#----- 检测、校验并输出结果 -----

import cv2

# 准备好识别方法

recognizer = cv2.face.LBPHFaceRecognizer_create()

# 使用之前训练好的模型

recognizer.read('trainner/trainner.yml')

# 再次调用人脸分类器

cascade_path = "haarcascade_frontalface_default.xml"

face_cascade = cv2.CascadeClassifier(cascade_path)

# 加载一个字体，用于识别后，在图片上标注出对象的名字

font = cv2.FONT_HERSHEY_SIMPLEX

idnum = 0

# 设置好与 ID 号码对应的用户名，如下，如 0 对应的就是初始

names = [' 初始 ','admin','user1','user2','user3']

# 调用摄像头

cam = cv2.VideoCapture(0)

minW =跟单网gendan5.com 0.1*cam.get(3)

minH = 0.1*cam.get(4)

while True:

     ret,img = cam.read()

     gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

     # 识别人脸

     faces = face_cascade.detectMultiScale(

             gray,

             scaleFactor = 1.2,

             minNeighbors = 5,

             minSize = (int(minW),int(minH))

             )

     # 进行校验

     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])

         # 计算出一个检验结果

         if confidence < 100:

             idum = names[idnum]

             confidence = "{0}%",format(round(100-confidence))

         else:

             idum = "unknown"

             confidence = "{0}%",format(round(100-confidence))

         # 输出检验结果以及用户名

         cv2.putText(img,str(idum),(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)

         # 展示结果

         cv2.imshow('camera',img)

         k = cv2.waitKey(20)

         if k == 27:

             break

# 释放资源

cam.release()

cv2.destroyAllWindows()