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added option to converter --output-face-scale-modifier

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iperov 2018-11-28 20:38:48 +04:00
commit 64c3e57f1c
3 changed files with 29 additions and 19 deletions
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26
models/ConverterMasked.py
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@ -5,12 +5,7 @@ import cv2
import numpy as np
from utils import image_utils
'''
predictor:
input: [predictor_input_size, predictor_input_size, BGRA]
output: [predictor_input_size, predictor_input_size, BGRA]
'''
class ConverterMasked(ConverterBase):
#override
@ -24,9 +19,10 @@ class ConverterMasked(ConverterBase):
masked_hist_match = False,
mode='seamless',
erode_mask_modifier=0,
blur_mask_modifier=0,
blur_mask_modifier=0,
output_face_scale_modifier=0.0,
alpha=False,
transfercolor=False,
transfercolor=False,
**in_options):
super().__init__(predictor)
@ -41,6 +37,7 @@ class ConverterMasked(ConverterBase):
self.mode = mode
self.erode_mask_modifier = erode_mask_modifier
self.blur_mask_modifier = blur_mask_modifier
self.output_face_scale = np.clip(1.0 + output_face_scale_modifier*0.01, 0.5, 1.0)
self.alpha = alpha
self.transfercolor = transfercolor
@ -68,6 +65,8 @@ class ConverterMasked(ConverterBase):
img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr, img_face_landmarks)
face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, self.output_size, face_type=self.face_type)
face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, self.output_size, face_type=self.face_type, scale=self.output_face_scale)
dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4 )
dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4 )
@ -84,7 +83,7 @@ class ConverterMasked(ConverterBase):
prd_face_mask_a = np.expand_dims (prd_face_mask_a_0, axis=-1)
prd_face_mask_aaa = np.repeat (prd_face_mask_a, (3,), axis=-1)
img_prd_face_mask_aaa = cv2.warpAffine( prd_face_mask_aaa, face_mat, img_size, np.zeros(img_bgr.shape, dtype=float), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4 )
img_prd_face_mask_aaa = cv2.warpAffine( prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=float), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4 )
img_prd_face_mask_aaa = np.clip (img_prd_face_mask_aaa, 0.0, 1.0)
img_face_mask_aaa = img_prd_face_mask_aaa
@ -146,7 +145,7 @@ class ConverterMasked(ConverterBase):
if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
if debug:
debugs += [ cv2.warpAffine( prd_face_bgr, face_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ) ]
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=prd_face_bgr.dtype)
@ -159,8 +158,9 @@ class ConverterMasked(ConverterBase):
if self.mode == 'hist-match-bw':
prd_face_bgr = prd_face_bgr.astype(np.float32)
out_img = cv2.warpAffine( prd_face_bgr, face_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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 debug:
debugs += [out_img.copy()]
@ -177,7 +177,7 @@ class ConverterMasked(ConverterBase):
debugs += [out_img.copy()]
if self.clip_border_mask_per > 0:
img_prd_border_rect_mask_a = cv2.warpAffine( prd_border_rect_mask_a, face_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
img_prd_border_rect_mask_a = cv2.warpAffine( prd_border_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
img_prd_border_rect_mask_a = np.expand_dims (img_prd_border_rect_mask_a, -1)
out_img = out_img * img_prd_border_rect_mask_a + img_bgr * (1.0 - img_prd_border_rect_mask_a)
@ -186,7 +186,7 @@ class ConverterMasked(ConverterBase):
out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (out_img*img_mask_blurry_aaa) , 0, 1.0 )
if self.mode == 'seamless-hist-match':
out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size) )
out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size) )
new_out_face_bgr = image_utils.color_hist_match(out_face_bgr, dst_face_bgr )
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 )