C# OpenCvSharp DNN HybridNets 同时处理车辆检测、可驾驶区域分割、车道线分割

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C# OpenCvSharp DNN HybridNets 同时处理车辆检测、可驾驶区域分割、车道线分割

效果

项目

代码

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Numerics;
using System.Text;
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;string anchorpath;int inpHeight;int inpWidth;float[] mean = { 0.485f, 0.456f, 0.406f };float[] std = { 0.229f, 0.224f, 0.225f };List<string> det_class_names = new List<string>() { "car" };List<string> seg_class_names = new List<string>() { "Background", "Lane", "Line" };List<Vec3b> class_colors = new List<Vec3b> { new Vec3b(0, 0, 0), new Vec3b(0, 255, 0), new Vec3b(255, 0, 0) };int det_num_class = 1;int seg_numclass = 3;float[] anchors;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/hybridnets_256x384.onnx";anchorpath = "model/anchors_73656.bin";inpHeight = 256;inpWidth = 384;opencv_net = CvDnn.ReadNetFromOnnx(modelpath);FileStream fileStream = new FileStream(anchorpath, FileMode.Open);//读二进制文件类BinaryReader br = new BinaryReader(fileStream, Encoding.UTF8);int len = 73656;anchors = new float[len];byte[] byteTemp;float fTemp;for (int i = 0; i < len; i++){byteTemp = br.ReadBytes(4);fTemp = BitConverter.ToSingle(byteTemp, 0);anchors[i] = fTemp;}br.Close();image_path = "test_img/test.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);int newh = 0, neww = 0, padh = 0, padw = 0;Mat resize_img = Common.ResizeImage(image, inpHeight, inpWidth, ref newh, ref neww, ref padh, ref padw);float ratioh = (float)image.Rows / newh;float ratiow = (float)image.Cols / neww;Mat normalize = Common.Normalize(resize_img, mean, std);dt1 = DateTime.Now;BN_image = CvDnn.BlobFromImage(normalize);//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();opencv_net.Forward(outs, outBlobNames);dt2 = DateTime.Now;float* classification = (float*)outs[0].Data;float* box_regression = (float*)outs[1].Data;float* seg = (float*)outs[2].Data;List<Rect> boxes = new List<Rect>();List<float> confidences = new List<float>();List<int> classIds = new List<int>();int num_proposal = outs[1].Size(1);  //输入的是单张图, 第0维batchsize忽略for (int n = 0; n < num_proposal; n++){float conf = classification[n];if (conf > confThreshold){int row_ind = n * 4;float x_centers = box_regression[row_ind + 1] * anchors[row_ind + 2] + anchors[row_ind];float y_centers = box_regression[row_ind] * anchors[row_ind + 3] + anchors[row_ind + 1];float w = (float)(Math.Exp(box_regression[row_ind + 3]) * anchors[row_ind + 2]);float h = (float)(Math.Exp(box_regression[row_ind + 2]) * anchors[row_ind + 3]);float xmin = (float)((x_centers - w * 0.5 - padw) * ratiow);float ymin = (float)((y_centers - h * 0.5 - padh) * ratioh);w *= ratiow;h *= ratioh;Rect box = new Rect((int)xmin, (int)ymin, (int)w, (int)h);boxes.Add(box);confidences.Add(conf);classIds.Add(0);}}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 = det_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);}int area = inpHeight * inpWidth;int i = 0, j = 0, c = 0;for (i = 0; i < result_image.Rows; i++){for (j = 0; j < result_image.Cols; j++){int x = (int)((j / ratiow) + padw);  ///从原图映射回到输出特征图int y = (int)((i / ratioh) + padh);int max_id = -1;float max_conf = -10000;for (c = 0; c < seg_numclass; c++){float seg_conf = seg[c * area + y * inpWidth + x];if (seg_conf > max_conf){max_id = c;max_conf = seg_conf;}}if (max_id > 0){result_image.Set<Vec3b>(i, j, class_colors[max_id]);}}}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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