.. _how-to-save-images-and-3d-reprojection: Saving rendered images and 3D re-projection =========================================== .. currentmodule:: isaaclab This guide accompanied with the ``run_usd_camera.py`` script in the ``IsaacLab/scripts/tutorials/04_sensors`` directory. .. dropdown:: Code for run_usd_camera.py :icon: code .. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py :language: python :emphasize-lines: 174-182, 232-250, 254-267 :linenos: Saving using Replicator Basic Writer ------------------------------------ .. note:: The BasicWriter is part of the Omniverse Replicator ecosystem and is specific to the default Isaac RTX renderer backend. Other renderer backends may require different save workflows. .. note:: The ``colorize_*`` arguments below are set on :attr:`~isaaclab.sensors.camera.CameraCfg.renderer_cfg` (an :class:`~isaaclab_physx.renderers.IsaacRtxRendererCfg`); the same-named fields on :class:`~isaaclab.sensors.camera.CameraCfg` are deprecated. To save camera outputs, we use the basic write class from Omniverse Replicator. This class allows us to save the images in a numpy format. For more information on the basic writer, please check the `documentation `_. .. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py :language: python :start-at: rep_writer = rep.BasicWriter( :end-before: # Camera positions, targets, orientations While stepping the simulator, the images can be saved to the defined folder. Since the BasicWriter only supports saving data using NumPy format, we first need to convert the PyTorch sensors to NumPy arrays before packing them in a dictionary and writing with the BasicWriter. .. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py :language: python :start-at: # Save images from camera at camera_index :end-at: rep_writer.write(rep_output) Projection into 3D Space ------------------------ We include utilities to project the depth image into 3D Space. The re-projection operations are done using PyTorch operations which allows faster computation. .. code-block:: python from isaaclab.utils.math import transform_points, unproject_depth # Pointcloud in world frame points_3d_cam = unproject_depth( camera.data.output["distance_to_image_plane"], camera.data.intrinsic_matrices ) points_3d_world = transform_points(points_3d_cam, camera.data.pos_w, camera.data.quat_w_ros) Alternately, we can use the :meth:`isaaclab.sensors.camera.utils.create_pointcloud_from_depth` function to create a point cloud from the depth image and transform it to the world frame. .. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py :language: python :start-at: # Derive pointcloud from camera at camera_index :end-before: # In the first few steps, things are still being instanced and Camera.data The resulting point cloud can be visualized using the :mod:`isaacsim.util.debug_draw` extension from Isaac Sim. This makes it easy to visualize the point cloud in the 3D space. .. literalinclude:: ../../../scripts/tutorials/04_sensors/run_usd_camera.py :language: python :start-at: # In the first few steps, things are still being instanced and Camera.data :end-at: pc_markers.visualize(translations=pointcloud) Executing the script -------------------- To run the accompanying script, execute the following command: .. code-block:: bash # Usage with saving and drawing python scripts/tutorials/04_sensors/run_usd_camera.py --save --draw --enable_cameras # Usage with saving only in headless mode python scripts/tutorials/04_sensors/run_usd_camera.py --save --enable_cameras The simulation should start, and you can observe different objects falling down. An output folder will be created in the ``IsaacLab/scripts/tutorials/04_sensors`` directory, where the images will be saved. Additionally, you should see the point cloud in the 3D space drawn on the viewport. To stop the simulation, close the window, or use ``Ctrl+C`` in the terminal.