Visualization

Rendering with Tier4

If you want to visualize annotation results, Tier4 supports some rendering methods as below.

Scene

>>> t4.render_scene()

Specific Instance(s)

>>> instance_token = t4.instance[0].token
>>> t4.render_instance(instance_token)

Note

You can also render multiple instances at once:

>>> instance_tokens = [inst.token for inst in t4.instance[:3]]
>>> t4.render_instance(instance_tokens)

PointCloud

>>> t4.render_pointcloud()

Note

In case of you want to ignore camera distortion, please specify ignore_distortion=True.

>>> t4.render_pointcloud(ignore_distortion=True)

Save Recording

You can save the rendering result as follows:

>>> t4.render_scene(scene_token, save_dir=<DIR_TO_SAVE>)

When you specify save_dir, viewer will not be spawned on your screen.

Rendering with RerunViewer

If you want to visualize your components, such as boxes that your ML-model estimated, RerunViewer allows you to visualize these components. For details, please refer to the API references.

>>> from t4_devkit.viewer import RerunViewer
# You need to specify `cameras` if you want to 2D spaces
>>> viewer = RerunViewer("foo", cameras=<CAMERA_NAMES:[str;N]>)

# Timestamp in seconds
>>> seconds: int | float = ...

Rendering 3D boxes

# Rendering 3D boxes
>>> from t4_devkit.dataclass import Box3D
>>> box3ds = [Box3D(...)...]
>>> viewer.render_box3ds(seconds, box3ds)

It allows you to render boxes by specifying elements of boxes directly.

# Rendering 3D boxes
>>> centers = [[i, i, i] for i in range(10)]
>>> rotations = [[1, 0, 0, 0] for _ in range(10)]
>>> sizes = [[1, 1, 1] for _ in range(10)]
>>> class_ids = [0 for _ in range(10)]
>>> viewer.render_box3ds(seconds, centers, rotations, sizes, class_ids)

Rendering 2D boxes

For 2D spaces, you need to specify camera names in the viewer constructor, and render images by specifying camera names:

# RerunViewer(<APP_ID:str>, cameras=<CAMERA_NAMES:[str;N]>)
>>> viewer = RerunViewer("foo", cameras=["camera1"])

>>> import numpy as np
>>> image = np.zeros((100, 100, 3), dtype=np.uint8)
>>> viewer.render_image(seconds, "camera1", image)

# Rendering 2D boxes
>>> from t4_devkit.dataclass import Box2D
>>> box2ds = [Box2D(...)...]
>>> viewer.render_box2ds(seconds, "camera1", box2ds)

It allows you to render boxes by specifying elements of boxes directly:

# Rendering 2D boxes
>>> rois = [[0, 0, 10 * i, 10 * i] for i in range(10)]
>>> viewer.render_box2ds(seconds, "camera1", rois, class_ids)

Rendering point cloud

from t4_devkit.dataclass import LidarPointCloud
# Point cloud channel name
>>> lidar_channel = "LIDAR_TOP"
# Load point cloud from file
>>> pointcloud = LidarPointCloud.from_file(<PATH_TO_POINTCLOUD.pcd.bin>)
>>> viewer.render_pointcloud(seconds, lidar_channel, pointcloud)

Rendering lanelet map

# Rendering lanelet map
>>> viewer.render_map(<PATH_TO_LANELET.osm>)