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a/google_drive_export/dataset_annotated/labels/ffeac303cb5ada59.txt b/google_drive_export/dataset_annotated/labels/ffeac303cb5ada59.txt new file mode 100644 index 0000000000000000000000000000000000000000..eff97720e8b60f7ca21cfc9b6133e8abd0565683 --- /dev/null +++ b/google_drive_export/dataset_annotated/labels/ffeac303cb5ada59.txt @@ -0,0 +1 @@ +0 0.488530 0.502639 0.955919 0.993613 diff --git a/google_drive_export/docs/PROJECT_CONTEXT_AND_ACTION_PLAN.md b/google_drive_export/docs/PROJECT_CONTEXT_AND_ACTION_PLAN.md new file mode 100644 index 0000000000000000000000000000000000000000..81edc0d639d6b670228c83e2c0cdebb6d9980394 --- /dev/null +++ b/google_drive_export/docs/PROJECT_CONTEXT_AND_ACTION_PLAN.md @@ -0,0 +1,127 @@ +# E-Waste PCB Component Purity & Cannibalization Index: Project Context, System Architecture & Master Action Plan + +## 1. Project Overview & Business Context + +The **Component Purity & Cannibalization Index** is an edge-AI system built for Kabadiwala Connect (e-waste recycling platform). It detects real-time, on-device PCB cannibalization (the illicit stripping of valuable components like CPU sockets, gold edge connector fingers, IC microchips, and SMD tantalum capacitors) to: +1. Calculate a dynamic **Board Integrity Score (BIS)**. +2. Emit **8-language vernacular voice warnings** to scrap collectors for fair purchasing price adjustments. +3. Propagate component integrity data across the **Collector → Dealer → Recycler** supply chain to catch mid-chain cannibalization fraud. + +--- + +## 2. Current Project State & Progress Summary + +| Category | Current Status | Details & Artifacts | +| :--- | :--- | :--- | +| **Dataset Size** | **3,923 Normalized JPEGs** | Located in `data/images/` and `data/annotated_auto/images/` (6,864 total tracked). Average resolution: $1315 \times 1029\text{ px}$. | +| **Auto-Annotation** | **3,784 Draft Bounding Boxes** | Auto-labeled via YOLO-World zero-shot model (`pipeline/auto_annotate.py`, `conf=0.02`). | +| **Dataset Spec** | **YOLO Format Ready** | Specification saved to [`data/annotated_auto/data.yaml`](file:///home/taruns/Downloads/scraper/data/annotated_auto/data.yaml). Summary in [`data/annotated_auto/auto_annotation_summary.json`](file:///home/taruns/Downloads/scraper/data/annotated_auto/auto_annotation_summary.json). | +| **Updated Taxonomy** | **7 Fine-Grained Classes** | Defined in [`config/taxonomy.yaml`](file:///home/taruns/Downloads/scraper/config/taxonomy.yaml) (separates fraud desoldered pads from factory empty pads). | +| **Scraper Status** | **Harvest Completed (+681 Net New)** | Incremental harvest executed across 298 queries (`config/queries.yaml`). | +| **Pipeline Tests** | **11 / 11 Passed** | Test suite (`tests/test_pipeline.py`) fully compliant. | + +--- + +## 3. Architecture & Dual-Track Execution Strategy + +Development is decoupled into two parallel tracks linked by a frozen contract ([`model_contract.md`](file:///home/taruns/Downloads/scraper/implementation_plan_component_purity_index.md#L61)): + +``` + ┌────────────────────────────────────────────────────────┐ + │ model_contract.md (Frozen Schema) │ + └──────────────────────────┬─────────────────────────────┘ + │ + ┌────────────────────────┴────────────────────────┐ + │ │ +┌────────▼───────────────────────┐ ┌─────────────────▼─────────────────────┐ +│ Track A: Model & Dataset │ │ Track B: Application & Integration │ +├────────────────────────────────┤ ├───────────────────────────────────────┤ +│ • 7-Class Refined Taxonomy │ │ • `MockModelClient` for Dev/Testing │ +│ • Multi-Scale YOLOv8 Training │ │ • Integrity Engine (`purity_assay.py`) │ +│ • Focal Loss for Desoldered │ │ • Offline Pre-Recorded Audio Warnings │ +│ • TFLite INT8 Quantization │ │ • Collector Lite & Recycler Portal │ +│ • Real-World Test Evaluation │ │ • BIS-Delta Fraud Discrepancy UI │ +└────────────────┬───────────────┘ └─────────────────┬─────────────────────┘ + │ │ + └─────────────────┬───────────────────────┘ + ▼ + Swap Mock for Quantized Model (`TFLiteModelClient`) + (1-Line Config Change) +``` + +--- + +## 4. System Requirements & Architectural Adjustments + +Based on technical analysis and field realities, the following system-level changes are enforced: + +### A. Edge Hardware & Memory Optimization (Low-End Android 2GB–3GB RAM) +* **Sequential Model Execution**: Running the 21-class category model and YOLOv8 micro-model simultaneously triggers browser tab memory crashes. +* **Fix**: Enforce explicit tensor disposal (`model.dispose()`) after Stage 1 classification before loading Stage 2 micro-component detection. + +### B. Taxonomy Refinement (Eliminating False Fraud Signals) +* **Fix**: Split generic desoldered footprints into: + 1. `forcibly_desoldered_pad`: Exposed pads with jagged solder peaks, torn traces, or blowtorch/burnt flux residue (**fraud signal**). + 2. `factory_unpopulated_pad`: Clean silkscreen outline with smooth solder dots left unpopulated from factory (**NOT fraud**). + 3. `occluded_region`: Footprints covered by heatsinks, metal shields, or thick dust. + +### C. Dynamic Scrap Grade Pricing Floor +* **Fix**: Replace rigid 35% hardcoded floor with category scrap grade floors: + $$\text{Adjusted Price} = \text{Base Price} \times \left( \text{Floor}_{\text{category}} + (1 - \text{Floor}_{\text{category}}) \times \frac{\text{BIS}}{100} \right)$$ + * Server/GPU PCBs: $\text{Floor} = 0.15$ + * Desktop PCBs: $\text{Floor} = 0.30$ + * Low-grade Power PCBs: $\text{Floor} = 0.60$ + +### D. Multi-Angle 2-Sided Inspection Protocol +* **Fix**: Standardize a 2-photo workflow (Front Top-Down + Reverse Bottom-Down / Edge) in Collector Lite to inspect both sides of the PCB. + +### E. Supply Chain Fraud Tolerance Bands +* **Fix**: Update Recycler Portal intake discrepancy bands to: + * $< 12\%$ BIS Drop $\rightarrow$ Normal capture variance (Pass). + * $12\text{--}25\%$ BIS Drop $\rightarrow$ Advisory Warning / Discrepancy Flag. + * $> 25\%$ BIS Drop $\rightarrow$ Critical Fraud Alert & CPCB Form 6 PDF Audit Log Entry. + +### F. Offline Vernacular Audio Reliability +* **Fix**: Standardize pre-recorded `.opus` audio playback via Service Worker `CacheStorage` as a fallback when Web Speech API regional voice packs are missing offline. + +--- + +## 5. Master Plan of Action (Step-by-Step Roadmap) + +### Phase 1: Baseline Model Training (Track A) +- [x] Auto-annotate dataset with 3,130 draft boxes (`pipeline/auto_annotate.py`). +- [ ] Fine-tune baseline **YOLOv8n / YOLOv8s** on `data/annotated_auto/data.yaml` (`imgsz=960` or `1280`). +- [ ] Evaluate per-class mAP@50 and fraud recall on `forcibly_desoldered_pad`. +- [ ] Quantize model to TFLite INT8 ($<15\text{ MB}$, target $<1\text{s}$ latency). + +### Phase 2: Incremental Harvest & Synthetic Data (Optional Extension) +- [ ] Execute optional incremental scraper batch (~500–1,000 images) using updated [`config/queries.yaml`](file:///home/taruns/Downloads/scraper/config/queries.yaml). +- [ ] Apply CutMix / Copy-Paste synthetic desoldered pad data synthesis during training. + +### Phase 3: Purity Engine Module Build (Track B) +- [ ] Build `purity_engine/model_client.py` (`MockModelClient` & `TFLiteModelClient`). +- [ ] Build `purity_engine/purity_assay.py` (Form-factor baseline matching + dynamic scrap pricing formula). +- [ ] Build `purity_engine/vernacular_voice.py` (Web Speech API + `.opus` fallback). + +### Phase 4: App Integration & End-to-End Verification +- [ ] Extend Collector Lite (`mobile/kabadiwala-lite.html`) with 2-photo workflow & inline BIS display. +- [ ] Extend Recycler Portal (`recycler-dashboard-frontend/`) with BIS-delta view ($12\% / 25\%$ flags). +- [ ] Run automated test suite (`tests/test_purity_assay.py`, `tests/test_bis_delta_api.py`). + +--- + +## 6. Key Configuration & Pipeline Commands Quick Reference + +* **Run Automated Tests**: + ```bash + .venv/bin/python -m pytest + ``` +* **Run Auto-Annotation Script**: + ```bash + .venv/bin/python pipeline/auto_annotate.py + ``` +* **Run Incremental Web Scraper**: + ```bash + .venv/bin/python scraper.py harvest + ``` +* **Dataset Location**: `data/annotated_auto/` diff --git a/google_drive_export/docs/implementation_plan.md b/google_drive_export/docs/implementation_plan.md new file mode 100644 index 0000000000000000000000000000000000000000..d3a2f2161ba3c476afce94880091adbc006add3f --- /dev/null +++ b/google_drive_export/docs/implementation_plan.md @@ -0,0 +1,72 @@ +# Implementation Plan: E-Waste Dataset Scraper & Pipeline (v2 Architecture) + +Build a modular, production-grade Python web scraper and image preprocessing pipeline following `scraper_plan_v2.md` to harvest, clean, deduplicate, and prepare high-resolution e-waste PCB dataset images for fine-tuning YOLOv8 micro-component detectors. + +## User Review Required + +> [!IMPORTANT] +> **Scraping Target & Volume:** The pilot harvest target will be set to **500 images per query batch** across Bing and Flickr to keep the pilot run under 10–15 minutes while establishing real attrition rates. +> **Dependency Note:** `icrawler`, `pillow`, `imagehash`, and `open_clip_torch` (or lightweight CLIP filtering) will be installed in the environment. + +## Open Questions + +> [!NOTE] +> **CLIP Relevance Filter Mode:** Should the initial script execute CLIP zero-shot relevance filtering locally via PyTorch, or fall back to rule-based aspect-ratio/color-histogram heuristic filtering if PyTorch GPU memory is constrained during background runs? (Default: Rule-based + optional CLIP module). + +--- + +## Proposed Changes + +### Scraping & Pipeline Package (`ewaste_dataset_pipeline/`) + +Creation of a self-contained, modular Python CLI pipeline under `ewaste_dataset_pipeline/`. + +#### [NEW] [`config/taxonomy.yaml`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/config/taxonomy.yaml) +- Defines the 5 target YOLO detection classes: `cpu_socket`, `gold_finger`, `ic_chip`, `tantalum_capacitor`, `desoldered_zone`. +- Defines image-level tags: `board_type`, `condition`, `source_type`. + +#### [NEW] [`config/queries.yaml`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/config/queries.yaml) +- Search query expansion matrix across search terms, condition variants, board types, and multilingual terms. + +#### [NEW] [`pipeline/manifest.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/manifest.py) +- SQLite database abstraction layer (`manifest.db`) tracking URL provenance, download status, MD5 checksums, resolution, pHash values, cluster IDs, and split assignments. + +#### [NEW] [`pipeline/crawler.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/crawler.py) +- URL harvester using `icrawler` (Bing, Flickr, Baidu) with exponential backoff retries, custom headers, and rate limiting. Saves raw bytes to `data/originals/`. + +#### [NEW] [`pipeline/validator.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/validator.py) +- Validation engine checking file integrity (`verify()` + `load()`), short/long resolution cutoffs ($\ge 480\text{ px}$ short side), and aspect ratio bounds (flag $> 3:1$, drop $> 6:1$). + +#### [NEW] [`pipeline/processor.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/processor.py) +- Image normalizer handling EXIF auto-rotation (`exif_transpose`), RGBA alpha white-background compositing, CMYK to RGB conversion, and JPEG 95 quality encoding. + +#### [NEW] [`pipeline/dedup.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/dedup.py) +- Multi-tier deduplication engine (SHA-256 exact byte hash + perceptual `pHash`/`dHash` with horizontal flip awareness). + +#### [NEW] [`scraper.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/scraper.py) +- Unified CLI entry point supporting subcommands: `harvest`, `validate`, `process`, `dedup`, `report`. + +#### [NEW] [`requirements.txt`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/requirements.txt) +- Dependency specifications (`icrawler`, `Pillow`, `imagehash`, `pyyaml`, `tqdm`). + +--- + +## Verification Plan + +### Automated Verification +1. **Unit & Module Tests:** + - Test image validation on corrupted, truncated, transparent PNG, CMYK, and EXIF-rotated sample images. + - Verify pHash deduplication on identical vs resized/flipped images. +2. **CLI Execution Runs:** + - Execute pilot harvest run: + ```bash + cd ewaste_dataset_pipeline + python3 scraper.py harvest --max-num 50 + python3 scraper.py validate + python3 scraper.py process + python3 scraper.py dedup + python3 scraper.py report + ``` + +### Manual Verification +- Inspect generated `reports/summary.json` to verify image yield, discard counts by reason, and clean `dataset_raw/` outputs. diff --git a/google_drive_export/docs/implementation_plan_component_purity_index.md b/google_drive_export/docs/implementation_plan_component_purity_index.md new file mode 100644 index 0000000000000000000000000000000000000000..aa06b519807da0937a2be778706971c1769ad39f --- /dev/null +++ b/google_drive_export/docs/implementation_plan_component_purity_index.md @@ -0,0 +1,148 @@ +# Implementation Plan: Edge "Component Purity & Cannibalization Index" (Updated) + +This feature delivers real-time, on-device detection of PCB cannibalization (stripping/depopulation of valuable components like CPU sockets, gold edge fingers, and tantalum capacitors) to calculate a **Board Integrity Score (BIS)**, generate vernacular voice warnings for scrap pricing adjustments, and propagate integrity data through the Dealer → Recycler chain to catch mid-chain stripping fraud. + +Model training data collection is still in progress, so this plan is split into two tracks joined by a single frozen interface (`model_contract.md`): **Track A** builds and trains the detection model; **Track B** builds the full app pipeline against a mock model conforming to that same contract, so integration is not blocked on model readiness. Swapping the mock for the trained model later is a one-line config change, not a rewrite. + +--- + +## User Review Required + +> [!IMPORTANT] +> - **Labeling taxonomy refinement**: Split desoldered footprints into `forcibly_desoldered_pad` (jagged, torn traces, burnt flux) and `factory_unpopulated_pad` (pristine silkscreen, smooth solder dots) to eliminate false cannibalization alerts on unpopulated board regions. Added `occluded_region` to prevent penalizing components covered by heatsinks/dust. +> - **Form-factor baselines**: Replaced static class-wide component counts with form-factor sub-profiles (`desktop_atx`, `desktop_micro_atx`, `server_1u`, `gpu_pcb`, `ram_dimm`) in `board_profiles.json` to prevent small intact boards from receiving low BIS scores. +> - **Dynamic price floor**: Replaced the rigid 35% hardcoded price floor with category-based scrap grade floors ($\text{Floor}_{\text{category}}$ = 15% for high-grade server/GPU boards up to 60% for low-grade power boards). +> - **Sequential edge execution**: Adopted sequential model loading (`dispose()` calls between 21-class category classifier and YOLOv8 micro-model) to prevent memory crashes on 2GB–3GB RAM Android phones. +> - **BIS-delta tolerance bands**: Updated recycler intake fraud threshold to $>12\%$ for advisory flags and $>25\%$ for critical alerts (up from 5%) to accommodate field-vs-warehouse camera & lighting variances. +> - **Offline voice fallback**: Standardized pre-recorded `.opus` audio snippets via Service Worker `CacheStorage` as a fallback when Web Speech API offline regional voice packs are missing. +> - **Data sourcing strategy**: Confirmed that existing open datasets can bootstrap the generic (`*_intact`) component classes, removing most of the annotation burden for those; the fraud-specific classes (`forcibly_desoldered_pad`, `occluded_region`) still require primarily field-staged photography, supplemented — not replaced — by mined web sources. See Phase 1, Component 2a. **Requires a decision on whether forum/marketplace-sourced supplemental images are approved for use** (see licensing note in Component 2a) before any scraping work begins. + +--- + +## Track A: Micro-Component Detection Model + +### Phase 1: Data Collection & Labeling (Updated Guidelines) + +#### Component 1: Refined Labeling Taxonomy (`taxonomy/component_classes.yaml`) +- [NEW] Defines fine-grained class states to eliminate false positives: + - `bga_cpu_socket_intact`, `bga_cpu_socket_missing` + - `gold_edge_finger_intact`, `gold_edge_finger_stripped` + - `tantalum_cap_intact`, `tantalum_cap_missing` + - `ram_dimm_slot_intact`, `ram_dimm_slot_stripped` + - `forcibly_desoldered_pad` (jagged solder, torn traces, blowtorch/burnt flux residue — primary fraud signal) + - `factory_unpopulated_pad` (clean silkscreen outline, smooth solder dots — unpopulated from factory, NOT fraud) + - `occluded_region` (heatsinks, metal shields, heavy dirt/dust covering component pads) + +#### Component 2: Data Collection & Sourcing Protocol (`data_collection/`) +- [NEW] `capture_protocol.md` — multi-angle capture protocol (Front side top-down, Reverse side top-down, and macro gold edge connector shot) to handle 2-sided PCB inspection. +- [NEW] `board_sourcing_log.csv` — logs board type, source, form factor (`desktop_atx`, `micro_atx`, `server_4u`, etc.), and intentional stripping state (pristine, blowtorched, organically degraded, factory unpopulated). +- Sourcing requirements: Must harvest dirty, corroded, dusty, and factory-unpopulated boards alongside pristine and stripped examples to match field scrap yard conditions. + +#### Component 2a: Data Sourcing Strategy — Open Datasets, Mined Web Sources, and Field Photography (`data_collection/sourcing_strategy.md`) + +Research into publicly available data confirms a **three-tier sourcing strategy**, since scraping alone cannot cover the fraud-specific classes: + +**Tier 1 — Open datasets, for bootstrapping `*_intact` classes (use directly, no scraping needed):** +- A Dataset Ninja "PCB Component Detection" set — 1,410 images, 11,119 bounding-box-labeled objects across 9 classes (capacitor, transformer, MOSFET, resistor, etc.), released CC0 and explicitly tagged for the waste-recycling use case. +- HuggingFace `PCB_COMPONENTS_LABELLED` — 17-class YOLO-format set (IC, capacitor, connector, crystal oscillator, diode, ferrite bead, fuse, inductor, LED, resistor, switch, transistor, pins, pads, test points). +- HuggingFace `pcb-object-detection-dataset` — 21-class YOLO set including heatsinks and connectors. +- Roboflow Universe community PCB datasets (component-level bounding boxes, CC BY 4.0). +- **Use**: pretrain the detection backbone and pad out the `*_intact` and generic-component classes; re-map class names to `taxonomy/component_classes.yaml`. This does not reduce the need for field data on fraud-specific classes below, but meaningfully cuts the annotation volume needed for the "board still has its parts" side of the taxonomy. + +**Tier 2 — Mined web sources, as a supplemental seed set for `forcibly_desoldered_pad` only (not a primary source):** +- Gold-recovery/e-waste hobbyist forums (e.g. Gold Refining Forum, finishing.com) contain user-posted photos of boards before, during, and after chip/pin removal, and active community discussion distinguishing "unpopulated" from "depopulated" boards — the closest organic analog to the `forcibly_desoldered_pad` class available online. +- Hobbyist "gold recovery" / heat-gun chip-pulling YouTube videos are a higher-yield variant of the same source: frame-extraction from a single video can produce many more usable images than a forum thread. +- **Known limitations, by design not treated as a primary source**: + - Low volume per source and inconsistent photo quality/angle — doesn't match the phone-camera, scrap-yard conditions defined in `capture_protocol.md`. + - No existing bounding boxes — still requires full manual annotation, same as field photos. + - Licensing: forum and video content is user-generated and not freely licensed for reuse; scraping is low-risk for an internal/hackathon prototype but is **not cleared for production training data** without explicit permission or a licensed dataset. Flagged in User Review Required above — treat as blocked until a decision is made. +- **Use**: a small supplemental set to diversify what "stripped" looks like beyond staged photos, mined and annotated in parallel with, not instead of, Tier 3. + +**Tier 3 — Deliberately staged field photography, mandatory and primary for fraud classes (this is the existing Component 2 protocol above):** +- Only source that guarantees: domain match to real collector phone conditions, controlled multi-angle capture, and clean separation between `forcibly_desoldered_pad`, `factory_unpopulated_pad`, and `occluded_region` at capture time (know the ground truth because you staged it, rather than inferring it after the fact from a forum photo). +- Remains the dataset of record for Phase 4 evaluation thresholds; Tier 1/2 data is for pretraining and augmentation only, not held-out test data. + +#### Component 3: Annotation & QA +- [NEW] Bounding-box annotation via CVAT or Label Studio, exported in COCO/YOLO format. +- [NEW] `qa_annotation_review.py` — inter-annotator agreement check (min. 2 annotators per image, flag disagreement >10% of boxes for adjudication). +- Target: minimum 300 images per class, with at least 30% deliberately-stripped/burnt negative examples, before first training run. + +### Phase 2: Form-Factor Baseline Definitions + +#### Component 4: Dynamic Board Profiles (`baselines/board_profiles.json`) +- [NEW] Defines expected component counts/area ratios per board class **and form factor**: + - `desktop_atx`, `desktop_micro_atx`, `desktop_itx` + - `server_1u_2u`, `server_blade_4u` + - `gpu_pcb_high_end`, `gpu_pcb_budget` + - `ram_dimm_desktop`, `ram_so_dimm_laptop` +- Sourced from recycling domain experts — consumed downstream by `purity_assay.py`. + +### Phase 3: Model Training + +#### Component 5: Training Pipeline (`training/train.py`) +- [NEW] YOLOv8n/s fine-tuning with multi-scale training (`imgsz=960` or `1280`) and custom anchor ratios for elongated `gold_finger` boxes ($>4:1$). +- Heavy augmentations for field realities: specular glare on gold fingers, lighting variance, dirt noise, and angle tilt. +- [NEW] `train_config.yaml` — Focal loss & elevated class weights for `forcibly_desoldered_pad` vs `factory_unpopulated_pad`. + +#### Component 6: Auxiliary Desoldered Pad Classifier Head +- [NEW] Secondary classification head specifically trained to distinguish desoldered blowtorch pads from factory empty pads. + +#### Component 7: Export & Quantization (`export/export_tflite.py`) +- [NEW] Export to TFLite INT8 quantized, target size <15MB, target inference <1s on low-end Android hardware. +- [NEW] `model_contract.md` — freezes input/output tensor schema. + +### Phase 4: Evaluation + +#### Component 8: Model Evaluation (`eval/evaluate.py`) +- [NEW] Per-class mAP, plus dedicated fraud recall on `forcibly_desoldered_pad` and false-positive rate on `factory_unpopulated_pad`. +- Thresholds: $\ge 90\%$ recall on intact classes, $\ge 85\%$ recall on desoldered pad classes, and $\le 5\%$ false positive rate on factory unpopulated pads. + +--- + +## Track B: Integration into Kabadiwala Connect + +### Component 9: Purity Engine & Memory-Conscious Orchestrator (`purity_engine/`) + +- [NEW] `purity_engine/model_client.py` — wrapper supporting `MockModelClient` and `TFLiteModelClient` with explicit tensor memory management (`model.dispose()` between Stage 1 and Stage 2). +- [NEW] `purity_engine/purity_assay.py` — implements `calculate_board_integrity_score(detected_components, form_factor)`: + - Dynamic price formula: + $$\text{Adjusted Price} = \text{Base Price} \times \left( \text{Floor}_{\text{category}} + (1 - \text{Floor}_{\text{category}}) \times \frac{\text{BIS}}{100} \right)$$ + - Scrap Grade Floors: Server/GPU = 15%, Desktop = 30%, Low-grade Power = 60%. +- [NEW] `purity_engine/pcb_analyzer.py` — single entry point for Collector Lite & Recycler Portal. + +### Component 10: Vernacular Voice Alerts with Audio Fallback (`purity_engine/vernacular_voice.py`) + +- [NEW] Extends Collector Lite's 8-language Web Speech API audio system with Service Worker pre-recorded `.opus` audio fallbacks for offline devices lacking regional voice packs. +- Message templates parameterized by BIS band (Pristine $\ge 85\%$, Partial $50\text{--}84\%$, Severe $<50\%$). + +### Component 11: Collector Lite Multi-Angle UI Integration (`mobile/kabadiwala-lite.html`) + +- Extend Step 1 of Collector Lite: Stage 1 21-class classifier detects `PCB` $\rightarrow$ prompts guided Front/Back photo capture $\rightarrow$ triggers `pcb_analyzer`. +- Displays BIS score, missing-component breakdown, and transparent price adjustment in Step 3. +- Persists assay JSON into IndexedDB with the scrap slip. + +### Component 12: QR Handover Payload Enrichment (`mobile/dealer.html`, backend) + +- QR payload includes collector BIS score, form factor, and assay report hash. + +### Component 13: Recycler Portal BIS-Delta View (`recycler-dashboard-frontend/`) + +- Re-scan intake lot via `pcb_analyzer`. +- Tolerance Bands: + - $< 12\%$ BIS Drop $\rightarrow$ Normal capture variance (Pass). + - $12\text{--}25\%$ BIS Drop $\rightarrow$ Discrepancy Flagged. + - $> 25\%$ BIS Drop $\rightarrow$ Critical Fraud Alert & CPCB Form 6 PDF Audit Log Entry. + +### Component 14: Backend API Changes (`backend/`) + +- [NEW] `POST /api/lots/{lot_id}/assay` & `GET /api/lots/{lot_id}/bis-delta`. + +--- + +## Sequencing Summary + +| | Track A (Model & Dataset) | Track B (App Integration) | +|---|---|---| +| Now | Tier 1: bootstrap from open datasets. Tier 3: harvest dirty/stripped/unpopulated boards; annotate fine-grained taxonomy. Tier 2 (mined web sources) blocked pending licensing decision | Build multi-angle UI & pricing engine against `MockModelClient` | +| Parallel | Multi-scale YOLOv8 training with focal loss & TFLite INT8 quantization | Collector Lite, audio fallbacks, Recycler Portal BIS-delta tolerance UI | +| Hand-off | Ship model clearing Phase 4 thresholds | Swap `TFLiteModelClient` in with zero code changes | diff --git a/google_drive_export/docs/scraper_plan_v2.md b/google_drive_export/docs/scraper_plan_v2.md new file mode 100644 index 0000000000000000000000000000000000000000..f37ea5c197a4a62c5f3da2716f3d462851059be8 --- /dev/null +++ b/google_drive_export/docs/scraper_plan_v2.md @@ -0,0 +1,485 @@ +# E-Waste Image Dataset Pipeline: Revised Plan (v2) + +## 0. What changed from v1 + +v1 framed the work as a scraping problem. v2 treats it as a **dataset-quality problem** in which scraping is one input. The main changes: + +| Area | v1 | v2 | +| :--- | :--- | :--- | +| Class definitions | Scrape folders and annotation classes disagreed | One taxonomy defined first; queries derived from it | +| Deduplication | MD5 only (byte-identical) | Exact hash + perceptual hash (flip-aware) + embedding clusters, global across classes | +| Aspect-ratio filter | Discarded > 3:1 (removes RAM/edge-finger images) | Flag > 3:1, discard only > 6:1 | +| Min resolution | 250 px | Short side ≥ 480 px, long side ≥ 800 px | +| Relevance | None | CLIP zero-shot filter + manual review queue | +| Domain gap | Web images only | Real-world capture set, held-out real-world test split | +| Splits | Random train/val | Cluster-aware train/val + separate real-world test | +| Small objects | Not addressed | Resolution policy, `imgsz=1280`, tiling/two-stage inference | +| Image handling | `verify()` only, JPEG q90 | `verify()` + `load()`, EXIF fix, alpha compositing, q95, originals preserved | +| Resilience | Claimed backoff/fallback that `icrawler` lacks | Custom downloader with retries, backoff, per-domain rate limits | +| Provenance | Sequential filenames | Hash filenames + SQLite manifest, resumable stages | +| Targets & metrics | None | Per-class image/instance targets, evaluation plan | +| BIS | Undefined | Proposed definition (needs sign-off) | + +--- + +## 1. Objective & Scope + +Build a repeatable pipeline that produces a clean, deduplicated, well-documented image dataset for a YOLO detector that finds components on PCBs and supports an on-device **Board Integrity Score (BIS)** in a mobile app. + +**In scope:** collection, validation, normalization, deduplication, relevance filtering, manifesting, splitting, annotation guidelines, training/evaluation plan, export. +**Out of scope for this document:** app UI, backend, pricing/valuation logic beyond BIS inputs. + +### Success criteria + +- ≥ 1,500 labeled images per class and ≥ 10,000 labeled instances per class in the training set (common YOLO guideline; revisit after the first training run). +- Zero train/val/test leakage (no near-duplicate or same-board pairs across splits). +- ≥ 40% of the final set is "in-the-wild" imagery (dirty, cluttered, phone-shot), not clean listing photos. +- Held-out real-world test set of ≥ 300 images that is never used for tuning. +- Every image traceable to its source URL, query, and processing history via the manifest. + +--- + +## 2. Phase 0: Decisions to lock before writing code + +### 2.1 Detection taxonomy (fixes the v1 class mismatch) + +Define **object classes** (what YOLO detects) separately from **image-level tags** (metadata used for search and stratification). + +**Detection classes (v1 of the label set):** + +| Class | Definition | Notes | +| :--- | :--- | :--- | +| `cpu_socket` | LGA, PGA, or ZIF processor socket footprint (populated or empty) | Sockets only; see BGA note below | +| `gold_finger` | Gold-plated edge-connector contacts (PCIe, RAM, edge cards) | Long, thin objects; boxes must not be forced square | +| `ic_chip` | Packaged ICs, including BGA-package chips soldered to the board | Optional sub-attribute `bga` in annotation tool | +| `tantalum_capacitor` | SMD tantalum capacitors (typically yellow/orange/black molded bodies with polarity bar) | Small object; needs labeling rules | +| `desoldered_zone` | Region with exposed pads/solder residue where a component was removed | Ambiguous; needs a precise definition and example gallery | + +> **BGA correction:** BGA (ball grid array) is a chip *package* soldered onto the board. LGA/PGA are *sockets*. v1's `bga_cpu_socket` conflated them. In v2, BGA chips fall under `ic_chip`, and socket queries target LGA/PGA only. + +**Image-level tags (manifest metadata, not YOLO classes):** + +- `board_type`: desktop, laptop, phone, server, GPU, other +- `condition`: intact, stripped, burnt/damaged +- `source_type`: listing (white background), in_the_wild, recycling_yard, render/diagram (to be discarded) + +Folders are **not labels**. Search-query hints are noisy and are stored only as `class_hint` metadata. + +### 2.2 Target counts and attrition budget + +Boards contain several classes per image, so ~3,000-4,000 unique labeled images may cover all classes, but the rare/small classes drive the budget. + +| Class | Target images | Target instances | Difficulty | +| :--- | :--- | :--- | :--- | +| `ic_chip` | 1,500 | 15,000+ | Easy (abundant) | +| `cpu_socket` | 1,500 | 3,000+ | Medium | +| `gold_finger` | 1,500 | 5,000+ | Medium | +| `tantalum_capacitor` | 1,500 | 10,000+ | Hard (small, visually varied) | +| `desoldered_zone` | 1,500 | 5,000+ | Hard (ambiguous) | + +Expect **50-70% attrition** across validation, dedup, and relevance filtering, so plan to harvest roughly **3x the target** (about 10,000-12,000 raw downloads). Set the exact harvest budget after a 500-image pilot measures the real attrition rate. + +### 2.3 Board Integrity Score (BIS): proposed definition + +BIS must be defined now because it determines which labels are needed. + +Proposal (needs domain-owner sign-off): + +``` +V_present = Σ over detected high-value classes c of w_c × count_c + (cpu_socket, gold_finger, ic_chip, tantalum_capacitor) +V_missing = w_d × (number or area-normalized sum of desoldered_zone detections) + +BIS = 100 × V_present / (V_present + V_missing) +``` + +Open questions: value weights `w_c` (need recycler input), whether to use counts or area, per-board-type normalization, and how detector confidence thresholds affect BIS stability. Log these as decisions in `docs/decisions.md`. + +--- + +## 3. System Architecture + +```mermaid +flowchart TD + A["Config: taxonomy + query matrix"] --> B["URL harvest via icrawler: Bing / Baidu / Flickr"] + B --> C["Resilient downloader: retry, backoff, rate limit"] + RW["Real-world photo capture"] --> E + C --> D[("Originals store: untouched bytes, sha256 names")] + D --> E{"Validation"} + E -- "fail: corrupt, under 20 KB, below min resolution, over 6:1" --> X["Discard and log reason"] + E -- "pass (flag if over 3:1)" --> F["Normalize: EXIF fix, RGB, JPEG q95"] + F --> G{"Dedup: exact + perceptual, global"} + G -- "duplicate" --> X + G -- "unique" --> H{"CLIP relevance filter"} + H -- "irrelevant" --> X + H -- "borderline" --> R["Manual review queue"] + H -- "relevant" --> M["Manifest: SQLite"] + R --> M + M --> S["Cluster-aware split: train / val / test"] + S --> L["Annotation: CVAT or Roboflow"] + L --> T["YOLO training and evaluation"] +``` + +Design principles: + +1. **Stages are idempotent and resumable.** Each image has a `status` in the manifest; reruns skip completed work. +2. **Originals are never modified.** Normalized copies are derived artifacts and can be regenerated with different settings. +3. **Every discard is logged with a reason** so thresholds can be tuned from data instead of guesses. + +--- + +## 4. Directory & Dataset Schema + +``` +ewaste_dataset_pipeline/ +├── scraper.py # CLI entry point (subcommands: harvest, validate, process, dedup, filter, split, report) +├── config/ +│ ├── config.yaml # thresholds, engines, rate limits, seeds +│ ├── taxonomy.yaml # classes, definitions, image-level tags +│ └── queries.yaml # query matrix (see §5.1) +├── pipeline/ +│ ├── crawler.py # URL harvest + custom downloader +│ ├── validator.py +│ ├── processor.py # normalization +│ ├── dedup.py # exact + perceptual + embedding clusters +│ ├── relevance.py # CLIP zero-shot filter +│ ├── manifest.py # SQLite state store + JSONL/CSV export +│ ├── split.py # cluster-aware splitting +│ └── report.py # summary statistics +├── tests/ +│ ├── fixtures/ # truncated JPEG, RGBA PNG, EXIF-rotated, CMYK, animated GIF, 5:1 RAM image +│ └── test_*.py +├── data/ +│ ├── originals/ # untouched downloaded bytes: . +│ ├── images/ # normalized JPEGs: .jpg (flat store) +│ ├── review/ # borderline / flagged images for manual review +│ ├── realworld/ # own captures, organized by session_id +│ ├── manifest.db # SQLite source of truth +│ └── manifest.jsonl # export for tooling +├── splits/ +│ └── splits.json # frozen image_id -> split mapping (versioned) +├── reports/ +│ └── summary.json +├── dataset_annotated/ # YOLO export after labeling +│ ├── data.yaml +│ ├── train/ +│ ├── val/ +│ └── test/ # real-world sessions only +├── docs/ +│ ├── decisions.md +│ └── labeling_guide.md +└── requirements.txt +``` + +The flat `images/` store replaces per-class folders because one image can match several queries. Per-hint browsing views can be generated from the manifest on demand. + +--- + +## 5. Core Modules + +### 5.1 Query & Category Registry (`queries.yaml`) + +Queries are **derived from the taxonomy**, then expanded along diversity axes. Two or three queries per class, as in v1, will not produce enough variety. + +**Expansion matrix:** `class term × board_type × condition × view/lighting × language` + +| Axis | Example values | +| :--- | :--- | +| Board type | desktop motherboard, laptop mainboard, smartphone PCB, server board, GPU PCB | +| Condition | intact, stripped, burnt, corroded, dusty | +| View / setting | top view, close-up macro, angled, recycling bin, workbench, e-waste pile, hand-held | +| Language | English, plus Chinese, Spanish, German, Hindi, Vietnamese (recycling hubs and local marketplaces) | + +Example seed terms per class: + +- `cpu_socket`: "LGA 1151 socket close up", "LGA 1700 socket bent pins", "PGA socket AM4 motherboard", "empty CPU socket motherboard scrap" +- `gold_finger`: "PCIe x16 gold fingers PCB", "RAM module gold contacts scrap", "edge connector gold plated board" +- `ic_chip`: "BGA chip on motherboard close up", "SMD IC laptop motherboard", "chipset heatsink removed motherboard" +- `tantalum_capacitor`: "SMD tantalum capacitor yellow PCB", "tantalum capacitor polarity stripe board", "tantalum capacitors recycling" +- `desoldered_zone`: "desoldered components exposed pads PCB", "cannibalized motherboard ewaste scrap", "hot air removed chips board", "burnt circuit board blowtorch" + +Notes: + +- Search engines cap results per query (often a few hundred at best), so **volume comes from query variety**, not from raising `max_num`. +- Record `query`, `engine`, and `class_hint` per URL in the manifest. + +### 5.2 Harvesting & Download Engine (`crawler.py`) + +- **Engines:** `icrawler` Bing, Baidu, and Flickr crawlers. The Google crawler is unreliable and optional. **DuckDuckGo is not natively supported by `icrawler`** and is removed from v1 scope; add it later only through a separate adapter. +- **Two-step design:** + 1. Use `icrawler` to harvest URLs and metadata into the manifest with status `pending`. + 2. A custom downloader (subclass of `icrawler`'s `ImageDownloader`, or a `requests` session pool) performs the fetch. +- **Resilience (implemented explicitly, since `icrawler` does not provide it):** + - Exponential backoff with jitter (e.g., `tenacity`), max 4 retries + - Per-domain rate limiting and concurrency caps + - Connect/read timeouts (e.g., 5s / 20s), maximum download size (e.g., 25 MB) + - Content-Type and magic-byte sniffing before saving + - Rotating realistic User-Agent strings + - Respect HTTP 429 / `Retry-After` +- **Concurrency:** 4-8 downloader threads per query, global cap across queries. +- **Resumability:** URL-normalized dedup at the manifest level so reruns never re-fetch a URL already tried (successful or permanently failed). +- **Storage:** save untouched bytes to `data/originals/` under `.`. + +### 5.3 Validation Engine (`validator.py`) + +Checks run in a fixed order so cheap checks fail first: + +| # | Check | Rule | Action | +| :--- | :--- | :--- | :--- | +| 1 | File size | < 20 KB | Discard (`too_small_file`) | +| 2 | Integrity | `Image.open()` → `verify()` → **reopen** → `load()` (catches truncation that `verify()` misses) | Discard (`corrupt`) | +| 3 | Animated | Multi-frame GIF/WEBP | Use first frame; flag `animated` | +| 4 | Resolution | Short side < 480 px **or** long side < 800 px | Discard (`low_resolution`) | +| 5 | Aspect ratio | > 3:1 or < 1:3 | **Flag** `extreme_aspect`, send to review | +| 6 | Aspect ratio (hard) | > 6:1 or < 1:6 | Discard (`extreme_aspect_hard`) | + +Why the aspect-ratio change: RAM modules and edge-finger crops are naturally ~4.5-5:1. A 3:1 hard cutoff would remove exactly the images the `gold_finger` class needs. + +Why the resolution change: tantalum capacitors and desoldered zones occupy very few pixels; a 250 px image contains almost no usable detail for them. + +### 5.4 Normalization Engine (`processor.py`) + +Applied to a **copy** of each validated image: + +1. **`ImageOps.exif_transpose()`** first, so phone photos are not rotated incorrectly. +2. **Mode conversion to RGB:** + - RGBA / LA / P-with-transparency: composite onto a **white** background (a naive `convert("RGB")` turns transparency black). + - CMYK: convert via embedded ICC profile when present (`ImageCms`), otherwise standard conversion. + - Grayscale: replicate to 3 channels. +3. **Size cap:** if the long side > 2560 px, downscale with LANCZOS (never upscale). This bounds storage while preserving detail for small objects. +4. **Encode:** JPEG **quality 95**, 4:4:4 chroma subsampling (`subsampling=0`), `optimize=True`. This limits the extra lossy generation and keeps fine solder/pad detail that q90 with default 4:2:0 would blur. +5. **Metadata:** strip EXIF (privacy and size); store dimensions and original format in the manifest. +6. Output name: `data/images/.jpg`, where the hash is of the original file bytes. + +### 5.5 Deduplication Engine (`dedup.py`) + +MD5 catches only byte-identical files. A resized or recompressed copy of the same photo gets a different hash, and near-duplicates that straddle train/val inflate metrics. v2 uses three layers, applied **globally across all classes and all queries**: + +| Layer | Method | Catches | +| :--- | :--- | :--- | +| 1. Exact | SHA-256 of original bytes (pre-decode) and of decoded pixel array | Byte-identical and re-saved-identical images | +| 2. Near-duplicate | Perceptual hash (`imagehash` pHash, 64-bit, plus dHash) with Hamming distance ≤ 6; also hash the **horizontally flipped** image and compare | Resizes, recompression, minor crops/edits, mirror copies | +| 3. Semantic clusters | CLIP image embeddings; cosine similarity ≥ 0.95 → same cluster (agglomerative or connected components) | Same board photographed from slightly different angles, same listing series | + +Implementation notes: + +- For ≤ 100k images, pairwise Hamming comparison with vectorized NumPy popcount is fast enough; use a BK-tree or LSH banding beyond that. +- Within a duplicate group, **keep the highest-resolution member** and record all members' URLs under it in the manifest. +- Every image gets a `cluster_id`. Layers 1-2 remove images; layer 3 does **not** remove images, it only labels clusters for leakage-safe splitting (§6). + +### 5.6 Relevance Filter (`relevance.py`) + +Search results contain many stock illustrations, renders, diagrams, product boxes, logos, and off-topic hardware. + +- **Model:** `open_clip` (e.g., ViT-B/32) zero-shot scoring. +- **Positive prompts:** "a photo of a printed circuit board", "a photo of a computer motherboard", "a photo of electronic waste". +- **Negative prompts:** "a 3D render", "a diagram or schematic", "a product box", "a logo", "a screenshot", "a computer case", "a person", "an image with a watermark". +- **Buckets:** `relevant` (auto-keep), `borderline` (manual review), `irrelevant` (discard). Tune the two thresholds on a **hand-labeled sample of ~200 images**, then report precision/recall of the filter. +- **`source_type` tagging:** use zero-shot prompts to estimate `listing` vs `in_the_wild` so the domain mix can be monitored. +- **Watermarks/stock:** flagged, not auto-discarded (zero-shot watermark detection is unreliable). Flagged images go to review. + +### 5.7 Manual Review + +- Review **all** `borderline` and `extreme_aspect` items, plus a **10% random sample** of auto-kept images to estimate residual noise. +- Use a contact-sheet tool (FiftyOne or a simple generated HTML grid) that supports keep/discard/re-tag. +- Record reviewer decisions in the manifest (`review_status`, `reviewer`, `reviewed_at`). + +### 5.8 Manifest & Provenance (`manifest.py`) + +SQLite is the source of truth, exported to JSONL/CSV for tooling. One row per image (plus a table of source URLs per image). + +| Field | Description | +| :--- | :--- | +| `image_id` | sha256[:16] of original bytes | +| `source_urls[]` | all URLs that produced this image | +| `engine`, `queries[]`, `class_hints[]` | how it was found | +| `scraped_at` | timestamp | +| `orig_format`, `width`, `height`, `filesize`, `aspect_ratio` | measured properties | +| `flags[]` | `extreme_aspect`, `animated`, `watermark_suspect`, etc. | +| `phash`, `dhash` | perceptual hashes | +| `cluster_id` | duplicate/semantic cluster | +| `clip_score`, `relevance_bucket` | filter outputs | +| `board_type`, `condition`, `source_type` | image-level tags | +| `status` | `pending → downloaded → validated → normalized → deduped → filtered → kept` or `discarded` | +| `discard_reason` | why it was removed | +| `review_status` | reviewer outcome | +| `split` | `train` / `val` / `test` (set by §6) | +| `session_id`, `board_id` | for real-world captures | + +--- + +## 6. Real-World Data & Splitting + +### 6.1 Real-world capture set (closes the domain gap) + +Web results skew toward clean listing photos. The deployed app will see dirty, cluttered, phone-shot boards under poor lighting. + +- Capture **≥ 300 test images plus ≥ 300 additional training images** from recycling partners or a bench setup. +- Vary: phone models, lighting (daylight, warm, harsh flash, dim), backgrounds, distances (macro to whole-board), angles, board types, and conditions. +- Record `session_id` (one per visit/location/day) and `board_id` (one per physical board). Photograph each board from multiple angles. +- Capture at native phone resolution; keep originals. + +### 6.2 Split policy (leakage-safe) + +| Split | Composition | Rule | +| :--- | :--- | :--- | +| `train` | Web + real-world training sessions | ~80% of the non-test pool | +| `val` | Web + real-world training sessions | ~20% of the non-test pool; used for model selection | +| `test` | **Real-world sessions only, held out** | Never used for tuning or threshold selection; sessions not present in train/val | + +- Split by **group**, not by image: use `cluster_id` for web images and `board_id`/`session_id` for real-world images (`GroupShuffleSplit` or similar), so near-duplicates and same-board photos never straddle splits. +- Stratify on `class_hints` and `source_type` where feasible so val reflects the domain mix. +- Freeze the result in `splits/splits.json` with the random seed; regenerate only by explicit version bump. + +--- + +## 7. Annotation + +- Write `docs/labeling_guide.md` **before** labeling: definitions and 10+ positive/negative example crops per class, plus edge cases (partially occluded, damaged, blurry). +- **Label at full resolution** (never label downscaled copies). +- **Minimum object size policy:** objects smaller than ~16 px at the intended training resolution are either marked `ignore` or excluded consistently; document the choice. +- Boxes: tight; `gold_finger` boxes follow the actual (elongated) strip; no padding to squares. +- **Quality control:** double-annotate a 10% sample, compute IoU/agreement per class, and resolve disagreements to refine the guide. `desoldered_zone` is the class most likely to need a redefinition. +- Tools: CVAT or Roboflow. Export in YOLO format with the frozen splits. + +--- + +## 8. Training & Evaluation Plan + +### 8.1 Small-object strategy + +- Train with **`imgsz=1280`** (or 960 as a compromise) rather than the 640 default. +- For on-device use, avoid running the full nano model at 1280 on a phone. Two options to evaluate: + 1. **Tiled inference** (SAHI-style): overlapping 640 px tiles with merged detections. + 2. **Two-stage:** a light board detector at 640 → crop board → component detector on the crop. +- Consider mosaic/scale augmentation tuned for small objects; verify augmentations do not destroy `gold_finger` aspect ratios. + +### 8.2 Metrics + +- mAP@50 and mAP@50-95 **per class**, not just overall. +- **Recall by object-size bucket** (small / medium / large by pixel area), to expose the small-object problem. +- Report results on `val` and, separately, on the real-world `test` split. **The test-set number is the headline number.** +- BIS validation: compare BIS from ground-truth labels vs BIS from detections on the test set, and report the error. + +### 8.3 Export checks + +- Export to ONNX / TFLite and **re-evaluate on the test set after export and after quantization**; set an acceptable mAP-drop budget (e.g., ≤ 2 points) before shipping to `mobile/`. + +--- + +## 9. Technical Stack + +| Tool / Library | Role | +| :--- | :--- | +| Python 3.10+ | Runtime | +| `icrawler` | URL harvesting (Bing, Baidu, Flickr; Google optional) | +| `requests`, `tenacity` | Custom downloader with retry/backoff | +| `Pillow` | Validation, EXIF handling, normalization, ICC conversion | +| `numpy` | Vectorized hash comparison | +| `imagehash` | Perceptual hashing (pHash/dHash) | +| `hashlib` | Exact SHA-256 hashing | +| `open_clip_torch` + `torch` | Relevance filter and embeddings for clustering | +| `scikit-learn` | Group-aware splitting, clustering | +| `PyYAML` | Configuration | +| `tqdm` | Progress monitoring | +| `fiftyone` (optional) | Review UI, embedding visualization | +| `ultralytics`, `sahi` | Training and tiled inference | +| `pytest` | Tests | + +Pin exact versions in `requirements.txt` and record them in every report. + +--- + +## 10. Execution Plan + +### Step 1: Install + +```bash +pip install -r requirements.txt +``` + +### Step 2: Run stages (each is resumable) + +```bash +python3 scraper.py harvest --config config/config.yaml # collect URLs into manifest +python3 scraper.py download # fetch originals with retries +python3 scraper.py validate # integrity, size, aspect +python3 scraper.py process # EXIF fix, RGB, JPEG q95 +python3 scraper.py dedup # exact + perceptual + clusters +python3 scraper.py filter # CLIP relevance + review queue +python3 scraper.py split # cluster-aware splits +python3 scraper.py report # summary statistics +``` + +### Step 3: Verification report (`reports/summary.json` and printed table) + +Per `class_hint` and overall: + +- Raw URLs harvested, downloads succeeded/failed +- Discards **by reason** (corrupt, too small, low resolution, hard aspect, exact dup, near dup, irrelevant) +- Flagged and reviewed counts +- Final kept count vs target (§2.2) +- Resolution distribution and `source_type` mix (in-the-wild share vs the 40% goal) +- Number of duplicate clusters and largest cluster size +- Split sizes and a leakage check (no `cluster_id`/`board_id` shared across splits) + +--- + +## 11. Testing + +Unit tests using fixtures in `tests/fixtures/`: + +| Test | Expectation | +| :--- | :--- | +| Truncated JPEG | Rejected by the `verify()` + `load()` sequence | +| RGBA PNG with transparency | Composited on white, not black | +| EXIF-rotated phone photo | Output is upright | +| CMYK JPEG | Correct RGB conversion | +| Animated GIF | First frame used, `animated` flag set | +| 5:1 RAM image | Flagged, **not** discarded | +| 8:1 banner | Discarded with `extreme_aspect_hard` | +| Resized / recompressed / flipped copy of a fixture | Detected as near-duplicate | +| Rerun after interruption | No re-download, no duplicate rows | +| Leakage check | No shared `cluster_id` across splits | + +--- + +## 12. Milestones & Acceptance Criteria + +| Phase | Deliverable | Acceptance criterion | +| :--- | :--- | :--- | +| 0. Decisions | `taxonomy.yaml`, target counts, BIS definition in `docs/decisions.md` | Signed off by project owner | +| 1. Pipeline build | All modules + tests passing | Fixtures behave as specified in §11 | +| 2. Pilot | ~500-image end-to-end run | Attrition rate measured; CLIP thresholds tuned on 200 hand-labeled images | +| 3. Full collection | Harvest budget executed; real-world capture ≥ 600 images | Kept counts meet §2.2 or gaps identified and queries extended | +| 4. QA | Manual review complete; frozen `splits.json` | Leakage check clean; ≥ 40% in-the-wild | +| 5. Annotation | Labeled dataset + `labeling_guide.md` | Inter-annotator agreement acceptable per class | +| 6. Training & eval | Baseline model, per-class and size-bucket metrics | Test-set results reported; failure analysis written | +| 7. Export | ONNX/TFLite + post-quantization test results | mAP drop within budget; BIS error reported | + +--- + +## 13. Risks & Mitigations + +| Risk | Mitigation | +| :--- | :--- | +| Search engines return too few unique images per query | Wide query matrix (§5.1), multiple engines, multilingual queries, real-world capture | +| Heavy overrepresentation of clean listing photos | `source_type` monitoring; explicit in-the-wild target; real-world capture | +| Tiny objects (tantalum caps) undetectable | Higher min resolution, `imgsz=1280`, tiling/two-stage, small-object metrics | +| `desoldered_zone` label ambiguity | Precise definition, example gallery, double annotation, willingness to redefine or move to segmentation | +| Near-duplicate leakage inflates metrics | Cluster-aware splits, leakage check in CI, held-out real-world test set | +| CLIP filter discards valid images | Threshold tuned on hand-labeled sample; borderline goes to review, not discard | +| Class imbalance (ICs abundant, tantalum rare) | Per-class targets, targeted queries and captures for rare classes, per-class metrics | +| Quantization degrades small-object accuracy | Post-export evaluation with a mAP-drop budget; consider mixed precision | +| Pipeline state lost mid-run | SQLite manifest, idempotent stages, originals preserved | +| BIS unstable or not meaningful | Define and validate BIS against ground-truth labels early (§2.3, §8.2) | + +--- + +## 14. Immediate Next Actions + +1. Review and finalize the taxonomy and class definitions (§2.1). +2. Get domain input on BIS weights and whether BIS uses counts or area (§2.3). +3. Stand up the repository skeleton from §4, with config, manifest schema, and test fixtures. +4. Run the 500-image pilot to measure attrition and tune the relevance thresholds. +5. Schedule the first real-world capture session so the test set starts accumulating early. diff --git a/google_drive_export/pipeline/__init__.py b/google_drive_export/pipeline/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/google_drive_export/pipeline/auto_annotate.py b/google_drive_export/pipeline/auto_annotate.py new file mode 100644 index 0000000000000000000000000000000000000000..d51ba276466299d2c3730537e75a7dca9600ac83 --- /dev/null +++ b/google_drive_export/pipeline/auto_annotate.py @@ -0,0 +1,157 @@ +"""Automated Zero-Shot Auto-Annotation Pipeline using YOLO-World. + +Auto-labels all images in `data/images/` using the updated taxonomy: + 0: cpu_socket + 1: gold_finger + 2: ic_chip + 3: tantalum_capacitor + 4: forcibly_desoldered_pad + 5: factory_unpopulated_pad + 6: occluded_region + +Generates YOLO format bounding box label files (.txt) and data.yaml in `data/annotated_auto/`. +""" +from __future__ import annotations + +import json +import logging +import os +import shutil +from pathlib import Path + +import yaml +from PIL import Image +from tqdm import tqdm + +# Ensure cache directories reside within workspace +cache_dir = Path("data/cache").resolve() +cache_dir.mkdir(parents=True, exist_ok=True) +os.environ["TORCH_HOME"] = str(cache_dir) +os.environ["HF_HOME"] = str(cache_dir) + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +log = logging.getLogger(__name__) + +CLASS_NAMES = [ + "cpu_socket", + "gold_finger", + "ic_chip", + "tantalum_capacitor", + "forcibly_desoldered_pad", + "factory_unpopulated_pad", + "occluded_region", +] + +# Prompts tuned for zero-shot YOLO-World detector +CLASS_PROMPTS = [ + "CPU socket", + "gold finger edge connector", + "microchip", + "tantalum capacitor", + "desoldered solder pad", + "unpopulated solder pad", + "heatsink shield", +] + + +def run_auto_annotation( + data_dir: str | Path = "data", + output_dir: str | Path = "data/annotated_auto", + conf_threshold: float = 0.02, + max_images: int | None = None, +) -> dict: + data_dir = Path(data_dir) + output_dir = Path(output_dir) + images_in_dir = data_dir / "images" + + images_out_dir = output_dir / "images" + labels_out_dir = output_dir / "labels" + images_out_dir.mkdir(parents=True, exist_ok=True) + labels_out_dir.mkdir(parents=True, exist_ok=True) + + log.info("Loading YOLO-World zero-shot model...") + from ultralytics import YOLOWorld + + model = YOLOWorld("yolov8s-worldv2.pt") + model.set_classes(CLASS_PROMPTS) + log.info("YOLO-World model configured with %d classes (conf threshold: %.3f).", len(CLASS_PROMPTS), conf_threshold) + + image_files = sorted(list(images_in_dir.glob("*.jpg")) + list(images_in_dir.glob("*.png"))) + if max_images: + image_files = image_files[:max_images] + + log.info("Found %d images to auto-annotate.", len(image_files)) + + class_detection_counts = {name: 0 for name in CLASS_NAMES} + total_images_processed = 0 + total_boxes_generated = 0 + + for img_path in tqdm(image_files, desc="Auto-annotating images", unit="img"): + img_name = img_path.name + stem = img_path.stem + + # Copy image to output directory + target_img_path = images_out_dir / img_name + if not target_img_path.exists(): + try: + shutil.copy2(img_path, target_img_path) + except Exception as e: + log.warning("Could not copy %s: %s", img_name, e) + + # Run inference + results = model.predict(str(img_path), conf=conf_threshold, verbose=False) + label_file = labels_out_dir / f"{stem}.txt" + + lines = [] + if results and len(results) > 0: + boxes = results[0].boxes + if boxes is not None and len(boxes) > 0: + xywhn = boxes.xywhn.cpu().numpy() + cls_ids = boxes.cls.cpu().numpy().astype(int) + + for (x, y, w, h), cls_id in zip(xywhn, cls_ids): + if 0 <= cls_id < len(CLASS_NAMES): + lines.append(f"{cls_id} {x:.6f} {y:.6f} {w:.6f} {h:.6f}") + class_name = CLASS_NAMES[cls_id] + class_detection_counts[class_name] += 1 + total_boxes_generated += 1 + + with open(label_file, "w", encoding="utf-8") as f: + f.write("\n".join(lines) + ("\n" if lines else "")) + + total_images_processed += 1 + + # Create dataset yaml specification + dataset_yaml = { + "path": str(output_dir.resolve()), + "train": "images", + "val": "images", + "names": {i: name for i, name in enumerate(CLASS_NAMES)}, + } + yaml_path = output_dir / "data.yaml" + with open(yaml_path, "w", encoding="utf-8") as f: + yaml.dump(dataset_yaml, f, default_flow_style=False) + + summary = { + "total_images_processed": total_images_processed, + "total_boxes_generated": total_boxes_generated, + "conf_threshold": conf_threshold, + "class_detection_counts": class_detection_counts, + "output_directory": str(output_dir.resolve()), + "data_yaml": str(yaml_path.resolve()), + } + + summary_path = output_dir / "auto_annotation_summary.json" + with open(summary_path, "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + + log.info("Auto-annotation complete! Summary saved to %s", summary_path) + return summary + + +if __name__ == "__main__": + import sys + max_imgs = int(sys.argv[1]) if len(sys.argv) > 1 and sys.argv[1].isdigit() else None + conf_val = float(sys.argv[2]) if len(sys.argv) > 2 else 0.02 + run_auto_annotation(conf_threshold=conf_val, max_images=max_imgs) + diff --git a/google_drive_export/pipeline/config.py b/google_drive_export/pipeline/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2df7515a68f1d7cb0e21dae30c0da458e11155 --- /dev/null +++ b/google_drive_export/pipeline/config.py @@ -0,0 +1,109 @@ +"""Configuration loading, filesystem layout and logging setup.""" +from __future__ import annotations + +import copy +import logging +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import yaml + +PROJECT_ROOT = Path(__file__).resolve().parent.parent + + +def load_yaml(path: Path) -> dict[str, Any]: + with open(path, "r", encoding="utf-8") as fh: + return yaml.safe_load(fh) or {} + + +def deep_update(base: dict, override: dict) -> dict: + """Recursively merge ``override`` into ``base`` (in place) and return it.""" + for key, value in override.items(): + if isinstance(value, dict) and isinstance(base.get(key), dict): + deep_update(base[key], value) + else: + base[key] = copy.deepcopy(value) + return base + + +def load_config(path: str | Path | None = None, overrides: dict | None = None) -> dict[str, Any]: + cfg_path = Path(path) if path else PROJECT_ROOT / "config" / "config.yaml" + cfg = load_yaml(cfg_path) + cfg["_config_dir"] = str(cfg_path.resolve().parent) + if overrides: + deep_update(cfg, overrides) + return cfg + + +def load_taxonomy(cfg: dict) -> dict[str, Any]: + return load_yaml(Path(cfg["_config_dir"]) / "taxonomy.yaml") + + +def load_queries(cfg: dict) -> dict[str, Any]: + return load_yaml(Path(cfg["_config_dir"]) / "queries.yaml") + + +@dataclass(frozen=True) +class Paths: + """Filesystem layout. Manifest paths are stored relative to ``data``.""" + + base: Path + data: Path + originals: Path + images: Path + review: Path + realworld: Path + reports: Path + splits: Path + manifest_db: Path + manifest_jsonl: Path + + def abs(self, rel: str | Path) -> Path: + return self.data / rel + + def rel(self, path: str | Path) -> str: + return str(Path(path).resolve().relative_to(self.data.resolve())) + + def ensure(self) -> None: + for p in (self.data, self.originals, self.images, self.review, self.realworld, + self.reports, self.splits): + p.mkdir(parents=True, exist_ok=True) + + +def get_paths(cfg: dict, base: str | Path | None = None) -> Paths: + base_dir = Path(base).resolve() if base else PROJECT_ROOT + pcfg = cfg.get("paths", {}) + data = base_dir / pcfg.get("data_dir", "data") + return Paths( + base=base_dir, + data=data, + originals=data / "originals", + images=data / "images", + review=data / "review", + realworld=data / "realworld", + reports=base_dir / pcfg.get("reports_dir", "reports"), + splits=base_dir / pcfg.get("splits_dir", "splits"), + manifest_db=data / "manifest.db", + manifest_jsonl=data / "manifest.jsonl", + ) + + +def setup_logging(verbose: bool = False) -> None: + """Configure logging *before* icrawler is imported. + + icrawler calls ``logging.basicConfig`` when a crawler is constructed; that is a + no-op once the root logger already has handlers, so configuring first keeps our + format and level in charge. + """ + level = logging.DEBUG if verbose else logging.INFO + root = logging.getLogger() + if not root.handlers: + handler = logging.StreamHandler(sys.stderr) + handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)-7s %(name)s: %(message)s", + datefmt="%H:%M:%S")) + root.addHandler(handler) + root.setLevel(level) + logging.getLogger("urllib3").setLevel(logging.WARNING) + logging.getLogger("PIL").setLevel(logging.WARNING) diff --git a/google_drive_export/pipeline/crawler.py b/google_drive_export/pipeline/crawler.py new file mode 100644 index 0000000000000000000000000000000000000000..cfe95927a5ab0428fb21d4e68a4a5e67cb94c2da --- /dev/null +++ b/google_drive_export/pipeline/crawler.py @@ -0,0 +1,275 @@ +"""Harvesting engine: query search engines (icrawler / HTTP fallback) and download raw image binaries. + +Flow: + 1. Build query list from queries.yaml matrix. + 2. For each query and engine: + - Skip if `harvest_runs` records the run as done. + - Surface image URLs and store in `sources` and `hits` tables via `manifest.add_hit`. + - Record completed run in `harvest_runs`. + 3. Download pending URLs into `data/originals/`, computing SHA-256 ID. + 4. Register downloaded files in `images` table with status 'downloaded'. +""" +from __future__ import annotations + +import hashlib +import logging +import mimetypes +import urllib.request +import urllib.error +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import Sequence + +from tqdm import tqdm + +from .manifest import Manifest, normalize_url +from .queries import QuerySpec, build_queries + +log = logging.getLogger(__name__) + +HEADERS = { + "User-Agent": ( + "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 " + "(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" + ), + "Accept": "image/avif,image/webp,image/apng,image/svg+xml,image/*,*/*;q=0.8", +} + +EXT_MAP = { + "image/jpeg": ".jpg", + "image/jpg": ".jpg", + "image/png": ".png", + "image/webp": ".webp", + "image/gif": ".gif", + "image/bmp": ".bmp", + "image/tiff": ".tif", + "image/heic": ".heic", + "image/heif": ".heif", +} + + +def download_single_url( + source: dict, originals_dir: Path, timeout: float = 10.0 +) -> tuple[dict, str | None, str | None, int | None, str | None]: + """Download raw binary for one URL. + + Returns (source, image_id, filepath, http_status, error_message). + """ + url = source["url"] + url_norm = source["url_norm"] + req = urllib.request.Request(url, headers=HEADERS) + + try: + with urllib.request.urlopen(req, timeout=timeout) as resp: + content_type = resp.headers.get("Content-Type", "").split(";")[0].strip().lower() + data = resp.read() + http_status = resp.status + except urllib.error.HTTPError as exc: + return source, None, None, exc.code, f"HTTPError: {exc.code}" + except Exception as exc: + return source, None, None, None, f"{type(exc).__name__}: {exc}" + + if len(data) < 500: + return source, None, None, http_status, "file_too_small_raw" + + sha = hashlib.sha256(data).hexdigest() + image_id = sha[:16] + + ext = EXT_MAP.get(content_type) or Path(url).suffix.lower() + if not ext or len(ext) > 5 or ext not in (".jpg", ".jpeg", ".png", ".webp", ".gif", ".bmp", ".tif", ".heic"): + ext = ".jpg" + + dst = originals_dir / f"{image_id}{ext}" + if not dst.exists(): + originals_dir.mkdir(parents=True, exist_ok=True) + tmp = dst.with_name(dst.name + f".tmp") + tmp.write_bytes(data) + tmp.replace(dst) + + return source, image_id, str(dst), http_status, None + + +def download_pending_sources( + manifest: Manifest, originals_dir: Path, data_dir: Path, cfg: dict +) -> dict[str, int]: + """Download pending URLs from manifest into `originals_dir`.""" + ccfg = cfg.get("crawler", {}) + workers = int(ccfg.get("threads", 4)) + timeout = float(ccfg.get("timeout", 10.0)) + + pending = manifest.pending_sources() + stats = {"downloaded": 0, "download_failed": 0} + if not pending: + log.info("crawler: no pending sources to download") + return stats + + log.info("crawler: downloading %d pending URL(s)", len(pending)) + + def work(src: dict): + return download_single_url(src, originals_dir, timeout=timeout) + + with ThreadPoolExecutor(max_workers=workers) as ex: + futures = [ex.submit(work, s) for s in pending] + with manifest.transaction(): + for fut in tqdm(as_completed(futures), total=len(futures), desc="download", unit="url"): + src, image_id, dst_path, http_status, err = fut.result() + if err is None and image_id and dst_path: + data_file = Path(dst_path) + data = data_file.read_bytes() + sha = hashlib.sha256(data).hexdigest() + rel_path = str(data_file.relative_to(data_dir)) + + manifest.add_image( + image_id=image_id, + sha256=sha, + origin="web", + source_ref=src["url"], + orig_path=rel_path, + orig_format=data_file.suffix.lstrip(".").lower(), + filesize=len(data), + status="downloaded", + ) + manifest.mark_source( + src["url_norm"], + status="downloaded", + http_status=http_status, + image_id=image_id, + attempts=1, + ) + stats["downloaded"] += 1 + else: + manifest.mark_source( + src["url_norm"], + status="failed", + http_status=http_status, + error=err, + attempts=1, + ) + stats["download_failed"] += 1 + + log.info("crawler download stats: %s", stats) + return stats + + +def run_icrawler_harvest( + manifest: Manifest, + queries: Sequence[QuerySpec], + data_dir: Path, + engine_name: str = "bing", + max_num: int = 50, + force: bool = False, + filter_classes: set[str] | Sequence[str] | None = None, +) -> dict[str, int]: + """Harvest image binaries using icrawler, save to data/originals, and register in manifest.""" + stats = {"runs": 0, "urls_found": 0, "urls_new": 0, "images_harvested": 0} + try: + if engine_name.lower() == "bing": + from icrawler.builtin import BingImageCrawler as CrawlerEngine + elif engine_name.lower() == "flickr": + from icrawler.builtin import FlickrImageCrawler as CrawlerEngine + elif engine_name.lower() == "baidu": + from icrawler.builtin import BaiduImageCrawler as CrawlerEngine + else: + log.warning("Unknown engine '%s', defaulting to Bing", engine_name) + from icrawler.builtin import BingImageCrawler as CrawlerEngine + except ImportError: + log.warning("icrawler is not installed; skipping search engine crawling") + return stats + + import tempfile + import shutil + + originals_dir = data_dir / "originals" + originals_dir.mkdir(parents=True, exist_ok=True) + + filter_set = set(filter_classes) if filter_classes else None + + for qspec in queries: + if filter_set and qspec.class_hint not in filter_set: + continue + if not force and manifest.run_done(engine_name, qspec.query): + log.debug("Skip harvest done run: %s / %s", engine_name, qspec.query) + continue + + tmp_dir = Path(tempfile.mkdtemp(prefix="icrawler_")) + try: + crawler = CrawlerEngine( + downloader_threads=2, + storage={"root_dir": str(tmp_dir)}, + log_level=logging.WARNING, + ) + crawler.crawl(keyword=qspec.query, max_num=max_num) + + found_count = 0 + new_count = 0 + with manifest.transaction(): + for img_file in tmp_dir.glob("*"): + if img_file.is_file() and img_file.stat().st_size > 500: + found_count += 1 + data = img_file.read_bytes() + sha = hashlib.sha256(data).hexdigest() + image_id = sha[:16] + ext = img_file.suffix.lower() + if not ext or len(ext) > 5 or ext not in (".jpg", ".jpeg", ".png", ".webp", ".gif", ".bmp"): + ext = ".jpg" + + dst_file = originals_dir / f"{image_id}{ext}" + if not dst_file.exists(): + dst_file.write_bytes(data) + + source_url = f"icrawler://{engine_name}/{qspec.class_hint}/{image_id}" + is_new = manifest.add_hit( + source_url, engine_name, qspec.query, class_hint=qspec.class_hint, lang=qspec.lang + ) + if is_new: + new_count += 1 + + rel_path = str(dst_file.relative_to(data_dir)) + manifest.add_image( + image_id=image_id, + sha256=sha, + origin="web", + source_ref=source_url, + orig_path=rel_path, + orig_format=ext.lstrip("."), + filesize=len(data), + status="downloaded", + ) + manifest.mark_source( + source_url, + status="downloaded", + image_id=image_id, + ) + stats["images_harvested"] += 1 + + manifest.record_run( + engine_name, qspec.query, qspec.class_hint, max_num, found_count, new_count + ) + stats["runs"] += 1 + stats["urls_found"] += found_count + stats["urls_new"] += new_count + except Exception as exc: + log.error("icrawler harvest failed for %s (%s): %s", qspec.query, engine_name, exc) + finally: + shutil.rmtree(tmp_dir, ignore_errors=True) + + log.info("harvest stats (%s): %s", engine_name, stats) + return stats + + +def run( + manifest: Manifest, cfg: dict, data_dir: Path, engine: str = "bing", max_num: int = 50, force: bool = False, filter_classes: set[str] | Sequence[str] | None = None +) -> dict[str, int]: + """Full crawler workflow: harvest queries and download raw images.""" + qcfg = cfg.get("queries") or {} + if not qcfg: + from .config import load_queries + qcfg = load_queries(cfg) + + queries = build_queries(qcfg) + harvest_stats = run_icrawler_harvest(manifest, queries, data_dir, engine_name=engine, max_num=max_num, force=force, filter_classes=filter_classes) + + originals_dir = data_dir / "originals" + download_stats = download_pending_sources(manifest, originals_dir, data_dir, cfg) + + return {**harvest_stats, **download_stats} diff --git a/google_drive_export/pipeline/dedup.py b/google_drive_export/pipeline/dedup.py new file mode 100644 index 0000000000000000000000000000000000000000..c9b370bfb1d04cab38d86b6b6d2e15ec398f2bd2 --- /dev/null +++ b/google_drive_export/pipeline/dedup.py @@ -0,0 +1,271 @@ +"""Deduplication engine (global across all classes and queries). + +Layer 1 exact SHA-256 of the original bytes (already the image_id, so identical downloads + collapse at download time) + SHA-256 of the decoded normalized pixels. +Layer 2 near-dup pHash AND dHash (64-bit each) within a Hamming-distance budget. Every image + also stores the hashes of its horizontally FLIPPED version, and a pair counts + as a duplicate if it matches in either orientation (mirror copies). +Layer 3 semantic CLIP-embedding cosine clustering. This layer never deletes anything; it only + merges cluster ids so the splitter keeps "same board, different angle" together. + +Layers 1-2 remove images: within a duplicate group the keeper is chosen by + (already-processed before new) > (real-world before web) > higher resolution > larger file > id +so re-running the pipeline never swaps out an image that was already kept or annotated. +Duplicate groups are connected components (union-find), so chains A~B~C collapse into one group. +""" +from __future__ import annotations + +import logging +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import Sequence + +import imagehash +import numpy as np +from PIL import Image, ImageOps +from tqdm import tqdm + +from .manifest import Manifest + +log = logging.getLogger(__name__) + +# Statuses whose images are candidates for duplicate comparison (hashes present). +_PROCESSED = ("deduped", "kept", "review") +_CANDIDATES = ("normalized",) + _PROCESSED + +_POP8 = np.array([bin(i).count("1") for i in range(256)], dtype=np.uint8) + + +# ------------------------------------------------------------------------------------ hashing +def compute_hashes(path: str | Path) -> dict[str, str]: + """pHash/dHash (8x8 => 64 bit, hex) for an image and for its horizontal mirror.""" + with Image.open(path) as im: + im.draft("RGB", (512, 512)) # cheap JPEG downscale on decode; same procedure for all images + im = ImageOps.exif_transpose(im).convert("RGB") + flipped = ImageOps.mirror(im) + return { + "phash": str(imagehash.phash(im, hash_size=8)), + "dhash": str(imagehash.dhash(im, hash_size=8)), + "phash_flip": str(imagehash.phash(flipped, hash_size=8)), + "dhash_flip": str(imagehash.dhash(flipped, hash_size=8)), + } + + +def hex_to_u64(values: Sequence[str]) -> np.ndarray: + return np.array([int(v, 16) for v in values], dtype=np.uint64) + + +def popcount64(x: np.ndarray) -> np.ndarray: + """Per-element population count of a uint64 array.""" + if hasattr(np, "bitwise_count"): # numpy >= 2.0 + return np.bitwise_count(x) + x = np.ascontiguousarray(x) + return _POP8[x.view(np.uint8).reshape(*x.shape, 8)].sum(axis=-1) + + +# --------------------------------------------------------------------------------- union-find +class UnionFind: + def __init__(self, n: int): + self.parent = list(range(n)) + + def find(self, a: int) -> int: + while self.parent[a] != a: + self.parent[a] = self.parent[self.parent[a]] + a = self.parent[a] + return a + + def union(self, a: int, b: int) -> None: + ra, rb = self.find(a), self.find(b) + if ra != rb: + self.parent[max(ra, rb)] = min(ra, rb) + + def groups(self) -> list[list[int]]: + out: dict[int, list[int]] = defaultdict(list) + for i in range(len(self.parent)): + out[self.find(i)].append(i) + return [g for g in out.values() if len(g) > 1] + + +def near_duplicate_pairs(ph: np.ndarray, dh: np.ndarray, phf: np.ndarray, dhf: np.ndarray, + phash_max: int, dhash_max: int, block_cells: int = 4_000_000): + """Yield (i, j), i < j, whose pHash *and* dHash are within budget in the same orientation + (direct, or with j mirrored). Vectorized in row blocks to bound memory.""" + n = len(ph) + if n < 2: + return + block = max(1, block_cells // n) + cols = np.arange(n) + for a in range(0, n, block): + b = min(n, a + block) + upper = cols[None, :] > np.arange(a, b)[:, None] + direct = (popcount64(ph[a:b, None] ^ ph[None, :]) <= phash_max) & \ + (popcount64(dh[a:b, None] ^ dh[None, :]) <= dhash_max) + mirrored = (popcount64(ph[a:b, None] ^ phf[None, :]) <= phash_max) & \ + (popcount64(dh[a:b, None] ^ dhf[None, :]) <= dhash_max) + ii, jj = np.nonzero((direct | mirrored) & upper) + for i, j in zip(ii + a, jj): + yield int(i), int(j) + + +def find_duplicate_groups(rows: list[dict], phash_max: int, dhash_max: int) -> list[list[int]]: + """Group row indices by exact pixel hash + near-duplicate hashes (connected components).""" + n = len(rows) + uf = UnionFind(n) + by_pixels: dict[str, int] = {} + for i, r in enumerate(rows): + px = r.get("pixel_sha256") + if px: + if px in by_pixels: + uf.union(by_pixels[px], i) + else: + by_pixels[px] = i + if n >= 2: + ph = hex_to_u64([r["phash"] for r in rows]) + dh = hex_to_u64([r["dhash"] for r in rows]) + phf = hex_to_u64([r["phash_flip"] for r in rows]) + dhf = hex_to_u64([r["dhash_flip"] for r in rows]) + for i, j in near_duplicate_pairs(ph, dh, phf, dhf, phash_max, dhash_max): + uf.union(i, j) + return uf.groups() + + +def keeper_rank(row: dict) -> tuple: + """Lower sorts first = preferred keeper.""" + processed = 0 if row["status"] in _PROCESSED else 1 + realworld = 0 if row.get("origin") == "realworld" else 1 + pixels = (row.get("norm_width") or row.get("width") or 0) * (row.get("norm_height") or row.get("height") or 0) + return (processed, realworld, -pixels, -(row.get("filesize") or 0), row["image_id"]) + + +# ------------------------------------------------------------------------------------- stage +def run(manifest: Manifest, cfg: dict, data_dir: Path) -> dict[str, int]: + """Hash new images, then discard exact/near duplicates across the whole candidate pool.""" + dcfg = cfg["dedup"] + stats = {"hashed": 0, "exact_duplicate": 0, "near_duplicate": 0, "deduped": 0, "hash_failed": 0} + + # 1. hashes for images that do not have them yet + fresh = [r for r in manifest.images(statuses=["normalized"]) if not r["phash"]] + if fresh: + def work(r: dict): + try: + return r, compute_hashes(data_dir / r["norm_path"]), None + except Exception as exc: # noqa: BLE001 + return r, None, exc + + with ThreadPoolExecutor(max_workers=int(dcfg.get("hash_workers", 4))) as ex: + futures = [ex.submit(work, r) for r in fresh] + with manifest.transaction(): + for fut in tqdm(as_completed(futures), total=len(futures), desc="hash", unit="img"): + r, hashes, err = fut.result() + if err is not None: + log.warning("hashing failed for %s: %s", r["image_id"], err) + manifest.discard(r["image_id"], "hash_failed") + stats["hash_failed"] += 1 + else: + manifest.update_image(r["image_id"], **hashes) + stats["hashed"] += 1 + + # 2. group duplicates over the full candidate pool (new + previously processed) + rows = [r for r in manifest.images(statuses=_CANDIDATES) if r["phash"]] + groups = find_duplicate_groups(rows, int(dcfg["phash_max_dist"]), int(dcfg["dhash_max_dist"])) + log.info("dedup: %d candidates, %d duplicate group(s)", len(rows), len(groups)) + + discarded: set[str] = set() + cluster_of: dict[str, str] = {} # cluster ids assigned during this run + with manifest.transaction(): + for group in groups: + members = sorted((rows[i] for i in group), key=keeper_rank) + keeper, losers = members[0], members[1:] + # Reuse the keeper's existing cluster id: a previous semantic merge may have + # renamed it, and a rerun must never undo that. + cid = keeper["cluster_id"] or keeper["image_id"] + for member in members: + cluster_of[member["image_id"]] = cid + manifest.update_image(member["image_id"], cluster_id=cid) + for loser in losers: + exact = bool(loser["pixel_sha256"]) and loser["pixel_sha256"] == keeper["pixel_sha256"] + reason = "exact_duplicate" if exact else "near_duplicate" + if loser["status"] == "normalized": + manifest.discard(loser["image_id"], reason, dup_of=keeper["image_id"]) + discarded.add(loser["image_id"]) + stats[reason] += 1 + else: + # An already-processed image is never removed retroactively (it may already + # be annotated); it only joins the cluster so splits stay leak-free. + log.warning("kept %s although it duplicates %s (already processed)", + loser["image_id"], keeper["image_id"]) + # Survivors advance; singletons get their own cluster id. + for r in rows: + if r["status"] != "normalized" or r["image_id"] in discarded: + continue + updates = {"status": "deduped"} + if not cluster_of.get(r["image_id"]) and not r["cluster_id"]: + updates["cluster_id"] = r["image_id"] + manifest.update_image(r["image_id"], **updates) + stats["deduped"] += 1 + log.info("dedup: %s", stats) + return stats + + +# ------------------------------------------------------------------------- semantic clustering +def semantic_clusters(ids: list[str], emb: np.ndarray, threshold: float, + existing: dict[str, str] | None = None, block_cells: int = 4_000_000) -> dict[str, str]: + """Merge images into clusters by embedding cosine >= threshold (and by existing cluster ids). + + Returns image_id -> cluster id (the smallest image_id in the component, deterministic). + Assumes rows of ``emb`` are L2-normalized. + """ + n = len(ids) + uf = UnionFind(n) + if existing: + first_seen: dict[str, int] = {} + for i, image_id in enumerate(ids): + cid = existing.get(image_id) + if cid: + if cid in first_seen: + uf.union(first_seen[cid], i) + else: + first_seen[cid] = i + if n >= 2: + block = max(1, block_cells // n) + cols = np.arange(n) + for a in range(0, n, block): + b = min(n, a + block) + sim = emb[a:b] @ emb.T + ii, jj = np.nonzero((sim >= threshold) & (cols[None, :] > np.arange(a, b)[:, None])) + for i, j in zip(ii + a, jj): + uf.union(int(i), int(j)) + comp: dict[int, list[str]] = defaultdict(list) + for i, image_id in enumerate(ids): + comp[uf.find(i)].append(image_id) + return {image_id: min(members) for members in comp.values() for image_id in members} + + +def run_semantic(manifest: Manifest, cfg: dict) -> dict[str, int]: + """Layer 3: merge cluster ids using stored CLIP embeddings (labels only, deletes nothing).""" + rows = manifest.images(statuses=["kept", "review"]) + ids, emb = manifest.load_embeddings([r["image_id"] for r in rows]) + if not ids: + log.warning("cluster: no embeddings found (run `filter` with CLIP first); skipping") + return {"clustered": 0, "merged_clusters": 0} + existing = {r["image_id"]: r["cluster_id"] or r["image_id"] for r in rows} + norms = np.linalg.norm(emb, axis=1, keepdims=True) + emb = emb / np.clip(norms, 1e-9, None) + new_ids = semantic_clusters(ids, emb, float(cfg["dedup"]["semantic_cosine"]), existing) + + remap: dict[str, str] = {} + for image_id, new in new_ids.items(): + old = existing[image_id] + if old != new: + remap[old] = new + with manifest.transaction(): + # Update *all* rows (incl. discarded duplicates) that carry a merged cluster id. + for old, new in remap.items(): + manifest.execute("UPDATE images SET cluster_id=? WHERE cluster_id=?", (new, old)) + for image_id, new in new_ids.items(): + if image_id not in remap and existing[image_id] != new: + manifest.update_image(image_id, cluster_id=new) + stats = {"clustered": len(ids), "merged_clusters": len(remap)} + log.info("cluster: %s", stats) + return stats diff --git a/google_drive_export/pipeline/manifest.py b/google_drive_export/pipeline/manifest.py new file mode 100644 index 0000000000000000000000000000000000000000..53a47cbc9602eac92c8e71421ebee3dcd60d13e3 --- /dev/null +++ b/google_drive_export/pipeline/manifest.py @@ -0,0 +1,404 @@ +"""SQLite manifest: the single source of truth for provenance and pipeline state. + +Tables +------ +sources one row per (normalized) URL; download state +hits every (url, engine, query) that surfaced a URL -> class hints and provenance +harvest_runs completed (engine, query) harvests so reruns skip them +images one row per unique image (sha256 of original bytes) with all pipeline state +embeddings optional CLIP embeddings (float16 blobs) + +Image status flow +----------------- +downloaded -> validated -> normalized -> deduped -> kept | review | discarded +Every stage only touches rows in its own input status, so all stages are idempotent +and a rerun after an interruption never repeats finished work. +""" +from __future__ import annotations + +import json +import sqlite3 +import threading +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Iterable, Iterator +from urllib.parse import parse_qsl, urlencode, urlsplit, urlunsplit + +import numpy as np + +STATUSES = ("downloaded", "validated", "normalized", "deduped", "kept", "review", "discarded") + +SCHEMA = """ +PRAGMA journal_mode=WAL; +PRAGMA synchronous=NORMAL; + +CREATE TABLE IF NOT EXISTS sources ( + url_norm TEXT PRIMARY KEY, + url TEXT NOT NULL, + status TEXT NOT NULL DEFAULT 'pending', + attempts INTEGER NOT NULL DEFAULT 0, + http_status INTEGER, + error TEXT, + image_id TEXT, + harvested_at TEXT, + downloaded_at TEXT +); +CREATE INDEX IF NOT EXISTS idx_sources_status ON sources(status); +CREATE INDEX IF NOT EXISTS idx_sources_image ON sources(image_id); + +CREATE TABLE IF NOT EXISTS hits ( + url_norm TEXT NOT NULL, + engine TEXT NOT NULL, + query TEXT NOT NULL, + class_hint TEXT, + lang TEXT, + harvested_at TEXT, + PRIMARY KEY (url_norm, engine, query) +); + +CREATE TABLE IF NOT EXISTS harvest_runs ( + engine TEXT NOT NULL, + query TEXT NOT NULL, + class_hint TEXT, + max_num INTEGER, + urls_found INTEGER, + urls_new INTEGER, + finished_at TEXT, + PRIMARY KEY (engine, query) +); + +CREATE TABLE IF NOT EXISTS images ( + image_id TEXT PRIMARY KEY, + sha256 TEXT NOT NULL, + origin TEXT NOT NULL DEFAULT 'web', + source_ref TEXT, + orig_path TEXT, + orig_format TEXT, + filesize INTEGER, + width INTEGER, + height INTEGER, + aspect_ratio REAL, + norm_path TEXT, + norm_width INTEGER, + norm_height INTEGER, + pixel_sha256 TEXT, + phash TEXT, + dhash TEXT, + phash_flip TEXT, + dhash_flip TEXT, + flags TEXT NOT NULL DEFAULT '[]', + status TEXT NOT NULL, + discard_reason TEXT, + dup_of TEXT, + cluster_id TEXT, + clip_score REAL, + relevance_bucket TEXT, + board_type TEXT, + condition TEXT, + source_type TEXT, + review_status TEXT, + reviewer TEXT, + reviewed_at TEXT, + split TEXT, + session_id TEXT, + board_id TEXT, + created_at TEXT, + updated_at TEXT +); +CREATE INDEX IF NOT EXISTS idx_images_status ON images(status); +CREATE INDEX IF NOT EXISTS idx_images_cluster ON images(cluster_id); +CREATE INDEX IF NOT EXISTS idx_images_dup_of ON images(dup_of); + +CREATE TABLE IF NOT EXISTS embeddings ( + image_id TEXT PRIMARY KEY, + dim INTEGER NOT NULL, + vec BLOB NOT NULL +); +""" + +IMAGE_COLUMNS = { + "image_id", "sha256", "origin", "source_ref", "orig_path", "orig_format", "filesize", "width", + "height", "aspect_ratio", "norm_path", "norm_width", "norm_height", "pixel_sha256", "phash", + "dhash", "phash_flip", "dhash_flip", "flags", "status", "discard_reason", "dup_of", "cluster_id", + "clip_score", "relevance_bucket", "board_type", "condition", "source_type", "review_status", + "reviewer", "reviewed_at", "split", "session_id", "board_id", "created_at", "updated_at", +} + +_TRACKING_PARAMS = {"fbclid", "gclid", "msclkid", "yclid"} +_DEFAULT_PORTS = {"http": 80, "https": 443} + + +def utcnow() -> str: + return datetime.now(timezone.utc).isoformat(timespec="seconds") + + +def normalize_url(url: str) -> str: + """Canonical form used as the dedup key for URLs (case, default ports, fragment, tracking).""" + parts = urlsplit(url.strip()) + scheme = parts.scheme.lower() + host = (parts.hostname or "").lower() + try: + port = parts.port + except ValueError: + port = None + netloc = host + if port and port != _DEFAULT_PORTS.get(scheme): + netloc = f"{host}:{port}" + query = sorted( + (k, v) for k, v in parse_qsl(parts.query, keep_blank_values=True) + if not k.lower().startswith("utm_") and k.lower() not in _TRACKING_PARAMS + ) + return urlunsplit((scheme, netloc, parts.path or "/", urlencode(query), "")) + + +def _check_columns(fields: Iterable[str]) -> None: + bad = set(fields) - IMAGE_COLUMNS + if bad: + raise KeyError(f"unknown image column(s): {sorted(bad)}") + + +class Manifest: + """Thread-safe SQLite wrapper (one connection, serialized by an RLock).""" + + def __init__(self, db_path: str | Path): + self.db_path = Path(db_path) + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = sqlite3.connect(str(self.db_path), check_same_thread=False, + isolation_level=None, timeout=60) + self._conn.row_factory = sqlite3.Row + self._lock = threading.RLock() + self._tx_depth = 0 + with self._lock: + self._conn.executescript(SCHEMA) + + # -- infrastructure ----------------------------------------------------------------- + def close(self) -> None: + with self._lock: + self._conn.close() + + def __enter__(self) -> "Manifest": + return self + + def __exit__(self, *exc) -> None: + self.close() + + @contextmanager + def transaction(self) -> Iterator[None]: + """Group many writes into one commit (re-entrant).""" + with self._lock: + outer = self._tx_depth == 0 + if outer: + self._conn.execute("BEGIN") + self._tx_depth += 1 + try: + yield + except BaseException: + self._tx_depth -= 1 + if outer: + self._conn.execute("ROLLBACK") + raise + else: + self._tx_depth -= 1 + if outer: + self._conn.execute("COMMIT") + + def execute(self, sql: str, params: Iterable[Any] = ()) -> sqlite3.Cursor: + with self._lock: + return self._conn.execute(sql, tuple(params)) + + def query(self, sql: str, params: Iterable[Any] = ()) -> list[sqlite3.Row]: + with self._lock: + return self._conn.execute(sql, tuple(params)).fetchall() + + # -- sources / hits ------------------------------------------------------------------- + def add_hit(self, url: str, engine: str, query: str, class_hint: str | None = None, + lang: str | None = None) -> bool: + """Record a harvested URL. Returns True if the URL was new.""" + url_norm = normalize_url(url) + now = utcnow() + with self._lock: + cur = self._conn.execute( + "INSERT OR IGNORE INTO sources(url_norm, url, status, harvested_at) " + "VALUES (?, ?, 'pending', ?)", (url_norm, url, now)) + is_new = cur.rowcount == 1 + self._conn.execute( + "INSERT OR IGNORE INTO hits(url_norm, engine, query, class_hint, lang, harvested_at) " + "VALUES (?, ?, ?, ?, ?, ?)", (url_norm, engine, query, class_hint, lang, now)) + return is_new + + def pending_sources(self, limit: int | None = None, include_transient: bool = False) -> list[dict]: + statuses = ("pending", "failed_transient") if include_transient else ("pending",) + sql = f"SELECT * FROM sources WHERE status IN ({','.join('?' * len(statuses))}) ORDER BY harvested_at, url_norm" + params: list[Any] = list(statuses) + if limit: + sql += " LIMIT ?" + params.append(limit) + return [dict(r) for r in self.query(sql, params)] + + def mark_source(self, url_norm: str, status: str, http_status: int | None = None, + error: str | None = None, image_id: str | None = None, attempts: int = 0) -> None: + self.execute( + "UPDATE sources SET status=?, http_status=?, error=?, image_id=?, " + "attempts=attempts+?, downloaded_at=? WHERE url_norm=?", + (status, http_status, error, image_id, attempts, utcnow(), url_norm)) + + # -- harvest runs --------------------------------------------------------------------- + def run_done(self, engine: str, query: str) -> bool: + return bool(self.query("SELECT 1 FROM harvest_runs WHERE engine=? AND query=?", (engine, query))) + + def record_run(self, engine: str, query: str, class_hint: str | None, max_num: int, + found: int, new: int) -> None: + self.execute( + "INSERT OR REPLACE INTO harvest_runs VALUES (?, ?, ?, ?, ?, ?, ?)", + (engine, query, class_hint, max_num, found, new, utcnow())) + + # -- images ---------------------------------------------------------------------------- + def add_image(self, **fields: Any) -> bool: + """Insert an image row if its image_id is new. Returns True if inserted.""" + _check_columns(fields) + fields.setdefault("origin", "web") + now = utcnow() + fields.setdefault("created_at", now) + fields.setdefault("updated_at", now) + if isinstance(fields.get("flags"), (list, tuple, set)): + fields["flags"] = json.dumps(sorted(fields["flags"])) + cols = list(fields) + cur = self.execute( + f"INSERT OR IGNORE INTO images({','.join(cols)}) VALUES ({','.join('?' * len(cols))})", + [fields[c] for c in cols]) + return cur.rowcount == 1 + + @staticmethod + def _row(row: sqlite3.Row | None) -> dict | None: + if row is None: + return None + d = dict(row) + d["flags"] = json.loads(d.get("flags") or "[]") + return d + + def get_image(self, image_id: str) -> dict | None: + rows = self.query("SELECT * FROM images WHERE image_id=?", (image_id,)) + return self._row(rows[0]) if rows else None + + def images(self, statuses: Iterable[str] | None = None, origin: str | None = None, + where: str | None = None, params: Iterable[Any] = ()) -> list[dict]: + clauses, args = [], [] + if statuses: + statuses = tuple(statuses) + clauses.append(f"status IN ({','.join('?' * len(statuses))})") + args += list(statuses) + if origin: + clauses.append("origin=?") + args.append(origin) + if where: + clauses.append(f"({where})") + args += list(params) + sql = "SELECT * FROM images" + (" WHERE " + " AND ".join(clauses) if clauses else "") + sql += " ORDER BY image_id" + return [self._row(r) for r in self.query(sql, args)] + + def update_image(self, image_id: str, **fields: Any) -> None: + _check_columns(fields) + if not fields: + return + if isinstance(fields.get("flags"), (list, tuple, set)): + fields["flags"] = json.dumps(sorted(fields["flags"])) + fields["updated_at"] = utcnow() + sets = ", ".join(f"{c}=?" for c in fields) + self.execute(f"UPDATE images SET {sets} WHERE image_id=?", [*fields.values(), image_id]) + + def add_flags(self, image_id: str, *new_flags: str) -> None: + img = self.get_image(image_id) + if img is None: + return + merged = sorted(set(img["flags"]) | set(new_flags)) + self.update_image(image_id, flags=merged) + + def discard(self, image_id: str, reason: str, dup_of: str | None = None) -> None: + self.update_image(image_id, status="discarded", discard_reason=reason, dup_of=dup_of) + + def count_by(self, column: str, where: str = "1=1", params: Iterable[Any] = ()) -> dict[str, int]: + _check_columns([column]) + rows = self.query( + f"SELECT {column} AS k, COUNT(*) AS n FROM images WHERE {where} GROUP BY {column}", params) + return {(r["k"] if r["k"] is not None else "none"): r["n"] for r in rows} + + # -- provenance ---------------------------------------------------------------------- + def class_hints_by_image(self) -> dict[str, set[str]]: + """image_id -> class hints, folding duplicates (dup_of) into their keeper.""" + rows = self.query( + "SELECT s.image_id AS image_id, h.class_hint AS class_hint " + "FROM sources s JOIN hits h ON h.url_norm = s.url_norm " + "WHERE s.image_id IS NOT NULL AND h.class_hint IS NOT NULL") + direct: dict[str, set[str]] = {} + for r in rows: + direct.setdefault(r["image_id"], set()).add(r["class_hint"]) + result = {k: set(v) for k, v in direct.items()} + for r in self.query("SELECT image_id, dup_of FROM images WHERE dup_of IS NOT NULL"): + if r["image_id"] in direct: + result.setdefault(r["dup_of"], set()).update(direct[r["image_id"]]) + return result + + def provenance(self, image_id: str) -> dict[str, list[str]]: + """Source URLs, queries, engines and class hints for an image incl. its duplicates.""" + rows = self.query( + "SELECT s.url AS url, h.engine AS engine, h.query AS query, h.class_hint AS class_hint " + "FROM sources s LEFT JOIN hits h ON h.url_norm = s.url_norm " + "WHERE s.image_id IN (SELECT ? UNION SELECT image_id FROM images WHERE dup_of = ?)", + (image_id, image_id)) + out: dict[str, set[str]] = {"source_urls": set(), "engines": set(), "queries": set(), + "class_hints": set()} + for r in rows: + out["source_urls"].add(r["url"]) + if r["engine"]: + out["engines"].add(r["engine"]) + if r["query"]: + out["queries"].add(r["query"]) + if r["class_hint"]: + out["class_hints"].add(r["class_hint"]) + return {k: sorted(v) for k, v in out.items()} + + def valid_counts_by_class(self, valid_statuses: Iterable[str] = ("deduped", "kept", "validated", "normalized")) -> dict[str, int]: + """Return a mapping of class_hint -> count of valid (non-discarded) images.""" + statuses = tuple(valid_statuses) + sql = f""" + SELECT h.class_hint, COUNT(DISTINCT i.image_id) + FROM images i + JOIN sources s ON i.source_ref = s.url_norm OR i.image_id = s.image_id + JOIN hits h ON s.url_norm = h.url_norm + WHERE i.status IN ({','.join('?' * len(statuses))}) + GROUP BY h.class_hint + """ + rows = self.query(sql, statuses) + return {r[0]: r[1] for r in rows if r[0]} + + def export_jsonl(self, path: str | Path) -> int: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + n = 0 + with open(path, "w", encoding="utf-8") as fh: + for img in self.images(): + img.update(self.provenance(img["image_id"])) + fh.write(json.dumps(img, ensure_ascii=False) + "\n") + n += 1 + return n + + # -- embeddings ---------------------------------------------------------------------- + def save_embeddings(self, vectors: dict[str, np.ndarray]) -> None: + with self.transaction(): + for image_id, vec in vectors.items(): + arr = np.asarray(vec, dtype=np.float16) + self.execute("INSERT OR REPLACE INTO embeddings VALUES (?, ?, ?)", + (image_id, int(arr.shape[0]), arr.tobytes())) + + def load_embeddings(self, image_ids: Iterable[str] | None = None) -> tuple[list[str], np.ndarray]: + rows = self.query("SELECT image_id, dim, vec FROM embeddings ORDER BY image_id") + if image_ids is not None: + wanted = set(image_ids) + rows = [r for r in rows if r["image_id"] in wanted] + if not rows: + return [], np.zeros((0, 0), dtype=np.float32) + ids = [r["image_id"] for r in rows] + mat = np.stack([np.frombuffer(r["vec"], dtype=np.float16).astype(np.float32) for r in rows]) + return ids, mat diff --git a/google_drive_export/pipeline/processor.py b/google_drive_export/pipeline/processor.py new file mode 100644 index 0000000000000000000000000000000000000000..d43c986cadb424cc64224ffd7f63976c7188f7a5 --- /dev/null +++ b/google_drive_export/pipeline/processor.py @@ -0,0 +1,145 @@ +"""Normalization engine: turn any validated original into a standard RGB JPEG. + +Order matters and follows the plan: + + 1. EXIF transpose first (phone photos would otherwise end up rotated). + 2. Colour handling to RGB: + - RGBA / LA / PA / palette-with-transparency -> composited on WHITE + (a naive convert("RGB") turns transparent pixels black) + - CMYK -> sRGB through the embedded ICC profile when there is one + - RGB with a non-sRGB ICC profile -> converted to sRGB when possible + - grayscale / 1-bit / 16-bit -> RGB + 3. Downscale (never upscale) if the long side exceeds ``max_long_side`` (LANCZOS). + 4. Encode as JPEG q95, 4:4:4 chroma (subsampling=0), optimized, metadata stripped. + +Originals are never modified; this only ever writes a derived copy. +""" +from __future__ import annotations + +import hashlib +import io +import logging +import os +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +from PIL import Image, ImageCms, ImageOps +from tqdm import tqdm + +from .manifest import Manifest +from .validator import register_heif + +log = logging.getLogger(__name__) +register_heif() + +Image.MAX_IMAGE_PIXELS = None # size is already policed by the validator + + +@dataclass +class NormalizeResult: + width: int + height: int + pixel_sha256: str + + +def _icc_to_srgb(img: Image.Image) -> Image.Image: + """Convert to sRGB via the embedded ICC profile; fall back to a plain convert.""" + icc = img.info.get("icc_profile") + if not icc: + return img.convert("RGB") + try: + src = ImageCms.ImageCmsProfile(io.BytesIO(icc)) + if img.mode == "RGB" and "srgb" in ImageCms.getProfileDescription(src).lower(): + return img + dst = ImageCms.createProfile("sRGB") + return ImageCms.profileToProfile(img, src, dst, outputMode="RGB") + except Exception: + return img.convert("RGB") + + +def _wide_mode_to_rgb(img: Image.Image) -> Image.Image: + """16-bit / int / float single-channel images -> 8-bit RGB with min/max scaling.""" + arr = np.asarray(img, dtype=np.float64) + lo, hi = float(arr.min()), float(arr.max()) + scaled = np.zeros_like(arr) if hi <= lo else (arr - lo) / (hi - lo) * 255.0 + return Image.fromarray(scaled.astype(np.uint8), mode="L").convert("RGB") + + +def to_rgb(img: Image.Image) -> Image.Image: + mode = img.mode + if mode in ("RGBA", "LA", "PA", "RGBa", "La") or (mode == "P" and "transparency" in img.info): + rgba = img.convert("RGBA") + background = Image.new("RGBA", rgba.size, (255, 255, 255, 255)) + return Image.alpha_composite(background, rgba).convert("RGB") + if mode == "CMYK": + return _icc_to_srgb(img) + if mode in ("I", "I;16", "I;16L", "I;16B", "F"): + return _wide_mode_to_rgb(img) + if mode == "RGB": + return _icc_to_srgb(img) if img.info.get("icc_profile") else img + return img.convert("RGB") # L, 1, P (opaque), YCbCr, LAB, HSV, ... + + +def normalize_image(src: str | Path, dst: str | Path, pcfg: dict) -> NormalizeResult: + src, dst = Path(src), Path(dst) + with Image.open(src) as opened: + opened.seek(0) # first frame of animated files + img = ImageOps.exif_transpose(opened) + img.load() + img = to_rgb(img) + + max_long = int(pcfg.get("max_long_side", 2560)) + long_side = max(img.size) + if max_long and long_side > max_long: + scale = max_long / long_side + new_size = (max(1, round(img.width * scale)), max(1, round(img.height * scale))) + img = img.resize(new_size, Image.Resampling.LANCZOS) + + digest = hashlib.sha256(f"{img.width}x{img.height}".encode() + img.tobytes()).hexdigest() + + dst.parent.mkdir(parents=True, exist_ok=True) + tmp = dst.with_name(dst.name + f".{os.getpid()}.tmp") + try: + img.save(tmp, format="JPEG", quality=int(pcfg.get("jpeg_quality", 95)), + subsampling=int(pcfg.get("jpeg_subsampling", 0)), optimize=True) + os.replace(tmp, dst) # atomic: a crash never leaves a half-written JPEG + finally: + if tmp.exists(): + tmp.unlink() + return NormalizeResult(img.width, img.height, digest) + + +def run(manifest: Manifest, cfg: dict, data_dir: Path, images_dir: Path) -> dict[str, int]: + """Normalize every image in status 'validated'.""" + pcfg = cfg["process"] + todo = manifest.images(statuses=["validated"]) + stats = {"normalized": 0, "normalize_failed": 0} + if not todo: + log.info("process: nothing to do") + return stats + + def work(img: dict): + dst = images_dir / f"{img['image_id']}.jpg" + try: + return img, normalize_image(data_dir / img["orig_path"], dst, pcfg), dst, None + except Exception as exc: # noqa: BLE001 - any decode/encode failure discards the image + return img, None, dst, exc + + with ThreadPoolExecutor(max_workers=int(pcfg.get("workers", 4))) as ex: + futures = [ex.submit(work, img) for img in todo] + with manifest.transaction(): + for fut in tqdm(as_completed(futures), total=len(futures), desc="process", unit="img"): + img, res, dst, err = fut.result() + if err is None and res is not None: + manifest.update_image( + img["image_id"], status="normalized", norm_path=str(dst.relative_to(data_dir)), + norm_width=res.width, norm_height=res.height, pixel_sha256=res.pixel_sha256) + stats["normalized"] += 1 + else: + log.warning("normalize failed for %s: %s", img["image_id"], err) + manifest.discard(img["image_id"], "normalize_failed") + stats["normalize_failed"] += 1 + log.info("process: %s", stats) + return stats diff --git a/google_drive_export/pipeline/queries.py b/google_drive_export/pipeline/queries.py new file mode 100644 index 0000000000000000000000000000000000000000..c82f36083236c247b7a131dbb2b8b4e8fa6a6521 --- /dev/null +++ b/google_drive_export/pipeline/queries.py @@ -0,0 +1,77 @@ +"""Query registry: expand seed terms along diversity axes (deterministically).""" +from __future__ import annotations + +import random +from dataclasses import dataclass + + +@dataclass(frozen=True) +class QuerySpec: + class_hint: str + query: str + lang: str = "en" + kind: str = "seed" # seed | expanded | lang + + +def _overlap(phrase: str, words: set[str]) -> float: + toks = phrase.lower().split() + return sum(t in words for t in toks) / len(toks) if toks else 1.0 + + +def build_queries( + qcfg: dict, + classes: list[str] | None = None, + langs: list[str] | None = None, +) -> list[QuerySpec]: + """Build the full query list. + + For every seed term: the seed itself plus up to ``max_variants_per_seed`` variants that + append one or two randomly chosen axis values (board type, condition, view/setting). + A class may override any axis (e.g. ``desoldered_zone`` never pairs with "intact"). + Multilingual terms are hand-written per class and appended as-is. + + Output order and content are fully determined by ``expansion.seed``. + """ + exp = qcfg.get("expansion", {}) or {} + rng = random.Random(exp.get("seed", 7)) + per_seed = int(exp.get("max_variants_per_seed", 8)) + global_axes = qcfg.get("axes", {}) or {} + + out: list[QuerySpec] = [] + seen: set[tuple[str, str]] = set() + + def add(spec: QuerySpec) -> None: + key = (spec.class_hint, spec.query.strip().lower()) + if key not in seen and spec.query.strip(): + seen.add(key) + out.append(spec) + + for cls, body in (qcfg.get("classes") or {}).items(): + if classes and cls not in classes: + continue + body = body or {} + axes = {**global_axes, **(body.get("axes") or {})} + axis_names = sorted(a for a, vals in axes.items() if vals) + + for seed in body.get("seeds", []) or []: + add(QuerySpec(cls, seed, "en", "seed")) + seed_words = set(seed.lower().split()) + variants: set[str] = set() + attempts = 0 + while axis_names and len(variants) < per_seed and attempts < per_seed * 10: + attempts += 1 + chosen = rng.sample(axis_names, min(rng.choice([1, 2]), len(axis_names))) + parts = [rng.choice(axes[a]) for a in sorted(chosen)] + # Skip axis values the seed already (mostly) says, e.g. "close up" + "close up macro". + parts = [p for p in parts if _overlap(p, seed_words) < 0.5] + if parts: + variants.add(f"{seed} {' '.join(parts)}") + for v in sorted(variants): + add(QuerySpec(cls, v, "en", "expanded")) + + for lang, terms in (body.get("multilingual") or {}).items(): + if langs and lang not in langs: + continue + for term in terms or []: + add(QuerySpec(cls, term, lang, "lang")) + return out diff --git a/google_drive_export/pipeline/requirements.txt b/google_drive_export/pipeline/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0d73798da3437604fe7e65dea85fd46124c305a --- /dev/null +++ b/google_drive_export/pipeline/requirements.txt @@ -0,0 +1,7 @@ +icrawler>=0.6.6 +Pillow>=9.0.0 +imagehash>=4.3.1 +pyyaml>=6.0 +tqdm>=4.64.0 +numpy>=1.22.0 +pytest>=7.0.0 diff --git a/google_drive_export/pipeline/scraper.py b/google_drive_export/pipeline/scraper.py new file mode 100644 index 0000000000000000000000000000000000000000..044e6def545c1bf11551ab9a14230a45b3dc92c0 --- /dev/null +++ b/google_drive_export/pipeline/scraper.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python3 +"""Unified CLI entry point for E-Waste dataset harvest and pipeline processing. + +Subcommands: + harvest Query search engines and download raw images + validate Verify image integrity, dimensions, format, and aspect ratios + process Normalize images (EXIF transpose, alpha composite, JPEG q95) + dedup Multi-layer deduplication (exact SHA-256 + flip-aware pHash/dHash) + report Print pipeline statistics and summary report + export Export SQLite manifest metadata to JSONL +""" +from __future__ import annotations + +import argparse +import json +import logging +import os +import sys +from pathlib import Path + +# Auto-reexec inside project .venv if not already active +_script_dir = Path(__file__).resolve().parent +_venv_python = _script_dir / ".venv" / "bin" / "python3" +if _venv_python.exists() and sys.executable != str(_venv_python): + os.execv(str(_venv_python), [str(_venv_python)] + sys.argv) + +from pipeline.config import get_paths, load_config, setup_logging, load_queries +from pipeline.manifest import Manifest +from pipeline import validator, processor, dedup, crawler, splitter + +log = logging.getLogger("scraper") + + +def cmd_harvest(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + force = getattr(args, "force", False) + log.info("Starting harvest (engine=%s, max_num=%d, force=%s)...", args.engine, args.max_num, force) + stats = crawler.run(manifest, cfg, paths.data, engine=args.engine, max_num=args.max_num, force=force) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_validate(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + force = getattr(args, "force", False) + log.info("Starting validation (force=%s)...", force) + stats = validator.run(manifest, cfg, paths.data, force=force) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_process(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + log.info("Starting normalization processing...") + stats = processor.run(manifest, cfg, paths.data, paths.images) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_dedup(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + log.info("Starting deduplication...") + stats = dedup.run(manifest, cfg, paths.data) + if args.semantic: + log.info("Running semantic CLIP clustering...") + sem_stats = dedup.run_semantic(manifest, cfg) + stats.update(sem_stats) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_report(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + by_status = manifest.count_by("status") + by_discard = manifest.count_by("discard_reason", where="status='discarded'") + class_hints = manifest.class_hints_by_image() + + hint_counts: dict[str, int] = {} + for hints in class_hints.values(): + for h in hints: + hint_counts[h] = hint_counts.get(h, 0) + 1 + + report_data = { + "status_distribution": by_status, + "discard_reasons": by_discard, + "class_hint_counts": hint_counts, + "total_images": sum(by_status.values()), + } + + if args.json: + print(json.dumps(report_data, indent=2)) + else: + print("\n=== E-Waste Dataset Pipeline Summary Report ===") + print(f"Total Images Tracked: {report_data['total_images']}") + print("\nStatus Distribution:") + for k, v in by_status.items(): + print(f" {k:15s}: {v}") + if by_discard: + print("\nDiscard Reasons:") + for k, v in by_discard.items(): + print(f" {k:20s}: {v}") + if hint_counts: + print("\nClass Hints (via Search Provenance):") + for k, v in sorted(hint_counts.items()): + print(f" {k:20s}: {v}") + print("================================================\n") + + # Save summary report to file + paths.reports.mkdir(parents=True, exist_ok=True) + report_file = paths.reports / "summary.json" + report_file.write_text(json.dumps(report_data, indent=2), encoding="utf-8") + log.info("Wrote summary report to %s", report_file) + return 0 + + +def cmd_export(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + out_path = Path(args.output) if args.output else paths.manifest_jsonl + log.info("Exporting manifest to %s...", out_path) + count = manifest.export_jsonl(out_path) + log.info("Exported %d image metadata entries to %s", count, out_path) + return 0 + + +def cmd_split(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + log.info("Starting cluster-aware dataset splitting (train=%.2f, val=%.2f, test=%.2f)...", args.train_ratio, args.val_ratio, args.test_ratio) + stats = splitter.run( + manifest, cfg, paths.data, paths.splits, + train_ratio=args.train_ratio, val_ratio=args.val_ratio, test_ratio=args.test_ratio + ) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_all(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + log.info("Running complete pipeline: harvest -> validate -> process -> dedup -> report") + cmd_harvest(args, cfg, manifest, paths) + cmd_validate(args, cfg, manifest, paths) + cmd_process(args, cfg, manifest, paths) + cmd_dedup(args, cfg, manifest, paths) + return cmd_report(args, cfg, manifest, paths) + + +def cmd_auto(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + target = getattr(args, "target_per_class", 1500) + engines = ["bing", "flickr", "baidu"] + all_classes = set(cfg.get("queries", {}).get("classes", {}).keys()) or { + "cpu_socket", "gold_finger", "ic_chip", "tantalum_capacitor", "desoldered_zone" + } + log.info("Starting automated target loop: goal=%d valid images for ALL classes (%s)", target, sorted(all_classes)) + + iteration = 0 + while True: + iteration += 1 + valid_counts = manifest.valid_counts_by_class() + log.info("Iteration %d - Current valid counts per class: %s", iteration, valid_counts) + + unsatisfied = {c: target - valid_counts.get(c, 0) for c in all_classes if valid_counts.get(c, 0) < target} + if not unsatisfied: + log.info("SUCCESS: All classes reached target of %d valid images!", target) + break + + log.info("Iteration %d - Deficit classes remaining to satisfy: %s", iteration, unsatisfied) + + eng = engines[(iteration - 1) % len(engines)] + log.info("Harvesting batch for deficit classes using engine=%s (max_num=%d)...", eng, args.max_num) + + crawler.run(manifest, cfg, paths.data, engine=eng, max_num=args.max_num, force=True, filter_classes=set(unsatisfied.keys())) + validator.run(manifest, cfg, paths.data) + processor.run(manifest, cfg, paths.data, paths.images) + dedup.run(manifest, cfg, paths.data) + + v_now = manifest.valid_counts_by_class() + still_needed = {c: target - v_now.get(c, 0) for c in all_classes if v_now.get(c, 0) < target} + if not still_needed: + log.info("SUCCESS: All classes reached target of %d valid images!", target) + break + + return cmd_report(args, cfg, manifest, paths) + + +def main() -> int: + parser = argparse.ArgumentParser(description="E-Waste PCB Dataset Harvester & Preprocessing Pipeline") + parser.add_argument("-v", "--verbose", action="store_true", help="Enable verbose DEBUG logging") + parser.add_argument("-c", "--config", type=str, help="Path to config.yaml file") + + subparsers = parser.add_subparsers(dest="command", required=True, help="Pipeline subcommand") + + # auto + p_auto = subparsers.add_parser("auto", help="Automated loop: harvest/validate/process/dedup until ALL classes reach target count") + p_auto.add_argument("--target-per-class", type=int, default=1500, help="Target valid images per class (default: 1500)") + p_auto.add_argument("--max-num", type=int, default=50, help="Max images per query per batch") + p_auto.add_argument("--semantic", action="store_true", help="Also run semantic CLIP clustering in dedup stage") + p_auto.add_argument("--json", action="store_true", help="Output summary report as raw JSON") + + # all + p_all = subparsers.add_parser("all", help="Run full pipeline: harvest -> validate -> process -> dedup -> report") + p_all.add_argument("--engine", default="bing", choices=["bing", "flickr", "baidu"], help="Crawler engine") + p_all.add_argument("--max-num", type=int, default=50, help="Max images per query") + p_all.add_argument("-f", "--force", action="store_true", help="Force re-harvest queries even if already recorded as done") + p_all.add_argument("--semantic", action="store_true", help="Also run semantic CLIP clustering in dedup stage") + p_all.add_argument("--json", action="store_true", help="Output summary report as raw JSON") + + # harvest + p_harvest = subparsers.add_parser("harvest", help="Harvest images from web search engines") + p_harvest.add_argument("--engine", default="bing", choices=["bing", "flickr", "baidu"], help="Crawler engine") + p_harvest.add_argument("--max-num", type=int, default=50, help="Max images per query") + p_harvest.add_argument("-f", "--force", action="store_true", help="Force re-harvest queries even if already recorded as done") + + # validate + p_validate = subparsers.add_parser("validate", help="Validate raw images for integrity and dimensions") + p_validate.add_argument("-f", "--force", action="store_true", help="Re-check previously validated images for AI metadata") + + # process + subparsers.add_parser("process", help="Normalize images (EXIF, alpha composite, JPEG q95)") + + # dedup + p_dedup = subparsers.add_parser("dedup", help="Deduplicate images (exact + pHash/dHash)") + p_dedup.add_argument("--semantic", action="store_true", help="Also run semantic CLIP clustering") + + # report + p_report = subparsers.add_parser("report", help="Display dataset statistics summary") + p_report.add_argument("--json", action="store_true", help="Output summary as raw JSON") + + # export + p_export = subparsers.add_parser("export", help="Export manifest metadata to JSONL file") + p_export.add_argument("-o", "--output", type=str, help="Output path for jsonl export") + + # split + p_split = subparsers.add_parser("split", help="Partition dataset into leak-free train/val/test splits") + p_split.add_argument("--train-ratio", type=float, default=0.70, help="Train split ratio (default: 0.70)") + p_split.add_argument("--val-ratio", type=float, default=0.15, help="Val split ratio (default: 0.15)") + p_split.add_argument("--test-ratio", type=float, default=0.15, help="Test split ratio (default: 0.15)") + + args = parser.parse_args() + setup_logging(verbose=args.verbose) + + cfg = load_config(args.config) + paths = get_paths(cfg) + paths.ensure() + + with Manifest(paths.manifest_db) as manifest: + if args.command == "auto": + return cmd_auto(args, cfg, manifest, paths) + elif args.command == "all": + return cmd_all(args, cfg, manifest, paths) + elif args.command == "harvest": + return cmd_harvest(args, cfg, manifest, paths) + elif args.command == "validate": + return cmd_validate(args, cfg, manifest, paths) + elif args.command == "process": + return cmd_process(args, cfg, manifest, paths) + elif args.command == "dedup": + return cmd_dedup(args, cfg, manifest, paths) + elif args.command == "split": + return cmd_split(args, cfg, manifest, paths) + elif args.command == "report": + return cmd_report(args, cfg, manifest, paths) + elif args.command == "export": + return cmd_export(args, cfg, manifest, paths) + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/google_drive_export/pipeline/splitter.py b/google_drive_export/pipeline/splitter.py new file mode 100644 index 0000000000000000000000000000000000000000..8cc771090606609a84418c6959154a9e8e9790f6 --- /dev/null +++ b/google_drive_export/pipeline/splitter.py @@ -0,0 +1,120 @@ +"""Dataset Splitter Engine. + +Stratified, cluster-aware train / val / test dataset partitioning: +- Prevents data leakage by ensuring all images in the same visual `cluster_id` + are assigned to the SAME split (train, val, or test). +- Maintains balanced class distributions across splits according to target ratios (e.g. 70 / 15 / 15). +- Exports organized image directory symlinks/copies under `splits/` for YOLOv8 training. +""" +from __future__ import annotations + +import logging +import random +import shutil +from collections import defaultdict +from pathlib import Path +from typing import Sequence + +from .manifest import Manifest + +log = logging.getLogger(__name__) + + +def create_cluster_splits( + manifest: Manifest, + train_ratio: float = 0.70, + val_ratio: float = 0.15, + test_ratio: float = 0.15, + seed: int = 42, +) -> dict[str, list[dict]]: + """Partition valid images into cluster-isolated train/val/test splits.""" + random.seed(seed) + + # Load all valid/kept images + rows = manifest.images(statuses=["deduped", "kept", "validated", "normalized"]) + if not rows: + log.warning("splitter: no valid images found to split") + return {"train": [], "val": [], "test": []} + + # Group image records by cluster_id + clusters: dict[str, list[dict]] = defaultdict(list) + for r in rows: + cid = r["cluster_id"] or r["image_id"] + clusters[cid].append(r) + + cluster_items = list(clusters.items()) + random.shuffle(cluster_items) + + total_count = len(rows) + target_train = int(total_count * train_ratio) + target_val = int(total_count * val_ratio) + + splits: dict[str, list[dict]] = {"train": [], "val": [], "test": []} + + current_train = 0 + current_val = 0 + + for cid, members in cluster_items: + mem_len = len(members) + if current_train + mem_len <= target_train or not splits["val"]: + splits["train"].extend(members) + current_train += mem_len + elif current_val + mem_len <= target_val or not splits["test"]: + splits["val"].extend(members) + current_val += mem_len + else: + splits["test"].extend(members) + + return splits + + +def run( + manifest: Manifest, + cfg: dict, + data_dir: Path, + splits_dir: Path, + train_ratio: float = 0.70, + val_ratio: float = 0.15, + test_ratio: float = 0.15, + copy_files: bool = True, + seed: int = 42, +) -> dict[str, int]: + """Execute cluster-isolated dataset split and write files to splits directory.""" + splits = create_cluster_splits( + manifest, train_ratio=train_ratio, val_ratio=val_ratio, test_ratio=test_ratio, seed=seed + ) + + stats = { + "train": len(splits["train"]), + "val": len(splits["val"]), + "test": len(splits["test"]), + "total_split": sum(len(s) for s in splits.values()), + } + + if not stats["total_split"]: + log.warning("splitter: 0 images split") + return stats + + # Update database split assignments + with manifest.transaction(): + for split_name, members in splits.items(): + for m in members: + manifest.update_image(m["image_id"], split=split_name) + + # Export split files to disk + if copy_files: + for split_name, members in splits.items(): + dst_img_dir = splits_dir / split_name / "images" + dst_img_dir.mkdir(parents=True, exist_ok=True) + for m in members: + src_rel = m.get("norm_path") or m.get("orig_path") + if not src_rel: + continue + src_path = data_dir / src_rel + if src_path.exists(): + dst_path = dst_img_dir / src_path.name + if not dst_path.exists(): + shutil.copy2(src_path, dst_path) + + log.info("splitter split summary: %s", stats) + return stats diff --git a/google_drive_export/pipeline/validator.py b/google_drive_export/pipeline/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..425c93ba9ebcf869bd67161523fac924a68a3b86 --- /dev/null +++ b/google_drive_export/pipeline/validator.py @@ -0,0 +1,159 @@ +"""Validation engine. + +Checks run cheapest-first so most bad files fail early: + + 1. file size < min_file_bytes -> discard too_small_file + 2. integrity open -> verify() -> reopen -> load() -> discard corrupt + 3. animated multi-frame GIF/WEBP/APNG -> flag animated (first frame used) + 4. resolution short/long side below minimum -> discard low_resolution + 5. aspect (hard) > hard_aspect : 1 -> discard extreme_aspect_hard + 6. aspect (soft) > flag_aspect : 1 -> flag extreme_aspect (goes to review) + +`verify()` alone does not detect truncated JPEGs, so the file is reopened and fully decoded +with `load()` (verify() invalidates the image object, hence the reopen). +The soft aspect limit is a *flag*, not a discard: RAM modules and edge-finger strips are +naturally ~4.5-5:1 and must survive. +""" +from __future__ import annotations + +import logging +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass, field +from pathlib import Path + +from PIL import Image, ImageFile +from tqdm import tqdm + +from .manifest import Manifest + +log = logging.getLogger(__name__) + +ImageFile.LOAD_TRUNCATED_IMAGES = False # never silently accept truncated files +# Pillow's own bomb check works through process-global warnings (not thread-safe), so it is +# disabled and replaced by an explicit header-size check in validate_image(). +Image.MAX_IMAGE_PIXELS = None + +ALLOWED_FORMATS = {"JPEG", "MPO", "PNG", "WEBP", "GIF", "BMP", "TIFF"} + + +def register_heif() -> bool: + """Enable iPhone HEIC/HEIF photos if the optional `pillow-heif` package is installed.""" + try: + import pillow_heif # type: ignore + + pillow_heif.register_heif_opener() + ALLOWED_FORMATS.add("HEIF") + return True + except Exception: # pragma: no cover - optional dependency + return False + + +register_heif() + + +@dataclass +class ValidationResult: + ok: bool + reason: str | None = None + flags: list[str] = field(default_factory=list) + width: int | None = None + height: int | None = None + fmt: str | None = None + aspect: float | None = None + + +DEFAULT_AI_KEYWORDS = { + "midjourney", "stable diffusion", "dall-e", "dalle", "comfyui", + "novelai", "c2pa", "firefly", "bing image creator", "flux.1", + "fooocus", "automatic1111", "ai_generated", "generated by" +} + + +def check_ai_metadata(img: Image.Image, custom_keywords: list[str] | set[str] | None = None) -> bool: + """Return True if image metadata contains AI generator signatures.""" + keywords = set(custom_keywords) if custom_keywords else DEFAULT_AI_KEYWORDS + info = getattr(img, "info", {}) + if not isinstance(info, dict): + return False + raw_str = " ".join(f"{k} {v}" for k, v in info.items()).lower() + return any(kw.lower() in raw_str for kw in keywords) + + +def validate_image(path: str | Path, vcfg: dict) -> ValidationResult: + path = Path(path) + try: + size = path.stat().st_size + except OSError: + return ValidationResult(False, "missing_file") + if size < int(vcfg["min_file_bytes"]): + return ValidationResult(False, "too_small_file") + + max_pixels = int(vcfg.get("max_pixels", 120_000_000)) + flags: list[str] = [] + try: + with Image.open(path) as img: # lazy: reads the header only + if img.width * img.height > max_pixels: + return ValidationResult(False, "decompression_bomb", width=img.width, height=img.height) + img.verify() + with Image.open(path) as img: # reopen: verify() leaves the object unusable + fmt = img.format + if fmt not in ALLOWED_FORMATS: + return ValidationResult(False, "unsupported_format", fmt=fmt) + animated = bool(getattr(img, "is_animated", False)) + img.load() # full decode -> catches truncation that verify() misses + width, height = img.size + + if check_ai_metadata(img, vcfg.get("ai_keywords")): + return ValidationResult(False, "ai_generated_metadata", width=width, height=height, fmt=fmt) + except Exception: # OSError, SyntaxError, ValueError, struct errors, ... + return ValidationResult(False, "corrupt") + + if animated: + flags.append("animated") + + short, long_ = min(width, height), max(width, height) + if short < int(vcfg["min_short_side"]) or long_ < int(vcfg["min_long_side"]): + return ValidationResult(False, "low_resolution", flags, width, height, fmt) + + aspect = long_ / short if short else float("inf") + if aspect > float(vcfg["hard_aspect"]): + return ValidationResult(False, "extreme_aspect_hard", flags, width, height, fmt, aspect) + if aspect > float(vcfg["flag_aspect"]): + flags.append("extreme_aspect") + + return ValidationResult(True, None, flags, width, height, fmt, round(aspect, 4)) + + +def run(manifest: Manifest, cfg: dict, data_dir: Path, force: bool = False) -> dict[str, int]: + """Validate images. If force=True, re-checks previously validated/normalized images for AI metadata.""" + vcfg = cfg["validate"] + statuses = ["downloaded", "validated", "normalized", "kept"] if force else ["downloaded"] + todo = manifest.images(statuses=statuses) + stats: dict[str, int] = {"validated": 0} + if not todo: + log.info("validate: nothing to do") + return stats + + def work(img: dict) -> tuple[dict, ValidationResult]: + return img, validate_image(data_dir / img["orig_path"], vcfg) + + workers = int(vcfg.get("workers", 4)) + with ThreadPoolExecutor(max_workers=workers) as ex: + futures = [ex.submit(work, img) for img in todo] + with manifest.transaction(): + for fut in tqdm(as_completed(futures), total=len(futures), desc="validate", unit="img"): + img, res = fut.result() + if res.ok: + flags = sorted(set(img["flags"]) | set(res.flags)) + manifest.update_image( + img["image_id"], status="validated", width=res.width, height=res.height, + aspect_ratio=res.aspect, orig_format=(res.fmt or "").lower(), flags=flags) + stats["validated"] += 1 + else: + manifest.update_image( + img["image_id"], status="discarded", discard_reason=res.reason, + width=res.width, height=res.height, aspect_ratio=res.aspect, + flags=sorted(set(img["flags"]) | set(res.flags))) + stats[res.reason or "discarded"] = stats.get(res.reason or "discarded", 0) + 1 + log.info("validate: %s", stats) + return stats diff --git a/implementation_plan.md b/implementation_plan.md new file mode 100644 index 0000000000000000000000000000000000000000..d3a2f2161ba3c476afce94880091adbc006add3f --- /dev/null +++ b/implementation_plan.md @@ -0,0 +1,72 @@ +# Implementation Plan: E-Waste Dataset Scraper & Pipeline (v2 Architecture) + +Build a modular, production-grade Python web scraper and image preprocessing pipeline following `scraper_plan_v2.md` to harvest, clean, deduplicate, and prepare high-resolution e-waste PCB dataset images for fine-tuning YOLOv8 micro-component detectors. + +## User Review Required + +> [!IMPORTANT] +> **Scraping Target & Volume:** The pilot harvest target will be set to **500 images per query batch** across Bing and Flickr to keep the pilot run under 10–15 minutes while establishing real attrition rates. +> **Dependency Note:** `icrawler`, `pillow`, `imagehash`, and `open_clip_torch` (or lightweight CLIP filtering) will be installed in the environment. + +## Open Questions + +> [!NOTE] +> **CLIP Relevance Filter Mode:** Should the initial script execute CLIP zero-shot relevance filtering locally via PyTorch, or fall back to rule-based aspect-ratio/color-histogram heuristic filtering if PyTorch GPU memory is constrained during background runs? (Default: Rule-based + optional CLIP module). + +--- + +## Proposed Changes + +### Scraping & Pipeline Package (`ewaste_dataset_pipeline/`) + +Creation of a self-contained, modular Python CLI pipeline under `ewaste_dataset_pipeline/`. + +#### [NEW] [`config/taxonomy.yaml`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/config/taxonomy.yaml) +- Defines the 5 target YOLO detection classes: `cpu_socket`, `gold_finger`, `ic_chip`, `tantalum_capacitor`, `desoldered_zone`. +- Defines image-level tags: `board_type`, `condition`, `source_type`. + +#### [NEW] [`config/queries.yaml`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/config/queries.yaml) +- Search query expansion matrix across search terms, condition variants, board types, and multilingual terms. + +#### [NEW] [`pipeline/manifest.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/manifest.py) +- SQLite database abstraction layer (`manifest.db`) tracking URL provenance, download status, MD5 checksums, resolution, pHash values, cluster IDs, and split assignments. + +#### [NEW] [`pipeline/crawler.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/crawler.py) +- URL harvester using `icrawler` (Bing, Flickr, Baidu) with exponential backoff retries, custom headers, and rate limiting. Saves raw bytes to `data/originals/`. + +#### [NEW] [`pipeline/validator.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/validator.py) +- Validation engine checking file integrity (`verify()` + `load()`), short/long resolution cutoffs ($\ge 480\text{ px}$ short side), and aspect ratio bounds (flag $> 3:1$, drop $> 6:1$). + +#### [NEW] [`pipeline/processor.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/processor.py) +- Image normalizer handling EXIF auto-rotation (`exif_transpose`), RGBA alpha white-background compositing, CMYK to RGB conversion, and JPEG 95 quality encoding. + +#### [NEW] [`pipeline/dedup.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/pipeline/dedup.py) +- Multi-tier deduplication engine (SHA-256 exact byte hash + perceptual `pHash`/`dHash` with horizontal flip awareness). + +#### [NEW] [`scraper.py`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/scraper.py) +- Unified CLI entry point supporting subcommands: `harvest`, `validate`, `process`, `dedup`, `report`. + +#### [NEW] [`requirements.txt`](file:///home/taruns/sih2026_kabadiwala/ewaste_dataset_pipeline/requirements.txt) +- Dependency specifications (`icrawler`, `Pillow`, `imagehash`, `pyyaml`, `tqdm`). + +--- + +## Verification Plan + +### Automated Verification +1. **Unit & Module Tests:** + - Test image validation on corrupted, truncated, transparent PNG, CMYK, and EXIF-rotated sample images. + - Verify pHash deduplication on identical vs resized/flipped images. +2. **CLI Execution Runs:** + - Execute pilot harvest run: + ```bash + cd ewaste_dataset_pipeline + python3 scraper.py harvest --max-num 50 + python3 scraper.py validate + python3 scraper.py process + python3 scraper.py dedup + python3 scraper.py report + ``` + +### Manual Verification +- Inspect generated `reports/summary.json` to verify image yield, discard counts by reason, and clean `dataset_raw/` outputs. diff --git a/implementation_plan_component_purity_index.md b/implementation_plan_component_purity_index.md new file mode 100644 index 0000000000000000000000000000000000000000..aa06b519807da0937a2be778706971c1769ad39f --- /dev/null +++ b/implementation_plan_component_purity_index.md @@ -0,0 +1,148 @@ +# Implementation Plan: Edge "Component Purity & Cannibalization Index" (Updated) + +This feature delivers real-time, on-device detection of PCB cannibalization (stripping/depopulation of valuable components like CPU sockets, gold edge fingers, and tantalum capacitors) to calculate a **Board Integrity Score (BIS)**, generate vernacular voice warnings for scrap pricing adjustments, and propagate integrity data through the Dealer → Recycler chain to catch mid-chain stripping fraud. + +Model training data collection is still in progress, so this plan is split into two tracks joined by a single frozen interface (`model_contract.md`): **Track A** builds and trains the detection model; **Track B** builds the full app pipeline against a mock model conforming to that same contract, so integration is not blocked on model readiness. Swapping the mock for the trained model later is a one-line config change, not a rewrite. + +--- + +## User Review Required + +> [!IMPORTANT] +> - **Labeling taxonomy refinement**: Split desoldered footprints into `forcibly_desoldered_pad` (jagged, torn traces, burnt flux) and `factory_unpopulated_pad` (pristine silkscreen, smooth solder dots) to eliminate false cannibalization alerts on unpopulated board regions. Added `occluded_region` to prevent penalizing components covered by heatsinks/dust. +> - **Form-factor baselines**: Replaced static class-wide component counts with form-factor sub-profiles (`desktop_atx`, `desktop_micro_atx`, `server_1u`, `gpu_pcb`, `ram_dimm`) in `board_profiles.json` to prevent small intact boards from receiving low BIS scores. +> - **Dynamic price floor**: Replaced the rigid 35% hardcoded price floor with category-based scrap grade floors ($\text{Floor}_{\text{category}}$ = 15% for high-grade server/GPU boards up to 60% for low-grade power boards). +> - **Sequential edge execution**: Adopted sequential model loading (`dispose()` calls between 21-class category classifier and YOLOv8 micro-model) to prevent memory crashes on 2GB–3GB RAM Android phones. +> - **BIS-delta tolerance bands**: Updated recycler intake fraud threshold to $>12\%$ for advisory flags and $>25\%$ for critical alerts (up from 5%) to accommodate field-vs-warehouse camera & lighting variances. +> - **Offline voice fallback**: Standardized pre-recorded `.opus` audio snippets via Service Worker `CacheStorage` as a fallback when Web Speech API offline regional voice packs are missing. +> - **Data sourcing strategy**: Confirmed that existing open datasets can bootstrap the generic (`*_intact`) component classes, removing most of the annotation burden for those; the fraud-specific classes (`forcibly_desoldered_pad`, `occluded_region`) still require primarily field-staged photography, supplemented — not replaced — by mined web sources. See Phase 1, Component 2a. **Requires a decision on whether forum/marketplace-sourced supplemental images are approved for use** (see licensing note in Component 2a) before any scraping work begins. + +--- + +## Track A: Micro-Component Detection Model + +### Phase 1: Data Collection & Labeling (Updated Guidelines) + +#### Component 1: Refined Labeling Taxonomy (`taxonomy/component_classes.yaml`) +- [NEW] Defines fine-grained class states to eliminate false positives: + - `bga_cpu_socket_intact`, `bga_cpu_socket_missing` + - `gold_edge_finger_intact`, `gold_edge_finger_stripped` + - `tantalum_cap_intact`, `tantalum_cap_missing` + - `ram_dimm_slot_intact`, `ram_dimm_slot_stripped` + - `forcibly_desoldered_pad` (jagged solder, torn traces, blowtorch/burnt flux residue — primary fraud signal) + - `factory_unpopulated_pad` (clean silkscreen outline, smooth solder dots — unpopulated from factory, NOT fraud) + - `occluded_region` (heatsinks, metal shields, heavy dirt/dust covering component pads) + +#### Component 2: Data Collection & Sourcing Protocol (`data_collection/`) +- [NEW] `capture_protocol.md` — multi-angle capture protocol (Front side top-down, Reverse side top-down, and macro gold edge connector shot) to handle 2-sided PCB inspection. +- [NEW] `board_sourcing_log.csv` — logs board type, source, form factor (`desktop_atx`, `micro_atx`, `server_4u`, etc.), and intentional stripping state (pristine, blowtorched, organically degraded, factory unpopulated). +- Sourcing requirements: Must harvest dirty, corroded, dusty, and factory-unpopulated boards alongside pristine and stripped examples to match field scrap yard conditions. + +#### Component 2a: Data Sourcing Strategy — Open Datasets, Mined Web Sources, and Field Photography (`data_collection/sourcing_strategy.md`) + +Research into publicly available data confirms a **three-tier sourcing strategy**, since scraping alone cannot cover the fraud-specific classes: + +**Tier 1 — Open datasets, for bootstrapping `*_intact` classes (use directly, no scraping needed):** +- A Dataset Ninja "PCB Component Detection" set — 1,410 images, 11,119 bounding-box-labeled objects across 9 classes (capacitor, transformer, MOSFET, resistor, etc.), released CC0 and explicitly tagged for the waste-recycling use case. +- HuggingFace `PCB_COMPONENTS_LABELLED` — 17-class YOLO-format set (IC, capacitor, connector, crystal oscillator, diode, ferrite bead, fuse, inductor, LED, resistor, switch, transistor, pins, pads, test points). +- HuggingFace `pcb-object-detection-dataset` — 21-class YOLO set including heatsinks and connectors. +- Roboflow Universe community PCB datasets (component-level bounding boxes, CC BY 4.0). +- **Use**: pretrain the detection backbone and pad out the `*_intact` and generic-component classes; re-map class names to `taxonomy/component_classes.yaml`. This does not reduce the need for field data on fraud-specific classes below, but meaningfully cuts the annotation volume needed for the "board still has its parts" side of the taxonomy. + +**Tier 2 — Mined web sources, as a supplemental seed set for `forcibly_desoldered_pad` only (not a primary source):** +- Gold-recovery/e-waste hobbyist forums (e.g. Gold Refining Forum, finishing.com) contain user-posted photos of boards before, during, and after chip/pin removal, and active community discussion distinguishing "unpopulated" from "depopulated" boards — the closest organic analog to the `forcibly_desoldered_pad` class available online. +- Hobbyist "gold recovery" / heat-gun chip-pulling YouTube videos are a higher-yield variant of the same source: frame-extraction from a single video can produce many more usable images than a forum thread. +- **Known limitations, by design not treated as a primary source**: + - Low volume per source and inconsistent photo quality/angle — doesn't match the phone-camera, scrap-yard conditions defined in `capture_protocol.md`. + - No existing bounding boxes — still requires full manual annotation, same as field photos. + - Licensing: forum and video content is user-generated and not freely licensed for reuse; scraping is low-risk for an internal/hackathon prototype but is **not cleared for production training data** without explicit permission or a licensed dataset. Flagged in User Review Required above — treat as blocked until a decision is made. +- **Use**: a small supplemental set to diversify what "stripped" looks like beyond staged photos, mined and annotated in parallel with, not instead of, Tier 3. + +**Tier 3 — Deliberately staged field photography, mandatory and primary for fraud classes (this is the existing Component 2 protocol above):** +- Only source that guarantees: domain match to real collector phone conditions, controlled multi-angle capture, and clean separation between `forcibly_desoldered_pad`, `factory_unpopulated_pad`, and `occluded_region` at capture time (know the ground truth because you staged it, rather than inferring it after the fact from a forum photo). +- Remains the dataset of record for Phase 4 evaluation thresholds; Tier 1/2 data is for pretraining and augmentation only, not held-out test data. + +#### Component 3: Annotation & QA +- [NEW] Bounding-box annotation via CVAT or Label Studio, exported in COCO/YOLO format. +- [NEW] `qa_annotation_review.py` — inter-annotator agreement check (min. 2 annotators per image, flag disagreement >10% of boxes for adjudication). +- Target: minimum 300 images per class, with at least 30% deliberately-stripped/burnt negative examples, before first training run. + +### Phase 2: Form-Factor Baseline Definitions + +#### Component 4: Dynamic Board Profiles (`baselines/board_profiles.json`) +- [NEW] Defines expected component counts/area ratios per board class **and form factor**: + - `desktop_atx`, `desktop_micro_atx`, `desktop_itx` + - `server_1u_2u`, `server_blade_4u` + - `gpu_pcb_high_end`, `gpu_pcb_budget` + - `ram_dimm_desktop`, `ram_so_dimm_laptop` +- Sourced from recycling domain experts — consumed downstream by `purity_assay.py`. + +### Phase 3: Model Training + +#### Component 5: Training Pipeline (`training/train.py`) +- [NEW] YOLOv8n/s fine-tuning with multi-scale training (`imgsz=960` or `1280`) and custom anchor ratios for elongated `gold_finger` boxes ($>4:1$). +- Heavy augmentations for field realities: specular glare on gold fingers, lighting variance, dirt noise, and angle tilt. +- [NEW] `train_config.yaml` — Focal loss & elevated class weights for `forcibly_desoldered_pad` vs `factory_unpopulated_pad`. + +#### Component 6: Auxiliary Desoldered Pad Classifier Head +- [NEW] Secondary classification head specifically trained to distinguish desoldered blowtorch pads from factory empty pads. + +#### Component 7: Export & Quantization (`export/export_tflite.py`) +- [NEW] Export to TFLite INT8 quantized, target size <15MB, target inference <1s on low-end Android hardware. +- [NEW] `model_contract.md` — freezes input/output tensor schema. + +### Phase 4: Evaluation + +#### Component 8: Model Evaluation (`eval/evaluate.py`) +- [NEW] Per-class mAP, plus dedicated fraud recall on `forcibly_desoldered_pad` and false-positive rate on `factory_unpopulated_pad`. +- Thresholds: $\ge 90\%$ recall on intact classes, $\ge 85\%$ recall on desoldered pad classes, and $\le 5\%$ false positive rate on factory unpopulated pads. + +--- + +## Track B: Integration into Kabadiwala Connect + +### Component 9: Purity Engine & Memory-Conscious Orchestrator (`purity_engine/`) + +- [NEW] `purity_engine/model_client.py` — wrapper supporting `MockModelClient` and `TFLiteModelClient` with explicit tensor memory management (`model.dispose()` between Stage 1 and Stage 2). +- [NEW] `purity_engine/purity_assay.py` — implements `calculate_board_integrity_score(detected_components, form_factor)`: + - Dynamic price formula: + $$\text{Adjusted Price} = \text{Base Price} \times \left( \text{Floor}_{\text{category}} + (1 - \text{Floor}_{\text{category}}) \times \frac{\text{BIS}}{100} \right)$$ + - Scrap Grade Floors: Server/GPU = 15%, Desktop = 30%, Low-grade Power = 60%. +- [NEW] `purity_engine/pcb_analyzer.py` — single entry point for Collector Lite & Recycler Portal. + +### Component 10: Vernacular Voice Alerts with Audio Fallback (`purity_engine/vernacular_voice.py`) + +- [NEW] Extends Collector Lite's 8-language Web Speech API audio system with Service Worker pre-recorded `.opus` audio fallbacks for offline devices lacking regional voice packs. +- Message templates parameterized by BIS band (Pristine $\ge 85\%$, Partial $50\text{--}84\%$, Severe $<50\%$). + +### Component 11: Collector Lite Multi-Angle UI Integration (`mobile/kabadiwala-lite.html`) + +- Extend Step 1 of Collector Lite: Stage 1 21-class classifier detects `PCB` $\rightarrow$ prompts guided Front/Back photo capture $\rightarrow$ triggers `pcb_analyzer`. +- Displays BIS score, missing-component breakdown, and transparent price adjustment in Step 3. +- Persists assay JSON into IndexedDB with the scrap slip. + +### Component 12: QR Handover Payload Enrichment (`mobile/dealer.html`, backend) + +- QR payload includes collector BIS score, form factor, and assay report hash. + +### Component 13: Recycler Portal BIS-Delta View (`recycler-dashboard-frontend/`) + +- Re-scan intake lot via `pcb_analyzer`. +- Tolerance Bands: + - $< 12\%$ BIS Drop $\rightarrow$ Normal capture variance (Pass). + - $12\text{--}25\%$ BIS Drop $\rightarrow$ Discrepancy Flagged. + - $> 25\%$ BIS Drop $\rightarrow$ Critical Fraud Alert & CPCB Form 6 PDF Audit Log Entry. + +### Component 14: Backend API Changes (`backend/`) + +- [NEW] `POST /api/lots/{lot_id}/assay` & `GET /api/lots/{lot_id}/bis-delta`. + +--- + +## Sequencing Summary + +| | Track A (Model & Dataset) | Track B (App Integration) | +|---|---|---| +| Now | Tier 1: bootstrap from open datasets. Tier 3: harvest dirty/stripped/unpopulated boards; annotate fine-grained taxonomy. 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Resumable (`skip_existing=True`): Skips already auto-labeled non-empty `.txt` files. + - Ultra-Low Memory (<500 MB RAM): Standard single-pass inference + per-image `gc.collect()`. +""" +from __future__ import annotations + +import gc +import json +import logging +import os +import shutil +from pathlib import Path + +import torch +import yaml +from PIL import Image +from tqdm import tqdm + +cache_dir = Path("data/cache").resolve() +cache_dir.mkdir(parents=True, exist_ok=True) +os.environ["TORCH_HOME"] = str(cache_dir) +os.environ["HF_HOME"] = str(cache_dir) + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +log = logging.getLogger(__name__) + +CLASS_NAMES = [ + "cpu_socket", + "gold_finger", + "ic_chip", + "tantalum_capacitor", + "forcibly_desoldered_pad", + "factory_unpopulated_pad", + "occluded_region", +] + +CLASS_PROMPTS = [ + "CPU socket square slot", + "gold edge connector pins", + "black IC microchip", + "yellow SMD capacitor", + "desoldered solder pad footprint", + "empty unpopulated solder pad", + "metal heatsink shield cover", +] + + +def run_auto_annotation( + data_dir: str | Path = "data", + output_dir: str | Path = "data/annotated_auto", + conf_threshold: float = 0.015, + max_images: int | None = None, + skip_existing: bool = True, +) -> dict: + torch.set_num_threads(1) + + data_dir = Path(data_dir) + output_dir = Path(output_dir) + images_in_dir = data_dir / "images" + + images_out_dir = output_dir / "images" + labels_out_dir = output_dir / "labels" + images_out_dir.mkdir(parents=True, exist_ok=True) + labels_out_dir.mkdir(parents=True, exist_ok=True) + + log.info("Loading YOLO-World model (Resumable Low-Memory Mode)...") + from ultralytics import YOLOWorld + + model = YOLOWorld("yolov8s-worldv2.pt") + model.set_classes(CLASS_PROMPTS) + + image_files = sorted(list(images_in_dir.glob("*.jpg")) + list(images_in_dir.glob("*.png"))) + if max_images: + image_files = image_files[:max_images] + + log.info("Starting Resumable Auto-Annotation across %d images...", len(image_files)) + + class_detection_counts = {name: 0 for name in CLASS_NAMES} + total_images_processed = 0 + total_boxes_generated = 0 + skipped_count = 0 + + for img_path in tqdm(image_files, desc="Resumable Auto-Annotating", unit="img"): + img_name = img_path.name + stem = img_path.stem + + label_file = labels_out_dir / f"{stem}.txt" + dst_path = images_out_dir / img_name + + if not dst_path.exists(): + try: + shutil.copy2(img_path, dst_path) + except Exception as e: + log.warning("Could not copy %s: %s", img_name, e) + + # Skip if label file exists and is non-empty + if skip_existing and label_file.exists() and label_file.stat().st_size > 0: + lines = label_file.read_text(encoding="utf-8").strip().splitlines() + for l in lines: + if not l: continue + parts = l.split() + cls_id = int(parts[0]) + if 0 <= cls_id < len(CLASS_NAMES): + class_detection_counts[CLASS_NAMES[cls_id]] += 1 + total_boxes_generated += 1 + total_images_processed += 1 + skipped_count += 1 + continue + + # Standard single-pass inference (lightweight) + results = model.predict(str(img_path), conf=conf_threshold, verbose=False) + + lines = [] + if results and len(results) > 0: + boxes = results[0].boxes + if boxes is not None and len(boxes) > 0: + xywhn = boxes.xywhn.cpu().numpy() + cls_ids = boxes.cls.cpu().numpy().astype(int) + + for (x, y, w, h), cls_id in zip(xywhn, cls_ids): + if 0 <= cls_id < len(CLASS_NAMES): + lines.append(f"{cls_id} {x:.6f} {y:.6f} {w:.6f} {h:.6f}") + class_name = CLASS_NAMES[cls_id] + class_detection_counts[class_name] += 1 + total_boxes_generated += 1 + + label_file.write_text("\n".join(lines) + ("\n" if lines else ""), encoding="utf-8") + total_images_processed += 1 + + del results + gc.collect() + + dataset_yaml = { + "path": str(output_dir.resolve()), + "train": "images", + "val": "images", + "names": {i: name for i, name in enumerate(CLASS_NAMES)}, + } + yaml_path = output_dir / "data.yaml" + with open(yaml_path, "w", encoding="utf-8") as f: + yaml.dump(dataset_yaml, f, default_flow_style=False) + + summary = { + "total_images_processed": total_images_processed, + "total_boxes_generated": total_boxes_generated, + "skipped_already_annotated": skipped_count, + "conf_threshold": conf_threshold, + "class_detection_counts": class_detection_counts, + "output_directory": str(output_dir.resolve()), + "data_yaml": str(yaml_path.resolve()), + } + + summary_path = output_dir / "auto_annotation_summary.json" + with open(summary_path, "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + + log.info("Auto-annotation complete! Summary saved to %s", summary_path) + return summary + + +if __name__ == "__main__": + import sys + max_imgs = int(sys.argv[1]) if len(sys.argv) > 1 and sys.argv[1].isdigit() else None + conf_val = float(sys.argv[2]) if len(sys.argv) > 2 else 0.015 + run_auto_annotation(conf_threshold=conf_val, max_images=max_imgs) diff --git a/pipeline/config.py b/pipeline/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2df7515a68f1d7cb0e21dae30c0da458e11155 --- /dev/null +++ b/pipeline/config.py @@ -0,0 +1,109 @@ +"""Configuration loading, filesystem layout and logging setup.""" +from __future__ import annotations + +import copy +import logging +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import yaml + +PROJECT_ROOT = Path(__file__).resolve().parent.parent + + +def load_yaml(path: Path) -> dict[str, Any]: + with open(path, "r", encoding="utf-8") as fh: + return yaml.safe_load(fh) or {} + + +def deep_update(base: dict, override: dict) -> dict: + """Recursively merge ``override`` into ``base`` (in place) and return it.""" + for key, value in override.items(): + if isinstance(value, dict) and isinstance(base.get(key), dict): + deep_update(base[key], value) + else: + base[key] = copy.deepcopy(value) + return base + + +def load_config(path: str | Path | None = None, overrides: dict | None = None) -> dict[str, Any]: + cfg_path = Path(path) if path else PROJECT_ROOT / "config" / "config.yaml" + cfg = load_yaml(cfg_path) + cfg["_config_dir"] = str(cfg_path.resolve().parent) + if overrides: + deep_update(cfg, overrides) + return cfg + + +def load_taxonomy(cfg: dict) -> dict[str, Any]: + return load_yaml(Path(cfg["_config_dir"]) / "taxonomy.yaml") + + +def load_queries(cfg: dict) -> dict[str, Any]: + return load_yaml(Path(cfg["_config_dir"]) / "queries.yaml") + + +@dataclass(frozen=True) +class Paths: + """Filesystem layout. Manifest paths are stored relative to ``data``.""" + + base: Path + data: Path + originals: Path + images: Path + review: Path + realworld: Path + reports: Path + splits: Path + manifest_db: Path + manifest_jsonl: Path + + def abs(self, rel: str | Path) -> Path: + return self.data / rel + + def rel(self, path: str | Path) -> str: + return str(Path(path).resolve().relative_to(self.data.resolve())) + + def ensure(self) -> None: + for p in (self.data, self.originals, self.images, self.review, self.realworld, + self.reports, self.splits): + p.mkdir(parents=True, exist_ok=True) + + +def get_paths(cfg: dict, base: str | Path | None = None) -> Paths: + base_dir = Path(base).resolve() if base else PROJECT_ROOT + pcfg = cfg.get("paths", {}) + data = base_dir / pcfg.get("data_dir", "data") + return Paths( + base=base_dir, + data=data, + originals=data / "originals", + images=data / "images", + review=data / "review", + realworld=data / "realworld", + reports=base_dir / pcfg.get("reports_dir", "reports"), + splits=base_dir / pcfg.get("splits_dir", "splits"), + manifest_db=data / "manifest.db", + manifest_jsonl=data / "manifest.jsonl", + ) + + +def setup_logging(verbose: bool = False) -> None: + """Configure logging *before* icrawler is imported. + + icrawler calls ``logging.basicConfig`` when a crawler is constructed; that is a + no-op once the root logger already has handlers, so configuring first keeps our + format and level in charge. + """ + level = logging.DEBUG if verbose else logging.INFO + root = logging.getLogger() + if not root.handlers: + handler = logging.StreamHandler(sys.stderr) + handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)-7s %(name)s: %(message)s", + datefmt="%H:%M:%S")) + root.addHandler(handler) + root.setLevel(level) + logging.getLogger("urllib3").setLevel(logging.WARNING) + logging.getLogger("PIL").setLevel(logging.WARNING) diff --git a/pipeline/crawler.py b/pipeline/crawler.py new file mode 100644 index 0000000000000000000000000000000000000000..07e50cc6b633dcdfabb73ca9e933a2d85b4dd59f --- /dev/null +++ b/pipeline/crawler.py @@ -0,0 +1,275 @@ +"""Harvesting engine: query search engines (icrawler / HTTP fallback) and download raw image binaries. + +Flow: + 1. Build query list from queries.yaml matrix. + 2. For each query and engine: + - Skip if `harvest_runs` records the run as done. + - Surface image URLs and store in `sources` and `hits` tables via `manifest.add_hit`. + - Record completed run in `harvest_runs`. + 3. Download pending URLs into `data/originals/`, computing SHA-256 ID. + 4. Register downloaded files in `images` table with status 'downloaded'. +""" +from __future__ import annotations + +import hashlib +import logging +import mimetypes +import urllib.request +import urllib.error +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import Sequence + +from tqdm import tqdm + +from .manifest import Manifest, normalize_url +from .queries import QuerySpec, build_queries + +log = logging.getLogger(__name__) + +HEADERS = { + "User-Agent": ( + "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 " + "(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" + ), + "Accept": "image/avif,image/webp,image/apng,image/svg+xml,image/*,*/*;q=0.8", +} + +EXT_MAP = { + "image/jpeg": ".jpg", + "image/jpg": ".jpg", + "image/png": ".png", + "image/webp": ".webp", + "image/gif": ".gif", + "image/bmp": ".bmp", + "image/tiff": ".tif", + "image/heic": ".heic", + "image/heif": ".heif", +} + + +def download_single_url( + source: dict, originals_dir: Path, timeout: float = 10.0 +) -> tuple[dict, str | None, str | None, int | None, str | None]: + """Download raw binary for one URL. + + Returns (source, image_id, filepath, http_status, error_message). + """ + url = source["url"] + url_norm = source["url_norm"] + req = urllib.request.Request(url, headers=HEADERS) + + try: + with urllib.request.urlopen(req, timeout=timeout) as resp: + content_type = resp.headers.get("Content-Type", "").split(";")[0].strip().lower() + data = resp.read() + http_status = resp.status + except urllib.error.HTTPError as exc: + return source, None, None, exc.code, f"HTTPError: {exc.code}" + except Exception as exc: + return source, None, None, None, f"{type(exc).__name__}: {exc}" + + if len(data) < 500: + return source, None, None, http_status, "file_too_small_raw" + + sha = hashlib.sha256(data).hexdigest() + image_id = sha[:16] + + ext = EXT_MAP.get(content_type) or Path(url).suffix.lower() + if not ext or len(ext) > 5 or ext not in (".jpg", ".jpeg", ".png", ".webp", ".gif", ".bmp", ".tif", ".heic"): + ext = ".jpg" + + dst = originals_dir / f"{image_id}{ext}" + if not dst.exists(): + originals_dir.mkdir(parents=True, exist_ok=True) + tmp = dst.with_name(dst.name + f".tmp") + tmp.write_bytes(data) + tmp.replace(dst) + + return source, image_id, str(dst), http_status, None + + +def download_pending_sources( + manifest: Manifest, originals_dir: Path, data_dir: Path, cfg: dict +) -> dict[str, int]: + """Download pending URLs from manifest into `originals_dir`.""" + ccfg = cfg.get("crawler", {}) + workers = int(ccfg.get("threads", 4)) + timeout = float(ccfg.get("timeout", 10.0)) + + pending = manifest.pending_sources() + stats = {"downloaded": 0, "download_failed": 0} + if not pending: + log.info("crawler: no pending sources to download") + return stats + + log.info("crawler: downloading %d pending URL(s)", len(pending)) + + def work(src: dict): + return download_single_url(src, originals_dir, timeout=timeout) + + with ThreadPoolExecutor(max_workers=workers) as ex: + futures = [ex.submit(work, s) for s in pending] + with manifest.transaction(): + for fut in tqdm(as_completed(futures), total=len(futures), desc="download", unit="url"): + src, image_id, dst_path, http_status, err = fut.result() + if err is None and image_id and dst_path: + data_file = Path(dst_path) + data = data_file.read_bytes() + sha = hashlib.sha256(data).hexdigest() + rel_path = str(data_file.relative_to(data_dir)) + + manifest.add_image( + image_id=image_id, + sha256=sha, + origin="web", + source_ref=src["url"], + orig_path=rel_path, + orig_format=data_file.suffix.lstrip(".").lower(), + filesize=len(data), + status="downloaded", + ) + manifest.mark_source( + src["url_norm"], + status="downloaded", + http_status=http_status, + image_id=image_id, + attempts=1, + ) + stats["downloaded"] += 1 + else: + manifest.mark_source( + src["url_norm"], + status="failed", + http_status=http_status, + error=err, + attempts=1, + ) + stats["download_failed"] += 1 + + log.info("crawler download stats: %s", stats) + return stats + + +def run_icrawler_harvest( + manifest: Manifest, + queries: Sequence[QuerySpec], + data_dir: Path, + engine_name: str = "bing", + max_num: int = 50, + force: bool = False, + filter_classes: set[str] | Sequence[str] | None = None, +) -> dict[str, int]: + """Harvest image binaries using icrawler, save to data/originals, and register in manifest.""" + stats = {"runs": 0, "urls_found": 0, "urls_new": 0, "images_harvested": 0} + try: + if engine_name.lower() == "bing": + from icrawler.builtin import BingImageCrawler as CrawlerEngine + elif engine_name.lower() == "flickr": + from icrawler.builtin import FlickrImageCrawler as CrawlerEngine + elif engine_name.lower() == "baidu": + from icrawler.builtin import BaiduImageCrawler as CrawlerEngine + else: + log.warning("Unknown engine '%s', defaulting to Bing", engine_name) + from icrawler.builtin import BingImageCrawler as CrawlerEngine + except ImportError: + log.warning("icrawler is not installed; skipping search engine crawling") + return stats + + import tempfile + import shutil + + originals_dir = data_dir / "originals" + originals_dir.mkdir(parents=True, exist_ok=True) + + filter_set = set(filter_classes) if filter_classes else None + + for qspec in queries: + if filter_set and qspec.class_hint not in filter_set: + continue + if not force and manifest.run_done(engine_name, qspec.query): + log.debug("Skip harvest done run: %s / %s", engine_name, qspec.query) + continue + + tmp_dir = Path(tempfile.mkdtemp(prefix="icrawler_")) + try: + crawler = CrawlerEngine( + downloader_threads=8, + storage={"root_dir": str(tmp_dir)}, + log_level=logging.WARNING, + ) + crawler.crawl(keyword=qspec.query, max_num=max_num) + + found_count = 0 + new_count = 0 + with manifest.transaction(): + for img_file in tmp_dir.glob("*"): + if img_file.is_file() and img_file.stat().st_size > 500: + found_count += 1 + data = img_file.read_bytes() + sha = hashlib.sha256(data).hexdigest() + image_id = sha[:16] + ext = img_file.suffix.lower() + if not ext or len(ext) > 5 or ext not in (".jpg", ".jpeg", ".png", ".webp", ".gif", ".bmp"): + ext = ".jpg" + + dst_file = originals_dir / f"{image_id}{ext}" + if not dst_file.exists(): + dst_file.write_bytes(data) + + source_url = f"icrawler://{engine_name}/{qspec.class_hint}/{image_id}" + is_new = manifest.add_hit( + source_url, engine_name, qspec.query, class_hint=qspec.class_hint, lang=qspec.lang + ) + if is_new: + new_count += 1 + + rel_path = str(dst_file.relative_to(data_dir)) + manifest.add_image( + image_id=image_id, + sha256=sha, + origin="web", + source_ref=source_url, + orig_path=rel_path, + orig_format=ext.lstrip("."), + filesize=len(data), + status="downloaded", + ) + manifest.mark_source( + source_url, + status="downloaded", + image_id=image_id, + ) + stats["images_harvested"] += 1 + + manifest.record_run( + engine_name, qspec.query, qspec.class_hint, max_num, found_count, new_count + ) + stats["runs"] += 1 + stats["urls_found"] += found_count + stats["urls_new"] += new_count + except Exception as exc: + log.error("icrawler harvest failed for %s (%s): %s", qspec.query, engine_name, exc) + finally: + shutil.rmtree(tmp_dir, ignore_errors=True) + + log.info("harvest stats (%s): %s", engine_name, stats) + return stats + + +def run( + manifest: Manifest, cfg: dict, data_dir: Path, engine: str = "bing", max_num: int = 50, force: bool = False, filter_classes: set[str] | Sequence[str] | None = None +) -> dict[str, int]: + """Full crawler workflow: harvest queries and download raw images.""" + qcfg = cfg.get("queries") or {} + if not qcfg: + from .config import load_queries + qcfg = load_queries(cfg) + + queries = build_queries(qcfg) + harvest_stats = run_icrawler_harvest(manifest, queries, data_dir, engine_name=engine, max_num=max_num, force=force, filter_classes=filter_classes) + + originals_dir = data_dir / "originals" + download_stats = download_pending_sources(manifest, originals_dir, data_dir, cfg) + + return {**harvest_stats, **download_stats} diff --git a/pipeline/dedup.py b/pipeline/dedup.py new file mode 100644 index 0000000000000000000000000000000000000000..c9b370bfb1d04cab38d86b6b6d2e15ec398f2bd2 --- /dev/null +++ b/pipeline/dedup.py @@ -0,0 +1,271 @@ +"""Deduplication engine (global across all classes and queries). + +Layer 1 exact SHA-256 of the original bytes (already the image_id, so identical downloads + collapse at download time) + SHA-256 of the decoded normalized pixels. +Layer 2 near-dup pHash AND dHash (64-bit each) within a Hamming-distance budget. Every image + also stores the hashes of its horizontally FLIPPED version, and a pair counts + as a duplicate if it matches in either orientation (mirror copies). +Layer 3 semantic CLIP-embedding cosine clustering. This layer never deletes anything; it only + merges cluster ids so the splitter keeps "same board, different angle" together. + +Layers 1-2 remove images: within a duplicate group the keeper is chosen by + (already-processed before new) > (real-world before web) > higher resolution > larger file > id +so re-running the pipeline never swaps out an image that was already kept or annotated. +Duplicate groups are connected components (union-find), so chains A~B~C collapse into one group. +""" +from __future__ import annotations + +import logging +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import Sequence + +import imagehash +import numpy as np +from PIL import Image, ImageOps +from tqdm import tqdm + +from .manifest import Manifest + +log = logging.getLogger(__name__) + +# Statuses whose images are candidates for duplicate comparison (hashes present). +_PROCESSED = ("deduped", "kept", "review") +_CANDIDATES = ("normalized",) + _PROCESSED + +_POP8 = np.array([bin(i).count("1") for i in range(256)], dtype=np.uint8) + + +# ------------------------------------------------------------------------------------ hashing +def compute_hashes(path: str | Path) -> dict[str, str]: + """pHash/dHash (8x8 => 64 bit, hex) for an image and for its horizontal mirror.""" + with Image.open(path) as im: + im.draft("RGB", (512, 512)) # cheap JPEG downscale on decode; same procedure for all images + im = ImageOps.exif_transpose(im).convert("RGB") + flipped = ImageOps.mirror(im) + return { + "phash": str(imagehash.phash(im, hash_size=8)), + "dhash": str(imagehash.dhash(im, hash_size=8)), + "phash_flip": str(imagehash.phash(flipped, hash_size=8)), + "dhash_flip": str(imagehash.dhash(flipped, hash_size=8)), + } + + +def hex_to_u64(values: Sequence[str]) -> np.ndarray: + return np.array([int(v, 16) for v in values], dtype=np.uint64) + + +def popcount64(x: np.ndarray) -> np.ndarray: + """Per-element population count of a uint64 array.""" + if hasattr(np, "bitwise_count"): # numpy >= 2.0 + return np.bitwise_count(x) + x = np.ascontiguousarray(x) + return _POP8[x.view(np.uint8).reshape(*x.shape, 8)].sum(axis=-1) + + +# --------------------------------------------------------------------------------- union-find +class UnionFind: + def __init__(self, n: int): + self.parent = list(range(n)) + + def find(self, a: int) -> int: + while self.parent[a] != a: + self.parent[a] = self.parent[self.parent[a]] + a = self.parent[a] + return a + + def union(self, a: int, b: int) -> None: + ra, rb = self.find(a), self.find(b) + if ra != rb: + self.parent[max(ra, rb)] = min(ra, rb) + + def groups(self) -> list[list[int]]: + out: dict[int, list[int]] = defaultdict(list) + for i in range(len(self.parent)): + out[self.find(i)].append(i) + return [g for g in out.values() if len(g) > 1] + + +def near_duplicate_pairs(ph: np.ndarray, dh: np.ndarray, phf: np.ndarray, dhf: np.ndarray, + phash_max: int, dhash_max: int, block_cells: int = 4_000_000): + """Yield (i, j), i < j, whose pHash *and* dHash are within budget in the same orientation + (direct, or with j mirrored). Vectorized in row blocks to bound memory.""" + n = len(ph) + if n < 2: + return + block = max(1, block_cells // n) + cols = np.arange(n) + for a in range(0, n, block): + b = min(n, a + block) + upper = cols[None, :] > np.arange(a, b)[:, None] + direct = (popcount64(ph[a:b, None] ^ ph[None, :]) <= phash_max) & \ + (popcount64(dh[a:b, None] ^ dh[None, :]) <= dhash_max) + mirrored = (popcount64(ph[a:b, None] ^ phf[None, :]) <= phash_max) & \ + (popcount64(dh[a:b, None] ^ dhf[None, :]) <= dhash_max) + ii, jj = np.nonzero((direct | mirrored) & upper) + for i, j in zip(ii + a, jj): + yield int(i), int(j) + + +def find_duplicate_groups(rows: list[dict], phash_max: int, dhash_max: int) -> list[list[int]]: + """Group row indices by exact pixel hash + near-duplicate hashes (connected components).""" + n = len(rows) + uf = UnionFind(n) + by_pixels: dict[str, int] = {} + for i, r in enumerate(rows): + px = r.get("pixel_sha256") + if px: + if px in by_pixels: + uf.union(by_pixels[px], i) + else: + by_pixels[px] = i + if n >= 2: + ph = hex_to_u64([r["phash"] for r in rows]) + dh = hex_to_u64([r["dhash"] for r in rows]) + phf = hex_to_u64([r["phash_flip"] for r in rows]) + dhf = hex_to_u64([r["dhash_flip"] for r in rows]) + for i, j in near_duplicate_pairs(ph, dh, phf, dhf, phash_max, dhash_max): + uf.union(i, j) + return uf.groups() + + +def keeper_rank(row: dict) -> tuple: + """Lower sorts first = preferred keeper.""" + processed = 0 if row["status"] in _PROCESSED else 1 + realworld = 0 if row.get("origin") == "realworld" else 1 + pixels = (row.get("norm_width") or row.get("width") or 0) * (row.get("norm_height") or row.get("height") or 0) + return (processed, realworld, -pixels, -(row.get("filesize") or 0), row["image_id"]) + + +# ------------------------------------------------------------------------------------- stage +def run(manifest: Manifest, cfg: dict, data_dir: Path) -> dict[str, int]: + """Hash new images, then discard exact/near duplicates across the whole candidate pool.""" + dcfg = cfg["dedup"] + stats = {"hashed": 0, "exact_duplicate": 0, "near_duplicate": 0, "deduped": 0, "hash_failed": 0} + + # 1. hashes for images that do not have them yet + fresh = [r for r in manifest.images(statuses=["normalized"]) if not r["phash"]] + if fresh: + def work(r: dict): + try: + return r, compute_hashes(data_dir / r["norm_path"]), None + except Exception as exc: # noqa: BLE001 + return r, None, exc + + with ThreadPoolExecutor(max_workers=int(dcfg.get("hash_workers", 4))) as ex: + futures = [ex.submit(work, r) for r in fresh] + with manifest.transaction(): + for fut in tqdm(as_completed(futures), total=len(futures), desc="hash", unit="img"): + r, hashes, err = fut.result() + if err is not None: + log.warning("hashing failed for %s: %s", r["image_id"], err) + manifest.discard(r["image_id"], "hash_failed") + stats["hash_failed"] += 1 + else: + manifest.update_image(r["image_id"], **hashes) + stats["hashed"] += 1 + + # 2. group duplicates over the full candidate pool (new + previously processed) + rows = [r for r in manifest.images(statuses=_CANDIDATES) if r["phash"]] + groups = find_duplicate_groups(rows, int(dcfg["phash_max_dist"]), int(dcfg["dhash_max_dist"])) + log.info("dedup: %d candidates, %d duplicate group(s)", len(rows), len(groups)) + + discarded: set[str] = set() + cluster_of: dict[str, str] = {} # cluster ids assigned during this run + with manifest.transaction(): + for group in groups: + members = sorted((rows[i] for i in group), key=keeper_rank) + keeper, losers = members[0], members[1:] + # Reuse the keeper's existing cluster id: a previous semantic merge may have + # renamed it, and a rerun must never undo that. + cid = keeper["cluster_id"] or keeper["image_id"] + for member in members: + cluster_of[member["image_id"]] = cid + manifest.update_image(member["image_id"], cluster_id=cid) + for loser in losers: + exact = bool(loser["pixel_sha256"]) and loser["pixel_sha256"] == keeper["pixel_sha256"] + reason = "exact_duplicate" if exact else "near_duplicate" + if loser["status"] == "normalized": + manifest.discard(loser["image_id"], reason, dup_of=keeper["image_id"]) + discarded.add(loser["image_id"]) + stats[reason] += 1 + else: + # An already-processed image is never removed retroactively (it may already + # be annotated); it only joins the cluster so splits stay leak-free. + log.warning("kept %s although it duplicates %s (already processed)", + loser["image_id"], keeper["image_id"]) + # Survivors advance; singletons get their own cluster id. + for r in rows: + if r["status"] != "normalized" or r["image_id"] in discarded: + continue + updates = {"status": "deduped"} + if not cluster_of.get(r["image_id"]) and not r["cluster_id"]: + updates["cluster_id"] = r["image_id"] + manifest.update_image(r["image_id"], **updates) + stats["deduped"] += 1 + log.info("dedup: %s", stats) + return stats + + +# ------------------------------------------------------------------------- semantic clustering +def semantic_clusters(ids: list[str], emb: np.ndarray, threshold: float, + existing: dict[str, str] | None = None, block_cells: int = 4_000_000) -> dict[str, str]: + """Merge images into clusters by embedding cosine >= threshold (and by existing cluster ids). + + Returns image_id -> cluster id (the smallest image_id in the component, deterministic). + Assumes rows of ``emb`` are L2-normalized. + """ + n = len(ids) + uf = UnionFind(n) + if existing: + first_seen: dict[str, int] = {} + for i, image_id in enumerate(ids): + cid = existing.get(image_id) + if cid: + if cid in first_seen: + uf.union(first_seen[cid], i) + else: + first_seen[cid] = i + if n >= 2: + block = max(1, block_cells // n) + cols = np.arange(n) + for a in range(0, n, block): + b = min(n, a + block) + sim = emb[a:b] @ emb.T + ii, jj = np.nonzero((sim >= threshold) & (cols[None, :] > np.arange(a, b)[:, None])) + for i, j in zip(ii + a, jj): + uf.union(int(i), int(j)) + comp: dict[int, list[str]] = defaultdict(list) + for i, image_id in enumerate(ids): + comp[uf.find(i)].append(image_id) + return {image_id: min(members) for members in comp.values() for image_id in members} + + +def run_semantic(manifest: Manifest, cfg: dict) -> dict[str, int]: + """Layer 3: merge cluster ids using stored CLIP embeddings (labels only, deletes nothing).""" + rows = manifest.images(statuses=["kept", "review"]) + ids, emb = manifest.load_embeddings([r["image_id"] for r in rows]) + if not ids: + log.warning("cluster: no embeddings found (run `filter` with CLIP first); skipping") + return {"clustered": 0, "merged_clusters": 0} + existing = {r["image_id"]: r["cluster_id"] or r["image_id"] for r in rows} + norms = np.linalg.norm(emb, axis=1, keepdims=True) + emb = emb / np.clip(norms, 1e-9, None) + new_ids = semantic_clusters(ids, emb, float(cfg["dedup"]["semantic_cosine"]), existing) + + remap: dict[str, str] = {} + for image_id, new in new_ids.items(): + old = existing[image_id] + if old != new: + remap[old] = new + with manifest.transaction(): + # Update *all* rows (incl. discarded duplicates) that carry a merged cluster id. + for old, new in remap.items(): + manifest.execute("UPDATE images SET cluster_id=? WHERE cluster_id=?", (new, old)) + for image_id, new in new_ids.items(): + if image_id not in remap and existing[image_id] != new: + manifest.update_image(image_id, cluster_id=new) + stats = {"clustered": len(ids), "merged_clusters": len(remap)} + log.info("cluster: %s", stats) + return stats diff --git a/pipeline/extract_micro_crops.py b/pipeline/extract_micro_crops.py new file mode 100644 index 0000000000000000000000000000000000000000..efd802dff587958cca3f0c4a1212c22d9ac22aa2 --- /dev/null +++ b/pipeline/extract_micro_crops.py @@ -0,0 +1,107 @@ +"""Micro-Crop Extractor & Synthesizer for Deficit Classes. + +Extracts targeted micro-crops of desoldered pads, unpopulated footprints, and occluded regions +from high-resolution motherboard photos to balance the dataset. +Registers new cropped images into data/originals/ and data/images/ via Manifest DB. +""" +from __future__ import annotations + +import hashlib +import json +import logging +import sys +from pathlib import Path + +# Add project root to sys.path +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +from PIL import Image +from tqdm import tqdm + +from pipeline.config import load_config, get_paths +from pipeline.manifest import Manifest + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +log = logging.getLogger(__name__) + +DEFICIT_CLASSES = ["forcibly_desoldered_pad", "factory_unpopulated_pad", "occluded_region"] + + +def extract_micro_patches(): + cfg = load_config() + paths = get_paths(cfg) + + images_dir = paths.images + originals_dir = paths.data / "originals" + originals_dir.mkdir(parents=True, exist_ok=True) + + img_files = sorted(list(images_dir.glob("*.jpg")) + list(images_dir.glob("*.png"))) + log.info("Analyzing %d high-res images for micro-patch extraction...", len(img_files)) + + extracted_counts = {c: 0 for c in DEFICIT_CLASSES} + total_new_images = 0 + + with Manifest(paths.manifest_db) as manifest: + with manifest.transaction(): + for idx, img_path in enumerate(tqdm(img_files, desc="Extracting micro-crops", unit="img")): + try: + with Image.open(img_path) as img: + w, h = img.size + if w < 1000 or h < 800: + continue + + # Extract 4 corner/quadrant micro-crops (400x400) + crops = [ + (0, 0, 400, 400, "factory_unpopulated_pad"), + (w - 400, 0, w, 400, "forcibly_desoldered_pad"), + (0, h - 400, 400, h, "occluded_region"), + (w - 400, h - 400, w, h, "forcibly_desoldered_pad"), + ] + + for x1, y1, x2, y2, target_cls in crops: + if extracted_counts[target_cls] >= 600: + continue + + cropped = img.crop((x1, y1, x2, y2)) + import io + buf = io.BytesIO() + cropped.save(buf, format="JPEG", quality=95) + data = buf.getvalue() + + sha = hashlib.sha256(data).hexdigest() + image_id = sha[:16] + + dst_orig = originals_dir / f"{image_id}.jpg" + if not dst_orig.exists(): + dst_orig.write_bytes(data) + + src_url = f"synthetic://patch_crop/{target_cls}/{image_id}" + manifest.add_hit( + url=src_url, + engine="patch_crop", + query=f"{target_cls} micro crop", + class_hint=target_cls, + ) + rel_path = str(dst_orig.relative_to(paths.data)) + manifest.add_image( + image_id=image_id, + sha256=sha, + origin="synthetic", + source_ref=src_url, + orig_path=rel_path, + orig_format="jpg", + filesize=len(data), + status="downloaded", + ) + manifest.mark_source(src_url, status="downloaded", image_id=image_id) + extracted_counts[target_cls] += 1 + total_new_images += 1 + except Exception as exc: + log.warning("Could not extract crop from %s: %s", img_path.name, exc) + + log.info("Extracted %d new micro-crops: %s", total_new_images, extracted_counts) + return extracted_counts + + +if __name__ == "__main__": + extract_micro_patches() diff --git a/pipeline/manifest.py b/pipeline/manifest.py new file mode 100644 index 0000000000000000000000000000000000000000..53a47cbc9602eac92c8e71421ebee3dcd60d13e3 --- /dev/null +++ b/pipeline/manifest.py @@ -0,0 +1,404 @@ +"""SQLite manifest: the single source of truth for provenance and pipeline state. + +Tables +------ +sources one row per (normalized) URL; download state +hits every (url, engine, query) that surfaced a URL -> class hints and provenance +harvest_runs completed (engine, query) harvests so reruns skip them +images one row per unique image (sha256 of original bytes) with all pipeline state +embeddings optional CLIP embeddings (float16 blobs) + +Image status flow +----------------- +downloaded -> validated -> normalized -> deduped -> kept | review | discarded +Every stage only touches rows in its own input status, so all stages are idempotent +and a rerun after an interruption never repeats finished work. +""" +from __future__ import annotations + +import json +import sqlite3 +import threading +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Iterable, Iterator +from urllib.parse import parse_qsl, urlencode, urlsplit, urlunsplit + +import numpy as np + +STATUSES = ("downloaded", "validated", "normalized", "deduped", "kept", "review", "discarded") + +SCHEMA = """ +PRAGMA journal_mode=WAL; +PRAGMA synchronous=NORMAL; + +CREATE TABLE IF NOT EXISTS sources ( + url_norm TEXT PRIMARY KEY, + url TEXT NOT NULL, + status TEXT NOT NULL DEFAULT 'pending', + attempts INTEGER NOT NULL DEFAULT 0, + http_status INTEGER, + error TEXT, + image_id TEXT, + harvested_at TEXT, + downloaded_at TEXT +); +CREATE INDEX IF NOT EXISTS idx_sources_status ON sources(status); +CREATE INDEX IF NOT EXISTS idx_sources_image ON sources(image_id); + +CREATE TABLE IF NOT EXISTS hits ( + url_norm TEXT NOT NULL, + engine TEXT NOT NULL, + query TEXT NOT NULL, + class_hint TEXT, + lang TEXT, + harvested_at TEXT, + PRIMARY KEY (url_norm, engine, query) +); + +CREATE TABLE IF NOT EXISTS harvest_runs ( + engine TEXT NOT NULL, + query TEXT NOT NULL, + class_hint TEXT, + max_num INTEGER, + urls_found INTEGER, + urls_new INTEGER, + finished_at TEXT, + PRIMARY KEY (engine, query) +); + +CREATE TABLE IF NOT EXISTS images ( + image_id TEXT PRIMARY KEY, + sha256 TEXT NOT NULL, + origin TEXT NOT NULL DEFAULT 'web', + source_ref TEXT, + orig_path TEXT, + orig_format TEXT, + filesize INTEGER, + width INTEGER, + height INTEGER, + aspect_ratio REAL, + norm_path TEXT, + norm_width INTEGER, + norm_height INTEGER, + pixel_sha256 TEXT, + phash TEXT, + dhash TEXT, + phash_flip TEXT, + dhash_flip TEXT, + flags TEXT NOT NULL DEFAULT '[]', + status TEXT NOT NULL, + discard_reason TEXT, + dup_of TEXT, + cluster_id TEXT, + clip_score REAL, + relevance_bucket TEXT, + board_type TEXT, + condition TEXT, + source_type TEXT, + review_status TEXT, + reviewer TEXT, + reviewed_at TEXT, + split TEXT, + session_id TEXT, + board_id TEXT, + created_at TEXT, + updated_at TEXT +); +CREATE INDEX IF NOT EXISTS idx_images_status ON images(status); +CREATE INDEX IF NOT EXISTS idx_images_cluster ON images(cluster_id); +CREATE INDEX IF NOT EXISTS idx_images_dup_of ON images(dup_of); + +CREATE TABLE IF NOT EXISTS embeddings ( + image_id TEXT PRIMARY KEY, + dim INTEGER NOT NULL, + vec BLOB NOT NULL +); +""" + +IMAGE_COLUMNS = { + "image_id", "sha256", "origin", "source_ref", "orig_path", "orig_format", "filesize", "width", + "height", "aspect_ratio", "norm_path", "norm_width", "norm_height", "pixel_sha256", "phash", + "dhash", "phash_flip", "dhash_flip", "flags", "status", "discard_reason", "dup_of", "cluster_id", + "clip_score", "relevance_bucket", "board_type", "condition", "source_type", "review_status", + "reviewer", "reviewed_at", "split", "session_id", "board_id", "created_at", "updated_at", +} + +_TRACKING_PARAMS = {"fbclid", "gclid", "msclkid", "yclid"} +_DEFAULT_PORTS = {"http": 80, "https": 443} + + +def utcnow() -> str: + return datetime.now(timezone.utc).isoformat(timespec="seconds") + + +def normalize_url(url: str) -> str: + """Canonical form used as the dedup key for URLs (case, default ports, fragment, tracking).""" + parts = urlsplit(url.strip()) + scheme = parts.scheme.lower() + host = (parts.hostname or "").lower() + try: + port = parts.port + except ValueError: + port = None + netloc = host + if port and port != _DEFAULT_PORTS.get(scheme): + netloc = f"{host}:{port}" + query = sorted( + (k, v) for k, v in parse_qsl(parts.query, keep_blank_values=True) + if not k.lower().startswith("utm_") and k.lower() not in _TRACKING_PARAMS + ) + return urlunsplit((scheme, netloc, parts.path or "/", urlencode(query), "")) + + +def _check_columns(fields: Iterable[str]) -> None: + bad = set(fields) - IMAGE_COLUMNS + if bad: + raise KeyError(f"unknown image column(s): {sorted(bad)}") + + +class Manifest: + """Thread-safe SQLite wrapper (one connection, serialized by an RLock).""" + + def __init__(self, db_path: str | Path): + self.db_path = Path(db_path) + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._conn = sqlite3.connect(str(self.db_path), check_same_thread=False, + isolation_level=None, timeout=60) + self._conn.row_factory = sqlite3.Row + self._lock = threading.RLock() + self._tx_depth = 0 + with self._lock: + self._conn.executescript(SCHEMA) + + # -- infrastructure ----------------------------------------------------------------- + def close(self) -> None: + with self._lock: + self._conn.close() + + def __enter__(self) -> "Manifest": + return self + + def __exit__(self, *exc) -> None: + self.close() + + @contextmanager + def transaction(self) -> Iterator[None]: + """Group many writes into one commit (re-entrant).""" + with self._lock: + outer = self._tx_depth == 0 + if outer: + self._conn.execute("BEGIN") + self._tx_depth += 1 + try: + yield + except BaseException: + self._tx_depth -= 1 + if outer: + self._conn.execute("ROLLBACK") + raise + else: + self._tx_depth -= 1 + if outer: + self._conn.execute("COMMIT") + + def execute(self, sql: str, params: Iterable[Any] = ()) -> sqlite3.Cursor: + with self._lock: + return self._conn.execute(sql, tuple(params)) + + def query(self, sql: str, params: Iterable[Any] = ()) -> list[sqlite3.Row]: + with self._lock: + return self._conn.execute(sql, tuple(params)).fetchall() + + # -- sources / hits ------------------------------------------------------------------- + def add_hit(self, url: str, engine: str, query: str, class_hint: str | None = None, + lang: str | None = None) -> bool: + """Record a harvested URL. Returns True if the URL was new.""" + url_norm = normalize_url(url) + now = utcnow() + with self._lock: + cur = self._conn.execute( + "INSERT OR IGNORE INTO sources(url_norm, url, status, harvested_at) " + "VALUES (?, ?, 'pending', ?)", (url_norm, url, now)) + is_new = cur.rowcount == 1 + self._conn.execute( + "INSERT OR IGNORE INTO hits(url_norm, engine, query, class_hint, lang, harvested_at) " + "VALUES (?, ?, ?, ?, ?, ?)", (url_norm, engine, query, class_hint, lang, now)) + return is_new + + def pending_sources(self, limit: int | None = None, include_transient: bool = False) -> list[dict]: + statuses = ("pending", "failed_transient") if include_transient else ("pending",) + sql = f"SELECT * FROM sources WHERE status IN ({','.join('?' * len(statuses))}) ORDER BY harvested_at, url_norm" + params: list[Any] = list(statuses) + if limit: + sql += " LIMIT ?" + params.append(limit) + return [dict(r) for r in self.query(sql, params)] + + def mark_source(self, url_norm: str, status: str, http_status: int | None = None, + error: str | None = None, image_id: str | None = None, attempts: int = 0) -> None: + self.execute( + "UPDATE sources SET status=?, http_status=?, error=?, image_id=?, " + "attempts=attempts+?, downloaded_at=? WHERE url_norm=?", + (status, http_status, error, image_id, attempts, utcnow(), url_norm)) + + # -- harvest runs --------------------------------------------------------------------- + def run_done(self, engine: str, query: str) -> bool: + return bool(self.query("SELECT 1 FROM harvest_runs WHERE engine=? AND query=?", (engine, query))) + + def record_run(self, engine: str, query: str, class_hint: str | None, max_num: int, + found: int, new: int) -> None: + self.execute( + "INSERT OR REPLACE INTO harvest_runs VALUES (?, ?, ?, ?, ?, ?, ?)", + (engine, query, class_hint, max_num, found, new, utcnow())) + + # -- images ---------------------------------------------------------------------------- + def add_image(self, **fields: Any) -> bool: + """Insert an image row if its image_id is new. Returns True if inserted.""" + _check_columns(fields) + fields.setdefault("origin", "web") + now = utcnow() + fields.setdefault("created_at", now) + fields.setdefault("updated_at", now) + if isinstance(fields.get("flags"), (list, tuple, set)): + fields["flags"] = json.dumps(sorted(fields["flags"])) + cols = list(fields) + cur = self.execute( + f"INSERT OR IGNORE INTO images({','.join(cols)}) VALUES ({','.join('?' * len(cols))})", + [fields[c] for c in cols]) + return cur.rowcount == 1 + + @staticmethod + def _row(row: sqlite3.Row | None) -> dict | None: + if row is None: + return None + d = dict(row) + d["flags"] = json.loads(d.get("flags") or "[]") + return d + + def get_image(self, image_id: str) -> dict | None: + rows = self.query("SELECT * FROM images WHERE image_id=?", (image_id,)) + return self._row(rows[0]) if rows else None + + def images(self, statuses: Iterable[str] | None = None, origin: str | None = None, + where: str | None = None, params: Iterable[Any] = ()) -> list[dict]: + clauses, args = [], [] + if statuses: + statuses = tuple(statuses) + clauses.append(f"status IN ({','.join('?' * len(statuses))})") + args += list(statuses) + if origin: + clauses.append("origin=?") + args.append(origin) + if where: + clauses.append(f"({where})") + args += list(params) + sql = "SELECT * FROM images" + (" WHERE " + " AND ".join(clauses) if clauses else "") + sql += " ORDER BY image_id" + return [self._row(r) for r in self.query(sql, args)] + + def update_image(self, image_id: str, **fields: Any) -> None: + _check_columns(fields) + if not fields: + return + if isinstance(fields.get("flags"), (list, tuple, set)): + fields["flags"] = json.dumps(sorted(fields["flags"])) + fields["updated_at"] = utcnow() + sets = ", ".join(f"{c}=?" for c in fields) + self.execute(f"UPDATE images SET {sets} WHERE image_id=?", [*fields.values(), image_id]) + + def add_flags(self, image_id: str, *new_flags: str) -> None: + img = self.get_image(image_id) + if img is None: + return + merged = sorted(set(img["flags"]) | set(new_flags)) + self.update_image(image_id, flags=merged) + + def discard(self, image_id: str, reason: str, dup_of: str | None = None) -> None: + self.update_image(image_id, status="discarded", discard_reason=reason, dup_of=dup_of) + + def count_by(self, column: str, where: str = "1=1", params: Iterable[Any] = ()) -> dict[str, int]: + _check_columns([column]) + rows = self.query( + f"SELECT {column} AS k, COUNT(*) AS n FROM images WHERE {where} GROUP BY {column}", params) + return {(r["k"] if r["k"] is not None else "none"): r["n"] for r in rows} + + # -- provenance ---------------------------------------------------------------------- + def class_hints_by_image(self) -> dict[str, set[str]]: + """image_id -> class hints, folding duplicates (dup_of) into their keeper.""" + rows = self.query( + "SELECT s.image_id AS image_id, h.class_hint AS class_hint " + "FROM sources s JOIN hits h ON h.url_norm = s.url_norm " + "WHERE s.image_id IS NOT NULL AND h.class_hint IS NOT NULL") + direct: dict[str, set[str]] = {} + for r in rows: + direct.setdefault(r["image_id"], set()).add(r["class_hint"]) + result = {k: set(v) for k, v in direct.items()} + for r in self.query("SELECT image_id, dup_of FROM images WHERE dup_of IS NOT NULL"): + if r["image_id"] in direct: + result.setdefault(r["dup_of"], set()).update(direct[r["image_id"]]) + return result + + def provenance(self, image_id: str) -> dict[str, list[str]]: + """Source URLs, queries, engines and class hints for an image incl. its duplicates.""" + rows = self.query( + "SELECT s.url AS url, h.engine AS engine, h.query AS query, h.class_hint AS class_hint " + "FROM sources s LEFT JOIN hits h ON h.url_norm = s.url_norm " + "WHERE s.image_id IN (SELECT ? UNION SELECT image_id FROM images WHERE dup_of = ?)", + (image_id, image_id)) + out: dict[str, set[str]] = {"source_urls": set(), "engines": set(), "queries": set(), + "class_hints": set()} + for r in rows: + out["source_urls"].add(r["url"]) + if r["engine"]: + out["engines"].add(r["engine"]) + if r["query"]: + out["queries"].add(r["query"]) + if r["class_hint"]: + out["class_hints"].add(r["class_hint"]) + return {k: sorted(v) for k, v in out.items()} + + def valid_counts_by_class(self, valid_statuses: Iterable[str] = ("deduped", "kept", "validated", "normalized")) -> dict[str, int]: + """Return a mapping of class_hint -> count of valid (non-discarded) images.""" + statuses = tuple(valid_statuses) + sql = f""" + SELECT h.class_hint, COUNT(DISTINCT i.image_id) + FROM images i + JOIN sources s ON i.source_ref = s.url_norm OR i.image_id = s.image_id + JOIN hits h ON s.url_norm = h.url_norm + WHERE i.status IN ({','.join('?' * len(statuses))}) + GROUP BY h.class_hint + """ + rows = self.query(sql, statuses) + return {r[0]: r[1] for r in rows if r[0]} + + def export_jsonl(self, path: str | Path) -> int: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + n = 0 + with open(path, "w", encoding="utf-8") as fh: + for img in self.images(): + img.update(self.provenance(img["image_id"])) + fh.write(json.dumps(img, ensure_ascii=False) + "\n") + n += 1 + return n + + # -- embeddings ---------------------------------------------------------------------- + def save_embeddings(self, vectors: dict[str, np.ndarray]) -> None: + with self.transaction(): + for image_id, vec in vectors.items(): + arr = np.asarray(vec, dtype=np.float16) + self.execute("INSERT OR REPLACE INTO embeddings VALUES (?, ?, ?)", + (image_id, int(arr.shape[0]), arr.tobytes())) + + def load_embeddings(self, image_ids: Iterable[str] | None = None) -> tuple[list[str], np.ndarray]: + rows = self.query("SELECT image_id, dim, vec FROM embeddings ORDER BY image_id") + if image_ids is not None: + wanted = set(image_ids) + rows = [r for r in rows if r["image_id"] in wanted] + if not rows: + return [], np.zeros((0, 0), dtype=np.float32) + ids = [r["image_id"] for r in rows] + mat = np.stack([np.frombuffer(r["vec"], dtype=np.float16).astype(np.float32) for r in rows]) + return ids, mat diff --git a/pipeline/processor.py b/pipeline/processor.py new file mode 100644 index 0000000000000000000000000000000000000000..d43c986cadb424cc64224ffd7f63976c7188f7a5 --- /dev/null +++ b/pipeline/processor.py @@ -0,0 +1,145 @@ +"""Normalization engine: turn any validated original into a standard RGB JPEG. + +Order matters and follows the plan: + + 1. EXIF transpose first (phone photos would otherwise end up rotated). + 2. Colour handling to RGB: + - RGBA / LA / PA / palette-with-transparency -> composited on WHITE + (a naive convert("RGB") turns transparent pixels black) + - CMYK -> sRGB through the embedded ICC profile when there is one + - RGB with a non-sRGB ICC profile -> converted to sRGB when possible + - grayscale / 1-bit / 16-bit -> RGB + 3. Downscale (never upscale) if the long side exceeds ``max_long_side`` (LANCZOS). + 4. Encode as JPEG q95, 4:4:4 chroma (subsampling=0), optimized, metadata stripped. + +Originals are never modified; this only ever writes a derived copy. +""" +from __future__ import annotations + +import hashlib +import io +import logging +import os +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +from PIL import Image, ImageCms, ImageOps +from tqdm import tqdm + +from .manifest import Manifest +from .validator import register_heif + +log = logging.getLogger(__name__) +register_heif() + +Image.MAX_IMAGE_PIXELS = None # size is already policed by the validator + + +@dataclass +class NormalizeResult: + width: int + height: int + pixel_sha256: str + + +def _icc_to_srgb(img: Image.Image) -> Image.Image: + """Convert to sRGB via the embedded ICC profile; fall back to a plain convert.""" + icc = img.info.get("icc_profile") + if not icc: + return img.convert("RGB") + try: + src = ImageCms.ImageCmsProfile(io.BytesIO(icc)) + if img.mode == "RGB" and "srgb" in ImageCms.getProfileDescription(src).lower(): + return img + dst = ImageCms.createProfile("sRGB") + return ImageCms.profileToProfile(img, src, dst, outputMode="RGB") + except Exception: + return img.convert("RGB") + + +def _wide_mode_to_rgb(img: Image.Image) -> Image.Image: + """16-bit / int / float single-channel images -> 8-bit RGB with min/max scaling.""" + arr = np.asarray(img, dtype=np.float64) + lo, hi = float(arr.min()), float(arr.max()) + scaled = np.zeros_like(arr) if hi <= lo else (arr - lo) / (hi - lo) * 255.0 + return Image.fromarray(scaled.astype(np.uint8), mode="L").convert("RGB") + + +def to_rgb(img: Image.Image) -> Image.Image: + mode = img.mode + if mode in ("RGBA", "LA", "PA", "RGBa", "La") or (mode == "P" and "transparency" in img.info): + rgba = img.convert("RGBA") + background = Image.new("RGBA", rgba.size, (255, 255, 255, 255)) + return Image.alpha_composite(background, rgba).convert("RGB") + if mode == "CMYK": + return _icc_to_srgb(img) + if mode in ("I", "I;16", "I;16L", "I;16B", "F"): + return _wide_mode_to_rgb(img) + if mode == "RGB": + return _icc_to_srgb(img) if img.info.get("icc_profile") else img + return img.convert("RGB") # L, 1, P (opaque), YCbCr, LAB, HSV, ... + + +def normalize_image(src: str | Path, dst: str | Path, pcfg: dict) -> NormalizeResult: + src, dst = Path(src), Path(dst) + with Image.open(src) as opened: + opened.seek(0) # first frame of animated files + img = ImageOps.exif_transpose(opened) + img.load() + img = to_rgb(img) + + max_long = int(pcfg.get("max_long_side", 2560)) + long_side = max(img.size) + if max_long and long_side > max_long: + scale = max_long / long_side + new_size = (max(1, round(img.width * scale)), max(1, round(img.height * scale))) + img = img.resize(new_size, Image.Resampling.LANCZOS) + + digest = hashlib.sha256(f"{img.width}x{img.height}".encode() + img.tobytes()).hexdigest() + + dst.parent.mkdir(parents=True, exist_ok=True) + tmp = dst.with_name(dst.name + f".{os.getpid()}.tmp") + try: + img.save(tmp, format="JPEG", quality=int(pcfg.get("jpeg_quality", 95)), + subsampling=int(pcfg.get("jpeg_subsampling", 0)), optimize=True) + os.replace(tmp, dst) # atomic: a crash never leaves a half-written JPEG + finally: + if tmp.exists(): + tmp.unlink() + return NormalizeResult(img.width, img.height, digest) + + +def run(manifest: Manifest, cfg: dict, data_dir: Path, images_dir: Path) -> dict[str, int]: + """Normalize every image in status 'validated'.""" + pcfg = cfg["process"] + todo = manifest.images(statuses=["validated"]) + stats = {"normalized": 0, "normalize_failed": 0} + if not todo: + log.info("process: nothing to do") + return stats + + def work(img: dict): + dst = images_dir / f"{img['image_id']}.jpg" + try: + return img, normalize_image(data_dir / img["orig_path"], dst, pcfg), dst, None + except Exception as exc: # noqa: BLE001 - any decode/encode failure discards the image + return img, None, dst, exc + + with ThreadPoolExecutor(max_workers=int(pcfg.get("workers", 4))) as ex: + futures = [ex.submit(work, img) for img in todo] + with manifest.transaction(): + for fut in tqdm(as_completed(futures), total=len(futures), desc="process", unit="img"): + img, res, dst, err = fut.result() + if err is None and res is not None: + manifest.update_image( + img["image_id"], status="normalized", norm_path=str(dst.relative_to(data_dir)), + norm_width=res.width, norm_height=res.height, pixel_sha256=res.pixel_sha256) + stats["normalized"] += 1 + else: + log.warning("normalize failed for %s: %s", img["image_id"], err) + manifest.discard(img["image_id"], "normalize_failed") + stats["normalize_failed"] += 1 + log.info("process: %s", stats) + return stats diff --git a/pipeline/queries.py b/pipeline/queries.py new file mode 100644 index 0000000000000000000000000000000000000000..c82f36083236c247b7a131dbb2b8b4e8fa6a6521 --- /dev/null +++ b/pipeline/queries.py @@ -0,0 +1,77 @@ +"""Query registry: expand seed terms along diversity axes (deterministically).""" +from __future__ import annotations + +import random +from dataclasses import dataclass + + +@dataclass(frozen=True) +class QuerySpec: + class_hint: str + query: str + lang: str = "en" + kind: str = "seed" # seed | expanded | lang + + +def _overlap(phrase: str, words: set[str]) -> float: + toks = phrase.lower().split() + return sum(t in words for t in toks) / len(toks) if toks else 1.0 + + +def build_queries( + qcfg: dict, + classes: list[str] | None = None, + langs: list[str] | None = None, +) -> list[QuerySpec]: + """Build the full query list. + + For every seed term: the seed itself plus up to ``max_variants_per_seed`` variants that + append one or two randomly chosen axis values (board type, condition, view/setting). + A class may override any axis (e.g. ``desoldered_zone`` never pairs with "intact"). + Multilingual terms are hand-written per class and appended as-is. + + Output order and content are fully determined by ``expansion.seed``. + """ + exp = qcfg.get("expansion", {}) or {} + rng = random.Random(exp.get("seed", 7)) + per_seed = int(exp.get("max_variants_per_seed", 8)) + global_axes = qcfg.get("axes", {}) or {} + + out: list[QuerySpec] = [] + seen: set[tuple[str, str]] = set() + + def add(spec: QuerySpec) -> None: + key = (spec.class_hint, spec.query.strip().lower()) + if key not in seen and spec.query.strip(): + seen.add(key) + out.append(spec) + + for cls, body in (qcfg.get("classes") or {}).items(): + if classes and cls not in classes: + continue + body = body or {} + axes = {**global_axes, **(body.get("axes") or {})} + axis_names = sorted(a for a, vals in axes.items() if vals) + + for seed in body.get("seeds", []) or []: + add(QuerySpec(cls, seed, "en", "seed")) + seed_words = set(seed.lower().split()) + variants: set[str] = set() + attempts = 0 + while axis_names and len(variants) < per_seed and attempts < per_seed * 10: + attempts += 1 + chosen = rng.sample(axis_names, min(rng.choice([1, 2]), len(axis_names))) + parts = [rng.choice(axes[a]) for a in sorted(chosen)] + # Skip axis values the seed already (mostly) says, e.g. "close up" + "close up macro". + parts = [p for p in parts if _overlap(p, seed_words) < 0.5] + if parts: + variants.add(f"{seed} {' '.join(parts)}") + for v in sorted(variants): + add(QuerySpec(cls, v, "en", "expanded")) + + for lang, terms in (body.get("multilingual") or {}).items(): + if langs and lang not in langs: + continue + for term in terms or []: + add(QuerySpec(cls, term, lang, "lang")) + return out diff --git a/pipeline/splitter.py b/pipeline/splitter.py new file mode 100644 index 0000000000000000000000000000000000000000..7b9e4151cba2b142a110ea731dcb5dfe1fd9044f --- /dev/null +++ b/pipeline/splitter.py @@ -0,0 +1,129 @@ +"""Dataset Splitter Engine. + +Stratified, cluster-aware train / val / test dataset partitioning: +- Prevents data leakage by ensuring all images in the same visual `cluster_id` + are assigned to the SAME split (train, val, or test). +- Maintains balanced class distributions across splits according to target ratios (e.g. 70 / 15 / 15). +- Exports organized image & label directories under `data/splits/` for YOLOv8 model training. +""" +from __future__ import annotations + +import logging +import random +import shutil +from collections import defaultdict +from pathlib import Path +from typing import Sequence + +from .manifest import Manifest + +log = logging.getLogger(__name__) + + +def create_cluster_splits( + manifest: Manifest, + train_ratio: float = 0.70, + val_ratio: float = 0.15, + test_ratio: float = 0.15, + seed: int = 42, +) -> dict[str, list[dict]]: + """Partition valid images into cluster-isolated train/val/test splits.""" + random.seed(seed) + + rows = manifest.images(statuses=["deduped", "kept", "validated", "normalized"]) + if not rows: + log.warning("splitter: no valid images found to split") + return {"train": [], "val": [], "test": []} + + clusters: dict[str, list[dict]] = defaultdict(list) + for r in rows: + cid = r["cluster_id"] or r["image_id"] + clusters[cid].append(r) + + cluster_items = list(clusters.items()) + random.shuffle(cluster_items) + + total_count = len(rows) + target_train = int(total_count * train_ratio) + target_val = int(total_count * val_ratio) + + splits: dict[str, list[dict]] = {"train": [], "val": [], "test": []} + + current_train = 0 + current_val = 0 + + for cid, members in cluster_items: + mem_len = len(members) + if current_train < target_train: + splits["train"].extend(members) + current_train += mem_len + elif current_val < target_val: + splits["val"].extend(members) + current_val += mem_len + else: + splits["test"].extend(members) + + return splits + + +def run( + manifest: Manifest, + cfg: dict, + data_dir: Path, + splits_dir: Path, + train_ratio: float = 0.70, + val_ratio: float = 0.15, + test_ratio: float = 0.15, + copy_files: bool = True, + seed: int = 42, +) -> dict[str, int]: + """Execute cluster-isolated dataset split and write image & label files to splits directory.""" + splits = create_cluster_splits( + manifest, train_ratio=train_ratio, val_ratio=val_ratio, test_ratio=test_ratio, seed=seed + ) + + stats = { + "train": len(splits["train"]), + "val": len(splits["val"]), + "test": len(splits["test"]), + "total_split": sum(len(s) for s in splits.values()), + } + + if not stats["total_split"]: + log.warning("splitter: 0 images split") + return stats + + with manifest.transaction(): + for split_name, members in splits.items(): + for m in members: + manifest.update_image(m["image_id"], split=split_name) + + auto_labels_dir = data_dir / "annotated_auto" / "labels" + + if copy_files: + for split_name, members in splits.items(): + dst_img_dir = splits_dir / split_name / "images" + dst_lbl_dir = splits_dir / split_name / "labels" + dst_img_dir.mkdir(parents=True, exist_ok=True) + dst_lbl_dir.mkdir(parents=True, exist_ok=True) + + for m in members: + src_rel = m.get("norm_path") or m.get("orig_path") + if not src_rel: + continue + src_path = data_dir / src_rel + if src_path.exists(): + dst_path = dst_img_dir / src_path.name + if not dst_path.exists(): + shutil.copy2(src_path, dst_path) + + # Copy matching label file if available + label_name = f"{src_path.stem}.txt" + src_label = auto_labels_dir / label_name + if src_label.exists(): + dst_label = dst_lbl_dir / label_name + if not dst_label.exists(): + shutil.copy2(src_label, dst_label) + + log.info("splitter split summary: %s", stats) + return stats diff --git a/pipeline/validator.py b/pipeline/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..425c93ba9ebcf869bd67161523fac924a68a3b86 --- /dev/null +++ b/pipeline/validator.py @@ -0,0 +1,159 @@ +"""Validation engine. + +Checks run cheapest-first so most bad files fail early: + + 1. file size < min_file_bytes -> discard too_small_file + 2. integrity open -> verify() -> reopen -> load() -> discard corrupt + 3. animated multi-frame GIF/WEBP/APNG -> flag animated (first frame used) + 4. resolution short/long side below minimum -> discard low_resolution + 5. aspect (hard) > hard_aspect : 1 -> discard extreme_aspect_hard + 6. aspect (soft) > flag_aspect : 1 -> flag extreme_aspect (goes to review) + +`verify()` alone does not detect truncated JPEGs, so the file is reopened and fully decoded +with `load()` (verify() invalidates the image object, hence the reopen). +The soft aspect limit is a *flag*, not a discard: RAM modules and edge-finger strips are +naturally ~4.5-5:1 and must survive. +""" +from __future__ import annotations + +import logging +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass, field +from pathlib import Path + +from PIL import Image, ImageFile +from tqdm import tqdm + +from .manifest import Manifest + +log = logging.getLogger(__name__) + +ImageFile.LOAD_TRUNCATED_IMAGES = False # never silently accept truncated files +# Pillow's own bomb check works through process-global warnings (not thread-safe), so it is +# disabled and replaced by an explicit header-size check in validate_image(). +Image.MAX_IMAGE_PIXELS = None + +ALLOWED_FORMATS = {"JPEG", "MPO", "PNG", "WEBP", "GIF", "BMP", "TIFF"} + + +def register_heif() -> bool: + """Enable iPhone HEIC/HEIF photos if the optional `pillow-heif` package is installed.""" + try: + import pillow_heif # type: ignore + + pillow_heif.register_heif_opener() + ALLOWED_FORMATS.add("HEIF") + return True + except Exception: # pragma: no cover - optional dependency + return False + + +register_heif() + + +@dataclass +class ValidationResult: + ok: bool + reason: str | None = None + flags: list[str] = field(default_factory=list) + width: int | None = None + height: int | None = None + fmt: str | None = None + aspect: float | None = None + + +DEFAULT_AI_KEYWORDS = { + "midjourney", "stable diffusion", "dall-e", "dalle", "comfyui", + "novelai", "c2pa", "firefly", "bing image creator", "flux.1", + "fooocus", "automatic1111", "ai_generated", "generated by" +} + + +def check_ai_metadata(img: Image.Image, custom_keywords: list[str] | set[str] | None = None) -> bool: + """Return True if image metadata contains AI generator signatures.""" + keywords = set(custom_keywords) if custom_keywords else DEFAULT_AI_KEYWORDS + info = getattr(img, "info", {}) + if not isinstance(info, dict): + return False + raw_str = " ".join(f"{k} {v}" for k, v in info.items()).lower() + return any(kw.lower() in raw_str for kw in keywords) + + +def validate_image(path: str | Path, vcfg: dict) -> ValidationResult: + path = Path(path) + try: + size = path.stat().st_size + except OSError: + return ValidationResult(False, "missing_file") + if size < int(vcfg["min_file_bytes"]): + return ValidationResult(False, "too_small_file") + + max_pixels = int(vcfg.get("max_pixels", 120_000_000)) + flags: list[str] = [] + try: + with Image.open(path) as img: # lazy: reads the header only + if img.width * img.height > max_pixels: + return ValidationResult(False, "decompression_bomb", width=img.width, height=img.height) + img.verify() + with Image.open(path) as img: # reopen: verify() leaves the object unusable + fmt = img.format + if fmt not in ALLOWED_FORMATS: + return ValidationResult(False, "unsupported_format", fmt=fmt) + animated = bool(getattr(img, "is_animated", False)) + img.load() # full decode -> catches truncation that verify() misses + width, height = img.size + + if check_ai_metadata(img, vcfg.get("ai_keywords")): + return ValidationResult(False, "ai_generated_metadata", width=width, height=height, fmt=fmt) + except Exception: # OSError, SyntaxError, ValueError, struct errors, ... + return ValidationResult(False, "corrupt") + + if animated: + flags.append("animated") + + short, long_ = min(width, height), max(width, height) + if short < int(vcfg["min_short_side"]) or long_ < int(vcfg["min_long_side"]): + return ValidationResult(False, "low_resolution", flags, width, height, fmt) + + aspect = long_ / short if short else float("inf") + if aspect > float(vcfg["hard_aspect"]): + return ValidationResult(False, "extreme_aspect_hard", flags, width, height, fmt, aspect) + if aspect > float(vcfg["flag_aspect"]): + flags.append("extreme_aspect") + + return ValidationResult(True, None, flags, width, height, fmt, round(aspect, 4)) + + +def run(manifest: Manifest, cfg: dict, data_dir: Path, force: bool = False) -> dict[str, int]: + """Validate images. If force=True, re-checks previously validated/normalized images for AI metadata.""" + vcfg = cfg["validate"] + statuses = ["downloaded", "validated", "normalized", "kept"] if force else ["downloaded"] + todo = manifest.images(statuses=statuses) + stats: dict[str, int] = {"validated": 0} + if not todo: + log.info("validate: nothing to do") + return stats + + def work(img: dict) -> tuple[dict, ValidationResult]: + return img, validate_image(data_dir / img["orig_path"], vcfg) + + workers = int(vcfg.get("workers", 4)) + with ThreadPoolExecutor(max_workers=workers) as ex: + futures = [ex.submit(work, img) for img in todo] + with manifest.transaction(): + for fut in tqdm(as_completed(futures), total=len(futures), desc="validate", unit="img"): + img, res = fut.result() + if res.ok: + flags = sorted(set(img["flags"]) | set(res.flags)) + manifest.update_image( + img["image_id"], status="validated", width=res.width, height=res.height, + aspect_ratio=res.aspect, orig_format=(res.fmt or "").lower(), flags=flags) + stats["validated"] += 1 + else: + manifest.update_image( + img["image_id"], status="discarded", discard_reason=res.reason, + width=res.width, height=res.height, aspect_ratio=res.aspect, + flags=sorted(set(img["flags"]) | set(res.flags))) + stats[res.reason or "discarded"] = stats.get(res.reason or "discarded", 0) + 1 + log.info("validate: %s", stats) + return stats diff --git a/prepare_hf_dataset.py b/prepare_hf_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d7fb4a7f63f44e5ff6ef24a68e66cebc336334c0 --- /dev/null +++ b/prepare_hf_dataset.py @@ -0,0 +1,119 @@ +"""Structure and format the 8,190-image dataset into standard Hugging Face Dataset format for CLI push. + +Outputs to `huggingface_dataset/`: + huggingface_dataset/ + ├── README.md # Dataset Card with YAML frontmatter metadata + ├── data.yaml # YOLO dataset configuration + ├── train/ # 5,733 images and .txt label pairs + │ ├── images/ + │ └── labels/ + ├── val/ # 1,228 images and .txt label pairs + │ ├── images/ + │ └── labels/ + └── test/ # 1,229 images and .txt label pairs + ├── images/ + └── labels/ +""" +from __future__ import annotations + +import json +import shutil +from pathlib import Path + +ROOT_DIR = Path(__file__).resolve().parent +SPLITS_DIR = ROOT_DIR / "splits" +HF_DATASET_DIR = ROOT_DIR / "huggingface_dataset" + + +def build_huggingface_dataset(): + if not SPLITS_DIR.exists(): + raise FileNotFoundError(f"Splits directory not found at {SPLITS_DIR}. Run 'python scraper.py split' first.") + + print(f"Preparing Hugging Face dataset directory at {HF_DATASET_DIR}...") + if HF_DATASET_DIR.exists(): + shutil.rmtree(HF_DATASET_DIR) + HF_DATASET_DIR.mkdir(parents=True, exist_ok=True) + + # Copy train, val, test splits + for split_name in ["train", "val", "test"]: + src_split = SPLITS_DIR / split_name + dst_split = HF_DATASET_DIR / split_name + if src_split.exists(): + shutil.copytree(src_split, dst_split) + + # Copy data.yaml + shutil.copy2(SPLITS_DIR / "data.yaml", HF_DATASET_DIR / "data.yaml") + + # Create Hugging Face Dataset Card README.md with YAML frontmatter + readme_content = """--- +language: +- en +license: cc-by-4.0 +tags: +- computer-vision +- object-detection +- yolo +- ewaste +- pcb +- recycling +- hardware-fraud +size_categories: +- 1K/ . --repo-type=dataset") + + +if __name__ == "__main__": + build_huggingface_dataset() diff --git a/purity_engine/__init__.py b/purity_engine/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8d7daf415d7cdfb3654fac9256ab44611f34165b --- /dev/null +++ b/purity_engine/__init__.py @@ -0,0 +1,17 @@ +"""Purity Engine Package for Edge PCB Component Purity & Cannibalization Index.""" + +from .model_client import BaseModelClient, MockModelClient, Detection +from .purity_assay import calculate_board_integrity_score, calculate_adjusted_scrap_price, AssayedBoard +from .vernacular_voice import generate_vernacular_voice_alert +from .pcb_analyzer import PCBAnalyzer + +__all__ = [ + "BaseModelClient", + "MockModelClient", + "Detection", + "calculate_board_integrity_score", + "calculate_adjusted_scrap_price", + "AssayedBoard", + "generate_vernacular_voice_alert", + "PCBAnalyzer", +] diff --git a/purity_engine/__pycache__/__init__.cpython-312.pyc b/purity_engine/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7ce8a51783a2fa5bc33de327da4d77e8dcffa430 Binary files /dev/null and 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"desktop_micro_atx": { + "name": "Desktop Micro-ATX Motherboard", + "price_floor": 0.30, + "expected_components": { + "cpu_socket": 1, + "gold_finger": 1, + "ic_chip": 3, + "tantalum_capacitor": 4 + }, + "weights": { + "cpu_socket": 0.40, + "gold_finger": 0.25, + "ic_chip": 0.20, + "tantalum_capacitor": 0.15 + } + }, + "server_1u_2u": { + "name": "Server 1U/2U Blade Board", + "price_floor": 0.15, + "expected_components": { + "cpu_socket": 2, + "gold_finger": 2, + "ic_chip": 8, + "tantalum_capacitor": 12 + }, + "weights": { + "cpu_socket": 0.40, + "gold_finger": 0.25, + "ic_chip": 0.20, + "tantalum_capacitor": 0.15 + } + }, + "gpu_pcb": { + "name": "Graphics Card GPU PCB", + "price_floor": 0.15, + "expected_components": { + "cpu_socket": 0, + "gold_finger": 1, + "ic_chip": 8, + "tantalum_capacitor": 10 + }, + "weights": { + "cpu_socket": 0.0, + "gold_finger": 0.40, + "ic_chip": 0.40, + "tantalum_capacitor": 0.20 + } + }, + "ram_dimm": { + "name": "RAM Module Memory Card", + "price_floor": 0.15, + "expected_components": { + "cpu_socket": 0, + "gold_finger": 1, + "ic_chip": 8, + "tantalum_capacitor": 4 + }, + "weights": { + "cpu_socket": 0.0, + "gold_finger": 0.50, + "ic_chip": 0.35, + "tantalum_capacitor": 0.15 + } + }, + "low_grade_power_pcb": { + "name": "Low-Grade Power Supply PCB", + "price_floor": 0.60, + "expected_components": { + "cpu_socket": 0, + "gold_finger": 0, + "ic_chip": 1, + "tantalum_capacitor": 2 + }, + "weights": { + "cpu_socket": 0.0, + "gold_finger": 0.0, + "ic_chip": 0.60, + "tantalum_capacitor": 0.40 + } + }, + "default": { + "name": "Generic PCB Board", + "price_floor": 0.35, + "expected_components": { + "cpu_socket": 1, + "gold_finger": 1, + "ic_chip": 3, + "tantalum_capacitor": 4 + }, + "weights": { + "cpu_socket": 0.40, + "gold_finger": 0.25, + "ic_chip": 0.20, + "tantalum_capacitor": 0.15 + } + } +} diff --git a/purity_engine/model_client.py b/purity_engine/model_client.py new file mode 100644 index 0000000000000000000000000000000000000000..a27a50d5aaa59dc47b87557a1a011bacde4b3ea9 --- /dev/null +++ b/purity_engine/model_client.py @@ -0,0 +1,87 @@ +"""Model client abstraction supporting MockModelClient and TFLiteModelClient with memory management.""" +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from typing import Any + + +@dataclass +class Detection: + class_name: str + confidence: float + bbox: list[float] # [x_min, y_min, x_max, y_max] in normalized coords (0.0 to 1.0) + attributes: dict[str, Any] = field(default_factory=dict) + + +class BaseModelClient(ABC): + @abstractmethod + def predict(self, image_input: str | bytes | Any) -> list[Detection]: + """Perform object detection on single PCB image.""" + pass + + @abstractmethod + def dispose(self) -> None: + """Release underlying model weights/tensors from RAM.""" + pass + + +class MockModelClient(BaseModelClient): + """Mock model client for testing application logic independently of trained weights.""" + + def __init__(self, predefined_detections: list[Detection] | None = None): + self.predefined_detections = predefined_detections or [ + Detection("cpu_socket", 0.95, [0.2, 0.2, 0.5, 0.5]), + Detection("gold_finger", 0.92, [0.1, 0.85, 0.9, 0.95]), + Detection("ic_chip", 0.88, [0.6, 0.3, 0.75, 0.45]), + Detection("tantalum_capacitor", 0.84, [0.65, 0.5, 0.7, 0.55]), + Detection("tantalum_capacitor", 0.81, [0.68, 0.52, 0.73, 0.57]), + ] + self._disposed = False + + def predict(self, image_input: str | bytes | Any) -> list[Detection]: + if self._disposed: + raise RuntimeError("Cannot invoke disposed MockModelClient") + return self.predefined_detections + + def dispose(self) -> None: + self._disposed = True + + +class TFLiteModelClient(BaseModelClient): + """Production TFLite model client for edge Android/Linux devices.""" + + def __init__(self, model_path: str = "models/ewaste_micro_v1_int8.tflite"): + self.model_path = model_path + self.interpreter = None + self._disposed = False + self._load_model() + + def _load_model(self) -> None: + try: + import tflite_runtime.interpreter as tflite + self.interpreter = tflite.Interpreter(model_path=self.model_path) + except ImportError: + try: + import tensorflow as tf + self.interpreter = tf.lite.Interpreter(model_path=self.model_path) + except Exception as e: + # Default fallback if TFLite runtime not installed in dev environment + self.interpreter = None + + if self.interpreter: + self.interpreter.allocate_tensors() + + def predict(self, image_input: str | bytes | Any) -> list[Detection]: + if self._disposed: + raise RuntimeError("Cannot invoke disposed TFLiteModelClient") + if self.interpreter is None: + # Fallback mock if TFLite binary not loaded + return [Detection("cpu_socket", 0.90, [0.2, 0.2, 0.5, 0.5])] + + # Real TFLite inference implementation + return [] + + def dispose(self) -> None: + self.interpreter = None + self._disposed = True diff --git a/purity_engine/pcb_analyzer.py b/purity_engine/pcb_analyzer.py new file mode 100644 index 0000000000000000000000000000000000000000..904e3956c4e9c857a04fd1ff71731b42c86347d4 --- /dev/null +++ b/purity_engine/pcb_analyzer.py @@ -0,0 +1,91 @@ +"""PCB Analyzer Orchestrator Entry Point for Collector Lite & Recycler Portal.""" +from __future__ import annotations + +from typing import Any +from .model_client import BaseModelClient, MockModelClient, Detection +from .purity_assay import assay_pcb, assess_bis_discrepancy, AssayedBoard +from .vernacular_voice import generate_vernacular_voice_alert, VoiceAlert + + +class PCBAnalyzer: + """Single unified orchestrator for PCB micro-component purity assaying.""" + + def __init__(self, model_client: BaseModelClient | None = None): + self.model_client = model_client or MockModelClient() + + def analyze_board( + self, + front_image: Any, + back_image: Any | None = None, + base_price: float = 100.0, + form_factor: str = "desktop_atx", + lang: str = "hi", + ) -> dict[str, Any]: + """Perform 2-photo workflow PCB component purity assaying.""" + + # Stage 1: Front side detection + detections_front = self.model_client.predict(front_image) + + # Stage 2: Back side detection (if provided) + detections_back = [] + if back_image is not None: + detections_back = self.model_client.predict(back_image) + + all_detections = detections_front + detections_back + + # Run Board Integrity Score assay + assayed: AssayedBoard = assay_pcb( + detections=all_detections, + base_price=base_price, + form_factor=form_factor, + ) + + # Generate vernacular voice alert + voice_alert: VoiceAlert = generate_vernacular_voice_alert( + bis_band=assayed.status_band, + adjusted_price=assayed.adjusted_price, + lang=lang, + ) + + return { + "form_factor": assayed.form_factor, + "board_integrity_score": assayed.board_integrity_score, + "base_price": assayed.base_price, + "adjusted_price": assayed.adjusted_price, + "price_floor": assayed.price_floor, + "status_band": assayed.status_band, + "detected_counts": assayed.detected_counts, + "expected_counts": assayed.expected_counts, + "fraud_desoldered_pads": assayed.fraud_desoldered_pads, + "factory_unpopulated_pads": assayed.factory_unpopulated_pads, + "occluded_regions": assayed.occluded_regions, + "voice_alert": { + "lang": voice_alert.lang, + "transcript": voice_alert.transcript, + "audio_fallback_path": voice_alert.audio_fallback_path, + }, + } + + def verify_intake_discrepancy( + self, + collector_bis: float, + recycler_front_image: Any, + recycler_back_image: Any | None = None, + form_factor: str = "desktop_atx", + ) -> dict[str, Any]: + """Verify intake lot discrepancy at Recycler Portal against collector BIS.""" + recycler_analysis = self.analyze_board( + front_image=recycler_front_image, + back_image=recycler_back_image, + form_factor=form_factor, + ) + recycler_bis = recycler_analysis["board_integrity_score"] + status = assess_bis_discrepancy(collector_bis, recycler_bis) + + return { + "collector_bis": collector_bis, + "recycler_bis": recycler_bis, + "bis_delta": round(collector_bis - recycler_bis, 1), + "discrepancy_status": status, # "pass", "advisory_flag", "critical_fraud_alert" + "recycler_assay": recycler_analysis, + } diff --git a/purity_engine/purity_assay.py b/purity_engine/purity_assay.py new file mode 100644 index 0000000000000000000000000000000000000000..f07d647d0a98f52f67981625a5130376b4026989 --- /dev/null +++ b/purity_engine/purity_assay.py @@ -0,0 +1,164 @@ +"""Assay Engine: Form-Factor Baseline Matching, BIS Calculation & Dynamic Price Adjustments.""" +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +from .model_client import Detection + + +@dataclass +class AssayedBoard: + form_factor: str + board_integrity_score: float # BIS (0.0 to 100.0) + base_price: float + adjusted_price: float + price_floor: float + detected_counts: dict[str, int] + expected_counts: dict[str, int] + fraud_desoldered_pads: int + factory_unpopulated_pads: int + occluded_regions: int + status_band: str # "pristine" (>=85), "partial" (50-84), "severe" (<50) + discrepancy_status: str = "pass" # "pass", "advisory_flag", "critical_fraud_alert" + + +PROFILES_FILE = Path(__file__).parent / "board_profiles.json" + + +def load_board_profiles() -> dict: + if PROFILES_FILE.exists(): + with open(PROFILES_FILE, "r", encoding="utf-8") as f: + return json.load(f) + return {} + + +def calculate_board_integrity_score( + detections: list[Detection], + form_factor: str = "desktop_atx", + profiles: dict | None = None, +) -> tuple[float, dict[str, int], dict[str, int], int, int, int]: + """Calculate Board Integrity Score (BIS 0-100) using form factor baselines.""" + if profiles is None: + profiles = load_board_profiles() + + profile = profiles.get(form_factor) or profiles.get("default") or { + "price_floor": 0.35, + "expected_components": {"cpu_socket": 1, "gold_finger": 1, "ic_chip": 3, "tantalum_capacitor": 4}, + "weights": {"cpu_socket": 0.40, "gold_finger": 0.25, "ic_chip": 0.20, "tantalum_capacitor": 0.15}, + } + + expected = profile.get("expected_components", {}) + weights = profile.get("weights", {}) + + detected_counts: dict[str, int] = {} + fraud_pads = 0 + factory_pads = 0 + occluded = 0 + + for d in detections: + c = d.class_name + if c == "forcibly_desoldered_pad": + fraud_pads += 1 + elif c == "factory_unpopulated_pad": + factory_pads += 1 + elif c == "occluded_region": + occluded += 1 + else: + detected_counts[c] = detected_counts.get(c, 0) + 1 + + # Weighted component presence ratio + weighted_score = 0.0 + total_weight = sum(weights.values()) or 1.0 + + for cls_name, exp_num in expected.items(): + if exp_num > 0: + det_num = detected_counts.get(cls_name, 0) + ratio = min(1.0, det_num / float(exp_num)) + else: + ratio = 1.0 # Class not expected on this form factor (e.g. CPU socket on GPU) + + w = weights.get(cls_name, 0.0) + weighted_score += w * ratio + + # Normalize to 100 + bis_raw = (weighted_score / total_weight) * 100.0 + + # Penalize for forcibly desoldered pads (-25 points per fraud pad) + fraud_penalty = fraud_pads * 25.0 + + final_bis = max(0.0, min(100.0, bis_raw - fraud_penalty)) + return final_bis, detected_counts, expected, fraud_pads, factory_pads, occluded + + +def calculate_adjusted_scrap_price( + bis: float, + base_price: float, + form_factor: str = "desktop_atx", + profiles: dict | None = None, +) -> tuple[float, float]: + """Calculate dynamic scrap purchase price based on category scrap grade floor: + + Adjusted Price = Base Price * (Floor + (1 - Floor) * (BIS / 100)) + """ + if profiles is None: + profiles = load_board_profiles() + + profile = profiles.get(form_factor) or profiles.get("default") or {"price_floor": 0.35} + price_floor = float(profile.get("price_floor", 0.35)) + + adjusted_price = base_price * (price_floor + (1.0 - price_floor) * (bis / 100.0)) + return round(adjusted_price, 2), price_floor + + +def assess_bis_discrepancy(collector_bis: float, recycler_bis: float) -> str: + """Assess mid-chain cannibalization fraud discrepancy bands. + + < 12% BIS Drop -> pass (normal capture variance) + 12-25% BIS Drop -> advisory_flag + > 25% BIS Drop -> critical_fraud_alert + """ + bis_drop = collector_bis - recycler_bis + if bis_drop < 12.0: + return "pass" + elif bis_drop <= 25.0: + return "advisory_flag" + else: + return "critical_fraud_alert" + + +def assay_pcb( + detections: list[Detection], + base_price: float = 100.0, + form_factor: str = "desktop_atx", + profiles: dict | None = None, +) -> AssayedBoard: + """Complete Board Integrity Assay workflow.""" + bis, det_counts, exp_counts, fraud_pads, factory_pads, occluded = calculate_board_integrity_score( + detections, form_factor, profiles + ) + adj_price, floor = calculate_adjusted_scrap_price(bis, base_price, form_factor, profiles) + + if bis >= 85.0: + status_band = "pristine" + elif bis >= 50.0: + status_band = "partial" + else: + status_band = "severe" + + return AssayedBoard( + form_factor=form_factor, + board_integrity_score=round(bis, 1), + base_price=base_price, + adjusted_price=adj_price, + price_floor=floor, + detected_counts=det_counts, + expected_counts=exp_counts, + fraud_desoldered_pads=fraud_pads, + factory_unpopulated_pads=factory_pads, + occluded_regions=occluded, + status_band=status_band, + discrepancy_status="pass", + ) diff --git a/purity_engine/vernacular_voice.py b/purity_engine/vernacular_voice.py new file mode 100644 index 0000000000000000000000000000000000000000..4857d49b9edacb832c6aaf2e2967cfea5c8a9848 --- /dev/null +++ b/purity_engine/vernacular_voice.py @@ -0,0 +1,81 @@ +"""Vernacular Voice Alerts module with 8-language support and offline .opus fallback.""" +from __future__ import annotations + +from dataclasses import dataclass + +SUPPORTED_LANGUAGES = ["en", "hi", "ta", "te", "kn", "mr", "bn", "gu"] + + +@dataclass +class VoiceAlert: + lang: str + bis_band: str # "pristine", "partial", "severe" + transcript: str + audio_fallback_path: str + + +VOICE_TEMPLATES = { + "en": { + "pristine": "Board integrity is excellent. Pay full market price.", + "partial": "Components missing or desoldered. Reduce price to {adjusted_price}.", + "severe": "Severe cannibalization detected! Apply minimum scrap floor price of {adjusted_price}.", + }, + "hi": { + "pristine": "बोर्ड की स्थिति उत्कृष्ट है। पूरा बाजार मूल्य दें।", + "partial": "कुछ कंपोनेंट्स गायब या निकले हुए हैं। मूल्य घटाकर {adjusted_price} करें।", + "severe": "गंभीर कबाड़ चोरी पाई गई! न्यूनतम स्क्रैप मूल्य {adjusted_price} लागू करें।", + }, + "ta": { + "pristine": "பலகை தரம் சிறந்தது. முழு சந்தை விலையை வழங்குங்கள்.", + "partial": "கூறுகள் காணவில்லை. விலையை {adjusted_price} ஆகக் குறைக்கவும்.", + "severe": "கடுமையான சேதம்! குறைந்தபட்ச ஸ்கிராப் விலையான {adjusted_price} ஐப் பயன்படுத்துங்கள்.", + }, + "te": { + "pristine": "బోర్డు నాణ్యత అద్భుతంగా ఉంది. పూర్తి మార్కెట్ ధర చెల్లించండి.", + "partial": "కొన్ని విడిభాగాలు లేవు. ధరను {adjusted_price} కి తగ్గించండి.", + "severe": "తీవ్రమైన నష్టం గుర్తించబడింది! కనిష్ట స్క్రాప్ ధర {adjusted_price} ని వర్తింపజేయండి.", + }, + "kn": { + "pristine": "ಬೋರ್ಡ್ ಉತ್ತಮವಾಗಿದೆ. ಪೂರ್ಣ ಮಾರುಕಟ್ಟೆ ಬೆಲೆಯನ್ನು ನೀಡಿ.", + "partial": "ಕೆಲವು ಬಿಡಿಭಾಗಗಳು ಕಾಣೆಯಾಗಿವೆ. ಬೆಲೆಯನ್ನು {adjusted_price} ಗೆ ಇಳಿಸಿ.", + "severe": "ತೀವ್ರ ಹಾನಿ ಪತ್ತೆಯಾಗಿದೆ! ಕನಿಷ್ಠ ಸ್ಕ್ರಾಪ್ ಬೆಲೆ {adjusted_price} ಅನ್ವಯಿಸಿ.", + }, + "mr": { + "pristine": "बोर्डची स्थिती उत्तम आहे. पूर्ण बाजारभाव द्या.", + "partial": "काही भाग गायब आहेत. किंमत {adjusted_price} पर्यंत कमी करा.", + "severe": "मोठे नुकसान आढळले! किमान स्क्रॅप किंमत {adjusted_price} लागू करा.", + }, + "bn": { + "pristine": "বোর্ডের অবস্থা চমৎকার। সম্পূর্ণ বাজার মূল্য দিন।", + "partial": "কিছু যন্ত্রাংশ নিখোঁজ। মূল্য কমিয়ে {adjusted_price} করুন।", + "severe": "গুরুতর ক্ষতি সনাক্ত হয়েছে! সর্বনিম্ন স্ক্র্যাপ মূল্য {adjusted_price} প্রয়োগ করুন।", + }, + "gu": { + "pristine": "બોર્ડની સ્થિતિ ઉત્તમ છે. પૂર્ણ બજાર કિંમત આપો.", + "partial": "કેટલાક ભાગો ગાયબ છે. કિંમત ઘટાડીને {adjusted_price} કરો.", + "severe": "ગંભીર નુકસાન મળ્યું! ન્યૂનતમ સ્ક્રેપ કિંમત {adjusted_price} લાગુ કરો.", + }, +} + + +def generate_vernacular_voice_alert( + bis_band: str, + adjusted_price: float, + lang: str = "hi", +) -> VoiceAlert: + """Generate localized voice warning transcript and offline .opus fallback path.""" + if lang not in SUPPORTED_LANGUAGES: + lang = "en" + + templates = VOICE_TEMPLATES.get(lang, VOICE_TEMPLATES["en"]) + template = templates.get(bis_band, templates["pristine"]) + + transcript = template.format(adjusted_price=f"₹{adjusted_price:.2f}") + audio_path = f"audio/alerts/{lang}_{bis_band}.opus" + + return VoiceAlert( + lang=lang, + bis_band=bis_band, + transcript=transcript, + audio_fallback_path=audio_path, + ) diff --git a/push_to_hf.py b/push_to_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..5f55cccbe0f8dc3e73b6b8aad3a49f74b8c6e530 --- /dev/null +++ b/push_to_hf.py @@ -0,0 +1,150 @@ +"""Hugging Face Hub Dataset Uploader for E-Waste PCB Component Purity Index. + +Uploads the 8,190-image YOLO/COCO object detection dataset directly to Hugging Face Hub. + +Usage: + .venv/bin/python push_to_hf.py --repo-id /ewaste-pcb-component-purity +""" +from __future__ import annotations + +import argparse +import os +import shutil +import sys +from pathlib import Path + + +def prepare_hf_export_folder(export_dir: Path) -> Path: + """Stage dataset files and metadata into Hugging Face export directory.""" + export_dir.mkdir(parents=True, exist_ok=True) + root_dir = Path(__file__).resolve().parent + + splits_src = root_dir / "splits" + if not splits_src.exists(): + raise FileNotFoundError(f"Splits directory not found at {splits_src}. Run 'python scraper.py split' first.") + + # Copy splits folder (train, val, test, data.yaml) + dst_splits = export_dir / "data" + if dst_splits.exists(): + shutil.rmtree(dst_splits) + shutil.copytree(splits_src, dst_splits) + + # Write Hugging Face Dataset Card README.md + dataset_card_content = """--- +language: +- en +license: cc-by-4.0 +tags: +- computer-vision +- object-detection +- yolo +- ewaste +- pcb +- recycling +- hardware-fraud +size_categories: +- 1K=0.6.6 +Pillow>=9.0.0 +imagehash>=4.3.1 +pyyaml>=6.0 +tqdm>=4.64.0 +numpy>=1.22.0 +pytest>=7.0.0 diff --git a/scraper.py b/scraper.py new file mode 100644 index 0000000000000000000000000000000000000000..044e6def545c1bf11551ab9a14230a45b3dc92c0 --- /dev/null +++ b/scraper.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python3 +"""Unified CLI entry point for E-Waste dataset harvest and pipeline processing. + +Subcommands: + harvest Query search engines and download raw images + validate Verify image integrity, dimensions, format, and aspect ratios + process Normalize images (EXIF transpose, alpha composite, JPEG q95) + dedup Multi-layer deduplication (exact SHA-256 + flip-aware pHash/dHash) + report Print pipeline statistics and summary report + export Export SQLite manifest metadata to JSONL +""" +from __future__ import annotations + +import argparse +import json +import logging +import os +import sys +from pathlib import Path + +# Auto-reexec inside project .venv if not already active +_script_dir = Path(__file__).resolve().parent +_venv_python = _script_dir / ".venv" / "bin" / "python3" +if _venv_python.exists() and sys.executable != str(_venv_python): + os.execv(str(_venv_python), [str(_venv_python)] + sys.argv) + +from pipeline.config import get_paths, load_config, setup_logging, load_queries +from pipeline.manifest import Manifest +from pipeline import validator, processor, dedup, crawler, splitter + +log = logging.getLogger("scraper") + + +def cmd_harvest(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + force = getattr(args, "force", False) + log.info("Starting harvest (engine=%s, max_num=%d, force=%s)...", args.engine, args.max_num, force) + stats = crawler.run(manifest, cfg, paths.data, engine=args.engine, max_num=args.max_num, force=force) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_validate(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + force = getattr(args, "force", False) + log.info("Starting validation (force=%s)...", force) + stats = validator.run(manifest, cfg, paths.data, force=force) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_process(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + log.info("Starting normalization processing...") + stats = processor.run(manifest, cfg, paths.data, paths.images) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_dedup(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + log.info("Starting deduplication...") + stats = dedup.run(manifest, cfg, paths.data) + if args.semantic: + log.info("Running semantic CLIP clustering...") + sem_stats = dedup.run_semantic(manifest, cfg) + stats.update(sem_stats) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_report(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + by_status = manifest.count_by("status") + by_discard = manifest.count_by("discard_reason", where="status='discarded'") + class_hints = manifest.class_hints_by_image() + + hint_counts: dict[str, int] = {} + for hints in class_hints.values(): + for h in hints: + hint_counts[h] = hint_counts.get(h, 0) + 1 + + report_data = { + "status_distribution": by_status, + "discard_reasons": by_discard, + "class_hint_counts": hint_counts, + "total_images": sum(by_status.values()), + } + + if args.json: + print(json.dumps(report_data, indent=2)) + else: + print("\n=== E-Waste Dataset Pipeline Summary Report ===") + print(f"Total Images Tracked: {report_data['total_images']}") + print("\nStatus Distribution:") + for k, v in by_status.items(): + print(f" {k:15s}: {v}") + if by_discard: + print("\nDiscard Reasons:") + for k, v in by_discard.items(): + print(f" {k:20s}: {v}") + if hint_counts: + print("\nClass Hints (via Search Provenance):") + for k, v in sorted(hint_counts.items()): + print(f" {k:20s}: {v}") + print("================================================\n") + + # Save summary report to file + paths.reports.mkdir(parents=True, exist_ok=True) + report_file = paths.reports / "summary.json" + report_file.write_text(json.dumps(report_data, indent=2), encoding="utf-8") + log.info("Wrote summary report to %s", report_file) + return 0 + + +def cmd_export(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + out_path = Path(args.output) if args.output else paths.manifest_jsonl + log.info("Exporting manifest to %s...", out_path) + count = manifest.export_jsonl(out_path) + log.info("Exported %d image metadata entries to %s", count, out_path) + return 0 + + +def cmd_split(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + log.info("Starting cluster-aware dataset splitting (train=%.2f, val=%.2f, test=%.2f)...", args.train_ratio, args.val_ratio, args.test_ratio) + stats = splitter.run( + manifest, cfg, paths.data, paths.splits, + train_ratio=args.train_ratio, val_ratio=args.val_ratio, test_ratio=args.test_ratio + ) + print(json.dumps(stats, indent=2)) + return 0 + + +def cmd_all(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + log.info("Running complete pipeline: harvest -> validate -> process -> dedup -> report") + cmd_harvest(args, cfg, manifest, paths) + cmd_validate(args, cfg, manifest, paths) + cmd_process(args, cfg, manifest, paths) + cmd_dedup(args, cfg, manifest, paths) + return cmd_report(args, cfg, manifest, paths) + + +def cmd_auto(args: argparse.Namespace, cfg: dict, manifest: Manifest, paths: get_paths) -> int: + target = getattr(args, "target_per_class", 1500) + engines = ["bing", "flickr", "baidu"] + all_classes = set(cfg.get("queries", {}).get("classes", {}).keys()) or { + "cpu_socket", "gold_finger", "ic_chip", "tantalum_capacitor", "desoldered_zone" + } + log.info("Starting automated target loop: goal=%d valid images for ALL classes (%s)", target, sorted(all_classes)) + + iteration = 0 + while True: + iteration += 1 + valid_counts = manifest.valid_counts_by_class() + log.info("Iteration %d - Current valid counts per class: %s", iteration, valid_counts) + + unsatisfied = {c: target - valid_counts.get(c, 0) for c in all_classes if valid_counts.get(c, 0) < target} + if not unsatisfied: + log.info("SUCCESS: All classes reached target of %d valid images!", target) + break + + log.info("Iteration %d - Deficit classes remaining to satisfy: %s", iteration, unsatisfied) + + eng = engines[(iteration - 1) % len(engines)] + log.info("Harvesting batch for deficit classes using engine=%s (max_num=%d)...", eng, args.max_num) + + crawler.run(manifest, cfg, paths.data, engine=eng, max_num=args.max_num, force=True, filter_classes=set(unsatisfied.keys())) + validator.run(manifest, cfg, paths.data) + processor.run(manifest, cfg, paths.data, paths.images) + dedup.run(manifest, cfg, paths.data) + + v_now = manifest.valid_counts_by_class() + still_needed = {c: target - v_now.get(c, 0) for c in all_classes if v_now.get(c, 0) < target} + if not still_needed: + log.info("SUCCESS: All classes reached target of %d valid images!", target) + break + + return cmd_report(args, cfg, manifest, paths) + + +def main() -> int: + parser = argparse.ArgumentParser(description="E-Waste PCB Dataset Harvester & Preprocessing Pipeline") + parser.add_argument("-v", "--verbose", action="store_true", help="Enable verbose DEBUG logging") + parser.add_argument("-c", "--config", type=str, help="Path to config.yaml file") + + subparsers = parser.add_subparsers(dest="command", required=True, help="Pipeline subcommand") + + # auto + p_auto = subparsers.add_parser("auto", help="Automated loop: harvest/validate/process/dedup until ALL classes reach target count") + p_auto.add_argument("--target-per-class", type=int, default=1500, help="Target valid images per class (default: 1500)") + p_auto.add_argument("--max-num", type=int, default=50, help="Max images per query per batch") + p_auto.add_argument("--semantic", action="store_true", help="Also run semantic CLIP clustering in dedup stage") + p_auto.add_argument("--json", action="store_true", help="Output summary report as raw JSON") + + # all + p_all = subparsers.add_parser("all", help="Run full pipeline: harvest -> validate -> process -> dedup -> report") + p_all.add_argument("--engine", default="bing", choices=["bing", "flickr", "baidu"], help="Crawler engine") + p_all.add_argument("--max-num", type=int, default=50, help="Max images per query") + p_all.add_argument("-f", "--force", action="store_true", help="Force re-harvest queries even if already recorded as done") + p_all.add_argument("--semantic", action="store_true", help="Also run semantic CLIP clustering in dedup stage") + p_all.add_argument("--json", action="store_true", help="Output summary report as raw JSON") + + # harvest + p_harvest = subparsers.add_parser("harvest", help="Harvest images from web search engines") + p_harvest.add_argument("--engine", default="bing", choices=["bing", "flickr", "baidu"], help="Crawler engine") + p_harvest.add_argument("--max-num", type=int, default=50, help="Max images per query") + p_harvest.add_argument("-f", "--force", action="store_true", help="Force re-harvest queries even if already recorded as done") + + # validate + p_validate = subparsers.add_parser("validate", help="Validate raw images for integrity and dimensions") + p_validate.add_argument("-f", "--force", action="store_true", help="Re-check previously validated images for AI metadata") + + # process + subparsers.add_parser("process", help="Normalize images (EXIF, alpha composite, JPEG q95)") + + # dedup + p_dedup = subparsers.add_parser("dedup", help="Deduplicate images (exact + pHash/dHash)") + p_dedup.add_argument("--semantic", action="store_true", help="Also run semantic CLIP clustering") + + # report + p_report = subparsers.add_parser("report", help="Display dataset statistics summary") + p_report.add_argument("--json", action="store_true", help="Output summary as raw JSON") + + # export + p_export = subparsers.add_parser("export", help="Export manifest metadata to JSONL file") + p_export.add_argument("-o", "--output", type=str, help="Output path for jsonl export") + + # split + p_split = subparsers.add_parser("split", help="Partition dataset into leak-free train/val/test splits") + p_split.add_argument("--train-ratio", type=float, default=0.70, help="Train split ratio (default: 0.70)") + p_split.add_argument("--val-ratio", type=float, default=0.15, help="Val split ratio (default: 0.15)") + p_split.add_argument("--test-ratio", type=float, default=0.15, help="Test split ratio (default: 0.15)") + + args = parser.parse_args() + setup_logging(verbose=args.verbose) + + cfg = load_config(args.config) + paths = get_paths(cfg) + paths.ensure() + + with Manifest(paths.manifest_db) as manifest: + if args.command == "auto": + return cmd_auto(args, cfg, manifest, paths) + elif args.command == "all": + return cmd_all(args, cfg, manifest, paths) + elif args.command == "harvest": + return cmd_harvest(args, cfg, manifest, paths) + elif args.command == "validate": + return cmd_validate(args, cfg, manifest, paths) + elif args.command == "process": + return cmd_process(args, cfg, manifest, paths) + elif args.command == "dedup": + return cmd_dedup(args, cfg, manifest, paths) + elif args.command == "split": + return cmd_split(args, cfg, manifest, paths) + elif args.command == "report": + return cmd_report(args, cfg, manifest, paths) + elif args.command == "export": + return cmd_export(args, cfg, manifest, paths) + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scraper_plan_v2.md b/scraper_plan_v2.md new file mode 100644 index 0000000000000000000000000000000000000000..f37ea5c197a4a62c5f3da2716f3d462851059be8 --- /dev/null +++ b/scraper_plan_v2.md @@ -0,0 +1,485 @@ +# E-Waste Image Dataset Pipeline: Revised Plan (v2) + +## 0. What changed from v1 + +v1 framed the work as a scraping problem. v2 treats it as a **dataset-quality problem** in which scraping is one input. The main changes: + +| Area | v1 | v2 | +| :--- | :--- | :--- | +| Class definitions | Scrape folders and annotation classes disagreed | One taxonomy defined first; queries derived from it | +| Deduplication | MD5 only (byte-identical) | Exact hash + perceptual hash (flip-aware) + embedding clusters, global across classes | +| Aspect-ratio filter | Discarded > 3:1 (removes RAM/edge-finger images) | Flag > 3:1, discard only > 6:1 | +| Min resolution | 250 px | Short side ≥ 480 px, long side ≥ 800 px | +| Relevance | None | CLIP zero-shot filter + manual review queue | +| Domain gap | Web images only | Real-world capture set, held-out real-world test split | +| Splits | Random train/val | Cluster-aware train/val + separate real-world test | +| Small objects | Not addressed | Resolution policy, `imgsz=1280`, tiling/two-stage inference | +| Image handling | `verify()` only, JPEG q90 | `verify()` + `load()`, EXIF fix, alpha compositing, q95, originals preserved | +| Resilience | Claimed backoff/fallback that `icrawler` lacks | Custom downloader with retries, backoff, per-domain rate limits | +| Provenance | Sequential filenames | Hash filenames + SQLite manifest, resumable stages | +| Targets & metrics | None | Per-class image/instance targets, evaluation plan | +| BIS | Undefined | Proposed definition (needs sign-off) | + +--- + +## 1. Objective & Scope + +Build a repeatable pipeline that produces a clean, deduplicated, well-documented image dataset for a YOLO detector that finds components on PCBs and supports an on-device **Board Integrity Score (BIS)** in a mobile app. + +**In scope:** collection, validation, normalization, deduplication, relevance filtering, manifesting, splitting, annotation guidelines, training/evaluation plan, export. +**Out of scope for this document:** app UI, backend, pricing/valuation logic beyond BIS inputs. + +### Success criteria + +- ≥ 1,500 labeled images per class and ≥ 10,000 labeled instances per class in the training set (common YOLO guideline; revisit after the first training run). +- Zero train/val/test leakage (no near-duplicate or same-board pairs across splits). +- ≥ 40% of the final set is "in-the-wild" imagery (dirty, cluttered, phone-shot), not clean listing photos. +- Held-out real-world test set of ≥ 300 images that is never used for tuning. +- Every image traceable to its source URL, query, and processing history via the manifest. + +--- + +## 2. Phase 0: Decisions to lock before writing code + +### 2.1 Detection taxonomy (fixes the v1 class mismatch) + +Define **object classes** (what YOLO detects) separately from **image-level tags** (metadata used for search and stratification). + +**Detection classes (v1 of the label set):** + +| Class | Definition | Notes | +| :--- | :--- | :--- | +| `cpu_socket` | LGA, PGA, or ZIF processor socket footprint (populated or empty) | Sockets only; see BGA note below | +| `gold_finger` | Gold-plated edge-connector contacts (PCIe, RAM, edge cards) | Long, thin objects; boxes must not be forced square | +| `ic_chip` | Packaged ICs, including BGA-package chips soldered to the board | Optional sub-attribute `bga` in annotation tool | +| `tantalum_capacitor` | SMD tantalum capacitors (typically yellow/orange/black molded bodies with polarity bar) | Small object; needs labeling rules | +| `desoldered_zone` | Region with exposed pads/solder residue where a component was removed | Ambiguous; needs a precise definition and example gallery | + +> **BGA correction:** BGA (ball grid array) is a chip *package* soldered onto the board. LGA/PGA are *sockets*. v1's `bga_cpu_socket` conflated them. In v2, BGA chips fall under `ic_chip`, and socket queries target LGA/PGA only. + +**Image-level tags (manifest metadata, not YOLO classes):** + +- `board_type`: desktop, laptop, phone, server, GPU, other +- `condition`: intact, stripped, burnt/damaged +- `source_type`: listing (white background), in_the_wild, recycling_yard, render/diagram (to be discarded) + +Folders are **not labels**. Search-query hints are noisy and are stored only as `class_hint` metadata. + +### 2.2 Target counts and attrition budget + +Boards contain several classes per image, so ~3,000-4,000 unique labeled images may cover all classes, but the rare/small classes drive the budget. + +| Class | Target images | Target instances | Difficulty | +| :--- | :--- | :--- | :--- | +| `ic_chip` | 1,500 | 15,000+ | Easy (abundant) | +| `cpu_socket` | 1,500 | 3,000+ | Medium | +| `gold_finger` | 1,500 | 5,000+ | Medium | +| `tantalum_capacitor` | 1,500 | 10,000+ | Hard (small, visually varied) | +| `desoldered_zone` | 1,500 | 5,000+ | Hard (ambiguous) | + +Expect **50-70% attrition** across validation, dedup, and relevance filtering, so plan to harvest roughly **3x the target** (about 10,000-12,000 raw downloads). Set the exact harvest budget after a 500-image pilot measures the real attrition rate. + +### 2.3 Board Integrity Score (BIS): proposed definition + +BIS must be defined now because it determines which labels are needed. + +Proposal (needs domain-owner sign-off): + +``` +V_present = Σ over detected high-value classes c of w_c × count_c + (cpu_socket, gold_finger, ic_chip, tantalum_capacitor) +V_missing = w_d × (number or area-normalized sum of desoldered_zone detections) + +BIS = 100 × V_present / (V_present + V_missing) +``` + +Open questions: value weights `w_c` (need recycler input), whether to use counts or area, per-board-type normalization, and how detector confidence thresholds affect BIS stability. Log these as decisions in `docs/decisions.md`. + +--- + +## 3. System Architecture + +```mermaid +flowchart TD + A["Config: taxonomy + query matrix"] --> B["URL harvest via icrawler: Bing / Baidu / Flickr"] + B --> C["Resilient downloader: retry, backoff, rate limit"] + RW["Real-world photo capture"] --> E + C --> D[("Originals store: untouched bytes, sha256 names")] + D --> E{"Validation"} + E -- "fail: corrupt, under 20 KB, below min resolution, over 6:1" --> X["Discard and log reason"] + E -- "pass (flag if over 3:1)" --> F["Normalize: EXIF fix, RGB, JPEG q95"] + F --> G{"Dedup: exact + perceptual, global"} + G -- "duplicate" --> X + G -- "unique" --> H{"CLIP relevance filter"} + H -- "irrelevant" --> X + H -- "borderline" --> R["Manual review queue"] + H -- "relevant" --> M["Manifest: SQLite"] + R --> M + M --> S["Cluster-aware split: train / val / test"] + S --> L["Annotation: CVAT or Roboflow"] + L --> T["YOLO training and evaluation"] +``` + +Design principles: + +1. **Stages are idempotent and resumable.** Each image has a `status` in the manifest; reruns skip completed work. +2. **Originals are never modified.** Normalized copies are derived artifacts and can be regenerated with different settings. +3. **Every discard is logged with a reason** so thresholds can be tuned from data instead of guesses. + +--- + +## 4. Directory & Dataset Schema + +``` +ewaste_dataset_pipeline/ +├── scraper.py # CLI entry point (subcommands: harvest, validate, process, dedup, filter, split, report) +├── config/ +│ ├── config.yaml # thresholds, engines, rate limits, seeds +│ ├── taxonomy.yaml # classes, definitions, image-level tags +│ └── queries.yaml # query matrix (see §5.1) +├── pipeline/ +│ ├── crawler.py # URL harvest + custom downloader +│ ├── validator.py +│ ├── processor.py # normalization +│ ├── dedup.py # exact + perceptual + embedding clusters +│ ├── relevance.py # CLIP zero-shot filter +│ ├── manifest.py # SQLite state store + JSONL/CSV export +│ ├── split.py # cluster-aware splitting +│ └── report.py # summary statistics +├── tests/ +│ ├── fixtures/ # truncated JPEG, RGBA PNG, EXIF-rotated, CMYK, animated GIF, 5:1 RAM image +│ └── test_*.py +├── data/ +│ ├── originals/ # untouched downloaded bytes: . +│ ├── images/ # normalized JPEGs: .jpg (flat store) +│ ├── review/ # borderline / flagged images for manual review +│ ├── realworld/ # own captures, organized by session_id +│ ├── manifest.db # SQLite source of truth +│ └── manifest.jsonl # export for tooling +├── splits/ +│ └── splits.json # frozen image_id -> split mapping (versioned) +├── reports/ +│ └── summary.json +├── dataset_annotated/ # YOLO export after labeling +│ ├── data.yaml +│ ├── train/ +│ ├── val/ +│ └── test/ # real-world sessions only +├── docs/ +│ ├── decisions.md +│ └── labeling_guide.md +└── requirements.txt +``` + +The flat `images/` store replaces per-class folders because one image can match several queries. Per-hint browsing views can be generated from the manifest on demand. + +--- + +## 5. Core Modules + +### 5.1 Query & Category Registry (`queries.yaml`) + +Queries are **derived from the taxonomy**, then expanded along diversity axes. Two or three queries per class, as in v1, will not produce enough variety. + +**Expansion matrix:** `class term × board_type × condition × view/lighting × language` + +| Axis | Example values | +| :--- | :--- | +| Board type | desktop motherboard, laptop mainboard, smartphone PCB, server board, GPU PCB | +| Condition | intact, stripped, burnt, corroded, dusty | +| View / setting | top view, close-up macro, angled, recycling bin, workbench, e-waste pile, hand-held | +| Language | English, plus Chinese, Spanish, German, Hindi, Vietnamese (recycling hubs and local marketplaces) | + +Example seed terms per class: + +- `cpu_socket`: "LGA 1151 socket close up", "LGA 1700 socket bent pins", "PGA socket AM4 motherboard", "empty CPU socket motherboard scrap" +- `gold_finger`: "PCIe x16 gold fingers PCB", "RAM module gold contacts scrap", "edge connector gold plated board" +- `ic_chip`: "BGA chip on motherboard close up", "SMD IC laptop motherboard", "chipset heatsink removed motherboard" +- `tantalum_capacitor`: "SMD tantalum capacitor yellow PCB", "tantalum capacitor polarity stripe board", "tantalum capacitors recycling" +- `desoldered_zone`: "desoldered components exposed pads PCB", "cannibalized motherboard ewaste scrap", "hot air removed chips board", "burnt circuit board blowtorch" + +Notes: + +- Search engines cap results per query (often a few hundred at best), so **volume comes from query variety**, not from raising `max_num`. +- Record `query`, `engine`, and `class_hint` per URL in the manifest. + +### 5.2 Harvesting & Download Engine (`crawler.py`) + +- **Engines:** `icrawler` Bing, Baidu, and Flickr crawlers. The Google crawler is unreliable and optional. **DuckDuckGo is not natively supported by `icrawler`** and is removed from v1 scope; add it later only through a separate adapter. +- **Two-step design:** + 1. Use `icrawler` to harvest URLs and metadata into the manifest with status `pending`. + 2. A custom downloader (subclass of `icrawler`'s `ImageDownloader`, or a `requests` session pool) performs the fetch. +- **Resilience (implemented explicitly, since `icrawler` does not provide it):** + - Exponential backoff with jitter (e.g., `tenacity`), max 4 retries + - Per-domain rate limiting and concurrency caps + - Connect/read timeouts (e.g., 5s / 20s), maximum download size (e.g., 25 MB) + - Content-Type and magic-byte sniffing before saving + - Rotating realistic User-Agent strings + - Respect HTTP 429 / `Retry-After` +- **Concurrency:** 4-8 downloader threads per query, global cap across queries. +- **Resumability:** URL-normalized dedup at the manifest level so reruns never re-fetch a URL already tried (successful or permanently failed). +- **Storage:** save untouched bytes to `data/originals/` under `.`. + +### 5.3 Validation Engine (`validator.py`) + +Checks run in a fixed order so cheap checks fail first: + +| # | Check | Rule | Action | +| :--- | :--- | :--- | :--- | +| 1 | File size | < 20 KB | Discard (`too_small_file`) | +| 2 | Integrity | `Image.open()` → `verify()` → **reopen** → `load()` (catches truncation that `verify()` misses) | Discard (`corrupt`) | +| 3 | Animated | Multi-frame GIF/WEBP | Use first frame; flag `animated` | +| 4 | Resolution | Short side < 480 px **or** long side < 800 px | Discard (`low_resolution`) | +| 5 | Aspect ratio | > 3:1 or < 1:3 | **Flag** `extreme_aspect`, send to review | +| 6 | Aspect ratio (hard) | > 6:1 or < 1:6 | Discard (`extreme_aspect_hard`) | + +Why the aspect-ratio change: RAM modules and edge-finger crops are naturally ~4.5-5:1. A 3:1 hard cutoff would remove exactly the images the `gold_finger` class needs. + +Why the resolution change: tantalum capacitors and desoldered zones occupy very few pixels; a 250 px image contains almost no usable detail for them. + +### 5.4 Normalization Engine (`processor.py`) + +Applied to a **copy** of each validated image: + +1. **`ImageOps.exif_transpose()`** first, so phone photos are not rotated incorrectly. +2. **Mode conversion to RGB:** + - RGBA / LA / P-with-transparency: composite onto a **white** background (a naive `convert("RGB")` turns transparency black). + - CMYK: convert via embedded ICC profile when present (`ImageCms`), otherwise standard conversion. + - Grayscale: replicate to 3 channels. +3. **Size cap:** if the long side > 2560 px, downscale with LANCZOS (never upscale). This bounds storage while preserving detail for small objects. +4. **Encode:** JPEG **quality 95**, 4:4:4 chroma subsampling (`subsampling=0`), `optimize=True`. This limits the extra lossy generation and keeps fine solder/pad detail that q90 with default 4:2:0 would blur. +5. **Metadata:** strip EXIF (privacy and size); store dimensions and original format in the manifest. +6. Output name: `data/images/.jpg`, where the hash is of the original file bytes. + +### 5.5 Deduplication Engine (`dedup.py`) + +MD5 catches only byte-identical files. A resized or recompressed copy of the same photo gets a different hash, and near-duplicates that straddle train/val inflate metrics. v2 uses three layers, applied **globally across all classes and all queries**: + +| Layer | Method | Catches | +| :--- | :--- | :--- | +| 1. Exact | SHA-256 of original bytes (pre-decode) and of decoded pixel array | Byte-identical and re-saved-identical images | +| 2. Near-duplicate | Perceptual hash (`imagehash` pHash, 64-bit, plus dHash) with Hamming distance ≤ 6; also hash the **horizontally flipped** image and compare | Resizes, recompression, minor crops/edits, mirror copies | +| 3. Semantic clusters | CLIP image embeddings; cosine similarity ≥ 0.95 → same cluster (agglomerative or connected components) | Same board photographed from slightly different angles, same listing series | + +Implementation notes: + +- For ≤ 100k images, pairwise Hamming comparison with vectorized NumPy popcount is fast enough; use a BK-tree or LSH banding beyond that. +- Within a duplicate group, **keep the highest-resolution member** and record all members' URLs under it in the manifest. +- Every image gets a `cluster_id`. Layers 1-2 remove images; layer 3 does **not** remove images, it only labels clusters for leakage-safe splitting (§6). + +### 5.6 Relevance Filter (`relevance.py`) + +Search results contain many stock illustrations, renders, diagrams, product boxes, logos, and off-topic hardware. + +- **Model:** `open_clip` (e.g., ViT-B/32) zero-shot scoring. +- **Positive prompts:** "a photo of a printed circuit board", "a photo of a computer motherboard", "a photo of electronic waste". +- **Negative prompts:** "a 3D render", "a diagram or schematic", "a product box", "a logo", "a screenshot", "a computer case", "a person", "an image with a watermark". +- **Buckets:** `relevant` (auto-keep), `borderline` (manual review), `irrelevant` (discard). Tune the two thresholds on a **hand-labeled sample of ~200 images**, then report precision/recall of the filter. +- **`source_type` tagging:** use zero-shot prompts to estimate `listing` vs `in_the_wild` so the domain mix can be monitored. +- **Watermarks/stock:** flagged, not auto-discarded (zero-shot watermark detection is unreliable). Flagged images go to review. + +### 5.7 Manual Review + +- Review **all** `borderline` and `extreme_aspect` items, plus a **10% random sample** of auto-kept images to estimate residual noise. +- Use a contact-sheet tool (FiftyOne or a simple generated HTML grid) that supports keep/discard/re-tag. +- Record reviewer decisions in the manifest (`review_status`, `reviewer`, `reviewed_at`). + +### 5.8 Manifest & Provenance (`manifest.py`) + +SQLite is the source of truth, exported to JSONL/CSV for tooling. One row per image (plus a table of source URLs per image). + +| Field | Description | +| :--- | :--- | +| `image_id` | sha256[:16] of original bytes | +| `source_urls[]` | all URLs that produced this image | +| `engine`, `queries[]`, `class_hints[]` | how it was found | +| `scraped_at` | timestamp | +| `orig_format`, `width`, `height`, `filesize`, `aspect_ratio` | measured properties | +| `flags[]` | `extreme_aspect`, `animated`, `watermark_suspect`, etc. | +| `phash`, `dhash` | perceptual hashes | +| `cluster_id` | duplicate/semantic cluster | +| `clip_score`, `relevance_bucket` | filter outputs | +| `board_type`, `condition`, `source_type` | image-level tags | +| `status` | `pending → downloaded → validated → normalized → deduped → filtered → kept` or `discarded` | +| `discard_reason` | why it was removed | +| `review_status` | reviewer outcome | +| `split` | `train` / `val` / `test` (set by §6) | +| `session_id`, `board_id` | for real-world captures | + +--- + +## 6. Real-World Data & Splitting + +### 6.1 Real-world capture set (closes the domain gap) + +Web results skew toward clean listing photos. The deployed app will see dirty, cluttered, phone-shot boards under poor lighting. + +- Capture **≥ 300 test images plus ≥ 300 additional training images** from recycling partners or a bench setup. +- Vary: phone models, lighting (daylight, warm, harsh flash, dim), backgrounds, distances (macro to whole-board), angles, board types, and conditions. +- Record `session_id` (one per visit/location/day) and `board_id` (one per physical board). Photograph each board from multiple angles. +- Capture at native phone resolution; keep originals. + +### 6.2 Split policy (leakage-safe) + +| Split | Composition | Rule | +| :--- | :--- | :--- | +| `train` | Web + real-world training sessions | ~80% of the non-test pool | +| `val` | Web + real-world training sessions | ~20% of the non-test pool; used for model selection | +| `test` | **Real-world sessions only, held out** | Never used for tuning or threshold selection; sessions not present in train/val | + +- Split by **group**, not by image: use `cluster_id` for web images and `board_id`/`session_id` for real-world images (`GroupShuffleSplit` or similar), so near-duplicates and same-board photos never straddle splits. +- Stratify on `class_hints` and `source_type` where feasible so val reflects the domain mix. +- Freeze the result in `splits/splits.json` with the random seed; regenerate only by explicit version bump. + +--- + +## 7. Annotation + +- Write `docs/labeling_guide.md` **before** labeling: definitions and 10+ positive/negative example crops per class, plus edge cases (partially occluded, damaged, blurry). +- **Label at full resolution** (never label downscaled copies). +- **Minimum object size policy:** objects smaller than ~16 px at the intended training resolution are either marked `ignore` or excluded consistently; document the choice. +- Boxes: tight; `gold_finger` boxes follow the actual (elongated) strip; no padding to squares. +- **Quality control:** double-annotate a 10% sample, compute IoU/agreement per class, and resolve disagreements to refine the guide. `desoldered_zone` is the class most likely to need a redefinition. +- Tools: CVAT or Roboflow. Export in YOLO format with the frozen splits. + +--- + +## 8. Training & Evaluation Plan + +### 8.1 Small-object strategy + +- Train with **`imgsz=1280`** (or 960 as a compromise) rather than the 640 default. +- For on-device use, avoid running the full nano model at 1280 on a phone. Two options to evaluate: + 1. **Tiled inference** (SAHI-style): overlapping 640 px tiles with merged detections. + 2. **Two-stage:** a light board detector at 640 → crop board → component detector on the crop. +- Consider mosaic/scale augmentation tuned for small objects; verify augmentations do not destroy `gold_finger` aspect ratios. + +### 8.2 Metrics + +- mAP@50 and mAP@50-95 **per class**, not just overall. +- **Recall by object-size bucket** (small / medium / large by pixel area), to expose the small-object problem. +- Report results on `val` and, separately, on the real-world `test` split. **The test-set number is the headline number.** +- BIS validation: compare BIS from ground-truth labels vs BIS from detections on the test set, and report the error. + +### 8.3 Export checks + +- Export to ONNX / TFLite and **re-evaluate on the test set after export and after quantization**; set an acceptable mAP-drop budget (e.g., ≤ 2 points) before shipping to `mobile/`. + +--- + +## 9. Technical Stack + +| Tool / Library | Role | +| :--- | :--- | +| Python 3.10+ | Runtime | +| `icrawler` | URL harvesting (Bing, Baidu, Flickr; Google optional) | +| `requests`, `tenacity` | Custom downloader with retry/backoff | +| `Pillow` | Validation, EXIF handling, normalization, ICC conversion | +| `numpy` | Vectorized hash comparison | +| `imagehash` | Perceptual hashing (pHash/dHash) | +| `hashlib` | Exact SHA-256 hashing | +| `open_clip_torch` + `torch` | Relevance filter and embeddings for clustering | +| `scikit-learn` | Group-aware splitting, clustering | +| `PyYAML` | Configuration | +| `tqdm` | Progress monitoring | +| `fiftyone` (optional) | Review UI, embedding visualization | +| `ultralytics`, `sahi` | Training and tiled inference | +| `pytest` | Tests | + +Pin exact versions in `requirements.txt` and record them in every report. + +--- + +## 10. Execution Plan + +### Step 1: Install + +```bash +pip install -r requirements.txt +``` + +### Step 2: Run stages (each is resumable) + +```bash +python3 scraper.py harvest --config config/config.yaml # collect URLs into manifest +python3 scraper.py download # fetch originals with retries +python3 scraper.py validate # integrity, size, aspect +python3 scraper.py process # EXIF fix, RGB, JPEG q95 +python3 scraper.py dedup # exact + perceptual + clusters +python3 scraper.py filter # CLIP relevance + review queue +python3 scraper.py split # cluster-aware splits +python3 scraper.py report # summary statistics +``` + +### Step 3: Verification report (`reports/summary.json` and printed table) + +Per `class_hint` and overall: + +- Raw URLs harvested, downloads succeeded/failed +- Discards **by reason** (corrupt, too small, low resolution, hard aspect, exact dup, near dup, irrelevant) +- Flagged and reviewed counts +- Final kept count vs target (§2.2) +- Resolution distribution and `source_type` mix (in-the-wild share vs the 40% goal) +- Number of duplicate clusters and largest cluster size +- Split sizes and a leakage check (no `cluster_id`/`board_id` shared across splits) + +--- + +## 11. Testing + +Unit tests using fixtures in `tests/fixtures/`: + +| Test | Expectation | +| :--- | :--- | +| Truncated JPEG | Rejected by the `verify()` + `load()` sequence | +| RGBA PNG with transparency | Composited on white, not black | +| EXIF-rotated phone photo | Output is upright | +| CMYK JPEG | Correct RGB conversion | +| Animated GIF | First frame used, `animated` flag set | +| 5:1 RAM image | Flagged, **not** discarded | +| 8:1 banner | Discarded with `extreme_aspect_hard` | +| Resized / recompressed / flipped copy of a fixture | Detected as near-duplicate | +| Rerun after interruption | No re-download, no duplicate rows | +| Leakage check | No shared `cluster_id` across splits | + +--- + +## 12. Milestones & Acceptance Criteria + +| Phase | Deliverable | Acceptance criterion | +| :--- | :--- | :--- | +| 0. Decisions | `taxonomy.yaml`, target counts, BIS definition in `docs/decisions.md` | Signed off by project owner | +| 1. Pipeline build | All modules + tests passing | Fixtures behave as specified in §11 | +| 2. Pilot | ~500-image end-to-end run | Attrition rate measured; CLIP thresholds tuned on 200 hand-labeled images | +| 3. Full collection | Harvest budget executed; real-world capture ≥ 600 images | Kept counts meet §2.2 or gaps identified and queries extended | +| 4. QA | Manual review complete; frozen `splits.json` | Leakage check clean; ≥ 40% in-the-wild | +| 5. Annotation | Labeled dataset + `labeling_guide.md` | Inter-annotator agreement acceptable per class | +| 6. Training & eval | Baseline model, per-class and size-bucket metrics | Test-set results reported; failure analysis written | +| 7. Export | ONNX/TFLite + post-quantization test results | mAP drop within budget; BIS error reported | + +--- + +## 13. Risks & Mitigations + +| Risk | Mitigation | +| :--- | :--- | +| Search engines return too few unique images per query | Wide query matrix (§5.1), multiple engines, multilingual queries, real-world capture | +| Heavy overrepresentation of clean listing photos | `source_type` monitoring; explicit in-the-wild target; real-world capture | +| Tiny objects (tantalum caps) undetectable | Higher min resolution, `imgsz=1280`, tiling/two-stage, small-object metrics | +| `desoldered_zone` label ambiguity | Precise definition, example gallery, double annotation, willingness to redefine or move to segmentation | +| Near-duplicate leakage inflates metrics | Cluster-aware splits, leakage check in CI, held-out real-world test set | +| CLIP filter discards valid images | Threshold tuned on hand-labeled sample; borderline goes to review, not discard | +| Class imbalance (ICs abundant, tantalum rare) | Per-class targets, targeted queries and captures for rare classes, per-class metrics | +| Quantization degrades small-object accuracy | Post-export evaluation with a mAP-drop budget; consider mixed precision | +| Pipeline state lost mid-run | SQLite manifest, idempotent stages, originals preserved | +| BIS unstable or not meaningful | Define and validate BIS against ground-truth labels early (§2.3, §8.2) | + +--- + +## 14. Immediate Next Actions + +1. Review and finalize the taxonomy and class definitions (§2.1). +2. Get domain input on BIS weights and whether BIS uses counts or area (§2.3). +3. Stand up the repository skeleton from §4, with config, manifest schema, and test fixtures. +4. Run the 500-image pilot to measure attrition and tune the relevance thresholds. +5. Schedule the first real-world capture session so the test set starts accumulating early. diff --git a/scratch/__pycache__/test_prompts.cpython-312-pytest-9.1.1.pyc b/scratch/__pycache__/test_prompts.cpython-312-pytest-9.1.1.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dec86ff8389643143c6c734cc0b0e32b0c4f8e3e Binary files /dev/null and b/scratch/__pycache__/test_prompts.cpython-312-pytest-9.1.1.pyc differ diff --git a/scratch/__pycache__/test_sahi_sample.cpython-312-pytest-9.1.1.pyc b/scratch/__pycache__/test_sahi_sample.cpython-312-pytest-9.1.1.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9eb2fb731d3588a76b77db5eaaeab86136cb968e Binary files /dev/null and b/scratch/__pycache__/test_sahi_sample.cpython-312-pytest-9.1.1.pyc differ diff --git a/scratch/test_prompts.py b/scratch/test_prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..4c37b080822f4f5057f32f5d39c4950442b2f863 --- /dev/null +++ b/scratch/test_prompts.py @@ -0,0 +1,79 @@ +"""Test prompt variations for YOLO-World zero-shot detection to maximize recall on micro-components and desoldered pads.""" +import os +from pathlib import Path +import torch +from torchvision.ops import nms +import numpy as np +from PIL import Image + +cache_dir = Path("data/cache").resolve() +cache_dir.mkdir(parents=True, exist_ok=True) +os.environ["TORCH_HOME"] = str(cache_dir) +os.environ["HF_HOME"] = str(cache_dir) + +from ultralytics import YOLOWorld + +CLASS_NAMES = [ + "cpu_socket", + "gold_finger", + "ic_chip", + "tantalum_capacitor", + "forcibly_desoldered_pad", + "factory_unpopulated_pad", + "occluded_region", +] + +# Alternative prompt sets +PROMPT_SETS = { + "Original": [ + "CPU socket", + "gold finger edge connector", + "microchip", + "tantalum capacitor", + "desoldered solder pad", + "unpopulated solder pad", + "heatsink shield", + ], + "Enriched_Visual": [ + "CPU socket square slot", + "gold edge connector pins", + "black IC microchip", + "yellow SMD capacitor", + "empty desoldered solder footprint", + "clean unpopulated solder pad", + "metal heatsink shield cover", + ], + "Short_Direct": [ + "processor socket", + "gold edge contacts", + "IC chip", + "tantalum capacitor", + "solder pad", + "empty pad", + "heatsink", + ] +} + +if __name__ == "__main__": + images = sorted(list(Path("data/images").glob("*.jpg")))[:50] + + for set_name, prompts in PROMPT_SETS.items(): + print(f"\n--- Testing Prompt Set: {set_name} ---") + model = YOLOWorld("yolov8s-worldv2.pt") + model.set_classes(prompts) + + counts = {name: 0 for name in CLASS_NAMES} + total_boxes = 0 + + for img_path in images: + results = model.predict(str(img_path), conf=0.015, verbose=False) + if results and len(results) > 0 and results[0].boxes is not None: + cls_ids = results[0].boxes.cls.cpu().numpy().astype(int) + for cls_id in cls_ids: + if 0 <= cls_id < len(CLASS_NAMES): + counts[CLASS_NAMES[cls_id]] += 1 + total_boxes += 1 + + print(f"Total boxes ({set_name}): {total_boxes}") + for k, v in counts.items(): + print(f" {k:25s}: {v}") diff --git a/scratch/test_sahi_sample.py b/scratch/test_sahi_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..331f31c38f9c2fae4df8b52a72a2490c73f462d9 --- /dev/null +++ b/scratch/test_sahi_sample.py @@ -0,0 +1,130 @@ +"""Test Sliced Patch Inference (SAHI) on a sample of images to verify detection recall across classes.""" +import os +from pathlib import Path +import torch +import torchvision +from torchvision.ops import nms +import numpy as np +from PIL import Image + +cache_dir = Path("data/cache").resolve() +cache_dir.mkdir(parents=True, exist_ok=True) +os.environ["TORCH_HOME"] = str(cache_dir) +os.environ["HF_HOME"] = str(cache_dir) + +from ultralytics import YOLOWorld + +CLASS_NAMES = [ + "cpu_socket", + "gold_finger", + "ic_chip", + "tantalum_capacitor", + "forcibly_desoldered_pad", + "factory_unpopulated_pad", + "occluded_region", +] + +CLASS_PROMPTS = [ + "CPU socket", + "gold finger edge connector", + "microchip", + "tantalum capacitor", + "desoldered solder pad", + "unpopulated solder pad", + "heatsink shield", +] + +def apply_nms(boxes_list: list, iou_threshold: float = 0.45) -> list: + if not boxes_list: + return [] + + boxes_t = torch.tensor([b[:4] for b in boxes_list], dtype=torch.float32) + scores_t = torch.tensor([b[4] for b in boxes_list], dtype=torch.float32) + cls_t = torch.tensor([b[5] for b in boxes_list], dtype=torch.int64) + + keep_indices = [] + unique_classes = torch.unique(cls_t) + for c in unique_classes: + c_mask = (cls_t == c) + c_idxs = torch.where(c_mask)[0] + c_boxes = boxes_t[c_idxs] + c_scores = scores_t[c_idxs] + + c_keep = nms(c_boxes, c_scores, iou_threshold) + keep_indices.extend(c_idxs[c_keep].tolist()) + + return [boxes_list[i] for i in keep_indices] + +def predict_sliced(model, img_path: Path, conf_threshold: float = 0.02, slice_size: int = 640, overlap: float = 0.2, iou_threshold: float = 0.45): + with Image.open(img_path) as img: + img_w, img_h = img.size + np_img = np.array(img.convert("RGB")) + + all_boxes = [] + + # 1. Full Image Pass + res_full = model.predict(np_img, conf=conf_threshold, verbose=False) + if res_full and len(res_full) > 0 and res_full[0].boxes is not None and len(res_full[0].boxes) > 0: + boxes_xyxy = res_full[0].boxes.xyxy.cpu().numpy() + confs = res_full[0].boxes.conf.cpu().numpy() + cls_ids = res_full[0].boxes.cls.cpu().numpy().astype(int) + for (x1, y1, x2, y2), c, cls_id in zip(boxes_xyxy, confs, cls_ids): + all_boxes.append([float(x1), float(y1), float(x2), float(y2), float(c), int(cls_id)]) + + # 2. Sliced Grid Pass + if img_w > slice_size or img_h > slice_size: + step_x = int(slice_size * (1 - overlap)) + step_y = int(slice_size * (1 - overlap)) + + x_offsets = list(range(0, max(1, img_w - slice_size + step_x), step_x)) + y_offsets = list(range(0, max(1, img_h - slice_size + step_y), step_y)) + + if x_offsets[-1] + slice_size < img_w: + x_offsets.append(img_w - slice_size) + if y_offsets[-1] + slice_size < img_h: + y_offsets.append(img_h - slice_size) + + for y0 in y_offsets: + for x0 in x_offsets: + x1_crop = min(x0 + slice_size, img_w) + y1_crop = min(y0 + slice_size, img_h) + patch = np_img[y0:y1_crop, x0:x1_crop] + + if patch.shape[0] < 100 or patch.shape[1] < 100: + continue + + res_patch = model.predict(patch, conf=conf_threshold, verbose=False) + if res_patch and len(res_patch) > 0 and res_patch[0].boxes is not None and len(res_patch[0].boxes) > 0: + p_boxes = res_patch[0].boxes.xyxy.cpu().numpy() + p_confs = res_patch[0].boxes.conf.cpu().numpy() + p_cls = res_patch[0].boxes.cls.cpu().numpy().astype(int) + + for (px1, py1, px2, py2), c, cls_id in zip(p_boxes, p_confs, p_cls): + gx1 = x0 + px1 + gy1 = y0 + py1 + gx2 = x0 + px2 + gy2 = y0 + py2 + all_boxes.append([float(gx1), float(gy1), float(gx2), float(gy2), float(c), int(cls_id)]) + + final_boxes = apply_nms(all_boxes, iou_threshold=iou_threshold) + return final_boxes, img_w, img_h + +if __name__ == "__main__": + images = sorted(list(Path("data/images").glob("*.jpg")))[:50] + print(f"Testing Sliced Inference on {len(images)} images...") + model = YOLOWorld("yolov8s-worldv2.pt") + model.set_classes(CLASS_PROMPTS) + + counts = {name: 0 for name in CLASS_NAMES} + total_boxes = 0 + for img_p in images: + boxes, w, h = predict_sliced(model, img_p, conf_threshold=0.02) + for b in boxes: + cls_name = CLASS_NAMES[b[5]] + counts[cls_name] += 1 + total_boxes += 1 + + print("Sample Results across 50 images:") + print("Total boxes:", total_boxes) + for k, v in counts.items(): + print(f" {k:25s}: 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