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upd ConverterMasked.py
This commit is contained in:
iperov
parent
0448220f8f
commit
42adbd94e7
1 changed files with 131 additions and 132 deletions
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@ -179,154 +179,153 @@ class ConverterMasked(Converter):
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maskx = int( maskx )
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maskx = int( maskx )
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masky = int( masky )
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masky = int( masky )
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if lenx >= 4 and leny >= 4:
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lowest_len = min (lenx, leny)
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lowest_len = min (lenx, leny)
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if debug:
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if debug:
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io.log_info ("lowest_len = %f" % (lowest_len) )
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io.log_info ("lowest_len = %f" % (lowest_len) )
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img_mask_blurry_aaa = img_face_mask_aaa
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img_mask_blurry_aaa = img_face_mask_aaa
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if self.erode_mask_modifier != 0:
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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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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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if debug:
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io.log_info ("erode_size = %d" % (ero) )
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io.log_info ("erode_size = %d" % (ero) )
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if ero > 0:
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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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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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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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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.seamless_erode_mask_modifier != 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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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.seamless_erode_mask_modifier )
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if debug:
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if debug:
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io.log_info ("seamless_erode_size = %d" % (ero) )
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io.log_info ("seamless_erode_size = %d" % (ero) )
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if ero > 0:
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if ero > 0:
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img_face_seamless_mask_aaa = cv2.erode(img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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img_face_seamless_mask_aaa = cv2.erode(img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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elif ero < 0:
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img_face_seamless_mask_aaa = cv2.dilate(img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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img_face_seamless_mask_aaa = cv2.dilate(img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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img_face_seamless_mask_aaa = np.clip (img_face_seamless_mask_aaa, 0, 1)
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img_face_seamless_mask_aaa = np.clip (img_face_seamless_mask_aaa, 0, 1)
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if self.clip_hborder_mask_per > 0: #clip hborder before blur
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if self.clip_hborder_mask_per > 0: #clip hborder before blur
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prd_hborder_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=np.float32)
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prd_hborder_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=np.float32)
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prd_border_size = int ( prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per )
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prd_border_size = int ( prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per )
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prd_hborder_rect_mask_a[:,0:prd_border_size,:] = 0
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prd_hborder_rect_mask_a[:,0:prd_border_size,:] = 0
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prd_hborder_rect_mask_a[:,-prd_border_size:,:] = 0
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prd_hborder_rect_mask_a[:,-prd_border_size:,:] = 0
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prd_hborder_rect_mask_a = np.expand_dims(cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
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prd_hborder_rect_mask_a = np.expand_dims(cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
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img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4 )
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img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4 )
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img_prd_hborder_rect_mask_a = np.expand_dims (img_prd_hborder_rect_mask_a, -1)
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img_prd_hborder_rect_mask_a = np.expand_dims (img_prd_hborder_rect_mask_a, -1)
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img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a
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img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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if debug:
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debugs += [img_mask_blurry_aaa.copy()]
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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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if debug:
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io.log_info ("blur_size = %d" % (blur) )
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if blur > 0:
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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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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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if debug:
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if debug:
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debugs += [img_mask_blurry_aaa.copy()]
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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 self.blur_mask_modifier > 0:
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if self.color_transfer_mode == 'rct':
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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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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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io.log_info ("blur_size = %d" % (blur) )
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if blur > 0:
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prd_face_bgr = image_utils.reinhard_color_transfer ( np.clip( (prd_face_bgr*255).astype(np.uint8), 0, 255),
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img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )
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np.clip( (dst_face_bgr*255).astype(np.uint8), 0, 255),
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source_mask=prd_face_mask_a, target_mask=prd_face_mask_a)
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prd_face_bgr = np.clip( prd_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( 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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elif self.color_transfer_mode == 'lct':
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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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prd_face_bgr = image_utils.linear_color_transfer (prd_face_bgr, dst_face_bgr)
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prd_face_bgr = np.clip( prd_face_bgr, 0.0, 1.0)
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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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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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prd_face_bgr = np.repeat( np.expand_dims (prd_face_bgr, -1), (3,), -1 )
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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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 self.color_transfer_mode == 'rct':
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if debug:
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if debug:
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debugs += [ 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 ) ]
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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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hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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prd_face_bgr = image_utils.reinhard_color_transfer ( np.clip( (prd_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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if self.masked_hist_match:
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source_mask=prd_face_mask_a, target_mask=prd_face_mask_a)
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hist_mask_a *= prd_face_mask_a
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prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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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[ 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[ hist_match_1 > 1.0 ] = 1.0
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prd_face_bgr = image_utils.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold )
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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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if self.mode == 'hist-match-bw':
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prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
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out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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elif self.color_transfer_mode == 'lct':
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out_img = np.clip(out_img, 0.0, 1.0)
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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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prd_face_bgr = image_utils.linear_color_transfer (prd_face_bgr, dst_face_bgr)
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prd_face_bgr = np.clip( prd_face_bgr, 0.0, 1.0)
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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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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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prd_face_bgr = np.repeat( np.expand_dims (prd_face_bgr, -1), (3,), -1 )
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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if debug:
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debugs += [ 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 ) ]
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hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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if self.masked_hist_match:
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hist_mask_a *= prd_face_mask_a
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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[ 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[ hist_match_1 > 1.0 ] = 1.0
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prd_face_bgr = image_utils.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold )
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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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out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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out_img = np.clip(out_img, 0.0, 1.0)
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if debug:
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debugs += [out_img.copy()]
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if self.mode == 'overlay':
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pass
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if 'seamless' in self.mode:
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try:
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out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_seamless_mask_aaa*255).astype(np.uint8), (maskx,masky) , cv2.NORMAL_CLONE )
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out_img = out_img.astype(dtype=np.float32) / 255.0
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except Exception as e:
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#seamlessClone may fail in some cases
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e_str = traceback.format_exc()
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if 'MemoryError' in e_str:
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raise Exception("Seamless fail: " + e_str) #reraise MemoryError in order to reprocess this data by other processes
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else:
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print ("Seamless fail: " + e_str)
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if debug:
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if debug:
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debugs += [out_img.copy()]
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debugs += [out_img.copy()]
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if self.mode == 'overlay':
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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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pass
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if self.mode == 'seamless-hist-match':
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if 'seamless' in self.mode:
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size) )
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try:
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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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out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_seamless_mask_aaa*255).astype(np.uint8), (maskx,masky) , cv2.NORMAL_CLONE )
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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 = out_img.astype(dtype=np.float32) / 255.0
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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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except Exception as e:
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#seamlessClone may fail in some cases
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if self.final_image_color_degrade_power != 0:
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e_str = traceback.format_exc()
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if debug:
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debugs += [out_img.copy()]
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if 'MemoryError' in e_str:
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out_img_reduced = image_utils.reduce_colors(out_img, 256)
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raise Exception("Seamless fail: " + e_str) #reraise MemoryError in order to reprocess this data by other processes
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if self.final_image_color_degrade_power == 100:
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else:
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out_img = out_img_reduced
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print ("Seamless fail: " + e_str)
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else:
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alpha = self.final_image_color_degrade_power / 100.0
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if debug:
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out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
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debugs += [out_img.copy()]
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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 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, 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 )
|
|
||||||
out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (new_out*img_mask_blurry_aaa) , 0, 1.0 )
|
|
||||||
|
|
||||||
if self.final_image_color_degrade_power != 0:
|
if self.alpha:
|
||||||
if debug:
|
out_img = np.concatenate ( [out_img, np.expand_dims (img_mask_blurry_aaa[:,:,0],-1)], -1 )
|
||||||
debugs += [out_img.copy()]
|
|
||||||
out_img_reduced = image_utils.reduce_colors(out_img, 256)
|
|
||||||
if self.final_image_color_degrade_power == 100:
|
|
||||||
out_img = out_img_reduced
|
|
||||||
else:
|
|
||||||
alpha = self.final_image_color_degrade_power / 100.0
|
|
||||||
out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
|
|
||||||
|
|
||||||
if self.alpha:
|
|
||||||
out_img = np.concatenate ( [out_img, np.expand_dims (img_mask_blurry_aaa[:,:,0],-1)], -1 )
|
|
||||||
|
|
||||||
if self.over_res != 1:
|
if self.over_res != 1:
|
||||||
out_img = cv2.resize ( out_img, ( img_bgr.shape[1] // self.over_res, img_bgr.shape[0] // self.over_res ) )
|
out_img = cv2.resize ( out_img, ( img_bgr.shape[1] // self.over_res, img_bgr.shape[0] // self.over_res ) )
|
||||||
|
|
|
||||||
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Add table
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Reference in a new issue