def ConvertMaskedFace(cfg, frame_info, img_bgr_uint8, img_bgr,
img_face_landmarks):
#if debug:
# debugs = [img_bgr.copy()]
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)
if cfg.mode == 'original':
if cfg.export_mask_alpha:
img_bgr = np.concatenate([img_bgr, img_face_mask_a], -1)
return img_bgr, img_face_mask_a
out_img = img_bgr.copy()
out_merging_mask = None
output_size = cfg.predictor_input_shape[0]
if cfg.super_resolution_mode != 0:
output_size *= 2
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)
dst_face_bgr = cv2.warpAffine(img_bgr,
face_mat, (output_size, output_size),
flags=cv2.INTER_CUBIC)
dst_face_mask_a_0 = cv2.warpAffine(img_face_mask_a,
face_mat, (output_size, output_size),
flags=cv2.INTER_CUBIC)
predictor_input_bgr = cv2.resize(dst_face_bgr,
cfg.predictor_input_shape[0:2])
if cfg.predictor_masked:
prd_face_bgr, prd_face_mask_a_0 = cfg.predictor_func(
predictor_input_bgr)
prd_face_bgr = np.clip(prd_face_bgr, 0, 1.0)
prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0.0, 1.0)
else:
predicted = cfg.predictor_func(predictor_input_bgr)
prd_face_bgr = np.clip(predicted, 0, 1.0)
prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0,
cfg.predictor_input_shape[0:2])
if cfg.super_resolution_mode:
#if debug:
# tmp = cv2.resize (prd_face_bgr, (output_size,output_size), cv2.INTER_CUBIC)
# debugs += [ np.clip( cv2.warpAffine( tmp, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
prd_face_bgr = cfg.superres_func(cfg.super_resolution_mode,
prd_face_bgr)
#if debug:
# 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) ]
if cfg.predictor_masked:
prd_face_mask_a_0 = cv2.resize(prd_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
else:
prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
if cfg.mask_mode == 2: #dst
prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
elif cfg.mask_mode >= 3 and cfg.mask_mode <= 7:
if cfg.mask_mode == 3 or cfg.mask_mode == 5 or cfg.mask_mode == 6:
prd_face_fanseg_bgr = cv2.resize(prd_face_bgr,
(cfg.fanseg_input_size, ) * 2)
prd_face_fanseg_mask = cfg.fanseg_extract_func(
FaceType.FULL, prd_face_fanseg_bgr)
FAN_prd_face_mask_a_0 = cv2.resize(prd_face_fanseg_mask,
(output_size, output_size),
cv2.INTER_CUBIC)
if cfg.mask_mode >= 4 or cfg.mask_mode <= 7:
full_face_fanseg_mat = LandmarksProcessor.get_transform_mat(
img_face_landmarks,
cfg.fanseg_input_size,
face_type=FaceType.FULL)
dst_face_fanseg_bgr = cv2.warpAffine(img_bgr,
full_face_fanseg_mat,
(cfg.fanseg_input_size, ) * 2,
flags=cv2.INTER_CUBIC)
dst_face_fanseg_mask = cfg.fanseg_extract_func(
FaceType.FULL, dst_face_fanseg_bgr)
if cfg.face_type == FaceType.FULL:
FAN_dst_face_mask_a_0 = cv2.resize(dst_face_fanseg_mask,
(output_size, output_size),
cv2.INTER_CUBIC)
elif cfg.face_type == FaceType.HALF:
half_face_fanseg_mat = LandmarksProcessor.get_transform_mat(
img_face_landmarks,
cfg.fanseg_input_size,
face_type=FaceType.HALF)
fanseg_rect_corner_pts = np.array(
[[0, 0], [cfg.fanseg_input_size - 1, 0],
[0, cfg.fanseg_input_size - 1]],
dtype=np.float32)
a = LandmarksProcessor.transform_points(fanseg_rect_corner_pts,
half_face_fanseg_mat,
invert=True)
b = LandmarksProcessor.transform_points(
a, full_face_fanseg_mat)
m = cv2.getAffineTransform(b, fanseg_rect_corner_pts)
FAN_dst_face_mask_a_0 = cv2.warpAffine(
dst_face_fanseg_mask,
m, (cfg.fanseg_input_size, ) * 2,
flags=cv2.INTER_CUBIC)
FAN_dst_face_mask_a_0 = cv2.resize(FAN_dst_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
else:
raise ValueError("cfg.face_type unsupported")
if cfg.mask_mode == 3: #FAN-prd
prd_face_mask_a_0 = FAN_prd_face_mask_a_0
elif cfg.mask_mode == 4: #FAN-dst
prd_face_mask_a_0 = FAN_dst_face_mask_a_0
elif cfg.mask_mode == 5:
prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0
elif cfg.mask_mode == 6:
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
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
#if debug:
# debugs += [img_face_mask_aaa.copy()]
if 'raw' in cfg.mode:
face_corner_pts = np.array(
[[0, 0], [output_size - 1, 0], [output_size - 1, output_size - 1],
[0, output_size - 1]],
dtype=np.float32)
square_mask = np.zeros(img_bgr.shape, dtype=np.float32)
cv2.fillConvexPoly(square_mask, \
LandmarksProcessor.transform_points (face_corner_pts, face_output_mat, invert=True ).astype(np.int), \
(1,1,1) )
if cfg.mode == 'raw-rgb':
out_merging_mask = square_mask
if cfg.mode == 'raw-rgb' or cfg.mode == 'raw-rgb-mask':
out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size,
out_img,
cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC,
cv2.BORDER_TRANSPARENT)
if cfg.mode == 'raw-rgb-mask':
out_img = np.concatenate(
[out_img,
np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1)
out_merging_mask = square_mask
elif cfg.mode == 'raw-mask-only':
out_img = img_face_mask_aaa
out_merging_mask = img_face_mask_aaa
elif cfg.mode == 'raw-predicted-only':
out_img = 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)
out_merging_mask = square_mask
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)
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:
lenx, leny = np.mean(ar, axis=0)
lowest_len = min(lenx, leny)
#if debug:
# io.log_info ("lenx/leny:(%d/%d) " % (lenx, leny ) )
# io.log_info ("lowest_len = %f" % (lowest_len) )
if cfg.erode_mask_modifier != 0:
ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) *
0.01 * cfg.erode_mask_modifier)
#if debug:
# io.log_info ("erode_size = %d" % (ero) )
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 debug:
# debugs += [img_face_mask_aaa.copy()]
if cfg.blur_mask_modifier > 0:
blur = int(lowest_len * 0.10 * 0.01 * cfg.blur_mask_modifier)
#if debug:
# io.log_info ("blur_size = %d" % (blur) )
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 debug:
# debugs += [img_face_mask_aaa.copy()]
if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0:
if cfg.color_transfer_mode == 1:
#if debug:
# 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) ]
prd_face_bgr = imagelib.reinhard_color_transfer(
np.clip((prd_face_bgr * 255).astype(np.uint8), 0, 255),
np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255),
source_mask=prd_face_mask_a,
target_mask=prd_face_mask_a)
prd_face_bgr = np.clip(
prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
#if debug:
# 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) ]
elif cfg.color_transfer_mode == 2:
#if debug:
# 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) ]
prd_face_bgr = imagelib.linear_color_transfer(
prd_face_bgr, dst_face_bgr)
prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0)
#if debug:
# 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) ]
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 debug:
# debugs += [ 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 ) ]
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
white = (1.0 - hist_mask_a) * np.ones(
prd_face_bgr.shape[:2] + (1, ), dtype=np.float32)
hist_match_1 = prd_face_bgr * hist_mask_a + white
hist_match_1[hist_match_1 > 1.0] = 1.0
hist_match_2 = dst_face_bgr * hist_mask_a + white
hist_match_2[hist_match_1 > 1.0] = 1.0
prd_face_bgr = imagelib.color_hist_match(
hist_match_1, hist_match_2, cfg.hist_match_threshold)
#if cfg.masked_hist_match:
# prd_face_bgr -= white
if cfg.mode == 'hist-match-bw':
prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
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 = np.clip(out_img, 0.0, 1.0)
#if debug:
# debugs += [out_img.copy()]
if cfg.mode == 'overlay':
pass
if 'seamless' in cfg.mode:
#mask used for cv2.seamlessClone
img_face_seamless_mask_a = None
img_face_mask_a = img_face_mask_aaa[..., 0:1]
for i in range(1, 10):
a = img_face_mask_a > i / 10.0
if len(np.argwhere(a)) == 0:
continue
img_face_seamless_mask_a = img_face_mask_aaa[...,
0:1].copy()
img_face_seamless_mask_a[a] = 1.0
img_face_seamless_mask_a[img_face_seamless_mask_a <= i /
10.0] = 0.0
break
try:
#calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering (not flickering)
l, t, w, h = cv2.boundingRect(
(img_face_seamless_mask_a * 255).astype(np.uint8))
s_maskx, s_masky = int(l + w / 2), int(t + h / 2)
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)
out_img = out_img.astype(dtype=np.float32) / 255.0
except Exception as e:
#seamlessClone may fail in some cases
e_str = traceback.format_exc()
if 'MemoryError' in e_str:
raise Exception(
"Seamless fail: " + e_str
) #reraise MemoryError in order to reprocess this data by other processes
else:
print("Seamless fail: " + e_str)
#if debug:
# debugs += [out_img.copy()]
out_img = img_bgr * (1 - img_face_mask_aaa) + (out_img *
img_face_mask_aaa)
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:
#if debug:
# 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) ]
face_mask_aaa = cv2.warpAffine(img_face_mask_aaa, face_mat,
(output_size, output_size))
out_face_bgr = imagelib.reinhard_color_transfer(
np.clip((out_face_bgr * 255).astype(np.uint8), 0, 255),
np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255),
source_mask=face_mask_aaa,
target_mask=face_mask_aaa)
out_face_bgr = np.clip(
out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
#if debug:
# 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) ]
elif cfg.color_transfer_mode == 2:
#if debug:
# 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) ]
out_face_bgr = imagelib.linear_color_transfer(
out_face_bgr, dst_face_bgr)
out_face_bgr = np.clip(out_face_bgr, 0.0, 1.0)
#if debug:
# 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) ]
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_mode:
k_size *= 2
out_face_bgr = imagelib.LinearMotionBlur(
out_face_bgr, k_size, frame_info.motion_deg)
if cfg.sharpen_mode != 0 and cfg.sharpen_amount != 0:
out_face_bgr = cfg.sharpen_func(out_face_bgr, cfg.sharpen_mode,
3, cfg.sharpen_amount)
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)
if cfg.color_degrade_power != 0:
#if debug:
# debugs += [out_img.copy()]
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)
if cfg.export_mask_alpha:
out_img = np.concatenate(
[out_img, img_face_mask_aaa[:, :, 0:1]], -1)
out_merging_mask = img_face_mask_aaa
#if debug:
# debugs += [out_img.copy()]
return out_img, out_merging_mask
def cli_convert_face(self, img_bgr, img_face_landmarks, debug):
if debug:
debugs = [img_bgr.copy()]
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)
output_size = self.predictor_input_size
if self.super_resolution:
output_size *= 2
face_mat = LandmarksProcessor.get_transform_mat(
img_face_landmarks, output_size, face_type=self.face_type)
face_output_mat = LandmarksProcessor.get_transform_mat(
img_face_landmarks,
output_size,
face_type=self.face_type,
scale=self.output_face_scale)
dst_face_bgr = cv2.warpAffine(img_bgr,
face_mat, (output_size, output_size),
flags=cv2.INTER_LANCZOS4)
dst_face_mask_a_0 = cv2.warpAffine(img_face_mask_a,
face_mat,
(output_size, output_size),
flags=cv2.INTER_LANCZOS4)
predictor_input_bgr = cv2.resize(
dst_face_bgr,
(self.predictor_input_size, self.predictor_input_size))
if self.predictor_masked:
prd_face_bgr, prd_face_mask_a_0 = self.predictor_func(
predictor_input_bgr)
prd_face_bgr = np.clip(prd_face_bgr, 0, 1.0)
prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0.0, 1.0)
else:
predicted = self.predictor_func(predictor_input_bgr)
prd_face_bgr = np.clip(predicted, 0, 1.0)
prd_face_mask_a_0 = cv2.resize(
dst_face_mask_a_0,
(self.predictor_input_size, self.predictor_input_size))
if self.super_resolution:
if debug:
tmp = cv2.resize(prd_face_bgr, (output_size, output_size),
cv2.INTER_CUBIC)
debugs += [
np.clip(
cv2.warpAffine(
tmp, face_output_mat, img_size, img_bgr.copy(),
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
prd_face_bgr = self.dc_upscale(prd_face_bgr)
if debug:
debugs += [
np.clip(
cv2.warpAffine(
prd_face_bgr, face_output_mat, img_size,
img_bgr.copy(),
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
if self.predictor_masked:
prd_face_mask_a_0 = cv2.resize(prd_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
else:
prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
if self.mask_mode == 2: #dst
prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
elif self.mask_mode >= 3 and self.mask_mode <= 6:
if self.mask_mode == 3 or self.mask_mode == 5 or self.mask_mode == 6:
prd_face_bgr_256 = cv2.resize(prd_face_bgr, (256, 256))
prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr(
prd_face_bgr_256[np.newaxis, ...])[0]
FAN_prd_face_mask_a_0 = cv2.resize(prd_face_bgr_256_mask,
(output_size, output_size),
cv2.INTER_CUBIC)
if self.mask_mode == 4 or self.mask_mode == 5 or self.mask_mode == 6:
face_256_mat = LandmarksProcessor.get_transform_mat(
img_face_landmarks, 256, face_type=FaceType.FULL)
dst_face_256_bgr = cv2.warpAffine(img_bgr,
face_256_mat, (256, 256),
flags=cv2.INTER_LANCZOS4)
dst_face_256_mask = self.fan_seg.extract_from_bgr(
dst_face_256_bgr[np.newaxis, ...])[0]
FAN_dst_face_mask_a_0 = cv2.resize(dst_face_256_mask,
(output_size, output_size),
cv2.INTER_CUBIC)
if self.mask_mode == 3: #FAN-prd
prd_face_mask_a_0 = FAN_prd_face_mask_a_0
elif self.mask_mode == 4: #FAN-dst
prd_face_mask_a_0 = FAN_dst_face_mask_a_0
elif self.mask_mode == 5:
prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0
elif self.mask_mode == 6:
prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_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_LANCZOS4)
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
if debug:
debugs += [img_face_mask_aaa.copy()]
out_img = img_bgr.copy()
if self.mode == 'raw':
if self.raw_mode == 'rgb' or self.raw_mode == 'rgb-mask':
out_img = cv2.warpAffine(
prd_face_bgr, face_output_mat, img_size, out_img,
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT)
if self.raw_mode == 'rgb-mask':
out_img = np.concatenate(
[out_img,
np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1)
if self.raw_mode == 'mask-only':
out_img = img_face_mask_aaa
if self.raw_mode == 'predicted-only':
out_img = cv2.warpAffine(
prd_face_bgr, face_output_mat, img_size,
np.zeros(out_img.shape, dtype=np.float32),
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT)
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)
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:
lenx, leny = np.mean(ar, axis=0)
lowest_len = min(lenx, leny)
if debug:
io.log_info("lenx/leny:(%d/%d) " % (lenx, leny))
io.log_info("lowest_len = %f" % (lowest_len))
if self.erode_mask_modifier != 0:
ero = int(lowest_len *
(0.126 - lowest_len * 0.00004551365) * 0.01 *
self.erode_mask_modifier)
if debug:
io.log_info("erode_size = %d" % (ero))
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)
img_mask_blurry_aaa = img_face_mask_aaa
if self.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] *
self.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 = 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_LANCZOS4)
img_prd_hborder_rect_mask_a = np.expand_dims(
img_prd_hborder_rect_mask_a, -1)
img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a
img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0)
if debug:
debugs += [img_mask_blurry_aaa.copy()]
if self.blur_mask_modifier > 0:
blur = int(lowest_len * 0.10 * 0.01 *
self.blur_mask_modifier)
if debug:
io.log_info("blur_size = %d" % (blur))
if blur > 0:
img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa,
(blur, blur))
img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0)
face_mask_blurry_aaa = cv2.warpAffine(
img_mask_blurry_aaa, face_mat, (output_size, output_size))
if debug:
debugs += [img_mask_blurry_aaa.copy()]
if 'seamless' not in self.mode and self.color_transfer_mode is not None:
if self.color_transfer_mode == 'rct':
if debug:
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_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
prd_face_bgr = imagelib.reinhard_color_transfer(
np.clip((prd_face_bgr * 255).astype(np.uint8), 0,
255),
np.clip((dst_face_bgr * 255).astype(np.uint8), 0,
255),
source_mask=prd_face_mask_a,
target_mask=prd_face_mask_a)
prd_face_bgr = np.clip(
prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
if debug:
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_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
elif self.color_transfer_mode == 'lct':
if debug:
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_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
prd_face_bgr = imagelib.linear_color_transfer(
prd_face_bgr, dst_face_bgr)
prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0)
if debug:
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_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
if self.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 self.mode == 'hist-match' or self.mode == 'hist-match-bw':
if debug:
debugs += [
cv2.warpAffine(
prd_face_bgr, face_output_mat, img_size,
np.zeros(img_bgr.shape, dtype=np.float32),
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT)
]
hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ),
dtype=np.float32)
if self.masked_hist_match:
hist_mask_a *= prd_face_mask_a
white = (1.0 - hist_mask_a) * np.ones(
prd_face_bgr.shape[:2] + (1, ), dtype=np.float32)
hist_match_1 = prd_face_bgr * hist_mask_a + white
hist_match_1[hist_match_1 > 1.0] = 1.0
hist_match_2 = dst_face_bgr * hist_mask_a + white
hist_match_2[hist_match_1 > 1.0] = 1.0
prd_face_bgr = imagelib.color_hist_match(
hist_match_1, hist_match_2, self.hist_match_threshold)
#if self.masked_hist_match:
# prd_face_bgr -= white
if self.mode == 'hist-match-bw':
prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
out_img = cv2.warpAffine(
prd_face_bgr, face_output_mat, img_size, out_img,
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT)
out_img = np.clip(out_img, 0.0, 1.0)
if debug:
debugs += [out_img.copy()]
if self.mode == 'overlay':
pass
if 'seamless' in self.mode:
#mask used for cv2.seamlessClone
img_face_seamless_mask_a = None
img_face_mask_a = img_mask_blurry_aaa[..., 0:1]
for i in range(1, 10):
a = img_face_mask_a > i / 10.0
if len(np.argwhere(a)) == 0:
continue
img_face_seamless_mask_a = img_mask_blurry_aaa[
..., 0:1].copy()
img_face_seamless_mask_a[a] = 1.0
img_face_seamless_mask_a[
img_face_seamless_mask_a <= i / 10.0] = 0.0
break
try:
#calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering
l, t, w, h = cv2.boundingRect(
(img_face_seamless_mask_a * 255).astype(np.uint8))
s_maskx, s_masky = int(l + w / 2), int(t + h / 2)
out_img = cv2.seamlessClone(
(out_img * 255).astype(np.uint8),
(img_bgr * 255).astype(np.uint8),
(img_face_seamless_mask_a * 255).astype(np.uint8),
(s_maskx, s_masky), cv2.NORMAL_CLONE)
out_img = out_img.astype(dtype=np.float32) / 255.0
except Exception as e:
#seamlessClone may fail in some cases
e_str = traceback.format_exc()
if 'MemoryError' in e_str:
raise Exception(
"Seamless fail: " + e_str
) #reraise MemoryError in order to reprocess this data by other processes
else:
print("Seamless fail: " + e_str)
if debug:
debugs += [out_img.copy()]
out_img = np.clip(
img_bgr * (1 - img_mask_blurry_aaa) +
(out_img * img_mask_blurry_aaa), 0, 1.0)
if 'seamless' in self.mode and self.color_transfer_mode is not None:
out_face_bgr = cv2.warpAffine(out_img, face_mat,
(output_size, output_size))
if self.color_transfer_mode == 'rct':
if debug:
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_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
new_out_face_bgr = imagelib.reinhard_color_transfer(
np.clip((out_face_bgr * 255).astype(np.uint8), 0,
255),
np.clip((dst_face_bgr * 255).astype(np.uint8), 0,
255),
source_mask=face_mask_blurry_aaa,
target_mask=face_mask_blurry_aaa)
new_out_face_bgr = np.clip(
new_out_face_bgr.astype(np.float32) / 255.0, 0.0,
1.0)
if debug:
debugs += [
np.clip(
cv2.warpAffine(
new_out_face_bgr, face_output_mat,
img_size,
np.zeros(img_bgr.shape,
dtype=np.float32),
cv2.WARP_INVERSE_MAP
| cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
elif self.color_transfer_mode == 'lct':
if debug:
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_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
new_out_face_bgr = imagelib.linear_color_transfer(
out_face_bgr, dst_face_bgr)
new_out_face_bgr = np.clip(new_out_face_bgr, 0.0, 1.0)
if debug:
debugs += [
np.clip(
cv2.warpAffine(
new_out_face_bgr, face_output_mat,
img_size,
np.zeros(img_bgr.shape,
dtype=np.float32),
cv2.WARP_INVERSE_MAP
| cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT), 0, 1.0)
]
new_out = cv2.warpAffine(
new_out_face_bgr, face_mat, img_size, img_bgr.copy(),
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT)
out_img = np.clip(
img_bgr * (1 - img_mask_blurry_aaa) +
(new_out * img_mask_blurry_aaa), 0, 1.0)
if self.mode == 'seamless-hist-match':
out_face_bgr = cv2.warpAffine(out_img, face_mat,
(output_size, output_size))
new_out_face_bgr = imagelib.color_hist_match(
out_face_bgr, dst_face_bgr, self.hist_match_threshold)
new_out = cv2.warpAffine(
new_out_face_bgr, face_mat, img_size, img_bgr.copy(),
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4,
cv2.BORDER_TRANSPARENT)
out_img = np.clip(
img_bgr * (1 - img_mask_blurry_aaa) +
(new_out * img_mask_blurry_aaa), 0, 1.0)
if self.final_image_color_degrade_power != 0:
if debug:
debugs += [out_img.copy()]
out_img_reduced = imagelib.reduce_colors(out_img, 256)
if self.final_image_color_degrade_power == 100:
out_img = out_img_reduced
else:
alpha = self.final_image_color_degrade_power / 100.0
out_img = (out_img * (1.0 - alpha) +
out_img_reduced * alpha)
if self.alpha:
out_img = np.concatenate([
out_img,
np.expand_dims(img_mask_blurry_aaa[:, :, 0], -1)
], -1)
out_img = np.clip(out_img, 0.0, 1.0)
if debug:
debugs += [out_img.copy()]
return debugs if debug else out_img
