python人脸识别截图_Python使用opencv人脸识别剪裁图片

#-*-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')