ea0dd7456c
Backend implementation for add-layout-preprocessing proposal:
- Add LayoutPreprocessingService with CLAHE, sharpen, binarize
- Add auto-detection: analyze_image_quality() for contrast/edge metrics
- Integrate preprocessing into OCR pipeline (analyze_layout)
- Add Preview API: POST /api/v2/tasks/{id}/preview/preprocessing
- Add config options: layout_preprocessing_mode, thresholds
- Add schemas: PreprocessingConfig, PreprocessingPreviewResponse
Preprocessing only affects layout detection input.
Original images preserved for element extraction.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
126 lines
4.7 KiB
Markdown
126 lines
4.7 KiB
Markdown
# Tasks: Add Image Preprocessing for Layout Detection
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## 1. Configuration
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- [ ] 1.1 Add preprocessing configuration to `backend/app/core/config.py`
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- `layout_preprocessing_mode: str = "auto"` - Options: auto, manual, disabled
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- `layout_preprocessing_contrast: str = "clahe"` - Options: none, histogram, clahe
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- `layout_preprocessing_sharpen: bool = True` - Enable sharpening for faint lines
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- `layout_preprocessing_binarize: bool = False` - Optional binarization (aggressive)
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- [ ] 1.2 Add preprocessing schema to `backend/app/schemas/task.py`
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- `PreprocessingMode` enum: auto, manual, disabled
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- `PreprocessingConfig` schema for API request/response
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## 2. Preprocessing Service
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- [ ] 2.1 Create `backend/app/services/preprocessing_service.py`
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- Image loading utility (supports PIL, OpenCV)
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- Contrast enhancement methods (histogram equalization, CLAHE)
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- Sharpening filter for line enhancement
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- Optional adaptive binarization
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- Return preprocessed image as numpy array or PIL Image
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- [ ] 2.2 Implement `enhance_for_layout_detection()` function
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- Input: Original image path or PIL Image + config
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- Output: Preprocessed image (same format as input)
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- Steps: contrast → sharpen → (optional) binarize
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- [ ] 2.3 Implement `analyze_image_quality()` function (Auto mode)
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- Calculate contrast level (standard deviation of grayscale)
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- Detect edge clarity (Sobel/Canny edge strength)
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- Return recommended `PreprocessingConfig` based on analysis
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- Thresholds:
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- Low contrast < 40: Apply CLAHE
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- Faint edges < 0.1: Apply sharpen
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- Very low contrast < 20: Consider binarize
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## 3. Integration with OCR Service
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- [ ] 3.1 Update `backend/app/services/ocr_service.py`
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- Import preprocessing service
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- Check preprocessing mode (auto/manual/disabled)
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- If auto: call `analyze_image_quality()` first
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- Before `_run_ppstructure()`, preprocess image based on config
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- Pass preprocessed image to PP-Structure for layout detection
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- Keep original image reference for image extraction
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- [ ] 3.2 Ensure image element extraction uses original
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- Verify `saved_path` and `img_path` in elements reference original
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- Bbox coordinates from preprocessed detection applied to original crop
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- [ ] 3.3 Update task start API to accept preprocessing options
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- Add `preprocessing_mode` parameter to start request
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- Add `preprocessing_config` for manual mode overrides
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## 4. Preview API
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- [ ] 4.1 Create `backend/app/api/v2/endpoints/preview.py`
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- `POST /api/v2/tasks/{task_id}/preview/preprocessing`
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- Input: page number, preprocessing config (optional)
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- Output:
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- Original image (base64 or URL)
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- Preprocessed image (base64 or URL)
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- Auto-detected config (if mode=auto)
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- Image quality metrics (contrast, edge_strength)
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- [ ] 4.2 Add preview router to API
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- Register in `backend/app/api/v2/api.py`
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- Add appropriate authentication/authorization
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## 5. Frontend UI
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- [ ] 5.1 Create `frontend/src/components/PreprocessingSettings.tsx`
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- Radio buttons: Auto / Manual / Disabled
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- Manual mode shows:
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- Contrast dropdown: None / Histogram / CLAHE
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- Sharpen checkbox
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- Binarize checkbox
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- Preview button to trigger comparison view
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- [ ] 5.2 Create `frontend/src/components/PreprocessingPreview.tsx`
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- Side-by-side image comparison (original vs preprocessed)
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- Display detected quality metrics
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- Show which auto settings would be applied
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- Slider or toggle to switch between views
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- [ ] 5.3 Integrate with task start flow
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- Add PreprocessingSettings to OCR track options
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- Pass selected config to task start API
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- Store user preference in localStorage
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- [ ] 5.4 Add i18n translations
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- `frontend/src/i18n/locales/zh-TW.json` - Traditional Chinese
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- `frontend/src/i18n/locales/en.json` - English (if exists)
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## 6. Testing (with env)
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- [ ] 6.1 Unit tests for preprocessing_service
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- Test contrast enhancement methods
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- Test sharpening filter
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- Test binarization
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- Test `analyze_image_quality()` with various images
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- Test with various image formats (PNG, JPEG)
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- [ ] 6.2 Unit tests for preview API
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- Test preview endpoint returns correct images
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- Test auto-detection returns sensible config
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- [ ] 6.3 Integration tests (accountL ymirliu@panjit.com.tw ; password: 4RFV5tgb6yhn)
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- Test OCR track with preprocessing modes (auto/manual/disabled)
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- Verify image element quality is preserved
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- Test with known problematic documents (faint table borders)
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- Verify auto mode improves detection for low-quality images
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## 7. Documentation
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- [ ] 7.1 Update API documentation
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- Document new configuration options
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- Document preview endpoint
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- Explain preprocessing behavior and modes
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- [ ] 7.2 Add user guide section
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- When to use auto vs manual
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- How to interpret quality metrics
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- Troubleshooting tips
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