#-*-coding:utf8-*-
import os
import cv2
import time
import shutil
def getAllPath(dirpath, *suffix):
PathArray = []
for r, ds, fs in os.walk(dirpath):
for fn in fs:
if os.path.splitext(fn)[1] in suffix:
fname = os.path.join(r, fn)
PathArray.append(fname)
return PathArray
def readPicSaveFace_1(sourcePath,targetPath,invalidPath,*suffix):
try:
ImagePaths=getAllPath(sourcePath, *suffix)
#对list中图片逐一进行检查,找出其中的人脸然后写到目标文件夹下
count = 1
# haarcascade_frontalface_alt.xml为库训练好的分类器文件,下载opencv,安装目录中可找到
face_cascade = cv2.CascadeClassifier('haarcascades\haarcascade_frontalface_alt.xml')
for imagePath in ImagePaths:
try:
img = cv2.imread(imagePath)
if type(img) != str:
faces = face_cascade.detectMultiScale(img, 1.1, 5)
if len(faces):
for (x, y, w, h) in faces:
# 设置人脸宽度大于16像素,去除较小的人脸
if w>=64 and h>=64:
# 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)
# 扩大图片,可根据坐标调整
X = int(x)
W = min(int(x + w),img.shape[1])
Y = int(y)
H = min(int(y + h),img.shape[0])
f = cv2.resize(img[Y:H, X:W], (W-X,H-Y))
cv2.imwrite(targetPath+os.sep+'%s.JPG' % fileName, f)
count += 1
print (imagePath + "have face")
#else:
# shutil.move(imagePath, invalidPath)
except:
continue
except IOError:
print ("Error")
else:
print ('Find '+str(count-1)+' faces to Destination '+targetPath)
if __name__ == '__main__':
invalidPath = r'无效输出'
sourcePath = r'源输入'
targetPath1 = r'目标输出'
readPicSaveFace_1(sourcePath,targetPath1,invalidPath,'.jpg','.JPG','png','PNG')