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yolov10--C#接口_yolov10 requirement.txt

作者：人工智能uu | 2024-06-22 09:04:20

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yolov10 requirement.txt

一、前言

     本章主要讲解yolov10的C#接口，主要是使用微软开发的openvinocsharp工具加载yolov10模型，并做推理。

二、yolov10模型转换

     这里为了演示，使用官方yolov10m模型（其他大小的模型同理）做演示，可从下方下载，当然也可以是自己训练好的模型

https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt

      该原始模型，需要被转换为openvinocsharp所支持的模型格式，为此需要建立一个yolov10的python环境，使用conda创建，requirements.txt 为 yolov10官方代码下的所需包

  1. conda create -n yolov10 python=3.9
  2. conda activate yolov10
  3. pip install -r requirements.txt
  4. pip install -e .

然后安装OpenVINO™环境，输入以下指令

pip install openvino==2024.1.0

在该创建好的虚拟环境，cd 至下载好的yolov10m.pt 所在目录，执行

  1. yolo export model=yolov10m.pt format=onnx opset=11 simplify
  2. ovc yolov10m.onnx

这样，pt文件的模型将转换为onnx文件，再换换为 openvino 所需的 bin 和 xml 文件格式

三、C#端openvinosharp相关包安装

         首先需使用VS2022构建项目，其次将openvinosharp相关包安装上

  1. <Project Sdk="Microsoft.NET.Sdk">
  2.  
  3.   <PropertyGroup>
  4.     <OutputType>Exe</OutputType>
  5.     <TargetFramework>net6.0</TargetFramework>
  6.     <ImplicitUsings>enable</ImplicitUsings>
  7.     <Nullable>enable</Nullable>
  8.   </PropertyGroup>
  9.  
  10.   <ItemGroup>
  11.     <PackageReference Include="OpenCvSharp4" Version="4.9.0.20240103" />
  12.     <PackageReference Include="OpenCvSharp4.Extensions" Version="4.9.0.20240103" />
  13.     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.9.0.20240103" />
  14.     <PackageReference Include="OpenVINO.CSharp.API" Version="2024.1.0.1" />
  15.     <PackageReference Include="OpenVINO.CSharp.API.Extensions.OpenCvSharp" Version="1.0.4" />
  16.     <PackageReference Include="OpenVINO.runtime.win" Version="2024.1.0.1" />
  17.   </ItemGroup>
  18.  
  19. </Project>

也就是  OpenCvSharp4、OpenCvSharp4.Extensions、OpenCvSharp4.runtime.win、OpenVINO.CSharp.API、OpenVINO.CSharp.API.Extensions.OpenCvSharp、OpenVINO.runtime.win，这6个包给装上

这部分参考自下面博客

【OpenVINO™】在C#中使用 OpenVINO™ 部署 YOLOv10 模型实现目标_yolov10 openvino-CSDN博客

四、C#加载yolo10推理代码

这样就可以创建C# winform项目，愉快地加载前面转换好的模型文件做前向推理了

  1. // See https://aka.ms/new-console-template for more information
  2. //Console.WriteLine("Hello, World!");
  3.  
  4. using System.Reflection;
  5. using System.Runtime.InteropServices;
  6. using System;
  7. using OpenVinoSharp;
  8. using OpenVinoSharp.Extensions.utility;
  9. using OpenVinoSharp.Extensions;
  10. using OpenCvSharp;
  11. using OpenCvSharp.Dnn;
  12. using OpenVinoSharp.preprocess;
  13.  
  14. namespace yolov10_det_opencvsharp
  15. {
  16.     internal class Program
  17.     {
  18.         static void Main(string[] args)
  19.         {
  20.             string model_path = "./model_demo/yolov10m.xml";
  21.             string image_path = "./model_demo/cat.png";
  22.             string device = "AUTO";  //CPU GPU AUTO，可选AUTO模式
  23.  
  24.       
  25.  
  26.             // -------- Get OpenVINO runtime version --------
  27.  
  28.             OpenVinoSharp.Version version = Ov.get_openvino_version();
  29.  
  30.             Slog.INFO("---- OpenVINO INFO----");
  31.             Slog.INFO("Description : " + version.description);
  32.             Slog.INFO("Build number: " + version.buildNumber);
  33.  
  34.             Slog.INFO("Predict model files: " + model_path);
  35.             Slog.INFO("Predict image  files: " + image_path);
  36.             Slog.INFO("Inference device: " + device);
  37.             Slog.INFO("Start yolov10 model inference.");
  38.  
  39.             //yolov10_det(model_path, image_path, device);
  40.             yolov10_det_process(model_path, image_path , device);
  41.         }
  42.         static void yolov10_det(string model_path, string image_path, string device)
  43.         {
  44.             DateTime start = DateTime.Now;
  45.             // -------- Step 1. Initialize OpenVINO Runtime Core --------
  46.             Core core = new Core();
  47.             DateTime end = DateTime.Now;
  48.             Slog.INFO("1. Initialize OpenVINO Runtime Core success, time spend: " + (end - start).TotalMilliseconds + "ms.");
  49.             // -------- Step 2. Read inference model --------
  50.             start = DateTime.Now;
  51.             Model model = core.read_model(model_path);
  52.             end = DateTime.Now;
  53.             Slog.INFO("2. Read inference model success, time spend: " + (end - start).TotalMilliseconds + "ms.");
  54.             OvExtensions.printf_model_info(model);
  55.             // -------- Step 3. Loading a model to the device --------
  56.             start = DateTime.Now;
  57.             CompiledModel compiled_model = core.compile_model(model, device);
  58.             end = DateTime.Now;
  59.             Slog.INFO("3. Loading a model to the device success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  60.             // -------- Step 4. Create an infer request --------
  61.             start = DateTime.Now;
  62.             InferRequest infer_request = compiled_model.create_infer_request();
  63.             end = DateTime.Now;
  64.             Slog.INFO("4. Create an infer request success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  65.             // -------- Step 5. Process input images --------
  66.             start = DateTime.Now;
  67.             Mat image = new Mat(image_path); // Read image by opencvsharp
  68.             int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
  69.             Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
  70.             Rect roi = new Rect(0, 0, image.Cols, image.Rows);
  71.             image.CopyTo(new Mat(max_image, roi));
  72.             float factor = (float)(max_image_length / 640.0);
  73.             end = DateTime.Now;
  74.             Slog.INFO("5. Process input images success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  75.             // -------- Step 6. Set up input data --------
  76.             start = DateTime.Now;
  77.             Tensor input_tensor = infer_request.get_input_tensor();
  78.             Shape input_shape = input_tensor.get_shape();
  79.             Mat input_mat = CvDnn.BlobFromImage(max_image, 1.0 / 255.0, new OpenCvSharp.Size(input_shape[2], input_shape[3]), 0, true, false);
  80.             float[] input_data = new float[input_shape[1] * input_shape[2] * input_shape[3]];
  81.             Marshal.Copy(input_mat.Ptr(0), input_data, 0, input_data.Length);
  82.             input_tensor.set_data<float>(input_data);
  83.  
  84.             end = DateTime.Now;
  85.             Slog.INFO("6. Set up input data success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  86.             // -------- Step 7. Do inference synchronously --------
  87.             infer_request.infer();
  88.             start = DateTime.Now;
  89.             infer_request.infer();
  90.             end = DateTime.Now;
  91.             Slog.INFO("7. Do inference synchronously success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  92.             // -------- Step 8. Get infer result data --------
  93.             start = DateTime.Now;
  94.             Tensor output_tensor = infer_request.get_output_tensor();
  95.             int output_length = (int)output_tensor.get_size();
  96.             float[] output_data = output_tensor.get_data<float>(output_length);
  97.             end = DateTime.Now;
  98.             Slog.INFO("8. Get infer result data success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  99.  
  100.              -------- Step 9. Process reault  --------
  101.             start = DateTime.Now;
  102.  
  103.             List<Rect> position_boxes = new List<Rect>();
  104.             List<int> class_ids = new List<int>();
  105.             List<float> confidences = new List<float>();
  106.  
  107.             // Preprocessing output results
  108.             for (int i = 0; i < output_data.Length / 6; i++)
  109.             {
  110.                 int s = 6 * i;
  111.                 if ((float)output_data[s + 4] > 0.5)
  112.                 {
  113.                     float cx = output_data[s + 0];
  114.                     float cy = output_data[s + 1];
  115.                     float dx = output_data[s + 2];
  116.                     float dy = output_data[s + 3];
  117.                     int x = (int)((cx) * factor);
  118.                     int y = (int)((cy) * factor);
  119.                     int width = (int)((dx - cx) * factor);
  120.                     int height = (int)((dy - cy) * factor);
  121.                     Rect box = new Rect();
  122.                     box.X = x;
  123.                     box.Y = y;
  124.                     box.Width = width;
  125.                     box.Height = height;
  126.  
  127.                     position_boxes.Add(box);
  128.                     class_ids.Add((int)output_data[s + 5]);
  129.                     confidences.Add((float)output_data[s + 4]);
  130.                 }
  131.             }
  132.  
  133.  
  134.             end = DateTime.Now;
  135.             Slog.INFO("9. Process reault  success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  136.             for (int i = 0; i < class_ids.Count; i++)
  137.             {
  138.                 int index = i;
  139.                 Cv2.Rectangle(image, position_boxes[index], new Scalar(0, 0, 255), 2, LineTypes.Link8);
  140.                 Cv2.Rectangle(image, new OpenCvSharp.Point(position_boxes[index].TopLeft.X, position_boxes[index].TopLeft.Y + 30),
  141.                     new OpenCvSharp.Point(position_boxes[index].BottomRight.X, position_boxes[index].TopLeft.Y), new Scalar(0, 255, 255), -1);
  142.                 Cv2.PutText(image, class_ids[index] + "-" + confidences[index].ToString("0.00"),
  143.                     new OpenCvSharp.Point(position_boxes[index].X, position_boxes[index].Y + 25),
  144.                     HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 0, 0), 2);
  145.             }
  146.             string output_path = Path.Combine(Path.GetDirectoryName(Path.GetFullPath(image_path)),
  147.                 Path.GetFileNameWithoutExtension(image_path) + "_result.jpg");
  148.             Cv2.ImWrite(output_path, image);
  149.             Slog.INFO("The result save to " + output_path);
  150.             Cv2.ImShow("Result", image);
  151.             Cv2.WaitKey(0);
  152.         }
  153.         static void yolov10_det_process(string model_path, string image_path, string device)
  154.         {
  155.             DateTime start = DateTime.Now;
  156.             // -------- Step 1. Initialize OpenVINO Runtime Core --------
  157.             Core core = new Core();
  158.             DateTime end = DateTime.Now;
  159.             Slog.INFO("1. Initialize OpenVINO Runtime Core success, time spend: " + (end - start).TotalMilliseconds + "ms.");
  160.             // -------- Step 2. Read inference model --------
  161.             start = DateTime.Now;
  162.             Model model = core.read_model(model_path);
  163.             end = DateTime.Now;
  164.             Slog.INFO("2. Read inference model success, time spend: " + (end - start).TotalMilliseconds + "ms.");
  165.             OvExtensions.printf_model_info(model);
  166.             PrePostProcessor processor = new PrePostProcessor(model);
  167.             Tensor input_tensor_pro = new Tensor(new OvType(ElementType.U8), new Shape(1, 640, 640, 3));   //注意这个地方要改和模型窗口一致，模型是640，这里也要640
  168.             InputInfo input_info = processor.input(0);
  169.             InputTensorInfo input_tensor_info = input_info.tensor();
  170.             input_tensor_info.set_from(input_tensor_pro).set_layout(new Layout("NHWC")).set_color_format(ColorFormat.BGR);
  171.  
  172.             PreProcessSteps process_steps = input_info.preprocess();
  173.             process_steps.convert_color(ColorFormat.RGB).resize(ResizeAlgorithm.RESIZE_LINEAR)
  174.                 .convert_element_type(new OvType(ElementType.F32)).scale(255.0f).convert_layout(new Layout("NCHW"));
  175.             Model new_model = processor.build();
  176.             // -------- Step 3. Loading a model to the device --------
  177.             start = DateTime.Now;
  178.             CompiledModel compiled_model = core.compile_model(new_model, device);
  179.             end = DateTime.Now;
  180.             Slog.INFO("3. Loading a model to the device success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  181.             // -------- Step 4. Create an infer request --------
  182.             start = DateTime.Now;
  183.             InferRequest infer_request = compiled_model.create_infer_request();
  184.             end = DateTime.Now;
  185.             Slog.INFO("4. Create an infer request success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  186.             // -------- Step 5. Process input images --------
  187.             start = DateTime.Now;
  188.             Mat image = new Mat(image_path); // Read image by opencvsharp
  189.             int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
  190.             Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
  191.             Rect roi = new Rect(0, 0, image.Cols, image.Rows);
  192.             image.CopyTo(new Mat(max_image, roi));
  193.             Cv2.Resize(max_image, max_image, new OpenCvSharp.Size(640, 640));   //注意这个地方要改和模型窗口一致，模型是640，这里也要640
  194.             float factor = (float)(max_image_length / 640.0);
  195.             end = DateTime.Now;
  196.             Slog.INFO("5. Process input images success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  197.             // -------- Step 6. Set up input data --------
  198.             start = DateTime.Now;
  199.             Tensor input_tensor = infer_request.get_input_tensor();
  200.             Shape input_shape = input_tensor.get_shape();
  201.             byte[] input_data = new byte[input_shape[1] * input_shape[2] * input_shape[3]];
  202.             //max_image.GetArray<int>(out input_data);
  203.             Marshal.Copy(max_image.Ptr(0), input_data, 0, input_data.Length);
  204.             IntPtr destination = input_tensor.data();
  205.             Marshal.Copy(input_data, 0, destination, input_data.Length);
  206.  
  207.             end = DateTime.Now;
  208.             Slog.INFO("6. Set up input data success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  209.             // -------- Step 7. Do inference synchronously --------
  210.             infer_request.infer();
  211.             start = DateTime.Now;
  212.             infer_request.infer();
  213.   
  214.             end = DateTime.Now;
  215.             Slog.INFO("7. Do inference synchronously success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  216.  
  217.             // -------- Step 8. Get infer result data --------
  218.             start = DateTime.Now;
  219.             Tensor output_tensor = infer_request.get_output_tensor();
  220.             int output_length = (int)output_tensor.get_size();
  221.             float[] output_data = output_tensor.get_data<float>(output_length);
  222.             end = DateTime.Now;
  223.             Slog.INFO("8. Get infer result data success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  224.  
  225.              -------- Step 9. Process reault  --------
  226.             start = DateTime.Now;
  227.  
  228.             List<Rect> position_boxes = new List<Rect>();
  229.             List<int> class_ids = new List<int>();
  230.             List<float> confidences = new List<float>();
  231.  
  232.             // Preprocessing output results
  233.             for (int i = 0; i < output_data.Length / 6; i++)
  234.             {
  235.                 int s = 6 * i;
  236.                 if ((float)output_data[s + 4] > 0.2)
  237.                 {
  238.                     float cx = output_data[s + 0];
  239.                     float cy = output_data[s + 1];
  240.                     float dx = output_data[s + 2];
  241.                     float dy = output_data[s + 3];
  242.                     int x = (int)((cx) * factor);
  243.                     int y = (int)((cy) * factor);
  244.                     int width = (int)((dx - cx) * factor);
  245.                     int height = (int)((dy - cy) * factor);
  246.                     Rect box = new Rect();
  247.                     box.X = x;
  248.                     box.Y = y;
  249.                     box.Width = width;
  250.                     box.Height = height;
  251.  
  252.                     position_boxes.Add(box);
  253.                     class_ids.Add((int)output_data[s + 5]);
  254.                     confidences.Add((float)output_data[s + 4]);
  255.                 }
  256.             }
  257.  
  258.  
  259.             end = DateTime.Now;
  260.             Slog.INFO("9. Process reault  success, time spend:" + (end - start).TotalMilliseconds + "ms.");
  261.             for (int i = 0; i < class_ids.Count; i++)
  262.             {
  263.                 int index = i;
  264.                 Cv2.Rectangle(image, position_boxes[index], new Scalar(0, 0, 255), 2, LineTypes.Link8);
  265.                 Cv2.Rectangle(image, new OpenCvSharp.Point(position_boxes[index].TopLeft.X, position_boxes[index].TopLeft.Y + 30),
  266.                     new OpenCvSharp.Point(position_boxes[index].BottomRight.X, position_boxes[index].TopLeft.Y), new Scalar(0, 255, 255), -1);
  267.                 Cv2.PutText(image, class_ids[index] + "-" + confidences[index].ToString("0.00"),
  268.                     new OpenCvSharp.Point(position_boxes[index].X, position_boxes[index].Y + 25),
  269.                     HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 0, 0), 2);
  270.             }
  271.             string output_path = Path.Combine(Path.GetDirectoryName(Path.GetFullPath(image_path)),
  272.                 Path.GetFileNameWithoutExtension(image_path) + "_result.jpg");
  273.             Cv2.ImWrite(output_path, image);
  274.             Slog.INFO("The result save to " + output_path);
  275.             Cv2.ImShow("Result", image);
  276.             Cv2.WaitKey(0);
  277.         }
  278.     }
  279. }
  280.  
  281.

五、输出结果

        运行代码，可以得到统计的代码加载、预处理、推理的运行时间，并且得到识别结果，类别号、置信度、以及位置

       有显卡的话，可将模型AUTO改为GPU，运行时间会更快些。。。

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