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/modules/train.md

@@ -0,0 +1,61 @@
+# Module: train
+
+## Purpose
+Main training pipeline. Forms YOLO datasets from processed annotations, trains YOLOv11 models, and exports/uploads the trained model.
+
+## Public Interface
+
+| Function | Signature | Returns | Description |
+|----------|-----------|---------|-------------|
+| `form_dataset` | `()` | — | Creates train/valid/test split from processed images |
+| `copy_annotations` | `(images, folder: str)` | — | Copies image+label pairs to a dataset split folder (concurrent) |
+| `check_label` | `(label_path: str) -> bool` | bool | Validates YOLO label file (all coords ≤ 1.0) |
+| `create_yaml` | `()` | — | Generates YOLO `data.yaml` with class names from `classes.json` |
+| `resume_training` | `(last_pt_path: str)` | — | Resumes training from a checkpoint |
+| `train_dataset` | `()` | — | Full pipeline: form_dataset → create_yaml → train YOLOv11 → save model |
+| `export_current_model` | `()` | — | Exports current .pt to ONNX, encrypts, uploads as split resource |
+
+## Internal Logic
+- **Dataset formation**: Shuffles all processed images, splits 70/20/10 (train/valid/test). Copies in parallel via ThreadPoolExecutor. Corrupted labels (coords > 1.0) are moved to `/azaion/data-corrupted/`.
+- **YAML generation**: Reads annotation classes from `classes.json`, builds `data.yaml` with 80 class names (17 actual + 63 placeholders "Class-N"), sets train/valid/test paths.
+- **Training**: YOLOv11 medium (`yolo11m.yaml`), 120 epochs, batch=11 (tuned for 24GB VRAM), 1280px input, save every epoch, 24 workers.
+- **Post-training**: Copies results to `/azaion/models/{date}/`, removes intermediate epoch checkpoints, copies `best.pt` to `CURRENT_PT_MODEL`.
+- **Export**: Calls `export_onnx`, reads the ONNX file, encrypts with model key, uploads via `upload_big_small_resource`.
+- **Dataset naming**: `azaion-{YYYY-MM-DD}` using current date.
+- **`__main__`**: Runs `train_dataset()` then `export_current_model()`.
+
+## Dependencies
+- `constants` — all directory/path constants
+- `api_client` — ApiClient for model upload
+- `cdn_manager` — CDNCredentials, CDNManager (imported but CDN init done via api_client)
+- `dto/annotationClass` — AnnotationClass for class name generation
+- `inference/onnx_engine` — OnnxEngine (imported but unused in current code)
+- `security` — model encryption key
+- `utils` — Dotdict
+- `exports` — export_tensorrt, upload_model, export_onnx
+- `ultralytics` (external) — YOLO training and export
+- `yaml`, `concurrent.futures`, `glob`, `os`, `random`, `shutil`, `subprocess`, `datetime`, `pathlib`, `time` (stdlib)
+
+## Consumers
+manual_run
+
+## Data Models
+Uses AnnotationClass for class definitions.
+
+## Configuration
+- Training hyperparameters hardcoded: epochs=120, batch=11, imgsz=1280, save_period=1, workers=24
+- Dataset split ratios: train_set=70, valid_set=20, test_set=10
+- old_images_percentage=75 (declared but unused)
+- DEFAULT_CLASS_NUM=80
+
+## External Integrations
+- Ultralytics YOLOv11 training pipeline
+- Azaion API + CDN for model upload
+- Filesystem: `/azaion/datasets/`, `/azaion/models/`, `/azaion/data-processed/`, `/azaion/data-corrupted/`
+
+## Security
+- Trained models are encrypted before upload
+- Uses `Security.get_model_encryption_key()` for encryption
+
+## Tests
+None.