def extract_vgg(video_directory, max_num_captions):
"""Extract VGG features."""
vgg_features = list()
# Session config.
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess_config.gpu_options.visible_device_list = '0'
resFile_video = '/home/yulia/github/Activity_net_whole/train10_qa.json'
data = json.load(open(resFile_video))
caption_info = data['caption_info']
with tf.Graph().as_default(), tf.Session(config=sess_config) as sess:
extractor = VideoVGGExtractor(max_num_captions, sess)
video_id_train = open("video_id_train.txt", "r")
for video_id in video_id_train:
video_id_cap = 'v_' + video_id[:-1]
if video_id_cap in caption_info.keys():
list_timestamps = caption_info[video_id_cap]['timestamps']
video_path = os.path.join(video_directory, video_id[:-1] + '.avi')
print('[VGG]', video_path)
vgg_features.append(
extractor.extract(video_path, list_timestamps,
max_num_captions))
# print(vgg_features[-1])
video_id_train.close()
return vgg_features
def extract_vgg(video_directory):
"""Extract VGG features."""
vgg_features = list()
# Session config.
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess_config.gpu_options.visible_device_list = '0'
with tf.Graph().as_default(), tf.Session(config=sess_config) as sess:
extractor = VideoVGGExtractor(20, sess)
for i in range(1, 1971):
video_path = os.path.join(video_directory, 'vid' + str(i) + '.avi')
print('[VGG]', video_path)
vgg_features.append(extractor.extract(video_path))
# print(vgg_features[-1])
return vgg_features