mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-14 02:37:00 -07:00
Merger:
increased speed improved quality
This commit is contained in:
Colombo
parent
3f813d5611
commit
123c015fdc
2 changed files with 86 additions and 80 deletions
|
|
@ -16,22 +16,30 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
return img_bgr, img_face_mask_a
|
||||
|
||||
out_img = img_bgr.copy()
|
||||
out_merging_mask = None
|
||||
out_merging_mask_a = None
|
||||
|
||||
output_size = predictor_input_shape[0]
|
||||
mask_subres = 4
|
||||
input_size = predictor_input_shape[0]
|
||||
mask_subres_size = input_size*4
|
||||
output_size = input_size
|
||||
if cfg.super_resolution_mode != 0:
|
||||
output_size *= 4
|
||||
|
||||
face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type)
|
||||
face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type)
|
||||
face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale )
|
||||
|
||||
if mask_subres_size == output_size:
|
||||
face_mask_output_mat = face_output_mat
|
||||
else:
|
||||
face_mask_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, mask_subres_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale )
|
||||
|
||||
dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
|
||||
dst_face_bgr = np.clip(dst_face_bgr, 0, 1)
|
||||
|
||||
dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
|
||||
dst_face_mask_a_0 = np.clip(dst_face_mask_a_0, 0, 1)
|
||||
|
||||
predictor_input_bgr = cv2.resize (dst_face_bgr, predictor_input_shape[0:2] )
|
||||
predictor_input_bgr = cv2.resize (dst_face_bgr, (input_size,input_size) )
|
||||
|
||||
predicted = predictor_func (predictor_input_bgr)
|
||||
if isinstance(predicted, tuple):
|
||||
|
|
@ -42,7 +50,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
else:
|
||||
#merger return bgr only, using dst mask
|
||||
prd_face_bgr = np.clip (predicted, 0, 1.0 )
|
||||
prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, predictor_input_shape[0:2] )
|
||||
prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (input_size,input_size) )
|
||||
predictor_masked = False
|
||||
|
||||
if cfg.super_resolution_mode != 0:
|
||||
|
|
@ -91,29 +99,65 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0
|
||||
elif cfg.mask_mode == 7:
|
||||
prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_dst_face_mask_a_0
|
||||
#elif cfg.mask_mode == 8: #FANCHQ-dst
|
||||
# prd_face_mask_a_0 = FANCHQ_dst_face_mask_a_0
|
||||
|
||||
prd_face_mask_a_0[ prd_face_mask_a_0 < 0.001 ] = 0.0
|
||||
|
||||
prd_face_mask_a = prd_face_mask_a_0[...,np.newaxis]
|
||||
prd_face_mask_aaa = np.repeat (prd_face_mask_a, (3,), axis=-1)
|
||||
|
||||
img_face_mask_aaa = cv2.warpAffine( prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )
|
||||
img_face_mask_aaa = np.clip (img_face_mask_aaa, 0.0, 1.0)
|
||||
img_face_mask_aaa [ img_face_mask_aaa <= 0.1 ] = 0.0 #get rid of noise
|
||||
# process mask in local predicted space
|
||||
if 'raw' not in cfg.mode:
|
||||
# resize to mask_subres_size
|
||||
if prd_face_mask_a_0.shape[0] != mask_subres_size:
|
||||
prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (mask_subres_size, mask_subres_size), cv2.INTER_CUBIC)
|
||||
|
||||
# add zero pad
|
||||
prd_face_mask_a_0 = np.pad (prd_face_mask_a_0, input_size)
|
||||
|
||||
ero = cfg.erode_mask_modifier
|
||||
blur = cfg.blur_mask_modifier
|
||||
|
||||
if ero > 0:
|
||||
prd_face_mask_a_0 = cv2.erode(prd_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
|
||||
elif ero < 0:
|
||||
prd_face_mask_a_0 = cv2.dilate(prd_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
|
||||
|
||||
# clip eroded/dilated mask in actual predict area
|
||||
# pad with half blur size in order to accuratelly fade to zero at the boundary
|
||||
clip_size = input_size + blur // 2
|
||||
|
||||
prd_face_mask_a_0[:clip_size,:] = 0
|
||||
prd_face_mask_a_0[-clip_size:,:] = 0
|
||||
prd_face_mask_a_0[:,:clip_size] = 0
|
||||
prd_face_mask_a_0[:,-clip_size:] = 0
|
||||
|
||||
if blur > 0:
|
||||
blur = blur + (1-blur % 2)
|
||||
prd_face_mask_a_0 = cv2.GaussianBlur(prd_face_mask_a_0, (blur, blur) , 0)
|
||||
|
||||
prd_face_mask_a_0 = prd_face_mask_a_0[input_size:-input_size,input_size:-input_size]
|
||||
prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0, 1)
|
||||
|
||||
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]
|
||||
img_face_mask_a = np.clip (img_face_mask_a, 0.0, 1.0)
|
||||
img_face_mask_a [ img_face_mask_a <= 0.1 ] = 0.0 #get rid of noise
|
||||
|
||||
if prd_face_mask_a_0.shape[0] != output_size:
|
||||
prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
|
||||
|
||||
prd_face_mask_a = prd_face_mask_a_0[...,None]
|
||||
prd_face_mask_area_a = prd_face_mask_a.copy()
|
||||
prd_face_mask_area_a[prd_face_mask_area_a>0] = 1.0
|
||||
|
||||
if 'raw' in cfg.mode:
|
||||
if cfg.mode == 'raw-rgb':
|
||||
out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
|
||||
out_merging_mask = img_face_mask_aaa
|
||||
|
||||
out_merging_mask_a = img_face_mask_a
|
||||
|
||||
out_img = np.clip (out_img, 0.0, 1.0 )
|
||||
else:
|
||||
#averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask
|
||||
ar = []
|
||||
for i in range(1, 10):
|
||||
maxregion = np.argwhere( img_face_mask_aaa > i / 10.0 )
|
||||
maxregion = np.argwhere( img_face_mask_a > i / 10.0 )
|
||||
if maxregion.size != 0:
|
||||
miny,minx = maxregion.min(axis=0)[:2]
|
||||
maxy,maxx = maxregion.max(axis=0)[:2]
|
||||
|
|
@ -123,67 +167,34 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
ar += [ [ lenx, leny] ]
|
||||
|
||||
if len(ar) > 0:
|
||||
lenx, leny = np.mean ( ar, axis=0 )
|
||||
lowest_len = min (lenx, leny)
|
||||
|
||||
if cfg.erode_mask_modifier != 0:
|
||||
ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*cfg.erode_mask_modifier )
|
||||
if ero > 0:
|
||||
img_face_mask_aaa = cv2.erode(img_face_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
|
||||
elif ero < 0:
|
||||
img_face_mask_aaa = cv2.dilate(img_face_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
|
||||
|
||||
if cfg.clip_hborder_mask_per > 0: #clip hborder before blur
|
||||
prd_hborder_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=np.float32)
|
||||
prd_border_size = int ( prd_hborder_rect_mask_a.shape[1] * cfg.clip_hborder_mask_per )
|
||||
prd_hborder_rect_mask_a[:,0:prd_border_size,:] = 0
|
||||
prd_hborder_rect_mask_a[:,-prd_border_size:,:] = 0
|
||||
prd_hborder_rect_mask_a[-prd_border_size:,:,:] = 0
|
||||
prd_hborder_rect_mask_a = np.expand_dims(cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
|
||||
|
||||
img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )
|
||||
img_prd_hborder_rect_mask_a = np.expand_dims (img_prd_hborder_rect_mask_a, -1)
|
||||
img_face_mask_aaa *= img_prd_hborder_rect_mask_a
|
||||
img_face_mask_aaa = np.clip( img_face_mask_aaa, 0, 1.0 )
|
||||
|
||||
if cfg.blur_mask_modifier > 0:
|
||||
blur = int( lowest_len * 0.10 * 0.01*cfg.blur_mask_modifier )
|
||||
if blur > 0:
|
||||
img_face_mask_aaa = cv2.blur(img_face_mask_aaa, (blur, blur) )
|
||||
|
||||
img_face_mask_aaa = np.clip( img_face_mask_aaa, 0, 1.0 )
|
||||
|
||||
if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0:
|
||||
if cfg.color_transfer_mode == 1: #rct
|
||||
prd_face_bgr = imagelib.reinhard_color_transfer ( np.clip( prd_face_bgr*255, 0, 255).astype(np.uint8),
|
||||
np.clip( dst_face_bgr*255, 0, 255).astype(np.uint8),
|
||||
source_mask=prd_face_mask_a, target_mask=prd_face_mask_a)
|
||||
source_mask=prd_face_mask_area_a, target_mask=prd_face_mask_area_a)
|
||||
prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
elif cfg.color_transfer_mode == 2: #lct
|
||||
prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr)
|
||||
elif cfg.color_transfer_mode == 3: #mkl
|
||||
prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr, dst_face_bgr)
|
||||
elif cfg.color_transfer_mode == 4: #mkl-m
|
||||
prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a)
|
||||
prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
|
||||
elif cfg.color_transfer_mode == 5: #idt
|
||||
prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr, dst_face_bgr)
|
||||
elif cfg.color_transfer_mode == 6: #idt-m
|
||||
prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a)
|
||||
prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
|
||||
elif cfg.color_transfer_mode == 7: #sot-m
|
||||
prd_face_bgr = imagelib.color_transfer_sot (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a)
|
||||
prd_face_bgr = imagelib.color_transfer_sot (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
|
||||
prd_face_bgr = np.clip (prd_face_bgr, 0.0, 1.0)
|
||||
elif cfg.color_transfer_mode == 8: #mix-m
|
||||
prd_face_bgr = imagelib.color_transfer_mix (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a)
|
||||
prd_face_bgr = imagelib.color_transfer_mix (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a)
|
||||
|
||||
if cfg.mode == 'hist-match-bw':
|
||||
prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY)
|
||||
prd_face_bgr = np.repeat( np.expand_dims (prd_face_bgr, -1), (3,), -1 )
|
||||
|
||||
if cfg.mode == 'hist-match' or cfg.mode == 'hist-match-bw':
|
||||
if cfg.mode == 'hist-match':
|
||||
hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
|
||||
|
||||
if cfg.masked_hist_match:
|
||||
hist_mask_a *= prd_face_mask_a
|
||||
hist_mask_a *= prd_face_mask_area_a
|
||||
|
||||
white = (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
|
||||
|
||||
|
|
@ -195,13 +206,8 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
|
||||
prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, cfg.hist_match_threshold ).astype(dtype=np.float32)
|
||||
|
||||
if cfg.mode == 'hist-match-bw':
|
||||
prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
|
||||
|
||||
if 'seamless' in cfg.mode:
|
||||
#mask used for cv2.seamlessClone
|
||||
img_face_mask_a = img_face_mask_aaa[...,0:1]
|
||||
|
||||
img_face_seamless_mask_a = None
|
||||
for i in range(1,10):
|
||||
a = img_face_mask_a > i / 10.0
|
||||
|
|
@ -233,33 +239,33 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
print ("Seamless fail: " + e_str)
|
||||
|
||||
|
||||
out_img = img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa)
|
||||
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) )
|
||||
|
||||
if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0:
|
||||
if cfg.color_transfer_mode == 1:
|
||||
face_mask_aaa = cv2.warpAffine( img_face_mask_aaa, face_mat, (output_size, output_size) )
|
||||
face_mask_a = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size) )[...,None]
|
||||
|
||||
out_face_bgr = imagelib.reinhard_color_transfer ( (out_face_bgr*255).astype(np.uint8),
|
||||
(dst_face_bgr*255).astype(np.uint8),
|
||||
source_mask=face_mask_aaa, target_mask=face_mask_aaa)
|
||||
source_mask=face_mask_a, target_mask=face_mask_a)
|
||||
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*prd_face_mask_a, dst_face_bgr*prd_face_mask_a)
|
||||
out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_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_a, dst_face_bgr*prd_face_mask_a)
|
||||
out_face_bgr = imagelib.color_transfer_idt (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_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_a, dst_face_bgr*prd_face_mask_a)
|
||||
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 = 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_a, dst_face_bgr*prd_face_mask_a)
|
||||
out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_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)
|
||||
|
|
@ -294,7 +300,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
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_aaa) + (new_out*img_face_mask_aaa) , 0, 1.0 )
|
||||
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)
|
||||
|
|
@ -304,9 +310,9 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
alpha = cfg.color_degrade_power / 100.0
|
||||
out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
|
||||
|
||||
out_merging_mask = img_face_mask_aaa
|
||||
out_merging_mask_a = img_face_mask_a
|
||||
|
||||
return out_img, out_merging_mask[...,0:1]
|
||||
return out_img, out_merging_mask_a
|
||||
|
||||
|
||||
def MergeMasked (predictor_func, predictor_input_shape, cfg, frame_info):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue