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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
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11
converters/Converter.py
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@ -5,8 +5,9 @@ You can implement your own Converter, check example ConverterMasked.py
class Converter(object):
TYPE_FACE = 0 #calls convert_face
TYPE_IMAGE = 1 #calls convert_image without landmarks
TYPE_IMAGE_WITH_LANDMARKS = 2 #calls convert_image with landmarks
TYPE_FACE_AVATAR = 1 #calls convert_face with avatar_operator_face
TYPE_IMAGE = 2 #calls convert_image without landmarks
TYPE_IMAGE_WITH_LANDMARKS = 3 #calls convert_image with landmarks
#overridable
def __init__(self, predictor_func, type):
@ -23,13 +24,13 @@ class Converter(object):
pass
#overridable
def cli_convert_face (self, img_bgr, img_face_landmarks, debug):
def cli_convert_face (self, img_bgr, img_face_landmarks, debug, avaperator_face_bgr=None, **kwargs):
#return float32 image
#if debug , return tuple ( images of any size and channels, ...)
return image
#overridable
def convert_image (self, img_bgr, img_landmarks, debug):
def cli_convert_image (self, img_bgr, img_landmarks, debug):
#img_landmarks not None, if input image is png with embedded data
#return float32 image
#if debug , return tuple ( images of any size and channels, ...)