Function affine_func

monai/transforms/spatial/functional.py:590–655

Functional implementation of affine. This function operates eagerly or lazily according to ``lazy`` (default ``False``). Args: img: data to be changed, assuming `img` is channel-first. affine: the affine transformation to be applied, it can be a 3x3 or 4x4 matrix. T

(
    img, affine, grid, resampler, sp_size, mode, padding_mode, do_resampling, image_only, lazy, transform_info
)

def affine_func(
    img, affine, grid, resampler, sp_size, mode, padding_mode, do_resampling, image_only, lazy, transform_info
):
    """
    Functional implementation of affine.
    This function operates eagerly or lazily according to
    ``lazy`` (default ``False``).

    Args:
        img: data to be changed, assuming `img` is channel-first.
        affine: the affine transformation to be applied, it can be a 3x3 or 4x4 matrix. This should be defined
            for the voxel space spatial centers (``float(size - 1)/2``).
        grid: used in non-lazy mode to pre-compute the grid to do the resampling.
        resampler: the resampler function, see also: :py:class:`monai.transforms.Resample`.
        sp_size: output image spatial size.
        mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers).
            Interpolation mode to calculate output values.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
            When it'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.
            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
        do_resampling: whether to do the resampling, this is a flag for the use case of updating metadata but
            skipping the actual (potentially heavy) resampling operation.
        image_only: if True return only the image volume, otherwise return (image, affine).
        lazy: a flag that indicates whether the operation should be performed lazily or not
        transform_info: a dictionary with the relevant information pertaining to an applied transform.

    """

    # resampler should carry the align_corners and type info
    img_size = img.peek_pending_shape() if isinstance(img, MetaTensor) else img.shape[1:]
    rank = img.peek_pending_rank() if isinstance(img, MetaTensor) else torch.tensor(3.0, dtype=torch.double)
    extra_info = {
        "affine": affine,
        "mode": mode,
        "padding_mode": padding_mode,
        "do_resampling": do_resampling,
        "align_corners": resampler.align_corners,
    }
    affine = monai.transforms.Affine.compute_w_affine(rank, affine, img_size, sp_size)
    meta_info = TraceableTransform.track_transform_meta(
        img,
        sp_size=sp_size,
        affine=affine,
        extra_info=extra_info,
        orig_size=img_size,
        transform_info=transform_info,
        lazy=lazy,
    )
    if lazy:
        out = _maybe_new_metatensor(img)
        out = out.copy_meta_from(meta_info) if isinstance(out, MetaTensor) else meta_info