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()
