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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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@ -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
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out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 4: #mkl-m
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out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
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out_face_bgr = imagelib.color_transfer_mkl (out_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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out_face_bgr = imagelib.color_transfer_idt (out_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 6: #idt-m
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out_face_bgr = imagelib.color_transfer_idt (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
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out_face_bgr = imagelib.color_transfer_idt (out_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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out_face_bgr = imagelib.color_transfer_sot (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
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out_face_bgr = imagelib.color_transfer_sot (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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out_face_bgr = np.clip (out_face_bgr, 0.0, 1.0)
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elif cfg.color_transfer_mode == 8: #mix-m
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out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
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out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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if cfg.mode == 'seamless-hist-match':
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out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, cfg.hist_match_threshold)
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Reference in a new issue