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/acceptance_criteria.md

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+# Acceptance Criteria
+
+## Training
+
+- Dataset split: 70% train, 20% validation, 10% test (hardcoded in train.py).
+- Training parameters: YOLOv11 medium, 120 epochs, batch size 11, image size 1280px, save_period=1.
+- Corrupted labels (bounding box coordinates > 1.0) are filtered to `/azaion/data-corrupted/`.
+- Model export to ONNX: 1280px resolution, batch size 4, NMS baked in.
+- Trained model encrypted with AES-256-CBC before upload.
+- Encrypted model split: small part ≤3KB or 20% of total → API server; remainder → CDN.
+- Post-training: model uploaded to both API and CDN endpoints.
+
+## Augmentation
+
+- Each validated image produces exactly 8 outputs (1 original + 7 augmented variants).
+- Augmentation runs every 5 minutes, processing only unprocessed images.
+- Bounding boxes clipped to [0, 1] range; boxes with area < 0.01% of image discarded.
+- Processing is parallelized per image using ThreadPoolExecutor.
+
+## Annotation Ingestion
+
+- Created/Edited annotations from Validators/Admins → `/azaion/data/`.
+- Created/Edited annotations from Operators → `/azaion/data-seed/`.
+- Validated (bulk) events → move from `/data-seed/` to `/data/`.
+- Deleted (bulk) events → move to `/data_deleted/`.
+- Queue consumer offset persisted to `offset.yaml` after each message.
+
+## Inference
+
+- TensorRT inference: ~54s for 200s video, ~3.7GB VRAM.
+- ONNX inference: ~81s for 200s video, ~6.3GB VRAM.
+- Frame sampling: every 4th frame.
+- Batch size: 4 (for both ONNX and TensorRT).
+- Confidence threshold: 0.3 (hardcoded in inference/inference.py).
+- NMS IoU threshold: 0.3 (hardcoded in inference/inference.py).
+- Overlapping detection removal: IoU > 0.3 with lower confidence removed.
+
+## Security
+
+- API authentication via JWT (email/password login).
+- Model encryption: AES-256-CBC with static key.
+- Resource encryption: AES-256-CBC with hardware-derived key (CPU+GPU+RAM+drive serial hash).
+- CDN access: separate read/write S3 credentials.
+- Split-model storage: prevents model theft from single storage compromise.
+
+## Data Format
+
+- Annotation format: YOLO (class_id center_x center_y width height — all normalized 0–1).
+- 17 base annotation classes × 3 weather modes = 51 active classes (80 total slots).
+- Image format: JPEG.
+- Queue message format: msgpack with positional integer keys.