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update == 04.20.2019 == (#242)

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* superb improved fanseg

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* added FANseg extractor for src and dst faces to use it in training

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* update to 'partial' func

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* trained FANSeg_256_full_face.h5,
new experimental models: AVATAR, RecycleGAN

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* fix for TCC mode cards(tesla), was conflict with plaidML initialization.

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* update manuals

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iperov 2019-04-20 08:23:08 +04:00 • committed by GitHub
commit 046649e6be
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32 changed files with 1152 additions and 329 deletions
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20
converters/ConverterMasked.py
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@ -30,7 +30,8 @@ class ConverterMasked(Converter):
base_blur_mask_modifier = 0,
default_erode_mask_modifier = 0,
default_blur_mask_modifier = 0,
clip_hborder_mask_per = 0):
clip_hborder_mask_per = 0,
force_mask_mode=-1):
super().__init__(predictor_func, Converter.TYPE_FACE)
@ -76,10 +77,13 @@ class ConverterMasked(Converter):
if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255)
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 )
if force_mask_mode != -1:
self.mask_mode = force_mask_mode
else:
self.mask_mode = np.clip ( io.input_int ("Mask mode: (1) learned, (2) dst . Default - %d : " % (1) , 1), 1, 2 )
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 )
else:
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:
self.fan_seg = None
@ -118,10 +122,10 @@ class ConverterMasked(Converter):
#overridable
def on_cli_initialize(self):
if (self.mask_mode >= 3 and self.mask_mode <= 6) and self.fan_seg == None:
self.fan_seg = FANSegmentator(256, FaceType.toString(FaceType.FULL) )
self.fan_seg = FANSegmentator(256, FaceType.toString( self.face_type ) )
#override
def cli_convert_face (self, img_bgr, img_face_landmarks, debug):
def cli_convert_face (self, img_bgr, img_face_landmarks, debug, **kwargs):
if debug:
debugs = [img_bgr.copy()]
@ -171,13 +175,13 @@ class ConverterMasked(Converter):
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]
prd_face_bgr_256_mask = self.fan_seg.extract( prd_face_bgr_256 )
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]
dst_face_256_mask = self.fan_seg.extract( dst_face_256_bgr )
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