关于Yolo5训练自定义数据集，请参考此教程：YOLO5训练自定义数据集
完整项目（VS2022）
链接：https://pan.baidu.com/s/17jQUx-Dp0YYC4YWzYLs0AA?pwd=yzj9
提取码：yzj9
一 YOLO 导出ONNX模型
在export.py 中修改参数（如下图），运行导出ONNX
二 Yolov5Net.Scorer.dll文件设置及编译
C# ONNX模型推理dll库：
https://github.com/mentalstack/yolov5-net
根据模型修改model文件后，再编译dll，在项目中引用此dll
public class YoloCocoP5Model : YoloModel
{
//输入图像大小
public override int Width {
get; set; } = 640;
public override int Height {
get; set; } = 640;
public override int Depth {
get; set; } = 3;
/// <summary>
/// 对象种类数 注意：此值=对象实际种类数+5
/// </summary>
public override int Dimensions {
get; set; } = 6;
public override int[] Strides {
get; set; } = new int[] {
8, 16, 32 };
public override int[][][] Anchors {
get; set; } = new int[][][]
{
new int[][] {
new int[] {
010, 13 }, new int[] {
016, 030 }, new int[] {
033, 023 } },
new int[][] {
new int[] {
030, 61 }, new int[] {
062, 045 }, new int[] {
059, 119 } },
new int[][] {
new int[] {
116, 90 }, new int[] {
156, 198 }, new int[] {
373, 326 } }
};
public override int[] Shapes {
get; set; } = new int[] {
80, 40, 20 };
//阈值设定，大于此阈值才识别
public override float Confidence {
get; set; } = 0.40f;
public override float MulConfidence {
get; set; } = 0.25f;
public override float Overlap {
get; set; } = 0.45f;
//ONNX模型的输出变量名称，可在https://netron.app/中查看
public override string[] Outputs {
get; set; } = new[] {
"output0" };
public override List<YoloLabel> Labels {
get; set; } = new List<YoloLabel>()
{
//模型中的对象的种类信息
new YoloLabel {
Id = 0, Name = "Stop-Sign" },
};
public override bool UseDetect {
get; set; } = true;
public YoloCocoP5Model()
{
}
}
三 C# 中模型进行推理
C# 项目编译环境为>=.NET5,本案例使用.NET6
需要安装以下库：
Microsoft.ML.OnnxRuntime （ONNX运行时）
OpenCvSharp4.Extensions （输出图像框定对象使用）
OpenCvSharp4.Windows （输出图像框定对象使用）
同时需要引用上一步编译的：Yolov5Net.Scorer.dll
测试代码：
using Yolov5Net.Scorer.Models;
using Yolov5Net.Scorer;
using OpenCvSharp;
using System.Windows.Forms;
using Point = OpenCvSharp.Point;
namespace YOLO_WinformDemo
{
/// <summary>
/// 此项目使用.NET6编译
/// </summary>
public partial class Form1 : Form
{
YoloScorer<YoloCocoP5Model> scorer;
string filePath = "";
public Form1()
{
InitializeComponent();
//加载模型地址
scorer = new YoloScorer<YoloCocoP5Model>(Application.StartupPath+ "\\onnx\\best.onnx");
}
private void btn_selectFile_Click(object sender, EventArgs e)
{
OpenFileDialog openFile=new OpenFileDialog();
if(openFile.ShowDialog() == DialogResult.OK)
{
filePath = openFile.FileName;
using var image = Image.FromFile(openFile.FileName);
picbox_Display.BackgroundImage = AddInfoToImage(image);
}
}
/// <summary>
/// 使用Opensharp4给识别对象加框和标注
/// </summary>
/// <param name="inputImage"></param>
/// <returns></returns>
public Image AddInfoToImage(Image inputImage)
{
List<YoloPrediction> predictions = scorer.Predict(inputImage);
if (predictions.Count == 0) {
return inputImage;
}
else
{
Mat inputMat = Cv2.ImRead(filePath, ImreadModes.Color);
foreach (YoloPrediction prediction in predictions)
{
Point p1 = new Point(prediction.Rectangle.X, prediction.Rectangle.Y);//矩形左上顶点
Point p2 = new Point(prediction.Rectangle.X+ prediction.Rectangle.Width, prediction.Rectangle.Y + prediction.Rectangle.Height);//矩形右下顶点
Point p3 = new Point(prediction.Rectangle.X, prediction.Rectangle.Y - 60);
Scalar scalar = new Scalar(0, 0, 255);
Cv2.Rectangle(inputMat, p1, p2, scalar, 7);
Cv2.PutText(inputMat, prediction.Label.Name+" "+ Math.Round(prediction.Score,2), p3, HersheyFonts.HersheyDuplex, 2, scalar, 4);
}
return OpenCvSharp.Extensions.BitmapConverter.ToBitmap(inputMat);
}
}
}
}
测试效果：