def extract_all_features(video_id):
#using Inception_V3
def create_graph():
# Creates a graph from saved GraphDef file and returns a saver
if not path.exists(path.join(FLAGS.model_dir, 'classify_image_graph_def.pb')):
print("Graph definition " + path.join(FLAGS.model_dir, 'classify_image_graph_def.pb') + " not found")
exit(1)
else:
with tf.gfile.FastGFile(path.join(FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
dst = path.join(frames_path, video_id)
extract_frames(path.join(data_path, "TestVideo/", video_id + ".mp4"), dst)
with tf.Session() as sess:
create_graph()
video_frames = sorted(glob.glob(path.join(dst,'*.jpg')))
if not path.exists(descriptors_save_path):
makedirs(descriptors_save_path)
mixed_10_tensor = sess.graph.get_tensor_by_name('mixed_10/join:0')
video_descriptors = []
for image_path in tqdm(video_frames):
with open(image_path, 'rb') as image_data:
descriptors = sess.run(mixed_10_tensor, {'DecodeJpeg/contents:0': image_data.read()})
video_descriptors.append(np.squeeze(descriptors))
video_descriptors = np.asarray(video_descriptors, dtype=np.float32)
np.save( path.join(descriptors_save_path, video_id + '_incp_v3.npy'), video_descriptors)
#cleanup
tf.reset_default_graph()
return video_descriptors
print('Unzip dataset')
print('==========================')
preprocessing.unzip_dataset(temp)
useful_frame = [[[] for i in range(10)] for i in range(4)]
print('==========================')
print('Extract all frames')
print('==========================')
video_list = preprocessing.get_filelist(temp, '.avi')
for video in video_list:
class_num = os.path.basename(video).split('_')[0]
iter_num = os.path.basename(video).split('.')[0].split('_')[-1]
print("{:10} size(MB): {:>8.2f}".format(os.path.basename(video),
os.path.getsize(video) / 1000000))
preprocessing.extract_frames(
video, os.path.join(Dagger_folder, f"{class_num}/video/{iter_num}"))
print('==========================')
print('Decay command')
print('==========================')
def decay(diff, len, index, options):
if options == 'ex':
sign = -1 if diff < 0 else 1
w = np.log(abs(int(diff))) / len
return sign * np.exp(w * (index + 1))
elif options == 'sigmoid':
sign = -1 if diff < 0 else 1
w = np.exp(abs(int(diff))) / len
return np.log(w * (index + 1)) * sign
elif options == 'linear':
print('==========================')
print('Extract all frames')
print('==========================')
for classnum in rawdata.category:
print('Processing Class:' + str(classnum) + ' data...')
if len(rawdata.video_list[classnum]) > 0:
for index, video in enumerate(rawdata.video_list[classnum]):
print("{:10} size(MB): {:>8.2f}".format(
split_name(video),
os.path.getsize(video) / 1000000))
dest_name = os.path.join(dataset.video_list['root'][classnum],
split_name(video))
archieve_path = os.path.join(
rawdata.video_list['root'][classnum],
'archieve/' + split_name(video) + '.avi')
preprocessing.extract_frames(video, dest_name)
os.rename(video, archieve_path)
print('Finish extract frames')
dataset.update()
# ==========================================================================
# Merge all command
# merge command from each video together
# ==========================================================================
if MERGE_COMMAND:
print('==========================')
print('Merge command')
print('==========================')
for classnum in rawdata.category:
if len(rawdata.command_list[classnum]) > 0:
print('Processing Class:' + str(classnum) + ' data...', end=' ')
original_file_list = rawdata.command_list[classnum]