# Component: Data Models

## Overview
Shared data transfer objects for the training pipeline: annotation class definitions (with weather modes) and image+label containers for visualization and augmentation.

**Pattern**: Plain data classes / value objects
**Upstream**: None (leaf)
**Downstream**: Data Pipeline (augmentation, dataset-visualiser), Training (YAML generation)

## Modules
- `dto/annotationClass` — AnnotationClass, WeatherMode enum, classes.json reader
- `dto/imageLabel` — ImageLabel container with bbox visualization

## Internal Interfaces

### WeatherMode (Enum)
| Member | Value | Description |
|--------|-------|-------------|
| Norm | 0 | Normal weather |
| Wint | 20 | Winter |
| Night | 40 | Night |

### AnnotationClass
```python
AnnotationClass(id: int, name: str, color: str)
AnnotationClass.read_json() -> dict[int, AnnotationClass]  # static
AnnotationClass.color_tuple -> tuple  # property, RGB ints
```

### ImageLabel
```python
ImageLabel(image_path: str, image: np.ndarray, labels_path: str, labels: list)
ImageLabel.visualize(annotation_classes: dict) -> None
```

## Data Access Patterns
- `AnnotationClass.read_json()` reads `classes.json` from project root (relative to `dto/` parent)
- `ImageLabel.visualize()` renders to matplotlib window (no disk I/O)

## Implementation Details
- 17 base annotation classes × 3 weather modes = 51 classes with offset IDs (0–16, 20–36, 40–56)
- System reserves 80 class slots (DEFAULT_CLASS_NUM in train.py)
- YOLO label format: [x_center, y_center, width, height, class_id] — all normalized 0–1
- `color_tuple` parsing strips first 3 chars (assumes "#ff" prefix format) — fragile if color format changes

## Caveats
- `AnnotationClass` duplicated in 3 locations (dto, inference/dto, annotation-queue/annotation_queue_dto) with slight differences
- `color_tuple` property has a non-obvious parsing approach that may break on different color string formats
- Empty files: `dto/annotation_bulk_message.py` and `dto/annotation_message.py` suggest planned but unimplemented DTOs

## Dependency Graph
```mermaid
graph TD
    dto_annotationClass[dto/annotationClass] --> train
    dto_annotationClass --> dataset-visualiser
    dto_imageLabel[dto/imageLabel] --> augmentation
    dto_imageLabel --> dataset-visualiser
```

## Logging Strategy
None.