- Introduced VideoPlayer class to handle local video playback, emitting frames via frame_ready signal.
- Updated MainWindow to switch between camera and video sources, integrating video playback controls.
- Enhanced AppMenuBar with options to open video files and manage inference models.
- Implemented BboxOverlay for displaying detection results on video frames.
- Added InferenceManager to manage YOLO inference in a separate process, with error handling and restart logic.
- Created tests for BboxOverlay and InferenceManager to ensure functionality and robustness.
- Updated pyproject.toml to include optional dependencies for inference support.
- Implemented CameraSettingsDialog to manage UVC and Qt camera controls.
- Integrated UVC parameter sliders and auto controls for brightness, contrast, saturation, hue, sharpness, gamma, white balance, backlight compensation, and exposure.
- Added functionality to change white balance and exposure settings via Qt controls.
- Updated MainWindow to open CameraSettingsDialog and manage UVC controller lifecycle.
- Enhanced AppMenuBar to include a Camera Settings option.
- Created tests for UVC controller abstraction layer and parameter info.
- Documented camera specifications and supported features in new markdown files.
- Add FrameDispatcher for distributing QVideoFrames to subscribers
- Implement TelemetryCollector to measure video pipeline performance metrics
- Create MainWindow as the main application interface with video rendering
- Develop AppMenuBar for camera selection, resolution, and FPS settings
- Establish overlay system for displaying telemetry metrics
- Set up project structure and configuration files
- Add unit tests for FrameDispatcher and TelemetryCollector
