Class BasePoints

embodiedscan/structures/points/base_points.py:14–522

Base class for Points. Args: tensor (Tensor or np.ndarray or Sequence[Sequence[float]]): The points data with shape (N, points_dim). points_dim (int): Integer indicating the dimension of a point. Each row is (x, y, z, ...). Defaults to 3. attribut

class BasePoints:
    """Base class for Points.

    Args:
        tensor (Tensor or np.ndarray or Sequence[Sequence[float]]): The points
            data with shape (N, points_dim).
        points_dim (int): Integer indicating the dimension of a point. Each row
            is (x, y, z, ...). Defaults to 3.
        attribute_dims (dict, optional): Dictionary to indicate the meaning of
            extra dimension. Defaults to None.

    Attributes:
        tensor (Tensor): Float matrix with shape (N, points_dim).
        points_dim (int): Integer indicating the dimension of a point. Each row
            is (x, y, z, ...).
        attribute_dims (dict, optional): Dictionary to indicate the meaning of
            extra dimension. Defaults to None.
        rotation_axis (int): Default rotation axis for points rotation.
    """

    def __init__(self,
                 tensor: Union[Tensor, np.ndarray, Sequence[Sequence[float]]],
                 points_dim: int = 3,
                 attribute_dims: Optional[dict] = None) -> None:
        if isinstance(tensor, Tensor):
            device = tensor.device
        else:
            device = torch.device('cpu')
        tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
        if tensor.numel() == 0:
            # Use reshape, so we don't end up creating a new tensor that does
            # not depend on the inputs (and consequently confuses jit)
            tensor = tensor.reshape((-1, points_dim))
        assert tensor.dim() == 2 and tensor.size(-1) == points_dim, \
            ('The points dimension must be 2 and the length of the last '
             f'dimension must be {points_dim}, but got points with shape '
             f'{tensor.shape}.')

        self.tensor = tensor.clone()
        self.points_dim = points_dim
        self.attribute_dims = attribute_dims
        self.rotation_axis = 0

    @property
    def coord(self) -> Tensor:
        """Tensor: Coordinates of each point in shape (N, 3)."""
        return self.tensor[:, :3]

    @coord.setter
    def coord(self, tensor: Union[Tensor, np.ndarray]) -> None:
        """Set the coordinates of each point.

        Args:
            tensor (Tensor or np.ndarray): Coordinates of each point with shape
                (N, 3).
        """
        try:
            tensor = tensor.reshape(self.shape[0], 3)