Class Spacing

monai/transforms/spatial/array.py:341–550

Resample input image into the specified `pixdim`. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information.

class Spacing(InvertibleTransform, LazyTransform):
    """
    Resample input image into the specified `pixdim`.

    This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic<lazy_resampling>`
    for more information.
    """

    backend = SpatialResample.backend

    def __init__(
        self,
        pixdim: Sequence[float] | float | np.ndarray,
        diagonal: bool = False,
        mode: str | int = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.BORDER,
        align_corners: bool = False,
        dtype: DtypeLike = np.float64,
        scale_extent: bool = False,
        recompute_affine: bool = False,
        min_pixdim: Sequence[float] | float | np.ndarray | None = None,
        max_pixdim: Sequence[float] | float | np.ndarray | None = None,
        lazy: bool = False,
    ) -> None:
        """
        Args:
            pixdim: output voxel spacing. if providing a single number, will use it for the first dimension.
                items of the pixdim sequence map to the spatial dimensions of input image, if length
                of pixdim sequence is longer than image spatial dimensions, will ignore the longer part,
                if shorter, will pad with the last value. For example, for 3D image if pixdim is [1.0, 2.0] it
                will be padded to [1.0, 2.0, 2.0]
                if the components of the `pixdim` are non-positive values, the transform will use the
                corresponding components of the original pixdim, which is computed from the `affine`
                matrix of input image.
            diagonal: whether to resample the input to have a diagonal affine matrix.
                If True, the input data is resampled to the following affine::

                    np.diag((pixdim_0, pixdim_1, ..., pixdim_n, 1))

                This effectively resets the volume to the world coordinate system (RAS+ in nibabel).
                The original orientation, rotation, shearing are not preserved.

                If False, this transform preserves the axes orientation, orthogonal rotation and
                translation components from the original affine. This option will not flip/swap axes
                of the original data.
            mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers).
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                When it&#x27;s an integer, the numpy (cpu tensor)/cupy (cuda tensor) backends will be used
                and the value represents the order of the spline interpolation.
                See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"border"``.
                See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                When `mode` is an integer, using numpy/cupy backends, this argument accepts
                {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', 'mirror', 'grid-wrap', 'wrap'}.
                See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.