C# Onnx CenterNet实现目标检测的示例详解
作者:天天代码码天天
这篇文章主要为大家详细介绍了C# Onnx CenterNet实现目标检测的相关知识,文中的示例代码讲解详细,感兴趣的小伙伴可以跟随小编一起学习一下
效果
模型信息
Inputs
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name:input.1
tensor:Float[1, 3, 384, 384]
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Outputs
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name:508
tensor:Float[1, 80, 96, 96]
name:511
tensor:Float[1, 2, 96, 96]
name:514
tensor:Float[1, 2, 96, 96]
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项目
代码
using Microsoft.ML.OnnxRuntime.Tensors; using Microsoft.ML.OnnxRuntime; using OpenCvSharp; using System; using System.Collections.Generic; using System.Windows.Forms; using System.Linq; using System.Drawing; using System.IO; using OpenCvSharp.Dnn; using System.Text; using OpenCvSharp.Flann; namespace Onnx_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 = 0.4f; float nmsThreshold = 0.5f; int inpWidth; int inpHeight; Mat image; string model_path = ""; SessionOptions options; InferenceSession onnx_session; Tensor<float> input_tensor; Tensor<float> input_tensor_scale; List<NamedOnnxValue> input_container; IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer; DisposableNamedOnnxValue[] results_onnxvalue; List<string> class_names; int num_class; StringBuilder sb = new StringBuilder(); float[] mean = { 0.406f, 0.456f, 0.485f }; float[] std = { 0.225f, 0.224f, 0.229f }; int num_grid_y; int num_grid_x; float sigmoid(float x) { return (float)(1.0 / (1.0 + Math.Exp(-x))); } 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 System.Drawing.Bitmap(image_path); image = new Mat(image_path); } private void Form1_Load(object sender, EventArgs e) { // 创建输入容器 input_container = new List<NamedOnnxValue>(); // 创建输出会话 options = new SessionOptions(); options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO; options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行 // 创建推理模型类,读取本地模型文件 model_path = "model/ctdet_coco_dlav0_384.onnx"; inpHeight = 384; inpWidth = 384; num_grid_y = 96; num_grid_x = 96; onnx_session = new InferenceSession(model_path, options); // 创建输入容器 input_container = new List<NamedOnnxValue>(); image_path = "test_img/person.jpg"; pictureBox1.Image = new Bitmap(image_path); class_names = new List<string>(); StreamReader sr = new StreamReader("coco.names"); string line; while ((line = sr.ReadLine()) != null) { class_names.Add(line); } num_class = class_names.Count(); } private unsafe void button2_Click(object sender, EventArgs e) { if (image_path == "") { return; } textBox1.Text = "检测中,请稍等……"; pictureBox2.Image = null; sb.Clear(); System.Windows.Forms.Application.DoEvents(); image = new Mat(image_path); //-----------------前处理-------------------------- Mat dstimg = new Mat(); Cv2.CvtColor(image, dstimg, ColorConversionCodes.BGR2RGB); Cv2.Resize(dstimg, dstimg, new OpenCvSharp.Size(inpWidth, inpHeight)); Mat[] mv = new Mat[3]; Cv2.Split(dstimg, out mv); for (int i = 0; i < mv.Length; i++) { mv[i].ConvertTo(mv[i], MatType.CV_32FC1, 1.0 / (255.0 * std[i]), (0.0 - mean[i]) / std[i]); } Cv2.Merge(mv, dstimg); int row = dstimg.Rows; int col = dstimg.Cols; float[] input_tensor_data = new float[1 * 3 * row * col]; for (int c = 0; c < 3; c++) { for (int i = 0; i < row; i++) { for (int j = 0; j < col; j++) { float pix = ((float*)(dstimg.Ptr(i).ToPointer()))[j * 3 + c]; input_tensor_data[c * row * col + i * col + j] = pix; } } } input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 1, 3, inpHeight, inpWidth }); input_container.Add(NamedOnnxValue.CreateFromTensor("input.1", input_tensor)); //-----------------推理-------------------------- dt1 = DateTime.Now; result_infer = onnx_session.Run(input_container);//运行 Inference 并获取结果 dt2 = DateTime.Now; //-----------------后处理-------------------------- results_onnxvalue = result_infer.ToArray(); float ratioh = (float)image.Rows / inpHeight; float ratiow = (float)image.Cols / inpWidth; float stride = inpHeight / num_grid_y; float[] pscore = results_onnxvalue[0].AsTensor<float>().ToArray(); float[] pxy = results_onnxvalue[1].AsTensor<float>().ToArray(); float[] pwh = results_onnxvalue[2].AsTensor<float>().ToArray(); int area = num_grid_y * num_grid_x; List<float> confidences = new List<float>(); List<Rect> position_boxes = new List<Rect>(); List<int> class_ids = new List<int>(); Result result = new Result(); for (int i = 0; i < num_grid_y; i++) { for (int j = 0; j < num_grid_x; j++) { float max_class_score = -1000; int class_id = -1; for (int c = 0; c < num_class; c++) { float score = sigmoid(pscore[c * area + i * num_grid_x + j]); if (score > max_class_score) { max_class_score = score; class_id = c; } } if (max_class_score > confThreshold) { float cx = (pxy[i * num_grid_x + j] + j) * stride * ratiow; ///cx float cy = (pxy[area + i * num_grid_x + j] + i) * stride * ratioh; ///cy float w = pwh[i * num_grid_x + j] * stride * ratiow; ///w float h = pwh[area + i * num_grid_x + j] * stride * ratioh; ///h int x = (int)Math.Max(cx - 0.5 * w, 0); int y = (int)Math.Max(cy - 0.5 * h, 0); int width = (int)Math.Min(w, image.Cols - 1); int height = (int)Math.Min(h, image.Rows - 1); position_boxes.Add(new Rect(x, y, width, height)); class_ids.Add(class_id); confidences.Add(max_class_score); } } } // NMS非极大值抑制 int[] indexes = new int[position_boxes.Count]; CvDnn.NMSBoxes(position_boxes, confidences, confThreshold, nmsThreshold, out indexes); for (int i = 0; i < indexes.Length; i++) { int index = indexes[i]; result.add(confidences[index], position_boxes[index], class_names[class_ids[index]]); } if (pictureBox2.Image != null) { pictureBox2.Image.Dispose(); } sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms"); sb.AppendLine("------------------------------"); // 将识别结果绘制到图片上 Mat result_image = image.Clone(); for (int i = 0; i < result.length; i++) { Cv2.Rectangle(result_image, result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8); Cv2.Rectangle(result_image, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20), new OpenCvSharp.Point(result.rects[i].BottomRight.X, result.rects[i].TopLeft.Y), new Scalar(0, 0, 255), -1); Cv2.PutText(result_image, result.classes[i] + "-" + result.scores[i].ToString("0.00"), new OpenCvSharp.Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1); sb.AppendLine(string.Format("{0}:{1},({2},{3},{4},{5})" , result.classes[i] , result.scores[i].ToString("0.00") , result.rects[i].TopLeft.X , result.rects[i].TopLeft.Y , result.rects[i].BottomRight.X , result.rects[i].BottomRight.Y )); } textBox1.Text = sb.ToString(); pictureBox2.Image = new System.Drawing.Bitmap(result_image.ToMemoryStream()); result_image.Dispose(); dstimg.Dispose(); image.Dispose(); } 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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