到这一步就是进行人脸识别了。

流程图：

 

代码：

import cv2def recognize(cam):    recognizer = cv2.face.LBPHFaceRecognizer_create()    recognizer.read('face_trainer/trainer.yml')    cascadePath = "haarcascade_frontalface_default.xml"    faceCascade = cv2.CascadeClassifier(cascadePath)    # 字体    font = cv2.FONT_HERSHEY_DUPLEX    names = ['zhiqiang', 'jiayu', 'jinze']    cam = cv2.VideoCapture(0)    minW = 0.1*cam.get(3)    minH = 0.1*cam.get(4)    while True:        ret, img = cam.read()        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)        faces = faceCascade.detectMultiScale(            gray,            scaleFactor=1.2,            minNeighbors=5,            minSize=(int(minW), int(minH))        )        for (x, y, w, h) in faces:            cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)            idnum, confidence = recognizer.predict(gray[y:y+h, x:x+w])            print(idnum)            if confidence < 100:                idnum = names[idnum]                confidence = "{0}%".format(round(100 - confidence))            else:                idnum = "unknown"                confidence = "{0}%".format(round(100 - confidence))            cv2.putText(img, str(idnum), (x+5, y-5), font, 1, (0, 0, 255), 1)            cv2.putText(img, str(confidence), (x+5, y+h-5), font, 1, (0, 0, 0), 1)        cv2.imshow('camera', img)        k = cv2.waitKey(10)        if k == 27:            break    cam.release()    cv2.destroyAllWindows()if __name__ == '__main__':    cam = cv2.VideoCapture(0)    recognize(cam)