Kit visualizer: interactive viewport
Kit visualizer: streaming camera view
Example 2: Streaming from Robot-Mounted Cameras ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. tab-set:: :sync-group: os .. tab-item:: :icon:`fa-brands fa-linux` Linux :sync: linux .. code-block:: bash uv run --extra teleop python scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py \ --task IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor --num_envs 25 --viz newton_gl .. tab-item:: :icon:`fa-brands fa-windows` Windows :sync: windows .. code-block:: batch uv run --extra teleop python scripts/tutorials/07_visualizers/run_tiled_camera_visualizer.py ^ --task IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor --num_envs 25 --viz newton_gl The Galbot cube-stacking environment ships with wrist-mounted cameras giving an egocentric view of the gripper, table, and cubes. The script's ``NewtonGLVisualizerCfg`` streams from the existing sensor at ``/World/envs/env_.*/Robot/head_camera_sim_view_frame/head_camera``; edit ``streaming_sensor_prim_path`` to show a different camera. Of the 25 environments, 12 camera feeds are shown by default. .. raw:: htmlNewton visualizer: interactive viewport
Newton visualizer: streaming camera view
Configuration ------------- To customize streaming camera behavior, edit the highlighted ``VisualizerCfg`` fields in ``run_tiled_camera_visualizer.py``: * For auto-created cameras, ``streaming_cam_target_prim_path`` chooses the followed prim and ``streaming_cam_eye`` sets the camera offset from that prim. Defaults to ``None``, which causes the visualizer to adopt the first scene camera it discovers at init; no explicit path is needed when a ``TiledCamera`` sensor is already in the scene. * For existing scene cameras, ``streaming_sensor_prim_path`` must match an Isaac Lab :class:`~isaaclab.sensors.Camera` sensor prim path in the selected task. * ``streaming_envs`` controls how many environment tiles are shown. Pass an ``int`` to randomly sample that many environments, or a ``list[int]`` to pin specific environment indices. * ``streaming_gt_types`` selects which ground-truth types are shown, e.g. ``["rgb", "depth", "segmentation", "normals"]``. * ``streaming_depth_min`` / ``streaming_depth_max`` set the depth colormap range in metres. Troubleshooting --------------- * If a generated view fails with a missing prim error, verify that ``streaming_cam_target_prim_path`` resolves in each selected environment; common template forms are ``/World/envs/*/...`` and ``/World/envs/env_.*/...``. In most cases you can leave it as ``None`` and let the visualizer adopt an existing scene camera automatically. * If an existing-camera view reports that no Isaac Lab camera owns the prim, check that ``streaming_sensor_prim_path`` matches a :class:`~isaaclab.sensors.Camera` sensor in the task. * If the depth panel shows a flat color, adjust ``streaming_depth_min`` and ``streaming_depth_max`` to bracket the expected depth range in your scene. * If the view is too expensive, reduce ``streaming_envs``, ``--num_envs``, or the camera resolution. .. warning:: **Newton MJWarp with** ``replicate_physics=True`` **and auto-created cameras** With ``replicate_physics=True``, only ``env_0`` has a USD prim after physics initialization. Cameras for the remaining environments (``env_1`` through ``env_{N-1}``) are dropped, causing initialization to fail:: RuntimeError: Number of camera prims in the view (1) does not match the number of environments (N). **Workaround**: set ``streaming_sensor_prim_path`` to a scene camera that was declared in the scene config before physics init (for example, a ``TiledCamera`` on a vision-based task). See also -------- * :ref:`visualization-streaming-camera-view`: how the streaming view works and which visualizers support it * :doc:`/source/concepts/visualization`: visualizer configuration and UI controls * :doc:`/source/how-to/record_video`: recording the streaming view to video * :doc:`/source/how-to/configure_rendering`: customizing RTX rendering settings