Refactor constants management to use Pydantic BaseModel for configuration

- Replaced module-level path variables in constants.py with a structured Pydantic Config class.
- Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure.
- Fixed bugs related to image processing and model saving.
- Enhanced test infrastructure to accommodate the new configuration approach.

This refactor improves code maintainability and clarity by centralizing configuration management.

_docs/02_document/components/04_data_models/description.md

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+# 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.