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synced 2025-07-06 13:02:15 -07:00
added converter --seamless-erode-mask-modifier . This mask used to define area for opencv seamless cloning function. So if you erode it little bit , you can decrease flickering effect on some frames.
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iperov
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commit
ffddfeabdb
2 changed files with 47 additions and 36 deletions
9
main.py
9
main.py
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@ -147,6 +147,12 @@ if __name__ == "__main__":
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except:
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arguments.blur_mask_modifier = 0
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if arguments.mode == 'seamless' or arguments.mode == 'seamless-hist-match':
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try:
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arguments.seamless_erode_mask_modifier = int ( input ("Choose seamless erode mask modifier [-20..20] (default 0) : ") )
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except:
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arguments.seamless_erode_mask_modifier = 0
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try:
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arguments.output_face_scale_modifier = int ( input ("Choose output face scale modifier [-50..50] (default 0) : ") )
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except:
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@ -169,6 +175,7 @@ if __name__ == "__main__":
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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.seamless_erode_mask_modifier = np.clip ( int(arguments.seamless_erode_mask_modifier), -20, 20)
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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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@ -185,6 +192,7 @@ if __name__ == "__main__":
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use_predicted_mask = arguments.use_predicted_mask,
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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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seamless_erode_mask_modifier = arguments.seamless_erode_mask_modifier,
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output_face_scale_modifier = arguments.output_face_scale_modifier,
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final_image_color_degrade_power = arguments.final_image_color_degrade_power,
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transfercolor = arguments.transfercolor,
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@ -205,6 +213,7 @@ if __name__ == "__main__":
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convert_parser.add_argument('--use-predicted-mask', action="store_true", dest="use_predicted_mask", default=True, help="Use predicted mask by model. Default - True.")
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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('--seamless-erode-mask-modifier', type=int, dest="seamless_erode_mask_modifier", default=0, help="Automatic seamless erode 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 face to converted final face.")
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@ -12,8 +12,6 @@ class ConverterMasked(ConverterBase):
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predictor_input_size=0,
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output_size=0,
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face_type=FaceType.FULL,
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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 = True,
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hist_match_threshold = 255,
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@ -21,6 +19,7 @@ class ConverterMasked(ConverterBase):
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use_predicted_mask = True,
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erode_mask_modifier=0,
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blur_mask_modifier=0,
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seamless_erode_mask_modifier=0,
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output_face_scale_modifier=0.0,
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transfercolor=False,
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final_image_color_degrade_power=0,
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@ -33,26 +32,19 @@ class ConverterMasked(ConverterBase):
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self.output_size = output_size
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self.face_type = face_type
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self.use_predicted_mask = use_predicted_mask
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self.erode_mask = erode_mask
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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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self.seamless_erode_mask_modifier = seamless_erode_mask_modifier
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self.output_face_scale = np.clip(1.0 + output_face_scale_modifier*0.01, 0.5, 1.5)
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self.transfercolor = transfercolor
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self.TFLabConverter = None
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self.final_image_color_degrade_power = np.clip (final_image_color_degrade_power, 0, 100)
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self.alpha = alpha
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if self.erode_mask_modifier != 0 and not self.erode_mask:
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print ("Erode mask modifier not used in this model.")
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if self.blur_mask_modifier != 0 and not self.blur_mask:
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print ("Blur modifier not used in this model.")
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#override
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def get_mode(self):
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return ConverterBase.MODE_FACE
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@ -126,33 +118,43 @@ class ConverterMasked(ConverterBase):
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if debug:
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print ("lowest_len = %f" % (lowest_len) )
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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.erode_mask_modifier )
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blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier )
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if debug:
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print ("erode_size = %d, blur_size = %d" % (ero, blur) )
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img_mask_blurry_aaa = img_face_mask_aaa
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if self.erode_mask:
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if self.erode_mask_modifier != 0:
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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.erode_mask_modifier )
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if debug:
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print ("erode_size = %d" % (ero) )
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if ero > 0:
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img_mask_blurry_aaa = cv2.erode(img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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img_mask_blurry_aaa = cv2.dilate(img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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if self.blur_mask and blur > 0:
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if self.seamless_erode_mask_modifier != 0:
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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.seamless_erode_mask_modifier )
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if ero > 0:
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img_face_mask_flatten_aaa = cv2.erode(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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img_face_mask_flatten_aaa = cv2.dilate(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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if debug:
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print ("seamless_erode_size = %d" % (ero) )
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if self.blur_mask_modifier > 0:
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blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier )
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img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )
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if debug:
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print ("blur_size = %d" % (blur) )
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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if self.clip_border_mask_per > 0:
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prd_border_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=prd_face_mask_a.dtype)
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prd_border_size = int ( prd_border_rect_mask_a.shape[1] * self.clip_border_mask_per )
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prd_border_rect_mask_a[0:prd_border_size,:,:] = 0
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prd_border_rect_mask_a[-prd_border_size:,:,:] = 0
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prd_border_rect_mask_a[:,0:prd_border_size,:] = 0
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prd_border_rect_mask_a[:,-prd_border_size:,:] = 0
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prd_border_rect_mask_a = np.expand_dims(cv2.blur(prd_border_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
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#if self.clip_border_mask_per > 0:
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# prd_border_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=prd_face_mask_a.dtype)
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# prd_border_size = int ( prd_border_rect_mask_a.shape[1] * self.clip_border_mask_per )
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#
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# prd_border_rect_mask_a[0:prd_border_size,:,:] = 0
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# prd_border_rect_mask_a[-prd_border_size:,:,:] = 0
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# prd_border_rect_mask_a[:,0:prd_border_size,:] = 0
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# prd_border_rect_mask_a[:,-prd_border_size:,:] = 0
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# prd_border_rect_mask_a = np.expand_dims(cv2.blur(prd_border_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
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if self.mode == 'hist-match-bw':
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prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY)
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@ -199,12 +201,12 @@ class ConverterMasked(ConverterBase):
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if debug:
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debugs += [out_img.copy()]
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if self.clip_border_mask_per > 0:
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img_prd_border_rect_mask_a = cv2.warpAffine( prd_border_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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img_prd_border_rect_mask_a = np.expand_dims (img_prd_border_rect_mask_a, -1)
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out_img = out_img * img_prd_border_rect_mask_a + img_bgr * (1.0 - img_prd_border_rect_mask_a)
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img_mask_blurry_aaa *= img_prd_border_rect_mask_a
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#if self.clip_border_mask_per > 0:
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# img_prd_border_rect_mask_a = cv2.warpAffine( prd_border_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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# img_prd_border_rect_mask_a = np.expand_dims (img_prd_border_rect_mask_a, -1)
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#
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# out_img = out_img * img_prd_border_rect_mask_a + img_bgr * (1.0 - img_prd_border_rect_mask_a)
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# img_mask_blurry_aaa *= img_prd_border_rect_mask_a
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out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (out_img*img_mask_blurry_aaa) , 0, 1.0 )
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