Method __init__

monai/transforms/spatial/dictionary.py:904–993

Args: keys: keys of the corresponding items to be transformed. rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D. Defaults to no rotation. shear_params: shearing factors for affine matrix, take a

(
        self,
        keys: KeysCollection,
        rotate_params: Sequence[float] | float | None = None,
        shear_params: Sequence[float] | float | None = None,
        translate_params: Sequence[float] | float | None = None,
        scale_params: Sequence[float] | float | None = None,
        affine: NdarrayOrTensor | None = None,
        spatial_size: Sequence[int] | int | None = None,
        mode: SequenceStr = GridSampleMode.BILINEAR,
        padding_mode: SequenceStr = GridSamplePadMode.REFLECTION,
        device: torch.device | None = None,
        dtype: DtypeLike | torch.dtype = np.float32,
        align_corners: bool = False,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    )

    backend = Affine.backend

    def __init__(
        self,
        keys: KeysCollection,
        rotate_params: Sequence[float] | float | None = None,
        shear_params: Sequence[float] | float | None = None,
        translate_params: Sequence[float] | float | None = None,
        scale_params: Sequence[float] | float | None = None,
        affine: NdarrayOrTensor | None = None,
        spatial_size: Sequence[int] | int | None = None,
        mode: SequenceStr = GridSampleMode.BILINEAR,
        padding_mode: SequenceStr = GridSamplePadMode.REFLECTION,
        device: torch.device | None = None,
        dtype: DtypeLike | torch.dtype = np.float32,
        align_corners: bool = False,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    ) -> None:
        """
        Args:
            keys: keys of the corresponding items to be transformed.
            rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D.
                Defaults to no rotation.
            shear_params: shearing factors for affine matrix, take a 3D affine as example::

                [
                    [1.0, params[0], params[1], 0.0],
                    [params[2], 1.0, params[3], 0.0],
                    [params[4], params[5], 1.0, 0.0],
                    [0.0, 0.0, 0.0, 1.0],
                ]

                a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing.
            translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in
                pixel/voxel relative to the center of the input image. Defaults to no translation.
            scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D,
                a tuple of 3 floats for 3D. Defaults to `1.0`.
            affine: if applied, ignore the params (`rotate_params`, etc.) and use the
                supplied matrix. Should be square with each side = num of image spatial
                dimensions + 1.
            spatial_size: output image spatial size.
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
                if some components of the `spatial_size` are non-positive values, the transform will use the
                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
                to `(32, 64)` if the second spatial dimension size of img is `64`.
            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'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
                It also can be a sequence, each element corresponds to a key in ``keys``.
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"reflection"``.
                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