# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import numpy as np
import re
import torch
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Literal
import carb
import omni.isaac.core.utils.stage as stage_utils
import omni.kit.commands
import omni.usd
from omni.isaac.core.prims import XFormPrimView
from pxr import UsdGeom
import omni.isaac.lab.sim as sim_utils
from omni.isaac.lab.utils import to_camel_case
from omni.isaac.lab.utils.array import convert_to_torch
from omni.isaac.lab.utils.math import (
convert_camera_frame_orientation_convention,
create_rotation_matrix_from_view,
quat_from_matrix,
)
from ..sensor_base import SensorBase
from .camera_data import CameraData
if TYPE_CHECKING:
from .camera_cfg import CameraCfg
[文档]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.
- ``"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"``.
- ``"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."""
[文档] 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.
"""
# check if sensor path is valid
# note: currently we do not handle environment indices if there is a regex pattern in the leaf
# For example, if the prim path is "/World/Sensor_[1,2]".
sensor_path = cfg.prim_path.split("/")[-1]
sensor_path_is_regex = re.match(r"^[a-zA-Z0-9/_]+$", sensor_path) is None
if sensor_path_is_regex:
raise RuntimeError(
f"Invalid prim path for the camera sensor: {self.cfg.prim_path}."
"\n\tHint: Please ensure that the prim path does not contain any regex patterns in the leaf."
)
# 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
carb_settings_iface = carb.settings.get_settings()
carb_settings_iface.set_bool("/isaaclab/render/rtx_sensors", True)
# spawn the asset
if self.cfg.spawn is not None:
# compute the rotation offset
rot = torch.tensor(self.cfg.offset.rot, dtype=torch.float32).unsqueeze(0)
rot_offset = convert_camera_frame_orientation_convention(
rot, origin=self.cfg.offset.convention, target="opengl"
)
rot_offset = rot_offset.squeeze(0).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
# spawn the asset
self.cfg.spawn.func(
self.cfg.prim_path, self.cfg.spawn, translation=self.cfg.offset.pos, orientation=rot_offset
)
# check that spawn was successful
matching_prims = sim_utils.find_matching_prims(self.cfg.prim_path)
if len(matching_prims) == 0:
raise RuntimeError(f"Could not find prim with path {self.cfg.prim_path}.")
# UsdGeom Camera prim for the sensor
self._sensor_prims: list[UsdGeom.Camera] = list()
# Create empty variables for storing output data
self._data = CameraData()
def __del__(self):
"""Unsubscribes from callbacks and detach from the replicator registry."""
# unsubscribe callbacks
super().__del__()
# delete from replicator registry
for _, annotators in self._rep_registry.items():
for annotator, render_product_path in zip(annotators, self._render_product_paths):
annotator.detach([render_product_path])
annotator = None
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 render_product_paths(self) -> list[str]:
"""The path of the render products for the cameras.
This can be used via replicator interfaces to attach to writes or external annotator registry.
"""
return self._render_product_paths
@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
"""
[文档] def set_intrinsic_matrices(
self, matrices: torch.Tensor, focal_length: float = 1.0, env_ids: Sequence[int] | None = None
):
"""Set parameters of the USD camera from its intrinsic matrix.
The intrinsic matrix and focal length are 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: Focal length to use when computing aperture values (in cm). Defaults to 1.0.
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):
# extract parameters from matrix
f_x = intrinsic_matrix[0, 0]
c_x = intrinsic_matrix[0, 2]
f_y = intrinsic_matrix[1, 1]
c_y = intrinsic_matrix[1, 2]
# get viewport parameters
height, width = self.image_shape
height, width = float(height), float(width)
# resolve parameters for usd camera
params = {
"focal_length": focal_length,
"horizontal_aperture": width * focal_length / f_x,
"vertical_aperture": height * focal_length / f_y,
"horizontal_aperture_offset": (c_x - width / 2) / f_x,
"vertical_aperture_offset": (c_y - height / 2) / f_y,
}
# TODO: Adjust to handle aperture offsets once supported by omniverse
# Internal ticket from rendering team: OM-42611
if params["horizontal_aperture_offset"] > 1e-4 or params["vertical_aperture_offset"] > 1e-4:
omni.log.warn("Camera aperture offsets are not supported by Omniverse. These parameters are ignored.")
# 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")
# set value
# note: We have to do it this way because the camera might be on a different
# layer (default cameras are on session layer), and this is the simplest
# way to set the property on the right layer.
omni.usd.set_prop_val(param_attr(), param_value)
# update the internal buffers
self._update_intrinsic_matrices(env_ids)
"""
Operations - Set pose.
"""
[文档] 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:`omni.isaac.lab.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 (w, x, y, z). 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)
[文档] 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 = stage_utils.get_stage_up_axis()
# 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
"""
[文档] def reset(self, env_ids: Sequence[int] | 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)
# resolve None
# note: cannot do smart indexing here since we do a for loop over data.
if 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 handles and registers the provided data types with the replicator registry to
be able to access the data from the sensor. 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 replicator was not found.
"""
carb_settings_iface = carb.settings.get_settings()
if not carb_settings_iface.get("/isaaclab/cameras_enabled"):
raise RuntimeError(
"A camera was spawned without the --enable_cameras flag. Please use --enable_cameras to enable"
" rendering."
)
import omni.replicator.core as rep
from omni.syntheticdata.scripts.SyntheticData import SyntheticData
# Initialize parent class
super()._initialize_impl()
# Create a view for the sensor
self._view = XFormPrimView(self.cfg.prim_path, reset_xform_properties=False)
self._view.initialize()
# 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})."
)
# WAR: use DLAA antialiasing to avoid frame offset issue at small resolutions
if self.cfg.width < 265 or self.cfg.height < 265:
rep.settings.set_render_rtx_realtime(antialiasing="DLAA")
# 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)
# Attach the sensor data types to render node
self._render_product_paths: list[str] = list()
self._rep_registry: dict[str, list[rep.annotators.Annotator]] = {name: list() for name in self.cfg.data_types}
# Obtain current stage
stage = omni.usd.get_context().get_stage()
# Convert all encapsulated prims to Camera
for cam_prim_path in self._view.prim_paths:
# Get camera prim
cam_prim = stage.GetPrimAtPath(cam_prim_path)
# 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
sensor_prim = UsdGeom.Camera(cam_prim)
self._sensor_prims.append(sensor_prim)
# Get render product
# From Isaac Sim 2023.1 onwards, render product is a HydraTexture so we need to extract the path
render_prod_path = rep.create.render_product(cam_prim_path, resolution=(self.cfg.width, self.cfg.height))
if not isinstance(render_prod_path, str):
render_prod_path = render_prod_path.path
self._render_product_paths.append(render_prod_path)
# Check if semantic types or semantic filter predicate is provided
if isinstance(self.cfg.semantic_filter, list):
semantic_filter_predicate = ":*; ".join(self.cfg.semantic_filter) + ":*"
elif isinstance(self.cfg.semantic_filter, str):
semantic_filter_predicate = self.cfg.semantic_filter
else:
raise ValueError(f"Semantic types must be a list or a string. Received: {self.cfg.semantic_filter}.")
# set the semantic filter predicate
# copied from rep.scripts.writes_default.basic_writer.py
SyntheticData.Get().set_instance_mapping_semantic_filter(semantic_filter_predicate)
# Iterate over each data type and create annotator
# TODO: This will move out of the loop once Replicator supports multiple render products within a single
# annotator, i.e.: rep_annotator.attach(self._render_product_paths)
for name in self.cfg.data_types:
# note: we are verbose here to make it easier to understand the code.
# if colorize is true, the data is mapped to colors and a uint8 4 channel image is returned.
# if colorize is false, the data is returned as a uint32 image with ids as values.
if name == "semantic_segmentation":
init_params = {"colorize": self.cfg.colorize_semantic_segmentation}
elif name == "instance_segmentation_fast":
init_params = {"colorize": self.cfg.colorize_instance_segmentation}
elif name == "instance_id_segmentation_fast":
init_params = {"colorize": self.cfg.colorize_instance_id_segmentation}
else:
init_params = None
# Resolve device name
if "cuda" in self._device:
device_name = self._device.split(":")[0]
else:
device_name = "cpu"
# Map special cases to their corresponding annotator names
special_cases = {"rgba": "rgb", "depth": "distance_to_image_plane"}
# Get the annotator name, falling back to the original name if not a special case
annotator_name = special_cases.get(name, name)
# Create the annotator node
rep_annotator = rep.AnnotatorRegistry.get_annotator(annotator_name, init_params, device=device_name)
# attach annotator to render product
rep_annotator.attach(render_prod_path)
# add to registry
self._rep_registry[name].append(rep_annotator)
# Create internal buffers
self._create_buffers()
self._update_intrinsic_matrices(self._ALL_INDICES)
def _update_buffers_impl(self, env_ids: Sequence[int]):
# Increment frame count
self._frame[env_ids] += 1
# -- pose
self._update_poses(env_ids)
# -- read the data from annotator registry
# check if buffer is called for the first time. If so then, allocate the memory
if len(self._data.output) == 0:
# this is the first time buffer is called
# it allocates memory for all the sensors
self._create_annotator_data()
else:
# iterate over all the data types
for name, annotators in self._rep_registry.items():
# iterate over all the annotators
for index in env_ids:
# get the output
output = annotators[index].get_data()
# process the output
data, info = self._process_annotator_output(name, output)
# add data to output
self._data.output[name][index] = data
# add info to output
self._data.info[index][name] = info
"""
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."""
# create the data object
# -- 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)
# -- intrinsic matrix
self._data.intrinsic_matrices = torch.zeros((self._view.count, 3, 3), device=self._device)
self._data.image_shape = self.image_shape
# -- output data
# lazy allocation of data dictionary
# since the size of the output data is not known in advance, we leave it as None
# the memory will be allocated when the buffer() function is called for the first time.
self._data.output = {}
self._data.info = [{name: None for name in self.cfg.data_types} for _ in range(self._view.count)]
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
focal_length = sensor_prim.GetFocalLengthAttr().Get()
horiz_aperture = sensor_prim.GetHorizontalApertureAttr().Get()
vert_aperture = sensor_prim.GetVerticalApertureAttr().Get()
horiz_aperture_offset = sensor_prim.GetHorizontalApertureOffsetAttr().Get()
vert_aperture_offset = sensor_prim.GetVerticalApertureOffsetAttr().Get()
# get viewport parameters
height, width = self.image_shape
# extract intrinsic parameters
f_x = (width * focal_length) / horiz_aperture
f_y = (height * focal_length) / vert_aperture
c_x = width * 0.5 + horiz_aperture_offset * f_x
c_y = height * 0.5 + vert_aperture_offset * f_y
# 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 (w, x, y, z).
"""
# 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"
)
def _create_annotator_data(self):
"""Create the buffers to store the annotator data.
We create a buffer for each annotator and store the data in a dictionary. Since the data
shape is not known beforehand, we create a list of buffers and concatenate them later.
This is an expensive operation and should be called only once.
"""
# add data from the annotators
for name, annotators in self._rep_registry.items():
# create a list to store the data for each annotator
data_all_cameras = list()
# iterate over all the annotators
for index in self._ALL_INDICES:
# get the output
output = annotators[index].get_data()
# process the output
data, info = self._process_annotator_output(name, output)
# append the data
data_all_cameras.append(data)
# store the info
self._data.info[index][name] = info
# concatenate the data along the batch dimension
self._data.output[name] = torch.stack(data_all_cameras, dim=0)
def _process_annotator_output(self, name: str, output: Any) -> tuple[torch.tensor, dict | None]:
"""Process the annotator output.
This function is called after the data has been collected from all the cameras.
"""
# extract info and data from the output
if isinstance(output, dict):
data = output["data"]
info = output["info"]
else:
data = output
info = None
# convert data into torch tensor
data = convert_to_torch(data, device=self.device)
# process data for different segmentation types
# Note: Replicator returns raw buffers of dtype int32 for segmentation types
# so we need to convert them to uint8 4 channel images for colorized types
height, width = self.image_shape
if name == "semantic_segmentation":
if self.cfg.colorize_semantic_segmentation:
data = data.view(torch.uint8).reshape(height, width, -1)
else:
data = data.view(height, width, 1)
elif name == "instance_segmentation_fast":
if self.cfg.colorize_instance_segmentation:
data = data.view(torch.uint8).reshape(height, width, -1)
else:
data = data.view(height, width, 1)
elif name == "instance_id_segmentation_fast":
if self.cfg.colorize_instance_id_segmentation:
data = data.view(torch.uint8).reshape(height, width, -1)
else:
data = data.view(height, width, 1)
# make sure buffer dimensions are consistent as (H, W, C)
elif name == "distance_to_camera" or name == "distance_to_image_plane" or name == "depth":
data = data.view(height, width, 1)
# we only return the RGB channels from the RGBA output if rgb is required
# normals return (x, y, z) in first 3 channels, 4th channel is unused
elif name == "rgb" or name == "normals":
data = data[..., :3]
# motion vectors return (x, y) in first 2 channels, 3rd and 4th channels are unused
elif name == "motion_vectors":
data = data[..., :2]
# return the data and info
return data, info
"""
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
