C# Onnx Yolov8 Detect 路面坑洼检测

效果

项目

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;namespace Onnx_Yolov8_Detect
{public partial class Form1 : Form{public Form1(){InitializeComponent();}string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";string image_path = "";string startupPath;string classer_path;string model_path;DateTime dt1 = DateTime.Now;DateTime dt2 = DateTime.Now;Mat image;Mat result_image;SessionOptions options;InferenceSession onnx_session;Tensor<float> input_tensor;List<NamedOnnxValue> input_ontainer;IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;DisposableNamedOnnxValue[] results_onnxvalue;Tensor<float> result_tensors;float[] result_array;float[] factors = new float[2];Result result;DetectionResult result_pro;StringBuilder sb = new StringBuilder();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){startupPath = Application.StartupPath + "\\model\\";model_path = startupPath + "pothole.onnx";classer_path = startupPath + "lable.txt";// 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onnx_session = new InferenceSession(model_path, options);// 输入Tensorinput_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });// 创建输入容器input_ontainer = new List<NamedOnnxValue>();}private void button2_Click(object sender, EventArgs e){if (image_path == ""){return;}textBox1.Text = "检测中，请稍等……";pictureBox2.Image = null;Application.DoEvents();//图片缩放image = new Mat(image_path);int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);Rect roi = new Rect(0, 0, image.Cols, image.Rows);image.CopyTo(new Mat(max_image, roi));factors[0] = factors[1] = (float)(max_image_length / 640.0);//将图片转为RGB通道Mat image_rgb = new Mat();Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);Mat resize_image = new Mat();Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));//输入Tensorfor (int y = 0; y < resize_image.Height; y++){for (int x = 0; x < resize_image.Width; x++){input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;}}//将 input_tensor 放入一个输入参数的容器，并指定名称input_ontainer.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));dt1 = DateTime.Now;//运行 Inference 并获取结果result_infer = onnx_session.Run(input_ontainer);dt2 = DateTime.Now;//将输出结果转为DisposableNamedOnnxValue数组results_onnxvalue = result_infer.ToArray();//读取第一个节点输出并转为Tensor数据result_tensors = results_onnxvalue[0].AsTensor<float>();result_array = result_tensors.ToArray();resize_image.Dispose();image_rgb.Dispose();result_pro = new DetectionResult(classer_path, factors);result = result_pro.process_result(result_array);result_image = result_pro.draw_result(result, image.Clone());if (!result_image.Empty()){pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());sb.Clear();sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");sb.AppendLine("------------------------------");for (int i = 0; i < result.length; i++){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();}else{textBox1.Text = "无信息";}}}
}