def __call__(self, data): d = dict(data) self.randomize() if not self._do_transform: return d adjuster = AdjustContrast(self.gamma_value) for key in self.keys: d[key] = adjuster(d[key]) return d
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) self.randomize() assert self.gamma_value is not None if not self._do_transform: return d adjuster = AdjustContrast(self.gamma_value) for key in self.keys: d[key] = adjuster(d[key]) return d
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) self.randomize() if self.gamma_value is None: raise AssertionError if not self._do_transform: return d adjuster = AdjustContrast(self.gamma_value) for key in self.key_iterator(d): d[key] = adjuster(d[key]) return d
def __init__(self, keys: KeysCollection, gamma: float) -> None: super().__init__(keys) self.adjuster = AdjustContrast(gamma)
def __init__(self, keys, gamma): super().__init__(keys) self.adjuster = AdjustContrast(gamma)
def __init__(self, keys: KeysCollection, gamma: float, allow_missing_keys: bool = False) -> None: super().__init__(keys, allow_missing_keys) self.adjuster = AdjustContrast(gamma)
def __init__(self, keys: Hashable, gamma: Union[int, float]): super().__init__(keys) self.adjuster = AdjustContrast(gamma)