# 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
from __future__ import annotations
import logging
from collections.abc import Sequence
from typing import TYPE_CHECKING, Literal
import numpy as np
import torch
import warp as wp
from packaging import version
from pxr import Sdf, UsdGeom
import isaaclab.sim as sim_utils
import isaaclab.utils.sensors as sensor_utils
from isaaclab.app.settings_manager import get_settings_manager
from isaaclab.renderers import BaseRenderer, Renderer
from isaaclab.sim.views import XformPrimView
from isaaclab.utils import has_kit, to_camel_case
from isaaclab.utils.math import (
convert_camera_frame_orientation_convention,
create_rotation_matrix_from_view,
quat_from_matrix,
)
from isaaclab.utils.version import get_isaac_sim_version
from ..sensor_base import SensorBase
from .camera_data import CameraData
if TYPE_CHECKING:
from .camera_cfg import CameraCfg
# import logger
logger = logging.getLogger(__name__)
[docs]
class Camera(SensorBase):
r"""The camera sensor for acquiring visual data.
This class wraps over the `UsdGeom Camera`_ for providing a consistent API for acquiring visual data.
It ensures that the camera follows the ROS convention for the coordinate system.
Summarizing from the `replicator extension`_, the following sensor types are supported:
- ``"rgb"``: A 3-channel rendered color image.
- ``"rgba"``: A 4-channel rendered color image with alpha channel.
- ``"albedo"``: A 4-channel fast diffuse-albedo only path for color image.
Note that this path will achieve the best performance when used alone or with depth only.
- ``"distance_to_camera"``: An image containing the distance to camera optical center.
- ``"distance_to_image_plane"``: An image containing distances of 3D points from camera plane along camera's z-axis.
- ``"depth"``: The same as ``"distance_to_image_plane"``.
- ``"simple_shading_constant_diffuse"``: Simple shading (constant diffuse) RGB approximation.
- ``"simple_shading_diffuse_mdl"``: Simple shading (diffuse MDL) RGB approximation.
- ``"simple_shading_full_mdl"``: Simple shading (full MDL) RGB approximation.
- ``"normals"``: An image containing the local surface normal vectors at each pixel.
- ``"motion_vectors"``: An image containing the motion vector data at each pixel.
- ``"semantic_segmentation"``: The semantic segmentation data.
- ``"instance_segmentation_fast"``: The instance segmentation data.
- ``"instance_id_segmentation_fast"``: The instance id segmentation data.
.. note::
Currently the following sensor types are not supported in a "view" format:
- ``"instance_segmentation"``: The instance segmentation data. Please use the fast counterparts instead.
- ``"instance_id_segmentation"``: The instance id segmentation data. Please use the fast counterparts instead.
- ``"bounding_box_2d_tight"``: The tight 2D bounding box data (only contains non-occluded regions).
- ``"bounding_box_2d_tight_fast"``: The tight 2D bounding box data (only contains non-occluded regions).
- ``"bounding_box_2d_loose"``: The loose 2D bounding box data (contains occluded regions).
- ``"bounding_box_2d_loose_fast"``: The loose 2D bounding box data (contains occluded regions).
- ``"bounding_box_3d"``: The 3D view space bounding box data.
- ``"bounding_box_3d_fast"``: The 3D view space bounding box data.
.. _replicator extension: https://docs.omniverse.nvidia.com/extensions/latest/ext_replicator/annotators_details.html#annotator-output
.. _USDGeom Camera: https://graphics.pixar.com/usd/docs/api/class_usd_geom_camera.html
"""
cfg: CameraCfg
"""The configuration parameters."""
UNSUPPORTED_TYPES: set[str] = {
"instance_id_segmentation",
"instance_segmentation",
"bounding_box_2d_tight",
"bounding_box_2d_loose",
"bounding_box_3d",
"bounding_box_2d_tight_fast",
"bounding_box_2d_loose_fast",
"bounding_box_3d_fast",
}
"""The set of sensor types that are not supported by the camera class."""
SIMPLE_SHADING_TYPES: set[str] = {
"simple_shading_constant_diffuse",
"simple_shading_diffuse_mdl",
"simple_shading_full_mdl",
}
[docs]
def __init__(self, cfg: CameraCfg):
"""Initializes the camera sensor.
Args:
cfg: The configuration parameters.
Raises:
RuntimeError: If no camera prim is found at the given path.
ValueError: If the provided data types are not supported by the camera.
"""
# perform check on supported data types
self._check_supported_data_types(cfg)
# initialize base class
super().__init__(cfg)
# toggle rendering of rtx sensors as True
# this flag is read by SimulationContext to determine if rtx sensors should be rendered
settings = get_settings_manager()
settings.set_bool("/isaaclab/render/rtx_sensors", True)
# spawn the asset
if self.cfg.spawn is not None:
# Use spawn_path when set (points to template location for scene-cloned sensors).
# This allows the camera to be spawned inside the asset template (e.g. inside
# proto_asset_0) before clone_environments replicates it to all env paths.
spawn_target = (
self.cfg.spawn.spawn_path
if getattr(self.cfg.spawn, "spawn_path", None) is not None
else self.cfg.prim_path
)
# compute the rotation offset
rot = torch.tensor(self.cfg.offset.rot, dtype=torch.float32, device="cpu").unsqueeze(0)
rot_offset = convert_camera_frame_orientation_convention(
rot, origin=self.cfg.offset.convention, target="opengl"
)
rot_offset = rot_offset.squeeze(0).cpu().numpy()
# ensure vertical aperture is set, otherwise replace with default for squared pixels
if self.cfg.spawn.vertical_aperture is None:
self.cfg.spawn.vertical_aperture = self.cfg.spawn.horizontal_aperture * self.cfg.height / self.cfg.width
self.cfg.spawn.func(spawn_target, self.cfg.spawn, translation=self.cfg.offset.pos, orientation=rot_offset)
# check that spawn was successful; use spawn_path if set (template location) since env
# paths are not yet populated at init time — they are filled in by clone_environments.
check_path = (
self.cfg.spawn.spawn_path
if self.cfg.spawn is not None and getattr(self.cfg.spawn, "spawn_path", None) is not None
else self.cfg.prim_path
)
matching_prims = sim_utils.find_matching_prims(check_path)
if len(matching_prims) == 0:
raise RuntimeError(f"Could not find prim with path {check_path}.")
# UsdGeom Camera prim for the sensor
self._sensor_prims: list[UsdGeom.Camera] = list()
# Create empty variables for storing output data
self._data = CameraData()
# Renderer and render data — assigned in _initialize_impl.
self._renderer: BaseRenderer | None = None
self._render_data = None
if not has_kit():
return
# HACK: We need to disable instancing for semantic_segmentation and instance_segmentation_fast to work
# checks for Isaac Sim v4.5 as this issue exists there
if get_isaac_sim_version() == version.parse("4.5"):
if "semantic_segmentation" in self.cfg.data_types or "instance_segmentation_fast" in self.cfg.data_types:
logger.warning(
"Isaac Sim 4.5 introduced a bug in Camera and TiledCamera when outputting instance and semantic"
" segmentation outputs for instanceable assets. As a workaround, the instanceable flag on assets"
" will be disabled in the current workflow and may lead to longer load times and increased memory"
" usage."
)
with Sdf.ChangeBlock():
for prim in self.stage.Traverse():
prim.SetInstanceable(False)
def __del__(self):
"""Unsubscribes from callbacks and cleans up renderer resources."""
# unsubscribe callbacks
super().__del__()
# cleanup render resources (renderer may be None if never initialized)
if self._renderer is not None:
self._renderer.cleanup(self._render_data)
def __str__(self) -> str:
"""Returns: A string containing information about the instance."""
# message for class
return (
f"Camera @ '{self.cfg.prim_path}': \n"
f"\tdata types : {list(self.data.output.keys())} \n"
f"\tsemantic filter : {self.cfg.semantic_filter}\n"
f"\tcolorize semantic segm. : {self.cfg.colorize_semantic_segmentation}\n"
f"\tcolorize instance segm. : {self.cfg.colorize_instance_segmentation}\n"
f"\tcolorize instance id segm.: {self.cfg.colorize_instance_id_segmentation}\n"
f"\tupdate period (s): {self.cfg.update_period}\n"
f"\tshape : {self.image_shape}\n"
f"\tnumber of sensors : {self._view.count}"
)
"""
Properties
"""
@property
def num_instances(self) -> int:
return self._view.count
@property
def data(self) -> CameraData:
# update sensors if needed
self._update_outdated_buffers()
# return the data
return self._data
@property
def frame(self) -> torch.tensor:
"""Frame number when the measurement took place."""
return self._frame
@property
def image_shape(self) -> tuple[int, int]:
"""A tuple containing (height, width) of the camera sensor."""
return (self.cfg.height, self.cfg.width)
"""
Configuration
"""
[docs]
def set_intrinsic_matrices(
self, matrices: torch.Tensor, focal_length: float | None = None, env_ids: Sequence[int] | None = None
):
"""Set parameters of the USD camera from its intrinsic matrix.
The intrinsic matrix is used to set the following parameters to the USD camera:
- ``focal_length``: The focal length of the camera.
- ``horizontal_aperture``: The horizontal aperture of the camera.
- ``vertical_aperture``: The vertical aperture of the camera.
- ``horizontal_aperture_offset``: The horizontal offset of the camera.
- ``vertical_aperture_offset``: The vertical offset of the camera.
.. warning::
Due to limitations of Omniverse camera, we need to assume that the camera is a spherical lens,
i.e. has square pixels, and the optical center is centered at the camera eye. If this assumption
is not true in the input intrinsic matrix, then the camera will not set up correctly.
Args:
matrices: The intrinsic matrices for the camera. Shape is (N, 3, 3).
focal_length: Perspective focal length (in cm) used to calculate pixel size. Defaults to None. If None,
focal_length will be calculated 1 / width.
env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices.
"""
# resolve env_ids
if env_ids is None:
env_ids = self._ALL_INDICES
# convert matrices to numpy tensors
if isinstance(matrices, torch.Tensor):
matrices = matrices.cpu().numpy()
else:
matrices = np.asarray(matrices, dtype=float)
# iterate over env_ids
for i, intrinsic_matrix in zip(env_ids, matrices):
height, width = self.image_shape
params = sensor_utils.convert_camera_intrinsics_to_usd(
intrinsic_matrix=intrinsic_matrix.reshape(-1), height=height, width=width, focal_length=focal_length
)
# change data for corresponding camera index
sensor_prim = self._sensor_prims[i]
# set parameters for camera
for param_name, param_value in params.items():
# convert to camel case (CC)
param_name = to_camel_case(param_name, to="CC")
# get attribute from the class
param_attr = getattr(sensor_prim, f"Get{param_name}Attr")
# convert numpy scalar to Python float for USD compatibility (NumPy 2.0+)
if isinstance(param_value, np.floating):
param_value = float(param_value)
# set value using pure USD API
param_attr().Set(param_value)
# update the internal buffers
self._update_intrinsic_matrices(env_ids)
"""
Operations - Set pose.
"""
[docs]
def set_world_poses(
self,
positions: torch.Tensor | None = None,
orientations: torch.Tensor | None = None,
env_ids: Sequence[int] | None = None,
convention: Literal["opengl", "ros", "world"] = "ros",
):
r"""Set the pose of the camera w.r.t. the world frame using specified convention.
Since different fields use different conventions for camera orientations, the method allows users to
set the camera poses in the specified convention. Possible conventions are:
- :obj:`"opengl"` - forward axis: -Z - up axis +Y - Offset is applied in the OpenGL (Usd.Camera) convention
- :obj:`"ros"` - forward axis: +Z - up axis -Y - Offset is applied in the ROS convention
- :obj:`"world"` - forward axis: +X - up axis +Z - Offset is applied in the World Frame convention
See :meth:`isaaclab.sensors.camera.utils.convert_camera_frame_orientation_convention` for more details
on the conventions.
Args:
positions: The cartesian coordinates (in meters). Shape is (N, 3).
Defaults to None, in which case the camera position in not changed.
orientations: The quaternion orientation in (x, y, z, w). Shape is (N, 4).
Defaults to None, in which case the camera orientation in not changed.
env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices.
convention: The convention in which the poses are fed. Defaults to "ros".
Raises:
RuntimeError: If the camera prim is not set. Need to call :meth:`initialize` method first.
"""
# resolve env_ids
if env_ids is None:
env_ids = self._ALL_INDICES
# convert to backend tensor
if positions is not None:
if isinstance(positions, np.ndarray):
positions = torch.from_numpy(positions).to(device=self._device)
elif not isinstance(positions, torch.Tensor):
positions = torch.tensor(positions, device=self._device)
# convert rotation matrix from input convention to OpenGL
if orientations is not None:
if isinstance(orientations, np.ndarray):
orientations = torch.from_numpy(orientations).to(device=self._device)
elif not isinstance(orientations, torch.Tensor):
orientations = torch.tensor(orientations, device=self._device)
orientations = convert_camera_frame_orientation_convention(orientations, origin=convention, target="opengl")
# set the pose
self._view.set_world_poses(positions, orientations, env_ids)
[docs]
def set_world_poses_from_view(
self, eyes: torch.Tensor, targets: torch.Tensor, env_ids: Sequence[int] | None = None
):
"""Set the poses of the camera from the eye position and look-at target position.
Args:
eyes: The positions of the camera's eye. Shape is (N, 3).
targets: The target locations to look at. Shape is (N, 3).
env_ids: A sensor ids to manipulate. Defaults to None, which means all sensor indices.
Raises:
RuntimeError: If the camera prim is not set. Need to call :meth:`initialize` method first.
NotImplementedError: If the stage up-axis is not "Y" or "Z".
"""
# resolve env_ids
if env_ids is None:
env_ids = self._ALL_INDICES
# get up axis of current stage
up_axis = UsdGeom.GetStageUpAxis(self.stage)
# set camera poses using the view
orientations = quat_from_matrix(create_rotation_matrix_from_view(eyes, targets, up_axis, device=self._device))
self._view.set_world_poses(eyes, orientations, env_ids)
"""
Operations
"""
[docs]
def reset(self, env_ids: Sequence[int] | None = None, env_mask: wp.array | None = None):
if not self._is_initialized:
raise RuntimeError(
"Camera could not be initialized. Please ensure --enable_cameras is used to enable rendering."
)
# reset the timestamps
super().reset(env_ids, env_mask)
# resolve to indices for torch indexing
if env_ids is None and env_mask is not None:
env_ids = wp.to_torch(env_mask).nonzero(as_tuple=False).squeeze(-1)
elif env_ids is None:
env_ids = self._ALL_INDICES
# reset the data
# note: this recomputation is useful if one performs events such as randomizations on the camera poses.
self._update_poses(env_ids)
# Reset the frame count
self._frame[env_ids] = 0
"""
Implementation.
"""
def _initialize_impl(self):
"""Initializes the sensor handles and internal buffers.
This function creates a :class:`~isaaclab.renderers.Renderer` from the configured
:attr:`~isaaclab.sensors.camera.CameraCfg.renderer_cfg` and delegates all render-product
and annotator management to it. It also initializes the internal buffers to store the data.
Raises:
RuntimeError: If the number of camera prims in the view does not match the number of environments.
RuntimeError: If cameras are not enabled (missing ``--enable_cameras`` flag).
"""
renderer_type = getattr(self.cfg.renderer_cfg, "renderer_type", "default")
needs_kit_cameras = renderer_type in ("default", "isaac_rtx")
if needs_kit_cameras and not get_settings_manager().get("/isaaclab/cameras_enabled"):
raise RuntimeError(
"A camera was spawned without the --enable_cameras flag. Please use --enable_cameras to enable"
" rendering."
)
# Initialize parent class
super()._initialize_impl()
self._renderer = Renderer(self.cfg.renderer_cfg)
logger.info("Using renderer: %s", type(self._renderer).__name__)
# Stage preprocessing must happen before creating the view because the view keeps
# references to prims located in the stage.
self._renderer.prepare_stage(self.stage, self._num_envs)
# Create a view for the sensor with Fabric enabled for fast pose queries.
# TODO: remove sync_usd_on_fabric_write=True once the GPU Fabric sync bug is fixed.
self._view = XformPrimView(
self.cfg.prim_path, device=self._device, stage=self.stage, sync_usd_on_fabric_write=True
)
# Check that sizes are correct
if self._view.count != self._num_envs:
raise RuntimeError(
f"Number of camera prims in the view ({self._view.count}) does not match"
f" the number of environments ({self._num_envs})."
)
# Create all env_ids buffer
self._ALL_INDICES = torch.arange(self._view.count, device=self._device, dtype=torch.long)
# Create frame count buffer
self._frame = torch.zeros(self._view.count, device=self._device, dtype=torch.long)
# Convert all encapsulated prims to Camera
for cam_prim in self._view.prims:
# Obtain the prim path
cam_prim_path = cam_prim.GetPath().pathString
# Check if prim is a camera
if not cam_prim.IsA(UsdGeom.Camera):
raise RuntimeError(f"Prim at path '{cam_prim_path}' is not a Camera.")
# Add to list
self._sensor_prims.append(UsdGeom.Camera(cam_prim))
# View needs to exist before creating render data
self._render_data = self._renderer.create_render_data(self)
# Create internal buffers (includes intrinsic matrix and pose init)
self._create_buffers()
def _update_buffers_impl(self, env_mask: wp.array):
env_ids = wp.to_torch(env_mask).nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) == 0:
return
# Increment frame count
self._frame[env_ids] += 1
# update latest camera pose if requested
if self.cfg.update_latest_camera_pose:
self._update_poses(env_ids)
self._renderer.update_transforms()
self._renderer.render(self._render_data)
self._renderer.read_output(self._render_data, self._data)
"""
Private Helpers
"""
def _check_supported_data_types(self, cfg: CameraCfg):
"""Checks if the data types are supported by the ray-caster camera."""
# check if there is any intersection in unsupported types
# reason: these use np structured data types which we can't yet convert to torch tensor
common_elements = set(cfg.data_types) & Camera.UNSUPPORTED_TYPES
if common_elements:
# provide alternative fast counterparts
fast_common_elements = []
for item in common_elements:
if "instance_segmentation" in item or "instance_id_segmentation" in item:
fast_common_elements.append(item + "_fast")
# raise error
raise ValueError(
f"Camera class does not support the following sensor types: {common_elements}."
"\n\tThis is because these sensor types output numpy structured data types which"
"can't be converted to torch tensors easily."
"\n\tHint: If you need to work with these sensor types, we recommend using their fast counterparts."
f"\n\t\tFast counterparts: {fast_common_elements}"
)
def _create_buffers(self):
"""Create buffers for storing data."""
# -- intrinsic matrix
self._data.intrinsic_matrices = torch.zeros((self._view.count, 3, 3), device=self._device)
self._update_intrinsic_matrices(self._ALL_INDICES)
# -- pose of the cameras
self._data.pos_w = torch.zeros((self._view.count, 3), device=self._device)
self._data.quat_w_world = torch.zeros((self._view.count, 4), device=self._device)
self._update_poses(self._ALL_INDICES)
self._data.image_shape = self.image_shape
# -- output data (eagerly pre-allocated so renderer.set_outputs() can hold tensor references)
data_dict = dict()
if "rgba" in self.cfg.data_types or "rgb" in self.cfg.data_types:
data_dict["rgba"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8
).contiguous()
if "rgb" in self.cfg.data_types:
data_dict["rgb"] = data_dict["rgba"][..., :3]
if "albedo" in self.cfg.data_types:
data_dict["albedo"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8
).contiguous()
for data_type in self.SIMPLE_SHADING_TYPES:
if data_type in self.cfg.data_types:
data_dict[data_type] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 3), device=self.device, dtype=torch.uint8
).contiguous()
if "distance_to_image_plane" in self.cfg.data_types:
data_dict["distance_to_image_plane"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.float32
).contiguous()
if "depth" in self.cfg.data_types:
data_dict["depth"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.float32
).contiguous()
if "distance_to_camera" in self.cfg.data_types:
data_dict["distance_to_camera"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.float32
).contiguous()
if "normals" in self.cfg.data_types:
data_dict["normals"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 3), device=self.device, dtype=torch.float32
).contiguous()
if "motion_vectors" in self.cfg.data_types:
data_dict["motion_vectors"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 2), device=self.device, dtype=torch.float32
).contiguous()
if "semantic_segmentation" in self.cfg.data_types:
if self.cfg.colorize_semantic_segmentation:
data_dict["semantic_segmentation"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8
).contiguous()
else:
data_dict["semantic_segmentation"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.int32
).contiguous()
if "instance_segmentation_fast" in self.cfg.data_types:
if self.cfg.colorize_instance_segmentation:
data_dict["instance_segmentation_fast"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8
).contiguous()
else:
data_dict["instance_segmentation_fast"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.int32
).contiguous()
if "instance_id_segmentation_fast" in self.cfg.data_types:
if self.cfg.colorize_instance_id_segmentation:
data_dict["instance_id_segmentation_fast"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 4), device=self.device, dtype=torch.uint8
).contiguous()
else:
data_dict["instance_id_segmentation_fast"] = torch.zeros(
(self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.int32
).contiguous()
self._data.output = data_dict
self._data.info = {name: None for name in self.cfg.data_types}
self._renderer.set_outputs(self._render_data, self._data.output)
def _update_intrinsic_matrices(self, env_ids: Sequence[int]):
"""Compute camera's matrix of intrinsic parameters.
Also called calibration matrix. This matrix works for linear depth images. We assume square pixels.
.. note::
The calibration matrix projects points in the 3D scene onto an imaginary screen of the camera.
The coordinates of points on the image plane are in the homogeneous representation.
"""
# iterate over all cameras
for i in env_ids:
# Get corresponding sensor prim
sensor_prim = self._sensor_prims[i]
# get camera parameters
# currently rendering does not use aperture offsets or vertical aperture
focal_length = sensor_prim.GetFocalLengthAttr().Get()
horiz_aperture = sensor_prim.GetHorizontalApertureAttr().Get()
# get viewport parameters
height, width = self.image_shape
# extract intrinsic parameters
f_x = (width * focal_length) / horiz_aperture
f_y = f_x
c_x = width * 0.5
c_y = height * 0.5
# create intrinsic matrix for depth linear
self._data.intrinsic_matrices[i, 0, 0] = f_x
self._data.intrinsic_matrices[i, 0, 2] = c_x
self._data.intrinsic_matrices[i, 1, 1] = f_y
self._data.intrinsic_matrices[i, 1, 2] = c_y
self._data.intrinsic_matrices[i, 2, 2] = 1
def _update_poses(self, env_ids: Sequence[int]):
"""Computes the pose of the camera in the world frame with ROS convention.
This methods uses the ROS convention to resolve the input pose. In this convention,
we assume that the camera front-axis is +Z-axis and up-axis is -Y-axis.
Returns:
A tuple of the position (in meters) and quaternion (x, y, z, w).
"""
# check camera prim exists
if len(self._sensor_prims) == 0:
raise RuntimeError("Camera prim is None. Please call 'sim.play()' first.")
# get the poses from the view
poses, quat = self._view.get_world_poses(env_ids)
self._data.pos_w[env_ids] = poses
self._data.quat_w_world[env_ids] = convert_camera_frame_orientation_convention(
quat, origin="opengl", target="world"
)
# notify renderer of updated poses (guarded in case called before initialization completes)
if self._render_data is not None:
self._renderer.update_camera(
self._render_data, self._data.pos_w, self._data.quat_w_world, self._data.intrinsic_matrices
)
"""
Internal simulation callbacks.
"""
def _invalidate_initialize_callback(self, event):
"""Invalidates the scene elements."""
# call parent
super()._invalidate_initialize_callback(event)
# set all existing views to None to invalidate them
self._view = None
