Method __call__

monai/transforms/intensity/array.py:1469–1496

Args: mask_data: if mask data is single channel, apply to every channel of input image. if multiple channels, the channel number must match input data. mask_data will be converted to `bool` values by `mask_data > 0` before applying

(self, img: NdarrayOrTensor, mask_data: NdarrayOrTensor | None = None)

        self.select_fn = select_fn

    def __call__(self, img: NdarrayOrTensor, mask_data: NdarrayOrTensor | None = None) -> NdarrayOrTensor:
        """
        Args:
            mask_data: if mask data is single channel, apply to every channel
                of input image. if multiple channels, the channel number must
                match input data. mask_data will be converted to `bool` values
                by `mask_data > 0` before applying transform to input image.

        Raises:
            - ValueError: When both ``mask_data`` and ``self.mask_data`` are None.
            - ValueError: When ``mask_data`` and ``img`` channels differ and ``mask_data`` is not single channel.

        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        mask_data = self.mask_data if mask_data is None else mask_data
        if mask_data is None:
            raise ValueError("must provide the mask_data when initializing the transform or at runtime.")

        mask_data_, *_ = convert_to_dst_type(src=mask_data, dst=img)

        mask_data_ = self.select_fn(mask_data_)
        if mask_data_.shape[0] != 1 and mask_data_.shape[0] != img.shape[0]:
            raise ValueError(
                "When mask_data is not single channel, mask_data channels must match img, "
                f"got img channels={img.shape[0]} mask_data channels={mask_data_.shape[0]}."
            )

        return convert_to_dst_type(img * mask_data_, dst=img)[0]


class SavitzkyGolaySmooth(Transform):