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FastDeploy/examples/vision/detection/yolov5/csharp/README_CN.md
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[English](README.md) | 简体中文
# YOLOv5 C#部署示例
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。
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. 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)
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)
## 1. 下载C#包管理程序nuget客户端
> https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe
下载完成后将该程序添加到环境变量**PATH**中
## 2. 下载模型文件和测试图片
> https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
> https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
## 3. 编译示例代码
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
```shell
cd D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\detection\yolov5\csharp
mkdir build && cd build
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"
nuget restore
msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
```
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
- [在 Windows 使用 FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
- [FastDeploy C++库在Windows上的多种使用方式](../../../../../docs/cn/faq/use_sdk_on_windows_build.md)
## 4. 运行可执行程序
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)
```shell
cd D:\Download\fastdeploy-win-x64-gpu-x.x.x
fastdeploy_init.bat install %cd% D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\detection\yolov5\csharp\build\Release
```
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
```shell
cd Release
infer_demo yolov5s.onnx 000000014439.jpg 0 # CPU
infer_demo yolov5s.onnx 000000014439.jpg 1 # GPU
```
## YOLOv5 C#接口
### 模型
```c#
fastdeploy.vision.detection.YOLOv5(
string model_file,
string params_file,
fastdeploy.RuntimeOption runtime_option = null,
fastdeploy.ModelFormat model_format = ModelFormat.ONNX)
```
> YOLOv5 模型加载和初始化。
> **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时此参数传入空字符串即可
>> * **runtime_option**(RuntimeOption): 后端推理配置默认为null即采用默认配置
>> * **model_format**(ModelFormat): 模型格式默认为ONNX格式
#### Predict函数
```c#
fastdeploy.DetectionResult Predict(OpenCvSharp.Mat im)
```
> 模型预测接口,输入图像直接输出检测结果。
>
> **参数**
>
>> * **im**(Mat): 输入图像注意需为HWCBGR格式
>
> **返回值**
>
>> * **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/)
- [模型介绍](../../)
- [Python部署](../python)
- [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)