def __call__(self, data):
d = dict(data)
self.randomize()
if not self._do_transform:
return d
scaler = ScaleIntensity(minv=None, maxv=None, factor=self.factor)
for key in self.keys:
d[key] = scaler(d[key])
return d
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
d = dict(data)
self.randomize()
if not self._do_transform:
return d
scaler = ScaleIntensity(minv=None, maxv=None, factor=self.factor)
for key in self.keys:
d[key] = scaler(d[key])
return d
def __init__(self, keys, minv=0.0, maxv=1.0, factor=None):
"""
Args:
keys (hashable items): keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
minv (int or float): minimum value of output data.
maxv (int or float): maximum value of output data.
factor (float): factor scale by ``v = v * (1 + factor)``.
"""
super().__init__(keys)
self.scaler = ScaleIntensity(minv, maxv, factor)
def __init__(
self,
min_perc: float,
max_perc: float,
minmax: bool = False,
) -> None:
super().__init__()
self.min_perc = min_perc
self.max_perc = max_perc
if minmax:
self.converter = ScaleIntensity(minv=0.0, maxv=1.0)
else:
self.converter = NormalizeIntensity()
def __init__(
self, keys: KeysCollection, minv: float = 0.0, maxv: float = 1.0, factor: Optional[float] = None
) -> None:
"""
Args:
keys: keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
minv: minimum value of output data.
maxv: maximum value of output data.
factor: factor scale by ``v = v * (1 + factor)``.
"""
super().__init__(keys)
self.scaler = ScaleIntensity(minv, maxv, factor)
def __init__(
self,
keys: KeysCollection,
minv: Optional[float] = 0.0,
maxv: Optional[float] = 1.0,
factor: Optional[float] = None,
allow_missing_keys: bool = False,
) -> None:
"""
Args:
keys: keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
minv: minimum value of output data.
maxv: maximum value of output data.
factor: factor scale by ``v = v * (1 + factor)``. In order to use
this parameter, please set `minv` and `maxv` into None.
allow_missing_keys: don't raise exception if key is missing.
"""
super().__init__(keys, allow_missing_keys)
self.scaler = ScaleIntensity(minv, maxv, factor)