Class RandAdjustContrast

monai/transforms/intensity/array.py:1247–1320

Randomly changes image intensity with gamma transform. Each pixel/voxel intensity is updated as: x = ((x - min) / intensity_range) ^ gamma * intensity_range + min Args: prob: Probability of adjustment. gamma: Range of gamma values. If single number, val

class RandAdjustContrast(RandomizableTransform):
    """
    Randomly changes image intensity with gamma transform. Each pixel/voxel intensity is updated as:

        x = ((x - min) / intensity_range) ^ gamma * intensity_range + min

    Args:
        prob: Probability of adjustment.
        gamma: Range of gamma values.
            If single number, value is picked from (0.5, gamma), default is (0.5, 4.5).
        invert_image: whether to invert the image before applying gamma augmentation. If True, multiply all intensity
            values with -1 before the gamma transform and again after the gamma transform. This behaviour is mimicked
            from `nnU-Net <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
            function.
        retain_stats: if True, applies a scaling factor and an offset to all intensity values after gamma transform to
            ensure that the output intensity distribution has the same mean and standard deviation as the intensity
            distribution of the input. This behaviour is mimicked from `nnU-Net
            <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
            function.
    """

    backend = AdjustContrast.backend

    def __init__(
        self,
        prob: float = 0.1,
        gamma: Sequence[float] | float = (0.5, 4.5),
        invert_image: bool = False,
        retain_stats: bool = False,
    ) -> None:
        RandomizableTransform.__init__(self, prob)

        if isinstance(gamma, (int, float)):
            if gamma <= 0.5:
                raise ValueError(
                    f"if gamma is a number, must greater than 0.5 and value is picked from (0.5, gamma), got {gamma}"
                )
            self.gamma = (0.5, gamma)
        elif len(gamma) != 2:
            raise ValueError("gamma should be a number or pair of numbers.")
        else:
            self.gamma = (min(gamma), max(gamma))

        self.gamma_value: float = 1.0
        self.invert_image: bool = invert_image
        self.retain_stats: bool = retain_stats

        self.adjust_contrast = AdjustContrast(
            self.gamma_value, invert_image=self.invert_image, retain_stats=self.retain_stats
        )

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        self.gamma_value = self.R.uniform(low=self.gamma[0], high=self.gamma[1])