# SAM3 Metrics Evaluation - Implementation Status

**Date**: 2025-11-23
**Status**: Framework Complete - Ready for Implementation

## Summary

The metrics evaluation subproject has been fully planned and structured. All necessary components from the Road AI Analysis project have been copied, and the project is ready for systematic implementation.

## What Has Been Completed

### ✅ Project Structure
```
metrics_evaluation/
├── README.md                      # Complete documentation
├── TODO.md                        # Detailed 8-phase implementation plan
├── IMPLEMENTATION_STATUS.md       # This file
├── config/
│   ├── config.json               # Configuration with all parameters
│   ├── config_models.py          # Pydantic validation models
│   └── config_loader.py          # Config loading with validation
├── cvat_api/                     # Complete CVAT API client (copied)
├── schema/
│   └── cvat/                     # CVAT schemas (copied)
│   └── core/annotation/          # Mask and BoundingBox schemas (copied)
├── extraction/                   # Directory for CVAT extraction module
├── inference/                    # Directory for SAM3 inference module
├── metrics/                      # Directory for metrics calculation
├── visualization/                # Directory for visualization
└── utils/                        # Directory for utilities
```

### ✅ Dependencies Copied
- **CVAT API modules**: Complete client with auth, projects, tasks, annotations
- **CVAT schemas**: Pydantic models for all CVAT data structures
- **Mask schema**: Complete Mask class with CVAT RLE conversion methods
- **BoundingBox schema**: For bbox handling
- **.env file**: CVAT credentials at project root
- **CODE_GUIDE.md**: Development guidelines

### ✅ Configuration System
- JSON configuration file with all parameters
- Pydantic models for validation
- Config loader with clear error messages
- Supports:
  - CVAT connection settings
  - Class selection (Fissure, Nid de poule, Road)
  - SAM3 endpoint configuration
  - IoU thresholds for metrics
  - Output paths

### ✅ Planning Documents
- **README.md**: Complete user documentation
- **TODO.md**: Actionable 8-phase implementation plan with 40+ specific tasks
- Task breakdown for:
  - CVAT data extraction
  - SAM3 inference
  - Metrics computation
  - Visualization
  - Pipeline integration

## What Needs to Be Implemented

The TODO.md contains the complete implementation roadmap. Here's the high-level summary:

### Phase 1: CVAT Data Extraction (Priority 1)
**File**: `extraction/cvat_extractor.py`

**Key Functions**:
```python
def connect_to_cvat(config: CVATConfig) -> CVATClient:
    """Connect and authenticate to CVAT."""

def find_training_project(client: CVATClient, filter: str) -> Project:
    """Find project designated for AI training."""

def discover_images(client: CVATClient, project: Project, classes: dict) -> list[ImageMetadata]:
    """Find images with target labels, stratified sampling."""

def download_image(client: CVATClient, image_meta: ImageMetadata, output_dir: Path) -> Path:
    """Download JPG image, check cache first."""

def extract_ground_truth_masks(client: CVATClient, image_meta: ImageMetadata, output_dir: Path) -> list[Mask]:
    """Extract mask annotations, convert CVAT RLE to PNG."""
```

**Logic Flow**:
1. Load config and connect to CVAT using credentials from .env
2. List all projects, filter by name containing "training"
3. Query tasks/jobs in selected project
4. For each class (Fissure, Nid de poule, Road):
   - Find all images with that label
   - Randomly sample N images
5. For each selected image:
   - Check if already in cache (`.cache/test/metrics/{label}/{image_name}/`)
   - If not cached: download JPG
   - Extract annotations with type="mask"
   - For each mask annotation:
     - Get CVAT RLE data
     - Convert using `Mask.from_cvat_api_rle()`
     - Save as `ground_truth/mask_{label}_{idx}.png`
6. Create `metadata.json` for each image listing all masks

### Phase 2: SAM3 Inference (Priority 2)
**File**: `inference/sam3_inference.py`

**Key Functions**:
```python
def call_sam3_endpoint(image_path: Path, classes: list[str], config: SAM3Config) -> list[dict]:
    """Call SAM3 endpoint, handle retries."""

def convert_sam3_masks(response: list[dict], output_dir: Path) -> list[Mask]:
    """Convert base64 masks from SAM3 to PNG format."""

def run_inference_batch(image_paths: list[Path], config: EvaluationConfig) -> dict[str, list[Mask]]:
    """Run SAM3 inference on all images, check cache."""
```

**Logic Flow**:
1. For each image in cache:
   - Check if inference results exist
   - If not: load image, encode to base64
   - Call SAM3 endpoint with classes from config
   - Parse response, extract masks
   - Convert base64 masks to numpy arrays
   - Save as `inference/mask_{label}_{idx}.png`
2. Create metadata.json matching ground truth structure
3. Log API latency and errors

### Phase 3: Metrics Computation (Priority 3)
**File**: `metrics/metrics_calculator.py`

**Key Functions**:
```python
def match_instances(ground_truth: list[Mask], predicted: list[Mask], iou_threshold: float) -> dict:
    """Match GT to predictions using Hungarian algorithm."""

def compute_map_mar(matches: dict, ground_truth_count: int) -> tuple[float, float]:
    """Compute mAP and mAR."""

def compute_confusion_matrix(matches: dict, classes: list[str]) -> np.ndarray:
    """Generate confusion matrix."""

def compute_all_metrics(cache_dir: Path, config: EvaluationConfig) -> dict:
    """Compute all metrics across all images."""
```

**Metrics to Compute**:
- mAP (mean Average Precision)
- mAR (mean Average Recall)
- True Positives, False Positives, False Negatives at each IoU threshold
- Confusion matrices at 4 IoU thresholds
- Per-class precision, recall, F1-score
- Overall statistics

### Phase 4: Visualization (Priority 4)
**File**: `visualization/visual_comparison.py`

**Key Functions**:
```python
def create_comparison_image(image_path: Path, ground_truth_dir: Path, inference_dir: Path, output_path: Path):
    """Create side-by-side comparison with overlays."""
```

**Visualization**:
- Original image
- Ground truth masks (green overlay)
- Predicted masks (red overlay)
- Highlight TP (yellow), FP (red), FN (blue)

### Phase 5: Main Pipeline (Priority 5)
**File**: `run_evaluation.py`

**Main Function**:
```python
def main():
    # Load config
    # Connect to CVAT
    # Extract ground truth
    # Run SAM3 inference
    # Compute metrics
    # Generate report
    # Create visualizations
```

**Command-Line Interface**:
```python
parser.add_argument('--config', default='config/config.json')
parser.add_argument('--force-download', action='store_true')
parser.add_argument('--force-inference', action='store_true')
parser.add_argument('--skip-inference', action='store_true')
parser.add_argument('--visualize', action='store_true')
```

## Implementation Guidelines

### Code Quality (from CODE_GUIDE.md)
1. **Fail Fast**: Raise clear errors, never silently degrade
2. **Type Hints**: All function parameters and returns
3. **Pydantic**: Use for all data structures
4. **Validation**: Validate inputs and outputs
5. **Logging**: Extensive logging at INFO, WARNING, ERROR levels
6. **Error Messages**: Specific, actionable, contextual

### Example Implementation Pattern
```python
def extract_ground_truth_masks(
    annotations: list[dict],
    image_width: int,
    image_height: int,
    output_dir: Path
) -> list[Mask]:
    """Extract ground truth masks from CVAT annotations.

    Args:
        annotations: CVAT annotation data
        image_width: Image width in pixels
        image_height: Image height in pixels
        output_dir: Directory to save mask PNG files

    Returns:
        List of Mask instances with PNG files saved

    Raises:
        ValueError: If annotations are empty or invalid
        FileNotFoundError: If output directory cannot be created
    """
    if not annotations:
        raise ValueError("No annotations provided")

    if image_width <= 0 or image_height <= 0:
        raise ValueError(f"Invalid image dimensions: {image_width}x{image_height}")

    output_dir.mkdir(parents=True, exist_ok=True)

    masks = []
    for idx, ann in enumerate(annotations):
        if ann['type'] != 'mask':
            continue

        cvat_rle = ann['points']  # CVAT RLE format
        label = ann['label']

        # Convert CVAT RLE to Mask
        mask_path = output_dir / f"mask_{label}_{idx}.png"
        mask = Mask.from_cvat_api_rle(
            cvat_rle=cvat_rle,
            width=image_width,
            height=image_height,
            file_path=mask_path
        )
        masks.append(mask)

    if not masks:
        raise ValueError("No mask annotations found in provided data")

    return masks
```

## Testing Strategy

1. **Unit Tests**: Test each module independently
   - Mock CVAT API responses
   - Test mask conversion
   - Test metrics calculation

2. **Integration Test**: Small dataset (5 images per class)
   - Verify end-to-end pipeline
   - Check output file generation
   - Validate metrics ranges

3. **Full Evaluation**: Complete dataset (50 images per class)
   - Monitor execution
   - Review results
   - Generate report

## Expected Issues and Solutions

### Issue: CVAT Project Not Found
**Solution**: Log all project names, check filter string in config.json

### Issue: Few Images With Target Labels
**Solution**: Log available counts, proceed with available data

### Issue: SAM3 API Timeouts
**Solution**: Implement retry with exponential backoff, continue with remaining images

### Issue: Mask Dimension Mismatch
**Solution**: Validate dimensions, resize if needed, log warnings

### Issue: Low Metrics Values
**Solution**: Expected initially, document in report, recommend fine-tuning

## Next Steps for Autonomous Implementation

1. **Start with extraction module** (`extraction/cvat_extractor.py`)
   - Test CVAT connection first
   - Implement project discovery
   - Add image download with caching
   - Test on 1-2 images before full extraction

2. **Then inference module** (`inference/sam3_inference.py`)
   - Test endpoint connectivity
   - Implement single image inference
   - Add batch processing with progress
   - Test on extraction results

3. **Then metrics module** (`metrics/metrics_calculator.py`)
   - Implement instance matching
   - Add metric computation functions
   - Test on sample data

4. **Then visualization** (`visualization/visual_comparison.py`)
   - Create basic overlay function
   - Test on few images

5. **Finally main pipeline** (`run_evaluation.py`)
   - Integrate all modules
   - Add CLI
   - Add logging
   - Run full evaluation

## Success Criteria

- [ ] Successfully extract 150 images from CVAT
- [ ] All ground truth masks saved correctly
- [ ] SAM3 inference completes for all images
- [ ] Metrics computed without errors
- [ ] Confusion matrices generated for all IoU thresholds
- [ ] Visual comparisons created
- [ ] Comprehensive report generated
- [ ] All results reviewed and validated

## Time Estimate

- **Implementation**: 8-10 hours
- **Testing**: 2-3 hours
- **Full Evaluation Run**: 20-30 minutes
- **Results Review**: 1-2 hours
- **Report Writing**: 1-2 hours
- **Total**: 12-18 hours

## Files to Create

1. `extraction/cvat_extractor.py` (~300-400 lines)
2. `inference/sam3_inference.py` (~200-300 lines)
3. `metrics/metrics_calculator.py` (~400-500 lines)
4. `metrics/confusion_matrix.py` (~150-200 lines)
5. `visualization/visual_comparison.py` (~200-250 lines)
6. `utils/logging_config.py` (~100 lines)
7. `run_evaluation.py` (~300-400 lines)

**Total**: ~1,650-2,250 lines of quality Python code

## Current Status: READY FOR IMPLEMENTATION

All planning, structure, and dependencies are in place. The implementation can proceed systematically following the TODO.md roadmap.

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**Note**: This is a senior-level autonomous task. Implementation should follow CODE_GUIDE.md principles, include extensive error handling and logging, and produce production-quality code that will be maintained long-term.