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C# OnnxRuntime yolov8 纸箱检测

news 2025/8/10 7:59:46

目录

效果

模型信息

项目

代码

数据集

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效果

模型信息

Model Properties
-------------------------
date：2025-08-09T09:30:23.475375
description：Ultralytics YOLOv8n model trained on C:\Work\yolov8\config\carton.yaml
author：Ultralytics
version：8.1.29
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: 'carton'}
---------------------------------------------------------------

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

Outputs
-------------------------
name：output0
tensor：Float[1, 5, 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.Linq;
using System.Text;
using System.Windows.Forms;

namespace Onnx_Yolov8_Demo
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        string classer_path;
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
        string model_path;
        Mat image;
        DetectionResult result_pro;
        Mat result_image;
        Result result;

        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<float> input_tensor;
        List<NamedOnnxValue> input_container;
        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
        DisposableNamedOnnxValue[] results_onnxvalue;

        Tensor<float> result_tensors;

        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 = "";
            image = new Mat(image_path);
            pictureBox2.Image = null;
        }

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

            float score_threshold = 0.5f;
            float nms_threshold = 0.5f;

            button2.Enabled = false;
            pictureBox2.Image = null;
            textBox1.Text = "";

            Application.DoEvents();

            //图片缩放
            image = new Mat(image_path);
            int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
            Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
            Rect roi = new Rect(0, 0, image.Cols, image.Rows);
            image.CopyTo(new Mat(max_image, roi));

            float[] result_array = new float[8400 * 84];
            float[] factors = new float[2];
            factors[0] = factors[1] = (float)(max_image_length / 640.0);

            // 将图片转为RGB通道
            Mat image_rgb = new Mat();
            Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);
            Mat resize_image = new Mat();
            Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));

            // 输入Tensor
            for (int y = 0; y < resize_image.Height; y++)
            {
                for (int x = 0; x < resize_image.Width; x++)
                {
                    input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;
                    input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;
                    input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;
                }
            }

            //将 input_tensor 放入一个输入参数的容器，并指定名称
            input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));

            dt1 = DateTime.Now;
            //运行 Inference 并获取结果
            result_infer = onnx_session.Run(input_container);
            dt2 = DateTime.Now;

            // 将输出结果转为DisposableNamedOnnxValue数组
            results_onnxvalue = result_infer.ToArray();

            // 读取第一个节点输出并转为Tensor数据
            result_tensors = results_onnxvalue[0].AsTensor<float>();

            result_array = result_tensors.ToArray();

            resize_image.Dispose();
            image_rgb.Dispose();

            result_pro = new DetectionResult(classer_path, factors, score_threshold, nms_threshold);
            result = result_pro.process_result(result_array);
            result_image =  result_pro.draw_result(result, image.Clone());

            StringBuilder sb = new StringBuilder();
            if (!result_image.Empty())
            {
                pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
                //textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
                sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");

                sb.AppendLine("--------------------------------------------------");
                sb.AppendLine("{lable}{scores}({X},{Y},{Width},{Height})");
                sb.AppendLine("--------------------------------------------------");
                // 识别结果

                for (int i = 0; i < result.length; i++)
                {
                    //Scalar color= Scalar.RandomColor();
                    Scalar color = new Scalar(0, 0, 255);

                    string lable = string.Format("{0}\t{1}\t({2},{3},{4},{5})"
                        , result.classes[i]
                        , result.scores[i].ToString("P2")
                        , result.rects[i].X
                        , result.rects[i].Y
                        , result.rects[i].Width
                        , result.rects[i].Height
                        ); 

                    sb.AppendLine(lable);

                    //Cv2.Rectangle(image, result.rects[i], color, 2, LineTypes.Link8);

                    //Cv2.Rectangle(image
                    //    , new Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20)
                    //    , new Point(result.rects[i].TopLeft.X - 1 + lable.Length * 12, result.rects[i].TopLeft.Y)
                    //    , color
                    //    , -1);

                    //Cv2.PutText(image, lable, new Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);
                
                
                }


                textBox1.Text = sb.ToString();
            }
            else
            {
                textBox1.Text = "无信息";
            }

            button2.Enabled = true;
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            startupPath = System.Windows.Forms.Application.StartupPath;

            model_path = "model/carton.onnx";
            classer_path = "model/lable.txt";

            // 创建输出会话，用于输出模型读取信息
            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，读取本地模型文件
            onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径

            // 输入Tensor
            input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
            // 创建输入容器
            input_container = new List<NamedOnnxValue>();

            image_path = "test_img/4.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|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);
            }
        }
    }

    public class DetectionResult : ResultBase
    {
        /// <summary>
        /// 结果处理类构造
        /// </summary>
        /// <param name="path">识别类别文件地址</param>
        /// <param name="scales">缩放比例</param>
        /// <param name="score_threshold">分数阈值</param>
        /// <param name="nms_threshold">非极大值抑制阈值</param>
        public DetectionResult(string path, float[] scales, float score_threshold = 0.25f, float nms_threshold = 0.5f)
        {
            read_class_names(path);
            this.scales = scales;
            this.score_threshold = score_threshold;
            this.nms_threshold = nms_threshold;
        }

        /// <summary>
        /// 结果处理
        /// </summary>
        /// <param name="result">模型预测输出</param>
        /// <returns>模型识别结果</returns>
        public Result process_result(float[] result)
        {
            Mat result_data = new Mat(4 + class_num, 8400, MatType.CV_32F, result);
            result_data = result_data.T();

            // 存放结果list
            List<Rect> position_boxes = new List<Rect>();
            List<int> class_ids = new List<int>();
            List<float> confidences = new List<float>();
            // 预处理输出结果
            for (int i = 0; i < result_data.Rows; i++)
            {
                Mat classes_scores = result_data.Row(i).ColRange(4, 4 + class_num);
                OpenCvSharp.Point max_classId_point, min_classId_point;
                double max_score, min_score;
                // 获取一组数据中最大值及其位置
                Cv2.MinMaxLoc(classes_scores, out min_score, out max_score,
                    out min_classId_point, out max_classId_point);
                // 置信度 0～1之间
                // 获取识别框信息
                if (max_score > this.score_threshold)
                {
                    float cx = result_data.At<float>(i, 0);
                    float cy = result_data.At<float>(i, 1);
                    float ow = result_data.At<float>(i, 2);
                    float oh = result_data.At<float>(i, 3);
                    int x = (int)((cx - 0.5 * ow) * this.scales[0]);
                    int y = (int)((cy - 0.5 * oh) * this.scales[1]);
                    int width = (int)(ow * this.scales[0]);
                    int height = (int)(oh * this.scales[1]);
                    Rect box = new Rect();
                    box.X = x;
                    box.Y = y;
                    box.Width = width;
                    box.Height = height;

                    position_boxes.Add(box);
                    class_ids.Add(max_classId_point.X);
                    confidences.Add((float)max_score);
                }
            }

            // NMS非极大值抑制
            int[] indexes = new int[position_boxes.Count];
            CvDnn.NMSBoxes(position_boxes, confidences, this.score_threshold, this.nms_threshold, out indexes);

            Result re_result = new Result();
            // 将识别结果绘制到图片上
            for (int i = 0; i < indexes.Length; i++)
            {
                int index = indexes[i];
                int idx = class_ids[index];
                re_result.add(confidences[index], position_boxes[index], this.class_names[class_ids[index]]);
            }
            return re_result;
        }

        /// <summary>
        /// 结果绘制
        /// </summary>
        /// <param name="result">识别结果</param>
        /// <param name="image">绘制图片</param>
        /// <returns></returns>
        public Mat draw_result(Result result, Mat image)
        {
            // 将识别结果绘制到图片上
            for (int i = 0; i < result.length; i++)
            {
                //Scalar color= Scalar.RandomColor();
                Scalar color = new Scalar(0, 0, 255);

                string lable = result.classes[i] + "-" + result.scores[i].ToString("0.00");

                Cv2.Rectangle(image, result.rects[i], color, 2, LineTypes.Link8);
               
                Cv2.Rectangle(image
                    , new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20)
                    , new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1 + lable.Length * 12, result.rects[i].TopLeft.Y)
                    , color
                    , -1);

                Cv2.PutText(image, lable, new OpenCvSharp.Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);
            }
            return image;
        }

    }
}
 

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.Linq;
using System.Text;
using System.Windows.Forms;namespace Onnx_Yolov8_Demo
{public partial class Form1 : Form{public Form1(){InitializeComponent();}string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";string image_path = "";string startupPath;string classer_path;DateTime dt1 = DateTime.Now;DateTime dt2 = DateTime.Now;string model_path;Mat image;DetectionResult result_pro;Mat result_image;Result result;SessionOptions options;InferenceSession onnx_session;Tensor<float> input_tensor;List<NamedOnnxValue> input_container;IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;DisposableNamedOnnxValue[] results_onnxvalue;Tensor<float> result_tensors;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 = "";image = new Mat(image_path);pictureBox2.Image = null;}private void button2_Click(object sender, EventArgs e){if (image_path == ""){return;}float score_threshold = 0.5f;float nms_threshold = 0.5f;button2.Enabled = false;pictureBox2.Image = null;textBox1.Text = "";Application.DoEvents();//图片缩放image = new Mat(image_path);int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);Rect roi = new Rect(0, 0, image.Cols, image.Rows);image.CopyTo(new Mat(max_image, roi));float[] result_array = new float[8400 * 84];float[] factors = new float[2];factors[0] = factors[1] = (float)(max_image_length / 640.0);// 将图片转为RGB通道Mat image_rgb = new Mat();Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);Mat resize_image = new Mat();Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));// 输入Tensorfor (int y = 0; y < resize_image.Height; y++){for (int x = 0; x < resize_image.Width; x++){input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;}}//将 input_tensor 放入一个输入参数的容器，并指定名称input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));dt1 = DateTime.Now;//运行 Inference 并获取结果result_infer = onnx_session.Run(input_container);dt2 = DateTime.Now;// 将输出结果转为DisposableNamedOnnxValue数组results_onnxvalue = result_infer.ToArray();// 读取第一个节点输出并转为Tensor数据result_tensors = results_onnxvalue[0].AsTensor<float>();result_array = result_tensors.ToArray();resize_image.Dispose();image_rgb.Dispose();result_pro = new DetectionResult(classer_path, factors, score_threshold, nms_threshold);result = result_pro.process_result(result_array);result_image =  result_pro.draw_result(result, image.Clone());StringBuilder sb = new StringBuilder();if (!result_image.Empty()){pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());//textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");sb.AppendLine("--------------------------------------------------");sb.AppendLine("{lable}{scores}({X},{Y},{Width},{Height})");sb.AppendLine("--------------------------------------------------");// 识别结果for (int i = 0; i < result.length; i++){//Scalar color= Scalar.RandomColor();Scalar color = new Scalar(0, 0, 255);string lable = string.Format("{0}\t{1}\t({2},{3},{4},{5})", result.classes[i], result.scores[i].ToString("P2"), result.rects[i].X, result.rects[i].Y, result.rects[i].Width, result.rects[i].Height); sb.AppendLine(lable);//Cv2.Rectangle(image, result.rects[i], color, 2, LineTypes.Link8);//Cv2.Rectangle(image//    , new Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20)//    , new Point(result.rects[i].TopLeft.X - 1 + lable.Length * 12, result.rects[i].TopLeft.Y)//    , color//    , -1);//Cv2.PutText(image, lable, new Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);}textBox1.Text = sb.ToString();}else{textBox1.Text = "无信息";}button2.Enabled = true;}private void Form1_Load(object sender, EventArgs e){startupPath = System.Windows.Forms.Application.StartupPath;model_path = "model/carton.onnx";classer_path = "model/lable.txt";// 创建输出会话，用于输出模型读取信息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，读取本地模型文件onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径// 输入Tensorinput_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });// 创建输入容器input_container = new List<NamedOnnxValue>();image_path = "test_img/4.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|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);}}}public class DetectionResult : ResultBase{/// <summary>/// 结果处理类构造/// </summary>/// <param name="path">识别类别文件地址</param>/// <param name="scales">缩放比例</param>/// <param name="score_threshold">分数阈值</param>/// <param name="nms_threshold">非极大值抑制阈值</param>public DetectionResult(string path, float[] scales, float score_threshold = 0.25f, float nms_threshold = 0.5f){read_class_names(path);this.scales = scales;this.score_threshold = score_threshold;this.nms_threshold = nms_threshold;}/// <summary>/// 结果处理/// </summary>/// <param name="result">模型预测输出</param>/// <returns>模型识别结果</returns>public Result process_result(float[] result){Mat result_data = new Mat(4 + class_num, 8400, MatType.CV_32F, result);result_data = result_data.T();// 存放结果listList<Rect> position_boxes = new List<Rect>();List<int> class_ids = new List<int>();List<float> confidences = new List<float>();// 预处理输出结果for (int i = 0; i < result_data.Rows; i++){Mat classes_scores = result_data.Row(i).ColRange(4, 4 + class_num);OpenCvSharp.Point max_classId_point, min_classId_point;double max_score, min_score;// 获取一组数据中最大值及其位置Cv2.MinMaxLoc(classes_scores, out min_score, out max_score,out min_classId_point, out max_classId_point);// 置信度 0～1之间// 获取识别框信息if (max_score > this.score_threshold){float cx = result_data.At<float>(i, 0);float cy = result_data.At<float>(i, 1);float ow = result_data.At<float>(i, 2);float oh = result_data.At<float>(i, 3);int x = (int)((cx - 0.5 * ow) * this.scales[0]);int y = (int)((cy - 0.5 * oh) * this.scales[1]);int width = (int)(ow * this.scales[0]);int height = (int)(oh * this.scales[1]);Rect box = new Rect();box.X = x;box.Y = y;box.Width = width;box.Height = height;position_boxes.Add(box);class_ids.Add(max_classId_point.X);confidences.Add((float)max_score);}}// NMS非极大值抑制int[] indexes = new int[position_boxes.Count];CvDnn.NMSBoxes(position_boxes, confidences, this.score_threshold, this.nms_threshold, out indexes);Result re_result = new Result();// 将识别结果绘制到图片上for (int i = 0; i < indexes.Length; i++){int index = indexes[i];int idx = class_ids[index];re_result.add(confidences[index], position_boxes[index], this.class_names[class_ids[index]]);}return re_result;}/// <summary>/// 结果绘制/// </summary>/// <param name="result">识别结果</param>/// <param name="image">绘制图片</param>/// <returns></returns>public Mat draw_result(Result result, Mat image){// 将识别结果绘制到图片上for (int i = 0; i < result.length; i++){//Scalar color= Scalar.RandomColor();Scalar color = new Scalar(0, 0, 255);string lable = result.classes[i] + "-" + result.scores[i].ToString("0.00");Cv2.Rectangle(image, result.rects[i], color, 2, LineTypes.Link8);Cv2.Rectangle(image, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20), new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1 + lable.Length * 12, result.rects[i].TopLeft.Y), color, -1);Cv2.PutText(image, lable, new OpenCvSharp.Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);}return image;}}
}

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