# Tasks: Add Image Preprocessing for Layout Detection

## 1. Configuration

- [x] 1.1 Add preprocessing configuration to `backend/app/core/config.py`
  - `layout_preprocessing_mode: str = "auto"` - Options: auto, manual, disabled
  - `layout_preprocessing_contrast: str = "clahe"` - Options: none, histogram, clahe
  - `layout_preprocessing_sharpen: bool = True` - Enable sharpening for faint lines
  - `layout_preprocessing_binarize: bool = False` - Optional binarization (aggressive)

- [x] 1.2 Add preprocessing schema to `backend/app/schemas/task.py`
  - `PreprocessingMode` enum: auto, manual, disabled
  - `PreprocessingConfig` schema for API request/response

## 2. Preprocessing Service

- [x] 2.1 Create `backend/app/services/layout_preprocessing_service.py`
  - Image loading utility (supports PIL, OpenCV)
  - Contrast enhancement methods (histogram equalization, CLAHE)
  - Sharpening filter for line enhancement
  - Optional adaptive binarization
  - Return preprocessed image as numpy array or PIL Image

- [x] 2.2 Implement `preprocess()` and `preprocess_to_pil()` functions
  - Input: Original image path or PIL Image + config
  - Output: Preprocessed image (same format as input) + PreprocessingResult
  - Steps: contrast → sharpen → (optional) binarize

- [x] 2.3 Implement `analyze_image_quality()` function (Auto mode)
  - Calculate contrast level (standard deviation of grayscale)
  - Detect edge clarity (Sobel gradient mean)
  - Return ImageQualityMetrics based on analysis
  - `get_auto_config()` returns PreprocessingConfig based on thresholds:
    - Low contrast < 40: Apply CLAHE
    - Faint edges < 15: Apply sharpen
    - Very low contrast < 20: Consider binarize

## 3. Integration with OCR Service

- [x] 3.1 Update `backend/app/services/ocr_service.py`
  - Import preprocessing service
  - Check preprocessing mode (auto/manual/disabled)
  - If auto: call `analyze_image_quality()` first
  - Before PP-Structure prediction, preprocess image based on config
  - Pass preprocessed PIL Image to PP-Structure for layout detection
  - Keep original image reference for image extraction

- [x] 3.2 Update `backend/app/services/pp_structure_enhanced.py`
  - Add `preprocessed_image` parameter to `analyze_with_full_structure()`
  - When preprocessed_image provided, convert to BGR numpy array and pass to PP-Structure
  - Bbox coordinates from preprocessed detection applied to original image crop

- [x] 3.3 Update task start API to accept preprocessing options
  - Add `preprocessing_mode` parameter to ProcessingOptions
  - Add `preprocessing_config` for manual mode overrides

## 4. Preview API

- [x] 4.1 Create preview endpoints in `backend/app/routers/tasks.py`
  - `POST /api/v2/tasks/{task_id}/preview/preprocessing`
  - Input: page number, preprocessing mode/config
  - Output: PreprocessingPreviewResponse with:
    - Original image URL
    - Preprocessed image URL
    - Auto-detected config
    - Image quality metrics (contrast, edge_strength)
  - `GET /api/v2/tasks/{task_id}/preview/image` - Serve preview images

- [x] 4.2 Add preview router functionality
  - Integrated into tasks router
  - Uses task authentication/authorization

## 5. Frontend UI

- [x] 5.1 Create `frontend/src/components/PreprocessingSettings.tsx`
  - Radio buttons with icons: Auto / Manual / Disabled
  - Manual mode shows:
    - Contrast dropdown: None / Histogram / CLAHE
    - Sharpen checkbox
    - Binarize checkbox (with warning)
  - Preview button integration (onPreview prop)

- [ ] 5.2 Create `frontend/src/components/PreprocessingPreview.tsx` (optional)
  - Side-by-side image comparison (original vs preprocessed)
  - Display detected quality metrics
  - Note: Preview functionality available via API, UI modal is optional enhancement

- [x] 5.3 Integrate with task start flow
  - Added PreprocessingSettings to ProcessingPage.tsx
  - Pass selected config to task start API
  - Note: localStorage preference storage is optional enhancement

- [x] 5.4 Add i18n translations
  - `frontend/src/i18n/locales/zh-TW.json` - Traditional Chinese

## 6. Testing

- [x] 6.1 Unit tests for preprocessing_service
  - Validated imports and service creation
  - Tested `analyze_image_quality()` with test images
  - Tested `get_auto_config()` returns sensible config
  - Tested `preprocess()` produces correct output shape

- [ ] 6.2 Integration tests for preview API (optional)
  - Manual testing recommended with actual documents

- [ ] 6.3 End-to-end testing
  - Test OCR track with preprocessing modes (auto/manual/disabled)
  - Test with known problematic documents (faint table borders)

## 7. Documentation

- [x] 7.1 Update API documentation
  - Schemas documented in task.py with Field descriptions
  - Preview endpoint accessible via /docs

- [ ] 7.2 Add user guide section (optional)
  - When to use auto vs manual
  - How to interpret quality metrics

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## Implementation Summary

**Backend commits:**
1. `feat: implement layout preprocessing backend` - Core service, OCR integration, preview API

**Frontend commits:**
1. `feat: add preprocessing UI components and integration` - PreprocessingSettings, i18n, ProcessingPage integration

**Key files created/modified:**
- `backend/app/services/layout_preprocessing_service.py` (new)
- `backend/app/core/config.py` (updated)
- `backend/app/schemas/task.py` (updated)
- `backend/app/services/ocr_service.py` (updated)
- `backend/app/services/pp_structure_enhanced.py` (updated)
- `backend/app/routers/tasks.py` (updated)
- `frontend/src/components/PreprocessingSettings.tsx` (new)
- `frontend/src/types/apiV2.ts` (updated)
- `frontend/src/pages/ProcessingPage.tsx` (updated)
- `frontend/src/i18n/locales/zh-TW.json` (updated)