Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` factor: factor shift by ``v = v + factor * std(v)``. nonzero: whether only count non-zero values. channe
(
self,
keys: KeysCollection,
factor: float,
nonzero: bool = False,
channel_wise: bool = False,
dtype: DtypeLike = np.float32,
allow_missing_keys: bool = False,
)
| 461 | backend = StdShiftIntensity.backend |
| 462 | |
| 463 | def __init__( |
| 464 | self, |
| 465 | keys: KeysCollection, |
| 466 | factor: float, |
| 467 | nonzero: bool = False, |
| 468 | channel_wise: bool = False, |
| 469 | dtype: DtypeLike = np.float32, |
| 470 | allow_missing_keys: bool = False, |
| 471 | ) -> None: |
| 472 | """ |
| 473 | Args: |
| 474 | keys: keys of the corresponding items to be transformed. |
| 475 | See also: :py:class:`monai.transforms.compose.MapTransform` |
| 476 | factor: factor shift by ``v = v + factor * std(v)``. |
| 477 | nonzero: whether only count non-zero values. |
| 478 | channel_wise: if True, calculate on each channel separately. Please ensure |
| 479 | that the first dimension represents the channel of the image if True. |
| 480 | dtype: output data type, if None, same as input image. defaults to float32. |
| 481 | allow_missing_keys: don't raise exception if key is missing. |
| 482 | """ |
| 483 | super().__init__(keys, allow_missing_keys) |
| 484 | self.shifter = StdShiftIntensity(factor, nonzero, channel_wise, dtype) |
| 485 | |
| 486 | def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]: |
| 487 | d = dict(data) |
nothing calls this directly
no test coverage detected