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* add yolov5cls * fixed bugs * fixed bugs * fixed preprocess bug * add yolov5cls readme * deal with comments * Add YOLOv5Cls Note * add yolov5cls test * add rvm support * support rvm model * add rvm demo * fixed bugs * add rvm readme * add TRT support * add trt support * add rvm test * add EXPORT.md * rename export.md * rm poros doxyen * deal with comments * deal with comments * add rvm video_mode note Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
89 lines
4.0 KiB
Markdown
Executable File
89 lines
4.0 KiB
Markdown
Executable File
# RobustVideoMatting Python部署示例
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`infer.py`快速完成RobustVideoMatting在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/matting/rvm/python
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# 下载RobustVideoMatting模型文件和测试图片以及视频
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## 原版ONNX模型
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wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx
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## 为加载TRT特殊处理ONNX模型
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wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_trt.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4
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# CPU推理
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## 图片
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python infer.py --model rvm_mobilenetv3_fp32.onnx --image matting_input.jpg --bg matting_bgr.jpg --device cpu
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## 视频
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python infer.py --model rvm_mobilenetv3_fp32.onnx --video video.mp4 --bg matting_bgr.jpg --device cpu
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# GPU推理
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## 图片
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python infer.py --model rvm_mobilenetv3_fp32.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu
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## 视频
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python infer.py --model rvm_mobilenetv3_fp32.onnx --video video.mp4 --bg matting_bgr.jpg --device gpu
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# TRT推理
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## 图片
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python infer.py --model rvm_mobilenetv3_trt.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
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## 视频
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python infer.py --model rvm_mobilenetv3_trt.onnx --video video.mp4 --bg matting_bgr.jpg --device gpu --use_trt True
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```
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运行完成可视化结果如下图所示
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<div width="1240">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852587-48895efc-d24a-43c9-aeec-d7b0362ab2b9.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852554-6960659f-4fd7-4506-b33b-54e1a9dd89bf.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/19977378/196653716-f7043bd5-dfc2-4e7d-be0f-e12a6af4c55b.gif">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/19977378/196654529-866bff5d-47a2-4584-9627-39b587799228.gif">
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</div>
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## RobustVideoMatting Python接口
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```python
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fd.vision.matting.RobustVideoMatting(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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RobustVideoMatting模型加载和初始化，其中model_file为导出的ONNX模型格式
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为ONNX
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### predict函数
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> ```python
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> RobustVideoMatting.predict(input_image)
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> ```
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>
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> 模型预测结口，输入图像直接输出抠图结果。
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>
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> **参数**
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>
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> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> **返回**
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>
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> > 返回`fastdeploy.vision.MattingResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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## 其它文档
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- [RobustVideoMatting 模型介绍](..)
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- [RobustVideoMatting C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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