# 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.
