def Convert(config):
# collect_files(get_ray_nodes())
reps = ReprStorage(os.path.join(config.repr.directory))
# print('Extracting Signatures from Video representations')
sm = SimilarityModel()
vid_level_iterator = bulk_read(reps.video_level)
print("Prepare to update database! vid_num :" +
str(len(vid_level_iterator)))
if len(vid_level_iterator) > 0:
signatures = sm.predict(
vid_level_iterator) # Get {ReprKey => signature} dict
if config.database.use:
# Convert dict to list of (path, sha256, url, signature) tuples
entries = [(key.path, key.hash, key.url, sig)
for key, sig in signatures.items()]
# Connect to database
database = Database(uri=config.database.uri)
database.create_tables()
try:
# Save signatures
result_storage = DBResultStorage(database)
result_storage.add_signatures(entries)
# after writen to db, remove.
for key, sig in signatures.items():
remove_file("/project/data/representations/video_level/" +
key.path + ".npy")
except Exception as e:
print("save db ERROR!")
print(e)
def main(config, list_of_files, frame_sampling, save_frames):
config = resolve_config(config_path=config,
frame_sampling=frame_sampling,
save_frames=save_frames)
reps = ReprStorage(os.path.join(config.repr.directory))
reprkey = reprkey_resolver(config)
print('Searching for Dataset Video Files')
if len(list_of_files) == 0:
videos = scan_videos(config.sources.root,
'**',
extensions=config.sources.extensions)
else:
videos = scan_videos_from_txt(list_of_files,
extensions=config.sources.extensions)
print('Number of files found: {}'.format(len(videos)))
remaining_videos_path = [
path for path in videos if not reps.frame_level.exists(reprkey(path))
]
print('There are {} videos left'.format(len(remaining_videos_path)))
VIDEOS_LIST = create_video_list(remaining_videos_path,
config.proc.video_list_filename)
print('Processed video List saved on :{}'.format(VIDEOS_LIST))
if len(remaining_videos_path) > 0:
# Instantiates the extractor
model_path = default_model_path(
config.proc.pretrained_model_local_path)
extractor = IntermediateCnnExtractor(
video_src=VIDEOS_LIST,
reprs=reps,
reprkey=reprkey,
frame_sampling=config.proc.frame_sampling,
save_frames=config.proc.save_frames,
model=(load_featurizer(model_path)))
# Starts Extracting Frame Level Features
extractor.start(batch_size=16, cores=4)
print('Converting Frame by Frame representations to Video Representations')
converter = FrameToVideoRepresentation(reps)
converter.start()
print('Extracting Signatures from Video representations')
sm = SimilarityModel()
vid_level_iterator = bulk_read(reps.video_level)
assert len(vid_level_iterator) > 0, 'No Signatures left to be processed'
signatures = sm.predict(
vid_level_iterator) # Get {ReprKey => signature} dict
print('Saving Video Signatures on :{}'.format(reps.signature.directory))
if config.database.use:
# Convert dict to list of (path, sha256, signature) tuples
entries = [(key.path, key.hash, key.url, sig)
for key, sig in signatures.items()]
# Connect to database
database = Database(uri=config.database.uri)
database.create_tables()
# Save signatures
result_storage = DBResultStorage(database)
result_storage.add_signatures(entries)
if config.save_files:
bulk_write(reps.signature, signatures)