Method resample_if_needed

monai/data/image_writer.py:208–281

Convert the ``data_array`` into the coordinate system specified by ``target_affine``, from the current coordinate definition of ``affine``. If the transform between ``affine`` and ``target_affine`` could be achieved by simply transposing and flipping ``data_array``,

(
        cls,
        data_array: NdarrayOrTensor,
        affine: NdarrayOrTensor | None = None,
        target_affine: NdarrayOrTensor | None = None,
        output_spatial_shape: Sequence[int] | int | None = None,
        mode: str = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.BORDER,
        align_corners: bool = False,
        dtype: DtypeLike = np.float64,
    )

    @classmethod
    def resample_if_needed(
        cls,
        data_array: NdarrayOrTensor,
        affine: NdarrayOrTensor | None = None,
        target_affine: NdarrayOrTensor | None = None,
        output_spatial_shape: Sequence[int] | int | None = None,
        mode: str = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.BORDER,
        align_corners: bool = False,
        dtype: DtypeLike = np.float64,
    ):
        """
        Convert the ``data_array`` into the coordinate system specified by
        ``target_affine``, from the current coordinate definition of ``affine``.

        If the transform between ``affine`` and ``target_affine`` could be
        achieved by simply transposing and flipping ``data_array``, no resampling
        will happen.  Otherwise, this function resamples ``data_array`` using the
        transformation computed from ``affine`` and ``target_affine``.

        This function assumes the NIfTI dimension notations. Spatially it
        supports up to three dimensions, that is, H, HW, HWD for 1D, 2D, 3D
        respectively. When saving multiple time steps or multiple channels,
        time and/or modality axes should be appended after the first three
        dimensions. For example, shape of 2D eight-class segmentation
        probabilities to be saved could be `(64, 64, 1, 8)`. Also, data in
        shape `(64, 64, 8)` or `(64, 64, 8, 1)` will be considered as a
        single-channel 3D image. The ``convert_to_channel_last`` method can be
        used to convert the data to the format described here.

        Note that the shape of the resampled ``data_array`` may subject to some
        rounding errors. For example, resampling a 20x20 pixel image from pixel
        size (1.5, 1.5)-mm to (3.0, 3.0)-mm space will return a 10x10-pixel
        image. However, resampling a 20x20-pixel image from pixel size (2.0,
        2.0)-mm to (3.0, 3.0)-mm space will output a 14x14-pixel image, where
        the image shape is rounded from 13.333x13.333 pixels. In this case
        ``output_spatial_shape`` could be specified so that this function
        writes image data to a designated shape.

        Args:
            data_array: input data array to be converted.
            affine: the current affine of ``data_array``. Defaults to identity
            target_affine: the designated affine of ``data_array``.
                The actual output affine might be different from this value due to precision changes.
            output_spatial_shape: spatial shape of the output image.
                This option is used when resampling is needed.
            mode: available options are {``"bilinear"``, ``"nearest"``, ``"bicubic"``}.
                This option is used when resampling is needed.
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: available options are {``"zeros"``, ``"border"``, ``"reflection"``}.
                This option is used when resampling is needed.
                Padding mode for outside grid values. Defaults to ``"border"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            align_corners: boolean option of ``grid_sample`` to handle the corner convention.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            dtype: data type for resampling computation. Defaults to
                ``np.float64`` for best precision. If ``None``, use the data type of input data.