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
(
self,
keys: KeysCollection,
minv: float | None = 0.0,
maxv: float | None = 1.0,
factor: float | None = None,
channel_wise: bool = False,
dtype: DtypeLike = np.float32,
allow_missing_keys: bool = False,
)
| 557 | backend = ScaleIntensity.backend |
| 558 | |
| 559 | def __init__( |
| 560 | self, |
| 561 | keys: KeysCollection, |
| 562 | minv: float | None = 0.0, |
| 563 | maxv: float | None = 1.0, |
| 564 | factor: float | None = None, |
| 565 | channel_wise: bool = False, |
| 566 | dtype: DtypeLike = np.float32, |
| 567 | allow_missing_keys: bool = False, |
| 568 | ) -> None: |
| 569 | """ |
| 570 | Args: |
| 571 | keys: keys of the corresponding items to be transformed. |
| 572 | See also: :py:class:`monai.transforms.compose.MapTransform` |
| 573 | minv: minimum value of output data. |
| 574 | maxv: maximum value of output data. |
| 575 | factor: factor scale by ``v = v * (1 + factor)``. In order to use |
| 576 | this parameter, please set both `minv` and `maxv` into None. |
| 577 | channel_wise: if True, scale on each channel separately. Please ensure |
| 578 | that the first dimension represents the channel of the image if True. |
| 579 | dtype: output data type, if None, same as input image. defaults to float32. |
| 580 | allow_missing_keys: don't raise exception if key is missing. |
| 581 | |
| 582 | """ |
| 583 | super().__init__(keys, allow_missing_keys) |
| 584 | self.scaler = ScaleIntensity(minv, maxv, factor, channel_wise, dtype) |
| 585 | |
| 586 | def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]: |
| 587 | d = dict(data) |
nothing calls this directly
no test coverage detected