- Added a new Cython extension for the engine factory to the setup configuration.
- Updated the inference module to include additional logging for video batch processing and annotation callbacks.
- Refactored test cases to standardize the detection endpoint responses and include channel IDs in headers for better event handling.
- Updated the detection image endpoint to require a channel ID for event streaming.
- Introduced a new endpoint for streaming detection events, allowing clients to receive real-time updates.
- Enhanced the internal buffering mechanism for detection events to manage multiple channels.
- Refactored the inference module to support the new event handling structure.
Made-with: Cursor
- Introduced `AIAvailabilityStatus` class to manage the availability status of AI models, including methods for setting status and logging messages.
- Added `AIRecognitionConfig` class to encapsulate configuration parameters for AI recognition, with a static method for creating instances from dictionaries.
- Implemented enums for AI availability states to enhance clarity and maintainability.
- Updated related Cython files to support the new classes and ensure proper type handling.
These changes aim to improve the structure and functionality of the AI model management system, facilitating better status tracking and configuration handling.
- Updated various Cython files to explicitly cast types, enhancing type safety and readability.
- Adjusted the `engine_name` property in `InferenceEngine` and its subclasses to be set directly in the constructor.
- Modified the `request` method in `_SessionWithBase` to accept `*args` for better flexibility.
- Ensured proper type casting for return values in methods across multiple classes, including `Inference`, `CoreMLEngine`, and `TensorRTEngine`.
These changes aim to streamline the codebase and improve maintainability by enforcing consistent type usage.
Oleksandr Bezdieniezhnykh