`dataclass`

Box3D `dataclass`

Box3D(unix_time: int, frame_id: str, semantic_label: SemanticLabel, position: TranslationType, rotation: RotationType, shape: Shape, velocity: VelocityType | None = None, num_points: int | None = None, *, confidence: float = 1.0, uuid: str | None = None)

A class to represent 3D box.

Attributes:

unix_time (int) –

Unix timestamp.
frame_id (str) –

Coordinates frame ID where the box is with respect to.
semantic_label (SemanticLabel) –

SemanticLabel object.
confidence (float) –

Confidence score of the box.
uuid (str | None) –

Unique box identifier.
position (TranslationType) –

Box center position (x, y, z).
rotation (RotationType) –

Box rotation quaternion.
shape (Shape) –

Shape object.
velocity (VelocityType | None) –

Box velocity (vx, vy, vz).
num_points (int | None) –

The number of points inside the box.
future (list[Trajectory] | None) –

Box trajectory in the future of each mode.

Examples:

>>> # without future
>>> box3d = Box3D(
...     unix_time=100,
...     frame_id="base_link",
...     semantic_label=SemanticLabel(LabelID.CAR),
...     position=(1.0, 1.0, 1.0),
...     rotation=Quaternion([0.0, 0.0, 0.0, 1.0]),
...     shape=Shape(shape_type=ShapeType.BOUNDING_BOX, size=(1.0, 1.0, 1.0)),
...     velocity=(1.0, 1.0, 1.0),
...     confidence=1.0,
...     uuid="car3d_0",
... )
>>> # with future
>>> box3d = box3d.with_future(
...     waypoints=[[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]],
...     confidences=[1.0],
... )

with_future

with_future(waypoints: list[TrajectoryType], confidences: list[float]) -> Self

Return a self instance setting future attribute.

Parameters:

waypoints (list[TrajectoryType]) –

List of waypoints for each mode.
confidences (list[float]) –

List of confidences for each mode.

Returns:

Self –

Self instance after setting future.

corners

corners(box_scale: float = 1.0) -> NDArrayF64

Return the bounding box corners.

Parameters:

box_scale (float, default: 1.0 ) –

Multiply size by this factor to scale the box.

Returns:

NDArrayF64 –

First four corners are the ones facing forward. The last four are the ones facing backwards, in the shape of (8, 3).

Box2D `dataclass`

Box2D(unix_time: int, frame_id: str, semantic_label: SemanticLabel, roi: Roi | None = None, *, confidence: float = 1.0, uuid: str | None = None)

A class to represent 2D box.

Attributes:

unix_time (int) –

Unix timestamp.
frame_id (str) –

Coordinates frame ID where the box is with respect to.
semantic_label (SemanticLabel) –

SemanticLabel object.
confidence (float) –

Confidence score of the box.
uuid (str | None) –

Unique box identifier.
roi (Roi | None) –

Roi object.
position (TranslationType | None) –

3D position (x, y, z).

Examples:

>>> # without 3D position
>>> box2d = Box2D(
...     unix_time=100,
...     frame_id="camera",
...     semantic_label=SemanticLabel(LabelID.CAR),
...     roi=(100, 100, 50, 50),
...     confidence=1.0,
...     uuid="car2d_0",
... )
>>> # with 3D position
>>> box2d = box2d.with_position(position=(1.0, 1.0, 1.0))

with_position

with_position(position: TranslationType) -> Self

Return a self instance setting position attribute.

Parameters:

position (TranslationType) –

3D position.

Returns:

Self –

Self instance after setting position.

distance_box

distance_box(box: BoxType, tf_matrix: HomogeneousMatrix) -> float | None

Return a box distance from base_link.

Parameters:

box (BoxType) –

A box.
tf_matrix (HomogeneousMatrix) –

Transformation matrix.

Raises:

TypeError –

Expecting type of box is Box2D or Box3D.

Returns:

float | None –

float | None: Return None if the type of box is Box2D and its position is None, otherwise returns distance from base_link.

LabelID

Enum of label elements.

SemanticLabel `dataclass`

SemanticLabel(label: LabelID, original: str | None = None, attributes: list[str] = list())

A dataclass to represent semantic labels.

Attributes:

label (LabelID) –

Label ID.
original (str | None) –

Original name of the label.
attributes (list[str]) –

List of attribute names.

convert_label

convert_label(original: str, attributes: list[str] | None = None, *, name_mapping: dict[str, str] | None = None, update_default_mapping: bool = False) -> SemanticLabel

Covert string original label name to SemanticLabel object.

Parameters:

original (str) –

Original label name. For example, vehicle.car.
attributes (list[str] | None, default: None ) –

List of label attributes.
name_mapping (dict[str, str] | None, default: None ) –

Name mapping for original and label. If None, DEFAULT_NAME_MAPPING will be used.
update_default_mapping (bool, default: False ) –

Whether to update DEFAULT_NAME_MAPPING by name_mapping. If False and name_mapping is specified, the specified name_mapping is used instead of DEFAULT_NAME_MAPPING completely. Note that, this parameter works only if name_mapping is specified.

Returns:

SemanticLabel –

Converted SemanticLabel object.

PointCloud `dataclass`

PointCloud(points: NDArrayFloat)

Abstract base dataclass for pointcloud data.

num_dims `abstractmethod` `staticmethod`

num_dims() -> int

Return the number of the point dimensions.

Returns:

int ( int ) –

The number of the point dimensions.

from_file `abstractmethod` `classmethod`

from_file(filepath: str) -> Self

Create an object from pointcloud file.

Parameters:

filepath (str) –

File path of the pointcloud file.

Returns:

Self –

Self instance.

num_points

num_points() -> int

Return the number of points.

Returns:

int ( int ) –

description

LidarPointCloud `dataclass`

LidarPointCloud(points: NDArrayFloat)

A dataclass to represent lidar pointcloud.

ShapeType

Bases: Enum

from_name `classmethod`

from_name(name: str) -> Self

Return an enum object from the name of the member.

Parameters:

name (str) –

Name of enum member.

Returns:

Self –

Enum object.

Shape `dataclass`

Shape(shape_type: ShapeType, size: SizeType, footprint: Polygon = None)

A dataclass to represent the 3D box shape.

Examples:

>>> shape = Shape(
...     shape_type=ShapeType.BOUNDING_BOX,
...     size=[1.0, 1.0, 1.0]
... )

Trajectory `dataclass`

Trajectory(waypoints: TrajectoryType, confidence: float = 1.0)

A dataclass to represent trajectory.

Attributes:

waypoints (TrajectoryType) –

Waypoints matrix in the shape of (N, 3).
confidence (float) –

Confidence score the trajectory.

Examples:

>>> trajectory = Trajectory(
...     waypoints=[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]],
...     confidence=1.0,
... )
# Get the number of waypoints.
>>> len(trajectory)
2
# Access the shape of waypoints matrix: (N, 3).
>>> trajectory.shape
(2, 3)
# Access each point as subscriptable.
>>> trajectory[0]
array([1., 1., 1.])
# Access each point as iterable.
>>> for point in trajectory:
...     print(point)
...
[1. 1. 1.]
[2. 2. 2.]

shape `property`

shape: tuple[int, ...]

Return the shape of the waypoints matrix.

Returns:

tuple[int, ...] –

Shape of the matrix (N, 3).

to_trajectories

to_trajectories(waypoints: list[TrajectoryType], confidences: list[float]) -> list[Trajectory]

Convert a list of waypoints and confidences to a list of Trajectorys for each mode.

Parameters:

waypoints (list[TrajectoryType]) –

List of waypoints for each mode.
confidences (list[float]) –

List of confidences for each mode.

Returns:

list[Trajectory] –

List of Trajectorys for each mode.

TransformBuffer `dataclass`

TransformBuffer()

set_transform

set_transform(matrix: HomogeneousMatrix) -> None

Set transform matrix to the buffer. Also, if its inverse transformation has not been registered, registers it too.

Parameters:

matrix (HomogeneousMatrix) –

Transformation matrix.

HomogeneousMatrix `dataclass`

HomogeneousMatrix(position: ArrayLike, rotation: ArrayLike | RotationType, src: str, dst: str)

Construct a new object.

Parameters:

position (ArrayLike) –

3D position.
rotation (ArrayLike | RotationType) –

3x3 rotation matrix or quaternion.
src (str) –

Source frame ID.
dst (str) –

Destination frame ID.

shape `property`

shape: tuple[int, ...]

Return a shape of the homogeneous matrix.

Returns:

tuple[int, ...] –

Return the shape of (4, 4).

yaw_pitch_roll `property`

yaw_pitch_roll: tuple[float, float, float]

Return yaw, pitch and roll.

NOTE

yaw: Rotation angle around the z-axis in [rad], in the range [-pi, pi]. pitch: Rotation angle around the y'-axis in [rad], in the range [-pi/2, pi/2]. roll: Rotation angle around the x"-axis in [rad], in the range [-pi, pi].

Returns:

tuple[float, float, float] –

Yaw, pitch and roll in [rad].

rotation_matrix `property`

rotation_matrix: NDArray

Return a 3x3 rotation matrix.

Returns:

NDArray –

3x3 rotation matrix.

as_identity `classmethod`

as_identity(frame_id: str) -> Self

Construct a new object with identity.

Parameters:

frame_id (str) –

Frame ID.

Returns:

Self –

Constructed self instance.

from_matrix `classmethod`

from_matrix(matrix: NDArray | HomogeneousMatrix, src: str | None = None, dst: str | None = None) -> Self

Construct a new object from a homogeneous matrix.

Parameters:

matrix (NDArray | HomogeneousMatrix) –

4x4 homogeneous matrix.
src (str | None, default: None ) –

Source frame ID. This must be specified only if the input matrix is NDArray.
dst (str | None, default: None ) –

Destination frame ID. This must be specified only if the input matrix is NDArray.

Returns:

Self –

Constructed self instance.

dot

dot(other: HomogeneousMatrix) -> HomogeneousMatrix

Return a dot product of myself and another.

Parameters:

other (HomogeneousMatrix) –

HomogeneousMatrix object.

Raises:

ValueError –

self.src and other.dst must be the same frame ID.

Returns:

HomogeneousMatrix –

Result of a dot product.

inv

inv() -> HomogeneousMatrix

Return a inverse matrix of myself.

Returns:

HomogeneousMatrix –

Inverse matrix.

dataclass

Box3D dataclass

with_future

corners

Box2D dataclass

with_position

distance_box

LabelID

SemanticLabel dataclass

convert_label

PointCloud dataclass

num_dims abstractmethod staticmethod

from_file abstractmethod classmethod

num_points

LidarPointCloud dataclass

ShapeType

from_name classmethod

Shape dataclass

Trajectory dataclass

shape property

to_trajectories

TransformBuffer dataclass

set_transform

HomogeneousMatrix dataclass

shape property

yaw_pitch_roll property

rotation_matrix property

as_identity classmethod

from_matrix classmethod

dot

inv

`dataclass`

Box3D `dataclass`

Box2D `dataclass`

SemanticLabel `dataclass`

PointCloud `dataclass`

num_dims `abstractmethod` `staticmethod`

from_file `abstractmethod` `classmethod`

LidarPointCloud `dataclass`

from_name `classmethod`

Shape `dataclass`

Trajectory `dataclass`

shape `property`

TransformBuffer `dataclass`

HomogeneousMatrix `dataclass`

shape `property`

yaw_pitch_roll `property`

rotation_matrix `property`

as_identity `classmethod`

from_matrix `classmethod`