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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 05:22:06 -07:00
Converter:
Session is now saved to the model folder. blur and erode ranges are increased to -400+400 hist-match-bw is now replaced with seamless2 mode. Added 'ebs' color transfer mode (works only on Windows). FANSEG model (used in FAN-x mask modes) is retrained with new model configuration and now produces better precision and less jitter
This commit is contained in:
Colombo
parent
70dada42ea
commit
7ed38a8097
29 changed files with 768 additions and 314 deletions
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@ -9,7 +9,7 @@ from interact import interact as io
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from utils.cv2_utils import *
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def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks):
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def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks):
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#if debug:
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# debugs = [img_bgr.copy()]
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@ -26,7 +26,7 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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out_img = img_bgr.copy()
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out_merging_mask = None
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output_size = cfg.predictor_input_shape[0]
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output_size = predictor_input_shape[0]
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if cfg.super_resolution_mode != 0:
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output_size *= 2
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@ -36,17 +36,19 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
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dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
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predictor_input_bgr = cv2.resize (dst_face_bgr, cfg.predictor_input_shape[0:2] )
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predictor_input_bgr = cv2.resize (dst_face_bgr, predictor_input_shape[0:2] )
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if cfg.predictor_masked:
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prd_face_bgr, prd_face_mask_a_0 = cfg.predictor_func (predictor_input_bgr)
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prd_face_bgr = np.clip (prd_face_bgr, 0, 1.0 )
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prd_face_mask_a_0 = np.clip (prd_face_mask_a_0, 0.0, 1.0)
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predicted = predictor_func (predictor_input_bgr)
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if isinstance(predicted, tuple):
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#converter 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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predicted = cfg.predictor_func (predictor_input_bgr)
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#converter 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, cfg.predictor_input_shape[0:2] )
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, predictor_input_shape[0:2] )
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predictor_masked = False
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if cfg.super_resolution_mode:
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#if debug:
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@ -57,7 +59,7 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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#if debug:
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# debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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if cfg.predictor_masked:
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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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@ -198,7 +200,7 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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# debugs += [img_face_mask_aaa.copy()]
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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:
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if cfg.color_transfer_mode == 1: #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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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@ -211,8 +213,8 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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elif cfg.color_transfer_mode == 2:
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#if debug:
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elif cfg.color_transfer_mode == 2: #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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr)
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@ -220,7 +222,14 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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elif cfg.color_transfer_mode == 3: #ebs
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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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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prd_face_bgr = cfg.ebs_ct_func ( np.clip( (dst_face_bgr*255), 0, 255).astype(np.uint8),
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np.clip( (prd_face_bgr*255), 0, 255).astype(np.uint8), )#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 cfg.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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@ -249,34 +258,39 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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if cfg.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_CUBIC, 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 cfg.mode == 'overlay':
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pass
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if 'seamless' in cfg.mode:
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#mask used for cv2.seamlessClone
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img_face_seamless_mask_a = None
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#mask used for cv2.seamlessClone
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img_face_mask_a = img_face_mask_aaa[...,0:1]
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if cfg.mode == 'seamless2':
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img_face_mask_a = cv2.warpAffine( img_face_mask_a, face_output_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
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img_face_seamless_mask_a = None
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for i in range(1,10):
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a = img_face_mask_a > i / 10.0
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if len(np.argwhere(a)) == 0:
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continue
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img_face_seamless_mask_a = img_face_mask_aaa[...,0:1].copy()
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img_face_seamless_mask_a = img_face_mask_a.copy()
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img_face_seamless_mask_a[a] = 1.0
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img_face_seamless_mask_a[img_face_seamless_mask_a <= i / 10.0] = 0.0
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break
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if cfg.mode == 'seamless2':
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face_seamless = imagelib.seamless_clone ( prd_face_bgr, dst_face_bgr, img_face_seamless_mask_a )
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out_img = cv2.warpAffine( face_seamless, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
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else:
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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_img = np.clip(out_img, 0.0, 1.0)
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if 'seamless' in cfg.mode and cfg.mode != 'seamless2':
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try:
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#calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering (not flickering)
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l,t,w,h = cv2.boundingRect( (img_face_seamless_mask_a*255).astype(np.uint8) )
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s_maskx, s_masky = int(l+w/2), int(t+h/2)
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out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), img_bgr_uint8, (img_face_seamless_mask_a*255).astype(np.uint8), (s_maskx,s_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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@ -301,8 +315,8 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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face_mask_aaa = cv2.warpAffine( img_face_mask_aaa, face_mat, (output_size, output_size) )
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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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out_face_bgr = imagelib.reinhard_color_transfer ( np.clip( (out_face_bgr*255), 0, 255).astype(np.uint8),
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np.clip( (dst_face_bgr*255), 0, 255).astype(np.uint8),
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source_mask=face_mask_aaa, target_mask=face_mask_aaa)
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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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@ -318,7 +332,15 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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elif cfg.color_transfer_mode == 3: #ebs
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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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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out_face_bgr = cfg.ebs_ct_func ( np.clip( (dst_face_bgr*255), 0, 255).astype(np.uint8),
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np.clip( (out_face_bgr*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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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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@ -359,14 +381,14 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
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return out_img, out_merging_mask
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def ConvertMasked (cfg, frame_info):
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def ConvertMasked (predictor_func, predictor_input_shape, cfg, frame_info):
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img_bgr_uint8 = cv2_imread(frame_info.filename)
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img_bgr_uint8 = imagelib.normalize_channels (img_bgr_uint8, 3)
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img_bgr = img_bgr_uint8.astype(np.float32) / 255.0
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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 = ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks)
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out_img, out_img_merging_mask = ConvertMaskedFace (predictor_func, predictor_input_shape, 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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