Class RandAffine

monai/transforms/spatial/array.py:2386–2652

Random affine transform. A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information.

class RandAffine(RandomizableTransform, InvertibleTransform, LazyTransform):
    """
    Random affine transform.
    A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.

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

    backend = Affine.backend

    def __init__(
        self,
        prob: float = 0.1,
        rotate_range: RandRange = None,
        shear_range: RandRange = None,
        translate_range: RandRange = None,
        scale_range: RandRange = None,
        spatial_size: Sequence[int] | int | None = None,
        mode: str | int = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.REFLECTION,
        cache_grid: bool = False,
        device: torch.device | None = None,
        lazy: bool = False,
    ) -> None:
        """
        Args:
            prob: probability of returning a randomized affine grid.
                defaults to 0.1, with 10% chance returns a randomized grid.
            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then
                `uniform[rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.
                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be
                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`
                for dim0 and nothing for the remaining dimensions.
            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select
                shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) 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],
                    ]

            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly
                select pixel/voxel to translate for every spatial dims.
            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select
                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.
                This allows 0 to correspond to no change (i.e., a scaling of 1.0).
            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).