def process(self):
# Original & LowMem models go with Adjust or Masked converter
# GAN converter & model must go together
# Note: GAN prediction outputs a mask + an image, while other predicts only an image
model_name = self.arguments.trainer
conv_name = self.arguments.converter
if conv_name.startswith("GAN"):
assert model_name.startswith(
"GAN"
) is True, "GAN converter can only be used with GAN model!"
else:
assert model_name.startswith(
"GAN"
) is False, "GAN model can only be used with GAN converter!"
model = PluginLoader.get_model(model_name)(get_folder(
self.arguments.model_dir))
if not model.load(self.arguments.swap_model):
print(
'Model Not Found! A valid model must be provided to continue!')
exit(1)
converter = PluginLoader.get_converter(conv_name)(
model.converter(False),
blur_size=self.arguments.blur_size,
seamless_clone=self.arguments.seamless_clone,
mask_type=self.arguments.mask_type,
erosion_kernel_size=self.arguments.erosion_kernel_size,
smooth_mask=self.arguments.smooth_mask,
avg_color_adjust=self.arguments.avg_color_adjust)
batch = BackgroundGenerator(self.prepare_images(), 1)
# frame ranges stuff...
self.frame_ranges = None
# split out the frame ranges and parse out "min" and "max" values
minmax = {
"min": 0, # never any frames less than 0
"max": float("inf")
}
if self.arguments.frame_ranges:
self.frame_ranges = [
tuple(
map(lambda q: minmax[q] if q in minmax.keys() else int(q),
v.split("-"))) for v in self.arguments.frame_ranges
]
# last number regex. I know regex is hacky, but its reliablyhacky(tm).
self.imageidxre = re.compile(r'(\d+)(?!.*\d)')
for item in batch.iterator():
self.convert(converter, item)
def process(self):
self.convert_cache = self.input_A_path + '_convert'
try:
os.mkdir(self.convert_cache)
except:
import shutil
shutil.rmtree(self.convert_cache)
os.mkdir(self.convert_cache)
# model_name = 'Original'
# conv_name = 'Masked' #'Masked'
# model = PluginLoader.get_model(model_name)(self.model_path)
model = Model_Original.Model(self.model_path)
if not model.load(swapped = True):
exit(1)
converter = Convert_Masked.Convert(model.converter(False),
blur_size=2,
seamless_clone=True,
mask_type="facehullandrect",
erosion_kernel_size=None,
smooth_mask=True,
avg_color_adjust=True
)
batch = BackgroundGenerator(self.prepare_images(), 1)
self.frame_ranges = None
minmax = {
"min": 0, # never any frames less than 0
"max": float("inf")
}
if self.frame_ranges:
self.frame_ranges = [tuple(map(lambda q: minmax[q] if q in minmax.keys() else int(q), v.split("-"))) for v in self.arguments.frame_ranges]
self.imageidxre = re.compile(r'(\d+)(?!.*\d)')
try:
for item in batch.iterator():
if not self.state.empty():
if self.state.get() == 'stop_con':
print('取消合成，正在清理缓存文件')
break
else:
self.convert(converter, item)
else:
self.convert(converter, item)
finally:
keras.backend.clear_session()
def process(self):
# Original model goes with Adjust or Masked converter
# does the LowMem one work with only one?
# seems to work with both in testing - although Adjust with LowMem
# looks a real mess - you can see that it is "working"
model_name = self.arguments.trainer
conv_name = self.arguments.converter
model = PluginLoader.get_model(model_name)(self.arguments.model_dir)
if not model.load(self.arguments.swap_model):
print(
'Model Not Found! A valid model must be provided to continue!')
exit(1)
converter = PluginLoader.get_converter(conv_name)(
model.converter(False),
blur_size=self.arguments.blur_size,
seamless_clone=self.arguments.seamless_clone,
mask_type=self.arguments.mask_type,
erosion_kernel_size=self.arguments.erosion_kernel_size,
smooth_mask=self.arguments.smooth_mask,
avg_color_adjust=self.arguments.avg_color_adjust)
batch = BackgroundGenerator(self.prepare_images(), 1)
# frame ranges stuff...
self.frame_ranges = None
# split out the frame ranges and parse out "min" and "max" values
minmax = {
"min": 0, # never any frames less than 0
"max": float("inf")
}
if self.arguments.frame_ranges:
self.frame_ranges = [
tuple(
map(lambda q: minmax[q] if q in minmax.keys() else int(q),
v.split("-"))) for v in self.arguments.frame_ranges
]
# last number regex. I know regex is hacky, but its reliablyhacky(tm).
self.imageidxre = re.compile(r'(\d+)(?!.*\d)')
for item in batch.iterator():
self.convert(converter, item)
def process(self):
# Original model goes with Adjust or Masked converter
model_name = "Original" # TODO Pass as argument
conv_name = "Masked" # TODO Pass as argument
model = PluginLoader.get_model(model_name)(self.arguments.model_dir)
if not model.load(self.arguments.swap_model):
print(
'Model Not Found! A valid model must be provided to continue!')
exit(1)
converter = PluginLoader.get_converter(conv_name)(
model.converter(False))
batch = BackgroundGenerator(self.prepare_images(), 1)
for item in batch.iterator():
self.convert(converter, item)
def process(self):
model_name = self.arguments.trainer
conv_name = self.arguments.converter
if conv_name.startswith("GAN"):
assert model_name.startswith("GAN") is True, "GAN converter can only be used with GAN model"
else:
assert model_name.startswith("GAN") is False, "GAN model can only be used with GAN Converter"
model = PluginLoader.get_model(model_name)(get_folder(self.arguments.model_dir))
if not model.load(self.arguments.swap_model):
print("Model Not Found! A valid model must be provided to continue!")
exit(1)
converter = PluginLoader.get_converter(conv_name)(model.converter(False),
blur_size = self.arguments.blur_size,
seamless_clone=self.arguments.seamless_clone,
mask_type=self.arguments.mask_type,
erosion_kernel_size=self.arguments.erosion_kernel_size,
smooth_mask=self.arguments.smooth_mask,
avg_color_adjust=self.arguments.avg_color_adjust)
batch = BackgroundGenerator(self.prepare_images(),1)
#frame ranges stuffs
self.frame_ranges = None
minmax = {
"min":0,
"max":float("inf")
}
if self.arguments.frame_ranges:
self.frame_ranges = [tuple(map(lamda q: minmax[q] if q in minmax.keys() else int(q),v.split("-"))) for v in self.arguments.frame_ranges]
self.imageidxre - re.compile(r'(\d+)(?!.*\d)')
for item in batch.iterator():
self.convert(converter,item)
def process(self):
""" Original & LowMem models go with Adjust or Masked converter
Note: GAN prediction outputs a mask + an image, while other predicts only an image """
Utils.set_verbosity(self.args.verbose)
if not self.alignments.have_alignments_file:
self.generate_alignments()
self.faces.faces_detected = self.alignments.read_alignments()
model = self.load_model()
converter = self.load_converter(model)
batch = BackgroundGenerator(self.prepare_images(), 1)
for item in batch.iterator():
self.convert(converter, item)
Utils.finalize(self.images.images_found,
self.faces.num_faces_detected,
self.faces.verify_output)
def process(self):
""" Original & LowMem models go with Adjust or Masked converter
Note: GAN prediction outputs a mask + an image, while other
predicts only an image. """
Utils.set_verbosity(self.args.verbose)
if not self.alignments.have_alignments_file:
self.generate_alignments()
self.faces.faces_detected = self.alignments.read_alignments()
model = self.load_model()
converter = self.load_converter(model)
batch = BackgroundGenerator(self.prepare_images(), 1)
for item in batch.iterator():
self.convert(converter, item)
Utils.finalize(self.images.images_found,
self.faces.num_faces_detected,
self.faces.verify_output)
def process(self):
# Original model goes with Adjust or Masked converter
# does the LowMem one work with only one?
model_name = "Original" # TODO Pass as argument
conv_name = self.arguments.converter
model = PluginLoader.get_model(model_name)(self.arguments.model_dir)
if not model.load(self.arguments.swap_model):
print('Model Not Found! A valid model must be provided to continue!')
exit(1)
converter = PluginLoader.get_converter(conv_name)(model.converter(False),
blur_size=self.arguments.blur_size,
seamless_clone=self.arguments.seamless_clone,
mask_type=self.arguments.mask_type,
erosion_kernel_size=self.arguments.erosion_kernel_size,
smooth_mask=self.arguments.smooth_mask,
avg_color_adjust=self.arguments.avg_color_adjust
)
batch = BackgroundGenerator(self.prepare_images(), 1)
# frame ranges stuff...
self.frame_ranges = None
# split out the frame ranges and parse out "min" and "max" values
minmax = {
"min": 0, # never any frames less than 0
"max": float("inf")
}
if self.arguments.frame_ranges:
self.frame_ranges = [tuple(map(lambda q: minmax[q] if q in minmax.keys() else int(q), v.split("-"))) for v in self.arguments.frame_ranges]
# last number regex. I know regex is hacky, but its reliablyhacky(tm).
self.imageidxre = re.compile(r'(\d+)(?!.*\d)')
for item in batch.iterator():
self.convert(converter, item)
def process(self):
# Original & LowMem models go with Adjust or Masked converter
# Note: GAN prediction outputs a mask + an image, while other predicts only an image
model_name = self.arguments.trainer
conv_name = self.arguments.converter
self.input_aligned_dir = None
model = PluginLoader.get_model(model_name)(get_folder(
self.arguments.model_dir))
if not model.load(self.arguments.swap_model):
print(
'Model Not Found! A valid model must be provided to continue!')
exit(1)
input_aligned_dir = Path(self.arguments.input_dir) / Path('aligned')
if self.arguments.input_aligned_dir is not None:
input_aligned_dir = self.arguments.input_aligned_dir
try:
self.input_aligned_dir = [
Path(path) for path in get_image_paths(input_aligned_dir)
]
if len(self.input_aligned_dir) == 0:
print(
'Aligned directory is empty, no faces will be converted!')
elif len(self.input_aligned_dir) <= len(self.input_dir) / 3:
print(
'Aligned directory contains an amount of images much less than the input, are you sure this is the right directory?'
)
except:
print(
'Aligned directory not found. All faces listed in the alignments file will be converted.'
)
converter = PluginLoader.get_converter(conv_name)(
model.converter(False),
trainer=self.arguments.trainer,
blur_size=self.arguments.blur_size,
seamless_clone=self.arguments.seamless_clone,
mask_type=self.arguments.mask_type,
erosion_kernel_size=self.arguments.erosion_kernel_size,
match_histogram=self.arguments.match_histogram,
smooth_mask=self.arguments.smooth_mask,
avg_color_adjust=self.arguments.avg_color_adjust)
batch = BackgroundGenerator(self.prepare_images(), 1)
# frame ranges stuff...
self.frame_ranges = None
# split out the frame ranges and parse out "min" and "max" values
minmax = {
"min": 0, # never any frames less than 0
"max": float("inf")
}
if self.arguments.frame_ranges:
self.frame_ranges = [
tuple(
map(lambda q: minmax[q] if q in minmax.keys() else int(q),
v.split("-"))) for v in self.arguments.frame_ranges
]
# last number regex. I know regex is hacky, but its reliablyhacky(tm).
self.imageidxre = re.compile(r'(\d+)(?!.*\d)')
for item in batch.iterator():
self.convert(converter, item)
