C# OpenCvSharp DNN FreeYOLO 密集行人检测

目录

效果

模型信息

项目

代码

下载

---

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];  //cxfloat cy = (0.5f + i + pdata[1]) * strides[n];   //cyfloat w = (float)(Math.Exp(pdata[2]) * strides[n]);   //wfloat h = (float)(Math.Exp(pdata[3]) * strides[n]);  //hfloat 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);}}
}

下载

可执行程序exe下载

源码下载