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[Docs] Pick seg fastdeploy docs from PaddleSeg (#1482)
* [Docs] Pick seg fastdeploy docs from PaddleSeg * [Docs] update seg docs * [Docs] Add c&csharp examples for seg * [Docs] Add c&csharp examples for seg * [Doc] Update paddleseg README.md * Update README.md
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DefTruth
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PROJECT(infer_demo CSharp)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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# Set the C# language version (defaults to 3.0 if not set).
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set(CMAKE_CSharp_FLAGS "/langversion:10")
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set(CMAKE_DOTNET_TARGET_FRAMEWORK "net6.0")
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set(CMAKE_DOTNET_SDK "Microsoft.NET.Sdk")
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeployCSharp.cmake)
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cs)
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set_property(TARGET infer_demo PROPERTY VS_DOTNET_REFERENCES
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${FASTDEPLOY_DOTNET_REFERENCES}
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)
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set_property(TARGET infer_demo
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PROPERTY VS_PACKAGE_REFERENCES ${FASTDEPLOY_PACKAGE_REFERENCES})
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English | [简体中文](README_CN.md)
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# PaddleSeg C# Deployment Example
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This directory provides `infer.cs` to finish the deployment of PaddleSeg 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](https://github.com/PaddlePaddle/FastDeploy/blob/develop/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](https://github.com/PaddlePaddle/FastDeploy/blob/develop/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/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz # (Decompress it)
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> https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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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\segmentation\paddleseg\cpu-gpu\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](https://github.com/PaddlePaddle/FastDeploy/blob/develop/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\segmentation\paddleseg\cpu-gpu\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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# CPU inference
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infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 0
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# GPU inference
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infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 1
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```
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## PaddleSeg C# Interface
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### Model Class
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```c#
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fastdeploy.vision.segmentation.PaddleSeg(
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string model_file,
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string params_file,
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string config_file,
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fastdeploy.RuntimeOption runtime_option = null,
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fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
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```
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> PaddleSeg 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
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>> * **config_file**(str): Config file path
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>> * **runtime_option**(RuntimeOption): Backend inference configuration. null 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.SegmentationResult 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**: Segmentation prediction results, refer to [Vision Model Prediction Results](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/) for SegmentationResult
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## Other Documents
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- [PPSegmentation Model Description](../../)
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- [PaddleSeg Python Deployment](../python)
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- [Model Prediction Results](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/)
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- [How to switch the model inference backend engine](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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[English](README.md) | 简体中文
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# PaddleSeg CPU-GPU 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PaddleSeg模型在CPU/GPU上部署的示例。
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## 1. 说明
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PaddleSeg支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署Segmentation模型。
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## 2. 部署环境准备
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在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库。
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## 3. 部署模型准备
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在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleSeg部署模型](../README.md)，如果你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)。
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在本目录执行如下命令即可在Windows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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## 4. 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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## 4. 下载模型文件和测试图片
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> https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz # (下载后解压缩)
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> https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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## 6. 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\segmentation\paddleseg\cpu-gpu\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](https://github.com/PaddlePaddle/FastDeploy/blob/develop/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](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_windows_build.md)
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## 7. 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\segmentation\paddleseg\cpu-gpu\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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# CPU推理
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infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 0
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# GPU推理
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infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 1
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```
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## 8. PaddleSeg C#接口
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### 模型
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```c#
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fastdeploy.vision.segmentation.PaddleSeg(
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string model_file,
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string params_file,
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string config_file,
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fastdeploy.RuntimeOption runtime_option = null,
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fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
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```
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> PaddleSeg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
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>> * **config_file**(str): 配置文件路径
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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PADDLE格式
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#### Predict函数
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```c#
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fastdeploy.SegmentationResult 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**: Segmentation检测结果，SegmentationResult说明参考[视觉模型预测结果](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/)
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## 9. 常见问题
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- [PPSegmentation 系列模型介绍](../../)
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- [PaddleSeg Python部署](../python)
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- [模型预测结果说明](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/)
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- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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using System;
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using System.IO;
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using System.Runtime.InteropServices;
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using OpenCvSharp;
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using fastdeploy;
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namespace Test
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{
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public class TestPaddleSegModel
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{
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public static void Main(string[] args)
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{
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if (args.Length < 3) {
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Console.WriteLine(
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"Usage: infer_demo path/to/model_dir path/to/image run_option" +
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"e.g ./infer_model ./ppseg_model_dir ./test.jpeg 0"
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);
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Console.WriteLine( "The data type of run_option is int, 0: run with cpu; 1: run with gpu");
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return;
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}
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string model_dir = args[0];
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string image_path = args[1];
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string model_file = model_dir + "\\" + "model.pdmodel";
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string params_file = model_dir + "\\" + "model.pdiparams";
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string config_file = model_dir + "\\" + "deploy.yaml";
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RuntimeOption runtimeoption = new RuntimeOption();
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int device_option = Int32.Parse(args[2]);
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if(device_option==0){
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runtimeoption.UseCpu();
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}else{
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runtimeoption.UseGpu();
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}
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fastdeploy.vision.segmentation.PaddleSegModel model = new fastdeploy.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtimeoption, ModelFormat.PADDLE);
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if(!model.Initialized()){
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Console.WriteLine("Failed to initialize.\n");
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}
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Mat image = Cv2.ImRead(image_path);
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fastdeploy.vision.SegmentationResult res = model.Predict(image);
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Console.WriteLine(res.ToString());
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Mat res_img = fastdeploy.vision.Visualize.VisSegmentation(image, res, 0.5f);
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Cv2.ImShow("result.png", res_img);
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Cv2.WaitKey(0);
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}
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}
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}
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