viewer

RerunViewer

RerunViewer(app_id: str, *, cameras: Sequence[str] | None = None, with_3d: bool = True, spawn: bool = True)

A viewer class that renders some components powered by rerun.

Construct a new object.

Parameters:

• app_id (str) –

  Application ID.

• cameras (Sequence[str] | None, default: None ) –

  Sequence of camera names. If None, any 2D spaces will not be visualized.

• with_3d (bool, default: True ) –

  Whether to render objects with the 3D space.

• spawn (bool, default: True ) –

  Whether to spawn the viewer.

Examples:

>>> from t4_devkit.viewer import RerunViewer
# Rendering both 3D/2D spaces
>>> viewer = RerunViewer("myapp", cameras=["camera0", "camera1"])
# Rendering 3D space only
>>> viewer = RerunViewer("myapp")
# Rendering 2D space only
>>> viewer = RerunViewer("myapp", cameras=["camera0", "camera1"], with_3d=False)

with_labels

with_labels(label2id: dict[str, int]) -> Self

Return myself after creating rr.AnnotationContext on the recording.

Parameters:

• label2id (dict[str, int]) –

  Key-value mapping which maps label name to its class ID.

Returns:

• Self –

  Self instance.

Examples:

>>> label2id = {"car": 0, "pedestrian": 1}
>>> viewer = RerunViewer("myapp").with_labels(label2id)

save

save(save_dir: str) -> None

Save recording result as save_dir/{app_id}.rrd.

Parameters:

• save_dir (str) –

  Directory path to save the result.

render_box3ds

render_box3ds(*args, **kwargs) -> None

Render 3D boxes.

render_box2ds

render_box2ds(*args, **kwargs) -> None

Render 2D boxes.

render_segmentation2d

render_segmentation2d(seconds: float, camera: str, masks: Sequence[NDArrayU8], class_ids: Sequence[int], uuids: Sequence[str | None] | None = None) -> None

Render 2D segmentation image.

Parameters:

• seconds (float) –

  Timestamp in [sec].

• camera (str) –

  Name of camera channel.

• masks (Sequence[NDArrayU8]) –

  Sequence of segmentation mask of each instance, each mask is the shape of (W, H).

• class_ids (Sequence[int]) –

  Sequence of label ids.

• uuids (Sequence[str | None] | None, default: None ) –

  Sequence of each instance ID.

render_pointcloud

render_pointcloud(seconds: float, channel: str, pointcloud: PointCloudLike) -> None

Render pointcloud.

Parameters:

• seconds (float) –

  Timestamp in [sec].

• channel (str) –

  Name of the pointcloud sensor channel.

• pointcloud (PointCloudLike) –

  Inherence object of PointCloud.

render_image

render_image(seconds: float, camera: str, image: str | NDArrayU8) -> None

Render an image.

Parameters:

• seconds (float) –

  Timestamp in [sec].

• camera (str) –

  Name of the camera channel.

• image (str | NDArrayU8) –

  Image tensor or path of the image file.

render_ego

render_ego(*args, **kwargs) -> None

Render an ego pose.

render_calibration

render_calibration(*args, **kwargs) -> None

Render a sensor calibration.

distance_color

distance_color(distances: Number | ArrayLike, cmap: str | None = None, v_min: float = 3.0, v_max: float = 75.0) -> tuple[float, float, float] | NDArrayF64

Return color map depending on distance values.

Parameters:

• distances (Number | ArrayLike) –

  Array of distances in the shape of (N,).

• cmap (str | None, default: None ) –

  Color map name in matplotlib. If None, turbo_r will be used.

• v_min (float, default: 3.0 ) –

  Min value to normalize.

• v_max (float, default: 75.0 ) –

  Max value to normalize.

Returns:

• tuple[float, float, float] | NDArrayF64 –

  Color map in the shape of (N,). If input type is any number, returns a color as tuple[float, float, float]. Otherwise, returns colors as NDArrayF64.

format_entity

format_entity(root: str, *entities) -> str

Format entity path.

Parameters:

• root (str) –

  Root entity path.

• *entities –

  Entity path(s).

Returns:

• str –

  Formatted entity path.

Examples:

>>> format_entity("map")
>>> "map"
>>> format_entity("map", "map/base_link")
"map/base_link"
>>> format_entity("map", "map/base_link", "camera")
"map/base_link/camera"