Method __init__

monai/transforms/intensity/array.py:268–290

Args: offsets: offset range to randomly shift. if single number, offset value is picked from (-offsets, offsets). safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`. E.g., `[256, -12]` -> `[array(0)

(
        self, offsets: tuple[float, float] | float, safe: bool = False, prob: float = 0.1, channel_wise: bool = False
    )

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self, offsets: tuple[float, float] | float, safe: bool = False, prob: float = 0.1, channel_wise: bool = False
    ) -> None:
        """
        Args:
            offsets: offset range to randomly shift.
                if single number, offset value is picked from (-offsets, offsets).
            safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`.
                E.g., `[256, -12]` -> `[array(0), array(244)]`. If `True`, then `[256, -12]` -> `[array(255), array(0)]`.
            prob: probability of shift.
            channel_wise: if True, shift intensity on each channel separately. For each channel, a random offset will be chosen.
                Please ensure that the first dimension represents the channel of the image if True.
        """
        RandomizableTransform.__init__(self, prob)
        if isinstance(offsets, (int, float)):
            self.offsets = (min(-offsets, offsets), max(-offsets, offsets))
        elif len(offsets) != 2:
            raise ValueError(f"offsets should be a number or pair of numbers, got {offsets}.")
        else:
            self.offsets = (min(offsets), max(offsets))
        self._offset = self.offsets[0]
        self.channel_wise = channel_wise
        self._shifter = ShiftIntensity(self._offset, safe)

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)