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New script:
5.XSeg) data_dst/src mask for XSeg trainer - fetch.bat Copies faces containing XSeg polygons to aligned_xseg\ dir. Useful only if you want to collect labeled faces and reuse them in other fakes. Now you can use trained XSeg mask in the SAEHD training process. It’s mean default ‘full_face’ mask obtained from landmarks will be replaced with the mask obtained from the trained XSeg model. use 5.XSeg.optional) trained mask for data_dst/data_src - apply.bat 5.XSeg.optional) trained mask for data_dst/data_src - remove.bat Normally you don’t need it. You can use it, if you want to use ‘face_style’ and ‘bg_style’ with obstructions. XSeg trainer : now you can choose type of face XSeg trainer : now you can restart training in “override settings” Merger: XSeg-* modes now can be used with all types of faces. Therefore old MaskEditor, FANSEG models, and FAN-x modes have been removed, because the new XSeg solution is better, simpler and more convenient, which costs only 1 hour of manual masking for regular deepfake.
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Colombo
parent
e5bad483ca
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30 changed files with 279 additions and 1520 deletions
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@ -8,12 +8,10 @@ from facelib import FaceType, LandmarksProcessor
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from core.interact import interact as io
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from core.cv2ex import *
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fanseg_input_size = 256
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xseg_input_size = 256
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def MergeMaskedFace (predictor_func, predictor_input_shape,
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face_enhancer_func,
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fanseg_full_face_256_extract_func,
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xseg_256_extract_func,
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cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks):
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img_size = img_bgr.shape[1], img_bgr.shape[0]
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@ -73,61 +71,27 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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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 <= 7:
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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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prd_face_fanseg_bgr = cv2.resize (prd_face_bgr, (fanseg_input_size,)*2 )
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prd_face_fanseg_mask = fanseg_full_face_256_extract_func(prd_face_fanseg_bgr)
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FAN_prd_face_mask_a_0 = cv2.resize ( prd_face_fanseg_mask, (output_size, output_size), cv2.INTER_CUBIC)
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if cfg.mask_mode >= 4 and cfg.mask_mode <= 7:
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full_face_fanseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, fanseg_input_size, face_type=FaceType.FULL)
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dst_face_fanseg_bgr = cv2.warpAffine(img_bgr, full_face_fanseg_mat, (fanseg_input_size,)*2, flags=cv2.INTER_CUBIC )
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dst_face_fanseg_mask = fanseg_full_face_256_extract_func(dst_face_fanseg_bgr )
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if cfg.face_type == FaceType.FULL:
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FAN_dst_face_mask_a_0 = cv2.resize (dst_face_fanseg_mask, (output_size,output_size), cv2.INTER_CUBIC)
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else:
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face_fanseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, fanseg_input_size, face_type=cfg.face_type)
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fanseg_rect_corner_pts = np.array ( [ [0,0], [fanseg_input_size-1,0], [0,fanseg_input_size-1] ], dtype=np.float32 )
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a = LandmarksProcessor.transform_points (fanseg_rect_corner_pts, face_fanseg_mat, invert=True )
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b = LandmarksProcessor.transform_points (a, full_face_fanseg_mat )
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m = cv2.getAffineTransform(b, fanseg_rect_corner_pts)
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FAN_dst_face_mask_a_0 = cv2.warpAffine(dst_face_fanseg_mask, m, (fanseg_input_size,)*2, flags=cv2.INTER_CUBIC )
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FAN_dst_face_mask_a_0 = cv2.resize (FAN_dst_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
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if cfg.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 cfg.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 cfg.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 cfg.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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elif cfg.mask_mode == 7:
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prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_dst_face_mask_a_0
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elif cfg.mask_mode >= 8 and cfg.mask_mode <= 11:
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if cfg.mask_mode == 8 or cfg.mask_mode == 10 or cfg.mask_mode == 11:
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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 >= 9 and cfg.mask_mode <= 11:
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whole_face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=FaceType.WHOLE_FACE)
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dst_face_xseg_bgr = cv2.warpAffine(img_bgr, whole_face_mat, (xseg_input_size,)*2, flags=cv2.INTER_CUBIC )
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if cfg.mask_mode >= 4 and cfg.mask_mode <= 6:
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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 == 8: #'XSeg-prd',
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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 == 9: #'XSeg-dst',
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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 == 10: #'XSeg-prd*XSeg-dst',
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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 == 11: #learned*XSeg-prd*XSeg-dst'
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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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prd_face_mask_a_0[ prd_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise
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@ -346,7 +310,6 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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def MergeMasked (predictor_func,
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predictor_input_shape,
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face_enhancer_func,
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fanseg_full_face_256_extract_func,
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xseg_256_extract_func,
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cfg,
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frame_info):
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@ -356,7 +319,7 @@ def MergeMasked (predictor_func,
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outs = []
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for face_num, img_landmarks in enumerate( frame_info.landmarks_list ):
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out_img, out_img_merging_mask = MergeMaskedFace (predictor_func, predictor_input_shape, face_enhancer_func, fanseg_full_face_256_extract_func, xseg_256_extract_func, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks)
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out_img, out_img_merging_mask = MergeMaskedFace (predictor_func, predictor_input_shape, face_enhancer_func, xseg_256_extract_func, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks)
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outs += [ (out_img, out_img_merging_mask) ]
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#Combining multiple face outputs
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