FastDeploy/tutorials/vision_processor

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Vision Processor

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++

TODO(guxukai)

Python

Python API, Currently supported operators are as follows:

• ResizeByShort
• NormalizeAndPermute

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.

Demo

• Python Demo

Performance comparison between CV-CUDA and OpenCV:

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%