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Merger: optimizations

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This commit is contained in:
Colombo 2020-04-14 10:25:54 +04:00
parent e549624eeb
commit cd8919d95b
1 changed files with 159 additions and 159 deletions
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42
merger/MergeMasked.py
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@ -14,13 +14,10 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
face_enhancer_func,
xseg_256_extract_func,
cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks):
img_size = img_bgr.shape[1], img_bgr.shape[0]
img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
out_img = img_bgr.copy()
out_merging_mask_a = None
input_size = predictor_input_shape[0]
mask_subres_size = input_size*4
output_size = input_size
@ -129,43 +126,40 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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]
img_face_mask_a = np.clip (img_face_mask_a, 0.0, 1.0)
img_face_mask_a [ img_face_mask_a < (1.0/255.0) ] = 0.0 # get rid of noise
if wrk_face_mask_a_0.shape[0] != output_size:
wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
wrk_face_mask_a = wrk_face_mask_a_0[...,None]
wrk_face_mask_area_a = wrk_face_mask_a.copy()
wrk_face_mask_area_a[wrk_face_mask_area_a>0] = 1.0
out_merging_mask_a = None
if cfg.mode == 'original':
return img_bgr, img_face_mask_a
elif '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_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
out_merging_mask_a = img_face_mask_a
elif cfg.mode == 'raw-predict':
out_img = prd_face_bgr
out_merging_mask_a = wrk_face_mask_a
else:
raise ValueError(f"undefined raw type {cfg.mode}")
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_a > i / 10.0 )
# Process if the mask meets minimum size
maxregion = np.argwhere( img_face_mask_a >= 0.1 )
if maxregion.size != 0:
miny,minx = maxregion.min(axis=0)[:2]
maxy,maxx = maxregion.max(axis=0)[:2]
lenx = maxx - minx
leny = maxy - miny
if min(lenx,leny) >= 4:
ar += [ [ lenx, leny] ]
if len(ar) > 0:
wrk_face_mask_area_a = wrk_face_mask_a.copy()
wrk_face_mask_area_a[wrk_face_mask_area_a>0] = 1.0
if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0:
if cfg.color_transfer_mode == 1: #rct
@ -217,8 +211,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
img_face_seamless_mask_a[img_face_seamless_mask_a <= i / 10.0] = 0.0
break
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_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 )
out_img = np.clip(out_img, 0.0, 1.0)
if 'seamless' in cfg.mode:
@ -237,9 +230,17 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
else:
print ("Seamless fail: " + e_str)
cfg_mp = cfg.motion_blur_power / 100.0
out_img = img_bgr*(1-img_face_mask_a) + (out_img*img_face_mask_a)
if ('seamless' in cfg.mode and cfg.color_transfer_mode != 0) or \
cfg.mode == 'seamless-hist-match' or \
cfg_mp != 0 or \
cfg.blursharpen_amount != 0 or \
cfg.image_denoise_power != 0 or \
cfg.bicubic_degrade_power != 0:
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:
@ -266,7 +267,6 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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:
@ -278,7 +278,6 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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:
@ -295,7 +294,8 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
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 )
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: