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最近在做人脸识别，需要对人脸数据集进行处理，对一张或批量图像的人脸进行切割分离，并且另保存下来。受网上开源工具的启发，在借鉴他人的基础上进行了改进，使得更加方便实用。

以下代码是改进版，分为两部分功能：

1. 一张人脸图片切割并显示，不保存
2. 一张/批量人脸图像切割并保存

在这里需要申明一下，尊重原著！（转发需要标注一下原著信息）

原创版：

  Author:    coneypo
  Blog:       http://www.cnblogs.com/AdaminXie
  GitHub:   https://github.com/coneypo/Dlib_face_cut

改进版：

Improver:  Cai_90hou

Blog:         https://blog.csdn.net/qq_38677310/article/details/84702662

Github:      

#crop_faces_show.pyimport 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 正向人脸检测器
detector = dlib.get_frontal_face_detector()# 读取图像
path = "faces_for_test/"img = cv2.imread(path+"test_faces_1.jpg")# Dlib 检测
dets = detector(img, 1)print("检测到的人脸数 / faces ：", len(dets), "\n")# 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 = heightelse:height_max = height_max# 绘制用来显示人脸的图像的大小
print("窗口大小：", '\n', "高度 / height :", height_max, '\n', "宽度 / width : ", 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 += widthcv2.namedWindow("img_faces", 0)
cv2.imshow("img_faces", img_blank)
cv2.waitKey(0)

#crop_faces_save.pyimport 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
import os#切割后的人脸序号，为全局变量
image_num = 0# Dlib 正向人脸检测器
detector = dlib.get_frontal_face_detector()# 读取图像的路径
path_read = "faces_for_test/"
#img = cv2.imread(path_read+"test_faces_3.jpg")# 用来存储生成的单张人脸的路径
path_save = "faces_separated/"#将文件夹中待处理的图片存于列表中
imgs_read  = os.listdir(path_read)   #源地址文件夹
imgs_write = os.listdir(path_save)   #存储地址文件夹# Delete old images
def clear_images():for img in imgs_write:os.remove(path_save + img)print("clean finish", '\n')#处理一张图片：
def cut_one_photo(img):global image_num# Dlib 检测faces = detector(img, 1)print("人脸数：", len(faces))for k, d in enumerate(faces):# 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()# 根据人脸大小生成空的图像img_blank = np.zeros((height, width, 3), np.uint8)#复制人脸for i in range(height):for j in range(width):img_blank[i][j] = img[d.top()+i][d.left()+j]# cv2.imshow("face_"+str(k+1), img_blank)# 保存在本地#print("Save to:", path_save+"CWH"+str(image_num+1)+".jpg")print("Save to:",      "img_face" + str(image_num + 1) + ".jpg")cv2.imwrite(path_save + "img_face" + str(image_num + 1) + ".jpg", img_blank)image_num += 1#批量处理图片
def cut_batch_photoes():clear_images() #清除原有图片for img in imgs_read:try:path = os.path.join(path_read, img)image = cv2.imread(path)cut_one_photo(image)print('\n')except:continueprint('Successful operation!\n')print('total number of faces: ', image_num)def main():# 批量处理图片并保存cut_batch_photoes()if __name__ == '__main__':main()