1 from scipy import misc
2 import tensorflow as tf
3 import detect_face
4 import cv2
5 import matplotlib.pyplot as plt
6 # %pylab inline
7
8 minsize = 20 # minimum size of face
9 threshold = [0.6, 0.7, 0.7] # three steps's threshold
10 factor = 0.709 # scale factor
11 margin = 44
12 frame_interval = 3
13 batch_size = 1000
14 image_size = 182
15 input_image_size = 160
16
17 print('Creating networks and loading parameters')
18
19 with tf.Graph().as_default():
20 gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
21 sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
22 with sess.as_default():
23 pnet, rnet, onet = detect_face.create_mtcnn(sess, 'D:\\pycode\\real-time-deep-face-recognition-master\\20170512-110547')
24
25 image_path = 'D:\\Users\\a\\Pictures\\test_pho\\5.jpg'
26
27 img = misc.imread(image_path)
28 bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
29 nrof_faces = bounding_boxes.shape[0] # 人脸数目
30 print('找到人脸数目为：{}'.format(nrof_faces))
31
32 print(bounding_boxes)
33
34 crop_faces = []
35 for face_position in bounding_boxes:
36 face_position = face_position.astype(int)
37 print(face_position[0:4])
38 cv2.rectangle(img, (face_position[0], face_position[1]), (face_position[2], face_position[3]), (0, 255, 0), 2)
39 crop = img[face_position[1]:face_position[3],
40 face_position[0]:face_position[2], ]
41
42 crop = cv2.resize(crop, (96, 96), interpolation=cv2.INTER_CUBIC)
43 print(crop.shape)
44 crop_faces.append(crop)
45 print(crop)
46 plt.imshow(crop)
47 plt.show()
48
49 plt.imshow(img)
50 plt.show()
