目录

效果

模型信息

项目

代码

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C# OpenCvSharp DNN 部署yoloX

效果

模型信息

Inputs
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name：images
tensor：Float[1, 3, 640, 640]
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Outputs
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name：output
tensor：Float[1, 8400, 85]
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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.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 prob_threshold;float nms_threshold;float[] stride = new float[3] { 8.0f, 16.0f, 32.0f };int[] input_shape = new int[] { 640, 640 };   // height, widthfloat[] mean = new float[3] { 0.485f, 0.456f, 0.406f };float[] std = new float[3] { 0.229f, 0.224f, 0.225f };float scale = 1.0f;string modelpath;int inpHeight;int inpWidth;List<string> class_names;int num_class;Net opencv_net;Mat BN_image;Mat image;Mat result_image;public Mat Normalize(Mat src){Cv2.CvtColor(src, src, ColorConversionCodes.BGR2RGB);Mat[] bgr = src.Split();for (int i = 0; i < bgr.Length; ++i){bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1.0 / (255.0 * std[i]), (0.0 - mean[i]) / std[i]);}Cv2.Merge(bgr, src);foreach (Mat channel in bgr){channel.Dispose();}return src;}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){prob_threshold = 0.6f;nms_threshold = 0.6f;modelpath = "model/yolox_s.onnx";inpHeight = 640;inpWidth = 640;opencv_net = CvDnn.ReadNetFromOnnx(modelpath);class_names = new List<string>();StreamReader sr = new StreamReader("model/coco.names");string line;while ((line = sr.ReadLine()) != null){class_names.Add(line);}num_class = class_names.Count();image_path = "test_img/dog.jpg";pictureBox1.Image = new Bitmap(image_path);}Mat ResizeImage(Mat srcimg){float r = (float)Math.Min(input_shape[1] / (srcimg.Cols * 1.0), input_shape[0] / (srcimg.Rows * 1.0));scale = r;int unpad_w = (int)(r * srcimg.Cols);int unpad_h = (int)(r * srcimg.Rows);Mat re = new Mat(unpad_h, unpad_w, MatType.CV_8UC3);Cv2.Resize(srcimg, re, new OpenCvSharp.Size(unpad_w, unpad_h));Mat outMat = new Mat(input_shape[1], input_shape[0], MatType.CV_8UC3, new Scalar(114, 114, 114));re.CopyTo(new Mat(outMat, new Rect(0, 0, re.Cols, re.Rows)));return outMat;}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);Mat dstimg = ResizeImage(image);dstimg = Normalize(dstimg);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[] { 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);outs[0] = outs[0].Reshape(0, num_proposal);float* pdata = (float*)outs[0].Data;int row_ind = 0;int nout = num_class + 5;List<Rect> boxes = new List<Rect>();List<float> confidences = new List<float>();List<int> classIds = new List<int>();for (int n = 0; n < 3; n++){int num_grid_x = (int)(inpWidth / stride[n]);int num_grid_y = (int)(inpHeight / stride[n]);for (int i = 0; i < num_grid_y; i++){for (int j = 0; j < num_grid_x; j++){float box_score = pdata[4];Mat scores = outs[0].Row(row_ind).ColRange(5, outs[0].Cols);double minVal, max_class_socre;OpenCvSharp.Point minLoc, classIdPoint;// Get the value and location of the maximum scoreCv2.MinMaxLoc(scores, out minVal, out max_class_socre, out minLoc, out classIdPoint);int class_idx = classIdPoint.X;float cls_score = pdata[5 + class_idx];float box_prob = box_score * cls_score;if (box_prob > prob_threshold){float x_center = (pdata[0] + j) * stride[n];float y_center = (pdata[1] + i) * stride[n];float w = (float)(Math.Exp(pdata[2]) * stride[n]);float h = (float)(Math.Exp(pdata[3]) * stride[n]);float x0 = x_center - w * 0.5f;float y0 = y_center - h * 0.5f;classIds.Add(class_idx);confidences.Add(box_prob);boxes.Add(new Rect((int)x0, (int)y0, (int)w, (int)h));}pdata += nout;row_ind++;}}}int[] indices;CvDnn.NMSBoxes(boxes, confidences, prob_threshold, nms_threshold, out indices);result_image = image.Clone();for (int ii = 0; ii < indices.Length; ++ii){int idx = indices[ii];Rect box = boxes[idx];// adjust offset to original unpaddedfloat x0 = box.X / scale; ;float y0 = box.Y / scale; ;float x1 = (box.X + box.Width) / scale;float y1 = (box.Y + box.Height) / scale;// clipx0 = Math.Max(Math.Min(x0, (float)(image.Cols - 1)), 0.0f);y0 = Math.Max(Math.Min(y0, (float)(image.Rows - 1)), 0.0f);x1 = Math.Max(Math.Min(x1, (float)(image.Cols - 1)), 0.0f);y1 = Math.Max(Math.Min(y1, (float)(image.Rows - 1)), 0.0f);Cv2.Rectangle(result_image, new OpenCvSharp.Point(x0, y0), new OpenCvSharp.Point(x1, y1), new Scalar(0, 255, 0), 2);string label = class_names[classIds[idx]] + ":" + confidences[idx].ToString("0.00");Cv2.PutText(result_image, label, new OpenCvSharp.Point(x0, y0 - 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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