Python3结合Dlib实现人脸识别和剪切

import dlib  # 人脸识别的库dlib
import numpy as np # 数据处理的库numpy
import cv2  # 图像处理的库OpenCv

img_blank = np.zeros((height_max, width_sum, 3), np.uint8)

for i in range(height):
for j in range(width):
img_blank[i][blank_start+j] = img[d.top()+i][d.left()+j]

# 2018-01-22
# By TimeStamp
# #cnblogs: http://www.cnblogs.com/AdaminXie/
import dlib  # 人脸识别的库dlib
import numpy as np # 数据处理的库numpy
import cv2  # 图像处理的库OpenCv
# dlib预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
# 读取图像
path = "F:/code/python/***/pic/"
img = cv2.imread(path+"test.jpg")
#print("img/shape:", img.shape)
# dlib检测
dets = detector(img, 1)
print("人脸数：", len(dets))
# 记录人脸矩阵大小
height_max = 0
width_sum = 0
# 计算要生成的图像img_blank大小
for k, d in enumerate(dets):
# 计算矩形大小
# (x,y), (宽度width, 高度height)
pos_start = tuple([d.left(), d.top()])
pos_end = tuple([d.right(), d.bottom()])
# 计算矩形框大小
height = d.bottom()-d.top()
width = d.right()-d.left()
# 处理宽度
width_sum += width
# 处理高度
if height > height_max:
height_max = height
else:
height_max = height_max
# 绘制用来显示人脸的图像的大小
print("img_blank的大小：")
print("高度", height_max, "宽度", width_sum) 
# 生成用来显示的图像
img_blank = np.zeros((height_max, width_sum, 3), np.uint8)
# 记录每次开始写入人脸像素的宽度位置
blank_start = 0 
# 将人脸填充到img_blank
for k, d in enumerate(dets):
height = d.bottom()-d.top()
width = d.right()-d.left()
# 填充
for i in range(height):
for j in range(width):
img_blank[i][blank_start+j] = img[d.top()+i][d.left()+j]
# 调整图像
blank_start += width
cv2.namedWindow("img_faces", 2)
cv2.imshow("img_faces", img_blank)
cv2.waitKey(0)