Function rotate

monai/transforms/spatial/functional.py:385–453

Functional implementation of rotate. This function operates eagerly or lazily according to ``lazy`` (default ``False``). Args: img: data to be changed, assuming `img` is channel-first. angle: Rotation angle(s) in radians. should a float for 2D, three floats for 3D.

(img, angle, output_shape, mode, padding_mode, align_corners, dtype, lazy, transform_info)

def rotate(img, angle, output_shape, mode, padding_mode, align_corners, dtype, lazy, transform_info):
    """
    Functional implementation of rotate.
    This function operates eagerly or lazily according to
    ``lazy`` (default ``False``).

    Args:
        img: data to be changed, assuming `img` is channel-first.
        angle: Rotation angle(s) in radians. should a float for 2D, three floats for 3D.
        output_shape: output shape of the rotated data.
        mode: {``"bilinear"``, ``"nearest"``}
            Interpolation mode to calculate output values.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.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
        align_corners: See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
        dtype: data type for resampling computation.
            If None, use the data type of input data. To be compatible with other modules,
            the output data type is always ``float32``.
        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.

    """

    im_shape = img.peek_pending_shape() if isinstance(img, MetaTensor) else img.shape[1:]
    input_ndim = len(im_shape)
    if input_ndim not in (2, 3):
        raise ValueError(f"Unsupported image dimension: {input_ndim}, available options are [2, 3].")
    _angle = ensure_tuple_rep(angle, 1 if input_ndim == 2 else 3)
    transform = create_rotate(input_ndim, _angle)
    if output_shape is None:
        corners = np.asarray(np.meshgrid(*[(0, dim) for dim in im_shape], indexing="ij")).reshape((len(im_shape), -1))
        corners = transform[:-1, :-1] @ corners  # type: ignore
        output_shape = np.asarray(np.ptp(corners, axis=1) + 0.5, dtype=int)
    else:
        output_shape = np.asarray(output_shape, dtype=int)
    shift = create_translate(input_ndim, ((np.array(im_shape) - 1) / 2).tolist())
    shift_1 = create_translate(input_ndim, (-(np.asarray(output_shape, dtype=int) - 1) / 2).tolist())
    transform = shift @ transform @ shift_1
    extra_info = {
        "rot_mat": transform,
        "mode": mode,
        "padding_mode": padding_mode,
        "align_corners": align_corners if align_corners is not None else TraceKeys.NONE,
        "dtype": str(dtype)[6:],  # dtype as string; remove "torch": torch.float32 -> float32
    }
    meta_info = TraceableTransform.track_transform_meta(
        img,
        sp_size=output_shape,
        affine=transform,
        extra_info=extra_info,
        orig_size=im_shape,
        transform_info=transform_info,
        lazy=lazy,
    )
    out = _maybe_new_metatensor(img)
    if lazy: