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[Doc] Add docs for ppocr ppseg examples (#1429)
* add docs for examples * add english doc * fix * fix docs
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chenjian
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examples/vision/detection/yolov5/csharp/README.md
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examples/vision/detection/yolov5/csharp/README.md
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English | [简体中文](README_CN.md)
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# YOLOv5 C# Deployment Example
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This directory provides `infer.cs` to finish the deployment of YOLOv5 on CPU/GPU.
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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. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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Please follow below instructions to compile and test in Windows. FastDeploy version 1.0.4 or above (x.x.x>=1.0.4) is required to support this model.
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## 1. Download C# package management tool nuget client
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> https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe
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Add nuget program into system variable **PATH**
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## 2. Download model and image for test
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> https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
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> https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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## 3. Compile example code
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Open `x64 Native Tools Command Prompt for VS 2019` command tool on Windows, cd to the demo path of ppyoloe and execute commands
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```shell
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cd D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\detection\yolov5\csharp
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mkdir build && cd build
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cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=D:\Download\fastdeploy-win-x64-gpu-x.x.x -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"
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nuget restore
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msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
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```
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For more information about how to use FastDeploy SDK to compile a project with Visual Studio 2019. Please refer to
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- [Using the FastDeploy C++ SDK on Windows Platform](../../../../../docs/en/faq/use_sdk_on_windows.md)
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## 4. Execute compiled program
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fastdeploy.dll and related dynamic libraries are required by the program. FastDeploy provide a script to copy all required dll to your program path.
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```shell
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cd D:\Download\fastdeploy-win-x64-gpu-x.x.x
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fastdeploy_init.bat install %cd% D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\detection\yolov5\csharp\build\Release
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```
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Then you can run your program and test the model with image
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```shell
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cd Release
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infer_demo yolov5s.onnx 000000014439.jpg 0 # CPU
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infer_demo yolov5s.onnx 000000014439.jpg 1 # GPU
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```
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## YOLOv5 C# Interface
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### Model Class
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```c#
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fastdeploy.vision.detection.YOLOv5(
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string model_file,
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string params_file,
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fastdeploy.RuntimeOption runtime_option = null,
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fastdeploy.ModelFormat model_format = ModelFormat.ONNX)
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```
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> YOLOv5 initialization.
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> **Params**
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>> * **model_file**(str): Model file path
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>> * **params_file**(str): Parameter file path，when model format is onnx，this can be empty string
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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.
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#### Predict Function
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```c#
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fastdeploy.DetectionResult Predict(OpenCvSharp.Mat im)
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```
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> Model prediction interface. Input images and output results directly.
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>
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> **Params**
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>
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>> * **im**(Mat): Input images in HWC or BGR format
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>
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> **Return**
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>
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>> * **result**(DetectionResult): Detection result, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for DetectionResult
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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/en/faq/how_to_change_backend.md)
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98
examples/vision/detection/yolov5/csharp/README_CN.md
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examples/vision/detection/yolov5/csharp/README_CN.md
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[English](README.md) | 简体中文
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# YOLOv5 C#部署示例
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本目录下提供`infer.cs`来调用C# API快速完成YOLOv5模型在CPU/GPU上部署的示例。
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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. 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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在Windows下执行如下操作完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
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## 1. 下载C#包管理程序nuget客户端
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> https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe
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下载完成后将该程序添加到环境变量**PATH**中
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## 2. 下载模型文件和测试图片
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> https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
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> https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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## 3. 编译示例代码
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本文档编译的示例代码可在解压的库中找到，编译工具依赖VS 2019的安装，**Windows打开x64 Native Tools Command Prompt for VS 2019命令工具**，通过如下命令开始编译
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```shell
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cd D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\detection\yolov5\csharp
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mkdir build && cd build
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cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=D:\Download\fastdeploy-win-x64-gpu-x.x.x -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"
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nuget restore
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msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
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```
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关于使用Visual Studio 2019创建sln工程，或者CMake工程等方式编译的更详细信息，可参考如下文档
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- [在 Windows 使用 FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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- [FastDeploy C++库在Windows上的多种使用方式](../../../../../docs/cn/faq/use_sdk_on_windows_build.md)
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## 4. 运行可执行程序
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注意Windows上运行时，需要将FastDeploy依赖的库拷贝至可执行程序所在目录, 或者配置环境变量。FastDeploy提供了工具帮助我们快速将所有依赖库拷贝至可执行程序所在目录,通过如下命令将所有依赖的dll文件拷贝至可执行程序所在的目录(可能生成的可执行文件在Release下还有一层目录，这里假设生成的可执行文件在Release处)
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```shell
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cd D:\Download\fastdeploy-win-x64-gpu-x.x.x
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fastdeploy_init.bat install %cd% D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\detection\yolov5\csharp\build\Release
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```
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将dll拷贝到当前路径后，准备好模型和图片，使用如下命令运行可执行程序即可
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```shell
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cd Release
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infer_demo yolov5s.onnx 000000014439.jpg 0 # CPU
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infer_demo yolov5s.onnx 000000014439.jpg 1 # GPU
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```
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## YOLOv5 C#接口
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### 模型
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```c#
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fastdeploy.vision.detection.YOLOv5(
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string model_file,
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string params_file,
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fastdeploy.RuntimeOption runtime_option = null,
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fastdeploy.ModelFormat model_format = ModelFormat.ONNX)
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```
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> YOLOv5 模型加载和初始化。
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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时，此参数传入空字符串即可
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>> * **runtime_option**(RuntimeOption): 后端推理配置，默认为null，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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fastdeploy.DetectionResult Predict(OpenCvSharp.Mat im)
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```
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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>> * **im**(Mat): 输入图像，注意需为HWC，BGR格式
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>
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> **返回值**
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>
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>> * **result**(DetectionResult): 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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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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