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[Doc]Add English version of documents in examples (#1070)
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rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update README.md * Update and rename README_EN.md to README_CN.md * Update 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This commit is contained in:
Hu Chuqi
@@ -1,41 +1,42 @@
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# RobustVideoMatting C++部署示例
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English | [简体中文](README_CN.md)
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# RobustVideoMatting C++ Deployment Example
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在部署前，需确认以下两个步骤
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Before deployment, two steps require confirmation
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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. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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以Linux上 RobustVideoMatting 推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
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Taking the RobustVideoMatting inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
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本目录下提供`infer.cc`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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This directory provides examples that `infer.cc` 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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mkdir build
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cd build
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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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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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# 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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## 为加载TRT特殊处理ONNX模型
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## The ONNX model is specially processed 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推理
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# CPU inference
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./infer_demo rvm_mobilenetv3_fp32.onnx matting_input.jpg matting_bgr.jpg 0
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# GPU推理
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# GPU inference
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./infer_demo rvm_mobilenetv3_fp32.onnx matting_input.jpg matting_bgr.jpg 1
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# TRT推理
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# TRT inference
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./infer_demo rvm_mobilenetv3_trt.onnx matting_input.jpg matting_bgr.jpg 2
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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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The visualized result after running is as follows
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<div width="840">
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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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@@ -43,10 +44,10 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4
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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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</div>
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
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- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## RobustVideoMatting C++接口
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## RobustVideoMatting C++ Interface
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```c++
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fastdeploy::vision::matting::RobustVideoMatting(
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@@ -56,32 +57,32 @@ fastdeploy::vision::matting::RobustVideoMatting(
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const ModelFormat& 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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RobustVideoMatting model loading and initialization, among which model_file is the exported ONNX model format.
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**参数**
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**Parameter**
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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格式
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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函数
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#### Predict Function
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> ```c++
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> RobustVideoMatting::Predict(cv::Mat* im, MattingResult* result)
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> ```
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>
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> 模型预测接口，输入图像直接输出抠图结果。
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> Model prediction interface. Input images and output matting results.
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>
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> **参数**
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> **Parameter**
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>
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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 抠图结果， MattingResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > * **im**: Input images in HWC or BGR format
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> > * **result**: Matting result. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of MattingResult
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## 其它文档
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## Other Documents
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../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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- [Model Description](../../)
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- [Python Deployment](../python)
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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/cn/faq/how_to_change_backend.md)
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88
examples/vision/matting/rvm/cpp/README_CN.md
Normal file
88
examples/vision/matting/rvm/cpp/README_CN.md
Normal file
@@ -0,0 +1,88 @@
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[English](README.md) | 简体中文
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# RobustVideoMatting C++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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|
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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. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
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|
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以Linux上 RobustVideoMatting 推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
||||
|
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本目录下提供`infer.cc`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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mkdir build
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cd build
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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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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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./infer_demo rvm_mobilenetv3_fp32.onnx matting_input.jpg matting_bgr.jpg 0
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# GPU推理
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./infer_demo rvm_mobilenetv3_fp32.onnx matting_input.jpg matting_bgr.jpg 1
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# TRT推理
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./infer_demo rvm_mobilenetv3_trt.onnx matting_input.jpg matting_bgr.jpg 2
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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="840">
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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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</div>
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## RobustVideoMatting C++接口
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```c++
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fastdeploy::vision::matting::RobustVideoMatting(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& 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格式
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#### Predict函数
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> ```c++
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> RobustVideoMatting::Predict(cv::Mat* im, MattingResult* result)
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> ```
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>
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> 模型预测接口，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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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 抠图结果， MattingResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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## 其它文档
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
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