# -*- codeing: utf-8 -*-
import sys
import os
import cv2
import dlib
input_dir = sys.argv[1]
output_dir = sys.argv[2]
print
(input_dir)
print
(output_dir)
size = 64
if
not os.path.exists(output_dir):
os.makedirs(output_dir)
# 使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
index = 1
for
(path, dirnames, filenames) in os.walk(input_dir):
for
filename in filenames:
if
filename.endswith(
'.jpg'
):
print
(
'Being processed picture %s'
% index)
img_path = path +
'/'
+ filename
# 从文件读取图片
img = cv2.imread(img_path)
# 转为灰度图片
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用detector进行人脸检测 dets为返回的结果
dets = detector(gray_img, 1)
# 使用enumerate 函数遍历序列中的元素以及它们的下标
# 下标i即为人脸序号
# left:人脸左边距离图片左边界的距离 ;right:人脸右边距离图片左边界的距离
# top:人脸上边距离图片上边界的距离 ;bottom:人脸下边距离图片上边界的距离
for
i, d in enumerate(dets):
x1 = d.top()
if
d.top() > 0
else
0
y1 = d.bottom()
if
d.bottom() > 0
else
0
x2 = d.left()
if
d.left() > 0
else
0
y2 = d.right()
if
d.right() > 0
else
0
# img[y:y+h,x:x+w]
face = img[x1:y1, x2:y2]
# 调整图片的尺寸
face = cv2.resize(face, (size, size))
#cv2.imshow(
'image'
, face)
# 保存图片
cv2.imwrite(output_dir +
'/'
+ str(index) +
'.jpg'
, face)
index += 1
key = cv2.waitKey(30) & 0xff
if
key == 27:
sys.
exit
(0) # -*- codeing: utf-8 -*-