| | import numpy as np |
| | from numpy.linalg import norm as l2norm |
| | |
| |
|
| | class Face(dict): |
| |
|
| | def __init__(self, d=None, **kwargs): |
| | if d is None: |
| | d = {} |
| | if kwargs: |
| | d.update(**kwargs) |
| | for k, v in d.items(): |
| | setattr(self, k, v) |
| | |
| | |
| | |
| | |
| |
|
| | def __setattr__(self, name, value): |
| | if isinstance(value, (list, tuple)): |
| | value = [self.__class__(x) |
| | if isinstance(x, dict) else x for x in value] |
| | elif isinstance(value, dict) and not isinstance(value, self.__class__): |
| | value = self.__class__(value) |
| | super(Face, self).__setattr__(name, value) |
| | super(Face, self).__setitem__(name, value) |
| |
|
| | __setitem__ = __setattr__ |
| |
|
| | def __getattr__(self, name): |
| | return None |
| |
|
| | @property |
| | def embedding_norm(self): |
| | if self.embedding is None: |
| | return None |
| | return l2norm(self.embedding) |
| |
|
| | @property |
| | def normed_embedding(self): |
| | if self.embedding is None: |
| | return None |
| | return self.embedding / self.embedding_norm |
| |
|
| | @property |
| | def sex(self): |
| | if self.gender is None: |
| | return None |
| | return 'M' if self.gender==1 else 'F' |
| |
|