run_usd_camera.py

# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

"""
This script shows how to use the camera sensor from the Isaac Lab framework.

The camera sensor is created and interfaced through the Omniverse Replicator API. However, instead of using
the simulator or OpenGL convention for the camera, we use the robotics or ROS convention.

.. code-block:: bash

    # Usage with GUI
    ./isaaclab.sh -p scripts/tutorials/04_sensors/run_usd_camera.py --enable_cameras

    # Usage with headless
    ./isaaclab.sh -p scripts/tutorials/04_sensors/run_usd_camera.py --headless --enable_cameras

"""

"""Launch Isaac Sim Simulator first."""

import argparse

from isaaclab.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="This script demonstrates how to use the camera sensor.")
parser.add_argument(
    "--draw",
    action="store_true",
    default=False,
    help="Draw the pointcloud from camera at index specified by ``--camera_id``.",
)
parser.add_argument(
    "--save",
    action="store_true",
    default=False,
    help="Save the data from camera at index specified by ``--camera_id``.",
)
parser.add_argument(
    "--camera_id",
    type=int,
    choices={0, 1},
    default=0,
    help=(
        "The camera ID to use for displaying points or saving the camera data. Default is 0."
        " The viewport will always initialize with the perspective of camera 0."
    ),
)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()

# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app

"""Rest everything follows."""

import os
import random

import numpy as np
import torch
import warp as wp
from isaaclab_physx.renderers import IsaacRtxRendererCfg

import omni.replicator.core as rep

import isaaclab.sim as sim_utils
from isaaclab.assets import RigidObject, RigidObjectCfg
from isaaclab.markers import VisualizationMarkers
from isaaclab.markers.config import RAY_CASTER_MARKER_CFG
from isaaclab.sensors.camera import Camera, CameraCfg
from isaaclab.sensors.camera.utils import create_pointcloud_from_depth
from isaaclab.utils import convert_dict_to_backend
from isaaclab.utils.array import convert_to_torch


def define_sensor() -> Camera:
    """Defines the camera sensor to add to the scene."""
    # Setup camera sensor
    # In contrast to the ray-cast camera, we spawn the prim at these locations.
    # This means the camera sensor will be attached to these prims.
    sim_utils.create_prim("/World/Origin_00", "Xform")
    sim_utils.create_prim("/World/Origin_01", "Xform")
    camera_cfg = CameraCfg(
        prim_path="/World/Origin_.*/CameraSensor",
        update_period=0,
        height=480,
        width=640,
        data_types=[
            "rgb",
            "distance_to_image_plane",
            "normals",
            "semantic_segmentation",
            "instance_segmentation_fast",
            "instance_id_segmentation_fast",
        ],
        renderer_cfg=IsaacRtxRendererCfg(
            colorize_semantic_segmentation=True,
            colorize_instance_id_segmentation=True,
            colorize_instance_segmentation=True,
        ),
        spawn=sim_utils.PinholeCameraCfg(
            focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5)
        ),
    )
    # Create camera
    camera = Camera(cfg=camera_cfg)

    return camera


def design_scene() -> dict:
    """Design the scene."""
    # Populate scene
    # -- Ground-plane
    cfg = sim_utils.GroundPlaneCfg()
    cfg.func("/World/defaultGroundPlane", cfg)
    # -- Lights
    cfg = sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
    cfg.func("/World/Light", cfg)

    # Create a dictionary for the scene entities
    scene_entities = {}

    # Xform to hold objects
    sim_utils.create_prim("/World/Objects", "Xform")
    # Random objects
    for i in range(8):
        # sample random position
        position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0])
        position *= np.asarray([1.5, 1.5, 0.5])
        # sample random color
        color = (random.random(), random.random(), random.random())
        # choose random prim type
        prim_type = random.choice(["Cube", "Cone", "Cylinder"])
        common_properties = {
            "rigid_props": sim_utils.RigidBodyPropertiesCfg(),
            "mass_props": sim_utils.MassPropertiesCfg(mass=5.0),
            "collision_props": sim_utils.CollisionPropertiesCfg(),
            "visual_material": sim_utils.PreviewSurfaceCfg(diffuse_color=color, metallic=0.5),
            "semantic_tags": [("class", prim_type)],
        }
        if prim_type == "Cube":
            shape_cfg = sim_utils.CuboidCfg(size=(0.25, 0.25, 0.25), **common_properties)
        elif prim_type == "Cone":
            shape_cfg = sim_utils.ConeCfg(radius=0.1, height=0.25, **common_properties)
        elif prim_type == "Cylinder":
            shape_cfg = sim_utils.CylinderCfg(radius=0.25, height=0.25, **common_properties)
        # Rigid Object
        obj_cfg = RigidObjectCfg(
            prim_path=f"/World/Objects/Obj_{i:02d}",
            spawn=shape_cfg,
            init_state=RigidObjectCfg.InitialStateCfg(pos=position),
        )
        scene_entities[f"rigid_object{i}"] = RigidObject(cfg=obj_cfg)

    # Sensors
    camera = define_sensor()

    # return the scene information
    scene_entities["camera"] = camera
    return scene_entities


def run_simulator(sim: sim_utils.SimulationContext, scene_entities: dict):
    """Run the simulator."""
    # extract entities for simplified notation
    camera: Camera = scene_entities["camera"]

    # Create replicator writer
    output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output", "camera")
    rep_writer = rep.BasicWriter(
        output_dir=output_dir,
        frame_padding=0,
        colorize_instance_id_segmentation=camera.cfg.renderer_cfg.colorize_instance_id_segmentation,
        colorize_instance_segmentation=camera.cfg.renderer_cfg.colorize_instance_segmentation,
        colorize_semantic_segmentation=camera.cfg.renderer_cfg.colorize_semantic_segmentation,
    )

    # Camera positions, targets, orientations
    camera_positions = torch.tensor([[2.5, 2.5, 2.5], [-2.5, -2.5, 2.5]], device=sim.device)
    camera_targets = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=sim.device)
    # These orientations are in ROS-convention, and will position the cameras to view the origin
    camera_orientations = torch.tensor(  # noqa: F841
        [[-0.1759, 0.3399, 0.8205, -0.4247], [-0.4247, 0.8205, -0.3399, 0.1759]], device=sim.device
    )

    # Set pose: There are two ways to set the pose of the camera.
    # -- Option-1: Set pose using view
    camera.set_world_poses_from_view(camera_positions, camera_targets)
    # -- Option-2: Set pose using ROS
    # camera.set_world_poses(camera_positions, camera_orientations, convention="ros")

    # Index of the camera to use for visualization and saving
    camera_index = args_cli.camera_id

    # Create the markers for the --draw option outside of is_running() loop
    if sim.get_setting("/isaaclab/has_gui") and args_cli.draw:
        cfg = RAY_CASTER_MARKER_CFG.replace(prim_path="/Visuals/CameraPointCloud")
        cfg.markers["hit"].radius = 0.002
        pc_markers = VisualizationMarkers(cfg)

    # Simulate physics
    while simulation_app.is_running():
        # Step simulation
        sim.step()
        # Update camera data
        camera.update(dt=sim.get_physics_dt())

        # Print camera info
        print(camera)
        if "rgb" in camera.data.output.keys():
            print("Received shape of rgb image        : ", camera.data.output["rgb"].shape)
        if "distance_to_image_plane" in camera.data.output.keys():
            print("Received shape of depth image      : ", camera.data.output["distance_to_image_plane"].shape)
        if "normals" in camera.data.output.keys():
            print("Received shape of normals          : ", camera.data.output["normals"].shape)
        if "semantic_segmentation" in camera.data.output.keys():
            print("Received shape of semantic segm.   : ", camera.data.output["semantic_segmentation"].shape)
        if "instance_segmentation_fast" in camera.data.output.keys():
            print("Received shape of instance segm.   : ", camera.data.output["instance_segmentation_fast"].shape)
        if "instance_id_segmentation_fast" in camera.data.output.keys():
            print("Received shape of instance id segm.: ", camera.data.output["instance_id_segmentation_fast"].shape)
        print("-------------------------------")

        # Extract camera data
        if args_cli.save:
            # Save images from camera at camera_index
            # note: BasicWriter only supports saving data in numpy format, so we need to convert the data to numpy.
            single_cam_data = convert_dict_to_backend(
                {k: v[camera_index] for k, v in camera.data.output.items()}, backend="numpy"
            )

            # Pack data back into replicator format to save them using its writer
            rep_output = {"annotators": {}}
            for key, data in single_cam_data.items():
                info = camera.data.info.get(key)
                if info is not None:
                    rep_output["annotators"][key] = {"render_product": {"data": data, **info}}
                else:
                    rep_output["annotators"][key] = {"render_product": {"data": data}}
            # Save images
            # Note: We need to provide On-time data for Replicator to save the images.
            rep_output["trigger_outputs"] = {"on_time": camera.frame[camera_index]}
            rep_writer.write(rep_output)

        # Draw pointcloud if there is a GUI and --draw has been passed
        if (
            sim.get_setting("/isaaclab/has_gui")
            and args_cli.draw
            and "distance_to_image_plane" in camera.data.output.keys()
        ):
            # Derive pointcloud from camera at camera_index; lift buffers to torch.
            pointcloud = create_pointcloud_from_depth(
                intrinsic_matrix=convert_to_torch(camera.data.intrinsic_matrices)[camera_index],
                depth=wp.to_torch(camera.data.output["distance_to_image_plane"])[camera_index],
                position=convert_to_torch(camera.data.pos_w)[camera_index],
                orientation=convert_to_torch(camera.data.quat_w_ros)[camera_index],
                device=sim.device,
            )

            # In the first few steps, things are still being instanced and Camera.data
            # can be empty. If we attempt to visualize an empty pointcloud it will crash
            # the sim, so we check that the pointcloud is not empty.
            if pointcloud.size()[0] > 0:
                pc_markers.visualize(translations=pointcloud)


def main():
    """Main function."""
    # Load simulation context
    sim_cfg = sim_utils.SimulationCfg(device=args_cli.device)
    sim = sim_utils.SimulationContext(sim_cfg)
    # Set main camera
    sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0])
    # Design scene
    scene_entities = design_scene()
    # Play simulator
    sim.reset()
    # Now we are ready!
    print("[INFO]: Setup complete...")
    # Run simulator
    run_simulator(sim, scene_entities)


if __name__ == "__main__":
    # run the main function
    main()
    # close sim app
    simulation_app.close()