OpenCvSharp打造智能考勤系统，实现高效人脸录入和精准考勤识别

Install-Package OpenCvSharp4
1.

using System;
using System.Collections.Generic;
using OpenCvSharp;
using OpenCvSharp.CPlusPlus; // 或者使用OpenCvSharp4

class Program
{
    // 全局变量用于存储员工的人脸特征
    static Dictionary<string, List<float>> employeeFaceFeatures = new Dictionary<string, List<float>>();

    static void Main()
    {
        // 步骤3：员工人脸录入
        EmployeeFaceEnrollment("Employee1", "path/to/employee1.jpg");
        EmployeeFaceEnrollment("Employee2", "path/to/employee2.jpg");

        // 步骤4：上下班考勤人脸识别
        FaceRecognition("path/to/attendance_face.jpg");
    }

    // 步骤3：员工人脸录入的方法
    static void EmployeeFaceEnrollment(string employeeName, string imagePath)
    {
        Mat faceImage = Cv2.ImRead(imagePath, ImreadModes.Color);

        // 提取人脸特征
        List<float> faceFeature = ExtractFaceFeature(faceImage);

        // 存储人脸特征到全局变量中
        employeeFaceFeatures[employeeName] = faceFeature;

        Console.WriteLine($"{employeeName}的人脸特征已录入。");
    }

    // 步骤4：上下班考勤人脸识别的方法
    static void FaceRecognition(string attendanceImagePath)
    {
        Mat attendanceFaceImage = Cv2.ImRead(attendanceImagePath, ImreadModes.Color);

        // 提取考勤人脸的特征
        List<float> attendanceFaceFeature = ExtractFaceFeature(attendanceFaceImage);

        // 与员工人脸特征进行比对
        string recognizedEmployee = RecognizeEmployee(attendanceFaceFeature);

        // 输出考勤结果
        if (!string.IsNullOrEmpty(recognizedEmployee))
        {
            Console.WriteLine($"识别到员工：{recognizedEmployee}，考勤成功。");
        }
        else
        {
            Console.WriteLine("未识别到员工，考勤失败。");
        }
    }

    // 提取人脸特征的方法
    static List<float> ExtractFaceFeature(Mat faceImage)
    {
        // 使用OpenCV的方法提取人脸特征，例如人脸识别模型
        // 这里简单地使用ORB方法提取特征向量
        using (var orb = new ORB())
        {
            KeyPoint[] keyPoints;
            Mat descriptors = new Mat();
            orb.DetectAndCompute(faceImage, null, out keyPoints, descriptors);

            // 返回特征向量
            return descriptors.ToFloatArray();
        }
    }

    // 识别员工的方法
    static string RecognizeEmployee(List<float> attendanceFaceFeature)
    {
        foreach (var employee in employeeFaceFeatures)
        {
            double similarity = CalculateSimilarity(employee.Value, attendanceFaceFeature);

            // 设置相似度阈值，可以根据实际情况调整
            double similarityThreshold = 0.7;

            if (similarity > similarityThreshold)
            {
                return employee.Key;
            }
        }

        return null;
    }

    // 计算相似度的方法
    static double CalculateSimilarity(List<float> featureVector1, List<float> featureVector2)
    {
        // 使用OpenCV的方法计算相似度，例如欧氏距离、余弦相似度等
        // 这里简单地使用余弦相似度计算
        double dotProduct = 0;
        double magnitude1 = 0;
        double magnitude2 = 0;

        for (int i = 0; i < featureVector1.Count; i++)
        {
            dotProduct += featureVector1[i] * featureVector2[i];
            magnitude1 += Math.Pow(featureVector1[i], 2);
            magnitude2 += Math.Pow(featureVector2[i], 2);
        }

        if (magnitude1 == 0 || magnitude2 == 0)
            return 0;

        return dotProduct / (Math.Sqrt(magnitude1) * Math.Sqrt(magnitude2));
    }
}
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