mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 21:56:36 +00:00
Oleksandr Bezdieniezhnykh
142c6c4de8
- 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.
49 lines
2.4 KiB
Markdown
49 lines
2.4 KiB
Markdown
# Module: inference/inference
|
|
|
|
## Purpose
|
|
High-level video inference pipeline. Orchestrates preprocessing → engine inference → postprocessing → visualization for object detection on video streams.
|
|
|
|
## Public Interface
|
|
|
|
### Inference
|
|
| Method | Signature | Returns | Description |
|
|
|--------|-----------|---------|-------------|
|
|
| `__init__` | `(engine: InferenceEngine, confidence_threshold, iou_threshold)` | — | Stores engine, thresholds, loads annotation classes |
|
|
| `preprocess` | `(frames: list) -> np.ndarray` | Batched blob tensor | Normalizes, resizes, and stacks frames into NCHW blob |
|
|
| `postprocess` | `(batch_frames, batch_timestamps, output) -> list[Annotation]` | Annotations per frame | Extracts detections from raw output, applies confidence filter and NMS |
|
|
| `process` | `(video: str)` | — | End-to-end: reads video → batched inference → draws + displays results |
|
|
| `draw` | `(annotation: Annotation)` | — | Draws bounding boxes with class labels on frame, shows via cv2.imshow |
|
|
| `remove_overlapping_detections` | `(detections: list[Detection]) -> list[Detection]` | Filtered list | Custom NMS: removes overlapping detections keeping higher confidence |
|
|
|
|
## Internal Logic
|
|
- **Video processing**: Reads video via cv2.VideoCapture, processes every 4th frame (frame_count % 4), batches frames to engine batch size.
|
|
- **Preprocessing**: `cv2.dnn.blobFromImage` with 1/255 scaling, model input size, BGR→RGB swap.
|
|
- **Postprocessing**: Iterates raw output, filters by confidence threshold, normalizes coordinates from model space to [0,1], creates Detection objects, applies custom NMS.
|
|
- **Custom NMS**: Pairwise IoU comparison. When two detections overlap above threshold, keeps the one with higher confidence (ties broken by lower class ID).
|
|
- **Visualization**: Draws colored rectangles and confidence labels using annotation class colors in OpenCV window.
|
|
|
|
## Dependencies
|
|
- `inference/dto` — Detection, Annotation, AnnotationClass
|
|
- `inference/onnx_engine` — InferenceEngine ABC (type hint)
|
|
- `cv2` (external) — video I/O, image processing, display
|
|
- `numpy` (external) — tensor operations
|
|
|
|
## Consumers
|
|
start_inference
|
|
|
|
## Data Models
|
|
Uses Detection, Annotation from `inference/dto`.
|
|
|
|
## Configuration
|
|
`confidence_threshold` and `iou_threshold` set at construction.
|
|
|
|
## External Integrations
|
|
- OpenCV video capture (file or stream input)
|
|
- OpenCV GUI window for real-time display
|
|
|
|
## Security
|
|
None.
|
|
|
|
## Tests
|
|
None.
|