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 
gpu_memory_fraction=1.0 
 
 
print('Creating networks and loading parameters') 
 
with tf.Graph().as_default(): 
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) 
    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, None) 
 
image_path = '/home/cqh/faceData/multi_face/multi_face3.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) 
  plt.imshow(crop) 
  plt.show() 
   
plt.imshow(img) 
plt.show()