用Python写一个人脸识别考勤签到的程序

可以使用Python中的OpenCV库和人脸识别算法来实现人脸识别考勤签到程序。该程序可以通过摄像头捕获学生/员工的照片，检测并识别脸部，将脸部特征保存到数据库中，并在签到时对照数据库中的脸部特征进行身份验证。下面是一个示例代码：

import cv2
import numpy as np
import os

def face_recognition(directory):
    recognizer = cv2.face.LBPHFaceRecognizer_create() #使用LBPH算法创建人脸识别器
    detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")#使用Haar级联分类器检测人脸
    image_paths = [os.path.join(directory, f) for f in os.listdir(directory)]
    faces = []
    labels = []
    for image_path in image_paths:
        image = cv2.imread(image_path)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        face_rects = detector.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)
        for (x, y, w, h) in face_rects:
            faces.append(gray[y:y+h, x:x+w])
            labels.append(int(os.path.split(image_path)[1].split(".")[0]))
    recognizer.train(faces, np.array(labels)) #训练识别器
    return recognizer

def attendence_recognition(recognizer):
    cap = cv2.VideoCapture(0, cv2.CAP_DSHOW) #使用摄像头获取图像
    detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
    font = cv2.FONT_HERSHEY_SIMPLEX
    while True:
        ret, frame = cap.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        face_rects = detector.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)
        for (x, y, w, h) in face_rects:
            face = gray[y:y+h, x:x+w]
            label, confidence = recognizer.predict(face)
            if confidence < 50: # 50是可调整的置信度阈值
                cv2.putText(frame, "Identified: " + str(confidence) + "%", (x, y-10), font, 1, (0, 255, 0), 2)
            else:
                cv2.putText(frame, "Unidentified", (x, y-10), font, 1, (0, 0, 255), 2)
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
        cv2.imshow("Real-time capture", frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    directory = "att_faces"
    recognizer = face_recognition(directory)
    attendence_recognition(recognizer)

当用户运行程序时，会使用“att_faces”目录下的单独的图像来训练分类器。该程序作为一个实时视频捕捉器运行，并将训练数据与程序捕捉的视频帧中发现的人脸数据库进行比对。如果发现人的脸并且可以在数据库中匹配，程序将显示匹配的姓名，并将该人员标记为“签到成功”；否则，将显示“签到失败”。