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Formatting, adds mask fix

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This commit is contained in:
Jeremy Hummel 2019-08-10 13:35:36 -07:00
commit 659ef67f29
1 changed files with 17 additions and 14 deletions
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31
converters/ConverterMasked.py
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@ -90,23 +90,26 @@ class ConverterMasked(Converter):
else:
if face_type == FaceType.FULL:
self.mask_mode = np.clip(io.input_int(
"Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) learned*FAN-prd*FAN-dst (?) help. Default - %d : " % (
1), 1,
help_message="If you learned mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained FAN-model from dst face. 'FAN-prd*FAN-dst' or 'learned*FAN-prd*FAN-dst' - using multiplied masks."),
1, 6)
"Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) "
"learned*FAN-prd*FAN-dst (?) help. Default - %d : " % 1, 1,
help_message="If you learned mask, then option 1 should be choosed. 'dst' mask is raw shaky mask "
"from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained "
"FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained "
"FAN-model from dst face. 'FAN-prd*FAN-dst' or 'learned*FAN-prd*FAN-dst' - using "
"multiplied masks."), 1, 6)
else:
self.mask_mode = np.clip(io.input_int("Mask mode: (1) learned, (2) dst . Default - %d : " % (1), 1), 1,
self.mask_mode = np.clip(io.input_int("Mask mode: (1) learned, (2) dst . Default - %d : " % 1, 1), 1,
2)
if self.mask_mode >= 3 and self.mask_mode <= 6:
if 3 <= self.mask_mode <= 6:
self.fan_seg = None
if self.mode != 'raw':
self.erode_mask_modifier = base_erode_mask_modifier + np.clip(
io.input_int("Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier),
io.input_int("Choose erode mask modifier [-200..200] (skip:%d) : " % default_erode_mask_modifier,
default_erode_mask_modifier), -200, 200)
self.blur_mask_modifier = base_blur_mask_modifier + np.clip(
io.input_int("Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier),
io.input_int("Choose blur mask modifier [-200..200] (skip:%d) : " % default_blur_mask_modifier,
default_blur_mask_modifier), -200, 200)
self.output_face_scale = np.clip(
@ -142,7 +145,7 @@ class ConverterMasked(Converter):
# overridable
def on_cli_initialize(self):
if (self.mask_mode >= 3 and self.mask_mode <= 6) and self.fan_seg == None:
if (3 <= self.mask_mode <= 6) and self.fan_seg is None:
self.fan_seg = FANSegmentator(256, FaceType.toString(self.face_type))
# override
@ -198,7 +201,7 @@ class ConverterMasked(Converter):
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:
elif 3 <= 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))
@ -270,12 +273,12 @@ class ConverterMasked(Converter):
lowest_len = min(lenx, leny)
if debug:
io.log_info("lenx/leny:(%d/%d) " % (lenx, leny))
io.log_info("lowest_len = %f" % (lowest_len))
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))
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)),
@ -292,6 +295,7 @@ class ConverterMasked(Converter):
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[-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)
@ -308,7 +312,7 @@ class ConverterMasked(Converter):
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))
io.log_info("blur_size = %d" % blur)
if blur > 0:
img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur))
@ -453,7 +457,6 @@ class ConverterMasked(Converter):
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,