def parse_arguments():
p = argparse.ArgumentParser(description="Generate labels based on programmatic movement", fromfile_prefix_chars='@')
p.add_argument('--dataroot', type=str)
p.add_argument('-n', '--nframes', help='Number of frames to generate', required=True, type=int)
p.add_argument('--frame-offset', help='The frame offset for the two datasets', required=True, type=int)
p = Labeller.add_arguments(p)
return p.parse_args()
def parse_arguments():
p = argparse.ArgumentParser(description="Generate labels based on programmatic movement", fromfile_prefix_chars='@')
p.add_argument('--dataroot', type=str)
p.add_argument('-i', '--input', help='Path to a frame image that provides the basis for the generation', required=True)
p.add_argument('-n', '--nframes', help='Number of frames to generate', required=True, type=int)
p = Labeller.add_arguments(p)
return p.parse_args()
def parse_arguments():
p = argparse.ArgumentParser(
description="Transfer motion from a source video to a target video",
fromfile_prefix_chars='@')
p.add_argument('--dataroot', type=str)
p.add_argument('-i', '--input', help='Path to the video', required=True)
p.add_argument(
'--trim',
help=
'Decimal, colon separated seconds to trim the input video to. -1 indicates the end of the video',
type=str,
default='0:-1')
p.add_argument(
'--subsample',
help=
'Factor to subsample the source frames, every Nth frame will be selected',
type=int,
default=1)
p.add_argument(
'--subsample-offset',
help=
'Offset for subsampling the source frames, every Nth+i frame will be selected',
type=int,
default=0)
p.add_argument('--resize', help='Resize source to the given size')
p.add_argument('--crop', help='After resizing, crop to the given size')
p.add_argument('--crop-center',
help='Center to use for cropping',
choices=[c.name for c in CropCenter],
default=CropCenter.body.name)
p.add_argument('--flip',
help='Flip vertically, horizontally, or both',
choices=['v', 'h', 'vh', 'hv'])
p.add_argument('--normalize',
help='Output frame data for normalization',
action='store_true')
p = Labeller.add_arguments(p)
p.add_argument(
'--face-size',
help='The size (squared) of faces extracted to train the face network',
type=int,
default=128)
p.add_argument(
'--directory-prefix',
help='Image and label directory prefixes for label training',
default='train')
p.add_argument('--no-label', help='Disable labeling', action='store_true')
p.add_argument(
'--train-a',
help="Put images in the train_A directory for non-label training",
action='store_true')
p.add_argument(
'--train-b',
help="Put images in the train_B directory for non-label training",
action='store_true')
p.add_argument(
'--test-a',
help="Put images in the test_A directory for non-label training",
action='store_true')
p.add_argument('--frame-offset',
help="Offset all frame numbers by this number",
type=int,
default=0)
p.set_defaults(normalize=False)
return p.parse_args()