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
 # %pylab inline

 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(per_process_gpu_memory_fraction=0.6)
     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:\\pycode\\real-time-deep-face-recognition-master\\20170512-110547')

 image_path = 'D:\\Users\\a\\Pictures\\test_pho\\5.jpg'

 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('找到人脸数目为：{}'.format(nrof_faces))

 print(bounding_boxes)

 crop_faces = []
 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], ]

     crop = cv2.resize(crop, (96, 96), interpolation=cv2.INTER_CUBIC)
     print(crop.shape)
     crop_faces.append(crop)
     print(crop)
     plt.imshow(crop)
     plt.show()

 plt.imshow(img)
 plt.show()