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/07_inference/description.md

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+# Component: Inference Engine
+
+## Overview
+Real-time object detection inference subsystem supporting ONNX Runtime and TensorRT backends. Processes video streams with batched inference, custom NMS, and live visualization.
+
+**Pattern**: Strategy pattern (InferenceEngine ABC) + pipeline orchestrator
+**Upstream**: Core, Security, API & CDN (for model download)
+**Downstream**: None (end-user facing — processes video input)
+
+## Modules
+- `inference/dto` — Detection, Annotation, AnnotationClass data classes
+- `inference/onnx_engine` — InferenceEngine ABC + OnnxEngine implementation
+- `inference/tensorrt_engine` — TensorRTEngine implementation with CUDA memory management + ONNX converter
+- `inference/inference` — Video processing pipeline (preprocess → infer → postprocess → draw)
+- `start_inference` — Entry point: downloads model, initializes engine, runs on video
+
+## Internal Interfaces
+
+### InferenceEngine (ABC)
+```python
+InferenceEngine.__init__(model_path: str, batch_size: int = 1, **kwargs)
+InferenceEngine.get_input_shape() -> Tuple[int, int]
+InferenceEngine.get_batch_size() -> int
+InferenceEngine.run(input_data: np.ndarray) -> List[np.ndarray]
+```
+
+### OnnxEngine (extends InferenceEngine)
+Constructor takes `model_bytes` (not path). Uses CUDAExecutionProvider + CPUExecutionProvider.
+
+### TensorRTEngine (extends InferenceEngine)
+Constructor takes `model_bytes: bytes`. Additional static methods:
+```python
+TensorRTEngine.get_gpu_memory_bytes(device_id=0) -> int
+TensorRTEngine.get_engine_filename(device_id=0) -> str | None
+TensorRTEngine.convert_from_onnx(onnx_model: bytes) -> bytes | None
+```
+
+### Inference
+```python
+Inference(engine: InferenceEngine, confidence_threshold, iou_threshold)
+Inference.preprocess(frames: list) -> np.ndarray
+Inference.postprocess(batch_frames, batch_timestamps, output) -> list[Annotation]
+Inference.process(video: str) -> None
+Inference.draw(annotation: Annotation) -> None
+Inference.remove_overlapping_detections(detections) -> list[Detection]
+```
+
+## Data Access Patterns
+- Model bytes loaded by caller (start_inference via ApiClient.load_big_small_resource)
+- Video input via cv2.VideoCapture (file path)
+- No disk writes during inference
+
+## Implementation Details
+- **Video processing**: Every 4th frame processed (25% frame sampling), batched to engine batch size
+- **Preprocessing**: cv2.dnn.blobFromImage (1/255 scale, model input size, BGR→RGB)
+- **Postprocessing**: Raw detections filtered by confidence, coordinates normalized to [0,1], custom NMS applied
+- **Custom NMS**: Pairwise IoU comparison. Keeps higher confidence; ties broken by lower class ID.
+- **TensorRT**: Async CUDA execution (memcpy_htod_async → execute_async_v3 → synchronize → memcpy_dtoh)
+- **TensorRT shapes**: Default 1280×1280 input, 300 max detections, 6 values per detection (x1,y1,x2,y2,conf,cls)
+- **ONNX conversion**: TensorRT builder with 90% GPU memory workspace, FP16 if supported
+- **Engine filename**: GPU-architecture-specific: `azaion.cc_{major}.{minor}_sm_{sm_count}.engine`
+- **start_inference flow**: ApiClient → load encrypted TensorRT model (big/small split) → decrypt → TensorRTEngine → Inference.process()
+
+## Caveats
+- `start_inference.get_engine_filename()` duplicates `TensorRTEngine.get_engine_filename()`
+- Video path hardcoded in `start_inference` (`tests/ForAI_test.mp4`)
+- `inference/dto` has its own AnnotationClass — duplicated from `dto/annotationClass`
+- cv2.imshow display requires a GUI environment — won't work headless
+- TensorRT `batch_size` attribute used before assignment if engine input shape has dynamic batch — potential NameError
+
+## Dependency Graph
+```mermaid
+graph TD
+    inference_dto[inference/dto] --> inference_inference[inference/inference]
+    inference_onnx[inference/onnx_engine] --> inference_inference
+    inference_onnx --> inference_trt[inference/tensorrt_engine]
+    inference_trt --> start_inference
+    inference_inference --> start_inference
+    constants --> start_inference
+    api_client --> start_inference
+    security --> start_inference
+```
+
+## Logging Strategy
+Print statements for metadata, download progress, timing. cv2.imshow for visual output.