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SAE: increased speed of training by 10-18%,

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increased clipping border mask in full face mode results better transition of cheeks,
default archi now 'df'
This commit is contained in:
iperov 2019-02-03 20:32:46 +04:00
parent 41abda42d2
commit e226ab5385
2 changed files with 65 additions and 70 deletions
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44
models/ConverterMasked.py
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@ -15,14 +15,14 @@ class ConverterMasked(ConverterBase):
face_type=FaceType.FULL,
base_erode_mask_modifier = 0,
base_blur_mask_modifier = 0,
clip_border_mask_per = 0,
clip_hborder_mask_per = 0,
**in_options):
super().__init__(predictor)
self.predictor_input_size = predictor_input_size
self.output_size = output_size
self.face_type = face_type
self.clip_border_mask_per = clip_border_mask_per
self.clip_hborder_mask_per = clip_hborder_mask_per
self.TFLabConverter = None
mode = input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless (default), (5) seamless hist match, (6) raw : ", 4)
@ -155,6 +155,7 @@ class ConverterMasked(ConverterBase):
print ("lowest_len = %f" % (lowest_len) )
img_mask_blurry_aaa = img_face_mask_aaa
if self.erode_mask_modifier != 0:
ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.erode_mask_modifier )
if debug:
@ -172,7 +173,19 @@ class ConverterMasked(ConverterBase):
img_face_mask_flatten_aaa = cv2.erode(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
elif ero < 0:
img_face_mask_flatten_aaa = cv2.dilate(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
if self.clip_hborder_mask_per > 0: #clip hborder before blur
prd_hborder_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=prd_face_mask_a.dtype)
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, cv2.BORDER_TRANSPARENT )
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
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 )
@ -182,17 +195,10 @@ class ConverterMasked(ConverterBase):
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 )
if self.clip_border_mask_per > 0:
prd_border_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=prd_face_mask_a.dtype)
prd_border_size = int ( prd_border_rect_mask_a.shape[1] * self.clip_border_mask_per )
prd_border_rect_mask_a[0:prd_border_size,:,:] = 0
prd_border_rect_mask_a[-prd_border_size:,:,:] = 0
prd_border_rect_mask_a[:,0:prd_border_size,:] = 0
prd_border_rect_mask_a[:,-prd_border_size:,:] = 0
prd_border_rect_mask_a = np.expand_dims(cv2.blur(prd_border_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
if debug:
debugs += [img_mask_blurry_aaa.copy()]
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 )
@ -221,7 +227,6 @@ class ConverterMasked(ConverterBase):
if debug:
debugs += [out_img.copy()]
debugs += [img_mask_blurry_aaa.copy()]
if self.mode == 'overlay':
pass
@ -236,14 +241,9 @@ class ConverterMasked(ConverterBase):
if debug:
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_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)
img_mask_blurry_aaa *= img_prd_border_rect_mask_a
out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (out_img*img_mask_blurry_aaa) , 0, 1.0 )
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) )
new_out_face_bgr = image_utils.color_hist_match(out_face_bgr, dst_face_bgr, self.hist_match_threshold)