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Added new face type : head
Now you can replace the head. Example: https://www.youtube.com/watch?v=xr5FHd0AdlQ Requirements: Post processing skill in Adobe After Effects or Davinci Resolve. Usage: 1) Find suitable dst footage with the monotonous background behind head 2) Use “extract head” script 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor 5) Train XSeg 6) Apply trained XSeg mask for src and dst headsets 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. You can use pretrained model for head. Minimum recommended resolution for head is 224. 8) Extract multiple tracks, using Merger: a. Raw-rgb b. XSeg-prd mask c. XSeg-dst mask 9) Using AAE or DavinciResolve, do: a. Hide source head using XSeg-prd mask: content-aware-fill, clone-stamp, background retraction, or other technique b. Overlay new head using XSeg-dst mask Warning: Head faceset can be used for whole_face or less types of training only with XSeg masking. XSegEditor: added button ‘view trained XSeg mask’, so you can see which frames should be masked to improve mask quality.
This commit is contained in:
Colombo
parent
383d4d3736
commit
2b7364005d
21 changed files with 506 additions and 413 deletions
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@ -84,14 +84,19 @@ class InteractiveMergerSubprocessor(Subprocessor):
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filepath = frame_info.filepath
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if len(frame_info.landmarks_list) == 0:
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self.log_info (f'no faces found for {filepath.name}, copying without faces')
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img_bgr = cv2_imread(filepath)
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imagelib.normalize_channels(img_bgr, 3)
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if cfg.mode == 'raw-predict':
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h,w,c = self.predictor_input_shape
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img_bgr = np.zeros( (h,w,3), dtype=np.uint8)
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img_mask = np.zeros( (h,w,1), dtype=np.uint8)
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else:
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self.log_info (f'no faces found for {filepath.name}, copying without faces')
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img_bgr = cv2_imread(filepath)
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imagelib.normalize_channels(img_bgr, 3)
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h,w,c = img_bgr.shape
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img_mask = np.zeros( (h,w,1), dtype=img_bgr.dtype)
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cv2_imwrite (pf.output_filepath, img_bgr)
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h,w,c = img_bgr.shape
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img_mask = np.zeros( (h,w,1), dtype=img_bgr.dtype)
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cv2_imwrite (pf.output_mask_filepath, img_mask)
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if pf.need_return_image:
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@ -300,6 +305,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
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'3' : lambda cfg,shift_pressed: cfg.set_mode(3),
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'4' : lambda cfg,shift_pressed: cfg.set_mode(4),
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'5' : lambda cfg,shift_pressed: cfg.set_mode(5),
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'6' : lambda cfg,shift_pressed: cfg.set_mode(6),
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'q' : lambda cfg,shift_pressed: cfg.add_hist_match_threshold(1 if not shift_pressed else 5),
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'a' : lambda cfg,shift_pressed: cfg.add_hist_match_threshold(-1 if not shift_pressed else -5),
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'w' : lambda cfg,shift_pressed: cfg.add_erode_mask_modifier(1 if not shift_pressed else 5),
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@ -17,8 +17,6 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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img_size = img_bgr.shape[1], img_bgr.shape[0]
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img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
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if cfg.mode == 'original':
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return img_bgr, img_face_mask_a
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out_img = img_bgr.copy()
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out_merging_mask_a = None
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@ -45,17 +43,10 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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predictor_input_bgr = cv2.resize (dst_face_bgr, (input_size,input_size) )
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predicted = predictor_func (predictor_input_bgr)
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if isinstance(predicted, tuple):
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#merger return bgr,mask
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prd_face_bgr = np.clip (predicted[0], 0, 1.0)
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prd_face_mask_a_0 = np.clip (predicted[1], 0, 1.0)
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predictor_masked = True
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else:
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#merger return bgr only, using dst mask
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prd_face_bgr = np.clip (predicted, 0, 1.0 )
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (input_size,input_size) )
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predictor_masked = False
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predicted = predictor_func (predictor_input_bgr)
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prd_face_bgr = np.clip (predicted[0], 0, 1.0)
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prd_face_mask_a_0 = np.clip (predicted[1], 0, 1.0)
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prd_face_dst_mask_a_0 = np.clip (predicted[2], 0, 1.0)
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if cfg.super_resolution_power != 0:
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prd_face_bgr_enhanced = face_enhancer_func(prd_face_bgr, is_tanh=True, preserve_size=False)
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@ -64,89 +55,100 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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prd_face_bgr = np.clip(prd_face_bgr, 0, 1)
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if cfg.super_resolution_power != 0:
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if predictor_masked:
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prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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else:
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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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prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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prd_face_dst_mask_a_0 = cv2.resize (prd_face_dst_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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if cfg.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 cfg.mask_mode >= 3 and cfg.mask_mode <= 6: #XSeg modes
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if cfg.mask_mode == 3 or cfg.mask_mode == 5 or cfg.mask_mode == 6:
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if cfg.mask_mode == 1: #dst
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wrk_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
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elif cfg.mask_mode == 2: #learned-prd
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wrk_face_mask_a_0 = prd_face_mask_a_0
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elif cfg.mask_mode == 3: #learned-dst
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wrk_face_mask_a_0 = prd_face_dst_mask_a_0
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elif cfg.mask_mode == 4: #learned-prd*learned-dst
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wrk_face_mask_a_0 = prd_face_mask_a_0*prd_face_dst_mask_a_0
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elif cfg.mask_mode >= 5 and cfg.mask_mode <= 8: #XSeg modes
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if cfg.mask_mode == 5 or cfg.mask_mode == 7 or cfg.mask_mode == 8:
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# obtain XSeg-prd
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prd_face_xseg_bgr = cv2.resize (prd_face_bgr, (xseg_input_size,)*2, cv2.INTER_CUBIC)
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prd_face_xseg_mask = xseg_256_extract_func(prd_face_xseg_bgr)
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X_prd_face_mask_a_0 = cv2.resize ( prd_face_xseg_mask, (output_size, output_size), cv2.INTER_CUBIC)
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if cfg.mask_mode >= 4 and cfg.mask_mode <= 6:
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if cfg.mask_mode >= 6 and cfg.mask_mode <= 8:
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# obtain XSeg-dst
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xseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=cfg.face_type)
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dst_face_xseg_bgr = cv2.warpAffine(img_bgr, xseg_mat, (xseg_input_size,)*2, flags=cv2.INTER_CUBIC )
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dst_face_xseg_mask = xseg_256_extract_func(dst_face_xseg_bgr)
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X_dst_face_mask_a_0 = cv2.resize (dst_face_xseg_mask, (output_size,output_size), cv2.INTER_CUBIC)
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if cfg.mask_mode == 3: #'XSeg-prd',
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prd_face_mask_a_0 = X_prd_face_mask_a_0
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elif cfg.mask_mode == 4: #'XSeg-dst',
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prd_face_mask_a_0 = X_dst_face_mask_a_0
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elif cfg.mask_mode == 5: #'XSeg-prd*XSeg-dst',
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prd_face_mask_a_0 = X_prd_face_mask_a_0 * X_dst_face_mask_a_0
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elif cfg.mask_mode == 6: #learned*XSeg-prd*XSeg-dst'
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prd_face_mask_a_0 = prd_face_mask_a_0 * X_prd_face_mask_a_0 * X_dst_face_mask_a_0
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if cfg.mask_mode == 5: #'XSeg-prd'
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wrk_face_mask_a_0 = X_prd_face_mask_a_0
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elif cfg.mask_mode == 6: #'XSeg-dst'
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wrk_face_mask_a_0 = X_dst_face_mask_a_0
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elif cfg.mask_mode == 7: #'XSeg-prd*XSeg-dst'
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wrk_face_mask_a_0 = X_prd_face_mask_a_0 * X_dst_face_mask_a_0
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elif cfg.mask_mode == 8: #learned-prd*learned-dst*XSeg-prd*XSeg-dst
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wrk_face_mask_a_0 = prd_face_mask_a_0 * prd_face_dst_mask_a_0 * X_prd_face_mask_a_0 * X_dst_face_mask_a_0
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prd_face_mask_a_0[ prd_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise
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wrk_face_mask_a_0[ wrk_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise
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# resize to mask_subres_size
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if prd_face_mask_a_0.shape[0] != mask_subres_size:
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prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (mask_subres_size, mask_subres_size), cv2.INTER_CUBIC)
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if wrk_face_mask_a_0.shape[0] != mask_subres_size:
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wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (mask_subres_size, mask_subres_size), cv2.INTER_CUBIC)
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# process mask in local predicted space
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if 'raw' not in cfg.mode:
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# add zero pad
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prd_face_mask_a_0 = np.pad (prd_face_mask_a_0, input_size)
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wrk_face_mask_a_0 = np.pad (wrk_face_mask_a_0, input_size)
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ero = cfg.erode_mask_modifier
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blur = cfg.blur_mask_modifier
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if ero > 0:
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prd_face_mask_a_0 = cv2.erode(prd_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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wrk_face_mask_a_0 = cv2.erode(wrk_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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prd_face_mask_a_0 = cv2.dilate(prd_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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wrk_face_mask_a_0 = cv2.dilate(wrk_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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# clip eroded/dilated mask in actual predict area
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# pad with half blur size in order to accuratelly fade to zero at the boundary
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clip_size = input_size + blur // 2
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prd_face_mask_a_0[:clip_size,:] = 0
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prd_face_mask_a_0[-clip_size:,:] = 0
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prd_face_mask_a_0[:,:clip_size] = 0
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prd_face_mask_a_0[:,-clip_size:] = 0
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wrk_face_mask_a_0[:clip_size,:] = 0
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wrk_face_mask_a_0[-clip_size:,:] = 0
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wrk_face_mask_a_0[:,:clip_size] = 0
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wrk_face_mask_a_0[:,-clip_size:] = 0
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if blur > 0:
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blur = blur + (1-blur % 2)
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prd_face_mask_a_0 = cv2.GaussianBlur(prd_face_mask_a_0, (blur, blur) , 0)
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wrk_face_mask_a_0 = cv2.GaussianBlur(wrk_face_mask_a_0, (blur, blur) , 0)
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prd_face_mask_a_0 = prd_face_mask_a_0[input_size:-input_size,input_size:-input_size]
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wrk_face_mask_a_0 = wrk_face_mask_a_0[input_size:-input_size,input_size:-input_size]
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prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0, 1)
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wrk_face_mask_a_0 = np.clip(wrk_face_mask_a_0, 0, 1)
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img_face_mask_a = cv2.warpAffine( prd_face_mask_a_0, face_mask_output_mat, img_size, np.zeros(img_bgr.shape[0:2], dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )[...,None]
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img_face_mask_a = cv2.warpAffine( wrk_face_mask_a_0, face_mask_output_mat, img_size, np.zeros(img_bgr.shape[0:2], dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )[...,None]
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img_face_mask_a = np.clip (img_face_mask_a, 0.0, 1.0)
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img_face_mask_a [ img_face_mask_a < (1.0/255.0) ] = 0.0 # get rid of noise
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if prd_face_mask_a_0.shape[0] != output_size:
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prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
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if wrk_face_mask_a_0.shape[0] != output_size:
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wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
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prd_face_mask_a = prd_face_mask_a_0[...,None]
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prd_face_mask_area_a = prd_face_mask_a.copy()
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prd_face_mask_area_a[prd_face_mask_area_a>0] = 1.0
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wrk_face_mask_a = wrk_face_mask_a_0[...,None]
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wrk_face_mask_area_a = wrk_face_mask_a.copy()
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wrk_face_mask_area_a[wrk_face_mask_area_a>0] = 1.0
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if 'raw' in cfg.mode:
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if cfg.mode == 'original':
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return img_bgr, img_face_mask_a
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elif 'raw' in cfg.mode:
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if cfg.mode == 'raw-rgb':
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out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
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out_merging_mask_a = img_face_mask_a
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elif cfg.mode == 'raw-predict':
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out_img = prd_face_bgr
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out_merging_mask_a = wrk_face_mask_a
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out_img = np.clip (out_img, 0.0, 1.0 )
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else:
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#averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask
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@ -165,8 +167,8 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0:
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if cfg.color_transfer_mode == 1: #rct
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prd_face_bgr = imagelib.reinhard_color_transfer ( np.clip( prd_face_bgr*prd_face_mask_area_a*255, 0, 255).astype(np.uint8),
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np.clip( dst_face_bgr*prd_face_mask_area_a*255, 0, 255).astype(np.uint8), )
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prd_face_bgr = imagelib.reinhard_color_transfer ( np.clip( prd_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8),
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np.clip( dst_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8), )
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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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elif cfg.color_transfer_mode == 2: #lct
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@ -174,22 +176,22 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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elif cfg.color_transfer_mode == 3: #mkl
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prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 4: #mkl-m
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prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
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prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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elif cfg.color_transfer_mode == 5: #idt
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prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 6: #idt-m
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prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
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prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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elif cfg.color_transfer_mode == 7: #sot-m
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prd_face_bgr = imagelib.color_transfer_sot (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
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prd_face_bgr = imagelib.color_transfer_sot (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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prd_face_bgr = np.clip (prd_face_bgr, 0.0, 1.0)
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elif cfg.color_transfer_mode == 8: #mix-m
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prd_face_bgr = imagelib.color_transfer_mix (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
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prd_face_bgr = imagelib.color_transfer_mix (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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if cfg.mode == 'hist-match':
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hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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if cfg.masked_hist_match:
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hist_mask_a *= prd_face_mask_area_a
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hist_mask_a *= wrk_face_mask_area_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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@ -240,24 +242,24 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0:
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if cfg.color_transfer_mode == 1:
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out_face_bgr = imagelib.reinhard_color_transfer ( np.clip(out_face_bgr*prd_face_mask_area_a*255, 0, 255).astype(np.uint8),
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np.clip(dst_face_bgr*prd_face_mask_area_a*255, 0, 255).astype(np.uint8) )
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out_face_bgr = imagelib.reinhard_color_transfer ( np.clip(out_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8),
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np.clip(dst_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8) )
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out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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elif cfg.color_transfer_mode == 2: #lct
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out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 3: #mkl
|
||||
out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr, dst_face_bgr)
|
||||
elif cfg.color_transfer_mode == 4: #mkl-m
|
||||
out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
|
||||
out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
|
||||
elif cfg.color_transfer_mode == 5: #idt
|
||||
out_face_bgr = imagelib.color_transfer_idt (out_face_bgr, dst_face_bgr)
|
||||
elif cfg.color_transfer_mode == 6: #idt-m
|
||||
out_face_bgr = imagelib.color_transfer_idt (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
|
||||
out_face_bgr = imagelib.color_transfer_idt (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
|
||||
elif cfg.color_transfer_mode == 7: #sot-m
|
||||
out_face_bgr = imagelib.color_transfer_sot (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
|
||||
out_face_bgr = imagelib.color_transfer_sot (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
|
||||
out_face_bgr = np.clip (out_face_bgr, 0.0, 1.0)
|
||||
elif cfg.color_transfer_mode == 8: #mix-m
|
||||
out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
|
||||
out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
|
||||
|
||||
if cfg.mode == 'seamless-hist-match':
|
||||
out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, cfg.hist_match_threshold)
|
||||
|
|
|
|||
|
|
@ -76,21 +76,21 @@ mode_dict = {0:'original',
|
|||
2:'hist-match',
|
||||
3:'seamless',
|
||||
4:'seamless-hist-match',
|
||||
5:'raw-rgb',}
|
||||
5:'raw-rgb',
|
||||
6:'raw-predict'}
|
||||
|
||||
mode_str_dict = {}
|
||||
mode_str_dict = { mode_dict[key] : key for key in mode_dict.keys() }
|
||||
|
||||
for key in mode_dict.keys():
|
||||
mode_str_dict[ mode_dict[key] ] = key
|
||||
|
||||
mask_mode_dict = {1:'learned',
|
||||
2:'dst',
|
||||
3:'XSeg-prd',
|
||||
4:'XSeg-dst',
|
||||
5:'XSeg-prd*XSeg-dst',
|
||||
6:'learned*XSeg-prd*XSeg-dst'
|
||||
mask_mode_dict = {1:'dst',
|
||||
2:'learned-prd',
|
||||
3:'learned-dst',
|
||||
4:'learned-prd*learned-dst',
|
||||
5:'XSeg-prd',
|
||||
6:'XSeg-dst',
|
||||
7:'XSeg-prd*XSeg-dst',
|
||||
8:'learned-prd*learned-dst*XSeg-prd*XSeg-dst'
|
||||
}
|
||||
|
||||
|
||||
|
||||
ctm_dict = { 0: "None", 1:"rct", 2:"lct", 3:"mkl", 4:"mkl-m", 5:"idt", 6:"idt-m", 7:"sot-m", 8:"mix-m" }
|
||||
ctm_str_dict = {None:0, "rct":1, "lct":2, "mkl":3, "mkl-m":4, "idt":5, "idt-m":6, "sot-m":7, "mix-m":8 }
|
||||
|
|
@ -102,7 +102,7 @@ class MergerConfigMasked(MergerConfig):
|
|||
mode='overlay',
|
||||
masked_hist_match=True,
|
||||
hist_match_threshold = 238,
|
||||
mask_mode = 1,
|
||||
mask_mode = 4,
|
||||
erode_mask_modifier = 0,
|
||||
blur_mask_modifier = 0,
|
||||
motion_blur_power = 0,
|
||||
|
|
@ -118,7 +118,7 @@ class MergerConfigMasked(MergerConfig):
|
|||
super().__init__(type=MergerConfig.TYPE_MASKED, **kwargs)
|
||||
|
||||
self.face_type = face_type
|
||||
if self.face_type not in [FaceType.HALF, FaceType.MID_FULL, FaceType.FULL, FaceType.WHOLE_FACE ]:
|
||||
if self.face_type not in [FaceType.HALF, FaceType.MID_FULL, FaceType.FULL, FaceType.WHOLE_FACE, FaceType.HEAD ]:
|
||||
raise ValueError("MergerConfigMasked does not support this type of face.")
|
||||
|
||||
self.default_mode = default_mode
|
||||
|
|
@ -262,9 +262,9 @@ class MergerConfigMasked(MergerConfig):
|
|||
|
||||
if self.mode == 'hist-match' or self.mode == 'seamless-hist-match':
|
||||
r += f"""hist_match_threshold: {self.hist_match_threshold}\n"""
|
||||
|
||||
|
||||
r += f"""mask_mode: { mask_mode_dict[self.mask_mode] }\n"""
|
||||
|
||||
|
||||
if 'raw' not in self.mode:
|
||||
r += (f"""erode_mask_modifier: {self.erode_mask_modifier}\n"""
|
||||
f"""blur_mask_modifier: {self.blur_mask_modifier}\n"""
|
||||
|
|
@ -274,8 +274,8 @@ class MergerConfigMasked(MergerConfig):
|
|||
|
||||
if 'raw' not in self.mode:
|
||||
r += f"""color_transfer_mode: {ctm_dict[self.color_transfer_mode]}\n"""
|
||||
r += super().to_string(filename)
|
||||
|
||||
r += super().to_string(filename)
|
||||
r += f"""super_resolution_power: {self.super_resolution_power}\n"""
|
||||
|
||||
if 'raw' not in self.mode:
|
||||
|
|
|
|||
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Reference in a new issue