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)