C# OnnxRuntime yolov11 detection

目录

说明

效果

模型信息

项目

代码

下载

说明

官网地址：

https://github.com/ultralytics/ultralytics

效果

模型信息

Model Properties
-------------------------
date：2024-10-06T16:52:12.968917
description：Ultralytics YOLO11n model trained on /usr/src/ultralytics/ultralytics/cfg/datasets/coco.yaml
author：Ultralytics
version：8.3.5
task：detect
license：AGPL-3.0 License (https://ultralytics.com/license)
docs：https://docs.ultralytics.com
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
---------------------------------------------------------------

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

Outputs
-------------------------
name：output0
tensor：Float[1, 84, 8400]
---------------------------------------------------------------

项目

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.IO;
using System.Linq;
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 model_path;
        string classer_path;
        public string[] class_names;
        public int class_num;

        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;

        int input_height;
        int input_width;
        float ratio_height;
        float ratio_width;

        InferenceSession onnx_session;

        int box_num;
        float conf_threshold;
        float nms_threshold;

        ///


        /// 选择图片
        ///
        ///
        ///
        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;
        }

        ///


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

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

            Mat image = new Mat(image_path);
            
            //图片缩放
            int height = image.Rows;
            int width = image.Cols;
            Mat temp_image = image.Clone();
            if (height > input_height || width > input_width)
            {
                float scale = Math.Min((float)input_height / height, (float)input_width / width);
                OpenCvSharp.Size new_size = new OpenCvSharp.Size((int)(width * scale), (int)(height * scale));
                Cv2.Resize(image, temp_image, new_size);
            }
            ratio_height = (float)height / temp_image.Rows;
            ratio_width = (float)width / temp_image.Cols;
            Mat input_img = new Mat();
            Cv2.CopyMakeBorder(temp_image, input_img, 0, input_height - temp_image.Rows, 0, input_width - temp_image.Cols, BorderTypes.Constant, 0);

            //Cv2.ImShow("input_img", input_img);

            //输入Tensor
            Tensor input_tensor = new DenseTensor(new[] { 1, 3, 640, 640 });
            for (int y = 0; y < input_img.Height; y++)
            {
                for (int x = 0; x < input_img.Width; x++)
                {
                    input_tensor[0, 0, y, x] = input_img.At(y, x)[0] / 255f;
                    input_tensor[0, 1, y, x] = input_img.At(y, x)[1] / 255f;
                    input_tensor[0, 2, y, x] = input_img.At(y, x)[2] / 255f;
                }
            }

            List input_container = new List
            {
                NamedOnnxValue.CreateFromTensor("images", input_tensor)
            };

            //推理
            dt1 = DateTime.Now;
            var ort_outputs = onnx_session.Run(input_container).ToArray();
            dt2 = DateTime.Now;

            float[] data = Transpose(ort_outputs[0].AsTensor().ToArray(), 4 + class_num, box_num);

            float[] confidenceInfo = new float[class_num];
            float[] rectData = new float[4];

            List detResults = new List();

            for (int i = 0; i < box_num; i++)
            {
                Array.Copy(data, i * (class_num + 4), rectData, 0, 4);
                Array.Copy(data, i * (class_num + 4) + 4, confidenceInfo, 0, class_num);

                float score = confidenceInfo.Max(); // 获取最大值

                int maxIndex = Array.IndexOf(confidenceInfo, score); // 获取最大值的位置

                int _centerX = (int)(rectData[0] * ratio_width);
                int _centerY = (int)(rectData[1] * ratio_height);
                int _width = (int)(rectData[2] * ratio_width);
                int _height = (int)(rectData[3] * ratio_height);

                detResults.Add(new DetectionResult(
                   maxIndex,
                   class_names[maxIndex],
                   new Rect(_centerX - _width / 2, _centerY - _height / 2, _width, _height),
                   score));
            }

            //NMS
            CvDnn.NMSBoxes(detResults.Select(x => x.Rect), detResults.Select(x => x.Confidence), conf_threshold, nms_threshold, out int[] indices);
            detResults = detResults.Where((x, index) => indices.Contains(index)).ToList();

            //绘制结果
            Mat result_image = image.Clone();
            foreach (DetectionResult r in detResults)
            {
                Cv2.PutText(result_image, $"{r.Class}:{r.Confidence:P0}", 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);
            }

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

            button2.Enabled = true;
        }

        ///


        ///窗体加载
        ///
        ///
        ///
        private void Form1_Load(object sender, EventArgs e)
        {
            model_path = "model/yolo11n.onnx";

            //创建输出会话，用于输出模型读取信息
            SessionOptions options = new SessionOptions();
            options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
            options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行

            // 创建推理模型类，读取模型文件
            onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径

            input_height = 640;
            input_width = 640;

            box_num = 8400;
            conf_threshold = 0.25f;
            nms_threshold = 0.5f;

            classer_path = "model/lable.txt";
            class_names = File.ReadAllLines(classer_path, Encoding.UTF8);
            class_num = class_names.Length;

            image_path = "test_img/zidane.jpg";
            pictureBox1.Image = new Bitmap(image_path);

        }

        ///


        /// 保存
        ///
        ///
        ///
        private void button3_Click(object sender, EventArgs e)
        {
            if (pictureBox2.Image == null)
            {
                return;
            }
            Bitmap output = new Bitmap(pictureBox2.Image);
            SaveFileDialog sdf = new SaveFileDialog();
            sdf.Title = "保存";
            sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
            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;
                        }
                    case 4:
                        {
                            output.Save(sdf.FileName, ImageFormat.Emf);
                            break;
                        }
                    case 5:
                        {
                            output.Save(sdf.FileName, ImageFormat.Exif);
                            break;
                        }
                    case 6:
                        {
                            output.Save(sdf.FileName, ImageFormat.Gif);
                            break;
                        }
                    case 7:
                        {
                            output.Save(sdf.FileName, ImageFormat.Icon);
                            break;
                        }

                    case 8:
                        {
                            output.Save(sdf.FileName, ImageFormat.Tiff);
                            break;
                        }
                    case 9:
                        {
                            output.Save(sdf.FileName, ImageFormat.Wmf);
                            break;
                        }
                }
                MessageBox.Show("保存成功，位置：" + sdf.FileName);
            }
        }

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

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

        public  void ShowNormalImg(Image img)
        {
            if (img == null) return;

            frmShow frm = new frmShow();

            frm.Width = Screen.PrimaryScreen.Bounds.Width;
            frm.Height = Screen.PrimaryScreen.Bounds.Height;

            if (frm.Width > img.Width)
            {
                frm.Width = img.Width;
            }

            if (frm.Height > img.Height)
            {
                frm.Height = img.Height;
            }

            bool b = frm.richTextBox1.ReadOnly;
            Clipboard.SetDataObject(img, true);
            frm.richTextBox1.ReadOnly = false;
            frm.richTextBox1.Paste(DataFormats.GetFormat(DataFormats.Bitmap));
            frm.richTextBox1.ReadOnly = b;

            frm.ShowDialog();

        }

        public unsafe float[] Transpose(float[] tensorData, int rows, int cols)
        {
            float[] transposedTensorData = new float[tensorData.Length];

            fixed (float* pTensorData = tensorData)
            {
                fixed (float* pTransposedData = transposedTensorData)
                {
                    for (int i = 0; i < rows; i++)
                    {
                        for (int j = 0; j < cols; j++)
                        {
                            int index = i * cols + j;
                            int transposedIndex = j * rows + i;
                            pTransposedData[transposedIndex] = pTensorData[index];
                        }
                    }
                }
            }
            return transposedTensorData;
        }
    }

    public class DetectionResult
    {
        public DetectionResult(int ClassId, string Class, Rect Rect, float Confidence)
        {
            this.ClassId = ClassId;
            this.Confidence = Confidence;
            this.Rect = Rect;
            this.Class = Class;
        }

        public string Class { get; set; }

        public int ClassId { get; set; }

        public float Confidence { get; set; }

        public Rect Rect { get; set; }

    }

}
 

  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.Drawing;
  8. using System.Drawing.Imaging;
  9. using System.IO;
  10. using System.Linq;
  11. using System.Text;
  12. using System.Windows.Forms;
  13.  
  14. namespace Onnx_Demo
  15. {
  16.     public partial class Form1 : Form
  17.     {
  18.         public Form1()
  19.         {
  20.             InitializeComponent();
  21.         }
  22.  
  23.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
  24.         string image_path = ""; 
  25.         string model_path;
  26.         string classer_path;
  27.         public string[] class_names;
  28.         public int class_num;
  29.  
  30.         DateTime dt1 = DateTime.Now;
  31.         DateTime dt2 = DateTime.Now;
  32.  
  33.         int input_height;
  34.         int input_width;
  35.         float ratio_height;
  36.         float ratio_width;
  37.  
  38.         InferenceSession onnx_session;
  39.  
  40.         int box_num;
  41.         float conf_threshold;
  42.         float nms_threshold;
  43.  
  44.         /// <summary>
  45.         /// 选择图片
  46.         /// </summary>
  47.         /// <param name="sender"></param>
  48.         /// <param name="e"></param>
  49.         private void button1_Click(object sender, EventArgs e)
  50.         {
  51.             OpenFileDialog ofd = new OpenFileDialog();
  52.             ofd.Filter = fileFilter;
  53.             if (ofd.ShowDialog() != DialogResult.OK) return;
  54.  
  55.             pictureBox1.Image = null;
  56.  
  57.             image_path = ofd.FileName;
  58.             pictureBox1.Image = new Bitmap(image_path);
  59.  
  60.             textBox1.Text = "";
  61.             pictureBox2.Image = null;
  62.         }
  63.  
  64.         /// <summary>
  65.         /// 推理
  66.         /// </summary>
  67.         /// <param name="sender"></param>
  68.         /// <param name="e"></param>
  69.         private void button2_Click(object sender, EventArgs e)
  70.         {
  71.             if (image_path == "")
  72.             {
  73.                 return;
  74.             }
  75.  
  76.             button2.Enabled = false;
  77.             pictureBox2.Image = null;
  78.             textBox1.Text = "";
  79.             Application.DoEvents();
  80.  
  81.             Mat image = new Mat(image_path);
  82.             
  83.             //图片缩放
  84.             int height = image.Rows;
  85.             int width = image.Cols;
  86.             Mat temp_image = image.Clone();
  87.             if (height > input_height || width > input_width)
  88.             {
  89.                 float scale = Math.Min((float)input_height / height, (float)input_width / width);
  90.                 OpenCvSharp.Size new_size = new OpenCvSharp.Size((int)(width * scale), (int)(height * scale));
  91.                 Cv2.Resize(image, temp_image, new_size);
  92.             }
  93.             ratio_height = (float)height / temp_image.Rows;
  94.             ratio_width = (float)width / temp_image.Cols;
  95.             Mat input_img = new Mat();
  96.             Cv2.CopyMakeBorder(temp_image, input_img, 0, input_height - temp_image.Rows, 0, input_width - temp_image.Cols, BorderTypes.Constant, 0);
  97.  
  98.             //Cv2.ImShow("input_img", input_img);
  99.  
  100.             //输入Tensor
  101.             Tensor<float> input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
  102.             for (int y = 0; y < input_img.Height; y++)
  103.             {
  104.                 for (int x = 0; x < input_img.Width; x++)
  105.                 {
  106.                     input_tensor[0, 0, y, x] = input_img.At<Vec3b>(y, x)[0] / 255f;
  107.                     input_tensor[0, 1, y, x] = input_img.At<Vec3b>(y, x)[1] / 255f;
  108.                     input_tensor[0, 2, y, x] = input_img.At<Vec3b>(y, x)[2] / 255f;
  109.                 }
  110.             }
  111.  
  112.             List<NamedOnnxValue> input_container = new List<NamedOnnxValue>
  113.             {
  114.                 NamedOnnxValue.CreateFromTensor("images", input_tensor)
  115.             };
  116.  
  117.             //推理
  118.             dt1 = DateTime.Now;
  119.             var ort_outputs = onnx_session.Run(input_container).ToArray();
  120.             dt2 = DateTime.Now;
  121.  
  122.             float[] data = Transpose(ort_outputs[0].AsTensor<float>().ToArray(), 4 + class_num, box_num);
  123.  
  124.             float[] confidenceInfo = new float[class_num];
  125.             float[] rectData = new float[4];
  126.  
  127.             List<DetectionResult> detResults = new List<DetectionResult>();
  128.  
  129.             for (int i = 0; i < box_num; i++)
  130.             {
  131.                 Array.Copy(data, i * (class_num + 4), rectData, 0, 4);
  132.                 Array.Copy(data, i * (class_num + 4) + 4, confidenceInfo, 0, class_num);
  133.  
  134.                 float score = confidenceInfo.Max(); // 获取最大值
  135.  
  136.                 int maxIndex = Array.IndexOf(confidenceInfo, score); // 获取最大值的位置
  137.  
  138.                 int _centerX = (int)(rectData[0] * ratio_width);
  139.                 int _centerY = (int)(rectData[1] * ratio_height);
  140.                 int _width = (int)(rectData[2] * ratio_width);
  141.                 int _height = (int)(rectData[3] * ratio_height);
  142.  
  143.                 detResults.Add(new DetectionResult(
  144.                    maxIndex,
  145.                    class_names[maxIndex],
  146.                    new Rect(_centerX - _width / 2, _centerY - _height / 2, _width, _height),
  147.                    score));
  148.             }
  149.  
  150.             //NMS
  151.             CvDnn.NMSBoxes(detResults.Select(x => x.Rect), detResults.Select(x => x.Confidence), conf_threshold, nms_threshold, out int[] indices);
  152.             detResults = detResults.Where((x, index) => indices.Contains(index)).ToList();
  153.  
  154.             //绘制结果
  155.             Mat result_image = image.Clone();
  156.             foreach (DetectionResult r in detResults)
  157.             {
  158.                 Cv2.PutText(result_image, $"{r.Class}:{r.Confidence:P0}", new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
  159.                 Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2);
  160.             }
  161.  
  162.             pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
  163.             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
  164.  
  165.             button2.Enabled = true;
  166.         }
  167.  
  168.         /// <summary>
  169.         ///窗体加载
  170.         /// </summary>
  171.         /// <param name="sender"></param>
  172.         /// <param name="e"></param>
  173.         private void Form1_Load(object sender, EventArgs e)
  174.         {
  175.             model_path = "model/yolo11n.onnx";
  176.  
  177.             //创建输出会话，用于输出模型读取信息
  178.             SessionOptions options = new SessionOptions();
  179.             options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
  180.             options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
  181.  
  182.             // 创建推理模型类，读取模型文件
  183.             onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径
  184.  
  185.             input_height = 640;
  186.             input_width = 640;
  187.  
  188.             box_num = 8400;
  189.             conf_threshold = 0.25f;
  190.             nms_threshold = 0.5f;
  191.  
  192.             classer_path = "model/lable.txt";
  193.             class_names = File.ReadAllLines(classer_path, Encoding.UTF8);
  194.             class_num = class_names.Length;
  195.  
  196.             image_path = "test_img/zidane.jpg";
  197.             pictureBox1.Image = new Bitmap(image_path);
  198.  
  199.         }
  200.  
  201.         /// <summary>
  202.         /// 保存
  203.         /// </summary>
  204.         /// <param name="sender"></param>
  205.         /// <param name="e"></param>
  206.         private void button3_Click(object sender, EventArgs e)
  207.         {
  208.             if (pictureBox2.Image == null)
  209.             {
  210.                 return;
  211.             }
  212.             Bitmap output = new Bitmap(pictureBox2.Image);
  213.             SaveFileDialog sdf = new SaveFileDialog();
  214.             sdf.Title = "保存";
  215.             sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
  216.             if (sdf.ShowDialog() == DialogResult.OK)
  217.             {
  218.                 switch (sdf.FilterIndex)
  219.                 {
  220.                     case 1:
  221.                         {
  222.                             output.Save(sdf.FileName, ImageFormat.Jpeg);
  223.                             break;
  224.                         }
  225.                     case 2:
  226.                         {
  227.                             output.Save(sdf.FileName, ImageFormat.Png);
  228.                             break;
  229.                         }
  230.                     case 3:
  231.                         {
  232.                             output.Save(sdf.FileName, ImageFormat.Bmp);
  233.                             break;
  234.                         }
  235.                     case 4:
  236.                         {
  237.                             output.Save(sdf.FileName, ImageFormat.Emf);
  238.                             break;
  239.                         }
  240.                     case 5:
  241.                         {
  242.                             output.Save(sdf.FileName, ImageFormat.Exif);
  243.                             break;
  244.                         }
  245.                     case 6:
  246.                         {
  247.                             output.Save(sdf.FileName, ImageFormat.Gif);
  248.                             break;
  249.                         }
  250.                     case 7:
  251.                         {
  252.                             output.Save(sdf.FileName, ImageFormat.Icon);
  253.                             break;
  254.                         }
  255.  
  256.                     case 8:
  257.                         {
  258.                             output.Save(sdf.FileName, ImageFormat.Tiff);
  259.                             break;
  260.                         }
  261.                     case 9:
  262.                         {
  263.                             output.Save(sdf.FileName, ImageFormat.Wmf);
  264.                             break;
  265.                         }
  266.                 }
  267.                 MessageBox.Show("保存成功，位置：" + sdf.FileName);
  268.             }
  269.         }
  270.  
  271.         private void pictureBox1_DoubleClick(object sender, EventArgs e)
  272.         {
  273.             ShowNormalImg(pictureBox1.Image);
  274.         }
  275.  
  276.         private void pictureBox2_DoubleClick(object sender, EventArgs e)
  277.         {
  278.             ShowNormalImg(pictureBox2.Image);
  279.         }
  280.  
  281.         public  void ShowNormalImg(Image img)
  282.         {
  283.             if (img == null) return;
  284.  
  285.             frmShow frm = new frmShow();
  286.  
  287.             frm.Width = Screen.PrimaryScreen.Bounds.Width;
  288.             frm.Height = Screen.PrimaryScreen.Bounds.Height;
  289.  
  290.             if (frm.Width > img.Width)
  291.             {
  292.                 frm.Width = img.Width;
  293.             }
  294.  
  295.             if (frm.Height > img.Height)
  296.             {
  297.                 frm.Height = img.Height;
  298.             }
  299.  
  300.             bool b = frm.richTextBox1.ReadOnly;
  301.             Clipboard.SetDataObject(img, true);
  302.             frm.richTextBox1.ReadOnly = false;
  303.             frm.richTextBox1.Paste(DataFormats.GetFormat(DataFormats.Bitmap));
  304.             frm.richTextBox1.ReadOnly = b;
  305.  
  306.             frm.ShowDialog();
  307.  
  308.         }
  309.  
  310.         public unsafe float[] Transpose(float[] tensorData, int rows, int cols)
  311.         {
  312.             float[] transposedTensorData = new float[tensorData.Length];
  313.  
  314.             fixed (float* pTensorData = tensorData)
  315.             {
  316.                 fixed (float* pTransposedData = transposedTensorData)
  317.                 {
  318.                     for (int i = 0; i < rows; i++)
  319.                     {
  320.                         for (int j = 0; j < cols; j++)
  321.                         {
  322.                             int index = i * cols + j;
  323.                             int transposedIndex = j * rows + i;
  324.                             pTransposedData[transposedIndex] = pTensorData[index];
  325.                         }
  326.                     }
  327.                 }
  328.             }
  329.             return transposedTensorData;
  330.         }
  331.     }
  332.  
  333.     public class DetectionResult
  334.     {
  335.         public DetectionResult(int ClassId, string Class, Rect Rect, float Confidence)
  336.         {
  337.             this.ClassId = ClassId;
  338.             this.Confidence = Confidence;
  339.             this.Rect = Rect;
  340.             this.Class = Class;
  341.         }
  342.  
  343.         public string Class { get; set; }
  344.  
  345.         public int ClassId { get; set; }
  346.  
  347.         public float Confidence { get; set; }
  348.  
  349.         public Rect Rect { get; set; }
  350.  
  351.     }
  352.  
  353. }

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