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)
