C# OnnxRuntime YoloV5 Demo

目录

效果

模型信息

项目

代码

Form1.cs

YoloV5.cs

下载

效果

模型信息

Model Properties
-------------------------
---------------------------------------------------------------

Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
---------------------------------------------------------------

Outputs
-------------------------
name：output
tensor：Float[1, 25200, 85]
name：350
tensor：Float[1, 3, 80, 80, 85]
name：416
tensor：Float[1, 3, 40, 40, 85]
name：482
tensor：Float[1, 3, 20, 20, 85]
---------------------------------------------------------------

项目

代码

Form1.cs

using OpenCvSharp;
using System;
using System.Drawing;
using System.Drawing.Imaging;
using System.Text;
using System.Windows.Forms;

namespace Onnx_Demo
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
        Mat image;
        Mat result_image;
        YoloV5 yoloV5;

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;
            pictureBox1.Image = null;
            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            textBox1.Text = "";
            pictureBox2.Image = null;
        }

        private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }

            button2.Enabled = false;
            pictureBox2.Image = null;
            textBox1.Text = "";
            Application.DoEvents();

            //读图片
            image = Cv2.ImRead(image_path);
          
            float confidence = 0.4f;
            if (!float.TryParse(txtConfidence.Text,out confidence))
            {
                confidence = 0.4f;
            }

            dt1 = DateTime.Now;
            var detResults = yoloV5.Detect(image, confidence);
            dt2 = DateTime.Now;

            result_image = image.Clone();
            image.Dispose();

            StringBuilder sb=new StringBuilder();

            foreach (DetectionResult r in detResults)
            {
                string info = $"{r.Class}:{r.Confidence:P0}";
                //绘制
                Cv2.PutText(result_image, info, new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2);
                sb.AppendLine(info);
            }

            pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
            result_image.Dispose();
            textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms\r\n";
            textBox1.Text += "---------------------------\r\n";
            textBox1.Text += sb.ToString();

            button2.Enabled = true;

        }

        private void Form1_Load(object sender, EventArgs e)
        {
            startupPath = System.Windows.Forms.Application.StartupPath;
            yoloV5 = new YoloV5("model/yolov5n.onnx", "model/lable.txt");

            image_path = "test_img/dog.jpg";
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);
        }

        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }

        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }

        SaveFileDialog sdf = new SaveFileDialog();
        private void button3_Click(object sender, EventArgs e)
        {
            if (pictureBox2.Image == null)
            {
                return;
            }
            Bitmap output = new Bitmap(pictureBox2.Image);
            sdf.Title = "保存";
            sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp";
            if (sdf.ShowDialog() == DialogResult.OK)
            {
                switch (sdf.FilterIndex)
                {
                    case 1:
                        {
                            output.Save(sdf.FileName, ImageFormat.Jpeg);
                            break;
                        }
                    case 2:
                        {
                            output.Save(sdf.FileName, ImageFormat.Png);
                            break;
                        }
                    case 3:
                        {
                            output.Save(sdf.FileName, ImageFormat.Bmp);
                            break;
                        }
                }
                MessageBox.Show("保存成功，位置：" + sdf.FileName);
            }
        }
    }
}

  1. using OpenCvSharp;
  2. using System;
  3. using System.Drawing;
  4. using System.Drawing.Imaging;
  5. using System.Text;
  6. using System.Windows.Forms;
  7.  
  8. namespace Onnx_Demo
  9. {
  10.     public partial class Form1 : Form
  11.     {
  12.         public Form1()
  13.         {
  14.             InitializeComponent();
  15.         }
  16.  
  17.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
  18.         string image_path = "";
  19.         string startupPath;
  20.         DateTime dt1 = DateTime.Now;
  21.         DateTime dt2 = DateTime.Now;
  22.         Mat image;
  23.         Mat result_image;
  24.         YoloV5 yoloV5;
  25.  
  26.         private void button1_Click(object sender, EventArgs e)
  27.         {
  28.             OpenFileDialog ofd = new OpenFileDialog();
  29.             ofd.Filter = fileFilter;
  30.             if (ofd.ShowDialog() != DialogResult.OK) return;
  31.             pictureBox1.Image = null;
  32.             image_path = ofd.FileName;
  33.             pictureBox1.Image = new Bitmap(image_path);
  34.             textBox1.Text = "";
  35.             pictureBox2.Image = null;
  36.         }
  37.  
  38.         private void button2_Click(object sender, EventArgs e)
  39.         {
  40.             if (image_path == "")
  41.             {
  42.                 return;
  43.             }
  44.  
  45.             button2.Enabled = false;
  46.             pictureBox2.Image = null;
  47.             textBox1.Text = "";
  48.             Application.DoEvents();
  49.  
  50.             //读图片
  51.             image = Cv2.ImRead(image_path);
  52.           
  53.             float confidence = 0.4f;
  54.             if (!float.TryParse(txtConfidence.Text,out confidence))
  55.             {
  56.                 confidence = 0.4f;
  57.             }
  58.  
  59.             dt1 = DateTime.Now;
  60.             var detResults = yoloV5.Detect(image, confidence);
  61.             dt2 = DateTime.Now;
  62.  
  63.             result_image = image.Clone();
  64.             image.Dispose();
  65.  
  66.             StringBuilder sb=new StringBuilder();
  67.  
  68.             foreach (DetectionResult r in detResults)
  69.             {
  70.                 string info = $"{r.Class}:{r.Confidence:P0}";
  71.                 //绘制
  72.                 Cv2.PutText(result_image, info, new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
  73.                 Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2);
  74.                 sb.AppendLine(info);
  75.             }
  76.  
  77.             pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
  78.             result_image.Dispose();
  79.             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms\r\n";
  80.             textBox1.Text += "---------------------------\r\n";
  81.             textBox1.Text += sb.ToString();
  82.  
  83.             button2.Enabled = true;
  84.  
  85.         }
  86.  
  87.         private void Form1_Load(object sender, EventArgs e)
  88.         {
  89.             startupPath = System.Windows.Forms.Application.StartupPath;
  90.             yoloV5 = new YoloV5("model/yolov5n.onnx", "model/lable.txt");
  91.  
  92.             image_path = "test_img/dog.jpg";
  93.             pictureBox1.Image = new Bitmap(image_path);
  94.             image = new Mat(image_path);
  95.         }
  96.  
  97.         private void pictureBox1_DoubleClick(object sender, EventArgs e)
  98.         {
  99.             Common.ShowNormalImg(pictureBox1.Image);
  100.         }
  101.  
  102.         private void pictureBox2_DoubleClick(object sender, EventArgs e)
  103.         {
  104.             Common.ShowNormalImg(pictureBox2.Image);
  105.         }
  106.  
  107.         SaveFileDialog sdf = new SaveFileDialog();
  108.         private void button3_Click(object sender, EventArgs e)
  109.         {
  110.             if (pictureBox2.Image == null)
  111.             {
  112.                 return;
  113.             }
  114.             Bitmap output = new Bitmap(pictureBox2.Image);
  115.             sdf.Title = "保存";
  116.             sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp";
  117.             if (sdf.ShowDialog() == DialogResult.OK)
  118.             {
  119.                 switch (sdf.FilterIndex)
  120.                 {
  121.                     case 1:
  122.                         {
  123.                             output.Save(sdf.FileName, ImageFormat.Jpeg);
  124.                             break;
  125.                         }
  126.                     case 2:
  127.                         {
  128.                             output.Save(sdf.FileName, ImageFormat.Png);
  129.                             break;
  130.                         }
  131.                     case 3:
  132.                         {
  133.                             output.Save(sdf.FileName, ImageFormat.Bmp);
  134.                             break;
  135.                         }
  136.                 }
  137.                 MessageBox.Show("保存成功，位置：" + sdf.FileName);
  138.             }
  139.         }
  140.     }
  141. }

YoloV5.cs

  1. using Microsoft.ML.OnnxRuntime;
  2. using Microsoft.ML.OnnxRuntime.Tensors;
  3. using OpenCvSharp;
  4. using OpenCvSharp.Dnn;
  5. using System;
  6. using System.Collections.Generic;
  7. using System.IO;
  8. using System.Linq;
  9. using System.Text;
  10.  
  11. namespace Onnx_Demo
  12. {
  13.     internal class YoloV5
  14.     {
  15.         float[] input_image;
  16.  
  17.         int inpWidth = 640;
  18.         int inpHeight = 640;
  19.  
  20.         float confThreshold;
  21.         float nmsThreshold;
  22.  
  23.         List<string> classes;
  24.  
  25.         SessionOptions options;
  26.         InferenceSession onnx_session;
  27.  
  28.         public YoloV5(string modelPath, string classesPath)
  29.         {
  30.             confThreshold = 0.5f;
  31.             nmsThreshold = 0.5f;
  32.  
  33.             classes = File.ReadAllLines(classesPath, Encoding.UTF8).ToList();
  34.  
  35.             options = new SessionOptions();
  36.             options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
  37.             options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
  38.  
  39.             // 创建推理模型类，读取本地模型文件
  40.             onnx_session = new InferenceSession(modelPath, options);
  41.         }
  42.  
  43.         public List<DetectionResult> Detect(Mat frame, float confidence)
  44.         {
  45.             float ratio = 0.0f;
  46.             confThreshold = confidence;
  47.  
  48.             List<DetectionResult> detResults = new List<DetectionResult>();
  49.  
  50.             int max_image_length = frame.Cols > frame.Rows ? frame.Cols : frame.Rows;
  51.             Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
  52.             Rect roi = new Rect(0, 0, frame.Cols, frame.Rows);
  53.             frame.CopyTo(new Mat(max_image, roi));
  54.  
  55.             Cv2.CvtColor(max_image, max_image, ColorConversionCodes.BGR2RGB);
  56.  
  57.             Cv2.Resize(max_image, max_image, new Size(inpWidth, inpHeight));
  58.  
  59.             ratio = (float)(max_image_length / 640.0);
  60.  
  61.             max_image.ConvertTo(max_image, MatType.CV_32FC3, (float)(1 / 255.0));
  62.  
  63.             var input = new DenseTensor<float>(Common.ExtractMat(max_image), new[] { 1, 3, inpHeight, inpWidth });
  64.  
  65.             max_image.Dispose();
  66.  
  67.             // Setup inputs and outputs
  68.             var inputs = new List<NamedOnnxValue>
  69.             {
  70.                NamedOnnxValue.CreateFromTensor("images", input)
  71.             };
  72.  
  73.             var results = onnx_session.Run(inputs);
  74.  
  75.             //Postprocessing
  76.             var resultsArray = results.ToArray();
  77.             var pred_value = resultsArray[0].AsEnumerable<float>().ToArray();
  78.             var pred_dim = resultsArray[0].AsTensor<float>().Dimensions.ToArray();
  79.  
  80.             var nc = pred_dim[pred_dim.Length - 1] - 5;
  81.             var candidate = Common.GetCandidate(pred_value, pred_dim, confThreshold);
  82.  
  83.             //Compute conf
  84.             for (int i = 0; i < candidate.Count; i++)
  85.             {
  86.                 var obj_cnf = candidate[i][4];
  87.                 for (int j = 5; j < candidate[i].Count; j++)
  88.                 {
  89.                     candidate[i][j] *= obj_cnf;
  90.                 }
  91.             }
  92.  
  93.             float[] confidenceInfo = new float[nc];
  94.             float[] rectData = new float[4];
  95.  
  96.             for (int i = 0; i < candidate.Count; i++)
  97.             {
  98.                 Array.Copy(candidate[i].ToArray(), 0, rectData, 0, 4);
  99.                 Array.Copy(candidate[i].ToArray(), 5, confidenceInfo, 0, nc);
  100.  
  101.                 float score = confidenceInfo.Max(); // 获取最大值
  102.                 int maxIndex = Array.IndexOf(confidenceInfo, score); // 获取最大值的位置
  103.  
  104.                 int _centerX = (int)(rectData[0] * ratio);
  105.                 int _centerY = (int)(rectData[1] * ratio);
  106.                 int _width = (int)(rectData[2] * ratio);
  107.                 int _height = (int)(rectData[3] * ratio);
  108.  
  109.                 detResults.Add(new DetectionResult(
  110.                    maxIndex,
  111.                    classes[maxIndex],
  112.                    new Rect(_centerX - _width / 2, _centerY - _height / 2, _width, _height),
  113.                    score));
  114.             }
  115.  
  116.             //NMS
  117.             CvDnn.NMSBoxes(detResults.Select(x => x.Rect), detResults.Select(x => x.Confidence), confThreshold, nmsThreshold, out int[] indices);
  118.             detResults = detResults.Where((x, index) => indices.Contains(index)).ToList();
  119.  
  120.             return detResults;
  121.         }
  122.     }
  123. }

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