def get_optional_arguments():
""" Put the arguments in a list so that they are accessible from both argparse and gui """
argument_list = []
argument_list.append({"opts": ("-m", "--model-dir"),
"action": FullPaths,
"dest": "model_dir",
"default": "models",
"help": "Model directory. A directory containing the trained model "
"you wish to process. Defaults to 'models'"})
argument_list.append({"opts": ("-a", "--input-aligned-dir"),
"action": FullPaths,
"dest": "input_aligned_dir",
"default": None,
"help": "Input \"aligned directory\". A directory that should "
"contain the aligned faces extracted from the input files. "
"If you delete faces from this folder, they'll be skipped "
"during conversion. If no aligned dir is specified, all "
"faces will be converted"})
argument_list.append({"opts": ("-t", "--trainer"),
"type": str,
# case sensitive because this is used to load a plug-in.
"choices": PluginLoader.get_available_models(),
"default": PluginLoader.get_default_model(),
"help": "Select the trainer that was used to create the model"})
argument_list.append({"opts": ("-c", "--converter"),
"type": str,
# case sensitive because this is used to load a plugin.
"choices": ("Masked", "Adjust"),
"default": "Masked",
"help": "Converter to use"})
argument_list.append({"opts": ("-b", "--blur-size"),
"type": int,
"default": 2,
"help": "Blur size. (Masked converter only)"})
argument_list.append({"opts": ("-e", "--erosion-kernel-size"),
"dest": "erosion_kernel_size",
"type": int,
"default": None,
"help": "Erosion kernel size. Positive values apply erosion "
"which reduces the edge of the swapped face. Negative "
"values apply dilation which allows the swapped face "
"to cover more space. (Masked converter only)"})
argument_list.append({"opts": ("-M", "--mask-type"),
#lowercase this, because its just a string later on.
"type": str.lower,
"dest": "mask_type",
"choices": ["rect", "facehull", "facehullandrect"],
"default": "facehullandrect",
"help": "Mask to use to replace faces. (Masked converter only)"})
argument_list.append({"opts": ("-sh", "--sharpen"),
"type": str.lower,
"dest": "sharpen_image",
"choices": ["bsharpen", "gsharpen"],
"default": None,
"help": "Use Sharpen Image.bsharpen for Box Blur, gsharpen for "
"Gaussian Blur (Masked converter only)"})
argument_list.append({"opts": ("-g", "--gpus"),
"type": int,
"default": 1,
"help": "Number of GPUs to use for conversion"})
argument_list.append({"opts": ("-fr", "--frame-ranges"),
"nargs": "+",
"type": str,
"help": "frame ranges to apply transfer to e.g. For frames 10 to "
"50 and 90 to 100 use --frame-ranges 10-50 90-100. Files "
"must have the frame-number as the last number in the "
"name!"})
argument_list.append({"opts": ("-d", "--discard-frames"),
"action": "store_true",
"dest": "discard_frames",
"default": False,
"help": "When used with --frame-ranges discards frames that are "
"not processed instead of writing them out unchanged"})
argument_list.append({"opts": ("-s", "--swap-model"),
"action": "store_true",
"dest": "swap_model",
"default": False,
"help": "Swap the model. Instead of A -> B, swap B -> A"})
argument_list.append({"opts": ("-S", "--seamless"),
"action": "store_true",
"dest": "seamless_clone",
"default": False,
"help": "Use cv2's seamless clone. (Masked converter only)"})
argument_list.append({"opts": ("-mh", "--match-histogram"),
"action": "store_true",
"dest": "match_histogram",
"default": False,
"help": "Use histogram matching. (Masked converter only)"})
argument_list.append({"opts": ("-sm", "--smooth-mask"),
"action": "store_true",
"dest": "smooth_mask",
"default": True,
"help": "Smooth mask (Adjust converter only)"})
argument_list.append({"opts": ("-aca", "--avg-color-adjust"),
"action": "store_true",
"dest": "avg_color_adjust",
"default": True,
"help": "Average color adjust. (Adjust converter only)"})
return argument_list
def get_argument_list():
""" Put the arguments in a list so that they are accessible from both argparse and gui """
argument_list = []
argument_list.append({"opts": ("-A", "--input-A"),
"action": FullPaths,
"dest": "input_A",
"default": "input_A",
"help": "Input directory. A directory containing training images "
"for face A. Defaults to 'input'"})
argument_list.append({"opts": ("-B", "--input-B"),
"action": FullPaths,
"dest": "input_B",
"default": "input_B",
"help": "Input directory. A directory containing training images "
"for face B Defaults to 'input'"})
argument_list.append({"opts": ("-m", "--model-dir"),
"action": FullPaths,
"dest": "model_dir",
"default": "models",
"help": "Model directory. This is where the training data will "
"be stored. Defaults to 'model'"})
argument_list.append({"opts": ("-s", "--save-interval"),
"type": int,
"dest": "save_interval",
"default": 100,
"help": "Sets the number of iterations before saving the model"})
argument_list.append({"opts": ("-t", "--trainer"),
"type": str,
"choices": PluginLoader.get_available_models(),
"default": PluginLoader.get_default_model(),
"help": "Select which trainer to use, Use LowMem for cards with "
" less than 2GB of VRAM"})
argument_list.append({"opts": ("-bs", "--batch-size"),
"type": int,
"default": 64,
"help": "Batch size, as a power of 2 (64, 128, 256, etc)"})
argument_list.append({"opts": ("-ep", "--epochs"),
"type": int,
"default": 1000000,
"help": "Length of training in epochs"})
argument_list.append({"opts": ("-g", "--gpus"),
"type": int,
"default": 1,
"help": "Number of GPUs to use for training"})
argument_list.append({"opts": ("-p", "--preview"),
"action": "store_true",
"dest": "preview",
"default": False,
"help": "Show preview output. If not specified, write progress "
"to file"})
argument_list.append({"opts": ("-w", "--write-image"),
"action": "store_true",
"dest": "write_image",
"default": False,
"help": "Writes the training result to a file even on "
"preview mode"})
argument_list.append({"opts": ("-pl", "--use-perceptual-loss"),
"action": "store_true",
"dest": "perceptual_loss",
"default": False,
"help": "Use perceptual loss while training"})
argument_list.append({"opts": ("-ag", "--allow-growth"),
"action": "store_true",
"dest": "allow_growth",
"default": False,
"help": "Sets allow_growth option of Tensorflow to spare memory "
"on some configs"})
argument_list.append({"opts": ("-v", "--verbose"),
"action": "store_true",
"dest": "verbose",
"default": False,
"help": "Show verbose output"})
# This is a hidden argument to indicate that the GUI is being used,
# so the preview window should be redirected Accordingly
argument_list.append({"opts": ("-gui", "--gui"),
"action": "store_true",
"dest": "redirect_gui",
"default": False,
"help": argparse.SUPPRESS})
return argument_list
def get_optional_arguments():
''' Put the arguments in a list so that they are accessible from both argparse and gui '''
argument_list = []
argument_list.append({
"opts": ('-m', '--model-dir'),
"action":
FullPaths,
"dest":
"model_dir",
"default":
"models",
"help":
"Model directory. A directory containing the trained model \
you wish to process. Defaults to 'models'"
})
argument_list.append({
"opts": ('-a', '--input-aligned-dir'),
"action":
FullPaths,
"dest":
"input_aligned_dir",
"default":
None,
"help":
"Input \"aligned directory\". A directory that should contain the \
aligned faces extracted from the input files. If you delete faces from \
this folder, they'll be skipped during conversion. If no aligned dir is \
specified, all faces will be converted."
})
argument_list.append({
"opts": ('-t', '--trainer'),
"type":
str,
"choices":
PluginLoader.get_available_models(
), # case sensitive because this is used to load a plug-in.
"default":
PluginLoader.get_default_model(),
"help":
"Select the trainer that was used to create the model."
})
argument_list.append({
"opts": ('-s', '--swap-model'),
"action":
"store_true",
"dest":
"swap_model",
"default":
False,
"help":
"Swap the model. Instead of A -> B, swap B -> A."
})
argument_list.append({
"opts": ('-c', '--converter'),
"type": str,
"choices":
("Masked", "Adjust"
), # case sensitive because this is used to load a plugin.
"default": "Masked",
"help": "Converter to use."
})
argument_list.append({
"opts": ('-D', '--detector'),
"type":
str,
"choices":
("hog",
"cnn"), # case sensitive because this is used to load a plugin.
"default":
"hog",
"help":
"Detector to use. 'cnn' detects much more angles but will be much more resource intensive and may fail on large files."
})
argument_list.append({
"opts": ('-fr', '--frame-ranges'),
"nargs":
"+",
"type":
str,
"help":
"frame ranges to apply transfer to e.g. For frames 10 to 50 and 90 to 100 use --frame-ranges 10-50 90-100. \
Files must have the frame-number as the last number in the name!"
})
argument_list.append({
"opts": ('-d', '--discard-frames'),
"action":
"store_true",
"dest":
"discard_frames",
"default":
False,
"help":
"When used with --frame-ranges discards frames that are not processed instead of writing them out unchanged."
})
argument_list.append({
"opts": ('-l', '--ref_threshold'),
"type": float,
"dest": "ref_threshold",
"default": 0.6,
"help": "Threshold for positive face recognition"
})
argument_list.append({
"opts": ('-n', '--nfilter'),
"type":
str,
"dest":
"nfilter",
"nargs":
'+',
"default":
"nfilter.jpg",
"help":
"Reference image for the persons you do not want to process. Should be a front portrait"
})
argument_list.append({
"opts": ('-f', '--filter'),
"type":
str,
"dest":
"filter",
"nargs":
"+",
"default":
"filter.jpg",
"help":
"Reference images for the person you want to process. Should be a front portrait"
})
argument_list.append({
"opts": ('-b', '--blur-size'),
"type": int,
"default": 2,
"help": "Blur size. (Masked converter only)"
})
argument_list.append({
"opts": ('-S', '--seamless'),
"action":
"store_true",
"dest":
"seamless_clone",
"default":
False,
"help":
"Use cv2's seamless clone. (Masked converter only)"
})
argument_list.append({
"opts": ('-M', '--mask-type'),
"type":
str.lower, #lowercase this, because its just a string later on.
"dest":
"mask_type",
"choices": ["rect", "facehull", "facehullandrect"],
"default":
"facehullandrect",
"help":
"Mask to use to replace faces. (Masked converter only)"
})
argument_list.append({
"opts": ('-e', '--erosion-kernel-size'),
"dest":
"erosion_kernel_size",
"type":
int,
"default":
None,
"help":
"Erosion kernel size. (Masked converter only). Positive values apply erosion which reduces the edge of the swapped face. Negative values apply dilation which allows the swapped face to cover more space."
})
argument_list.append({
"opts": ('-mh', '--match-histgoram'),
"action":
"store_true",
"dest":
"match_histogram",
"default":
False,
"help":
"Use histogram matching. (Masked converter only)"
})
argument_list.append({
"opts": ('-sh', ),
"type":
str.lower,
"dest":
"sharpen_image",
"choices": ["bsharpen", "gsharpen"],
"default":
None,
"help":
"Use Sharpen Image - bsharpen = Box Blur, gsharpen = Gaussian Blur (Masked converter only)"
})
argument_list.append({
"opts": ('-sm', '--smooth-mask'),
"action": "store_true",
"dest": "smooth_mask",
"default": True,
"help": "Smooth mask (Adjust converter only)"
})
argument_list.append({
"opts": ('-aca', '--avg-color-adjust'),
"action":
"store_true",
"dest":
"avg_color_adjust",
"default":
True,
"help":
"Average color adjust. (Adjust converter only)"
})
argument_list.append({
"opts": ('-g', '--gpus'),
"type": int,
"default": 1,
"help": "Number of GPUs to use for conversion"
})
return argument_list
def add_optional_arguments(self, parser):
parser.add_argument(
'-m',
'--model-dir',
action=FullPaths,
dest="model_dir",
default="models",
help="Model directory. A directory containing the trained model \
you wish to process. Defaults to 'models'")
parser.add_argument(
'-a',
'--input-aligned-dir',
action=FullPaths,
dest="input_aligned_dir",
default=None,
help=
"Input \"aligned directory\". A directory that should contain the \
aligned faces extracted from the input files. If you delete faces from \
this folder, they'll be skipped during conversion. If no aligned dir is \
specified, all faces will be converted.")
parser.add_argument(
'-t',
'--trainer',
type=str,
choices=PluginLoader.get_available_models(
), # case sensitive because this is used to load a plug-in.
default=PluginLoader.get_default_model(),
help="Select the trainer that was used to create the model.")
parser.add_argument(
'-s',
'--swap-model',
action="store_true",
dest="swap_model",
default=False,
help="Swap the model. Instead of A -> B, swap B -> A.")
parser.add_argument(
'-c',
'--converter',
type=str,
choices=(
"Masked", "Adjust"
), # case sensitive because this is used to load a plugin.
default="Masked",
help="Converter to use.")
parser.add_argument(
'-D',
'--detector',
type=str,
choices=(
"hog", "cnn"
), # case sensitive because this is used to load a plugin.
default="hog",
help=
"Detector to use. 'cnn' detects much more angles but will be much more resource intensive and may fail on large files."
)
parser.add_argument(
'-fr',
'--frame-ranges',
nargs="+",
type=str,
help=
"frame ranges to apply transfer to e.g. For frames 10 to 50 and 90 to 100 use --frame-ranges 10-50 90-100. \
Files must have the frame-number as the last number in the name!"
)
parser.add_argument(
'-d',
'--discard-frames',
action="store_true",
dest="discard_frames",
default=False,
help=
"When used with --frame-ranges discards frames that are not processed instead of writing them out unchanged."
)
parser.add_argument('-l',
'--ref_threshold',
type=float,
dest="ref_threshold",
default=0.6,
help="Threshold for positive face recognition")
parser.add_argument(
'-n',
'--nfilter',
type=str,
dest="nfilter",
nargs='+',
default="nfilter.jpg",
help=
"Reference image for the persons you do not want to process. Should be a front portrait"
)
parser.add_argument(
'-f',
'--filter',
type=str,
dest="filter",
nargs="+",
default="filter.jpg",
help=
"Reference images for the person you want to process. Should be a front portrait"
)
parser.add_argument('-b',
'--blur-size',
type=int,
default=2,
help="Blur size. (Masked converter only)")
parser.add_argument(
'-S',
'--seamless',
action="store_true",
dest="seamless_clone",
default=False,
help="Use cv2's seamless clone. (Masked converter only)")
parser.add_argument(
'-M',
'--mask-type',
type=str.
lower, #lowercase this, because its just a string later on.
dest="mask_type",
choices=["rect", "facehull", "facehullandrect"],
default="facehullandrect",
help="Mask to use to replace faces. (Masked converter only)")
parser.add_argument(
'-e',
'--erosion-kernel-size',
dest="erosion_kernel_size",
type=int,
default=None,
help=
"Erosion kernel size. (Masked converter only). Positive values apply erosion which reduces the edge of the swapped face. Negative values apply dilation which allows the swapped face to cover more space."
)
parser.add_argument(
'-mh',
'--match-histgoram',
action="store_true",
dest="match_histogram",
default=False,
help="Use histogram matching. (Masked converter only)")
parser.add_argument('-sm',
'--smooth-mask',
action="store_true",
dest="smooth_mask",
default=True,
help="Smooth mask (Adjust converter only)")
parser.add_argument(
'-aca',
'--avg-color-adjust',
action="store_true",
dest="avg_color_adjust",
default=True,
help="Average color adjust. (Adjust converter only)")
return parser
def parse_arguments(self, description, subparser, command):
parser = subparser.add_parser(
command,
help="This command trains the model for the two faces A and B.",
description=description,
epilog="Questions and feedback: \
https://github.com/deepfakes/faceswap-playground")
parser.add_argument(
'-A',
'--input-A',
action=FullPaths,
dest="input_A",
default="input_A",
help=
"Input directory. A directory containing training images for face A.\
Defaults to 'input'")
parser.add_argument(
'-B',
'--input-B',
action=FullPaths,
dest="input_B",
default="input_B",
help=
"Input directory. A directory containing training images for face B.\
Defaults to 'input'")
parser.add_argument(
'-m',
'--model-dir',
action=FullPaths,
dest="model_dir",
default="models",
help="Model directory. This is where the training data will \
be stored. Defaults to 'model'")
parser.add_argument(
'-p',
'--preview',
action="store_true",
dest="preview",
default=False,
help="Show preview output. If not specified, write progress \
to file.")
parser.add_argument('-v',
'--verbose',
action="store_true",
dest="verbose",
default=False,
help="Show verbose output")
parser.add_argument(
'-s',
'--save-interval',
type=int,
dest="save_interval",
default=100,
help="Sets the number of iterations before saving the model.")
parser.add_argument(
'-w',
'--write-image',
action="store_true",
dest="write_image",
default=False,
help="Writes the training result to a file even on preview mode.")
parser.add_argument(
'-t',
'--trainer',
type=str,
choices=PluginLoader.get_available_models(),
default=PluginLoader.get_default_model(),
help="Select which trainer to use, LowMem for cards < 2gb.")
parser.add_argument('-pl',
'--use-perceptual-loss',
action="store_true",
dest="perceptual_loss",
default=False,
help="Use perceptual loss while training")
parser.add_argument(
'-bs',
'--batch-size',
type=int,
default=64,
help="Batch size, as a power of 2 (64, 128, 256, etc)")
parser.add_argument(
'-ag',
'--allow-growth',
action="store_true",
dest="allow_growth",
default=False,
help=
"Sets allow_growth option of Tensorflow to spare memory on some configs"
)
parser.add_argument('-ep',
'--epochs',
type=int,
default=1000000,
help="Length of training in epochs.")
parser = self.add_optional_arguments(parser)
parser.set_defaults(func=self.process_arguments)
def get_argument_list():
""" Put the arguments in a list so that they are accessible from both
argparse and gui """
argument_list = list()
argument_list.append({"opts": ("-A", "--input-A"),
"action": DirFullPaths,
"dest": "input_A",
"default": "input_A",
"help": "Input directory. A directory "
"containing training images for face A. "
"Defaults to 'input'"})
argument_list.append({"opts": ("-B", "--input-B"),
"action": DirFullPaths,
"dest": "input_B",
"default": "input_B",
"help": "Input directory. A directory "
"containing training images for face B. "
"Defaults to 'input'"})
argument_list.append({"opts": ("-m", "--model-dir"),
"action": DirFullPaths,
"dest": "model_dir",
"default": "models",
"help": "Model directory. This is where the "
"training data will be stored. "
"Defaults to 'model'"})
argument_list.append({"opts": ("-s", "--save-interval"),
"type": int,
"dest": "save_interval",
"default": 100,
"help": "Sets the number of iterations before "
"saving the model"})
argument_list.append({"opts": ("-t", "--trainer"),
"type": str,
"choices": PluginLoader.get_available_models(),
"default": PluginLoader.get_default_model(),
"help": "Select which trainer to use, Use "
"LowMem for cards with less than 2GB of "
"VRAM"})
argument_list.append({"opts": ("-bs", "--batch-size"),
"type": int,
"default": 64,
"help": "Batch size, as a power of 2 "
"(64, 128, 256, etc)"})
argument_list.append({"opts": ("-it", "--iterations"),
"type": int,
"default": 1000000,
"help": "Length of training in iterations"})
argument_list.append({"opts": ("-g", "--gpus"),
"type": int,
"default": 1,
"help": "Number of GPUs to use for training"})
argument_list.append({"opts": ("-p", "--preview"),
"action": "store_true",
"dest": "preview",
"default": False,
"help": "Show preview output. If not specified, "
"write progress to file"})
argument_list.append({"opts": ("-w", "--write-image"),
"action": "store_true",
"dest": "write_image",
"default": False,
"help": "Writes the training result to a file "
"even on preview mode"})
argument_list.append({"opts": ("-pl", "--use-perceptual-loss"),
"action": "store_true",
"dest": "perceptual_loss",
"default": False,
"help": "Use perceptual loss while training"})
argument_list.append({"opts": ("-ag", "--allow-growth"),
"action": "store_true",
"dest": "allow_growth",
"default": False,
"help": "Sets allow_growth option of Tensorflow "
"to spare memory on some configs"})
argument_list.append({"opts": ("-v", "--verbose"),
"action": "store_true",
"dest": "verbose",
"default": False,
"help": "Show verbose output"})
# This is a hidden argument to indicate that the GUI is being used,
# so the preview window should be redirected Accordingly
argument_list.append({"opts": ("-gui", "--gui"),
"action": "store_true",
"dest": "redirect_gui",
"default": False,
"help": argparse.SUPPRESS})
return argument_list
def get_optional_arguments():
""" Put the arguments in a list so that they are accessible from both
argparse and gui """
argument_list = []
argument_list.append({"opts": ("-m", "--model-dir"),
"action": DirFullPaths,
"dest": "model_dir",
"default": "models",
"help": "Model directory. A directory "
"containing the trained model you wish "
"to process. Defaults to 'models'"})
argument_list.append({"opts": ("-a", "--input-aligned-dir"),
"action": DirFullPaths,
"dest": "input_aligned_dir",
"default": None,
"help": "Input \"aligned directory\". A "
"directory that should contain the "
"aligned faces extracted from the input "
"files. If you delete faces from this "
"folder, they'll be skipped during "
"conversion. If no aligned dir is "
"specified, all faces will be "
"converted"})
argument_list.append({"opts": ("-t", "--trainer"),
"type": str,
# case sensitive because this is used to
# load a plug-in.
"choices": PluginLoader.get_available_models(),
"default": PluginLoader.get_default_model(),
"help": "Select the trainer that was used to "
"create the model"})
argument_list.append({"opts": ("-c", "--converter"),
"type": str,
# case sensitive because this is used
# to load a plugin.
"choices": ("Masked", "Adjust"),
"default": "Masked",
"help": "Converter to use"})
argument_list.append({"opts": ("-b", "--blur-size"),
"type": int,
"default": 2,
"help": "Blur size. (Masked converter only)"})
argument_list.append({"opts": ("-e", "--erosion-kernel-size"),
"dest": "erosion_kernel_size",
"type": int,
"default": None,
"help": "Erosion kernel size. Positive values "
"apply erosion which reduces the edge "
"of the swapped face. Negative values "
"apply dilation which allows the "
"swapped face to cover more space. "
"(Masked converter only)"})
argument_list.append({"opts": ("-M", "--mask-type"),
# lowercase this, because it's just a
# string later on.
"type": str.lower,
"dest": "mask_type",
"choices": ["rect",
"facehull",
"facehullandrect"],
"default": "facehullandrect",
"help": "Mask to use to replace faces. "
"(Masked converter only)"})
argument_list.append({"opts": ("-sh", "--sharpen"),
"type": str.lower,
"dest": "sharpen_image",
"choices": ["bsharpen", "gsharpen"],
"default": None,
"help": "Use Sharpen Image. bsharpen for Box "
"Blur, gsharpen for Gaussian Blur "
"(Masked converter only)"})
argument_list.append({"opts": ("-g", "--gpus"),
"type": int,
"default": 1,
"help": "Number of GPUs to use for conversion"})
argument_list.append({"opts": ("-fr", "--frame-ranges"),
"nargs": "+",
"type": str,
"help": "frame ranges to apply transfer to e.g. "
"For frames 10 to 50 and 90 to 100 use "
"--frame-ranges 10-50 90-100. Files "
"must have the frame-number as the last "
"number in the name!"})
argument_list.append({"opts": ("-d", "--discard-frames"),
"action": "store_true",
"dest": "discard_frames",
"default": False,
"help": "When used with --frame-ranges discards "
"frames that are not processed instead "
"of writing them out unchanged"})
argument_list.append({"opts": ("-s", "--swap-model"),
"action": "store_true",
"dest": "swap_model",
"default": False,
"help": "Swap the model. Instead of A -> B, "
"swap B -> A"})
argument_list.append({"opts": ("-S", "--seamless"),
"action": "store_true",
"dest": "seamless_clone",
"default": False,
"help": "Use cv2's seamless clone. "
"(Masked converter only)"})
argument_list.append({"opts": ("-mh", "--match-histogram"),
"action": "store_true",
"dest": "match_histogram",
"default": False,
"help": "Use histogram matching. "
"(Masked converter only)"})
argument_list.append({"opts": ("-sm", "--smooth-mask"),
"action": "store_true",
"dest": "smooth_mask",
"default": True,
"help": "Smooth mask (Adjust converter only)"})
argument_list.append({"opts": ("-aca", "--avg-color-adjust"),
"action": "store_true",
"dest": "avg_color_adjust",
"default": True,
"help": "Average color adjust. "
"(Adjust converter only)"})
return argument_list
