【测试环境】

vs2019

net framework4.7.2

onnxruntime==1.16.3

opencvsharp

注意源码运行在CPU上不支持GPU运行，由于net framework限制GPU会很慢因此没有GPU版本提供。

【运行步骤】

打开sln项目

选择x64 debug运行即可

如需要再x64 release运行可以将x64 debug文件夹里面所有文件复制到x64 release文件夹里面然后再vs2019切换到x64 release运行即可。

【效果展示】

【界面设计代码】

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Diagnostics;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using OpenCvSharp;namespace FIRC
{public partial class Form1 : Form{Mat src = new Mat();Yolov8SegManager ym = new Yolov8SegManager();public Form1(){InitializeComponent();}private void button1_Click(object sender, EventArgs e){OpenFileDialog openFileDialog = new OpenFileDialog();openFileDialog.Filter = "图文件(*.*)|*.jpg;*.png;*.jpeg;*.bmp";openFileDialog.RestoreDirectory = true;openFileDialog.Multiselect = false;if (openFileDialog.ShowDialog() == DialogResult.OK){src = Cv2.ImRead(openFileDialog.FileName);pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(src);}}private void button2_Click(object sender, EventArgs e){if(pictureBox1.Image==null){return;}Stopwatch sw = new Stopwatch();sw.Start();var result = ym.Inference(src);sw.Stop();this.Text = "耗时" + sw.Elapsed.TotalSeconds + "秒";var resultMat = ym.DrawImage(src,result);pictureBox2.Image= OpenCvSharp.Extensions.BitmapConverter.ToBitmap(resultMat); //Mat转Bitmap}private void Form1_Load(object sender, EventArgs e){ym.LoadWeights(Application.StartupPath+ "\\weights\\yolov8n-seg.onnx", Application.StartupPath + "\\weights\\labels.txt");}private void btn_video_Click(object sender, EventArgs e){var detector = new Yolov8SegManager();detector.LoadWeights(Application.StartupPath + "\\weights\\yolov8n-seg.onnx", Application.StartupPath + "\\weights\\labels.txt");VideoCapture capture = new VideoCapture(0);if (!capture.IsOpened()){Console.WriteLine("video not open!");return;}Mat frame = new Mat();var sw = new Stopwatch();int fps = 0;while (true){capture.Read(frame);if (frame.Empty()){Console.WriteLine("data is empty!");break;}sw.Start();var result = detector.Inference(frame);var resultImg = detector.DrawImage(frame,result);sw.Stop();fps = Convert.ToInt32(1 / sw.Elapsed.TotalSeconds);sw.Reset();Cv2.PutText(resultImg, "FPS=" + fps, new OpenCvSharp.Point(30, 30), HersheyFonts.HersheyComplex, 1.0, new Scalar(255, 0, 0), 3);//显示结果Cv2.ImShow("Result", resultImg);int key = Cv2.WaitKey(10);if (key == 27)break;}capture.Release();}}
}

【训练数据集介绍】

注意数据集中有增强图片

数据集格式：labelme格式(不包含mask文件，仅仅包含jpg图片和对应的json文件)
图片数量(jpg文件个数)：9339
标注数量(json文件个数)：9339
标注类别数：1
标注类别名称:["Nail"]
每个类别标注的框数：
Nail count = 38632
使用标注工具：labelme=5.5.0
所在仓库：firc-dataset
图片分辨率：640x640
标注规则：对类别进行画多边形框polygon
重要说明：可以将数据集用labelme打开编辑，json数据集需自己转成mask或者yolo格式或者coco格式作语义分割或者实例分割
特别声明：本数据集不对训练的模型或者权重文件精度作任何保证

图片预览：