Mtcnn进行人脸剪裁和对齐B

Mtcnn进行人脸剪裁和对齐

 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