说明
官网地址：
https://github.com/ultralytics/ultralytics
效果
项目
模型信息 Model Properties-------------------------date：2024-10-06T16:52:12.968917description：Ultralytics YOLO11n model trained on /usr/src/ultralytics/ultralytics/cfg/datasets/coco.yamlauthor：Ultralyticsversion：8.3.5task：detectlicense：AGPL-3.0 License (https://ultralytics.com/license)docs：https://docs.ultralytics.comstride：32batch：1imgsz：[640, 640]names：{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}---------------------------------------------------------------Inputs-------------------------name：imagestensor：Float[1, 3, 640, 640]---------------------------------------------------------------Outputs-------------------------name：output0tensor：Float[1, 84, 8400]---------------------------------------------------------------代码 using Microsoft.ML.OnnxRuntime;using Microsoft.ML.OnnxRuntime.Tensors;using OpenCvSharp;using OpenCvSharp.Dnn;using System;using System.Collections.Generic;using System.Drawing;using System.Drawing.Imaging;using System.IO;using System.Linq;using System.Text;using System.Windows.Forms;namespace Onnx_Demo{ public partial Form1 : Form { public Form1() { InitializeComponent(); } string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png"; string image_path = ""; string model_path; stringer_path; public string[]_names; public int_num; DateTime dt1 = DateTime.Now; DateTime dt2 = DateTime.Now; int input_height; int input_width;float ratio_height;float ratio_width; InferenceSession onnx_session; int box_num;float conf_threshold;float nms_threshold; /// <summary> /// 选择图片 /// </summary> /// <param name="sender"></param> /// <param name="e"></param> private void button1_Click(object sender, EventArgs e) { OpenFileDialog ofd = new OpenFileDialog(); ofd.Filter = fileFilter;if (ofd.ShowDialog() != DialogResult.OK) return; pictureBox1.Image = ; image_path = ofd.FileName; pictureBox1.Image = new Bitmap(image_path); textBox1.Text = ""; pictureBox2.Image = ; } /// <summary> /// 推理 /// </summary> /// <param name="sender"></param> /// <param name="e"></param> private void button2_Click(object sender, EventArgs e) {if (image_path == "") {return; } button2.Enabled = false; pictureBox2.Image = ; textBox1.Text = ""; Application.DoEvents(); Mat image = new Mat(image_path); //图片缩放 int height = image.Rows; int width = image.Cols; Mat temp_image = image.Clone();if (height > input_height || width > input_width) {float scale = Math.Min((float)input_height / height, (float)input_width / width); OpenCvSharp.Size new_size = new OpenCvSharp.Size((int)(width * scale), (int)(height * scale)); Cv2.Resize(image, temp_image, new_size); } ratio_height = (float)height / temp_image.Rows; ratio_width = (float)width / temp_image.Cols; Mat input_img = new Mat(); Cv2.CopyMakeBorder(temp_image, input_img, 0, input_height - temp_image.Rows, 0, input_width - temp_image.Cols, BorderTypes.Constant, 0); //Cv2.ImShow("input_img", input_img); //输入Tensor Tensor<float> input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });for (int y = 0; y < input_img.Height; y++) {for (int x = 0; x < input_img.Width; x++) { input_tensor[0, 0, y, x] = input_img.At<Vec3b>(y, x)[0] / 255f; input_tensor[0, 1, y, x] = input_img.At<Vec3b>(y, x)[1] / 255f; input_tensor[0, 2, y, x] = input_img.At<Vec3b>(y, x)[2] / 255f; } } List<NamedOnnxValue> input_container = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("images", input_tensor) }; //推理 dt1 = DateTime.Now; var ort_outputs = onnx_session.Run(input_container).ToArray(); dt2 = DateTime.Now;float[] data = Transpose(ort_outputs[0].AsTensor<float>().ToArray(), 4 +_num, box_num);float[] confidenceInfo = new float[class_num];float[] rectData = new float[4]; List<DetectionResult> detResults = new List<DetectionResult>();for (int i = 0; i < box_num; i++) { Array.Copy(data, i * (class_num + 4), rectData, 0, 4); Array.Copy(data, i * (class_num + 4) + 4, confidenceInfo, 0,_num);float score = confidenceInfo.Max(); // 获取最大值 int maxIndex = Array.IndexOf(confidenceInfo, score); // 获取最大值的位置 int _centerX = (int)(rectData[0] * ratio_width); int _centerY = (int)(rectData[1] * ratio_height); int _width = (int)(rectData[2] * ratio_width); int _height = (int)(rectData[3] * ratio_height); detResults.Add(new DetectionResult( maxIndex,_names[maxIndex], new Rect(_centerX - _width / 2, _centerY - _height / 2, _width, _height), score)); } //NMS CvDnn.NMSBoxes(detResults.Select(x => x.Rect), detResults.Select(x => x.Confidence), conf_threshold, nms_threshold, out int[] indices); detResults = detResults.Where((x, index) => indices.Contains(index)).ToList(); //绘制结果 Mat result_image = image.Clone(); foreach (DetectionResult r in detResults) { Cv2.PutText(result_image, $"{r.Class}:{r.Confidence:P0}", new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2); Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2); } pictureBox2.Image = new Bitmap(result_image.ToMemoryStream()); textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms"; button2.Enabled = true; } /// <summary> ///窗体加载 /// </summary> /// <param name="sender"></param> /// <param name="e"></param> private void Form1_Load(object sender, EventArgs e) { model_path = "model/yolo11n.onnx"; //创建输出会话，用于输出模型读取信息 SessionOptions 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，读取模型文件 onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径 input_height = 640; input_width = 640; box_num = 8400; conf_threshold = 0.25f; nms_threshold = 0.5f;er_path = "model/lable.txt";_names = File.ReadAllLines(classer_path, Encoding.UTF8);_num =_names.Length; image_path = "test_img/zidane.jpg"; pictureBox1.Image = new Bitmap(image_path); } /// <summary> /// 保存 /// </summary> /// <param name="sender"></param> /// <param name="e"></param> private void button3_Click(object sender, EventArgs e) {if (pictureBox2.Image == ) {return; } Bitmap output = new Bitmap(pictureBox2.Image); SaveFileDialog sdf = new SaveFileDialog(); sdf.Title = "保存"; sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";if (sdf.ShowDialog() == DialogResult.OK) { switch (sdf.FilterIndex) {case 1: { output.Save(sdf.FileName, ImageFormat.Jpeg);break; }case 2: { output.Save(sdf.FileName, ImageFormat.Png);break; }case 3: { output.Save(sdf.FileName, ImageFormat.Bmp);break; }case 4: { output.Save(sdf.FileName, ImageFormat.Emf);break; }case 5: { output.Save(sdf.FileName, ImageFormat.Exif);break; }case 6: { output.Save(sdf.FileName, ImageFormat.Gif);break; }case 7: { output.Save(sdf.FileName, ImageFormat.Icon);break; }case 8: { output.Save(sdf.FileName, ImageFormat.Tiff);break; }case 9: { output.Save(sdf.FileName, ImageFormat.Wmf);break; } } MessageBox.Show("保存成功，位置：" + sdf.FileName); } } private void pictureBox1_DoubleClick(object sender, EventArgs e) { ShowNormalImg(pictureBox1.Image); } private void pictureBox2_DoubleClick(object sender, EventArgs e) { ShowNormalImg(pictureBox2.Image); } public void ShowNormalImg(Image img) {if (img == ) return; frmShow frm = new frmShow(); frm.Width = Screen.PrimaryScreen.Bounds.Width; frm.Height = Screen.PrimaryScreen.Bounds.Height;if (frm.Width > img.Width) { frm.Width = img.Width; }if (frm.Height > img.Height) { frm.Height = img.Height; } bool b = frm.richTextBox1.ReadOnly; Clipboard.SetDataObject(img, true); frm.richTextBox1.ReadOnly = false; frm.richTextBox1.Paste(DataFormats.GetFormat(DataFormats.Bitmap)); frm.richTextBox1.ReadOnly = b; frm.ShowDialog(); } public unsafe float[] Transpose(float[] tensorData, int rows, int cols) {float[] transposedTensorData = new float[tensorData.Length]; fixed (float* pTensorData = tensorData) { fixed (float* pTransposedData = transposedTensorData) {for (int i = 0; i < rows; i++) {for (int j = 0; j < cols; j++) { int index = i * cols + j; int transposedIndex = j * rows + i; pTransposedData[transposedIndex] = pTensorData[index]; } } } }return transposedTensorData; } } public DetectionResult { public DetectionResult(int ClassId, string Class, Rect Rect, float Confidence) { this.ClassId = ClassId; this.Confidence = Confidence; this.Rect = Rect; this.Class = Class; } public string Class { get; set; } public int ClassId { get; set; } public float Confidence { get; set; } public Rect Rect { get; set; } }}
