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Merger: optimizations
This commit is contained in:
Colombo
parent
e549624eeb
commit
cd8919d95b
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@ -10,17 +10,14 @@ from core.cv2ex import *
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xseg_input_size = 256
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def MergeMaskedFace (predictor_func, predictor_input_shape,
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def MergeMaskedFace (predictor_func, predictor_input_shape,
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face_enhancer_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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img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
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out_img = img_bgr.copy()
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out_merging_mask_a = None
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input_size = predictor_input_shape[0]
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mask_subres_size = input_size*4
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output_size = input_size
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@ -43,7 +40,7 @@ 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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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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@ -68,14 +65,14 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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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: #learned-prd+learned-dst
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wrk_face_mask_a_0 = np.clip( prd_face_mask_a_0+prd_face_dst_mask_a_0, 0, 1)
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elif cfg.mask_mode >= 6 and cfg.mask_mode <= 9: #XSeg modes
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elif cfg.mask_mode >= 6 and cfg.mask_mode <= 9: #XSeg modes
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if cfg.mask_mode == 6 or cfg.mask_mode == 8 or cfg.mask_mode == 9:
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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 >= 7 and cfg.mask_mode <= 9:
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if cfg.mask_mode >= 7 and cfg.mask_mode <= 9:
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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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@ -90,7 +87,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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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 == 9: #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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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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@ -129,193 +126,196 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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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 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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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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out_merging_mask_a = None
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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_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), 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 = prd_face_bgr
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out_merging_mask_a = wrk_face_mask_a
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else:
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raise ValueError(f"undefined raw type {cfg.mode}")
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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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ar = []
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for i in range(1, 10):
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maxregion = np.argwhere( img_face_mask_a > i / 10.0 )
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if maxregion.size != 0:
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miny,minx = maxregion.min(axis=0)[:2]
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maxy,maxx = maxregion.max(axis=0)[:2]
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lenx = maxx - minx
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leny = maxy - miny
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if min(lenx,leny) >= 4:
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ar += [ [ lenx, leny] ]
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# Process if the mask meets minimum size
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maxregion = np.argwhere( img_face_mask_a >= 0.1 )
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if maxregion.size != 0:
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miny,minx = maxregion.min(axis=0)[:2]
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maxy,maxx = maxregion.max(axis=0)[:2]
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lenx = maxx - minx
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leny = maxy - miny
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if min(lenx,leny) >= 4:
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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 '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*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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if len(ar) > 0:
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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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prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr)
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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*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*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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a, steps=10, batch_size=30)
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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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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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*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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prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr)
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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*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*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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a, steps=10, batch_size=30)
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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*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.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 *= wrk_face_mask_area_a
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if cfg.masked_hist_match:
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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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white = (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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hist_match_1 = prd_face_bgr*hist_mask_a + white
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hist_match_1[ hist_match_1 > 1.0 ] = 1.0
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hist_match_1 = prd_face_bgr*hist_mask_a + white
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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 + white
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hist_match_2[ hist_match_1 > 1.0 ] = 1.0
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hist_match_2 = dst_face_bgr*hist_mask_a + white
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hist_match_2[ hist_match_1 > 1.0 ] = 1.0
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prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, cfg.hist_match_threshold ).astype(dtype=np.float32)
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prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, cfg.hist_match_threshold ).astype(dtype=np.float32)
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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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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_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 '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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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_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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out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.empty_like(img_bgr), 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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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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if 'seamless' in cfg.mode:
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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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#seamlessClone may fail in some cases
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e_str = traceback.format_exc()
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out_img = np.clip(out_img, 0.0, 1.0)
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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 'seamless' in cfg.mode:
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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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#seamlessClone may fail in some cases
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e_str = traceback.format_exc()
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cfg_mp = cfg.motion_blur_power / 100.0
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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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out_img = img_bgr*(1-img_face_mask_a) + (out_img*img_face_mask_a)
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if ('seamless' in cfg.mode and cfg.color_transfer_mode != 0) or \
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cfg.mode == 'seamless-hist-match' or \
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cfg_mp != 0 or \
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cfg.blursharpen_amount != 0 or \
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cfg.image_denoise_power != 0 or \
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cfg.bicubic_degrade_power != 0:
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
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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*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*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*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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a, steps=10, batch_size=30)
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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*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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if cfg_mp != 0:
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k_size = int(frame_info.motion_power*cfg_mp)
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if k_size >= 1:
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k_size = np.clip (k_size+1, 2, 50)
|
||||
if cfg.super_resolution_power != 0:
|
||||
k_size *= 2
|
||||
out_face_bgr = imagelib.LinearMotionBlur (out_face_bgr, k_size , frame_info.motion_deg)
|
||||
|
||||
if cfg.blursharpen_amount != 0:
|
||||
out_face_bgr = imagelib.blursharpen ( out_face_bgr, cfg.sharpen_mode, 3, cfg.blursharpen_amount)
|
||||
|
||||
if cfg.image_denoise_power != 0:
|
||||
n = cfg.image_denoise_power
|
||||
while n > 0:
|
||||
img_bgr_denoised = cv2.medianBlur(img_bgr, 5)
|
||||
if int(n / 100) != 0:
|
||||
img_bgr = img_bgr_denoised
|
||||
else:
|
||||
pass_power = (n % 100) / 100.0
|
||||
img_bgr = img_bgr*(1.0-pass_power)+img_bgr_denoised*pass_power
|
||||
n = max(n-10,0)
|
||||
|
||||
if cfg.bicubic_degrade_power != 0:
|
||||
p = 1.0 - cfg.bicubic_degrade_power / 101.0
|
||||
img_bgr_downscaled = cv2.resize (img_bgr, ( int(img_size[0]*p), int(img_size[1]*p ) ), cv2.INTER_CUBIC)
|
||||
img_bgr = cv2.resize (img_bgr_downscaled, img_size, cv2.INTER_CUBIC)
|
||||
|
||||
new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
|
||||
|
||||
out_img = np.clip( img_bgr*(1-img_face_mask_a) + (new_out*img_face_mask_a) , 0, 1.0 )
|
||||
|
||||
if cfg.color_degrade_power != 0:
|
||||
out_img_reduced = imagelib.reduce_colors(out_img, 256)
|
||||
if cfg.color_degrade_power == 100:
|
||||
out_img = out_img_reduced
|
||||
else:
|
||||
print ("Seamless fail: " + e_str)
|
||||
|
||||
|
||||
out_img = img_bgr*(1-img_face_mask_a) + (out_img*img_face_mask_a)
|
||||
|
||||
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
|
||||
|
||||
if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0:
|
||||
if cfg.color_transfer_mode == 1:
|
||||
out_face_bgr = imagelib.reinhard_color_transfer ( np.clip(out_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8),
|
||||
np.clip(dst_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8) )
|
||||
out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
elif cfg.color_transfer_mode == 2: #lct
|
||||
out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
|
||||
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*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*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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a, steps=10, batch_size=30)
|
||||
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*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)
|
||||
|
||||
cfg_mp = cfg.motion_blur_power / 100.0
|
||||
if cfg_mp != 0:
|
||||
k_size = int(frame_info.motion_power*cfg_mp)
|
||||
if k_size >= 1:
|
||||
k_size = np.clip (k_size+1, 2, 50)
|
||||
if cfg.super_resolution_power != 0:
|
||||
k_size *= 2
|
||||
out_face_bgr = imagelib.LinearMotionBlur (out_face_bgr, k_size , frame_info.motion_deg)
|
||||
|
||||
if cfg.blursharpen_amount != 0:
|
||||
out_face_bgr = imagelib.blursharpen ( out_face_bgr, cfg.sharpen_mode, 3, cfg.blursharpen_amount)
|
||||
|
||||
|
||||
if cfg.image_denoise_power != 0:
|
||||
n = cfg.image_denoise_power
|
||||
while n > 0:
|
||||
img_bgr_denoised = cv2.medianBlur(img_bgr, 5)
|
||||
if int(n / 100) != 0:
|
||||
img_bgr = img_bgr_denoised
|
||||
else:
|
||||
pass_power = (n % 100) / 100.0
|
||||
img_bgr = img_bgr*(1.0-pass_power)+img_bgr_denoised*pass_power
|
||||
n = max(n-10,0)
|
||||
|
||||
if cfg.bicubic_degrade_power != 0:
|
||||
p = 1.0 - cfg.bicubic_degrade_power / 101.0
|
||||
img_bgr_downscaled = cv2.resize (img_bgr, ( int(img_size[0]*p), int(img_size[1]*p ) ), cv2.INTER_CUBIC)
|
||||
img_bgr = cv2.resize (img_bgr_downscaled, img_size, cv2.INTER_CUBIC)
|
||||
|
||||
new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
|
||||
out_img = np.clip( img_bgr*(1-img_face_mask_a) + (new_out*img_face_mask_a) , 0, 1.0 )
|
||||
|
||||
if cfg.color_degrade_power != 0:
|
||||
out_img_reduced = imagelib.reduce_colors(out_img, 256)
|
||||
if cfg.color_degrade_power == 100:
|
||||
out_img = out_img_reduced
|
||||
else:
|
||||
alpha = cfg.color_degrade_power / 100.0
|
||||
out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
|
||||
alpha = cfg.color_degrade_power / 100.0
|
||||
out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
|
||||
|
||||
out_merging_mask_a = img_face_mask_a
|
||||
|
||||
return out_img, out_merging_mask_a
|
||||
|
||||
|
||||
def MergeMasked (predictor_func,
|
||||
def MergeMasked (predictor_func,
|
||||
predictor_input_shape,
|
||||
face_enhancer_func,
|
||||
xseg_256_extract_func,
|
||||
cfg,
|
||||
xseg_256_extract_func,
|
||||
cfg,
|
||||
frame_info):
|
||||
img_bgr_uint8 = cv2_imread(frame_info.filepath)
|
||||
img_bgr_uint8 = imagelib.normalize_channels (img_bgr_uint8, 3)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user