def main():
# Use first line of file docstring as description if it exists.
parser = argparse.ArgumentParser(
description=__doc__.split('\n')[0] if __doc__ else '',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', default=Path('./release/config.yaml'))
args = parser.parse_args()
with open(args.config, 'r') as f:
config = yaml.load(f)
output_dir = Path(config['davis17']['output_dir']) / 'tracks'
output_dir.mkdir(exist_ok=True, parents=True)
common_setup(__file__, output_dir, args)
split_dirs = link_splits(config)
for split in config['davis17']['splits']:
image_dir = split_dirs[split][0]
init_detections = (Path(config['davis17']['output_dir']) /
'detections' / split)
output_split = Path(output_dir) / split
args = [
'--images-dir', image_dir, '--init-detections-dir',
init_detections, '--output-dir', output_split, '--save-numpy',
True, '--save-images', False, '--bidirectional',
'--score-init-min', 0.9, '--remove-continue-overlap', 0.1, '--fps',
30, '--filename-format', 'frame', '--save-video',
config['tracker']['visualize'], '--recursive'
]
cmd = ['python', 'tracker/two_detector_track.py'] + args
msg(f'Running tracker on DAVIS {split}')
subprocess_call(cmd)
def evaluate_official(config):
track_output = Path(config['fbms']['output_dir'] / 'tracks')
check_tracks(track_output)
for split in config['fbms']['splits']:
np_dir = track_output / split
dat_dir = np_dir / 'dat'
if not check_dat(track_output, split):
cmd = [
'python', 'fbms/numpy_to_dat.py', '--numpy-dir', np_dir,
'--output-dir', dat_dir, '--fbms-groundtruth',
Path(config['fbms']['root']) / split
]
msg(f'Converting {split} numpy predictions to .dat')
logging.info('This may take a few minutes...')
subprocess_call(cmd)
assert check_dat(track_output, split)
eval_binary = Path(config['fbms']['eval_dir']) / 'MoSegEvalAllPR'
cmd = [
'python', 'fbms/eval_official.py', '--eval-binary', eval_binary,
'--predictions-dir', dat_dir, '--split', 'all'
]
msg(f'Evaluating {split}')
subprocess_call(cmd)
def main():
# Use first line of file docstring as description if it exists.
parser = argparse.ArgumentParser(
description=__doc__.split('\n')[0] if __doc__ else '',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', default=Path('./release/config.yaml'))
parser.add_argument('--frames-dir', required=True, type=Path)
parser.add_argument('--detections-dir', required=True, type=Path)
parser.add_argument('--output-dir', required=True, type=Path)
parser.add_argument('--save-numpy', type=parse_bool, default=True)
parser.add_argument('--save-images', type=parse_bool, default=False)
parser.add_argument('--save-video', type=parse_bool, default=True)
parser.add_argument('--fps', default=30, type=float)
parser.add_argument('--model',
choices=['joint', 'appearance', 'motion'],
default='joint')
parser.add_argument(
'--filename-format',
choices=[
'frame', 'frameN', 'sequence_frame', 'sequence-frame', 'fbms'
],
required=True,
help=('Specifies how to get frame number from the filename. '
'"frame": the filename is the frame number, '
'"frameN": format <frame><number>, '
'"sequence_frame": frame number is separated by an underscore, '
'"sequence-frame": frame number is separated by a dash, '
'"fbms": assume fbms style frame numbers'))
args = parser.parse_args()
with open(args.config, 'r') as f:
config = yaml.load(f)
args.output_dir.mkdir(exist_ok=True, parents=True)
common_setup(__file__, args.output_dir)
logging.debug('Args:\n%s', pprint.pformat(vars(args)))
if args.model == 'joint':
detections_args = ['--init-detections-dir', args.detections_dir]
script = 'tracker/two_detector_track.py'
extra_args = ['--remove-continue-overlap', 0.1, '--recursive']
else:
detections_args = ['--detections-dir', args.detections_dir]
script = 'tracker/track_multiple.py'
extra_args = []
args = ['--images-dir', args.frames_dir] + detections_args + [
'--output-dir', args.output_dir, '--save-numpy', args.save_numpy,
'--save-images', args.save_images, '--save-video', args.save_video,
'--bidirectional', '--score-init-min', 0.9, '--fps', args.fps,
'--filename-format', args.filename_format, '--quiet'
] + extra_args
cmd = ['python', script] + args
msg(f'Running tracker')
subprocess_call(cmd)
def main():
# Use first line of file docstring as description if it exists.
parser = argparse.ArgumentParser(
description=__doc__.split('\n')[0] if __doc__ else '',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', default=Path('./release/config.yaml'))
args = parser.parse_args()
with open(args.config, 'r') as f:
config = yaml.load(f)
args = parser.parse_args()
output_dir = Path(config['davis16']['output_dir']) / 'detections'
output_dir.mkdir(exist_ok=True, parents=True)
common_setup(__file__, output_dir, args)
detectron_dir = (
Path(__file__).resolve().parent.parent.parent / 'detectron_pytorch')
split_dirs = link_splits(config)
for split in config['davis16']['splits']:
image_dir = split_dirs[split][0]
flow_dir = Path(config['davis16']['output_dir']) / 'flow' / split
joint_config, joint_model = get_config_ckpt(
config['model']['joint']['dir'], config['model']['joint']['step'])
appearance_config, appearance_model = get_config_ckpt(
config['model']['appearance']['dir'],
config['model']['appearance']['step'])
cmd = ['python', 'tools/infer_simple.py']
args = [
'--cfg', joint_config,
'--num_classes', 2,
'--load_ckpt', joint_model,
'--load_appearance_ckpt', appearance_model,
'--set', 'MODEL.MERGE_WITH_APPEARANCE.ENABLED', 'True',
'--image_dirs', image_dir, flow_dir,
'--input_type', 'rgb', 'flow',
'--save_images', False,
'--output_dir', output_dir / split,
'--quiet',
'--recursive'
]
subprocess_call(cmd + args, cwd=str(detectron_dir))
cmd = ['python', 'detectron_to_fgbg_masks.py']
args = [
'--detections-root', output_dir / split,
'--images-dir', image_dir,
'--output-dir', output_dir / split,
'--recursive',
'--extension', '.jpg',
'--threshold', '0.7'
]
subprocess_call(cmd + args)
def evaluate_proposed(config, output_stage):
if output_stage == 'detection':
input_dir = (Path(config['davis16']['output_dir']) / 'detections')
elif output_stage == 'tracking':
input_dir = (Path(config['davis16']['output_dir']) / 'tracks')
else:
raise ValueError(f'Unknown output stage: {output_stage}')
for split in config['davis16']['splits']:
masks_dir = input_dir / split / 'masks' / 'masks'
cmd = ['python', 'davis/eval_fgbg.py', '--masks-dir', masks_dir]
msg(f'Evaluating {split}')
subprocess_call(cmd)
def evaluate_proposed(config):
track_output = Path(config['fbms']['output_dir']) / 'tracks'
check_tracks(track_output, config['fbms']['splits'])
for split in config['fbms']['splits']:
np_dir = track_output / split
cmd = [
'python', 'fbms/eval_custom.py', '--npy-extension', '.npz',
'--eval-type', 'fbms', '--background-id', 0, '--groundtruth-dir',
Path(config['fbms']['root']) / split, '--predictions-dir', np_dir
]
msg(f'Evaluating {split}')
subprocess_call(cmd)
def evaluate_proposed(config):
track_output = Path(config['davis17']['output_dir']) / 'tracks'
check_tracks(track_output, config['davis17']['splits'])
split_dirs = link_splits(config)
for split in config['davis17']['splits']:
np_dir = track_output / split
annotations_dir = split_dirs[split][1]
cmd = [
'python', 'fbms/eval_custom.py', '--npy-extension', '.npz',
'--eval-type', 'davis', '--background-id', 0, '--groundtruth-dir',
annotations_dir, '--predictions-dir', np_dir
]
msg(f'Evaluating {split}')
subprocess_call(cmd)
def compute_flow_helper(config,
input_dir,
output_dir,
extensions=None,
recursive=True):
flownet2_dir = Path(config['flow']['flownet2_dir'])
if isinstance(extensions, str):
extensions = [extensions]
maybe_recursive = ['--recursive'] if recursive else []
maybe_extensions = (['--extensions'] + extensions) if extensions else []
args = ['--input-dir', input_dir] + maybe_recursive + [
'--convert-to-angle-magnitude-png', 'on'
] + maybe_extensions + ['--gpus'] + config['general']['gpus'] + [
'--num-workers', config['general']['num_workers'], '--output-dir',
output_dir, '--flow-type', 'flownet2', '--flownet2-dir', flownet2_dir,
'--flownet2-model', 'kitti', '--quiet'
]
cmd = ['python', 'flow/compute_flow_sequences.py'] + args
subprocess_call(cmd)
def main():
# Use first line of file docstring as description if it exists.
parser = argparse.ArgumentParser(
description=__doc__.split('\n')[0] if __doc__ else '',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--frames-dir', type=Path, required=True)
parser.add_argument('--output-dir', type=Path, required=True)
parser.add_argument('--model',
default='joint',
choices=['joint', 'appearance', 'motion'])
parser.add_argument(
'--filename-format',
choices=[
'frame', 'frameN', 'sequence_frame', 'sequence-frame', 'fbms'
],
required=True,
help=('Specifies how to get frame number from the filename. '
'"frame": the filename is the frame number, '
'"frameN": format "frame<number>", '
'"sequence_frame": frame number is separated by an underscore, '
'"sequence-frame": frame number is separated by a dash, '
'"fbms": assume fbms style frame numbers'))
parser.add_argument('--config', default=Path('./release/config.yaml'))
args = parser.parse_args()
args.output_dir.mkdir(exist_ok=True, parents=True)
common_setup(__file__, args.output_dir, args)
flow_dir = args.output_dir / 'flow'
if args.model != 'appearance':
subprocess_call([
'python', 'release/custom/compute_flow.py', '--frames-dir',
args.frames_dir, '--config', args.config, '--output-dir', flow_dir
])
output_dir = args.output_dir / args.model
detections_dir = output_dir / 'detections'
subprocess_call([
'python', 'release/custom/infer.py', '--frames-dir', args.frames_dir,
'--flow-dir', flow_dir, '--model', args.model, '--config', args.config,
'--output-dir', detections_dir
])
tracks_dir = output_dir / 'tracks'
subprocess_call([
'python', 'release/custom/track.py', '--frames-dir', args.frames_dir,
'--detections-dir', detections_dir, '--filename-format',
args.filename_format, '--config', args.config, '--model', args.model,
'--output-dir', tracks_dir
])
msg(f'Output results to: {tracks_dir}')
def main():
# Use first line of file docstring as description if it exists.
parser = argparse.ArgumentParser(
description=__doc__.split('\n')[0] if __doc__ else '',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', default=Path('./release/config.yaml'))
parser.add_argument(
'--without-ytvos-train',
action='store_true',
help=("By default, we infer with model that was trained on YTVOS. "
"For fair evaluation of generalization, we use a model without "
"YTVOS training in our manuscript. Set this to True to use "
"the model without YTVOS training."))
args = parser.parse_args()
with open(args.config, 'r') as f:
config = yaml.load(f)
args = parser.parse_args()
output_dir = Path(config['ytvos']['output_dir']) / 'detections'
if args.without_ytvos_train:
output_dir = output_dir / 'without_ytvos'
else:
output_dir = output_dir / 'with_ytvos'
output_dir.mkdir(exist_ok=True, parents=True)
common_setup(__file__, output_dir, args)
detectron_dir = (
Path(__file__).resolve().parent.parent.parent / 'detectron_pytorch')
split_dirs = link_splits(config)
for split in config['ytvos']['splits']:
image_dir = split_dirs[split][0]
flow_dir = Path(config['ytvos']['output_dir']) / 'flow' / split
if args.with_ytvos_train:
joint_config, joint_model = get_config_ckpt(
config['model']['joint']['dir'],
config['model']['joint']['step'])
else:
joint_config, joint_model = get_config_ckpt(
config['model']['joint_no_ytvos']['dir'],
config['model']['joint_no_ytvos']['step'])
appearance_config, appearance_model = get_config_ckpt(
config['model']['appearance']['dir'],
config['model']['appearance']['step'])
cmd = ['python', 'tools/infer_simple.py']
args = [
'--cfg', joint_config,
'--num_classes', 2,
'--load_ckpt', joint_model,
'--load_appearance_ckpt', appearance_model,
'--set', 'MODEL.MERGE_WITH_APPEARANCE.ENABLED', 'True',
'--image_dirs', image_dir, flow_dir,
'--input_type', 'rgb', 'flow',
'--save_images', False,
'--output_dir', output_dir / split,
'--quiet',
'--recursive'
]
subprocess_call(cmd + args, cwd=str(detectron_dir))
def main():
# Use first line of file docstring as description if it exists.
parser = argparse.ArgumentParser(
description=__doc__.split('\n')[0] if __doc__ else '',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', default=Path('./release/config.yaml'))
parser.add_argument('--frames-dir',
type=Path,
help='Required unless --model set to "motion"')
parser.add_argument('--flow-dir',
type=Path,
help='Required unless --model set to "appearance".')
parser.add_argument('--output-dir', type=Path, required=True)
parser.add_argument('--visualize', action='store_true')
parser.add_argument('--model',
choices=['joint', 'appearance', 'motion'],
default='joint')
args = parser.parse_args()
if args.model != 'appearance':
assert args.flow_dir and args.flow_dir.exists(), (
f'--flow-dir must be specified for --model {args.model}')
if args.model != 'motion':
assert args.frames_dir and args.frames_dir.exists(), (
f'--frames-dir must be specified for --model {args.model}')
with open(args.config, 'r') as f:
config = yaml.load(f)
args = parser.parse_args()
args.output_dir.mkdir(exist_ok=True, parents=True)
common_setup(__file__, args.output_dir)
logging.debug('Args:\n%s', pprint.pformat(vars(args)))
detectron_dir = (Path(__file__).resolve().parent.parent.parent /
'detectron_pytorch')
model_config, model = get_config_ckpt(config['model'][args.model]['dir'],
config['model'][args.model]['step'])
maybe_combine_appearance = []
if args.model == 'joint':
appearance_config, appearance_model = get_config_ckpt(
config['model']['appearance']['dir'],
config['model']['appearance']['step'])
maybe_combine_appearance = [
'--load_appearance_ckpt', appearance_model, '--set',
'MODEL.MERGE_WITH_APPEARANCE.ENABLED', 'True'
]
image_dirs = ['--image_dirs', args.frames_dir, args.flow_dir]
input_type = ['--input_type', 'rgb', 'flow']
elif args.model == 'appearance':
maybe_combine_appearance = []
image_dirs = ['--image_dirs', args.frames_dir]
input_type = ['--input_type', 'rgb']
elif args.model == 'motion':
maybe_combine_appearance = []
image_dirs = ['--image_dirs', args.flow_dir]
input_type = ['--input_type', 'flow']
msg(f'Running {args.model} model on {image_dirs[1].resolve()}')
cmd = ['python', 'tools/infer_simple.py']
args = (['--cfg', model_config, '--num_classes', 2, '--load_ckpt', model] +
maybe_combine_appearance + image_dirs + input_type + [
'--save_images', args.visualize, '--output_dir',
args.output_dir, '--quiet', '--recursive'
])
subprocess_call(cmd + args, cwd=str(detectron_dir))
