def main():
config = anyfig.setup_config(default_config='DebugConfig')
joke = meta_utils.get_joke()
print(f'Here is a programming joke while you wait ;)\n\n{joke}\n')
prepare_xml(config.xml_file)
qwe
asset_paths = xml_utils.get_asset_paths(config.xml_file)
docker_asset_paths = set([f'{p.parent}:{p.parent}' for p in asset_paths])
docker_asset_paths = [f'-v {a}:ro' for a in docker_asset_paths]
docker_asset_paths = ' '.join(docker_asset_paths)
if config.bash:
command = '/bin/bash'
else:
command = 'python extract_info.py ' + ' '.join(sys.argv[1:])
project_root = meta_utils.get_project_root()
output_volume = f"{project_root/'output'}:/sticker/output"
args = f"docker run -it --rm --name stick -v {project_root}:/sticker -v {output_volume} {docker_asset_paths} sticker {command}"
args = args.split()
args = [a.replace('%20', ' ') for a in args] # Spaces can be weird in Mac
print(args)
completed = subprocess.run(args)
if completed.returncode == 0 and config.send_to_finalcut:
xml_outpath = Path('output') / config.xml_file.name
meta_utils.send_xml_to_finalcut(xml_outpath)
def main():
ffmpeg_utils.assert_installed()
joke = meta_utils.get_joke()
print(f'A programming joke while you wait ;)\n\n{joke}\n')
# config = anyfig.setup_config(default_config='DebugConfig')
config = anyfig.setup_config(default_config='DevConfig')
err_msg = "You have to specify an input file in the config.py file or send it as an input parameter to the program as such --xml_file=path/to/file.fcpxml"
assert config.xml_file != '', err_msg
if config.clear_outdir:
meta_utils.clear_outdir()
# If we're running from docker, some paths needs handling
if config.using_docker and not config.test_install:
path_base = '/host_root'
else:
path_base = ''
xml_path = Path(config.xml_file)
if xml_path.is_absolute():
xml_path = Path(path_base) / str(xml_path)[1:]
recognizer = config.recognizer
analyzed_metadatum = []
for asset in get_asset_files(xml_path):
asset_path = Path(path_base) / asset['src']
# Upload data to cloud
data, cloud_file_name = recognizer.prepare_data(
asset_path, config.google_bucket_name, config.unique_cloud_file_id)
# Speech -> text -> actions
actions = recognizer.find_actions(data, config.fake_data)
if config.delete_cloud_file:
print(f"Deleting file {cloud_file_name} from Google storage...")
google_utils.delete_blob(config.google_bucket_name,
cloud_file_name)
analyzed_metadatum.append(dict(id=asset['id'], actions=actions))
# Edit & save xml
xml_tree = edit_xml.main(xml_path, analyzed_metadatum)
output_dir = meta_utils.get_project_root() / 'output'
xml_outpath = output_dir / f'enriched_{xml_path.name}'
xml_utils.save_xml(xml_tree, str(xml_outpath))
# Send xml to final cut
if config.send_to_finalcut and not config.using_docker:
meta_utils.send_xml_to_finalcut(xml_outpath)
elif config.send_to_finalcut:
sys.exit(42)
def main():
config = anyfig.setup_config(default_config='Config')
print(config)
input_file = config.input_file
assert input_file.exists(), f'Wrong path to video: {input_file}'
qr_detections = find_detections(config)
qr_detections = squeeze_detections(qr_detections,
config.detection_squeeze_window)
action_frames = format_detections(qr_detections, config)
print(f"Actions: {action_frames}")
split_info = split_video(action_frames, input_file, config.fps)
save_split_info(split_info, input_file)
cv2.waitKey(1)
config.video_cap.release()
cv2.destroyAllWindows()
def main():
config = anyfig.setup_config(default_config='DebugConfig')
if config.clear_outdir:
meta_utils.clear_outdir()
print(config)
recognizer = config.recognizer
analyzed_metadatum = []
for asset in get_asset_files(config.xml_file):
# Upload data to cloud
data, cloud_file_name = recognizer.prepare_data(
asset['src'], config.google_bucket_name, config.unique_cloud_file_id)
# Speech -> text -> actions
actions = recognizer.find_actions(data, config.fake_data)
if config.delete_cloud_file:
print(f"Deleting file {cloud_file_name} from Google storage...")
google_utils.delete_blob(config.google_bucket_name, cloud_file_name)
analyzed_metadatum.append(dict(id=asset['id'], actions=actions))
edit_xml.main(config.xml_file, analyzed_metadatum, config.send_to_finalcut)
optimizer.zero_grad()
# Decrease learning rate
lr_scheduler.step()
def setup_dataloaders(config):
train_loader = get_trainloader(config)
val_loader = get_valloader(config)
return train_loader, val_loader
def setup_train(config):
model = get_model(config)
optimizer = torch.optim.Adam(model.parameters(), lr=config.start_lr)
lr_scheduler = CosineAnnealingLR(optimizer,
T_max=config.optim_steps,
eta_min=config.end_lr)
logger = Logger(config)
validator = Validator(config, logger)
return model, optimizer, lr_scheduler, logger, validator
if __name__ == '__main__':
config = anyfig.setup_config(default_config='Cookie')
print(config)
print('\n{}\n'.format(config.save_comment))
seed_program(config.seed)
train(config)
def main():
config = anyfig.setup_config(default_config='Train')
print(config)
labels = labels.to(model.device)
# Validation
# if optim_steps % val_freq == 0:
# validator.validate(model, dataloaders.val, optim_steps)
# Forward pass
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_fn(outputs, labels)
# Backward pass
loss.backward()
optimizer.step()
optim_steps += 1
# Decrease learning rate
lr_scheduler.step()
# Log
accuracy = metrics['accuracy'](outputs, labels)
logger.log_accuracy(accuracy.item(), optim_steps)
if __name__ == '__main__':
config = anyfig.setup_config(default_config=configs.TrainLaptop)
print(config) # Remove if you dont want to see config at start
print('\n{}\n'.format(config.misc.save_comment))
setup_utils.setup(config.misc)
train(config)