def get_fm(image_filepath, layer_name='conv5_1'):
"""
get feature map
:param image_filepath:
:param layer_name:
:return:
"""
graph = tf.Graph()
with graph.as_default():
input_maps = tf.placeholder(tf.float32, [None, 224, 224, 3])
output, average_image, class_names = vgg_face(
config['vgg_face_model_mat_file'], input_maps)
# read sample image
img = imread(image_filepath, mode='RGB')
img = (img - img.mean()) / img.std()
img = imresize(img, [224, 224])
# run the graph
with tf.Session(graph=graph) as sess:
[out] = sess.run([output], feed_dict={input_maps: [img]})
feature = out[layer_name]
sess.close()
return feature
def extract_deep_feats(img_fname):
"""
extract deep features and save its features to a pickle file
:param img_fname:
:return:
"""
if not os.path.exists('./features'):
os.makedirs('./features')
img = imread(img_fname, mode='RGB')
img = (img - img.mean()) / img.std()
img = imresize(img, [224, 224])
graph = tf.Graph()
with graph.as_default():
input_maps = tf.placeholder(tf.float32, [None, 224, 224, 3])
output, average_image, class_names = vgg_face(
config['vgg_face_model_mat_file'], input_maps)
img = (img - img.mean()) / img.std()
img = imresize(img, [224, 224])
# run the graph
with tf.Session(graph=graph) as sess:
[out] = sess.run([output], feed_dict={input_maps: [img]})
feat = {}
print(out.keys())
for k, v in out.items():
if k in [
'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2',
'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1',
'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3',
'pool5', 'fc6', 'relu6', 'fc7', 'relu7', 'fc8'
]:
feat[k] = v.tolist()
print(out)
with open('./features/{0}.pkl'.format(
img_fname.split('/')[-1].split('.')[0]),
mode='wb') as f:
pickle.dump(feat, f)
sess.close()
def extract_conv_feature(img, layer_name='conv5_1'):
graph = tf.Graph()
with graph.as_default():
input_maps = tf.placeholder(tf.float32, [None, 224, 224, 3])
output, average_image, class_names = vgg_face(
config['vgg_face_model_mat_file'], input_maps)
img = (img - img.mean()) / img.std()
img = imresize(img, [224, 224])
# run the graph
with tf.Session(graph=graph) as sess:
[out] = sess.run([output], feed_dict={input_maps: [img]})
feature = out[layer_name]
sess.close()
return feature.ravel()