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()
'hv': -1,
'vh': -1
}[args.flip] if args.flip is not None else None
normalize = args.normalize
crop_center = CropCenter[args.crop_center]
print("Creating directory hierarchy")
create_directories(paths)
print("Fetching models")
fetch_models(paths)
if args.no_label:
print("Decimating video")
labeller = None
else:
print("Decimating video and labelling with {}".format(args.label_with))
labeller = Labeller.build_from_arguments(args, paths)
decimate_and_label_video(paths,
labeller,
trim=trim,
subsample=args.subsample,
subsample_offset=args.subsample_offset,
resize=resize,
crop=crop,
crop_center=crop_center,
flip=flip,
normalize=normalize,
frame_offset=args.frame_offset,
face_size=args.face_size)
help='The frame offset for the two datasets',
required=True,
type=int)
p.add_argument('--target-label-offset', required=True, type=int)
p = Labeller.add_arguments(p)
return p.parse_args()
print("Synthesizing face puppet")
args = parse_arguments()
paths_base = build_paths(args, directory_prefix='base')
paths_out = build_paths(args, directory_prefix='test')
create_directories(paths_out)
labeller = Labeller.build_from_arguments(args, paths_base)
labeller_t = Labeller.build_from_arguments(
args, paths_base, label_offset=args.target_label_offset)
del labeller.face_labeller.landmarks["right_eyebrow"]
del labeller.face_labeller.landmarks["left_eyebrow"]
del labeller.face_labeller.landmarks["right_eye"]
del labeller.face_labeller.landmarks["left_eye"]
del labeller_t.face_labeller.landmarks["mouth"]
del labeller_t.face_labeller.landmarks["inner_mouth"]
del labeller_t.face_labeller.landmarks["nose"]
del labeller_t.face_labeller.landmarks["jaw"]
base_image_fns = sorted(os.listdir(paths_base.img_dir))
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()
print("Synthesizing half and half faces")
args = parse_arguments()
paths_base = build_paths(args, directory_prefix='base')
paths_out = build_paths(args, directory_prefix='test')
create_directories(paths_out)
labeller_a = Labeller.build_from_arguments(args, paths_base, label_offset=0)
labeller_b = Labeller.build_from_arguments(args, paths_base)
base_image_fns = sorted(os.listdir(paths_base.img_dir))
for i in tqdm(range(0,args.nframes)):
t = float(i) / float(args.nframes)
paths = data_paths_for_idx(paths_out, i)
if paths.label.exists():
continue
path_base_image_a = paths_base.img_dir / base_image_fns[i]
path_base_image_b = paths_base.img_dir / base_image_fns[i + args.frame_offset]
base_image_a = cv.imread(str(path_base_image_a))