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
import os

# dlib预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('D:\\shape_predictor_68_face_landmarks.dat')

# 读取图像的路径
path_read = "C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files"
num=0
for file_name in os.listdir(path_read):
#aa是图片的全路径
aa=(path_read +"/"+file_name)
#读入的图片的路径中含非英文
img=cv2.imdecode(np.fromfile(aa, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
#获取图片的宽高
img_shape=img.shape
img_height=img_shape[0]
img_width=img_shape[1]

# 用来存储生成的单张人脸的路径
path_save="C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files1"
# dlib检测
dets = detector(img,1)
print("人脸数：", len(dets))
for k, d in enumerate(dets):
if len(dets)>1:
continue
num=num+1
# 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)
for i in range(height):
if d.top()+i>=img_height:# 防止越界
continue
for j in range(width):
if d.left()+j>=img_width:# 防止越界
continue
img_blank[i][j] = img[d.top()+i][d.left()+j]
img_blank = cv2.resize(img_blank, (200, 200), interpolation=cv2.INTER_CUBIC)

cv2.imencode('.jpg', img_blank)[1].tofile(path_save+"\\"+"file"+str(num)+".jpg") # 正确方法
————————————————
版权声明：本文为CSDN博主「机智的橙子」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_45659777/article/details/121728481