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90 lines
4.3 KiB
Markdown
Executable File
90 lines
4.3 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# RobustVideoMatting Python Deployment Example
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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This directory provides examples that `infer.py` fast finishes the deployment of RobustVideoMatting on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
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```bash
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# Download the deployment example code
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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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# Download RobustVideoMatting model files, test images and videos
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## Original ONNX Model
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wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx
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## Specially process the ONNX model for loading TRT
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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 inference
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## image
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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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## video
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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 inference
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## image
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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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## video
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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 inference
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## image
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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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## video
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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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The visualized result after running is as follows
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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 Interface
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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 model loading and initialization, among which model_file is the exported ONNX model format
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. ONNX format by default
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### predict function
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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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> Model prediction interface. Input images and output matting results.
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>
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> **Parameter**
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>
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> > * **input_image**(np.ndarray): Input data in HWC or BGR format
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> **Return**
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>
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> > Return `fastdeploy.vision.MattingResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
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## Other Documents
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- [RobustVideoMatting Model Description](..)
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- [RobustVideoMatting C++ Deployment](../cpp)
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- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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