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Basic usage instruction: https://i.imgur.com/w7LkId2.jpg 'whole_face' requires skill in Adobe After Effects. For using whole_face you have to extract whole_face's by using 4) data_src extract whole_face and 5) data_dst extract whole_face Images will be extracted in 512 resolution, so they can be used for regular full_face's and half_face's. 'whole_face' covers whole area of face include forehead in training square, but training mask is still 'full_face' therefore it requires manual final masking and composing in Adobe After Effects. added option 'masked_training' This option is available only for 'whole_face' type. Default is ON. Masked training clips training area to full_face mask, thus network will train the faces properly. When the face is trained enough, disable this option to train all area of the frame. Merge with 'raw-rgb' mode, then use Adobe After Effects to manually mask, tune color, and compose whole face include forehead.
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
from enum import IntEnum
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class FaceType(IntEnum):
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#enumerating in order "next contains prev"
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HALF = 0
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MID_FULL = 1
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FULL = 2
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FULL_NO_ALIGN = 3
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WHOLE_FACE = 4
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HEAD = 5
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HEAD_NO_ALIGN = 6
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MARK_ONLY = 10, #no align at all, just embedded faceinfo
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@staticmethod
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def fromString (s):
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r = from_string_dict.get (s.lower())
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if r is None:
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raise Exception ('FaceType.fromString value error')
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return r
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@staticmethod
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def toString (face_type):
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return to_string_dict[face_type]
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from_string_dict = {'half_face': FaceType.HALF,
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'midfull_face': FaceType.MID_FULL,
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'full_face': FaceType.FULL,
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'whole_face': FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD,
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'mark_only' : FaceType.MARK_ONLY,
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'full_face_no_align' : FaceType.FULL_NO_ALIGN,
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'head_no_align' : FaceType.HEAD_NO_ALIGN,
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}
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to_string_dict = { FaceType.HALF : 'half_face',
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FaceType.MID_FULL : 'midfull_face',
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FaceType.FULL : 'full_face',
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FaceType.WHOLE_FACE : 'whole_face',
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FaceType.HEAD : 'head',
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FaceType.MARK_ONLY :'mark_only',
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FaceType.FULL_NO_ALIGN : 'full_face_no_align',
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FaceType.HEAD_NO_ALIGN : 'head_no_align'
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}
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