C# OpenCvSharp DNN FreeYOLO 人脸检测

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效果

模型信息

项目

代码

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C# OpenCvSharp DNN FreeYOLO 人脸检测

效果

模型信息

Inputs

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name：input

tensor：Float[1, 3, 192, 320]

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Outputs

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name：output

tensor：Float[1, 1260, 6]

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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 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 = 0.8f;            nmsThreshold = 0.5f;            modelpath = "model/yolo_free_huge_widerface_192x320.onnx";            inpHeight = 192;            inpWidth = 320;            opencv_net = CvDnn.ReadNetFromOnnx(modelpath);            class_names = new List();            class_names.Add("face");            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 confidences = new List();            List boxes = new List();            List classIds = new List();            for (int n = 0; n  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  
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