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/00_problem/input_data/data_parameters.md

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+# Input Data Parameters
+
+## Annotation Images
+
+- **Format**: JPEG
+- **Naming**: UUID-based (`{uuid}.jpg`)
+- **Source**: Azaion annotation platform via RabbitMQ Streams
+- **Volume**: Up to 360K+ annotations observed in training comments
+- **Delivery**: Real-time streaming via annotation queue consumer
+
+## Annotation Labels
+
+- **Format**: YOLO text format (one detection per line)
+- **Schema**: `{class_id} {center_x} {center_y} {width} {height}`
+- **Coordinate system**: All values normalized to 0–1 relative to image dimensions
+- **Constraints**: Coordinates must be in [0, 1]; labels with coords > 1.0 are treated as corrupted
+
+## Annotation Classes
+
+- **Source file**: `classes.json` (static, 17 entries)
+- **Schema per class**: `{ Id: int, Name: str, ShortName: str, Color: hex_str }`
+- **Classes**: ArmorVehicle, Truck, Vehicle, Artillery, Shadow, Trenches, MilitaryMan, TyreTracks, AdditArmoredTank, Smoke, Plane, Moto, CamouflageNet, CamouflageBranches, Roof, Building, Caponier
+- **Weather expansion**: Each class × 3 modes (Norm offset 0, Wint offset 20, Night offset 40)
+- **Total class IDs**: 80 slots (51 used, 29 reserved as placeholders)
+
+## Queue Messages
+
+- **Protocol**: AMQP via RabbitMQ Streams (rstream library)
+- **Serialization**: msgpack with positional integer keys
+- **Message types**: AnnotationMessage (single), AnnotationBulkMessage (batch validate/delete)
+- **Fields**: createdDate, name, originalMediaName, time, imageExtension, detections (JSON string), image (raw bytes), createdRole, createdEmail, source, status
+
+## Configuration Files
+
+| File | Format | Key Contents |
+|------|--------|-------------|
+| `config.yaml` | YAML | API URL, email, password, queue host/port/username/password, directory paths |
+| `cdn.yaml` | YAML | CDN endpoint, read access key/secret, write access key/secret, bucket name |
+| `classes.json` | JSON | Annotation class definitions array |
+| `checkpoint.txt` | Plain text | Last training run timestamp |
+| `offset.yaml` | YAML | Queue consumer offset for resume |
+
+## Video Input (Inference)
+
+- **Format**: Any OpenCV-supported video format
+- **Processing**: Every 4th frame sampled, batched in groups of 4
+- **Resolution**: Resized to model input size (1280×1280) during preprocessing