Model\n Instance Segmentation\n Box Detection\n \n \n LVIS\n SA-Co/Gold\n LVIS\n COCO\n SA-Co/Gold\n \n \n cgF1\n AP\n cgF1\n cgF1\n AP\n AP\n APo\n\n cgF1\n \n \n \n \n Human\n -\n -\n 72.8\n -\n -\n -\n -\n 74.0\n \n \n OWLv2*\n 29.3\n 43.4\n 24.6\n 30.2\n 45.5\n 46.1\n 23.9\n 24.5\n \n \n DINO-X\n -\n 38.5\n 21.3\n -\n 52.4\n 56.0\n -\n 22.5\n \n \n Gemini 2.5\n 13.4\n -\n 13.0\n 16.1\n -\n -\n -\n 14.4\n \n \n SAM 3\n 37.2\n 48.5\n 54.1\n 40.6\n 53.6\n 56.4\n 55.7\n 55.7\n \n \n\n* Partially trained on LVIS, APo refers to COCO-O accuracy\n\nVideo Results\n\n\n \n \n Model\n SA-V test\n YT-Temporal-1B test\n SmartGlasses test\n LVVIS test\n BURST test\n \n \n cgF1\n pHOTA\n cgF1\n pHOTA\n cgF1\n pHOTA\n mAP\n HOTA\n \n \n \n \n Human\n 53.1\n 70.5\n 71.2\n 78.4\n 58.5\n 72.3\n -\n -\n \n \n SAM 3\n 30.3\n 58.0\n 50.8\n 69.9\n 36.4\n 63.6\n 36.3\n 44.5\n \n \n\n\nSA-Co Dataset\nWe release 2 image benchmarks, SA-Co/Gold and\nSA-Co/Silver, and a video benchmark\nSA-Co/VEval. The datasets contain images (or videos) with annotated noun phrases. Each image/video and noun phrase pair is annotated with instance masks and unique IDs of each object matching the phrase. Phrases that have no matching objects (negative prompts) have no masks, shown in red font in the figure. See the linked READMEs for more details on how to download and run evaluations on the datasets.\n\nHuggingFace host: SA-Co/Gold, SA-Co/Silver and SA-Co/VEval\nRoboflow host: SA-Co/Gold, SA-Co/Silver and SA-Co/VEval\n\n\nDevelopment\nTo set up the development environment:\npip install -e \".[dev,train]\"\nTo format the code:\nufmt format .\nContributing\nSee contributing and the\ncode of conduct.\nLicense\nThis project is licensed under the SAM License - see the LICENSE file\nfor details.\nAcknowledgements\nWe would like to thank the following people for their contributions to the SAM 3 project: Alex He, Alexander Kirillov,\nAlyssa Newcomb, Ana Paula Kirschner Mofarrej, Andrea Madotto, Andrew Westbury, Ashley Gabriel, Azita Shokpour,\nBen Samples, Bernie Huang, Carleigh Wood, Ching-Feng Yeh, Christian Puhrsch, Claudette Ward, Daniel Bolya,\nDaniel Li, Facundo Figueroa, Fazila Vhora, George Orlin, Hanzi Mao, Helen Klein, Hu Xu, Ida Cheng, Jake Kinney,\nJiale Zhi, Jo Sampaio, Joel Schlosser, Justin Johnson, Kai Brown, Karen Bergan, Karla Martucci, Kenny Lehmann,\nMaddie Mintz, Mallika Malhotra, Matt Ward, Michelle Chan, Michelle Restrepo, Miranda Hartley, Muhammad Maaz,\nNisha Deo, Peter Park, Phillip Thomas, Raghu Nayani, Rene Martinez Doehner, Robbie Adkins, Ross Girshik, Sasha\nMitts, Shashank Jain, Spencer Whitehead, Ty Toledano, Valentin Gabeur, Vincent Cho, Vivian Lee, William Ngan,\nXuehai He, Yael Yungster, Ziqi Pang, Ziyi Dou, Zoe Quake.\nCiting SAM 3\nIf you use SAM 3 or the SA-Co dataset in your research, please use the following BibTeX entry.\n@misc{carion2025sam3segmentconcepts,\n title={SAM 3: Segment Anything with Concepts},\n author={Nicolas Carion and Laura Gustafson and Yuan-Ting Hu and Shoubhik Debnath and Ronghang Hu and Didac Suris and Chaitanya Ryali and Kalyan Vasudev Alwala and Haitham Khedr and Andrew Huang and Jie Lei and Tengyu Ma and Baishan Guo and Arpit Kalla and Markus Marks and Joseph Greer and Meng Wang and Peize Sun and Roman Rädle and Triantafyllos Afouras and Effrosyni Mavroudi and Katherine Xu and Tsung-Han Wu and Yu Zhou and Liliane Momeni and Rishi Hazra and Shuangrui Ding and Sagar Vaze and Francois Porcher and Feng Li and Siyuan Li and Aishwarya Kamath and Ho Kei Cheng and Piotr Dollár and Nikhila Ravi and Kate Saenko and Pengchuan Zhang and Christoph Feichtenhofer},\n year={2025},\n eprint={2511.16719},\n archivePrefix={arXiv},\n primaryClass={cs.CV},\n url={https://arxiv.org/abs/2511.16719},\n}\n \n\n\n\n\n\n \n\n\n\n \n \n\n \n\n \n Footer\n\n \n\n\n \n \n \n \n \n\n\n \n © 2026 GitHub, Inc.\n \n \n\n \n Footer navigation\n\n \n\n\n \n Terms\n \n\n \n Privacy\n \n\n \n Security\n \n\n \n Status\n \n\n \n Community\n \n\n \n Docs\n \n\n \n Contact\n \n\n \n \n \n Manage cookies\n \n \n\n\n\n \n \n Do not share my personal information\n \n \n\n\n \n \n \n\n\n\n\n \n\n\n\n\n \n \n \n\n \n \n \n\n \n You can’t perform that action at this time.", + "metadata": { + "truncated": false + }, + "title": 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I verified it, and that page also confirms an important detail: **no Transformers integration for `facebook/sam3.1`**. So we’ll wire via the official `facebookresearch/sam3` package.\n\nHere’s the implementation plan (no edits yet):\n\n- Replace SAM1 backend in `poc_eval_notebook.ipynb` with a SAM3.1 backend adapter:\n - remove `SamModel/SamProcessor` usage for segmentation,\n - add SAM3 runtime init (`sam3` repo package + model builder + processor),\n - keep your notebook’s selection UI (box + brush hints) unchanged.\n\n- Keep prompt flow the same from user perspective:\n - `box` mode -> box prompt into SAM3,\n - `brush` mode -> derived bbox + sampled positive points into SAM3,\n - preserve your accept/re-draw loop.\n\n- Keep your hint-aware ranking layer:\n - if SAM3 returns multiple candidates, rank by hint overlap/point coverage (same logic you now use),\n - if SAM3 returns a single mask, compute diagnostics only and continue.\n\n- Keep downstream compatibility:\n - same `seg` contract (`mask`, `bbox_xywh`, scores/metrics),\n - no changes to SigCLIP pipeline shape, CSV columns, or passport schema expectations.\n\n- Add SAM3-specific setup guards:\n - Hugging Face auth/token check for gated model,\n - clear error if checkpoint not accessible,\n - backend/version printouts for reproducibility.\n\nOne blocking choice before I implement:\n1. Which checkpoint should I target first for notebook use? \n - `sam3.1` default/full (recommended for quality) \n - smallest/fastest available 3.1 checkpoint (recommended for Colab speed)\n\n2. Are you staying on **Google Colab runtime** for this step? \n (SAM3 repo lists stricter env requirements; I can tune the install path accordingly.)", + "time": { + "start": 1783081499154, + "end": 1783081505089 + }, + "metadata": { + "openai": { + "itemId": "msg_0acbfb836a95b894016a47aa1ae67081a1a694543f5661aca2", + "phase": "final_answer" + } + }, + "id": "prt_f27f07a12001uMvzxpOlVKfIoT", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f051cd002Bp3HGnwiQRMVhR" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 151147, + "input": 9078, + "output": 411, + "reasoning": 474, + "cache": { + "write": 0, + "read": 141184 + } + }, + "cost": 0.0529837, + "id": "prt_f27f091b2001p3w6ThyjwSqwRl", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f051cd002Bp3HGnwiQRMVhR" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783081600964 + }, + "agent": "build", + "model": { + "providerID": "openai", + 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io\\n\",\n39: \"from datetime import datetime, timezone\\n\",\n40: \"import json\\n\",\n41: \"\\n\",\n42: \"import numpy as np\\n\",\n43: \"import pandas as pd\\n\",\n44: \"import torch\\n\",\n45: \"import matplotlib.pyplot as plt\\n\",\n46: \"import matplotlib.patches as patches\\n\",\n47: \"from PIL import Image\\n\",\n48: \"\\n\",\n49: \"from transformers import (\\n\",\n50: \" SamModel,\\n\",\n51: \" SamProcessor,\\n\",\n52: \" AutoModel,\\n\",\n53: \" AutoProcessor,\\n\",\n54: \")\\n\",\n55: \"\\n\",\n56: \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n57: \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n58: \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n59: \"\\n\",\n60: \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n61: \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n62: \"PASSPORT_DIR = Path(\\\"/content/passports\\\")\\n\",\n63: \"PASSPORT_DIR.mkdir(parents=True, exist_ok=True)\\n\",\n64: \"\\n\",\n65: \"THRESHOLDS = {\\n\",\n66: \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n67: \" \\\"top1_accuracy\\\": 0.70,\\n\",\n68: \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n69: \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n70: \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n71: \"}\\n\",\n72: \"\\n\",\n73: \"MATERIAL_TAXONOMY = {\\n\",\n74: \" \\\"concrete\\\": [\\\"concrete\\\", \\\"cement\\\"],\\n\",\n75: \" \\\"brick\\\": [\\\"brick\\\", \\\"masonry brick\\\"],\\n\",\n76: \" \\\"wood\\\": [\\\"wood\\\", \\\"timber\\\", \\\"plywood\\\"],\\n\",\n77: \" \\\"steel\\\": [\\\"steel\\\", \\\"iron metal\\\"],\\n\",\n78: \" \\\"aluminum\\\": [\\\"aluminum\\\", \\\"aluminium\\\"],\\n\",\n79: \" \\\"glass\\\": [\\\"glass\\\", \\\"window glass\\\"],\\n\",\n80: \" \\\"plastic\\\": [\\\"plastic\\\", \\\"polymer\\\", \\\"pvc\\\"],\\n\",\n81: \" \\\"ceramic\\\": [\\\"ceramic\\\", \\\"tile\\\", \\\"porcelain\\\"],\\n\",\n82: \" \\\"gypsum\\\": [\\\"gypsum\\\", 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indent=2), encoding=\\\"utf-8\\\")\\n\",\n \" return out_path\\n\",\n \"\\n\",\n \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n \" df_out.to_csv(csv_path, index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n \"print(\\\"Loading SAM model...\\\")\\n\",\n \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n \"sam_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = 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All Rights Reserved\n\n# pyre-unsafe\nfrom typing import Dict, List\n\nimport numpy as np\nimport PIL\nimport torch\nfrom sam3.model import box_ops\nfrom sam3.model.data_misc import FindStage, interpolate\nfrom torchvision.transforms import v2\n\n\nclass Sam3Processor:\n \"\"\" \"\"\"\n\n def __init__(self, model, resolution=1008, device=\"cuda\", confidence_threshold=0.5):\n self.model = model\n self.resolution = resolution\n self.device = device\n self.transform = v2.Compose(\n [\n v2.ToDtype(torch.uint8, scale=True),\n v2.Resize(size=(resolution, resolution)),\n v2.ToDtype(torch.float32, scale=True),\n v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n ]\n )\n self.confidence_threshold = confidence_threshold\n\n self.find_stage = FindStage(\n img_ids=torch.tensor([0], device=device, dtype=torch.long),\n text_ids=torch.tensor([0], device=device, dtype=torch.long),\n input_boxes=None,\n input_boxes_mask=None,\n input_boxes_label=None,\n input_points=None,\n input_points_mask=None,\n )\n\n @torch.inference_mode()\n def set_image(self, image, state=None):\n \"\"\"Sets the image on which we want to do predictions.\"\"\"\n if state is None:\n state = {}\n\n if isinstance(image, PIL.Image.Image):\n width, height = image.size\n elif isinstance(image, (torch.Tensor, np.ndarray)):\n height, width = image.shape[-2:]\n else:\n raise ValueError(\"Image must be a PIL image or a tensor\")\n\n image = v2.functional.to_image(image).to(self.device)\n image = self.transform(image).unsqueeze(0)\n\n state[\"original_height\"] = height\n state[\"original_width\"] = width\n state[\"backbone_out\"] = self.model.backbone.forward_image(image)\n inst_interactivity_en = self.model.inst_interactive_predictor is not None\n if inst_interactivity_en and \"sam2_backbone_out\" in state[\"backbone_out\"]:\n sam2_backbone_out = state[\"backbone_out\"][\"sam2_backbone_out\"]\n sam2_backbone_out[\"backbone_fpn\"][0] = (\n self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s0(\n sam2_backbone_out[\"backbone_fpn\"][0]\n )\n )\n sam2_backbone_out[\"backbone_fpn\"][1] = (\n self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s1(\n sam2_backbone_out[\"backbone_fpn\"][1]\n )\n )\n return state\n\n @torch.inference_mode()\n def set_image_batch(self, images: List[np.ndarray], state=None):\n \"\"\"Sets the image batch on which we want to do predictions.\"\"\"\n if state is None:\n state = {}\n\n if not isinstance(images, list):\n raise ValueError(\"Images must be a list of PIL images or tensors\")\n assert len(images) > 0, \"Images list must not be empty\"\n assert isinstance(images[0], PIL.Image.Image), (\n \"Images must be a list of PIL images\"\n )\n\n state[\"original_heights\"] = [image.height for image in images]\n state[\"original_widths\"] = [image.width for image in images]\n\n images = [\n self.transform(v2.functional.to_image(image).to(self.device))\n for image in images\n ]\n images = torch.stack(images, dim=0)\n state[\"backbone_out\"] = self.model.backbone.forward_image(images)\n inst_interactivity_en = self.model.inst_interactive_predictor is not None\n if inst_interactivity_en and \"sam2_backbone_out\" in state[\"backbone_out\"]:\n sam2_backbone_out = state[\"backbone_out\"][\"sam2_backbone_out\"]\n sam2_backbone_out[\"backbone_fpn\"][0] = (\n self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s0(\n sam2_backbone_out[\"backbone_fpn\"][0]\n )\n )\n sam2_backbone_out[\"backbone_fpn\"][1] = (\n self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s1(\n sam2_backbone_out[\"backbone_fpn\"][1]\n )\n )\n return state\n\n @torch.inference_mode()\n def set_text_prompt(self, prompt: str, state: Dict):\n \"\"\"Sets the text prompt and run the inference\"\"\"\n\n if \"backbone_out\" not in state:\n raise ValueError(\"You must call set_image before set_text_prompt\")\n\n text_outputs = self.model.backbone.forward_text([prompt], device=self.device)\n # will erase the previous text prompt if any\n state[\"backbone_out\"].update(text_outputs)\n if \"geometric_prompt\" not in state:\n state[\"geometric_prompt\"] = self.model._get_dummy_prompt()\n\n return self._forward_grounding(state)\n\n @torch.inference_mode()\n def add_geometric_prompt(self, box: List, label: bool, state: Dict):\n \"\"\"Adds a box prompt and run the inference.\n The image needs to be set, but not necessarily the text prompt.\n The box is assumed to be in [center_x, center_y, width, height] format and normalized in [0, 1] range.\n The label is True for a positive box, False for a negative box.\n \"\"\"\n if \"backbone_out\" not in state:\n raise ValueError(\"You must call set_image before set_text_prompt\")\n\n if \"language_features\" not in state[\"backbone_out\"]:\n # Looks like we don't have a text prompt yet. This is allowed, but we need to set the text prompt to \"visual\" for the model to rely only on the geometric prompt\n dummy_text_outputs = self.model.backbone.forward_text(\n [\"visual\"], device=self.device\n )\n state[\"backbone_out\"].update(dummy_text_outputs)\n\n if \"geometric_prompt\" not in state:\n state[\"geometric_prompt\"] = self.model._get_dummy_prompt()\n\n # adding a batch and sequence dimension\n boxes = torch.tensor(box, device=self.device, dtype=torch.float32).view(1, 1, 4)\n labels = torch.tensor([label], device=self.device, dtype=torch.bool).view(1, 1)\n state[\"geometric_prompt\"].append_boxes(boxes, labels)\n\n return self._forward_grounding(state)\n\n def reset_all_prompts(self, state: Dict):\n \"\"\"Removes all the prompts and results\"\"\"\n if \"backbone_out\" in state:\n backbone_keys_to_del = [\n \"language_features\",\n \"language_mask\",\n \"language_embeds\",\n ]\n for key in backbone_keys_to_del:\n if key in state[\"backbone_out\"]:\n del state[\"backbone_out\"][key]\n\n keys_to_del = [\"geometric_prompt\", \"boxes\", \"masks\", \"masks_logits\", \"scores\"]\n for key in keys_to_del:\n if key in state:\n del state[key]\n\n @torch.inference_mode()\n def set_confidence_threshold(self, threshold: float, state=None):\n \"\"\"Sets the confidence threshold for the masks\"\"\"\n self.confidence_threshold = threshold\n if state is not None and \"boxes\" in state:\n # we need to filter the boxes again\n # In principle we could do this more efficiently since we would only need\n # to rerun the heads. But this is simpler and not too inefficient\n return self._forward_grounding(state)\n return state\n\n @torch.inference_mode()\n def _forward_grounding(self, state: Dict):\n outputs = self.model.forward_grounding(\n backbone_out=state[\"backbone_out\"],\n find_input=self.find_stage,\n geometric_prompt=state[\"geometric_prompt\"],\n find_target=None,\n )\n\n out_bbox = outputs[\"pred_boxes\"]\n out_logits = outputs[\"pred_logits\"]\n out_masks = outputs[\"pred_masks\"]\n out_probs = out_logits.sigmoid()\n presence_score = outputs[\"presence_logit_dec\"].sigmoid().unsqueeze(1)\n out_probs = (out_probs * presence_score).squeeze(-1)\n\n keep = out_probs > self.confidence_threshold\n out_probs = out_probs[keep]\n out_masks = out_masks[keep]\n out_bbox = out_bbox[keep]\n\n # convert to [x0, y0, x1, y1] format\n boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)\n\n img_h = state[\"original_height\"]\n img_w = state[\"original_width\"]\n scale_fct = torch.tensor([img_w, img_h, img_w, img_h]).to(self.device)\n boxes = boxes * scale_fct[None, :]\n\n out_masks = interpolate(\n out_masks.unsqueeze(1),\n (img_h, img_w),\n mode=\"bilinear\",\n align_corners=False,\n ).sigmoid()\n\n state[\"masks_logits\"] = out_masks\n state[\"masks\"] = out_masks > 0.5\n state[\"boxes\"] = boxes\n state[\"scores\"] = out_probs\n return state\n", + "metadata": { + "truncated": false + }, + "title": "https://raw.githubusercontent.com/facebookresearch/sam3/main/sam3/model/sam3_image_processor.py (text/plain; charset=utf-8)", + "time": { + "start": 1783081622793, + "end": 1783081623182 + } + }, + "metadata": { + "openai": { + "itemId": "fc_087fb1994f52caf7016a47aa96506881929737868d543c1d00" + } + }, + "id": "prt_f27f25b57001J62BCALDwfhMjH", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f2351a002OzK8N1ixLcdIr9" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 153255, + "input": 1118, + "output": 44, + "reasoning": 157, + "cache": { + "write": 0, + "read": 151936 + } + }, + "cost": 0.0313593, + "id": "prt_f27f25e91001ladtnNRzaIDfYh", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f2351a002OzK8N1ixLcdIr9" + } + ] + }, + { + "info": { + "parentID": "msg_f27f207c40019BcuAqaBu96PbB", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.03261965, + "tokens": { + "total": 155411, + "input": 2179, + "output": 40, + "reasoning": 104, + "cache": { + "write": 0, + "read": 153088 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783081623204, + "completed": 1783081627657 + }, + "finish": "tool-calls", + "id": "msg_f27f25ea4002dTIhhgNeUr03n0", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27f264dc001ucKR1KPBJgIKX7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f25ea4002dTIhhgNeUr03n0" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783081625107, + "end": 1783081627087 + }, + "metadata": { + "openai": { + "itemId": "rs_0c7292499a5a0cf5016a47aa98f5d881a0ab38d0e08b91b89b", + "reasoningEncryptedContent": "gAAAAABqR6qaXdRrWRyfVsDt2ZmAC_hG6r_4U9YRZAdDzFpZxN7G6_FRWDCpT7p46DZxaheBXgDzH8JJx877A-aEk3DYTBCGNPukgEREBKfc4GggIU5Hsv-xqIlrXanKPZXCtJvawXg3rAebxM3Oqcz5lRQ8YUYFYNhgTh0YzidbUrSexuxj4jElRQF2IRDemDhMeHZs8jx0ie_ElgZqqgitVLXwgGEPVii13hqn6THelbxMZFrZlEC2TDaZivHHt2kTnBfFYA4QC8cQwixm2g2FWovWhHRHbLhcqduqoO7fhgK5W9vK67g-HkA9yxUrYXsJWcnSVrPxn5iXqnrqpnMtpkuqLDekwF3RHAO3Z2gjvyWe9IW8Pn2I6CyraFoMgN6MOHATRWlG1y-q5tHG9DAWIsD5y0nSC0bN54mIZ5ZgLJ0KpOEMpxoe1NBQ-IM42lLK4b3cwpbnS8UrxxhRJSueJNnuEeBfQqzL27dL_yZ2C6akMrjS3-PeI2WDCK6IzNVTUqRQ9uAyfSsNf0zQVMeIf9ihO6rnG45Z7Zun0xrq0oIDzg7Qd_kwHTFlopoGNEDcf1sk2dqXMKqeoQECoeyT3ziyCvYpktL0WHCfbFhlka3jqD6rB-W2j4FqwcOFdlMJ481hwBKwh1x_scMGD1zY1ZNJBeQuOYEepg_pY2BbbZvzZPBM7g-8PEpRBEf7RqSlhPzKfjoO8zI7Dk9_wxcwSQnlLGAehg7sH0sI8N1wfZhDMBoz7oXXGD8pcRscxpNGa1u5QoZ1ReauKgZ4PUzd4sBqGBz4rW28R-yVlnk2nGpoBXDjq3BmC0jY1kPclizShgV9_48BHXCoSQ4HgSXQe5MykqpOYHDA_147uDltd5v8cnsIpQfsZkyVe0Pr9nk_77z3sWQa2wIBy7_Bk_VN9PAaB2np6k7JiCKq25vQgDnsjZzdyoHYhqHIa52MVv2gykCAjLrMsS1UdzvctBlkVImypfZ4TZCSpHlrqq1JTql4AjVkHArt8q1a-z3yE8n7X_zZmntRa-jMnxzxNgbz2Dr--zfU0Wc5w85dN1t1BFRcVhueDZzQGGTzhsUolTtArGlNu3-kAnOj379R6TXTvqZt4JZN6aYXF8KugpspSgqPy_mYC5CmG9YOIph-2k1nmVTedh0Xk3OLeHoLbFBRlcUYy2k0Ysc6gG7ioZ-Be-VLDWpdfkshCRQaCSrQ3rKV7HOnmhpwRuU-a5YlTWGHNXlrxlPxAUaQ2KHjm5leky9QmVF3oeMtjzc2IrfwQatuVre2GogifCPDaAgT3f4CZtieXCHKCP77vzEKWoFQL2V51nh2cV6Ha94bm6dZx7H2vymB3qYwdzj_iF1GyHmc6ZjnR-7BtmNn8JoSHvc820s7faAtnaRX4t-MmlfXpG2aKHT8PpWgwJpsOugQCrdD6fSyQhQz0go0gnUcxxfvyvcg_pP2M164_Iac4bYUqSUNinf1XOyc_uK0kDLz9r1nSSAo0sRnW5_phkiM2CqiQYSq2lGAOesS5jpyKVy7FtzdxwqGojpcnkhMGt0VHpM0DzZ2c3H9mEj1cZZas65hWi-8dImtYBUDzOc4XT-IEwhD_sVcFMR7" + } + }, + "id": "prt_f27f26613001sdWkhCwZmbWhm7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f25ea4002dTIhhgNeUr03n0" + }, + { + "type": "tool", + "tool": "webfetch", + "callID": "call_WEPjJC83ZJFfC1r0Ty1h2MN4", + "state": { + "status": "completed", + "input": { + "url": "https://raw.githubusercontent.com/facebookresearch/sam3/main/sam3/model_builder.py", + "format": "text" + }, + "output": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport os\nfrom typing import Optional\n\nimport pkg_resources\nimport torch\nimport torch.nn as nn\nfrom huggingface_hub import hf_hub_download\nfrom iopath.common.file_io import g_pathmgr\nfrom sam3.model.decoder import (\n DecoupledTransformerDecoderLayerv2,\n SimpleRoPEAttention,\n TransformerDecoder,\n TransformerDecoderLayer,\n TransformerDecoderLayerv2,\n TransformerEncoderCrossAttention,\n TransformerEncoderDecoupledCrossAttention,\n)\nfrom sam3.model.encoder import TransformerEncoderFusion, TransformerEncoderLayer\nfrom sam3.model.geometry_encoders import SequenceGeometryEncoder\nfrom sam3.model.maskformer_segmentation import PixelDecoder, UniversalSegmentationHead\nfrom sam3.model.memory import (\n CXBlock,\n SimpleFuser,\n SimpleMaskDownSampler,\n SimpleMaskEncoder,\n)\nfrom sam3.model.model_misc import (\n DotProductScoring,\n MLP,\n MultiheadAttentionWrapper as MultiheadAttention,\n TransformerWrapper,\n)\nfrom sam3.model.multiplex_utils import MultiplexController\nfrom sam3.model.necks import Sam3DualViTDetNeck, Sam3TriViTDetNeck\nfrom sam3.model.position_encoding import PositionEmbeddingSine\nfrom sam3.model.sam1_task_predictor import SAM3InteractiveImagePredictor\nfrom sam3.model.sam3_image import Sam3Image, Sam3ImageOnVideoMultiGPU\nfrom sam3.model.sam3_tracking_predictor import Sam3TrackerPredictor\nfrom sam3.model.sam3_video_inference import Sam3VideoInferenceWithInstanceInteractivity\nfrom sam3.model.sam3_video_predictor import Sam3VideoPredictorMultiGPU\nfrom sam3.model.text_encoder_ve import VETextEncoder\nfrom sam3.model.tokenizer_ve import SimpleTokenizer\nfrom sam3.model.video_tracking_multiplex import VideoTrackingDynamicMultiplex\nfrom sam3.model.vitdet import ViT\nfrom sam3.model.vl_combiner import SAM3VLBackbone, SAM3VLBackboneTri, TriHeadVisionOnly\nfrom sam3.sam.transformer import RoPEAttention\n\n\n# Setup TensorFloat-32 for Ampere GPUs if available\ndef _setup_tf32() -> None:\n \"\"\"Enable TensorFloat-32 for Ampere GPUs if available.\"\"\"\n if torch.cuda.is_available():\n device_props = torch.cuda.get_device_properties(0)\n if device_props.major >= 8:\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.allow_tf32 = True\n\n\n_setup_tf32()\n\n\ndef _create_position_encoding(precompute_resolution=None):\n \"\"\"Create position encoding for visual backbone.\"\"\"\n return PositionEmbeddingSine(\n num_pos_feats=256,\n normalize=True,\n scale=None,\n temperature=10000,\n precompute_resolution=precompute_resolution,\n )\n\n\ndef _create_vit_backbone(compile_mode=None, use_fa3=False, use_rope_real=False):\n \"\"\"Create ViT backbone for visual feature extraction.\"\"\"\n return ViT(\n img_size=1008,\n pretrain_img_size=336,\n patch_size=14,\n embed_dim=1024,\n depth=32,\n num_heads=16,\n mlp_ratio=4.625,\n norm_layer=\"LayerNorm\",\n drop_path_rate=0.1,\n qkv_bias=True,\n use_abs_pos=True,\n tile_abs_pos=True,\n global_att_blocks=(7, 15, 23, 31),\n rel_pos_blocks=(),\n use_rope=True,\n use_interp_rope=True,\n window_size=24,\n pretrain_use_cls_token=True,\n retain_cls_token=False,\n ln_pre=True,\n ln_post=False,\n return_interm_layers=False,\n bias_patch_embed=False,\n compile_mode=compile_mode,\n use_fa3=use_fa3,\n use_rope_real=use_rope_real,\n )\n\n\ndef _create_vit_neck(position_encoding, vit_backbone, enable_inst_interactivity=False):\n \"\"\"Create ViT neck for feature pyramid.\"\"\"\n return Sam3DualViTDetNeck(\n position_encoding=position_encoding,\n d_model=256,\n scale_factors=[4.0, 2.0, 1.0, 0.5],\n trunk=vit_backbone,\n add_sam2_neck=enable_inst_interactivity,\n )\n\n\ndef _create_vl_backbone(vit_neck, text_encoder):\n \"\"\"Create visual-language backbone.\"\"\"\n return SAM3VLBackbone(visual=vit_neck, text=text_encoder, scalp=1)\n\n\ndef _create_transformer_encoder(use_fa3=False) -> TransformerEncoderFusion:\n \"\"\"Create transformer encoder with its layer.\"\"\"\n encoder_layer = TransformerEncoderLayer(\n activation=\"relu\",\n d_model=256,\n dim_feedforward=2048,\n dropout=0.1,\n pos_enc_at_attn=True,\n pos_enc_at_cross_attn_keys=False,\n pos_enc_at_cross_attn_queries=False,\n pre_norm=True,\n self_attention=MultiheadAttention(\n num_heads=8,\n dropout=0.1,\n embed_dim=256,\n batch_first=True,\n use_fa3=use_fa3,\n ),\n cross_attention=MultiheadAttention(\n num_heads=8,\n dropout=0.1,\n embed_dim=256,\n batch_first=True,\n use_fa3=use_fa3,\n ),\n )\n\n encoder = TransformerEncoderFusion(\n layer=encoder_layer,\n num_layers=6,\n d_model=256,\n num_feature_levels=1,\n frozen=False,\n use_act_checkpoint=True,\n add_pooled_text_to_img_feat=False,\n pool_text_with_mask=True,\n )\n return encoder\n\n\ndef _create_transformer_decoder(use_fa3=False) -> TransformerDecoder:\n \"\"\"Create transformer decoder with its layer.\"\"\"\n decoder_layer = TransformerDecoderLayer(\n activation=\"relu\",\n d_model=256,\n dim_feedforward=2048,\n dropout=0.1,\n cross_attention=MultiheadAttention(\n num_heads=8,\n dropout=0.1,\n embed_dim=256,\n use_fa3=use_fa3,\n ),\n n_heads=8,\n use_text_cross_attention=True,\n )\n\n decoder = TransformerDecoder(\n layer=decoder_layer,\n num_layers=6,\n num_queries=200,\n return_intermediate=True,\n box_refine=True,\n num_o2m_queries=0,\n dac=True,\n boxRPB=\"log\",\n d_model=256,\n frozen=False,\n interaction_layer=None,\n dac_use_selfatt_ln=True,\n resolution=1008,\n stride=14,\n use_act_checkpoint=True,\n presence_token=True,\n )\n return decoder\n\n\ndef _create_dot_product_scoring():\n \"\"\"Create dot product scoring module.\"\"\"\n prompt_mlp = MLP(\n input_dim=256,\n hidden_dim=2048,\n output_dim=256,\n num_layers=2,\n dropout=0.1,\n residual=True,\n out_norm=nn.LayerNorm(256),\n )\n return DotProductScoring(d_model=256, d_proj=256, prompt_mlp=prompt_mlp)\n\n\ndef _create_segmentation_head(compile_mode=None, use_fa3=False):\n \"\"\"Create segmentation head with pixel decoder.\"\"\"\n pixel_decoder = PixelDecoder(\n num_upsampling_stages=3,\n interpolation_mode=\"nearest\",\n hidden_dim=256,\n compile_mode=compile_mode,\n )\n\n cross_attend_prompt = MultiheadAttention(\n num_heads=8,\n dropout=0,\n embed_dim=256,\n use_fa3=use_fa3,\n )\n\n segmentation_head = UniversalSegmentationHead(\n hidden_dim=256,\n upsampling_stages=3,\n aux_masks=False,\n presence_head=False,\n dot_product_scorer=None,\n act_ckpt=True,\n cross_attend_prompt=cross_attend_prompt,\n pixel_decoder=pixel_decoder,\n )\n return segmentation_head\n\n\ndef _create_geometry_encoder():\n \"\"\"Create geometry encoder with all its components.\"\"\"\n # Create position encoding for geometry encoder\n geo_pos_enc = _create_position_encoding()\n # Create CX block for fuser\n cx_block = CXBlock(\n dim=256,\n kernel_size=7,\n padding=3,\n layer_scale_init_value=1.0e-06,\n use_dwconv=True,\n )\n # Create geometry encoder layer\n geo_layer = TransformerEncoderLayer(\n activation=\"relu\",\n d_model=256,\n dim_feedforward=2048,\n dropout=0.1,\n pos_enc_at_attn=False,\n pre_norm=True,\n self_attention=MultiheadAttention(\n num_heads=8,\n dropout=0.1,\n embed_dim=256,\n batch_first=False,\n ),\n pos_enc_at_cross_attn_queries=False,\n pos_enc_at_cross_attn_keys=True,\n cross_attention=MultiheadAttention(\n num_heads=8,\n dropout=0.1,\n embed_dim=256,\n batch_first=False,\n ),\n )\n\n # Create geometry encoder\n input_geometry_encoder = SequenceGeometryEncoder(\n pos_enc=geo_pos_enc,\n encode_boxes_as_points=False,\n points_direct_project=True,\n points_pool=True,\n points_pos_enc=True,\n boxes_direct_project=True,\n boxes_pool=True,\n boxes_pos_enc=True,\n d_model=256,\n num_layers=3,\n layer=geo_layer,\n use_act_ckpt=True,\n add_cls=True,\n add_post_encode_proj=True,\n )\n return input_geometry_encoder\n\n\ndef _create_sam3_model(\n backbone,\n transformer,\n input_geometry_encoder,\n segmentation_head,\n dot_prod_scoring,\n inst_interactive_predictor,\n eval_mode,\n):\n \"\"\"Create the SAM3 image model.\"\"\"\n common_params = {\n \"backbone\": backbone,\n \"transformer\": transformer,\n \"input_geometry_encoder\": input_geometry_encoder,\n \"segmentation_head\": segmentation_head,\n \"num_feature_levels\": 1,\n \"o2m_mask_predict\": True,\n \"dot_prod_scoring\": dot_prod_scoring,\n \"use_instance_query\": False,\n \"multimask_output\": True,\n \"inst_interactive_predictor\": inst_interactive_predictor,\n }\n\n matcher = None\n if not eval_mode:\n from sam3.train.matcher import BinaryHungarianMatcherV2\n\n matcher = BinaryHungarianMatcherV2(\n focal=True,\n cost_class=2.0,\n cost_bbox=5.0,\n cost_giou=2.0,\n alpha=0.25,\n gamma=2,\n stable=False,\n )\n common_params[\"matcher\"] = matcher\n model = Sam3Image(**common_params)\n\n return model\n\n\ndef _create_tracker_maskmem_backbone():\n \"\"\"Create the SAM3 Tracker memory encoder.\"\"\"\n # Position encoding for mask memory backbone\n position_encoding = PositionEmbeddingSine(\n num_pos_feats=64,\n normalize=True,\n scale=None,\n temperature=10000,\n precompute_resolution=1008,\n )\n\n # Mask processing components\n mask_downsampler = SimpleMaskDownSampler(\n kernel_size=3, stride=2, padding=1, interpol_size=[1152, 1152]\n )\n\n cx_block_layer = CXBlock(\n dim=256,\n kernel_size=7,\n padding=3,\n layer_scale_init_value=1.0e-06,\n use_dwconv=True,\n )\n\n fuser = SimpleFuser(layer=cx_block_layer, num_layers=2)\n\n maskmem_backbone = SimpleMaskEncoder(\n out_dim=64,\n position_encoding=position_encoding,\n mask_downsampler=mask_downsampler,\n fuser=fuser,\n )\n\n return maskmem_backbone\n\n\ndef _create_tracker_transformer():\n \"\"\"Create the SAM3 Tracker transformer components.\"\"\"\n # Self attention\n self_attention = RoPEAttention(\n embedding_dim=256,\n num_heads=1,\n downsample_rate=1,\n dropout=0.1,\n rope_theta=10000.0,\n feat_sizes=[72, 72],\n use_fa3=False,\n use_rope_real=False,\n )\n\n # Cross attention\n cross_attention = RoPEAttention(\n embedding_dim=256,\n num_heads=1,\n downsample_rate=1,\n dropout=0.1,\n kv_in_dim=64,\n rope_theta=10000.0,\n feat_sizes=[72, 72],\n rope_k_repeat=True,\n use_fa3=False,\n use_rope_real=False,\n )\n\n # Encoder layer\n encoder_layer = TransformerDecoderLayerv2(\n cross_attention_first=False,\n activation=\"relu\",\n dim_feedforward=2048,\n dropout=0.1,\n pos_enc_at_attn=False,\n pre_norm=True,\n self_attention=self_attention,\n d_model=256,\n pos_enc_at_cross_attn_keys=True,\n pos_enc_at_cross_attn_queries=False,\n cross_attention=cross_attention,\n )\n\n # Encoder\n encoder = TransformerEncoderCrossAttention(\n remove_cross_attention_layers=[],\n batch_first=True,\n d_model=256,\n frozen=False,\n pos_enc_at_input=True,\n layer=encoder_layer,\n num_layers=4,\n use_act_checkpoint=False,\n )\n\n # Transformer wrapper\n transformer = TransformerWrapper(\n encoder=encoder,\n decoder=None,\n d_model=256,\n )\n\n return transformer\n\n\ndef build_tracker(\n apply_temporal_disambiguation: bool, with_backbone: bool = False, compile_mode=None\n) -> Sam3TrackerPredictor:\n \"\"\"\n Build the SAM3 Tracker module for video tracking.\n\n Returns:\n Sam3TrackerPredictor: Wrapped SAM3 Tracker module\n \"\"\"\n\n # Create model components\n maskmem_backbone = _create_tracker_maskmem_backbone()\n transformer = _create_tracker_transformer()\n backbone = None\n if with_backbone:\n vision_backbone = _create_vision_backbone(compile_mode=compile_mode)\n backbone = SAM3VLBackbone(scalp=1, visual=vision_backbone, text=None)\n # Create the Tracker module\n model = Sam3TrackerPredictor(\n image_size=1008,\n num_maskmem=7,\n backbone=backbone,\n backbone_stride=14,\n transformer=transformer,\n maskmem_backbone=maskmem_backbone,\n # SAM parameters\n multimask_output_in_sam=True,\n # Evaluation\n forward_backbone_per_frame_for_eval=True,\n trim_past_non_cond_mem_for_eval=False,\n # Multimask\n multimask_output_for_tracking=True,\n multimask_min_pt_num=0,\n multimask_max_pt_num=1,\n # Additional settings\n always_start_from_first_ann_frame=False,\n # Mask overlap\n non_overlap_masks_for_mem_enc=False,\n non_overlap_masks_for_output=False,\n max_cond_frames_in_attn=4,\n offload_output_to_cpu_for_eval=False,\n # SAM decoder settings\n sam_mask_decoder_extra_args={\n \"dynamic_multimask_via_stability\": True,\n \"dynamic_multimask_stability_delta\": 0.05,\n \"dynamic_multimask_stability_thresh\": 0.98,\n },\n clear_non_cond_mem_around_input=True,\n fill_hole_area=0,\n use_memory_selection=apply_temporal_disambiguation,\n )\n\n return model\n\n\ndef _create_text_encoder(bpe_path: str) -> VETextEncoder:\n \"\"\"Create SAM3 text encoder.\"\"\"\n tokenizer = SimpleTokenizer(bpe_path=bpe_path)\n return VETextEncoder(\n tokenizer=tokenizer,\n d_model=256,\n width=1024,\n heads=16,\n layers=24,\n )\n\n\ndef _create_vision_backbone(\n compile_mode=None, enable_inst_interactivity=True\n) -> Sam3DualViTDetNeck:\n \"\"\"Create SAM3 visual backbone with ViT and neck.\"\"\"\n # Position encoding\n position_encoding = _create_position_encoding(precompute_resolution=1008)\n # ViT backbone\n vit_backbone: ViT = _create_vit_backbone(compile_mode=compile_mode)\n vit_neck: Sam3DualViTDetNeck = _create_vit_neck(\n position_encoding,\n vit_backbone,\n enable_inst_interactivity=enable_inst_interactivity,\n )\n # Visual neck\n return vit_neck\n\n\ndef _create_sam3_transformer(\n has_presence_token: bool = True, use_fa3: bool = False\n) -> TransformerWrapper:\n \"\"\"Create SAM3 transformer encoder and decoder.\"\"\"\n encoder: TransformerEncoderFusion = _create_transformer_encoder(use_fa3=use_fa3)\n decoder: TransformerDecoder = _create_transformer_decoder(use_fa3=use_fa3)\n\n return TransformerWrapper(encoder=encoder, decoder=decoder, d_model=256)\n\n\ndef _load_checkpoint(model, checkpoint_path):\n \"\"\"Load model checkpoint from file.\"\"\"\n with g_pathmgr.open(checkpoint_path, \"rb\") as f:\n ckpt = torch.load(f, map_location=\"cpu\", weights_only=True)\n if \"model\" in ckpt and isinstance(ckpt[\"model\"], dict):\n ckpt = ckpt[\"model\"]\n sam3_image_ckpt = {\n k.replace(\"detector.\", \"\"): v for k, v in ckpt.items() if \"detector\" in k\n }\n if model.inst_interactive_predictor is not None:\n sam3_image_ckpt.update(\n {\n k.replace(\"tracker.\", \"inst_interactive_predictor.model.\"): v\n for k, v in ckpt.items()\n if \"tracker\" in k\n }\n )\n missing_keys, _ = model.load_state_dict(sam3_image_ckpt, strict=False)\n if len(missing_keys) > 0:\n print(\n f\"loaded {checkpoint_path} and found \"\n f\"missing and/or unexpected keys:\\n{missing_keys=}\"\n )\n\n\ndef _setup_device_and_mode(model, device, eval_mode):\n \"\"\"Setup model device and evaluation mode.\"\"\"\n if device == \"cuda\":\n model = model.cuda()\n if eval_mode:\n model.eval()\n return model\n\n\ndef build_sam3_image_model(\n bpe_path=None,\n device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n eval_mode=True,\n checkpoint_path=None,\n load_from_HF=True,\n enable_segmentation=True,\n enable_inst_interactivity=False,\n compile=False,\n):\n \"\"\"\n Build SAM3 image model\n\n Args:\n bpe_path: Path to the BPE tokenizer vocabulary\n device: Device to load the model on ('cuda' or 'cpu')\n eval_mode: Whether to set the model to evaluation mode\n checkpoint_path: Optional path to model checkpoint\n enable_segmentation: Whether to enable segmentation head\n enable_inst_interactivity: Whether to enable instance interactivity (SAM 1 task)\n compile_mode: To enable compilation, set to \"default\"\n\n Returns:\n A SAM3 image model\n \"\"\"\n if bpe_path is None:\n bpe_path = pkg_resources.resource_filename(\n \"sam3\", \"assets/bpe_simple_vocab_16e6.txt.gz\"\n )\n\n # Create visual components\n compile_mode = \"default\" if compile else None\n vision_encoder = _create_vision_backbone(\n compile_mode=compile_mode, enable_inst_interactivity=enable_inst_interactivity\n )\n\n # Create text components\n text_encoder = _create_text_encoder(bpe_path)\n\n # Create visual-language backbone\n backbone = _create_vl_backbone(vision_encoder, text_encoder)\n\n # Create transformer components\n transformer = _create_sam3_transformer()\n\n # Create dot product scoring\n dot_prod_scoring = _create_dot_product_scoring()\n\n # Create segmentation head if enabled\n segmentation_head = (\n _create_segmentation_head(compile_mode=compile_mode)\n if enable_segmentation\n else None\n )\n\n # Create geometry encoder\n input_geometry_encoder = _create_geometry_encoder()\n if enable_inst_interactivity:\n sam3_pvs_base = build_tracker(apply_temporal_disambiguation=False)\n inst_predictor = SAM3InteractiveImagePredictor(sam3_pvs_base)\n else:\n inst_predictor = None\n # Create the SAM3 model\n model = _create_sam3_model(\n backbone,\n transformer,\n input_geometry_encoder,\n segmentation_head,\n dot_prod_scoring,\n inst_predictor,\n eval_mode,\n )\n if load_from_HF and checkpoint_path is None:\n checkpoint_path = download_ckpt_from_hf(version=\"sam3\")\n # Load checkpoint if provided\n if checkpoint_path is not None:\n _load_checkpoint(model, checkpoint_path)\n\n # Setup device and mode\n model = _setup_device_and_mode(model, device, eval_mode)\n\n return model\n\n\ndef download_ckpt_from_hf(version=\"sam3\"):\n \"\"\"Download model checkpoint from HuggingFace Hub.\n\n Args:\n version: \"sam3\" or \"sam3.1\"\n \"\"\"\n if version == \"sam3.1\":\n repo_id = \"facebook/sam3.1\"\n ckpt_name = \"sam3.1_multiplex.pt\"\n cfg_name = \"config.json\"\n else:\n repo_id = \"facebook/sam3\"\n ckpt_name = \"sam3.pt\"\n cfg_name = \"config.json\"\n _ = hf_hub_download(repo_id=repo_id, filename=cfg_name)\n checkpoint_path = hf_hub_download(repo_id=repo_id, filename=ckpt_name)\n return checkpoint_path\n\n\ndef build_sam3_video_model(\n checkpoint_path: Optional[str] = None,\n load_from_HF=True,\n bpe_path: Optional[str] = None,\n has_presence_token: bool = True,\n geo_encoder_use_img_cross_attn: bool = True,\n strict_state_dict_loading: bool = True,\n apply_temporal_disambiguation: bool = True,\n device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n compile=False,\n) -> Sam3VideoInferenceWithInstanceInteractivity:\n \"\"\"\n Build SAM3 dense tracking model.\n\n Args:\n checkpoint_path: Optional path to checkpoint file\n bpe_path: Path to the BPE tokenizer file\n\n Returns:\n Sam3VideoInferenceWithInstanceInteractivity: The instantiated dense tracking model\n \"\"\"\n if bpe_path is None:\n bpe_path = pkg_resources.resource_filename(\n \"sam3\", \"assets/bpe_simple_vocab_16e6.txt.gz\"\n )\n\n # Build Tracker module\n tracker = build_tracker(apply_temporal_disambiguation=apply_temporal_disambiguation)\n\n # Build Detector components\n visual_neck = _create_vision_backbone()\n text_encoder = _create_text_encoder(bpe_path)\n backbone = SAM3VLBackbone(scalp=1, visual=visual_neck, text=text_encoder)\n transformer = _create_sam3_transformer(has_presence_token=has_presence_token)\n segmentation_head: UniversalSegmentationHead = _create_segmentation_head()\n input_geometry_encoder = _create_geometry_encoder()\n\n # Create main dot product scoring\n main_dot_prod_mlp = MLP(\n input_dim=256,\n hidden_dim=2048,\n output_dim=256,\n num_layers=2,\n dropout=0.1,\n residual=True,\n out_norm=nn.LayerNorm(256),\n )\n main_dot_prod_scoring = DotProductScoring(\n d_model=256, d_proj=256, prompt_mlp=main_dot_prod_mlp\n )\n\n # Build Detector module\n detector = Sam3ImageOnVideoMultiGPU(\n num_feature_levels=1,\n backbone=backbone,\n transformer=transformer,\n segmentation_head=segmentation_head,\n semantic_segmentation_head=None,\n input_geometry_encoder=input_geometry_encoder,\n use_early_fusion=True,\n use_dot_prod_scoring=True,\n dot_prod_scoring=main_dot_prod_scoring,\n supervise_joint_box_scores=has_presence_token,\n )\n\n # Build the main SAM3 video model\n if apply_temporal_disambiguation:\n model = Sam3VideoInferenceWithInstanceInteractivity(\n detector=detector,\n tracker=tracker,\n score_threshold_detection=0.5,\n assoc_iou_thresh=0.1,\n det_nms_thresh=0.1,\n new_det_thresh=0.7,\n hotstart_delay=15,\n hotstart_unmatch_thresh=8,\n hotstart_dup_thresh=8,\n suppress_unmatched_only_within_hotstart=True,\n min_trk_keep_alive=-1,\n max_trk_keep_alive=30,\n init_trk_keep_alive=30,\n suppress_overlapping_based_on_recent_occlusion_threshold=0.7,\n suppress_det_close_to_boundary=False,\n fill_hole_area=16,\n recondition_every_nth_frame=16,\n masklet_confirmation_enable=False,\n decrease_trk_keep_alive_for_empty_masklets=False,\n image_size=1008,\n image_mean=(0.5, 0.5, 0.5),\n image_std=(0.5, 0.5, 0.5),\n compile_model=compile,\n )\n else:\n # a version without any heuristics for ablation studies\n model = Sam3VideoInferenceWithInstanceInteractivity(\n detector=detector,\n tracker=tracker,\n score_threshold_detection=0.5,\n assoc_iou_thresh=0.1,\n det_nms_thresh=0.1,\n new_det_thresh=0.7,\n hotstart_delay=0,\n hotstart_unmatch_thresh=0,\n hotstart_dup_thresh=0,\n suppress_unmatched_only_within_hotstart=True,\n min_trk_keep_alive=-1,\n max_trk_keep_alive=30,\n init_trk_keep_alive=30,\n suppress_overlapping_based_on_recent_occlusion_threshold=0.7,\n suppress_det_close_to_boundary=False,\n fill_hole_area=16,\n recondition_every_nth_frame=0,\n masklet_confirmation_enable=False,\n decrease_trk_keep_alive_for_empty_masklets=False,\n image_size=1008,\n image_mean=(0.5, 0.5, 0.5),\n image_std=(0.5, 0.5, 0.5),\n compile_model=compile,\n )\n\n # Load checkpoint if provided\n if load_from_HF and checkpoint_path is None:\n checkpoint_path = download_ckpt_from_hf(version=\"sam3\")\n if checkpoint_path is not None:\n with g_pathmgr.open(checkpoint_path, \"rb\") as f:\n ckpt = torch.load(f, map_location=\"cpu\", weights_only=True)\n if \"model\" in ckpt and isinstance(ckpt[\"model\"], dict):\n ckpt = ckpt[\"model\"]\n\n missing_keys, unexpected_keys = model.load_state_dict(\n ckpt, strict=strict_state_dict_loading\n )\n if missing_keys:\n print(f\"Missing keys: {missing_keys}\")\n if unexpected_keys:\n print(f\"Unexpected keys: {unexpected_keys}\")\n\n model.to(device=device)\n return model\n\n\ndef build_sam3_video_predictor(*model_args, gpus_to_use=None, **model_kwargs):\n return Sam3VideoPredictorMultiGPU(\n *model_args, gpus_to_use=gpus_to_use, **model_kwargs\n )\n\n\ndef _create_multiplex_maskmem_backbone(multiplex_count=16):\n \"\"\"Create the multiplex memory encoder with per-object mask channels.\"\"\"\n position_encoding = PositionEmbeddingSine(\n num_pos_feats=256,\n normalize=True,\n scale=None,\n temperature=10000,\n precompute_resolution=1008,\n )\n\n mask_downsampler = SimpleMaskDownSampler(\n kernel_size=3,\n stride=2,\n padding=1,\n interpol_size=[1152, 1152],\n multiplex_count=multiplex_count,\n starting_out_chan=4,\n input_channel_multiplier=2,\n )\n\n cx_block_layer = CXBlock(\n dim=256,\n kernel_size=7,\n padding=3,\n layer_scale_init_value=1.0e-06,\n use_dwconv=True,\n )\n\n fuser = SimpleFuser(layer=cx_block_layer, num_layers=2)\n\n maskmem_backbone = SimpleMaskEncoder(\n out_dim=256,\n position_encoding=position_encoding,\n mask_downsampler=mask_downsampler,\n fuser=fuser,\n )\n\n return maskmem_backbone\n\n\ndef _create_multiplex_transformer(use_fa3=False, use_rope_real=False):\n \"\"\"Create the decoupled transformer for multiplex memory attention.\"\"\"\n self_attention_rope = SimpleRoPEAttention(\n d_model=256,\n num_heads=8,\n dropout_p=0.1,\n rope_theta=10000.0,\n feat_sizes=[72, 72],\n use_fa3=use_fa3,\n use_rope_real=use_rope_real,\n )\n\n cross_attention_rope = SimpleRoPEAttention(\n d_model=256,\n num_heads=8,\n dropout_p=0.1,\n rope_theta=10000.0,\n feat_sizes=[72, 72],\n rope_k_repeat=True,\n use_fa3=use_fa3,\n use_rope_real=use_rope_real,\n )\n\n encoder_layer = DecoupledTransformerDecoderLayerv2(\n activation=\"gelu\",\n d_model=256,\n num_heads=8,\n dropout=0.1,\n dim_feedforward=2048,\n pos_enc_at_attn=False,\n pre_norm=True,\n pos_enc_at_cross_attn_keys=True,\n pos_enc_at_cross_attn_queries=False,\n self_attention_rope=self_attention_rope,\n cross_attention_rope=cross_attention_rope,\n )\n\n encoder = TransformerEncoderDecoupledCrossAttention(\n d_model=256,\n frozen=False,\n pos_enc_at_input=True,\n use_image_in_output=False,\n layer=encoder_layer,\n num_layers=4,\n use_act_checkpoint=False,\n batch_first=True,\n )\n\n transformer = TransformerWrapper(\n encoder=encoder,\n decoder=None,\n d_model=256,\n )\n\n return transformer\n\n\ndef _create_multiplex_tri_backbone(\n compile_mode=None, use_fa3=False, use_rope_real=False\n):\n \"\"\"Create the TriHead vision backbone for multiplex model.\"\"\"\n position_encoding = _create_position_encoding(precompute_resolution=1008)\n vit_backbone = _create_vit_backbone(\n compile_mode=compile_mode, use_fa3=use_fa3, use_rope_real=use_rope_real\n )\n tri_neck = Sam3TriViTDetNeck(\n trunk=vit_backbone,\n position_encoding=position_encoding,\n d_model=256,\n scale_factors=[4.0, 2.0, 1.0],\n )\n return tri_neck\n\n\ndef build_sam3_multiplex_video_model(\n checkpoint_path: Optional[str] = None,\n load_from_HF=True,\n multiplex_count: int = 16,\n use_fa3: bool = False,\n use_rope_real: bool = False,\n strict_state_dict_loading: bool = True,\n device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n compile=False,\n):\n \"\"\"\n Build SAM3 multiplex video tracking model.\n\n Args:\n checkpoint_path: Optional path to checkpoint file\n multiplex_count: Number of objects per multiplex bucket\n use_fa3: Whether to use FlashAttention 3\n use_rope_real: Whether to use real-valued RoPE (for compile compat)\n strict_state_dict_loading: Whether to use strict state dict loading\n device: Device to place model on\n compile: Whether to compile model components\n\n Returns:\n VideoTrackingDynamicMultiplex: The instantiated multiplex tracking model\n \"\"\"\n # Build multiplex-specific components\n maskmem_backbone = _create_multiplex_maskmem_backbone(\n multiplex_count=multiplex_count\n )\n transformer = _create_multiplex_transformer(\n use_fa3=use_fa3, use_rope_real=use_rope_real\n )\n tri_neck = _create_multiplex_tri_backbone(\n compile_mode=\"max-autotune\" if compile else None\n )\n backbone = TriHeadVisionOnly(\n visual=tri_neck,\n n_features=256,\n scalp=0,\n )\n multiplex_controller = MultiplexController(\n multiplex_count=multiplex_count,\n eval_multiplex_count=multiplex_count,\n )\n\n # Build the multiplex model (use demo class for init_state and other demo methods)\n from sam3.model.video_tracking_multiplex_demo import Sam3VideoTrackingMultiplexDemo\n\n model = Sam3VideoTrackingMultiplexDemo(\n backbone=backbone,\n transformer=transformer,\n maskmem_backbone=maskmem_backbone,\n multiplex_controller=multiplex_controller,\n image_size=1008,\n backbone_stride=14,\n num_maskmem=7,\n # Multiplex-specific settings\n use_high_res_features_in_sam=True,\n use_obj_ptrs_in_encoder=True,\n max_obj_ptrs_in_encoder=16,\n add_tpos_enc_to_obj_ptrs=True,\n proj_tpos_enc_in_obj_ptrs=True,\n use_mlp_for_obj_ptr_proj=True,\n pred_obj_scores=True,\n pred_obj_scores_mlp=True,\n fixed_no_obj_ptr=True,\n use_no_obj_ptr=True,\n use_linear_no_obj_ptr=True,\n no_obj_embed_spatial=True,\n sincos_tpos_enc=True,\n # Multimask settings\n multimask_output_in_sam=True,\n multimask_output_for_tracking=True,\n multimask_min_pt_num=0,\n multimask_max_pt_num=1,\n use_multimask_token_for_obj_ptr=True,\n num_multimask_outputs=3,\n # Memory encoder settings\n apply_sigmoid_to_mask_logits_for_mem_enc=True,\n sigmoid_scale_for_mem_enc=2.0,\n sigmoid_bias_for_mem_enc=-1.0,\n non_overlap_masks_for_mem_enc=False,\n # Suppression/conditional embeddings\n add_output_suppression_embeddings=True,\n add_object_conditional_embeddings=False,\n condition_as_mask_input=True,\n condition_as_mask_input_fg=1.0,\n condition_as_mask_input_bg=0.0,\n # Memory settings\n use_maskmem_tpos_v2=True,\n save_image_features=True,\n randomness_fix=True,\n # Interaction settings\n use_mask_input_as_output_without_sam=True,\n directly_add_no_mem_embed=True,\n iou_prediction_use_sigmoid=False,\n forward_backbone_per_frame_for_eval=True,\n offload_output_to_cpu_for_eval=False,\n trim_past_non_cond_mem_for_eval=False,\n max_cond_frames_in_attn=4,\n # Dynamic multiplex settings\n is_dynamic_model=True,\n # SAM mask decoder extra args\n sam_mask_decoder_extra_args={\n \"dynamic_multimask_via_stability\": True,\n \"dynamic_multimask_stability_delta\": 0.05,\n \"dynamic_multimask_stability_thresh\": 0.98,\n },\n compile_all_components=compile,\n use_memory_selection=False,\n )\n\n # Load checkpoint if provided\n if load_from_HF and checkpoint_path is None:\n checkpoint_path = download_ckpt_from_hf(version=\"sam3.1\")\n if checkpoint_path is not None:\n with g_pathmgr.open(checkpoint_path, \"rb\") as f:\n ckpt = torch.load(f, map_location=\"cpu\", weights_only=True)\n if \"model\" in ckpt and isinstance(ckpt[\"model\"], dict):\n ckpt = ckpt[\"model\"]\n\n missing_keys, unexpected_keys = model.load_state_dict(\n ckpt, strict=strict_state_dict_loading\n )\n if missing_keys:\n print(f\"Missing keys: {missing_keys}\")\n if unexpected_keys:\n print(f\"Unexpected keys: {unexpected_keys}\")\n\n model.to(device=device)\n return model\n\n\ndef build_sam3_multiplex_video_predictor(\n checkpoint_path: Optional[str] = None,\n bpe_path: Optional[str] = None,\n max_num_objects: int = 16,\n multiplex_count: int = 16,\n use_fa3: bool = True,\n use_rope_real: bool = True,\n compile: bool = False,\n warm_up: bool = False,\n session_expiration_sec: int = 1200,\n default_output_prob_thresh: float = 0.5,\n async_loading_frames: bool = True,\n):\n \"\"\"\n Build a fully-initialized Sam3MultiplexVideoPredictor.\n\n This is the recommended entry point for SAM 3.1 multiplex video tracking.\n It builds the full model stack (tracker + detector + demo model), loads\n the checkpoint, and wraps everything in Sam3MultiplexVideoPredictor with\n handle_request / handle_stream_request API.\n\n Args:\n checkpoint_path: Path to the merged multiplex checkpoint\n bpe_path: Path to the BPE tokenizer vocabulary\n max_num_objects: Maximum number of tracked objects\n multiplex_count: Number of objects per multiplex bucket\n use_fa3: Whether to use FlashAttention 3\n use_rope_real: Whether to use real-valued RoPE (for compile compat)\n compile: Whether to enable torch.compile on model components\n warm_up: Whether to run warm-up compilation (requires compile=True)\n session_expiration_sec: Session expiration timeout in seconds\n default_output_prob_thresh: Default probability threshold for output masks\n async_loading_frames: Whether to load frames asynchronously\n\n Returns:\n Sam3MultiplexVideoPredictor: The fully-initialized predictor\n \"\"\"\n if bpe_path is None:\n bpe_path = pkg_resources.resource_filename(\n \"sam3\", \"assets/bpe_simple_vocab_16e6.txt.gz\"\n )\n\n from sam3.model.sam3_multiplex_base import Sam3MultiplexPredictorWrapper\n from sam3.model.sam3_multiplex_detector import Sam3MultiplexDetector\n from sam3.model.sam3_multiplex_tracking import (\n Sam3MultiplexTrackingWithInteractivity,\n )\n from sam3.model.sam3_multiplex_video_predictor import Sam3MultiplexVideoPredictor\n\n # Build tracker\n tracker_model = build_sam3_multiplex_video_model(\n checkpoint_path=checkpoint_path,\n load_from_HF=False,\n multiplex_count=multiplex_count,\n use_fa3=use_fa3,\n use_rope_real=use_rope_real,\n compile=False,\n strict_state_dict_loading=False,\n )\n del tracker_model.backbone\n tracker_model.backbone = None\n\n sam2_predictor = Sam3MultiplexPredictorWrapper(\n model=tracker_model,\n per_obj_inference=False,\n fill_hole_area=0,\n is_multiplex=True,\n is_multiplex_dynamic=True,\n )\n\n # Build detector\n tri_neck = _create_multiplex_tri_backbone(\n compile_mode=None, use_fa3=use_fa3, use_rope_real=use_rope_real\n )\n text_encoder = _create_text_encoder(bpe_path)\n backbone = SAM3VLBackboneTri(scalp=0, visual=tri_neck, text=text_encoder)\n transformer = _create_sam3_transformer(use_fa3=use_fa3)\n segmentation_head = _create_segmentation_head(use_fa3=use_fa3)\n geometry_encoder = _create_geometry_encoder()\n dot_prod_scoring = _create_dot_product_scoring()\n\n detector = Sam3MultiplexDetector(\n num_feature_levels=1,\n backbone=backbone,\n transformer=transformer,\n segmentation_head=segmentation_head,\n semantic_segmentation_head=None,\n input_geometry_encoder=geometry_encoder,\n use_early_fusion=True,\n use_dot_prod_scoring=True,\n dot_prod_scoring=dot_prod_scoring,\n supervise_joint_box_scores=True,\n is_multiplex=True,\n )\n\n # Assemble demo model\n demo_model = Sam3MultiplexTrackingWithInteractivity(\n tracker=sam2_predictor,\n detector=detector,\n score_threshold_detection=0.4,\n det_nms_thresh=0.1,\n det_nms_use_iom=True,\n assoc_iou_thresh=0.1,\n new_det_thresh=0.65,\n hotstart_delay=15,\n hotstart_unmatch_thresh=8,\n hotstart_dup_thresh=8,\n suppress_unmatched_only_within_hotstart=False,\n suppress_overlapping_based_on_recent_occlusion_threshold=0.7,\n suppress_det_close_to_boundary=True,\n fill_hole_area=0, # OV effectively 0 (Sam3MultiplexTrackerPredictor Hydra override clobbers yaml's 16)\n recondition_every_nth_frame=16,\n use_iom_recondition=True,\n iom_thresh_recondition=0.5,\n masklet_confirmation_enable=True,\n reconstruction_bbox_iou_thresh=-1,\n reconstruction_bbox_det_score=0.8,\n max_num_objects=max_num_objects,\n postprocess_batch_size=16,\n use_batched_grounding=True,\n batched_grounding_batch_size=16,\n max_num_kboxes=0,\n sprinkle_removal_area=0,\n is_multiplex=True,\n image_size=1008,\n image_mean=(0.5, 0.5, 0.5),\n image_std=(0.5, 0.5, 0.5),\n compile_model=compile,\n )\n\n # Load checkpoint (auto-download from HuggingFace if not provided)\n if checkpoint_path is None:\n checkpoint_path = download_ckpt_from_hf(version=\"sam3.1\")\n if checkpoint_path is not None:\n ckpt = torch.load(checkpoint_path, map_location=\"cpu\", weights_only=True)\n if \"model\" in ckpt and isinstance(ckpt[\"model\"], dict):\n ckpt = ckpt[\"model\"]\n # Remap checkpoint keys if needed (internal naming -> OSS naming)\n # HF checkpoints are already remapped; local checkpoints may use old naming\n needs_remap = any(\n k.startswith(\"sam3_model.\") or k.startswith(\"sam2_predictor.\") for k in ckpt\n )\n if needs_remap:\n remapped_ckpt = {}\n for k, v in ckpt.items():\n new_k = k\n if k.startswith(\"sam3_model.\"):\n new_k = \"detector.\" + k[len(\"sam3_model.\") :]\n elif k.startswith(\"sam2_predictor.\"):\n new_k = \"tracker.\" + k[len(\"sam2_predictor.\") :]\n remapped_ckpt[new_k] = v\n ckpt = remapped_ckpt\n missing_keys, unexpected_keys = demo_model.load_state_dict(ckpt, strict=False)\n if missing_keys:\n print(f\"Missing keys ({len(missing_keys)}): {missing_keys[:10]}...\")\n if unexpected_keys:\n print(\n f\"Unexpected keys ({len(unexpected_keys)}): {unexpected_keys[:10]}...\"\n )\n\n demo_model.cuda().eval()\n\n # Wrap in predictor\n predictor = Sam3MultiplexVideoPredictor(\n model=demo_model,\n session_expiration_sec=session_expiration_sec,\n default_output_prob_thresh=default_output_prob_thresh,\n async_loading_frames=async_loading_frames,\n warm_up=warm_up,\n )\n return predictor\n\n\ndef build_sam3_predictor(\n checkpoint_path: Optional[str] = None,\n bpe_path: Optional[str] = None,\n version: str = \"sam3.1\", # \"sam3\" or \"sam3.1\"\n compile: bool = False,\n warm_up: bool = False,\n # SAM 3.1 specific\n max_num_objects: int = 16,\n multiplex_count: int = 16,\n # Common\n use_fa3: bool = True,\n use_rope_real: bool = True,\n async_loading_frames: bool = True,\n **kwargs,\n):\n \"\"\"\n Build a SAM3 video predictor.\n\n Args:\n checkpoint_path: Path to model checkpoint\n bpe_path: Path to BPE tokenizer vocabulary\n version: Model version - \"sam3\" for base or \"sam3.1\" for multiplex\n compile: Enable torch.compile for ~2x speedup (SAM 3.1 only currently)\n warm_up: Run warm-up compilation passes\n max_num_objects: Maximum tracked objects (SAM 3.1 only)\n multiplex_count: Objects per multiplex bucket (SAM 3.1 only)\n use_fa3: Use Flash Attention 3\n use_rope_real: Use real-valued RoPE\n async_loading_frames: Load video frames asynchronously\n **kwargs: Additional arguments passed to the underlying builder\n\n Returns:\n A predictor with handle_request() and handle_stream_request() API.\n Both versions support: start_session, add_prompt, propagate_in_video,\n remove_object, reset_session, close_session.\n\n Example:\n # SAM 3.1 (auto-downloads from HuggingFace):\n predictor = build_sam3_predictor(version=\"sam3.1\", compile=True)\n\n # SAM 3 (auto-downloads from HuggingFace):\n predictor = build_sam3_predictor(version=\"sam3\")\n\n # Or with a local checkpoint:\n predictor = build_sam3_predictor(checkpoint_path=\"path/to/ckpt.pt\", version=\"sam3.1\")\n\n # Both use the same API:\n response = predictor.handle_request({\"type\": \"start_session\", \"resource_path\": video_dir})\n session_id = response[\"session_id\"]\n predictor.handle_request({\"type\": \"add_prompt\", \"session_id\": session_id, \"frame_index\": 0, \"text\": \"person\"})\n for out in predictor.handle_stream_request({\"type\": \"propagate_in_video\", \"session_id\": session_id}):\n masks = out[\"out_binary_masks\"]\n \"\"\"\n if version == \"sam3.1\":\n return build_sam3_multiplex_video_predictor(\n checkpoint_path=checkpoint_path,\n bpe_path=bpe_path,\n max_num_objects=max_num_objects,\n multiplex_count=multiplex_count,\n use_fa3=use_fa3,\n use_rope_real=use_rope_real,\n compile=compile,\n warm_up=warm_up,\n async_loading_frames=async_loading_frames,\n **kwargs,\n )\n elif version == \"sam3\":\n return build_sam3_video_predictor(\n checkpoint_path=checkpoint_path,\n bpe_path=bpe_path,\n compile=compile,\n async_loading_frames=async_loading_frames,\n **kwargs,\n )\n else:\n raise ValueError(f\"Unknown version: {version!r}. 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+ } + }, + "id": "prt_f27f2de67001Wp5oUvw03AFtWj", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f27023002F9HLMI2sxCnszJ" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_gbS8P67rcs1QioxA6TTXZsli", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"- box-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+ \"- box/brush guided segmentation with SAM 3.1 (`facebook/sam3.1`)\\n\",\n@@\n- \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n+ \"Requires access to the gated SAM 3.1 checkpoint on Hugging Face.\"\n@@\n- \"%pip -q install transformers accelerate pillow matplotlib pandas\"\n+ \"%pip -q install transformers accelerate pillow matplotlib pandas huggingface_hub\\n\",\n+ \"%pip -q install git+https://github.com/facebookresearch/sam3.git\"\n@@\n- \"from transformers import (\\n\",\n- \" SamModel,\\n\",\n- \" SamProcessor,\\n\",\n- \" AutoModel,\\n\",\n- \" AutoProcessor,\\n\",\n- \")\\n\",\n+ \"from transformers import AutoModel, AutoProcessor\\n\",\n+ \"\\n\",\n+ \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n+ \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n@@\n- \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n+ \"SAM3_REPO_ID = \\\"facebook/sam3.1\\\"\\n\",\n+ \"SAM3_CHECKPOINT_PATH = None # set a local path like /content/checkpoints/sam3.1_multiplex.pt to skip HF download\\n\",\n+ \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n+ \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n+ \"SAM3_MAX_POINT_HINTS = 4\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -6,16 +6,16 @@\n \"source\": [\n \"# Material Hunters POC Notebook\\n\",\n \"\\n\",\n \"This notebook runs a minimal end-to-end baseline:\\n\",\n- \"- box-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+ \"- box/brush guided segmentation with SAM 3.1 (`facebook/sam3.1`)\\n\",\n \"- optional brush-hint guidance (converted to SAM box + positive points)\\n\",\n \"- material classification with SigCLIP\\n\",\n \"- optional user edits\\n\",\n \"- passport JSON export\\n\",\n \"- evaluation row append + go/no-go metrics\\n\",\n \"\\n\",\n- \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n+ \"Requires access to the gated SAM 3.1 checkpoint on Hugging Face.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n@@ -23,9 +23,10 @@\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run once in Colab if needed.\\n\",\n- \"%pip -q install transformers accelerate pillow matplotlib pandas\"\n+ \"%pip -q install transformers accelerate pillow matplotlib pandas huggingface_hub\\n\",\n+ \"%pip -q install git+https://github.com/facebookresearch/sam3.git\"\n ]\n },\n {\n \"cell_type\": \"code\",\n@@ -45,17 +46,19 @@\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.patches as patches\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n- \"from transformers import (\\n\",\n- \" SamModel,\\n\",\n- \" SamProcessor,\\n\",\n- \" AutoModel,\\n\",\n- \" AutoProcessor,\\n\",\n- \")\\n\",\n+ \"from transformers import AutoModel, AutoProcessor\\n\",\n \"\\n\",\n+ \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n+ \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n+ \"\\n\",\n \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n- \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n+ \"SAM3_REPO_ID = \\\"facebook/sam3.1\\\"\\n\",\n+ \"SAM3_CHECKPOINT_PATH = None # set a local path like /content/checkpoints/sam3.1_multiplex.pt to skip HF download\\n\",\n+ \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n+ \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n+ \"SAM3_MAX_POINT_HINTS = 4\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n \"\\n\",\n \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -6,16 +6,16 @@\n \"source\": [\n \"# Material Hunters POC Notebook\\n\",\n \"\\n\",\n \"This notebook runs a minimal end-to-end baseline:\\n\",\n- \"- box-prompt segmentation with SAM (`facebook/sam-vit-base`)\\n\",\n+ \"- box/brush guided segmentation with SAM 3.1 (`facebook/sam3.1`)\\n\",\n \"- optional brush-hint guidance (converted to SAM box + positive points)\\n\",\n \"- material classification with SigCLIP\\n\",\n \"- optional user edits\\n\",\n \"- passport JSON export\\n\",\n \"- evaluation row append + go/no-go metrics\\n\",\n \"\\n\",\n- \"When SAM2/SAM3 integration is ready, replace the SAM helper function in this notebook.\"\n+ \"Requires access to the gated SAM 3.1 checkpoint on Hugging Face.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n@@ -23,9 +23,10 @@\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run once in Colab if needed.\\n\",\n- \"%pip -q install transformers accelerate pillow matplotlib pandas\"\n+ \"%pip -q install transformers accelerate pillow matplotlib pandas huggingface_hub\\n\",\n+ \"%pip -q install git+https://github.com/facebookresearch/sam3.git\"\n ]\n },\n {\n \"cell_type\": \"code\",\n@@ -45,17 +46,19 @@\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.patches as patches\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n- \"from transformers import (\\n\",\n- \" SamModel,\\n\",\n- \" SamProcessor,\\n\",\n- \" AutoModel,\\n\",\n- \" AutoProcessor,\\n\",\n- \")\\n\",\n+ \"from transformers import AutoModel, AutoProcessor\\n\",\n \"\\n\",\n+ \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n+ \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n+ \"\\n\",\n \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n- \"SAM_MODEL_ID = \\\"facebook/sam-vit-base\\\"\\n\",\n+ \"SAM3_REPO_ID = \\\"facebook/sam3.1\\\"\\n\",\n+ \"SAM3_CHECKPOINT_PATH = None # set a local path like /content/checkpoints/sam3.1_multiplex.pt to skip HF download\\n\",\n+ \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n+ \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n+ \"SAM3_MAX_POINT_HINTS = 4\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n \"\\n\",\n \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n", + "additions": 13, + "deletions": 10 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783081678377, + "end": 1783081678411 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0658585c7de328d2016a47aac905d881928038bdfca879f904" + } + }, + "id": "prt_f27f32176001MT6izKOfw8gq8O", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f27023002F9HLMI2sxCnszJ" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 167252, + "input": 10305, + "output": 504, + "reasoning": 1179, + "cache": { + "write": 0, + "read": 155264 + } + }, + "cost": 0.06876695, + "id": "prt_f27f336a0001oW1xQDDmMSemtQ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f27023002F9HLMI2sxCnszJ" + } + ] + }, + { + "info": { + "parentID": "msg_f27f207c40019BcuAqaBu96PbB", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + 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+ } + }, + "id": "prt_f27f3421f00112AbhicWeaeAsi", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f3371d0020uHbVC3AyQ7suT" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_gmuKpxr1HRwGhpa2iGRavuEv", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 640, + "limit": 250 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n640: \" for material, aliases in material_taxonomy.items():\\n\",\n641: \" for alias in aliases:\\n\",\n642: \" for template in prompt_templates:\\n\",\n643: \" prompts.append(template.format(material=alias))\\n\",\n644: \" prompt_materials.append(material)\\n\",\n645: \" return prompts, prompt_materials\\n\",\n646: \"\\n\",\n647: \"\\n\",\n648: \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n649: \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n650: \"\\n\",\n651: \"\\n\",\n652: \"def resolve_candidate_materials(object_label: str | None):\\n\",\n653: \" if not object_label:\\n\",\n654: \" return ALL_CANONICAL_MATERIALS\\n\",\n655: \" key = object_label.strip().lower()\\n\",\n656: \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n657: \" if len(candidates) < 2:\\n\",\n658: \" return ALL_CANONICAL_MATERIALS\\n\",\n659: \" return candidates\\n\",\n660: \"\\n\",\n661: \"\\n\",\n662: \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n663: \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n664: \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n665: \"\\n\",\n666: \"\\n\",\n667: \"def build_brush_hint_mask(image_shape_hw, prompt_bbox_xyxy, prompt_points_xy, point_radius_px: int = 16):\\n\",\n668: \" h, w = image_shape_hw\\n\",\n669: \" hint = np.zeros((h, w), dtype=bool)\\n\",\n670: \"\\n\",\n671: \" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n672: \" x0 = max(0, min(w - 1, x0))\\n\",\n673: \" y0 = max(0, min(h - 1, y0))\\n\",\n674: \" x1 = max(0, min(w - 1, x1))\\n\",\n675: \" y1 = max(0, min(h - 1, y1))\\n\",\n676: \"\\n\",\n677: \" if not prompt_points_xy:\\n\",\n678: \" if x1 > x0 and y1 > y0:\\n\",\n679: \" hint[y0:y1, x0:x1] = True\\n\",\n680: \" return hint\\n\",\n681: \"\\n\",\n682: \" yy, xx = np.ogrid[:h, :w]\\n\",\n683: \" for px, py in prompt_points_xy:\\n\",\n684: \" px = int(max(0, min(w - 1, px)))\\n\",\n685: \" py = int(max(0, min(h - 1, py)))\\n\",\n686: \" disk = (xx - px) ** 2 + (yy - py) ** 2 <= point_radius_px ** 2\\n\",\n687: \" hint |= disk\\n\",\n688: \"\\n\",\n689: \" return hint\\n\",\n690: \"\\n\",\n691: \"\\n\",\n692: \"def score_sam_candidate(mask, sam_score, prompt_bbox_xyxy, prompt_points_xy, hint_mask):\\n\",\n693: \" mask = mask.astype(bool)\\n\",\n694: \" mask_area = int(mask.sum())\\n\",\n695: \"\\n\",\n696: \" if mask_area == 0:\\n\",\n697: \" return {\\n\",\n698: \" \\\"total\\\": -1.0,\\n\",\n699: \" \\\"sam_score\\\": float(sam_score),\\n\",\n700: \" \\\"point_coverage\\\": 0.0,\\n\",\n701: \" \\\"hint_iou\\\": 0.0,\\n\",\n702: \" \\\"inside_box_ratio\\\": 0.0,\\n\",\n703: \" \\\"mask_area\\\": 0,\\n\",\n704: \" }\\n\",\n705: \"\\n\",\n706: \" h, w = mask.shape\\n\",\n707: \" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n708: \" x0 = max(0, min(w - 1, x0))\\n\",\n709: \" y0 = max(0, min(h - 1, y0))\\n\",\n710: \" x1 = max(0, min(w, x1))\\n\",\n711: \" y1 = max(0, min(h, y1))\\n\",\n712: \"\\n\",\n713: \" box_mask = np.zeros_like(mask)\\n\",\n714: \" if x1 > x0 and y1 > y0:\\n\",\n715: \" box_mask[y0:y1, x0:x1] = True\\n\",\n716: \"\\n\",\n717: \" inside_box_ratio = float((mask & box_mask).sum() / max(mask_area, 1))\\n\",\n718: \"\\n\",\n719: \" if prompt_points_xy:\\n\",\n720: \" hits = 0\\n\",\n721: \" for px, py in prompt_points_xy:\\n\",\n722: \" if 0 <= int(py) < h and 0 <= int(px) < w and mask[int(py), int(px)]:\\n\",\n723: \" hits += 1\\n\",\n724: \" point_coverage = float(hits / len(prompt_points_xy))\\n\",\n725: \" else:\\n\",\n726: \" point_coverage = 0.0\\n\",\n727: \"\\n\",\n728: \" if hint_mask is not None:\\n\",\n729: \" inter = float((mask & hint_mask).sum())\\n\",\n730: \" union = float((mask | hint_mask).sum())\\n\",\n731: \" hint_iou = inter / max(union, 1.0)\\n\",\n732: \" else:\\n\",\n733: \" hint_iou = 0.0\\n\",\n734: \"\\n\",\n735: \" if prompt_points_xy:\\n\",\n736: \" total = 0.50 * hint_iou + 0.30 * point_coverage + 0.10 * inside_box_ratio + 0.10 * float(sam_score)\\n\",\n737: \" else:\\n\",\n738: \" total = 0.80 * float(sam_score) + 0.20 * inside_box_ratio\\n\",\n739: \"\\n\",\n740: \" return {\\n\",\n741: \" \\\"total\\\": float(total),\\n\",\n742: \" \\\"sam_score\\\": float(sam_score),\\n\",\n743: \" \\\"point_coverage\\\": float(point_coverage),\\n\",\n744: \" \\\"hint_iou\\\": float(hint_iou),\\n\",\n745: \" \\\"inside_box_ratio\\\": float(inside_box_ratio),\\n\",\n746: \" \\\"mask_area\\\": int(mask_area),\\n\",\n747: \" }\\n\",\n748: \"\\n\",\n749: \"\\n\",\n750: \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n751: \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n752: \" if prompt_points_xy:\\n\",\n753: \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n754: \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n755: \" inputs = sam_processor(\\n\",\n756: \" image,\\n\",\n757: \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n758: \" input_points=pts,\\n\",\n759: \" input_labels=labels,\\n\",\n760: \" return_tensors=\\\"pt\\\",\\n\",\n761: \" )\\n\",\n762: \" else:\\n\",\n763: \" inputs = sam_processor(\\n\",\n764: \" image,\\n\",\n765: \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n766: \" return_tensors=\\\"pt\\\",\\n\",\n767: \" )\\n\",\n768: \"\\n\",\n769: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n770: \"\\n\",\n771: \" with torch.no_grad():\\n\",\n772: \" outputs = sam_model(**inputs)\\n\",\n773: \"\\n\",\n774: \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n775: \" outputs.pred_masks.cpu(),\\n\",\n776: \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n777: \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n778: \" )\\n\",\n779: \"\\n\",\n780: \" sam_scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n781: \" candidate_masks = masks[0][0].numpy().astype(bool)\\n\",\n782: \"\\n\",\n783: \" img_arr = np.array(image)\\n\",\n784: \" hint_mask = None\\n\",\n785: \" if prompt_points_xy:\\n\",\n786: \" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n787: \"\\n\",\n788: \" candidate_debug = []\\n\",\n789: \" for idx in range(candidate_masks.shape[0]):\\n\",\n790: \" metrics = score_sam_candidate(\\n\",\n791: \" mask=candidate_masks[idx],\\n\",\n792: \" sam_score=float(sam_scores[idx]),\\n\",\n793: \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n794: \" prompt_points_xy=prompt_points_xy,\\n\",\n795: \" hint_mask=hint_mask,\\n\",\n796: \" )\\n\",\n797: \" metrics[\\\"candidate_idx\\\"] = int(idx)\\n\",\n798: \" candidate_debug.append(metrics)\\n\",\n799: \"\\n\",\n800: \" best = max(candidate_debug, key=lambda d: d[\\\"total\\\"])\\n\",\n801: \" best_idx = int(best[\\\"candidate_idx\\\"])\\n\",\n802: \" mask = candidate_masks[best_idx]\\n\",\n803: \" bbox = mask_bbox_xywh(mask)\\n\",\n804: \"\\n\",\n805: \" return {\\n\",\n806: \" \\\"mask\\\": mask,\\n\",\n807: \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n808: \" \\\"bbox_xywh\\\": bbox,\\n\",\n809: \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n810: \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n811: \" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n812: \" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n813: \" \\\"selection_metrics\\\": {\\n\",\n814: \" \\\"point_coverage\\\": float(best[\\\"point_coverage\\\"]),\\n\",\n815: \" \\\"hint_iou\\\": float(best[\\\"hint_iou\\\"]),\\n\",\n816: \" \\\"inside_box_ratio\\\": float(best[\\\"inside_box_ratio\\\"]),\\n\",\n817: \" },\\n\",\n818: \" \\\"sam_candidates\\\": candidate_debug,\\n\",\n819: \" }\\n\",\n820: \"\\n\",\n821: \"\\n\",\n822: \"def classify_material_sigclip(\\n\",\n823: \" image_views: list,\\n\",\n824: \" object_label: str | None,\\n\",\n825: \" sigclip_model,\\n\",\n826: \" sigclip_processor,\\n\",\n827: \" top_k: int = 3,\\n\",\n828: \" view_weights: list | None = None,\\n\",\n829: \"):\\n\",\n830: \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n831: \"\\n\",\n832: \" selected_idx = [\\n\",\n833: \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n834: \" ]\\n\",\n835: \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n836: \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n837: \"\\n\",\n838: \" if not selected_prompts:\\n\",\n839: \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n840: \"\\n\",\n841: \" if view_weights is None:\\n\",\n842: \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n843: \" if len(view_weights) != len(image_views):\\n\",\n844: \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n845: \"\\n\",\n846: \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n847: \"\\n\",\n848: \" for image, weight in zip(image_views, view_weights):\\n\",\n849: \" inputs = sigclip_processor(\\n\",\n850: \" text=selected_prompts,\\n\",\n851: \" images=image,\\n\",\n852: \" return_tensors=\\\"pt\\\",\\n\",\n853: \" padding=\\\"max_length\\\",\\n\",\n854: \" )\\n\",\n855: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n856: \"\\n\",\n857: \" with torch.no_grad():\\n\",\n858: \" outputs = sigclip_model(**inputs)\\n\",\n859: \"\\n\",\n860: \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n861: \"\\n\",\n862: \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n863: \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n864: \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n865: \"\\n\",\n866: \" for m in candidate_materials:\\n\",\n867: \" agg_scores[m] += weight * per_material_max[m]\\n\",\n868: \"\\n\",\n869: \" total = sum(agg_scores.values())\\n\",\n870: \" if total > 0:\\n\",\n871: \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n872: \"\\n\",\n873: \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n874: \" top_k = min(top_k, len(ranked))\\n\",\n875: \" top_items = ranked[:top_k]\\n\",\n876: \"\\n\",\n877: \" top_labels = [x[0] for x in top_items]\\n\",\n878: \" top_scores = [x[1] for x in top_items]\\n\",\n879: \"\\n\",\n880: \" return {\\n\",\n881: \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n882: \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n883: \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n884: \" \\\"topk_labels\\\": top_labels,\\n\",\n885: \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n886: \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n887: \" }\\n\",\n888: \"\\n\",\n889: \"\\n\",\n\n(Showing lines 640-889 of 1409. Use offset=890 to continue.)\n</content>", + "metadata": { + "preview": " \" for material, aliases in material_taxonomy.items():\\n\",\n \" for alias in aliases:\\n\",\n \" for template in prompt_templates:\\n\",\n \" prompts.append(template.format(material=alias))\\n\",\n \" prompt_materials.append(material)\\n\",\n \" return prompts, prompt_materials\\n\",\n \"\\n\",\n \"\\n\",\n \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n \"\\n\",\n \"\\n\",\n \"def resolve_candidate_materials(object_label: str | None):\\n\",\n \" if not object_label:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" key = object_label.strip().lower()\\n\",\n \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n \" if len(candidates) < 2:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" return candidates\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" for material, aliases in material_taxonomy.items():\\n\",\n \" for alias in aliases:\\n\",\n \" for template in prompt_templates:\\n\",\n \" prompts.append(template.format(material=alias))\\n\",\n \" prompt_materials.append(material)\\n\",\n \" return prompts, prompt_materials\\n\",\n \"\\n\",\n \"\\n\",\n \"ALL_PROMPTS, ALL_PROMPT_MATERIALS = build_material_prompt_table(MATERIAL_TAXONOMY, PROMPT_TEMPLATES)\\n\",\n \"ALL_CANONICAL_MATERIALS = list(MATERIAL_TAXONOMY.keys())\\n\",\n \"\\n\",\n \"\\n\",\n \"def resolve_candidate_materials(object_label: str | None):\\n\",\n \" if not object_label:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" key = object_label.strip().lower()\\n\",\n \" candidates = OBJECT_MATERIAL_PRIORS.get(key, [])\\n\",\n \" if len(candidates) < 2:\\n\",\n \" return ALL_CANONICAL_MATERIALS\\n\",\n \" return candidates\\n\",\n \"\\n\",\n \"\\n\",\n \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n \"def build_brush_hint_mask(image_shape_hw, prompt_bbox_xyxy, prompt_points_xy, point_radius_px: int = 16):\\n\",\n \" h, w = image_shape_hw\\n\",\n \" hint = np.zeros((h, w), dtype=bool)\\n\",\n \"\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n \" x0 = max(0, min(w - 1, x0))\\n\",\n \" y0 = max(0, min(h - 1, y0))\\n\",\n \" x1 = max(0, min(w - 1, x1))\\n\",\n \" y1 = max(0, min(h - 1, y1))\\n\",\n \"\\n\",\n \" if not prompt_points_xy:\\n\",\n \" if x1 > x0 and y1 > y0:\\n\",\n \" hint[y0:y1, x0:x1] = True\\n\",\n \" return hint\\n\",\n \"\\n\",\n \" yy, xx = np.ogrid[:h, :w]\\n\",\n \" for px, py in prompt_points_xy:\\n\",\n \" px = int(max(0, min(w - 1, px)))\\n\",\n \" py = int(max(0, min(h - 1, py)))\\n\",\n \" disk = (xx - px) ** 2 + (yy - py) ** 2 <= point_radius_px ** 2\\n\",\n \" hint |= disk\\n\",\n \"\\n\",\n \" return hint\\n\",\n \"\\n\",\n \"\\n\",\n \"def score_sam_candidate(mask, sam_score, prompt_bbox_xyxy, prompt_points_xy, hint_mask):\\n\",\n \" mask = mask.astype(bool)\\n\",\n \" mask_area = int(mask.sum())\\n\",\n \"\\n\",\n \" if mask_area == 0:\\n\",\n \" return {\\n\",\n \" \\\"total\\\": -1.0,\\n\",\n \" \\\"sam_score\\\": float(sam_score),\\n\",\n \" \\\"point_coverage\\\": 0.0,\\n\",\n \" \\\"hint_iou\\\": 0.0,\\n\",\n \" \\\"inside_box_ratio\\\": 0.0,\\n\",\n \" \\\"mask_area\\\": 0,\\n\",\n \" }\\n\",\n \"\\n\",\n \" h, w = mask.shape\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in prompt_bbox_xyxy]\\n\",\n \" x0 = max(0, min(w - 1, x0))\\n\",\n \" y0 = max(0, min(h - 1, y0))\\n\",\n \" x1 = max(0, min(w, x1))\\n\",\n \" y1 = max(0, min(h, y1))\\n\",\n \"\\n\",\n \" box_mask = np.zeros_like(mask)\\n\",\n \" if x1 > x0 and y1 > y0:\\n\",\n \" box_mask[y0:y1, x0:x1] = True\\n\",\n \"\\n\",\n \" inside_box_ratio = float((mask & box_mask).sum() / max(mask_area, 1))\\n\",\n \"\\n\",\n \" if prompt_points_xy:\\n\",\n \" hits = 0\\n\",\n \" for px, py in prompt_points_xy:\\n\",\n \" if 0 <= int(py) < h and 0 <= int(px) < w and mask[int(py), int(px)]:\\n\",\n \" hits += 1\\n\",\n \" point_coverage = float(hits / len(prompt_points_xy))\\n\",\n \" else:\\n\",\n \" point_coverage = 0.0\\n\",\n \"\\n\",\n \" if hint_mask is not None:\\n\",\n \" inter = float((mask & hint_mask).sum())\\n\",\n \" union = float((mask | hint_mask).sum())\\n\",\n \" hint_iou = inter / max(union, 1.0)\\n\",\n \" else:\\n\",\n \" hint_iou = 0.0\\n\",\n \"\\n\",\n \" if prompt_points_xy:\\n\",\n \" total = 0.50 * hint_iou + 0.30 * point_coverage + 0.10 * inside_box_ratio + 0.10 * float(sam_score)\\n\",\n \" else:\\n\",\n \" total = 0.80 * float(sam_score) + 0.20 * inside_box_ratio\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"total\\\": float(total),\\n\",\n \" \\\"sam_score\\\": float(sam_score),\\n\",\n \" \\\"point_coverage\\\": float(point_coverage),\\n\",\n \" \\\"hint_iou\\\": float(hint_iou),\\n\",\n \" \\\"inside_box_ratio\\\": float(inside_box_ratio),\\n\",\n \" \\\"mask_area\\\": int(mask_area),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n \" if prompt_points_xy:\\n\",\n \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" input_points=pts,\\n\",\n \" input_labels=labels,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" inputs = sam_processor(\\n\",\n \" image,\\n\",\n \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" )\\n\",\n \"\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sam_model(**inputs)\\n\",\n \"\\n\",\n \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n \" outputs.pred_masks.cpu(),\\n\",\n \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n \" )\\n\",\n \"\\n\",\n \" sam_scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n \" candidate_masks = masks[0][0].numpy().astype(bool)\\n\",\n \"\\n\",\n \" img_arr = np.array(image)\\n\",\n \" hint_mask = None\\n\",\n \" if prompt_points_xy:\\n\",\n \" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n \"\\n\",\n \" candidate_debug = []\\n\",\n \" for idx in range(candidate_masks.shape[0]):\\n\",\n \" metrics = score_sam_candidate(\\n\",\n \" mask=candidate_masks[idx],\\n\",\n \" sam_score=float(sam_scores[idx]),\\n\",\n \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n \" prompt_points_xy=prompt_points_xy,\\n\",\n \" hint_mask=hint_mask,\\n\",\n \" )\\n\",\n \" metrics[\\\"candidate_idx\\\"] = int(idx)\\n\",\n \" candidate_debug.append(metrics)\\n\",\n \"\\n\",\n \" best = max(candidate_debug, key=lambda d: d[\\\"total\\\"])\\n\",\n \" best_idx = int(best[\\\"candidate_idx\\\"])\\n\",\n \" mask = candidate_masks[best_idx]\\n\",\n \" bbox = mask_bbox_xywh(mask)\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n \" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n \" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n \" \\\"selection_metrics\\\": {\\n\",\n \" \\\"point_coverage\\\": float(best[\\\"point_coverage\\\"]),\\n\",\n \" \\\"hint_iou\\\": float(best[\\\"hint_iou\\\"]),\\n\",\n \" \\\"inside_box_ratio\\\": float(best[\\\"inside_box_ratio\\\"]),\\n\",\n \" },\\n\",\n \" \\\"sam_candidates\\\": candidate_debug,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def classify_material_sigclip(\\n\",\n \" image_views: list,\\n\",\n \" object_label: str | None,\\n\",\n \" sigclip_model,\\n\",\n \" sigclip_processor,\\n\",\n \" top_k: int = 3,\\n\",\n \" view_weights: list | None = None,\\n\",\n \"):\\n\",\n \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n \"\\n\",\n \" selected_idx = [\\n\",\n \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n \" ]\\n\",\n \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n \"\\n\",\n \" if not selected_prompts:\\n\",\n \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n \"\\n\",\n \" if view_weights is None:\\n\",\n \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n \" if len(view_weights) != len(image_views):\\n\",\n \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n \"\\n\",\n \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n \"\\n\",\n \" for image, weight in zip(image_views, view_weights):\\n\",\n \" inputs = sigclip_processor(\\n\",\n \" text=selected_prompts,\\n\",\n \" images=image,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" padding=\\\"max_length\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sigclip_model(**inputs)\\n\",\n \"\\n\",\n \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n \"\\n\",\n \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n \"\\n\",\n \" for m in candidate_materials:\\n\",\n \" agg_scores[m] += weight * per_material_max[m]\\n\",\n \"\\n\",\n \" total = sum(agg_scores.values())\\n\",\n \" if total > 0:\\n\",\n \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n \"\\n\",\n \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n \" top_k = min(top_k, len(ranked))\\n\",\n \" top_items = ranked[:top_k]\\n\",\n \"\\n\",\n \" top_labels = [x[0] for x in top_items]\\n\",\n \" top_scores = [x[1] for x in top_items]\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" 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"gAAAAABqR6rjq9MHdykUKrNr4NfQSrqp52kfXh5-AO2uC41UvAvmLpeeHOHIudn58nEwJjbLpi0Ft1h0PaOE09lvGCFhmVLYwWczHQMw0L_EB27mzKZNqNXhYdkeKeRHjNcKmTFes0WuuLxI55StKzNd_DQKEcIsXvFavt3cW2bAo3WVvM_4YpQrZ80ZRjke4vl3kwVoZVfztawZlQC55jnXX6s6FT_jC13aMMMyNs84hCHEAQBhVkpy5bW-dxeLsFhvaz03oL3cD0pOakUhtnUkoBM8JCqboC-mUs-HbxnrIsEyljEe_SVw7_hF4_Jc7Gw_OOXGUEd_YkSz-_i8QEl5ipDQZm7PTwBN7uVdAHJJoA6XgMli5UK4ILk-N8SpwUKQnBn1qfHLSBe8M1L9iveNP3FlBwP_RFhRMuxUzuMaBSYjhSHbvfrQN2nTO6Khy9dprCLPoCBFBtJwGYVedTuMMCNRPoJteu1vypVRS62lFVgyDfmydM3DIw5XZayeADUR-LBStD5r_Z8KBf5DU1yutlqH8o7i1Lri9nDd0d4l_Jcm6RHsXiR3VBUCNxS2R4wNjPXuSSkeMkple8f_o7vsuE-Ejj2OR9xAIO3REgpJz0-lFa77dz1hveS29KFJ1iYYhXfl6c79cAx477gD6z3hyQ2sflXloqibXhhcCyFuN88w7HWxHgk3jAOk3p7mIQaalEHWxg4ZYKpXPY06eWIoPrzaAjU-9xtWppH5dHqgKyOnHXgWFAoSmpv92mSFhPOKTIn4p5aDuG_1plUZbUZwmlO_zpQZl9bbiO_nk5bI2cqb_ajWTbVsHwWTcUqbKUO2oeG88vpuC3mezLcxlTMyehO9HyeGr_91DPdY45aEzuDhbWTSbyqRzdJBNddbvLb7-AI43kWNWhQJH_JG2J4Jt3Oc44mjGciXKc6xyfBAL3dMMR4TLepkg7slHZ1aQR71PY-5EAFFLeSJotSD3R9L1RYcGVwZzaFnrKSUv09ImAWgh8qB4ms7Y5H09F0EFciXG2GjA93iD0atP09LLFveBOWb5QbLBVytr_0-idHT5iu2Br8vOYA1B6L9v50r7XoqBqKWnUYfJtZQ39KsD8fZdpEgPQmQmXarwfI7yKGeY3Obgp_ot0lWja5ZHmgewGVzI149pxCWUWBHJctKwlCdWAN72mVqzHo6MnllLGnyj2Dpw-XPB0AFLIcFi7oCALM2zabSZQryMTWwnLkXumswU5Cq-UqhsmGPRLFPIqsDGCxHeLsi5hy_mGjVyno9ycJ20GWvIft6j_GfPmnm6SqohZkIOMHaXDHr28EKiu_KoDeWq3yXwHrZ5a01ESMe8FR-B5x267vom2MfEZt7vNmqb2yzo2B29ae1wZtsEWVHP7J6LztuM1p1344btdjWjsqbo8WHVcUwlQ4VJ0Z8uTUoMLDZS_yfiz19NbyUmyyYq5JOdT67nX4=" + } + }, + "id": "prt_f27f3831e001wypgwY5cQEC4kt", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f349f80029PYkQ8eSsWv4Yn" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_4Sj04Yk1yXOxFq8wiDah54Tz", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def bbox_xyxy_to_norm_cxcywh(bbox_xyxy, image_width: int, image_height: int):\\n\",\n+ \" x0, y0, x1, y1 = [float(v) for v in bbox_xyxy]\\n\",\n+ \" x0 = max(0.0, min(float(image_width - 1), x0))\\n\",\n+ \" y0 = max(0.0, min(float(image_height - 1), y0))\\n\",\n+ \" x1 = max(0.0, min(float(image_width - 1), x1))\\n\",\n+ \" y1 = max(0.0, min(float(image_height - 1), y1))\\n\",\n+ \"\\n\",\n+ \" cx = ((x0 + x1) / 2.0) / max(float(image_width), 1.0)\\n\",\n+ \" cy = ((y0 + y1) / 2.0) / max(float(image_height), 1.0)\\n\",\n+ \" w = max(1.0, abs(x1 - x0)) / max(float(image_width), 1.0)\\n\",\n+ \" h = max(1.0, abs(y1 - y0)) / max(float(image_height), 1.0)\\n\",\n+ \"\\n\",\n+ \" return [float(cx), float(cy), float(w), float(h)]\\n\",\n+ \"\\n\",\n+ \"\\n\",\n@@\n- \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n- \" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n- \" if prompt_points_xy:\\n\",\n- \" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n- \" labels = [[1 for _ in prompt_points_xy]]\\n\",\n- \" inputs = sam_processor(\\n\",\n- \" image,\\n\",\n- \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n- \" input_points=pts,\\n\",\n- \" input_labels=labels,\\n\",\n- \" return_tensors=\\\"pt\\\",\\n\",\n- \" )\\n\",\n- \" else:\\n\",\n- \" inputs = sam_processor(\\n\",\n- \" image,\\n\",\n- \" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n- \" return_tensors=\\\"pt\\\",\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n- \"\\n\",\n- \" with torch.no_grad():\\n\",\n- \" outputs = sam_model(**inputs)\\n\",\n- \"\\n\",\n- \" masks = sam_processor.image_processor.post_process_masks(\\n\",\n- \" outputs.pred_masks.cpu(),\\n\",\n- \" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n- \" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n- \" )\\n\",\n- \"\\n\",\n- \" sam_scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n- \" candidate_masks = masks[0][0].numpy().astype(bool)\\n\",\n+ \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n+ \" img_w, img_h = image.size\\n\",\n+ \"\\n\",\n+ \" state = sam_processor.set_image(image, state={})\\n\",\n+ \"\\n\",\n+ \" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n+ \" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n+ \"\\n\",\n+ \" if prompt_points_xy:\\n\",\n+ \" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n+ \" for px, py in hint_points:\\n\",\n+ \" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n+ \" pbox = [\\n\",\n+ \" int(max(0, px - r)),\\n\",\n+ \" int(max(0, py - r)),\\n\",\n+ \" int(min(img_w - 1, px + r)),\\n\",\n+ \" int(min(img_h - 1, py + r)),\\n\",\n+ \" ]\\n\",\n+ \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n+ \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n+ \"\\n\",\n+ \" masks_t = state.get(\\\"masks\\\", None)\\n\",\n+ \" scores_t = state.get(\\\"scores\\\", None)\\n\",\n+ \"\\n\",\n+ \" if masks_t is None or scores_t is None:\\n\",\n+ \" raise RuntimeError(\\\"SAM3 inference did not return masks/scores.\\\")\\n\",\n+ \"\\n\",\n+ \" masks_np = masks_t.detach().cpu().numpy()\\n\",\n+ \" if masks_np.ndim == 4 and masks_np.shape[1] == 1:\\n\",\n+ \" candidate_masks = masks_np[:, 0].astype(bool)\\n\",\n+ \" elif masks_np.ndim == 3:\\n\",\n+ \" candidate_masks = masks_np.astype(bool)\\n\",\n+ \" else:\\n\",\n+ \" raise RuntimeError(f\\\"Unexpected SAM3 mask shape: {masks_np.shape}\\\")\\n\",\n+ \"\\n\",\n+ \" sam_scores = scores_t.detach().cpu().numpy().astype(float)\\n\",\n+ \" if sam_scores.ndim > 1:\\n\",\n+ \" sam_scores = sam_scores.reshape(-1)\\n\",\n+ \"\\n\",\n+ \" if len(candidate_masks) == 0:\\n\",\n+ \" raise RuntimeError(\\\"SAM3 returned zero candidate masks. Try redrawing the prompt.\\\")\\n\",\n@@\n- \" for idx in range(candidate_masks.shape[0]):\\n\",\n+ \" for idx in range(candidate_masks.shape[0]):\\n\",\n \" metrics = score_sam_candidate(\\n\",\n \" mask=candidate_masks[idx],\\n\",\n- \" sam_score=float(sam_scores[idx]),\\n\",\n+ \" sam_score=float(sam_scores[min(idx, len(sam_scores) - 1)]),\\n\",\n \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n \" prompt_points_xy=prompt_points_xy,\\n\",\n \" hint_mask=hint_mask,\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -663,8 +663,23 @@\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def bbox_xyxy_to_norm_cxcywh(bbox_xyxy, image_width: int, image_height: int):\\n\",\n+\" x0, y0, x1, y1 = [float(v) for v in bbox_xyxy]\\n\",\n+\" x0 = max(0.0, min(float(image_width - 1), x0))\\n\",\n+\" y0 = max(0.0, min(float(image_height - 1), y0))\\n\",\n+\" x1 = max(0.0, min(float(image_width - 1), x1))\\n\",\n+\" y1 = max(0.0, min(float(image_height - 1), y1))\\n\",\n+\"\\n\",\n+\" cx = ((x0 + x1) / 2.0) / max(float(image_width), 1.0)\\n\",\n+\" cy = ((y0 + y1) / 2.0) / max(float(image_height), 1.0)\\n\",\n+\" w = max(1.0, abs(x1 - x0)) / max(float(image_width), 1.0)\\n\",\n+\" h = max(1.0, abs(y1 - y0)) / max(float(image_height), 1.0)\\n\",\n+\"\\n\",\n+\" return [float(cx), float(cy), float(w), float(h)]\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def build_brush_hint_mask(image_shape_hw, prompt_bbox_xyxy, prompt_points_xy, point_radius_px: int = 16):\\n\",\n \" h, w = image_shape_hw\\n\",\n \" hint = np.zeros((h, w), dtype=bool)\\n\",\n \"\\n\",\n@@ -747,40 +762,49 @@\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n-\" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n+\" img_w, img_h = image.size\\n\",\n+\"\\n\",\n+\" state = sam_processor.set_image(image, state={})\\n\",\n+\"\\n\",\n+\" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n+\" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n+\"\\n\",\n \" if prompt_points_xy:\\n\",\n-\" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n-\" labels = [[1 for _ in prompt_points_xy]]\\n\",\n-\" inputs = sam_processor(\\n\",\n-\" image,\\n\",\n-\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n-\" input_points=pts,\\n\",\n-\" input_labels=labels,\\n\",\n-\" return_tensors=\\\"pt\\\",\\n\",\n-\" )\\n\",\n-\" else:\\n\",\n-\" inputs = sam_processor(\\n\",\n-\" image,\\n\",\n-\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n-\" return_tensors=\\\"pt\\\",\\n\",\n-\" )\\n\",\n+\" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n+\" for px, py in hint_points:\\n\",\n+\" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n+ \" pbox = [\\n\",\n+ \" int(max(0, px - r)),\\n\",\n+ \" int(max(0, py - r)),\\n\",\n+ \" int(min(img_w - 1, px + r)),\\n\",\n+ \" int(min(img_h - 1, py + r)),\\n\",\n+ \" ]\\n\",\n+ \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n+ \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n \"\\n\",\n-\" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+\" masks_t = state.get(\\\"masks\\\", None)\\n\",\n+\" scores_t = state.get(\\\"scores\\\", None)\\n\",\n \"\\n\",\n-\" with torch.no_grad():\\n\",\n-\" outputs = sam_model(**inputs)\\n\",\n+\" if masks_t is None or scores_t is None:\\n\",\n+\" raise RuntimeError(\\\"SAM3 inference did not return masks/scores.\\\")\\n\",\n \"\\n\",\n-\" masks = sam_processor.image_processor.post_process_masks(\\n\",\n-\" outputs.pred_masks.cpu(),\\n\",\n-\" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n-\" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n-\" )\\n\",\n+\" masks_np = masks_t.detach().cpu().numpy()\\n\",\n+\" if masks_np.ndim == 4 and masks_np.shape[1] == 1:\\n\",\n+\" candidate_masks = masks_np[:, 0].astype(bool)\\n\",\n+\" elif masks_np.ndim == 3:\\n\",\n+\" candidate_masks = masks_np.astype(bool)\\n\",\n+\" else:\\n\",\n+\" raise RuntimeError(f\\\"Unexpected SAM3 mask shape: {masks_np.shape}\\\")\\n\",\n \"\\n\",\n-\" sam_scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n-\" candidate_masks = masks[0][0].numpy().astype(bool)\\n\",\n+\" sam_scores = scores_t.detach().cpu().numpy().astype(float)\\n\",\n+\" if sam_scores.ndim > 1:\\n\",\n+\" sam_scores = sam_scores.reshape(-1)\\n\",\n \"\\n\",\n+\" if len(candidate_masks) == 0:\\n\",\n+\" raise RuntimeError(\\\"SAM3 returned zero candidate masks. Try redrawing the prompt.\\\")\\n\",\n+\"\\n\",\n \" img_arr = np.array(image)\\n\",\n \" hint_mask = None\\n\",\n \" if prompt_points_xy:\\n\",\n \" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n@@ -788,9 +812,9 @@\n \" candidate_debug = []\\n\",\n \" for idx in range(candidate_masks.shape[0]):\\n\",\n \" metrics = score_sam_candidate(\\n\",\n \" mask=candidate_masks[idx],\\n\",\n-\" sam_score=float(sam_scores[idx]),\\n\",\n+\" sam_score=float(sam_scores[min(idx, len(sam_scores) - 1)]),\\n\",\n \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n \" prompt_points_xy=prompt_points_xy,\\n\",\n \" hint_mask=hint_mask,\\n\",\n \" )\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -663,8 +663,23 @@\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def bbox_xyxy_to_norm_cxcywh(bbox_xyxy, image_width: int, image_height: int):\\n\",\n+\" x0, y0, x1, y1 = [float(v) for v in bbox_xyxy]\\n\",\n+\" x0 = max(0.0, min(float(image_width - 1), x0))\\n\",\n+\" y0 = max(0.0, min(float(image_height - 1), y0))\\n\",\n+\" x1 = max(0.0, min(float(image_width - 1), x1))\\n\",\n+\" y1 = max(0.0, min(float(image_height - 1), y1))\\n\",\n+\"\\n\",\n+\" cx = ((x0 + x1) / 2.0) / max(float(image_width), 1.0)\\n\",\n+\" cy = ((y0 + y1) / 2.0) / max(float(image_height), 1.0)\\n\",\n+\" w = max(1.0, abs(x1 - x0)) / max(float(image_width), 1.0)\\n\",\n+\" h = max(1.0, abs(y1 - y0)) / max(float(image_height), 1.0)\\n\",\n+\"\\n\",\n+\" return [float(cx), float(cy), float(w), float(h)]\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def build_brush_hint_mask(image_shape_hw, prompt_bbox_xyxy, prompt_points_xy, point_radius_px: int = 16):\\n\",\n \" h, w = image_shape_hw\\n\",\n \" hint = np.zeros((h, w), dtype=bool)\\n\",\n \"\\n\",\n@@ -747,40 +762,49 @@\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n-\" x0, y0, x1, y1 = [float(v) for v in prompt_bbox_xyxy]\\n\",\n+\" img_w, img_h = image.size\\n\",\n+\"\\n\",\n+\" state = sam_processor.set_image(image, state={})\\n\",\n+\"\\n\",\n+\" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n+\" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n+\"\\n\",\n \" if prompt_points_xy:\\n\",\n-\" pts = [[[float(x), float(y)] for x, y in prompt_points_xy]]\\n\",\n-\" labels = [[1 for _ in prompt_points_xy]]\\n\",\n-\" inputs = sam_processor(\\n\",\n-\" image,\\n\",\n-\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n-\" input_points=pts,\\n\",\n-\" input_labels=labels,\\n\",\n-\" return_tensors=\\\"pt\\\",\\n\",\n-\" )\\n\",\n-\" else:\\n\",\n-\" inputs = sam_processor(\\n\",\n-\" image,\\n\",\n-\" input_boxes=[[[x0, y0, x1, y1]]],\\n\",\n-\" return_tensors=\\\"pt\\\",\\n\",\n-\" )\\n\",\n+\" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n+\" for px, py in hint_points:\\n\",\n+\" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n+ \" pbox = [\\n\",\n+ \" int(max(0, px - r)),\\n\",\n+ \" int(max(0, py - r)),\\n\",\n+ \" int(min(img_w - 1, px + r)),\\n\",\n+ \" int(min(img_h - 1, py + r)),\\n\",\n+ \" ]\\n\",\n+ \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n+ \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n \"\\n\",\n-\" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+\" masks_t = state.get(\\\"masks\\\", None)\\n\",\n+\" scores_t = state.get(\\\"scores\\\", None)\\n\",\n \"\\n\",\n-\" with torch.no_grad():\\n\",\n-\" outputs = sam_model(**inputs)\\n\",\n+\" if masks_t is None or scores_t is None:\\n\",\n+\" raise RuntimeError(\\\"SAM3 inference did not return masks/scores.\\\")\\n\",\n \"\\n\",\n-\" masks = sam_processor.image_processor.post_process_masks(\\n\",\n-\" outputs.pred_masks.cpu(),\\n\",\n-\" inputs[\\\"original_sizes\\\"].cpu(),\\n\",\n-\" inputs[\\\"reshaped_input_sizes\\\"].cpu(),\\n\",\n-\" )\\n\",\n+\" masks_np = masks_t.detach().cpu().numpy()\\n\",\n+\" if masks_np.ndim == 4 and masks_np.shape[1] == 1:\\n\",\n+\" candidate_masks = masks_np[:, 0].astype(bool)\\n\",\n+\" elif masks_np.ndim == 3:\\n\",\n+\" candidate_masks = masks_np.astype(bool)\\n\",\n+\" else:\\n\",\n+\" raise RuntimeError(f\\\"Unexpected SAM3 mask shape: {masks_np.shape}\\\")\\n\",\n \"\\n\",\n-\" sam_scores = outputs.iou_scores[0, 0].detach().cpu().numpy()\\n\",\n-\" candidate_masks = masks[0][0].numpy().astype(bool)\\n\",\n+\" sam_scores = scores_t.detach().cpu().numpy().astype(float)\\n\",\n+\" if sam_scores.ndim > 1:\\n\",\n+\" sam_scores = sam_scores.reshape(-1)\\n\",\n \"\\n\",\n+\" if len(candidate_masks) == 0:\\n\",\n+\" raise RuntimeError(\\\"SAM3 returned zero candidate masks. Try redrawing the prompt.\\\")\\n\",\n+\"\\n\",\n \" img_arr = np.array(image)\\n\",\n \" hint_mask = None\\n\",\n \" if prompt_points_xy:\\n\",\n \" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n@@ -788,9 +812,9 @@\n \" candidate_debug = []\\n\",\n \" for idx in range(candidate_masks.shape[0]):\\n\",\n \" metrics = score_sam_candidate(\\n\",\n \" mask=candidate_masks[idx],\\n\",\n-\" sam_score=float(sam_scores[idx]),\\n\",\n+\" sam_score=float(sam_scores[min(idx, len(sam_scores) - 1)]),\\n\",\n \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n \" prompt_points_xy=prompt_points_xy,\\n\",\n \" hint_mask=hint_mask,\\n\",\n \" )\\n\",\n", + "additions": 51, + "deletions": 27 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783081718759, + "end": 1783081718791 + } + }, + "metadata": { + "openai": { + "itemId": "fc_08480d6a369f455d016a47aae37b8c81a3935f08c2e6febfbd" + } + }, + "id": "prt_f27f388e4001U14EDUuGFhI5hQ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f349f80029PYkQ8eSsWv4Yn" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 173319, + "input": 4129, + "output": 1929, + "reasoning": 93, + "cache": { + "write": 0, + "read": 167168 + } + }, + "cost": 0.06478815, + "id": "prt_f27f3d465001Q4xMnujFOdB7D7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f349f80029PYkQ8eSsWv4Yn" + } + ] + }, + { + "info": { + "parentID": "msg_f27f207c40019BcuAqaBu96PbB", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.04107355, + "tokens": { + "total": 173874, + "input": 2221, + "output": 461, + "reasoning": 56, + "cache": { + "write": 0, + "read": 171136 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783081718918, + "completed": 1783081726589 + }, + "finish": "tool-calls", + "id": "msg_f27f3d486002FiN35vf7nCj5wI", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27f3dce9001aIOO1kMzzbYbCn", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f3d486002FiN35vf7nCj5wI" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783081721436, + "end": 1783081722895 + }, + "metadata": { + "openai": { + "itemId": "rs_03ddcf17ef9a2886016a47aaf954dc81a0961e91021ec6ae3b", + "reasoningEncryptedContent": "gAAAAABqR6r6q93uK-eXtyLdjcBDwVICUHbbdBFw2yoQbDIHEkwT2jtNy5A3kh2XNZvcBAwWz0X3odKFO7Hs1DR9QHO1P1ddpW62zJhGy4M0hoaXE1LdlEr7Ypgzq-d7S9MygWcUM7vsUbIj6OhKP5STJ3c6L3fPspIFCJ9VVjkGftMck8aKE1GXJipKiAHNvN0gyRJOPmj7_UiIqsy7zoXZoCEozrWN7SigysgZ2YlhswPAxfafO5KbCBJiUE-zJlNl7fe8fkwQKy3tvF-mj7L_2Hqj5hTP2GUUQAwXmoqPdnw4XOdrJGHDfCDCmEKwyJWCA9I_-okEVCNAR4zwwHDNw9jQXS-P6j_NFVCWPZvGcwjdO7Q1DMtCB9sO9pki9VUr3CnGWowq86LIro7GK5LUq0-iJ8D3ZnwDTkkSe2CGL1aesDwRI95VvoJBRQkG-bHi5exwlF0BNL9XNY2CmdDJBt6ARoKTdk6FXwFyC_7sekoy9asBEaxfuOb-BPz9FT_4YTLrkovfScSviwtleW44aQwi_9DypE95IU_gLcDsFYlkxliXI7lTiKE4GcEC9kv0_kThTdmzAWCy_4LvJMq-rwtAdvzqdHMWgvAk3p4i_mO16a4t1YDE1odLjZKhSmZ3twOqbOqrAvzUoZzORQBVa5jJUPHh29kx2SBcouG7ZygJCeoDAymmFazHUHzd7D8TjVIY7tnhjbCGvOOcwdDEF6IGG1LwRwSuC2rUe3dFpjn0oZlqF5azKhOyt8eG0Kxwuumvi3a3OB1qj0dQSaZYhAbLf2mIBBuDZgG2nUksix4zeNzjMisRqaUeYO5zD3r_uglp0-1IE5b1fFBQZWIPX0zFNwJxKnsD_0sKWT_4LyosRFN34b-ZfBr5upvVMIMmg5n0wODN8y5oAfd9XpSkOQJVedvYgLSvx92rZJbmAQrY_QXZsmal4Zf-tjxf_7gsjfphGb1yNcjrw3bev-dViIrERvQrrdQg6wYSV0VFXqItorXW9yU4786DAXnNXSwJsa4ajxdhcOk5FcG3pbgxQ46E0lpEYxS71Yjc4DSpO0AaDxXOHXHWA7Q17HqofCNBb2hdUElyvoosr0e7HkO59nAX_ibEzzRpid1OM4wIPGymMHYPrqFRHkCOkPacQrv_0CCKTIRDEVHlEsCyHH7E-AAeYrO7tWCw4QOzF1ZU2drQQ0-HFKUzpe-moRuJf4HdNbxamaJ4aEvFnF4j6yNf370jl4069RTLn9WXEFLnjpfKy2XMRiPsunDY_fO387nlosA56gGc8r45qZuFweUXRe2kkWO-AZEuGVPN0ETaw8MCPo8qA0M=" + } + }, + "id": "prt_f27f3de5c001kvF6JCkL9no9d7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f3d486002FiN35vf7nCj5wI" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_MCThjz0hMs6D7d6MxDN1YtFS", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n- \"print(\\\"Loading SAM model...\\\")\\n\",\n- \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n- \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n- \"sam_model.eval()\\n\",\n+ \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n+ \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n+ \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n+ \"else:\\n\",\n+ \" sam3_checkpoint_path = SAM3_CHECKPOINT_PATH\\n\",\n+ \"\\n\",\n+ \"sam_model = build_sam3_image_model(\\n\",\n+ \" device=DEVICE,\\n\",\n+ \" checkpoint_path=sam3_checkpoint_path,\\n\",\n+ \" load_from_HF=False,\\n\",\n+ \" enable_inst_interactivity=False,\\n\",\n+ \")\\n\",\n+ \"sam_processor = Sam3Processor(\\n\",\n+ \" model=sam_model,\\n\",\n+ \" device=DEVICE,\\n\",\n+ \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n+ \")\\n\",\n+ \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -937,13 +937,27 @@\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n- \"print(\\\"Loading SAM model...\\\")\\n\",\n- \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n- \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n- \"sam_model.eval()\\n\",\n+ \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n+ \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n+ \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n+ \"else:\\n\",\n+ \" sam3_checkpoint_path = SAM3_CHECKPOINT_PATH\\n\",\n \"\\n\",\n+ \"sam_model = build_sam3_image_model(\\n\",\n+ \" device=DEVICE,\\n\",\n+ \" checkpoint_path=sam3_checkpoint_path,\\n\",\n+ \" load_from_HF=False,\\n\",\n+ \" enable_inst_interactivity=False,\\n\",\n+ \")\\n\",\n+ \"sam_processor = Sam3Processor(\\n\",\n+ \" model=sam_model,\\n\",\n+ \" device=DEVICE,\\n\",\n+ \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n+ \")\\n\",\n+ \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n+ \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -937,13 +937,27 @@\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n- \"print(\\\"Loading SAM model...\\\")\\n\",\n- \"sam_processor = SamProcessor.from_pretrained(SAM_MODEL_ID)\\n\",\n- \"sam_model = SamModel.from_pretrained(SAM_MODEL_ID).to(DEVICE)\\n\",\n- \"sam_model.eval()\\n\",\n+ \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n+ \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n+ \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n+ \"else:\\n\",\n+ \" sam3_checkpoint_path = SAM3_CHECKPOINT_PATH\\n\",\n \"\\n\",\n+ \"sam_model = build_sam3_image_model(\\n\",\n+ \" device=DEVICE,\\n\",\n+ \" checkpoint_path=sam3_checkpoint_path,\\n\",\n+ \" load_from_HF=False,\\n\",\n+ \" enable_inst_interactivity=False,\\n\",\n+ \")\\n\",\n+ \"sam_processor = Sam3Processor(\\n\",\n+ \" model=sam_model,\\n\",\n+ \" device=DEVICE,\\n\",\n+ \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n+ \")\\n\",\n+ \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n+ \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n", + "additions": 18, + "deletions": 4 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783081726538, + "end": 1783081726563 + } + }, + "metadata": { + "openai": { + "itemId": "fc_03ddcf17ef9a2886016a47aafad5c081a09fd3c7c8ead50419" + } + }, + "id": "prt_f27f3e4140013sWegJwXh24dNt", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f3d486002FiN35vf7nCj5wI" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 173874, + "input": 2221, + "output": 461, + "reasoning": 56, + "cache": { + "write": 0, + "read": 171136 + } + }, + "cost": 0.04107355, + "id": "prt_f27f3f2760012OzTqKjP03iuPF", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f3d486002FiN35vf7nCj5wI" + } + ] + }, + { + "info": { + "parentID": "msg_f27f207c40019BcuAqaBu96PbB", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + 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normalized `cx,cy,w,h`,\n - brush points are converted into small positive hint boxes (so they influence SAM inference),\n - candidate masks are still ranked with your hint-aware logic (`hint_iou`, point coverage, etc.).\n\nValidation:\n- Notebook JSON parses and Python code cells parse cleanly.\n\nWhat you should do in Colab now:\n1. Re-upload `poc_eval_notebook.ipynb`.\n2. Run from top (or restart runtime + run all).\n3. If gated access isn’t already active in runtime, run `hf auth login` in a cell/terminal first.\n4. Optional: if you already downloaded checkpoint locally, set `SAM3_CHECKPOINT_PATH` to it to avoid re-download.\n\nIf you want, next I can add a tiny “environment check” cell that verifies HF auth + checkpoint availability before model loading, so failures are clearer.", + "time": { + "start": 1783081804433, + "end": 1783081809665 + }, + "metadata": { + "openai": { + "itemId": "msg_04c238cc9a523940016a47ab4c5d34819fad3c954e947eb060", + "phase": "final_answer" + } + }, + "id": "prt_f27f52291001XEuKU8nz1JwWs1", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f4ec7a0021Zqu00lQV1d26p" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 175037, + "input": 264, + "output": 410, + "reasoning": 27, + "cache": { + "write": 0, + "read": 174336 + } + }, + "cost": 0.0370888, + "id": "prt_f27f53712001zw4SkdAMA2VWNM", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f4ec7a0021Zqu00lQV1d26p" + } + ] + }, + { + "info": { + 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\"code\",\n934: \"execution_count\": null,\n935: \"metadata\": {},\n936: \"outputs\": [],\n937: \"source\": [\n938: \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n939: \"\\n\",\n940: \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n941: \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n942: \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n943: \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n944: \"else:\\n\",\n945: \" sam3_checkpoint_path = SAM3_CHECKPOINT_PATH\\n\",\n946: \"\\n\",\n947: \"sam_model = build_sam3_image_model(\\n\",\n948: \" device=DEVICE,\\n\",\n949: \" checkpoint_path=sam3_checkpoint_path,\\n\",\n950: \" load_from_HF=False,\\n\",\n951: \" enable_inst_interactivity=False,\\n\",\n952: \")\\n\",\n953: \"sam_processor = Sam3Processor(\\n\",\n954: \" model=sam_model,\\n\",\n955: \" device=DEVICE,\\n\",\n956: \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n957: \")\\n\",\n958: \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n959: \"\\n\",\n960: \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n961: \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n962: \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n963: \"sigclip_model.eval()\\n\",\n964: \"\\n\",\n965: \"print(\\\"Models ready.\\\")\"\n966: ]\n967: },\n968: {\n969: \"cell_type\": \"code\",\n970: \"execution_count\": null,\n971: \"metadata\": {},\n972: \"outputs\": [],\n973: \"source\": [\n974: \"# Configure one image and one-or-many objects in it.\\n\",\n975: \"image_id = \\\"IMG002\\\"\\n\",\n976: \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n977: \"\\n\",\n978: \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n979: \"USE_ACCEPT_REDRAW = True\\n\",\n980: \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n981: \"MULTI_OBJECT_MODE = True\\n\",\n982: \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n983: \"\\n\",\n984: \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n985: \"OBJECT_SPECS = [\\n\",\n986: \" {\\n\",\n987: \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n988: \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n989: \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n\n(Showing lines 920-989 of 1447. Use offset=990 to continue.)\n</content>", + "metadata": { + "preview": " \"\\n\",\n \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n \" df_out.to_csv(csv_path, index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \"\\n\",\n \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n \" df_out.to_csv(csv_path, index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n \"else:\\n\",\n \" sam3_checkpoint_path = SAM3_CHECKPOINT_PATH\\n\",\n \"\\n\",\n \"sam_model = build_sam3_image_model(\\n\",\n \" device=DEVICE,\\n\",\n \" checkpoint_path=sam3_checkpoint_path,\\n\",\n \" load_from_HF=False,\\n\",\n \" enable_inst_interactivity=False,\\n\",\n \")\\n\",\n \"sam_processor = Sam3Processor(\\n\",\n \" model=sam_model,\\n\",\n \" device=DEVICE,\\n\",\n \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n \")\\n\",\n \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n \"USE_ACCEPT_REDRAW = True\\n\",\n \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n \"\\n\",\n \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n \"OBJECT_SPECS = [\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",", + "lineStart": 920, + "lineEnd": 989, + "totalLines": 1447, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783081843578, + "end": 1783081843729 + } + }, + "metadata": { + "openai": { + "itemId": "fc_024b7499e610ec69016a47ab731654819da27d586e6dc7d72a" + } + }, + "id": "prt_f27f5b9d0001cqUrsaa2XRSaCg", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f59ea0002GDdUZEDFt6LAN0" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 173475, + "input": 23114, + "output": 47, + "reasoning": 298, + "cache": { + "write": 0, + "read": 150016 + } + }, + "cost": 0.0715323, + "id": "prt_f27f5bc1a001eoFGeNvN3ZLAFE", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f59ea0002GDdUZEDFt6LAN0" + } + ] + }, + { + "info": { + "parentID": "msg_f27f59e7a001Z0KSWlCqI4fWCl", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.03578085, + "tokens": { + "total": 174656, + "input": 1091, + "output": 156, + "reasoning": 97, + "cache": { + "write": 0, + "read": 173312 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783081843769, + "completed": 1783081850214 + }, + "finish": "tool-calls", + "id": "msg_f27f5bc39002m2BHttOK6atOzz", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27f5c4f2001Hu6xk9A67i8rAV", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f5bc39002m2BHttOK6atOzz" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783081847218, + "end": 1783081849063 + }, + "metadata": { + "openai": { + "itemId": "rs_0263aef625a4fe71016a47ab771088819f9958f5426c7084c7", + "reasoningEncryptedContent": "gAAAAABqR6t4lv4rIgPHdZqlyDZL0zjXzp5NZfrt19h67BEjyIg5jlM31nRJLrs_DxpQusXiHSyaDOulO3LFd8yNcMb_qlKKFN4lFIU9IPJMWF4u6Hb6DVAXevS66ZM-pDKO6AwlekaM5mkZcHeg_89aga3K813v-sNn0jbrQhAf3W9zdjYH_T23F0RW4AURMZa1gDMIj8svq9R9V_ymHYkuE23_moaj3GJ-ErriMq9pfwzapMvwpxh5YHxYIWufX_zQ0fkrl5dzwVnzmzd7-9Abn20C3H5dE6KYCWzTFHQZu8BPfib-CHLnAHODNZI54XF69gdjEyDrOoG64PExoXD443jvUoWnYWnQF0SFMoF6NodAAEuZzyNjk3LKSaFxRmgfaCh_76rVkYM8qri37P92Am3vu_SDZcQ8nVs2DyBFimA0XQ8OOgeGIvHh99ZJFACXTiqY-0pB_weNJvWAhIY_p046FGvZ9zfZzKXWX881qAVq00cyc0BJX8RyGyHN0XokzGayKhf21TPccnz_nf06hZuBv_RNYJX1UPaJYDRPUFbWTrUHFQWNgbt8sEcHqEGtVahm_wUt6NLPBMSHnb08DIETVqP-qj76hs9cHrQjnLzrnfByXksfbLbXovunSJ8TpHy06Dh_qXHy5-mMSrzJ8fafzquNSQQgp1y-xckU9x9fg7aSuZsk9Tm-ww90JB8-eGPZO9jmZugYPpxhznmad-KS6bKaE40j2i7AZyX8-UhM7uL3_g0QqMFTTrnElY1GYcdH8uJdr5sCTqFxIq9kk9NBvH3fER-m_uTRVvA7j1TouzqQv_LIDElNwP-a7ZELFD9zQj5eucQeOHBkpXZVVRFIv5KodfBXiv0fzm_VKXgj3bYcGVlfWT8zfQoCXGudysdZ799ePsLkIE4ARx64EUJiA-D3YxnoalIKgpIGQK8mdzvvgcN-7_7TmKAjC-b2sE9_xpfZE0e3PKHNAYh4xRtuWLaQPvoDOs3rWRUlBdRtNAUxG2ETmotISPHd1lkznRLQQeGLMHlYKHFPsDnSVSllAFfp9Om4xBZPeNp88Y10CboQN_aaDvW-R4JDXAfZlHdC3p76d9VASZnvyN8ySNteKmxzAOo-T-9YbgXkOBCp3qEuPOnm_jwXOym0zTx_kQO2u6qVl8VzhkForNWiKSzO2icuIaqNJ8zEKCrFfKDUUcs9CvYNHLgSUyF5_fwhLsFsufG6A9epyxws3Ks3ERDzFFJXwW8Ot7b-7rNn7tqPuW1TM5776r4y_XC0kHc2I-uJWhOYBd0DV4k10x2AckiqHdZmaH0mNoK9nNi_4aqayUNkM8Ou_2rTyDZV8XeItdSrNaSc2KfmhRDQQm5PS-xvMUS5CfBYk3-_COt5fEKXoW79c9og-FI1SVgrolxjE83qYrsrAmJPgAr10elmj_VKZ3lcf_0jmDDHp3kNU2eUGq0XOJkVacJUveLeLK6D7RZJAte1RAO0DR-C_vSUCQhq36LH5Q==" + } + }, + "id": "prt_f27f5c9b2001K4h954qWB3hND2", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f5bc39002m2BHttOK6atOzz" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_1bUA1Y1arvrDlo1d4WBE7v95", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"from pathlib import Path\\n\",\n+ \"from pathlib import Path\\n\",\n+ \"import os\\n\",\n@@\n- \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n+ \"from huggingface_hub import whoami\\n\",\n+ \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -34,8 +34,9 @@\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n+ \"import os\\n\",\n \"import base64\\n\",\n \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n \"import json\\n\",\n@@ -48,8 +49,9 @@\n \"from PIL import Image\\n\",\n \"\\n\",\n \"from transformers import AutoModel, AutoProcessor\\n\",\n \"\\n\",\n+ \"from huggingface_hub import whoami\\n\",\n \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n \"\\n\",\n \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -34,8 +34,9 @@\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n+ \"import os\\n\",\n \"import base64\\n\",\n \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n \"import json\\n\",\n@@ -48,8 +49,9 @@\n \"from PIL import Image\\n\",\n \"\\n\",\n \"from transformers import AutoModel, AutoProcessor\\n\",\n \"\\n\",\n+ \"from huggingface_hub import whoami\\n\",\n \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n \"\\n\",\n \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n", + "additions": 2, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783081850152, + "end": 1783081850200 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0263aef625a4fe71016a47ab78b21c819fbd73c82f5ef4f052" + } + }, + "id": "prt_f27f5d0ed0010ZDquwKrh0Re2u", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f5bc39002m2BHttOK6atOzz" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 174656, + "input": 1091, + "output": 156, + "reasoning": 97, + "cache": { + "write": 0, + "read": 173312 + } + }, + "cost": 0.03578085, + "id": "prt_f27f5d55f001jshEjBDvr4NtQ5", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f5bc39002m2BHttOK6atOzz" + } + ] + }, + { + "info": { + "parentID": "msg_f27f59e7a001Z0KSWlCqI4fWCl", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0389361, + "tokens": { + "total": 175280, + "input": 102, + "output": 586, + "reasoning": 0, + "cache": { + "write": 0, + "read": 174592 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783081850231, + "completed": 1783081871357 + }, + "finish": "tool-calls", + "id": "msg_f27f5d577002bG4DahH1j2elJD", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f27f5e0930015myaM0flugq9F0", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f5d577002bG4DahH1j2elJD" + }, + { + "type": "step-start", + "id": "prt_f27f5f287001iODgFdtGuZR0SQ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f5d577002bG4DahH1j2elJD" + }, + { + "type": "step-start", + "id": "prt_f27f60ee9001QO1vPh3ivFRjnW", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f27f5d577002bG4DahH1j2elJD" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_yXgekclAShbBcGv3mvHnOZgA", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n+ \"print(\\\"Running environment checks...\\\")\\n\",\n+ \"\\n\",\n+ \"try:\\n\",\n+ \" hf_info = whoami()\\n\",\n+ \" hf_name = hf_info.get(\\\"name\\\", \\\"<unknown>\\\") if isinstance(hf_info, dict) else str(hf_info)\\n\",\n+ \" print(f\\\"HF auth: ok (user={hf_name})\\\")\\n\",\n+ \"except Exception as exc:\\n\",\n+ \" token_hint = os.environ.get(\\\"HF_TOKEN\\\") or os.environ.get(\\\"HUGGING_FACE_HUB_TOKEN\\\")\\n\",\n+ \" if token_hint:\\n\",\n+ \" print(\\\"HF auth: token env var found, but whoami failed. You may need `hf auth login`.\\\")\\n\",\n+ \" else:\\n\",\n+ \" print(\\\"HF auth: not detected. Run `hf auth login` to access gated SAM3.1 checkpoints.\\\")\\n\",\n+ \" print(f\\\"HF auth check detail: {exc}\\\")\\n\",\n+ \"\\n\",\n+ \"if SAM3_CHECKPOINT_PATH is not None:\\n\",\n+ \" if not Path(SAM3_CHECKPOINT_PATH).exists():\\n\",\n+ \" raise FileNotFoundError(f\\\"SAM3_CHECKPOINT_PATH does not exist: {SAM3_CHECKPOINT_PATH}\\\")\\n\",\n+ \" print(f\\\"SAM3 checkpoint source: local ({SAM3_CHECKPOINT_PATH})\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(f\\\"SAM3 checkpoint source: Hugging Face gated repo ({SAM3_REPO_ID})\\\")\\n\",\n+ \"\\n\",\n+ \"if DEVICE != \\\"cuda\\\":\\n\",\n+ \" print(\\\"Warning: CUDA not available; SAM3.1 full checkpoint may be too slow on CPU.\\\")\\n\",\n+ \"\\n\",\n \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -939,8 +939,32 @@\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n+ \"print(\\\"Running environment checks...\\\")\\n\",\n+ \"\\n\",\n+ \"try:\\n\",\n+ \" hf_info = whoami()\\n\",\n+ \" hf_name = hf_info.get(\\\"name\\\", \\\"<unknown>\\\") if isinstance(hf_info, dict) else str(hf_info)\\n\",\n+ \" print(f\\\"HF auth: ok (user={hf_name})\\\")\\n\",\n+ \"except Exception as exc:\\n\",\n+ \" token_hint = os.environ.get(\\\"HF_TOKEN\\\") or os.environ.get(\\\"HUGGING_FACE_HUB_TOKEN\\\")\\n\",\n+ \" if token_hint:\\n\",\n+ \" print(\\\"HF auth: token env var found, but whoami failed. You may need `hf auth login`.\\\")\\n\",\n+ \" else:\\n\",\n+ \" print(\\\"HF auth: not detected. Run `hf auth login` to access gated SAM3.1 checkpoints.\\\")\\n\",\n+ \" print(f\\\"HF auth check detail: {exc}\\\")\\n\",\n+ \"\\n\",\n+ \"if SAM3_CHECKPOINT_PATH is not None:\\n\",\n+ \" if not Path(SAM3_CHECKPOINT_PATH).exists():\\n\",\n+ \" raise FileNotFoundError(f\\\"SAM3_CHECKPOINT_PATH does not exist: {SAM3_CHECKPOINT_PATH}\\\")\\n\",\n+ \" print(f\\\"SAM3 checkpoint source: local ({SAM3_CHECKPOINT_PATH})\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(f\\\"SAM3 checkpoint source: Hugging Face gated repo ({SAM3_REPO_ID})\\\")\\n\",\n+ \"\\n\",\n+ \"if DEVICE != \\\"cuda\\\":\\n\",\n+ \" print(\\\"Warning: CUDA not available; SAM3.1 full checkpoint may be too slow on CPU.\\\")\\n\",\n+ \"\\n\",\n \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n \"else:\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -939,8 +939,32 @@\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n+ \"print(\\\"Running environment checks...\\\")\\n\",\n+ \"\\n\",\n+ \"try:\\n\",\n+ \" hf_info = whoami()\\n\",\n+ \" hf_name = hf_info.get(\\\"name\\\", \\\"<unknown>\\\") if isinstance(hf_info, dict) else str(hf_info)\\n\",\n+ \" print(f\\\"HF auth: ok (user={hf_name})\\\")\\n\",\n+ \"except Exception as exc:\\n\",\n+ \" token_hint = os.environ.get(\\\"HF_TOKEN\\\") or os.environ.get(\\\"HUGGING_FACE_HUB_TOKEN\\\")\\n\",\n+ \" if token_hint:\\n\",\n+ \" print(\\\"HF auth: token env var found, but whoami failed. You may need `hf auth login`.\\\")\\n\",\n+ \" else:\\n\",\n+ \" print(\\\"HF auth: not detected. Run `hf auth login` to access gated SAM3.1 checkpoints.\\\")\\n\",\n+ \" print(f\\\"HF auth check detail: {exc}\\\")\\n\",\n+ \"\\n\",\n+ \"if SAM3_CHECKPOINT_PATH is not None:\\n\",\n+ \" if not Path(SAM3_CHECKPOINT_PATH).exists():\\n\",\n+ \" raise FileNotFoundError(f\\\"SAM3_CHECKPOINT_PATH does not exist: {SAM3_CHECKPOINT_PATH}\\\")\\n\",\n+ \" print(f\\\"SAM3 checkpoint source: local ({SAM3_CHECKPOINT_PATH})\\\")\\n\",\n+ \"else:\\n\",\n+ \" print(f\\\"SAM3 checkpoint source: Hugging Face gated repo ({SAM3_REPO_ID})\\\")\\n\",\n+ \"\\n\",\n+ \"if DEVICE != \\\"cuda\\\":\\n\",\n+ \" print(\\\"Warning: CUDA not available; SAM3.1 full checkpoint may be too slow on CPU.\\\")\\n\",\n+ \"\\n\",\n \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n \"else:\\n\",\n", + "additions": 24, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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"There are a few issues in the notebook relating to PyTorch dtype mismatches; For SAM3, this almost always happens because one of these occurred:\n\nthe model was loaded in torch.float32\nthe inputs are being autocast to bfloat16\npart of the model was converted but another part wasn't; please work through them and fix.", + "id": "prt_f2819a9f4001YQd6nUkGDdVSuz", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819a9f2001fdq3Q1r1CcVHvo" + } + ] + }, + { + "info": { + "parentID": "msg_f2819a9f2001fdq3Q1r1CcVHvo", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0437332, + "tokens": { + "total": 175947, + "input": 2784, + "output": 77, + "reasoning": 542, + "cache": { + "write": 0, + "read": 172544 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084198419, + "completed": 1783084213672 + }, + "finish": "tool-calls", 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+ } + }, + "id": "prt_f2819bc41001SdfvwQgbnVp9CM", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819aa13002P8N9Mr728X2HYk" + }, + { + "type": "tool", + "tool": "grep", + "callID": "call_tlXoKYutykZPzJVuDqzjqAVf", + "state": { + "status": "completed", + "input": { + "pattern": "DEVICE =|SAM3_CONFIDENCE_THRESHOLD|build_sam3_image_model\\(|def run_sam_guided_segmentation|with torch.no_grad\\(\\):", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb" + }, + "output": "Found 6 matches\n/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb:\n Line 57: \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n\n Line 60: \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n\n Line 767: \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n\n Line 883: \" with torch.no_grad():\\n\",\n\n Line 973: \"sam_model = build_sam3_image_model(\\n\",\n\n Line 982: \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n", + "metadata": { + "matches": 6, + "truncated": false + }, + "title": "DEVICE =|SAM3_CONFIDENCE_THRESHOLD|build_sam3_image_model\\(|def run_sam_guided_segmentation|with torch.no_grad\\(\\):", + "time": { + "start": 1783084213639, + "end": 1783084213661 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0a068ac225aef61c016a47b4b4a720819eb49c129ca3123e43" + } + }, + "id": "prt_f2819e321001gz8j6mXFueqkG4", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819aa13002P8N9Mr728X2HYk" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 175947, + "input": 2784, + "output": 77, + "reasoning": 542, + "cache": { + "write": 0, + "read": 172544 + } + }, + "cost": 0.0437332, + "id": "prt_f2819e5a1001sV60oWBk4jrGpp", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819aa13002P8N9Mr728X2HYk" + } + ] + }, + { + "info": { + "parentID": "msg_f2819a9f2001fdq3Q1r1CcVHvo", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0324688, + "tokens": { + "total": 176181, + "input": 504, + "output": 47, + "reasoning": 14, + "cache": { + "write": 0, + "read": 175616 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084213693, + "completed": 1783084217688 + }, + "finish": "tool-calls", + "id": "msg_f2819e5bd002Wm1iB6mmQiCVTa", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f2819ef17001p79Q5NWZRtjbP5", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819e5bd002Wm1iB6mmQiCVTa" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783084216696, + "end": 1783084216867 + }, + "metadata": { + "openai": { + "itemId": "rs_01e9f680a0394ddf016a47b4b804c881a3a82134e4ff00bb19", + "reasoningEncryptedContent": 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+ } + }, + "id": "prt_f2819f178001Jn6EKmvzo50Pmt", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819e5bd002Wm1iB6mmQiCVTa" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_2lUt2psFldQOSopEZqDUk3Yq", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 32, + "limit": 55 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n32: \"cell_type\": \"code\",\n33: \"execution_count\": null,\n34: \"metadata\": {},\n35: \"outputs\": [],\n36: \"source\": [\n37: \"from pathlib import Path\\n\",\n38: \"import os\\n\",\n39: \"import base64\\n\",\n40: \"import io\\n\",\n41: \"from datetime import datetime, timezone\\n\",\n42: \"import json\\n\",\n43: \"\\n\",\n44: \"import numpy as np\\n\",\n45: \"import pandas as pd\\n\",\n46: \"import torch\\n\",\n47: \"import matplotlib.pyplot as plt\\n\",\n48: \"import matplotlib.patches as patches\\n\",\n49: \"from PIL import Image\\n\",\n50: \"\\n\",\n51: \"from transformers import AutoModel, AutoProcessor\\n\",\n52: \"\\n\",\n53: \"from huggingface_hub import whoami\\n\",\n54: \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n55: \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n56: \"\\n\",\n57: \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n58: \"SAM3_REPO_ID = \\\"facebook/sam3.1\\\"\\n\",\n59: \"SAM3_CHECKPOINT_PATH = None # set a local path like /content/checkpoints/sam3.1_multiplex.pt to skip HF download\\n\",\n60: \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n61: \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n62: \"SAM3_MAX_POINT_HINTS = 4\\n\",\n63: \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n64: \"\\n\",\n65: \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n66: \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n67: \"PASSPORT_DIR = Path(\\\"/content/passports\\\")\\n\",\n68: \"PASSPORT_DIR.mkdir(parents=True, exist_ok=True)\\n\",\n69: \"\\n\",\n70: \"THRESHOLDS = {\\n\",\n71: \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n72: \" \\\"top1_accuracy\\\": 0.70,\\n\",\n73: \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n74: \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n75: \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n76: \"}\\n\",\n77: \"\\n\",\n78: \"MATERIAL_TAXONOMY = {\\n\",\n79: \" \\\"concrete\\\": [\\\"concrete\\\", \\\"cement\\\"],\\n\",\n80: \" \\\"brick\\\": [\\\"brick\\\", \\\"masonry brick\\\"],\\n\",\n81: \" \\\"wood\\\": [\\\"wood\\\", \\\"timber\\\", \\\"plywood\\\"],\\n\",\n82: \" \\\"steel\\\": [\\\"steel\\\", \\\"iron metal\\\"],\\n\",\n83: \" \\\"aluminum\\\": [\\\"aluminum\\\", \\\"aluminium\\\"],\\n\",\n84: \" \\\"glass\\\": [\\\"glass\\\", \\\"window glass\\\"],\\n\",\n85: \" \\\"plastic\\\": [\\\"plastic\\\", \\\"polymer\\\", \\\"pvc\\\"],\\n\",\n86: \" \\\"ceramic\\\": [\\\"ceramic\\\", \\\"tile\\\", \\\"porcelain\\\"],\\n\",\n\n(Showing lines 32-86 of 1473. Use offset=87 to continue.)\n</content>", + "metadata": { + "preview": " \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n \"import os\\n\",\n \"import base64\\n\",\n \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n \"import json\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.patches as patches\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n \"from transformers import AutoModel, AutoProcessor\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n \"import os\\n\",\n \"import base64\\n\",\n \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n \"import json\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.patches as patches\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n \"from transformers import AutoModel, AutoProcessor\\n\",\n \"\\n\",\n \"from huggingface_hub import whoami\\n\",\n \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n \"\\n\",\n \"DEVICE = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n \"SAM3_REPO_ID = \\\"facebook/sam3.1\\\"\\n\",\n \"SAM3_CHECKPOINT_PATH = None # set a local path like /content/checkpoints/sam3.1_multiplex.pt to skip HF download\\n\",\n \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n \"SAM3_MAX_POINT_HINTS = 4\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n \"\\n\",\n \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n \"PASSPORT_DIR = Path(\\\"/content/passports\\\")\\n\",\n \"PASSPORT_DIR.mkdir(parents=True, exist_ok=True)\\n\",\n \"\\n\",\n \"THRESHOLDS = {\\n\",\n \" \\\"segmentation_acceptance_rate\\\": 0.85,\\n\",\n \" \\\"top1_accuracy\\\": 0.70,\\n\",\n \" \\\"top3_hit_rate\\\": 0.90,\\n\",\n \" \\\"manual_edit_rate_max\\\": 0.35,\\n\",\n \" \\\"median_e2e_time_sec_max\\\": 12.0,\\n\",\n \"}\\n\",\n \"\\n\",\n \"MATERIAL_TAXONOMY = {\\n\",\n \" \\\"concrete\\\": [\\\"concrete\\\", \\\"cement\\\"],\\n\",\n \" \\\"brick\\\": [\\\"brick\\\", \\\"masonry brick\\\"],\\n\",\n \" \\\"wood\\\": [\\\"wood\\\", \\\"timber\\\", \\\"plywood\\\"],\\n\",\n \" \\\"steel\\\": [\\\"steel\\\", \\\"iron metal\\\"],\\n\",\n \" \\\"aluminum\\\": [\\\"aluminum\\\", \\\"aluminium\\\"],\\n\",\n \" \\\"glass\\\": [\\\"glass\\\", \\\"window glass\\\"],\\n\",\n \" \\\"plastic\\\": [\\\"plastic\\\", \\\"polymer\\\", \\\"pvc\\\"],\\n\",\n \" \\\"ceramic\\\": [\\\"ceramic\\\", \\\"tile\\\", \\\"porcelain\\\"],\\n\",", + "lineStart": 32, + "lineEnd": 86, + "totalLines": 1473, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783084217494, + "end": 1783084217540 + } + }, + "metadata": { + "openai": { + "itemId": "fc_01e9f680a0394ddf016a47b4b8cd2481a3aecf93c0645937d7" + } + }, + "id": "prt_f2819f228001kFAYFLBYDS38zx", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819e5bd002Wm1iB6mmQiCVTa" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 176181, + "input": 504, + "output": 47, + "reasoning": 14, + "cache": { + "write": 0, + "read": 175616 + } + }, + "cost": 0.0324688, + "id": "prt_f2819f552001E6Z56dqhaD95sp", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819e5bd002Wm1iB6mmQiCVTa" + } + ] + }, + { + "info": { + "parentID": "msg_f2819a9f2001fdq3Q1r1CcVHvo", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0336175, + "tokens": { + "total": 177120, + "input": 1050, + "output": 47, + "reasoning": 23, + "cache": { + "write": 0, + "read": 176000 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084217704, + "completed": 1783084221383 + }, + "finish": "tool-calls", + "id": "msg_f2819f568002qdKPOm2nDEuwEa", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f2819fc13001WoSiwFJMp3oYZo", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819f568002qdKPOm2nDEuwEa" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783084219734, + "end": 1783084220535 + }, + "metadata": { + "openai": { + 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+ } + }, + "id": "prt_f2819fd56001hEmWWPRa46X7ZP", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2819f568002qdKPOm2nDEuwEa" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_gTrx5rZvtGGo8F6ZOcoyTFvc", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 748, + "limit": 170 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n748: \" hint_iou = inter / max(union, 1.0)\\n\",\n749: \" else:\\n\",\n750: \" hint_iou = 0.0\\n\",\n751: \"\\n\",\n752: \" if prompt_points_xy:\\n\",\n753: \" total = 0.50 * hint_iou + 0.30 * point_coverage + 0.10 * inside_box_ratio + 0.10 * float(sam_score)\\n\",\n754: \" else:\\n\",\n755: \" total = 0.80 * float(sam_score) + 0.20 * inside_box_ratio\\n\",\n756: \"\\n\",\n757: \" return {\\n\",\n758: \" \\\"total\\\": float(total),\\n\",\n759: \" \\\"sam_score\\\": float(sam_score),\\n\",\n760: \" \\\"point_coverage\\\": float(point_coverage),\\n\",\n761: \" \\\"hint_iou\\\": float(hint_iou),\\n\",\n762: \" \\\"inside_box_ratio\\\": float(inside_box_ratio),\\n\",\n763: \" \\\"mask_area\\\": int(mask_area),\\n\",\n764: \" }\\n\",\n765: \"\\n\",\n766: \"\\n\",\n767: \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n768: \" img_w, img_h = image.size\\n\",\n769: \"\\n\",\n770: \" state = sam_processor.set_image(image, state={})\\n\",\n771: \"\\n\",\n772: \" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n773: \" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n774: \"\\n\",\n775: \" if prompt_points_xy:\\n\",\n776: \" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n777: \" for px, py in hint_points:\\n\",\n778: \" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n779: \" pbox = [\\n\",\n780: \" int(max(0, px - r)),\\n\",\n781: \" int(max(0, py - r)),\\n\",\n782: \" int(min(img_w - 1, px + r)),\\n\",\n783: \" int(min(img_h - 1, py + r)),\\n\",\n784: \" ]\\n\",\n785: \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n786: \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n787: \"\\n\",\n788: \" masks_t = state.get(\\\"masks\\\", None)\\n\",\n789: \" scores_t = state.get(\\\"scores\\\", None)\\n\",\n790: \"\\n\",\n791: \" if masks_t is None or scores_t is None:\\n\",\n792: \" raise RuntimeError(\\\"SAM3 inference did not return masks/scores.\\\")\\n\",\n793: \"\\n\",\n794: \" masks_np = masks_t.detach().cpu().numpy()\\n\",\n795: \" if masks_np.ndim == 4 and masks_np.shape[1] == 1:\\n\",\n796: \" candidate_masks = masks_np[:, 0].astype(bool)\\n\",\n797: \" elif masks_np.ndim == 3:\\n\",\n798: \" candidate_masks = masks_np.astype(bool)\\n\",\n799: \" else:\\n\",\n800: \" raise RuntimeError(f\\\"Unexpected SAM3 mask shape: {masks_np.shape}\\\")\\n\",\n801: \"\\n\",\n802: \" sam_scores = scores_t.detach().cpu().numpy().astype(float)\\n\",\n803: \" if sam_scores.ndim > 1:\\n\",\n804: \" sam_scores = sam_scores.reshape(-1)\\n\",\n805: \"\\n\",\n806: \" if len(candidate_masks) == 0:\\n\",\n807: \" raise RuntimeError(\\\"SAM3 returned zero candidate masks. Try redrawing the prompt.\\\")\\n\",\n808: \"\\n\",\n809: \" img_arr = np.array(image)\\n\",\n810: \" hint_mask = None\\n\",\n811: \" if prompt_points_xy:\\n\",\n812: \" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n813: \"\\n\",\n814: \" candidate_debug = []\\n\",\n815: \" for idx in range(candidate_masks.shape[0]):\\n\",\n816: \" metrics = score_sam_candidate(\\n\",\n817: \" mask=candidate_masks[idx],\\n\",\n818: \" sam_score=float(sam_scores[min(idx, len(sam_scores) - 1)]),\\n\",\n819: \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n820: \" prompt_points_xy=prompt_points_xy,\\n\",\n821: \" hint_mask=hint_mask,\\n\",\n822: \" )\\n\",\n823: \" metrics[\\\"candidate_idx\\\"] = int(idx)\\n\",\n824: \" candidate_debug.append(metrics)\\n\",\n825: \"\\n\",\n826: \" best = max(candidate_debug, key=lambda d: d[\\\"total\\\"])\\n\",\n827: \" best_idx = int(best[\\\"candidate_idx\\\"])\\n\",\n828: \" mask = candidate_masks[best_idx]\\n\",\n829: \" bbox = mask_bbox_xywh(mask)\\n\",\n830: \"\\n\",\n831: \" return {\\n\",\n832: \" \\\"mask\\\": mask,\\n\",\n833: \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n834: \" \\\"bbox_xywh\\\": bbox,\\n\",\n835: \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n836: \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n837: \" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n838: \" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n839: \" \\\"selection_metrics\\\": {\\n\",\n840: \" \\\"point_coverage\\\": float(best[\\\"point_coverage\\\"]),\\n\",\n841: \" \\\"hint_iou\\\": float(best[\\\"hint_iou\\\"]),\\n\",\n842: \" \\\"inside_box_ratio\\\": float(best[\\\"inside_box_ratio\\\"]),\\n\",\n843: \" },\\n\",\n844: \" \\\"sam_candidates\\\": candidate_debug,\\n\",\n845: \" }\\n\",\n846: \"\\n\",\n847: \"\\n\",\n848: \"def classify_material_sigclip(\\n\",\n849: \" image_views: list,\\n\",\n850: \" object_label: str | None,\\n\",\n851: \" sigclip_model,\\n\",\n852: \" sigclip_processor,\\n\",\n853: \" top_k: int = 3,\\n\",\n854: \" view_weights: list | None = None,\\n\",\n855: \"):\\n\",\n856: \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n857: \"\\n\",\n858: \" selected_idx = [\\n\",\n859: \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n860: \" ]\\n\",\n861: \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n862: \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n863: \"\\n\",\n864: \" if not selected_prompts:\\n\",\n865: \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n866: \"\\n\",\n867: \" if view_weights is None:\\n\",\n868: \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n869: \" if len(view_weights) != len(image_views):\\n\",\n870: \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n871: \"\\n\",\n872: \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n873: \"\\n\",\n874: \" for image, weight in zip(image_views, view_weights):\\n\",\n875: \" inputs = sigclip_processor(\\n\",\n876: \" text=selected_prompts,\\n\",\n877: \" images=image,\\n\",\n878: \" return_tensors=\\\"pt\\\",\\n\",\n879: \" padding=\\\"max_length\\\",\\n\",\n880: \" )\\n\",\n881: \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n882: \"\\n\",\n883: \" with torch.no_grad():\\n\",\n884: \" outputs = sigclip_model(**inputs)\\n\",\n885: \"\\n\",\n886: \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n887: \"\\n\",\n888: \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n889: \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n890: \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n891: \"\\n\",\n892: \" for m in candidate_materials:\\n\",\n893: \" agg_scores[m] += weight * per_material_max[m]\\n\",\n894: \"\\n\",\n895: \" total = sum(agg_scores.values())\\n\",\n896: \" if total > 0:\\n\",\n897: \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n898: \"\\n\",\n899: \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n900: \" top_k = min(top_k, len(ranked))\\n\",\n901: \" top_items = ranked[:top_k]\\n\",\n902: \"\\n\",\n903: \" top_labels = [x[0] for x in top_items]\\n\",\n904: \" top_scores = [x[1] for x in top_items]\\n\",\n905: \"\\n\",\n906: \" return {\\n\",\n907: \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n908: \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n909: \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n910: \" \\\"topk_labels\\\": top_labels,\\n\",\n911: \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n912: \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n913: \" }\\n\",\n914: \"\\n\",\n915: \"\\n\",\n916: \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n917: \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n\n(Showing lines 748-917 of 1473. Use offset=918 to continue.)\n</content>", + "metadata": { + "preview": " \" hint_iou = inter / max(union, 1.0)\\n\",\n \" else:\\n\",\n \" hint_iou = 0.0\\n\",\n \"\\n\",\n \" if prompt_points_xy:\\n\",\n \" total = 0.50 * hint_iou + 0.30 * point_coverage + 0.10 * inside_box_ratio + 0.10 * float(sam_score)\\n\",\n \" else:\\n\",\n \" total = 0.80 * float(sam_score) + 0.20 * inside_box_ratio\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"total\\\": float(total),\\n\",\n \" \\\"sam_score\\\": float(sam_score),\\n\",\n \" \\\"point_coverage\\\": float(point_coverage),\\n\",\n \" \\\"hint_iou\\\": float(hint_iou),\\n\",\n \" \\\"inside_box_ratio\\\": float(inside_box_ratio),\\n\",\n \" \\\"mask_area\\\": int(mask_area),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" hint_iou = inter / max(union, 1.0)\\n\",\n \" else:\\n\",\n \" hint_iou = 0.0\\n\",\n \"\\n\",\n \" if prompt_points_xy:\\n\",\n \" total = 0.50 * hint_iou + 0.30 * point_coverage + 0.10 * inside_box_ratio + 0.10 * float(sam_score)\\n\",\n \" else:\\n\",\n \" total = 0.80 * float(sam_score) + 0.20 * inside_box_ratio\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"total\\\": float(total),\\n\",\n \" \\\"sam_score\\\": float(sam_score),\\n\",\n \" \\\"point_coverage\\\": float(point_coverage),\\n\",\n \" \\\"hint_iou\\\": float(hint_iou),\\n\",\n \" \\\"inside_box_ratio\\\": float(inside_box_ratio),\\n\",\n \" \\\"mask_area\\\": int(mask_area),\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" img_w, img_h = image.size\\n\",\n \"\\n\",\n \" state = sam_processor.set_image(image, state={})\\n\",\n \"\\n\",\n \" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n \" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n \"\\n\",\n \" if prompt_points_xy:\\n\",\n \" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n \" for px, py in hint_points:\\n\",\n \" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n \" pbox = [\\n\",\n \" int(max(0, px - r)),\\n\",\n \" int(max(0, py - r)),\\n\",\n \" int(min(img_w - 1, px + r)),\\n\",\n \" int(min(img_h - 1, py + r)),\\n\",\n \" ]\\n\",\n \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n \"\\n\",\n \" masks_t = state.get(\\\"masks\\\", None)\\n\",\n \" scores_t = state.get(\\\"scores\\\", None)\\n\",\n \"\\n\",\n \" if masks_t is None or scores_t is None:\\n\",\n \" raise RuntimeError(\\\"SAM3 inference did not return masks/scores.\\\")\\n\",\n \"\\n\",\n \" masks_np = masks_t.detach().cpu().numpy()\\n\",\n \" if masks_np.ndim == 4 and masks_np.shape[1] == 1:\\n\",\n \" candidate_masks = masks_np[:, 0].astype(bool)\\n\",\n \" elif masks_np.ndim == 3:\\n\",\n \" candidate_masks = masks_np.astype(bool)\\n\",\n \" else:\\n\",\n \" raise RuntimeError(f\\\"Unexpected SAM3 mask shape: {masks_np.shape}\\\")\\n\",\n \"\\n\",\n \" sam_scores = scores_t.detach().cpu().numpy().astype(float)\\n\",\n \" if sam_scores.ndim > 1:\\n\",\n \" sam_scores = sam_scores.reshape(-1)\\n\",\n \"\\n\",\n \" if len(candidate_masks) == 0:\\n\",\n \" raise RuntimeError(\\\"SAM3 returned zero candidate masks. Try redrawing the prompt.\\\")\\n\",\n \"\\n\",\n \" img_arr = np.array(image)\\n\",\n \" hint_mask = None\\n\",\n \" if prompt_points_xy:\\n\",\n \" hint_mask = build_brush_hint_mask(img_arr.shape[:2], prompt_bbox_xyxy, prompt_points_xy)\\n\",\n \"\\n\",\n \" candidate_debug = []\\n\",\n \" for idx in range(candidate_masks.shape[0]):\\n\",\n \" metrics = score_sam_candidate(\\n\",\n \" mask=candidate_masks[idx],\\n\",\n \" sam_score=float(sam_scores[min(idx, len(sam_scores) - 1)]),\\n\",\n \" prompt_bbox_xyxy=prompt_bbox_xyxy,\\n\",\n \" prompt_points_xy=prompt_points_xy,\\n\",\n \" hint_mask=hint_mask,\\n\",\n \" )\\n\",\n \" metrics[\\\"candidate_idx\\\"] = int(idx)\\n\",\n \" candidate_debug.append(metrics)\\n\",\n \"\\n\",\n \" best = max(candidate_debug, key=lambda d: d[\\\"total\\\"])\\n\",\n \" best_idx = int(best[\\\"candidate_idx\\\"])\\n\",\n \" mask = candidate_masks[best_idx]\\n\",\n \" bbox = mask_bbox_xywh(mask)\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n \" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n \" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n \" \\\"selection_metrics\\\": {\\n\",\n \" \\\"point_coverage\\\": float(best[\\\"point_coverage\\\"]),\\n\",\n \" \\\"hint_iou\\\": float(best[\\\"hint_iou\\\"]),\\n\",\n \" \\\"inside_box_ratio\\\": float(best[\\\"inside_box_ratio\\\"]),\\n\",\n \" },\\n\",\n \" \\\"sam_candidates\\\": candidate_debug,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def classify_material_sigclip(\\n\",\n \" image_views: list,\\n\",\n \" object_label: str | None,\\n\",\n \" sigclip_model,\\n\",\n \" sigclip_processor,\\n\",\n \" top_k: int = 3,\\n\",\n \" view_weights: list | None = None,\\n\",\n \"):\\n\",\n \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n \"\\n\",\n \" selected_idx = [\\n\",\n \" i for i, m in enumerate(ALL_PROMPT_MATERIALS) if m in candidate_materials\\n\",\n \" ]\\n\",\n \" selected_prompts = [ALL_PROMPTS[i] for i in selected_idx]\\n\",\n \" selected_prompt_materials = [ALL_PROMPT_MATERIALS[i] for i in selected_idx]\\n\",\n \"\\n\",\n \" if not selected_prompts:\\n\",\n \" raise ValueError(\\\"No text prompts available for candidate materials.\\\")\\n\",\n \"\\n\",\n \" if view_weights is None:\\n\",\n \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n \" if len(view_weights) != len(image_views):\\n\",\n \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n \"\\n\",\n \" agg_scores = {m: 0.0 for m in candidate_materials}\\n\",\n \"\\n\",\n \" for image, weight in zip(image_views, view_weights):\\n\",\n \" inputs = sigclip_processor(\\n\",\n \" text=selected_prompts,\\n\",\n \" images=image,\\n\",\n \" return_tensors=\\\"pt\\\",\\n\",\n \" padding=\\\"max_length\\\",\\n\",\n \" )\\n\",\n \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n \"\\n\",\n \" with torch.no_grad():\\n\",\n \" outputs = sigclip_model(**inputs)\\n\",\n \"\\n\",\n \" probs = torch.softmax(outputs.logits_per_image[0], dim=0).detach().cpu().numpy()\\n\",\n \"\\n\",\n \" per_material_max = {m: 0.0 for m in candidate_materials}\\n\",\n \" for p, m in zip(probs, selected_prompt_materials):\\n\",\n \" per_material_max[m] = max(per_material_max[m], float(p))\\n\",\n \"\\n\",\n \" for m in candidate_materials:\\n\",\n \" agg_scores[m] += weight * per_material_max[m]\\n\",\n \"\\n\",\n \" total = sum(agg_scores.values())\\n\",\n \" if total > 0:\\n\",\n \" agg_scores = {k: v / total for k, v in agg_scores.items()}\\n\",\n \"\\n\",\n \" ranked = sorted(agg_scores.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n \" top_k = min(top_k, len(ranked))\\n\",\n \" top_items = ranked[:top_k]\\n\",\n \"\\n\",\n \" top_labels = [x[0] for x in top_items]\\n\",\n \" top_scores = [x[1] for x in top_items]\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"pred_material_top1\\\": top_labels[0],\\n\",\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",", + "lineStart": 748, + "lineEnd": 917, + "totalLines": 1473, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783084221327, + "end": 1783084221370 + } + }, + "metadata": { + "openai": { + "itemId": 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+ } + }, + "id": "prt_f281a0ed0001IQsudCc8mEGWk7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a03dc002yzi8m5vl6t50ov" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_vEToVym5YuqPa0VM1hd40uc6", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"from pathlib import Path\\n\",\n+ \"from contextlib import nullcontext\\n\",\n \"import os\\n\",\n@@\n \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n \"SAM3_MAX_POINT_HINTS = 4\\n\",\n+ \"SAM3_TORCH_DTYPE = torch.bfloat16 if DEVICE == \\\"cuda\\\" else torch.float32\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -34,8 +34,9 @@\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n+ \"from contextlib import nullcontext\\n\",\n \"import os\\n\",\n \"import base64\\n\",\n \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n@@ -59,8 +60,9 @@\n \"SAM3_CHECKPOINT_PATH = None # set a local path like /content/checkpoints/sam3.1_multiplex.pt to skip HF download\\n\",\n \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n \"SAM3_MAX_POINT_HINTS = 4\\n\",\n+ \"SAM3_TORCH_DTYPE = torch.bfloat16 if DEVICE == \\\"cuda\\\" else torch.float32\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n \"\\n\",\n \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -34,8 +34,9 @@\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n+ \"from contextlib import nullcontext\\n\",\n \"import os\\n\",\n \"import base64\\n\",\n \"import io\\n\",\n \"from datetime import datetime, timezone\\n\",\n@@ -59,8 +60,9 @@\n \"SAM3_CHECKPOINT_PATH = None # set a local path like /content/checkpoints/sam3.1_multiplex.pt to skip HF download\\n\",\n \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n \"SAM3_MAX_POINT_HINTS = 4\\n\",\n+ \"SAM3_TORCH_DTYPE = torch.bfloat16 if DEVICE == \\\"cuda\\\" else torch.float32\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n \"\\n\",\n \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n", + "additions": 2, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783084230242, + "end": 1783084230255 + } + }, + "metadata": { + "openai": { + "itemId": "fc_015b2a125f684509016a47b4c48c98819ca6b0914965eff8a2" + } + }, + "id": "prt_f281a2012001Hu2y7kSjUL1GBr", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a03dc002yzi8m5vl6t50ov" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 180333, + "input": 2939, + "output": 195, + "reasoning": 303, + "cache": { + "write": 0, + "read": 176896 + } + }, + "cost": 0.04307205, + "id": "prt_f281a26d8001B3V8SKZpZxgyAR", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a03dc002yzi8m5vl6t50ov" + } + ] + }, + { + "info": { + "parentID": "msg_f2819a9f2001fdq3Q1r1CcVHvo", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.03571645, + "tokens": { + "total": 180651, + "input": 147, + "output": 280, + "reasoning": 0, + "cache": { + "write": 0, + "read": 180224 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084230382, + "completed": 1783084242695 + }, + "finish": "tool-calls", + "id": "msg_f281a26ee0028oZobxvtWw3BqF", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f281a31c9001LzJrcJ2Daknzwq", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a26ee0028oZobxvtWw3BqF" + }, + { + "type": "step-start", + "id": "prt_f281a48140019ReLNUKcUvzqdX", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a26ee0028oZobxvtWw3BqF" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_anL2S83Giw0uRfhlwsf2eu5r", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"def bbox_xyxy_to_center_xy(bbox_xyxy):\\n\",\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def sam3_autocast_context():\\n\",\n+ \" if DEVICE == \\\"cuda\\\" and SAM3_TORCH_DTYPE in (torch.float16, torch.bfloat16):\\n\",\n+ \" return torch.autocast(device_type=\\\"cuda\\\", dtype=SAM3_TORCH_DTYPE)\\n\",\n+ \" return nullcontext()\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def bbox_xyxy_to_norm_cxcywh(bbox_xyxy, image_width: int, image_height: int):\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -667,8 +667,14 @@\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def sam3_autocast_context():\\n\",\n+\" if DEVICE == \\\"cuda\\\" and SAM3_TORCH_DTYPE in (torch.float16, torch.bfloat16):\\n\",\n+\" return torch.autocast(device_type=\\\"cuda\\\", dtype=SAM3_TORCH_DTYPE)\\n\",\n+\" return nullcontext()\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def bbox_xyxy_to_norm_cxcywh(bbox_xyxy, image_width: int, image_height: int):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in bbox_xyxy]\\n\",\n \" x0 = max(0.0, min(float(image_width - 1), x0))\\n\",\n \" y0 = max(0.0, min(float(image_height - 1), y0))\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -667,8 +667,14 @@\n \" x0, y0, x1, y1 = [int(v) for v in bbox_xyxy]\\n\",\n \" return ((x0 + x1) // 2, (y0 + y1) // 2)\\n\",\n \"\\n\",\n \"\\n\",\n+\"def sam3_autocast_context():\\n\",\n+\" if DEVICE == \\\"cuda\\\" and SAM3_TORCH_DTYPE in (torch.float16, torch.bfloat16):\\n\",\n+\" return torch.autocast(device_type=\\\"cuda\\\", dtype=SAM3_TORCH_DTYPE)\\n\",\n+\" return nullcontext()\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def bbox_xyxy_to_norm_cxcywh(bbox_xyxy, image_width: int, image_height: int):\\n\",\n \" x0, y0, x1, y1 = [float(v) for v in bbox_xyxy]\\n\",\n \" x0 = max(0.0, min(float(image_width - 1), x0))\\n\",\n \" y0 = max(0.0, min(float(image_height - 1), y0))\\n\",\n", + "additions": 6, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783084242649, + "end": 1783084242674 + } + }, + "metadata": { + "openai": { + "itemId": "fc_04dd9e46dbe68ac6016a47b4d098b081a3a98c4728d04d2f1e" + } + }, + "id": "prt_f281a4f58001U8I0qtJd4HglST", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a26ee0028oZobxvtWw3BqF" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 180651, + "input": 147, + "output": 280, + "reasoning": 0, + "cache": { + "write": 0, + "read": 180224 + } + }, + "cost": 0.03571645, + "id": "prt_f281a570300132Ry97scR7rwuO", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a26ee0028oZobxvtWw3BqF" + } + ] + }, + { + "info": { + "parentID": "msg_f2819a9f2001fdq3Q1r1CcVHvo", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.04499775, + "tokens": { + "total": 181621, + "input": 209, + "output": 932, + "reasoning": 0, + "cache": { + "write": 0, + "read": 180480 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084242711, + "completed": 1783084253288 + }, + "finish": "tool-calls", + "id": "msg_f281a5717002CCcxBmRMX5IhIz", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f281a5da5001zVETGmR2N4OywS", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a5717002CCcxBmRMX5IhIz" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_w7jAboLCHEyiudZwb5IPXc68", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" img_w, img_h = image.size\\n\",\n \"\\n\",\n- \" state = sam_processor.set_image(image, state={})\\n\",\n- \"\\n\",\n- \" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n- \" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n- \"\\n\",\n- \" if prompt_points_xy:\\n\",\n- \" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n- \" for px, py in hint_points:\\n\",\n- \" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n- \" pbox = [\\n\",\n- \" int(max(0, px - r)),\\n\",\n- \" int(max(0, py - r)),\\n\",\n- \" int(min(img_w - 1, px + r)),\\n\",\n- \" int(min(img_h - 1, py + r)),\\n\",\n- \" ]\\n\",\n- \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n- \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n+ \" with torch.inference_mode(), sam3_autocast_context():\\n\",\n+ \" state = sam_processor.set_image(image, state={})\\n\",\n+ \"\\n\",\n+ \" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n+ \" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n+ \"\\n\",\n+ \" if prompt_points_xy:\\n\",\n+ \" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n+ \" for px, py in hint_points:\\n\",\n+ \" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n+ \" pbox = [\\n\",\n+ \" int(max(0, px - r)),\\n\",\n+ \" int(max(0, py - r)),\\n\",\n+ \" int(min(img_w - 1, px + r)),\\n\",\n+ \" int(min(img_h - 1, py + r)),\\n\",\n+ \" ]\\n\",\n+ \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n+ \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n@@\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n- \" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n+ \" \\\"prompt_bbox_xyxy\\\": [int(v) for v in prompt_bbox_xyxy],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -774,25 +774,26 @@\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" img_w, img_h = image.size\\n\",\n \"\\n\",\n-\" state = sam_processor.set_image(image, state={})\\n\",\n+\" with torch.inference_mode(), sam3_autocast_context():\\n\",\n+\" state = sam_processor.set_image(image, state={})\\n\",\n \"\\n\",\n-\" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n-\" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n+\" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n+\" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n \"\\n\",\n-\" if prompt_points_xy:\\n\",\n-\" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n-\" for px, py in hint_points:\\n\",\n-\" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n- \" pbox = [\\n\",\n- \" int(max(0, px - r)),\\n\",\n- \" int(max(0, py - r)),\\n\",\n- \" int(min(img_w - 1, px + r)),\\n\",\n- \" int(min(img_h - 1, py + r)),\\n\",\n- \" ]\\n\",\n- \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n- \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n+\" for px, py in hint_points:\\n\",\n+\" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n+\" pbox = [\\n\",\n+\" int(max(0, px - r)),\\n\",\n+\" int(max(0, py - r)),\\n\",\n+\" int(min(img_w - 1, px + r)),\\n\",\n+\" int(min(img_h - 1, py + r)),\\n\",\n+\" ]\\n\",\n+\" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n+\" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n \"\\n\",\n \" masks_t = state.get(\\\"masks\\\", None)\\n\",\n \" scores_t = state.get(\\\"scores\\\", None)\\n\",\n \"\\n\",\n@@ -839,9 +840,9 @@\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n-\" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": [int(v) for v in prompt_bbox_xyxy],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n \" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n \" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n \" \\\"selection_metrics\\\": {\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -774,25 +774,26 @@\n \"\\n\",\n \"def run_sam_guided_segmentation(image: Image.Image, prompt_bbox_xyxy, prompt_points_xy, sam_model, sam_processor):\\n\",\n \" img_w, img_h = image.size\\n\",\n \"\\n\",\n-\" state = sam_processor.set_image(image, state={})\\n\",\n+\" with torch.inference_mode(), sam3_autocast_context():\\n\",\n+\" state = sam_processor.set_image(image, state={})\\n\",\n \"\\n\",\n-\" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n-\" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n+\" box_norm = bbox_xyxy_to_norm_cxcywh(prompt_bbox_xyxy, image_width=img_w, image_height=img_h)\\n\",\n+\" state = sam_processor.add_geometric_prompt(box=box_norm, label=True, state=state)\\n\",\n \"\\n\",\n-\" if prompt_points_xy:\\n\",\n-\" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n-\" for px, py in hint_points:\\n\",\n-\" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n- \" pbox = [\\n\",\n- \" int(max(0, px - r)),\\n\",\n- \" int(max(0, py - r)),\\n\",\n- \" int(min(img_w - 1, px + r)),\\n\",\n- \" int(min(img_h - 1, py + r)),\\n\",\n- \" ]\\n\",\n- \" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n- \" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n+\" if prompt_points_xy:\\n\",\n+\" hint_points = prompt_points_xy[:SAM3_MAX_POINT_HINTS]\\n\",\n+\" for px, py in hint_points:\\n\",\n+\" r = int(SAM3_POINT_HINT_BOX_RADIUS_PX)\\n\",\n+\" pbox = [\\n\",\n+\" int(max(0, px - r)),\\n\",\n+\" int(max(0, py - r)),\\n\",\n+\" int(min(img_w - 1, px + r)),\\n\",\n+\" int(min(img_h - 1, py + r)),\\n\",\n+\" ]\\n\",\n+\" pbox_norm = bbox_xyxy_to_norm_cxcywh(pbox, image_width=img_w, image_height=img_h)\\n\",\n+\" state = sam_processor.add_geometric_prompt(box=pbox_norm, label=True, state=state)\\n\",\n \"\\n\",\n \" masks_t = state.get(\\\"masks\\\", None)\\n\",\n \" scores_t = state.get(\\\"scores\\\", None)\\n\",\n \"\\n\",\n@@ -839,9 +840,9 @@\n \" return {\\n\",\n \" \\\"mask\\\": mask,\\n\",\n \" \\\"mask_area_px\\\": int(mask.sum()),\\n\",\n \" \\\"bbox_xywh\\\": bbox,\\n\",\n-\" \\\"prompt_bbox_xyxy\\\": [int(x0), int(y0), int(x1), int(y1)],\\n\",\n+\" \\\"prompt_bbox_xyxy\\\": [int(v) for v in prompt_bbox_xyxy],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(x), int(y)] for x, y in prompt_points_xy],\\n\",\n \" \\\"sam_score\\\": float(sam_scores[best_idx]),\\n\",\n \" \\\"selection_score\\\": float(best[\\\"total\\\"]),\\n\",\n \" \\\"selection_metrics\\\": {\\n\",\n", + "additions": 17, + "deletions": 16 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783084253263, + "end": 1783084253277 + } + }, + "metadata": { + "openai": { + "itemId": "fc_02e8ea72c4e49e7b016a47b4d5f40081a38d72fc46e7541081" + } + }, + "id": "prt_f281a6470001hwYk5JKX6b25Vc", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a5717002CCcxBmRMX5IhIz" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 181621, + "input": 209, + "output": 932, + "reasoning": 0, + "cache": { + "write": 0, + "read": 180480 + } + }, + "cost": 0.04499775, + "id": "prt_f281a8062001KZXBfY6C5cy3nW", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a5717002CCcxBmRMX5IhIz" + } + ] + }, + { + "info": { + "parentID": "msg_f2819a9f2001fdq3Q1r1CcVHvo", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.03641645, + "tokens": { + "total": 181972, + "input": 155, + "output": 302, + "reasoning": 11, + "cache": { + "write": 0, + "read": 181504 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084253310, + "completed": 1783084307182 + }, + "finish": "tool-calls", + "id": "msg_f281a807e002MOwMrSSXy3Jhkt", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f281a908a001qCAkyVgewvaSql", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a807e002MOwMrSSXy3Jhkt" + }, + { + "type": "step-start", + "id": "prt_f281aa8cc001Mllx7PIBR4uQOn", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a807e002MOwMrSSXy3Jhkt" + }, + { + "type": "step-start", + "id": "prt_f281ac44a001MmOzaIfDP6G4Je", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a807e002MOwMrSSXy3Jhkt" + }, + { + "type": "step-start", + "id": "prt_f281afa1a001Tw9rpJDdHp4yml", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a807e002MOwMrSSXy3Jhkt" + }, + { + "type": "step-start", + "id": "prt_f281b4239001fRxAvh7IoQ7MVd", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a807e002MOwMrSSXy3Jhkt" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783084303198, + "end": 1783084304251 + }, + "metadata": { + "openai": { + "itemId": "rs_0747eb97b99a6a27016a47b50f199881a0a238a62c460a01db", + "reasoningEncryptedContent": "gAAAAABqR7UPPDAwtas5aHEJZ85YolWOL6cuUGFDn_meWNZJBQ0yTTTaVAkURRnDeQkl5M8R62fgMSMGgSxZSbmZSGpVRP9fZhBEPZzM7pZg17nuyKXOHkef_OsVu69YyhFU_2iY3JtsS3twbzWwotsJR9rKkRhAtlhS9orJPJwVsQuzPngD6yX6G6w6ZNsFMQmrGISKqXmAq78nmFdbPoEl-ty_CB7VfuTu6wIc3FW5nfpfzrMoj61UEXwK7dilzREpk1YoTA2VWcI52Eq1h3t0NqhI0Sm3rtZHESHdZ7HHopX0qfjubg-6iNKh_HGqfT_Xj-wv7-4FnVfFBrgb3OoTxKXLlADa-mNBnXVV3vWZs9-uKXH57beIqPZ4M4TQRfCknfIgdmQH6Zu0Md8bbgoBRxzPBijTaEwGyeS7mFKJpQe9m88AQjvFiQRjYkoA_w3sAOKpxnGJAcHX1IO-K5P_QYetv6izSW64TKr_SFPT1mhsj6b2ZQDEsv0eHSoGma05t6sJScbQOju4UERCWY3DjTEZfO1jYS3J-Sys5zg1xPcrGRpuFDSk1FkU7dP4AAArlr1P3IGfPxjIymoD0rAXGPT_I8k2yhWboyApdrgU66lFE0WEB3uRrqDtRRBVAICucFGPuTE_EKZlbSbWGTMajSUSwOVPjK7j2FPEjJntsH1ssd6yfuO93rJQcuexZ4-8K2HdzjKVEthsmswzpkyPZI4jP7K7hFLWemt3rjAG6WzWFmjtZ6ABRZHKvzfbp00EPeDCcRVf8DtpeEJi6VqbaFO8HUjjtbAD8xNXnWFpt0wbdnNTxIaBX_lvEIdUZEdEx7hyWmVabv-nfrQ7YFVZRX1GsDUz4aNOTXi1Pqkivr60yVy4mfpohOUvtYrCpOynz5CJ4AQYdiNmKiWD32U_UEml1VyGDWbPbaP7lJl6s7Co4ruiV8KLUc2VL35aTQ4xKOXgynSI_MZVxErNX7BXvh2zLRfk4A==" + } + }, + "id": "prt_f281b435e001RlZig7LJSZNlt3", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281a807e002MOwMrSSXy3Jhkt" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_mLihAyMb7k0QQVjOpQTBRLYW", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"sam_model = build_sam3_image_model(\\n\",\n \" device=DEVICE,\\n\",\n \" checkpoint_path=sam3_checkpoint_path,\\n\",\n \" load_from_HF=False,\\n\",\n \" enable_inst_interactivity=False,\\n\",\n \")\\n\",\n+ \"sam_model = sam_model.to(dtype=SAM3_TORCH_DTYPE)\\n\",\n+ \"sam_model.eval()\\n\",\n \"sam_processor = Sam3Processor(\\n\",\n \" model=sam_model,\\n\",\n \" device=DEVICE,\\n\",\n \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n \")\\n\",\n- \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n+ \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n+ \"print(f\\\"SAM dtype: {SAM3_TORCH_DTYPE}\\\")\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -984,14 +984,17 @@\n \" checkpoint_path=sam3_checkpoint_path,\\n\",\n \" load_from_HF=False,\\n\",\n \" enable_inst_interactivity=False,\\n\",\n \")\\n\",\n+\"sam_model = sam_model.to(dtype=SAM3_TORCH_DTYPE)\\n\",\n+\"sam_model.eval()\\n\",\n \"sam_processor = Sam3Processor(\\n\",\n \" model=sam_model,\\n\",\n \" device=DEVICE,\\n\",\n \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n \")\\n\",\n \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n+\"print(f\\\"SAM dtype: {SAM3_TORCH_DTYPE}\\\")\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -984,14 +984,17 @@\n \" checkpoint_path=sam3_checkpoint_path,\\n\",\n \" load_from_HF=False,\\n\",\n \" enable_inst_interactivity=False,\\n\",\n \")\\n\",\n+\"sam_model = sam_model.to(dtype=SAM3_TORCH_DTYPE)\\n\",\n+\"sam_model.eval()\\n\",\n \"sam_processor = Sam3Processor(\\n\",\n \" model=sam_model,\\n\",\n \" device=DEVICE,\\n\",\n \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n \")\\n\",\n \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n+\"print(f\\\"SAM dtype: {SAM3_TORCH_DTYPE}\\\")\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n", + "additions": 3, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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<<'PY'\nimport json,ast\nnb=json.load(open('poc_eval_notebook.ipynb'))\nfor c in nb['cells']:\n if c.get('cell_type')!='code':\n continue\n src=''.join(c.get('source',[]))\n if any(line.lstrip().startswith('%') for line in src.splitlines()):\n continue\n ast.parse(src)\nprint('ok')\nPY", + "workdir": "/home/magnus-bk/repos/material-hunters" + }, + "output": "ok\n", + "metadata": { + "output": "ok\n", + "exit": 0, + "truncated": false + }, + "title": "python3 -m json.tool \"poc_eval_notebook.ipynb\" >/tmp/opencode/poc_eval_notebook.pretty.json && python3 - <<'PY'\nimport json,ast\nnb=json.load(open('poc_eval_notebook.ipynb'))\nfor c in nb['cells']:\n if c.get('cell_type')!='code':\n continue\n src=''.join(c.get('source',[]))\n if any(line.lstrip().startswith('%') for line in src.splitlines()):\n continue\n ast.parse(src)\nprint('ok')\nPY", + "time": { + "start": 1783084312428, + "end": 1783084312435 + } + }, + "metadata": { + "openai": { + "itemId": "fc_03ccee6d5957d0a4016a47b51696a881a3b46d4b4cb2e572f4" + } + }, + "id": "prt_f281b60bb001P1hhkKRz0WrmCX", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281b5300002AhDJvuwNrKYlk2" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 182198, + "input": 122, + "output": 167, + "reasoning": 21, + "cache": { + "write": 0, + "read": 181888 + } + }, + "cost": 0.0346759, + "id": "prt_f281b6776001mhFpac5nwd2e6x", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281b5300002AhDJvuwNrKYlk2" + } + ] + }, + { + "info": { + "parentID": "msg_f2819a9f2001fdq3Q1r1CcVHvo", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.03652005, + "tokens": { + "total": 182520, + "input": 195, + "output": 288, + "reasoning": 21, + "cache": { + "write": 0, + "read": 182016 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084312462, + "completed": 1783084334715 + }, + "finish": "stop", + "id": "msg_f281b678e002sBnp4DDWkp1Fye", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f281b70d10015iqHO8SP3lFz2h", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281b678e002sBnp4DDWkp1Fye" + }, + { + "type": "step-start", + "id": "prt_f281b87fa0019fVyaphPs1Ggzq", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281b678e002sBnp4DDWkp1Fye" + }, + { + "type": "step-start", + "id": "prt_f281ba0ae001Y5zooft4SuuyrZ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281b678e002sBnp4DDWkp1Fye" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783084334423, + "end": 1783084334435 + }, + "metadata": { + "openai": { + "itemId": "rs_093857315f42fed6016a47b526e58081a1a5ca15d1b0c7f36f", + "reasoningEncryptedContent": "gAAAAABqR7Un_KA89yHAcxLAASrBV4k31quMBMJgC4t0O_q0comzFi0Npfv5mPLo-6phDPHR2aRToRf_Z_3dsYx96KNlzMvfc2zdf7Z48ufWTK8yM8v08rz8-akuCwJ7GiTh4aTZifOs2nuLrHZKpCYCm_tc0IS-zN55MymPqoogL-abHesE1huhBrmBxxMv_DBAcIG7gXplMRh7SsmQMifyuG3Is__WUhCyH59evcY2n3e5_21DmjuxLOp2_riv1WJv9_ILq0AGGYPQCxnvJQOCSH3pjNWp-x6xdlznqQEQfTpqsrWDGKyP_lXL7ssIE_stia-G0ElBwVr4SHD1Xna905dSxhXLC-zU6pjA6ZfmDgpeSTW_mUY7Y3kRGsGHdn7tlZUrppogjWNziziTbhpXqbei98zB4O6crkA4xEiz8AZ9TOv3NCx62iiJkXbPaOa_DCfH6plE5i2xuUQnGaJ04FDLLFJI_DiVDjGcwKjH5Mj4MIgtgcEwgeur7vHUq98eTWzMhpsPGMRIj8nuDjZXWFZsBP409Mt49nKKulZdn92fdfAakPEMBrqVigvAuPN9L2Wkqx2ebmhSd2ULN6ENhYLNCMiYnF1CyM9SXDYWgtECcGMSe36s7tLXQq_IqgeEHi6X02xNrNWB-daofa_TqsIwf5XRymXuAvFIfedu2WoaxNp0RV1vj-cNDA05flB5e8BwC_xr6rYGMAMKobvMUlKdJ0u2R71v9-xAiDPEhAH-UucKCyaqpSTu8oeTpel9b0zA4hGlXZ_G2xrSD8GCXJLW8UhZZ_CvomhYDCC4rdNjFqitxQYSVamhndBrGkSTk7giRfGgvMOAqcgJrgvgQTy44EHPYiJU_D73XVtRzPu2sLxfoZ6q9Wk4nhHOAFtrWZPLupTW4ERHUFiQsOhNG07l3JhhKmhTjJXr-k4YI7RPXB5KnT1Hb94JOjh0FMSYYhK6WNIXvVAIuR7bCFTqMg5DPGVIlHpK6-Ffu41Ep1LKK_sJqYyIFaxSWhuNcThJZHDt9SCVdmP_SMEBrGXqf3nxZlk2Dyw-EG1DWQSB5jItyX3yWbg=" + } + }, + "id": "prt_f281bbd57001f8v4Bc59chnNke", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281b678e002sBnp4DDWkp1Fye" + }, + { + "type": "text", + "text": "Good catch — I fixed the dtype path so SAM3 runs in one consistent precision mode.\n\nWhat I changed in `poc_eval_notebook.ipynb`:\n- Added explicit SAM dtype config:\n - `SAM3_TORCH_DTYPE = torch.bfloat16 if DEVICE == \"cuda\" else torch.float32`\n- Added a shared autocast helper:\n - `sam3_autocast_context()` uses CUDA autocast only when dtype is fp16/bf16, otherwise no autocast.\n- Wrapped SAM3 inference calls in:\n - `with torch.inference_mode(), sam3_autocast_context():`\n so image embedding + prompt additions run under the same precision policy.\n- Forced full model to one dtype after load:\n - `sam_model = sam_model.to(dtype=SAM3_TORCH_DTYPE)`\n - `sam_model.eval()`\n- Added startup print for dtype:\n - `SAM dtype: ...`\n- Also fixed a segmentation return bug while touching this area:\n - `prompt_bbox_xyxy` now uses the actual prompt values (previously referenced undefined vars).\n\nWhat to do in Colab now:\n1. Re-upload the updated notebook.\n2. Restart runtime.\n3. Run all cells from top.\n\nIf mismatches persist, send the exact traceback line and I’ll pin it to the specific module/op and patch that path directly.", + "time": { + "start": 1783084334439, + "end": 1783084334698 + }, + "metadata": { + "openai": { + "itemId": "msg_093857315f42fed6016a47b527bad881a1a6d1d1b8e9fab6ff", + "phase": "final_answer" + } + }, + "id": "prt_f281bbd67001ceNs3Poim64Bx4", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281b678e002sBnp4DDWkp1Fye" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 182520, + "input": 195, + "output": 288, + "reasoning": 21, + "cache": { + "write": 0, + "read": 182016 + } + }, + "cost": 0.03652005, + "id": "prt_f281bbe70001G50C8rIEo9umXz", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281b678e002sBnp4DDWkp1Fye" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783084456087 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f281d9896001KtY9YSPze8zDij", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "Let's bracket the SAM implementation for now, since it works quite well. Next bucket is the primary one for the functionality of the tool, since it involves the actual material classification -> the material passport.", + "id": "prt_f281d989c001wakprgCmxOL7Wm", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281d9896001KtY9YSPze8zDij" + } + ] + }, + { + "info": { + "parentID": "msg_f281d9896001KtY9YSPze8zDij", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.11007605, + "tokens": { + "total": 182693, + "input": 42059, + "output": 634, + "reasoning": 224, + "cache": { + "write": 0, + "read": 139776 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084456120, + "completed": 1783084476114 + }, + "finish": "stop", + "id": "msg_f281d98b8002y11y0kGL25HNTD", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f281da090001i5rKiz6VfJBdgu", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281d98b8002y11y0kGL25HNTD" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783084460724, + "end": 1783084466537 + }, + "metadata": { + "openai": { + "itemId": "rs_0848d0710d669df2016a47b5aca7d081a39e1a9e79c82abc7d", + "reasoningEncryptedContent": "gAAAAABqR7Wy7swEc6G-5XgUFUTLLohcbDRmChS5ucX8asyvqCE2y12g0vFotr7tL7QSbiDQcygs45I23m8DCbvxcL8CdVn70awUqpFTv_MVmdaaRgomwj0fpTdneKbVNnbDNWUs3WhTyldqetP4MEhAVKWo8DCCl2Z57WckvE7iStd2RtMO0IT9XXGfXIlGLZpK2CaI8ZIt4GWViEEbQnOz1wbKqybE2nTjjGKs3S9JB9QxndaBzQwoUEoY7uCzYp5PeFJQZzgd6Be5DLIzORAG7QaHBG2EmV4Fy3bEzhnz0HdeXIKq4O_imsHIL35SQNWhO9M9GbeR4c_bk2HSDne--dK08LgHtRycvMVW00IcrdVg5MoSIExW_OtT9bAEmPZeSiU7A7--qbFCLtwB2fCeHEIF_N6yVoAkhsb-YntG-IAE1XPNxHp5L10hfmMzq4NdgvQQ1UeqgmTeYjacJMzNMNeoqN0Ohy8WSDIjhbF95rhBMOmfJRVdHv_kSVbMiml1RGfevEzRIoEiflqQ2zHR0WDmFPi5NFsTjZl0ynRUFffYqxtGL-fLMNbSznfiquZ1vsyIOW1pcwC4ZMOtXUoC4oa_uzKVyuqPw1oT_OhILoef_XBC0HzHUkfBC3VCb_qsBbCpRX8yZVh7jCWdB1gNU46HCMXmQk6HcKcZm_8VrQQRXXY-KB_grS8ga9G4314gMzizLJkBzUAMd4lqSwjg-F36vDDYyCUJlmBasWQXiJl9KtkYsQCNE4wYk3IiQPS7mHVgf5UWQs8_dQMc2IisiDg3LEshEHPo1AThzlAgFLO1FOEyAI9X_zwO2du9vpRV0yNSlM6pYIdF5XPvNsD8p3TfbHyIDXTXBPJdIFmAWEBN0dGBgka07-61j4CY9eLsULoldxOgj5SbgwKEwzN2YBuwfiKzFdBKZmRjZuzqvmDfiZQUL9G-Tl6WCLjx73nyeb-lIiSItdVugZmEEJNB_8XuF3DQvJ0O3SFHQ3nj5bngS82EpBA0y44gEeS1iMk9fu_Jx4v4zRiOAPASPePMCdCqKAVaxcVk4Fu8Jdo8y_d4up3VYV41yXWJwq9m_boQXgqBLbOU97ef6w9lAFyxLXFbbBtWGGQnjLs6OK_OMfXjhLV05ds2SGjlVMEGbJBthtGPsWjuKh219id8AIJGxK4006Co8e3lKwFmpBBvlJHlCetUp4-FcSPUhdmCfyHDuMmi6KmP6IzYdXEsiYvOeClckbO6E0dkH7yiN89F4SQdYby0AYJ5RYldWiAdtYE1H1hqeyiX3IU2Vu0fUXkURLM9bt9-Ushf3xooHB2WyIHltp2j0X5TK6bhdpW0FAZjGebL_pZyVBJW_cD7KLuoFp7X-VQIk15TqM_Lsqj4dxzAhJgnwWtYrdn-Zds3nvTjgWo-6IydI1qQbibs88tV29XBTvGWgxvL4u8skfaE_2k1rXibBBUm7_SxYiVJ3XJCEak-DdqhL7qT--vSdex1Ke-vRfltBPrjzgf0igxYrt74DJT-OARrDbNk0h8ArrlAAb2fn8DN-MTlqWNAXYk1MTDqbH5iR3_Qwulwn5e5GsQd-u-3pWbjgqRzZjLpiHN0cSR1RGBEg-ieKcyKHKIVc5B12URw1h2lLEFJ3lUdLGJte8H8-tU0-66bk3e3CSLOLmjxl2ryn_-iKSVb-DqqfTBO7z5cl1vj0heu_-O12qodI1TxMkIlR05Hl9pBnXKowInhDRmcU-8FOvDC6NUbx9mf0RdjVweISe1a_mqdk_b1BjKddZIUye1T5DKGO_7nkz8RyNUcamvSjCWBew2faO2dY3R8uiFAtmgv1gRoSYLZ8B0Ykm7C_JXGsNKbmEGhgL167K0bmUA233gqz2hv47RhTd5iJW2uCziRhBEYMg88u23q5BVJAOiJ3lM6-08gNJr7quKnInJp6cib7Iar7jNi1Yx_ArEuLzL4mpKarSaTCaOg3GhMLS16j_trEVCQnD8icGCIGQZLqTkvzubnkPtnUBPWk7VI0WoHV6O9M288ucvMA4_gab5KBzN9YTylh4TeTV6J3FgI2X2cQi6_gBghhXn2jqhhala_qcEmkuwVYjSlyOL1AGQKCHPPPUwzljcLxdRHtPtJ6WuIXv1cNdufW0Gk9AbAHoBLeFkCwK1oZu_4n1BzjODUbJfQBGx-w0f6gsAaqGiMV8KdoktqOOZSDX0Oubymmt1SVXu_aArjH4G9AzOwbeJr_DkUQdYs3mxTJr_Mc_aukn_WyWrJ0mHuStWELHktoK6zchQFCVVPuuwX58S0IT6fGxtIPaf4wlMGWTmC6FT682rVMyUK8J_0_O7VsiKhUwW6MzAnCpt8JghvH_XM8oKazQ8f-Tr6tj-5AXzQKph0wai5_qyStogbo6IjcNP_PZTxP3hHBOQ6PZpt4z2YglJ0YoYhL_pvSg2uSZZ9VIkWJci0Kt5P9eZoUl2eS-BOntf3ZqWbmsb6EaCi2ArJ7X6urzr2ul-1ScxVHYYwUJbL6BKqgJ-B9hgLX4-03A==" + } + }, + "id": "prt_f281daab4001LtCYRQkQk67YpW", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281d98b8002y11y0kGL25HNTD" + }, + { + "type": "text", + "text": "Perfect — we’ll freeze segmentation and focus this bucket on **material classification -> material passport**.\n\nHere’s a concrete execution plan (read-only; no changes made yet), with checkpoints before each next step.\n\n**Bucket Plan**\n\n1) **Material Label System (foundation)**\n- Define canonical material classes for POC (e.g., 10–20) and alias mapping (synonyms -> canonical).\n- Add optional hierarchy: `category -> material -> subtype` (keep subtype optional for POC).\n- Define allowed values for uncertainty (`unknown`, `mixed`, `ambiguous`).\n- **Checkpoint:** you approve the exact class list and alias map.\n\n2) **Classification Engine v1 (SigCLIP-first)**\n- Keep current SigCLIP pipeline, but make output explicit as:\n - `top_k materials`, `confidence`, `candidate set used`, `reason metadata`.\n- Add deterministic post-processing:\n - class priors by object type,\n - confidence thresholding,\n - low-confidence fallback to `unknown`.\n- **Checkpoint:** review top-1/top-3 quality on a small fixed sample.\n\n3) **Human Review + Correction Contract**\n- Standardize review fields in notebook:\n - predicted material, final material, edit reason, confidence override.\n- Add explicit provenance tags:\n - `source=model`, `source=user`, `source=mixed`.\n- **Checkpoint:** ensure edits are captured fast and unambiguously.\n\n4) **Material Passport Schema (primary output)**\n- Lock JSON schema with required fields:\n - identity (`sample_id`, `image_id`, `object_id`)\n - geometry refs (`prompt bbox`, `segmentation bbox/mask ref`)\n - classification (`top_k`, `top1`, confidence, final label)\n - audit trail (`model_version`, `selection_mode`, `user_edits`, timestamp)\n- Add schema validation step before save.\n- **Checkpoint:** validate 5–10 passports and inspect for downstream usability.\n\n5) **Passport Write + Session Index**\n- Save one passport JSON per object.\n- Maintain optional session index file linking all passports for one run/image.\n- Add duplicate guard (`sample_id`/`object_id` collision policy).\n- **Checkpoint:** confirm file organization and naming are acceptable.\n\n6) **Acceptance Metrics for This Bucket**\n- Classification quality: top-1/top-3, edit rate.\n- Passport reliability: schema pass rate, missing-field rate, overwrite conflicts.\n- Usability: median review+save time per object.\n- **Checkpoint:** go/no-go on “classification + passport flow usable for pilot”.\n\n**Two decisions to lock before implementation**\n1. Do you want **single-label only** now, or allow `mixed_materials` (list with percentages) in the passport from day one? \n2. For low confidence, should default be:\n - A) `unknown` + required user confirmation (recommended), or\n - B) keep top-1 prediction and mark low-confidence?\n\nIf you answer those, I’ll turn this into an exact implementation checklist for the next execution pass.", + "time": { + "start": 1783084466543, + "end": 1783084476067 + }, + "metadata": { + "openai": { + "itemId": "msg_0848d0710d669df2016a47b5b277bc81a3bcede0aec51671ef", + "phase": "final_answer" + } + }, + "id": "prt_f281dc16f0015ZhBNqHekCpAKh", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281d98b8002y11y0kGL25HNTD" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 182693, + "input": 42059, + "output": 634, + "reasoning": 224, + "cache": { + "write": 0, + "read": 139776 + } + }, + "cost": 0.11007605, + "id": "prt_f281de6cd0013U5GevLz8bYQfn", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f281d98b8002y11y0kGL25HNTD" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783084620954 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f28201c9a0013GqqNYb4SV73sZ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "I want the mixed material (list with percentages); For low confidence, default should be unknown + required user confirmation.", + "id": "prt_f28201c9b001eVX7EH67vALa7q", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28201c9a0013GqqNYb4SV73sZ" + } + ] + }, + { + "info": { + "parentID": "msg_f28201c9a0013GqqNYb4SV73sZ", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0471128, + "tokens": { + "total": 183382, + "input": 1760, + "output": 632, + "reasoning": 254, + "cache": { + "write": 0, + "read": 180736 + } + }, + "modelID": "gpt-5.3-codex", + 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+ } + }, + "id": "prt_f28202553001gc8pdI8tIZQ6Wl", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28201cc60025mcftOFUvXgOai" + }, + { + "type": "text", + "text": "Perfect — locked decisions received:\n\n- `mixed_materials` in passport: **yes** (list with percentages)\n- low confidence behavior: **default = `unknown` + required user confirmation**\n\nHere is the execution plan for this bucket.\n\n**Plan**\n\n- **Material output contract**\n - Keep `pred_material_topk` from model, then derive `mixed_materials` as `[{\"material\": ..., \"percentage\": ...}]`.\n - Percentages always sum to `100` (post-rounding correction on last entry).\n - Add `final_material_mode` enum: `single | mixed | unknown`.\n\n- **Confidence policy + confirmation gate**\n - Add two thresholds:\n - `LOW_CONF_THRESHOLD` (below this -> force `unknown`)\n - `MIXED_TRIGGER_THRESHOLD` (if ambiguity is high -> suggest mixed instead of single).\n - If low-confidence: block save until user explicitly confirms one of:\n - keep `unknown`,\n - set single material,\n - set mixed materials.\n - Store `confirmation_required=true/false` and `confirmed_by_user=true/false`.\n\n- **Mixed-material computation**\n - From top-k probabilities, select candidates above cutoff (or top-N with cumulative mass rule).\n - Normalize selected scores into percentages.\n - Enforce practical limits (e.g., max 3 materials, min 5% each) to avoid noisy passports.\n - Preserve raw model scores separately for audit/debug.\n\n- **Passport schema update**\n - Add/standardize fields:\n - `material.predicted_topk` (raw model outputs),\n - `material.mixed_materials` (percent list),\n - `material.final_material_mode`,\n - `material.final_single_material` (nullable),\n - `material.final_mixed_materials` (nullable),\n - `material.confidence`,\n - `material.low_confidence_flag`,\n - `material.user_confirmation`.\n - Keep provenance: model output vs user-final values.\n\n- **Notebook flow changes**\n - Review cell becomes structured decision step:\n - shows suggested single + suggested mixed + confidence state.\n - Save/export cell validates:\n - required confirmation satisfied,\n - percentages valid,\n - schema-valid JSON before write.\n - Eval row still records single final label for compatibility, plus optional mixed summary field.\n\n- **Validation pass (this bucket)**\n - Run on 10–15 objects:\n - check mixed percentage quality,\n - check low-confidence gating behavior,\n - check no invalid passports written.\n - Acceptance target: `100% schema-valid passports`, `0 unconfirmed low-confidence saves`.\n\n**Need your preference on 3 defaults before I implement**\n1. Max mixed entries: `2` or `3`? (I recommend `3`)\n2. Minimum percentage per listed material: `5%` or `10%`? (I recommend `10%`)\n3. Initial low-confidence threshold: `0.45` or `0.50` top-1 confidence? (I recommend `0.50`)", + "time": { + "start": 1783084627836, + "end": 1783084636965 + }, + "metadata": { + "openai": { + "itemId": "msg_00a9775ca5716e45016a47b653c400819c819e5e64fa1dc3eb", + "phase": "final_answer" + } + }, + "id": "prt_f2820377c001yL5h6ZzSq0MEfY", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28201cc60025mcftOFUvXgOai" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 183382, + "input": 1760, + "output": 632, + "reasoning": 254, + "cache": { + "write": 0, + "read": 180736 + } + }, + "cost": 0.0471128, + "id": "prt_f28205b3900114XJnS5qYSjjbx", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28201cc60025mcftOFUvXgOai" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783084692784 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f28213530001pFa9bO2OikTtuB", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "1. max 3 mixed entries, 2. 10% is good, 3. 0.50 is good.", + "id": "prt_f28213532001yQ0l5Spq7GLFUF", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28213530001pFa9bO2OikTtuB" + } + ] + }, + { + "info": { + "parentID": "msg_f28213530001pFa9bO2OikTtuB", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.04401425, + "tokens": { + "total": 183856, + "input": 1399, + "output": 631, + "reasoning": 66, + "cache": { + "write": 0, + "read": 181760 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084692819, + "completed": 1783084705200 + }, + "finish": "stop", + "id": "msg_f28213553002RwtIZnA10yZc5w", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f28213f2b001JXJxVPuLTdl7iG", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28213553002RwtIZnA10yZc5w" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783084695936, + "end": 1783084697277 + }, + "metadata": { + "openai": { + "itemId": "rs_0f98d795f5fd5eb7016a47b697be3c819eb5bb3da18a7d17ae", + "reasoningEncryptedContent": "gAAAAABqR7aZiOXG01Xmx0eRoqq6SG-KGZT9Qlr9vLMlRS8uiKnQXu2utXC4VnEqEw7oQISyJA4lVMujxVbsydONPeExfXbrPlIsDoYnvm8Y7BdFlzHjEFqZ0__xHfi60OITPLdLmRfFE3fMICrGTL_m60y8VyIttMN0PQnGChUgcmLSuA3tUHUkN4nkc_RAnL1ivqGGcyQQbI1kB7mhGuxGbQ98Niqig6OVH24S3ZChFQxNKtz_zYV8ksLK-8ePZx5sjyW0Kpt17jplVHO8dIBV-aXQl1PK1IrOCC0GWel18xMlAQgJWDUDlhZfe_F5e_05_nGci4no7h58Kg2DqkOKqq1wd8sdO2RyLYAQ2bOASpl3xFzbEyemUOSGCc9vjLcW8ImwFQf4vG_7k-TVWHc1lQ0nz1KqZA3d8AmUujmaVVev56Lf2IbDihkGb2z3Qs53gxBqq5FSt3Pn2odS8Ur56_IBgs1OI6gLOMcBFLLt81EknZYUQaECSkXyMY9tYUXIIWbMB3CHbRuX0bFpXmUji9VMpOwq76IHNj9GuudBdpcvj1ZTS6PE2YhtG2_WnhAOvEdcCBsSXHJQxwDmnYhjrBanCScb9639p_pWTju35m1VIfwfGxxf3Zn8jLU8n1F3blJFzl40ozXoVD05V4Zt1nhIMjyamq_1uH6zlzvBcsNLuH92D9MiSHmq0LJSlMC_d1lfPRYfofE8QRdo5lWXG6E110sKbDcofqwMlQozvV6tNsR402rX4RWXrJLvDP55bcvJO2vEzsh17pIeZixylxRs5xy3RBJeZxj9cN9UPXpxH6aPpbHgT8zpuVXQGivAGBxdATZEQ5LIHYQ-bcbKaI_gTsvKRKonpy0t00DNJmwONjRRWa5JJxiz4aJs0B3GNjsyOLAmq4DEKb7qzgtaSJQpjxb4DUbM6QMZo6SRHhyLBG8nHyRrhdTJctTGX-OY7XcTofNqXPsA-t2q69aIY4wtKCl-apnf5R4tWpJCcZxBBJ2Fk1s1kdiKPQKIv4b_Y-8dzVQRZzE1nziRWpkpI73Otw64Z9pkEQRD7vbT0tq6tt01h4b9aWPjBMJWtFVvmx4RGIjGgA87Sl0YYgT0pVJhntHYBQqN-REShTd5zMCllzh00dNOHK_zLU5aiURG9fjZwVpW11EAm_9ka2EkCt0JO1GgUulAa0Tt4i9DgGBKSoCX7qhURrSWhluOC8GXqrdYUd2N6N1ACO5eeESRa__PvmmAD_YAI6-aCNEmtDjL4bsXABRppIJ4EsWU4TFghEqAr6YQ95kQjkK_USfjTb-YhiSkEXEWut6BDj34_V_JrOxkoui_yZxDSjKh2gMxpkaQYG_BWves8tPDG25hjXTvSTvEIMsyrrxiJ6OwKxjL46LZfh4=" + } + }, + "id": "prt_f28214180001Q8WeXoExaynmiL", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28213553002RwtIZnA10yZc5w" + }, + { + "type": "text", + "text": "Excellent — locked:\n\n- Max mixed entries: `3`\n- Min percentage per material: `10%`\n- Low-confidence threshold: `0.50`\n\nHere is the exact implementation plan for this bucket (no execution yet).\n\n**Bucket: Material Classification -> Passport**\n\n- **Config constants**\n - Add constants: `MAX_MIXED_ENTRIES=3`, `MIN_MIXED_PERCENT=10`, `LOW_CONF_THRESHOLD=0.50`\n - Add optional `MIXED_MARGIN` (e.g. top1-top2 gap threshold) to trigger mixed suggestion.\n\n- **Classification post-processing module**\n - Input: model top-k labels + scores.\n - Output:\n - `suggested_single_material`\n - `suggested_mixed_materials` (<=3 entries, each >=10%)\n - `low_confidence_flag`\n - `requires_user_confirmation` (true when low confidence).\n - Rules:\n - If top-1 < 0.50 -> default suggestion is `unknown`.\n - For mixed: keep up to 3 candidates, filter tiny tails, renormalize to 100.\n - If only one candidate survives mixed rules, treat as single suggestion.\n\n- **Review/confirmation flow (notebook)**\n - Show:\n - model top-k + confidence,\n - suggested single,\n - suggested mixed.\n - Require explicit user decision when `low_confidence_flag=true`:\n - `unknown`,\n - single material,\n - mixed materials.\n - Block export if low confidence and no confirmation recorded.\n\n- **Passport schema update**\n - Add material fields:\n - `predicted_topk`\n - `predicted_confidence_top1`\n - `suggested_single_material`\n - `suggested_mixed_materials`\n - `final_material_mode: single|mixed|unknown`\n - `final_single_material` (nullable)\n - `final_mixed_materials` (nullable)\n - `low_confidence_flag`\n - `requires_user_confirmation`\n - `confirmed_by_user`\n - Add validation checks:\n - mixed list length <= 3\n - each entry >= 10\n - percentages sum to 100 (+/- tiny rounding epsilon)\n\n- **CSV/eval compatibility**\n - Keep existing columns intact.\n - For mixed final mode:\n - `user_final_material` stores a compact string (e.g. `mixed:wood=60|steel=40`) for traceability.\n - Keep top-1/top-3 metrics as-is; mixed handling can be evaluated separately later.\n\n- **Acceptance checks for this bucket**\n - `100%` passport schema validity.\n - `0` low-confidence records saved without confirmation.\n - Mixed percentage constraints always satisfied.\n - Manual spot-check on 10–15 objects.\n\n**Checkpoint before coding**\nIf this plan looks good, next step is I implement it in the notebook as one cohesive change set, then return with exactly what changed and how to test it quickly.", + "time": { + "start": 1783084697284, + "end": 1783084705173 + }, + "metadata": { + "openai": { + "itemId": "msg_0f98d795f5fd5eb7016a47b69933a4819ea3ed2c0e48e4232b", + "phase": "final_answer" + } + }, + "id": "prt_f282146c4001JGyZLGdqGCKUfE", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28213553002RwtIZnA10yZc5w" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 183856, + "input": 1399, + "output": 631, + "reasoning": 66, + "cache": { + "write": 0, + "read": 181760 + } + }, + "cost": 0.04401425, + "id": "prt_f282165a9001dHoc41Bx267GBY", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28213553002RwtIZnA10yZc5w" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783084791732 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f2822b7b4001jEUY7pDcVHbzyt", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "let's discuss the classification implementation more, since this is primary to the whole project. Is SigCLIP enough/valid for a stable material classification, can we combine it with other tooling?", + "id": "prt_f2822b7b600123ivmMLlyzOVkY", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2822b7b4001jEUY7pDcVHbzyt" + } + ] + }, + { + "info": { + "parentID": "msg_f2822b7b4001jEUY7pDcVHbzyt", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0430892, + "tokens": { + "total": 184494, + "input": 1048, + "output": 520, + "reasoning": 142, + "cache": { + "write": 0, + "read": 182784 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084791767, + "completed": 1783084805464 + }, + "finish": "stop", + "id": "msg_f2822b7d70024ZpuV8rCPCH06u", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f2822be82001VwU1n5YwXnXqnH", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + 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+ } + }, + "id": "prt_f2822bf280012hnx9dIIO3nmIU", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2822b7d70024ZpuV8rCPCH06u" + }, + { + "type": "text", + "text": "Great call to pause here — this is the core risk in the whole POC.\n\nShort answer:\n- **SigCLIP alone is valid for a POC baseline**, especially for top-k suggestions.\n- **SigCLIP alone is usually not enough for stable production-grade material classification** in field photos (lighting, dirt, paint, composites, occlusion, object context leakage).\n\nSo yes, you should combine it with other tooling.\n\n## What SigCLIP does well vs poorly\n\n- Good at broad semantic matching (`wood`, `glass`, `metal`) with prompt engineering.\n- Fast to stand up, no task-specific training required.\n- Struggles on fine-grained material distinctions (`aluminum vs steel`, `gypsum vs painted concrete`), especially from small/dirty crops.\n- Confidence is not calibrated by default, so “0.7” is not necessarily true 70% reliability.\n\n## Recommended stacked approach (practical)\n\n1. **SigCLIP as retrieval/prior layer**\n- Keep top-k candidate materials.\n\n2. **Feature-bank refinement**\n- Build a small labeled embedding bank from your own domain images (few-shot per material).\n- Re-rank SigCLIP outputs by nearest prototypes (cosine similarity).\n- This usually improves stability a lot with low effort.\n\n3. **Rule/constraint layer**\n- Object-conditioned priors (you already started this).\n- Scene sanity checks (e.g., impossible combos for certain object types).\n- Low-confidence gate -> `unknown` + required confirmation (already your chosen policy).\n\n4. **Calibration layer**\n- Temperature scaling / isotonic calibration on your eval set.\n- Converts raw scores into better “decision confidence.”\n\nThat combo is typically much more reliable than swapping backbone alone.\n\n## Where DINOv3 fits\n\n- DINOv3 is strong for visual features; often useful as a second embedding branch.\n- Good pattern: **SigCLIP (text-aligned) + DINOv3 (visual discrimination)** ensemble.\n- Use disagreement as uncertainty signal: if they disagree strongly, default to `unknown`.\n\n## Suggested decision rule (simple and strong)\n\n- If top-1 confidence < threshold OR model disagreement high -> `unknown`.\n- Else if top-1/top-2 margin small -> suggest `mixed`.\n- Else suggest single material.\n- Always allow user override and log provenance.\n\nIf you want, next I can give you a concrete **classification architecture spec** for the notebook (inputs, scoring formula, calibration step, and exact fallback logic) before we implement anything.", + "time": { + "start": 1783084797012, + "end": 1783084805420 + }, + "metadata": { + "openai": { + "itemId": "msg_0ad640b2b17a1ffd016a47b6fcefa0819c814481ad28b080ef", + "phase": "final_answer" + } + }, + "id": "prt_f2822cc54001Ifk8IIF29JvsKN", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2822b7d70024ZpuV8rCPCH06u" + }, + { + "reason": "stop", 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+ } + }, + "id": "prt_f282443a3001nrd5D8qkEmqr1z", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28243afe002sNc08ZqMNa6tTY" + }, + { + "type": "text", + "text": "Perfect. Here is a concrete **classification spec v1** for your POC (SigCLIP-first), aligned with your decisions.\n\n**Scope**\n- Goal: robust material suggestion for passport creation, with safe fallback.\n- Output modes: `single | mixed | unknown`.\n- Hard rules:\n - `LOW_CONF_THRESHOLD = 0.50`\n - `MAX_MIXED_ENTRIES = 3`\n - `MIN_MIXED_PERCENT = 10`\n - low confidence => `unknown` + required user confirmation.\n\n---\n\n**1) Inputs**\n\nPer object:\n- `image_views`:\n - masked crop (primary),\n - bbox crop (secondary),\n - full image (context).\n- `object_label` (if available, for priors).\n- `material taxonomy`:\n - canonical materials + aliases (already present).\n- `prompt templates` (already present).\n\nOptional (recommended soon):\n- prototype embedding bank per canonical material from labeled examples.\n\n---\n\n**2) Core Model Stack (v1)**\n\n- Base scorer: SigCLIP text-image scoring across prompts.\n- Prompt aggregation:\n - for each canonical material, take max score across aliases/templates.\n- View fusion:\n - weighted average of per-view material scores.\n - default: `[0.5 masked, 0.35 bbox, 0.15 full]`.\n- Prior filtering:\n - if `object_label` has priors, restrict/reweight to candidate set.\n- Normalize to probability distribution over canonical materials.\n\n---\n\n**3) Decision Logic**\n\nLet sorted probabilities be:\n- `p1, p2, p3...`\n- labels `m1, m2, m3...`\n\nDerived signals:\n- `top1_conf = p1`\n- `margin12 = p1 - p2`\n- `entropy = -sum(p_i log p_i)` (normalized optional)\n\nRules:\n\n1. **Low confidence gate**\n- If `top1_conf < 0.50`:\n - `suggested_mode = \"unknown\"`\n - `requires_user_confirmation = true`\n - still show top-k for operator context.\n\n2. **Mixed suggestion**\n- Else if ambiguity is high (recommended initial rule):\n - `margin12 < 0.15` OR cumulative of top2 > 0.75\n - then suggest `mixed`.\n- Build mixed list:\n - take descending candidates,\n - keep max 3 entries,\n - drop entries < 10% after normalization,\n - renormalize to 100,\n - ensure sum=100 via last-entry correction.\n\n3. **Single suggestion**\n- Else `suggested_mode = \"single\"` with `m1`.\n\n---\n\n**4) Mixed Percentage Algorithm**\n\nGiven selected scores `s_i`:\n- Normalize: `q_i = s_i / sum(s)`\n- Convert percent: `pct_i = round(100 * q_i)`\n- Enforce min:\n - remove entries `<10`, renormalize remaining, repeat once.\n- Cap to max 3 entries.\n- Final sum fix:\n - `pct_last += (100 - sum(pct))`.\n\nIf fewer than 2 valid entries remain => fallback to `single`.\n\n---\n\n**5) Confirmation Policy**\n\n- If `requires_user_confirmation = true`, block save until user picks one:\n - `unknown`, or\n - `single` (explicit material), or\n - `mixed` (explicit list).\n- Store:\n - `confirmed_by_user`\n - `confirmation_reason` (optional, recommended)\n - `source = model | user | mixed`.\n\n---\n\n**6) Passport Contract (material block)**\n\n```json\n{\n \"material\": {\n \"predicted_topk\": [{\"material\":\"wood\",\"score\":0.42}, {\"material\":\"steel\",\"score\":0.31}],\n \"predicted_confidence_top1\": 0.42,\n \"suggested_mode\": \"unknown\",\n \"suggested_single_material\": null,\n \"suggested_mixed_materials\": [],\n \"final_material_mode\": \"mixed\",\n \"final_single_material\": null,\n \"final_mixed_materials\": [{\"material\":\"wood\",\"percentage\":60},{\"material\":\"steel\",\"percentage\":40}],\n \"low_confidence_flag\": true,\n \"requires_user_confirmation\": true,\n \"confirmed_by_user\": true\n }\n}\n```\n\nValidation:\n- mixed length `<=3`\n- each percentage `>=10`\n- sum `==100`\n- low-confidence requires confirmation.\n\n---\n\n**7) Minimal Notebook Function Interfaces**\n\n- `compute_material_scores(image_views, object_label) -> dict[material]prob`\n- `suggest_material_decision(probs, thresholds) -> decision_dict`\n- `build_mixed_materials(probs, max_entries=3, min_pct=10) -> list`\n- `validate_material_payload(material_block) -> (ok, errors)`\n\n---\n\n**8) v1.1 Upgrade (next, not required now)**\n\n- Add prototype-bank reranking:\n - blend `SigCLIP_prob` and `prototype_similarity_prob`.\n- Add calibration on eval set:\n - temperature scaling for confidence reliability.\n- Keep same decision interface/output schema.\n\n---\n\nIf you want, next I can turn this into a **test matrix** (10 concrete edge cases) so you can verify the implementation behavior before coding.", + "time": { + "start": 1783084896246, + "end": 1783084909625 + }, + "metadata": { + "openai": { + "itemId": "msg_034ca3ca65d0501a016a47b75ffa788191a0bc935c9a23bca9", + "phase": "final_answer" + } + }, + "id": "prt_f28244ff6001jzzy2211u7Sbp3", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28243afe002sNc08ZqMNa6tTY" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 185654, + "input": 1581, + "output": 1112, + "reasoning": 177, + "cache": { + "write": 0, + "read": 182784 + } + }, + "cost": 0.05279995, + "id": "prt_f28248442001Vhd6PlmbLFiw1Y", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28243afe002sNc08ZqMNa6tTY" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783084973358 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f28257d2e001f76v8waM9RQdc4", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "No, I think we can begin the implementation. But please make this into an agile bucket (AGENTS.md file for reference)", + "id": "prt_f28257d2e002sjmXXN853VsQz5", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28257d2e001f76v8waM9RQdc4" + } + ] + }, + { + "info": { + "parentID": "msg_f28257d2e001f76v8waM9RQdc4", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0479619, + "tokens": { + "total": 186422, + "input": 1698, + "output": 617, + "reasoning": 299, + "cache": { + "write": 0, + "read": 183808 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783084973395, + "completed": 1783084988448 + }, + "finish": "stop", + "id": "msg_f28257d53002W6XLREaJ4NTZGN", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f2825853f001lbNSfx7ItDktyA", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28257d53002W6XLREaJ4NTZGN" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783084975429, + "end": 1783084980412 + }, + "metadata": { + "openai": { + "itemId": "rs_0a625d141e3ce4e7016a47b7af40e08191bb100f401bb5f80a", + "reasoningEncryptedContent": "gAAAAABqR7e0RKU02uFo7YCapxWo3TkGSptgxid-muHfxUONrqxcBp87PmAnlHb-SzcOkoZ5DCFpL0yCYrDkcd2R8bUNkExQHnpyufneafLIP1pu_-nBe46WWE9Dh6fGCzaUjVdUYypzf31DhljKTcoEgxz2C636CvnpTqo42bFTq4GFWDr86qDIJZirdT-uyFCU1wsPGh6jH2GjZbC-8zZRLxJoRQVeqiLBXrGw1xARwM-ZGg-JZHefXPnalYDex8JYna2spzct8Hp7vwK6dH89Hq8XHGfB3-3OqsKxu6iRtFaIdEJp2H4c9l51bLo0zQQUneoVJo7km0kiN_o2wJMBfvLYuvgqthI3GokMU-UfDWnNwN2eD2oJfkppF6y0oQiceikiKZWWx_CDl-7BZzggHpqvo8z3vqp4Gu2VI43wx4nzkyP1ZH06EcXd-jplxEz9j-C97mYrraU9fibFvmYuJRZm-MEUkXWSgfksz1LEA9GrFnY8uqlonS40VD4alZITqBOzV8M4udGICPVCcuGOp2s7R659cj5EpMwgwrOAv_r92FQ1-zrmsY7EsmYLLFuT1ZpCRAeXWZ4yTGB3BOHgPi3DACBCg1LdZ_aCbRq753GUlZOjQoZ3J25CwFBKD72uvWlbt4xBuvb8UmCkfG16n9J8WmaNAZTy7uGh1WzAgohRZIe7XcvjmdqqLGdvKhasjpbiHF1UPht9ffVbV36En7RSeft0A2S3wFHXatndqHFNDs66FKS0vbwgtgJzd-9qdHGM8ZvI7HtYvU9rGf1e-uG9Jh05ISF_iR4yatYIXCPYkpOj14z4lTqSV3nNIt8Fez1nKqBwxJuiJVSfAXfp6n6RWVc4MFVTzJT94yDQqfM09wfPLPorT6IOomAvfUSCsB6uKfDtFMy6tve_Fkw56XgJ3-0IKY2oCx7IVpWZGSakqQXID0bjo9NNcf4rI0ys93a5zFIQ0GUDEB1unapIMiPsd2R-rieWKKk0zIkAbjcb6HDeZeNg_W71QmhFKeEh1Yx_l7RkFLJ-VAKKhmRYKqyBKbpnNH7DTkqLn-6ozAM5r0HCfh5sFMvkqjx2XaDH6yzwQeG2Zohy4IPbVl8L6uhenlZL_CKmmKkEjOmpxO89QbQ6aUV6nrFkt6vdRYORLu7KA-QN0xcJw2L7RAmnBHUcQtmz6g9ErLCAQZsqz-JeIfm0vwXk_Cp4cKqxze1o-RUwS7rG1NB2FSZ1b9i4cRGvsj_5D5jnxTjxAn976MSyhttznMgRhER-ib-VW6SM0N8UhnqYfkFIXFPS6_StLovpdS70TgVOfsAmn68VnIQeznCD2xOU0hgytZ9OYuBwmCiXlNZXUXCYiOT6a9G7BE6IS-sq1n1LAYcLALpD-4QHm3tKc1kMsHPocKWa6zATMq6aqdqo6sld3QTpLmvWheD_8OGR0WfW9RusHEAoICVHindo6kqT4f2WhZXd7Y4peEHzO7vzGW2Ep6btTAypp7B2CljOvitIgyi37-DW5LD7OzbhduiF0yEDXJSaP-qVVQVk5qunpiDEDuAQcn-Nty6bcw9_0SdHfx5YJqoa5ch5Bek5-jv6aEdwnvlSGH3ZO5THAMNt2YnXGarmmV_7SXwVtuDNCSXOFEwG1IYBIcKLt_HHDhMPbwbJHMqDz0s6zKpXx7VeBVROYTN-CCjjQ6qJnggpzIrGU_MAa3Cx9z9wV5hPcT7pHHm13Y-TRfE9Fo2SM8YUWLdbXZP3KWvaMMUDgSrMLQSf-iHAdFA9xOYCJnEibLtFwJXLk-7MR2v11eyUO4cioPYjqNt2OO7HxUH9pweM2x94DnZL93ci5Q8kseXggBvOZSuc61htT3YFwursOGUHnK86uqyCPCjPra1tqquYEwk25xfXauFM9a7qD8G0DOW41hzgT98ns1FIZVDZ4Jwnfdge9nKuJh2uh5ziuWyK7ASkdjgjL8836RiYa9qKGBRADSfNGIhuqknVDbkAlP_VuudmKPzA4Lz66IXpiXpcqVswXrP4hB0IGDB3ky_QrlT7fuh6kcptyliSQQyBUz41KwgSrGmsjavRF1f-z2yuIZVJaO-ohqbnNCqZqrCoU4dHh2ChmHCi6FyWcpu6iZ-NWfnvjf68oM_nkUWGaEJqwoPwkdk8_Cj9NF3hjUBmY2W6Z74n0V0pC6Y_C7yeSOFp1Zwm1FJAtjixr5AtBriuw0cKk0nJw_rSg2B9NUWnofniuMD0oGTrR_eRNUqOiWva38rrMCKoaPQWmZepA9DlR0wRy-RPWbQkmsMbgjkZFHDEYgPVxJ1fc4cnZmatp4U08SvDbuKi3vkenoa-ImJ3yV4dk2acNE2fOOgVXmM0zZh8EdJieDXvXs7feCTdngf62HPK7Jvgc7HGY3HuABsGxUeTe9m4FwD0TlO7zxhGEv4cjy__Dw_HSZvFq-2dcV99jJV6PW2bt9rZ1Lwr_2xDpofBqfok3b0MPpl4wLaIFEwaiP2C3WaPtO3wfX4VtUNgYr1d24QdwLy_pdsbJjoZUKKUOdlkkaUzFsG0S2_kGKTUKIBZP1ubgzD4WskcNx-PgFdyN8nd1CQwsK2S17T3wEFAnk0szrz-IFkslW50BOA_Y2VqNeNk7RxA9sVub4EOD8MHEYEVMbQ1mzkcjUKUozimiSKqgsrsT_L6Gj5sVkhjaaZRKbctcRF_MUfps-vgEkO6iJRKG-eGTkdXNwtE6odntLSVZX7R4k8nsxzlwhSe9bdrttItuENU3NvVWVbBvBaET2gS6os5ik-dpMFQgRvV83cyC3_dqLkl07rZs4opGAKQ2eeeo6_hstBB-vHA3skne_xQg0f01YweDb881p6NynyC56X6oQNHuBc8aN4T9PDAwQAZIimaEj3Ewe9sfv6c0URJdUUCY8lydgHJNogIT3cGA-vGatwNoFHNvAl8R4G59Qm2oEFeLAB6GiU6TbELk3DyeMA98i6y5h_KWddF5jt5S9MqwV5dQfn5Ollea1woJq3ufZNg2WY1kt33ywRq5mfylj2U-Q9gMDRL62Mt1OvOfkvViMjWsMUzzHw=" + } + }, + "id": "prt_f28258545001oKuTTbX4LiDz1D", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28257d53002W6XLREaJ4NTZGN" + }, + { + "type": "text", + "text": "Perfect — let’s run this as one agile bucket with explicit checkpoints.\n\nLocked decisions (restate):\n- Mixed materials enabled (`<=3` entries, each `>=10%`)\n- Low confidence (`top1 < 0.50`) => default `unknown` + required user confirmation\n- Segmentation stack stays as-is for now\n\n**Bucket: Material Decision + Passport Contract (v1)**\n\n- **Goal**\n - Implement stable material decision logic and passport material fields so every saved object has a valid, auditable material outcome (`single|mixed|unknown`).\n\n- **Scope**\n - In scope: classification post-processing, decision policy, confirmation gate, passport schema fields, validation, notebook flow wiring.\n - Out of scope: SAM A/B comparisons, DINOv3 integration, calibration training.\n\n- **Step 1 — Decision Engine**\n - Add pure functions:\n - `compute_material_scores(...)` (from current SigCLIP output)\n - `build_mixed_materials(...)` (max 3, min 10%, sum=100)\n - `suggest_material_decision(...)` (`single|mixed|unknown`)\n - **Checkpoint 1:** review outputs on 5 mocked score vectors (easy, ambiguous, low-conf, noisy tail, tie case).\n\n- **Step 2 — Confirmation Gate**\n - Add `requires_user_confirmation` logic.\n - Block save when low-confidence and unconfirmed.\n - Add explicit user decision fields in review cell.\n - **Checkpoint 2:** dry-run 3 examples showing save blocked/unblocked correctly.\n\n- **Step 3 — Passport Material Schema**\n - Extend material block with:\n - `predicted_topk`, `predicted_confidence_top1`\n - `suggested_mode`, `suggested_single`, `suggested_mixed`\n - `final_material_mode`, `final_single`, `final_mixed`\n - `low_confidence_flag`, `requires_user_confirmation`, `confirmed_by_user`\n - Add validator for mixed constraints and required fields.\n - **Checkpoint 3:** inspect 3 generated passports (single/mixed/unknown).\n\n- **Step 4 — CSV Compatibility + Quick Verification**\n - Keep current CSV columns; encode mixed final as compact string in `user_final_material`.\n - Run quick verification on 10–15 objects and report:\n - schema pass rate,\n - unconfirmed-low-conf save count,\n - mixed constraint violations.\n - **Checkpoint 4:** go/no-go to proceed to calibration/prototype improvements.\n\n**Definition of done**\n- 100% passport schema-valid saves\n- 0 low-confidence saves without confirmation\n- Mixed percentages always valid (`<=3`, each `>=10`, sum=100)\n- Existing notebook flow still executable end-to-end\n\nOne small rule to lock before implementation:\n- For suggesting `mixed` when not low-confidence, use `top1-top2 < 0.15` (recommended) — confirm?", + "time": { + "start": 1783084980421, + "end": 1783084988420 + }, + "metadata": { + "openai": { + "itemId": "msg_0a625d141e3ce4e7016a47b7b413dc8191911212213a549283", + "phase": "final_answer" + } + }, + "id": "prt_f282598c50011GwSXboQ65FMX0", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28257d53002W6XLREaJ4NTZGN" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 186422, + "input": 1698, + "output": 617, + "reasoning": 299, + "cache": { + "write": 0, + "read": 183808 + } + }, + "cost": 0.0479619, + "id": "prt_f2825b81a001o0LDOqvYzyqTgj", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28257d53002W6XLREaJ4NTZGN" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783085079768 + }, + "agent": "plan", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f28271cd8001UQVdkl7HtZGntj", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "is DINOv3 not required to get valid scores? I think we should prioritize a joint implementation", + "id": "prt_f28271cd9001TVN9UbsfwLs9lp", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28271cd8001UQVdkl7HtZGntj" + } + ] + }, + { + "info": { + "parentID": "msg_f28271cd8001UQVdkl7HtZGntj", + "role": "assistant", + "mode": "plan", + "agent": "plan", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0436506, + "tokens": { + "total": 186791, + "input": 1316, + "output": 496, + "reasoning": 147, + "cache": { + "write": 0, + "read": 184832 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783085079797, + "completed": 1783085091045 + }, + "finish": "stop", + "id": "msg_f28271cf5002PfpX4NeLx0xL5Y", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f282724c2001joCfw34wsbZ3LZ", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28271cf5002PfpX4NeLx0xL5Y" + 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+ } + }, + "id": "prt_f2827252b0015OaMdEgEOWfvp7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28271cf5002PfpX4NeLx0xL5Y" + }, + { + "type": "text", + "text": "Great point — and I agree with your priority shift.\n\nShort answer: **DINOv3 is not strictly required to produce valid scores**, but if this is the core value path, a **joint SigCLIP + DINOv3 pipeline is the right move now** for better stability and uncertainty handling.\n\n**Why joint now**\n- SigCLIP gives strong text-aligned semantics.\n- DINOv3 gives strong visual discrimination.\n- Their agreement/disagreement is a high-signal confidence feature.\n- This directly improves your `single|mixed|unknown` decision quality.\n\n**Agile Bucket: Joint Classifier v1 (SigCLIP + DINOv3)**\n- **Step 1: Dual-score extraction**\n - For each object crop, compute:\n - SigCLIP material probabilities (existing path)\n - DINOv3 similarity scores against material prototypes (new path)\n - Checkpoint: inspect per-sample top-k from both models on 10 objects.\n- **Step 2: Fusion layer**\n - Combine normalized scores with fixed weights (initial default: 0.6 SigCLIP / 0.4 DINOv3).\n - Add disagreement metric (`KL`/margin mismatch) for uncertainty.\n - Checkpoint: review fusion outputs vs each model alone.\n- **Step 3: Decision policy update**\n - Keep your locked rules:\n - low confidence => `unknown` + required confirmation\n - mixed allowed (`<=3`, `>=10%`, sum=100)\n - Add disagreement override: high disagreement pushes toward `unknown` or `mixed`.\n - Checkpoint: validate edge cases (ambiguous, low-light, partial mask).\n- **Step 4: Passport + provenance**\n - Store both model outputs + fused output + decision reason fields.\n - Keep schema-valid material passport contract.\n - Checkpoint: review 3 passports (`single`, `mixed`, `unknown`).\n- **Step 5: Acceptance gate for this bucket**\n - 0 unconfirmed low-confidence saves\n - 100% schema-valid passports\n - lower manual edit rate than SigCLIP-only baseline on same mini-set\n\nOne decision to lock before implementation:\n- Use **fixed fusion weights** first (`0.6/0.4`, recommended) or start with a **learned logistic fusion** (needs small labeled set)?", + "time": { + "start": 1783085084115, + "end": 1783085091013 + }, + "metadata": { + "openai": { + "itemId": "msg_072e84fc7686ad7d016a47b81c09c48192ac96816218b8dec3", + "phase": "final_answer" + } + }, + "id": "prt_f28272dd3001Qh2eUJJTClUtSR", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28271cf5002PfpX4NeLx0xL5Y" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 186791, + "input": 1316, + "output": 496, + "reasoning": 147, + "cache": { + "write": 0, + "read": 184832 + } + }, + "cost": 0.0436506, + "id": "prt_f282748dc001hE04016AyxTkp5", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28271cf5002PfpX4NeLx0xL5Y" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783085148778 + }, + 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+ } + }, + "id": "prt_f28283444001If7j3B62ad7UQE", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28282a8e002yNGzYLdsZaFOl3" + }, + { + "type": "text", + "text": "Yes — very similar.\n\n- A **learned logistic fusion** is basically a **linear classifier on top of features/scores** (often equivalent to a linear probe in practice).\n- If inputs are model scores (e.g., SigCLIP probs + DINOv3 similarities + disagreement metrics), logistic regression learns weights + bias to output class probabilities.\n- A classic **linear probe** usually means training a linear layer directly on frozen embeddings; logistic fusion is the same idea but often on engineered score features instead of raw embeddings.\n\nFor your case, I recommend this setup:\n- Use frozen SigCLIP + frozen DINOv3.\n- Build feature vector per object:\n - SigCLIP top-k probs or full class probs\n - DINOv3 class similarities\n - agreement features (top1 match flag, margin deltas, entropy/disagreement)\n- Train multinomial logistic regression to predict canonical material class.\n- Keep your policy layer on top:\n - low confidence -> `unknown` + required confirmation\n - mixed mode derivation from calibrated probabilities.\n\nIf you want, I can draft the exact training data schema and minimum sample size target for a stable first logistic fusion pass (e.g., per-class counts, split strategy, and overfitting checks).", + "time": { + "start": 1783085154351, + "end": 1783085157453 + }, + "metadata": { + "openai": { + "itemId": "msg_0872d83780ef2277016a47b8624934819c976802ab0b649394", + "phase": "final_answer" + } + }, + "id": "prt_f2828402f001XWXz8t6fiMQs13", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f28282a8e002yNGzYLdsZaFOl3" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 186972, + "input": 1828, + "output": 264, + "reasoning": 48, + "cache": { + "write": 0, + "read": 184832 + } + }, + "cost": 0.0399126, + "id": 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+ } + }, + "id": "prt_f2828e1ec001V6g7273qsqq8Vz", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2828d19c002UzPMxttngLkala" + }, + { + "type": "text", + "text": "Perfect — that is the right scope control.\n\nLet’s run this as a **single agile bucket** with fixed fusion weights first, then revisit learned logistic fusion later.\n\n**Bucket: Fixed Fusion Classifier v1 (SigCLIP + DINOv3)**\n\n- **Goal**\n - Improve material decision stability for passport generation using fused model scores, while preserving your `single|mixed|unknown` policy and confirmation gate.\n\n- **Step 1: Dual-model scoring (no policy changes yet)**\n - Keep current SigCLIP scoring.\n - Add DINOv3 material scoring on the same crops/views.\n - Normalize both to class-probability vectors over the same canonical material set.\n - **Checkpoint 1:** inspect side-by-side top-k outputs (SigCLIP vs DINOv3) on ~10 objects.\n\n- **Step 2: Fixed score fusion**\n - Add weighted fusion:\n - `p_fused = w_sigclip * p_sigclip + w_dino * p_dino`\n - Recommended initial weights: `w_sigclip=0.6`, `w_dino=0.4`.\n - Add disagreement diagnostics (top-1 mismatch, margin gap) for debugging only.\n - **Checkpoint 2:** compare fused top-k vs individual models on same samples.\n\n- **Step 3: Decision layer integration**\n - Run all decision rules on `p_fused`:\n - low confidence (`<0.50`) -> `unknown` + required user confirmation\n - mixed output allowed (`<=3`, each `>=10%`, sum=100)\n - Keep user override and confirmation logic intact.\n - **Checkpoint 3:** validate behavior on edge cases (ambiguous materials, low confidence, close top-2).\n\n- **Step 4: Passport + CSV wiring**\n - Passport stores:\n - SigCLIP scores, DINOv3 scores, fused scores, final decision mode.\n - CSV remains backward-compatible; mixed final encoded compactly in `user_final_material`.\n - **Checkpoint 4:** review 3 passports (`single`, `mixed`, `unknown`) for audit quality.\n\n- **Definition of done**\n - 100% schema-valid passports.\n - 0 low-confidence saves without confirmation.\n - Mixed constraints always valid.\n - Fused outputs visibly reduce unstable one-off predictions on your spot-check set.\n\nIf you’re good with this, I’ll use `0.6/0.4` as the starting weights and proceed with implementation in that order.", + "time": { + "start": 1783085197674, + "end": 1783085204669 + }, + "metadata": { + "openai": { + "itemId": "msg_04344b8d31ef132c016a47b88d995881a1bbbef006dbf4c483", + "phase": "final_answer" + } + }, + "id": "prt_f2828e96a001RDObmQOxqKpoN7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2828d19c002UzPMxttngLkala" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 187637, + "input": 1093, + "output": 531, + "reasoning": 157, + "cache": { + "write": 0, + "read": 185856 + } + }, + "cost": 0.04406955, + "id": "prt_f282904ce001Hf6w6P2eMFNWVm", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2828d19c002UzPMxttngLkala" + } + ] + }, + { + "info": { + "role": "user", + "time": { + "created": 1783085268010 + }, + "agent": "build", + "model": { + "providerID": "openai", + "modelID": "gpt-5.3-codex" + }, + "summary": { + "diffs": [] + }, + "id": "msg_f2829fc2a001ZJ23yIGmBb50tA", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "text", + "text": "alright, let's start building the bucket.", + "id": "prt_f2829fc310018rD4b3qXdb83de", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2829fc2a001ZJ23yIGmBb50tA" + } + ] + }, + { + "info": { + "parentID": "msg_f2829fc2a001ZJ23yIGmBb50tA", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.05733735, + "tokens": { + "total": 187601, + "input": 12681, + "output": 90, + "reasoning": 238, + "cache": { + "write": 0, + "read": 174592 + } + }, + "modelID": "gpt-5.3-codex", + 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+ } + }, + "id": "prt_f282a09220013CGX3Q4XCovUOD", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2829fc5000235jxvN3rcsPmGn" + }, + { + "type": "tool", + "tool": "webfetch", + "callID": "call_HtclKf06v39tmzrVL4NfqoWI", + "state": { + "status": "completed", + "input": { + "url": "https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m", + "format": "text" + }, + "output": "facebook/dinov3-vitb16-pretrain-lvd1689m · Hugging Face\n\n\t\t\n\n\t\t\n\n\t\t\n\t\t \n\t\n\t\n\t\t Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up facebook / dinov3-vitb16-pretrain-lvd1689m like 173 Follow AI at Meta 13.5k Image Feature Extraction Transformers Safetensors English dinov3_vit dino dinov3 arxiv: 2508.10104 License: dinov3-license Model card Files Files and versions xet Community 13 Deploy Copy to bucket new Use this model Instructions to use facebook/dinov3-vitb16-pretrain-lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers How to use facebook/dinov3-vitb16-pretrain-lvd1689m with Transformers: # Use a pipeline as a high-level helper\nfrom transformers import pipeline\n\npipe = pipeline(\"image-feature-extraction\", model=\"facebook/dinov3-vitb16-pretrain-lvd1689m\") # Load model directly\nfrom transformers import AutoImageProcessor, AutoModel\n\nprocessor = AutoImageProcessor.from_pretrained(\"facebook/dinov3-vitb16-pretrain-lvd1689m\")\nmodel = AutoModel.from_pretrained(\"facebook/dinov3-vitb16-pretrain-lvd1689m\") Notebooks Google Colab Kaggle You need to agree to share your contact information to access this model The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\n Log in or Sign Up to review the conditions and access this model content. Model Card for DINOv3 Model Details Model DescriptionModel SourcesUses Direct UseDownstream UseBias, Risks, and Limitations RecommendationsHow to Get Started with the Model Training Details Training DataTraining ProcedureEvaluation Environmental Impact Technical Specifications Model Architecture and ObjectiveCompute InfrastructureMore Information Citation \n\t\n\t\t\n\t\n\t\n\t\tModel Card for DINOv3\n\t\n\nDINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models.\n\n\t\n\t\t\n\t\n\t\n\t\tModel Details\n\t\n\nThese are Vision Transformer and ConvNeXt models trained following the method described in the DINOv3 paper. 12 models are provided:\n\n10 models pretrained on web data (LVD-1689M dataset)\n1 ViT-7B trained from scratch,\n5 ViT-S/S+/B/L/H+ models distilled from the ViT-7B,\n4 ConvNeXt-{T/S/B/L} models distilled from the ViT-7B,\n\n\n2 models pretrained on satellite data (SAT-493M dataset)\n1 ViT-7B trained from scratch\n1 ViT-L distilled from the ViT-7B\n\n\n\nEach Transformer-based model takes an image as input and returns a class token, patch tokens (and register tokens). These models follow a ViT architecture, with a patch size of 16. For a 224x224 image, this results in 1 class token + 4 register tokens + 196 patch tokens = 201 tokens (for DINOv2 with registers this resulted in 1 + 4 + 256 = 261 tokens).\nThe models can accept larger images provided the image shapes are multiples of the patch size (16). If this condition is not verified, the model will crop to the closest smaller multiple of the patch size.\n\n\t\n\t\t\n\t\n\t\n\t\tModel Description\n\t\n\n\nDeveloped by: Meta AI\nModel type: Vision Transformer, ConvNeXt\nLicense: DINOv3 License\n\n\n\t\n\t\t\n\t\n\t\n\t\tModel Sources\n\t\n\n\nRepository: https://github.com/facebookresearch/dinov3\nPaper: https://arxiv.org/abs/2508.10104\n\n\n\t\n\t\t\n\t\n\t\n\t\tUses\n\t\n\nThe models are vision backbones providing multi-purpose features for downstream tasks.\n\n\t\n\t\t\n\t\n\t\n\t\tDirect Use\n\t\n\nThe models can be used without fine-tuning, with downstream classifiers as simple as linear layers, to obtain competitive results:\n\non image classification, using k-NN classifiers on the class token\non image classification, with logistic regression classifiers applied on the class token\non image classification, with a linear layer applied on the class token and the average of the patch tokens\non image retrieval using nearest neighbors\non geometric and semantic 3D keypoint correspondances\non depth estimation, semantic segmentation, using linear layers\non unsupervised object discovery\non video segmentation tracking\non video classification, using a small 4-layer attentive probe\n\n\n\t\n\t\t\n\t\n\t\n\t\tDownstream Use\n\t\n\nWhile fine-tuning the models can yield some gains, it is recommended to keep this option as a last resort: the frozen features are expected to provide good performance out-of-the-box.\n\n\t\n\t\t\n\t\n\t\n\t\tBias, Risks, and Limitations\n\t\n\nCompared to DINOv2 and SEERv2, DINOv3 delivers somewhat consistent performance across income categories on geographical fairness and diversity, although with a notable performance drop in the low-income bucket compared to the highest-income bucket.\nDINOv3 also achieves relatively good scores across different regions, improving over its predecessor DINOv2. However, a relative difference is still observed between Europe and Africa.\n\n\t\n\t\t\n\t\n\t\n\t\tRecommendations\n\t\n\nFine-tuning is expected to increase the biases in the features produced by the model as they will be tuned to the fine-tuning labels.\n\n\t\n\t\t\n\t\n\t\n\t\tHow to Get Started with the Model\n\t\n\nThe example below demonstrates how to obtain an image embedding with [Pipeline] or the [AutoModel] class.\nfrom transformers import pipeline\nfrom transformers.image_utils import load_image\n\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg\"\nimage = load_image(url)\n\nfeature_extractor = pipeline(\n model=\"facebook/dinov3-vitb16-pretrain-lvd1689m\",\n task=\"image-feature-extraction\", \n)\nfeatures = feature_extractor(image)\n\nimport torch\nfrom transformers import AutoImageProcessor, AutoModel\nfrom transformers.image_utils import load_image\n\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\nimage = load_image(url)\n\npretrained_model_name = \"facebook/dinov3-vitb16-pretrain-lvd1689m\"\nprocessor = AutoImageProcessor.from_pretrained(pretrained_model_name)\nmodel = AutoModel.from_pretrained(\n pretrained_model_name, \n device_map=\"auto\", \n)\n\ninputs = processor(images=image, return_tensors=\"pt\").to(model.device)\nwith torch.inference_mode():\n outputs = model(**inputs)\n\npooled_output = outputs.pooler_output\nprint(\"Pooled output shape:\", pooled_output.shape)\n\n\n\t\n\t\t\n\t\n\t\n\t\tTraining Details\n\t\n\n\n\t\n\t\t\n\t\n\t\n\t\tTraining Data\n\t\n\n\nWeb dataset (LVD-1689M): a curated dataset of 1,689 millions of images extracted from a large data\npool of 17 billions web images collected from public posts on Instagram\n\nSatellite dataset (SAT-493M): a dataset of 493 millions of 512x512 images sampled randomly from Maxar RGB ortho-rectified imagery at 0.6 meter resolution\n\n\n\n\t\n\t\t\n\t\n\t\n\t\tTraining Procedure\n\t\n\nTraining objective:\n\nDINO self-distillation loss with multi-crop\n\niBOT masked-image modeling loss\n\nKoLeo regularization on [CLS] tokens\n\nGram anchoring\n\nTraining regime: PyTorch FSDP2 (with bf16 and fp8 matrix multiplications)\n\n\nDistillation:\n\nDistillation follows the standard DINOv3 pretraining procedure, except the teacher is a frozen pretrained ViT-7B.\n\n\n\t\n\t\t\n\t\n\t\n\t\tEvaluation\n\t\n\nResults\nThe reader is referred to the associated paper for details on the evaluation protocols\nResults for ViT backbones pretrained (or distilled) on web (LVD-1689M)\n\n \n \n \n Global Tasks\n Dense Tasks\n \n \n Model\n \n IN-ReaL\n IN-R\n Obj.Net\n Ox.-H\n ADE20k\n NYU↓\n DAVIS\n NAVI\n SPair\n \n \n DINOv3 ViT-S/16\n \n 87.0\n 60.4\n 50.9\n 49.5\n 47.0\n 0.403\n 72.7\n 56.3\n 50.4\n \n \n DINOv3 ViT-S+/16\n \n 88.0\n 68.8\n 54.6\n 50.0\n 48.8\n 0.399\n 75.5\n 57.1\n 55.2\n \n \n DINOv3 ViT-B/16\n \n 89.3\n 76.7\n 64.1\n 58.5\n 51.8\n 0.373\n 77.2\n 58.8\n 57.2\n \n \n DINOv3 ViT-L/16\n \n 90.2\n 88.1\n 74.8\n 63.1\n 54.9\n 0.352\n 79.9\n 62.3\n 61.3\n \n \n DINOv3 ViT-H+/16\n \n 90.3\n 90.0\n 78.6\n 64.5\n 54.8\n 0.352\n 79.3\n 63.3\n 56.3\n \n \n DINOv3 ViT-7B/16\n \n 90.4\n 91.1\n 91.1\n 72.8\n 55.9\n 0.309\n 79.7\n 64.4\n 58.7\n \n\n\nResults for ConvNeXt backbones distilled on web (LVD-1689M)\n\n \n \n Global Tasks\n Dense Tasks\n \n \n Model\n IN-ReaL\n IN-R\n Obj.Net\n ADE20k\n NYU↓\n \n \n \n @256px\n @512px\n @256px\n @512px\n @256px\n @512px\n \n \n \n DINOv3 ConvNeXt Tiny\n 86.6\n 87.7\n 73.7\n 74.1\n 52.6\n 58.7\n 42.7\n 0.448\n \n \n DINOv3 ConvNeXt Small\n 87.9\n 88.7\n 73.7\n 74.1\n 52.6\n 58.7\n 44.8\n 0.432\n \n \n DINOv3 ConvNeXt Base\n 88.5\n 89.2\n 77.2\n 78.2\n 56.2\n 61.3\n 46.3\n 0.420\n \n \n DINOv3 ConvNeXt Large\n 88.9\n 89.4\n 81.3\n 82.4\n 59.3\n 65.2\n 47.8\n 0.403\n \n\n\nResults for ViT backbones pretrained (or distilled) on satellite (SAT-493M)\n\n \n \n (GEO-Bench) Classification\n \n \n Model\n m-BEnet\n m-brick-kiln\n m-eurosat\n m-forestnet\n m-pv4ger\n m-so2sat\n mean\n \n \n DINOv3 ViT-L/16\n 73.0\n 96.5\n 94.1\n 60.6\n 96.0\n 57.4\n 79.6\n \n \n DINOv3 ViT-7B/16\n 74.0\n 97.2\n 94.8\n 62.3\n 96.1\n 62.1\n 81.1\n \n \n \n (GEO-Bench) Segmentation\n \n \n Model\n m-cashew\n m-chesapeake\n m-NeonTree\n m-nz-cattle\n m-pv4ger-seg\n m-SA-crop\n mean\n \n \n DINOv3 ViT-L/16\n 94.2\n 75.6\n 61.8\n 83.7\n 95.2\n 36.8\n 74.5\n \n \n DINOv3 ViT-7B/16\n 94.1\n 76.6\n 62.6\n 83.4\n 95.5\n 37.6\n 75.0\n \n\n\n\n\n\t\n\t\t\n\t\n\t\n\t\tEnvironmental Impact\n\t\n\n\nHardware Type: Nvidia H100\nHours used: 61,440 hours for ViT-7B model training\nCloud Provider: Private infrastructure\nCompute Region: USA\nCarbon Emitted: 18t CO2eq\n\n\n\t\n\t\t\n\t\n\t\n\t\tTechnical Specifications\n\t\n\n\n\t\n\t\t\n\t\n\t\n\t\tModel Architecture and Objective\n\t\n\nVision Transformer models:\n\nViT-S (21M parameters): patch size 16, embedding dimension 384, 4 register tokens, 6 heads, MLP FFN, RoPE\nViT-S+ (29M parameters): patch size 16, embedding dimension 384, 4 register tokens, 6 heads, SwiGLU FFN, RoPE\nViT-B (86M parameters): patch size 16, embedding dimension 768, 4 register tokens, 12 heads, MLP FFN, RoPE\nViT-L (300M parameters): patch size 16, embedding dimension 1024, 4 register tokens, 16 heads, MLP FFN, RoPE\nViT-H+ (840M parameters): patch size 16, embedding dimension 1280, 4 register tokens, 20 heads, SwiGLU FFN, RoPE\nViT-7B (6716M parameters): patch size 16, embedding dimension 4096, 4 register tokens, 32 heads, SwiGLU FFN, RoPE\n\nConvNeXt models:\n\nConvNeXt Tiny (29M parameters)\nConvNeXt Small (50M parameters)\nConvNeXt Base (89M parameters)\nConvNeXt Large (198M parameters)\n\n\n\t\n\t\t\n\t\n\t\n\t\tCompute Infrastructure\n\t\n\n\n\t\n\t\t\n\t\n\t\n\t\tHardware\n\t\n\nNvidia H100 GPUs\n\n\t\n\t\t\n\t\n\t\n\t\tSoftware\n\t\n\nPyTorch 2.7\n\n\t\n\t\t\n\t\n\t\n\t\tMore Information\n\t\n\nSee the blog post and the associated website.\n\n\t\n\t\t\n\t\n\t\n\t\tCitation\n\t\n\nBibTeX\n@misc{simeoni2025dinov3,\n title={{DINOv3}},\n author={Sim{\\'e}oni, Oriane and Vo, Huy V. and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{\\\"e}l and Massa, Francisco and Haziza, Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet, Timoth{\\'e}e and Moutakanni, Th{\\'e}o and Sentana, Leonel and Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie and Brandt, John and Couprie, Camille and Mairal, Julien and J{\\'e}gou, Herv{\\'e} and Labatut, Patrick and Bojanowski, Piotr},\n year={2025},\n eprint={2508.10104},\n archivePrefix={arXiv},\n primaryClass={cs.CV},\n url={https://arxiv.org/abs/2508.10104},\n}\n\n Downloads last month 690,958 \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\t\t\n\t\t Safetensors Model size 85.7M params Tensor type F32 · Files info Inference Providers NEW Image Feature Extraction This model isn't deployed by any Inference Provider. 🙋 7 Ask for provider support Model tree for facebook/dinov3-vitb16-pretrain-lvd1689m Base model facebook/dinov3-vit7b16-pretrain-lvd1689m Finetuned (36) this model Finetunes 16 models Quantizations 1 model Spaces using facebook/dinov3-vitb16-pretrain-lvd1689m 16 🦖 atalaydenknalbant/DINOv3 🌍 merve/DINOv3-keypoint-matching 🏆 pawlo2013/Dinov3_Image_Retrieval 🐢 fnnBsch/lotis-demo 🎮 methodw/xbgp 🐾 burlynb/petreunite-api 🦖 manu02/DINOv3-Interactive-Patch-Cosine-Similarity 🏃 Antuke/FaR-FT-PE + 11 Spaces + 8 Spaces Collection including facebook/dinov3-vitb16-pretrain-lvd1689m DINOv3 Collection DINOv3: foundation models producing excellent dense features, outperforming SotA w/o fine-tuning - https://arxiv.org/abs/2508.10104 • 15 items • Updated Mar 10 • 678 Paper for facebook/dinov3-vitb16-pretrain-lvd1689m DINOv3 Paper • 2508.10104 • Published Aug 13, 2025 • 311 System theme Company TOS Privacy About Careers Website Models Datasets Spaces Pricing Docs", + "metadata": { + "truncated": false + }, + "title": "https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m (text/html; charset=utf-8)", + "time": { + "start": 1783085276105, + "end": 1783085276635 + } + }, + "metadata": { + "openai": { + "itemId": "fc_026e4d9a4db46a76016a47b8dbff588192adb6cf6c3ba76e6e" + } + }, + "id": "prt_f282a1bb5001yH1ZioFpXN377i", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f2829fc5000235jxvN3rcsPmGn" + }, + { + "type": "tool", + "tool": "webfetch", + "callID": "call_axlxO4kfeGWwGFSaahdbPIQO", + "state": { + "status": "completed", + "input": { + "url": "https://huggingface.co/models?search=dinov3", + "format": "text" + }, + "output": "Models – Hugging Face\n\n\t\t\n\n\t\t\n\n\t\t\n\t\t \n\t\n\t\n\t\t Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Edit Models filters Main Tasks Libraries Languages Licenses Other Tasks Text Generation Any-to-Any Image-Text-to-Text Image-to-Text Image-to-Image Text-to-Image Text-to-Video Text-to-Speech + 44Parameters Reset Parameters < 1B 6B 12B 32B 128B > 500B < 1B > 500BLibraries PyTorch google-tensorflow TensorFlow JAX Transformers Diffusers GGUF MLX Transformers.js Safetensors + 44Apps vLLM llama.cpp MLX LM LM Studio Ollama Jan Draw Things + 10Inference Providers Groq Novita Cerebras Nscale fal Together AI Fireworks Featherless AI + 9Hardware Add your hardware Apply filters Models 418 Base only Inference Available Inference Add filters Sort: \n\t\tTrending facebook/dinov3-vitl16-pretrain-lvd1689m Image Feature Extraction • 0.3B • Updated Aug 19, 2025 • 529k • 374 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HqaQqpwQloJe1wftGv3mNVivgWaOB5qD9eNGHF-x7XafVSuGaQEPKLkQALAY1wOqhmeF6Hmha4Efz7jOVQ56r5aMit5HoN-aF0y7upFKFCQQALRaiPA6T7VsgjrvyZ7raNa1J7gFP8Rh7upfKWCYFueV_sYMvugnGYA_vxpzyAXckY3OOdO4wDaRF8TZ7v8GTloRbx1KmGci_c9uEACsMrsNeOVkGC7Ehrq591J5utozo_jBR_S_hXQd2UBPzFfvWmZCihSsZVsre6iKPqEgnhIqx6umGFExy_ikxY4j2Dkr2r6Sv0QHcR5uyr3vJqFw67oVeEXX9uNJZnMXh9_5_FY7oCHlF9ADCsfDRy5FHRWtkvgpQnG3xAHZIt-TbkRScY7bzNK8du1UDoJ6I6G--eeLxNxgJbxFfWTeYIfofrULdi3ssyx1i5fkbRDSch99h05eWsioW5Iu7eiz-iSqo322uCIa_34D5Nc0gkpi8EjnzpAT0CL76nN9crRTA5zur1WAIaZvOQdQF33ptupaJ4tVxS1OH4PCLdPUMRZt9NR0ll7prp4SrKQPPFoCLiCk87yzvQrhUiQHoF9jMqE1Urgwzuf-By7yQGaSUex5ef5wTBG7jchVyIKxhxCsKRKySnM70blQxqWrmRpLeetmQzizwNWA0HfZccqEh2-4hH7KtqSchMcVS6lJzBylohfVolYSHnN3QoC9GBTgq8V2C7xTpyZYdQRJcQbbFYT054YWUIXeZtx-iH7wtGArUPsZHt0kPcwQ_9B9GXeotaYTgQK2zXF_aWrAhgM9NC2bMFv2aVTwVJJdMffswvGb_Et1rGFiKP9Omv4YhXlacLTN6T7cO7ccR9wSEGRLH0OyMolgGji9WaS7yoM6qkHiRImhpQp0Sn6Px5x6QBfSAhAf3icN2nl3SShAR6DmC_HuF5n9glTAtLiPgXzFkFsnEY9vxFQvIF2pVIq6TlWO144hfszFNdUpxFrcBPDITVrZSoTteyFh2XBQsCS5ikPjPX_ltbT2-NTtxd4TZrjQ7DdWOAL3YlmHI0stESkMXFcI60nq9zjM4_Rup6q-lbiI4EON1_z1MHjAt2-4943-XrlT6lDrF8ZiukpeaTpin0ipQXkKZOIP6udELp3Mqw9Id4sCL1-5HZeLl2T1ExHHFIQV_pJLx8XHqq4v99UNl-H4Ox5gxIYu3KaxBEpJb9e45-OjzWIoQj4c98vyl90IPjHYFQv598RI6lopEpvtS2Vk7cO07x7vy_oELj940QkVdc4-AY0KjTamsfj8TNWjDd34eW0C9dj5VT1hjngTmfrU2GCc20g4UBmFOoLQfo4Mvb_2OcdJO4FhgGqE3cmdq2WuV7FZDD0iUatdVfIeiXYjAwzu1N_5fBtWdmeMmuE6tCdogMrc0pPvmFwtJmCGJ85WVHOlFdWDnLOJrXxTzAEdNcQFkcdhcj37KIzA1Ai5gPym_N0PZDbefSwKYgmklEEWclexE8i3009xj5Mwh82gjeBJ6UJ0-DXjBT6XHQn6dpLbqDZqNQIulUvmGY0hQXQDXNaE1okMCCUGnFx6UfplvUJkKv5Z4NY0zJUHh9fjLgEyusamBV55OJx7ig9BQZ8jBANixqkgmAhX_LMAg0XnvIb_C7TM3QAtcjMHCLGcHz5wKTFPqrljH-BFy7bQManKMU_Ctn-YkoqZIz_ipXznxW9qg-UdAbawmE56cVqaXjvcENHIVHs7M3hMlEoA0jTYu47Oh4NRFWVZ9EQyvtb0Dtcq6OXXd6r_qu2VCDNQMwDcR5BOZORuYllhnDWcItAbJFs-ZTw6GW2DfZgorJ3tGLGFXWkZpMEr-MrPx-74Rep7IsVRW7a3fnaN83dZA1soyTJP-dyjbdG3Kz9ZSIwB7n9l2k0Z8eBu44QCjLjfVhSTKDkNVR4RKi9mD3KvoEoMLNfBFAR1Jw9OXfxLr4NkSOtL6U7Egzx0j2iI0-UNqA-klv7sDfsyeLL7NFsa9Z7o-5-zhUCTVmRqjwKcxIEHAiJy136NhfjOdWDGC1hEIW10GQ0iA4GCjuq7Va3aGrIbn2ZOP7aO2BFUARQIo0UtWQb1f_qmb2tpbSUXa0W0fBmvP_GEAR5QQPb-sa5WuA7NxxGv66Nil9PTEhxy6SoP_gg3YGdIZJma-GoC6JijSYQ1weSSWKWh15SE-6gE7T2Hpr7dJ-n2x1NplUTw73nJH0zr_9sBm8u3xGaJcLlDB1u_01ntTsrE3fmXEYykTHsQ12MD-4MzOe4-8xgvm99hXORhlqQuoCc-r1KbOOZiPsd2CxapJ0JqbjTv5PgotB8YOZL2ep8Y9UCKUDkg7DkljM2o6t2iLm8L4t5zNrOAIdDye3CR7T85hs0Vx99X4YPdaneeG4LHQsve28RfjXxBkPJJ0p1LhcdR8F2m_CYqgbZk629QuQL2qZ9Jnv2RlIMM5D3TD-6dRSCfMxlsL0T0UAW0hs6Rk2I8aQRBbJw1FhpkfLAFyJ5otF9jYBC0F0TWUhuyC8R_qdWvuagFotz_8ljBJzwfH9ugsK3hrDErj88Bn1kLY4MAKiHf1mZnmC40OAkVbKTulsSFlfMWe-RpcaL3bP-XQvyCpg02r2BNuH0s7CcNVMGhh4rwN-JJLoczRI6WKRMHSZJOYGpcwIXNAMXpE1oKw-TwJnX0wZ02Ap6feblR_n27n_8HhVSuDuPJWgpsgwGJHEY5KkWzdEx0ovM9LcH5eWi8JKL-5hAtTU6xUId3YKEXlfvaRLqLhy98sEXNxmX5qrxwnEIT_0y4JbEF_ry8cGcKjJdYEoPTo3EeE4Qq8f8YNszuxT9X599kiTpht2of8XbJfx3oyZMsqS__JplenxCriMZnjayn3WJj_4jQEDMARDy85h0sHhUme8dtWvHb-hj9sH5KkqA6qLuO5h-LxHOFkuiG7dyJwRkUuepx5Gmot_wW0hQRxEoYNx11E4Mefgd_nu864p_biA4j70QO1qm0t8XMB2t9Ji86MChf-xme0igNfXQHVvDRDjUFwgvKbHVz7UnNwYQfVCxEHZ4_VlGuPHyPq_HZiWUiRw9TYoARCmbbnOv_PY7VwxJ6F04inkuCE5hVWbZKjkLP9qFtu6e_knBOajv-xPvJosbLUAGcqkwJZxNm4VkkH_A5PClag2tZd38sTAHz84t9nodLu05xDGQy3io7b-i29IxsbmJXYGa7053m02Xg5vGCNbN4OBS31bv3BqiFXAeQbgnDmk4VY9Ft76lb4PZbiIZVV24XNExPBDJqpHZcLQnOe23DonC5SMoOyRqymHxkTaVtvV3UDdCodcaQE-U9bTHGrddR2kHmv1nEmBAIpBImdQySvw7YbIr9sU-AHZczdza7ZMKwSAGIyW4pgEXE7Mm_Yiq6Da65qnQPy0fugFxMHPA77XDJfUjyMvCSiXkC0JYdZ6t7H0bfB24dSZyUUvODGwaxfS0NmrdlHZQf_kBbds-yVjW6M4XrO13EFjtZu1zZbCChDt7Rg8aCmnvSAGRSOWjiAyD0YuCNGNX_yJyeJa8i_2TrKU5bmZp1vR9lNuMraef-81pEtBhHaio3VKQYCTndXMpq85ohXtCT1I97tRK4fmcXr6fCFnGv1dTVkFS2nLR_2RXGkvgctI45jcF8FPeV-nAJaxXb6qqFeBRFJiPOuOAHaYGFR08aHAhQtlvSHtZqADnyybZGhzfQRFiZmrZch06Dwe3Uh9a0rlcZfp39kib9NQyJzsI2suLQ-Rc7A3iAusunwb51xI7c0pTEVR-rvWHDC-hPhC0irTw00yWU12t4LRzcYKaICiVg335vn5CutCE6XMaBvpuDY6EhVFHvxTJzfCr747mfH3UNihE47izvKcd7QDY6lscNEv_NTizrTjJkEXqpYAa3Fpkdco2RInoCWq72heLSUvpwyr5YWHoODRseEge2rTTAdXbcVQPwxmf_ZA_xN0OY611gCnk8uhSOZlEa9VVllhaT0Y1CpuZffplTs-aNdjtGOvldCJ_bWzuZxXofU_i6KVGrTFtHoKsGozjVZyflcA6HvFOcKEfStSEjXF8OZW8rInin735N02U_fTZkFL-NS7mX5EnSD3yKqoizZ1lj9IP9a85mII_xcbWHpNkkq8kGKakEiPFsQlTY1NOnHcTHEDEs5Jt9X010NV4dmsnUHev7YQMOgVmo7PMObqTVr5v2iqqQmc5P-IGvQ2JioHjuc0-INurfS3114LelhHPZxgkgvHzga_3WsxuakTdJU4whWwZCMQxehIXdtTB5j5CP0vUaOIErp2YxzkYUIsMyyS1zQsI5F8NCt4ZS0stqaNssUaceAlW8t2yF3foIRCPgWzvU0J4q9AElk1PUnBsZ0cErgA2vfJirHJ91BnLbF5cRqQKbMyKIdqejStBm6rhWOukdpTB-OYMtvnoYqK6Nz8MJz5XjtM4K_2IPyksETGZ6fB4_ohoOX2VNML53q9z5iAoYXumIeQOjYOowLBoGUYjLbaPohkeSZUDoetXaU60JDWEKI8u2giBhmwRGQH5JtS_Ul6OUppIMAu7iQwSCnwBuVUF4QA7k-eHXt1tcByjv-xJPOJvtO4xGsieyrMuRIvS5FGm7hTdVIQH-Fr9yV8rMWjrtU1xXVwp_RndLjFx8E83SEAVuGFy72rzxcScgbvUze08lHPqrMm7IwL62VMrp3WgaMPCjNbJ2TL8Yhoe-HWoI-iSdU4lPADfzQT22F_4yYKwjdnyn8V2IcEOAdgPQt4GS1aZ7EmZpFVwi4BGo_BFnsKeC1Eoybkr8qv7rZ-OS66rqUJ-H_AZ6zEMhXmUw8q9xbz1hNPBeU8szgho7QCZqyZUhJyJopRwysiu7LrwglPwUqIsnQmNE9Lg7rVqIGTsCYnvFUQRtuhRKVmCUk1YznwwWHHtXYgsxMwkr68uFh_vtay6-s90CnuxtyPvH6x4LqhzOcUEcAg8qTky_IV8KnWPTPqeKY3nKMYieZ3FXpy4BzOQekxYiD9cNAppawE8p6foJXUILP6dNuQDzQwj6_iM5adiEE3eGyfhXsT2de9LfqtI0uZ5L7039W6YfQXyEARdnu50ma6uRGUqbimmgSERzG09mOT5SakNSrOEP37Nc-uBfHGaap5hFR2yY__57PCI1AtJeNDPMM4AUl2BaXWMPVG6RAy_8O0WFYruVB_a76Gga0AGAbPGbat2wQc_LIOUEjKQnf-NFyOOWL47ImklTGCnrym0m2d-4MzJbPSIGQiL2Xl6-ZlRvFpdQtBWNm6kjIrcEDRbfBf-POR7RDAE29HtT42ObBEsXSWKhcKKnm-mYUivEE34V9_j8UnWTXF7sLTrQzm8YDrjpUWq0aVPDNN0kYCLypDgaQYmc62IPSqKAVvFh9zl_H1Lp-xFR9CRaLcCoGuqsvtupR5qfOmvZ-aED16ReLUQQ3IlA3V7PzH8QnSDFHg9NysvaAHZKRNQ9qoChif7vFn2bBHQPvnfsLYLT-AGFMd0UU2IaSBWXcwPckUckfaOQel_86mKxF0jLOcK5M-yRUS_Hi5OpaTQgZoQDOfd8LWJZS33hls_MxUpcgF3Bn1EdNYdAAHC5Ich-6eCSddRNCrzXxJCRt1G-A8WyjS0vndHREUfzHcCK18iNjoRHwb8Kh7oG884_-KkmrB518WE9Lq61Xqc6M_BbzrSUw3N28zlAPVMCDOcFsaOYwngwrW4K5JUMIjeYWUs45UFwXcoOKEzr2NqOj-Tm6yUImT_HXj8TgclmK7EBoS2ULwnIFauLWvAEjyR0eWrJyCJyfjnkOq0z8a7QAzoaZnPjKC7OfMRMQSglI_MuCAguq6rZPSgfVeIQSD4E4WdDG2mhWgqeeGf1p5KWbQnyXdsQPl6tgJ7HBykilkrHWKAi7QvtGnkq8ruPJJq7jTdlYM_X2wxnb78R5ZOmZEmIbOHcLHT2ccO7NKu43qCdwqRLCMXCbmF23mu4_89CelEb_2y614uH7IZfcxrjm13n6N597vbvzazfjjf0_E4YWTCx1WI98UvHJBKYhw9OTe8wVOz72jBC-DoHY-JZyahlKcxAxEJa32gd_OKeSqQa3Hq4ZHnzmXVsQTVclFbzU-aVrMhMi97qMO-lvxzK4NegrxM4dwA-Scy-axtGnLkKs-u6kcCtq04e49-S01l9Rx__yDVJceqYZ8ZbqpV4O9ZkcCbSdSEnBq68_-YdIxi4nMRXB_SvGTPWg3GCY_v_DSBuSKexfKBWOPVGsRFihQhZJ6x-zxrT0x4HWPlhmRvhkMM858H8g21diiN38AmBY3ToHofl1bcfJW9tQv0hxOlAhfnP4vp4GI2jwoOmfRb-qW06smg8GpyeUyvBQzg==" + } + }, + "id": "prt_f282a28790010P0q1m4sq08Y7U", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282a1e03002kq809aRzfYf8Wz" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_ctUS8t6IefR1pqBXva1Rvs4o", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"from transformers import AutoModel, AutoProcessor\\n\",\n+ \"from transformers import AutoImageProcessor, AutoModel, AutoProcessor\\n\",\n@@\n \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n \"SAM3_MAX_POINT_HINTS = 4\\n\",\n \"SAM3_TORCH_DTYPE = torch.bfloat16 if DEVICE == \\\"cuda\\\" else torch.float32\\n\",\n+ \"DINO_MODEL_ID = \\\"facebook/dinov3-vitb16-pretrain-lvd1689m\\\"\\n\",\n+ \"SIGCLIP_FUSION_WEIGHT = 0.60\\n\",\n+ \"DINO_FUSION_WEIGHT = 0.40\\n\",\n+ \"DINO_SIM_TEMPERATURE = 0.10\\n\",\n+ \"LOW_CONF_THRESHOLD = 0.50\\n\",\n+ \"MIXED_MARGIN_THRESHOLD = 0.15\\n\",\n+ \"MAX_MIXED_ENTRIES = 3\\n\",\n+ \"MIN_MIXED_PERCENT = 10\\n\",\n+ \"MATERIAL_PROTOTYPE_IMAGE_PATHS = {\\n\",\n+ \" # \\\"wood\\\": [\\\"/content/prototypes/wood_01.jpg\\\", \\\"/content/prototypes/wood_02.jpg\\\"],\\n\",\n+ \" # \\\"steel\\\": [\\\"/content/prototypes/steel_01.jpg\\\"],\\n\",\n+ \"}\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -48,9 +48,9 @@\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.patches as patches\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n-\"from transformers import AutoModel, AutoProcessor\\n\",\n+\"from transformers import AutoImageProcessor, AutoModel, AutoProcessor\\n\",\n \"\\n\",\n \"from huggingface_hub import whoami\\n\",\n \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n@@ -61,8 +61,20 @@\n \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n \"SAM3_MAX_POINT_HINTS = 4\\n\",\n \"SAM3_TORCH_DTYPE = torch.bfloat16 if DEVICE == \\\"cuda\\\" else torch.float32\\n\",\n+\"DINO_MODEL_ID = \\\"facebook/dinov3-vitb16-pretrain-lvd1689m\\\"\\n\",\n+\"SIGCLIP_FUSION_WEIGHT = 0.60\\n\",\n+\"DINO_FUSION_WEIGHT = 0.40\\n\",\n+\"DINO_SIM_TEMPERATURE = 0.10\\n\",\n+\"LOW_CONF_THRESHOLD = 0.50\\n\",\n+\"MIXED_MARGIN_THRESHOLD = 0.15\\n\",\n+\"MAX_MIXED_ENTRIES = 3\\n\",\n+\"MIN_MIXED_PERCENT = 10\\n\",\n+\"MATERIAL_PROTOTYPE_IMAGE_PATHS = {\\n\",\n+\" # \\\"wood\\\": [\\\"/content/prototypes/wood_01.jpg\\\", \\\"/content/prototypes/wood_02.jpg\\\"],\\n\",\n+\" # \\\"steel\\\": [\\\"/content/prototypes/steel_01.jpg\\\"],\\n\",\n+\"}\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n \"\\n\",\n \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -48,9 +48,9 @@\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.patches as patches\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n-\"from transformers import AutoModel, AutoProcessor\\n\",\n+\"from transformers import AutoImageProcessor, AutoModel, AutoProcessor\\n\",\n \"\\n\",\n \"from huggingface_hub import whoami\\n\",\n \"from sam3.model_builder import build_sam3_image_model, download_ckpt_from_hf\\n\",\n \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n@@ -61,8 +61,20 @@\n \"SAM3_CONFIDENCE_THRESHOLD = 0.10\\n\",\n \"SAM3_POINT_HINT_BOX_RADIUS_PX = 12\\n\",\n \"SAM3_MAX_POINT_HINTS = 4\\n\",\n \"SAM3_TORCH_DTYPE = torch.bfloat16 if DEVICE == \\\"cuda\\\" else torch.float32\\n\",\n+\"DINO_MODEL_ID = \\\"facebook/dinov3-vitb16-pretrain-lvd1689m\\\"\\n\",\n+\"SIGCLIP_FUSION_WEIGHT = 0.60\\n\",\n+\"DINO_FUSION_WEIGHT = 0.40\\n\",\n+\"DINO_SIM_TEMPERATURE = 0.10\\n\",\n+\"LOW_CONF_THRESHOLD = 0.50\\n\",\n+\"MIXED_MARGIN_THRESHOLD = 0.15\\n\",\n+\"MAX_MIXED_ENTRIES = 3\\n\",\n+\"MIN_MIXED_PERCENT = 10\\n\",\n+\"MATERIAL_PROTOTYPE_IMAGE_PATHS = {\\n\",\n+\" # \\\"wood\\\": [\\\"/content/prototypes/wood_01.jpg\\\", \\\"/content/prototypes/wood_02.jpg\\\"],\\n\",\n+\" # \\\"steel\\\": [\\\"/content/prototypes/steel_01.jpg\\\"],\\n\",\n+\"}\\n\",\n \"SIGCLIP_MODEL_ID = \\\"google/siglip-base-patch16-224\\\"\\n\",\n \"\\n\",\n \"CSV_PATH = Path(\\\"/content/poc_eval_sheet.csv\\\")\\n\",\n \"TEMPLATE_PATH = Path(\\\"/content/poc_eval_sheet_template.csv\\\")\\n\",\n", + "additions": 13, + "deletions": 1 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -929,8 +929,9 @@\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n+\" \\\"per_material_probs\\\": {k: float(v) for k, v in agg_scores.items()},\\n\",\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -929,8 +929,9 @@\n \" \\\"pred_material_top3\\\": \\\"|\\\".join(top_labels),\\n\",\n \" \\\"pred_material_conf_top1\\\": float(top_scores[0]),\\n\",\n \" \\\"topk_labels\\\": top_labels,\\n\",\n \" \\\"topk_scores\\\": [float(s) for s in top_scores],\\n\",\n+\" \\\"per_material_probs\\\": {k: float(v) for k, v in agg_scores.items()},\\n\",\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n", + "additions": 1, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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float = 1e-12):\\n\",\n+ \" n = float(np.linalg.norm(vec))\\n\",\n+ \" if n < eps:\\n\",\n+ \" return vec\\n\",\n+ \" return vec / n\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def extract_dinov3_embedding(image: Image.Image, dino_model, dino_processor):\\n\",\n+ \" inputs = dino_processor(images=image, return_tensors=\\\"pt\\\")\\n\",\n+ \" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+ \" with torch.inference_mode():\\n\",\n+ \" outputs = dino_model(**inputs)\\n\",\n+ \" emb = outputs.pooler_output[0].detach().float().cpu().numpy()\\n\",\n+ \" return l2_normalize_np(emb)\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def build_dino_prototype_bank(material_image_paths: dict, dino_model, dino_processor):\\n\",\n+ \" bank = {}\\n\",\n+ \" missing = []\\n\",\n+ \" for material in ALL_CANONICAL_MATERIALS:\\n\",\n+ \" paths = material_image_paths.get(material, [])\\n\",\n+ \" embs = []\\n\",\n+ \" for p in paths:\\n\",\n+ \" if not Path(p).exists():\\n\",\n+ \" missing.append(p)\\n\",\n+ \" continue\\n\",\n+ \" img = load_image(str(p))\\n\",\n+ \" embs.append(extract_dinov3_embedding(img, dino_model, dino_processor))\\n\",\n+ \" if embs:\\n\",\n+ \" bank[material] = l2_normalize_np(np.mean(np.stack(embs, axis=0), axis=0))\\n\",\n+ \" return bank, missing\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def normalize_prob_dict(score_dict: dict, keys: list):\\n\",\n+ \" vals = np.array([float(score_dict.get(k, 0.0)) for k in keys], dtype=np.float64)\\n\",\n+ \" vals = np.clip(vals, a_min=0.0, a_max=None)\\n\",\n+ \" s = float(vals.sum())\\n\",\n+ \" if s <= 0:\\n\",\n+ \" vals = np.ones_like(vals) / max(len(vals), 1)\\n\",\n+ \" else:\\n\",\n+ \" vals = vals / s\\n\",\n+ \" return {k: float(v) for k, v in zip(keys, vals)}\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def classify_material_dinov3(\\n\",\n+ \" image_views: list,\\n\",\n+ \" object_label: str | None,\\n\",\n+ \" dino_model,\\n\",\n+ \" dino_processor,\\n\",\n+ \" prototype_bank: dict,\\n\",\n+ \" top_k: int = 3,\\n\",\n+ \" view_weights: list | None = None,\\n\",\n+ \"):\\n\",\n+ \" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n+ \"\\n\",\n+ \" if view_weights is None:\\n\",\n+ \" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n+ \" if len(view_weights) != len(image_views):\\n\",\n+ \" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n+ \"\\n\",\n+ \" proto_materials = [m for m in candidate_materials if m in prototype_bank]\\n\",\n+ \" prototype_coverage = float(len(proto_materials) / max(len(candidate_materials), 1))\\n\",\n+ \"\\n\",\n+ \" if len(proto_materials) < 2:\\n\",\n+ \" probs = {m: 1.0 / len(candidate_materials) for m in candidate_materials}\\n\",\n+ \" ranked = sorted(probs.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+ \" top_items = ranked[: min(top_k, len(ranked))]\\n\",\n+ \" return {\\n\",\n+ \" \\\"pred_material_top1\\\": top_items[0][0],\\n\",\n+ \" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top_items]),\\n\",\n+ \" \\\"pred_material_conf_top1\\\": float(top_items[0][1]),\\n\",\n+ \" \\\"topk_labels\\\": [x[0] for x in top_items],\\n\",\n+ \" \\\"topk_scores\\\": [float(x[1]) for x in top_items],\\n\",\n+ \" \\\"per_material_probs\\\": probs,\\n\",\n+ \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n+ \" \\\"prototype_coverage\\\": prototype_coverage,\\n\",\n+ \" \\\"prototypes_available\\\": False,\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \" agg = {m: 0.0 for m in candidate_materials}\\n\",\n+ \" for image, weight in zip(image_views, view_weights):\\n\",\n+ \" emb = extract_dinov3_embedding(image, dino_model, dino_processor)\\n\",\n+ \" sims = []\\n\",\n+ \" for m in candidate_materials:\\n\",\n+ \" if m in prototype_bank:\\n\",\n+ \" sims.append(float(np.dot(emb, prototype_bank[m])))\\n\",\n+ \" else:\\n\",\n+ \" sims.append(-1e3)\\n\",\n+ \" sims = np.array(sims, dtype=np.float64)\\n\",\n+ \" probs = np.exp((sims - np.max(sims)) / max(DINO_SIM_TEMPERATURE, 1e-6))\\n\",\n+ \" probs = probs / max(probs.sum(), 1e-12)\\n\",\n+ \" for m, p in zip(candidate_materials, probs.tolist()):\\n\",\n+ \" agg[m] += float(weight) * float(p)\\n\",\n+ \"\\n\",\n+ \" agg = normalize_prob_dict(agg, candidate_materials)\\n\",\n+ \" ranked = sorted(agg.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+ \" top_items = ranked[: min(top_k, len(ranked))]\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"pred_material_top1\\\": top_items[0][0],\\n\",\n+ \" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top_items]),\\n\",\n+ \" \\\"pred_material_conf_top1\\\": float(top_items[0][1]),\\n\",\n+ \" \\\"topk_labels\\\": [x[0] for x in top_items],\\n\",\n+ \" \\\"topk_scores\\\": [float(x[1]) for x in top_items],\\n\",\n+ \" \\\"per_material_probs\\\": agg,\\n\",\n+ \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n+ \" \\\"prototype_coverage\\\": prototype_coverage,\\n\",\n+ \" \\\"prototypes_available\\\": True,\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def fuse_material_predictions(sigclip_out: dict, dino_out: dict):\\n\",\n+ \" mats = sorted(set(sigclip_out[\\\"candidate_materials\\\"]) | set(dino_out[\\\"candidate_materials\\\"]))\\n\",\n+ \" p_sig = normalize_prob_dict(sigclip_out.get(\\\"per_material_probs\\\", {}), mats)\\n\",\n+ \" p_dino = normalize_prob_dict(dino_out.get(\\\"per_material_probs\\\", {}), mats)\\n\",\n+ \"\\n\",\n+ \" w_sig = float(SIGCLIP_FUSION_WEIGHT)\\n\",\n+ \" w_dino = float(DINO_FUSION_WEIGHT) if dino_out.get(\\\"prototypes_available\\\", False) else 0.0\\n\",\n+ \" if w_sig + w_dino <= 0:\\n\",\n+ \" w_sig = 1.0\\n\",\n+ \" w_sum = w_sig + w_dino\\n\",\n+ \" w_sig, w_dino = w_sig / w_sum, w_dino / w_sum\\n\",\n+ \"\\n\",\n+ \" fused = {m: w_sig * p_sig[m] + w_dino * p_dino[m] for m in mats}\\n\",\n+ \" fused = normalize_prob_dict(fused, mats)\\n\",\n+ \"\\n\",\n+ \" ranked = sorted(fused.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+ \" top3 = ranked[: min(3, len(ranked))]\\n\",\n+ \"\\n\",\n+ \" sig_top1 = max(p_sig.items(), key=lambda kv: kv[1])[0]\\n\",\n+ \" dino_top1 = max(p_dino.items(), key=lambda kv: kv[1])[0]\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"pred_material_top1\\\": ranked[0][0],\\n\",\n+ \" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top3]),\\n\",\n+ \" \\\"pred_material_conf_top1\\\": float(ranked[0][1]),\\n\",\n+ \" \\\"topk_labels\\\": [x[0] for x in top3],\\n\",\n+ \" \\\"topk_scores\\\": [float(x[1]) for x in top3],\\n\",\n+ \" \\\"per_material_probs\\\": fused,\\n\",\n+ \" \\\"candidate_materials\\\": mats,\\n\",\n+ \" \\\"weights\\\": {\\\"sigclip\\\": w_sig, \\\"dinov3\\\": w_dino},\\n\",\n+ \" \\\"disagreement\\\": {\\n\",\n+ \" \\\"top1_match\\\": bool(sig_top1 == dino_top1),\\n\",\n+ \" \\\"sigclip_top1\\\": sig_top1,\\n\",\n+ \" \\\"dinov3_top1\\\": dino_top1,\\n\",\n+ \" },\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def build_mixed_materials(prob_dict: dict):\\n\",\n+ \" ranked = sorted(prob_dict.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+ \" selected = ranked[: max(2, MAX_MIXED_ENTRIES)]\\n\",\n+ \" vals = np.array([max(0.0, float(v)) for _, v in selected], dtype=np.float64)\\n\",\n+ \" vals = vals / max(vals.sum(), 1e-12)\\n\",\n+ \"\\n\",\n+ \" pairs = []\\n\",\n+ \" for (m, _), p in zip(selected, vals.tolist()):\\n\",\n+ \" pairs.append({\\\"material\\\": m, \\\"percentage\\\": int(round(100 * p))})\\n\",\n+ \"\\n\",\n+ \" pairs = [x for x in pairs if x[\\\"percentage\\\"] >= MIN_MIXED_PERCENT]\\n\",\n+ \" if len(pairs) < 2:\\n\",\n+ \" return []\\n\",\n+ \"\\n\",\n+ \" total = sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+ \" if total <= 0:\\n\",\n+ \" return []\\n\",\n+ \" for x in pairs:\\n\",\n+ \" x[\\\"percentage\\\"] = int(round(100 * x[\\\"percentage\\\"] / total))\\n\",\n+ \" diff = 100 - sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+ \" pairs[-1][\\\"percentage\\\"] += diff\\n\",\n+ \"\\n\",\n+ \" pairs = sorted(pairs, key=lambda x: x[\\\"percentage\\\"], reverse=True)[:MAX_MIXED_ENTRIES]\\n\",\n+ \" total2 = sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+ \" if total2 != 100:\\n\",\n+ \" pairs[-1][\\\"percentage\\\"] += 100 - total2\\n\",\n+ \" return pairs\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def suggest_material_decision(fused_out: dict):\\n\",\n+ \" probs = fused_out[\\\"per_material_probs\\\"]\\n\",\n+ \" ranked = sorted(probs.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+ \" top1_mat, top1_conf = ranked[0]\\n\",\n+ \" top2_conf = ranked[1][1] if len(ranked) > 1 else 0.0\\n\",\n+ \" margin12 = float(top1_conf - top2_conf)\\n\",\n+ \"\\n\",\n+ \" low_conf = float(top1_conf) < float(LOW_CONF_THRESHOLD)\\n\",\n+ \" mixed_suggestion = build_mixed_materials(probs)\\n\",\n+ \"\\n\",\n+ \" if low_conf:\\n\",\n+ \" suggested_mode = \\\"unknown\\\"\\n\",\n+ \" elif margin12 < float(MIXED_MARGIN_THRESHOLD) and len(mixed_suggestion) >= 2:\\n\",\n+ \" suggested_mode = \\\"mixed\\\"\\n\",\n+ \" else:\\n\",\n+ \" suggested_mode = \\\"single\\\"\\n\",\n+ \"\\n\",\n+ \" return {\\n\",\n+ \" \\\"suggested_mode\\\": suggested_mode,\\n\",\n+ \" \\\"suggested_single_material\\\": top1_mat if suggested_mode == \\\"single\\\" else None,\\n\",\n+ \" \\\"suggested_mixed_materials\\\": mixed_suggestion if suggested_mode == \\\"mixed\\\" else [],\\n\",\n+ \" \\\"low_confidence_flag\\\": bool(low_conf),\\n\",\n+ \" \\\"requires_user_confirmation\\\": bool(low_conf),\\n\",\n+ \" \\\"margin12\\\": margin12,\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -934,8 +934,213 @@\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n+\"def l2_normalize_np(vec: np.ndarray, eps: float = 1e-12):\\n\",\n+\" n = float(np.linalg.norm(vec))\\n\",\n+\" if n < eps:\\n\",\n+\" return vec\\n\",\n+\" return vec / n\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def extract_dinov3_embedding(image: Image.Image, dino_model, dino_processor):\\n\",\n+\" inputs = dino_processor(images=image, return_tensors=\\\"pt\\\")\\n\",\n+\" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+\" with torch.inference_mode():\\n\",\n+\" outputs = dino_model(**inputs)\\n\",\n+\" emb = outputs.pooler_output[0].detach().float().cpu().numpy()\\n\",\n+\" return l2_normalize_np(emb)\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def build_dino_prototype_bank(material_image_paths: dict, dino_model, dino_processor):\\n\",\n+\" bank = {}\\n\",\n+\" missing = []\\n\",\n+\" for material in ALL_CANONICAL_MATERIALS:\\n\",\n+\" paths = material_image_paths.get(material, [])\\n\",\n+\" embs = []\\n\",\n+\" for p in paths:\\n\",\n+\" if not Path(p).exists():\\n\",\n+\" missing.append(p)\\n\",\n+\" continue\\n\",\n+\" img = load_image(str(p))\\n\",\n+\" embs.append(extract_dinov3_embedding(img, dino_model, dino_processor))\\n\",\n+\" if embs:\\n\",\n+\" bank[material] = l2_normalize_np(np.mean(np.stack(embs, axis=0), axis=0))\\n\",\n+\" return bank, missing\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def normalize_prob_dict(score_dict: dict, keys: list):\\n\",\n+\" vals = np.array([float(score_dict.get(k, 0.0)) for k in keys], dtype=np.float64)\\n\",\n+\" vals = np.clip(vals, a_min=0.0, a_max=None)\\n\",\n+\" s = float(vals.sum())\\n\",\n+\" if s <= 0:\\n\",\n+\" vals = np.ones_like(vals) / max(len(vals), 1)\\n\",\n+\" else:\\n\",\n+\" vals = vals / s\\n\",\n+\" return {k: float(v) for k, v in zip(keys, vals)}\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def classify_material_dinov3(\\n\",\n+\" image_views: list,\\n\",\n+\" object_label: str | None,\\n\",\n+\" dino_model,\\n\",\n+\" dino_processor,\\n\",\n+\" prototype_bank: dict,\\n\",\n+\" top_k: int = 3,\\n\",\n+\" view_weights: list | None = None,\\n\",\n+\"):\\n\",\n+\" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n+\"\\n\",\n+\" if view_weights is None:\\n\",\n+\" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n+\" if len(view_weights) != len(image_views):\\n\",\n+\" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n+\"\\n\",\n+\" proto_materials = [m for m in candidate_materials if m in prototype_bank]\\n\",\n+\" prototype_coverage = float(len(proto_materials) / max(len(candidate_materials), 1))\\n\",\n+\"\\n\",\n+\" if len(proto_materials) < 2:\\n\",\n+\" probs = {m: 1.0 / len(candidate_materials) for m in candidate_materials}\\n\",\n+\" ranked = sorted(probs.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top_items = ranked[: min(top_k, len(ranked))]\\n\",\n+\" return {\\n\",\n+\" \\\"pred_material_top1\\\": top_items[0][0],\\n\",\n+\" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top_items]),\\n\",\n+\" \\\"pred_material_conf_top1\\\": float(top_items[0][1]),\\n\",\n+\" \\\"topk_labels\\\": [x[0] for x in top_items],\\n\",\n+\" \\\"topk_scores\\\": [float(x[1]) for x in top_items],\\n\",\n+\" \\\"per_material_probs\\\": probs,\\n\",\n+\" \\\"candidate_materials\\\": candidate_materials,\\n\",\n+\" \\\"prototype_coverage\\\": prototype_coverage,\\n\",\n+\" \\\"prototypes_available\\\": False,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" agg = {m: 0.0 for m in candidate_materials}\\n\",\n+\" for image, weight in zip(image_views, view_weights):\\n\",\n+\" emb = extract_dinov3_embedding(image, dino_model, dino_processor)\\n\",\n+\" sims = []\\n\",\n+\" for m in candidate_materials:\\n\",\n+\" if m in prototype_bank:\\n\",\n+\" sims.append(float(np.dot(emb, prototype_bank[m])))\\n\",\n+\" else:\\n\",\n+\" sims.append(-1e3)\\n\",\n+\" sims = np.array(sims, dtype=np.float64)\\n\",\n+\" probs = np.exp((sims - np.max(sims)) / max(DINO_SIM_TEMPERATURE, 1e-6))\\n\",\n+\" probs = probs / max(probs.sum(), 1e-12)\\n\",\n+\" for m, p in zip(candidate_materials, probs.tolist()):\\n\",\n+\" agg[m] += float(weight) * float(p)\\n\",\n+\"\\n\",\n+\" agg = normalize_prob_dict(agg, candidate_materials)\\n\",\n+\" ranked = sorted(agg.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top_items = ranked[: min(top_k, len(ranked))]\\n\",\n+\"\\n\",\n+\" return {\\n\",\n+\" \\\"pred_material_top1\\\": top_items[0][0],\\n\",\n+\" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top_items]),\\n\",\n+\" \\\"pred_material_conf_top1\\\": float(top_items[0][1]),\\n\",\n+\" \\\"topk_labels\\\": [x[0] for x in top_items],\\n\",\n+\" \\\"topk_scores\\\": [float(x[1]) for x in top_items],\\n\",\n+\" \\\"per_material_probs\\\": agg,\\n\",\n+\" \\\"candidate_materials\\\": candidate_materials,\\n\",\n+\" \\\"prototype_coverage\\\": prototype_coverage,\\n\",\n+\" \\\"prototypes_available\\\": True,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def fuse_material_predictions(sigclip_out: dict, dino_out: dict):\\n\",\n+\" mats = sorted(set(sigclip_out[\\\"candidate_materials\\\"]) | set(dino_out[\\\"candidate_materials\\\"]))\\n\",\n+\" p_sig = normalize_prob_dict(sigclip_out.get(\\\"per_material_probs\\\", {}), mats)\\n\",\n+\" p_dino = normalize_prob_dict(dino_out.get(\\\"per_material_probs\\\", {}), mats)\\n\",\n+\"\\n\",\n+\" w_sig = float(SIGCLIP_FUSION_WEIGHT)\\n\",\n+\" w_dino = float(DINO_FUSION_WEIGHT) if dino_out.get(\\\"prototypes_available\\\", False) else 0.0\\n\",\n+\" if w_sig + w_dino <= 0:\\n\",\n+\" w_sig = 1.0\\n\",\n+\" w_sum = w_sig + w_dino\\n\",\n+\" w_sig, w_dino = w_sig / w_sum, w_dino / w_sum\\n\",\n+\"\\n\",\n+\" fused = {m: w_sig * p_sig[m] + w_dino * p_dino[m] for m in mats}\\n\",\n+\" fused = normalize_prob_dict(fused, mats)\\n\",\n+\"\\n\",\n+\" ranked = sorted(fused.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top3 = ranked[: min(3, len(ranked))]\\n\",\n+\"\\n\",\n+\" sig_top1 = max(p_sig.items(), key=lambda kv: kv[1])[0]\\n\",\n+\" dino_top1 = max(p_dino.items(), key=lambda kv: kv[1])[0]\\n\",\n+\"\\n\",\n+\" return {\\n\",\n+\" \\\"pred_material_top1\\\": ranked[0][0],\\n\",\n+\" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top3]),\\n\",\n+\" \\\"pred_material_conf_top1\\\": float(ranked[0][1]),\\n\",\n+\" \\\"topk_labels\\\": [x[0] for x in top3],\\n\",\n+\" \\\"topk_scores\\\": [float(x[1]) for x in top3],\\n\",\n+\" \\\"per_material_probs\\\": fused,\\n\",\n+\" \\\"candidate_materials\\\": mats,\\n\",\n+\" \\\"weights\\\": {\\\"sigclip\\\": w_sig, \\\"dinov3\\\": w_dino},\\n\",\n+\" \\\"disagreement\\\": {\\n\",\n+\" \\\"top1_match\\\": bool(sig_top1 == dino_top1),\\n\",\n+\" \\\"sigclip_top1\\\": sig_top1,\\n\",\n+\" \\\"dinov3_top1\\\": dino_top1,\\n\",\n+\" },\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def build_mixed_materials(prob_dict: dict):\\n\",\n+\" ranked = sorted(prob_dict.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" selected = ranked[: max(2, MAX_MIXED_ENTRIES)]\\n\",\n+\" vals = np.array([max(0.0, float(v)) for _, v in selected], dtype=np.float64)\\n\",\n+\" vals = vals / max(vals.sum(), 1e-12)\\n\",\n+\"\\n\",\n+\" pairs = []\\n\",\n+\" for (m, _), p in zip(selected, vals.tolist()):\\n\",\n+\" pairs.append({\\\"material\\\": m, \\\"percentage\\\": int(round(100 * p))})\\n\",\n+\"\\n\",\n+\" pairs = [x for x in pairs if x[\\\"percentage\\\"] >= MIN_MIXED_PERCENT]\\n\",\n+\" if len(pairs) < 2:\\n\",\n+\" return []\\n\",\n+\"\\n\",\n+\" total = sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+\" if total <= 0:\\n\",\n+\" return []\\n\",\n+\" for x in pairs:\\n\",\n+\" x[\\\"percentage\\\"] = int(round(100 * x[\\\"percentage\\\"] / total))\\n\",\n+\" diff = 100 - sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+\" pairs[-1][\\\"percentage\\\"] += diff\\n\",\n+\"\\n\",\n+\" pairs = sorted(pairs, key=lambda x: x[\\\"percentage\\\"], reverse=True)[:MAX_MIXED_ENTRIES]\\n\",\n+\" total2 = sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+\" if total2 != 100:\\n\",\n+\" pairs[-1][\\\"percentage\\\"] += 100 - total2\\n\",\n+\" return pairs\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def suggest_material_decision(fused_out: dict):\\n\",\n+\" probs = fused_out[\\\"per_material_probs\\\"]\\n\",\n+\" ranked = sorted(probs.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top1_mat, top1_conf = ranked[0]\\n\",\n+\" top2_conf = ranked[1][1] if len(ranked) > 1 else 0.0\\n\",\n+\" margin12 = float(top1_conf - top2_conf)\\n\",\n+\"\\n\",\n+\" low_conf = float(top1_conf) < float(LOW_CONF_THRESHOLD)\\n\",\n+\" mixed_suggestion = build_mixed_materials(probs)\\n\",\n+\"\\n\",\n+\" if low_conf:\\n\",\n+\" suggested_mode = \\\"unknown\\\"\\n\",\n+\" elif margin12 < float(MIXED_MARGIN_THRESHOLD) and len(mixed_suggestion) >= 2:\\n\",\n+\" suggested_mode = \\\"mixed\\\"\\n\",\n+\" else:\\n\",\n+\" suggested_mode = \\\"single\\\"\\n\",\n+\"\\n\",\n+\" return {\\n\",\n+\" \\\"suggested_mode\\\": suggested_mode,\\n\",\n+\" \\\"suggested_single_material\\\": top1_mat if suggested_mode == \\\"single\\\" else None,\\n\",\n+\" \\\"suggested_mixed_materials\\\": mixed_suggestion if suggested_mode == \\\"mixed\\\" else [],\\n\",\n+\" \\\"low_confidence_flag\\\": bool(low_conf),\\n\",\n+\" \\\"requires_user_confirmation\\\": bool(low_conf),\\n\",\n+\" \\\"margin12\\\": margin12,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -934,8 +934,213 @@\n \" \\\"candidate_materials\\\": candidate_materials,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n+\"def l2_normalize_np(vec: np.ndarray, eps: float = 1e-12):\\n\",\n+\" n = float(np.linalg.norm(vec))\\n\",\n+\" if n < eps:\\n\",\n+\" return vec\\n\",\n+\" return vec / n\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def extract_dinov3_embedding(image: Image.Image, dino_model, dino_processor):\\n\",\n+\" inputs = dino_processor(images=image, return_tensors=\\\"pt\\\")\\n\",\n+\" inputs = {k: v.to(DEVICE) if hasattr(v, \\\"to\\\") else v for k, v in inputs.items()}\\n\",\n+\" with torch.inference_mode():\\n\",\n+\" outputs = dino_model(**inputs)\\n\",\n+\" emb = outputs.pooler_output[0].detach().float().cpu().numpy()\\n\",\n+\" return l2_normalize_np(emb)\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def build_dino_prototype_bank(material_image_paths: dict, dino_model, dino_processor):\\n\",\n+\" bank = {}\\n\",\n+\" missing = []\\n\",\n+\" for material in ALL_CANONICAL_MATERIALS:\\n\",\n+\" paths = material_image_paths.get(material, [])\\n\",\n+\" embs = []\\n\",\n+\" for p in paths:\\n\",\n+\" if not Path(p).exists():\\n\",\n+\" missing.append(p)\\n\",\n+\" continue\\n\",\n+\" img = load_image(str(p))\\n\",\n+\" embs.append(extract_dinov3_embedding(img, dino_model, dino_processor))\\n\",\n+\" if embs:\\n\",\n+\" bank[material] = l2_normalize_np(np.mean(np.stack(embs, axis=0), axis=0))\\n\",\n+\" return bank, missing\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def normalize_prob_dict(score_dict: dict, keys: list):\\n\",\n+\" vals = np.array([float(score_dict.get(k, 0.0)) for k in keys], dtype=np.float64)\\n\",\n+\" vals = np.clip(vals, a_min=0.0, a_max=None)\\n\",\n+\" s = float(vals.sum())\\n\",\n+\" if s <= 0:\\n\",\n+\" vals = np.ones_like(vals) / max(len(vals), 1)\\n\",\n+\" else:\\n\",\n+\" vals = vals / s\\n\",\n+\" return {k: float(v) for k, v in zip(keys, vals)}\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def classify_material_dinov3(\\n\",\n+\" image_views: list,\\n\",\n+\" object_label: str | None,\\n\",\n+\" dino_model,\\n\",\n+\" dino_processor,\\n\",\n+\" prototype_bank: dict,\\n\",\n+\" top_k: int = 3,\\n\",\n+\" view_weights: list | None = None,\\n\",\n+\"):\\n\",\n+\" candidate_materials = resolve_candidate_materials(object_label)\\n\",\n+\"\\n\",\n+\" if view_weights is None:\\n\",\n+\" view_weights = [1.0 / len(image_views)] * len(image_views)\\n\",\n+\" if len(view_weights) != len(image_views):\\n\",\n+\" raise ValueError(\\\"view_weights length must match image_views length\\\")\\n\",\n+\"\\n\",\n+\" proto_materials = [m for m in candidate_materials if m in prototype_bank]\\n\",\n+\" prototype_coverage = float(len(proto_materials) / max(len(candidate_materials), 1))\\n\",\n+\"\\n\",\n+\" if len(proto_materials) < 2:\\n\",\n+\" probs = {m: 1.0 / len(candidate_materials) for m in candidate_materials}\\n\",\n+\" ranked = sorted(probs.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top_items = ranked[: min(top_k, len(ranked))]\\n\",\n+\" return {\\n\",\n+\" \\\"pred_material_top1\\\": top_items[0][0],\\n\",\n+\" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top_items]),\\n\",\n+\" \\\"pred_material_conf_top1\\\": float(top_items[0][1]),\\n\",\n+\" \\\"topk_labels\\\": [x[0] for x in top_items],\\n\",\n+\" \\\"topk_scores\\\": [float(x[1]) for x in top_items],\\n\",\n+\" \\\"per_material_probs\\\": probs,\\n\",\n+\" \\\"candidate_materials\\\": candidate_materials,\\n\",\n+\" \\\"prototype_coverage\\\": prototype_coverage,\\n\",\n+\" \\\"prototypes_available\\\": False,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\" agg = {m: 0.0 for m in candidate_materials}\\n\",\n+\" for image, weight in zip(image_views, view_weights):\\n\",\n+\" emb = extract_dinov3_embedding(image, dino_model, dino_processor)\\n\",\n+\" sims = []\\n\",\n+\" for m in candidate_materials:\\n\",\n+\" if m in prototype_bank:\\n\",\n+\" sims.append(float(np.dot(emb, prototype_bank[m])))\\n\",\n+\" else:\\n\",\n+\" sims.append(-1e3)\\n\",\n+\" sims = np.array(sims, dtype=np.float64)\\n\",\n+\" probs = np.exp((sims - np.max(sims)) / max(DINO_SIM_TEMPERATURE, 1e-6))\\n\",\n+\" probs = probs / max(probs.sum(), 1e-12)\\n\",\n+\" for m, p in zip(candidate_materials, probs.tolist()):\\n\",\n+\" agg[m] += float(weight) * float(p)\\n\",\n+\"\\n\",\n+\" agg = normalize_prob_dict(agg, candidate_materials)\\n\",\n+\" ranked = sorted(agg.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top_items = ranked[: min(top_k, len(ranked))]\\n\",\n+\"\\n\",\n+\" return {\\n\",\n+\" \\\"pred_material_top1\\\": top_items[0][0],\\n\",\n+\" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top_items]),\\n\",\n+\" \\\"pred_material_conf_top1\\\": float(top_items[0][1]),\\n\",\n+\" \\\"topk_labels\\\": [x[0] for x in top_items],\\n\",\n+\" \\\"topk_scores\\\": [float(x[1]) for x in top_items],\\n\",\n+\" \\\"per_material_probs\\\": agg,\\n\",\n+\" \\\"candidate_materials\\\": candidate_materials,\\n\",\n+\" \\\"prototype_coverage\\\": prototype_coverage,\\n\",\n+\" \\\"prototypes_available\\\": True,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def fuse_material_predictions(sigclip_out: dict, dino_out: dict):\\n\",\n+\" mats = sorted(set(sigclip_out[\\\"candidate_materials\\\"]) | set(dino_out[\\\"candidate_materials\\\"]))\\n\",\n+\" p_sig = normalize_prob_dict(sigclip_out.get(\\\"per_material_probs\\\", {}), mats)\\n\",\n+\" p_dino = normalize_prob_dict(dino_out.get(\\\"per_material_probs\\\", {}), mats)\\n\",\n+\"\\n\",\n+\" w_sig = float(SIGCLIP_FUSION_WEIGHT)\\n\",\n+\" w_dino = float(DINO_FUSION_WEIGHT) if dino_out.get(\\\"prototypes_available\\\", False) else 0.0\\n\",\n+\" if w_sig + w_dino <= 0:\\n\",\n+\" w_sig = 1.0\\n\",\n+\" w_sum = w_sig + w_dino\\n\",\n+\" w_sig, w_dino = w_sig / w_sum, w_dino / w_sum\\n\",\n+\"\\n\",\n+\" fused = {m: w_sig * p_sig[m] + w_dino * p_dino[m] for m in mats}\\n\",\n+\" fused = normalize_prob_dict(fused, mats)\\n\",\n+\"\\n\",\n+\" ranked = sorted(fused.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top3 = ranked[: min(3, len(ranked))]\\n\",\n+\"\\n\",\n+\" sig_top1 = max(p_sig.items(), key=lambda kv: kv[1])[0]\\n\",\n+\" dino_top1 = max(p_dino.items(), key=lambda kv: kv[1])[0]\\n\",\n+\"\\n\",\n+\" return {\\n\",\n+\" \\\"pred_material_top1\\\": ranked[0][0],\\n\",\n+\" \\\"pred_material_top3\\\": \\\"|\\\".join([x[0] for x in top3]),\\n\",\n+\" \\\"pred_material_conf_top1\\\": float(ranked[0][1]),\\n\",\n+\" \\\"topk_labels\\\": [x[0] for x in top3],\\n\",\n+\" \\\"topk_scores\\\": [float(x[1]) for x in top3],\\n\",\n+\" \\\"per_material_probs\\\": fused,\\n\",\n+\" \\\"candidate_materials\\\": mats,\\n\",\n+\" \\\"weights\\\": {\\\"sigclip\\\": w_sig, \\\"dinov3\\\": w_dino},\\n\",\n+\" \\\"disagreement\\\": {\\n\",\n+\" \\\"top1_match\\\": bool(sig_top1 == dino_top1),\\n\",\n+\" \\\"sigclip_top1\\\": sig_top1,\\n\",\n+\" \\\"dinov3_top1\\\": dino_top1,\\n\",\n+\" },\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def build_mixed_materials(prob_dict: dict):\\n\",\n+\" ranked = sorted(prob_dict.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" selected = ranked[: max(2, MAX_MIXED_ENTRIES)]\\n\",\n+\" vals = np.array([max(0.0, float(v)) for _, v in selected], dtype=np.float64)\\n\",\n+\" vals = vals / max(vals.sum(), 1e-12)\\n\",\n+\"\\n\",\n+\" pairs = []\\n\",\n+\" for (m, _), p in zip(selected, vals.tolist()):\\n\",\n+\" pairs.append({\\\"material\\\": m, \\\"percentage\\\": int(round(100 * p))})\\n\",\n+\"\\n\",\n+\" pairs = [x for x in pairs if x[\\\"percentage\\\"] >= MIN_MIXED_PERCENT]\\n\",\n+\" if len(pairs) < 2:\\n\",\n+\" return []\\n\",\n+\"\\n\",\n+\" total = sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+\" if total <= 0:\\n\",\n+\" return []\\n\",\n+\" for x in pairs:\\n\",\n+\" x[\\\"percentage\\\"] = int(round(100 * x[\\\"percentage\\\"] / total))\\n\",\n+\" diff = 100 - sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+\" pairs[-1][\\\"percentage\\\"] += diff\\n\",\n+\"\\n\",\n+\" pairs = sorted(pairs, key=lambda x: x[\\\"percentage\\\"], reverse=True)[:MAX_MIXED_ENTRIES]\\n\",\n+\" total2 = sum(x[\\\"percentage\\\"] for x in pairs)\\n\",\n+\" if total2 != 100:\\n\",\n+\" pairs[-1][\\\"percentage\\\"] += 100 - total2\\n\",\n+\" return pairs\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def suggest_material_decision(fused_out: dict):\\n\",\n+\" probs = fused_out[\\\"per_material_probs\\\"]\\n\",\n+\" ranked = sorted(probs.items(), key=lambda kv: kv[1], reverse=True)\\n\",\n+\" top1_mat, top1_conf = ranked[0]\\n\",\n+\" top2_conf = ranked[1][1] if len(ranked) > 1 else 0.0\\n\",\n+\" margin12 = float(top1_conf - top2_conf)\\n\",\n+\"\\n\",\n+\" low_conf = float(top1_conf) < float(LOW_CONF_THRESHOLD)\\n\",\n+\" mixed_suggestion = build_mixed_materials(probs)\\n\",\n+\"\\n\",\n+\" if low_conf:\\n\",\n+\" suggested_mode = \\\"unknown\\\"\\n\",\n+\" elif margin12 < float(MIXED_MARGIN_THRESHOLD) and len(mixed_suggestion) >= 2:\\n\",\n+\" suggested_mode = \\\"mixed\\\"\\n\",\n+\" else:\\n\",\n+\" suggested_mode = \\\"single\\\"\\n\",\n+\"\\n\",\n+\" return {\\n\",\n+\" \\\"suggested_mode\\\": suggested_mode,\\n\",\n+\" \\\"suggested_single_material\\\": top1_mat if suggested_mode == \\\"single\\\" else None,\\n\",\n+\" \\\"suggested_mixed_materials\\\": mixed_suggestion if suggested_mode == \\\"mixed\\\" else [],\\n\",\n+\" \\\"low_confidence_flag\\\": bool(low_conf),\\n\",\n+\" \\\"requires_user_confirmation\\\": bool(low_conf),\\n\",\n+\" \\\"margin12\\\": margin12,\\n\",\n+\" }\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n", + "additions": 205, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783085358557, + "end": 1783085358584 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0c76f763c823e084016a47b907ceb0819da0c67707684913a9" + } + }, + "id": "prt_f282ac6d4001yHGgKrOmCcyhtk", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282ab943002gPfPpRvE0EdzML" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 199524, + "input": 444, + "output": 3968, + "reasoning": 40, + "cache": { + "write": 0, + "read": 195072 + } + }, + "cost": 0.0910266, + "id": "prt_f282b5dfe001ACWtqIWpbFbOwj", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282ab943002gPfPpRvE0EdzML" + } + ] + }, + { + "info": { + "parentID": "msg_f2829fc2a001ZJ23yIGmBb50tA", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0414547, + "tokens": { + "total": 200013, + "input": 138, + "output": 425, + "reasoning": 26, + "cache": { + "write": 0, + "read": 199424 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783085358615, + "completed": 1783085366178 + }, + "finish": "tool-calls", + "id": "msg_f282b5e17002UMK8T6ATJOdvf2", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f282b6835001dAhqFxKWrGQG0k", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282b5e17002UMK8T6ATJOdvf2" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783085361435, + "end": 1783085362486 + }, + "metadata": { + "openai": { + "itemId": "rs_0e1feee7931fd88a016a47b9315e84819193f2d0c2c416fa6b", + "reasoningEncryptedContent": "gAAAAABqR7kydqeabI39LiNWzrUyLo0z0ClfcqxHR3ctAQ3xAF8h117oUxTdtQgi-TOvqajrxmWh4j4KrhmGaoocbnqhgaUuppCQCrHBcOfS0MezW0a7WKTio96s-MwmYw-tQIllWihcHizJOa8mBseAP6Awcy3HT_TnV0gCpVVE31JGm-s99nb-qSby_F7vdONitBOAvdnQQPg6Oh9R7xWDrEFGNepebOdkkezWVdjcA2Nofg4_HMHLUMc_qZTCzOy6H_wlvEcl2UCMnasxzMhmYb8DperE56N_Krl6IJxOPuQCp7CDXS6kptvzSRpHDnjiXUASuECCeQKXxdtbeViQFysJ_U7ZOvFJVrB35PQhKL4vLapL4bYiUQeMzpryUE4k3huIAoc8E0bJk9INiRh8d2EHg6qW5sgboLevL6qV2dKh2Yfy05vmEeWzOrZ54QZHbfWPuDBwig96vPu7UkdlJvXS51zJyXDtmihtuSA5QnKGW3rKdVZ6vhWdd-4CEld6rSQFsOUcTVECfJo_f8YMCbtZMNxbas6KWfYJ9jImgBwoLqb9c6OowHTwc-N0qiiNRTJrbDeOJN_ToYRQc8jqtR-QdY8_MxR-ftccFi-xOEoid-osMSkHvlUVRWZVCFc9EJvu_uuufijmeNTW-gUujXeldbb-xvd26OLViCREyRPzQBosIX4fAFhRPo9jW9SQC8azGuFIrMPOMBJmJJi0XcPbaUxVdgqFlJPnJCWgYdoCW_tgQhXvwObNjmZ5jRAvcuCutkRfnLpoOgDdEXwri1wHmLMemFOARP_6eIwmr2zYbx7o1S81HRZ4Zs8I_bux46itMOw5cq-bm4w8b6W29ZtZLTlG9-N4p82XmB7wjnEeZ5aTLqNcuN2oMiLOaf5j76JDixFni5XSaIkzZec0srR94ES4Sz0SBNbV-vygsW908MwCnmJVRVnB2K-M3mb_YtJ2nGO1z61LlPmBmambARTaqegB8s9J0yaMn-a7khh8n_QNaj4c4d_7812MK8QSZquAJ0fyXegevn2JpLe0_UzLqXXRDAdqAvttoiXnyl-vbfI5zC5BwBqkuxRq2o2F-uDV4B7k" + } + }, + "id": "prt_f282b691b001QkRUTgtFnfWGaw", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282b5e17002UMK8T6ATJOdvf2" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_29dQsFXqELSkkIWG40KCsX83", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Loading DINOv3 model...\\\")\\n\",\n+ \"dino_processor = AutoImageProcessor.from_pretrained(DINO_MODEL_ID)\\n\",\n+ \"dino_model = AutoModel.from_pretrained(DINO_MODEL_ID).to(DEVICE)\\n\",\n+ \"dino_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Building DINO prototype bank...\\\")\\n\",\n+ \"dino_prototype_bank, missing_proto_paths = build_dino_prototype_bank(\\n\",\n+ \" MATERIAL_PROTOTYPE_IMAGE_PATHS,\\n\",\n+ \" dino_model=dino_model,\\n\",\n+ \" dino_processor=dino_processor,\\n\",\n+ \")\\n\",\n+ \"print(f\\\"DINO prototypes loaded: {len(dino_prototype_bank)} materials\\\")\\n\",\n+ \"if missing_proto_paths:\\n\",\n+ \" print(\\\"Missing prototype files:\\\")\\n\",\n+ \" for p in missing_proto_paths:\\n\",\n+ \" print(f\\\"- {p}\\\")\\n\",\n \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1217,8 +1217,25 @@\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n \"\\n\",\n+ \"print(\\\"Loading DINOv3 model...\\\")\\n\",\n+ \"dino_processor = AutoImageProcessor.from_pretrained(DINO_MODEL_ID)\\n\",\n+ \"dino_model = AutoModel.from_pretrained(DINO_MODEL_ID).to(DEVICE)\\n\",\n+ \"dino_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Building DINO prototype bank...\\\")\\n\",\n+ \"dino_prototype_bank, missing_proto_paths = build_dino_prototype_bank(\\n\",\n+ \" MATERIAL_PROTOTYPE_IMAGE_PATHS,\\n\",\n+ \" dino_model=dino_model,\\n\",\n+ \" dino_processor=dino_processor,\\n\",\n+ \")\\n\",\n+ \"print(f\\\"DINO prototypes loaded: {len(dino_prototype_bank)} materials\\\")\\n\",\n+ \"if missing_proto_paths:\\n\",\n+ \" print(\\\"Missing prototype files:\\\")\\n\",\n+ \" for p in missing_proto_paths:\\n\",\n+ \" print(f\\\"- {p}\\\")\\n\",\n+ \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n ]\n },\n {\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1217,8 +1217,25 @@\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n \"\\n\",\n+ \"print(\\\"Loading DINOv3 model...\\\")\\n\",\n+ \"dino_processor = AutoImageProcessor.from_pretrained(DINO_MODEL_ID)\\n\",\n+ \"dino_model = AutoModel.from_pretrained(DINO_MODEL_ID).to(DEVICE)\\n\",\n+ \"dino_model.eval()\\n\",\n+ \"\\n\",\n+ \"print(\\\"Building DINO prototype bank...\\\")\\n\",\n+ \"dino_prototype_bank, missing_proto_paths = build_dino_prototype_bank(\\n\",\n+ \" MATERIAL_PROTOTYPE_IMAGE_PATHS,\\n\",\n+ \" dino_model=dino_model,\\n\",\n+ \" dino_processor=dino_processor,\\n\",\n+ \")\\n\",\n+ \"print(f\\\"DINO prototypes loaded: {len(dino_prototype_bank)} materials\\\")\\n\",\n+ \"if missing_proto_paths:\\n\",\n+ \" print(\\\"Missing prototype files:\\\")\\n\",\n+ \" for p in missing_proto_paths:\\n\",\n+ \" print(f\\\"- {p}\\\")\\n\",\n+ \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n ]\n },\n {\n", + "additions": 17, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783085366135, + "end": 1783085366145 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0e1feee7931fd88a016a47b932696c8191ae109f876f400501" + } + }, + "id": "prt_f282b6d3d001q2y90s9Wlba31k", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282b5e17002UMK8T6ATJOdvf2" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 200013, + "input": 138, + "output": 425, + "reasoning": 26, + "cache": { + "write": 0, + "read": 199424 + } + }, + "cost": 0.0414547, + "id": "prt_f282b7b9e001nXHOQzdgUZMoq7", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282b5e17002UMK8T6ATJOdvf2" + } + ] + }, + { + "info": { + "parentID": "msg_f2829fc2a001ZJ23yIGmBb50tA", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.04605405, + "tokens": { + "total": 200827, + "input": 115, + "output": 739, + "reasoning": 37, + "cache": { + "write": 0, + "read": 199936 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783085366196, + "completed": 1783085378702 + }, + "finish": "tool-calls", + "id": "msg_f282b7bb40020Wy3atVljZ9eVd", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f282b8a6a001C1N3Ws2dE2NLiM", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282b7bb40020Wy3atVljZ9eVd" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783085370227, + "end": 1783085371479 + }, + "metadata": { + "openai": { + "itemId": "rs_01e2e2f41db6395b016a47b93a270c819d89c869dd31742122", + "reasoningEncryptedContent": 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+ } + }, + "id": "prt_f282b8b73001Nwa9H1kGxVPHbK", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282b7bb40020Wy3atVljZ9eVd" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_U58qiFakXSgVuiUzHuDWlRxH", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \" clf_start = pd.Timestamp.utcnow()\\n\",\n- \" clf = classify_material_sigclip(\\n\",\n+ \" clf_sig = classify_material_sigclip(\\n\",\n \" image_views=[crop_masked, crop_bbox, img],\\n\",\n \" object_label=pred_object,\\n\",\n \" sigclip_model=sigclip_model,\\n\",\n \" sigclip_processor=sigclip_processor,\\n\",\n \" top_k=3,\\n\",\n \" view_weights=[0.5, 0.35, 0.15],\\n\",\n \" )\\n\",\n+ \" clf_dino = classify_material_dinov3(\\n\",\n+ \" image_views=[crop_masked, crop_bbox],\\n\",\n+ \" object_label=pred_object,\\n\",\n+ \" dino_model=dino_model,\\n\",\n+ \" dino_processor=dino_processor,\\n\",\n+ \" prototype_bank=dino_prototype_bank,\\n\",\n+ \" top_k=3,\\n\",\n+ \" view_weights=[0.65, 0.35],\\n\",\n+ \" )\\n\",\n+ \" clf_fused = fuse_material_predictions(clf_sig, clf_dino)\\n\",\n+ \" decision = suggest_material_decision(clf_fused)\\n\",\n+ \"\\n\",\n+ \" clf = {\\n\",\n+ \" **clf_fused,\\n\",\n+ \" \\\"sigclip\\\": clf_sig,\\n\",\n+ \" \\\"dinov3\\\": clf_dino,\\n\",\n+ \" \\\"suggested_mode\\\": decision[\\\"suggested_mode\\\"],\\n\",\n+ \" \\\"suggested_single_material\\\": decision[\\\"suggested_single_material\\\"],\\n\",\n+ \" \\\"suggested_mixed_materials\\\": decision[\\\"suggested_mixed_materials\\\"],\\n\",\n+ \" \\\"low_confidence_flag\\\": decision[\\\"low_confidence_flag\\\"],\\n\",\n+ \" \\\"requires_user_confirmation\\\": decision[\\\"requires_user_confirmation\\\"],\\n\",\n+ \" \\\"margin12\\\": decision[\\\"margin12\\\"],\\n\",\n+ \" }\\n\",\n \" clf_end = pd.Timestamp.utcnow()\\n\",\n@@\n- \" f\\\"top3={clf['pred_material_top3']}\\\"\\n\",\n+ \" f\\\"top3={clf['pred_material_top3']} mode={clf['suggested_mode']} \\\"\\n\",\n+ \" f\\\"w=({clf['weights']['sigclip']:.2f},{clf['weights']['dinov3']:.2f})\\\"\\n\",\n \" )\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1401,16 +1401,39 @@\n \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n \"\\n\",\n \" clf_start = pd.Timestamp.utcnow()\\n\",\n-\" clf = classify_material_sigclip(\\n\",\n+\" clf_sig = classify_material_sigclip(\\n\",\n \" image_views=[crop_masked, crop_bbox, img],\\n\",\n \" object_label=pred_object,\\n\",\n \" sigclip_model=sigclip_model,\\n\",\n \" sigclip_processor=sigclip_processor,\\n\",\n \" top_k=3,\\n\",\n \" view_weights=[0.5, 0.35, 0.15],\\n\",\n \" )\\n\",\n+\" clf_dino = classify_material_dinov3(\\n\",\n+\" image_views=[crop_masked, crop_bbox],\\n\",\n+\" object_label=pred_object,\\n\",\n+\" dino_model=dino_model,\\n\",\n+\" dino_processor=dino_processor,\\n\",\n+\" prototype_bank=dino_prototype_bank,\\n\",\n+\" top_k=3,\\n\",\n+\" view_weights=[0.65, 0.35],\\n\",\n+\" )\\n\",\n+\" clf_fused = fuse_material_predictions(clf_sig, clf_dino)\\n\",\n+\" decision = suggest_material_decision(clf_fused)\\n\",\n+\"\\n\",\n+\" clf = {\\n\",\n+\" **clf_fused,\\n\",\n+\" \\\"sigclip\\\": clf_sig,\\n\",\n+\" \\\"dinov3\\\": clf_dino,\\n\",\n+\" \\\"suggested_mode\\\": decision[\\\"suggested_mode\\\"],\\n\",\n+\" \\\"suggested_single_material\\\": decision[\\\"suggested_single_material\\\"],\\n\",\n+\" \\\"suggested_mixed_materials\\\": decision[\\\"suggested_mixed_materials\\\"],\\n\",\n+\" \\\"low_confidence_flag\\\": decision[\\\"low_confidence_flag\\\"],\\n\",\n+\" \\\"requires_user_confirmation\\\": decision[\\\"requires_user_confirmation\\\"],\\n\",\n+\" \\\"margin12\\\": decision[\\\"margin12\\\"],\\n\",\n+\" }\\n\",\n \" clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n@@ -1443,9 +1466,10 @@\n \" f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} \\\"\\n\",\n \" f\\\"sam={seg['sam_score']:.3f} sel={seg['selection_score']:.3f} \\\"\\n\",\n \" f\\\"hint_iou={seg['selection_metrics']['hint_iou']:.3f} \\\"\\n\",\n \" f\\\"pt_cov={seg['selection_metrics']['point_coverage']:.3f} \\\"\\n\",\n-\" f\\\"top3={clf['pred_material_top3']}\\\"\\n\",\n+\" f\\\"top3={clf['pred_material_top3']} mode={clf['suggested_mode']} \\\"\\n\",\n+\" f\\\"w=({clf['weights']['sigclip']:.2f},{clf['weights']['dinov3']:.2f})\\\"\\n\",\n \" )\\n\",\n \"\\n\",\n \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \" axes[0].imshow(img)\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1401,16 +1401,39 @@\n \" crop_masked = crop_masked_rgb(img, seg[\\\"mask\\\"], seg[\\\"bbox_xywh\\\"], pad=8)\\n\",\n \" crop_bbox = crop_bbox_rgb(img, seg[\\\"bbox_xywh\\\"], pad=16)\\n\",\n \"\\n\",\n \" clf_start = pd.Timestamp.utcnow()\\n\",\n-\" clf = classify_material_sigclip(\\n\",\n+\" clf_sig = classify_material_sigclip(\\n\",\n \" image_views=[crop_masked, crop_bbox, img],\\n\",\n \" object_label=pred_object,\\n\",\n \" sigclip_model=sigclip_model,\\n\",\n \" sigclip_processor=sigclip_processor,\\n\",\n \" top_k=3,\\n\",\n \" view_weights=[0.5, 0.35, 0.15],\\n\",\n \" )\\n\",\n+\" clf_dino = classify_material_dinov3(\\n\",\n+\" image_views=[crop_masked, crop_bbox],\\n\",\n+\" object_label=pred_object,\\n\",\n+\" dino_model=dino_model,\\n\",\n+\" dino_processor=dino_processor,\\n\",\n+\" prototype_bank=dino_prototype_bank,\\n\",\n+\" top_k=3,\\n\",\n+\" view_weights=[0.65, 0.35],\\n\",\n+\" )\\n\",\n+\" clf_fused = fuse_material_predictions(clf_sig, clf_dino)\\n\",\n+\" decision = suggest_material_decision(clf_fused)\\n\",\n+\"\\n\",\n+\" clf = {\\n\",\n+\" **clf_fused,\\n\",\n+\" \\\"sigclip\\\": clf_sig,\\n\",\n+\" \\\"dinov3\\\": clf_dino,\\n\",\n+\" \\\"suggested_mode\\\": decision[\\\"suggested_mode\\\"],\\n\",\n+\" \\\"suggested_single_material\\\": decision[\\\"suggested_single_material\\\"],\\n\",\n+\" \\\"suggested_mixed_materials\\\": decision[\\\"suggested_mixed_materials\\\"],\\n\",\n+\" \\\"low_confidence_flag\\\": decision[\\\"low_confidence_flag\\\"],\\n\",\n+\" \\\"requires_user_confirmation\\\": decision[\\\"requires_user_confirmation\\\"],\\n\",\n+\" \\\"margin12\\\": decision[\\\"margin12\\\"],\\n\",\n+\" }\\n\",\n \" clf_end = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n \" t1 = pd.Timestamp.utcnow()\\n\",\n \"\\n\",\n@@ -1443,9 +1466,10 @@\n \" f\\\"[{sample_id}] bbox={seg['bbox_xywh']} area={seg['mask_area_px']} \\\"\\n\",\n \" f\\\"sam={seg['sam_score']:.3f} sel={seg['selection_score']:.3f} \\\"\\n\",\n \" f\\\"hint_iou={seg['selection_metrics']['hint_iou']:.3f} \\\"\\n\",\n \" f\\\"pt_cov={seg['selection_metrics']['point_coverage']:.3f} \\\"\\n\",\n-\" f\\\"top3={clf['pred_material_top3']}\\\"\\n\",\n+\" f\\\"top3={clf['pred_material_top3']} mode={clf['suggested_mode']} \\\"\\n\",\n+\" f\\\"w=({clf['weights']['sigclip']:.2f},{clf['weights']['dinov3']:.2f})\\\"\\n\",\n \" )\\n\",\n \"\\n\",\n \" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\\n\",\n \" axes[0].imshow(img)\\n\",\n", + "additions": 26, + "deletions": 2 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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You may need `hf auth login`.\\\")\\n\",\n1180: \" else:\\n\",\n1181: \" print(\\\"HF auth: not detected. Run `hf auth login` to access gated SAM3.1 checkpoints.\\\")\\n\",\n1182: \" print(f\\\"HF auth check detail: {exc}\\\")\\n\",\n1183: \"\\n\",\n1184: \"if SAM3_CHECKPOINT_PATH is not None:\\n\",\n1185: \" if not Path(SAM3_CHECKPOINT_PATH).exists():\\n\",\n1186: \" raise FileNotFoundError(f\\\"SAM3_CHECKPOINT_PATH does not exist: {SAM3_CHECKPOINT_PATH}\\\")\\n\",\n1187: \" print(f\\\"SAM3 checkpoint source: local ({SAM3_CHECKPOINT_PATH})\\\")\\n\",\n1188: \"else:\\n\",\n1189: \" print(f\\\"SAM3 checkpoint source: Hugging Face gated repo ({SAM3_REPO_ID})\\\")\\n\",\n1190: \"\\n\",\n1191: \"if DEVICE != \\\"cuda\\\":\\n\",\n1192: \" print(\\\"Warning: CUDA not available; SAM3.1 full checkpoint may be too slow on CPU.\\\")\\n\",\n1193: \"\\n\",\n1194: \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n1195: \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n1196: \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n1197: \"else:\\n\",\n1198: \" sam3_checkpoint_path = SAM3_CHECKPOINT_PATH\\n\",\n1199: \"\\n\",\n1200: \"sam_model = build_sam3_image_model(\\n\",\n1201: \" device=DEVICE,\\n\",\n1202: \" checkpoint_path=sam3_checkpoint_path,\\n\",\n1203: \" load_from_HF=False,\\n\",\n1204: \" enable_inst_interactivity=False,\\n\",\n1205: \")\\n\",\n1206: \"sam_model = sam_model.to(dtype=SAM3_TORCH_DTYPE)\\n\",\n1207: \"sam_model.eval()\\n\",\n1208: \"sam_processor = Sam3Processor(\\n\",\n1209: \" model=sam_model,\\n\",\n1210: \" device=DEVICE,\\n\",\n1211: \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n1212: \")\\n\",\n1213: \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n1214: \"print(f\\\"SAM dtype: {SAM3_TORCH_DTYPE}\\\")\\n\",\n1215: \"\\n\",\n1216: \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n1217: \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n1218: \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n1219: \"sigclip_model.eval()\\n\",\n1220: \"\\n\",\n1221: \"print(\\\"Loading DINOv3 model...\\\")\\n\",\n1222: \"dino_processor = AutoImageProcessor.from_pretrained(DINO_MODEL_ID)\\n\",\n1223: \"dino_model = AutoModel.from_pretrained(DINO_MODEL_ID).to(DEVICE)\\n\",\n1224: \"dino_model.eval()\\n\",\n1225: \"\\n\",\n1226: \"print(\\\"Building DINO prototype bank...\\\")\\n\",\n1227: \"dino_prototype_bank, missing_proto_paths = build_dino_prototype_bank(\\n\",\n1228: \" MATERIAL_PROTOTYPE_IMAGE_PATHS,\\n\",\n1229: \" dino_model=dino_model,\\n\",\n1230: \" dino_processor=dino_processor,\\n\",\n1231: \")\\n\",\n1232: \"print(f\\\"DINO prototypes loaded: {len(dino_prototype_bank)} materials\\\")\\n\",\n1233: \"if missing_proto_paths:\\n\",\n1234: \" print(\\\"Missing prototype files:\\\")\\n\",\n1235: \" for p in missing_proto_paths:\\n\",\n1236: \" print(f\\\"- {p}\\\")\\n\",\n1237: \"\\n\",\n1238: \"print(\\\"Models ready.\\\")\"\n1239: ]\n1240: },\n1241: {\n1242: \"cell_type\": \"code\",\n1243: \"execution_count\": null,\n1244: \"metadata\": {},\n1245: \"outputs\": [],\n1246: \"source\": [\n1247: \"# Configure one image and one-or-many objects in it.\\n\",\n1248: \"image_id = \\\"IMG002\\\"\\n\",\n1249: \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n1250: \"\\n\",\n1251: \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n1252: \"USE_ACCEPT_REDRAW = True\\n\",\n1253: \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n1254: \"MULTI_OBJECT_MODE = True\\n\",\n1255: \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n1256: \"\\n\",\n1257: \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n1258: \"OBJECT_SPECS = [\\n\",\n1259: \" {\\n\",\n1260: \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n1261: \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n1262: \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n1263: \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n1264: \" },\\n\",\n1265: \" {\\n\",\n1266: \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n1267: \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n1268: \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n1269: \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n1270: \" },\\n\",\n1271: \" {\\n\",\n1272: \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n1273: \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n1274: \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n1275: \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n1276: \" },\\n\",\n1277: \"]\\n\",\n1278: \"\\n\",\n1279: \"img = load_image(image_path)\\n\",\n\n(Showing lines 1150-1279 of 1744. Use offset=1280 to continue.)\n</content>", + "metadata": { + "preview": " \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n \" df_out.to_csv(csv_path, index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \"\\n\",\n \"def append_eval_row(csv_path: Path, row: dict):\\n\",\n \" df_new = pd.DataFrame([row])\\n\",\n \" if not csv_path.exists():\\n\",\n \" df_new.to_csv(csv_path, index=False)\\n\",\n \" return\\n\",\n \" df_existing = pd.read_csv(csv_path)\\n\",\n \" df_out = pd.concat([df_existing, df_new], ignore_index=True)\\n\",\n \" df_out.to_csv(csv_path, index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ensure_eval_csv(CSV_PATH, TEMPLATE_PATH)\\n\",\n \"\\n\",\n \"print(f\\\"Using device: {DEVICE}\\\")\\n\",\n \"print(\\\"Running environment checks...\\\")\\n\",\n \"\\n\",\n \"try:\\n\",\n \" hf_info = whoami()\\n\",\n \" hf_name = hf_info.get(\\\"name\\\", \\\"<unknown>\\\") if isinstance(hf_info, dict) else str(hf_info)\\n\",\n \" print(f\\\"HF auth: ok (user={hf_name})\\\")\\n\",\n \"except Exception as exc:\\n\",\n \" token_hint = os.environ.get(\\\"HF_TOKEN\\\") or os.environ.get(\\\"HUGGING_FACE_HUB_TOKEN\\\")\\n\",\n \" if token_hint:\\n\",\n \" print(\\\"HF auth: token env var found, but whoami failed. You may need `hf auth login`.\\\")\\n\",\n \" else:\\n\",\n \" print(\\\"HF auth: not detected. Run `hf auth login` to access gated SAM3.1 checkpoints.\\\")\\n\",\n \" print(f\\\"HF auth check detail: {exc}\\\")\\n\",\n \"\\n\",\n \"if SAM3_CHECKPOINT_PATH is not None:\\n\",\n \" if not Path(SAM3_CHECKPOINT_PATH).exists():\\n\",\n \" raise FileNotFoundError(f\\\"SAM3_CHECKPOINT_PATH does not exist: {SAM3_CHECKPOINT_PATH}\\\")\\n\",\n \" print(f\\\"SAM3 checkpoint source: local ({SAM3_CHECKPOINT_PATH})\\\")\\n\",\n \"else:\\n\",\n \" print(f\\\"SAM3 checkpoint source: Hugging Face gated repo ({SAM3_REPO_ID})\\\")\\n\",\n \"\\n\",\n \"if DEVICE != \\\"cuda\\\":\\n\",\n \" print(\\\"Warning: CUDA not available; SAM3.1 full checkpoint may be too slow on CPU.\\\")\\n\",\n \"\\n\",\n \"print(\\\"Loading SAM 3.1 model...\\\")\\n\",\n \"if SAM3_CHECKPOINT_PATH is None:\\n\",\n \" sam3_checkpoint_path = download_ckpt_from_hf(version=\\\"sam3.1\\\")\\n\",\n \"else:\\n\",\n \" sam3_checkpoint_path = SAM3_CHECKPOINT_PATH\\n\",\n \"\\n\",\n \"sam_model = build_sam3_image_model(\\n\",\n \" device=DEVICE,\\n\",\n \" checkpoint_path=sam3_checkpoint_path,\\n\",\n \" load_from_HF=False,\\n\",\n \" enable_inst_interactivity=False,\\n\",\n \")\\n\",\n \"sam_model = sam_model.to(dtype=SAM3_TORCH_DTYPE)\\n\",\n \"sam_model.eval()\\n\",\n \"sam_processor = Sam3Processor(\\n\",\n \" model=sam_model,\\n\",\n \" device=DEVICE,\\n\",\n \" confidence_threshold=SAM3_CONFIDENCE_THRESHOLD,\\n\",\n \")\\n\",\n \"print(f\\\"SAM backend: SAM3.1 checkpoint at {sam3_checkpoint_path}\\\")\\n\",\n \"print(f\\\"SAM dtype: {SAM3_TORCH_DTYPE}\\\")\\n\",\n \"\\n\",\n \"print(\\\"Loading SigCLIP model...\\\")\\n\",\n \"sigclip_processor = AutoProcessor.from_pretrained(SIGCLIP_MODEL_ID)\\n\",\n \"sigclip_model = AutoModel.from_pretrained(SIGCLIP_MODEL_ID).to(DEVICE)\\n\",\n \"sigclip_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Loading DINOv3 model...\\\")\\n\",\n \"dino_processor = AutoImageProcessor.from_pretrained(DINO_MODEL_ID)\\n\",\n \"dino_model = AutoModel.from_pretrained(DINO_MODEL_ID).to(DEVICE)\\n\",\n \"dino_model.eval()\\n\",\n \"\\n\",\n \"print(\\\"Building DINO prototype bank...\\\")\\n\",\n \"dino_prototype_bank, missing_proto_paths = build_dino_prototype_bank(\\n\",\n \" MATERIAL_PROTOTYPE_IMAGE_PATHS,\\n\",\n \" dino_model=dino_model,\\n\",\n \" dino_processor=dino_processor,\\n\",\n \")\\n\",\n \"print(f\\\"DINO prototypes loaded: {len(dino_prototype_bank)} materials\\\")\\n\",\n \"if missing_proto_paths:\\n\",\n \" print(\\\"Missing prototype files:\\\")\\n\",\n \" for p in missing_proto_paths:\\n\",\n \" print(f\\\"- {p}\\\")\\n\",\n \"\\n\",\n \"print(\\\"Models ready.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Configure one image and one-or-many objects in it.\\n\",\n \"image_id = \\\"IMG002\\\"\\n\",\n \"image_path = \\\"/content/images/example.jpg\\\"\\n\",\n \"\\n\",\n \"SELECTION_MODE = \\\"box\\\" # \\\"box\\\" or \\\"brush\\\"\\n\",\n \"USE_ACCEPT_REDRAW = True\\n\",\n \"MAX_SELECTION_ATTEMPTS = 5\\n\",\n \"MULTI_OBJECT_MODE = True\\n\",\n \"N_OBJECTS_TO_SELECT = 3 # used only in multi-object mode\\n\",\n \"\\n\",\n \"# Define object metadata in selection order. prompt_bbox_xyxy is optional in interactive mode.\\n\",\n \"OBJECT_SPECS = [\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S002\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"window\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"glass\\\",\\n\",\n \" \\\"pred_object\\\": \\\"window\\\",\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S003\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"door\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"wood\\\",\\n\",\n \" \\\"pred_object\\\": \\\"door\\\",\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": \\\"S004\\\",\\n\",\n \" \\\"ground_truth_object\\\": \\\"wall\\\",\\n\",\n \" \\\"ground_truth_material\\\": \\\"brick\\\",\\n\",\n \" \\\"pred_object\\\": \\\"wall\\\",\\n\",\n \" },\\n\",\n \"]\\n\",\n \"\\n\",\n \"img = load_image(image_path)\\n\",", + "lineStart": 1150, + "lineEnd": 1279, + "totalLines": 1744, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783085382623, + "end": 1783085382647 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0a4d0d435e30dfb0016a47b9460bc88192b3da1a9aa779273b" + } + }, + "id": "prt_f282bba600016k6jc3kXyBAJx3", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282baca3002JOfom4QfNsT8JT" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 200942, + "input": 161, + "output": 48, + "reasoning": 29, + "cache": { + "write": 0, + "read": 200704 + } + }, + "cost": 0.03648295, + "id": "prt_f282bbc160011yTHg4kSMoAHyo", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282baca3002JOfom4QfNsT8JT" + } + ] + }, + { + "info": { + "parentID": 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Set defaults and optional per-sample overrides.\\n\",\n1503: \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n1504: \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n1505: \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n1506: \"\\n\",\n1507: \"# Example override format:\\n\",\n1508: \"# USER_REVIEW_OVERRIDES = {\\n\",\n1509: \"# \\\"S003\\\": {\\n\",\n1510: \"# \\\"segmentation_accepted\\\": 0,\\n\",\n1511: \"# \\\"user_edited_material\\\": 1,\\n\",\n1512: \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n1513: \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n1514: \"# \\\"status\\\": \\\"ok\\\",\\n\",\n1515: \"# \\\"error_type\\\": \\\"\\\",\\n\",\n1516: \"# }\\n\",\n1517: \"# }\\n\",\n1518: \"USER_REVIEW_OVERRIDES = {}\\n\",\n1519: \"\\n\",\n1520: \"for run in RUN_ROWS:\\n\",\n1521: \" sid = run[\\\"sample_id\\\"]\\n\",\n1522: \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n1523: \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n1524: \"\\n\",\n1525: \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n1526: \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n1527: \"\\n\",\n1528: \" run[\\\"review\\\"] = {\\n\",\n1529: \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n1530: \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n1531: \" \\\"user_final_material\\\": user_final_material,\\n\",\n1532: \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n1533: \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n1534: \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n1535: \" }\\n\",\n1536: \"\\n\",\n1537: \"pd.DataFrame([\\n\",\n1538: \" {\\n\",\n1539: \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n1540: \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n1541: \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n1542: \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n1543: \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n1544: \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n1545: \" }\\n\",\n1546: \" for r in RUN_ROWS\\n\",\n1547: \"])\"\n1548: ]\n1549: },\n1550: {\n1551: \"cell_type\": \"code\",\n1552: \"execution_count\": null,\n1553: \"metadata\": {},\n1554: \"outputs\": [],\n1555: \"source\": [\n1556: \"for run in RUN_ROWS:\\n\",\n1557: \" review = run.get(\\\"review\\\", {})\\n\",\n1558: \" seg = run[\\\"seg\\\"]\\n\",\n1559: \" clf = run[\\\"clf\\\"]\\n\",\n1560: \" timing = run[\\\"timing\\\"]\\n\",\n1561: \"\\n\",\n1562: \" passport = {\\n\",\n1563: \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n1564: \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n1565: \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n1566: \" \\\"interaction\\\": {\\n\",\n1567: \" \\\"selection_mode\\\": SELECTION_MODE,\\n\",\n1568: \" \\\"prompt_bbox_xyxy\\\": [int(v) for v in run[\\\"prompt_bbox_xyxy\\\"]],\\n\",\n1569: \" \\\"prompt_points_xy\\\": [[int(p[0]), int(p[1])] for p in run.get(\\\"prompt_points_xy\\\", [])],\\n\",\n1570: \" \\\"bbox_center_xy\\\": [int(run[\\\"tap_x\\\"]), int(run[\\\"tap_y\\\"])],\\n\",\n1571: \" },\\n\",\n1572: \" \\\"object\\\": {\\n\",\n1573: \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n1574: \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n1575: \" },\\n\",\n1576: \" \\\"material\\\": {\\n\",\n1577: \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n1578: \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n1579: \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n1580: \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n1581: \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n1582: \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n1583: \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n1584: \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n1585: \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n1586: \" },\\n\",\n1587: \" \\\"segmentation\\\": {\\n\",\n1588: \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n1589: \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n1590: \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n1591: \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n1592: \" \\\"selection_score\\\": seg.get(\\\"selection_score\\\", seg[\\\"sam_score\\\"]),\\n\",\n1593: \" \\\"selection_metrics\\\": seg.get(\\\"selection_metrics\\\", {}),\\n\",\n1594: \" },\\n\",\n1595: \" \\\"timing\\\": {\\n\",\n1596: \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n1597: \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n1598: \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n1599: \" },\\n\",\n1600: \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n1601: \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n1602: \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n1603: \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n1604: \" }\\n\",\n1605: \"\\n\",\n1606: \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n1607: \"\\n\",\n1608: \" eval_row = {\\n\",\n1609: \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n1610: \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n1611: \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n1612: \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n1613: \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n1614: \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n1615: \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n1616: \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n1617: \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n1618: \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n1619: \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n1620: \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n1621: \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n1622: \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n1623: \" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n1624: \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n1625: \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n1626: \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n1627: \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n1628: \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n1629: \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n1630: \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n1631: \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n1632: \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n1633: \" }\\n\",\n1634: \" append_eval_row(CSV_PATH, eval_row)\\n\",\n1635: \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n1636: \"\\n\",\n1637: \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n1638: ]\n1639: },\n1640: {\n1641: \"cell_type\": \"code\",\n1642: \"execution_count\": null,\n1643: \"metadata\": {},\n1644: \"outputs\": [],\n1645: \"source\": [\n1646: \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n1647: \" df = df_in.copy()\\n\",\n1648: \" df = df[df[\\\"status\\\"].notna()]\\n\",\n1649: \"\\n\",\n1650: \" if df.empty:\\n\",\n1651: \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n1652: \"\\n\",\n1653: \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n1654: \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n1655: \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n1656: \" axis=1,\\n\",\n1657: \" )\\n\",\n1658: \"\\n\",\n1659: \" metrics = {\\n\",\n1660: \" \\\"n_samples\\\": int(len(df)),\\n\",\n1661: \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n1662: \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n1663: \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n1664: \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n1665: \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n1666: \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n1667: \" }\\n\",\n1668: \"\\n\",\n1669: \" go_no_go = (\\n\",\n1670: \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n1671: \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n1672: \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n1673: \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n1674: \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n1675: \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n1676: \" )\\n\",\n1677: \"\\n\",\n1678: \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n1679: \"\\n\",\n1680: \"\\n\",\n1681: \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n1682: \"result = compute_metrics(df_eval)\\n\",\n1683: \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n1684: \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n1685: \" print(f\\\"- {k}: {v}\\\")\\n\",\n1686: \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n1687: ]\n1688: },\n1689: {\n1690: \"cell_type\": \"code\",\n1691: \"execution_count\": null,\n1692: \"metadata\": {},\n1693: \"outputs\": [],\n1694: \"source\": [\n1695: \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n1696: \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n1697: \"\\n\",\n1698: \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n1699: \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n1700: \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n1701: \"\\n\",\n1702: \"if sigclip_df.empty:\\n\",\n1703: \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n1704: \"else:\\n\",\n1705: \" print(\\\"SigCLIP metrics:\\\")\\n\",\n1706: \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n1707: \"\\n\",\n1708: \"if dinov3_df.empty:\\n\",\n1709: \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n\n(Showing lines 1490-1709 of 1744. Use offset=1710 to continue.)\n</content>", + "metadata": { + "preview": " \" plt.tight_layout()\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n \"\\n\",\n \"# Example override format:\\n\",\n \"# USER_REVIEW_OVERRIDES = {\\n\",\n \"# \\\"S003\\\": {\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" plt.tight_layout()\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"print(f\\\"Processed {len(RUN_ROWS)} object(s).\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n \"\\n\",\n \"# Example override format:\\n\",\n \"# USER_REVIEW_OVERRIDES = {\\n\",\n \"# \\\"S003\\\": {\\n\",\n \"# \\\"segmentation_accepted\\\": 0,\\n\",\n \"# \\\"user_edited_material\\\": 1,\\n\",\n \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n \"# \\\"status\\\": \\\"ok\\\",\\n\",\n \"# \\\"error_type\\\": \\\"\\\",\\n\",\n \"# }\\n\",\n \"# }\\n\",\n \"USER_REVIEW_OVERRIDES = {}\\n\",\n \"\\n\",\n \"for run in RUN_ROWS:\\n\",\n \" sid = run[\\\"sample_id\\\"]\\n\",\n \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n \"\\n\",\n \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n \"\\n\",\n \" run[\\\"review\\\"] = {\\n\",\n \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n \" \\\"user_final_material\\\": user_final_material,\\n\",\n \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n \" }\\n\",\n \"\\n\",\n \"pd.DataFrame([\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n \" }\\n\",\n \" for r in RUN_ROWS\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for run in RUN_ROWS:\\n\",\n \" review = run.get(\\\"review\\\", {})\\n\",\n \" seg = run[\\\"seg\\\"]\\n\",\n \" clf = run[\\\"clf\\\"]\\n\",\n \" timing = run[\\\"timing\\\"]\\n\",\n \"\\n\",\n \" passport = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n \" \\\"interaction\\\": {\\n\",\n \" \\\"selection_mode\\\": SELECTION_MODE,\\n\",\n \" \\\"prompt_bbox_xyxy\\\": [int(v) for v in run[\\\"prompt_bbox_xyxy\\\"]],\\n\",\n \" \\\"prompt_points_xy\\\": [[int(p[0]), int(p[1])] for p in run.get(\\\"prompt_points_xy\\\", [])],\\n\",\n \" \\\"bbox_center_xy\\\": [int(run[\\\"tap_x\\\"]), int(run[\\\"tap_y\\\"])],\\n\",\n \" },\\n\",\n \" \\\"object\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n \" },\\n\",\n \" \\\"material\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" },\\n\",\n \" \\\"segmentation\\\": {\\n\",\n \" \\\"accepted\\\": bool(review.get(\\\"segmentation_accepted\\\", 1)),\\n\",\n \" \\\"mask_area_px\\\": int(seg[\\\"mask_area_px\\\"]),\\n\",\n \" \\\"bbox_xywh\\\": [int(v) for v in seg[\\\"bbox_xywh\\\"]],\\n\",\n \" \\\"sam_score\\\": seg[\\\"sam_score\\\"],\\n\",\n \" \\\"selection_score\\\": seg.get(\\\"selection_score\\\", seg[\\\"sam_score\\\"]),\\n\",\n \" \\\"selection_metrics\\\": seg.get(\\\"selection_metrics\\\", {}),\\n\",\n \" },\\n\",\n \" \\\"timing\\\": {\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" },\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n \" }\\n\",\n \"\\n\",\n \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n \"\\n\",\n \" eval_row = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n \" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n \" }\\n\",\n \" append_eval_row(CSV_PATH, eval_row)\\n\",\n \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n \"\\n\",\n \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n \" df = df_in.copy()\\n\",\n \" df = df[df[\\\"status\\\"].notna()]\\n\",\n \"\\n\",\n \" if df.empty:\\n\",\n \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n \"\\n\",\n \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n \" axis=1,\\n\",\n \" )\\n\",\n \"\\n\",\n \" metrics = {\\n\",\n \" \\\"n_samples\\\": int(len(df)),\\n\",\n \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n \" }\\n\",\n \"\\n\",\n \" go_no_go = (\\n\",\n \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n \" )\\n\",\n \"\\n\",\n \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n \"\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"result = compute_metrics(df_eval)\\n\",\n \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n \" print(f\\\"- {k}: {v}\\\")\\n\",\n \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n \"\\n\",\n \"if sigclip_df.empty:\\n\",\n \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"SigCLIP metrics:\\\")\\n\",\n \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n \"\\n\",\n \"if dinov3_df.empty:\\n\",\n \" print(\\\"No DINOv3 rows yet.\\\")\\n\",", + "lineStart": 1490, + "lineEnd": 1709, + "totalLines": 1744, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783085413356, + "end": 1783085413417 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0a8900429d6c0612016a47b964b8ac819ea6a8460678b94b57" + } + }, + "id": "prt_f282c325f001mfVCQpCH1A3iUx", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282bbc2e001u2bY4nMHR7SZL2" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 203008, + "input": 2112, + "output": 48, + "reasoning": 16, + "cache": { + "write": 0, + "read": 200832 + } + }, + "cost": 0.0397376, + "id": "prt_f282c342d001l5Zp9Y7Ho7G8Ir", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": 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+ } + }, + "id": "prt_f282c77be001LvnakUz2hat77p", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282c344e002WKrYU3W6TZy1a0" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_W4x5L8oUBUnXbnxecCPTzeh7", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"def suggest_material_decision(fused_out: dict):\\n\",\n@@\n \" return {\\n\",\n \" \\\"suggested_mode\\\": suggested_mode,\\n\",\n@@\n \" \\\"margin12\\\": margin12,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n+ \"def validate_mixed_materials(mixed_materials):\\n\",\n+ \" if not isinstance(mixed_materials, list):\\n\",\n+ \" return False, \\\"mixed_materials must be a list\\\"\\n\",\n+ \" if len(mixed_materials) < 2 or len(mixed_materials) > MAX_MIXED_ENTRIES:\\n\",\n+ \" return False, f\\\"mixed_materials must have 2..{MAX_MIXED_ENTRIES} entries\\\"\\n\",\n+ \"\\n\",\n+ \" total = 0\\n\",\n+ \" for item in mixed_materials:\\n\",\n+ \" if not isinstance(item, dict) or \\\"material\\\" not in item or \\\"percentage\\\" not in item:\\n\",\n+ \" return False, \\\"each mixed entry must be {material, percentage}\\\"\\n\",\n+ \" pct = int(item[\\\"percentage\\\"])\\n\",\n+ \" if pct < MIN_MIXED_PERCENT:\\n\",\n+ \" return False, f\\\"each mixed percentage must be >= {MIN_MIXED_PERCENT}\\\"\\n\",\n+ \" total += pct\\n\",\n+ \" if total != 100:\\n\",\n+ \" return False, \\\"mixed percentages must sum to 100\\\"\\n\",\n+ \" return True, \\\"\\\"\\n\",\n+ \"\\n\",\n+ \"\\n\",\n+ \"def encode_final_material_for_csv(final_mode, final_single, final_mixed):\\n\",\n+ \" if final_mode == \\\"single\\\":\\n\",\n+ \" return str(final_single)\\n\",\n+ \" if final_mode == \\\"mixed\\\":\\n\",\n+ \" parts = [f\\\"{x['material']}={x['percentage']}\\\" for x in final_mixed]\\n\",\n+ \" return \\\"mixed:\\\" + \\\"|\\\".join(parts)\\n\",\n+ \" return \\\"unknown\\\"\\n\",\n+ \"\\n\",\n+ \"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1139,8 +1139,36 @@\n \" \\\"margin12\\\": margin12,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n+\"def validate_mixed_materials(mixed_materials):\\n\",\n+\" if not isinstance(mixed_materials, list):\\n\",\n+\" return False, \\\"mixed_materials must be a list\\\"\\n\",\n+\" if len(mixed_materials) < 2 or len(mixed_materials) > MAX_MIXED_ENTRIES:\\n\",\n+\" return False, f\\\"mixed_materials must have 2..{MAX_MIXED_ENTRIES} entries\\\"\\n\",\n+\"\\n\",\n+\" total = 0\\n\",\n+\" for item in mixed_materials:\\n\",\n+\" if not isinstance(item, dict) or \\\"material\\\" not in item or \\\"percentage\\\" not in item:\\n\",\n+\" return False, \\\"each mixed entry must be {material, percentage}\\\"\\n\",\n+\" pct = int(item[\\\"percentage\\\"])\\n\",\n+\" if pct < MIN_MIXED_PERCENT:\\n\",\n+\" return False, f\\\"each mixed percentage must be >= {MIN_MIXED_PERCENT}\\\"\\n\",\n+\" total += pct\\n\",\n+\" if total != 100:\\n\",\n+\" return False, \\\"mixed percentages must sum to 100\\\"\\n\",\n+\" return True, \\\"\\\"\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def encode_final_material_for_csv(final_mode, final_single, final_mixed):\\n\",\n+\" if final_mode == \\\"single\\\":\\n\",\n+\" return str(final_single)\\n\",\n+\" if final_mode == \\\"mixed\\\":\\n\",\n+\" parts = [f\\\"{x['material']}={x['percentage']}\\\" for x in final_mixed]\\n\",\n+\" return \\\"mixed:\\\" + \\\"|\\\".join(parts)\\n\",\n+\" return \\\"unknown\\\"\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1139,8 +1139,36 @@\n \" \\\"margin12\\\": margin12,\\n\",\n \" }\\n\",\n \"\\n\",\n \"\\n\",\n+\"def validate_mixed_materials(mixed_materials):\\n\",\n+\" if not isinstance(mixed_materials, list):\\n\",\n+\" return False, \\\"mixed_materials must be a list\\\"\\n\",\n+\" if len(mixed_materials) < 2 or len(mixed_materials) > MAX_MIXED_ENTRIES:\\n\",\n+\" return False, f\\\"mixed_materials must have 2..{MAX_MIXED_ENTRIES} entries\\\"\\n\",\n+\"\\n\",\n+\" total = 0\\n\",\n+\" for item in mixed_materials:\\n\",\n+\" if not isinstance(item, dict) or \\\"material\\\" not in item or \\\"percentage\\\" not in item:\\n\",\n+\" return False, \\\"each mixed entry must be {material, percentage}\\\"\\n\",\n+\" pct = int(item[\\\"percentage\\\"])\\n\",\n+\" if pct < MIN_MIXED_PERCENT:\\n\",\n+\" return False, f\\\"each mixed percentage must be >= {MIN_MIXED_PERCENT}\\\"\\n\",\n+\" total += pct\\n\",\n+\" if total != 100:\\n\",\n+\" return False, \\\"mixed percentages must sum to 100\\\"\\n\",\n+\" return True, \\\"\\\"\\n\",\n+\"\\n\",\n+\"\\n\",\n+\"def encode_final_material_for_csv(final_mode, final_single, final_mixed):\\n\",\n+\" if final_mode == \\\"single\\\":\\n\",\n+\" return str(final_single)\\n\",\n+\" if final_mode == \\\"mixed\\\":\\n\",\n+\" parts = [f\\\"{x['material']}={x['percentage']}\\\" for x in final_mixed]\\n\",\n+\" return \\\"mixed:\\\" + \\\"|\\\".join(parts)\\n\",\n+\" return \\\"unknown\\\"\\n\",\n+\"\\n\",\n+\"\\n\",\n \"def save_passport(passport: dict, out_dir: Path) -> Path:\\n\",\n \" out_dir.mkdir(parents=True, exist_ok=True)\\n\",\n \" sample_id = passport[\\\"sample_id\\\"]\\n\",\n \" out_path = out_dir / f\\\"{sample_id}.json\\\"\\n\",\n", + "additions": 28, + "deletions": 0 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783085438093, + "end": 1783085438137 + } + }, + "metadata": { + "openai": { + "itemId": "fc_0cfc7cb221810951016a47b978117881a19c22443e9f548812" + } + }, + "id": "prt_f282c7df4001HJza5i6onKB3Vx", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282c344e002WKrYU3W6TZy1a0" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 207543, + "input": 3913, + "output": 667, + "reasoning": 83, + "cache": { + "write": 0, + "read": 202880 + } + }, + "cost": 0.05285175, + "id": "prt_f282c94be001U0qXi4AWqA3Frq", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282c344e002WKrYU3W6TZy1a0" + } + ] + }, + { + "info": { + "parentID": "msg_f2829fc2a001ZJ23yIGmBb50tA", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.06891675, + "tokens": { + "total": 209812, + "input": 861, + "output": 2195, + "reasoning": 36, + "cache": { + "write": 0, + "read": 206720 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783085438165, + "completed": 1783085488691 + }, + "finish": "tool-calls", + "id": "msg_f282c94d5002SBd0c0jYbtLkb3", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f282c9f9c00190bhffuYoTjjYP", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282c94d5002SBd0c0jYbtLkb3" + }, + { + "type": "step-start", + "id": "prt_f282cb59b001U9d7fpQTPjbvMw", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282c94d5002SBd0c0jYbtLkb3" + }, + { + "type": "step-start", + "id": "prt_f282cd4ed001JIWwKLmiRVFIHe", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282c94d5002SBd0c0jYbtLkb3" + }, + { + "type": "reasoning", + "text": "", + "time": { + "start": 1783085455715, + "end": 1783085457385 + }, + "metadata": { + "openai": { + "itemId": "rs_09e62184bb38a213016a47b98f88448192be4ecc26e71448f8", + "reasoningEncryptedContent": "gAAAAABqR7mRQjhMb11CFCxKbz2fp2IOAPWWxE-JMY69oX_L4JDe-lDxEOzDgdMhifrZ0_ioB_T_c7YZG4tc7n6loNky_3hT68Brs6uSlA9y0tUp77Gy2LF4U5JiBpoTykxOR-bSB83ZrnAh2Tv46JZN2BDh5xtSThRK1XTU3_Jc4XuRvYmc6CretCIbF1s3fCtNzza4eQFmAcin7muR8C-HjX8-9kAwO-SR2eLQxPGsydr4RA3V0NImlCjONuczYVKhmeApPkVR5AlNltqMvUqdfG7S5gaeBL4tIH0vOAJWXmLr32c9YWKUlSudm2NIoEPMJxsIaYN9p6oQdjRXvrFxxKBxxfYwx0f0y_9OGGfFpWKHUMlvRzfXJrPMBaNX4vz8dPzG4lnszqPUAQPFIXrR1Xlj-nKvaEJc_EG2ybnxU8BFu5HS7EBeMPTi7G7NttRMY5FXEO186y0Xh_H-I23dd18EIio-ZAtNO-6Q4HoG398rQ0X4cuiudimdIYMuV-STsjTDMKNsm3PpfUYaiDi984vtXO5Oteh-mLQWzfAXSWe4KdSiuLOxD48TJ5ZaYDMvs7Pw6DLRODpoIFBvXRJRZvHZD1P7JN6a4czhHa2lIf3CLZ20c0jusZe9BczAJ1p4z33xHmCpucTK8sG9EJE3gvDjLC14lXL6L5QI1snrbvscrmP3eTeqPuSM2aR0aXYcu_Z-lcRJBdH1QDqOHFudsh_wh_gqTHBiH9p2lY0F3sdAajx9FajCi9HYwC9HzdrL2J55lal57dz6PHm7aNKvRf2qiwFdEftVv1F5CqsN-HNLhln4p4ljPSG3wzVeWLpzNXd0pWdNNL7uDqPJQO96Bb5ujRjbMLJaWB2ywrO8Nb9DDFKRE8OBu_oGSyOGxgMLxLcPv_PBOyI8BZaqX5aig9scgW9tE6d98HV1xZerjGGajY_-ApB9PJzM6CtJtDUNcTxogL-QTZyOaIvlsxlTogxoxIPMA9BT5FdbWosRiv1HyJJ9CzoZKZo_BGhmCTV_Gb5csrAHsyMaJ-afH5DXKf3W-6a4tbG21FqgSu4-gU7sZ4-MjAEAV2KruDbJKrvE0FHlg9Vn78zqsfBAwaO_l5kYUsCrvskx4tqmF5pAm5T1Zqsh-YkGs7V1JUQViOV_wCjlBMRf" + } + }, + "id": "prt_f282cd963001bgSpiE6GQQf6ck", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282c94d5002SBd0c0jYbtLkb3" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_pmY2MRBRoPTkdqEISoTHWvtR", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n- \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n- \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n- \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n- \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n- \"\\n\",\n- \"# Example override format:\\n\",\n- \"# USER_REVIEW_OVERRIDES = {\\n\",\n- \"# \\\"S003\\\": {\\n\",\n- \"# \\\"segmentation_accepted\\\": 0,\\n\",\n- \"# \\\"user_edited_material\\\": 1,\\n\",\n- \"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n- \"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n- \"# \\\"status\\\": \\\"ok\\\",\\n\",\n- \"# \\\"error_type\\\": \\\"\\\",\\n\",\n- \"# }\\n\",\n- \"# }\\n\",\n- \"USER_REVIEW_OVERRIDES = {}\\n\",\n- \"\\n\",\n- \"for run in RUN_ROWS:\\n\",\n- \" sid = run[\\\"sample_id\\\"]\\n\",\n- \" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n- \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n- \"\\n\",\n- \" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n- \" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n- \"\\n\",\n- \" run[\\\"review\\\"] = {\\n\",\n- \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n- \" \\\"user_edited_material\\\": user_edited_material,\\n\",\n- \" \\\"user_final_material\\\": user_final_material,\\n\",\n- \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n- \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n- \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n- \" }\\n\",\n- \"\\n\",\n- \"pd.DataFrame([\\n\",\n- \" {\\n\",\n- \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n- \" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n- \" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n- \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n- \" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n- \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n- \" }\\n\",\n- \" for r in RUN_ROWS\\n\",\n- \"])\"\n+ \"# Human review/edit step. Set defaults and optional per-sample overrides.\\n\",\n+ \"DEFAULT_SEGMENTATION_ACCEPTED = 1\\n\",\n+ \"DEFAULT_STATUS = \\\"ok\\\"\\n\",\n+ \"DEFAULT_ERROR_TYPE = \\\"\\\"\\n\",\n+ \"\\n\",\n+ \"# Example override format:\\n\",\n+ \"# USER_REVIEW_OVERRIDES = {\\n\",\n+ \"# \\\"S003\\\": {\\n\",\n+ \"# \\\"segmentation_accepted\\\": 1,\\n\",\n+ \"# \\\"final_material_mode\\\": \\\"mixed\\\", # single|mixed|unknown\\n\",\n+ \"# \\\"final_single_material\\\": None,\\n\",\n+ \"# \\\"final_mixed_materials\\\": [{\\\"material\\\": \\\"wood\\\", \\\"percentage\\\": 60}, {\\\"material\\\": \\\"steel\\\", \\\"percentage\\\": 40}],\\n\",\n+ \"# \\\"confirmed_by_user\\\": True,\\n\",\n+ \"# \\\"edit_reason\\\": \\\"edge case\\\",\\n\",\n+ \"# \\\"status\\\": \\\"ok\\\",\\n\",\n+ \"# \\\"error_type\\\": \\\"\\\",\\n\",\n+ \"# }\\n\",\n+ \"# }\\n\",\n+ \"USER_REVIEW_OVERRIDES = {}\\n\",\n+ \"\\n\",\n+ \"for run in RUN_ROWS:\\n\",\n+ \" sid = run[\\\"sample_id\\\"]\\n\",\n+ \" clf = run[\\\"clf\\\"]\\n\",\n+ \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n+ \"\\n\",\n+ \" suggested_mode = clf[\\\"suggested_mode\\\"]\\n\",\n+ \" suggested_single = clf.get(\\\"suggested_single_material\\\")\\n\",\n+ \" suggested_mixed = clf.get(\\\"suggested_mixed_materials\\\", [])\\n\",\n+ \"\\n\",\n+ \" final_mode = override.get(\\\"final_material_mode\\\", suggested_mode)\\n\",\n+ \" final_single = override.get(\\\"final_single_material\\\", suggested_single if final_mode == \\\"single\\\" else None)\\n\",\n+ \" final_mixed = override.get(\\\"final_mixed_materials\\\", suggested_mixed if final_mode == \\\"mixed\\\" else [])\\n\",\n+ \"\\n\",\n+ \" requires_confirm = bool(clf.get(\\\"requires_user_confirmation\\\", False))\\n\",\n+ \" confirmed_by_user = bool(override.get(\\\"confirmed_by_user\\\", not requires_confirm))\\n\",\n+ \"\\n\",\n+ \" if final_mode == \\\"mixed\\\":\\n\",\n+ \" ok, err = validate_mixed_materials(final_mixed)\\n\",\n+ \" if not ok:\\n\",\n+ \" raise ValueError(f\\\"{sid}: invalid final_mixed_materials -> {err}\\\")\\n\",\n+ \"\\n\",\n+ \" user_edited = int(final_mode != suggested_mode)\\n\",\n+ \"\\n\",\n+ \" run[\\\"review\\\"] = {\\n\",\n+ \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n+ \" \\\"final_material_mode\\\": final_mode,\\n\",\n+ \" \\\"final_single_material\\\": final_single,\\n\",\n+ \" \\\"final_mixed_materials\\\": final_mixed,\\n\",\n+ \" \\\"requires_user_confirmation\\\": requires_confirm,\\n\",\n+ \" \\\"confirmed_by_user\\\": confirmed_by_user,\\n\",\n+ \" \\\"user_edited_material\\\": user_edited,\\n\",\n+ \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n+ \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n+ \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n+ \" }\\n\",\n+ \"\\n\",\n+ \"pd.DataFrame([\\n\",\n+ \" {\\n\",\n+ \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n+ \" \\\"suggested_mode\\\": r[\\\"clf\\\"][\\\"suggested_mode\\\"],\\n\",\n+ \" \\\"final_mode\\\": r[\\\"review\\\"][\\\"final_material_mode\\\"],\\n\",\n+ \" \\\"requires_confirm\\\": r[\\\"review\\\"][\\\"requires_user_confirmation\\\"],\\n\",\n+ \" \\\"confirmed\\\": r[\\\"review\\\"][\\\"confirmed_by_user\\\"],\\n\",\n+ \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n+ \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n+ \" }\\n\",\n+ \" for r in RUN_ROWS\\n\",\n+ \"])\"\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1534,42 +1534,64 @@\n \"\\n\",\n \"# Example override format:\\n\",\n \"# USER_REVIEW_OVERRIDES = {\\n\",\n \"# \\\"S003\\\": {\\n\",\n-\"# \\\"segmentation_accepted\\\": 0,\\n\",\n-\"# \\\"user_edited_material\\\": 1,\\n\",\n-\"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n-\"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n+\"# \\\"segmentation_accepted\\\": 1,\\n\",\n+\"# \\\"final_material_mode\\\": \\\"mixed\\\", # single|mixed|unknown\\n\",\n+\"# \\\"final_single_material\\\": None,\\n\",\n+\"# \\\"final_mixed_materials\\\": [{\\\"material\\\": \\\"wood\\\", \\\"percentage\\\": 60}, {\\\"material\\\": \\\"steel\\\", \\\"percentage\\\": 40}],\\n\",\n+\"# \\\"confirmed_by_user\\\": True,\\n\",\n+\"# \\\"edit_reason\\\": \\\"edge case\\\",\\n\",\n \"# \\\"status\\\": \\\"ok\\\",\\n\",\n \"# \\\"error_type\\\": \\\"\\\",\\n\",\n \"# }\\n\",\n \"# }\\n\",\n \"USER_REVIEW_OVERRIDES = {}\\n\",\n \"\\n\",\n \"for run in RUN_ROWS:\\n\",\n \" sid = run[\\\"sample_id\\\"]\\n\",\n-\" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n+\" clf = run[\\\"clf\\\"]\\n\",\n \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n \"\\n\",\n-\" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n-\" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n+\" suggested_mode = clf[\\\"suggested_mode\\\"]\\n\",\n+\" suggested_single = clf.get(\\\"suggested_single_material\\\")\\n\",\n+\" suggested_mixed = clf.get(\\\"suggested_mixed_materials\\\", [])\\n\",\n \"\\n\",\n+\" final_mode = override.get(\\\"final_material_mode\\\", suggested_mode)\\n\",\n+\" final_single = override.get(\\\"final_single_material\\\", suggested_single if final_mode == \\\"single\\\" else None)\\n\",\n+\" final_mixed = override.get(\\\"final_mixed_materials\\\", suggested_mixed if final_mode == \\\"mixed\\\" else [])\\n\",\n+\"\\n\",\n+\" requires_confirm = bool(clf.get(\\\"requires_user_confirmation\\\", False))\\n\",\n+\" confirmed_by_user = bool(override.get(\\\"confirmed_by_user\\\", not requires_confirm))\\n\",\n+\"\\n\",\n+\" if final_mode == \\\"mixed\\\":\\n\",\n+\" ok, err = validate_mixed_materials(final_mixed)\\n\",\n+\" if not ok:\\n\",\n+\" raise ValueError(f\\\"{sid}: invalid final_mixed_materials -> {err}\\\")\\n\",\n+\"\\n\",\n+\" user_edited = int(final_mode != suggested_mode)\\n\",\n+\"\\n\",\n \" run[\\\"review\\\"] = {\\n\",\n \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n-\" \\\"user_edited_material\\\": user_edited_material,\\n\",\n-\" \\\"user_final_material\\\": user_final_material,\\n\",\n+\" \\\"final_material_mode\\\": final_mode,\\n\",\n+\" \\\"final_single_material\\\": final_single,\\n\",\n+\" \\\"final_mixed_materials\\\": final_mixed,\\n\",\n+\" \\\"requires_user_confirmation\\\": requires_confirm,\\n\",\n+\" \\\"confirmed_by_user\\\": confirmed_by_user,\\n\",\n+\" \\\"user_edited_material\\\": user_edited,\\n\",\n \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n \" }\\n\",\n \"\\n\",\n \"pd.DataFrame([\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n-\" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n-\" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n+\" \\\"suggested_mode\\\": r[\\\"clf\\\"][\\\"suggested_mode\\\"],\\n\",\n+\" \\\"final_mode\\\": r[\\\"review\\\"][\\\"final_material_mode\\\"],\\n\",\n+\" \\\"requires_confirm\\\": r[\\\"review\\\"][\\\"requires_user_confirmation\\\"],\\n\",\n+\" \\\"confirmed\\\": r[\\\"review\\\"][\\\"confirmed_by_user\\\"],\\n\",\n \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n-\" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n \" }\\n\",\n \" for r in RUN_ROWS\\n\",\n \"])\"\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1534,42 +1534,64 @@\n \"\\n\",\n \"# Example override format:\\n\",\n \"# USER_REVIEW_OVERRIDES = {\\n\",\n \"# \\\"S003\\\": {\\n\",\n-\"# \\\"segmentation_accepted\\\": 0,\\n\",\n-\"# \\\"user_edited_material\\\": 1,\\n\",\n-\"# \\\"user_final_material\\\": \\\"aluminum\\\",\\n\",\n-\"# \\\"edit_reason\\\": \\\"frame dominates crop\\\",\\n\",\n+\"# \\\"segmentation_accepted\\\": 1,\\n\",\n+\"# \\\"final_material_mode\\\": \\\"mixed\\\", # single|mixed|unknown\\n\",\n+\"# \\\"final_single_material\\\": None,\\n\",\n+\"# \\\"final_mixed_materials\\\": [{\\\"material\\\": \\\"wood\\\", \\\"percentage\\\": 60}, {\\\"material\\\": \\\"steel\\\", \\\"percentage\\\": 40}],\\n\",\n+\"# \\\"confirmed_by_user\\\": True,\\n\",\n+\"# \\\"edit_reason\\\": \\\"edge case\\\",\\n\",\n \"# \\\"status\\\": \\\"ok\\\",\\n\",\n \"# \\\"error_type\\\": \\\"\\\",\\n\",\n \"# }\\n\",\n \"# }\\n\",\n \"USER_REVIEW_OVERRIDES = {}\\n\",\n \"\\n\",\n \"for run in RUN_ROWS:\\n\",\n \" sid = run[\\\"sample_id\\\"]\\n\",\n-\" pred_top1 = run[\\\"clf\\\"][\\\"pred_material_top1\\\"]\\n\",\n+\" clf = run[\\\"clf\\\"]\\n\",\n \" override = USER_REVIEW_OVERRIDES.get(sid, {})\\n\",\n \"\\n\",\n-\" user_edited_material = int(override.get(\\\"user_edited_material\\\", 0))\\n\",\n-\" user_final_material = override.get(\\\"user_final_material\\\", pred_top1)\\n\",\n+\" suggested_mode = clf[\\\"suggested_mode\\\"]\\n\",\n+\" suggested_single = clf.get(\\\"suggested_single_material\\\")\\n\",\n+\" suggested_mixed = clf.get(\\\"suggested_mixed_materials\\\", [])\\n\",\n \"\\n\",\n+\" final_mode = override.get(\\\"final_material_mode\\\", suggested_mode)\\n\",\n+\" final_single = override.get(\\\"final_single_material\\\", suggested_single if final_mode == \\\"single\\\" else None)\\n\",\n+\" final_mixed = override.get(\\\"final_mixed_materials\\\", suggested_mixed if final_mode == \\\"mixed\\\" else [])\\n\",\n+\"\\n\",\n+\" requires_confirm = bool(clf.get(\\\"requires_user_confirmation\\\", False))\\n\",\n+\" confirmed_by_user = bool(override.get(\\\"confirmed_by_user\\\", not requires_confirm))\\n\",\n+\"\\n\",\n+\" if final_mode == \\\"mixed\\\":\\n\",\n+\" ok, err = validate_mixed_materials(final_mixed)\\n\",\n+\" if not ok:\\n\",\n+\" raise ValueError(f\\\"{sid}: invalid final_mixed_materials -> {err}\\\")\\n\",\n+\"\\n\",\n+\" user_edited = int(final_mode != suggested_mode)\\n\",\n+\"\\n\",\n \" run[\\\"review\\\"] = {\\n\",\n \" \\\"segmentation_accepted\\\": int(override.get(\\\"segmentation_accepted\\\", DEFAULT_SEGMENTATION_ACCEPTED)),\\n\",\n-\" \\\"user_edited_material\\\": user_edited_material,\\n\",\n-\" \\\"user_final_material\\\": user_final_material,\\n\",\n+\" \\\"final_material_mode\\\": final_mode,\\n\",\n+\" \\\"final_single_material\\\": final_single,\\n\",\n+\" \\\"final_mixed_materials\\\": final_mixed,\\n\",\n+\" \\\"requires_user_confirmation\\\": requires_confirm,\\n\",\n+\" \\\"confirmed_by_user\\\": confirmed_by_user,\\n\",\n+\" \\\"user_edited_material\\\": user_edited,\\n\",\n \" \\\"edit_reason\\\": override.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"status\\\": override.get(\\\"status\\\", DEFAULT_STATUS),\\n\",\n \" \\\"error_type\\\": override.get(\\\"error_type\\\", DEFAULT_ERROR_TYPE),\\n\",\n \" }\\n\",\n \"\\n\",\n \"pd.DataFrame([\\n\",\n \" {\\n\",\n \" \\\"sample_id\\\": r[\\\"sample_id\\\"],\\n\",\n-\" \\\"pred_material_top1\\\": r[\\\"clf\\\"][\\\"pred_material_top1\\\"],\\n\",\n-\" \\\"user_final_material\\\": r[\\\"review\\\"][\\\"user_final_material\\\"],\\n\",\n+\" \\\"suggested_mode\\\": r[\\\"clf\\\"][\\\"suggested_mode\\\"],\\n\",\n+\" \\\"final_mode\\\": r[\\\"review\\\"][\\\"final_material_mode\\\"],\\n\",\n+\" \\\"requires_confirm\\\": r[\\\"review\\\"][\\\"requires_user_confirmation\\\"],\\n\",\n+\" \\\"confirmed\\\": r[\\\"review\\\"][\\\"confirmed_by_user\\\"],\\n\",\n \" \\\"user_edited_material\\\": r[\\\"review\\\"][\\\"user_edited_material\\\"],\\n\",\n-\" \\\"segmentation_accepted\\\": r[\\\"review\\\"][\\\"segmentation_accepted\\\"],\\n\",\n \" \\\"status\\\": r[\\\"review\\\"][\\\"status\\\"],\\n\",\n \" }\\n\",\n \" for r in RUN_ROWS\\n\",\n \"])\"\n", + "additions": 34, + "deletions": 12 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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+ } + }, + "id": "prt_f282d67f9001COPg12cQIGoiaw", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282d5a4d002odqX2UjjpnLztn" + }, + { + "type": "tool", + "tool": "apply_patch", + "callID": "call_XlMHeTd1aUHMZ5hVOXjqqRyo", + "state": { + "status": "completed", + "input": { + "patchText": "*** Begin Patch\n*** Update File: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@\n \"for run in RUN_ROWS:\\n\",\n \" review = run.get(\\\"review\\\", {})\\n\",\n \" seg = run[\\\"seg\\\"]\\n\",\n \" clf = run[\\\"clf\\\"]\\n\",\n \" timing = run[\\\"timing\\\"]\\n\",\n+ \"\\n\",\n+ \" if review.get(\\\"requires_user_confirmation\\\", False) and not review.get(\\\"confirmed_by_user\\\", False):\\n\",\n+ \" raise ValueError(f\\\"{run['sample_id']}: low-confidence result requires explicit user confirmation before save\\\")\\n\",\n+ \"\\n\",\n+ \" final_mode = review.get(\\\"final_material_mode\\\", \\\"unknown\\\")\\n\",\n+ \" final_single = review.get(\\\"final_single_material\\\", None)\\n\",\n+ \" final_mixed = review.get(\\\"final_mixed_materials\\\", [])\\n\",\n+ \"\\n\",\n+ \" if final_mode == \\\"mixed\\\":\\n\",\n+ \" ok, err = validate_mixed_materials(final_mixed)\\n\",\n+ \" if not ok:\\n\",\n+ \" raise ValueError(f\\\"{run['sample_id']}: invalid mixed output -> {err}\\\")\\n\",\n+ \" elif final_mode == \\\"single\\\":\\n\",\n+ \" if not final_single:\\n\",\n+ \" raise ValueError(f\\\"{run['sample_id']}: final_single_material is required for single mode\\\")\\n\",\n+ \" elif final_mode == \\\"unknown\\\":\\n\",\n+ \" final_single = None\\n\",\n+ \" final_mixed = []\\n\",\n+ \" else:\\n\",\n+ \" raise ValueError(f\\\"{run['sample_id']}: unknown final_material_mode={final_mode}\\\")\\n\",\n@@\n \" \\\"material\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n- \" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n- \" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n- \" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n- \" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n- \" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n- \" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n- \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n+ \" \\\"predicted_topk\\\": [\\n\",\n+ \" {\\\"material\\\": m, \\\"score\\\": float(s)}\\n\",\n+ \" for m, s in zip(clf[\\\"topk_labels\\\"], clf[\\\"topk_scores\\\"])\\n\",\n+ \" ],\\n\",\n+ \" \\\"predicted_confidence_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+ \" \\\"suggested_mode\\\": clf[\\\"suggested_mode\\\"],\\n\",\n+ \" \\\"suggested_single_material\\\": clf.get(\\\"suggested_single_material\\\"),\\n\",\n+ \" \\\"suggested_mixed_materials\\\": clf.get(\\\"suggested_mixed_materials\\\", []),\\n\",\n+ \" \\\"final_material_mode\\\": final_mode,\\n\",\n+ \" \\\"final_single_material\\\": final_single,\\n\",\n+ \" \\\"final_mixed_materials\\\": final_mixed,\\n\",\n+ \" \\\"low_confidence_flag\\\": bool(clf.get(\\\"low_confidence_flag\\\", False)),\\n\",\n+ \" \\\"requires_user_confirmation\\\": bool(review.get(\\\"requires_user_confirmation\\\", False)),\\n\",\n+ \" \\\"confirmed_by_user\\\": bool(review.get(\\\"confirmed_by_user\\\", False)),\\n\",\n+ \" \\\"fusion_weights\\\": clf.get(\\\"weights\\\", {}),\\n\",\n+ \" \\\"model_outputs\\\": {\\n\",\n+ \" \\\"sigclip\\\": {\\n\",\n+ \" \\\"topk_labels\\\": clf[\\\"sigclip\\\"].get(\\\"topk_labels\\\", []),\\n\",\n+ \" \\\"topk_scores\\\": clf[\\\"sigclip\\\"].get(\\\"topk_scores\\\", []),\\n\",\n+ \" },\\n\",\n+ \" \\\"dinov3\\\": {\\n\",\n+ \" \\\"topk_labels\\\": clf[\\\"dinov3\\\"].get(\\\"topk_labels\\\", []),\\n\",\n+ \" \\\"topk_scores\\\": clf[\\\"dinov3\\\"].get(\\\"topk_scores\\\", []),\\n\",\n+ \" \\\"prototype_coverage\\\": clf[\\\"dinov3\\\"].get(\\\"prototype_coverage\\\", 0.0),\\n\",\n+ \" \\\"prototypes_available\\\": bool(clf[\\\"dinov3\\\"].get(\\\"prototypes_available\\\", False)),\\n\",\n+ \" },\\n\",\n+ \" },\\n\",\n+ \" \\\"user_final\\\": encode_final_material_for_csv(final_mode, final_single, final_mixed),\\n\",\n+ \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" },\\n\",\n@@\n- \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n \" }\\n\",\n@@\n- \" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+ \" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n- \" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+ \" \\\"user_final_material\\\": encode_final_material_for_csv(final_mode, final_single, final_mixed),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1608,8 +1608,28 @@\n \" seg = run[\\\"seg\\\"]\\n\",\n \" clf = run[\\\"clf\\\"]\\n\",\n \" timing = run[\\\"timing\\\"]\\n\",\n \"\\n\",\n+\" if review.get(\\\"requires_user_confirmation\\\", False) and not review.get(\\\"confirmed_by_user\\\", False):\\n\",\n+\" raise ValueError(f\\\"{run['sample_id']}: low-confidence result requires explicit user confirmation before save\\\")\\n\",\n+\"\\n\",\n+\" final_mode = review.get(\\\"final_material_mode\\\", \\\"unknown\\\")\\n\",\n+\" final_single = review.get(\\\"final_single_material\\\", None)\\n\",\n+\" final_mixed = review.get(\\\"final_mixed_materials\\\", [])\\n\",\n+\"\\n\",\n+\" if final_mode == \\\"mixed\\\":\\n\",\n+\" ok, err = validate_mixed_materials(final_mixed)\\n\",\n+\" if not ok:\\n\",\n+\" raise ValueError(f\\\"{run['sample_id']}: invalid mixed output -> {err}\\\")\\n\",\n+\" elif final_mode == \\\"single\\\":\\n\",\n+\" if not final_single:\\n\",\n+\" raise ValueError(f\\\"{run['sample_id']}: final_single_material is required for single mode\\\")\\n\",\n+\" elif final_mode == \\\"unknown\\\":\\n\",\n+\" final_single = None\\n\",\n+\" final_mixed = []\\n\",\n+\" else:\\n\",\n+\" raise ValueError(f\\\"{run['sample_id']}: unknown final_material_mode={final_mode}\\\")\\n\",\n+\"\\n\",\n \" passport = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n@@ -1624,14 +1644,36 @@\n \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n \" },\\n\",\n \" \\\"material\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n-\" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n-\" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n-\" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n-\" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n-\" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n-\" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+\" \\\"predicted_topk\\\": [\\n\",\n+\" {\\\"material\\\": m, \\\"score\\\": float(s)}\\n\",\n+\" for m, s in zip(clf[\\\"topk_labels\\\"], clf[\\\"topk_scores\\\"])\\n\",\n+\" ],\\n\",\n+\" \\\"predicted_confidence_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+\" \\\"suggested_mode\\\": clf[\\\"suggested_mode\\\"],\\n\",\n+\" \\\"suggested_single_material\\\": clf.get(\\\"suggested_single_material\\\"),\\n\",\n+\" \\\"suggested_mixed_materials\\\": clf.get(\\\"suggested_mixed_materials\\\", []),\\n\",\n+\" \\\"final_material_mode\\\": final_mode,\\n\",\n+\" \\\"final_single_material\\\": final_single,\\n\",\n+\" \\\"final_mixed_materials\\\": final_mixed,\\n\",\n+\" \\\"low_confidence_flag\\\": bool(clf.get(\\\"low_confidence_flag\\\", False)),\\n\",\n+\" \\\"requires_user_confirmation\\\": bool(review.get(\\\"requires_user_confirmation\\\", False)),\\n\",\n+\" \\\"confirmed_by_user\\\": bool(review.get(\\\"confirmed_by_user\\\", False)),\\n\",\n+\" \\\"fusion_weights\\\": clf.get(\\\"weights\\\", {}),\\n\",\n+\" \\\"model_outputs\\\": {\\n\",\n+\" \\\"sigclip\\\": {\\n\",\n+\" \\\"topk_labels\\\": clf[\\\"sigclip\\\"].get(\\\"topk_labels\\\", []),\\n\",\n+\" \\\"topk_scores\\\": clf[\\\"sigclip\\\"].get(\\\"topk_scores\\\", []),\\n\",\n+\" },\\n\",\n+\" \\\"dinov3\\\": {\\n\",\n+\" \\\"topk_labels\\\": clf[\\\"dinov3\\\"].get(\\\"topk_labels\\\", []),\\n\",\n+\" \\\"topk_scores\\\": clf[\\\"dinov3\\\"].get(\\\"topk_scores\\\", []),\\n\",\n+\" \\\"prototype_coverage\\\": clf[\\\"dinov3\\\"].get(\\\"prototype_coverage\\\", 0.0),\\n\",\n+\" \\\"prototypes_available\\\": bool(clf[\\\"dinov3\\\"].get(\\\"prototypes_available\\\", False)),\\n\",\n+\" },\\n\",\n+\" },\\n\",\n+\" \\\"user_final\\\": encode_final_material_for_csv(final_mode, final_single, final_mixed),\\n\",\n \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" },\\n\",\n \" \\\"segmentation\\\": {\\n\",\n@@ -1648,9 +1690,9 @@\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" },\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n-\" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+\" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n \" }\\n\",\n \"\\n\",\n \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n@@ -1666,12 +1708,12 @@\n \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n-\" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+\" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n-\" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+\" \\\"user_final_material\\\": encode_final_material_for_csv(final_mode, final_single, final_mixed),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1608,8 +1608,28 @@\n \" seg = run[\\\"seg\\\"]\\n\",\n \" clf = run[\\\"clf\\\"]\\n\",\n \" timing = run[\\\"timing\\\"]\\n\",\n \"\\n\",\n+\" if review.get(\\\"requires_user_confirmation\\\", False) and not review.get(\\\"confirmed_by_user\\\", False):\\n\",\n+\" raise ValueError(f\\\"{run['sample_id']}: low-confidence result requires explicit user confirmation before save\\\")\\n\",\n+\"\\n\",\n+\" final_mode = review.get(\\\"final_material_mode\\\", \\\"unknown\\\")\\n\",\n+\" final_single = review.get(\\\"final_single_material\\\", None)\\n\",\n+\" final_mixed = review.get(\\\"final_mixed_materials\\\", [])\\n\",\n+\"\\n\",\n+\" if final_mode == \\\"mixed\\\":\\n\",\n+\" ok, err = validate_mixed_materials(final_mixed)\\n\",\n+\" if not ok:\\n\",\n+\" raise ValueError(f\\\"{run['sample_id']}: invalid mixed output -> {err}\\\")\\n\",\n+\" elif final_mode == \\\"single\\\":\\n\",\n+\" if not final_single:\\n\",\n+\" raise ValueError(f\\\"{run['sample_id']}: final_single_material is required for single mode\\\")\\n\",\n+\" elif final_mode == \\\"unknown\\\":\\n\",\n+\" final_single = None\\n\",\n+\" final_mixed = []\\n\",\n+\" else:\\n\",\n+\" raise ValueError(f\\\"{run['sample_id']}: unknown final_material_mode={final_mode}\\\")\\n\",\n+\"\\n\",\n \" passport = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n@@ -1624,14 +1644,36 @@\n \" \\\"predicted\\\": run[\\\"pred_object\\\"],\\n\",\n \" },\\n\",\n \" \\\"material\\\": {\\n\",\n \" \\\"ground_truth\\\": run[\\\"ground_truth_material\\\"],\\n\",\n-\" \\\"pred_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n-\" \\\"pred_top3\\\": clf[\\\"pred_material_top3\\\"].split(\\\"|\\\"),\\n\",\n-\" \\\"pred_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n-\" \\\"candidate_materials\\\": clf[\\\"candidate_materials\\\"],\\n\",\n-\" \\\"topk_scores\\\": clf[\\\"topk_scores\\\"],\\n\",\n-\" \\\"user_final\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+\" \\\"predicted_topk\\\": [\\n\",\n+\" {\\\"material\\\": m, \\\"score\\\": float(s)}\\n\",\n+\" for m, s in zip(clf[\\\"topk_labels\\\"], clf[\\\"topk_scores\\\"])\\n\",\n+\" ],\\n\",\n+\" \\\"predicted_confidence_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n+\" \\\"suggested_mode\\\": clf[\\\"suggested_mode\\\"],\\n\",\n+\" \\\"suggested_single_material\\\": clf.get(\\\"suggested_single_material\\\"),\\n\",\n+\" \\\"suggested_mixed_materials\\\": clf.get(\\\"suggested_mixed_materials\\\", []),\\n\",\n+\" \\\"final_material_mode\\\": final_mode,\\n\",\n+\" \\\"final_single_material\\\": final_single,\\n\",\n+\" \\\"final_mixed_materials\\\": final_mixed,\\n\",\n+\" \\\"low_confidence_flag\\\": bool(clf.get(\\\"low_confidence_flag\\\", False)),\\n\",\n+\" \\\"requires_user_confirmation\\\": bool(review.get(\\\"requires_user_confirmation\\\", False)),\\n\",\n+\" \\\"confirmed_by_user\\\": bool(review.get(\\\"confirmed_by_user\\\", False)),\\n\",\n+\" \\\"fusion_weights\\\": clf.get(\\\"weights\\\", {}),\\n\",\n+\" \\\"model_outputs\\\": {\\n\",\n+\" \\\"sigclip\\\": {\\n\",\n+\" \\\"topk_labels\\\": clf[\\\"sigclip\\\"].get(\\\"topk_labels\\\", []),\\n\",\n+\" \\\"topk_scores\\\": clf[\\\"sigclip\\\"].get(\\\"topk_scores\\\", []),\\n\",\n+\" },\\n\",\n+\" \\\"dinov3\\\": {\\n\",\n+\" \\\"topk_labels\\\": clf[\\\"dinov3\\\"].get(\\\"topk_labels\\\", []),\\n\",\n+\" \\\"topk_scores\\\": clf[\\\"dinov3\\\"].get(\\\"topk_scores\\\", []),\\n\",\n+\" \\\"prototype_coverage\\\": clf[\\\"dinov3\\\"].get(\\\"prototype_coverage\\\", 0.0),\\n\",\n+\" \\\"prototypes_available\\\": bool(clf[\\\"dinov3\\\"].get(\\\"prototypes_available\\\", False)),\\n\",\n+\" },\\n\",\n+\" },\\n\",\n+\" \\\"user_final\\\": encode_final_material_for_csv(final_mode, final_single, final_mixed),\\n\",\n \" \\\"user_edited\\\": bool(review.get(\\\"user_edited_material\\\", 0)),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" },\\n\",\n \" \\\"segmentation\\\": {\\n\",\n@@ -1648,9 +1690,9 @@\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" },\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n-\" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+\" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n \" }\\n\",\n \"\\n\",\n \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n@@ -1666,12 +1708,12 @@\n \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n-\" \\\"model_name\\\": \\\"SigCLIP\\\",\\n\",\n+\" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n-\" \\\"user_final_material\\\": review.get(\\\"user_final_material\\\", clf[\\\"pred_material_top1\\\"]),\\n\",\n+\" \\\"user_final_material\\\": encode_final_material_for_csv(final_mode, final_single, final_mixed),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n", + "additions": 51, + "deletions": 9 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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save_passport(passport, PASSPORT_DIR)\\n\",\n1699: \"\\n\",\n1700: \" eval_row = {\\n\",\n1701: \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n1702: \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n1703: \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n1704: \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n1705: \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n1706: \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n1707: \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n1708: \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n1709: \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n1710: \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n1711: \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n1712: \" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n1713: \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n1714: \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n1715: \" \\\"user_final_material\\\": encode_final_material_for_csv(final_mode, final_single, final_mixed),\\n\",\n1716: \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n1717: \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n1718: \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n1719: \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n1720: \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n1721: \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n1722: \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n1723: \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n1724: \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n1725: \" }\\n\",\n1726: \" append_eval_row(CSV_PATH, eval_row)\\n\",\n1727: \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n1728: \"\\n\",\n1729: \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n1730: ]\n1731: },\n1732: {\n1733: \"cell_type\": \"code\",\n1734: \"execution_count\": null,\n1735: \"metadata\": {},\n1736: \"outputs\": [],\n1737: \"source\": [\n1738: \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n1739: \" df = df_in.copy()\\n\",\n1740: \" df = df[df[\\\"status\\\"].notna()]\\n\",\n1741: \"\\n\",\n1742: \" if df.empty:\\n\",\n1743: \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n1744: \"\\n\",\n1745: \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n1746: \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n1747: \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n1748: \" axis=1,\\n\",\n1749: \" )\\n\",\n1750: \"\\n\",\n1751: \" metrics = {\\n\",\n1752: \" \\\"n_samples\\\": int(len(df)),\\n\",\n1753: \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n1754: \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n1755: \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n1756: \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n1757: \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n1758: \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n1759: \" }\\n\",\n1760: \"\\n\",\n1761: \" go_no_go = (\\n\",\n1762: \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n1763: \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n1764: \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n1765: \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n1766: \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n1767: \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n1768: \" )\\n\",\n1769: \"\\n\",\n1770: \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n1771: \"\\n\",\n1772: \"\\n\",\n1773: \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n1774: \"result = compute_metrics(df_eval)\\n\",\n1775: \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n1776: \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n1777: \" print(f\\\"- {k}: {v}\\\")\\n\",\n1778: \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n1779: ]\n\n(Showing lines 1690-1779 of 1836. Use offset=1780 to continue.)\n</content>", + "metadata": { + "preview": " \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" },\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n \" }\\n\",\n \"\\n\",\n \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n \"\\n\",\n \" eval_row = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" },\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"created_at_utc\\\": datetime.now(timezone.utc).isoformat(),\\n\",\n \" }\\n\",\n \"\\n\",\n \" passport_path = save_passport(passport, PASSPORT_DIR)\\n\",\n \"\\n\",\n \" eval_row = {\\n\",\n \" \\\"sample_id\\\": run[\\\"sample_id\\\"],\\n\",\n \" \\\"image_id\\\": run[\\\"image_id\\\"],\\n\",\n \" \\\"image_path\\\": run[\\\"image_path\\\"],\\n\",\n \" \\\"tap_x\\\": run[\\\"tap_x\\\"],\\n\",\n \" \\\"tap_y\\\": run[\\\"tap_y\\\"],\\n\",\n \" \\\"ground_truth_object\\\": run[\\\"ground_truth_object\\\"],\\n\",\n \" \\\"ground_truth_material\\\": run[\\\"ground_truth_material\\\"],\\n\",\n \" \\\"pred_object\\\": run[\\\"pred_object\\\"],\\n\",\n \" \\\"pred_material_top1\\\": clf[\\\"pred_material_top1\\\"],\\n\",\n \" \\\"pred_material_top3\\\": clf[\\\"pred_material_top3\\\"],\\n\",\n \" \\\"pred_material_conf_top1\\\": clf[\\\"pred_material_conf_top1\\\"],\\n\",\n \" \\\"model_name\\\": \\\"SigCLIP+DINOv3\\\",\\n\",\n \" \\\"segmentation_accepted\\\": review.get(\\\"segmentation_accepted\\\", 1),\\n\",\n \" \\\"user_edited_material\\\": review.get(\\\"user_edited_material\\\", 0),\\n\",\n \" \\\"user_final_material\\\": encode_final_material_for_csv(final_mode, final_single, final_mixed),\\n\",\n \" \\\"edit_reason\\\": review.get(\\\"edit_reason\\\", \\\"\\\"),\\n\",\n \" \\\"end_to_end_time_sec\\\": timing[\\\"end_to_end_time_sec\\\"],\\n\",\n \" \\\"segmentation_time_sec\\\": timing[\\\"segmentation_time_sec\\\"],\\n\",\n \" \\\"classification_time_sec\\\": timing[\\\"classification_time_sec\\\"],\\n\",\n \" \\\"status\\\": review.get(\\\"status\\\", \\\"ok\\\"),\\n\",\n \" \\\"error_type\\\": review.get(\\\"error_type\\\", \\\"\\\"),\\n\",\n \" \\\"mask_area_px\\\": seg[\\\"mask_area_px\\\"],\\n\",\n \" \\\"bbox_xywh\\\": \\\",\\\".join(str(v) for v in seg[\\\"bbox_xywh\\\"]),\\n\",\n \" \\\"passport_json_path\\\": str(passport_path),\\n\",\n \" }\\n\",\n \" append_eval_row(CSV_PATH, eval_row)\\n\",\n \" print(f\\\"[{run['sample_id']}] saved passport + eval row\\\")\\n\",\n \"\\n\",\n \"print(f\\\"Updated eval CSV: {CSV_PATH}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def compute_metrics(df_in: pd.DataFrame) -> dict:\\n\",\n \" df = df_in.copy()\\n\",\n \" df = df[df[\\\"status\\\"].notna()]\\n\",\n \"\\n\",\n \" if df.empty:\\n\",\n \" raise ValueError(\\\"No rows with non-empty status.\\\")\\n\",\n \"\\n\",\n \" df[\\\"top1_correct\\\"] = (df[\\\"pred_material_top1\\\"] == df[\\\"ground_truth_material\\\"]).astype(int)\\n\",\n \" df[\\\"top3_hit\\\"] = df.apply(\\n\",\n \" lambda r: int(str(r[\\\"ground_truth_material\\\"]) in str(r[\\\"pred_material_top3\\\"]).split(\\\"|\\\")),\\n\",\n \" axis=1,\\n\",\n \" )\\n\",\n \"\\n\",\n \" metrics = {\\n\",\n \" \\\"n_samples\\\": int(len(df)),\\n\",\n \" \\\"segmentation_acceptance_rate\\\": float(df[\\\"segmentation_accepted\\\"].mean()),\\n\",\n \" \\\"top1_accuracy\\\": float(df[\\\"top1_correct\\\"].mean()),\\n\",\n \" \\\"top3_hit_rate\\\": float(df[\\\"top3_hit\\\"].mean()),\\n\",\n \" \\\"manual_edit_rate\\\": float(df[\\\"user_edited_material\\\"].mean()),\\n\",\n \" \\\"median_e2e_time_sec\\\": float(df[\\\"end_to_end_time_sec\\\"].median()),\\n\",\n \" \\\"failure_rate\\\": float((df[\\\"status\\\"] != \\\"ok\\\").mean()),\\n\",\n \" }\\n\",\n \"\\n\",\n \" go_no_go = (\\n\",\n \" metrics[\\\"segmentation_acceptance_rate\\\"] >= THRESHOLDS[\\\"segmentation_acceptance_rate\\\"]\\n\",\n \" and metrics[\\\"top1_accuracy\\\"] >= THRESHOLDS[\\\"top1_accuracy\\\"]\\n\",\n \" and metrics[\\\"top3_hit_rate\\\"] >= THRESHOLDS[\\\"top3_hit_rate\\\"]\\n\",\n \" and metrics[\\\"manual_edit_rate\\\"] <= THRESHOLDS[\\\"manual_edit_rate_max\\\"]\\n\",\n \" and metrics[\\\"median_e2e_time_sec\\\"] <= THRESHOLDS[\\\"median_e2e_time_sec_max\\\"]\\n\",\n \" and metrics[\\\"failure_rate\\\"] == 0.0\\n\",\n \" )\\n\",\n \"\\n\",\n \" return {\\\"metrics\\\": metrics, \\\"go_no_go\\\": bool(go_no_go)}\\n\",\n \"\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"result = compute_metrics(df_eval)\\n\",\n \"print(\\\"Thresholds:\\\", THRESHOLDS)\\n\",\n \"for k, v in result[\\\"metrics\\\"].items():\\n\",\n \" print(f\\\"- {k}: {v}\\\")\\n\",\n \"print(f\\\"Go/No-Go: {'GO' if result['go_no_go'] else 'NO-GO'}\\\")\"\n ]", + "lineStart": 1690, + "lineEnd": 1779, + "totalLines": 1836, + "truncated": true + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783085516200, + "end": 1783085516212 + } + }, + "metadata": { + "openai": { + "itemId": "fc_00486c81caed7e74016a47b9cb5c34819ca5807ff7e1a23082" + } + }, + "id": "prt_f282dc58f001QEPl4vYJXG1w1I", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282dacad002axz1EHRoxPb2hD" + }, + { + "reason": "tool-calls", + "type": "step-finish", + "tokens": { + "total": 211928, + "input": 1917, + "output": 48, + "reasoning": 171, + "cache": { + "write": 0, + "read": 209792 + } + }, + "cost": 0.04313435, + "id": "prt_f282dc5fd001NzMVRPplruOY3j", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282dacad002axz1EHRoxPb2hD" + } + ] + }, + { + "info": { + "parentID": "msg_f2829fc2a001ZJ23yIGmBb50tA", + "role": "assistant", + "mode": "build", + "agent": "build", + "path": { + "cwd": "/home/magnus-bk/repos/material-hunters", + "root": "/" + }, + "cost": 0.0409045, + "tokens": { + "total": 213708, + "input": 1822, + "output": 46, + "reasoning": 0, + "cache": { + "write": 0, + "read": 211840 + } + }, + "modelID": "gpt-5.3-codex", + "providerID": "openai", + "time": { + "created": 1783085516312, + "completed": 1783085526682 + }, + "finish": "tool-calls", + "id": "msg_f282dc618002zSLifgK7GxflxI", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd" + }, + "parts": [ + { + "type": "step-start", + "id": "prt_f282dd1840015djZVkBHlIrNku", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282dc618002zSLifgK7GxflxI" + }, + { + "type": "step-start", + "id": "prt_f282de41c001ruEHQXRZNcCJai", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282dc618002zSLifgK7GxflxI" + }, + { + "type": "tool", + "tool": "read", + "callID": "call_00tNY73yYozdk1SlYvD6ZKJy", + "state": { + "status": "completed", + "input": { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "offset": 1780, + "limit": 80 + }, + "output": "<path>/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb</path>\n<type>file</type>\n<content>\n1780: },\n1781: {\n1782: \"cell_type\": \"code\",\n1783: \"execution_count\": null,\n1784: \"metadata\": {},\n1785: \"outputs\": [],\n1786: \"source\": [\n1787: \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n1788: \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n1789: \"\\n\",\n1790: \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n1791: \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n1792: \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n1793: \"\\n\",\n1794: \"if sigclip_df.empty:\\n\",\n1795: \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n1796: \"else:\\n\",\n1797: \" print(\\\"SigCLIP metrics:\\\")\\n\",\n1798: \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n1799: \"\\n\",\n1800: \"if dinov3_df.empty:\\n\",\n1801: \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n1802: \"else:\\n\",\n1803: \" print(\\\"DINOv3 metrics:\\\")\\n\",\n1804: \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n1805: \"\\n\",\n1806: \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n1807: \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n1808: \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n1809: \"\\n\",\n1810: \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n1811: \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n1812: \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n1813: \"\\n\",\n1814: \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n1815: \"\\n\",\n1816: \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n1817: \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n1818: \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n1819: \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n1820: ]\n1821: }\n1822: ],\n1823: \"metadata\": {\n1824: \"kernelspec\": {\n1825: \"display_name\": \"Python 3\",\n1826: \"language\": \"python\",\n1827: \"name\": \"python3\"\n1828: },\n1829: \"language_info\": {\n1830: \"name\": \"python\",\n1831: \"version\": \"3\"\n1832: }\n1833: },\n1834: \"nbformat\": 4,\n1835: \"nbformat_minor\": 5\n1836: }\n\n(End of file - total 1836 lines)\n</content>", + "metadata": { + "preview": " },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n \"\\n\",\n \"if sigclip_df.empty:\\n\",\n \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"SigCLIP metrics:\\\")\\n\",\n \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n \"\\n\",", + "truncated": false, + "loaded": [], + "display": { + "type": "file", + "path": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "text": " },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n \"\\n\",\n \"if sigclip_df.empty:\\n\",\n \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"SigCLIP metrics:\\\")\\n\",\n \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n \"\\n\",\n \"if dinov3_df.empty:\\n\",\n \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"DINOv3 metrics:\\\")\\n\",\n \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n \"\\n\",\n \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n \"\\n\",\n \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n \"\\n\",\n \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n \"\\n\",\n \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\",\n \"version\": \"3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}", + "lineStart": 1780, + "lineEnd": 1836, + "totalLines": 1836, + "truncated": false + } + }, + "title": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "time": { + "start": 1783085526621, 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\"\\n\",\n- \"if dinov3_df.empty:\\n\",\n- \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n- \"else:\\n\",\n- \" print(\\\"DINOv3 metrics:\\\")\\n\",\n- \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n- \"\\n\",\n- \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n- \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n- \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n- \"\\n\",\n- \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n- \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n- \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n- \"\\n\",\n- \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n- \"\\n\",\n- \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n- \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n- \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n- \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n+ \"if df_eval.empty:\\n\",\n+ \" print(\\\"No evaluation rows yet.\\\")\\n\",\n+ \"else:\\n\",\n+ \" for name in sorted(df_eval[\\\"model_name\\\"].dropna().unique().tolist()):\\n\",\n+ \" sub = df_eval[df_eval[\\\"model_name\\\"] == name]\\n\",\n+ \" print(f\\\"Model={name}\\\")\\n\",\n+ \" print(compute_metrics(sub)[\\\"metrics\\\"])\"\n*** End Patch" + }, + "output": "Success. Updated the following files:\nM home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "metadata": { + "diff": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1783,41 +1783,16 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n- \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n- \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n- \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n- \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n- \"\\n\",\n- \"if sigclip_df.empty:\\n\",\n- \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n+ \"if df_eval.empty:\\n\",\n+ \" print(\\\"No evaluation rows yet.\\\")\\n\",\n \"else:\\n\",\n- \" print(\\\"SigCLIP metrics:\\\")\\n\",\n- \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n- \"\\n\",\n- \"if dinov3_df.empty:\\n\",\n- \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n- \"else:\\n\",\n- \" print(\\\"DINOv3 metrics:\\\")\\n\",\n- \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n- \"\\n\",\n- \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n- \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n- \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n- \"\\n\",\n- \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n- \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n- \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n- \"\\n\",\n- \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n- \"\\n\",\n- \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n- \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n- \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n- \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ \" for name in sorted(df_eval[\\\"model_name\\\"].dropna().unique().tolist()):\\n\",\n+ \" sub = df_eval[df_eval[\\\"model_name\\\"] == name]\\n\",\n+ \" print(f\\\"Model={name}\\\")\\n\",\n+ \" print(compute_metrics(sub)[\\\"metrics\\\"])\"\n ]\n }\n ],\n \"metadata\": {\n\n", + "files": [ + { + "filePath": "/home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "relativePath": "home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb", + "type": "update", + "patch": "Index: /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n===================================================================\n--- /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n+++ /home/magnus-bk/repos/material-hunters/poc_eval_notebook.ipynb\n@@ -1783,41 +1783,16 @@\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n- \"def model_slice(df_in: pd.DataFrame, model_name: str) -> pd.DataFrame:\\n\",\n- \" return df_in[df_in[\\\"model_name\\\"].fillna(\\\"\\\").str.lower() == model_name.lower()]\\n\",\n- \"\\n\",\n \"df_eval = pd.read_csv(CSV_PATH)\\n\",\n- \"sigclip_df = model_slice(df_eval, \\\"SigCLIP\\\")\\n\",\n- \"dinov3_df = model_slice(df_eval, \\\"DINOv3\\\")\\n\",\n- \"\\n\",\n- \"if sigclip_df.empty:\\n\",\n- \" print(\\\"No SigCLIP rows yet.\\\")\\n\",\n+ \"if df_eval.empty:\\n\",\n+ \" print(\\\"No evaluation rows yet.\\\")\\n\",\n \"else:\\n\",\n- \" print(\\\"SigCLIP metrics:\\\")\\n\",\n- \" print(compute_metrics(sigclip_df)[\\\"metrics\\\"])\\n\",\n- \"\\n\",\n- \"if dinov3_df.empty:\\n\",\n- \" print(\\\"No DINOv3 rows yet.\\\")\\n\",\n- \"else:\\n\",\n- \" print(\\\"DINOv3 metrics:\\\")\\n\",\n- \" print(compute_metrics(dinov3_df)[\\\"metrics\\\"])\\n\",\n- \"\\n\",\n- \"if not sigclip_df.empty and not dinov3_df.empty:\\n\",\n- \" m_sig = compute_metrics(sigclip_df)[\\\"metrics\\\"]\\n\",\n- \" m_dino = compute_metrics(dinov3_df)[\\\"metrics\\\"]\\n\",\n- \"\\n\",\n- \" top1_gain = m_dino[\\\"top1_accuracy\\\"] - m_sig[\\\"top1_accuracy\\\"]\\n\",\n- \" edit_drop = m_sig[\\\"manual_edit_rate\\\"] - m_dino[\\\"manual_edit_rate\\\"]\\n\",\n- \" time_increase = (m_dino[\\\"median_e2e_time_sec\\\"] - m_sig[\\\"median_e2e_time_sec\\\"]) / max(m_sig[\\\"median_e2e_time_sec\\\"], 1e-9)\\n\",\n- \"\\n\",\n- \" promote = (top1_gain >= 0.05 or edit_drop >= 0.10) and time_increase <= 0.20\\n\",\n- \"\\n\",\n- \" print(f\\\"Top-1 gain (DINOv3 - SigCLIP): {top1_gain:.3f}\\\")\\n\",\n- \" print(f\\\"Edit-rate drop (SigCLIP - DINOv3): {edit_drop:.3f}\\\")\\n\",\n- \" print(f\\\"Median time increase: {time_increase:.3f}\\\")\\n\",\n- \" print(f\\\"Promote DINOv3: {'YES' if promote else 'NO'}\\\")\"\n+ \" for name in sorted(df_eval[\\\"model_name\\\"].dropna().unique().tolist()):\\n\",\n+ \" sub = df_eval[df_eval[\\\"model_name\\\"] == name]\\n\",\n+ \" print(f\\\"Model={name}\\\")\\n\",\n+ \" print(compute_metrics(sub)[\\\"metrics\\\"])\"\n ]\n }\n ],\n \"metadata\": {\n", + "additions": 6, + "deletions": 31 + } + ], + "diagnostics": {}, + "truncated": false + }, + "title": "Success. 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fusion diagnostics.\n- Reworked review logic in `poc_eval_notebook.ipynb:1502`:\n - supports `final_material_mode`, `final_single_material`, `final_mixed_materials`, and required confirmation for low-confidence cases.\n- Reworked export/passport in `poc_eval_notebook.ipynb:1556`:\n - blocks save if low-confidence is unconfirmed,\n - enforces mixed constraints (`<=3`, each `>=10`, sum `100`),\n - writes full material provenance (SigCLIP output, DINO output, fused output, suggestion/final mode).\n- Kept CSV compatibility by encoding mixed output into `user_final_material` (e.g. `mixed:wood=60|steel=40`) in `poc_eval_notebook.ipynb:1700`.\n\nI also updated the final metrics cell to report by `model_name` generically (since rows are now `SigCLIP+DINOv3`) in `poc_eval_notebook.ipynb:1787`.\n\nNext steps to validate in Colab:\n1. Add a few prototype images in `MATERIAL_PROTOTYPE_IMAGE_PATHS` (without this, DINO part falls back and fusion effectively favors SigCLIP).\n2. Run notebook end-to-end on 10–15 objects.\n3. Confirm:\n - no low-confidence saves without explicit confirmation,\n - mixed outputs pass constraints,\n - passport material block contains fused + provenance fields as expected.", + "time": { + "start": 1783085647050, + "end": 1783085663713 + }, + "metadata": { + "openai": { + "itemId": "msg_00754581bae14e98016a47ba4ed6fc81a39f8ef34341ec0a03", + "phase": "final_answer" + } + }, + "id": "prt_f282fc4ca001v8ZeeNDj5xOS5k", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282f39bf0022Q9Zvua3xoGYD5" + }, + { + "reason": "stop", + "type": "step-finish", + "tokens": { + "total": 216869, + "input": 232, + "output": 526, + "reasoning": 47, + "cache": { + "write": 0, + "read": 216064 + } + }, + "cost": 0.0462392, + "id": "prt_f2830061e001Mir0yBK0ehw3LD", + "sessionID": "ses_0dcaec232ffe29bNbkePnXKhbd", + "messageID": "msg_f282f39bf0022Q9Zvua3xoGYD5" + } + ] + } + ] +} |