#-*- coding:utf-8 -*-
import cv2
import os
import time
import base64
import numpy as np
 
save_path = 'E:\\opencv\\2018-04-24OpenCv\\RAR\\savetest'
faceCascade = cv2.CascadeClassifier(
  './haarcascade_frontalface_alt.xml')
 
cap = cv2.VideoCapture(0)
 
count = 0
 
while True:
  ret,frame = cap.read()
  gray = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
  rect = faceCascade.detectMultiScale(gray,
                  scaleFactor=1.3,
                  minNeighbors=9,
                  minSize=(50,50),
                  flags=cv2.CASCADE_SCALE_IMAGE
                  )
 
  if not rect is ():
    for x,y,w,h in rect:
      roiImg = frame[y:y+h,x:x+w]
      # 以时间戳和读取的排序作为文件名称
      listStr = [str(int(time.time())), str(count)]
      fileName = ''.join(listStr)
      # 图片存储
      cv2.imwrite(save_path + os.sep + '%s.jpg' % fileName, roiImg)
      # print (roiImg)
      # roiTobase64 = cv2.imencode(roiImg,np.uint8)
      # print (base64.b64encode(roiTobase64))
      cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
 
 
      count += 1
 
  cv2.imshow('opencvCut',frame)
  k = cv2.waitKey(30) & 0xff
  if k == 27:
    break
cap.release()
cv2.destroyAllWindows()

import cv2
import sys
import os
from PIL import Image
cascPath = "haarcascade_frontalface_default.xml" #训练参数文件
faceCascade = cv2.CascadeClassifier(cascPath)#分类器
 
base = 'new_dir\\'
for img in os.listdir(base):
  image = cv2.imread(base + img)#读取图片
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  gray = cv2.equalizeHist(gray) #直方图均衡化，提高分类效果
  faces = faceCascade.detectMultiScale(
    gray,
    scaleFactor=1.1,
    minNeighbors=5,
    minSize=(10, 10)
  )
  num = 0
  for (x, y, w, h) in faces:
    cv2.imwrite("face_dir\\" + img,image[y:y+h,x:x+w])
    num += 1
#   cv2.imshow("Faces found", image)
  cv2.waitKey(0)