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Viewer

Tier4Viewer 

Tier4Viewer(app_id: str, cameras: Sequence[str] | None = None, *, without_3d: bool = False, 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.

  • without_3d (bool, default: False ) –

    Whether to render objects without the 3D space.

  • spawn (bool, default: True ) –

    Whether to spawn the viewer.

Examples:

>>> from t4_devkit.viewer import Tier4Viewer
# Rendering both 3D/2D spaces
>>> viewer = Tier4Viewer("myapp", cameras=["camera0", "camera1"])
# Rendering 3D space only
>>> viewer = Tier4Viewer("myapp")
# Rendering 2D space only
>>> viewer = Tier4Viewer("myapp", cameras=["camera0", "camera1"], without_3d=True)

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 ID.

Returns:

  • Self –

    Self instance.

Examples:

>>> label2id = {"car": 0, "pedestrian": 1}
>>> viewer = Tier4Viewer("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(seconds: float, boxes: Sequence[Box3D]) -> None

Render 3D boxes.

Parameters:

  • seconds (float) –

    Timestamp in [sec].

  • boxes (Sequence[Box3D]) –

    Sequence of Box3Ds.

render_box2ds 

render_box2ds(seconds: float, boxes: Sequence[Box2D]) -> None

Render 2D boxes.

Parameters:

  • seconds (float) –

    Timestamp in [sec].

  • boxes (Sequence[Box2D]) –

    Sequence of Box2Ds.

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.

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"