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效果 det_class_names = new List() {"car"}; List seg_class_names = new List() {"Background","Lane","Line"}; List class_colors = new List { 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 boxes = new List(); List confidences = new List(); List classIds = new List(); 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(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); } } } 下载