def get_converter(self, **in_options):
from models import ConverterMasked
base_erode_mask_modifier = 30 if self.options[
'face_type'] == 'f' else 100
base_blur_mask_modifier = 0 if self.options['face_type'] == 'f' else 100
default_erode_mask_modifier = 0
default_blur_mask_modifier = 100 if (self.options['face_style_power'] or self.options['bg_style_power']) and \
self.options['face_type'] == 'f' else 0
face_type = FaceType.FULL if self.options[
'face_type'] == 'f' else FaceType.HALF
return ConverterMasked(
self.predictor_func,
predictor_input_size=self.options['resolution'],
output_size=self.options['resolution'],
face_type=face_type,
default_mode=1 if self.options['face_style_power']
or self.options['bg_style_power'] else 4,
base_erode_mask_modifier=base_erode_mask_modifier,
base_blur_mask_modifier=base_blur_mask_modifier,
default_erode_mask_modifier=default_erode_mask_modifier,
default_blur_mask_modifier=default_blur_mask_modifier,
clip_hborder_mask_per=0.0625
if self.options['face_type'] == 'f' else 0,
**in_options)
def get_converter(self, **in_options):
from models import ConverterMasked
return ConverterMasked(self.predictor_func,
predictor_input_size=64,
output_size=64,
face_type=FaceType.HALF,
base_erode_mask_modifier=100,
base_blur_mask_modifier=100,
**in_options)
def get_converter(self, **in_options):
from models import ConverterMasked
if 'erode_mask_modifier' not in in_options.keys():
in_options['erode_mask_modifier'] = 0
in_options['erode_mask_modifier'] += 30
if 'blur_mask_modifier' not in in_options.keys():
in_options['blur_mask_modifier'] = 0
return ConverterMasked(self.predictor_func, predictor_input_size=128, output_size=128, face_type=FaceType.FULL, clip_border_mask_per=0.046875, **in_options)
def get_converter(self, **in_options):
from models import ConverterMasked
if 'erode_mask_modifier' not in in_options.keys():
in_options['erode_mask_modifier'] = 0
in_options['erode_mask_modifier'] += 100
if 'blur_mask_modifier' not in in_options.keys():
in_options['blur_mask_modifier'] = 0
in_options['blur_mask_modifier'] += 100
return ConverterMasked(self.predictor_func, predictor_input_size=128, output_size=128, face_type=FaceType.HALF, **in_options)
def get_converter(self, **in_options):
from models import ConverterMasked
if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] == None:
in_options['masked_hist_match'] = False
if 'erode_mask_modifier' not in in_options.keys():
in_options['erode_mask_modifier'] = 0
in_options['erode_mask_modifier'] += 30
if 'blur_mask_modifier' not in in_options.keys():
in_options['blur_mask_modifier'] = 0
return ConverterMasked(self.predictor_func, predictor_input_size=128, output_size=128, face_type='full_face', clip_border_mask_per=0.046875, **in_options)
def get_converter(self, **in_options):
from models import ConverterMasked
base_erode_mask_modifier = 30 if self.options['face_type'] == 'f' else 100
base_blur_mask_modifier = 0 if self.options['face_type'] == 'f' else 100
face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
return ConverterMasked(self.predictor_func,
predictor_input_size=self.options['resolution'],
output_size=self.options['resolution'],
face_type=face_type,
base_erode_mask_modifier=base_erode_mask_modifier,
base_blur_mask_modifier=base_blur_mask_modifier,
**in_options)
def get_converter(self, **in_options):
from models import ConverterMasked
if 'masked_hist_match' not in in_options.keys(
) or in_options['masked_hist_match'] == None:
in_options['masked_hist_match'] = True
if 'erode_mask_modifier' not in in_options.keys():
in_options['erode_mask_modifier'] = 0
in_options['erode_mask_modifier'] += 100
if 'blur_mask_modifier' not in in_options.keys():
in_options['blur_mask_modifier'] = 0
in_options['blur_mask_modifier'] += 100
return ConverterMasked(self.predictor_func,
predictor_input_size=64,
output_size=64,
face_type='half_face',
**in_options)
def get_converter(self, **in_options):
from models import ConverterMasked
return ConverterMasked(self.predictor_func, 64, 64, 'half_face',
**in_options)
def get_converter(self, **in_options):
from models import ConverterMasked
return ConverterMasked(self.predictor_func, 128, 128, 'full_face',
**in_options)