Function apply_affine_to_points

monai/transforms/utils.py:2582–2600

apply affine transformation to a set of points. Args: data: input data to apply affine transformation, should be a tensor of shape (C, N, 2 or 3), where C represents the number of channels and N denotes the number of points. affine: affine matrix to be applied,

(data: torch.Tensor, affine: torch.Tensor, dtype: DtypeLike | torch.dtype | None = None)

def apply_affine_to_points(data: torch.Tensor, affine: torch.Tensor, dtype: DtypeLike | torch.dtype | None = None):
    """
    apply affine transformation to a set of points.

    Args:
        data: input data to apply affine transformation, should be a tensor of shape (C, N, 2 or 3),
            where C represents the number of channels and N denotes the number of points.
        affine: affine matrix to be applied, should be a tensor of shape (3, 3) or (4, 4).
        dtype: output data dtype.
    """
    data_: torch.Tensor = convert_to_tensor(data, track_meta=False, dtype=torch.float64)
    affine = to_affine_nd(data_.shape[-1], affine)

    homogeneous: torch.Tensor = concatenate((data_, torch.ones((data_.shape[0], data_.shape[1], 1))), axis=2)  # type: ignore
    transformed_homogeneous = torch.matmul(homogeneous, affine.T)
    transformed_coordinates = transformed_homogeneous[:, :, :-1]
    out, *_ = convert_to_dst_type(transformed_coordinates, data, dtype=dtype)

    return out


if __name__ == "__main__":