using OpenCvSharp;
 using OpenCvSharp.Dnn;
 using System;
 using System.Collections.Generic;
 using System.Drawing;
 using System.IO;
 using System.Linq;
 using System.Windows.Forms;namespace OpenCvSharp_DNN_Demo
 {
     public partial class frmMain : Form
     {
         public frmMain()
         {
             InitializeComponent();
         }        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
         string image_path = "";        DateTime dt1 = DateTime.Now;
         DateTime dt2 = DateTime.Now;        float confThreshold;
         float nmsThreshold;
         string modelpath;        int inpHeight;
         int inpWidth;        List<string> class_names;
         int num_class;        Net opencv_net;
         Mat BN_image;        Mat image;
         Mat result_image;        private void button1_Click(object sender, EventArgs e)
         {
             OpenFileDialog ofd = new OpenFileDialog();
             ofd.Filter = fileFilter;
             if (ofd.ShowDialog() != DialogResult.OK) return;            pictureBox1.Image = null;
             pictureBox2.Image = null;
             textBox1.Text = "";            image_path = ofd.FileName;
             pictureBox1.Image = new Bitmap(image_path);
             image = new Mat(image_path);
         }        private void Form1_Load(object sender, EventArgs e)
         {
             confThreshold = 0.3f;
             nmsThreshold = 0.5f;
             modelpath = "model/yolov6s.onnx";            inpHeight = 640;
             inpWidth = 640;            opencv_net = CvDnn.ReadNetFromOnnx(modelpath);
            class_names = new List<string>();
             StreamReader sr = new StreamReader("model/coco.names");
             string line;
             while ((line = sr.ReadLine()) != null)
             {
                 class_names.Add(line);
             }
             num_class = class_names.Count();            image_path = "test_img/image3.jpg";
             pictureBox1.Image = new Bitmap(image_path);        }
        float sigmoid(float x)
         {
             return (float)(1.0 / (1 + Math.Exp(-x)));
         }        Mat ResizeImage(Mat srcimg, out int newh, out int neww, out int top, out int left)
         {
             int srch = srcimg.Rows, srcw = srcimg.Cols;
             top = 0;
             left = 0;
             newh = inpHeight;
             neww = inpWidth;
             Mat dstimg = new Mat();
             if (srch != srcw)
             {
                 float hw_scale = (float)srch / srcw;
                 if (hw_scale > 1)
                 {
                     newh = inpHeight;
                     neww = (int)(inpWidth / hw_scale);
                     Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh), 0, 0, InterpolationFlags.Area);
                     left = (int)((inpWidth - neww) * 0.5);
                     Cv2.CopyMakeBorder(dstimg, dstimg, 0, 0, left, inpWidth - neww - left, BorderTypes.Constant);
                 }
                 else
                 {
                     newh = (int)(inpHeight * hw_scale);
                     neww = inpWidth;
                     Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh), 0, 0, InterpolationFlags.Area);
                     top = (int)((inpHeight - newh) * 0.5);
                     Cv2.CopyMakeBorder(dstimg, dstimg, top, inpHeight - newh - top, 0, 0, BorderTypes.Constant);
                 }
             }
             else
             {
                 Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh));
             }
             return dstimg;
         }        private unsafe void button2_Click(object sender, EventArgs e)
         {
             if (image_path == "")
             {
                 return;
             }
             textBox1.Text = "检测中，请稍等……";
             pictureBox2.Image = null;
             Application.DoEvents();            image = new Mat(image_path);
            int newh = 0, neww = 0, padh = 0, padw = 0;
             Mat dstimg = ResizeImage(image, out newh, out neww, out padh, out padw);            BN_image = CvDnn.BlobFromImage(dstimg, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), true, false);
            //配置图片输入数据
             opencv_net.SetInput(BN_image);            //模型推理，读取推理结果
             Mat[] outs = new Mat[3] { new Mat(), new Mat(), new Mat() };
             string[] outBlobNames = opencv_net.GetUnconnectedOutLayersNames().ToArray();            dt1 = DateTime.Now;
            opencv_net.Forward(outs, outBlobNames);
            dt2 = DateTime.Now;
            int num_proposal = outs[0].Size(0);
             int nout = outs[0].Size(1);            if (outs[0].Dims > 2)
             {
                 num_proposal = outs[0].Size(1);
                 nout = outs[0].Size(2);
                 outs[0] = outs[0].Reshape(0, num_proposal);
             }            float ratioh = 1.0f * image.Rows / newh, ratiow = 1.0f * image.Cols / neww;
             int n = 0, row_ind = 0; ///cx,cy,w,h,box_score,class_score
             float* pdata = (float*)outs[0].Data;            List<Rect> boxes = new List<Rect>();
             List<float> confidences = new List<float>();
             List<int> classIds = new List<int>();            for (n = 0; n < num_proposal; n++)
             {
                 float box_score = pdata[4];                if (box_score > confThreshold)
                 {
                     Mat scores = outs[0].Row(row_ind).ColRange(5, nout);
                     double minVal, max_class_socre;
                     OpenCvSharp.Point minLoc, classIdPoint;
                     // Get the value and location of the maximum score
                     Cv2.MinMaxLoc(scores, out minVal, out max_class_socre, out minLoc, out classIdPoint);
                     max_class_socre *= box_score;                    int class_idx = classIdPoint.X;
                    float cx = (pdata[0] - padw) * ratiow;  //cx
                     float cy = (pdata[1] - padh) * ratioh;   //cy
                     float w = pdata[2] * ratiow;   //w
                     float h = pdata[3] * ratioh;  //h                    int left = (int)(cx - 0.5 * w);
                     int top = (int)(cy - 0.5 * h);                    confidences.Add((float)max_class_socre);
                     boxes.Add(new Rect(left, top, (int)w, (int)h));
                     classIds.Add(class_idx);
                 }
                 row_ind++;
                 pdata += nout;            }
            int[] indices;
             CvDnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, out indices);            result_image = image.Clone();
            for (int ii = 0; ii < indices.Length; ++ii)
             {
                 int idx = indices[ii];
                 Rect box = boxes[idx];
                 Cv2.Rectangle(result_image, new OpenCvSharp.Point(box.X, box.Y), new OpenCvSharp.Point(box.X + box.Width, box.Y + box.Height), new Scalar(0, 0, 255), 2);
                 string label = class_names[classIds[idx]] + ":" + confidences[idx].ToString("0.00");
                 Cv2.PutText(result_image, label, new OpenCvSharp.Point(box.X, box.Y - 5), HersheyFonts.HersheySimplex, 0.75, new Scalar(0, 0, 255), 1);
             }            pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
         }        private void pictureBox2_DoubleClick(object sender, EventArgs e)
         {
             Common.ShowNormalImg(pictureBox2.Image);
         }        private void pictureBox1_DoubleClick(object sender, EventArgs e)
         {
             Common.ShowNormalImg(pictureBox1.Image);
         }
     }
 }

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Windows.Forms;

namespace OpenCvSharp_DNN_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";

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

        float confThreshold;
        float nmsThreshold;
        string modelpath;

        int inpHeight;
        int inpWidth;

        List<string> class_names;
        int num_class;

        Net opencv_net;
        Mat BN_image;

        Mat image;
        Mat result_image;

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;
            pictureBox2.Image = null;
            textBox1.Text = "";

            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            confThreshold = 0.3f;
            nmsThreshold = 0.5f;
            modelpath = "model/yolov6s.onnx";

            inpHeight = 640;
            inpWidth = 640;

            opencv_net = CvDnn.ReadNetFromOnnx(modelpath);

            class_names = new List<string>();
            StreamReader sr = new StreamReader("model/coco.names");
            string line;
            while ((line = sr.ReadLine()) != null)
            {
                class_names.Add(line);
            }
            num_class = class_names.Count();

            image_path = "test_img/image3.jpg";
            pictureBox1.Image = new Bitmap(image_path);

        }

        float sigmoid(float x)
        {
            return (float)(1.0 / (1 + Math.Exp(-x)));
        }

        Mat ResizeImage(Mat srcimg, out int newh, out int neww, out int top, out int left)
        {
            int srch = srcimg.Rows, srcw = srcimg.Cols;
            top = 0;
            left = 0;
            newh = inpHeight;
            neww = inpWidth;
            Mat dstimg = new Mat();
            if (srch != srcw)
            {
                float hw_scale = (float)srch / srcw;
                if (hw_scale > 1)
                {
                    newh = inpHeight;
                    neww = (int)(inpWidth / hw_scale);
                    Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh), 0, 0, InterpolationFlags.Area);
                    left = (int)((inpWidth - neww) * 0.5);
                    Cv2.CopyMakeBorder(dstimg, dstimg, 0, 0, left, inpWidth - neww - left, BorderTypes.Constant);
                }
                else
                {
                    newh = (int)(inpHeight * hw_scale);
                    neww = inpWidth;
                    Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh), 0, 0, InterpolationFlags.Area);
                    top = (int)((inpHeight - newh) * 0.5);
                    Cv2.CopyMakeBorder(dstimg, dstimg, top, inpHeight - newh - top, 0, 0, BorderTypes.Constant);
                }
            }
            else
            {
                Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh));
            }
            return dstimg;
        }

        private unsafe void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            textBox1.Text = "检测中，请稍等……";
            pictureBox2.Image = null;
            Application.DoEvents();

            image = new Mat(image_path);

            int newh = 0, neww = 0, padh = 0, padw = 0;
            Mat dstimg = ResizeImage(image, out newh, out neww, out padh, out padw);

            BN_image = CvDnn.BlobFromImage(dstimg, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), true, false);

            //配置图片输入数据
            opencv_net.SetInput(BN_image);

            //模型推理，读取推理结果
            Mat[] outs = new Mat[3] { new Mat(), new Mat(), new Mat() };
            string[] outBlobNames = opencv_net.GetUnconnectedOutLayersNames().ToArray();

            dt1 = DateTime.Now;

            opencv_net.Forward(outs, outBlobNames);

            dt2 = DateTime.Now;

            int num_proposal = outs[0].Size(0);
            int nout = outs[0].Size(1);

            if (outs[0].Dims > 2)
            {
                num_proposal = outs[0].Size(1);
                nout = outs[0].Size(2);
                outs[0] = outs[0].Reshape(0, num_proposal);
            }

            float ratioh = 1.0f * image.Rows / newh, ratiow = 1.0f * image.Cols / neww;
            int n = 0, row_ind = 0; ///cx,cy,w,h,box_score,class_score
            float* pdata = (float*)outs[0].Data;

            List<Rect> boxes = new List<Rect>();
            List<float> confidences = new List<float>();
            List<int> classIds = new List<int>();

            for (n = 0; n < num_proposal; n++)
            {
                float box_score = pdata[4];

                if (box_score > confThreshold)
                {
                    Mat scores = outs[0].Row(row_ind).ColRange(5, nout);
                    double minVal, max_class_socre;
                    OpenCvSharp.Point minLoc, classIdPoint;
                    // Get the value and location of the maximum score
                    Cv2.MinMaxLoc(scores, out minVal, out max_class_socre, out minLoc, out classIdPoint);
                    max_class_socre *= box_score;

                    int class_idx = classIdPoint.X;

                    float cx = (pdata[0] - padw) * ratiow;  //cx
                    float cy = (pdata[1] - padh) * ratioh;   //cy
                    float w = pdata[2] * ratiow;   //w
                    float h = pdata[3] * ratioh;  //h

                    int left = (int)(cx - 0.5 * w);
                    int top = (int)(cy - 0.5 * h);

                    confidences.Add((float)max_class_socre);
                    boxes.Add(new Rect(left, top, (int)w, (int)h));
                    classIds.Add(class_idx);
                }
                row_ind++;
                pdata += nout;

            }

            int[] indices;
            CvDnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, out indices);

            result_image = image.Clone();

            for (int ii = 0; ii < indices.Length; ++ii)
            {
                int idx = indices[ii];
                Rect box = boxes[idx];
                Cv2.Rectangle(result_image, new OpenCvSharp.Point(box.X, box.Y), new OpenCvSharp.Point(box.X + box.Width, box.Y + box.Height), new Scalar(0, 0, 255), 2);
                string label = class_names[classIds[idx]] + ":" + confidences[idx].ToString("0.00");
                Cv2.PutText(result_image, label, new OpenCvSharp.Point(box.X, box.Y - 5), HersheyFonts.HersheySimplex, 0.75, new Scalar(0, 0, 255), 1);
            }

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

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

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