C# OpenCvSharp DNN FreeYOLO 密集行人检测

目录

效果

模型信息

项目

代码

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C# OpenCvSharp DNN FreeYOLO 密集行人检测

效果

模型信息

Inputs
-------------------------
name：input
tensor：Float[1, 3, 192, 320]
---------------------------------------------------------------

Outputs
-------------------------
name：output
tensor：Float[1, 1260, 6]
---------------------------------------------------------------

项目

代码

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
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;

? ? ? ? int num_stride = 3;
? ? ? ? float[] strides = new float[3] { 8.0f, 16.0f, 32.0f };

? ? ? ? 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 = 0.6f;
? ? ? ? ? ? nmsThreshold = 0.5f;

? ? ? ? ? ? modelpath = "model/yolo_free_huge_crowdhuman_192x320.onnx";

? ? ? ? ? ? inpHeight = 192;
? ? ? ? ? ? inpWidth = 320;

? ? ? ? ? ? opencv_net = CvDnn.ReadNetFromOnnx(modelpath);

? ? ? ? ? ? class_names = new List<string>();
? ? ? ? ? ? class_names.Add("person");
? ? ? ? ? ? num_class = 1;

? ? ? ? ? ? image_path = "test_img/1.jpg";
? ? ? ? ? ? pictureBox1.Image = new Bitmap(image_path);

? ? ? ? }

? ? ? ? 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);

? ? ? ? ? ? float ratio = Math.Min(1.0f * inpHeight / image.Rows, 1.0f * inpWidth / image.Cols);
? ? ? ? ? ? int neww = (int)(image.Cols * ratio);
? ? ? ? ? ? int newh = (int)(image.Rows * ratio);

? ? ? ? ? ? Mat dstimg = new Mat();
? ? ? ? ? ? Cv2.Resize(image, dstimg, new OpenCvSharp.Size(neww, newh));

? ? ? ? ? ? Cv2.CopyMakeBorder(dstimg, dstimg, 0, inpHeight - newh, 0, inpWidth - neww, BorderTypes.Constant);

? ? ? ? ? ? BN_image = CvDnn.BlobFromImage(dstimg);

? ? ? ? ? ? //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[1] { 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(1);
? ? ? ? ? ? int nout = outs[0].Size(2);

? ? ? ? ? ? float* pdata = (float*)outs[0].Data;

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

? ? ? ? ? ? for (int n = 0; n < num_stride; n++)
? ? ? ? ? ? {
? ? ? ? ? ? ? ? int num_grid_x = (int)Math.Ceiling(inpWidth / strides[n]);
? ? ? ? ? ? ? ? int num_grid_y = (int)Math.Ceiling(inpHeight / strides[n]);

? ? ? ? ? ? ? ? for (int i = 0; i < num_grid_y; i++)
? ? ? ? ? ? ? ? {
? ? ? ? ? ? ? ? ? ? for (int j = 0; j < num_grid_x; j++)
? ? ? ? ? ? ? ? ? ? {
? ? ? ? ? ? ? ? ? ? ? ? float box_score = pdata[4];
? ? ? ? ? ? ? ? ? ? ? ? int max_ind = 0;
? ? ? ? ? ? ? ? ? ? ? ? float max_class_socre = 0;
? ? ? ? ? ? ? ? ? ? ? ? for (int k = 0; k < num_class; k++)
? ? ? ? ? ? ? ? ? ? ? ? {
? ? ? ? ? ? ? ? ? ? ? ? ? ? if (pdata[k + 5] > max_class_socre)
? ? ? ? ? ? ? ? ? ? ? ? ? ? {
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? max_class_socre = pdata[k + 5];
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? max_ind = k;
? ? ? ? ? ? ? ? ? ? ? ? ? ? }
? ? ? ? ? ? ? ? ? ? ? ? }
? ? ? ? ? ? ? ? ? ? ? ? max_class_socre = max_class_socre* box_score;
? ? ? ? ? ? ? ? ? ? ? ? max_class_socre = (float)Math.Sqrt(max_class_socre);

? ? ? ? ? ? ? ? ? ? ? ? if (max_class_socre > confThreshold)
? ? ? ? ? ? ? ? ? ? ? ? {
? ? ? ? ? ? ? ? ? ? ? ? ? ? float cx = (0.5f + j + pdata[0]) * strides[n]; ?//cx
? ? ? ? ? ? ? ? ? ? ? ? ? ? float cy = (0.5f + i + pdata[1]) * strides[n]; ? //cy
? ? ? ? ? ? ? ? ? ? ? ? ? ? float w = (float)(Math.Exp(pdata[2]) * strides[n]); ? //w
? ? ? ? ? ? ? ? ? ? ? ? ? ? float h = (float)(Math.Exp(pdata[3]) * strides[n]); ?//h

? ? ? ? ? ? ? ? ? ? ? ? ? ? float xmin = (float)((cx - 0.5 * w) / ratio);
? ? ? ? ? ? ? ? ? ? ? ? ? ? float ymin = (float)((cy - 0.5 * h) / ratio);
? ? ? ? ? ? ? ? ? ? ? ? ? ? float xmax = (float)((cx + 0.5 * w) / ratio);
? ? ? ? ? ? ? ? ? ? ? ? ? ? float ymax = (float)((cy + 0.5 * h) / ratio);

? ? ? ? ? ? ? ? ? ? ? ? ? ? int left = (int)((cx - 0.5 * w) / ratio);
? ? ? ? ? ? ? ? ? ? ? ? ? ? int top = (int)((cy - 0.5 * h) / ratio);
? ? ? ? ? ? ? ? ? ? ? ? ? ? int width = (int)(w / ratio);
? ? ? ? ? ? ? ? ? ? ? ? ? ? int height = (int)(h / ratio);

? ? ? ? ? ? ? ? ? ? ? ? ? ? confidences.Add(max_class_socre);
? ? ? ? ? ? ? ? ? ? ? ? ? ? boxes.Add(new Rect(left, top, width, height));
? ? ? ? ? ? ? ? ? ? ? ? ? ? classIds.Add(max_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, 1, new Scalar(0, 0, 255), 2);
? ? ? ? ? ? }

? ? ? ? ? ? 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.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;

        int num_stride = 3;
        float[] strides = new float[3] { 8.0f, 16.0f, 32.0f };

        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 = 0.6f;
            nmsThreshold = 0.5f;

            modelpath = "model/yolo_free_huge_crowdhuman_192x320.onnx";

            inpHeight = 192;
            inpWidth = 320;

            opencv_net = CvDnn.ReadNetFromOnnx(modelpath);

            class_names = new List<string>();
            class_names.Add("person");
            num_class = 1;

            image_path = "test_img/1.jpg";
            pictureBox1.Image = new Bitmap(image_path);

        }

        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);

            float ratio = Math.Min(1.0f * inpHeight / image.Rows, 1.0f * inpWidth / image.Cols);
            int neww = (int)(image.Cols * ratio);
            int newh = (int)(image.Rows * ratio);

            Mat dstimg = new Mat();
            Cv2.Resize(image, dstimg, new OpenCvSharp.Size(neww, newh));

            Cv2.CopyMakeBorder(dstimg, dstimg, 0, inpHeight - newh, 0, inpWidth - neww, BorderTypes.Constant);

            BN_image = CvDnn.BlobFromImage(dstimg);

            //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[1] { 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(1);
            int nout = outs[0].Size(2);

            float* pdata = (float*)outs[0].Data;

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

            for (int n = 0; n < num_stride; n++)
            {
                int num_grid_x = (int)Math.Ceiling(inpWidth / strides[n]);
                int num_grid_y = (int)Math.Ceiling(inpHeight / strides[n]);

                for (int i = 0; i < num_grid_y; i++)
                {
                    for (int j = 0; j < num_grid_x; j++)
                    {
                        float box_score = pdata[4];
                        int max_ind = 0;
                        float max_class_socre = 0;
                        for (int k = 0; k < num_class; k++)
                        {
                            if (pdata[k + 5] > max_class_socre)
                            {
                                max_class_socre = pdata[k + 5];
                                max_ind = k;
                            }
                        }
                        max_class_socre = max_class_socre* box_score;
                        max_class_socre = (float)Math.Sqrt(max_class_socre);

                        if (max_class_socre > confThreshold)
                        {
                            float cx = (0.5f + j + pdata[0]) * strides[n];  //cx
                            float cy = (0.5f + i + pdata[1]) * strides[n];   //cy
                            float w = (float)(Math.Exp(pdata[2]) * strides[n]);   //w
                            float h = (float)(Math.Exp(pdata[3]) * strides[n]);  //h

                            float xmin = (float)((cx - 0.5 * w) / ratio);
                            float ymin = (float)((cy - 0.5 * h) / ratio);
                            float xmax = (float)((cx + 0.5 * w) / ratio);
                            float ymax = (float)((cy + 0.5 * h) / ratio);

                            int left = (int)((cx - 0.5 * w) / ratio);
                            int top = (int)((cy - 0.5 * h) / ratio);
                            int width = (int)(w / ratio);
                            int height = (int)(h / ratio);

                            confidences.Add(max_class_socre);
                            boxes.Add(new Rect(left, top, width, height));
                            classIds.Add(max_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, 1, new Scalar(0, 0, 255), 2);
            }

            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);
        }
    }
}

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