[C#][winform]基于yolov8的8种人脸表情检测系统C#源码+onnx模型+评估指标曲线+精美GUI界面

【重要说明】

该系统以opencvsharp作图像处理,onnxruntime做推理引擎，使用CPU进行推理，适合有显卡或者没有显卡windows x64系统均可，不支持macOS和Linux系统，不支持x86的windows操作系统。由于采用CPU推理，要比GPU慢。为了适合大部分操作系统我们暂时只写了CPU推理源码，GPU推理源码后期根据需要可能会调整，目前只考虑CPU推理，主要是为了照顾现在大部分使用该源码是学生，很多人并没有显卡的电脑情况。

【算法介绍】

基于YOLOv8的人脸表情检测系统是一个结合了先进目标检测算法（YOLOv8）与深度学习技术的项目，旨在实时或离线地识别并分类人脸表情（如快乐、悲伤、愤怒、惊讶、恐惧、厌恶、中立等）。以下是一个简短的介绍，概述了该系统Python源码的核心要点：

该系统直接利用YOLOv8模型进行人脸表情识别。YOLOv8以其高效的速度和准确性著称，非常适合实时应用。

Python源码实现通常包括以下几个关键部分：

1. 环境配置：安装必要的库，如onnxruntime（用于YOLOv8模型推理）、OpenCV（用于图像处理）等。
2. 模型加载：加载预训练的YOLOv8人脸表情识别模型。
3. 图像/视频处理：读取输入图像或视频，进行预处理（如缩放、归一化）。
4. 人脸检测与表情识别：使用YOLOv8直接输出人脸表情检测结果。
5. 结果展示：在图像或视频帧上标注检测到的人脸和表情，并显示结果。

这样的系统可以广泛应用于人机交互、情感分析、安全监控等领域。

【效果演示】

【测试环境】

windows10 x64系统
VS2019
netframework4.7.2
opencvsharp4.8.0
onnxruntime1.16.3

【模型可以检测出类别】

anger
content
disgust
fear
happy
neutral
sad
surprise

【相关参考训练数据集】

https://blog.csdn.net/FL1623863129/article/details/128648951

【训练信息】

【部分实现源码】

using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;
using System.Windows.Forms;
 
namespace FIRC
{
    public partial class Form1 : Form
    {
 
        public bool videoStart = false;//视频停止标志
        string weightsPath = Application.StartupPath + "\\weights";//模型目录
        string labelTxt= Application.StartupPath + "\\weights\\class_names.txt";//类别文件
        Yolov8Manager detetor = new Yolov8Manager();//推理引擎
        public Form1()
        {
            InitializeComponent();
            CheckForIllegalCrossThreadCalls = false;//线程更新控件不报错
        }
        private void LoadWeightsFromDir()
        {
            var di = new DirectoryInfo(weightsPath);
            foreach(var fi in di.GetFiles("*.onnx"))
            {
                comboBox1.Items.Add(fi.Name);
            }
            if(comboBox1.Items.Count>0)
            {
                comboBox1.SelectedIndex = 0;
            }
            else
            {
                tssl_show.Text = "未找到模型,请关闭程序，放入模型到weights文件夹!";
                tsb_pic.Enabled = false;
                tsb_video.Enabled = false;
                tsb_camera.Enabled = false;
            }
        }
        private void Form1_Load(object sender, EventArgs e)
        {
            LoadWeightsFromDir();//从目录加载模型
        }
        public string GetResultString(Result result)
        {
            Dictionary<string, int> resultDict = new Dictionary<string, int>();
            for (int i = 0; i < result.length; i++)
            {
                if(resultDict.ContainsKey( result.classes[i]) )
                {
                    resultDict[result.classes[i]]++;
                }
                else
                {
                    resultDict[result.classes[i]]=1;
                }
            }
 
            var resultStr = "";
            foreach(var item in resultDict)
            {
                resultStr += string.Format("{0}:{1}\n",item.Key,item.Value);
            }
            return resultStr;
        }
        private void tsb_pic_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
            if (ofd.ShowDialog() != DialogResult.OK) return;
            tssl_show.Text = "正在检测中...";
            Task.Run(() => {
                var sw = new Stopwatch();
                sw.Start();
                Mat image = Cv2.ImRead(ofd.FileName);
                detetor.Confidence =Convert.ToSingle(numericUpDown1.Value);
                detetor.IOU = Convert.ToSingle(numericUpDown2.Value);
                var results=detetor.Inference(image);
                
                var resultImage = detetor.DrawImage(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(image), results);
    
                sw.Stop();
                pb_show.Image = resultImage;
                tb_res.Text = GetResultString(results);
                tssl_show.Text = "检测已完成!总计耗时"+sw.Elapsed.TotalSeconds+"秒";
            });
           
 
 
        }
 
        public void VideoProcess(string videoPath)
        {
            Task.Run(() => {
 
                detetor.Confidence = Convert.ToSingle(numericUpDown1.Value);
                detetor.IOU = Convert.ToSingle(numericUpDown2.Value);
                VideoCapture capture = new VideoCapture(videoPath);
                if (!capture.IsOpened())
                {
                    tssl_show.Text="视频打开失败!";
                    return;
                }
                Mat frame = new Mat();
                var sw = new Stopwatch();
                int fps = 0;
                while (videoStart)
                {
 
                    capture.Read(frame);
                    if (frame.Empty())
                    {
                        Console.WriteLine("data is empty!");
                        break;
                    }
                    sw.Start();
                    var results = detetor.Inference(frame);
                    var resultImg = detetor.DrawImage(frame,results);
                    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
                    pb_show.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(resultImg);
                    tb_res.Text = GetResultString(results);
                    Thread.Sleep(5);
 
 
                }
 
                capture.Release();
 
                pb_show.Image = null;
                tssl_show.Text = "视频已停止!";
                tsb_video.Text = "选择视频";
 
            });
        }
        public void CameraProcess(int cameraIndex=0)
        {
            Task.Run(() => {
 
                detetor.Confidence = Convert.ToSingle(numericUpDown1.Value);
                detetor.IOU = Convert.ToSingle(numericUpDown2.Value);
                VideoCapture capture = new VideoCapture(cameraIndex);
                if (!capture.IsOpened())
                {
                    tssl_show.Text = "摄像头打开失败!";
                    return;
                }
                Mat frame = new Mat();
                var sw = new Stopwatch();
                int fps = 0;
                while (videoStart)
                {
 
                    capture.Read(frame);
                    if (frame.Empty())
                    {
                        Console.WriteLine("data is empty!");
                        break;
                    }
                    sw.Start();
                    var results = detetor.Inference(frame);
                    var resultImg = detetor.DrawImage(frame, results);
                    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
                    pb_show.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(resultImg);
                    tb_res.Text = GetResultString(results);
                    Thread.Sleep(5);
 
 
                }
 
                capture.Release();
 
                pb_show.Image = null;
                tssl_show.Text = "摄像头已停止!";
                tsb_camera.Text = "打开摄像头";
 
            });
        }
        private void tsb_video_Click(object sender, EventArgs e)
        {
            if(tsb_video.Text=="选择视频")
            {
                OpenFileDialog ofd = new OpenFileDialog();
                ofd.Filter = "视频文件(*.*)|*.mp4;*.avi";
                if (ofd.ShowDialog() != DialogResult.OK) return;
                videoStart = true;
                VideoProcess(ofd.FileName);
                tsb_video.Text = "停止";
                tssl_show.Text = "视频正在检测中...";
 
            }
            else
            {
                videoStart = false;
               
            }
        }
 
        private void tsb_camera_Click(object sender, EventArgs e)
        {
            if (tsb_camera.Text == "打开摄像头")
            {
                videoStart = true;
                CameraProcess(0);
                tsb_camera.Text = "停止";
                tssl_show.Text = "摄像头正在检测中...";
 
            }
            else
            {
                videoStart = false;
 
            }
        }
 
        private void tsb_exit_Click(object sender, EventArgs e)
        {
            videoStart = false;
            this.Close();
        }
 
        private void trackBar1_Scroll(object sender, EventArgs e)
        {
            numericUpDown1.Value = Convert.ToDecimal(trackBar1.Value / 100.0f);
        }
 
        private void trackBar2_Scroll(object sender, EventArgs e)
        {
            numericUpDown2.Value = Convert.ToDecimal(trackBar2.Value / 100.0f);
        }
 
        private void numericUpDown1_ValueChanged(object sender, EventArgs e)
        {
            trackBar1.Value = (int)(Convert.ToSingle(numericUpDown1.Value) * 100);
        }
 
        private void numericUpDown2_ValueChanged(object sender, EventArgs e)
        {
            trackBar2.Value = (int)(Convert.ToSingle(numericUpDown2.Value) * 100);
        }
 
        private void comboBox1_SelectedIndexChanged(object sender, EventArgs e)
        {
            tssl_show.Text="加载模型:"+comboBox1.Text;
            detetor.LoadWeights(weightsPath+"\\"+comboBox1.Text,labelTxt);
            tssl_show.Text = "模型加载已完成!";
        }
    }
}

【使用步骤】

使用步骤：
（1）首先根据官方框架https://github.com/ultralytics/ultralytics安装教程安装好yolov8环境，并根据官方export命令将自己pt模型转成onnx模型
（2）使用vs2019打开sln项目，选择x64 release并且修改一些必要的参数，比如输入shape等，点击运行即可查看最后效果

特别注意如果运行报错了，请参考我的博文进行重新引用我源码的DLL：[C#]opencvsharp报错System.Memory，Version=4.0.1.2，Culture=neutral，PublicKeyToken=cc7b13fcd2ddd51“版本高于所引_未能加载文件或程序集“system.memory, version=4.0.1.2, culture-CSDN博客

【提供文件】

C#源码
yolov8n.onnx模型（不提供pytorch模型）
训练的map,P,R曲线图(在weights\results.png)
测试图片（在test_img文件夹下面）

【源码下载地址】

关注下方名片并回复【firc7】即可获取下载方式