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# RobustVideoMatting Python部署示例
English | [简体中文](README_CN.md)
# RobustVideoMatting Python Deployment Example
在部署前，需确认以下两个步骤
Before deployment, two steps require confirmation
- 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)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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
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
```bash
#下载部署示例代码
# Download the deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/matting/rvm/python
# 下载RobustVideoMatting模型文件和测试图片以及视频
## 原版ONNX模型
# Download RobustVideoMatting model files, test images and videos
## Original ONNX Model
wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx
## 为加载TRT特殊处理ONNX模型
## Specially process the ONNX model for loading TRT
wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_trt.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4
# CPU推理
## 图片
# CPU inference
## image
python infer.py --model rvm_mobilenetv3_fp32.onnx --image matting_input.jpg --bg matting_bgr.jpg --device cpu
## 视频
## video
python infer.py --model rvm_mobilenetv3_fp32.onnx --video video.mp4 --bg matting_bgr.jpg --device cpu
# GPU推理
## 图片
# GPU inference
## image
python infer.py --model rvm_mobilenetv3_fp32.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu
## 视频
## video
python infer.py --model rvm_mobilenetv3_fp32.onnx --video video.mp4 --bg matting_bgr.jpg --device gpu
# TRT推理
## 图片
# TRT inference
## image
python infer.py --model rvm_mobilenetv3_trt.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
## 视频
## video
python infer.py --model rvm_mobilenetv3_trt.onnx --video video.mp4 --bg matting_bgr.jpg --device gpu --use_trt True
```
运行完成可视化结果如下图所示
The visualized result after running is as follows
<div width="1240">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852587-48895efc-d24a-43c9-aeec-d7b0362ab2b9.jpg">
@@ -48,41 +49,41 @@ python infer.py --model rvm_mobilenetv3_trt.onnx --video video.mp4 --bg matting_
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/19977378/196654529-866bff5d-47a2-4584-9627-39b587799228.gif">
</div>
## RobustVideoMatting Python接口
## RobustVideoMatting Python Interface
```python
fd.vision.matting.RobustVideoMatting(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
RobustVideoMatting模型加载和初始化，其中model_file为导出的ONNX模型格式
RobustVideoMatting model loading and initialization, among which model_file is the exported ONNX model format
**参数**
**Parameter**
> * **model_file**(str): 模型文件路径
> * **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
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
> * **model_format**(ModelFormat): 模型格式，默认为ONNX
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(ModelFormat): Model format. ONNX format by default
### predict函数
### predict function
> ```python
> RobustVideoMatting.predict(input_image)
> ```
>
> 模型预测结口，输入图像直接输出抠图结果。
> Model prediction interface. Input images and output matting results.
>
> **参数**
> **Parameter**
>
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> > * **input_image**(np.ndarray): Input data in HWC or BGR format
> **返回**
> **Return**
>
> > 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/)
> > Return `fastdeploy.vision.MattingResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
## 其它文档
## Other Documents
- [RobustVideoMatting 模型介绍](..)
- [RobustVideoMatting C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
- [RobustVideoMatting Model Description](..)
- [RobustVideoMatting C++ Deployment](../cpp)
- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)

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[English](README.md) | 简体中文
# RobustVideoMatting Python部署示例
在部署前，需确认以下两个步骤
- 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)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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
```bash
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/matting/rvm/python
# 下载RobustVideoMatting模型文件和测试图片以及视频
## 原版ONNX模型
wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx
## 为加载TRT特殊处理ONNX模型
wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_trt.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4
# CPU推理
## 图片
python infer.py --model rvm_mobilenetv3_fp32.onnx --image matting_input.jpg --bg matting_bgr.jpg --device cpu
## 视频
python infer.py --model rvm_mobilenetv3_fp32.onnx --video video.mp4 --bg matting_bgr.jpg --device cpu
# GPU推理
## 图片
python infer.py --model rvm_mobilenetv3_fp32.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu
## 视频
python infer.py --model rvm_mobilenetv3_fp32.onnx --video video.mp4 --bg matting_bgr.jpg --device gpu
# TRT推理
## 图片
python infer.py --model rvm_mobilenetv3_trt.onnx --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
## 视频
python infer.py --model rvm_mobilenetv3_trt.onnx --video video.mp4 --bg matting_bgr.jpg --device gpu --use_trt True
```
运行完成可视化结果如下图所示
<div width="1240">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852587-48895efc-d24a-43c9-aeec-d7b0362ab2b9.jpg">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852554-6960659f-4fd7-4506-b33b-54e1a9dd89bf.jpg">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/19977378/196653716-f7043bd5-dfc2-4e7d-be0f-e12a6af4c55b.gif">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/19977378/196654529-866bff5d-47a2-4584-9627-39b587799228.gif">
</div>
## RobustVideoMatting Python接口
```python
fd.vision.matting.RobustVideoMatting(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
RobustVideoMatting模型加载和初始化，其中model_file为导出的ONNX模型格式
**参数**
> * **model_file**(str): 模型文件路径
> * **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
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
> * **model_format**(ModelFormat): 模型格式，默认为ONNX
### predict函数
> ```python
> RobustVideoMatting.predict(input_image)
> ```
>
> 模型预测结口，输入图像直接输出抠图结果。
>
> **参数**
>
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> **返回**
>
> > 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/)
## 其它文档
- [RobustVideoMatting 模型介绍](..)
- [RobustVideoMatting C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)