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]#人臉數目 print('找到人臉數目為：{}'.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()