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synced 2025-08-22 14:24:40 -07:00
changed help message for pixel loss:
Pixel loss may help to enhance fine details and stabilize face color. Use it only if quality does not improve over time. SAE: previous SAE model will not work with this update. Greatly decreased chance of model collapse. Increased model accuracy. Residual blocks now default and this option has been removed. Improved 'learn mask'. Added masked preview (switch by space key) Converter: fixed rct/lct in seamless mode added mask mode (6) learned*FAN-prd*FAN-dst added mask editor, its created for refining dataset for FANSeg model, and not for production, but you can spend your time and test it in regular fakes with face obstructions
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iperov
parent
01e98cde8e
commit
5ac7e5d7f1
22 changed files with 715 additions and 387 deletions
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@ -77,11 +77,11 @@ class ConverterMasked(Converter):
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self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255)
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if face_type == FaceType.FULL:
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self.mask_mode = np.clip ( io.input_int ("Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd&dst (?) help. Default - %d : " % (1) , 1, help_message="If you learned mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained FAN-model from dst face. 'FAN-prd&dst' - using multiplied FAN prd and dst mask. "), 1, 5 )
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self.mask_mode = np.clip ( io.input_int ("Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) learned*FAN-prd*FAN-dst (?) help. Default - %d : " % (1) , 1, help_message="If you learned mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained FAN-model from dst face. 'FAN-prd*FAN-dst' or 'learned*FAN-prd*FAN-dst' - using multiplied masks."), 1, 6 )
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else:
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self.mask_mode = np.clip ( io.input_int ("Mask mode: (1) learned, (2) dst . Default - %d : " % (1) , 1), 1, 2 )
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if self.mask_mode == 3 or self.mask_mode == 4 or self.mask_mode == 5:
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if self.mask_mode >= 3 or self.mask_mode <= 6:
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self.fan_seg = None
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if self.mode != 'raw':
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@ -117,7 +117,7 @@ class ConverterMasked(Converter):
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#overridable
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def on_cli_initialize(self):
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if (self.mask_mode == 3 or self.mask_mode == 4 or self.mask_mode == 5) and self.fan_seg == None:
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if (self.mask_mode >= 3 and self.mask_mode <= 6) and self.fan_seg == None:
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self.fan_seg = FANSegmentator(256, FaceType.toString(FaceType.FULL) )
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#override
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@ -167,26 +167,28 @@ class ConverterMasked(Converter):
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if self.mask_mode == 2: #dst
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
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elif self.mask_mode >= 3 and self.mask_mode <= 5:
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elif self.mask_mode >= 3 and self.mask_mode <= 6:
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if self.mask_mode == 3 or self.mask_mode == 5: #FAN-prd
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if self.mask_mode == 3 or self.mask_mode == 5 or self.mask_mode == 6:
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prd_face_bgr_256 = cv2.resize (prd_face_bgr, (256,256) )
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prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr( prd_face_bgr_256[np.newaxis,...] ) [0]
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FAN_prd_face_mask_a_0 = cv2.resize (prd_face_bgr_256_mask, (output_size,output_size), cv2.INTER_CUBIC)
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if self.mask_mode == 4 or self.mask_mode == 5: #FAN-dst
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if self.mask_mode == 4 or self.mask_mode == 5 or self.mask_mode == 6:
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face_256_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, 256, face_type=FaceType.FULL)
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dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4 )
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dst_face_256_mask = self.fan_seg.extract_from_bgr( dst_face_256_bgr[np.newaxis,...] ) [0]
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FAN_dst_face_mask_a_0 = cv2.resize (dst_face_256_mask, (output_size,output_size), cv2.INTER_CUBIC)
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if self.mask_mode == 3:
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if self.mask_mode == 3: #FAN-prd
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prd_face_mask_a_0 = FAN_prd_face_mask_a_0
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elif self.mask_mode == 4:
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elif self.mask_mode == 4: #FAN-dst
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prd_face_mask_a_0 = FAN_dst_face_mask_a_0
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elif self.mask_mode == 5:
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prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0
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elif self.mask_mode == 6:
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prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0
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prd_face_mask_a_0[ prd_face_mask_a_0 < 0.001 ] = 0.0
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prd_face_mask_a = prd_face_mask_a_0[...,np.newaxis]
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@ -269,11 +271,12 @@ class ConverterMasked(Converter):
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img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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face_mask_blurry_aaa = cv2.warpAffine( img_mask_blurry_aaa, face_mat, (output_size, output_size) )
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if debug:
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debugs += [img_mask_blurry_aaa.copy()]
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if self.color_transfer_mode is not None:
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if 'seamless' not in self.mode and self.color_transfer_mode is not None:
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if self.color_transfer_mode == 'rct':
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if debug:
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debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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@ -309,15 +312,20 @@ class ConverterMasked(Converter):
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if self.masked_hist_match:
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hist_mask_a *= prd_face_mask_a
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white = (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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hist_match_1 = prd_face_bgr*hist_mask_a + (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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hist_match_1 = prd_face_bgr*hist_mask_a + white
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hist_match_1[ hist_match_1 > 1.0 ] = 1.0
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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=np.float32)
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hist_match_2 = dst_face_bgr*hist_mask_a + white
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hist_match_2[ hist_match_1 > 1.0 ] = 1.0
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prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold )
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#if self.masked_hist_match:
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# prd_face_bgr -= white
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if self.mode == 'hist-match-bw':
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prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
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@ -364,6 +372,35 @@ class ConverterMasked(Converter):
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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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if 'seamless' in self.mode and self.color_transfer_mode is not None:
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
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if self.color_transfer_mode == 'rct':
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if debug:
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debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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new_out_face_bgr = imagelib.reinhard_color_transfer ( np.clip( (out_face_bgr*255).astype(np.uint8), 0, 255),
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np.clip( (dst_face_bgr*255).astype(np.uint8), 0, 255),
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source_mask=face_mask_blurry_aaa, target_mask=face_mask_blurry_aaa)
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new_out_face_bgr = np.clip( new_out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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if debug:
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debugs += [ np.clip( cv2.warpAffine( new_out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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elif self.color_transfer_mode == 'lct':
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if debug:
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debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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new_out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
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new_out_face_bgr = np.clip( new_out_face_bgr, 0.0, 1.0)
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if debug:
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debugs += [ np.clip( cv2.warpAffine( new_out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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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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if self.mode == 'seamless-hist-match':
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
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new_out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, self.hist_match_threshold)
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