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FastDeploy/tutorials/vision_processor/README_CN.md
guxukai c6480de736 [CVCUDA] Vision Processor Python API and Tutorial (#1394) 
* bind success

* bind success fix

* FDMat pybind, ResizeByShort pybind

* FDMat pybind, ResizeByShort pybind, remove initialized_

* override BindProcessorManager::Run in python is available

* PyProcessorManager done

* vision_pybind fix

* manager.py fix

* add tutorials

* remove Apply() bind

* remove Apply() bind and fix

* fix reviewed problem

* fix reviewed problem

* fix reviewed problem readme

* fix reviewed problem readme etc

* apply return outputs

* nits

* update readme

* fix FDMatbatch

* add op pybind: CenterCrop, Pad

* add op overload for pass FDMatBatch

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Co-authored-by: Wang Xinyu <shaywxy@gmail.com>
2023-03-10 14:42:32 +08:00

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中文 | English

多硬件图像处理库

多硬件图像处理库（Vision Processor）可用于实现模型的预处理、后处理等图像操作，底层封装了多个第三方图像处理库，包括：

  • OpenCV，用于通用CPU图像处理
  • FlyCV，主要针对ARM CPU加速
  • CV-CUDA，用于NVIDIA GPU

C++

待编写

Python

Python API目前支持的算子如下：

  • ResizeByShort
  • NormalizeAndPermute

用户可通过继承PyProcessorManager类，实现自己的图像处理模块。基类PyProcessorManager实现了GPU内存管理、CUDA stream管理等，用户仅需要实现apply()函数，在其中调用多硬件图像处理库中的算子、实现处理逻辑即可，具体实现可参考示例代码。

示例代码

CV-CUDA与OpenCV性能对比

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%