import cv2

cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

#-*- coding: utf-8 -*-
# import openCV的库
import cv2
import os, math, operator
from PIL import Image
from functools import reduce


###调用电脑摄像头检测人脸并截图

def CatchPICFromVideo(window_name, path_name):
 cv2.namedWindow(window_name)

 #电脑摄像头
 cap = cv2.VideoCapture(0)

 #告诉OpenCV使用人脸识别分类器
 classfier = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

 #检测人脸后要画的边框的颜色
 color = (0, 255, 0)

 while cap.isOpened():
 ok, frame = cap.read() #读取一帧数据
 if not ok:
  break

 grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #将当前桢图像转换成灰度图像

 #人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数
 faceRects = classfier.detectMultiScale(grey, scaleFactor = 1.2, minNeighbors = 3, minSize = (32, 32))
 if len(faceRects) > 0:  #大于0则检测到人脸
  for faceRect in faceRects: #单独框出每一张人脸
  x, y, w, h = faceRect

   #画出矩形框
  cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)
  
  k = cv2.waitKey(100) #每0.1秒读一次键盘
  if k == ord("z") or k == ord("Z"): #如果输入z
   #将当前帧保存为图片
   img_name = path_name
   print(img_name)
   image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
   cv2.imwrite(img_name, image,[int(cv2.IMWRITE_PNG_COMPRESSION), 9])
   break 
   
 #显示图像
 cv2.imshow(window_name, frame)
 #退出摄像头界面
 c = cv2.waitKey(100)
 if c == ord("q") or c == ord("Q"): 
  break

 #释放摄像头并销毁所有窗口
 cap.release()
 cv2.destroyAllWindows()


os.system("cls") #清屏
recogname = "recogface.jpg" #预存的人脸文件
CatchPICFromVideo("get face",recogname)