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Vision Processor is used to implement model preprocessing, postprocessing, etc. The following 3rd party vision libraries are integrated:
- OpenCV, general CPU image processing
- FlyCV, mainly optimized for ARM CPU
- CV-CUDA, for NVIDIA GPU
C++ API, Currently supported operators are as follows:
- Cast
- CenterCrop
- HWC2CHW
- Resize
- ResizeByShort
- NormalizeAndPermute
- Normalize
- Pad
- PadToSize
- StridePad
Users can inherit ProcessorManager when creating a Preprocessor class in the C++ deployment of the visual class model, and can choose to use OpenCV or CV-CUDA through UseCuda() in the ProcessorManager base class. The base class ProcessorManager implements GPU memory management, CUDA stream management, etc. Users only need to implement the Apply() function, in which operators in the multi-hardware image processing library are called to implement processing logic. For specific implementation, please refer to the sample code.
Python API, Currently supported operators are as follows:
- Cast
- CenterCrop
- HWC2CHW
- Resize
- ResizeByShort
- NormalizeAndPermute
- Normalize
- Pad
- PadToSize
- StridePad
Users can implement a image processing modules by inheriting the PyProcessorManager class. The base class PyProcessorManager implements GPU memory management, CUDA stream management, etc. Users only need to implement the apply() function by calling vision processors in this library and implements processing logic. For specific implementation, please refer to the demo code.
CPU: Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz
GPU: T4
CUDA: 11.6
Processing logic: Resize -> NormalizeAndPermute
Warmup 100 rounds，tested 1000 rounds and get avg. latency.
| Input Image Shape | Target shape | Batch Size | OpenCV | CV-CUDA | Gain |
|---|---|---|---|---|---|
| 1920x1080 | 640x360 | 1 | 1.1572ms | 0.9067ms | 16.44% |
| 1280x720 | 640x360 | 1 | 2.7551ms | 0.5296ms | 80.78% |
| 360x240 | 640x360 | 1 | 3.3450ms | 0.2421ms | 92.76% |