mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
Added option hist-match threshold, used for decrease artifacts of hist-match.
Fix terminating converter thread in debug mode. Added DeepFaceLab converter overview to readme.
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iperov
parent
016791bb6f
commit
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11 changed files with 36 additions and 42 deletions
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@ -102,6 +102,8 @@ dst - controller face (your face)
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converter --input-dir must contains *extracted dst faces* in sequence to be converted, its mean you can train on for example 1500 dst faces, but use for example 100 faces for convert.
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- Video comparison of different Cage facesets.
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Vertical: 1 - mix of various Cage face shape and light conditions. 2,3,4 - without mix.
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Horizontal: 1 - DF, 2 - LIAEF128.
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34
main.py
34
main.py
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@ -121,18 +121,24 @@ if __name__ == "__main__":
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if arguments.mode == 'hist-match' or arguments.mode == 'hist-match-bw':
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try:
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choice = int ( input ("Masked hist match? [0..1] (default - model choice) : ") )
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arguments.masked_hist_match = (choice != 0)
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arguments.masked_hist_match = bool ( {"1":True,"0":False}[input("Masked hist match? [0 or 1] (default 1) : ").lower()] )
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except:
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arguments.masked_hist_match = None
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arguments.masked_hist_match = True
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if arguments.mode == 'hist-match' or arguments.mode == 'hist-match-bw' or arguments.mode == 'seamless-hist-match':
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try:
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hist_match_threshold = int ( input ("Hist match threshold. [0..255] (default - 255) : ") )
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arguments.hist_match_threshold = hist_match_threshold
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except:
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arguments.hist_match_threshold = 255
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try:
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arguments.erode_mask_modifier = int ( input ("Choose erode mask modifier [-100..100] (default 0) : ") )
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arguments.erode_mask_modifier = int ( input ("Choose erode mask modifier [-200..200] (default 0) : ") )
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except:
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arguments.erode_mask_modifier = 0
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try:
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arguments.blur_mask_modifier = int ( input ("Choose blur mask modifier [-100..200] (default 0) : ") )
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arguments.blur_mask_modifier = int ( input ("Choose blur mask modifier [-200..200] (default 0) : ") )
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except:
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arguments.blur_mask_modifier = 0
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@ -142,7 +148,7 @@ if __name__ == "__main__":
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arguments.output_face_scale_modifier = 0
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try:
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arguments.transfercolor = bool ( {"1":True,"0":False}[input("Transfer color from original DST image? [0..1] (default 0) : ").lower()] )
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arguments.transfercolor = bool ( {"1":True,"0":False}[input("Transfer color from dst face to converted final face? [0 or 1] (default 0) : ").lower()] )
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except:
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arguments.transfercolor = False
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@ -158,8 +164,8 @@ if __name__ == "__main__":
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arguments.erode_mask_modifier = np.clip ( int(arguments.erode_mask_modifier), -100, 100)
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arguments.blur_mask_modifier = np.clip ( int(arguments.blur_mask_modifier), -100, 200)
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arguments.erode_mask_modifier = np.clip ( int(arguments.erode_mask_modifier), -200, 200)
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arguments.blur_mask_modifier = np.clip ( int(arguments.blur_mask_modifier), -200, 200)
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arguments.output_face_scale_modifier = np.clip ( int(arguments.output_face_scale_modifier), -50, 50)
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from mainscripts import Converter
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@ -172,6 +178,7 @@ if __name__ == "__main__":
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debug = arguments.debug,
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mode = arguments.mode,
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masked_hist_match = arguments.masked_hist_match,
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hist_match_threshold = arguments.hist_match_threshold,
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erode_mask_modifier = arguments.erode_mask_modifier,
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blur_mask_modifier = arguments.blur_mask_modifier,
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output_face_scale_modifier = arguments.output_face_scale_modifier,
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@ -189,13 +196,14 @@ if __name__ == "__main__":
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convert_parser.add_argument('--model', required=True, dest="model_name", choices=Path_utils.get_all_dir_names_startswith ( Path(__file__).parent / 'models' , 'Model_'), help="Type of model")
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convert_parser.add_argument('--ask-for-params', action="store_true", dest="ask_for_params", default=False, help="Ask for params.")
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convert_parser.add_argument('--mode', dest="mode", choices=['seamless','hist-match', 'hist-match-bw','seamless-hist-match'], default='seamless', help="Face overlaying mode. Seriously affects result.")
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convert_parser.add_argument('--masked-hist-match', type=str2bool, nargs='?', const=True, default=None, help="True or False. Excludes background for hist match. Default - model decide.")
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convert_parser.add_argument('--erode-mask-modifier', type=int, dest="erode_mask_modifier", default=0, help="Automatic erode mask modifier. Valid range [-100..100].")
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convert_parser.add_argument('--blur-mask-modifier', type=int, dest="blur_mask_modifier", default=0, help="Automatic blur mask modifier. Valid range [-100..200].")
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convert_parser.add_argument('--masked-hist-match', type=str2bool, nargs='?', const=True, default=True, help="True or False. Excludes background for hist match. Default - True.")
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convert_parser.add_argument('--hist-match-threshold', type=int, dest="hist_match_threshold", default=255, help="Hist match threshold. Decrease to hide artifacts of hist match. Valid range [0..255]. Default 255")
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convert_parser.add_argument('--erode-mask-modifier', type=int, dest="erode_mask_modifier", default=0, help="Automatic erode mask modifier. Valid range [-200..200].")
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convert_parser.add_argument('--blur-mask-modifier', type=int, dest="blur_mask_modifier", default=0, help="Automatic blur mask modifier. Valid range [-200..200].")
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convert_parser.add_argument('--output-face-scale-modifier', type=int, dest="output_face_scale_modifier", default=0, help="Output face scale modifier. Valid range [-50..50].")
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convert_parser.add_argument('--final-image-color-degrade-power', type=int, dest="final_image_color_degrade_power", default=0, help="Degrades colors of final image to hide face problems. Valid range [0..100].")
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convert_parser.add_argument('--transfercolor', action="store_true", dest="transfercolor", default=False, help="transfer color from dst to merged.")
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convert_parser.add_argument('--alpha', action="store_true", dest="alpha", default=False, help="alpha channel.")
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convert_parser.add_argument('--transfercolor', action="store_true", dest="transfercolor", default=False, help="Transfer color from dst face to converted final face.")
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convert_parser.add_argument('--alpha', action="store_true", dest="alpha", default=False, help="Embeds alpha channel of face mask to final PNG. Used in manual composing video by editors such as Sony Vegas or After Effects.")
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convert_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug converter.")
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convert_parser.add_argument('--force-best-gpu-idx', type=int, dest="force_best_gpu_idx", default=-1, help="Force to choose this GPU idx as best.")
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@ -65,7 +65,7 @@ class ConvertSubprocessor(SubprocessorBase):
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#override
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def __init__(self, converter, input_path_image_paths, output_path, alignments, debug = False, **in_options):
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super().__init__('Converter')
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super().__init__('Converter', 86400 if debug == True else 60)
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self.converter = converter
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self.input_path_image_paths = input_path_image_paths
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self.output_path = output_path
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@ -15,7 +15,8 @@ class ConverterMasked(ConverterBase):
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erode_mask = True,
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blur_mask = True,
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clip_border_mask_per = 0,
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masked_hist_match = False,
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masked_hist_match = True,
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hist_match_threshold = 255,
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mode='seamless',
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erode_mask_modifier=0,
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blur_mask_modifier=0,
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@ -34,6 +35,7 @@ class ConverterMasked(ConverterBase):
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self.blur_mask = blur_mask
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self.clip_border_mask_per = clip_border_mask_per
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self.masked_hist_match = masked_hist_match
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self.hist_match_threshold = hist_match_threshold
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self.mode = mode
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self.erode_mask_modifier = erode_mask_modifier
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self.blur_mask_modifier = blur_mask_modifier
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@ -165,7 +167,7 @@ class ConverterMasked(ConverterBase):
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hist_match_2 = dst_face_bgr*hist_mask_a + (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=prd_face_bgr.dtype)
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hist_match_2[ hist_match_1 > 1.0 ] = 1.0
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new_prd_face_bgr = image_utils.color_hist_match(hist_match_1, hist_match_2 )
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new_prd_face_bgr = image_utils.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold )
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prd_face_bgr = new_prd_face_bgr
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@ -202,7 +204,7 @@ class ConverterMasked(ConverterBase):
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if self.mode == 'seamless-hist-match':
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size) )
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new_out_face_bgr = image_utils.color_hist_match(out_face_bgr, dst_face_bgr )
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new_out_face_bgr = image_utils.color_hist_match(out_face_bgr, dst_face_bgr, self.hist_match_threshold)
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new_out = cv2.warpAffine( new_out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (new_out*img_mask_blurry_aaa) , 0, 1.0 )
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@ -109,9 +109,6 @@ class Model(ModelBase):
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def get_converter(self, **in_options):
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from models import ConverterMasked
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if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] is None:
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in_options['masked_hist_match'] = True
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if 'erode_mask_modifier' not in in_options.keys():
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in_options['erode_mask_modifier'] = 0
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in_options['erode_mask_modifier'] += 30
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@ -113,9 +113,6 @@ class Model(ModelBase):
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def get_converter(self, **in_options):
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from models import ConverterMasked
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if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] is None:
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in_options['masked_hist_match'] = True
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if 'erode_mask_modifier' not in in_options.keys():
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in_options['erode_mask_modifier'] = 0
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in_options['erode_mask_modifier'] += 100
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@ -113,9 +113,6 @@ class Model(ModelBase):
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def get_converter(self, **in_options):
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from models import ConverterMasked
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if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] is None:
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in_options['masked_hist_match'] = True
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if 'erode_mask_modifier' not in in_options.keys():
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in_options['erode_mask_modifier'] = 0
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in_options['erode_mask_modifier'] += 100
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@ -117,9 +117,6 @@ class Model(ModelBase):
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def get_converter(self, **in_options):
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from models import ConverterMasked
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if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] is None:
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in_options['masked_hist_match'] = True
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if 'erode_mask_modifier' not in in_options.keys():
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in_options['erode_mask_modifier'] = 0
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in_options['erode_mask_modifier'] += 30
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@ -117,9 +117,6 @@ class Model(ModelBase):
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def get_converter(self, **in_options):
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from models import ConverterMasked
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if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] is None:
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in_options['masked_hist_match'] = True
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if 'erode_mask_modifier' not in in_options.keys():
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in_options['erode_mask_modifier'] = 0
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in_options['erode_mask_modifier'] += 30
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@ -153,9 +153,6 @@ class Model(ModelBase):
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def get_converter(self, **in_options):
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from models import ConverterMasked
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if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] is None:
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in_options['masked_hist_match'] = False
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if 'erode_mask_modifier' not in in_options.keys():
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in_options['erode_mask_modifier'] = 0
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in_options['erode_mask_modifier'] += 30
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@ -6,7 +6,7 @@ import localization
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from scipy.spatial import Delaunay
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from PIL import Image, ImageDraw, ImageFont
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def channel_hist_match(source, template, mask=None):
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def channel_hist_match(source, template, hist_match_threshold=255, mask=None):
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# Code borrowed from:
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# https://stackoverflow.com/questions/32655686/histogram-matching-of-two-images-in-python-2-x
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masked_source = source
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@ -29,18 +29,18 @@ def channel_hist_match(source, template, mask=None):
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mt_values, mt_counts = np.unique(template, return_counts=True)
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s_quantiles = np.cumsum(s_counts).astype(np.float64)
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s_quantiles /= s_quantiles[-1]
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s_quantiles = hist_match_threshold * s_quantiles / s_quantiles[-1]
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t_quantiles = np.cumsum(t_counts).astype(np.float64)
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t_quantiles /= t_quantiles[-1]
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t_quantiles = 255 * t_quantiles / t_quantiles[-1]
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interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
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return interp_t_values[bin_idx].reshape(oldshape)
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def color_hist_match(src_im, tar_im, mask=None):
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def color_hist_match(src_im, tar_im, hist_match_threshold=255):
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h,w,c = src_im.shape
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matched_R = channel_hist_match(src_im[:,:,0], tar_im[:,:,0], mask)
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matched_G = channel_hist_match(src_im[:,:,1], tar_im[:,:,1], mask)
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matched_B = channel_hist_match(src_im[:,:,2], tar_im[:,:,2], mask)
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matched_R = channel_hist_match(src_im[:,:,0], tar_im[:,:,0], hist_match_threshold, None)
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matched_G = channel_hist_match(src_im[:,:,1], tar_im[:,:,1], hist_match_threshold, None)
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matched_B = channel_hist_match(src_im[:,:,2], tar_im[:,:,2], hist_match_threshold, None)
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to_stack = (matched_R, matched_G, matched_B)
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for i in range(3, c):
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