from scipy import misc

 import tensorflow as tf

 import detect_face

 import cv2

 # import matplotlib.pyplot as plt

 from PIL import Image

 import os

 # import scipy.misc

 # %pylab inline

 fin = 'D:\data\male'

 fout = 'D:\data\\rain\male'

 minsize = 20  # minimum size of face

 threshold = [0.6, 0.7, 0.7]  # three steps's threshold

 factor = 0.709  # scale factor

 margin = 44

 frame_interval = 3

 batch_size = 1000

 image_size = 182

 input_image_size = 160

 print('Creating networks and loading parameters')

 with tf.Graph().as_default():

     gpu_options = tf.GPUOptions(allow_growth=True)

     sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))

     with sess.as_default():

         pnet, rnet, onet = detect_face.create_mtcnn(sess, 'D:\\code\\real-time-deep-face-recognition-master\\20170512-110547')

 i= 0

 for file in os.listdir(fin):

     try:

         file_fullname = fin + '/' + file

         img = misc.imread(file_fullname)

         # i+= 1

         # img = misc.imread(image_path)

         bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)

         nrof_faces = bounding_boxes.shape[0]  # 人脸数目

         print(nrof_faces)

         #print('找到人脸数目为：{}'.format(nrof_faces))

         # print(bounding_boxes)

         crop_faces = []

         if nrof_faces != 0 :

             for face_position in bounding_boxes:

                 face_position = face_position.astype(int)

                 print(face_position[0:4])

                 cv2.rectangle(img, (face_position[0], face_position[1]), (face_position[2], face_position[3]), (0, 255, 0), 2)

                 crop = img[face_position[1]:face_position[3],

                        face_position[0]:face_position[2], ]

                 # print(crop)

                 # crop = cv2.resize(crop, (96, 96), interpolation=cv2.INTER_CUBIC)

                 crop_faces.append(crop)

                 img2 = Image.open(file_fullname)

                 a = face_position[0:4]

                 # print('crop_faces:',crop_faces)

                 # a = [face_position[0:4]]

                 box = (a)

                 roi = img2.crop(box)

                 i = roi.resize((224, 224))

                 out_path = fout + '/' + file

                 i.save(out_path)

                 print('success')

         else:

             pass

     except:

         pass