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NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/wrapTokens.cpp
|
//
// Copyright 2016 Pixar
//
// Licensed under the Apache License, Version 2.0 (the "Apache License")
// with the following modification; you may not use this file except in
// compliance with the Apache License and the following modification to it:
// Section 6. Trademarks. is deleted and replaced with:
//
// 6. Trademarks. This License does not grant permission to use the trade
// names, trademarks, service marks, or product names of the Licensor
// and its affiliates, except as required to comply with Section 4(c) of
// the License and to reproduce the content of the NOTICE file.
//
// You may obtain a copy of the Apache License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the Apache License with the above modification is
// distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the Apache License for the specific
// language governing permissions and limitations under the Apache License.
//
// GENERATED FILE. DO NOT EDIT.
#include <boost/python/class.hpp>
#include ".//tokens.h"
PXR_NAMESPACE_USING_DIRECTIVE
namespace {
// Helper to return a static token as a string. We wrap tokens as Python
// strings and for some reason simply wrapping the token using def_readonly
// bypasses to-Python conversion, leading to the error that there's no
// Python type for the C++ TfToken type. So we wrap this functor instead.
class _WrapStaticToken {
public:
_WrapStaticToken(const TfToken* token) : _token(token) { }
std::string operator()() const
{
return _token->GetString();
}
private:
const TfToken* _token;
};
template <typename T>
void
_AddToken(T& cls, const char* name, const TfToken& token)
{
cls.add_static_property(name,
boost::python::make_function(
_WrapStaticToken(&token),
boost::python::return_value_policy<
boost::python::return_by_value>(),
boost::mpl::vector1<std::string>()));
}
} // anonymous
void wrapOmniWarpSceneIndexTokens()
{
boost::python::class_<OmniWarpSceneIndexTokensType, boost::noncopyable>
cls("Tokens", boost::python::no_init);
_AddToken(cls, "warpDependentPrims", OmniWarpSceneIndexTokens->warpDependentPrims);
_AddToken(cls, "warpSourceFile", OmniWarpSceneIndexTokens->warpSourceFile);
_AddToken(cls, "OmniWarpComputationAPI", OmniWarpSceneIndexTokens->OmniWarpComputationAPI);
}
| 2,626 |
C++
| 35.999999 | 95 | 0.690023 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/warpComputationAPIAdapter.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
#include <pxr/base/tf/stringUtils.h>
#include <pxr/imaging/hd/retainedDataSource.h>
#include <pxr/usdImaging/usdImaging/dataSourceAttribute.h>
#include "warpComputationAPIAdapter.h"
#include "warpComputationAPI.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_DEFINE_PRIVATE_TOKENS(
_tokens,
(warpComputation)
(sourceFile)
(dependentPrims)
(simulationParams)
);
TF_REGISTRY_FUNCTION(TfType)
{
typedef WarpComputationAPIAdapter Adapter;
TfType t = TfType::Define<Adapter, TfType::Bases<Adapter::BaseAdapter> >();
t.SetFactory< UsdImagingAPISchemaAdapterFactory<Adapter> >();
}
// ----------------------------------------------------------------------------
namespace
{
class SimulationParamsDataSource : public HdSampledDataSource
{
public:
HD_DECLARE_DATASOURCE(SimulationParamsDataSource);
SimulationParamsDataSource(
const VtDictionary &dict)
: _customData(dict)
{
}
VtValue
GetValue(Time shutterOffset)
{
return VtValue(_customData);
}
bool
GetContributingSampleTimesForInterval(
Time startTime,
Time endTime,
std::vector<Time> * outSampleTimes)
{
return false;
}
VtDictionary _customData;
};
class DependentPrimsDataSource : public HdPathArrayDataSource
{
public:
HD_DECLARE_DATASOURCE(DependentPrimsDataSource);
DependentPrimsDataSource(
const UsdRelationship &rel)
: _usdRel(rel)
{
}
VtValue
GetValue(
HdSampledDataSource::Time shutterOffset)
{
return VtValue(GetTypedValue(shutterOffset));
}
VtArray<SdfPath>
GetTypedValue(
HdSampledDataSource::Time shutterOffset)
{
SdfPathVector paths;
_usdRel.GetForwardedTargets(&paths);
VtArray<SdfPath> vtPaths(paths.begin(), paths.end());
return vtPaths;
}
bool
GetContributingSampleTimesForInterval(
HdSampledDataSource::Time startTime,
HdSampledDataSource::Time endTime,
std::vector<HdSampledDataSource::Time> *outSampleTimes)
{
return false;
}
private:
UsdRelationship _usdRel;
};
HD_DECLARE_DATASOURCE_HANDLES(DependentPrimsDataSource);
class _WarpComputationDataSource : public HdContainerDataSource
{
public:
HD_DECLARE_DATASOURCE(_WarpComputationDataSource);
_WarpComputationDataSource(
const UsdPrim &prim,
const UsdImagingDataSourceStageGlobals &stageGlobals)
: _api(prim)
, _stageGlobals(stageGlobals)
{
}
TfTokenVector GetNames() override
{
TfTokenVector result;
result.reserve(4);
result.push_back(_tokens->warpComputation);
if (UsdAttribute attr = _api.GetSourceFileAttr()) {
result.push_back(_tokens->sourceFile);
VtDictionary customData = attr.GetCustomData();
VtDictionary::iterator iter = customData.begin();
if (iter != customData.end())
{
result.push_back(_tokens->simulationParams);
}
}
if (_api.GetDependentPrimsRel()) {
result.push_back(_tokens->dependentPrims);
}
return result;
}
HdDataSourceBaseHandle Get(const TfToken &name) override {
if (name == _tokens->sourceFile)
{
if (UsdAttribute attr = _api.GetSourceFileAttr())
{
return UsdImagingDataSourceAttributeNew(attr, _stageGlobals);
}
}
else if (name == _tokens->dependentPrims)
{
if (UsdRelationship rel = _api.GetDependentPrimsRel())
{
return DependentPrimsDataSource::New(rel);
}
}
else if (name == _tokens->simulationParams)
{
if (UsdAttribute attr = _api.GetSourceFileAttr())
{
VtDictionary customData = attr.GetCustomData();
VtDictionary::iterator iter = customData.begin();
if (iter != customData.end())
{
return SimulationParamsDataSource::New(customData);
}
}
}
return nullptr;
}
private:
OmniWarpSceneIndexWarpComputationAPI _api;
const UsdImagingDataSourceStageGlobals &_stageGlobals;
};
HD_DECLARE_DATASOURCE_HANDLES(_WarpComputationDataSource);
} // anonymous namespace
// ----------------------------------------------------------------------------
HdContainerDataSourceHandle
WarpComputationAPIAdapter::GetImagingSubprimData(
UsdPrim const& prim,
TfToken const& subprim,
TfToken const& appliedInstanceName,
const UsdImagingDataSourceStageGlobals &stageGlobals)
{
OmniWarpSceneIndexWarpComputationAPI _api(prim);
std::string pythonModuleName;
UsdAttribute attr = _api.GetSourceFileAttr();
attr.Get(&pythonModuleName, 0.f);
if (pythonModuleName.length())
{
return HdRetainedContainerDataSource::New(
_tokens->warpComputation,
_WarpComputationDataSource::New(
prim, stageGlobals));
}
return nullptr;
}
#if PXR_VERSION < 2308
HdDataSourceLocatorSet
WarpComputationAPIAdapter::InvalidateImagingSubprim(
UsdPrim const& prim,
TfToken const& subprim,
TfToken const& appliedInstanceName,
TfTokenVector const& properties)
#else
HdDataSourceLocatorSet
WarpComputationAPIAdapter::InvalidateImagingSubprim(
UsdPrim const& prim,
TfToken const& subprim,
TfToken const& appliedInstanceName,
TfTokenVector const& properties,
const UsdImagingPropertyInvalidationType invalidationType)
#endif
{
#if 0
if (!subprim.IsEmpty() || appliedInstanceName.IsEmpty()) {
return HdDataSourceLocatorSet();
}
std::string prefix = TfStringPrintf(
"collections:%s:", appliedInstanceName.data());
for (const TfToken &propertyName : properties) {
if (TfStringStartsWith(propertyName.GetString(), prefix)) {
return HdDataSourceLocator(
_tokens->usdCollections, appliedInstanceName);
}
}
#endif
return HdDataSourceLocatorSet();
}
PXR_NAMESPACE_CLOSE_SCOPE
| 6,767 |
C++
| 25.4375 | 79 | 0.645338 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/warpPythonModule.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
#include <pxr/base/tf/pyInvoke.h>
#include <pxr/base/tf/errorMark.h>
#include <pxr/base/tf/pyExceptionState.h>
#include <pxr/base/tf/pyInterpreter.h>
#include <pxr/imaging/hd/tokens.h>
#include "warpPythonModule.h"
#include "tokens.h"
PXR_NAMESPACE_OPEN_SCOPE
OmniWarpPythonModule::OmniWarpPythonModule(const SdfPath &primPath,
const std::string& moduleName, UsdImagingStageSceneIndexConstRefPtr usdImagingSi)
: _primPath(primPath),
_moduleName(moduleName),
_usdImagingSi(usdImagingSi)
{
}
OmniWarpPythonModule::~OmniWarpPythonModule()
{
TfPyLock pyLock;
boost::python::object result;
TfPyInvokeAndReturn(_moduleName.c_str(), "terminate_sim", &result, _primPath);
}
void OmniWarpPythonModule::InitMesh(VtIntArray indices, VtVec3fArray vertices,
VtIntArray depIndices, VtVec3fArray depVertices, VtDictionary simParams)
{
TfPyLock pyLock;
boost::python::object result;
TfPyInvokeAndReturn(_moduleName.c_str(), "initialize_sim_mesh", &result, _primPath, indices, vertices,
depIndices, depVertices, simParams);
}
void OmniWarpPythonModule::InitParticles(
VtVec3fArray positions, VtIntArray depIndices, VtVec3fArray depVertices, VtDictionary simParams)
{
TfPyLock pyLock;
boost::python::object result;
TfPyInvokeAndReturn(_moduleName.c_str(), "initialize_sim_particles", &result,
_primPath, positions, depIndices, depVertices, simParams);
}
VtVec3fArray OmniWarpPythonModule::ExecSim(VtDictionary simParams)
{
return ExecSim(simParams, VtVec3fArray());
}
VtVec3fArray OmniWarpPythonModule::ExecSim(VtDictionary simParams, VtVec3fArray dependentVertices)
{
TfPyLock pyLock;
boost::python::object result;
float dt = 0.f;
if (_usdImagingSi)
{
dt = _usdImagingSi->GetTime().GetValue();
}
if (TfPyInvokeAndReturn(_moduleName.c_str(), "exec_sim", &result, _primPath, dt, dependentVertices, simParams))
{
boost::python::extract<VtVec3fArray> theResults(result);
if (theResults.check())
{
return theResults();
}
}
return VtVec3fArray();
}
PXR_NAMESPACE_CLOSE_SCOPE
| 2,735 |
C++
| 30.090909 | 115 | 0.729068 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/warpModules/particles.py
|
# Copyright 2023 NVIDIA CORPORATION
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import warp as wp
import warp.sim
import warp.sim.render
import numpy as np
from pxr import Vt, Sdf
wp.init()
global_examples = {}
# need radius of spehere
class Example2:
def __init__(self):
self.frame_dt = 1.0 / 60
self.frame_count = 400
self.sim_substeps = 64
self.sim_dt = self.frame_dt / self.sim_substeps
self.sim_steps = self.frame_count * self.sim_substeps
self.sim_time = 0.0
self.radius = 0.1
self.builder = wp.sim.ModelBuilder()
self.builder.default_particle_radius = self.radius
def update(self):
self.model.particle_grid.build(self.state_0.particle_q, self.radius * 2.0)
for s in range(self.sim_substeps):
self.state_0.clear_forces()
self.integrator.simulate(self.model, self.state_0, self.state_1, self.sim_dt)
# swap states
(self.state_0, self.state_1) = (self.state_1, self.state_0)
def terminate_sim(primPath: Sdf.Path):
global global_examples
global_examples[primPath] = None
def initialize_sim_particles(primPath: Sdf.Path,
src_positions: Vt.Vec3fArray, dep_mesh_indices: Vt.IntArray = None, dep_mesh_points: Vt.Vec3fArray = None, sim_params: dict = None):
global global_examples
global_examples[primPath] = Example2()
for pt in src_positions:
global_examples[primPath].builder.add_particle(pt, (5.0, 0.0, 0.0), 0.1)
global_examples[primPath].model = global_examples[primPath].builder.finalize()
global_examples[primPath].model.particle_kf = 25.0
global_examples[primPath].model.soft_contact_kd = 100.0
global_examples[primPath].model.soft_contact_kf *= 2.0
global_examples[primPath].state_0 = global_examples[primPath].model.state()
global_examples[primPath].state_1 = global_examples[primPath].model.state()
global_examples[primPath].integrator = wp.sim.SemiImplicitIntegrator()
def exec_sim(primPath: Sdf.Path, sim_dt: float, dep_mesh_points: Vt.Vec3fArray = None, sim_params: dict = None):
# Not respecting sim_dt at all, using internal time
global global_examples
global_examples[primPath].update()
return Vt.Vec3fArray.FromNumpy(global_examples[primPath].state_0.particle_q.numpy())
| 2,841 |
Python
| 33.658536 | 136 | 0.697994 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/warpModules/cloth.py
|
# Copyright (c) 2022 NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
###########################################################################
# Example Sim Cloth
#
# Shows a simulation of an FEM cloth model colliding against a static
# rigid body mesh using the wp.sim.ModelBuilder().
#
###########################################################################
import os
import math
import numpy as np
import warp as wp
import warp.sim
import warp.sim.render
from pxr import Usd, UsdGeom, Vt, Sdf
import sys
wp.init()
global_examples = {}
class Example:
def __init__(self, indices: Vt.IntArray, points: Vt.Vec3fArray):
self.sim_width = 64
self.sim_height = 64
self.frame_dt = 1.0 / 60
self.frame_count = 400
self.sim_substeps = 32
self.sim_dt = self.frame_dt / self.sim_substeps
self.sim_steps = self.frame_count * self.sim_substeps
self.sim_time = 0.0
builder = wp.sim.ModelBuilder()
# sim BCs
clothEdgeBendingStiffness = 0.01
clothEdgeDampingStiffness = 0.0
clothTriAreaStiffness = 1000000.0
clothTriDampingStiffness = 100.0
clothTriElasticStiffness = 1000000.0
colliderContactDistance = 1.0
colliderContactQueryRange = 100.0
contactDampingStiffness = 10000.0
contactElasticStiffness = 500000.0
contactFrictionCoeff = 0.75
contactFrictionStiffness = 10000.0
globalScale = 0.01
# cloth grid
builder.add_cloth_grid(
pos=(0.0, 50.0, -25.0),
rot=wp.quat_from_axis_angle((1.0, 0.0, 0.0), math.pi * 0.5),
vel=(0.0, 0.0, 0.0),
dim_x=self.sim_width,
dim_y=self.sim_height,
cell_x=1.0,
cell_y=1.0,
mass=0.1,
fix_left=True,
tri_ke=clothTriElasticStiffness * globalScale,
tri_ka=clothTriAreaStiffness * globalScale,
tri_kd=clothTriDampingStiffness * globalScale,
edge_ke=clothEdgeBendingStiffness * globalScale,
edge_kd=clothEdgeDampingStiffness * globalScale
)
# add collider (must have identity transform until we xforms piped through Hydra plugin)
mesh = wp.sim.Mesh(points, indices)
builder.add_shape_mesh(
body=-1,
mesh=mesh,
pos=(0.0, 0.0, 0.0),
rot=wp.quat_identity(),
scale=(1.0, 1.0, 1.0),
ke=1.0e2,
kd=1.0e2,
kf=1.0e1,
)
# set sim BCs
self.model = builder.finalize()
self.model.ground = True
self.model.allocate_soft_contacts(self.model.particle_count)
self.model.gravity = (0, -980, 0)
self.model.soft_contact_ke = contactElasticStiffness * globalScale
self.model.soft_contact_kf = contactFrictionStiffness * globalScale
self.model.soft_contact_mu = contactFrictionCoeff
self.model.soft_contact_kd = contactDampingStiffness * globalScale
self.model.soft_contact_margin = colliderContactDistance * colliderContactQueryRange
self.model.particle_radius = colliderContactDistance
self.integrator = wp.sim.SemiImplicitIntegrator()
self.state_0 = self.model.state()
self.state_1 = self.model.state()
def update(self, sim_time: float):
wp.sim.collide(self.model, self.state_0)
for s in range(self.sim_substeps):
self.state_0.clear_forces()
self.integrator.simulate(self.model, self.state_0, self.state_1, self.sim_dt)
(self.state_0, self.state_1) = (self.state_1, self.state_0)
def terminate_sim(primPath: Sdf.Path):
global global_examples
global_examples[primPath] = None
def initialize_sim_mesh(primPath: Sdf.Path, src_indices: Vt.IntArray, src_points: Vt.Vec3fArray,
dep_mesh_indices: Vt.IntArray = None, dep_mesh_points: Vt.Vec3fArray = None, sim_params: dict = None):
global global_examples
global_examples[primPath] = Example(dep_mesh_indices, dep_mesh_points)
def exec_sim(primPath: Sdf.Path, sim_dt: float, dep_mesh_points: Vt.Vec3fArray = None, sim_params: dict = None):
# Not respecting sim_dt at all, using internal time
global global_examples
global_examples[primPath].update(sim_dt)
return Vt.Vec3fArray.FromNumpy(global_examples[primPath].state_0.particle_q.numpy())
| 4,791 |
Python
| 33.978102 | 112 | 0.625339 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/warpModules/deform01.py
|
# Copyright 2023 NVIDIA CORPORATION
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import warp as wp
import numpy as np
from pxr import Vt, Sdf
@wp.kernel
def deform(positions: wp.array(dtype=wp.vec3), t: float):
tid = wp.tid()
x = positions[tid]
offset = -wp.sin(x[0]) * 0.06
scale = wp.sin(t)
x = x + wp.vec3(0.0, offset * scale, 0.0)
positions[tid] = x
class Example:
def __init__(self, indices: Vt.IntArray, points: Vt.Vec3fArray):
self.mesh = wp.Mesh(
points=wp.array(points, dtype=wp.vec3),
indices=wp.array(indices, dtype=int),
)
def update(self, sim_time: float):
wp.launch(kernel=deform, dim=len(self.mesh.points), inputs=[self.mesh.points, sim_time])
# refit the mesh BVH to account for the deformation
self.mesh.refit()
wp.init()
global_examples = {}
def terminate_sim(primPath: Sdf.Path):
global global_examples
global_examples[primPath] = None
def initialize_sim_mesh(primPath: Sdf.Path, src_indices: Vt.IntArray, src_points: Vt.Vec3fArray,
dep_mesh_indices: Vt.IntArray = None, dep_mesh_points: Vt.Vec3fArray = None, sim_params: dict = None):
global global_examples
global_examples[primPath] = Example(src_indices, src_points)
def exec_sim(primPath: Sdf.Path, sim_dt: float, dep_mesh_points: Vt.Vec3fArray = None, sim_params: dict = None):
global global_examples
# Sim expects 60 samples per second (or hydra time of 1.0)
global_examples[primPath].update(sim_dt / 60.0)
return Vt.Vec3fArray.FromNumpy(global_examples[primPath].mesh.points.numpy())
def is_enabled():
return True
| 2,140 |
Python
| 31.439393 | 112 | 0.693458 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/warpModules/ocean.py
|
# Copyright 2023 NVIDIA CORPORATION
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import warp as wp
import numpy as np
from pxr import Vt, Sdf
wp.init()
sim_params_global = {
'wave_amplitude': 1.5,
'wave_directionality': 0.0,
'wind_speed': 10.0,
'water_depth': 50.0,
'scale': 1.0,
'direction': 0.0,
}
#warp function definitions
# fractional part of a (w.r.t. floor(a))
@wp.func
def frac(a: float):
return a - wp.floor(a)
# square of a
@wp.func
def sqr(a: float):
return a * a
@wp.func
def alpha_beta_spectrum(omega: float,
peak_omega: float,
alpha: float,
beta: float,
gravity: float):
return ( (alpha * gravity * gravity / wp.pow(omega, 5.0)) * wp.exp(- beta * wp.pow(peak_omega/omega, 4.0)) )
@wp.func
def jonswap_peak_sharpening(omega: float,
peak_omega: float,
gamma: float):
sigma = float(0.07)
if omega > peak_omega:
sigma = float(0.09)
return wp.pow(gamma, wp.exp(- 0.5 * sqr( (omega - peak_omega) / (sigma * peak_omega)) ))
@wp.func
def jonswap_spectrum(omega: float,
gravity: float,
wind_speed: float,
fetch_km: float,
gamma: float):
#https://wikiwaves.org/Ocean-Wave_Spectra#JONSWAP_Spectrum
fetch = 1000.0 * fetch_km
alpha = 0.076 * wp.pow(wind_speed * wind_speed / (gravity * fetch), 0.22)
peak_omega = 22.0 * wp.pow(wp.abs(gravity * gravity / (wind_speed * fetch)), 1.0/3.0)
return (jonswap_peak_sharpening(omega, peak_omega, gamma) * alpha_beta_spectrum(omega, peak_omega, alpha, 1.25, gravity))
@wp.func
def TMA_spectrum(omega: float,
gravity: float,
wind_speed: float,
fetch_km: float,
gamma: float,
waterdepth: float):
#https://dl.acm.org/doi/10.1145/2791261.2791267
omegaH = omega * wp.sqrt(waterdepth/gravity)
omegaH = wp.max(0.0, wp.min(2.2, omegaH))
phi = 0.5 * omegaH * omegaH
if omegaH > 1.0:
phi = 1.0 - 0.5 * sqr(2.0 - omegaH)
return phi * jonswap_spectrum(omega, gravity, wind_speed, fetch_km, gamma);
#warp kernel definitions
@wp.kernel
def update_profile(profile: wp.array(dtype=wp.vec3),
profile_res: int,
profile_data_num: int,
lambdaMin: float,
lambdaMax: float,
profile_extend: float,
time: float,
windspeed: float,
waterdepth: float
):
x = wp.tid()
randself = wp.rand_init(7)
# sampling parameters
omega0 = wp.sqrt(2.0 * 3.14159 * 9.80665 / lambdaMin)
omega1 = wp.sqrt(2.0 * 3.14159 * 9.80665 / lambdaMax)
omega_delta = wp.abs(omega1 - omega0) / float(profile_data_num)
# we blend three displacements for seamless spatial profile tiling
space_pos_1 = profile_extend * float(x) / float(profile_res)
space_pos_2 = space_pos_1 + profile_extend
space_pos_3 = space_pos_1 - profile_extend
p1 = wp.vec2(0.0,0.0)
p2 = wp.vec2(0.0,0.0)
p3 = wp.vec2(0.0,0.0)
for i in range(0, profile_data_num):
omega = wp.abs(omega0 + (omega1 - omega0) * float(i) / float(profile_data_num)) # linear sampling of omega
k = omega * omega / 9.80665
phase = -time * omega + wp.randf(randself) * 2.0 * 3.14159
amplitude = float(10000.0) * wp.sqrt(wp.abs(2.0 * omega_delta * TMA_spectrum(omega, 9.80665, windspeed, 100.0, 3.3, waterdepth)))
p1 = wp.vec2( p1[0] + amplitude * wp.sin(phase + space_pos_1 * k), p1[1] - amplitude * wp.cos(phase + space_pos_1 * k) )
p2 = wp.vec2( p2[0] + amplitude * wp.sin(phase + space_pos_2 * k), p2[1] - amplitude * wp.cos(phase + space_pos_2 * k) )
p3 = wp.vec2( p3[0] + amplitude * wp.sin(phase + space_pos_3 * k), p3[1] - amplitude * wp.cos(phase + space_pos_3 * k) )
# cubic blending coefficients
s = float(float(x) / float(profile_res))
c1 = float(2.0 * s * s * s - 3.0 * s * s + 1.0)
c2 = float(-2.0 * s * s * s + 3.0 * s * s)
disp_out = wp.vec3( (p1[0] + c1 * p2[0] + c2 * p3[0]) / float(profile_data_num), (p1[1] + c1 * p2[1] + c2 * p3[1]) / float(profile_data_num), 0. )
wp.store(profile, x, disp_out)
@wp.kernel
def update_points(out_points: wp.array(dtype=wp.vec3),
in_points: wp.array(dtype=wp.vec3),
profile: wp.array(dtype=wp.vec3),
profile_res: int,
profile_extent: float,
amplitude: float,
directionality: float,
direction: float,
antiAlias: int,
camPosX: float,
camPosY: float,
camPosZ: float):
tid = wp.tid()
p_crd = in_points[tid]
p_crd = wp.vec3(p_crd[0], p_crd[2], p_crd[1])
randself = wp.rand_init(7)
disp_x = float(0.)
disp_y = float(0.)
disp_z = float(0.)
w_sum = float(0.)
direction_count = (int)(128)
for d in range(0, direction_count):
r = float(d) * 2. * 3.14159265359 / float(direction_count) + 0.02
dir_x = wp.cos(r)
dir_y = wp.sin(r)
# directional amplitude
t = wp.abs( direction - r )
if (t > 3.14159265359):
t = 2.0 * 3.14159265359 - t
t = pow(t, 1.2)
dirAmp = (2.0 * t * t * t - 3.0 * t * t + 1.0) * 1.0 + (- 2.0 * t * t * t + 3.0 * t * t) * (1.0 - directionality)
dirAmp = dirAmp / (1.0 + 10.0 * directionality)
rand_phase = wp.randf(randself)
x_crd = (p_crd[0] * dir_x + p_crd[2] * dir_y) / profile_extent + rand_phase
pos_0 = int(wp.floor(x_crd * float(profile_res))) % profile_res
if x_crd < 0.:
pos_0 = pos_0 + profile_res - 1
pos_1 = int(pos_0 + 1) % profile_res
p_disp_0 = profile[pos_0]
p_disp_1 = profile[pos_1]
w = frac( x_crd * float(profile_res) )
prof_height_x = dirAmp * float((1. - w) * p_disp_0[0] + w * p_disp_1[0])
prof_height_y = dirAmp * float((1. - w) * p_disp_0[1] + w * p_disp_1[1])
disp_x = disp_x + dir_x * prof_height_x
disp_y = disp_y + prof_height_y
disp_z = disp_z + dir_y * prof_height_x
w_sum = w_sum + 1.
# simple anti-aliasing: reduce amplitude with increasing distance to viewpoint
if (antiAlias > 0):
v1 = wp.normalize( wp.vec3( p_crd[0] - camPosX, max( 100.0, wp.abs(p_crd[1] - camPosY)), p_crd[2] - camPosZ) )
amplitude *= wp.sqrt( wp.abs(v1[1]) )
# write output vertex position
outP = wp.vec3(p_crd[0] + amplitude * disp_x / w_sum, p_crd[1] + amplitude * disp_y / w_sum, p_crd[2] + amplitude * disp_z / w_sum)
wp.store(out_points, tid, wp.vec3(outP[0], outP[2], outP[1]))
class Example:
def __init__(self, indices: Vt.IntArray, points: Vt.Vec3fArray):
# profile buffer intializations
print('[Ocean deformer] Initializing profile buffer.')
self.profile_extent = 410.0 #physical size of profile, should be around half the resolution
self.profile_res = int(8192)
self.profile_wavenum = int(1000)
self.profile_CUDA = wp.zeros(self.profile_res, dtype=wp.vec3, device="cuda:0")
self.points_in = wp.array(points, dtype=wp.vec3, device="cuda:0")
self.points_out = wp.array(points, dtype=wp.vec3, device="cuda:0")
print(self.points_in)
print(self.points_out)
def update(self, sim_time: float):
global sim_params_global
# params
wave_amplitude = sim_params_global["wave_amplitude"]
wave_directionality = sim_params_global["wave_directionality"]
wind_speed = sim_params_global["wind_speed"]
water_depth = sim_params_global["water_depth"]
scale = sim_params_global["scale"]
direction = sim_params_global["direction"]
# Parameters
time = float(sim_time)
amplitude = max(0.0001, min(1000.0, float(wave_amplitude)))
minWavelength = 0.1
maxWavelength = 250.0
direction = float(direction) % 6.28318530718
directionality = max(0.0, min(1.0, 0.02 * float(wave_directionality)))
windspeed = max(0.0, min(30.0, float(wind_speed)))
waterdepth = max(1.0, min(1000.0, float(water_depth)))
scale = min(10000.0, max(0.001, float(scale)))
antiAlias = int(0)
campos = [0.0, 0.0, 0.0]
# create 1D profile buffer for this timestep using wave paramters stored in internal self CUDA memory
wp.launch(
kernel=update_profile,
dim=self.profile_res,
inputs=[self.profile_CUDA, int(self.profile_res), int(self.profile_wavenum), float(minWavelength), float(maxWavelength), float(self.profile_extent), float(time), float(windspeed), float(waterdepth)],
outputs=[],
device="cuda:0")
# update point positions using the profile buffer created above
wp.launch(
kernel=update_points,
dim=len(self.points_out),
inputs=[self.points_out, self.points_in, self.profile_CUDA, int(self.profile_res), float(self.profile_extent*scale), float(amplitude), float(directionality), float(direction), int(antiAlias), float(campos[0]), float(campos[1]), float(campos[2]) ],
outputs=[],
device="cuda:0")
global_examples = {}
def terminate_sim(primPath: Sdf.Path):
global global_examples
global_examples[primPath] = None
def initialize_sim_mesh(primPath: Sdf.Path, src_indices: Vt.IntArray, src_points: Vt.Vec3fArray,
dep_mesh_indices: Vt.IntArray = None, dep_mesh_points: Vt.Vec3fArray = None, sim_params: dict = None):
global global_examples
global sim_params_global
if sim_params:
sim_params_global = sim_params
global_examples[primPath] = Example(src_indices, src_points)
def exec_sim(primPath: Sdf.Path, sim_dt: float, dep_mesh_points: Vt.Vec3fArray = None, sim_params: dict = None):
global global_examples
global sim_params_global
if sim_params:
sim_params_global = sim_params
# Sim expects 60 samples per second (or hydra time of 1.0)
global_examples[primPath].update(sim_dt / 60.0)
return Vt.Vec3fArray.FromNumpy(global_examples[primPath].points_out.numpy())
| 11,029 |
Python
| 37.838028 | 260 | 0.580288 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/oceanSim/preferences.py
|
#
# Copyright 2016 Pixar
#
# Licensed under the Apache License, Version 2.0 (the "Apache License")
# with the following modification; you may not use this file except in
# compliance with the Apache License and the following modification to it:
# Section 6. Trademarks. is deleted and replaced with:
#
# 6. Trademarks. This License does not grant permission to use the trade
# names, trademarks, service marks, or product names of the Licensor
# and its affiliates, except as required to comply with Section 4(c) of
# the License and to reproduce the content of the NOTICE file.
#
# You may obtain a copy of the Apache License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the Apache License with the above modification is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the Apache License for the specific
# language governing permissions and limitations under the Apache License.
#
from pxr.Usdviewq.qt import QtCore, QtGui, QtWidgets
from .preferencesUI import Ui_Preferences
class Preferences(QtWidgets.QDialog):
def __init__(self, parent, attr):
super(Preferences, self).__init__(parent)
self._ui = Ui_Preferences()
self._ui.setupUi(self)
self._attr = attr
metadata = self._attr.GetMetadata("customData")
self._ui.scaleSpinBox.setValue(metadata["scale"])
self._ui.directionSpinBox.setValue(metadata["direction"])
self._ui.windSpeedSpinBox.setValue(metadata["wind_speed"])
self._ui.waterDepthSpinBox.setValue(metadata["water_depth"])
self._ui.waveAmplitudeSpinBox.setValue(metadata["wave_amplitude"])
self._ui.waveDirectionalitySpinBox.setValue(metadata["wave_directionality"])
self._ui.buttonBox.clicked.connect(self._buttonBoxButtonClicked)
def _apply(self):
self._attr.SetMetadataByDictKey('customData', 'scale', self._ui.scaleSpinBox.value())
self._attr.SetMetadataByDictKey('customData', 'direction', self._ui.directionSpinBox.value())
self._attr.SetMetadataByDictKey('customData', 'wind_speed', self._ui.windSpeedSpinBox.value())
self._attr.SetMetadataByDictKey('customData', 'water_depth', self._ui.waterDepthSpinBox.value())
self._attr.SetMetadataByDictKey('customData', 'wave_amplitude', self._ui.waveAmplitudeSpinBox.value())
self._attr.SetMetadataByDictKey('customData', 'wave_directionality', self._ui.waveDirectionalitySpinBox.value())
def _buttonBoxButtonClicked(self, button):
role = self._ui.buttonBox.buttonRole(button)
Roles = QtWidgets.QDialogButtonBox.ButtonRole
if role == Roles.AcceptRole or role == Roles.ApplyRole:
self._apply()
if role == Roles.AcceptRole or role == Roles.RejectRole:
self.close()
| 2,923 |
Python
| 46.16129 | 120 | 0.718782 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/oceanSim/__init__.py
|
from pxr import Tf
from pxr.Usdviewq.plugin import PluginContainer
from .preferences import Preferences
def launchPreferences(usdviewApi):
prim = usdviewApi.stage.GetPrimAtPath("/World/grid/Grid")
attr = prim.GetAttribute("warp:sourceFile")
_preferencesDlg = Preferences(usdviewApi.qMainWindow, attr)
_preferencesDlg.show()
_preferencesDlg = None
class OceanSimPluginContainer(PluginContainer):
def registerPlugins(self, plugRegistry, usdviewApi):
self._launchPreferences = plugRegistry.registerCommandPlugin(
"OceanSimPluginContainer.launchPreferences",
"Launch Preferences",
launchPreferences)
def configureView(self, plugRegistry, plugUIBuilder):
tutMenu = plugUIBuilder.findOrCreateMenu("OceanSim")
tutMenu.addItem(self._launchPreferences)
Tf.Type.Define(OceanSimPluginContainer)
| 878 |
Python
| 32.807691 | 69 | 0.749431 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniWarpSceneIndex/oceanSim/preferencesUI_pyside6.py
|
# -*- coding: utf-8 -*-
################################################################################
## Form generated from reading UI file 'preferencesUI.ui'
##
## Created by: Qt User Interface Compiler version 6.5.1
##
## WARNING! All changes made in this file will be lost when recompiling UI file!
################################################################################
from PySide6.QtCore import (QCoreApplication, QDate, QDateTime, QLocale,
QMetaObject, QObject, QPoint, QRect,
QSize, QTime, QUrl, Qt)
from PySide6.QtGui import (QBrush, QColor, QConicalGradient, QCursor,
QFont, QFontDatabase, QGradient, QIcon,
QImage, QKeySequence, QLinearGradient, QPainter,
QPalette, QPixmap, QRadialGradient, QTransform)
from PySide6.QtWidgets import (QAbstractButton, QApplication, QDialog, QDialogButtonBox,
QDoubleSpinBox, QFrame, QHBoxLayout, QLabel,
QSizePolicy, QSpacerItem, QVBoxLayout, QWidget)
class Ui_Preferences(object):
def setupUi(self, Ocean_Simulation_Settings):
if not Ocean_Simulation_Settings.objectName():
Ocean_Simulation_Settings.setObjectName(u"Ocean_Simulation_Settings")
Ocean_Simulation_Settings.resize(295, 99)
self.verticalLayout = QVBoxLayout()
self.verticalLayout.setObjectName(u"verticalLayout")
self.prefsOverButtonsLayout = QVBoxLayout()
self.prefsOverButtonsLayout.setObjectName(u"prefsOverButtonsLayout")
self.horizontalLayout_3 = QHBoxLayout()
self.horizontalLayout_3.setObjectName(u"horizontalLayout_3")
self.scaleLabel = QLabel()
self.scaleLabel.setObjectName(u"scaleLabel")
self.horizontalLayout_3.addWidget(self.scaleLabel)
self.horizontalSpacer_2a = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_3.addItem(self.horizontalSpacer_2a)
self.scaleSpinBox = QDoubleSpinBox()
self.scaleSpinBox.setObjectName(u"scaleSpinBox")
self.scaleSpinBox.setDecimals(2)
self.scaleSpinBox.setMinimum(0.000000000000000)
self.scaleSpinBox.setValue(1.000000000000000)
self.horizontalLayout_3.addWidget(self.scaleSpinBox)
self.prefsOverButtonsLayout.addLayout(self.horizontalLayout_3)
self.horizontalLayout_4 = QHBoxLayout()
self.horizontalLayout_4.setObjectName(u"horizontalLayout_4")
self.directionLabel = QLabel()
self.directionLabel.setObjectName(u"directionLabel")
self.horizontalLayout_4.addWidget(self.directionLabel)
self.horizontalSpacer_2b = QSpacerItem(26, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_4.addItem(self.horizontalSpacer_2b)
self.directionSpinBox = QDoubleSpinBox()
self.directionSpinBox.setObjectName(u"directionSpinBox")
self.directionSpinBox.setDecimals(2)
self.directionSpinBox.setMinimum(0.000000000000000)
self.directionSpinBox.setValue(0.000000000000000)
self.horizontalLayout_4.addWidget(self.directionSpinBox)
self.prefsOverButtonsLayout.addLayout(self.horizontalLayout_4)
self.horizontalLayout_5 = QHBoxLayout()
self.horizontalLayout_5.setObjectName(u"horizontalLayout_5")
self.windSpeedLabel = QLabel()
self.windSpeedLabel.setObjectName(u"windSpeedLabel")
self.horizontalLayout_5.addWidget(self.windSpeedLabel)
self.horizontalSpacer_2c = QSpacerItem(24, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_5.addItem(self.horizontalSpacer_2c)
self.windSpeedSpinBox = QDoubleSpinBox()
self.windSpeedSpinBox.setObjectName(u"windSpeedSpinBox")
self.windSpeedSpinBox.setDecimals(2)
self.windSpeedSpinBox.setMinimum(0.000000000000000)
self.windSpeedSpinBox.setValue(10.000000000000000)
self.horizontalLayout_5.addWidget(self.windSpeedSpinBox)
self.prefsOverButtonsLayout.addLayout(self.horizontalLayout_5)
self.horizontalLayout_6 = QHBoxLayout()
self.horizontalLayout_6.setObjectName(u"horizontalLayout_6")
self.waterDepthLabel = QLabel()
self.waterDepthLabel.setObjectName(u"waterDepthLabel")
self.horizontalLayout_6.addWidget(self.waterDepthLabel)
self.horizontalSpacer_2d = QSpacerItem(24, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_6.addItem(self.horizontalSpacer_2d)
self.waterDepthSpinBox = QDoubleSpinBox()
self.waterDepthSpinBox.setObjectName(u"waterDepthSpinBox")
self.waterDepthSpinBox.setDecimals(2)
self.waterDepthSpinBox.setMinimum(0.000000000000000)
self.waterDepthSpinBox.setValue(50.000000000000000)
self.horizontalLayout_6.addWidget(self.waterDepthSpinBox)
self.prefsOverButtonsLayout.addLayout(self.horizontalLayout_6)
self.horizontalLayout_7 = QHBoxLayout()
self.horizontalLayout_7.setObjectName(u"horizontalLayout_7")
self.waveAmplitudeLabel = QLabel()
self.waveAmplitudeLabel.setObjectName(u"waveAmplitudeLabel")
self.horizontalLayout_7.addWidget(self.waveAmplitudeLabel)
self.horizontalSpacer_2e = QSpacerItem(21, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_7.addItem(self.horizontalSpacer_2e)
self.waveAmplitudeSpinBox = QDoubleSpinBox()
self.waveAmplitudeSpinBox.setObjectName(u"waveAmplitudeSpinBox")
self.waveAmplitudeSpinBox.setDecimals(2)
self.waveAmplitudeSpinBox.setMinimum(0.000000000000000)
self.waveAmplitudeSpinBox.setValue(1.500000000000000)
self.horizontalLayout_7.addWidget(self.waveAmplitudeSpinBox)
self.prefsOverButtonsLayout.addLayout(self.horizontalLayout_7)
self.horizontalLayout_8 = QHBoxLayout()
self.horizontalLayout_8.setObjectName(u"horizontalLayout_8")
self.waveDirectionalityLabel = QLabel()
self.waveDirectionalityLabel.setObjectName(u"waveDirectionalityLabel")
self.horizontalLayout_8.addWidget(self.waveDirectionalityLabel)
self.horizontalSpacer_2f = QSpacerItem(17, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_8.addItem(self.horizontalSpacer_2f)
self.waveDirectionalitySpinBox = QDoubleSpinBox()
self.waveDirectionalitySpinBox.setObjectName(u"waveDirectionalitySpinBox")
self.waveDirectionalitySpinBox.setMinimum(0.000000000000000)
self.waveDirectionalitySpinBox.setValue(0.000000000000000)
self.horizontalLayout_8.addWidget(self.waveDirectionalitySpinBox)
self.prefsOverButtonsLayout.addLayout(self.horizontalLayout_8)
self.verticalSpacer = QSpacerItem(20, 40, QSizePolicy.Minimum, QSizePolicy.Expanding)
self.prefsOverButtonsLayout.addItem(self.verticalSpacer)
self.line = QFrame()
self.line.setObjectName(u"line")
self.line.setFrameShape(QFrame.HLine)
self.line.setFrameShadow(QFrame.Sunken)
self.prefsOverButtonsLayout.addWidget(self.line)
self.horizontalLayout_2 = QHBoxLayout()
self.horizontalLayout_2.setObjectName(u"horizontalLayout_2")
self.horizontalSpacer = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_2.addItem(self.horizontalSpacer)
self.buttonBox = QDialogButtonBox()
self.buttonBox.setObjectName(u"buttonBox")
self.buttonBox.setStandardButtons(QDialogButtonBox.Apply|QDialogButtonBox.Cancel|QDialogButtonBox.Ok)
self.horizontalLayout_2.addWidget(self.buttonBox)
self.prefsOverButtonsLayout.addLayout(self.horizontalLayout_2)
self.verticalLayout.addLayout(self.prefsOverButtonsLayout)
self.retranslateUi(Ocean_Simulation_Settings)
QMetaObject.connectSlotsByName(Ocean_Simulation_Settings)
# setupUi
def retranslateUi(self, Ocean_Simulation_Settings):
Ocean_Simulation_Settings.setWindowTitle(QCoreApplication.translate("Preferences", u"Ocean Simulation Settings", None))
Ocean_Simulation_Settings.setProperty("comment", QCoreApplication.translate("Preferences", u"\n"
" Copyright 2020 Pixar \n"
" \n"
" Licensed under the Apache License, Version 2.0 (the \"Apache License\") \n"
" with the following modification; you may not use this file except in \n"
" compliance with the Apache License and the following modification to it: \n"
" Section 6. Trademarks. is deleted and replaced with: \n"
" \n"
" 6. Trademarks. This License does not grant permission to use the trade \n"
" names, trademarks, service marks, or product names of the Licensor \n"
" and its affiliates, except as required to comply with Section 4(c) of \n"
" the License and to reproduce the content of the NOTI"
"CE file. \n"
" \n"
" You may obtain a copy of the Apache License at \n"
" \n"
" http://www.apache.org/licenses/LICENSE-2.0 \n"
" \n"
" Unless required by applicable law or agreed to in writing, software \n"
" distributed under the Apache License with the above modification is \n"
" distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY \n"
" KIND, either express or implied. See the Apache License for the specific \n"
" language governing permissions and limitations under the Apache License. \n"
" ", None))
self.scaleLabel.setText(QCoreApplication.translate("Preferences", u"Scale", None))
self.directionLabel.setText(QCoreApplication.translate("Preferences", u"Direction", None))
self.windSpeedLabel.setText(QCoreApplication.translate("Preferences", u"Wind Speed", None))
self.waterDepthLabel.setText(QCoreApplication.translate("Preferences", u"Water Depth", None))
self.waveAmplitudeLabel.setText(QCoreApplication.translate("Preferences", u"Wave Amplitude", None))
self.waveDirectionalityLabel.setText(QCoreApplication.translate("Preferences", u"Wave Directionality", None))
# retranslateUi
| 10,887 |
Python
| 46.134199 | 127 | 0.669055 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/computedPrimDataSource.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/base/gf/transform.h>
#include <pxr/usd/usdGeom/tokens.h>
#include <pxr/imaging/hd/xformSchema.h>
#include "computedPrimDataSource.h"
#include "localPositionSchema.h"
#include "referencePositionSchema.h"
PXR_NAMESPACE_OPEN_SCOPE
HdOmniGeospatialComputedPrimDataSource::HdOmniGeospatialComputedPrimDataSource(
HdContainerDataSourceHandle inputDataSource) :
_inputDataSource(inputDataSource)
{
_matrixDataSource =
HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::New(_inputDataSource);
}
#if PXR_VERSION < 2302
bool HdOmniGeospatialComputedPrimDataSource::Has(const TfToken& name)
{
return (name == HdXformSchemaTokens->resetXformStack) ||
(name == HdXformSchemaTokens->matrix);
}
#endif
TfTokenVector HdOmniGeospatialComputedPrimDataSource::GetNames()
{
// this container data source retrieves the xform tokens
TfTokenVector result;
result.push_back(HdXformSchemaTokens->resetXformStack);
result.push_back(HdXformSchemaTokens->matrix);
return result;
}
HdDataSourceBaseHandle HdOmniGeospatialComputedPrimDataSource::Get(const TfToken& name)
{
if (_inputDataSource != nullptr)
{
if (name == HdXformSchemaTokens->resetXformStack)
{
// we don't modify the underlying time-sampled data
// for resetXformStack, so return that directly
HdXformSchema xformSchema = HdXformSchema::GetFromParent(_inputDataSource);
return xformSchema.IsDefined() ? xformSchema.GetResetXformStack() : nullptr;
}
else if (name == HdXformSchemaTokens->matrix)
{
// note even if resetXformStack was true we consider
// the geospatial data to override that
return _matrixDataSource;
}
}
return nullptr;
}
HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GeospatialMatrixDataSource(
HdContainerDataSourceHandle inputDataSource) : _inputDataSource(inputDataSource)
{
}
VtValue HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::GetValue(Time shutterOffset)
{
return VtValue(this->GetTypedValue(shutterOffset));
}
GfMatrix4d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::GetTypedValue(Time shutterOffset)
{
return this->_ComputeTransformedMatrix(shutterOffset);
}
bool HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::GetContributingSampleTimesForInterval(
Time startTime,
Time endTime,
std::vector<Time>* outSampleTimes)
{
HdSampledDataSourceHandle sources[] = {
this->_GetMatrixSource(),
this->_GetLocalPositionSource()
};
return HdGetMergedContributingSampleTimesForInterval(
TfArraySize(sources),
sources,
startTime,
endTime,
outSampleTimes);
}
HdMatrixDataSourceHandle HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetMatrixSource() const
{
return HdXformSchema::GetFromParent(_inputDataSource).GetMatrix();
}
HdVec3dDataSourceHandle HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetLocalPositionSource() const
{
return HdOmniGeospatialWGS84LocalPositionSchema::GetFromParent(_inputDataSource).GetPosition();
}
HdTokenDataSourceHandle HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetTangentPlaneSource() const
{
return HdOmniGeospatialWGS84ReferencePositionSchema::GetFromParent(_inputDataSource).GetTangentPlane();
}
HdVec3dDataSourceHandle HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetReferencePositionSource() const
{
return HdOmniGeospatialWGS84ReferencePositionSchema::GetFromParent(_inputDataSource).GetReferencePosition();
}
HdVec3dDataSourceHandle HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetOrientationSource() const
{
return HdOmniGeospatialWGS84ReferencePositionSchema::GetFromParent(_inputDataSource).GetOrientation();
}
HdTokenDataSourceHandle HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetStageUpAxisSource() const
{
return HdOmniGeospatialWGS84ReferencePositionSchema::GetFromParent(_inputDataSource).GetStageUpAxis();
}
HdDoubleDataSourceHandle HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetStageMetersPerUnitSource() const
{
return HdOmniGeospatialWGS84ReferencePositionSchema::GetFromParent(_inputDataSource).GetStageMetersPerUnit();
}
GfMatrix4d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetMatrix(const Time shutterOffset) const
{
HdMatrixDataSourceHandle dataSource = this->_GetMatrixSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(shutterOffset);
}
return GfMatrix4d(1.0);
}
GfVec3d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetLocalPosition(const Time shutterOffset) const
{
HdVec3dDataSourceHandle dataSource = this->_GetLocalPositionSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(shutterOffset);
}
return GfVec3d(1.0);
}
TfToken HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetTangentPlane() const
{
HdTokenDataSourceHandle dataSource = this->_GetTangentPlaneSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(0.0f);
}
return TfToken();
}
GfVec3d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetReferencePosition() const
{
HdVec3dDataSourceHandle dataSource = this->_GetReferencePositionSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(0.0f);
}
return GfVec3d(1.0);
}
GfVec3d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetOrientation() const
{
HdVec3dDataSourceHandle dataSource = this->_GetOrientationSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(0.0f);
}
return GfVec3d(1.0);
}
TfToken HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetStageUpAxis() const
{
HdTokenDataSourceHandle dataSource = this->_GetStageUpAxisSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(0.0f);
}
return UsdGeomTokens->y;
}
double HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GetStageMetersPerUnit() const
{
HdDoubleDataSourceHandle dataSource = this->_GetStageMetersPerUnitSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(0.0f);
}
return 0.01;
}
GfMatrix4d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_ComputeTransformedMatrix(const Time shutterOffset) const
{
// NOTE: in the case of the geospatially applied prim, we are completely
// ignoring the fact that resetXformStack may be true at any given time sample
// that is, geospatial positioning takes priority over local transformation reset
// to compute the local position, we need to first get the geodetic reference
TfToken targetFrame = this->_GetTangentPlane();
GfVec3d tangentPosition = this->_GetReferencePosition();
GfVec3d orientation = this->_GetOrientation();
GfVec3d localPosition = this->_GetLocalPosition(shutterOffset);
double metersPerUnit = this->_GetStageMetersPerUnit();
TfToken upAxis = this->_GetStageUpAxis();
// calculate the new geodetic translation
auto enu = this->_EcefToEnu(this->_GeodeticToEcef(localPosition), tangentPosition);
GfVec3d translation = this->_EnuToCartesian(enu, upAxis, metersPerUnit, tangentPosition);
// we only want to replace the translation piece
// but since the transform may have orientation and scale
// information, we need to extract that from the existing
// matrix first
GfTransform currentTransform(this->_GetMatrix(shutterOffset));
GfVec3d existingScale = currentTransform.GetScale();
GfRotation existingRotation = currentTransform.GetRotation();
GfRotation existingPivotOrientation = currentTransform.GetPivotOrientation();
GfVec3d existingPivotPosition = currentTransform.GetPivotPosition();
// now combine the new translation with the existing scale / rotation
GfTransform newTransform(existingScale, existingPivotOrientation,
existingRotation, existingPivotPosition, translation);
return newTransform.GetMatrix();
}
// Geospatial transform functions
// For reference:
// https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470099728.app3
// https://en.wikipedia.org/wiki/Geographic_coordinate_conversion
// Implementation of Ferrari's solution
GfVec3d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_GeodeticToEcef(const GfVec3d & llh) const
{
double lambda = llh[0] * GeoConstants::radians;
double phi = llh[1] * GeoConstants::radians;
double sin_lambda = sin(lambda);
double N = GeoConstants::semiMajorAxis / sqrt(1 - GeoConstants::eccentricity * sin_lambda * sin_lambda);
double cos_lambda = cos(lambda);
double cos_phi = cos(phi);
double sin_phi = sin(phi);
return PXR_NS::GfVec3d((llh[2] + N) * cos_lambda * cos_phi, (llh[2] + N) * cos_lambda * sin_phi,
(llh[2] + (1 - GeoConstants::eccentricity) * N) * sin_lambda);
}
GfVec3d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_EcefToEnu(const GfVec3d& ecef, const GfVec3d& llh) const
{
double lambda = llh[0] * GeoConstants::radians;
double phi = llh[1] * GeoConstants::radians;
double sin_lambda = sin(lambda);
double N = GeoConstants::semiMajorAxis / sqrt(1 - GeoConstants::eccentricity * sin_lambda * sin_lambda);
double cos_lambda = cos(lambda);
double cos_phi = cos(phi);
double sin_phi = sin(phi);
PXR_NS::GfVec3d pt((llh[2] + N) * cos_lambda * cos_phi,
(llh[2] + N) * cos_lambda * sin_phi,
(llh[2] + (1 - GeoConstants::eccentricity) * N) * sin_lambda);
auto delta = ecef - pt;
return PXR_NS::GfVec3d(-sin_phi * delta[0] + cos_phi * delta[1],
-cos_phi * sin_lambda * delta[0] - sin_lambda * sin_phi * delta[1] + cos_lambda * delta[2],
cos_lambda * cos_phi * delta[0] + cos_lambda * sin_phi * delta[1] + sin_lambda * delta[2]);
}
GfVec3d HdOmniGeospatialComputedPrimDataSource::_GeospatialMatrixDataSource::_EnuToCartesian(
const GfVec3d& enu,
const TfToken& upAxis,
const double& metersPerUnit,
const GfVec3d& reference) const
{
auto cartesian = GfVec3d(reference[0] < 0.0 ? -enu[0] : enu[0],
upAxis == UsdGeomTokens->y ? enu[2] : enu[1],
upAxis == UsdGeomTokens->z ? enu[2] : enu[1]);
cartesian /= metersPerUnit;
return cartesian;
}
PXR_NAMESPACE_CLOSE_SCOPE
| 11,354 |
C++
| 35.394231 | 137 | 0.747314 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/referencePositionSchema.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef HD_OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_SCHEMA_H_
#define HD_OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_SCHEMA_H_
#include <pxr/imaging/hd/schema.h>
#include <pxr/imaging/hd/dataSourceLocator.h>
#include "api.h"
PXR_NAMESPACE_OPEN_SCOPE
//-----------------------------------------------------------------------------
#define HDOMNIGEOSPATIALWGS84REFERENCEPOSITION_SCHEMA_TOKENS \
(referencePositionApi) \
(tangentPlane) \
(referencePosition) \
(orientation) \
(stageUpAxis) \
(stageMetersPerUnit) \
TF_DECLARE_PUBLIC_TOKENS(HdOmniGeospatialWGS84ReferencePositionSchemaTokens, OMNIGEOSCENEINDEX_API,
HDOMNIGEOSPATIALWGS84REFERENCEPOSITION_SCHEMA_TOKENS);
//-----------------------------------------------------------------------------
class HdOmniGeospatialWGS84ReferencePositionSchema : public HdSchema
{
public:
HdOmniGeospatialWGS84ReferencePositionSchema(HdContainerDataSourceHandle container)
: HdSchema(container) { }
OMNIGEOSCENEINDEX_API
HdTokenDataSourceHandle GetTangentPlane();
OMNIGEOSCENEINDEX_API
HdVec3dDataSourceHandle GetReferencePosition();
OMNIGEOSCENEINDEX_API
HdVec3dDataSourceHandle GetOrientation();
OMNIGEOSCENEINDEX_API
HdTokenDataSourceHandle GetStageUpAxis();
OMNIGEOSCENEINDEX_API
HdDoubleDataSourceHandle GetStageMetersPerUnit();
OMNIGEOSCENEINDEX_API
static HdOmniGeospatialWGS84ReferencePositionSchema GetFromParent(
const HdContainerDataSourceHandle& fromParentContainer);
OMNIGEOSCENEINDEX_API
static const HdDataSourceLocator& GetDefaultLocator();
OMNIGEOSCENEINDEX_API
static HdContainerDataSourceHandle BuildRetained(
const HdTokenDataSourceHandle& tangentPlane,
const HdVec3dDataSourceHandle& referencePosition,
const HdVec3dDataSourceHandle& orientation,
const HdTokenDataSourceHandle& stageUpAxis,
const HdDoubleDataSourceHandle& stageMetersPerUnit
);
};
PXR_NAMESPACE_CLOSE_SCOPE
#endif // HD_OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_SCHEMA_H_
| 2,662 |
C
| 32.70886 | 99 | 0.730278 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/api.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef OMNI_GEO_SCENE_INDEX_API_H
#define OMNI_GEO_SCENE_INDEX_API_H
#include "pxr/base/arch/export.h"
#if defined(PXR_STATIC)
# define OMNIGEOSCENEINDEX_API
# define OMNIGEOSCENEINDEX_API_TEMPLATE_CLASS(...)
# define OMNIGEOSCENEINDEX_API_TEMPLATE_STRUCT(...)
# define OMNIGEOSCENEINDEX_LOCAL
#else
# if defined(OMNIGEOSCENEINDEX_EXPORTS)
# define OMNIGEOSCENEINDEX_API ARCH_EXPORT
# define OMNIGEOSCENEINDEX_API_TEMPLATE_CLASS(...) ARCH_EXPORT_TEMPLATE(class, __VA_ARGS__)
# define OMNIGEOSCENEINDEX_API_TEMPLATE_STRUCT(...) ARCH_EXPORT_TEMPLATE(struct, __VA_ARGS__)
# else
# define OMNIGEOSCENEINDEX_API ARCH_IMPORT
# define OMNIGEOSCENEINDEX_API_TEMPLATE_CLASS(...) ARCH_IMPORT_TEMPLATE(class, __VA_ARGS__)
# define OMNIGEOSCENEINDEX_API_TEMPLATE_STRUCT(...) ARCH_IMPORT_TEMPLATE(struct, __VA_ARGS__)
# endif
# define OMNIGEOSCENEINDEX_LOCAL ARCH_HIDDEN
#endif
#endif // OMNI_GEO_INDEX_API_H
| 1,544 |
C
| 39.657894 | 99 | 0.734456 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/localPositionDataSource.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef HD_OMNI_GEOSPATIAL_WGS84_LOCAL_POSITION_DATA_SOURCE_H_
#define HD_OMNI_GEOSPATIAL_WGS84_LOCAL_POSITION_DATA_SOURCE_H_
#include <pxr/imaging/hd/dataSource.h>
#include <pxr/usdImaging/usdImaging/dataSourceStageGlobals.h>
#include <omniGeospatial/wGS84LocalPositionAPI.h>
#include "localPositionSchema.h"
PXR_NAMESPACE_OPEN_SCOPE
class HdOmniGeospatialWGS84LocalPositionDataSource : public HdContainerDataSource
{
public:
HD_DECLARE_DATASOURCE(HdOmniGeospatialWGS84LocalPositionDataSource);
HdOmniGeospatialWGS84LocalPositionDataSource(const UsdPrim& prim,
const UsdImagingDataSourceStageGlobals& stageGlobals);
TfTokenVector GetNames() override;
HdDataSourceBaseHandle Get(const TfToken& name) override;
#if PXR_VERSION < 2302
bool Has(const TfToken& name) override;
#endif
private:
OmniGeospatialWGS84LocalPositionAPI _localPositionApi;
const UsdImagingDataSourceStageGlobals& _stageGlobals;
};
HD_DECLARE_DATASOURCE_HANDLES(HdOmniGeospatialWGS84LocalPositionDataSource);
PXR_NAMESPACE_CLOSE_SCOPE
#endif // HD_OMNI_GEOSPATIAL_WGS84_LOCAL_POSITION_DATA_SOURCE_H_
| 1,710 |
C
| 33.219999 | 81 | 0.792398 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/computedDependentDataSource.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef HD_OMNI_GEOSPATIAL_COMPUTED_DEPENDENT_DATA_SOURCE_H_
#define HD_OMNI_GEOSPATIAL_COMPUTED_DEPENDENT_DATA_SOURCE_H_
#include <pxr/imaging/hd/dataSource.h>
#include <pxr/imaging/hd/dataSourceTypeDefs.h>
PXR_NAMESPACE_OPEN_SCOPE
/// \class HdOmniGeospatialComputedDependentDataSource
///
/// A datasource representing a container data source mimicing
/// that of a container data source for xform data, but returning
/// computed values based on geospatial data applied to the parent
/// (or some parent in the hierarchy) of this prim.
///
class HdOmniGeospatialComputedDependentDataSource : public HdContainerDataSource
{
public:
HD_DECLARE_DATASOURCE(HdOmniGeospatialComputedDependentDataSource);
HdOmniGeospatialComputedDependentDataSource(HdContainerDataSourceHandle inputDataSource,
HdContainerDataSourceHandle parentDataSource);
// data source overrides
TfTokenVector GetNames() override;
HdDataSourceBaseHandle Get(const TfToken& name) override;
#if PXR_VERSION < 2302
bool Has(const TfToken& name) override;
#endif
private:
HdDataSourceBaseHandle _ComputeGeospatiallyAffectedXform();
private:
HdContainerDataSourceHandle _inputDataSource;
HdContainerDataSourceHandle _parentDataSource;
HdMatrixDataSourceHandle _matrixDataSource;
class _GeospatiallyAffectedMatrixDataSource : public HdMatrixDataSource
{
public:
HD_DECLARE_DATASOURCE(_GeospatiallyAffectedMatrixDataSource);
VtValue GetValue(Time shutterOffset) override;
GfMatrix4d GetTypedValue(Time shutterOffset) override;
bool GetContributingSampleTimesForInterval(
Time startTime,
Time endTime,
std::vector<Time>* outSampleTimes) override;
private:
_GeospatiallyAffectedMatrixDataSource(HdContainerDataSourceHandle inputDataSource,
HdContainerDataSourceHandle parentDataSource);
HdMatrixDataSourceHandle _GetMatrixSource() const;
HdBoolDataSourceHandle _GetResetXformStackSource() const;
HdMatrixDataSourceHandle _GetParentMatrixSource() const;
HdMatrixDataSourceHandle _GetParentOriginalMatrixSource() const;
GfMatrix4d _GetMatrix(const Time shutterOffset) const;
bool _GetResetXformStack(const Time shutterOffset) const;
GfMatrix4d _GetParentMatrix(const Time shutterOffset) const;
GfMatrix4d _GetParentOriginalMatrix(const Time shutterOffset) const;
// geospatial transform methods
GfMatrix4d _ComputeTransformedMatrix(const Time shutterOffset) const;
HdContainerDataSourceHandle _inputDataSource;
HdContainerDataSourceHandle _parentDataSource;
};
HD_DECLARE_DATASOURCE_HANDLES(_GeospatiallyAffectedMatrixDataSource);
};
HD_DECLARE_DATASOURCE_HANDLES(HdOmniGeospatialComputedDependentDataSource);
PXR_NAMESPACE_CLOSE_SCOPE
#endif // HD_OMNI_GEOSPATIAL_COMPUTED_DEPENDENT_DATA_SOURCE_H_
| 3,530 |
C
| 35.402061 | 92 | 0.768272 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/geospatialDataSource.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/imaging/hd/xformSchema.h>
#include "geospatialDataSource.h"
#include "computedPrimDataSource.h"
#include "computedDependentDataSource.h"
#include "localPositionSchema.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_DEFINE_PUBLIC_TOKENS(HdOmniGeospatialDataSourceTokens,
HDOMNIGEOSPATIALDATASOURCE_TOKENS);
HdOmniGeospatialDataSource::HdOmniGeospatialDataSource(const HdSceneIndexBase& index, const SdfPath& primPath,
HdContainerDataSourceHandle wrappedDataSource) :
_sceneIndex(index),
_primPath(primPath),
_wrappedDataSource(wrappedDataSource)
{
}
void HdOmniGeospatialDataSource::UpdateWrappedDataSource(
HdContainerDataSourceHandle wrappedDataSource)
{
_wrappedDataSource = wrappedDataSource;
}
#if PXR_VERSION < 2302
bool HdOmniGeospatialDataSource::Has(const TfToken& name)
{
if (name == HdOmniGeospatialDataSourceTokens->geospatialPreservedXform)
{
return true;
}
return (_wrappedDataSource != nullptr) ? _wrappedDataSource->Has(name) : false;
}
#endif
TfTokenVector HdOmniGeospatialDataSource::GetNames()
{
// since we only wrapped Xformables, this should
// also return HdXformSchemaTokens->xform
TfTokenVector result = (_wrappedDataSource == nullptr) ? TfTokenVector() : _wrappedDataSource->GetNames();
result.push_back(HdOmniGeospatialDataSourceTokens->geospatialPreservedXform);
return result;
}
HdDataSourceBaseHandle HdOmniGeospatialDataSource::Get(const TfToken& name)
{
if (name == HdXformSchemaTokens->xform)
{
// this is an intercept of the flattened transform matrix
// we need to dynamically compute a geospatial one
return this->_ComputeGeospatialXform();
}
else if (name == HdOmniGeospatialDataSourceTokens->geospatialPreservedXform)
{
// this would be the original flattened matrix of the wrapped data source
if (_wrappedDataSource != nullptr)
{
return _wrappedDataSource->Get(HdXformSchemaTokens->xform);
}
}
// all other token values should be defer to the wrapped data source (if any)
if (_wrappedDataSource != nullptr)
{
return _wrappedDataSource->Get(name);
}
return nullptr;
}
bool HdOmniGeospatialDataSource::IsPrimDirtied(const HdDataSourceLocatorSet& locators)
{
static const HdContainerDataSourceHandle containerNull(nullptr);
if (locators.Intersects(HdXformSchema::GetDefaultLocator()))
{
if (HdContainerDataSource::AtomicLoad(_computedGeospatialPrimDataSource) != nullptr ||
HdContainerDataSource::AtomicLoad(_computedGeospatialDependentDataSource) != nullptr)
{
HdContainerDataSource::AtomicStore(_computedGeospatialPrimDataSource, containerNull);
HdContainerDataSource::AtomicStore(_computedGeospatialDependentDataSource, containerNull);
return true;
}
}
return false;
}
HdDataSourceBaseHandle HdOmniGeospatialDataSource::_ComputeGeospatialXform()
{
// since matrices are time sampled, we actually don't compute anything
// here, we just setup the right HdMatrixDataSources to be able to
// compute a final value at a specific time sample when asked
// to do that, we have two cases:
// 1. The wrapped prim in question has a local geodetic position applied
// In this case, all of the information we need to compute the position
// is stored inside of the wrapped prim itself (i.e. the geodetic root
// tangentFrame and geodtic position from the applied API schema)
// 2. The wrapped prim in question does not have a local geodetic position
// applied, but it's parent in the stage hierarchy does, which means
// that we need the wrapped prim plus it's parent prim to be able to
// compute the new correct transform
//
// Case 1 is easy - we can detect whether we have the information or not
// and create the right data source to return.
//
// Case 2 is a bit more difficult to do performantly - at the moment
// we will walk the parent prim hierarchy to the root to determine
// this information, but likely you would want to cache this locally
// on the wrapped prim. We can certainly do that, but then we have to
// be concerned about invalidating it at the right time. We'll leave this
// as a TODO for the future.
//
if (this->_HasGeospatialInformation(_wrappedDataSource))
{
// this is case 1, and we can create a data source specifically
// catered to do that computation
HdContainerDataSourceHandle computedGeospatialPrimDataSource =
HdContainerDataSource::AtomicLoad(_computedGeospatialPrimDataSource);
if (computedGeospatialPrimDataSource != nullptr)
{
// we have a previously cached value so can return that directly
return computedGeospatialPrimDataSource;
}
// otherwise we have to compute a new one
// since the container responsible for the xform token
// needs to take into account both resetXform and matrix
// and since both of those can be time-sampled, we have to make
// sure we can respond appropriately to any query
// so we will need a complete view of the wrapped data source
// to perform the computation
computedGeospatialPrimDataSource = HdOmniGeospatialComputedPrimDataSource::New(_wrappedDataSource);
HdContainerDataSource::AtomicStore(_computedGeospatialPrimDataSource, computedGeospatialPrimDataSource);
return computedGeospatialPrimDataSource;
}
else
{
// this is case 2, in order to perform this transformation appropriately
// we have to walk the parent hierarchy to find the parent with a local position
// geospatial API attached to it - if none exists we can return the wrapped
// data source directly, but if one does exist we need a new data source capable
// of handling the dynamic compute at any time sample
HdContainerDataSourceHandle computedGeospatialDependentDataSource =
HdContainerDataSource::AtomicLoad(_computedGeospatialDependentDataSource);
if (computedGeospatialDependentDataSource != nullptr)
{
// we have a previously cached value and can return that directly
return computedGeospatialDependentDataSource;
}
// otherwise we have to compute a new one
// so we need to follow the prim hierarchy up until we reach
// a geospatially applied one (if any)
if (_primPath != SdfPath::AbsoluteRootPath())
{
HdContainerDataSourceHandle geospatialDataSource = nullptr;
for (SdfPath p = _primPath.GetParentPath(); p != SdfPath::AbsoluteRootPath(); p = p.GetParentPath())
{
HdSceneIndexPrim prim = _sceneIndex.GetPrim(p);
if (this->_HasGeospatialInformation(prim.dataSource))
{
// found it!
geospatialDataSource = prim.dataSource;
}
}
// if we didn't find a geospatially applied parent, we don't need to do anything
if (geospatialDataSource == nullptr)
{
if (_wrappedDataSource != nullptr)
{
HdContainerDataSourceHandle dataSource = HdContainerDataSource::Cast(_wrappedDataSource->Get(HdXformSchemaTokens->xform));
if (dataSource != nullptr)
{
HdContainerDataSource::AtomicStore(_computedGeospatialDependentDataSource, dataSource);
return _computedGeospatialDependentDataSource;
}
return nullptr;
}
return nullptr;
}
// otherwise we need a new datasource that can perform the compute between
// the immediate parent and the prim in question
SdfPath parentPath = _primPath.GetParentPath();
HdSceneIndexPrim parentSceneIndexPrim = _sceneIndex.GetPrim(parentPath);
computedGeospatialDependentDataSource = HdOmniGeospatialComputedDependentDataSource::New(_wrappedDataSource,
parentSceneIndexPrim.dataSource);
HdContainerDataSource::AtomicStore(_computedGeospatialDependentDataSource, computedGeospatialDependentDataSource);
return computedGeospatialDependentDataSource;
}
else
{
// it's the root path, and we don't have to do anything here
// NOTE: this makes the assumption that root never has geospatial information applied
if (_wrappedDataSource != nullptr)
{
return _wrappedDataSource->Get(HdXformSchemaTokens->xform);
}
}
}
return nullptr;
}
bool HdOmniGeospatialDataSource::_HasGeospatialInformation(HdContainerDataSourceHandle handle)
{
HdOmniGeospatialWGS84LocalPositionSchema localPositionSchema = HdOmniGeospatialWGS84LocalPositionSchema::GetFromParent(handle);
return localPositionSchema.IsDefined();
}
PXR_NAMESPACE_CLOSE_SCOPE
| 9,813 |
C++
| 40.235294 | 142 | 0.689086 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/localPositionDataSource.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/usdImaging/usdImaging/dataSourceAttribute.h>
#include "localPositionDataSource.h"
PXR_NAMESPACE_OPEN_SCOPE
HdOmniGeospatialWGS84LocalPositionDataSource::HdOmniGeospatialWGS84LocalPositionDataSource(
const UsdPrim& prim,
const UsdImagingDataSourceStageGlobals& stageGlobals) :
_stageGlobals(stageGlobals)
{
_localPositionApi = OmniGeospatialWGS84LocalPositionAPI(prim);
}
#if PXR_VERSION < 2302
bool HdOmniGeospatialWGS84LocalPositionDataSource::Has(const TfToken& name)
{
return (name == HdOmniGeospatialWGS84LocalPositionSchemaTokens->position);
}
#endif
TfTokenVector HdOmniGeospatialWGS84LocalPositionDataSource::GetNames()
{
// return the hydra attribute names this data source is responsible for
TfTokenVector names;
names.push_back(HdOmniGeospatialWGS84LocalPositionSchemaTokens->position);
return names;
}
HdDataSourceBaseHandle HdOmniGeospatialWGS84LocalPositionDataSource::Get(const TfToken& name)
{
// retrieves the data source values for the attributes this data source
// supports
if (name == HdOmniGeospatialWGS84LocalPositionSchemaTokens->position)
{
return UsdImagingDataSourceAttribute<GfVec3d>::New(
_localPositionApi.GetPositionAttr(), _stageGlobals);
}
// this is a name we don't support
return nullptr;
}
PXR_NAMESPACE_CLOSE_SCOPE
| 1,954 |
C++
| 32.135593 | 93 | 0.772262 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/computedPrimDataSource.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef HD_OMNI_GEOSPATIAL_COMPUTED_PRIM_DATA_SOURCE_H_
#define HD_OMNI_GEOSPATIAL_COMPUTED_PRIM_DATA_SOURCE_H_
#include <pxr/imaging/hd/dataSource.h>
#include <pxr/imaging/hd/dataSourceTypeDefs.h>
PXR_NAMESPACE_OPEN_SCOPE
/// \class HdOmniGeospatialComputedPrimDataSource
///
/// A datasource representing a container data source mimicing
/// that of a container data source for xform data, but returning
/// computed values based on geospatial data applied to the prim.
///
class HdOmniGeospatialComputedPrimDataSource : public HdContainerDataSource
{
public:
HD_DECLARE_DATASOURCE(HdOmniGeospatialComputedPrimDataSource);
HdOmniGeospatialComputedPrimDataSource(HdContainerDataSourceHandle inputDataSource);
// data source overrides
TfTokenVector GetNames() override;
HdDataSourceBaseHandle Get(const TfToken& name) override;
#if PXR_VERSION < 2302
bool Has(const TfToken& name) override;
#endif
private:
HdDataSourceBaseHandle _ComputeGeospatialXform();
GfVec3d _GeodeticToEcef(const GfVec3d& llh) const;
GfVec3d _EcefToEnu(const GfVec3d& ecef, const GfVec3d& llh) const;
GfVec3d _EnuToCartesian(const GfVec3d& enu, const TfToken& upAxis, const double& metersPerUnit, const GfVec3d& reference) const;
private:
HdContainerDataSourceHandle _inputDataSource;
HdMatrixDataSourceHandle _matrixDataSource;
class _GeospatialMatrixDataSource : public HdMatrixDataSource
{
public:
HD_DECLARE_DATASOURCE(_GeospatialMatrixDataSource);
VtValue GetValue(Time shutterOffset) override;
GfMatrix4d GetTypedValue(Time shutterOffset) override;
bool GetContributingSampleTimesForInterval(
Time startTime,
Time endTime,
std::vector<Time>* outSampleTimes) override;
private:
_GeospatialMatrixDataSource(HdContainerDataSourceHandle inputDataSource);
HdMatrixDataSourceHandle _GetMatrixSource() const;
HdVec3dDataSourceHandle _GetLocalPositionSource() const;
HdTokenDataSourceHandle _GetTangentPlaneSource() const;
HdVec3dDataSourceHandle _GetReferencePositionSource() const;
HdVec3dDataSourceHandle _GetOrientationSource() const;
HdTokenDataSourceHandle _GetStageUpAxisSource() const;
HdDoubleDataSourceHandle _GetStageMetersPerUnitSource() const;
GfMatrix4d _GetMatrix(const Time shutterOffset) const;
GfVec3d _GetLocalPosition(const Time shutterOffset) const;
TfToken _GetTangentPlane() const;
GfVec3d _GetReferencePosition() const;
GfVec3d _GetOrientation() const;
TfToken _GetStageUpAxis() const;
double _GetStageMetersPerUnit() const;
// geospatial transform methods
GfMatrix4d _ComputeTransformedMatrix(const Time shutterOffset) const;
GfVec3d _GeodeticToEcef(const GfVec3d& llh) const;
GfVec3d _EcefToEnu(const GfVec3d& ecef, const GfVec3d& llh) const;
GfVec3d _EnuToCartesian(const GfVec3d& enu, const TfToken& upAxis, const double& metersPerUnit, const GfVec3d& reference) const;
struct GeoConstants
{
static constexpr double semiMajorAxis = 6378137.0;
static constexpr double semiMinorAxis = 6356752.3142;
static constexpr double flattening = 1.0 / 298.257223563;
static constexpr double eccentricity = flattening * (2 - flattening);
static constexpr double radians = M_PI / 180.0;
static constexpr double degrees = 180.0 / M_PI;
};
HdContainerDataSourceHandle _inputDataSource;
};
HD_DECLARE_DATASOURCE_HANDLES(_GeospatialMatrixDataSource);
};
HD_DECLARE_DATASOURCE_HANDLES(HdOmniGeospatialComputedPrimDataSource);
PXR_NAMESPACE_CLOSE_SCOPE
#endif // HD_OMNI_GEOSPATIAL_COMPUTED_PRIM_DATA_SOURCE_H_
| 4,414 |
C
| 37.72807 | 136 | 0.738106 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/geospatialSceneIndex.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef OMNI_GEO_SCENE_INDEX_H_
#define OMNI_GEO_SCENE_INDEX_H_
#include <pxr/pxr.h>
#include <pxr/usd/sdf/pathTable.h>
#include <pxr/imaging/hd/filteringSceneIndex.h>
#include "api.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_DECLARE_REF_PTRS(OmniGeospatialSceneIndex);
///
/// \class OmniGeospatialSceneIndex
///
/// A scene index responsible for observing an input flattened scene
/// index and producing a comparable scene in which geospatial transforms
/// have been applied to prims with geospatial state attached to them
/// and for updating the transform of their children as needed.
///
/// Note that with Render Delegate 2.0 and the ability to pull data
/// from a non-flattened scene, this implementation will have to be
/// revisited to work with the unflattened xform representation of
/// the hydra prims.
///
class OmniGeospatialSceneIndex : public HdSingleInputFilteringSceneIndexBase
{
public:
OMNIGEOSCENEINDEX_API
static OmniGeospatialSceneIndexRefPtr New(const HdSceneIndexBaseRefPtr& inputSceneIndex,
const HdContainerDataSourceHandle& inputArgs = nullptr);
OMNIGEOSCENEINDEX_API
~OmniGeospatialSceneIndex() override;
OMNIGEOSCENEINDEX_API
HdSceneIndexPrim GetPrim(const SdfPath& primPath) const override;
OMNIGEOSCENEINDEX_API
SdfPathVector GetChildPrimPaths(const SdfPath& primPath) const override;
protected:
OmniGeospatialSceneIndex(const HdSceneIndexBaseRefPtr& inputSceneIndex,
const HdContainerDataSourceHandle& inputArgs);
// these three are provided by HdSingleInputFilteringSceneIndexBase
// and must be overridden by inheritors
virtual void _PrimsAdded(const HdSceneIndexBase& sender,
const HdSceneIndexObserver::AddedPrimEntries& entries) override;
virtual void _PrimsRemoved(const HdSceneIndexBase& sender,
const HdSceneIndexObserver::RemovedPrimEntries& entries) override;
virtual void _PrimsDirtied(const HdSceneIndexBase& sender,
const HdSceneIndexObserver::DirtiedPrimEntries& entries) override;
private:
SdfPathTable<HdSceneIndexPrim>::_IterBoolPair _IsPrimWrapped(const SdfPath& primPath) const;
HdSceneIndexPrim& _WrapPrim(const SdfPath& primPath, const HdSceneIndexPrim& hdPrim) const;
void _DirtyHierarchy(const SdfPath& primPath, const HdDataSourceLocatorSet& locators, HdSceneIndexObserver::DirtiedPrimEntries* dirtyEntries);
/*HdContainerDataSourceHandle _ComputeDataSource(
const SdfPath& primPath,
const HdContainerDataSourceHandle& primDataSource) const;
void _ComputeChildDataSources(const SdfPath& parentPath,
const HdContainerDataSourceHandle& parentDataSource) const;
HdContainerDataSourceHandle _ComputeMatrixDependenciesDataSource(
const SdfPath& primPath) const;*/
private:
// marked as mutable because it is an internal cache
// that is written to on-demand from the GetPrim method
// which is a const method by interface definition in HdSceneIndexBase
mutable SdfPathTable<HdSceneIndexPrim> _wrappedPrims;
};
PXR_NAMESPACE_CLOSE_SCOPE
#endif
| 3,668 |
C
| 36.438775 | 146 | 0.773446 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/referencePositionSchema.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/imaging/hd/retainedDataSource.h>
#include "referencePositionSchema.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_DEFINE_PUBLIC_TOKENS(HdOmniGeospatialWGS84ReferencePositionSchemaTokens,
HDOMNIGEOSPATIALWGS84REFERENCEPOSITION_SCHEMA_TOKENS);
HdTokenDataSourceHandle HdOmniGeospatialWGS84ReferencePositionSchema::GetTangentPlane()
{
return _GetTypedDataSource<HdTokenDataSource>(
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->tangentPlane);
}
HdVec3dDataSourceHandle HdOmniGeospatialWGS84ReferencePositionSchema::GetReferencePosition()
{
return _GetTypedDataSource<HdVec3dDataSource>(
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePosition);
}
HdVec3dDataSourceHandle HdOmniGeospatialWGS84ReferencePositionSchema::GetOrientation()
{
return _GetTypedDataSource<HdVec3dDataSource>(
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->orientation);
}
HdTokenDataSourceHandle HdOmniGeospatialWGS84ReferencePositionSchema::GetStageUpAxis()
{
return _GetTypedDataSource<HdTokenDataSource>(
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageUpAxis);
}
HdDoubleDataSourceHandle HdOmniGeospatialWGS84ReferencePositionSchema::GetStageMetersPerUnit()
{
return _GetTypedDataSource<HdDoubleDataSource>(
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageMetersPerUnit);
}
HdOmniGeospatialWGS84ReferencePositionSchema HdOmniGeospatialWGS84ReferencePositionSchema::GetFromParent(
const HdContainerDataSourceHandle& fromParentContainer)
{
if (fromParentContainer == nullptr)
{
return HdOmniGeospatialWGS84ReferencePositionSchema(nullptr);
}
return HdOmniGeospatialWGS84ReferencePositionSchema(
HdContainerDataSource::Cast(fromParentContainer->Get(
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePositionApi))
);
}
const HdDataSourceLocator& HdOmniGeospatialWGS84ReferencePositionSchema::GetDefaultLocator()
{
static const HdDataSourceLocator locator(
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePositionApi
);
return locator;
}
HdContainerDataSourceHandle HdOmniGeospatialWGS84ReferencePositionSchema::BuildRetained(
const HdTokenDataSourceHandle& tangentPlane,
const HdVec3dDataSourceHandle& referencePosition,
const HdVec3dDataSourceHandle& orientation,
const HdTokenDataSourceHandle& stageUpAxis,
const HdDoubleDataSourceHandle& stageMetersPerUnit)
{
TfToken names[5];
HdDataSourceBaseHandle values[5];
size_t count = 0;
if (tangentPlane != nullptr)
{
names[count] = HdOmniGeospatialWGS84ReferencePositionSchemaTokens->tangentPlane;
values[count] = tangentPlane;
count++;
}
if (referencePosition != nullptr)
{
names[count] = HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePosition;
values[count] = referencePosition;
count++;
}
if (orientation != nullptr)
{
names[count] = HdOmniGeospatialWGS84ReferencePositionSchemaTokens->orientation;
values[count] = orientation;
count++;
}
if (stageUpAxis != nullptr)
{
names[count] = HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageUpAxis;
values[count] = stageUpAxis;
count++;
}
if (stageMetersPerUnit != nullptr)
{
names[count] = HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageMetersPerUnit;
values[count] = stageMetersPerUnit;
count++;
}
return HdRetainedContainerDataSource::New(count, names, values);
}
PXR_NAMESPACE_CLOSE_SCOPE
| 4,221 |
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| 33.048387 | 105 | 0.773513 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/localPositionSchema.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef HD_OMNI_GEOSPATIAL_WGS84_LOCAL_POSITION_SCHEMA_H_
#define HD_OMNI_GEOSPATIAL_WGS84_LOCAL_POSITION_SCHEMA_H_
#include <pxr/imaging/hd/schema.h>
#include <pxr/imaging/hd/dataSourceLocator.h>
#include "api.h"
PXR_NAMESPACE_OPEN_SCOPE
//-----------------------------------------------------------------------------
#define HDOMNIGEOSPATIALWGS84LOCALPOSITION_SCHEMA_TOKENS \
(localPositionApi) \
(position) \
TF_DECLARE_PUBLIC_TOKENS(HdOmniGeospatialWGS84LocalPositionSchemaTokens, OMNIGEOSCENEINDEX_API,
HDOMNIGEOSPATIALWGS84LOCALPOSITION_SCHEMA_TOKENS);
//-----------------------------------------------------------------------------
class HdOmniGeospatialWGS84LocalPositionSchema : public HdSchema
{
public:
HdOmniGeospatialWGS84LocalPositionSchema(HdContainerDataSourceHandle container)
: HdSchema(container) { }
OMNIGEOSCENEINDEX_API
HdVec3dDataSourceHandle GetPosition();
OMNIGEOSCENEINDEX_API
static HdOmniGeospatialWGS84LocalPositionSchema GetFromParent(
const HdContainerDataSourceHandle& fromParentContainer);
OMNIGEOSCENEINDEX_API
static const HdDataSourceLocator& GetDefaultLocator();
OMNIGEOSCENEINDEX_API
static HdContainerDataSourceHandle BuildRetained(
const HdVec3dDataSourceHandle& position
);
};
PXR_NAMESPACE_CLOSE_SCOPE
#endif // HD_OMNI_GEOSPATIAL_WGS84_LOCAL_POSITION_SCHEMA_H_
| 1,985 |
C
| 32.661016 | 95 | 0.716877 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/referencePositionDataSource.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef HD_OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_DATA_SOURCE_H_
#define HD_OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_DATA_SOURCE_H_
#include <pxr/imaging/hd/dataSource.h>
#include <pxr/usdImaging/usdImaging/dataSourceStageGlobals.h>
#include <omniGeospatial/wGS84ReferencePositionAPI.h>
#include "referencePositionSchema.h"
PXR_NAMESPACE_OPEN_SCOPE
class HdOmniGeospatialWGS84ReferencePositionDataSource : public HdContainerDataSource
{
public:
HD_DECLARE_DATASOURCE(HdOmniGeospatialWGS84ReferencePositionDataSource);
HdOmniGeospatialWGS84ReferencePositionDataSource(const UsdPrim& prim,
const UsdImagingDataSourceStageGlobals& stageGlobals);
TfTokenVector GetNames() override;
HdDataSourceBaseHandle Get(const TfToken& name) override;
#if PXR_VERSION < 2302
bool Has(const TfToken& name) override;
#endif
private:
OmniGeospatialWGS84ReferencePositionAPI _referencePositionApi;
const UsdImagingDataSourceStageGlobals& _stageGlobals;
template <typename T>
class _StageDataSource : public HdTypedSampledDataSource<T>
{
public:
HD_DECLARE_DATASOURCE(_StageDataSource<T>);
VtValue GetValue(HdSampledDataSource::Time shutterOffset) override
{
return VtValue(GetTypedValue(shutterOffset));
}
T GetTypedValue(HdSampledDataSource::Time shutterOffset) override
{
return _value;
}
bool GetContributingSampleTimesForInterval(
HdSampledDataSource::Time startTime,
HdSampledDataSource::Time endTime,
std::vector<HdSampledDataSource::Time>* outSampleTimes) override
{
return false;
}
private:
_StageDataSource(const T& value);
T _value;
};
};
HD_DECLARE_DATASOURCE_HANDLES(HdOmniGeospatialWGS84ReferencePositionDataSource);
PXR_NAMESPACE_CLOSE_SCOPE
#endif // HD_OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_DATA_SOURCE_H_
| 2,546 |
C
| 30.060975 | 85 | 0.739199 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/referencePositionAPIAdapter.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/imaging/hd/retainedDataSource.h>
#include <omniGeospatial/wGS84ReferencePositionAPI.h>
#include "referencePositionAPIAdapter.h"
#include "referencePositionDataSource.h"
#include "referencePositionSchema.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_REGISTRY_FUNCTION(TfType)
{
typedef OmniGeospatialWGS84ReferencePositionAPIAdapter Adapter;
TfType t = TfType::Define<Adapter, TfType::Bases<Adapter::BaseAdapter> >();
t.SetFactory<UsdImagingAPISchemaAdapterFactory<Adapter> >();
}
#if PXR_VERSION >= 2302
HdContainerDataSourceHandle OmniGeospatialWGS84ReferencePositionAPIAdapter::GetImagingSubprimData(
const UsdPrim& prim,
const TfToken& subprim,
const TfToken& appliedInstanceName,
const UsdImagingDataSourceStageGlobals& stageGlobals)
#else
HdContainerDataSourceHandle OmniGeospatialWGS84ReferencePositionAPIAdapter::GetImagingSubprimData(
const TfToken& subprim,
const UsdPrim& prim,
const TfToken& appliedInstanceName,
const UsdImagingDataSourceStageGlobals& stageGlobals)
#endif
{
// at the point we are invoked here, the stage scene index has already determined
// that the API schema applies to the prim, so we can safely create our
// data source
if (!subprim.IsEmpty() || !appliedInstanceName.IsEmpty())
{
// there shouldn't be a subprim or an applied instance name
// if there is, we don't really know what to do with it
// so we return null to indicate there is no data source
// for this prim setup
return nullptr;
}
return HdRetainedContainerDataSource::New(
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePositionApi,
HdOmniGeospatialWGS84ReferencePositionDataSource::New(prim, stageGlobals)
);
}
#if PXR_VERSION >= 2302
HdDataSourceLocatorSet OmniGeospatialWGS84ReferencePositionAPIAdapter::InvalidateImagingSubprim(
const UsdPrim& prim,
const TfToken& subprim,
const TfToken& appliedInstanceName,
const TfTokenVector& properties)
#else
HdDataSourceLocatorSet OmniGeospatialWGS84ReferencePositionAPIAdapter::InvalidateImagingSubprim(
const TfToken& subprim,
const TfToken& appliedInstanceName,
const TfTokenVector& properties)
#endif
{
if (!subprim.IsEmpty() || !appliedInstanceName.IsEmpty())
{
return HdDataSourceLocatorSet();
}
TfToken geospatialPrefix("omni:geospatial:wgs84:reference");
for (const TfToken& propertyName : properties)
{
if (TfStringStartsWith(propertyName, geospatialPrefix))
{
return HdOmniGeospatialWGS84ReferencePositionSchema::GetDefaultLocator();
}
}
return HdDataSourceLocatorSet();
}
PXR_NAMESPACE_CLOSE_SCOPE
| 3,306 |
C++
| 33.810526 | 98 | 0.753781 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/geospatialDataSource.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef HD_OMNI_GEOSPATIAL_DATA_SOURCE_H_
#define HD_OMNI_GEOSPATIAL_DATA_SOURCE_H_
#include <pxr/imaging/hd/dataSource.h>
#include <pxr/imaging/hd/dataSourceTypeDefs.h>
#include <pxr/imaging/hd/sceneIndex.h>
#include "api.h"
PXR_NAMESPACE_OPEN_SCOPE
//-----------------------------------------------------------------------------
#define HDOMNIGEOSPATIALDATASOURCE_TOKENS \
(geospatialPreservedXform)
TF_DECLARE_PUBLIC_TOKENS(HdOmniGeospatialDataSourceTokens, OMNIGEOSCENEINDEX_API,
HDOMNIGEOSPATIALDATASOURCE_TOKENS);
//-----------------------------------------------------------------------------
/// \class HdOmniGeospatialDataSource
///
/// A datasource representing a wrapped view of an existing flattened
/// data source where the xform token is intercepted and a new geospatial
/// matrix dynamically calculated.
///
class HdOmniGeospatialDataSource : public HdContainerDataSource
{
public:
HD_DECLARE_DATASOURCE(HdOmniGeospatialDataSource);
HdOmniGeospatialDataSource(const HdSceneIndexBase& sceneIndex, const SdfPath& primPath,
HdContainerDataSourceHandle wrappedDataSource);
void UpdateWrappedDataSource(HdContainerDataSourceHandle wrappedDataSource);
// data source overrides
TfTokenVector GetNames() override;
HdDataSourceBaseHandle Get(const TfToken& name) override;
#if PXR_VERSION < 2302
bool Has(const TfToken& name) override;
#endif
// determines if the data source would be dirtied based on the locators given
bool IsPrimDirtied(const HdDataSourceLocatorSet& locators);
private:
bool _HasGeospatialInformation(HdContainerDataSourceHandle dataSource);
HdDataSourceBaseHandle _ComputeGeospatialXform();
private:
const HdSceneIndexBase& _sceneIndex;
SdfPath _primPath;
HdContainerDataSourceHandle _wrappedDataSource;
// cached computed datasources
HdContainerDataSourceAtomicHandle _computedGeospatialPrimDataSource;
HdContainerDataSourceAtomicHandle _computedGeospatialDependentDataSource;
};
HD_DECLARE_DATASOURCE_HANDLES(HdOmniGeospatialDataSource);
PXR_NAMESPACE_CLOSE_SCOPE
#endif // HD_OMNI_GEOSPATIAL_DATA_SOURCE_H_
| 2,737 |
C
| 31.987951 | 91 | 0.739496 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/localPositionAPIAdapter.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/imaging/hd/retainedDataSource.h>
#include <omniGeospatial/wGS84LocalPositionAPI.h>
#include <omniGeospatial/wGS84ReferencePositionAPI.h>
#include "localPositionAPIAdapter.h"
#include "localPositionDataSource.h"
#include "localPositionSchema.h"
#include "referencePositionDataSource.h"
#include "referencePositionSchema.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_REGISTRY_FUNCTION(TfType)
{
typedef OmniGeospatialWGS84LocalPositionAPIAdapter Adapter;
TfType t = TfType::Define<Adapter, TfType::Bases<Adapter::BaseAdapter> >();
t.SetFactory<UsdImagingAPISchemaAdapterFactory<Adapter> >();
}
#if PXR_VERSION >= 2302
HdContainerDataSourceHandle OmniGeospatialWGS84LocalPositionAPIAdapter::GetImagingSubprimData(
const UsdPrim& prim,
const TfToken& subprim,
const TfToken& appliedInstanceName,
const UsdImagingDataSourceStageGlobals& stageGlobals)
#else
HdContainerDataSourceHandle OmniGeospatialWGS84LocalPositionAPIAdapter::GetImagingSubprimData(
const TfToken& subprim,
const UsdPrim& prim,
const TfToken& appliedInstanceName,
const UsdImagingDataSourceStageGlobals& stageGlobals)
#endif
{
// at the point we are invoked here, the stage scene index has already determined
// that the API schema applies to the prim, so we can safely create our
// data source
if (!subprim.IsEmpty() || !appliedInstanceName.IsEmpty())
{
// there shouldn't be a subprim or an applied instance name
// if there is, we don't really know what to do with it
// so we return null to indicate there is no data source
// for this prim setup
return nullptr;
}
// to make it a bit easier, we will traverse the parent structure here to find a geodetic root
// rather than traversing it in the scene index - this is because we have all of the information
// we need at the point where this prim is getting processed
HdDataSourceBaseHandle referencePositionDataSource = nullptr;
for (UsdPrim parentPrim = prim; !parentPrim.IsPseudoRoot(); parentPrim = parentPrim.GetParent())
{
if (parentPrim.HasAPI<OmniGeospatialWGS84ReferencePositionAPI>())
{
// bake the geodetic root information into this local prim
referencePositionDataSource = HdOmniGeospatialWGS84ReferencePositionDataSource::New(parentPrim, stageGlobals);
break;
}
}
// only process local position if we found a geodetic root - if we didn't
// it means that this is an unrooted local position so we keep whatever
// transform information the prim would have had otherwise
if (referencePositionDataSource != nullptr)
{
return HdRetainedContainerDataSource::New(
HdOmniGeospatialWGS84LocalPositionSchemaTokens->localPositionApi,
HdOmniGeospatialWGS84LocalPositionDataSource::New(prim, stageGlobals),
HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePositionApi,
referencePositionDataSource
);
}
return nullptr;
}
#if PXR_VERSION >= 2302
HdDataSourceLocatorSet OmniGeospatialWGS84LocalPositionAPIAdapter::InvalidateImagingSubprim(
const UsdPrim& prim,
const TfToken& subprim,
const TfToken& appliedInstanceName,
const TfTokenVector& properties)
#else
HdDataSourceLocatorSet OmniGeospatialWGS84LocalPositionAPIAdapter::InvalidateImagingSubprim(
const TfToken& subprim,
const TfToken& appliedInstanceName,
const TfTokenVector& properties)
#endif
{
if (!subprim.IsEmpty() || !appliedInstanceName.IsEmpty())
{
return HdDataSourceLocatorSet();
}
TfToken geospatialPrefix("omni:geospatial:wgs84:local");
for (const TfToken& propertyName : properties)
{
if (TfStringStartsWith(propertyName, geospatialPrefix))
{
return HdOmniGeospatialWGS84LocalPositionSchema::GetDefaultLocator();
}
}
return HdDataSourceLocatorSet();
}
PXR_NAMESPACE_CLOSE_SCOPE
| 4,574 |
C++
| 36.809917 | 122 | 0.740927 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/referencePositionDataSource.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/usd/usdGeom/metrics.h>
#include <pxr/usd/usdGeom/tokens.h>
#include <pxr/usdImaging/usdImaging/dataSourceAttribute.h>
#include "referencePositionDataSource.h"
PXR_NAMESPACE_OPEN_SCOPE
HdOmniGeospatialWGS84ReferencePositionDataSource::HdOmniGeospatialWGS84ReferencePositionDataSource(
const UsdPrim& prim,
const UsdImagingDataSourceStageGlobals& stageGlobals) :
_stageGlobals(stageGlobals)
{
_referencePositionApi = OmniGeospatialWGS84ReferencePositionAPI(prim);
}
#if PXR_VERSION < 2302
bool HdOmniGeospatialWGS84ReferencePositionDataSource::Has(const TfToken& name)
{
return (name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->tangentPlane) ||
(name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePosition) ||
(name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->orientation) ||
(name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageUpAxis) ||
(name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageMetersPerUnit);
}
#endif
TfTokenVector HdOmniGeospatialWGS84ReferencePositionDataSource::GetNames()
{
// return the hydra attribute names this data source is responsible for
TfTokenVector names;
names.push_back(HdOmniGeospatialWGS84ReferencePositionSchemaTokens->tangentPlane);
names.push_back(HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePosition);
names.push_back(HdOmniGeospatialWGS84ReferencePositionSchemaTokens->orientation);
names.push_back(HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageUpAxis);
names.push_back(HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageMetersPerUnit);
return names;
}
HdDataSourceBaseHandle HdOmniGeospatialWGS84ReferencePositionDataSource::Get(const TfToken& name)
{
// retrieves the data source values for the attributes this data source
// supports
if (name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->tangentPlane)
{
return UsdImagingDataSourceAttribute<TfToken>::New(
_referencePositionApi.GetTangentPlaneAttr(), _stageGlobals);
}
else if (name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->referencePosition)
{
return UsdImagingDataSourceAttribute<GfVec3d>::New(
_referencePositionApi.GetReferencePositionAttr(), _stageGlobals);
}
else if (name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->orientation)
{
return UsdImagingDataSourceAttribute<GfVec3d>::New(
_referencePositionApi.GetOrientationAttr(), _stageGlobals);
}
else if (name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageUpAxis)
{
TfToken upAxis = UsdGeomTokens->y;
UsdStageWeakPtr stage = _referencePositionApi.GetPrim().GetStage();
if (stage != nullptr)
{
upAxis = UsdGeomGetStageUpAxis(stage);
}
return _StageDataSource<TfToken>::New(upAxis);
}
else if (name == HdOmniGeospatialWGS84ReferencePositionSchemaTokens->stageMetersPerUnit)
{
double mpu = 0.01;
UsdStageWeakPtr stage = _referencePositionApi.GetPrim().GetStage();
if (stage != nullptr)
{
mpu = UsdGeomGetStageMetersPerUnit(stage);
}
return _StageDataSource<double>::New(mpu);
}
// this is a name we don't support
return nullptr;
}
template <typename T>
HdOmniGeospatialWGS84ReferencePositionDataSource::_StageDataSource<T>::_StageDataSource(const T& value) : _value(value)
{
}
PXR_NAMESPACE_CLOSE_SCOPE
| 4,155 |
C++
| 38.207547 | 119 | 0.754513 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/referencePositionAPIAdapter.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_API_ADAPTER_H_
#define OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_API_ADAPTER_H_
#include <pxr/pxr.h>
#include <pxr/usdImaging/usdImaging/apiSchemaAdapter.h>
#include "api.h"
PXR_NAMESPACE_OPEN_SCOPE
class OmniGeospatialWGS84ReferencePositionAPIAdapter : public UsdImagingAPISchemaAdapter
{
public:
using BaseAdapter = UsdImagingAPISchemaAdapter;
#if PXR_VERSION >= 2302
OMNIGEOSCENEINDEX_API
HdContainerDataSourceHandle GetImagingSubprimData(
const UsdPrim& prim,
const TfToken& subprim,
const TfToken& appliedInstanceName,
const UsdImagingDataSourceStageGlobals& stageGlobals
) override;
#else
OMNIGEOSCENEINDEX_API
HdContainerDataSourceHandle GetImagingSubprimData(
const TfToken& subprim,
const UsdPrim& prim,
const TfToken& appliedInstanceName,
const UsdImagingDataSourceStageGlobals& stageGlobals
) override;
#endif
#if PXR_VERSION >= 2302
OMNIGEOSCENEINDEX_API
HdDataSourceLocatorSet InvalidateImagingSubprim(
const UsdPrim& prim,
const TfToken& subprim,
const TfToken& appliedInstanceName,
const TfTokenVector& properties
) override;
#else
OMNIGEOSCENEINDEX_API
HdDataSourceLocatorSet InvalidateImagingSubprim(
const TfToken& subprim,
const TfToken& appliedInstanceName,
const TfTokenVector& properties
) override;
#endif
};
PXR_NAMESPACE_CLOSE_SCOPE
#endif // OMNI_GEOSPATIAL_WGS84_REFERENCE_POSITION_API_ADAPTER_H_
| 2,144 |
C
| 30.544117 | 88 | 0.747201 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/computedDependentDataSource.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/imaging/hd/xformSchema.h>
#include "geospatialDataSource.h"
#include "computedDependentDataSource.h"
PXR_NAMESPACE_OPEN_SCOPE
HdOmniGeospatialComputedDependentDataSource::HdOmniGeospatialComputedDependentDataSource(
HdContainerDataSourceHandle inputDataSource,
HdContainerDataSourceHandle parentDataSource) :
_inputDataSource(inputDataSource),
_parentDataSource(parentDataSource)
{
_matrixDataSource =
HdOmniGeospatialComputedDependentDataSource::_GeospatiallyAffectedMatrixDataSource::New(
_inputDataSource, parentDataSource);
}
#if PXR_VERSION < 2302
bool HdOmniGeospatialComputedDependentDataSource::Has(const TfToken& name)
{
return (name == HdXformSchemaTokens->resetXformStack) ||
(name == HdXformSchemaTokens->matrix);
}
#endif
TfTokenVector HdOmniGeospatialComputedDependentDataSource::GetNames()
{
// this container data source retrieves the xform tokens
TfTokenVector result;
result.push_back(HdXformSchemaTokens->resetXformStack);
result.push_back(HdXformSchemaTokens->matrix);
return result;
}
HdDataSourceBaseHandle HdOmniGeospatialComputedDependentDataSource::Get(const TfToken& name)
{
if (_inputDataSource != nullptr)
{
if (name == HdXformSchemaTokens->resetXformStack)
{
// we don't modify the underlying time-sampled data
// for resetXformStack, so return that directly
HdXformSchema xformSchema = HdXformSchema::GetFromParent(_inputDataSource);
return xformSchema.IsDefined() ? xformSchema.GetResetXformStack() : nullptr;
}
else if (name == HdXformSchemaTokens->matrix)
{
return _matrixDataSource;
}
}
return nullptr;
}
HdOmniGeospatialComputedDependentDataSource::_GeospatiallyAffectedMatrixDataSource::_GeospatiallyAffectedMatrixDataSource(
HdContainerDataSourceHandle inputDataSource,
HdContainerDataSourceHandle parentDataSource) :
_inputDataSource(inputDataSource),
_parentDataSource(parentDataSource)
{
}
VtValue HdOmniGeospatialComputedDependentDataSource::_GeospatiallyAffectedMatrixDataSource::GetValue(Time shutterOffset)
{
return VtValue(this->GetTypedValue(shutterOffset));
}
GfMatrix4d HdOmniGeospatialComputedDependentDataSource::_GeospatiallyAffectedMatrixDataSource::GetTypedValue(Time shutterOffset)
{
return this->_ComputeTransformedMatrix(shutterOffset);
}
bool HdOmniGeospatialComputedDependentDataSource::_GeospatiallyAffectedMatrixDataSource::GetContributingSampleTimesForInterval(
Time startTime,
Time endTime,
std::vector<Time>* outSampleTimes)
{
HdSampledDataSourceHandle sources[] = {
this->_GetMatrixSource(),
this->_GetParentMatrixSource()
};
return HdGetMergedContributingSampleTimesForInterval(
TfArraySize(sources),
sources,
startTime,
endTime,
outSampleTimes);
}
HdMatrixDataSourceHandle HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_GetMatrixSource() const
{
return HdXformSchema::GetFromParent(_inputDataSource).GetMatrix();
}
HdBoolDataSourceHandle HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_GetResetXformStackSource() const
{
return HdXformSchema::GetFromParent(_inputDataSource).GetResetXformStack();
}
HdMatrixDataSourceHandle HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_GetParentMatrixSource() const
{
return HdXformSchema::GetFromParent(_parentDataSource).GetMatrix();
}
HdMatrixDataSourceHandle HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_GetParentOriginalMatrixSource() const
{
// the parent data source here should be a geospatial data source
// but in the even it is not, this method will simply return the same
// matrix as that of _GetParentMatrixSource
HdOmniGeospatialDataSourceHandle geospatialDataSource =
HdOmniGeospatialDataSource::Cast(_parentDataSource);
if (geospatialDataSource != nullptr)
{
HdContainerDataSourceHandle xformDataSource =
HdContainerDataSource::Cast(
geospatialDataSource->Get(HdOmniGeospatialDataSourceTokens->geospatialPreservedXform));
if (xformDataSource == nullptr)
{
TF_WARN("Parent data source could not retrieve preserved xform!");
return this->_GetParentMatrixSource();
}
HdMatrixDataSourceHandle matrixDataSource = HdMatrixDataSource::Cast(
xformDataSource->Get(HdXformSchemaTokens->matrix));
if (matrixDataSource == nullptr)
{
TF_WARN("Xform schema not defined on preserved container data source!");
}
return (matrixDataSource != nullptr) ? matrixDataSource : this->_GetParentMatrixSource();
}
else
{
TF_WARN("Parent data source has no geospatial data source!");
}
return this->_GetParentMatrixSource();
}
GfMatrix4d HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_GetMatrix(const Time shutterOffset) const
{
HdMatrixDataSourceHandle dataSource = this->_GetMatrixSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(shutterOffset);
}
return GfMatrix4d(1.0);
}
bool HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_GetResetXformStack(const Time shutterOffset) const
{
HdBoolDataSourceHandle dataSource = this->_GetResetXformStackSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(shutterOffset);
}
return false;
}
GfMatrix4d HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_GetParentMatrix(const Time shutterOffset) const
{
HdMatrixDataSourceHandle dataSource = this->_GetParentMatrixSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(shutterOffset);
}
return GfMatrix4d(1.0);
}
GfMatrix4d HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_GetParentOriginalMatrix(const Time shutterOffset) const
{
HdMatrixDataSourceHandle dataSource = this->_GetParentOriginalMatrixSource();
if (dataSource != nullptr)
{
return dataSource->GetTypedValue(shutterOffset);
}
return GfMatrix4d(1.0);
}
GfMatrix4d HdOmniGeospatialComputedDependentDataSource::
_GeospatiallyAffectedMatrixDataSource::_ComputeTransformedMatrix(const Time shutterOffset) const
{
// this prim did not have geospatial information applied to it,
// but it is the child of one that did, so we compute the updated
// value based on the recomputed value of the parent
// however, we actually only want to do this if this prim does
// not have a resetXformStack applied
bool resetXformStack = this->_GetResetXformStack(shutterOffset);
if (!resetXformStack)
{
// to compute the affected matrix, we first need to acquire the parent information
GfMatrix4d flattenedParentTransform = this->_GetParentMatrix(shutterOffset);
GfMatrix4d originalParentTransform = this->_GetParentOriginalMatrix(shutterOffset);
// since we are dealing with flattened transformations, we have to recover
// the local transform of the prim data source in question
// we can do this by knowing the prim's flattened transform
// and the original transform of its parent (the _dependsOnDataSource)
// Let FT be the flattened transform, P be the transform of the parent,
// and LT be the child's local transform. The flattened transform would
// then have been computed as FT = (P)(LT), thus to recover LT we divide
// out by P, which results in LT = (FT) / (P) = FT * (P)^-1
// so we need the inverse of the original parent transform
GfMatrix4d inverseParentTransform = originalParentTransform.GetInverse();
GfMatrix4d originalChildTransform = this->_GetMatrix(shutterOffset);
GfMatrix4d childLocalTransform = originalChildTransform * inverseParentTransform;
// once we have the local transform, we can re-apply the new
// flattened parent transform - this is the new geospatially affected transform
// of the child
return flattenedParentTransform * childLocalTransform;
}
// if resetXformStack was true, the original flattened transform of
// of the input data source is valid here and we don't recompute
return this->_GetMatrix(shutterOffset);
}
PXR_NAMESPACE_CLOSE_SCOPE
| 9,285 |
C++
| 35.996016 | 128 | 0.74238 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/localPositionSchema.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/imaging/hd/retainedDataSource.h>
#include "localPositionSchema.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_DEFINE_PUBLIC_TOKENS(HdOmniGeospatialWGS84LocalPositionSchemaTokens,
HDOMNIGEOSPATIALWGS84LOCALPOSITION_SCHEMA_TOKENS);
HdVec3dDataSourceHandle HdOmniGeospatialWGS84LocalPositionSchema::GetPosition()
{
return _GetTypedDataSource<HdVec3dDataSource>(
HdOmniGeospatialWGS84LocalPositionSchemaTokens->position);
}
HdOmniGeospatialWGS84LocalPositionSchema HdOmniGeospatialWGS84LocalPositionSchema::GetFromParent(
const HdContainerDataSourceHandle& fromParentContainer)
{
if (fromParentContainer == nullptr)
{
return HdOmniGeospatialWGS84LocalPositionSchema(nullptr);
}
return HdOmniGeospatialWGS84LocalPositionSchema(
HdContainerDataSource::Cast(fromParentContainer->Get(
HdOmniGeospatialWGS84LocalPositionSchemaTokens->localPositionApi))
);
}
const HdDataSourceLocator& HdOmniGeospatialWGS84LocalPositionSchema::GetDefaultLocator()
{
static const HdDataSourceLocator locator(
HdOmniGeospatialWGS84LocalPositionSchemaTokens->localPositionApi
);
return locator;
}
HdContainerDataSourceHandle HdOmniGeospatialWGS84LocalPositionSchema::BuildRetained(
const HdVec3dDataSourceHandle& position)
{
TfToken names[1];
HdDataSourceBaseHandle values[1];
size_t count = 0;
if (position != nullptr)
{
names[count] = HdOmniGeospatialWGS84LocalPositionSchemaTokens->position;
values[count] = position;
count++;
}
return HdRetainedContainerDataSource::New(count, names, values);
}
PXR_NAMESPACE_CLOSE_SCOPE
| 2,240 |
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| 31.955882 | 97 | 0.775 |
NVIDIA-Omniverse/usd-plugin-samples/src/hydra-plugins/omniGeoSceneIndex/geospatialSceneIndex.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/base/work/utils.h>
#include <pxr/imaging/hd/xformSchema.h>
#include <pxr/imaging/hd/retainedDataSource.h>
#include <pxr/imaging/hd/overlayContainerDataSource.h>
#include <pxr/imaging/hd/dependenciesSchema.h>
#include "geospatialSceneIndex.h"
#include "referencePositionSchema.h"
#include "localPositionSchema.h"
#include "geospatialDataSource.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_DEFINE_PRIVATE_TOKENS(
_tokens,
(positionToXform)
);
OmniGeospatialSceneIndexRefPtr OmniGeospatialSceneIndex::New(
const HdSceneIndexBaseRefPtr& inputSceneIndex,
const HdContainerDataSourceHandle& inputArgs)
{
return TfCreateRefPtr(new OmniGeospatialSceneIndex(inputSceneIndex, inputArgs));
}
OmniGeospatialSceneIndex::OmniGeospatialSceneIndex(const HdSceneIndexBaseRefPtr& inputSceneIndex,
const HdContainerDataSourceHandle& inputArgs) :
HdSingleInputFilteringSceneIndexBase(inputSceneIndex)
{
}
OmniGeospatialSceneIndex::~OmniGeospatialSceneIndex() = default;
HdSceneIndexPrim OmniGeospatialSceneIndex::GetPrim(const SdfPath &primPath) const
{
// lookup the prim to see if we have wrapped it yet
auto iterBoolPair = this->_IsPrimWrapped(primPath);
if (iterBoolPair.second)
{
// we have it wrapped already, so return the wrapped prim
return iterBoolPair.first->second;
}
// we haven't wrapped it yet, but we only need to wrap it
// if it is Xformable - geospatial transforms have the potential
// to affect anything that has a transform, so even if it is
// never affected (e.g. resetXform is true or it is not the child
// of a geospatially applied prim) we wrap it here for simplicity
// sake at the cost of an extra HdSceneIndexPrim (as in some cases
// it will even retain its original data source)
// note that unlike the flattening scene index we wrap lazily
// instead of walking the tree at construction time - this is because
// there is a low chance of geospatial information being attached
// to a prim and in cases where the scene isn't goesptially grounded
// but the scene index is still applied we don't want to walk the
// whole scene
HdSceneIndexPrim sceneIndexPrim = this->_GetInputSceneIndex()->GetPrim(primPath);
HdXformSchema xformSchema = HdXformSchema::GetFromParent(sceneIndexPrim.dataSource);
if (xformSchema.IsDefined() && !xformSchema.GetResetXformStack())
{
return this->_WrapPrim(primPath, sceneIndexPrim);
}
// otherwise we don't need to wrap it and can return it directly
return sceneIndexPrim;
}
SdfPathVector OmniGeospatialSceneIndex::GetChildPrimPaths(const SdfPath& primPath) const
{
// no change in topology occurs as part of this scene index
// so we can ask the input scene to get the child prim paths directly
return this->_GetInputSceneIndex()->GetChildPrimPaths(primPath);
}
SdfPathTable<HdSceneIndexPrim>::_IterBoolPair OmniGeospatialSceneIndex::_IsPrimWrapped(const SdfPath& primPath) const
{
bool result = false;
const auto it = _wrappedPrims.find(primPath);
if (it != _wrappedPrims.end())
{
// because SdfPathTable inserts all parents
// when a path gets inserted, there may be an empty
// entry in our cache if a child path was visited first
// to verify we have to check the prim type and data source
if (it->second.primType != TfToken() || it->second.dataSource != nullptr)
{
// not an auto-insertion of the parent
result = true;
}
}
return std::make_pair(it, result);
}
HdSceneIndexPrim& OmniGeospatialSceneIndex::_WrapPrim(const SdfPath& primPath, const HdSceneIndexPrim& hdPrim) const
{
// PRECONDITION: The table must not yet contain a wrapped prim, check via _IsPrimWrapped first!
// wrapping a scene index prim involves creating our geospatial data source to wrap the original
// scene index prim's data source - this will allow us to intercept the xform token to return
// a compute geospatial transform and still provide access to the original xform via the wrapped data source
HdContainerDataSourceHandle wrappedDataSource = HdOmniGeospatialDataSource::New(*this, primPath, hdPrim.dataSource);
const auto it = _wrappedPrims.find(primPath);
if (it != _wrappedPrims.end())
{
// in this case, the entry is there, but it was auto-created
// by SdfPathTable, meaning it should have empty entries
TF_VERIFY(it->second.primType == TfToken());
TF_VERIFY(it->second.dataSource == nullptr);
it->second.primType = hdPrim.primType;
it->second.dataSource = std::move(wrappedDataSource);
return it->second;
}
else
{
auto iterBoolPair = _wrappedPrims.insert(
{
primPath,
HdSceneIndexPrim {
hdPrim.primType,
std::move(wrappedDataSource)
}
}
);
return iterBoolPair.first->second;
}
}
void OmniGeospatialSceneIndex::_PrimsAdded(const HdSceneIndexBase& sender,
const HdSceneIndexObserver::AddedPrimEntries& entries)
{
HdSceneIndexObserver::DirtiedPrimEntries dirtyEntries;
for(const HdSceneIndexObserver::AddedPrimEntry& entry : entries)
{
HdSceneIndexPrim sceneIndexPrim = this->_GetInputSceneIndex()->GetPrim(entry.primPath);
// cache the prim if necessary
HdXformSchema xformSchema = HdXformSchema::GetFromParent(sceneIndexPrim.dataSource);
if (xformSchema.IsDefined() && !xformSchema.GetResetXformStack())
{
auto iterBoolPair = this->_IsPrimWrapped(entry.primPath);
if (iterBoolPair.second)
{
/// we already wrapped this prim, so we need to update it
HdSceneIndexPrim& wrappedPrim = iterBoolPair.first->second;
wrappedPrim.primType = entry.primType;
if (wrappedPrim.dataSource != nullptr)
{
HdOmniGeospatialDataSource::Cast(wrappedPrim.dataSource)->UpdateWrappedDataSource(sceneIndexPrim.dataSource);
}
// if we updated it, we have to now see if we need
// to dirty any cached values alreday in the hierarchy
static HdDataSourceLocatorSet locators = {
HdXformSchema::GetDefaultLocator()
};
this->_DirtyHierarchy(entry.primPath, locators, &dirtyEntries);
}
else
{
// we don't yet have this prim wrapped - do so now
this->_WrapPrim(entry.primPath, sceneIndexPrim);
}
}
}
// forward on the notification
this->_SendPrimsAdded(entries);
// also, if we had to dirty entries because of an insertion in the middle
// of the stage hierarchy, send those along too
if (!dirtyEntries.empty())
{
this->_SendPrimsDirtied(dirtyEntries);
}
}
void OmniGeospatialSceneIndex::_PrimsRemoved(const HdSceneIndexBase& sender,
const HdSceneIndexObserver::RemovedPrimEntries& entries)
{
for (const HdSceneIndexObserver::RemovedPrimEntry& entry : entries)
{
if (entry.primPath.IsAbsoluteRootPath())
{
// removing the whole scene
_wrappedPrims.ClearInParallel();
TfReset(_wrappedPrims);
}
else
{
auto startEndRangeIterator = _wrappedPrims.FindSubtreeRange(entry.primPath);
for (auto it = startEndRangeIterator.first; it != startEndRangeIterator.second; it++)
{
WorkSwapDestroyAsync(it->second.dataSource);
}
if(startEndRangeIterator.first != startEndRangeIterator.second)
{
_wrappedPrims.erase(startEndRangeIterator.first);
}
}
}
_SendPrimsRemoved(entries);
}
void OmniGeospatialSceneIndex::_PrimsDirtied(const HdSceneIndexBase& sender,
const HdSceneIndexObserver::DirtiedPrimEntries& entries)
{
HdSceneIndexObserver::DirtiedPrimEntries dirtyEntries;
for (const HdSceneIndexObserver::DirtiedPrimEntry& entry : entries)
{
HdDataSourceLocatorSet locators;
if (entry.dirtyLocators.Intersects(HdXformSchema::GetDefaultLocator()))
{
locators.insert(HdXformSchema::GetDefaultLocator());
}
if (!locators.IsEmpty())
{
this->_DirtyHierarchy(entry.primPath, locators, &dirtyEntries);
}
}
_SendPrimsDirtied(entries);
if (!dirtyEntries.empty())
{
_SendPrimsDirtied(dirtyEntries);
}
}
void OmniGeospatialSceneIndex::_DirtyHierarchy(const SdfPath& primPath, const HdDataSourceLocatorSet& locators,
HdSceneIndexObserver::DirtiedPrimEntries* dirtyEntries)
{
// find subtree range retrieves a start end pair of children
// in the subtree of the given prim path
auto startEndRangeIterator = _wrappedPrims.FindSubtreeRange(primPath);
for (auto it = startEndRangeIterator.first; it != startEndRangeIterator.second;)
{
// if we have a valid wrapper for the prim, we need to check
// whether it needs to be dirtied - this involves checking the
// data sources to see if they have cached data and if so
// this indicates it needs to be updated
if (it->second.dataSource != nullptr)
{
HdOmniGeospatialDataSourceHandle geospatialDataSource =
HdOmniGeospatialDataSource::Cast(it->second.dataSource);
if (geospatialDataSource != nullptr && geospatialDataSource->IsPrimDirtied(locators))
{
if (it->first != primPath)
{
dirtyEntries->emplace_back(it->first, locators);
}
it++;
}
else
{
it++;
}
}
else
{
it++;
}
}
}
PXR_NAMESPACE_CLOSE_SCOPE
| 10,650 |
C++
| 36.37193 | 129 | 0.665446 |
NVIDIA-Omniverse/usd-plugin-samples/src/usd-plugins/fileFormat/edfFileFormat/edfFileFormat.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "edfFileFormat.h"
#include "edfData.h"
PXR_NAMESPACE_OPEN_SCOPE
EdfFileFormat::EdfFileFormat() : SdfFileFormat(
EdfFileFormatTokens->Id,
EdfFileFormatTokens->Version,
EdfFileFormatTokens->Target,
EdfFileFormatTokens->Extension)
{
}
EdfFileFormat::~EdfFileFormat()
{
}
bool EdfFileFormat::CanRead(const std::string& filePath) const
{
return true;
}
bool EdfFileFormat::Read(SdfLayer* layer, const std::string& resolvedPath, bool metadataOnly) const
{
// these macros emit methods defined in the Pixar namespace
// but not properly scoped, so we have to use the namespace
// locally here - note this isn't strictly true since we had to open
// the namespace scope anyway because the macros won't allow non-Pixar namespaces
// to be used because of some auto-generated content
PXR_NAMESPACE_USING_DIRECTIVE
if (!TF_VERIFY(layer))
{
return false;
}
// construct the SdfAbstractData object from the file format args
// and set that as the layer data - note this is a different object
// from that constructed in the InitData method - this may or may
// not be an issue, something to be investigated in more detail when
// working through the backend - either way we associate it with the layer
// so we always have a mapping from the dynamic layer and the specific
// set of parameters that created it
const FileFormatArguments& args = layer->GetFileFormatArguments();
SdfAbstractDataRefPtr layerData = this->InitData(args);
// inform the data provider that it's time to read the content
// this is a good time for it to cache data that it needs to generate
// the prim / property specs when asked for them via the data apis
EdfData& edfData = dynamic_cast<EdfData&>(*layerData);
bool readSuccess = edfData.Read();
if (readSuccess)
{
this->_SetLayerData(layer, layerData);
// for now, this is dynamic content read one way from a source external system
// therefore we mark that the layer is read-only
// later we will remove this restriction and explore what it means to edit
// data that is sourced from external data formats
layer->SetPermissionToSave(false);
layer->SetPermissionToEdit(false);
}
return readSuccess;
}
bool EdfFileFormat::WriteToString(const SdfLayer& layer, std::string* str, const std::string& comment) const
{
// this POC doesn't support writing
return false;
}
bool EdfFileFormat::WriteToStream(const SdfSpecHandle& spec, std::ostream& out, size_t indent) const
{
// this POC doesn't support writing
return false;
}
SdfAbstractDataRefPtr EdfFileFormat::InitData(const FileFormatArguments& args) const
{
// create the data parameters object to capture what data was used to create the layer
EdfDataParameters parameters = EdfDataParameters::FromFileFormatArgs(args);
return EdfData::CreateFromParameters(parameters);
}
bool EdfFileFormat::_ShouldSkipAnonymousReload() const
{
return false;
}
bool EdfFileFormat::_ShouldReadAnonymousLayers() const
{
return true;
}
void EdfFileFormat::ComposeFieldsForFileFormatArguments(const std::string& assetPath, const PcpDynamicFileFormatContext& context, FileFormatArguments* args, VtValue* contextDependencyData) const
{
VtValue val;
if (context.ComposeValue(EdfFileFormatTokens->Params, &val) && val.IsHolding<VtDictionary>())
{
// the composition engine has composed the metadata values of the prim appropriately
// for the currently composed stage, we read these metadata values that were composed
// and make them part of the file format arguments to load the dependent layer
VtDictionary dict = val.UncheckedGet<VtDictionary>();
const VtValue* dictVal = TfMapLookupPtr(dict, EdfDataParametersTokens->dataProviderId);
if (dictVal != nullptr)
{
(*args)[EdfDataParametersTokens->dataProviderId] = dictVal->UncheckedGet<std::string>();
}
// unfortunately, FileFormatArguments is a typedef for a map<string, string>
// which means we have to unpack the provider arguments dictionary
// to keep the unpacking simple, we assume for now that the providerArgs
// is itself a dictionary containing only string paris and values
// we can remove this restriction later for simple types (using TfStringify)
// but would need some work (recursively) for embedded lists and dictionary values
dictVal = TfMapLookupPtr(dict, EdfDataParametersTokens->providerArgs);
if (dictVal != nullptr)
{
std::string prefix = EdfDataParametersTokens->providerArgs.GetString();
VtDictionary providerArgs = dictVal->UncheckedGet<VtDictionary>();
for (VtDictionary::iterator it = providerArgs.begin(); it != providerArgs.end(); it++)
{
(*args)[prefix + ":" + it->first] = it->second.UncheckedGet<std::string>();
}
}
}
}
bool EdfFileFormat::CanFieldChangeAffectFileFormatArguments(const TfToken& field, const VtValue& oldValue, const VtValue& newValue, const VtValue& contextDependencyData) const
{
const VtDictionary& oldDictionaryValue = oldValue.IsHolding<VtDictionary>() ?
oldValue.UncheckedGet<VtDictionary>() : VtGetEmptyDictionary();
const VtDictionary& newDictionaryValue = newValue.IsHolding<VtDictionary>() ?
newValue.UncheckedGet<VtDictionary>() : VtGetEmptyDictionary();
// nothing to do if both metadata values are empty
if (oldDictionaryValue.empty() && newDictionaryValue.empty())
{
return false;
}
// our layer is new if:
// 1. there is a new provider
// 2. there is a change to the value of the provider specific data
const VtValue* oldProviderId =
TfMapLookupPtr(oldDictionaryValue, EdfDataParametersTokens->dataProviderId);
const VtValue* newProviderId =
TfMapLookupPtr(newDictionaryValue, EdfDataParametersTokens->dataProviderId);
if (oldProviderId != nullptr && newProviderId != nullptr)
{
if (oldProviderId->UncheckedGet<std::string>() != newProviderId->UncheckedGet<std::string>())
{
// different providers!
return true;
}
else
{
// same provider, but the specific provider metadata may have changed
const VtValue* oldProviderDictionaryValue =
TfMapLookupPtr(oldDictionaryValue, EdfDataParametersTokens->providerArgs);
const VtValue* newProviderDictionaryValue =
TfMapLookupPtr(newDictionaryValue, EdfDataParametersTokens->providerArgs);
const VtDictionary& oldProviderDictionary = oldProviderDictionaryValue->IsHolding<VtDictionary>() ?
oldProviderDictionaryValue->UncheckedGet<VtDictionary>() : VtGetEmptyDictionary();
const VtDictionary& newProviderDictionary = newProviderDictionaryValue->IsHolding<VtDictionary>() ?
newProviderDictionaryValue->UncheckedGet<VtDictionary>() : VtGetEmptyDictionary();
return oldProviderDictionary != newProviderDictionary;
}
}
else
{
// one of them (or both) are nullptrs
if (oldProviderId == nullptr && newProviderId == nullptr)
{
// no change to provider, don't need to check parameters
return false;
}
// otherwise one changed
return true;
}
}
// these macros emit methods defined in the Pixar namespace
// but not properly scoped, so we have to use the namespace
// locally here
TF_DEFINE_PUBLIC_TOKENS(
EdfFileFormatTokens,
((Id, "edfFileFormat"))
((Version, "1.0"))
((Target, "usd"))
((Extension, "edf"))
((Params, "EdfDataParameters"))
);
TF_REGISTRY_FUNCTION(TfType)
{
SDF_DEFINE_FILE_FORMAT(EdfFileFormat, SdfFileFormat);
}
PXR_NAMESPACE_CLOSE_SCOPE
| 7,937 |
C++
| 35.75 | 194 | 0.754567 |
NVIDIA-Omniverse/usd-plugin-samples/src/usd-plugins/dynamicPayload/omniMetProvider/api.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef OMNI_OMNIMETPROVIDER_API_H_
#define OMNI_OMNIMETPROVIDER_API_H_
#include "pxr/base/arch/export.h"
#if defined(PXR_STATIC)
# define OMNIMETPROVIDER_API
# define OMNIMETPROVIDER_API_TEMPLATE_CLASS(...)
# define OMNIMETPROVIDER_API_TEMPLATE_STRUCT(...)
# define OMNIMETPROVIDER_LOCAL
#else
# if defined(OMNIMETPROVIDER_EXPORTS)
# define OMNIMETPROVIDER_API ARCH_EXPORT
# define OMNIMETPROVIDER_API_TEMPLATE_CLASS(...) ARCH_EXPORT_TEMPLATE(class, __VA_ARGS__)
# define OMNIMETPROVIDER_API_TEMPLATE_STRUCT(...) ARCH_EXPORT_TEMPLATE(struct, __VA_ARGS__)
# else
# define OMNIMETPROVIDER_API ARCH_IMPORT
# define OMNIMETPROVIDER_API_TEMPLATE_CLASS(...) ARCH_IMPORT_TEMPLATE(class, __VA_ARGS__)
# define OMNIMETPROVIDER_API_TEMPLATE_STRUCT(...) ARCH_IMPORT_TEMPLATE(struct, __VA_ARGS__)
# endif
# define OMNIMETPROVIDER_LOCAL ARCH_HIDDEN
#endif
#endif
| 1,498 |
C
| 38.447367 | 97 | 0.732977 |
NVIDIA-Omniverse/usd-plugin-samples/src/usd-plugins/dynamicPayload/omniMetProvider/omniMetProvider.cpp
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pxr/base/tf/token.h>
#include <pxr/base/vt/value.h>
#include <pxr/base/js/json.h>
#include <pxr/usd/sdf/path.h>
#include <pxr/usd/sdf/schema.h>
#include <pxr/usd/sdf/payload.h>
#include <pxr/usd/sdf/primSpec.h>
#include <pxr/usd/sdf/attributeSpec.h>
#include <pxr/usd/usd/tokens.h>
#include <edfDataProviderFactory.h>
#include "omniMetProvider.h"
#include <iostream>
#include <curl/curl.h>
PXR_NAMESPACE_OPEN_SCOPE
EDF_DEFINE_DATAPROVIDER(OmniMetProvider);
TF_DEFINE_PUBLIC_TOKENS(
OmniMetProviderProviderArgKeys,
(dataLodLevel)
(deferredRead)
(lod1Count)
);
TF_DEFINE_PRIVATE_TOKENS(
EdfFieldKeys,
(EdfDataParameters)
);
TF_DEFINE_PRIVATE_TOKENS(
OmniMetProviderTypeNames,
(AmaDepartment)
(AmaObject)
);
TF_DEFINE_PRIVATE_TOKENS(
OmniMetProviderFieldKeys,
(departmentId)
(displayName)
(objectID)
(isHighlight)
(accessionNumber)
(accessionYear)
(isPublicDomain)
(primaryImage)
(primaryImageSmall)
(additionalImages)
(constituents)
(department)
(objectName)
(title)
(culture)
(period)
(dynasty)
(reign)
(portfolio)
(artistRole)
(artistPrefix)
(artistDisplayName)
(artistDisplayBio)
(artistSuffix)
(artistAlphaSort)
(artistNationality)
(artistGender)
(artistWikidata_URL)
(artistULAN_URL)
(objectDate)
(objectBeginDate)
(objectEndDate)
(medium)
(dimensions)
(measurements)
(creditLine)
(geographyType)
(city)
(state)
(county)
(country)
(region)
(subregion)
(locale)
(locus)
(excavation)
(river)
(classification)
(rightsAndReproduction)
(linkResource)
(metadataDate)
(repository)
(objectURL)
(objectWikidataURL)
(isTimelineWork)
(galleryNumber)
);
enum struct DataLodLevel
{
Level0 = 0,
Level1 = 1,
Level2 = 2
};
// urls used to retrieve the data
static const std::string DEPARTMENT_URL = "https://collectionapi.metmuseum.org/public/collection/v1/departments";
static const std::string OBJECTS_IN_DEPARTMENT_URL = "https://collectionapi.metmuseum.org/public/collection/v1/objects?departmentIds=";
static const std::string OBJECT_URL = "https://collectionapi.metmuseum.org/public/collection/v1/objects/";
static const SdfPath DATA_ROOT_PATH("/Data");
OmniMetProvider::OmniMetProvider(const EdfDataParameters& parameters) : IEdfDataProvider(parameters)
{
curl_global_init(CURL_GLOBAL_DEFAULT);
}
OmniMetProvider::~OmniMetProvider()
{
curl_global_cleanup();
}
bool OmniMetProvider::Read(std::shared_ptr<IEdfSourceData> sourceData)
{
// this gives the provider a chance to load all data it needs to on first layer read
// if we are parameterized for a deferred read, we do nothing and read on demand
// at first ask, if it's not a deferred read, we load all appropriate content from the
// back-end here
if(!this->IsDeferredRead())
{
// it's not a deferred read, so determine how much data we want to really load
int lodLevel = this->GetDataLodLevel();
if (lodLevel == static_cast<int>(DataLodLevel::Level0))
{
// load the departments
this->_LoadData(false, 0, sourceData);
}
else if (lodLevel == static_cast<int>(DataLodLevel::Level1))
{
// load the departments and their children
// but cap the number of children at the specified level
this->_LoadData(true, this->GetLod1Count(), sourceData);
}
else
{
// max lod level, load everything
this->_LoadData(true, 0, sourceData);
}
}
return true;
}
void OmniMetProvider::_LoadData(bool includeObjects, size_t objectCount, std::shared_ptr<IEdfSourceData> sourceData)
{
// load the department data
std::string departmentData = this->_LoadDepartments();
std::vector<std::pair<std::string, int>> departments = this->_ParseDepartments(departmentData, sourceData);
// do we want to load objects as well?
if (includeObjects)
{
for (auto it = departments.begin(); it != departments.end(); it++)
{
std::vector<std::string> objectData = this->_LoadObjects(TfStringify(it->second), objectCount);
for (auto itt = objectData.begin(); itt != objectData.end(); itt++)
{
this->_ParseObject(*itt, it->first, sourceData);
}
}
}
}
std::string OmniMetProvider::_LoadDepartments()
{
std::string departments;
CURL* departmentCurl = curl_easy_init();
if (departmentCurl != nullptr)
{
CURLcode resultCode;
curl_easy_setopt(departmentCurl, CURLOPT_URL, DEPARTMENT_URL.c_str());
curl_easy_setopt(departmentCurl, CURLOPT_HTTPGET, 1L);
curl_easy_setopt(departmentCurl, CURLOPT_WRITEFUNCTION, OmniMetProvider::_CurlWriteCallback);
// allocate a string that we can append the result onto
std::string* result = new std::string();
curl_easy_setopt(departmentCurl, CURLOPT_WRITEDATA, reinterpret_cast<void*>(result));
resultCode = curl_easy_perform(departmentCurl);
if (resultCode == CURLE_OK)
{
departments = *result;
}
else
{
TF_CODING_ERROR("Unable to load departments from '%s'!", DEPARTMENT_URL.c_str());
}
// done with the callback data
delete result;
// done with the request handle
curl_easy_cleanup(departmentCurl);
}
return departments;
}
std::vector<int> OmniMetProvider::_ParseObjectIds(const std::string& response) const
{
std::vector<int> objectIds;
PXR_NS::JsValue jsValue = PXR_NS::JsParseString(response, nullptr);
if (!jsValue.IsNull())
{
PXR_NS::JsObject rootObject = jsValue.GetJsObject();
PXR_NS::JsObject::iterator it = rootObject.find("objectIDs");
if (it != rootObject.end())
{
PXR_NS::JsArray jsonObjectIdArray = it->second.GetJsArray();
for (auto objectIdIt = jsonObjectIdArray.begin(); objectIdIt != jsonObjectIdArray.end(); objectIdIt++)
{
objectIds.push_back((*objectIdIt).GetInt());
}
}
else
{
TF_CODING_ERROR("Unable to find 'objectIDs' array in returned data '%s'!", response.c_str());
}
}
else
{
TF_CODING_ERROR("Data returned '%s' was not JSON or was empty!", response.c_str());
}
return objectIds;
}
std::vector<std::string> OmniMetProvider::_LoadObjects(const std::string& departmentId, size_t objectCount)
{
// NOTE: this should be updated to make these requests in parallel in the case
// where we aren't doing deferred reads
// ideally we wouldn't want to initialize a new curl handle here, but since this
// call can be made in the parallel prim indexing, we can't share the easy handle
// across threads, so we take the overhead hit here
std::vector<std::string> objects;
CURL* objectCurl = curl_easy_init();
std::string url = OBJECTS_IN_DEPARTMENT_URL + departmentId;
std::string* result = new std::string();
CURLcode resultCode;
*result = "";
curl_easy_setopt(objectCurl, CURLOPT_URL, url.c_str());
curl_easy_setopt(objectCurl, CURLOPT_HTTPGET, 1L);
curl_easy_setopt(objectCurl, CURLOPT_WRITEFUNCTION, OmniMetProvider::_CurlWriteCallback);
curl_easy_setopt(objectCurl, CURLOPT_WRITEDATA, reinterpret_cast<void*>(result));
resultCode = curl_easy_perform(objectCurl);
if (resultCode == CURLE_OK)
{
// process result
std::vector<int> objectIds = this->_ParseObjectIds(*result);
// objectCount = 0 means load all objects
// objectCount > 0 means load max that many objects
size_t counter = 0;
for (auto objectIdIterator = objectIds.begin(); objectIdIterator != objectIds.end() && (objectCount == 0 || counter < objectCount); objectIdIterator++)
{
// reset the URL and result buffer
// NOTE: this should be updated to make these requests in parallel
url = OBJECT_URL + TfStringify(*objectIdIterator);
*result = "";
curl_easy_setopt(objectCurl, CURLOPT_URL, url.c_str());
resultCode = curl_easy_perform(objectCurl);
if (resultCode == CURLE_OK)
{
objects.push_back(*result);
}
counter++;
}
}
// done with the callback data
delete result;
// done with the request handle
curl_easy_cleanup(objectCurl);
return objects;
}
std::vector<std::pair<std::string, int>> OmniMetProvider::_ParseDepartments(const std::string& departmentJson,
std::shared_ptr<IEdfSourceData> sourceData)
{
std::vector<std::pair<std::string, int>> parsedDepartments;
JsValue jsValue = JsParseString(departmentJson, nullptr);
if (!jsValue.IsNull())
{
JsObject rootObject = jsValue.GetJsObject();
JsObject::iterator it = rootObject.find("departments");
if (it != rootObject.end())
{
JsArray departments = it->second.GetJsArray();
std::string parent = DATA_ROOT_PATH.GetAsString();
for (auto departmentIt = departments.begin(); departmentIt != departments.end(); departmentIt++)
{
// for each department, create a prim to represent it
JsObject department = (*departmentIt).GetJsObject();
int departmentId = department[OmniMetProviderFieldKeys->departmentId.GetString()].GetInt();
std::string displayName = department[OmniMetProviderFieldKeys->displayName.GetString()].GetString();
// create the prim
std::string primName = TfMakeValidIdentifier(displayName);
sourceData->CreatePrim(DATA_ROOT_PATH, primName, SdfSpecifier::SdfSpecifierDef,
OmniMetProviderTypeNames->AmaDepartment);
// create the attributes for the prim
SdfPath parentPrim = SdfPath(parent + "/" + primName);
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->departmentId.GetString(),
SdfValueTypeNames->Int, SdfVariability::SdfVariabilityUniform, VtValue(departmentId));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->displayName.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform, VtValue(displayName));
parsedDepartments.push_back(std::make_pair(parentPrim.GetAsString(), departmentId));
}
}
else
{
TF_CODING_ERROR("Unable to find 'departments' array in returned data '%s'!", departmentJson.c_str());
}
}
else
{
TF_CODING_ERROR("Data returned '%s' was not JSON or was empty!", departmentJson.c_str());
}
return parsedDepartments;
}
void OmniMetProvider::_ParseObject(const std::string& objectData, const std::string& parentPath,
std::shared_ptr<IEdfSourceData> sourceData)
{
// from the parent path given and the data contained in the JSON
// object retrieved from the server, we can create the full prim
JsValue jsValue = JsParseString(objectData, nullptr);
if (!jsValue.IsNull())
{
JsObject rootObject = jsValue.GetJsObject();
// the root object contains all of our properties that we now need
// to create a prim spec for the object and a set of property
// specs for it
// NOTE: this code uses the "default value" of a property spec
// to represent the authored value coming from the external system
// We don't need to do sub-composition over the data coming
// from the external system, so we ever only have a value or not
// so if HasDefaultValue is true on the property spec, it means
// there was an authored value that came from the remote system
// One optimization we could do in the layer above (EdfData) is
// to add schema acquisition and checking in the loop. This would allow us
// to create the property spec or not depending on if the value that came in
// is different from the true fallback declared in the schema
// (but we'd have to change the ask for the property to check whether
// the schema has the property rather than if the property spec exists)
std::string objectName = rootObject[OmniMetProviderFieldKeys->objectName.GetString()].GetString();
std::string primName = TfMakeValidIdentifier(objectName) +
TfStringify(rootObject[OmniMetProviderFieldKeys->objectID.GetString()].GetInt());
// create the prim
SdfPath newPrimParentPath(parentPath);
sourceData->CreatePrim(newPrimParentPath, primName, SdfSpecifier::SdfSpecifierDef,
OmniMetProviderTypeNames->AmaObject);
// set the fact that this prim has an API schema attached to it
// usdGenSchema doesn't generate a public token for the actual
// API schema class name, so we hard code that here
SdfPath parentPrim = SdfPath(parentPath + "/" + primName);
TfTokenVector apiSchemas;
apiSchemas.push_back(TfToken("OmniMetArtistAPI"));
VtValue apiSchemasValue(apiSchemas);
sourceData->SetField(parentPrim, UsdTokens->apiSchemas, apiSchemasValue);
// create the attributes for the prim
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->objectID.GetString(),
SdfValueTypeNames->Int, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->objectID.GetString()].GetInt()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->isHighlight.GetString(),
SdfValueTypeNames->Bool, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->isHighlight.GetString()].GetBool()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->accessionNumber.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->accessionNumber.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->accessionYear.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->accessionYear.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->isPublicDomain.GetString(),
SdfValueTypeNames->Bool, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->isPublicDomain.GetString()].GetBool()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->primaryImage.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->primaryImage.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->primaryImageSmall.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->primaryImageSmall.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->department.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->department.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->title.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->title.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->culture.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->culture.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->period.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->period.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->dynasty.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->dynasty.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->reign.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->reign.GetString()].GetString()));
sourceData->CreateAttribute(parentPrim, OmniMetProviderFieldKeys->portfolio.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->portfolio.GetString()].GetString()));
// artist information complying with sample API schema
std::string namespaceFieldPrefix = "omni:met:artist:";
JsObject::const_iterator i = rootObject.find(OmniMetProviderFieldKeys->artistRole.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistRole.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistRole.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistPrefix.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistPrefix.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistPrefix.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistDisplayName.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistDisplayName.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistDisplayName.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistDisplayBio.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistDisplayBio.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistDisplayBio.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistSuffix.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistSuffix.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistSuffix.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistAlphaSort.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistAlphaSort.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistAlphaSort.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistNationality.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistNationality.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistNationality.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistGender.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistGender.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistGender.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistWikidata_URL.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistWikidata_URL.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistWikidata_URL.GetString()].GetString()));
}
i = rootObject.find(OmniMetProviderFieldKeys->artistULAN_URL.GetString());
if (i != rootObject.end())
{
sourceData->CreateAttribute(parentPrim, namespaceFieldPrefix + OmniMetProviderFieldKeys->artistULAN_URL.GetString(),
SdfValueTypeNames->String, SdfVariability::SdfVariabilityUniform,
VtValue(rootObject[OmniMetProviderFieldKeys->artistULAN_URL.GetString()].GetString()));
}
// note that there are quite a few additional properties that could be pulled, the above
// represents only a sample of the data that is there - if you'd like to try the rest as an
// exercise, you can enhance the schema attributes and read the remaining ones here
}
else
{
TF_CODING_ERROR("Data returned '%s' was not JSON or was empty!", objectData.c_str());
}
}
bool OmniMetProvider::ReadChildren(const std::string& parentPath, std::shared_ptr<IEdfSourceData> sourceData)
{
// if the parent path is the root, we need to load the departments
// but only if we are in a deferred read scenario
if (this->IsDeferredRead())
{
SdfPath parentPrimPath = SdfPath(parentPath);
int lodLevel = this->GetDataLodLevel();
if (parentPrimPath == DATA_ROOT_PATH)
{
// load the department data
std::cout << "Loading department data..." << std::endl;
std::string departmentData = this->_LoadDepartments();
std::vector<std::pair<std::string, int>> departments = this->_ParseDepartments(departmentData,
sourceData);
}
else
{
VtValue typeNameValue;
if(sourceData->HasField(SdfPath(parentPath), SdfFieldKeys->TypeName, &typeNameValue))
{
if (typeNameValue.UncheckedGet<TfToken>() == OmniMetProviderTypeNames->AmaDepartment &&
this->GetDataLodLevel() != static_cast<int>(DataLodLevel::Level0))
{
// it's a department, we need to load the objects
// associated with the department
std::string departmentIdPath = parentPath + "." + OmniMetProviderFieldKeys->departmentId.GetString();
VtValue departmentId;
if (sourceData->HasAttribute(SdfPath(departmentIdPath), &departmentId))
{
size_t objectCount = 0;
if (lodLevel == static_cast<int>(DataLodLevel::Level1))
{
objectCount = this->GetLod1Count();
}
// load the object data
std::cout << "Loading object data for " + parentPath + "..." << std::endl;
std::vector<std::string> objectData = this->_LoadObjects(TfStringify(departmentId.UncheckedGet<int>()), objectCount);
for (auto it = objectData.begin(); it != objectData.end(); it++)
{
this->_ParseObject(*it, parentPath, sourceData);
}
}
}
}
}
return true;
}
return false;
}
bool OmniMetProvider::IsDataCached() const
{
return !this->IsDeferredRead();
}
int OmniMetProvider::GetDataLodLevel() const
{
int dataLodLevel = 0;
EdfDataParameters parameters = this->GetParameters();
std::unordered_map<std::string, std::string>::const_iterator it = parameters.providerArgs.find(OmniMetProviderProviderArgKeys->dataLodLevel);
if (it != parameters.providerArgs.end())
{
dataLodLevel = TfUnstringify<int>(it->second);
if (dataLodLevel < 0)
{
dataLodLevel = 0;
}
}
return dataLodLevel;
}
size_t OmniMetProvider::GetLod1Count() const
{
// although the incoming string from the parameter set
// might be interpretable as a negative integer
// it doesn't really make practical sense, so if
// it is interpreted as negative, we clamp to 0
// and return an unsigned version to the caller
size_t lod1Count = 0;
EdfDataParameters parameters = this->GetParameters();
std::unordered_map<std::string, std::string>::const_iterator it = parameters.providerArgs.find(OmniMetProviderProviderArgKeys->lod1Count);
if (it != parameters.providerArgs.end())
{
lod1Count = TfUnstringify<int>(it->second);
if (lod1Count < 0)
{
lod1Count = 0;
}
}
return static_cast<size_t>(lod1Count);
}
bool OmniMetProvider::IsDeferredRead() const
{
bool deferredRead = false;
EdfDataParameters parameters = this->GetParameters();
std::unordered_map<std::string, std::string>::const_iterator it = parameters.providerArgs.find(OmniMetProviderProviderArgKeys->deferredRead);
if (it != parameters.providerArgs.end())
{
deferredRead = TfUnstringify<bool>(it->second);
}
return deferredRead;
}
size_t OmniMetProvider::_CurlWriteCallback(void* data, size_t size, size_t nmemb, void* userp)
{
std::string* result = reinterpret_cast<std::string*>(userp);
result->append(reinterpret_cast<const char* const>(data), nmemb);
return nmemb;
}
PXR_NAMESPACE_CLOSE_SCOPE
| 27,507 |
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| 41.780715 | 159 | 0.662704 |
NVIDIA-Omniverse/usd-plugin-samples/src/usd-plugins/dynamicPayload/omniMetProvider/omniMetProvider.h
|
// Copyright 2023 NVIDIA CORPORATION
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef OMNI_OMNIMETPROVIDER_OMNIMETPROVIDER_H_
#define OMNI_OMNIMETPROVIDER_OMNIMETPROVIDER_H_
#include <string>
#include <vector>
#include <utility>
#include <pxr/pxr.h>
#include <pxr/base/tf/token.h>
#include <pxr/usd/sdf/layer.h>
#include <pxr/usd/sdf/schema.h>
#include <iEdfDataProvider.h>
PXR_NAMESPACE_OPEN_SCOPE
TF_DECLARE_PUBLIC_TOKENS(
OmniMetProviderProviderArgKeys,
(dataLodLevel)
(deferredRead)
(lod1Count)
);
/// \class OmniMetProvider
///
/// Defines a specific EDF back-end data provider for reading information
/// from the Metropolitan Museum of Art REST APIs and converting that
/// into prim and attribute data that can be processed by USD.
///
class OmniMetProvider : public IEdfDataProvider
{
public:
OmniMetProvider(const EdfDataParameters& parameters);
virtual ~OmniMetProvider();
virtual bool Read(std::shared_ptr<IEdfSourceData> sourceData) override;
virtual bool ReadChildren(const std::string& parentPath, std::shared_ptr<IEdfSourceData> sourceData) override;
virtual bool IsDataCached() const override;
private:
int GetDataLodLevel() const;
size_t GetLod1Count() const;
bool IsDeferredRead() const;
void _LoadData(bool includeObjects, size_t objectCount, std::shared_ptr<IEdfSourceData> sourceData);
std::string _LoadDepartments();
std::vector<std::string> _LoadObjects(const std::string& departmentId, size_t objectCount);
std::vector<std::pair<std::string, int>> _ParseDepartments(const std::string& departmentJson,
std::shared_ptr<IEdfSourceData> sourceData);
void _ParseObject(const std::string& objectData, const std::string& parentPath, std::shared_ptr<IEdfSourceData> sourceData);
// NOTE: these methods are not technically const, since they do change internal state
// in the edfData object's layer data. This is ok, because that object is a cache
// https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#es50-dont-cast-away-const
// the mutuable cache state is allowed to change internally and still keep the semantics
// of the object not changing from the outside
void _LoadDepartments(bool includeObjects) const;
void _LoadObjects(const std::string& departmentId, const std::string& parentPath) const;
bool _IsDepartmentDataCached() const;
bool _IsObjectDataCached(const std::string& parentPath) const;
void _ParseDepartments(const std::string& response) const;
std::vector<int> _ParseObjectIds(const std::string& response) const;
void _ParseObject(const std::string& parentPath, const std::string& response) const;
static size_t _CurlWriteCallback(void* data, size_t size, size_t nmemb, void* userp);
};
PXR_NAMESPACE_CLOSE_SCOPE
#endif
| 3,321 |
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| 37.627907 | 128 | 0.747666 |
NVIDIA-Omniverse/kit-osc/README.md
|
# OSC Omniverse Kit Extension [omni.osc]
Omniverse Kit extension for sending and receiving OSC (Open Sound Control) messages.
*The OSC control surface app running on the iPad is [TouchOSC](https://hexler.net/touchosc).*
# Getting Started
Open the Community tab under Extensions window (`Window > Extensions`), search for `OSC`, and install and enable the `omni.osc` extension.
## Running the server
After installing and enabling the extension, you should see the following window.
Enter the private IP address of the computer running your Kit application and the desired port, then click `Start`. If you are prompted to configure your Windows Firewall, ensure that the Kit application is allowed to communicate with other devices on the private network.
You can find the private IP address of your computer by running `ipconfig` in the Windows terminal.
If you run the server on `localhost`, that means the server can only receive messages from OSC clients running on the same machine. If you want to receive messages from OSC clients running on other devices on the same network, you must run the server on an IP address that is visible to those devices.
Once the server is running, confirm that it can successfully receive messages by inspecting the verbose console logs. It might be helpful to filter only the logs that originate from `omni.osc`.
## Receiving messages with Python
Below is a python snippet that demonstrates how to handle OSC messages received by the server. It assumes that the OSC server configured above is running. You can paste and run the below snippet directly into the Omniverse Script Editor for testing.
```python
import carb
import carb.events
import omni.osc
def on_event(event: carb.events.IEvent) -> None:
addr, args = omni.osc.osc_message_from_carb_event(event)
carb.log_info(f"Received OSC message: [{addr}, {args}]")
sub = omni.osc.subscribe_to_osc_event_stream(on_event)
```
## Receiving messages with ActionGraph
Search for `OSC` in the Action Graph nodes list and add the `On OSC Message` node to your graph. The node takes a single input,
the OSC address path that this node will handle. This input can be a valid regular expression. Note that this input field does *not* support
OSC pattern matching expressions. The node outputs an OmniGraph bundle with two attributes named `address` and `arguments` which you
can access by using the `Extract Attribute` node.
You can find example USD stages that demonstrate how to configure an ActionGraph using this extension at [exts/omni.osc/data/examples](/exts/omni.osc/data/examples).
## Sending messages from Python
Since `omni.osc` depends on [python-osc](https://pypi.org/project/python-osc/), you can import this module directly in
your own Python code to send OSC messages. Please see the [documentation](https://python-osc.readthedocs.io/en/latest/) for additional
information and support.
```python
import random
import time
from pythonosc import udp_client
client = udp_client.SimpleUDPClient("127.0.0.1", 3334)
client.send_message("/scale", [random.random(), random.random(), random.random()])
```
You can paste and run the above snippet directly into the Omniverse Script Editor for testing.
## Sending messages from ActionGraph
This is not currently implemented.
## Limitations & Known Issues
- OSC Bundles are currently not supported.
- The OmniGraph `On OSC Message` node can only handle OSC messages containing lists of floating-point arguments.
# Help
The below sections should help you diagnose any potential issues you may encounter while working with `omni.osc` extension.
## Unable to receive messages
1. First, enable verbose logs in the console (filter by the `omni.osc` extension). The server will log any messages received.
2. Confirm that the computer running the Kit application and the device sending the OSC messages are on the same network.
3. Confirm that kit.exe is allowed to communicate with the private network through the Windows Defender Firewall. Note that
you may have multiple instances of kit.exe on this list. When in doubt, ensure that all of them have the appropriate permission.
4. Confirm that the Windows Defender Firewall allows incoming UDP traffic to the port in use.
5. Confirm that the device sending the OSC messages is sending the messages via UDP to the correct IP address and port.
6. Use a tool such as [wireshark](https://www.wireshark.org/) to confirm that the computer running the Kit application is receiving UDP traffic from the device.
## Unable to send messages
1. Confirm that the computer running the Kit application and the device receiving the OSC messages are on the same network.
2. Confirm that kit.exe is allowed to communicate with the private network through the Windows Defender Firewall.
3. Confirm that the device receiving the OSC messages is able to receive incoming UDP traffic at the port in use.
# Contributing
The source code for this repository is provided as-is and we are not accepting outside contributions.
# License
- The code in this repository is licensed under the Apache License 2.0. See [LICENSE](/LICENSE).
- python-osc is licensed under the Unlicense. See [exts/omni.osc/vendor/LICENSE-python-osc](/exts/omni.osc/vendor/LICENSE-python-osc).
# Resources
- [https://opensoundcontrol.stanford.edu/spec-1_0.html](https://opensoundcontrol.stanford.edu/spec-1_0.html)
- [https://en.wikipedia.org/wiki/Open_Sound_Control](https://en.wikipedia.org/wiki/Open_Sound_Control)
- [https://python-osc.readthedocs.io/en/latest/](https://python-osc.readthedocs.io/en/latest/)
| 5,998 |
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| 46.992 | 301 | 0.779593 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/config/extension.toml
|
[package]
# Semantic Versionning is used: https://semver.org/
version = "0.3.1"
# The title and description fields are primarily for displaying extension info in UI
title = "OSC (Open Sound Control)"
description="Send and receive OSC (Open Sound Control) messages"
authors = ["NVIDIA"]
repository = "https://github.com/NVIDIA-Omniverse/kit-osc"
readme = "docs/README.md"
changelog = "docs/CHANGELOG.md"
icon = "data/icon.png"
preview_image = "data/preview.png"
# One of categories for UI.
category = "Other"
# Keywords for the extension
keywords = ["kit", "osc"]
[dependencies]
"omni.kit.uiapp" = {}
"omni.kit.pipapi" = {}
"omni.graph" = {}
"omni.graph.bundle.action" = {}
# Main python module this extension provides, it will be publicly available as "import omni.osc.core".
[[python.module]]
name = "omni.osc"
[python.pipapi]
archiveDirs = ["vendor"]
[settings.exts."omni.osc"]
address = "localhost"
port = 3334
[[test]]
dependencies = ["omni.graph", "omni.kit.test"]
| 983 |
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| 22.999999 | 102 | 0.703967 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/extension.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from typing import Any, List
import carb
import carb.events
import carb.profiler
import omni.ext
import omni.kit.app
from pythonosc.dispatcher import Dispatcher
from .core import carb_event_payload_from_osc_message, push_to_osc_event_stream
from .menu import OscMenu
from .server import DaemonOSCUDPServer
from .window import OscWindow
class OmniOscExt(omni.ext.IExt):
def on_startup(self, ext_id):
def on_start(host: str, port: int) -> bool:
return self.server.start(host, port)
def on_stop() -> bool:
return self.server.stop()
def toggle_window_visible(_arg0, _arg1) -> None:
"""
Toggle the window visibility from the editor menu item
"""
self.window.visible = not self.window.visible
self.server = OmniOscExt.create_server()
# The main UI window
default_addr = carb.settings.get_settings().get("exts/omni.osc/address")
default_port = carb.settings.get_settings().get("exts/omni.osc/port")
self.window = OscWindow(
on_start=on_start, on_stop=on_stop, default_addr=default_addr, default_port=default_port
)
# The editor menu entry that toggles the window visibility
self.menu = OscMenu(on_click=toggle_window_visible)
# Toggle the editor menu entry when the user closes the window
self.window.set_visibility_changed_fn(lambda visible: self.menu.set_item_value(visible))
def on_shutdown(self):
self.window = None
self.menu = None
if self.server is not None:
self.server.stop()
self.server = None
def create_server() -> DaemonOSCUDPServer:
"""
Create a server that routes all OSC messages to a carbonite event stream
"""
@carb.profiler.profile
def on_osc_msg(addr: str, *args: List[Any]) -> None:
"""
OSC message handler
"""
carb.log_verbose(f"OSC message: [{addr}, {args}]")
payload = carb_event_payload_from_osc_message(addr, args)
push_to_osc_event_stream(payload)
# Server
dispatcher = Dispatcher()
dispatcher.set_default_handler(on_osc_msg)
return DaemonOSCUDPServer(dispatcher)
| 2,714 |
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| 34.723684 | 100 | 0.658438 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/__init__.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import omni.kit.pipapi
# python-osc:
# - SWIPAT request: http://nvbugs/3684871
# - A copy of the source is forked to https://github.com/NVIDIA-Omniverse/python-osc
# - The dependency vendored and installed from exts/omni.osc/vendor/python_osc-1.8.0-py3-none-any.whl
omni.kit.pipapi.install(
package="python-osc", module="pythonosc", use_online_index=False, ignore_cache=True, ignore_import_check=False
)
from pythonosc import * # noqa: F401
from .core import * # noqa: F401,F403
from .extension import * # noqa: F401,F403
from .server import * # noqa: F401,F403
# NOTE(jshrake): omni.graph is an optional dependency so handle the case
# that the below import fails
try:
from .ogn import *
except Exception as e:
print(f"omni.osc failed to import OGN due to {e}")
pass
| 1,219 |
Python
| 37.124999 | 114 | 0.754717 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/core.py
|
## Copyright © 2022 NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
##
## This software product is a proprietary product of Nvidia Corporation and its affiliates
## (the "Company") and all right, title, and interest in and to the software
## product, including all associated intellectual property rights, are and
## shall remain exclusively with the Company.
##
## This software product is governed by the End User License Agreement
## provided with the software product.
from typing import Callable, Tuple
import carb
import carb.events
import omni.ext
import omni.kit.app
OSC_EVENT_TYPE_NAME: str = "omni.osc"
OSC_EVENT_TYPE: int = carb.events.type_from_string(OSC_EVENT_TYPE_NAME)
OSC_MESSAGE_ADDRESS_STR = "address"
OSC_MESSAGE_ARGUMENTS_STR = "arguments"
def get_osc_event_stream() -> carb.events._events.IEventStream:
"""
Returns the OSC event stream
"""
return omni.kit.app.get_app().get_message_bus_event_stream()
def push_to_osc_event_stream(payload: dict) -> None:
"""
Push a payload to the OSC event stream
"""
get_osc_event_stream().push(OSC_EVENT_TYPE, sender=0, payload=payload)
def subscribe_to_osc_event_stream(
cb: Callable[[carb.events._events.IEvent], None]
) -> carb.events._events.ISubscription:
"""
Returns a Carbonite event subscription to the OSC event stream
"""
return get_osc_event_stream().create_subscription_to_pop_by_type(OSC_EVENT_TYPE, cb)
def carb_event_payload_from_osc_message(address: str, args: list) -> dict:
"""
Return a carbonite event payload suitable for pushing to the OSC event stream
"""
return {OSC_MESSAGE_ADDRESS_STR: address, OSC_MESSAGE_ARGUMENTS_STR: args}
def osc_message_from_carb_event(e: carb.events.IEvent) -> Tuple[str, list]:
"""
Return the OSC message address and arguments extracted from a carbonite event payload
"""
return (e.payload[OSC_MESSAGE_ADDRESS_STR], e.payload[OSC_MESSAGE_ARGUMENTS_STR])
| 1,961 |
Python
| 34.672727 | 90 | 0.7231 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/server.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import threading
import carb
import carb.events
from pythonosc import osc_server
from pythonosc.dispatcher import Dispatcher
class DaemonOSCUDPServer:
"""
Run a python-osc BlockingOSCUDPServer in a separate thread.
Usage::
import omni.osc.core as osc
dispatcher = osc.Dispatcher()
dispatcher.set_default_handler(lambda(path, args): print(f"{path}: {args}"))
server = osc.DaemonOSCUDPServer(dispatcher)
server.start("192.168.0.1", 3434)
# ...
server.stop()
"""
def __init__(self, dispatcher: Dispatcher):
self.dispatcher: Dispatcher = dispatcher
self.server: osc_server.BlockingOSCUDPServer = None
self.thread: threading.Thread = None
def running(self) -> bool:
"""
Returns true if the server is running
"""
return self.thread is not None and self.thread.is_alive()
def start(self, addr: str, port: int) -> bool:
"""
Start the OSC server on the specified address and port.
Does nothing if the server is already running.
"""
if not self.running():
carb.log_info(f"Starting OSC server on {addr}:{port}")
try:
self.server = osc_server.BlockingOSCUDPServer((addr, port), dispatcher=self.dispatcher)
self.thread = threading.Thread(target=lambda: self.server.serve_forever())
# NOTE(jshrake): Running the thread in daemon mode ensures that the thread and server
# are properly disposed of in the event that the main thread exits unexpectedly.
self.thread.daemon = True
self.thread.start()
except Exception as e:
carb.log_error(f"Error starting OSC server: {e}")
else:
carb.log_info("OSC server already running")
return self.running()
def stop(self) -> bool:
"""
Stops the OSC server.
"""
if self.running():
carb.log_info("Stopping OSC server")
try:
self.server.shutdown()
self.thread.join()
except Exception as e:
carb.log_error(f"Error stopping OSC server: {e}")
finally:
self.server = None
self.thread = None
else:
carb.log_info("OSC server not running")
return self.running()
| 2,857 |
Python
| 34.28395 | 103 | 0.615681 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/menu.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import omni.kit.ui
MENU_PATH = "Window/OSC"
class OscMenu:
def __init__(self, on_click):
editor_menu = omni.kit.ui.get_editor_menu()
if not editor_menu:
return
editor_menu.add_item(menu_path=MENU_PATH, on_click=on_click, toggle=True, value=True)
def set_item_value(self, val: bool) -> None:
editor_menu = omni.kit.ui.get_editor_menu()
if not editor_menu:
return
editor_menu.set_value(MENU_PATH, val)
def __del__(self):
editor_menu = omni.kit.ui.get_editor_menu()
if not editor_menu:
return
if editor_menu.has_item(MENU_PATH):
editor_menu.remove_item(MENU_PATH)
| 1,125 |
Python
| 33.121211 | 93 | 0.672889 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/window.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from typing import Callable
import omni.ui as ui
OnStartCallback = Callable[[str, int], bool]
OnStopCallback = Callable[[], bool]
class OscWindow(ui.Window):
def __init__(
self, default_addr: str, default_port: int, on_start: OnStartCallback, on_stop: OnStopCallback
) -> None:
super().__init__("OSC UDP Server", width=300, height=300)
def start() -> None:
"""
Callback when the user presses the start button
"""
is_running = on_start(addr.as_string, port.as_int)
running.set_value(is_running)
def stop() -> None:
"""
Callback when the user presses the stop button
"""
is_running = on_stop()
running.set_value(is_running)
def update_running_label(label: ui.Label, running: bool) -> None:
"""
Keep the UI label up to date with the state of the server
"""
if running:
label.text = f"Running UDP server @ {addr.as_string}:{port.as_int}"
label.set_style({"color": "green"})
else:
label.text = "Stopped"
label.set_style({"color": "red"})
def toggle_enabled(field: ui.AbstractField, running: bool) -> None:
"""
Enable or disable the input field based on the state of the server
"""
field.enabled = not running
color = "gray" if running else "white"
field.set_style({"color": color})
# Settings
addr = ui.SimpleStringModel(default_addr)
port = ui.SimpleIntModel(default_port)
running = ui.SimpleBoolModel(False)
with self.frame:
with ui.VStack():
label = ui.Label("", height=20)
update_running_label(label, running.get_value_as_bool())
running.add_value_changed_fn(lambda m: update_running_label(label, m.get_value_as_bool()))
with ui.VStack(height=20):
with ui.HStack():
ui.Label("Address:")
addr_field = ui.StringField(addr)
toggle_enabled(addr_field, running.get_value_as_bool())
running.add_value_changed_fn(lambda m: toggle_enabled(addr_field, m.get_value_as_bool()))
ui.Spacer(height=2)
with ui.HStack():
ui.Label("Port:")
port_field = ui.IntField(port)
toggle_enabled(port_field, running.get_value_as_bool())
running.add_value_changed_fn(lambda m: toggle_enabled(port_field, m.get_value_as_bool()))
with ui.VStack():
ui.Button("Start", clicked_fn=start)
ui.Button("Stop", clicked_fn=stop)
| 3,323 |
Python
| 39.536585 | 113 | 0.560036 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/ogn/__init__.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
Dynamically import every file in a directory tree that looks like a Python Ogn Node.
This includes linked directories, which is the mechanism by which nodes can be hot-reloaded from the source tree.
"""
# Required to register nodes in Kit 104
try:
import omni.graph.core as og
og.register_ogn_nodes(__file__, "omni.osc")
except Exception:
# Swallow any exceptions
pass
| 817 |
Python
| 37.952379 | 113 | 0.774786 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/ogn/nodes/OgnOnOscEvent.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
This is the implementation of the OGN node defined in OgnOnOscEvent.ogn
This implementation is inspired by the OgnOnCustomEvent node
See https://gitlab-master.nvidia.com/omniverse/kit/-/blob/master/kit/source/extensions/omni.graph.action/nodes/OgnOnCustomEvent.py # noqa E501
"""
import re
from typing import Any, List, Union
import carb
import carb.events
import carb.profiler
import omni.graph.core as og
import omni.osc
from omni.osc.core import OSC_MESSAGE_ADDRESS_STR, OSC_MESSAGE_ARGUMENTS_STR
from .. import OgnOnOscEventDatabase
class OgnOnOscEventInternalState:
"""Convenience class for maintaining per-node state information"""
def __init__(self):
"""Instantiate the per-node state information."""
# This subscription object controls the lifetime of our callback, it will be
# cleaned up automatically when our node is destroyed
self.sub = None
# Set when the callback has triggered
self.is_set = False
# The last event received
self.event: Union[None, carb.events.IEvent] = None
# The node instance handle
self.node = None
# The regex used to match the OSC address path
self.osc_path_regex = ""
# The compiled regex pattern
self.osc_path_regex_pattern = None
@carb.profiler.profile
def on_event(self, event: carb.events.IEvent):
"""The event callback"""
if event is None:
return
# Only handle messages with a path that matches the OSC address path regex
osc_addr, _ = omni.osc.osc_message_from_carb_event(event)
if self.osc_path_regex_pattern is None or not self.osc_path_regex_pattern.match(osc_addr):
return
self.is_set = True
self.event = event
# Tell the evaluator we need to be computed
if self.node.is_valid():
self.node.request_compute()
@carb.profiler.profile
def first_time_subscribe(self, node: og.Node, osc_path_regex: str) -> bool:
"""Checked call to set up carb subscription
Args:
node: The node instance
event_name: The name of the carb event
Returns:
True if we subscribed, False if we are already subscribed
"""
if self.osc_path_regex != osc_path_regex:
# osc path regex changed since we last subscribed, re-compile
try:
self.osc_path_regex_pattern = re.compile(osc_path_regex)
self.osc_path_regex = osc_path_regex
except Exception as e:
carb.log_error(f"Error compiling OSC Address Path Regex '{osc_path_regex}': {e}")
if self.sub is None:
self.sub = omni.osc.subscribe_to_osc_event_stream(self.on_event)
self.node = node
return True
return False
def try_pop_event(self) -> Union[None, carb.events.IEvent]:
"""Pop the last event received, or None if there is no event to pop"""
if self.is_set:
self.is_set = False
event = self.event
self.event = None
return event
return None
# ======================================================================
class OgnOnOscEvent:
"""
This node triggers when an OSC event is received that matches the OSC address path regex.
"""
@staticmethod
def internal_state():
"""Returns an object that will contain per-node state information"""
return OgnOnOscEventInternalState()
@staticmethod
def release(node):
state = OgnOnOscEventDatabase.OgnOnOscEventDatabase.per_node_internal_state(node)
if state.sub:
state.sub.unsubscribe()
state.sub = None
@staticmethod
def check_all_args_are_floats(args: List[Any]) -> bool:
"""
Returns true if the OSC message arguments has the shape of List[float]
"""
all_args_are_float = all(isinstance(arg, float) for arg in args)
return all_args_are_float
@staticmethod
@carb.profiler.profile
def compute(db: og.Database) -> bool:
state: OgnOnOscEventInternalState = db.internal_state
osc_path_regex = db.inputs.path
state.first_time_subscribe(db.node, osc_path_regex)
event = state.try_pop_event()
if event is None:
return False
try:
addr, args = omni.osc.osc_message_from_carb_event(event)
# Populate the output bundle
bundle: og._impl.bundles.BundleContents = db.outputs.message
bundle.clear()
# Update the address attribute
addr_attribute = bundle.insert((og.Type(og.BaseDataType.TOKEN), OSC_MESSAGE_ADDRESS_STR))
addr_attribute.value = addr
# Update the arguments attribute
all_args_are_floats = OgnOnOscEvent.check_all_args_are_floats(args)
# NOTE(jshrake): This node currently only supports OSC arguments shaped like a List[Float]
if all_args_are_floats:
if len(args) == 1:
# Argument list contains a single element, write it as a double
args_attribute = bundle.insert((og.Type(og.BaseDataType.DOUBLE), OSC_MESSAGE_ARGUMENTS_STR))
args_attribute.value = args[0]
elif len(args) > 1:
# Argument list contains multiple element, write it as a list
args_attribute = bundle.insert((og.Type(og.BaseDataType.DOUBLE, tuple_count=len(args), array_depth=0), OSC_MESSAGE_ARGUMENTS_STR))
args_attribute.value = args
else:
carb.log_warn(f"OnOscMessage node expected OSC message arguments to be of type List[Float], instead got {args}")
return False
db.outputs.execOut = og.ExecutionAttributeState.ENABLED
except Exception as e:
carb.log_error(f"Error in OgnOnOscEvent::compute: {e}")
return False
return True
| 6,464 |
Python
| 37.254438 | 150 | 0.629332 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/tests/tests.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import asyncio
import omni.kit.test
import omni.osc
class Test(omni.kit.test.AsyncTestCase):
# Before running each test
async def setUp(self):
pass
# After running each test
async def tearDown(self):
pass
async def test_can_start_and_stop_server(self):
server = omni.osc.DaemonOSCUDPServer(None)
is_running = server.start("localhost", 12345)
self.assertTrue(is_running)
await asyncio.sleep(0.1)
is_running = server.running()
self.assertTrue(is_running)
is_running = server.stop()
self.assertFalse(is_running)
async def test_server_can_receive_messages(self):
server = omni.osc.OmniOscExt.create_server()
is_running = server.start("localhost", 3337)
self.assertTrue(is_running)
self.count = 0
def on_event(e) -> None:
addr, _ = omni.osc.osc_message_from_carb_event(e)
self.assertEqual(e.type, omni.osc.core.OSC_EVENT_TYPE)
self.assertEqual(addr, "/filter")
self.count += 1
sub = omni.osc.subscribe_to_osc_event_stream(on_event)
total_msg_count = 10
def send_messages():
import random
from pythonosc import udp_client
client = udp_client.SimpleUDPClient(address="127.0.0.1", port=3337)
self.assertTrue(client is not None)
for _ in range(total_msg_count):
client.send_message("/filter", random.random())
send_messages()
# Wait a few seconds for the server to receive the messages
await asyncio.sleep(3)
# Manually pump the stream so our subscription callback executes
omni.osc.get_osc_event_stream().pump()
self.assertEqual(self.count, total_msg_count)
| 2,226 |
Python
| 34.919354 | 79 | 0.655436 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/docs/CHANGELOG.md
|
# Changelog
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
## [0.3.1] - 2023-09-28
### Changed
- Update CHANGELOG
## [0.3.0] - 2023-09-26
### Changed
- Fix OGN node registration for Kit 105.1
## [0.2.0] - 2022-09-12
### Changed
- The `On OSC Message` OmniGraph node now outputs a Bundle typed value rather than an Unknown typed value.
- Users can extract the "address" and the "arguments" of the OSC message with the `Extract Attribute` node.
## [0.1.1] - 2022-09-12
### Changed
- Updated documentation.
## [0.1.0] - 2022-09-02
### Added
- Initial release.
| 600 |
Markdown
| 22.115384 | 107 | 0.671667 |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/docs/README.md
|
# omni.osc
Omniverse Kit extension for sending and receiving OSC (Open Sound Control) messages.
| 96 |
Markdown
| 31.333323 | 84 | 0.802083 |
AccelerationAgency/omniverse-extensions/exts/taa.google.spreadsheet.api/taa/google/spreadsheet/api/extension.py
|
import omni.ext
import omni.ui as ui
import omni.kit.commands
from typing import List
from pxr import Gf
omni.kit.pipapi.install('google-api-python-client')
omni.kit.pipapi.install('google-auth-httplib2')
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
SPACING = 4
LABEL_WIDTH = 120
class MyExtension(omni.ext.IExt):
data = {'translate_x': 0, 'translate_y': 0, 'translate_z': 0, 'rotate_x': 0, 'rotate_y': 0, 'rotate_z': 0, 'scale_x': 0, 'scale_y': 0, 'scale_z': 0}
subscription = None
stage = None
google_sheet = None
label_width = 50
_source_prim_model = ui.SimpleStringModel()
# lifecycle
def on_startup(self, ext_id):
print("[taa.google.spreadsheet.api] Extension starting up")
self.stage = omni.usd.get_context().get_stage()
self._window = ui.Window("TAA Google Spreadsheet API", width=400, height=270)
with self._window.frame:
with ui.VStack(height=0, spacing=SPACING):
with ui.CollapsableFrame("Source", name="group"):
with ui.VStack(height=0, spacing=SPACING):
with ui.HStack():
ui.Label("Prim", name="attribute_name", width=LABEL_WIDTH)
ui.StringField(model=self._source_prim_model)
ui.Button(" S ", width=0, height=0, style={"margin": 0}, clicked_fn=self._on_get_selection, tooltip="Get From Selection")
ui.Spacer(height= 12)
with ui.CollapsableFrame("Settings", name="group"):
with ui.VStack(height=0, spacing=SPACING):
ui.Label('Spreadsheet ID', height=20)
self.spreadsheet_id_field = ui.StringField(height=20)
ui.Label('Range', height=20)
self.range_field = ui.StringField(height=20)
ui.Label('API Key', height=20)
self.api_key_field = ui.StringField(height=20)
ui.Spacer(height= 12)
self.startButton = ui.Button("Start", height=54, clicked_fn=lambda: self.start(), style={"background_color": "green"})
self.stopButton = ui.Button("Stop", height=54, clicked_fn=lambda: self.stop(), style={"color": "red"})
ui.Spacer(height= 12)
self.statusLabel = ui.Label('Click start to begin', height=14, style={"font_size": 12})
self.stopButton.visible = False
print("[taa.google.spreadsheet.api] Extension start up complete")
def on_shutdown(self):
print("Extension shutting down")
self.stop()
print("Extension shutdown complete")
# custom methods
def _on_get_selection(self):
print('_on_get_selection', self.get_selection())
self._source_prim_model.as_string = ", ".join(self.get_selection())
def get_selection(self) -> List[str]:
return omni.usd.get_context().get_selection().get_selected_prim_paths()
def apply_changes(self, frame):
try:
# load the data from Google Spreadsheet ever few seconds; this API is rate limited
frameNumber = int(frame.payload["SWHFrameNumber"])
if(frameNumber % 180 != 0): return
print('applying changes')
self.read_data()
# act on all selected prims
paths = self.list_paths_of_selected_prims()
for path in paths:
# get reference to the prim on stage, making sure that it's valid
prim = self.stage.GetPrimAtPath(path)
if prim.IsValid() == False: continue
# transform the prim based on the settings in the Google Spreadsheet
self.move_prim(prim)
self.rotate_prim(prim)
self.scale_prim(prim)
print('changes applied successfully')
except Exception as err:
print(err)
def read_config(self):
try:
spreadsheetId = self.spreadsheet_id_field.model.get_value_as_string()
range = self.range_field.model.get_value_as_string()
api_key = self.api_key_field.model.get_value_as_string()
return (spreadsheetId, range, api_key)
except Exception as err:
print(err)
def read_data(self):
try:
spreadsheetId, range, api_key = self.read_config()
if self.google_sheet == None:
service = build('sheets', 'v4', developerKey=api_key)
self.google_sheet = service.spreadsheets()
result = self.google_sheet.values().get(spreadsheetId=spreadsheetId, range=range).execute()
values = result.get('values', [])
data = toJSON(values)
# normalize and clean data
self.data["shape"] = data.setdefault('shape', 'Cube')
self.data["size"] = float(data.setdefault('size', 100))
self.data["radius"] = float(data.setdefault('radius', 100))
self.data["translate_x"] = float(data.setdefault('translate_x', 0))
self.data["translate_y"] = float(data.setdefault('translate_y', 0))
self.data["translate_z"] = float(data.setdefault('translate_z', 0))
self.data["rotate_x"] = float(data.setdefault('rotate_x', 0))
self.data["rotate_y"] = float(data.setdefault('rotate_y', 0))
self.data["rotate_z"] = float(data.setdefault('rotate_z', 0))
self.data["scale_x"] = float(data.setdefault('scale_x', 1))
self.data["scale_y"] = float(data.setdefault('scale_y', 1))
self.data["scale_z"] = float(data.setdefault('scale_z', 1))
except HttpError as err:
print(err)
def move_prim(self, prim):
try:
x = self.data.get('translate_x')
y = self.data.get('translate_y')
z = self.data.get('translate_z')
omni.kit.commands.execute('TransformPrimSRT',
path=prim.GetPath(),
new_translation=Gf.Vec3d(x, y, z),
)
except Exception as err:
print("Failed to move prim", err)
def rotate_prim(self, prim):
try:
x = self.data.get('rotate_x')
y = self.data.get('rotate_y')
z = self.data.get('rotate_z')
omni.kit.commands.execute('TransformPrimSRT',
path=prim.GetPath(),
new_rotation_euler=Gf.Vec3d(x, y, z),
)
except Exception as err:
print("Failed to rotate prime", err)
def scale_prim(self, prim):
try:
x = self.data.get('scale_x')
y = self.data.get('scale_y')
z = self.data.get('scale_z')
omni.kit.commands.execute('TransformPrimSRT',
path=prim.GetPath(),
new_scale=Gf.Vec3d(x, y, z),
)
except Exception as err:
print("Failed to scale prim", err)
def list_paths_of_selected_prims(self):
try:
paths = [i.strip() for i in self._source_prim_model.as_string.split(",")]
if not paths:
paths = self.get_selection()
if not paths:
pass
return paths
except Exception as err:
print(err)
def start(self):
self.read_data()
def on_update_apply(frame): self.apply_changes(frame)
self.subscription = omni.kit.app.get_app().get_update_event_stream().create_subscription_to_pop(on_update_apply)
self.startButton.visible = False
self.stopButton.visible = True
self.statusLabel.text = "Status: started"
def stop(self):
if self.subscription: del self.subscription
self.startButton.visible = True
self.stopButton.visible = False
self.statusLabel.text = "Status: stopped"
"""
Utility functions
"""
def toJSON(values):
json = {}
if not values:
return json
for row in values:
key = row[0]
value = row[1]
if not key or not value:
continue
json[row[0]] = row[1]
return json
| 8,802 |
Python
| 27.124601 | 152 | 0.527153 |
AccelerationAgency/omniverse-extensions/exts/taa.google.spreadsheet.api/config/extension.toml
|
[package]
version = "1.0.0"
title = "TAA - Google Spreadsheet API"
description="An exploration into using Google Spreadsheet data to objects on the stage"
readme = "docs/README.md"
repository = ""
category = "Other"
keywords = ["taa", "google", "spreadsheet", "api", "example"]
icon = "data/taa-logo.png"
[dependencies]
"omni.kit.uiapp" = {}
[[python.module]]
name = "taa.google.spreadsheet.api"
| 399 |
TOML
| 23.999999 | 87 | 0.696742 |
AccelerationAgency/omniverse-extensions/exts/taa.omniverse.cameracreator/taa/omniverse/cameracreator/extension.py
|
import omni.ext
import omni.ui as ui
import omni.kit.commands as commands
class MyExtension(omni.ext.IExt):
# Lifecycle
def on_startup(self, ext_id):
print("[taa.omniverse.viewport] Extension starting up")
self._window = ui.Window("TAA Quick Camera", width=200, height = 200)
with self._window.frame:
with ui.VStack(height = 0, spacing = 4):
self.perspectiveButton = ui.Button("Perspective", height=40, clicked_fn=lambda: self.create_perspective_camera(), style={"background_color":"black"})
self.topButton = ui.Button("Top", height=40, clicked_fn=lambda: self.create_top_camera(), style={"background_color":"black"})
self.frontButton = ui.Button("Front", height=40, clicked_fn=lambda: self.create_front_camera(), style={"background_color":"black"})
self.rightButton = ui.Button("Right", height=40, clicked_fn=lambda: self.create_right_camera(), style={"background_color":"black"})
print("[taa.omniverse.viewport] Extension start up complete")
def on_shutdown(self):
print("[taa.omniverse.viewport] Extension shutting down")
self.stop()
print("[taa.omniverse.viewport] Extension shutdown complete")
# Custom methods
def set_camera(self, path):
omni.kit.viewport_legacy.get_viewport_interface().get_viewport_window().set_active_camera(path)
def rename_camera(self, name):
cameraPath = omni.kit.viewport_legacy.get_viewport_interface().get_viewport_window().get_active_camera()
omni.kit.commands.execute('MovePrims', paths_to_move={cameraPath: f'/World/Camera_{name}'})
def create_perspective_camera(self):
print("[taa.omniverse.viewport] Creating new perspective camera")
self.set_camera("/OmniverseKit_Persp")
commands.execute('DuplicateFromActiveViewportCameraCommand', viewport_name='Viewport')
self.rename_camera("Perspective")
def create_top_camera(self):
print("[taa.omniverse.viewport] Creating new top-down camera")
self.set_camera("/OmniverseKit_Top")
commands.execute('DuplicateFromActiveViewportCameraCommand', viewport_name='Viewport')
self.rename_camera("Top")
def create_front_camera(self):
print("[taa.omniverse.viewport] Creating new front view camera")
self.set_camera("/OmniverseKit_Front")
commands.execute('DuplicateFromActiveViewportCameraCommand', viewport_name='Viewport')
self.rename_camera("Front")
def create_right_camera(self):
print("[taa.omniverse.viewport] Creating new right view camera")
self.set_camera("/OmniverseKit_Right")
commands.execute('DuplicateFromActiveViewportCameraCommand', viewport_name='Viewport')
self.rename_camera("Right")
def start(self):
print("[taa.omniverse.viewport] Starting...")
def stop(self):
print("[taa.omniverse.viewport] Stopping...")
| 2,974 |
Python
| 44.76923 | 165 | 0.675521 |
AccelerationAgency/omniverse-extensions/exts/taa.omniverse.cameracreator/config/extension.toml
|
[package]
version = "1.0.0"
title = "TAA - Omniverse Camera Creator"
description = "An simple extension that lets you quickly create cameras with a single click."
readme = "docs/README.md"
repository = ""
category = "Other"
keywords = ["taa", "viewport", "create", "camera", "view"]
icon = "data/taa-logo.png"
[dependencies]
"omni.kit.uiapp" = {}
[[python.module]]
name = "taa.omniverse.cameracreator"
| 405 |
TOML
| 24.374998 | 93 | 0.693827 |
ilanhuang/audio2face-streamgpt-public/README.md
|
# Stream-GPT
Stream-GPT is an Omniverse Extension that uses OpenAI's GPT-3 model to create a virtual assistant. It allows users to interact with the assistant through both text and voice, and the assistant responds in kind. The extension uses OpenAI's Whisper ASR system to transcribe audio input and Eleven Labs' API to convert the assistant's text responses into audio.
## Getting Started
### Prerequisites
- Python 3.6 or higher
- Omniverse Kit
- Omniverse Audio2Face
- OpenAI API key
- Eleven Labs API key
### Installation
1. Clone the repository:
```bash
git clone https://github.com/ilanhuang/audio2face-stream-chatgpt.git
```
2. Install the required Python packages:
```bash
pip install -r requirements.txt
```
3. Update the `sys.path.append` in `extension.py` with the correct path to the `streaming_server` directory in your local clone of the repository.
```python
sys.path.append("C:\\Users\\YourUsername\\path\\to\\stream-gpt\\pkg\\audio2face-2022.2.1\\exts\\omni.audio2face.player\omni\\audio2face\\player\\scripts\\streaming_server")
```
4. Add the custom extension to Omniverse:
- Go to the "Windows" tab on the top of the screen.
- Scroll down to "Extensions".
- Click on the gear icon to open the Extensions settings.
- Click on the "+" button to add a new path to the custom extension.
- A window will pop up when you turn on the extension.
5. Set your OpenAI and Eleven Labs API keys, as well as the voice_id, model_id, and the Audio2Face's audioplayer's prim path (instance_name) in the extension's settings:
- Open the extension and click on the "Settings" button.
- Enter your OpenAI API key, Eleven Labs API key, voice_id, model_id and instance name in the corresponding fields. (A text file in the repository lists the available voice ids.)
## Usage
Once the application is running, you can interact with the virtual assistant through the UI. You can type your prompts into the text field and click on the "Send" button or use the "Record Audio" button to speak your prompts. The assistant will respond in the chat log and through your speakers.
You can also add a system to the GPT virtual assistant by typing it in the "System" field in the UI.
All interactions made with the extension are saved in a folder named "chat_logs" for future reference.
| 2,294 |
Markdown
| 40.727272 | 358 | 0.762424 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/recording_transcription.py
|
#Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import os
import pyaudio
import wave
import keyboard
import time
from time import sleep
import openai
import datetime
def open_file(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return infile.read()
def save_file(filepath, content):
with open(filepath, 'w', encoding='utf-8') as outfile:
outfile.write(content)
def timestamp_to_datetime(unix_time):
return datetime.datetime.fromtimestamp(unix_time).strftime("%A, %B %d, %Y at %I:%M%p %Z")
def record_client_voice(output_filename, recording_status):
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
frames = []
p = pyaudio.PyAudio()
stream = None
try:
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
start_time = time.time()
min_duration = 0.1
while recording_status() or time.time() - start_time < min_duration:
data = stream.read(CHUNK)
frames.append(data)
except Exception as e:
print(f"Error while recording audio: {e}")
finally:
if stream is not None:
stream.stop_stream()
stream.close()
p.terminate()
wf = wave.open(output_filename, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
wf.close()
return output_filename
def transcribe_audio_to_text(file_path):
with open(file_path, 'rb') as audio_file:
transcript_response = openai.Audio.transcribe("whisper-1", audio_file)
return transcript_response["text"]
| 2,508 |
Python
| 32.013157 | 240 | 0.64673 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/transmission.py
|
#Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import grpc
import os
import soundfile
import numpy as np
import audio2face_pb2
import audio2face_pb2_grpc
import sounddevice as sd
import time
from typing import Iterator
import requests
import queue
import threading
import carb
def generate_stream(text: str, voice_id: str, model_id: str, api_key: str, stream_chunk_size: int = 2048) -> Iterator[bytes]:
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream"
data = dict(text=text, model_id=model_id, voice_settings=None)
headers = {"xi-api-key": api_key}
response = requests.post(url, json=data, headers=headers, stream=True)
for chunk in response.iter_content(chunk_size=stream_chunk_size):
if chunk:
yield chunk
def read_api_key_from_file(file_path: str) -> str:
with open(file_path, 'r') as f:
return f.read().strip()
def text_to_audio_stream(text, instance_name, api_key):
print("text_to_audio_stream: start")
settings = carb.settings.get_settings()
voice_id = settings.get_as_string("/persistent/exts/omni.example.streamgpt/VOICE_ID")
model_id = settings.get_as_string("/persistent/exts/omni.example.streamgpt/MODEL_ID")
audio_stream = generate_stream(text, voice_id, model_id, api_key)
current_dir = os.path.dirname(os.path.realpath(__file__))
audio_filename = os.path.join(current_dir, "temp_audio_response.mp3")
with open(audio_filename, 'wb') as f:
for chunk in audio_stream:
f.write(chunk)
audio_data, samplerate = soundfile.read(audio_filename, dtype="float32")
if len(audio_data.shape) > 1:
audio_data = np.average(audio_data, axis=1)
url = "localhost:50051"
audio_queue = queue.Queue()
audio_queue.put(audio_data)
def audio_streamer():
while not audio_queue.empty():
audio_chunk = audio_queue.get()
push_audio_track_stream(url, audio_chunk, samplerate, instance_name)
audio_thread = threading.Thread(target=audio_streamer)
audio_thread.start()
os.remove(audio_filename)
print("text_to_audio_stream: end")
def push_audio_track_stream(url, audio_data, samplerate, instance_name):
print("push_audio_track_stream: start")
chunk_size = samplerate // 10
sleep_between_chunks = 0.04
with grpc.insecure_channel(url) as channel:
print("Channel created")
stub = audio2face_pb2_grpc.Audio2FaceStub(channel)
def make_generator():
start_marker = audio2face_pb2.PushAudioRequestStart(
samplerate=samplerate,
instance_name=instance_name,
block_until_playback_is_finished=False,
)
yield audio2face_pb2.PushAudioStreamRequest(start_marker=start_marker)
for i in range(len(audio_data) // chunk_size + 1):
try:
time.sleep(sleep_between_chunks)
chunk = audio_data[i * chunk_size : i * chunk_size + chunk_size]
yield audio2face_pb2.PushAudioStreamRequest(audio_data=chunk.astype(np.float32).tobytes())
except Exception as e:
print(f"Error in generator function: {e}")
break
request_generator = make_generator()
print("Sending audio data...")
response = stub.PushAudioStream(request_generator)
if response.success:
print("SUCCESS")
else:
print(f"ERROR: {response.message}")
print("Channel closed")
| 4,203 |
Python
| 39.038095 | 240 | 0.66738 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/extension.py
|
#Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import omni.ext
import sys
sys.path.append("C:\\Users\\ERKS 2\\Documents\\Omniverse\\ov\\pkg\\audio2face-2022.2.1\\exts\\omni.audio2face.player\omni\\audio2face\\player\\scripts\\streaming_server")
import openai
import carb
from .window import AudioChatWindow
def open_file(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return infile.read()
# Any class derived from `omni.ext.IExt` in top level module (defined in `python.modules` of `extension.toml`) will be
# instantiated when extension gets enabled and `on_startup(ext_id)` will be called. Later when extension gets disabled
# on_shutdown() is called.
class MyExtension(omni.ext.IExt):
# ext_id is current extension id. It can be used with extension manager to query additional information, like where
# this extension is located on filesystem.
def on_startup(self, ext_id):
openai.api_key = AudioChatWindow.get_openai_api_key()
self._window = AudioChatWindow("VIRTUAL ASSISTANT", width=400, height=525)
def on_shutdown(self):
self._window.destroy()
self._window = None
| 1,821 |
Python
| 55.937498 | 240 | 0.741351 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/chatbot.py
|
#Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import os
import openai
import json
import numpy as np
from numpy.linalg import norm
import re
from time import time,sleep
from uuid import uuid4
import datetime
def open_file(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return infile.read()
def save_file(filepath, content):
with open(filepath, 'w', encoding='utf-8') as outfile:
outfile.write(content)
def load_json(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return json.load(infile)
def save_json(filepath, payload):
with open(filepath, 'w', encoding='utf-8') as outfile:
json.dump(payload, outfile, ensure_ascii=False, sort_keys=True, indent=2)
def timestamp_to_datetime(unix_time):
return datetime.datetime.fromtimestamp(unix_time).strftime("%A, %B %d, %Y at %I:%M%p %Z")
def gpt3_embedding(content, engine='text-embedding-ada-002'):
content = content.encode(encoding='ASCII',errors='ignore').decode() # fix any UNICODE errors
response = openai.Embedding.create(input=content,engine=engine)
vector = response['data'][0]['embedding'] # this is a normal list
return vector
def chatgpt_completion(messages, model="gpt-4", temp=0.0, top_p=1.0, tokens=400, freq_pen=0.0, pres_pen=0.0):
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temp,
max_tokens=tokens,
top_p=top_p,
frequency_penalty=freq_pen,
presence_penalty=pres_pen,)
text = response['choices'][0]['message']['content']
tokens_used = response['usage']['total_tokens']
filename = 'chat_%s_aibot.json' % time()
script_dir = os.path.dirname(os.path.realpath(__file__))
chat_logs_path = os.path.join(script_dir, 'chat_logs')
if not os.path.exists(chat_logs_path):
os.makedirs(chat_logs_path)
input_message = messages[-1]['content']
log_content = f"User:\n{input_message}\n\nAi_Bot:\n{text}\n\nTokens used: {tokens_used}"
save_file(os.path.join(chat_logs_path, filename), log_content)
return text
def flatten_convo(conversation):
convo = ''
for i in conversation:
convo += '%s: %s\n' % (i['role'].upper(), i['content'])
return convo.strip()
def set_openai_api_key(api_key):
openai.api_key = api_key
def set_system_content(content):
global system_content
system_content = content
if __name__ == '__main__':
convo_length = 30
set_openai_api_key(api_key)
conversation = list()
conversation.append({'role': 'system', 'content': system_content})
counter = 0
while True:
# get user input, save to file
a = input('\n\nCLIENT: ')
conversation.append({'role': 'user', 'content': a})
filename = 'chat_%s_client.txt' % time()
if not os.path.exists('chat_logs'):
os.makedirs('chat_logs')
save_file('chat_logs/%s' % filename, a)
flat = flatten_convo(conversation)
# generate a response
response = chatgpt_completion(conversation)
conversation.append({'role': 'assistant', 'content': response})
print('\n\nAI_Bot: %s' % response)
# increment counter and consolidate memories
counter += 2
if counter >= 10:
# reset conversation
conversation = list()
conversation.append({'role': 'system', 'content': system_content})
| 4,226 |
Python
| 35.128205 | 240 | 0.643871 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/window.py
|
#Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import os
import omni.ui as ui
import omni.kit.commands
from omni.kit.window.popup_dialog.form_dialog import FormDialog
from time import time
from .recording_transcription import record_client_voice, transcribe_audio_to_text
from .chatbot import chatgpt_completion, set_system_content
from .transmission import text_to_audio_stream
import threading
import time
import tempfile
import datetime
import carb
def save_file(filepath, content):
with open(filepath, 'w', encoding='utf-8') as outfile:
outfile.write(content)
def timestamp_to_datetime(unix_time):
return datetime.datetime.fromtimestamp(unix_time).strftime("%A, %B %d, %Y at %I:%M%p %Z")
class AudioChatWindow(ui.Window):
def _build_fn(self):
with self.frame:
with ui.VStack():
with ui.ScrollingFrame(height=ui.Percent(75)):
self.chat_log = ui.Label("", word_wrap=True)
with ui.HStack(height=ui.Percent(10)):
ui.StringField(model=self._prompt_model, multiline=True)
with ui.HStack(height=ui.Percent(10)):
self.record_audio_button = ui.Button("Record Audio", height=40, clicked_fn=lambda *_args, **_kwargs: self._toggle_record_audio())
ui.Button("Send", height=40, clicked_fn=lambda: self._send_text_prompt())
with ui.HStack():
ui.Button("Settings", tooltip="Configure API Key, Instance name and Default System", width=0, height=0, clicked_fn=lambda: self._open_settings())
system_settings_button = ui.Button("System", height=0, width=0)
system_settings_button.set_clicked_fn(lambda: self.show_system_settings_menu())
def __init__(self, title: str, **kwargs) -> None:
self.conversation = [{"role": "system", "content": ""}]
self.system_content_model = ui.SimpleStringModel()
self.lock = threading.Lock()
super().__init__(title, **kwargs)
self._prompt_model = ui.SimpleStringModel()
self.frame.set_build_fn(self._build_fn)
def show_system_settings_menu(self):
self.system_settings_menu = ui.Menu("")
with self.system_settings_menu:
ui.StringField(model=self.system_content_model, multiline=True)
self.system_settings_menu.show()
def _toggle_record_audio(self):
if not hasattr(self, "recording"):
self.recording = False
if not self.recording:
self.recording = True
threading.Thread(target=self._record_and_transcribe_audio).start()
else:
self.recording = False
def _process_conversation(self, user_content):
current_system_content = self.system_content_model.get_value_as_string().strip()
if current_system_content != self.conversation[0]['content']:
self.reset_chat()
set_system_content(current_system_content)
self.conversation.append({"role": "user", "content": user_content})
response = chatgpt_completion(self.conversation)
self.chat_log.text += f"\nUser: {user_content}\nAssistant: {response}"
settings = carb.settings.get_settings()
instance_name = settings.get_as_string("/persistent/exts/omni.example.streamgpt/INSTANCE_NAME")
threading.Thread(target=text_to_audio_stream, args=(response, instance_name, self.get_elevenlabs_api_key())).start()
def _record_and_transcribe_audio(self):
output_filename = "recorded_audio.wav"
record_client_voice(output_filename)
transcript = transcribe_audio_to_text(output_filename)
self._send_audio_transcript(transcript)
def _send_audio_transcript(self, transcript):
self.chat_log.text += "\nThinking..."
threading.Thread(target=self._process_conversation, args=(transcript,)).start()
def reset_chat(self):
self.chat_log.text = ""
self.conversation = [{"role": "system", "content": self.system_content_model.get_value_as_string().strip()}]
def _save_settings(self, dialog):
values = dialog.get_values()
settings = carb.settings.get_settings()
settings.set_string("/persistent/exts/omni.example.streamgpt/APIKey_OPEN_AI", values["APIKey_OPEN_AI"])
settings.set_string("/persistent/exts/omni.example.streamgpt/APIKey_ELEVEN_LABS", values["APIKey_ELEVEN_LABS"])
settings.set_string("/persistent/exts/omni.example.streamgpt/VOICE_ID", values["ELEVEN_LABS_VOICE_ID"])
settings.set_string("/persistent/exts/omni.example.streamgpt/MODEL_ID", values["ELEVEN_LABS_MODEL_ID"])
settings.set_string("/persistent/exts/omni.example.streamgpt/INSTANCE_NAME", values["INSTANCE_NAME"])
dialog.hide()
def _open_settings(self):
settings = carb.settings.get_settings()
apikey_open_ai = settings.get_as_string("/persistent/exts/omni.example.streamgpt/APIKey_OPEN_AI")
apikey_eleven_labs = settings.get_as_string("/persistent/exts/omni.example.streamgpt/APIKey_ELEVEN_LABS")
voice_id = settings.get_as_string("/persistent/exts/omni.example.streamgpt/VOICE_ID")
model_id = settings.get_as_string("/persistent/exts/omni.example.streamgpt/MODEL_ID")
instance_name = settings.get_as_string("/persistent/exts/omni.example.streamgpt/INSTANCE_NAME")
if apikey_open_ai == "":
apikey_open_ai = "Enter OPEN-AI API Key Here"
if apikey_eleven_labs == "":
apikey_eleven_labs = "Enter ELEVEN-LABS API Key Here"
if instance_name == "":
instance_name = "Enter Instance Name Here"
if voice_id == "":
voice_id = "Enter Eleven Labs Voice ID Here"
if model_id == "":
model_id = "Enter Eleven Labs Model ID Here"
field_defs = [
FormDialog.FieldDef("APIKey_OPEN_AI", "OPEN-AI API Key: ", ui.StringField, apikey_open_ai),
FormDialog.FieldDef("APIKey_ELEVEN_LABS", "ELEVEN-LABS API Key: ", ui.StringField, apikey_eleven_labs),
FormDialog.FieldDef("ELEVEN_LABS_VOICE_ID", "Voice ID: ", ui.StringField, voice_id),
FormDialog.FieldDef("ELEVEN_LABS_MODEL_ID", "Model ID: ", ui.StringField, model_id),
FormDialog.FieldDef("INSTANCE_NAME", "Instance Name: ", ui.StringField, instance_name),
]
dialog = FormDialog(
title="Settings",
message="Your Settings: ",
field_defs=field_defs,
ok_handler=lambda dialog: self._save_settings(dialog))
dialog.show()
@staticmethod
def get_openai_api_key():
settings = carb.settings.get_settings()
return settings.get_as_string("/persistent/exts/omni.example.streamgpt/APIKey_OPEN_AI")
def get_elevenlabs_api_key(self):
settings = carb.settings.get_settings()
return settings.get_as_string("/persistent/exts/omni.example.streamgpt/APIKey_ELEVEN_LABS")
def _send_text_prompt(self):
prompt = self._prompt_model.get_value_as_string()
self.chat_log.text += "\nThinking..."
threading.Thread(target=self._process_conversation, args=(prompt,)).start()
self._prompt_model.set_value("")
def _toggle_record_audio(self):
if not hasattr(self, "recording"):
self.recording = False
self.recording = not self.recording
if self.recording:
self.record_audio_button.text = "Stop Recording"
else:
self.record_audio_button.text = "Record Audio"
threading.Thread(target=self._record_and_transcribe_audio_alternative).start()
def recording_status(self):
return self.recording
def _record_and_transcribe_audio_alternative(self):
with self.lock:
temp_audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
temp_audio_filename = temp_audio_file.name
temp_audio_file.close()
recorded_audio_filename = record_client_voice(temp_audio_filename, self.recording_status)
transcript = transcribe_audio_to_text(recorded_audio_filename)
os.remove(temp_audio_filename)
if transcript.strip():
self._send_audio_transcript(transcript)
def destroy(self):
super().destroy()
self._prompt_model = None
| 9,174 |
Python
| 47.036649 | 240 | 0.645193 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/pytransform/__init__.py
|
# These module alos are used by protection code, so that protection
# code needn't import anything
import os
import platform
import sys
import struct
# Because ctypes is new from Python 2.5, so pytransform doesn't work
# before Python 2.5
#
from ctypes import cdll, c_char, c_char_p, c_int, c_void_p, \
pythonapi, py_object, PYFUNCTYPE, CFUNCTYPE
from fnmatch import fnmatch
#
# Support Platforms
#
plat_path = 'platforms'
plat_table = (
('windows', ('windows', 'cygwin*')),
('darwin', ('darwin',)),
('ios', ('ios',)),
('linux', ('linux*',)),
('freebsd', ('freebsd*', 'openbsd*', 'isilon onefs')),
('poky', ('poky',)),
)
arch_table = (
('x86', ('i?86', )),
('x86_64', ('x64', 'x86_64', 'amd64', 'intel')),
('arm', ('armv5',)),
('armv6', ('armv6l',)),
('armv7', ('armv7l',)),
('ppc64', ('ppc64le',)),
('mips32', ('mips',)),
('aarch32', ('aarch32',)),
('aarch64', ('aarch64', 'arm64'))
)
#
# Hardware type
#
HT_HARDDISK, HT_IFMAC, HT_IPV4, HT_IPV6, HT_DOMAIN = range(5)
#
# Global
#
_pytransform = None
class PytransformError(Exception):
pass
def dllmethod(func):
def wrap(*args, **kwargs):
return func(*args, **kwargs)
return wrap
@dllmethod
def version_info():
prototype = PYFUNCTYPE(py_object)
dlfunc = prototype(('version_info', _pytransform))
return dlfunc()
@dllmethod
def init_pytransform():
major, minor = sys.version_info[0:2]
# Python2.5 no sys.maxsize but sys.maxint
# bitness = 64 if sys.maxsize > 2**32 else 32
prototype = PYFUNCTYPE(c_int, c_int, c_int, c_void_p)
init_module = prototype(('init_module', _pytransform))
ret = init_module(major, minor, pythonapi._handle)
if (ret & 0xF000) == 0x1000:
raise PytransformError('Initialize python wrapper failed (%d)'
% (ret & 0xFFF))
return ret
@dllmethod
def init_runtime():
prototype = PYFUNCTYPE(c_int, c_int, c_int, c_int, c_int)
_init_runtime = prototype(('init_runtime', _pytransform))
return _init_runtime(0, 0, 0, 0)
@dllmethod
def encrypt_code_object(pubkey, co, flags, suffix=''):
_pytransform.set_option(6, suffix.encode())
prototype = PYFUNCTYPE(py_object, py_object, py_object, c_int)
dlfunc = prototype(('encrypt_code_object', _pytransform))
return dlfunc(pubkey, co, flags)
@dllmethod
def generate_license_key(prikey, keysize, rcode):
prototype = PYFUNCTYPE(py_object, c_char_p, c_int, c_char_p)
dlfunc = prototype(('generate_license_key', _pytransform))
return dlfunc(prikey, keysize, rcode) if sys.version_info[0] == 2 \
else dlfunc(prikey, keysize, rcode.encode())
@dllmethod
def get_registration_code():
prototype = PYFUNCTYPE(py_object)
dlfunc = prototype(('get_registration_code', _pytransform))
return dlfunc()
@dllmethod
def get_expired_days():
prototype = PYFUNCTYPE(py_object)
dlfunc = prototype(('get_expired_days', _pytransform))
return dlfunc()
@dllmethod
def clean_obj(obj, kind):
prototype = PYFUNCTYPE(c_int, py_object, c_int)
dlfunc = prototype(('clean_obj', _pytransform))
return dlfunc(obj, kind)
def clean_str(*args):
tdict = {
'str': 0,
'bytearray': 1,
'unicode': 2
}
for obj in args:
k = tdict.get(type(obj).__name__)
if k is None:
raise RuntimeError('Can not clean object: %s' % obj)
clean_obj(obj, k)
def get_hd_info(hdtype, name=None):
if hdtype not in range(HT_DOMAIN + 1):
raise RuntimeError('Invalid parameter hdtype: %s' % hdtype)
size = 256
t_buf = c_char * size
buf = t_buf()
cname = c_char_p(0 if name is None
else name.encode('utf-8') if hasattr('name', 'encode')
else name)
if (_pytransform.get_hd_info(hdtype, buf, size, cname) == -1):
raise PytransformError('Get hardware information failed')
return buf.value.decode()
def show_hd_info():
return _pytransform.show_hd_info()
def assert_armored(*names):
prototype = PYFUNCTYPE(py_object, py_object)
dlfunc = prototype(('assert_armored', _pytransform))
def wrapper(func):
def wrap_execute(*args, **kwargs):
dlfunc(names)
return func(*args, **kwargs)
return wrap_execute
return wrapper
def check_armored(*names):
try:
prototype = PYFUNCTYPE(py_object, py_object)
prototype(('assert_armored', _pytransform))(names)
return True
except RuntimeError:
return False
def get_license_info():
info = {
'ISSUER': None,
'EXPIRED': None,
'HARDDISK': None,
'IFMAC': None,
'IFIPV4': None,
'DOMAIN': None,
'DATA': None,
'CODE': None,
}
rcode = get_registration_code().decode()
if rcode.startswith('*VERSION:'):
index = rcode.find('\n')
info['ISSUER'] = rcode[9:index].split('.')[0].replace('-sn-1.txt', '')
rcode = rcode[index+1:]
index = 0
if rcode.startswith('*TIME:'):
from time import ctime
index = rcode.find('\n')
info['EXPIRED'] = ctime(float(rcode[6:index]))
index += 1
if rcode[index:].startswith('*FLAGS:'):
index += len('*FLAGS:') + 1
info['FLAGS'] = ord(rcode[index - 1])
prev = None
start = index
for k in ['HARDDISK', 'IFMAC', 'IFIPV4', 'DOMAIN', 'FIXKEY', 'CODE']:
index = rcode.find('*%s:' % k)
if index > -1:
if prev is not None:
info[prev] = rcode[start:index]
prev = k
start = index + len(k) + 2
info['CODE'] = rcode[start:]
i = info['CODE'].find(';')
if i > 0:
info['DATA'] = info['CODE'][i+1:]
info['CODE'] = info['CODE'][:i]
return info
def get_license_code():
return get_license_info()['CODE']
def get_user_data():
return get_license_info()['DATA']
def _match_features(patterns, s):
for pat in patterns:
if fnmatch(s, pat):
return True
def _gnu_get_libc_version():
try:
prototype = CFUNCTYPE(c_char_p)
ver = prototype(('gnu_get_libc_version', cdll.LoadLibrary('')))()
return ver.decode().split('.')
except Exception:
pass
def format_platform(platid=None):
if platid:
return os.path.normpath(platid)
plat = platform.system().lower()
mach = platform.machine().lower()
for alias, platlist in plat_table:
if _match_features(platlist, plat):
plat = alias
break
if plat == 'linux':
cname, cver = platform.libc_ver()
if cname == 'musl':
plat = 'musl'
elif cname == 'libc':
plat = 'android'
elif cname == 'glibc':
v = _gnu_get_libc_version()
if v and len(v) >= 2 and (int(v[0]) * 100 + int(v[1])) < 214:
plat = 'centos6'
for alias, archlist in arch_table:
if _match_features(archlist, mach):
mach = alias
break
if plat == 'windows' and mach == 'x86_64':
bitness = struct.calcsize('P'.encode()) * 8
if bitness == 32:
mach = 'x86'
return os.path.join(plat, mach)
# Load _pytransform library
def _load_library(path=None, is_runtime=0, platid=None, suffix='', advanced=0):
path = os.path.dirname(__file__) if path is None \
else os.path.normpath(path)
plat = platform.system().lower()
for alias, platlist in plat_table:
if _match_features(platlist, plat):
plat = alias
break
name = '_pytransform' + suffix
if plat == 'linux':
filename = os.path.abspath(os.path.join(path, name + '.so'))
elif plat in ('darwin', 'ios'):
filename = os.path.join(path, name + '.dylib')
elif plat == 'windows':
filename = os.path.join(path, name + '.dll')
elif plat in ('freebsd', 'poky'):
filename = os.path.join(path, name + '.so')
else:
filename = None
if platid is not None and os.path.isfile(platid):
filename = platid
elif platid is not None or not os.path.exists(filename) or not is_runtime:
libpath = platid if platid is not None and os.path.isabs(platid) else \
os.path.join(path, plat_path, format_platform(platid))
filename = os.path.join(libpath, os.path.basename(filename))
if filename is None:
raise PytransformError('Platform %s not supported' % plat)
if not os.path.exists(filename):
raise PytransformError('Could not find "%s"' % filename)
try:
m = cdll.LoadLibrary(filename)
except Exception as e:
if sys.flags.debug:
print('Load %s failed:\n%s' % (filename, e))
raise
# Removed from v4.6.1
# if plat == 'linux':
# m.set_option(-1, find_library('c').encode())
if not os.path.abspath('.') == os.path.abspath(path):
m.set_option(1, path.encode() if sys.version_info[0] == 3 else path)
elif (not is_runtime) and sys.platform.startswith('cygwin'):
path = os.environ['PYARMOR_CYGHOME']
m.set_option(1, path.encode() if sys.version_info[0] == 3 else path)
# Required from Python3.6
m.set_option(2, sys.byteorder.encode())
if sys.flags.debug:
m.set_option(3, c_char_p(1))
m.set_option(4, c_char_p(not is_runtime))
# Disable advanced mode by default
m.set_option(5, c_char_p(not advanced))
# Set suffix for private package
if suffix:
m.set_option(6, suffix.encode())
return m
def pyarmor_init(path=None, is_runtime=0, platid=None, suffix='', advanced=0):
global _pytransform
_pytransform = _load_library(path, is_runtime, platid, suffix, advanced)
return init_pytransform()
def pyarmor_runtime(path=None, suffix='', advanced=0):
if _pytransform is not None:
return
try:
pyarmor_init(path, is_runtime=1, suffix=suffix, advanced=advanced)
init_runtime()
except Exception as e:
if sys.flags.debug or hasattr(sys, '_catch_pyarmor'):
raise
sys.stderr.write("%s\n" % str(e))
sys.exit(1)
# ----------------------------------------------------------
# End of pytransform
# ----------------------------------------------------------
#
# Unused
#
@dllmethod
def generate_license_file(filename, priname, rcode, start=-1, count=1):
prototype = PYFUNCTYPE(c_int, c_char_p, c_char_p, c_char_p, c_int, c_int)
dlfunc = prototype(('generate_project_license_files', _pytransform))
return dlfunc(filename.encode(), priname.encode(), rcode.encode(),
start, count) if sys.version_info[0] == 3 \
else dlfunc(filename, priname, rcode, start, count)
#
# Not available from v5.6
#
def generate_capsule(licfile):
prikey, pubkey, prolic = _generate_project_capsule()
capkey, newkey = _generate_pytransform_key(licfile, pubkey)
return prikey, pubkey, capkey, newkey, prolic
@dllmethod
def _generate_project_capsule():
prototype = PYFUNCTYPE(py_object)
dlfunc = prototype(('generate_project_capsule', _pytransform))
return dlfunc()
@dllmethod
def _generate_pytransform_key(licfile, pubkey):
prototype = PYFUNCTYPE(py_object, c_char_p, py_object)
dlfunc = prototype(('generate_pytransform_key', _pytransform))
return dlfunc(licfile.encode() if sys.version_info[0] == 3 else licfile,
pubkey)
#
# Deprecated functions from v5.1
#
@dllmethod
def encrypt_project_files(proname, filelist, mode=0):
prototype = PYFUNCTYPE(c_int, c_char_p, py_object, c_int)
dlfunc = prototype(('encrypt_project_files', _pytransform))
return dlfunc(proname.encode(), filelist, mode)
def generate_project_capsule(licfile):
prikey, pubkey, prolic = _generate_project_capsule()
capkey = _encode_capsule_key_file(licfile)
return prikey, pubkey, capkey, prolic
@dllmethod
def _encode_capsule_key_file(licfile):
prototype = PYFUNCTYPE(py_object, c_char_p, c_char_p)
dlfunc = prototype(('encode_capsule_key_file', _pytransform))
return dlfunc(licfile.encode(), None)
@dllmethod
def encrypt_files(key, filelist, mode=0):
t_key = c_char * 32
prototype = PYFUNCTYPE(c_int, t_key, py_object, c_int)
dlfunc = prototype(('encrypt_files', _pytransform))
return dlfunc(t_key(*key), filelist, mode)
@dllmethod
def generate_module_key(pubname, key):
t_key = c_char * 32
prototype = PYFUNCTYPE(py_object, c_char_p, t_key, c_char_p)
dlfunc = prototype(('generate_module_key', _pytransform))
return dlfunc(pubname.encode(), t_key(*key), None)
#
# Compatible for PyArmor v3.0
#
@dllmethod
def old_init_runtime(systrace=0, sysprofile=1, threadtrace=0, threadprofile=1):
'''Only for old version, before PyArmor 3'''
pyarmor_init(is_runtime=1)
prototype = PYFUNCTYPE(c_int, c_int, c_int, c_int, c_int)
_init_runtime = prototype(('init_runtime', _pytransform))
return _init_runtime(systrace, sysprofile, threadtrace, threadprofile)
@dllmethod
def import_module(modname, filename):
'''Only for old version, before PyArmor 3'''
prototype = PYFUNCTYPE(py_object, c_char_p, c_char_p)
_import_module = prototype(('import_module', _pytransform))
return _import_module(modname.encode(), filename.encode())
@dllmethod
def exec_file(filename):
'''Only for old version, before PyArmor 3'''
prototype = PYFUNCTYPE(c_int, c_char_p)
_exec_file = prototype(('exec_file', _pytransform))
return _exec_file(filename.encode())
| 13,587 |
Python
| 27.07438 | 79 | 0.60499 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/config/extension.toml
|
[package]
# Semantic Versioning is used: https://semver.org/
version = "1.0.2"
# Lists people or organizations that are considered the "authors" of the package.
authors = ["Huang I Lan - Erks Virtual Studio"]
# The title and description fields are primarily for displaying extension info in UI
title = "stream-gpt"
description="Extension for NVIDIA Omniverse that provides a simple chatbot UI to record audio inputs, transcribe them, use transcriptions as chat GPT prompts, generate responses, convert responses to audio, and transmit them to Audio2Face via gRPC, while maintaining your original scripting style and modular system.."
# Path (relative to the root) or content of readme markdown file for UI.
readme = "docs/README.md"
# URL of the extension source repository.
repository = ""
# One of categories for UI.
category = "Chatbot"
# Keywords for the extension
keywords = ["Chat_GPT", "AI_assistant"]
# Location of change log file in target (final) folder of extension, relative to the root.
# More info on writing changelog: https://keepachangelog.com/en/1.0.0/
changelog="docs/CHANGELOG.md"
# Preview image and icon. Folder named "data" automatically goes in git lfs (see .gitattributes file).
# Preview image is shown in "Overview" of Extensions window. Screenshot of an extension might be a good preview image.
preview_image = "data/preview.png"
# Icon is shown in Extensions window, it is recommended to be square, of size 256x256.
icon = "data/icon.png"
# Use omni.ui to build simple UI
[dependencies]
"omni.kit.uiapp" = {}
[python.pipapi]
requirements = [
"pyaudio",
"openai",
"keyboard",
"soundfile",
"elevenlabs",
"pydub",
"gtts",
]
# Allow going to online index if package can't be found locally (not recommended)
use_online_index = true
# Main python module this extension provides, it will be publicly available as "import stream.gptchat".
[[python.module]]
name = "stream.gptchat"
[[test]]
# Extra dependencies only to be used during test run
dependencies = [
"omni.kit.ui_test" # UI testing extension
]
| 2,071 |
TOML
| 32.967213 | 318 | 0.740222 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/docs/CHANGELOG.md
|
# Changelog
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
## [1.0.2] - 2023-07-06
- Upgraded the UI to allow users to add API keys, Voice_ID, Voice_Models, and Instance Name directly from the UI, eliminating the need for hardcoding.
## [1.0.0] - 2023-04-13
- Initial version of extension UI template with a window.
| 355 |
Markdown
| 28.666664 | 150 | 0.715493 |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/docs/README.md
|
# Stream-GPT
Stream-GPT is an Omniverse Extension that uses OpenAI's GPT-3 model to create a virtual assistant. It allows users to interact with the assistant through both text and voice, and the assistant responds in kind. The extension uses OpenAI's Whisper ASR system to transcribe audio input and Eleven Labs' API to convert the assistant's text responses into audio.
## Getting Started
### Prerequisites
- OpenAI API key
- Eleven Labs API key
### SET UP
1. Set your OpenAI and Eleven Labs API keys, as well as the voice_id, model_id, and the Audio2Face's audioplayer's prim path (instance_name) in the extension's settings:
- Open the extension and click on the "Settings" button.
- Enter your OpenAI API key, Eleven Labs API key, voice_id, model_id and instance name in the corresponding fields. (A text file in the repository lists the available voice ids.)
## Usage
Once the application is running, you can interact with the virtual assistant through the UI. You can type your prompts into the text field and click on the "Send" button or use the "Record Audio" button to speak your prompts. The assistant will respond in the chat log and through your speakers.
You can also add a system to the GPT virtual assistant by typing it in the "System" field in the UI.
All interactions made with the extension are saved in a folder named "chat_logs" for future reference.
| 1,389 |
Markdown
| 46.931033 | 358 | 0.773938 |
ilanhuang/audio2face-streamgpt-public/UE5_install_files/extension.toml
|
[package]
version = "104.10.8"
title = "Audio2Face Exporter"
authors = ["NVIDIA"]
description="Custom Kit exporter for audio2face"
repository = ""
keywords = ["audio2face"]
category = "Animation"
readme = "docs/README.md"
changelog = "docs/CHANGELOG.md"
preview_image = "data/preview.png"
icon = "data/icon.png"
[dependencies]
"omni.ui" = {optional = true}
"omni.kit.window.filepicker" = {optional = true}
"omni.graph" = {}
"omni.graph.tools" = {}
"omni.kit.menu.utils" = {optional = true}
"omni.kit.window.viewport" = {optional = true}
"omni.kit.viewport.utility" = {optional = true}
"omni.client" = {}
"omni.anim.shared" = {}
"omni.deform.shared" = {}
"omni.audio2face.common" = {}
"omni.audio2face.ui.common" = {optional = true}
"omni.audio2face.tool" = {}
"omni.services.core"={}
[[python.module]]
name = "omni.audio2face.exporter"
[[test]]
dependencies = [
"omni.kit.renderer.core",
"omni.ui",
"omni.kit.window.filepicker",
"omni.kit.menu.utils",
"omni.kit.window.viewport",
"omni.kit.viewport.utility",
"omni.audio2face.ui.common"
]
timeout = 900
stdoutFailPatterns.exclude = [
"*failed to upload minidump*", # Exclude grahics leaks until fixed
]
[package.writeTarget]
kit = true
platform = true
[python.pipapi]
requirements = ['python-osc']
use_online_index = true
| 1,310 |
TOML
| 22.836363 | 71 | 0.681679 |
ilanhuang/audio2face-streamgpt-public/UE5_install_files/from pythonosc import udp_client.py
|
from pythonosc import udp_client
blend = ["eyeBlinkLeft", "eyeLookDownLeft", "eyeLookInLeft", "eyeLookOutLeft", "eyeLookUpLeft", "eyeSquintLeft", "eyeWideLeft", "eyeBlinkRight", "eyeLookDownRight", "eyeLookInRight", "eyeLookOutRight", "eyeLookUpRight", "eyeSquintRight", "eyeWideRight", "jawForward", "jawLeft", "jawRight", "jawOpen", "mouthClose", "mouthFunnel", "mouthPucker", "mouthLeft", "mouthRight", "mouthSmileLeft", "mouthSmileRight", "mouthFrownLeft", "mouthFrownRight", "mouthDimpleLeft", "mouthDimpleRight", "mouthStretchLeft", "mouthStretchRight", "mouthRollLower", "mouthRollUpper", "mouthShrugLower", "mouthShrugUpper", "mouthPressLeft", "mouthPressRight", "mouthLowerDownLeft", "mouthLowerDownRight", "mouthUpperUpLeft", "mouthUpperUpRight", "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft", "browOuterUpRight", "cheekPuff", "cheekSquintLeft", "cheekSquintRight", "noseSneerLeft", "noseSneerRight", "tongueOut"]
client = udp_client.SimpleUDPClient('127.0.0.1', 5008)
osc_array = outWeight.tolist()
count = 0
for i in osc_array:
client.send_message('/' + str(blend[count]), i)
count += 1
[python.pipapi]
requirements = ['python-osc']
use_online_index = true
| 1,267 |
Python
| 89.571422 | 910 | 0.708761 |
ilanhuang/audio2face-streamgpt-public/UE5_install_files/facsSolver.py
|
import numpy as np
from omni.audio2face.common import log_error, log_info, log_warn
from scipy.optimize import lsq_linear
from pythonosc import udp_client
class FacsSolver:
def __init__(self, neutral_mat, delta_mat):
self.weightRegulCoeff = 3.5
self.weightRegulCoeff_scale = 10.0
self.prevRegulCoeff = 3.5
self.prevRegulCoeff_scale = 100.0
self.sparseRegulCoeff = 1.0
self.sparseRegulCoeff_scale = 0.25
self.symmetryRegulCoeff = 1.0
self.symmetryRegulCoeff_scale = 10.0
self.neutral_mat = neutral_mat
self.delta_mat_orig = delta_mat
self.delta_mat = delta_mat
self.numPoses_orig = self.delta_mat_orig.shape[1]
self.numPoses = self.numPoses_orig
self.lb_orig = np.zeros(self.numPoses_orig)
self.ub_orig = self.lb_orig + 1.0
self.lb = self.lb_orig.copy()
self.ub = self.ub_orig.copy()
self.activeIdxMap = range(self.numPoses_orig)
self.activePosesBool = np.array([True for pi in range(self.numPoses_orig)], dtype=bool)
self.cancelPoseIndices = np.array([-1 for pi in range(self.numPoses_orig)], dtype=int)
self.symmetryPoseIndices = np.array([-1 for pi in range(self.numPoses_orig)], dtype=int)
self.cancelList = []
self.symmetryList = []
self.symShapeMat = np.zeros((self.numPoses_orig, self.numPoses_orig))
self.prevWeights = np.zeros(self.numPoses_orig)
# TODO L1 implementation
l1RegulMat = np.ones((1, self.numPoses))
self.l1RegulMat = np.dot(l1RegulMat.T, l1RegulMat)
self.compute_A_mat()
def compute_A_mat(self):
self.A = (
np.dot(self.delta_mat.T, self.delta_mat)
+ self.weightRegulCoeff * self.weightRegulCoeff_scale * np.eye(self.numPoses)
+ self.prevRegulCoeff * self.prevRegulCoeff_scale * np.eye(self.numPoses)
+ self.sparseRegulCoeff ** 2 * self.sparseRegulCoeff_scale * self.l1RegulMat
+ self.symmetryRegulCoeff * self.symmetryRegulCoeff_scale * self.symShapeMat
)
self.A = self.A.astype(np.float64)
def set_activePoses(self, activePosesBool):
self.activePosesBool = activePosesBool
# 1 - simple approach
# self.ub *= np.array(self.activePosesBool)
# 2- less computation way
self.delta_mat = self.delta_mat_orig[:, self.activePosesBool]
self.numPoses = self.delta_mat.shape[1]
self.lb = self.lb_orig[self.activePosesBool]
self.ub = self.ub_orig[self.activePosesBool]
self.prevWeights = np.zeros(self.numPoses)
self.activeIdxMap = []
cnt = 0
for idx in range(self.numPoses_orig):
if self.activePosesBool[idx]:
self.activeIdxMap.append(cnt)
cnt += 1
else:
self.activeIdxMap.append(-1)
# update L1 regularization mat
l1RegulMat = np.ones((1, self.numPoses))
self.l1RegulMat = np.dot(l1RegulMat.T, l1RegulMat)
# update cancel pair index
self.set_cancelPoses(self.cancelPoseIndices)
# update symmetry pair index
self.set_symmetryPoses(self.symmetryPoseIndices) # update self.A here
def set_cancelPoses(self, cancelPoseIndices):
self.cancelPoseIndices = cancelPoseIndices
# filter out cancel shapes
self.cancelList = []
maxIdx = np.max(self.cancelPoseIndices)
if maxIdx < 0:
return
for ci in range(maxIdx + 1):
cancelIndices = np.where(self.cancelPoseIndices == ci)[0]
if len(cancelIndices) > 2:
log_warn("There is more than 2 poses for a cancel index %d" % ci)
break
elif len(cancelIndices) < 2:
log_warn("There is less than 2 poses for a cancel index %d" % ci)
break
self.cancelList.append(cancelIndices)
# print ('cancel shape list', self.cancelList)
activeCancelList = []
for pIdx1, pIdx2 in self.cancelList:
if self.activePosesBool[pIdx1] and self.activePosesBool[pIdx2]:
activeCancelList.append([self.activeIdxMap[pIdx1], self.activeIdxMap[pIdx2]])
# print (activeCancelList)
self.cancelList = activeCancelList
def set_symmetryPoses(self, symmetryPoseIndices):
self.symmetryPoseIndices = symmetryPoseIndices
self.symmetryList = []
maxIdx = np.max(self.symmetryPoseIndices)
if maxIdx < 0:
self.symShapeMat = np.zeros((self.numPoses, self.numPoses))
else:
for ci in range(maxIdx + 1):
symmetryIndices = np.where(self.symmetryPoseIndices == ci)[0]
if len(symmetryIndices) > 2:
log_warn("There is more than 2 poses for a cancel index %d" % ci)
break
elif len(symmetryIndices) < 2:
log_warn("There is less than 2 poses for a cancel index %d" % ci)
break
self.symmetryList.append(symmetryIndices)
activeSymmetryList = []
for pIdx1, pIdx2 in self.symmetryList:
if self.activePosesBool[pIdx1] and self.activePosesBool[pIdx2]:
activeSymmetryList.append([self.activeIdxMap[pIdx1], self.activeIdxMap[pIdx2]])
self.symmetryList = activeSymmetryList
symShapeMat = np.zeros((len(self.symmetryList), self.numPoses))
for si, [pose1Idx, pose2Idx] in enumerate(self.symmetryList):
symShapeMat[si, pose1Idx] = 1.0
symShapeMat[si, pose2Idx] = -1.0
self.symShapeMat = np.dot(symShapeMat.T, symShapeMat)
self.compute_A_mat()
def set_l2_regularization(self, L2=3.5):
self.weightRegulCoeff = L2
self.compute_A_mat()
def set_tempo_regularization(self, temporal=3.5):
self.prevRegulCoeff = temporal
self.compute_A_mat()
def set_l1_regularization(self, L1=1.0):
self.sparseRegulCoeff = L1
self.compute_A_mat()
def set_symmetry_regularization(self, value=1.0):
self.symmetryRegulCoeff = value
self.compute_A_mat()
def computeFacsWeights(self, point_mat):
target_delta_mat = point_mat - self.neutral_mat
B = (
np.dot(self.delta_mat.T, target_delta_mat).flatten()
+ self.prevRegulCoeff * self.prevRegulCoeff_scale * self.prevWeights
)
B = B.astype(np.float64)
res = lsq_linear(self.A, B, bounds=(self.lb, self.ub), lsmr_tol="auto", verbose=0, method="bvls")
# print ('first pass:', res.x)
if len(self.cancelList) > 0:
# check cancelling poses -
ub = self.ub.copy()
lb = self.lb.copy()
for pose1Idx, pose2Idx in self.cancelList:
if res.x[pose1Idx] >= res.x[pose2Idx]:
ub[pose2Idx] = 1e-10
else:
ub[pose1Idx] = 1e-10
res = lsq_linear(self.A, B, bounds=(lb, ub), lsmr_tol="auto", verbose=0, method="bvls")
self.prevWeights = res.x
# print ('second pass:', res.x)
outWeight = np.zeros(self.numPoses_orig)
outWeight[self.activePosesBool] = res.x
outWeight = outWeight * (outWeight > 1.0e-9)
# print (outWeight)
blend = ["eyeBlinkLeft", "eyeLookDownLeft", "eyeLookInLeft", "eyeLookOutLeft", "eyeLookUpLeft", "eyeSquintLeft", "eyeWideLeft", "eyeBlinkRight", "eyeLookDownRight", "eyeLookInRight", "eyeLookOutRight", "eyeLookUpRight", "eyeSquintRight", "eyeWideRight", "jawForward", "jawLeft", "jawRight", "jawOpen", "mouthClose", "mouthFunnel", "mouthPucker", "mouthLeft", "mouthRight", "mouthSmileLeft", "mouthSmileRight", "mouthFrownLeft", "mouthFrownRight", "mouthDimpleLeft", "mouthDimpleRight", "mouthStretchLeft", "mouthStretchRight", "mouthRollLower", "mouthRollUpper", "mouthShrugLower", "mouthShrugUpper", "mouthPressLeft", "mouthPressRight", "mouthLowerDownLeft", "mouthLowerDownRight", "mouthUpperUpLeft", "mouthUpperUpRight", "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft", "browOuterUpRight", "cheekPuff", "cheekSquintLeft", "cheekSquintRight", "noseSneerLeft", "noseSneerRight", "tongueOut"]
try:
client = udp_client.SimpleUDPClient('127.0.0.1', 27008)
osc_array = outWeight.tolist()
count = 0
for i in osc_array:
client.send_message('/' + str(blend[count]), i)
count += 1
except Exception as e:
log_error(f"Error in OSC communication: {e}")
| 8,708 |
Python
| 41.276699 | 918 | 0.614378 |
matthias-research/omni.fun/README.md
|
# omni.fun
A simple plugin for nvidia's omniverse
| 50 |
Markdown
| 15.999995 | 38 | 0.78 |
matthias-research/omni.fun/exts/omni.fun/config/extension.toml
|
[package]
# Semantic Versioning is used: https://semver.org/
version = "0.1.0"
authors = ["Ten Minute Physics"]
title = "Fun"
description="Ten Minute Physics domniverse extension"
readme = "docs/README.md"
repository="https://github.com/matthias-research/omni.fun"
category = "sim"
keywords = ["simulation"]
changelog="docs/CHANGELOG.md"
preview_image = "data/preview.png"
icon = "data/icon.png"
# Watch the .ogn files for hot reloading (only works for Python files)
[fswatcher.patterns]
include = ["*.ogn", "*.py"]
exclude = ["Ogn*Database.py", "*/ogn*"]
[dependencies]
"omni.kit.test" = {}
"omni.kit.menu.utils" = {}
"omni.timeline" = {}
"omni.usd" = {}
# Main python module this extension provides, it will be publicly available as "import omni.play".
[[python.module]]
name = "omni.fun"
| 797 |
TOML
| 24.741935 | 98 | 0.697616 |
matthias-research/omni.fun/exts/omni.fun/config/extension.gen.toml
|
[package]
[package.target]
python = ["cp37"]
[package.publish]
date = 1635811509
kitVersion = "103.0+master.0.75457a67.gitlab"
| 127 |
TOML
| 17.285712 | 45 | 0.732283 |
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/sim.py
|
# Copyright 2022 Matthias Müller - Ten Minute Physics,
# https://www.youtube.com/c/TenMinutePhysics
# www.matthiasMueller.info/tenMinutePhysics
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import numpy as np
import math
import warp as wp
from pxr import Usd, UsdGeom, Gf, Sdf
from .usdutils import *
gravity = -9.81
@wp.struct
class SimData:
sphere_radius: wp.array(dtype=float)
sphere_mass: wp.array(dtype=float)
sphere_pos: wp.array(dtype=wp.vec3)
sphere_rot: wp.array(dtype=wp.quat)
sphere_lin_vel: wp.array(dtype=wp.vec3)
sphere_ang_vel: wp.array(dtype=wp.vec3)
sphere_pos_corr: wp.array(dtype=wp.vec3)
sphere_lin_corr: wp.array(dtype=wp.vec3)
sphere_ang_corr: wp.array(dtype=wp.vec3)
sphere_num_corr: wp.array(dtype=int)
sphere_lower_bounds: wp.array(dtype=wp.vec3)
sphere_upper_bounds: wp.array(dtype=wp.vec3)
sphere_bvh_id: wp.uint64
obj_mesh_id: wp.uint64
obj_tri_ids: wp.array(dtype=int)
obj_orig_pos: wp.array(dtype=wp.vec3)
obj_pos: wp.array(dtype=wp.vec3)
obj_prev_pos: wp.array(dtype=wp.vec3)
obj_transforms: wp.array(dtype=wp.mat44)
obj_pos_transform_nr: wp.array(dtype=int)
@wp.kernel
def dev_integrate(
dt: float,
gravity: wp.vec3,
bounds_margin: float,
sim: SimData):
sphere_nr = wp.tid()
pos = sim.sphere_pos[sphere_nr]
lin_vel = sim.sphere_lin_vel[sphere_nr]
rot = sim.sphere_rot[sphere_nr]
ang_vel = sim.sphere_ang_vel[sphere_nr]
# move state forward in time
lin_vel = lin_vel + gravity * dt
pos = pos + lin_vel * dt
qt = wp.quat(ang_vel[0], ang_vel[1], ang_vel[2], 0.0) * (dt * 0.5)
rot = wp.normalize(rot + qt * rot)
sim.sphere_pos[sphere_nr] = pos
sim.sphere_lin_vel[sphere_nr] = lin_vel
sim.sphere_rot[sphere_nr] = rot
# compute bounding box for bvh
pred_pos = pos + lin_vel * dt
lower = wp.vec3(wp.min(pos[0], pred_pos[0]), wp.min(pos[1], pred_pos[1]), wp.min(pos[2], pred_pos[2]))
upper = wp.vec3(wp.max(pos[0], pred_pos[0]), wp.max(pos[1], pred_pos[1]), wp.max(pos[2], pred_pos[2]))
m = bounds_margin + sim.sphere_radius[sphere_nr]
sim.sphere_lower_bounds[sphere_nr] = lower - wp.vec3(m, m, m)
sim.sphere_upper_bounds[sphere_nr] = upper + wp.vec3(m, m, m)
@wp.kernel
def dev_handle_sphere_sphere_collisions(
restitution: float,
sim: SimData):
sphere0 = wp.tid()
eps = 0.00001
pos0 = sim.sphere_pos[sphere0]
radius0 = sim.sphere_radius[sphere0]
m0 = sim.sphere_mass[sphere0]
w0 = 1.0 / (m0 + eps)
vel0 = sim.lin_vel[sphere0]
ang0 = sim.ang_vel[sphere0]
lower = sim.sphere_lower_bounds[sphere0]
upper = sim.sphere_upper_bounds[sphere0]
query = wp.bvh_query_aabb(sim.spheres_bvh_id, lower, upper)
sphere1 = int(0)
while (wp.bvh_query_next(query, sphere1)):
if sphere1 < sphere0: # handle each pair only once!
pos1 = sim.sphere_pos[sphere1]
radius1 = sim.sphere_radius[sphere1]
m1 = sim.sphere_mass[sphere1]
w1 = 1.0 / (m1 + eps)
vel1 = sim.lin_vel[sphere1]
ang1 = sim.ang_vel[sphere1]
min_dist = radius0 + radius1
pos_normal = wp.normalize(pos1 - pos0)
dist = wp.dot(pos_normal, pos1 - pos0)
if dist < min_dist:
# bounce
wp.atomic_add(sim.sphere_num_corr, sphere0, 1)
wp.atomic_add(sim.sphere_num_corr, sphere1, 1)
pos_corr = pos_normal / (w0 + w1) * (min_dist - dist + eps)
wp.atomic_add(sim.pos_corr, sphere0, -w0 * pos_corr)
wp.atomic_add(sim.pos_corr, sphere1, +w1 * pos_corr)
vn0 = wp.dot(vel0, pos_normal)
vn1 = wp.dot(vel1, pos_normal)
new_vn0 = (m0 * vn0 + m1 * vn1 - m1 * (vn0 - vn1) * restitution) / (m0 + m1)
new_vn1 = (m0 * vn0 + m1 * vn1 - m0 * (vn1 - vn0) * restitution) / (m0 + m1)
new_vn0 = wp.min(0.0, new_vn0)
new_vn1 = wp.max(0.0, new_vn1)
lin_corr0 = pos_normal * (new_vn0 - vn0)
lin_corr1 = pos_normal * (new_vn1 - vn1)
wp.atomic_add(sim.sphere_lin_corr, sphere0, lin_corr0)
wp.atomic_add(sim.sphere_lin_corr, sphere1, lin_corr1)
vel0 = vel0 + lin_corr0
vel1 = vel1 + lin_corr1
# friction
ang_normal = wp.normalize(ang0 * m0 + ang1 * m1)
ang_normal = wp.nomralize(ang_normal - pos_normal * wp.dot(pos_normal, ang_normal))
vt0 = wp.dot(vel0, wp.cross(ang_normal, pos_normal))
vt1 = wp.dot(vel1, wp.cross(ang_normal, pos_normal))
omega0 = wp.dot(ang0, ang_normal)
omega1 = wp.dot(ang1, ang_normal)
# v0 + (o0 - do*w0) * r0 = v1 + (o1 + do*w1) * r1
domega = (vt1 + omega1 * radius1 - vt0 - omega0 * radius0) / (radius0 * w0 + radius1 * w1)
ang_corr0 = ang_normal * (omega0 - domega * w0) - ang0
ang_corr1 = ang_normal * (omega1 + domega * w1) - ang1
ang0 = ang0 + ang_corr0
ang1 = ang1 + ang_corr1
wp.atomic_add(sim.sphere_ang_corr, sphere0, ang_corr0)
wp.atomic_add(sim.sphere_ang_corr, sphere1, ang_corr1)
@wp.kernel
def dev_update_obj_pos(sim: SimData):
id = wp.tid()
trans_nr = sim.pos_transform_nr[id]
pos = sim.obj_transforms[trans_nr] * sim.orig_pos[id]
sim.prev_pos[id] = sim.pos[id]
sim.pos[id] = pos
@wp.kernel
def dev_handle_sphere_obj_collisions(
dt: float,
restitution: float,
sim: SimData):
sphere_nr = wp.tid()
pos = sim.sphere_pos[sphere_nr]
radius = sim.sphere_radius[sphere_nr]
vel = sim.lin_vel[sphere_nr]
ang = sim.ang_vel[sphere_nr]
inside = float(0.0)
face_nr = int(0)
u = float(0.0)
v = float(0.0)
found = wp.mesh_query_point(sim.obj_mesh_id, pos, radius, inside, face_nr, u, v)
if not found:
return
id0 = sim.obj_tri_ids[3 * face_nr]
id1 = sim.obj_tri_ids[3 * face_nr + 1]
id2 = sim.obj_tri_ids[3 * face_nr + 2]
p0 = sim.obj_pos[id0]
p1 = sim.obj_pos[id1]
p2 = sim.obj_pos[id2]
closest = u * p0 + v * p1 + (1.0 - u - v) * p2
pos_normal = wp.normalize(pos - closest)
dist = wp.dot(pos_normal, pos - closest)
if dist >= radius:
return
sim.sphere_pos[sphere_nr] = pos - pos_normal * (radius - dist)
v0 = (p0 - sim.mesh_prev_points[id0]) / dt
v1 = (p1 - sim.mesh_prev_points[id1]) / dt
v2 = (p2 - sim.mesh_prev_points[id2]) / dt
v_mesh = v0 + u * (v1 - v0) + v * (v2 - v0)
v_mesh = u * v0 + v * v1 + (1.0 - u - v) * v2
vn_sphere = wp.dot(sim.sphere_lin_vel[sphere_nr], pos_normal)
vn_mesh = wp.dot(v_mesh, pos_normal)
new_vn = wp.min(vn_mesh - (vn_sphere - vn_mesh) * restitution, 0.0)
sim.sphere_lin_vel[sphere_nr] = v + pos_normal * (new_vn - vn_sphere)
# friction
ang_normal = wp.normalize(ang)
ang_normal = wp.nomralize(ang - pos_normal * wp.dot(pos_normal, ang_normal))
vt = wp.dot(vel, wp.cross(ang_normal, pos_normal))
omega = wp.dot(ang, ang_normal)
# vel + (omega + do) * r = v_mesh
domega = (vt + omega * radius - v_mesh) / radius
ang = ang + ang_normal * (omega - domega)
sim.sphere_ang_vel[sphere_nr] = ang
@wp.kernel
def dev_apply_corrections(
sim: SimData):
sphere_nr = wp.tid()
num = sim.sphere_num_corr[sphere_nr]
if num > 0:
s = 1.0 / float(num)
sim.sphere_pos[sphere_nr] += sim.sphere_pos_corr[sphere_nr] * s
sim.sphere_lin_vel[sphere_nr] += sim.sphere_lin_corr[sphere_nr] * s
sim.sphere_ang_vel[sphere_nr] += sim.sphere_ang_corr[sphere_nr] * s
class Sim():
def __init__(self, stage):
self.paused = True
self.stage = stage
self.device = 'cuda'
self.prim_cache = UsdGeom.XformCache()
self.dev_sim_data = SimData()
self.host_sim_data = SimData()
self.spheres_bvh = None
self.obj_mesh = None
self.sphere_usd_meshes = []
self.obj_usd_prims = []
self.obj_usd_transforms = []
self.initialized = False
self.time_step = 1.0 / 30.0
self.num_substeps = 5
self.restitution = 0.1
self.jacobi_scale = 0.25
self.num_spheres = 0
self.frame_nr = 0
def init(self):
if not self.stage:
return
obj_pos = []
obj_pos_transform_nr = []
obj_tri_ids = []
sphere_pos = []
sphere_radius = []
sphere_inv_mass = []
self.sphere_usd_meshes = []
self.sphere_usd_transforms = []
s = 4.0 / 3.0 * 3.141592
print("traversing stage")
for prim in self.stage.Traverse():
if prim.GetTypeName() == "Mesh":
mesh = UsdGeom.Mesh(prim)
name = mesh.GetName()
points = mesh.GetPointsAttr().Get(0.0)
if name.find("sphere") != 0 or name.find("Sphere") != 0:
# create a sphere
trans_mat, trans_t = get_global_transform(prim, 0.0, False)
trans_points = points @ trans_mat
min = np.min(trans_points, axis = 0)
max = np.max(trans_points, axis = 0)
radius = np.max(max - min) * 0.5
sphere_radius.append(radius)
sphere_pos.append(trans_t)
mass = s * radius * radius * radius
sphere_inv_mass.append(1.0 / mass)
clone = clone_prim(self.stage, prim)
self.sphere_usd_meshes.append(UsdGeom.Mesh(clone))
self.sphere_usd_transforms.append(clone.Get)
else:
obj_nr = len(self.obj_usd_prims)
self.object_usd_prims.append(prim)
# create obstacle points
first_pos = len(obj_pos)
for i in range(len(mesh_points)):
p = mesh_points[i]
obj_pos.append(wp.vec3(*p))
obj_pos_transform_nr.append(obj_nr)
# create obstacle triangles
mesh_poly_indices = mesh.GetFaceVertexIndicesAttr().Get(0.0)
mesh_face_sizes = mesh.GetFaceVertexCountsAttr().Get(0.0)
mesh_points = np.array(points)
first_index = 0
for i in range(len(mesh_face_sizes)):
face_size = mesh_face_sizes[i]
for j in range(1, face_size-1):
obj_tri_ids.append(first_pos + mesh_poly_indices[first_index])
obj_tri_ids.append(first_pos + mesh_poly_indices[first_index + j])
obj_tri_ids.append(first_pos + mesh_poly_indices[first_index + j + 1])
first_index += face_size
# create objects warp buffers
if len(obj_pos) > 0:
self.dev_sim_data.obj_pos = wp.array(obj_pos, dtype=wp.vec3, device=self.device)
self.dev_sim_data.pbj_prev_pos = wp.array(obj_pos, dtype=wp.vec3, device=self.device)
self.dev_sim_data.obj_tri_ids = wp.array(obj_tri_ids, dtype=int, device=self.device)
self.obj_mesh = wp.Mesh(self.dev_sim_data.obj_pos, self.dev_sim_data.obj_tri_ids)
self.dev_sim_data.obj_mesh_id = self.obj_mesh.id
num_objs = len(self.object_usd_prims)
mat = wp.mat44()
self.obj_transforms = np.array([mat] * num_objs)
self.dev_sim_data.obj_transforms = wp.zeros(shape=(num_objs), dtype=wp.mat44, device=self.device)
# create sphere warp buffers
self.num_spheres = len(sphere_pos)
if self.num_spheres > 0:
self.dev_sim_data.sphere_radius = wp.array(sphere_radius, dtype=float, device=self.device)
self.dev_sim_data.sphere_pos = wp.array(sphere_pos, dtype=wp.vec3, device=self.device)
self.dev_sim_data.sphere_quat = wp.zeros(shape=(self.num_spheres), dtype=wp.quat, device=self.device)
self.dev_sim_data.sphere_vel = wp.zeros(shape=(self.num_spheres), dtype=wp.vec3, device=self.device)
self.dev_sim_data.sphere_omega = wp.zeros(shape=(self.num_spheres), dtype=wp.vec3, device=self.device)
self.dev_sim_data.sphere_lower_bounds = wp.zeros(shape=(self.num_spheres), dtype=wp.vec3, device=self.device)
self.dev_sim_data.sphere_upper_bounds = wp.zeros(shape=(self.num_spheres), dtype=wp.vec3, device=self.device)
self.host_sim_data.sphere_pos = wp.array(sphere_pos, dtype=wp.vec3, device="cpu")
self.host_sim_data.sphere_quat = wp.zeros(shape=(self.num_spheres), dtype=wp.quat, device="cpu")
# zero time step to initialize sphere bounds
wp.launch(kernel = self.dev_integrate,
inputs = [0.0, wp.vec3(0.0, 0.0, 0.0), self.dev_sim_data],
dim = self.num_spheres, device=self.device)
self.sphere_bvh = wp.Bvh(self.dev_sim_data.sphere_lower_bounds, self.dev_sim_data.sphere_upper_bounds)
self.dev_sim_data.sphere_bvh_id = self.sphere_bvh.id
def simulate(self):
if self.paused:
return
self.frame_nr += 1
print("simulating", self.frame_nr)
return
# update objects
for i in range(len(self.object_usd_prims)):
self.obj_transforms[i] = get_global_transform(self.object_usd_prims[i], 0.0, True)
wp.copy(self.dev_sim_data.obj_transforms, wp.array(self.obj_transforms, dtype=wp.array(wp.mat44), copy=False, device="cpu"))
wp.launch(kernel = dev_update_obj_pos,
inputs = [self.dev_sim_data],
dim = len(self.dev_sim_data.obj_pos), device=self.device)
self.obj_mesh.refit()
#simulate spheres
wp.launch(kernel = dev_integrate,
inputs = [self.time_step, self.gravity, self.dev_sim_data],
dim = self.num_spheres, device=self.device)
self.sphere_bvh.refit()
self.dev_sim_data.sphere_pos_corr.zero_()
self.dev_sim_data.sphere_lin_corr.zero_()
self.dev_sim_data.sphere_ang_corr.zero_()
self.dev_sim_data.sphere_num_corr.zero_()
wp.launch(kernel = dev_handle_sphere_sphere_collisions,
inputs = [self.restitution, self.dev_sim_data],
dim = self.num_spheres, device=self.device)
wp.launch(kernel = dev_apply_corrections,
inputs = [self.dev_sim_data],
dim = self.num_spheres, device=self.device)
wp.launch(kernel = dev_handle_sphere_obj_collisions,
inputs = [self.time_step, self.restitution, self.dev_sim_data],
dim = self.num_spheres, device=self.device)
# update stage
wp.copy(self.host_sim_data.sphere_pos, self.dev_sim_data.sphere_pos)
wp.copy(self.host_sim_data.sphere_quat, self.dev_sim_data.sphere_quat)
pos = self.host_sim_data.numpy()
quat = self.host_sim_data.numpy()
for i in range(self.num_spheres):
set_transform(self.sphere_usd_meshes, pos[i], quat[i])
def reset(self):
hide_clones(self.stage)
self.paused = True
| 16,580 |
Python
| 34.734914 | 462 | 0.5769 |
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/extension.py
|
# Copyright 2022 Matthias Müller - Ten Minute Physics,
# https://www.youtube.com/c/TenMinutePhysics
# www.matthiasMueller.info/tenMinutePhysics
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import omni.ext
import os
import omni.usd
from omni import ui
from pxr import Usd
from .controls import ControlsWindow
from .sim import Sim
EXAMPLES_PATH = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../../data/scenes"))
class OmniFunExtension(omni.ext.IExt):
def on_startup(self, ext_id):
print("fun on_startup")
setattr(self, "controls", None)
setattr(self, "sim", None)
stage = omni.usd.get_context().get_stage()
self.sim = Sim(stage)
self.sim.init()
editor_menu = omni.kit.ui.get_editor_menu()
self.menu_items = []
if editor_menu:
self.controls_menu = editor_menu.add_item(
f"Window/Fun/Controls",
lambda _, value: self.show_controls(value),
toggle=True, value=False
)
self.menu_items.append(editor_menu.add_item(
f"Window/Fun/SimpleScene",
lambda _, value: self.load_example("simple.usd"),
toggle=False, value=False
))
# self.show_controls(True)
# set callbacks
self.update_event_stream = omni.kit.app.get_app_interface().get_update_event_stream()
self.stage_event_sub = omni.usd.get_context().get_stage_event_stream().create_subscription_to_pop(self.on_event)
def on_shutdown(self):
print("fun on_shutdown")
self.menu_items = None
self.update_event_stream = None
self.stage_event_sub = None
if self.sim:
self.sim.reset()
self.show_controls(False)
def init_callback(self, state):
if state:
stage = omni.usd.get_context().get_stage()
if self.sim:
self.sim = Sim(stage)
self.update_event_sub = self.update_event_stream.create_subscription_to_pop(self.on_update)
else:
if self.sim:
self.sim.reset()
self.sim = None
def play_callback(self, state):
if self.sim:
self.sim.paused = not state
def on_update(self, dt):
if self.sim:
self.sim.simulate()
def set_controls_menu(self, visible):
omni.kit.ui.get_editor_menu().set_value(f"Window/Fun/Controls", visible)
def show_controls(self, is_visible):
if is_visible:
if not hasattr(self, "controls"):
setattr(self, "controls", None)
if self.controls is None:
self.controls = ControlsWindow(
init_callback=self.init_callback,
play_callback=self.play_callback)
self.controls.create_window(lambda visible: self.set_controls_menu(visible))
self.controls.show_window()
else:
self.controls.show_window()
elif self.controls:
self.controls.destroy_window()
self.controls = None
def on_event(self, event):
if event.type == int(omni.usd.StageEventType.CLOSED):
if self.sim:
self.sim.reset()
if event.type == int(omni.usd.StageEventType.OPENED):
if self.sim:
self.sim.init()
def load_example(self, scene_name):
def new_stage():
stage_path = os.path.normpath(os.path.join(EXAMPLES_PATH, scene_name))
omni.usd.get_context().open_stage(stage_path)
if self.sim:
self.sim.init()
omni.kit.window.file.prompt_if_unsaved_stage(new_stage)
| 4,788 |
Python
| 35.007519 | 462 | 0.618421 |
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/gpu.py
|
# Copyright 2022 Matthias Müller - Ten Minute Physics,
# https://www.youtube.com/c/TenMinutePhysics
# www.matthiasMueller.info/tenMinutePhysics
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import numpy as np
import warp as wp
@wp.struct
class SimData:
spheres_pos: wp.array(dtype=wp.vec3)
spheres_prev_pos: wp.array(dtype=wp.vec3)
spheres_pos_corr: wp.array(dtype=wp.vec3)
spheres_vel: wp.array(dtype=wp.vec3)
spheres_radius: wp.array(dtype=float)
spheres_inv_mass: wp.array(dtype=float)
mesh_id: wp.uint64
mesh_verts: wp.array(dtype=wp.vec3)
mesh_tri_ids: wp.array(dtype=int)
@wp.func
def closest_point_on_triangle(
p: wp.vec3, p0: wp.vec3, p1: wp.vec3, p2: wp.vec3):
e0 = p1 - p0
e1 = p2 - p0
tmp = p0 - p
a = wp.dot(e0, e0)
b = wp.dot(e0, e1)
c = wp.dot(e1, e1)
d = wp.dot(e0, tmp)
e = wp.dot(e1, tmp)
coords = wp.vec3(b*e - c*d, b*d - a*e, a*c - b*b)
x = 0.0
y = 0.0
z = 0.0
if coords[0] <= 0.0:
if c != 0.0:
y = -e / c
elif coords[1] <= 0.0:
if a != 0.0:
x = -d / a
elif coords[0] + coords[1] > coords[2]:
den = a + c - b - b
num = c + e - b - d
if den != 0.0:
x = num / den
y = 1.0 - x
else:
if coords[2] != 0.0:
x = coords[0] / coords[2]
y = coords[1] / coords[2]
x = wp.clamp(x, 0.0, 1.0)
y = wp.clamp(y, 0.0, 1.0)
bary = wp.vec3(1.0 - x - y, x, y)
return bary
@wp.kernel
def dev_integrate_spheres(
dt: float,
gravity: wp.vec3,
data: SimData):
sphere_nr = wp.tid()
w = data.spheres_inv_mass[sphere_nr]
if w > 0.0:
data.spheres_vel[sphere_nr] += gravity * dt
data.spheres_prev_pos[sphere_nr] = data.spheres_pos[sphere_nr]
data.spheres_pos[sphere_nr] += data.spheres_vel[sphere_nr] * dt
def integrate_spheres(num_spheres: int, dt: float, gravity: wp.vec3, data: SimData, device):
wp.launch(kernel = dev_integrate_spheres,
inputs = [dt, gravity, data], dim=num_spheres, device=device)
@wp.kernel
def dev_update_spheres(
dt: float,
jacobi_scale: float,
data: SimData):
sphere_nr = wp.tid()
w = data.spheres_inv_mass[sphere_nr]
if w > 0.0:
data.spheres_pos[sphere_nr] = data.spheres_pos[sphere_nr] + jacobi_scale * data.spheres_pos_corr
data.spheres_vel[sphere_nr] = (data.spheres_pos[sphere_nr] - data.spheres_prev_pos[sphere_nr]) / dt
def update_spheres(num_spheres: int, dt: float, jacobi_scale: float, data: SimData, device):
wp.launch(kernel = dev_update_spheres,
inputs = [dt, jacobi_scale, data], dim=num_spheres, device=device)
@wp.kernel
def dev_solve_mesh_collisions(
data: SimData):
sphere_nr = wp.tid()
w = data.spheres_inv_mass[sphere_nr]
if w > 0.0:
pos = data.spheres_pos[sphere_nr]
r = data.spheres_radius[sphere_nr]
# query bounding volume hierarchy
bounds_lower = pos - wp.vec3(r, r, r)
bounds_upper = pos + wp.vec3(r, r, r)
query = wp.mesh_query_aabb(data.mesh_id, bounds_lower, bounds_upper)
tri_nr = int(0)
while (wp.mesh_query_aabb_next(query, tri_nr)):
p0 = data.mesh_verts[data.mesh_tri_ids[3*tri_nr]]
p1 = data.mesh_verts[data.mesh_tri_ids[3*tri_nr + 1]]
p2 = data.mesh_verts[data.mesh_tri_ids[3*tri_nr + 2]]
hit = closest_point_on_triangle(pos, p0, p1, p2)
n = pos - hit
d = wp.length(n)
if d < r:
n = wp.normalize(n)
data.spheres_pos[sphere_nr] = data.spheres_pos[sphere_nr] + n * (r - d)
def solve_mesh_collisions(num_spheres: int, data: SimData, device):
wp.launch(kernel = dev_solve_mesh_collisions,
inputs = [data], dim=num_spheres, device=device)
@wp.kernel
def dev_solve_sphere_collisions(
num_spheres: int,
data: SimData):
i0 = wp.tid()
p0 = data.spheres_pos[i0]
r0 = data.spheres_radius[i0]
w0 = data.spheres_inv_mass[i0]
# simpe O(n^2) collision detection
for i1 in range(num_spheres):
if i1 > i0:
p1 = data.spheres_pos[i1]
r1 = data.spheres_radius[i1]
w1 = data.spheres_inv_mass[i1]
w = w0 + w1
if w > 0.0:
n = p1 - p0
d = wp.length(n)
n = wp.noramlize(n)
if d < r0 + r1:
corr = n * (r0 + r1 - d) / w
data.spheres_corr[i0] = data.spheres_corr[i0] - corr * w0
data.spheres_corr[i1] = data.spheres_corr[i1] - corr * w0
def solve_sphere_collisions(num_spheres: int, data: SimData, device):
wp.launch(kernel = dev_solve_sphere_collisions,
inputs = [num_spheres, data], dim=num_spheres, device=device)
| 6,034 |
Python
| 32.342541 | 462 | 0.586841 |
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/controls.py
|
import carb
import omni.ui
import omni.usd
import omni.kit.app
from pxr import Usd, Sdf
from .sim import gravity
class ControlsWindow:
def __init__(self, init_callback=None, play_callback=None):
self._window = None
self.buttons = [
[None, init_callback, False, "Init", "Reset"],
[None, play_callback, False, "Play", "Pause"]]
def __bool__(self):
return self._window is not None
def create_window(self, visibility_changed_fn):
window_flags = omni.ui.WINDOW_FLAGS_NO_SCROLLBAR
self._window = omni.ui.Window("Fun Controls", flags=window_flags, width=400, height=400, dockPreference=omni.ui.DockPreference.RIGHT_TOP)
self._window.set_visibility_changed_fn(visibility_changed_fn)
self.rebuild_ui()
def show_window(self):
self._window.visible = True
def hide_window(self):
self._window.visible = False
def destroy_window(self):
if self._window:
self._window.visible = False
self._window.destroy()
self._window = None
def button_pressed(self, button):
state = not button[2]
button[2] = state
button[0].text = button[4] if state else button[3]
button[1](state)
def set_parameter(self, param_name, val):
if param_name == "gravity":
gravity = val
def rebuild_ui(self):
ui = omni.ui
row_height = 20
v_spacing = 10
h_spacing = 20
if self._window and self._window.visible:
with self._window.frame:
with ui.VStack(spacing=v_spacing, padding=50):
with ui.HStack(spacing=h_spacing, height=row_height):
for button in self.buttons:
button[0] = ui.Button(
button[3], width=100, height=15, margin=10,
clicked_fn=lambda button=button: self.button_pressed(button))
with ui.HStack(spacing=h_spacing, height=row_height):
ui.Label("Gravity", width=ui.Percent(50), height=10, name="Gravity")
slider = ui.FloatSlider(min=0.0,max=10.0, width=ui.Percent(50))
slider.model.add_value_changed_fn(
lambda val, param_name="gravity": self.set_parameter(param_name, val.get_value_as_float()))
| 2,487 |
Python
| 29.341463 | 145 | 0.554483 |
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/usdutils.py
|
from pxr import Usd, UsdGeom, Gf, UsdShade
import numpy as np
import warp as wp
prim_cache = None
def get_global_transform(prim, time, return_mat44):
if prim_cache is None:
prim_cache = UsdGeom.XformCache()
prim_cache.SetTime(time)
m = prim_cache.GetLocalToWorldTransform(prim)
if return_mat44:
return wp.mat44(
m[0][0], m[1][0], m[2][0], m[3][0],
m[0][1], m[1][1], m[2][1], m[3][1],
m[0][2], m[1][2], m[2][2], m[3][2],
m[0][3], m[1][3], m[2][3], m[3][3])
else:
A = np.array([[m[0][0], m[0][1], m[0][2]], [m[1][0], m[1][1], m[1][2]], [m[2][0], m[2][1], m[2][2]]])
b = np.array([m[3][0], m[3][1], m[3][2]])
return A, b
def set_transform(mesh, trans, quat):
usd_mat = Gf.Matrix4d()
usd_mat.SetRotateOnly(Gf.Quatd(*quat))
usd_mat.SetTranslateOnly(Gf.Vec3d(*trans))
xform = UsdGeom.Xform(mesh)
xform.GetOrderedXformOps()[0].Set(usd_mat)
def clone_primvar(self, prim, prim_clone, name, time=0.0):
try:
attr = UsdGeom.Primvar(prim.GetAttribute(name))
prim_clone.CreatePrimvar(name, attr.GetTypeName(), attr.GetInterpolation()).Set(attr.Get(time))
except:
pass
def clone_prim(stage, prim):
vis = prim.GetAttribute("visibility")
if vis:
vis.Set("invisible")
mesh = UsdGeom.Mesh(prim)
clone_prim_path = '/' + str(prim.GetPath()).replace("/", "_") + '_clone'
UsdGeom.Mesh.Define(stage, clone_prim_path)
prim_clone = UsdGeom.Mesh(stage.GetPrimAtPath(clone_prim_path))
mesh_clone = UsdGeom.Mesh(prim_clone)
stage.GetPrimAtPath(clone_prim_path).SetActive(True)
xform = UsdGeom.Xform(mesh_clone)
xform.ClearXformOpOrder()
xform.AddXformOp(UsdGeom.XformOp.TypeTransform)
trans_mat, trans_t = get_global_transform(prim, 0.0, True)
trans_points = mesh.GetPointsAttr().Get(0.0) @ trans_mat + trans_t
normal_mat = np.array([\
trans_mat[0,:] / np.linalg.norm(trans_mat[0,:]), \
trans_mat[1,:] / np.linalg.norm(trans_mat[1,:]), \
trans_mat[2,:] / np.linalg.norm(trans_mat[2,:])])
trans_normals = mesh.GetNormalsAttr().Get(0.0) @ normal_mat
mesh_clone.GetPointsAttr().Set(trans_points)
mesh_clone.GetNormalsAttr().Set(trans_normals)
mesh_clone.SetNormalsInterpolation(mesh.GetNormalsInterpolation())
mesh_clone.GetFaceVertexIndicesAttr().Set(mesh.GetFaceVertexIndicesAttr().Get(0.0))
mesh_clone.GetFaceVertexCountsAttr().Set(mesh.GetFaceVertexCountsAttr().Get(0.0))
mesh_clone.GetCornerIndicesAttr().Set(mesh.GetCornerIndicesAttr().Get(0.0))
mesh_clone.GetCornerSharpnessesAttr().Set(mesh.GetCornerSharpnessesAttr().Get(0.0))
mesh_clone.GetCreaseIndicesAttr().Set(mesh.GetCreaseIndicesAttr().Get(0.0))
mesh_clone.GetCreaseLengthsAttr().Set(mesh.GetCreaseLengthsAttr().Get(0.0))
mesh_clone.GetCreaseSharpnessesAttr().Set(mesh.GetCreaseSharpnessesAttr().Get(0.0))
mesh_clone.GetSubdivisionSchemeAttr().Set(mesh.GetSubdivisionSchemeAttr().Get(0.0))
mesh_clone.GetInterpolateBoundaryAttr().Set(mesh.GetInterpolateBoundaryAttr().Get(0.0))
mesh_clone.GetFaceVaryingLinearInterpolationAttr().Set(mesh.GetFaceVaryingLinearInterpolationAttr().Get(0.0))
mesh_clone.GetTriangleSubdivisionRuleAttr().Set(mesh.GetTriangleSubdivisionRuleAttr().Get(0.0))
mesh_clone.GetHoleIndicesAttr().Set(mesh.GetHoleIndicesAttr().Get(0.0))
for attr in prim.GetAttributes():
type = str(attr.GetTypeName())
if type.find("texCoord") >= 0:
clone_primvar(prim, prim_clone, attr.GetName())
try:
mat = UsdShade.MaterialBindingAPI(prim).GetDirectBinding().GetMaterial()
UsdShade.MaterialBindingAPI(prim_clone).Bind(mat)
except:
pass
return prim_clone
def hide_clones(stage):
if stage is None:
return
for prim in stage.Traverse():
if str(prim.GetName()).find("_clone") >= 0:
prim.SetActive(False)
else:
vis = prim.GetAttribute("visibility")
if vis:
vis.Set("inherited")
| 4,122 |
Python
| 34.543103 | 113 | 0.643862 |
matthias-research/omni.fun/exts/omni.fun/docs/CHANGELOG.md
|
# CHANGELOG
## [0.1.0] - 2022-08-15
- Initial publish for alpha testing
| 77 |
Markdown
| 7.666666 | 35 | 0.636364 |
matthias-research/omni.fun/exts/omni.fun/docs/README.md
|
# Play [omni.ten]
A simple plugin from ten minute physics.
## Documentation
None
## Source Code
None
| 109 |
Markdown
| 6.333333 | 40 | 0.688073 |
qcr/benchbot_sim_omni/pip_package_fix.py
|
import subprocess
import sys
print("HACK FIX FOR BROKEN PACKAGES")
def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])
def uninstall(package):
subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "--yes", package])
uninstall("click")
install("click")
uninstall("typing-extensions")
install("typing-extensions")
| 375 |
Python
| 27.923075 | 87 | 0.717333 |
qcr/benchbot_sim_omni/run.py
|
import flask
import numpy as np
import os
import signal
from builtins import print as bprint
from gevent import event, pywsgi, signal
from pathlib import Path
from spatialmath import SE3, UnitQuaternion
print("STARTING RUN.PY IN BENCHBOT_SIM_OMNI")
DEFAULT_POSE = np.array([1, 0, 0, 0, 0, 0, 0])
DIRTY_EPSILON_DIST = 1
DIRTY_EPSILON_YAW = 2
DIRTY_FILE = '/tmp/benchbot_dirty'
MAP_PRIM_PATH = '/env'
ROBOT_NAME = 'robot'
ROBOT_PRIM_PATH = '/%s' % ROBOT_NAME
ROBOT_COMPONENTS = {
'clock': '/ROS_Clock',
'diff_base': '%s/ROS_DifferentialBase' % ROBOT_PRIM_PATH,
'lidar': '%s/ROS_Lidar' % ROBOT_PRIM_PATH,
'rgbd': '%s/ROS_Camera_Stereo_Left' % ROBOT_PRIM_PATH,
'tf_sensors': '%s/ROS_Carter_Sensors_Broadcaster' % ROBOT_PRIM_PATH,
'tf': '%s/ROS_Carter_Broadcaster' % ROBOT_PRIM_PATH
}
UPDATE_DELAY_SECS = 3.0
def _dc_tf_to_SE3(tf):
r = np.array(tf.r)
return SE3(np.array(tf.p)) * UnitQuaternion(r[3], r[0:3]).SE3()
def _to_SE3(pose):
return SE3(pose[4::]) * UnitQuaternion(pose[0], pose[1:4]).SE3()
def disable_component(prop_path):
from omni.kit.commands import execute
from pxr import Sdf
print("DISABLING '%s.enabled'" % prop_path)
execute("ChangeProperty",
prop_path=Sdf.Path("%s.enabled" % prop_path),
value=False,
prev=None)
def print(*args, **kwargs):
bprint(*args, **kwargs, flush=True)
class SimulatorDaemon:
def __init__(self, port):
self.address = 'localhost:%s' % port
self.inst = None
self.sim = None
self.sim_i = 0
self.sim_collided = False
self.sim_dirty = False
self.map_usd = None
self.robot_usd = None
self.start_pose = None
self._map_usd = None
self._robot_usd = None
self._start_pose = None
self._dc = None
self._robot = None
self._robot_dc = None
def check_dirty(self):
delta = (_to_SE3(self.start_pose).inv() *
_dc_tf_to_SE3(self._dc.get_rigid_body_pose(self._robot_dc)))
return (np.linalg.norm(delta.t[0:2]) > DIRTY_EPSILON_DIST or
np.abs(delta.rpy(unit='deg')[2]) > DIRTY_EPSILON_YAW)
def check_collided(self):
return False
def open_usd(self):
# Bail early if we can't act
if self.inst is None:
print("No simulator running. "
"Stored environment USD, but not opening.")
return
if self.map_usd is None:
print("No environment USD selected. Returning.")
return
# Imports must go after bail early checks pass as they throw errors
# when called in an "inappropriate state" (no idea what that
# corresponds to...)
from omni.isaac.core.utils.stage import open_stage, update_stage
# Stop simulation if running
self.stop_simulation()
# Update the map
if self.map_usd != self._map_usd:
self._dc = None
self._start_pose = None
self._robot = None
self._robot_dc = None
self._robot_usd = None
open_stage(usd_path=self.map_usd)
update_stage()
self._map_usd = self.map_usd
else:
print("Skipping map load; already loaded.")
# Attempt to replace the robot
self.place_robot()
def place_robot(self):
# Bail early if we can't act
if self.inst is None:
print("No simulator running. "
"Stored robot USD & pose, but not opening.")
return
if self.robot_usd is None:
print("No robot USD selected. Returning.")
return
# Imports must go after bail early checks pass as they throw errors
# when called in an "inappropriate state" (no idea what that
# corresponds to...)
from omni.isaac.core.robots import Robot
from omni.isaac.core.utils.stage import (add_reference_to_stage,
update_stage)
# Stop simulation if running
self.stop_simulation()
# Add robot to the environment at the requested pose
p = DEFAULT_POSE if self.start_pose is None else self.start_pose
if self.robot_usd != self._robot_usd:
add_reference_to_stage(usd_path=self.robot_usd,
prim_path=ROBOT_PRIM_PATH)
self._robot = Robot(prim_path=ROBOT_PRIM_PATH, name=ROBOT_NAME)
update_stage()
self._robot_usd = self.robot_usd
else:
print("Skipping robot load; already loaded.")
if (p != self._start_pose).any():
self._robot.set_world_pose(position=p[4::],
orientation=p[:4])
update_stage()
self._start_pose = p
else:
print("Skipping robot move; already at requested pose.")
# Disable auto-publishing of all robot components (we'll manually
# publish at varying frequencies instead)
for p in ROBOT_COMPONENTS.values():
disable_component(p)
# Attempt to start the simulation
self.start_simulation()
def run(self):
f = flask.Flask('benchbot_sim_omni')
@f.route('/', methods=['GET'])
def __hello():
return flask.jsonify("Hello, I am the Omniverse Sim Daemon")
@f.route('/open_environment', methods=['POST'])
def __open_env():
r = flask.request.json
if 'environment' in r:
self.map_usd = r['environment']
self.open_usd()
return flask.jsonify({})
@f.route('/place_robot', methods=['POST'])
def __place_robot():
r = flask.request.json
if 'robot' in r:
self.robot_usd = r['robot']
if 'start_pose' in r:
# Probably should be regexing...
self.start_pose = np.array([
float(x.strip()) for x in r['start_pose'].replace(
'[', '').replace(']', '').split(',')
])
self.place_robot()
return flask.jsonify({})
@f.route('/restart_sim', methods=['POST'])
def __restart_sim():
self.stop_simulation()
self.start_simulation()
return flask.jsonify({})
@f.route('/start', methods=['POST'])
def __start_inst():
self.start_instance()
return flask.jsonify({})
@f.route('/start_sim', methods=['POST'])
def __start_sim():
self.start_simulation()
return flask.jsonify({})
@f.route('/started', methods=['GET'])
def __started():
# TODO note there is a race condition (returns true before a /start
# job finishes)
return flask.jsonify({'started': self.inst is not None})
@f.route('/stop_sim', methods=['POST'])
def __stop_sim():
self.stop_simulation()
return flask.jsonify({})
# Start long-running server
server = pywsgi.WSGIServer(self.address, f)
evt = event.Event()
for s in [signal.SIGINT, signal.SIGQUIT, signal.SIGTERM]:
signal.signal(s, lambda n, frame: evt.set())
server.start()
while not evt.is_set():
evt.wait(0.001)
self.tick_simulator()
# Cleanup
self.stop_instance()
def start_instance(self):
print("STARTING INSTANCE!!")
if not self.inst is None:
print("Instance already running. Please /stop first.")
return
env = {} if self.map_usd is None else {"open_usd": self.map_usd}
from omni.isaac.kit import SimulationApp
# Start the simulator
self.inst = SimulationApp({
"renderer": "RayTracedLighting",
"headless": False,
**env
})
# Import all required modules, and configure application
from omni.isaac.core.utils.extensions import enable_extension
enable_extension("omni.isaac.ros_bridge")
# Attempt to place the robot if we had a map
if env:
self.place_robot()
def start_simulation(self):
if self.sim is not None:
self.stop_simulation()
if self.inst is None or self.map_usd is None or self.robot_usd is None:
print("Can't start simulation. Missing some required state.")
return
from omni.isaac.core import SimulationContext
self.sim_i = 0
self.sim_collided = False
self.sim_dirty = False
self.sim = SimulationContext()
self.sim.play()
from omni.isaac.dynamic_control import _dynamic_control
self._dc = _dynamic_control.acquire_dynamic_control_interface()
self._robot_dc = self._dc.get_articulation_root_body(
self._dc.get_object(ROBOT_PRIM_PATH))
def stop_instance(self):
if self.inst is None:
print("No instance is running to stop.")
return
self.stop_simulation()
self.inst.close()
self.inst = None
def stop_simulation(self):
if self.sim is None:
print("Skipping. No running simulation to stop")
return
if self.inst is None:
print("Skipping. No running simulator found.")
return
self.sim.stop()
self.sim = None # TODO maybe could reuse with more guarding logic?
def tick_simulator(self):
# Tick simulator steps. Does less now than in 2021.2.1 due to new action graph
if self.inst is None:
return
if self.sim is None:
self.inst.update()
return
self.sim.step()
# Tick at 10Hz CHECK DIRTY
if self.sim_i % 6 == 0:
if not self.sim_dirty:
self.sim_dirty = self.check_dirty()
if self.sim_dirty:
Path(DIRTY_FILE).touch()
# Tick at 1Hz CHECK COLLIDED
if self.sim_i % 60 == 0:
self.sim_collided = self.check_collided()
self.sim_i += 1
if __name__ == '__main__':
print("inside run.py __main__")
sd = SimulatorDaemon(port=os.environ.get('PORT'))
sd.run()
| 10,394 |
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| 30.122754 | 86 | 0.554166 |
qcr/benchbot_sim_omni/README.md
|
**NOTE: this software is part of the BenchBot software stack. For a complete working BenchBot system, please install the BenchBot software stack by following the instructions [here](https://github.com/qcr/benchbot).**
# BenchBot Simulator for Omniverse-powered Isaac Sim
[](http://benchbot.org)
[](https://qcr.github.io)
[](./LICENSE.txt)
The BenchBot Simulator bindings for Omniverse-powered Isaac Sim provide a simple `run` script that makes powerful photorealistic simulations available in ROS, and controllable through a basic HTTP API.
Through a single script, this package provides:
- creation of, and management of, a running [Omniverse-powered Isaac Sim](https://developer.nvidia.com/isaac-sim) instance
- a simple HTTP API for programmatically loading environments, placing robots, and controlling simulations
- ROS topics for common mobile robot topics: transforms, odometry, command velocity, RGB images, depth images, laser scans
The configuration is currently Carter specific, but could easily be extended in the future to target other robots. Also all simulator interactions come from a simple Python script that could be used as a starting point for more complex projects.
## Installation
**Please see the note at the top of the page; the BenchBot ecosystem contains much more than just these bindings**
There is no physical installation step for these bindings, simply install Isaac Sim, clone this repository, and install Python dependencies:
1. Follow the instructions on the [NVIDIA Isaac Sim documentation site](https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html) for [installing Isaac Sim](https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/install_basic.html)
2. Clone this repository:
```
git clone https://github.com/qcr/benchbot_sim_omni
```
3. Install declared Python dependencies:
```
pip install -r ./.custom_deps
```
## Running and using the simulator bindings
Simulator bindings are run through the `run` script, which will start a blank instance of the simulator with the HTTP API bound on port 10001 by default:
```
./run
```
A simulation in environment `my_env.usd`, with robot `my_robot.usd` at position `(0,0,0)` and quaternion (w,x,y,z) `(1,0,0,0)` can then be started by the following two CURL commands:
```
curl localhost:10001/open_environment \
-H "Content-Type: application/json" \
-d '{"environment": "my_env.usd"}'
curl localhost:10001/place_robot \
-H "Content-Type: application/json" \
-d '{"robot": "my_robot.usd", "start_pose": "1,0,0,0,0,0,0"}'
```
Full documentation of configuration options and HTTP API routes is available through the script's `--help` flag:
```
user@pc:~/benchbot_sim_omni/$ ./run --help
run -- BenchBot simulator daemon for Omniverse-powered Isaac Sim
USAGE:
Start the daemon:
run
run -p /path/to/python.sh -P 8080
Print this help information:
run [-h|--help]
OPTION DETAILS:
-h, --help
Show this help menu.
-P,--port
Port the daemon will bind to. Default port of 10001 will
be used if not provided.
-p,--python-sh-path
Path to the 'python.sh' environment script included with your Isaac
Sim installation. Will recursively search for the script in the
current directory if this flag is not provided.
INTERACTING WITH THE DAEMON:
The daemon responds to HTTP requests.
Following routes are supported:
/
Returns a greeting message
/open_environment
Opens a new environment, with USD path specified via 'environment'
data field
/place_robot
Places a robot at a specified pose. Robot USD is specified via
'robot' data field, and start pose via a comma-separated 7-tuple in
the 'pose' field. Format for pose is:
quat_w,quat_x,quat_y,quat_z,pos_x,pos_y,pos_z
/start
Starts a simulator instance (happens by default when first opened)
/stop
Stops a currently running simulator instance if it exists
/restart
Restarts the entire simulator (generally not needed)
FURTHER DETAILS:
Please contact the authors of BenchBot for support or to report bugs:
[email protected]
```
## Using this simulator with the BenchBot Robot Controller
The [BenchBot Robot Controller](https://github.com/qcr/benchbot_robot_controller) is a wrapping ROS / HTTP hybrid script that manages running robots and their required subprocesses. It is ultimately fed configurations from [BenchBot add-ons](https://github.com/qcr/benchbot_addons) through our [BenchBot supervisor](https://github.com/qcr/benchbot_supervisor) service.
These details are superfluous to these BenchBot simulator bindings, but are provided here for context. This context may be helpful if looking for examples of more complex interactions with the simulator bindings. For example, the `carter_sim_omni.yaml` file in the [robots_sim_omni](https://github.com/benchbot-addons/robots_sim_omni) BenchBot add-on may be of interest.
| 5,559 |
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| 41.442748 | 370 | 0.729808 |
AndrePatri/OmniRoboGym/pyproject.toml
|
[build-system]
requires = ["flit_core >=2,<4"]
build-backend = "flit_core.buildapi"
[project]
name = "omni_robo_gym"
version = "0.1.0"
description = ""
authors = [{name = "AndrePatri", email = "[email protected]"}]
readme = "README.md"
license = {file = "LICENSE"}
| 276 |
TOML
| 24.181816 | 73 | 0.666667 |
AndrePatri/OmniRoboGym/omnirobogym_mamba_env.yml
|
name: omni_robo_gym_isaac2023.1.1
channels:
- defaults
- pytorch
- nvidia
- conda-forge
- omnia
- robostack-staging
- AndrePatri
dependencies:
- python=3.10
- pip
- pytorch == 2.0.1
- torchvision
- torchaudio
- cuda-toolkit=11.7
- compilers
- cmake
- make
- quaternion
- anaconda-client
- yaml-cpp
- pybind11
- gtest
- eigen3
- posix_ipc=1.0.4
- rospkg=1.5.0
- ros-humble-xacro
- empy
- python-devtools
- perf_sleep
- pyqt
- pyqtgraph
- pip:
- flit
- nvidia-cublas-cu11==11.11.3.6
- gym==0.26.2
- gymnasium==0.28.1
- stable_baselines3[extra]==2.0.0a10
- box2d-py
- tensorboard
- tensorboard-plugin-wit
- protobuf
- matplotlib
- scipy
- urdf-parser-py
- multiprocess
| 789 |
YAML
| 15.122449 | 40 | 0.593156 |
AndrePatri/OmniRoboGym/meta.yaml
|
package:
name: omni_robo_gym
version: 0.1.0
source:
path: . # Path to the directory containing your built distribution artifacts
requirements:
build:
- python=3.7
- flit
run:
- python=3.7
about:
home: https://github.com/AndrePatri/CoClusterBridge
license: MIT
summary: Some custom implementations of Tasks and Gyms for Omniverse Isaac Sim based on Gymnasium. Easy URDF and SRDF import/cloning and simulation configuration exploiting Omniverse API
extra:
recipe-maintainers:
- AndrePatri
| 537 |
YAML
| 20.519999 | 189 | 0.722533 |
AndrePatri/OmniRoboGym/README.md
|
# OmniRoboGym
Wrapper on top of [Omniverse Isaac Sim](https://developer.nvidia.com/isaac-sim), a photo-realistic GPU accelerated simulator from NVIDIA.
The aim of the package is to a easy interface for loading floating-base robots and their configuration from URDF and SRDF into IsaacSim, cloning them with Isaac Sim API and, in general, simplify simulation setup for RL-based robotics applications.
| 402 |
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| 79.599984 | 248 | 0.80597 |
AndrePatri/OmniRoboGym/LICENSE.md
|
GNU GENERAL PUBLIC LICENSE
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of promoting the sharing and reuse of software generally.
NO WARRANTY
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
REPAIR OR CORRECTION.
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, the commands you use may
be called something other than `show w' and `show c'; they could even be
mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the program, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.
| 18,092 |
Markdown
| 52.214706 | 77 | 0.785541 |
AndrePatri/OmniRoboGym/omni_robo_gym/envs/isaac_env.py
|
# Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
from omni.isaac.kit import SimulationApp
import os
import signal
import carb
import torch
from abc import ABC, abstractmethod
from typing import Union, Tuple, Dict
from SharsorIPCpp.PySharsorIPC import VLevel
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
import numpy as np
# import gymnasium as gym
# class IsaacSimEnv(gym.Env):
class IsaacSimEnv():
def __init__(
self,
headless: bool,
sim_device: int = 0,
enable_livestream: bool = False,
enable_viewport: bool = False,
debug = False
) -> None:
""" Initializes RL and task parameters.
Args:
headless (bool): Whether to run training headless.
sim_device (int): GPU device ID for running physics simulation. Defaults to 0.
enable_livestream (bool): Whether to enable running with livestream.
enable_viewport (bool): Whether to enable rendering in headless mode.
"""
self.debug = debug
experience = f'{os.environ["EXP_PATH"]}/omni.isaac.sim.python.omnirobogym.kit'
# experience = ""
if headless:
info = f"Will run in headless mode."
Journal.log(self.__class__.__name__,
"__init__",
info,
LogType.STAT,
throw_when_excep = True)
if enable_livestream:
experience = ""
elif enable_viewport:
exception = f"Using viewport is not supported yet."
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
else:
experience = f'{os.environ["EXP_PATH"]}/omni.isaac.sim.python.omnirobogym.headless.kit'
# experience = f'{os.environ["EXP_PATH"]}/omni.isaac.sim.python.gym.headless.kit'
self._simulation_app = SimulationApp({"headless": headless,
"physics_gpu": sim_device},
experience=experience)
info = "Using IsaacSim experience file @ " + experience
Journal.log(self.__class__.__name__,
"__init__",
info,
LogType.STAT,
throw_when_excep = True)
# carb.settings.get_settings().set("/persistent/omnihydra/useSceneGraphInstancing", True)
if enable_livestream:
info = "Livestream enabled"
Journal.log(self.__class__.__name__,
"__init__",
info,
LogType.STAT,
throw_when_excep = True)
from omni.isaac.core.utils.extensions import enable_extension
self._simulation_app.set_setting("/app/livestream/enabled", True)
self._simulation_app.set_setting("/app/window/drawMouse", True)
self._simulation_app.set_setting("/app/livestream/proto", "ws")
self._simulation_app.set_setting("/app/livestream/websocket/framerate_limit", 120)
self._simulation_app.set_setting("/ngx/enabled", False)
enable_extension("omni.kit.livestream.native")
enable_extension("omni.services.streaming.manager")
# handle ctrl+c event
signal.signal(signal.SIGINT, self.signal_handler)
self._render = not headless or enable_livestream or enable_viewport
self._record = False
self.step_counter = 0 # step counter
self._world = None
self.metadata = None
self.gpu_pipeline_enabled = False
def signal_handler(self, sig, frame):
self.close()
def set_task(self,
task,
backend="torch",
sim_params=None,
init_sim=True) -> None:
""" Creates a World object and adds Task to World.
Initializes and registers task to the environment interface.
Triggers task start-up.
Args:
task (RLTask): The task to register to the env.
backend (str): Backend to use for task. Can be "numpy" or "torch". Defaults to "numpy".
sim_params (dict): Simulation parameters for physics settings. Defaults to None.
init_sim (Optional[bool]): Automatically starts simulation. Defaults to True.
"""
from omni.isaac.core.world import World
# parse device based on sim_param settings
if sim_params and "sim_device" in sim_params:
device = sim_params["sim_device"]
else:
device = "cpu"
physics_device_id = carb.settings.get_settings().get_as_int("/physics/cudaDevice")
gpu_id = 0 if physics_device_id < 0 else physics_device_id
if sim_params and "use_gpu_pipeline" in sim_params:
# GPU pipeline must use GPU simulation
if sim_params["use_gpu_pipeline"]:
device = "cuda:" + str(gpu_id)
elif sim_params and "use_gpu" in sim_params:
if sim_params["use_gpu"]:
device = "cuda:" + str(gpu_id)
self.gpu_pipeline_enabled = sim_params["use_gpu_pipeline"]
info = "Using device: " + str(device)
Journal.log(self.__class__.__name__,
"__init__",
info,
LogType.STAT,
throw_when_excep = True)
if (sim_params is None):
info = f"No sim params provided -> defaults will be used."
Journal.log(self.__class__.__name__,
"set_task",
info,
LogType.STAT,
throw_when_excep = True)
sim_params = {}
# defaults for integration and rendering dt
if not("physics_dt" in sim_params):
sim_params["physics_dt"] = 1.0/60.0
dt = sim_params["physics_dt"]
info = f"Using default integration_dt of {dt} s."
Journal.log(self.__class__.__name__,
"set_task",
info,
LogType.STAT,
throw_when_excep = True)
if not("rendering_dt" in sim_params):
sim_params["rendering_dt"] = sim_params["physics_dt"]
dt = sim_params["rendering_dt"]
info = f"Using default rendering_dt of {dt} s."
Journal.log(self.__class__.__name__,
"set_task",
info,
LogType.STAT,
throw_when_excep = True)
self._world = World(
stage_units_in_meters=1.0,
physics_dt=sim_params["physics_dt"],
rendering_dt=sim_params["rendering_dt"], # dt between rendering steps. Note: rendering means rendering a frame of
# the current application and not only rendering a frame to the viewports/ cameras.
# So UI elements of Isaac Sim will be refereshed with this dt as well if running non-headless
backend=backend,
device=str(device),
physics_prim_path="/physicsScene",
set_defaults = False, # set to True to use the defaults settings [physics_dt = 1.0/ 60.0,
# stage units in meters = 0.01 (i.e in cms), rendering_dt = 1.0 / 60.0, gravity = -9.81 m / s
# ccd_enabled, stabilization_enabled, gpu dynamics turned off,
# broadcast type is MBP, solver type is TGS]
sim_params=sim_params
)
self._sim_params = sim_params
big_info = "[World] Creating task " + task.name + "\n" + \
"use_gpu_pipeline: " + str(sim_params["use_gpu_pipeline"]) + "\n" + \
"device: " + str(device) + "\n" +\
"backend: " + str(backend) + "\n" +\
"integration_dt: " + str(sim_params["physics_dt"]) + "\n" + \
"rendering_dt: " + str(sim_params["rendering_dt"]) + "\n" \
Journal.log(self.__class__.__name__,
"set_task",
big_info,
LogType.STAT,
throw_when_excep = True)
## we get the physics context to expose additional low-level ##
# settings of the simulation
self._physics_context = self._world.get_physics_context()
self._physics_scene_path = self._physics_context.prim_path
self._physics_context.enable_gpu_dynamics(True)
self._physics_context.enable_stablization(True)
self._physics_scene_prim = self._physics_context.get_current_physics_scene_prim()
self._solver_type = self._physics_context.get_solver_type()
# we set parameters, depending on sim_params dict
if "gpu_max_rigid_contact_count" in sim_params:
self._physics_context.set_gpu_max_rigid_contact_count(sim_params["gpu_max_rigid_contact_count"])
if "gpu_max_rigid_patch_count" in sim_params:
self._physics_context.set_gpu_max_rigid_patch_count(sim_params["gpu_max_rigid_patch_count"])
if "gpu_found_lost_pairs_capacity" in sim_params:
self._physics_context.set_gpu_found_lost_pairs_capacity(sim_params["gpu_found_lost_pairs_capacity"])
if "gpu_found_lost_aggregate_pairs_capacity" in sim_params:
self._physics_context.set_gpu_found_lost_aggregate_pairs_capacity(sim_params["gpu_found_lost_aggregate_pairs_capacity"])
if "gpu_total_aggregate_pairs_capacity" in sim_params:
self._physics_context.set_gpu_total_aggregate_pairs_capacity(sim_params["gpu_total_aggregate_pairs_capacity"])
if "gpu_max_soft_body_contacts" in sim_params:
self._physics_context.set_gpu_max_soft_body_contacts(sim_params["gpu_max_soft_body_contacts"])
if "gpu_max_particle_contacts" in sim_params:
self._physics_context.set_gpu_max_particle_contacts(sim_params["gpu_max_particle_contacts"])
if "gpu_heap_capacity" in sim_params:
self._physics_context.set_gpu_heap_capacity(sim_params["gpu_heap_capacity"])
if "gpu_temp_buffer_capacity" in sim_params:
self._physics_context.set_gpu_temp_buffer_capacity(sim_params["gpu_temp_buffer_capacity"])
if "gpu_max_num_partitions" in sim_params:
self._physics_context.set_gpu_max_num_partitions(sim_params["gpu_max_num_partitions"])
# overwriting defaults
# self._physics_context.set_gpu_max_rigid_contact_count(2 * self._physics_context.get_gpu_max_rigid_contact_count())
# self._physics_context.set_gpu_max_rigid_patch_count(2 * self._physics_context.get_gpu_max_rigid_patch_count())
# self._physics_context.set_gpu_found_lost_pairs_capacity(2 * self._physics_context.get_gpu_found_lost_pairs_capacity())
# self._physics_context.set_gpu_found_lost_aggregate_pairs_capacity(20 * self._physics_context.get_gpu_found_lost_aggregate_pairs_capacity())
# self._physics_context.set_gpu_total_aggregate_pairs_capacity(20 * self._physics_context.get_gpu_total_aggregate_pairs_capacity())
# self._physics_context.set_gpu_heap_capacity(2 * self._physics_context.get_gpu_heap_capacity())
# self._physics_context.set_gpu_temp_buffer_capacity(20 * self._physics_context.get_gpu_heap_capacity())
# self._physics_context.set_gpu_max_num_partitions(20 * self._physics_context.get_gpu_temp_buffer_capacity())
# GPU buffers
self._gpu_max_rigid_contact_count = self._physics_context.get_gpu_max_rigid_contact_count()
self._gpu_max_rigid_patch_count = self._physics_context.get_gpu_max_rigid_patch_count()
self._gpu_found_lost_pairs_capacity = self._physics_context.get_gpu_found_lost_pairs_capacity()
self._gpu_found_lost_aggregate_pairs_capacity = self._physics_context.get_gpu_found_lost_aggregate_pairs_capacity()
self._gpu_total_aggregate_pairs_capacity = self._physics_context.get_gpu_total_aggregate_pairs_capacity()
self._gpu_max_soft_body_contacts = self._physics_context.get_gpu_max_soft_body_contacts()
self._gpu_max_particle_contacts = self._physics_context.get_gpu_max_particle_contacts()
self._gpu_heap_capacity = self._physics_context.get_gpu_heap_capacity()
self._gpu_temp_buffer_capacity = self._physics_context.get_gpu_temp_buffer_capacity()
# self._gpu_max_num_partitions = physics_context.get_gpu_max_num_partitions() # BROKEN->method does not exist
big_info2 = "[physics context]:" + "\n" + \
"gpu_max_rigid_contact_count: " + str(self._gpu_max_rigid_contact_count) + "\n" + \
"gpu_max_rigid_patch_count: " + str(self._gpu_max_rigid_patch_count) + "\n" + \
"gpu_found_lost_pairs_capacity: " + str(self._gpu_found_lost_pairs_capacity) + "\n" + \
"gpu_found_lost_aggregate_pairs_capacity: " + str(self._gpu_found_lost_aggregate_pairs_capacity) + "\n" + \
"gpu_total_aggregate_pairs_capacity: " + str(self._gpu_total_aggregate_pairs_capacity) + "\n" + \
"gpu_max_soft_body_contacts: " + str(self._gpu_max_soft_body_contacts) + "\n" + \
"gpu_max_particle_contacts: " + str(self._gpu_max_particle_contacts) + "\n" + \
"gpu_heap_capacity: " + str(self._gpu_heap_capacity) + "\n" + \
"gpu_temp_buffer_capacity: " + str(self._gpu_temp_buffer_capacity)
Journal.log(self.__class__.__name__,
"set_task",
big_info2,
LogType.STAT,
throw_when_excep = True)
self._scene = self._world.scene
from omni.usd import get_context
self._stage = get_context().get_stage()
from pxr import UsdLux, Sdf, Gf, UsdPhysics, PhysicsSchemaTools
# add lighting
distantLight = UsdLux.DistantLight.Define(self._stage, Sdf.Path("/World/DistantLight"))
distantLight.CreateIntensityAttr(500)
self._world._current_tasks = dict() # resets registered tasks
self._task = task
self._task.set_world(self._world)
self._task.configure_scene()
self._world.add_task(self._task)
self._num_envs = self._task.num_envs
if sim_params and "enable_viewport" in sim_params:
self._render = sim_params["enable_viewport"]
Journal.log(self.__class__.__name__,
"set_task",
"[render]: " + str(self._render),
LogType.STAT,
throw_when_excep = True)
# if init_sim:
# self._world.reset() # after the first reset we get get all quantities
# # from the scene
# self._task.post_initialization_steps() # performs initializations
# # steps after the fisrt world reset was called
def render(self, mode="human") -> None:
""" Step the renderer.
Args:
mode (str): Select mode of rendering based on OpenAI environments.
"""
if mode == "human":
self._world.render()
return None
elif mode == "rgb_array":
# check if viewport is enabled -- if not, then complain because we won't get any data
if not self._render or not self._record:
exception = f"Cannot render '{mode}' when rendering is not enabled. Please check the provided" + \
"arguments to the environment class at initialization."
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
# obtain the rgb data
rgb_data = self._rgb_annotator.get_data()
# convert to numpy array
rgb_data = np.frombuffer(rgb_data, dtype=np.uint8).reshape(*rgb_data.shape)
# return the rgb data
return rgb_data[:, :, :3]
else:
# gym.Env.render(self, mode=mode)
return None
def create_viewport_render_product(self, resolution=(1280, 720)):
"""Create a render product of the viewport for rendering."""
try:
import omni.replicator.core as rep
# create render product
self._render_product = rep.create.render_product("/OmniverseKit_Persp", resolution)
# create rgb annotator -- used to read data from the render product
self._rgb_annotator = rep.AnnotatorRegistry.get_annotator("rgb", device="cpu")
self._rgb_annotator.attach([self._render_product])
self._record = True
except Exception as e:
carb.log_info("omni.replicator.core could not be imported. Skipping creation of render product.")
carb.log_info(str(e))
def close(self) -> None:
""" Closes simulation.
"""
if self._simulation_app.is_running():
self._simulation_app.close()
return
@abstractmethod
def step(self,
actions = None):
""" Basic implementation for stepping simulation"""
pass
@abstractmethod
def reset(self):
""" Usually resets the task and updates observations +
# other custom operations. """
pass
@property
def num_envs(self):
""" Retrieves number of environments.
Returns:
num_envs(int): Number of environments.
"""
return self._num_envs
@property
def simulation_app(self):
"""Retrieves the SimulationApp object.
Returns:
simulation_app(SimulationApp): SimulationApp.
"""
return self._simulation_app
@property
def get_world(self):
"""Retrieves the World object for simulation.
Returns:
world(World): Simulation World.
"""
return self._world
@property
def task(self):
"""Retrieves the task.
Returns:
task(BaseTask): Task.
"""
return self._task
@property
def render_enabled(self):
"""Whether rendering is enabled.
Returns:
render(bool): is render enabled.
"""
return self._render
| 19,383 |
Python
| 39.299376 | 149 | 0.579735 |
AndrePatri/OmniRoboGym/omni_robo_gym/tasks/isaac_task.py
|
# Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
from omni.isaac.core.tasks.base_task import BaseTask
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.viewports import set_camera_view
from omni.isaac.core.world import World
import omni.kit
import numpy as np
import torch
from omni.importer.urdf import _urdf
from omni.isaac.core.utils.prims import move_prim
from omni.isaac.cloner import GridCloner
import omni.isaac.core.utils.prims as prim_utils
# from omni.isaac.sensor import ContactSensor
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.scenes.scene import Scene
from omni_robo_gym.utils.jnt_imp_cntrl import OmniJntImpCntrl
from omni_robo_gym.utils.homing import OmniRobotHomer
from omni_robo_gym.utils.contact_sensor import OmniContactSensors
from omni_robo_gym.utils.terrains import RlTerrains
from omni_robo_gym.utils.math_utils import quat_to_omega, quaternion_difference, rel_vel
from abc import abstractmethod
from typing import List, Dict
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
class IsaacTask(BaseTask):
def __init__(self,
name: str,
integration_dt: float,
robot_names: List[str],
robot_pkg_names: List[str] = None,
contact_prims: Dict[str, List] = None,
contact_offsets: Dict[str, Dict[str, np.ndarray]] = None,
sensor_radii: Dict[str, Dict[str, np.ndarray]] = None,
num_envs = 1,
device = "cuda",
cloning_offset: np.array = None,
fix_base: List[bool] = None,
self_collide: List[bool] = None,
merge_fixed: List[bool] = None,
replicate_physics: bool = True,
solver_position_iteration_count: int = 4,
solver_velocity_iteration_count: int = 1,
solver_stabilization_thresh: float = 1e-5,
offset=None,
env_spacing = 5.0,
spawning_radius = 1.0,
use_flat_ground = True,
default_jnt_stiffness = 300.0,
default_jnt_damping = 20.0,
default_wheel_stiffness = 0.0,
default_wheel_damping = 10.0,
override_art_controller = False,
dtype = torch.float64,
debug_enabled: bool = False,
verbose = False,
use_diff_velocities = False) -> None:
self.torch_dtype = dtype
self._debug_enabled = debug_enabled
self._verbose = verbose
self.use_diff_velocities = use_diff_velocities
self.num_envs = num_envs
self._override_art_controller = override_art_controller
self._integration_dt = integration_dt # just used for contact reporting
self.torch_device = torch.device(device) # defaults to "cuda" ("cpu" also valid)
self.using_gpu = False
if self.torch_device == torch.device("cuda"):
self.using_gpu = True
self.robot_names = robot_names # these are (potentially) custom names to
self.robot_pkg_names = robot_pkg_names # will be used to search for URDF and SRDF packages
self.scene_setup_completed = False
if self.robot_pkg_names is None:
self.robot_pkg_names = self.robot_names # if not provided, robot_names are the same as robot_pkg_names
else:
# check dimension consistency
if len(robot_names) != len(robot_pkg_names):
exception = "The provided robot names list must match the length " + \
"of the provided robot package names"
raise Exception(exception)
if fix_base is None:
self._fix_base = [False] * len(self.robot_names)
else:
# check dimension consistency
if len(fix_base) != len(robot_pkg_names):
exception = "The provided fix_base list of boolean must match the length " + \
"of the provided robot package names"
raise Exception(exception)
self._fix_base = fix_base
if self_collide is None:
self._self_collide = [False] * len(self.robot_names)
else:
# check dimension consistency
if len(self_collide) != len(robot_pkg_names):
exception = "The provided self_collide list of boolean must match the length " + \
"of the provided robot package names"
raise Exception(exception)
self._self_collide = self_collide
if merge_fixed is None:
self._merge_fixed = [False] * len(self.robot_names)
else:
# check dimension consistency
if len(merge_fixed) != len(robot_pkg_names):
exception = "The provided merge_fixed list of boolean must match the length " + \
"of the provided robot package names"
raise Exception(exception)
self._merge_fixed = merge_fixed
self._urdf_paths = {}
self._srdf_paths = {}
self._robots_art_views = {}
self._robots_articulations = {}
self._robots_geom_prim_views = {}
self._solver_position_iteration_count = solver_position_iteration_count # solver position iteration count
# -> higher number makes simulation more accurate
self._solver_velocity_iteration_count = solver_velocity_iteration_count
self._solver_stabilization_thresh = solver_stabilization_thresh # threshold for kin. energy below which an articulatiion
# "goes to sleep", i.e. it's not simulated anymore until some action wakes him up
# potentially, each robot could have its own setting for the solver (not supported yet)
self._solver_position_iteration_counts = {}
self._solver_velocity_iteration_counts = {}
self._solver_stabilization_threshs = {}
self.robot_bodynames = {}
self.robot_n_links = {}
self.robot_n_dofs = {}
self.robot_dof_names = {}
self._root_p = {}
self._root_q = {}
self._jnts_q = {}
self._root_p_prev = {} # used for num differentiation
self._root_q_prev = {} # used for num differentiation
self._jnts_q_prev = {} # used for num differentiation
self._root_p_default = {}
self._root_q_default = {}
self._jnts_q_default = {}
self._root_v = {}
self._root_v_default = {}
self._root_omega = {}
self._root_omega_default = {}
self._jnts_v = {}
self._jnts_v_default = {}
self._jnts_eff_default = {}
self._root_pos_offsets = {}
self._root_q_offsets = {}
self.distr_offset = {} # decribed how robots within each env are distributed
self.jnt_imp_controllers = {}
self.homers = {}
# default jnt impedance settings
self.default_jnt_stiffness = default_jnt_stiffness
self.default_jnt_damping = default_jnt_damping
self.default_wheel_stiffness = default_wheel_stiffness
self.default_wheel_damping = default_wheel_damping
self.use_flat_ground = use_flat_ground
self.spawning_radius = spawning_radius # [m] -> default distance between roots of robots in a single
# environment
self._calc_robot_distrib() # computes the offsets of robots withing each env.
self._env_ns = "/World/envs"
self._env_spacing = env_spacing # [m]
self._template_env_ns = self._env_ns + "/env_0"
self._cloner = GridCloner(spacing=self._env_spacing)
self._cloner.define_base_env(self._env_ns)
prim_utils.define_prim(self._template_env_ns)
self._envs_prim_paths = self._cloner.generate_paths(self._env_ns + "/env",
self.num_envs)
self._cloning_offset = cloning_offset
if self._cloning_offset is None:
self._cloning_offset = np.array([[0, 0, 0]] * self.num_envs)
self._replicate_physics = replicate_physics
self._world_initialized = False
self._ground_plane_prim_path = "/World/terrain"
self._world = None
self._world_scene = None
self._world_physics_context = None
self.omni_contact_sensors = {}
self.contact_prims = contact_prims
for robot_name in contact_prims:
self.omni_contact_sensors[robot_name] = OmniContactSensors(
name = robot_name,
n_envs = self.num_envs,
contact_prims = contact_prims,
contact_offsets = contact_offsets,
sensor_radii = sensor_radii,
device = self.torch_device,
dtype = self.torch_dtype,
enable_debug=self._debug_enabled)
# trigger __init__ of parent class
BaseTask.__init__(self,
name=name,
offset=offset)
self.xrdf_cmd_vals = [] # by default empty, needs to be overriden by
# child class
def update_jnt_imp_control_gains(self,
robot_name: str,
jnt_stiffness: float,
jnt_damping: float,
wheel_stiffness: float,
wheel_damping: float,
env_indxs: torch.Tensor = None):
# updates joint imp. controller with new impedance values
if self._debug_enabled:
for_robots = ""
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
msg = "Provided env_indxs should be a torch tensor of indexes!"
Journal.log(self.__class__.__name__,
"update_jnt_imp_control_gains",
msg,
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for_robots = f"for robot {robot_name}, indexes: " + str(env_indxs.tolist())
if self._verbose:
Journal.log(self.__class__.__name__,
"update_jnt_imp_control_gains",
f"updating joint impedances " + for_robots,
LogType.STAT,
throw_when_excep = True)
# set jnt imp gains for the whole robot
if env_indxs is None:
gains_pos = torch.full((self.num_envs, \
self.jnt_imp_controllers[robot_name].n_dofs),
jnt_stiffness,
device = self.torch_device,
dtype=self.torch_dtype)
gains_vel = torch.full((self.num_envs, \
self.jnt_imp_controllers[robot_name].n_dofs),
jnt_damping,
device = self.torch_device,
dtype=self.torch_dtype)
else:
gains_pos = torch.full((env_indxs.shape[0], \
self.jnt_imp_controllers[robot_name].n_dofs),
jnt_stiffness,
device = self.torch_device,
dtype=self.torch_dtype)
gains_vel = torch.full((env_indxs.shape[0], \
self.jnt_imp_controllers[robot_name].n_dofs),
jnt_damping,
device = self.torch_device,
dtype=self.torch_dtype)
self.jnt_imp_controllers[robot_name].set_gains(
pos_gains = gains_pos,
vel_gains = gains_vel,
robot_indxs = env_indxs)
# in case of wheels
wheels_indxs = self.jnt_imp_controllers[robot_name].get_jnt_idxs_matching(
name_pattern="wheel")
if wheels_indxs is not None:
if env_indxs is None:
# wheels are velocity-controlled
wheels_pos_gains = torch.full((self.num_envs, len(wheels_indxs)),
wheel_stiffness,
device = self.torch_device,
dtype=self.torch_dtype)
wheels_vel_gains = torch.full((self.num_envs, len(wheels_indxs)),
wheel_damping,
device = self.torch_device,
dtype=self.torch_dtype)
else:
# wheels are velocity-controlled
wheels_pos_gains = torch.full((env_indxs.shape[0], len(wheels_indxs)),
wheel_stiffness,
device = self.torch_device,
dtype=self.torch_dtype)
wheels_vel_gains = torch.full((env_indxs.shape[0], len(wheels_indxs)),
wheel_damping,
device = self.torch_device,
dtype=self.torch_dtype)
self.jnt_imp_controllers[robot_name].set_gains(
pos_gains = wheels_pos_gains,
vel_gains = wheels_vel_gains,
jnt_indxs=wheels_indxs,
robot_indxs = env_indxs)
def update_root_offsets(self,
robot_name: str,
env_indxs: torch.Tensor = None):
if self._debug_enabled:
for_robots = ""
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
msg = "Provided env_indxs should be a torch tensor of indexes!"
Journal.log(self.__class__.__name__,
"update_root_offsets",
msg,
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for_robots = f"for robot {robot_name}, indexes: " + str(env_indxs.tolist())
if self._verbose:
Journal.log(self.__class__.__name__,
"update_root_offsets",
f"updating root offsets " + for_robots,
LogType.STAT,
throw_when_excep = True)
# only planar position used
if env_indxs is None:
self._root_pos_offsets[robot_name][:, 0:2] = self._root_p[robot_name][:, 0:2]
self._root_q_offsets[robot_name][:, :] = self._root_q[robot_name]
else:
self._root_pos_offsets[robot_name][env_indxs, 0:2] = self._root_p[robot_name][env_indxs, 0:2]
self._root_q_offsets[robot_name][env_indxs, :] = self._root_q[robot_name][env_indxs, :]
def synch_default_root_states(self,
robot_name: str = None,
env_indxs: torch.Tensor = None):
if self._debug_enabled:
for_robots = ""
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
msg = "Provided env_indxs should be a torch tensor of indexes!"
Journal.log(self.__class__.__name__,
"synch_default_root_states",
msg,
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for_robots = f"for robot {robot_name}, indexes: " + str(env_indxs.tolist())
if self._verbose:
Journal.log(self.__class__.__name__,
"synch_default_root_states",
f"updating default root states " + for_robots,
LogType.STAT,
throw_when_excep = True)
if env_indxs is None:
self._root_p_default[robot_name][:, :] = self._root_p[robot_name]
self._root_q_default[robot_name][:, :] = self._root_q[robot_name]
else:
self._root_p_default[robot_name][env_indxs, :] = self._root_p[robot_name][env_indxs, :]
self._root_q_default[robot_name][env_indxs, :] = self._root_q[robot_name][env_indxs, :]
def post_initialization_steps(self):
print("Performing post-initialization steps")
self._world_initialized = True # used by other methods which nees to run
# only when the world was initialized
# populates robot info fields
self._fill_robot_info_from_world()
# initializes homing managers
self._init_homing_managers()
# initializes robot state data
self._init_robots_state()
# default robot state
self._set_robots_default_jnt_config()
self._set_robots_root_default_config()
# initializes joint impedance controllers
self._init_jnt_imp_control()
# update solver options
self._update_art_solver_options()
self.reset()
self._custom_post_init()
self._get_solver_info() # get again solver option before printing everything
self._print_envs_info() # debug prints
def apply_collision_filters(self,
physicscene_path: str,
coll_root_path: str):
self._cloner.filter_collisions(physicsscene_path = physicscene_path,
collision_root_path = coll_root_path,
prim_paths=self._envs_prim_paths,
global_paths=[self._ground_plane_prim_path] # can collide with these prims
)
def reset_jnt_imp_control(self,
robot_name: str,
env_indxs: torch.Tensor = None):
if self._debug_enabled:
for_robots = ""
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
Journal.log(self.__class__.__name__,
"reset_jnt_imp_control",
"Provided env_indxs should be a torch tensor of indexes!",
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for_robots = f"for robot {robot_name}, indexes: " + str(env_indxs)
if self._verbose:
Journal.log(self.__class__.__name__,
"reset_jnt_imp_control",
f"resetting joint impedances " + for_robots,
LogType.STAT,
throw_when_excep = True)
# resets all internal data, refs to defaults
self.jnt_imp_controllers[robot_name].reset(robot_indxs = env_indxs)
# restore current state
if env_indxs is None:
self.jnt_imp_controllers[robot_name].update_state(pos = self._jnts_q[robot_name][:, :],
vel = self._jnts_v[robot_name][:, :],
eff = None,
robot_indxs = None)
else:
self.jnt_imp_controllers[robot_name].update_state(pos = self._jnts_q[robot_name][env_indxs, :],
vel = self._jnts_v[robot_name][env_indxs, :],
eff = None,
robot_indxs = env_indxs)
# restore default gains
self.update_jnt_imp_control_gains(robot_name = robot_name,
jnt_stiffness = self.default_jnt_stiffness,
jnt_damping = self.default_jnt_damping,
wheel_stiffness = self.default_wheel_stiffness,
wheel_damping = self.default_wheel_damping,
env_indxs = env_indxs)
#restore jnt imp refs to homing
if env_indxs is None:
self.jnt_imp_controllers[robot_name].set_refs(pos_ref=self.homers[robot_name].get_homing()[:, :],
robot_indxs = None)
else:
self.jnt_imp_controllers[robot_name].set_refs(pos_ref=self.homers[robot_name].get_homing()[env_indxs, :],
robot_indxs = env_indxs)
# actually applies reset commands to the articulation
# self.jnt_imp_controllers[robot_name].apply_cmds()
def set_world(self,
world: World):
if not isinstance(world, World):
Journal.log(self.__class__.__name__,
"configure_scene",
"world should be an instance of omni.isaac.core.world.World!",
LogType.EXCEP,
throw_when_excep = True)
self._world = world
self._world_scene = self._world.scene
self._world_physics_context = self._world.get_physics_context()
def set_up_scene(self,
scene: Scene):
super().set_up_scene(scene)
def configure_scene(self) -> None:
# this is called automatically by the environment BEFORE
# initializing the simulation
if self._world is None:
Journal.log(self.__class__.__name__,
"configure_scene",
"Did you call the set_world() method??",
LogType.EXCEP,
throw_when_excep = True)
if not self.scene_setup_completed:
for i in range(len(self.robot_names)):
robot_name = self.robot_names[i]
robot_pkg_name = self.robot_pkg_names[i]
fix_base = self._fix_base[i]
self_collide = self._self_collide[i]
merge_fixed = self._merge_fixed[i]
self._generate_rob_descriptions(robot_name=robot_name,
robot_pkg_name=robot_pkg_name)
self._import_urdf(robot_name,
fix_base=fix_base,
self_collide=self_collide,
merge_fixed=merge_fixed)
Journal.log(self.__class__.__name__,
"set_up_scene",
"cloning environments...",
LogType.STAT,
throw_when_excep = True)
self._cloner.clone(
source_prim_path=self._template_env_ns,
prim_paths=self._envs_prim_paths,
replicate_physics=self._replicate_physics,
position_offsets = self._cloning_offset
) # we can clone the environment in which all the robos are
Journal.log(self.__class__.__name__,
"set_up_scene",
"finishing scene setup...",
LogType.STAT,
throw_when_excep = True)
for i in range(len(self.robot_names)):
robot_name = self.robot_names[i]
self._robots_art_views[robot_name] = ArticulationView(name = robot_name + "ArtView",
prim_paths_expr = self._env_ns + "/env_.*"+ "/" + robot_name + "/base_link",
reset_xform_properties=False)
self._robots_articulations[robot_name] = self._world_scene.add(self._robots_art_views[robot_name])
# self._robots_geom_prim_views[robot_name] = GeometryPrimView(name = robot_name + "GeomView",
# prim_paths_expr = self._env_ns + "/env*"+ "/" + robot_name,
# # prepare_contact_sensors = True
# )
# self._robots_geom_prim_views[robot_name].apply_collision_apis() # to be able to apply contact sensors
if self.use_flat_ground:
self._world_scene.add_default_ground_plane(z_position=0,
name="terrain",
prim_path= self._ground_plane_prim_path,
static_friction=1.0,
dynamic_friction=1.0,
restitution=0.2)
else:
self.terrains = RlTerrains(get_current_stage())
self.terrains.get_obstacles_terrain(terrain_size=40,
num_obs=100,
max_height=0.4,
min_size=0.5,
max_size=5.0)
# delete_prim(self._ground_plane_prim_path + "/SphereLight") # we remove the default spherical light
# set default camera viewport position and target
self._set_initial_camera_params()
self.apply_collision_filters(self._world_physics_context.prim_path,
"/World/collisions")
# init contact sensors
self._init_contact_sensors() # IMPORTANT: this has to be called
# after calling the clone() method and initializing articulation views!!!
self._world.reset() # reset world to make art views available
self.post_initialization_steps()
self.scene_setup_completed = True
def post_reset(self):
pass
def reset(self,
env_indxs: torch.Tensor = None,
robot_names: List[str] =None):
# we first reset all target articulations to their default state
rob_names = robot_names if (robot_names is not None) else self.robot_names
# resets the state of target robot and env to the defaults
self.reset_state(env_indxs=env_indxs,
robot_names=rob_names)
# and jnt imp. controllers
for i in range(len(rob_names)):
self.reset_jnt_imp_control(robot_name=rob_names[i],
env_indxs=env_indxs)
def reset_state(self,
env_indxs: torch.Tensor = None,
robot_names: List[str] =None):
rob_names = robot_names if (robot_names is not None) else self.robot_names
if env_indxs is not None:
if self._debug_enabled:
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for i in range(len(rob_names)):
robot_name = rob_names[i]
# root q
self._robots_art_views[robot_name].set_world_poses(positions = self._root_p_default[robot_name][env_indxs, :],
orientations=self._root_q_default[robot_name][env_indxs, :],
indices = env_indxs)
# jnts q
self._robots_art_views[robot_name].set_joint_positions(positions = self._jnts_q_default[robot_name][env_indxs, :],
indices = env_indxs)
# root v and omega
self._robots_art_views[robot_name].set_joint_velocities(velocities = self._jnts_v_default[robot_name][env_indxs, :],
indices = env_indxs)
# jnts v
concatenated_vel = torch.cat((self._root_v_default[robot_name][env_indxs, :],
self._root_omega_default[robot_name][env_indxs, :]), dim=1)
self._robots_art_views[robot_name].set_velocities(velocities = concatenated_vel,
indices = env_indxs)
# jnts eff
self._robots_art_views[robot_name].set_joint_efforts(efforts = self._jnts_eff_default[robot_name][env_indxs, :],
indices = env_indxs)
else:
for i in range(len(rob_names)):
robot_name = rob_names[i]
# root q
self._robots_art_views[robot_name].set_world_poses(positions = self._root_p_default[robot_name][:, :],
orientations=self._root_q_default[robot_name][:, :],
indices = None)
# jnts q
self._robots_art_views[robot_name].set_joint_positions(positions = self._jnts_q_default[robot_name][:, :],
indices = None)
# root v and omega
self._robots_art_views[robot_name].set_joint_velocities(velocities = self._jnts_v_default[robot_name][:, :],
indices = None)
# jnts v
concatenated_vel = torch.cat((self._root_v_default[robot_name][:, :],
self._root_omega_default[robot_name][:, :]), dim=1)
self._robots_art_views[robot_name].set_velocities(velocities = concatenated_vel,
indices = None)
# jnts eff
self._robots_art_views[robot_name].set_joint_efforts(efforts = self._jnts_eff_default[robot_name][:, :],
indices = None)
# we update the robots state
self.get_states(env_indxs=env_indxs,
robot_names=rob_names)
def close(self):
pass
def root_pos_offsets(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_pos_offsets[robot_name]
else:
return self._root_pos_offsets[robot_name][env_idxs, :]
def root_q_offsets(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_q_offsets[robot_name]
else:
return self._root_q_offsets[robot_name][env_idxs, :]
def root_p(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_p[robot_name]
else:
return self._root_p[robot_name][env_idxs, :]
def root_p_rel(self,
robot_name: str,
env_idxs: torch.Tensor = None):
rel_pos = torch.sub(self.root_p(robot_name=robot_name,
env_idxs=env_idxs),
self.root_pos_offsets(robot_name=robot_name,
env_idxs=env_idxs))
return rel_pos
def root_q(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_q[robot_name]
else:
return self._root_q[robot_name][env_idxs, :]
def root_q_rel(self,
robot_name: str,
env_idxs: torch.Tensor = None):
rel_q = quaternion_difference(self.root_q_offsets(robot_name=robot_name,
env_idxs=env_idxs),
self.root_q(robot_name=robot_name,
env_idxs=env_idxs))
return rel_q
def root_v(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_v[robot_name]
else:
return self._root_v[robot_name][env_idxs, :]
def root_v_rel(self,
robot_name: str,
env_idxs: torch.Tensor = None):
v_rel = rel_vel(offset_q0_q1=self.root_q_offsets(robot_name=robot_name,
env_idxs=env_idxs),
v0=self.root_v(robot_name=robot_name, env_idxs=env_idxs))
return v_rel
def root_omega(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_omega[robot_name]
else:
return self._root_omega[robot_name][env_idxs, :]
def root_omega_rel(self,
robot_name: str,
env_idxs: torch.Tensor = None):
omega_rel = rel_vel(offset_q0_q1=self.root_q_offsets(robot_name=robot_name,
env_idxs=env_idxs),
v0=self.root_omega(robot_name=robot_name, env_idxs=env_idxs))
return omega_rel
def jnts_q(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._jnts_q[robot_name]
else:
return self._jnts_q[robot_name][env_idxs, :]
def jnts_v(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._jnts_v[robot_name]
else:
return self._jnts_v[robot_name][env_idxs, :]
def integration_dt(self):
return self._integration_dt
@abstractmethod
def _xrdf_cmds(self) -> Dict:
# this has to be implemented by the user depending on the arguments
# the xacro description of the robot takes. The output is a list
# of xacro commands.
# Example implementation:
# def _xrdf_cmds():
# cmds = {}
# cmds{self.robot_names[0]} = []
# xrdf_cmd_vals = [True, True, True, False, False, True]
# legs = "true" if xrdf_cmd_vals[0] else "false"
# big_wheel = "true" if xrdf_cmd_vals[1] else "false"
# upper_body ="true" if xrdf_cmd_vals[2] else "false"
# velodyne = "true" if xrdf_cmd_vals[3] else "false"
# realsense = "true" if xrdf_cmd_vals[4] else "false"
# floating_joint = "true" if xrdf_cmd_vals[5] else "false" # horizon needs a floating joint
# cmds.append("legs:=" + legs)
# cmds.append("big_wheel:=" + big_wheel)
# cmds.append("upper_body:=" + upper_body)
# cmds.append("velodyne:=" + velodyne)
# cmds.append("realsense:=" + realsense)
# cmds.append("floating_joint:=" + floating_joint)
# return cmds
pass
@abstractmethod
def pre_physics_step(self,
actions,
robot_name: str) -> None:
# apply actions to simulated robot
# to be overriden by child class depending
# on specific needs
pass
def _generate_srdf(self,
robot_name: str,
robot_pkg_name: str):
# we generate the URDF where the description package is located
import rospkg
rospackage = rospkg.RosPack()
descr_path = rospackage.get_path(robot_pkg_name + "_srdf")
srdf_path = descr_path + "/srdf"
xacro_name = robot_pkg_name
xacro_path = srdf_path + "/" + xacro_name + ".srdf.xacro"
self._srdf_paths[robot_name] = self._descr_dump_path + "/" + robot_name + ".srdf"
if self._xrdf_cmds() is not None:
cmds = self._xrdf_cmds()[robot_name]
if cmds is None:
xacro_cmd = ["xacro"] + [xacro_path] + ["-o"] + [self._srdf_paths[robot_name]]
else:
xacro_cmd = ["xacro"] + [xacro_path] + cmds + ["-o"] + [self._srdf_paths[robot_name]]
if self._xrdf_cmds() is None:
xacro_cmd = ["xacro"] + [xacro_path] + ["-o"] + [self._srdf_paths[robot_name]]
import subprocess
try:
xacro_gen = subprocess.check_call(xacro_cmd)
except:
Journal.log(self.__class__.__name__,
"_generate_urdf",
"failed to generate " + robot_name + "\'S SRDF!!!",
LogType.EXCEP,
throw_when_excep = True)
def _generate_urdf(self,
robot_name: str,
robot_pkg_name: str):
# we generate the URDF where the description package is located
import rospkg
rospackage = rospkg.RosPack()
descr_path = rospackage.get_path(robot_pkg_name + "_urdf")
urdf_path = descr_path + "/urdf"
xacro_name = robot_pkg_name
xacro_path = urdf_path + "/" + xacro_name + ".urdf.xacro"
self._urdf_paths[robot_name] = self._descr_dump_path + "/" + robot_name + ".urdf"
if self._xrdf_cmds() is not None:
cmds = self._xrdf_cmds()[robot_name]
if cmds is None:
xacro_cmd = ["xacro"] + [xacro_path] + ["-o"] + [self._urdf_paths[robot_name]]
else:
xacro_cmd = ["xacro"] + [xacro_path] + cmds + ["-o"] + [self._urdf_paths[robot_name]]
if self._xrdf_cmds() is None:
xacro_cmd = ["xacro"] + [xacro_path] + ["-o"] + [self._urdf_paths[robot_name]]
import subprocess
try:
xacro_gen = subprocess.check_call(xacro_cmd)
# we also generate an updated SRDF
except:
Journal.log(self.__class__.__name__,
"_generate_urdf",
"Failed to generate " + robot_name + "\'s URDF!!!",
LogType.EXCEP,
throw_when_excep = True)
def _generate_rob_descriptions(self,
robot_name: str,
robot_pkg_name: str):
self._descr_dump_path = "/tmp/" + f"{self.__class__.__name__}"
Journal.log(self.__class__.__name__,
"update_root_offsets",
"generating URDF for robot "+ f"{robot_name}, of type {robot_pkg_name}...",
LogType.STAT,
throw_when_excep = True)
self._generate_urdf(robot_name=robot_name,
robot_pkg_name=robot_pkg_name)
Journal.log(self.__class__.__name__,
"update_root_offsets",
"generating SRDF for robot "+ f"{robot_name}, of type {robot_pkg_name}...",
LogType.STAT,
throw_when_excep = True)
# we also generate SRDF files, which are useful for control
self._generate_srdf(robot_name=robot_name,
robot_pkg_name=robot_pkg_name)
def _import_urdf(self,
robot_name: str,
import_config: omni.importer.urdf._urdf.ImportConfig = _urdf.ImportConfig(),
fix_base = False,
self_collide = False,
merge_fixed = True):
Journal.log(self.__class__.__name__,
"update_root_offsets",
"importing robot URDF",
LogType.STAT,
throw_when_excep = True)
_urdf.acquire_urdf_interface()
# we overwrite some settings which are bound to be fixed
import_config.merge_fixed_joints = merge_fixed # makes sim more stable
# in case of fixed joints with light objects
import_config.import_inertia_tensor = True
# import_config.convex_decomp = False
import_config.fix_base = fix_base
import_config.self_collision = self_collide
# import_config.distance_scale = 1
# import_config.make_default_prim = True
# import_config.create_physics_scene = True
# import_config.default_drive_strength = 1047.19751
# import_config.default_position_drive_damping = 52.35988
# import_config.default_drive_type = _urdf.UrdfJointTargetType.JOINT_DRIVE_POSITION
# import URDF
success, robot_prim_path_default = omni.kit.commands.execute(
"URDFParseAndImportFile",
urdf_path=self._urdf_paths[robot_name],
import_config=import_config,
)
robot_base_prim_path = self._template_env_ns + "/" + robot_name
# moving default prim to base prim path for cloning
move_prim(robot_prim_path_default, # from
robot_base_prim_path) # to
return success
def _init_contact_sensors(self):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
# creates base contact sensor (which is then cloned)
self.omni_contact_sensors[robot_name].create_contact_sensors(
self._world,
self._env_ns
)
def _init_robots_state(self):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
pose = self._robots_art_views[robot_name].get_world_poses(
clone = True) # tuple: (pos, quat)
# root p (measured, previous, default)
self._root_p[robot_name] = pose[0]
self._root_p_prev[robot_name] = torch.clone(pose[0])
self._root_p_default[robot_name] = torch.clone(pose[0]) + self.distr_offset[robot_name]
# root q (measured, previous, default)
self._root_q[robot_name] = pose[1] # root orientation
self._root_q_prev[robot_name] = torch.clone(pose[1])
self._root_q_default[robot_name] = torch.clone(pose[1])
# jnt q (measured, previous, default)
self._jnts_q[robot_name] = self._robots_art_views[robot_name].get_joint_positions(
clone = True) # joint positions
self._jnts_q_prev[robot_name] = self._robots_art_views[robot_name].get_joint_positions(
clone = True)
self._jnts_q_default[robot_name] = self.homers[robot_name].get_homing(clone=True)
# root v (measured, default)
self._root_v[robot_name] = self._robots_art_views[robot_name].get_linear_velocities(
clone = True) # root lin. velocity
self._root_v_default[robot_name] = torch.full((self._root_v[robot_name].shape[0], self._root_v[robot_name].shape[1]),
0.0,
dtype=self.torch_dtype,
device=self.torch_device)
# root omega (measured, default)
self._root_omega[robot_name] = self._robots_art_views[robot_name].get_angular_velocities(
clone = True) # root ang. velocity
self._root_omega_default[robot_name] = torch.full((self._root_omega[robot_name].shape[0], self._root_omega[robot_name].shape[1]),
0.0,
dtype=self.torch_dtype,
device=self.torch_device)
# joints v (measured, default)
self._jnts_v[robot_name] = self._robots_art_views[robot_name].get_joint_velocities(
clone = True) # joint velocities
self._jnts_v_default[robot_name] = torch.full((self._jnts_v[robot_name].shape[0], self._jnts_v[robot_name].shape[1]),
0.0,
dtype=self.torch_dtype,
device=self.torch_device)
self._jnts_eff_default[robot_name] = torch.full((self._jnts_v[robot_name].shape[0], self._jnts_v[robot_name].shape[1]),
0.0,
dtype=self.torch_dtype,
device=self.torch_device)
self._root_pos_offsets[robot_name] = torch.zeros((self.num_envs, 3),
device=self.torch_device) # reference position offses
self._root_q_offsets[robot_name] = torch.zeros((self.num_envs, 4),
device=self.torch_device)
self._root_q_offsets[robot_name][:, 0] = 1.0 # init to valid identity quaternion
self.update_root_offsets(robot_name)
def _calc_robot_distrib(self):
import math
# we distribute robots in a single env. along the
# circumference of a circle of given radius
n_robots = len(self.robot_names)
offset_baseangle = 2 * math.pi / n_robots
for i in range(n_robots):
offset_angle = offset_baseangle * (i + 1)
robot_offset_wrt_center = torch.tensor([self.spawning_radius * math.cos(offset_angle),
self.spawning_radius * math.sin(offset_angle), 0],
device=self.torch_device,
dtype=self.torch_dtype)
# list with n references to the original tensor
tensor_list = [robot_offset_wrt_center] * self.num_envs
self.distr_offset[self.robot_names[i]] = torch.stack(tensor_list, dim=0)
def _get_robots_state(self,
env_indxs: torch.Tensor = None,
robot_names: List[str] = None,
dt: float = None,
reset: bool = False):
rob_names = robot_names if (robot_names is not None) else self.robot_names
if env_indxs is not None:
for i in range(0, len(rob_names)):
robot_name = rob_names[i]
pose = self._robots_art_views[robot_name].get_world_poses(
clone = True,
indices=env_indxs) # tuple: (pos, quat)
self._root_p[robot_name][env_indxs, :] = pose[0]
self._root_q[robot_name][env_indxs, :] = pose[1] # root orientation
self._jnts_q[robot_name][env_indxs, :] = self._robots_art_views[robot_name].get_joint_positions(
clone = True,
indices=env_indxs) # joint positions
if dt is None:
# we get velocities from the simulation. This is not good since
# these can actually represent artifacts which do not have physical meaning.
# It's better to obtain them by differentiation to avoid issues with controllers, etc...
self._root_v[robot_name][env_indxs, :] = self._robots_art_views[robot_name].get_linear_velocities(
clone = True,
indices=env_indxs) # root lin. velocity
self._root_omega[robot_name][env_indxs, :] = self._robots_art_views[robot_name].get_angular_velocities(
clone = True,
indices=env_indxs) # root ang. velocity
self._jnts_v[robot_name][env_indxs, :] = self._robots_art_views[robot_name].get_joint_velocities(
clone = True,
indices=env_indxs) # joint velocities
else:
# differentiate numerically
if not reset:
self._root_v[robot_name][env_indxs, :] = (self._root_p[robot_name][env_indxs, :] - \
self._root_p_prev[robot_name][env_indxs, :]) / dt
self._root_omega[robot_name][env_indxs, :] = quat_to_omega(self._root_q[robot_name][env_indxs, :],
self._root_q_prev[robot_name][env_indxs, :],
dt)
self._jnts_v[robot_name][env_indxs, :] = (self._jnts_q[robot_name][env_indxs, :] - \
self._jnts_q_prev[robot_name][env_indxs, :]) / dt
else:
# to avoid issues when differentiating numerically
self._root_v[robot_name][env_indxs, :].zero_()
self._root_omega[robot_name][env_indxs, :].zero_()
self._jnts_v[robot_name][env_indxs, :].zero_()
# update "previous" data for numerical differentiation
self._root_p_prev[robot_name][env_indxs, :] = self._root_p[robot_name][env_indxs, :]
self._root_q_prev[robot_name][env_indxs, :] = self._root_q[robot_name][env_indxs, :]
self._jnts_q_prev[robot_name][env_indxs, :] = self._jnts_q[robot_name][env_indxs, :]
else:
# updating data for all environments
for i in range(0, len(rob_names)):
robot_name = rob_names[i]
pose = self._robots_art_views[robot_name].get_world_poses(
clone = True) # tuple: (pos, quat)
self._root_p[robot_name][:, :] = pose[0]
self._root_q[robot_name][:, :] = pose[1] # root orientation
self._jnts_q[robot_name][:, :] = self._robots_art_views[robot_name].get_joint_positions(
clone = True) # joint positions
if dt is None:
# we get velocities from the simulation. This is not good since
# these can actually represent artifacts which do not have physical meaning.
# It's better to obtain them by differentiation to avoid issues with controllers, etc...
self._root_v[robot_name][:, :] = self._robots_art_views[robot_name].get_linear_velocities(
clone = True) # root lin. velocity
self._root_omega[robot_name][:, :] = self._robots_art_views[robot_name].get_angular_velocities(
clone = True) # root ang. velocity
self._jnts_v[robot_name][:, :] = self._robots_art_views[robot_name].get_joint_velocities(
clone = True) # joint velocities
else:
# differentiate numerically
if not reset:
self._root_v[robot_name][:, :] = (self._root_p[robot_name][:, :] - \
self._root_p_prev[robot_name][:, :]) / dt
self._root_omega[robot_name][:, :] = quat_to_omega(self._root_q[robot_name][:, :],
self._root_q_prev[robot_name][:, :],
dt)
self._jnts_v[robot_name][:, :] = (self._jnts_q[robot_name][:, :] - \
self._jnts_q_prev[robot_name][:, :]) / dt
# self._jnts_v[robot_name][:, :].zero_()
else:
# to avoid issues when differentiating numerically
self._root_v[robot_name][:, :].zero_()
self._root_omega[robot_name][:, :].zero_()
self._jnts_v[robot_name][:, :].zero_()
# update "previous" data for numerical differentiation
self._root_p_prev[robot_name][:, :] = self._root_p[robot_name][:, :]
self._root_q_prev[robot_name][:, :] = self._root_q[robot_name][:, :]
self._jnts_q_prev[robot_name][:, :] = self._jnts_q[robot_name][:, :]
def get_states(self,
env_indxs: torch.Tensor = None,
robot_names: List[str] = None):
if self.use_diff_velocities:
self._get_robots_state(dt = self.integration_dt(),
env_indxs = env_indxs,
robot_names = robot_names) # updates robot states
# but velocities are obtained via num. differentiation
else:
self._get_robots_state(env_indxs = env_indxs,
robot_names = robot_names) # velocities directly from simulator (can
# introduce relevant artifacts, making them unrealistic)
def _custom_post_init(self):
# can be overridden by child class
pass
def _set_robots_default_jnt_config(self):
# setting Isaac's internal defaults. Useful is resetting
# whole scenes or views, but single env reset has to be implemented
# manueally
# we use the homing of the robots
if (self._world_initialized):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
homing = self.homers[robot_name].get_homing()
self._robots_art_views[robot_name].set_joints_default_state(positions= homing,
velocities = torch.zeros((homing.shape[0], homing.shape[1]), \
dtype=self.torch_dtype, device=self.torch_device),
efforts = torch.zeros((homing.shape[0], homing.shape[1]), \
dtype=self.torch_dtype, device=self.torch_device))
else:
Journal.log(self.__class__.__name__,
"_set_robots_default_jnt_config",
"Before calling __set_robots_default_jnt_config(), you need to reset the World" + \
" at least once and call post_initialization_steps()",
LogType.EXCEP,
throw_when_excep = True)
def _set_robots_root_default_config(self):
if (self._world_initialized):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
self._robots_art_views[robot_name].set_default_state(positions = self._root_p_default[robot_name],
orientations = self._root_q_default[robot_name])
else:
Journal.log(self.__class__.__name__,
"_generate_urdf",
"Before calling _set_robots_root_default_config(), you need to reset the World" + \
" at least once and call post_initialization_steps()",
LogType.EXCEP,
throw_when_excep = True)
return True
def _get_solver_info(self):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
self._solver_position_iteration_counts[robot_name] = self._robots_art_views[robot_name].get_solver_position_iteration_counts()
self._solver_velocity_iteration_counts[robot_name] = self._robots_art_views[robot_name].get_solver_velocity_iteration_counts()
self._solver_stabilization_threshs[robot_name] = self._robots_art_views[robot_name].get_stabilization_thresholds()
def _update_art_solver_options(self):
# sets new solver iteration options for specifc articulations
self._get_solver_info() # gets current solver info for the articulations of the
# environments, so that dictionaries are filled properly
if (self._world_initialized):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
# increase by a factor
self._solver_position_iteration_counts[robot_name] = torch.full((self.num_envs,), self._solver_position_iteration_count)
self._solver_velocity_iteration_counts[robot_name] = torch.full((self.num_envs,), self._solver_velocity_iteration_count)
self._solver_stabilization_threshs[robot_name] = torch.full((self.num_envs,), self._solver_stabilization_thresh)
self._robots_art_views[robot_name].set_solver_position_iteration_counts(self._solver_position_iteration_counts[robot_name])
self._robots_art_views[robot_name].set_solver_velocity_iteration_counts(self._solver_velocity_iteration_counts[robot_name])
self._robots_art_views[robot_name].set_stabilization_thresholds(self._solver_stabilization_threshs[robot_name])
self._get_solver_info() # gets again solver info for articulation, so that it's possible to debug if
# the operation was successful
else:
Journal.log(self.__class__.__name__,
"_set_robots_default_jnt_config",
"Before calling update_art_solver_options(), you need to reset the World at least once!",
LogType.EXCEP,
throw_when_excep = True)
def _print_envs_info(self):
if (self._world_initialized):
print("TASK INFO:")
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
task_info = f"[{robot_name}]" + "\n" + \
"bodies: " + str(self._robots_art_views[robot_name].body_names) + "\n" + \
"n. prims: " + str(self._robots_art_views[robot_name].count) + "\n" + \
"prims names: " + str(self._robots_art_views[robot_name].prim_paths) + "\n" + \
"n. bodies: " + str(self._robots_art_views[robot_name].num_bodies) + "\n" + \
"n. dofs: " + str(self._robots_art_views[robot_name].num_dof) + "\n" + \
"dof names: " + str(self._robots_art_views[robot_name].dof_names) + "\n" + \
"solver_position_iteration_counts: " + str(self._solver_position_iteration_counts[robot_name]) + "\n" + \
"solver_velocity_iteration_counts: " + str(self._solver_velocity_iteration_counts[robot_name]) + "\n" + \
"stabiliz. thresholds: " + str(self._solver_stabilization_threshs[robot_name])
# print("dof limits: " + str(self._robots_art_views[robot_name].get_dof_limits()))
# print("effort modes: " + str(self._robots_art_views[robot_name].get_effort_modes()))
# print("dof gains: " + str(self._robots_art_views[robot_name].get_gains()))
# print("dof max efforts: " + str(self._robots_art_views[robot_name].get_max_efforts()))
# print("dof gains: " + str(self._robots_art_views[robot_name].get_gains()))
# print("physics handle valid: " + str(self._robots_art_views[robot_name].is_physics_handle_valid())
Journal.log(self.__class__.__name__,
"_print_envs_info",
task_info,
LogType.STAT,
throw_when_excep = True)
else:
Journal.log(self.__class__.__name__,
"_set_robots_default_jnt_config",
"Before calling __print_envs_info(), you need to reset the World at least once!",
LogType.EXCEP,
throw_when_excep = True)
def _fill_robot_info_from_world(self):
if self._world_initialized:
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
self.robot_bodynames[robot_name] = self._robots_art_views[robot_name].body_names
self.robot_n_links[robot_name] = self._robots_art_views[robot_name].num_bodies
self.robot_n_dofs[robot_name] = self._robots_art_views[robot_name].num_dof
self.robot_dof_names[robot_name] = self._robots_art_views[robot_name].dof_names
else:
Journal.log(self.__class__.__name__,
"_fill_robot_info_from_world",
"Before calling _fill_robot_info_from_world(), you need to reset the World at least once!",
LogType.EXCEP,
throw_when_excep = True)
def _init_homing_managers(self):
if self._world_initialized:
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
self.homers[robot_name] = OmniRobotHomer(articulation=self._robots_art_views[robot_name],
srdf_path=self._srdf_paths[robot_name],
device=self.torch_device,
dtype=self.torch_dtype)
else:
exception = "you should reset the World at least once and call the " + \
"post_initialization_steps() method before initializing the " + \
"homing manager."
Journal.log(self.__class__.__name__,
"_init_homing_managers",
exception,
LogType.EXCEP,
throw_when_excep = True)
def _init_jnt_imp_control(self):
if self._world_initialized:
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
# creates impedance controller
self.jnt_imp_controllers[robot_name] = OmniJntImpCntrl(articulation=self._robots_art_views[robot_name],
default_pgain = self.default_jnt_stiffness, # defaults
default_vgain = self.default_jnt_damping,
override_art_controller=self._override_art_controller,
filter_dt = None,
filter_BW = 50,
device= self.torch_device,
dtype=self.torch_dtype,
enable_safety=True,
enable_profiling=self._debug_enabled,
urdf_path=self._urdf_paths[robot_name],
debug_checks = self._debug_enabled)
self.reset_jnt_imp_control(robot_name)
else:
exception = "you should reset the World at least once and call the " + \
"post_initialization_steps() method before initializing the " + \
"joint impedance controller."
Journal.log(self.__class__.__name__,
"_init_homing_managers",
exception,
LogType.EXCEP,
throw_when_excep = True)
def _set_initial_camera_params(self,
camera_position=[10, 10, 3],
camera_target=[0, 0, 0]):
set_camera_view(eye=camera_position,
target=camera_target,
camera_prim_path="/OmniverseKit_Persp")
| 68,642 |
Python
| 47.995717 | 142 | 0.49324 |
AndrePatri/OmniRoboGym/omni_robo_gym/tests/test_lunar_lander_stable_bs3.py
|
# Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
import gymnasium as gym
from stable_baselines3 import DQN
from stable_baselines3.common.evaluation import evaluate_policy
# Create environment
env = gym.make("LunarLander-v2", render_mode="rgb_array")
# Instantiate the agent
model = DQN("MlpPolicy", env, verbose=1)
# Train the agent and display a progress bar
model.learn(total_timesteps=int(2e5), progress_bar=True)
# Save the agent
model.save("dqn_lunar")
del model # delete trained model to demonstrate loading
# Load the trained agent
# NOTE: if you have loading issue, you can pass `print_system_info=True`
# to compare the system on which the model was trained vs the current one
# model = DQN.load("dqn_lunar", env=env, print_system_info=True)
model = DQN.load("dqn_lunar", env=env)
# Evaluate the agent
# NOTE: If you use wrappers with your environment that modify rewards,
# this will be reflected here. To evaluate with original rewards,
# wrap environment in a "Monitor" wrapper before other wrappers.
mean_reward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=10)
# Enjoy trained agent
vec_env = model.get_env()
obs = vec_env.reset()
n_pred_iterations = 100000
for i in range(n_pred_iterations):
action, _states = model.predict(obs, deterministic=True)
obs, rewards, dones, info = vec_env.step(action)
vec_env.render("human")
| 2,169 |
Python
| 38.454545 | 102 | 0.751498 |
AndrePatri/OmniRoboGym/omni_robo_gym/tests/create_terrain_demo.py
|
# Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os, sys
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(SCRIPT_DIR)
import omni
from omni.isaac.kit import SimulationApp
import numpy as np
simulation_app = SimulationApp({"headless": False})
from omni.isaac.core.tasks import BaseTask
from omni.isaac.core import World
from omni.isaac.core.objects import DynamicSphere
from omni.isaac.core.utils.prims import define_prim
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.materials import PreviewSurface
from omni.isaac.cloner import GridCloner
from pxr import UsdLux, UsdShade, Sdf
from omni_robo_gym.utils.terrain_utils import *
from omni_robo_gym.utils.terrains import RlTerrains
class TerrainsTest(BaseTask):
def __init__(self,
name) -> None:
BaseTask.__init__(self, name=name)
self._device = "cpu"
def set_up_scene(self,
scene) -> None:
self._stage = get_current_stage()
distantLight = UsdLux.DistantLight.Define(self._stage, Sdf.Path("/World/DistantLight"))
distantLight.CreateIntensityAttr(2000)
self.terrains = RlTerrains(self._stage)
self.terrains.get_obstacles_terrain(
terrain_size = 40.0,
num_obs = 200,
max_height = 0.5,
min_size = 0.5,
max_size = 5.0,)
super().set_up_scene(scene)
return
def post_reset(self):
a = 1
def get_observations(self):
pass
def calculate_metrics(self) -> None:
pass
def is_done(self) -> None:
pass
if __name__ == "__main__":
world = World(
stage_units_in_meters=1.0,
rendering_dt=1.0/60.0,
backend="torch",
device="cpu",
)
terrain_creation_task = TerrainsTest(name="CustomTerrain",
)
world.add_task(terrain_creation_task)
world.reset()
while simulation_app.is_running():
if world.is_playing():
if world.current_time_step_index == 0:
world.reset(soft=True)
world.step(render=True)
else:
world.step(render=True)
simulation_app.close()
| 4,763 |
Python
| 33.773722 | 102 | 0.672475 |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/contact_sensor.py
|
import torch
import numpy as np
from omni.isaac.sensor import ContactSensor
from typing import List, Dict
from omni.isaac.core.world import World
from omni.isaac.core.prims import RigidPrimView, RigidContactView
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
class OmniContactSensors:
def __init__(self,
name: str, # robot name for which contact sensors are to be created
n_envs: int, # number of environments
contact_prims: Dict[str, List] = None,
contact_offsets: Dict[str, Dict[str, np.ndarray]] = None,
sensor_radii: Dict[str, Dict[str, np.ndarray]] = None,
device = "cuda",
dtype = torch.float64,
enable_debug: bool = False,
filter_paths: List[str] = ["/World/terrain/GroundPlane/CollisionPlane"]):
# contact sensors abstraction for a single robot
# over multiple environments
self._filter_paths = filter_paths
self._enable_debug = enable_debug
self.n_envs = n_envs
self.device = device
if self.device == "cuda":
self.using_gpu = True
else:
self.using_gpu = False
self.dtype = dtype
self.name = name
self.contact_radius_default = 0.003
# parses contact dictionaries and checks for issues
self._parse_contact_dicts(self.name,
contact_prims,
contact_offsets,
sensor_radii)
self.n_sensors = len(self.contact_prims)
self.in_contact = torch.full((n_envs, self.n_sensors),
False,
device = self.device,
dtype=torch.bool)
self.force_norm = torch.full((n_envs, self.n_sensors),
-1.0,
device = self.device,
dtype=self.dtype)
self.n_contacts = torch.full((n_envs, self.n_sensors),
0,
device = self.device,
dtype=torch.int)
self.contact_sensors = [[None] * self.n_sensors] * n_envs # outer: environment,
# inner: contact sensor, ordered as in contact_prims
self.contact_geom_prim_views = [None] * self.n_sensors
# self.contact_views = [None] * self.n_sensors
def _parse_contact_dicts(self,
name: str,
contact_prims: Dict[str, List],
contact_offsets: Dict[str, Dict[str, np.ndarray]],
sensor_radii: Dict[str, Dict[str, np.ndarray]]):
try:
self.contact_prims = contact_prims[name]
except:
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
f"Could not find key {name} in contact_prims dictionary.",
LogType.EXCEP,
throw_when_excep = True)
try:
self.contact_offsets = contact_offsets[name]
except:
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
f"Could not find key {name} in contact_offsets dictionary.",
LogType.EXCEP,
throw_when_excep = True)
try:
self.sensor_radii = sensor_radii[name]
except:
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
f"Could not find key {name} in sensor_radii dictionary.",
LogType.EXCEP,
throw_when_excep = True)
contact_offsets_ok = all(item in self.contact_offsets for item in self.contact_prims)
sensor_radii_ok = all(item in self.sensor_radii for item in self.contact_prims)
if not contact_offsets_ok:
warning = f"Provided contact_offsets dictionary does not posses all the necessary keys. " + \
f"It should contain all of [{' '.join(self.contact_prims)}]. \n" + \
f"Resetting all offsets to zero..."
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
warning,
LogType.WARN,
throw_when_excep = True)
for i in range(0, len(self.contact_prims)):
self.contact_offsets[self.contact_prims[i]] = np.array([0.0, 0.0, 0.0])
if not sensor_radii_ok:
warning = f"Provided sensor_radii dictionary does not posses all the necessary keys. " + \
f"It should contain all of [{' '.join(self.contact_prims)}]. \n" + \
f"Resetting all radii to {self.contact_radius_default} ..."
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
warning,
LogType.WARN,
throw_when_excep = True)
for i in range(0, len(self.contact_prims)):
self.sensor_radii[self.contact_prims[i]] = self.contact_radius_default
def create_contact_sensors(self,
world: World,
envs_namespace: str):
robot_name = self.name
contact_link_names = self.contact_prims
for sensor_idx in range(0, self.n_sensors):
# we create views of the contact links for all envs
if self.contact_geom_prim_views[sensor_idx] is None:
self.contact_geom_prim_views[sensor_idx] = RigidPrimView(prim_paths_expr=envs_namespace + "/env_.*/" + robot_name + \
"/" + contact_link_names[sensor_idx],
name= self.name + "RigidPrimView" + contact_link_names[sensor_idx],
contact_filter_prim_paths_expr= self._filter_paths,
prepare_contact_sensors=True,
track_contact_forces = True,
disable_stablization = False,
reset_xform_properties=False,
max_contact_count = self.n_envs
)
world.scene.add(self.contact_geom_prim_views[sensor_idx])
# for env_idx in range(0, self.n_envs):
# # env_idx = 0 # create contact sensors for base env only
# for sensor_idx in range(0, self.n_sensors):
# contact_link_prim_path = envs_namespace + f"/env_{env_idx}" + \
# "/" + robot_name + \
# "/" + contact_link_names[sensor_idx]
# sensor_prim_path = contact_link_prim_path + \
# "/contact_sensor" # contact sensor prim path
# print(f"[{self.__class__.__name__}]" + f"[{self.journal.status}]" + ": creating contact sensor at " +
# f"{sensor_prim_path}...")
# contact_sensor = ContactSensor(
# prim_path=sensor_prim_path,
# name=f"{robot_name}{env_idx}_{contact_link_names[sensor_idx]}_contact_sensor",
# min_threshold=0,
# max_threshold=10000000,
# radius=self.sensor_radii[contact_link_names[sensor_idx]],
# translation=self.contact_offsets[contact_link_names[sensor_idx]],
# position=None
# )
# self.contact_sensors[env_idx][sensor_idx] = world.scene.add(contact_sensor)
# self.contact_sensors[env_idx][sensor_idx].add_raw_contact_data_to_frame()
# print(f"[{self.__class__.__name__}]" + f"[{self.journal.status}]" + ": contact sensor at " +
# f"{sensor_prim_path} created.")
def get(self,
dt: float,
contact_link: str,
env_indxs: torch.Tensor = None,
clone = False):
index = -1
try:
index = self.contact_prims.index(contact_link)
except:
exception = f"[{self.__class__.__name__}]" + f"[{self.journal.exception}]" + \
f"could not find contact link {contact_link} in contact list {' '.join(self.contact_prims)}."
Journal.log(self.__class__.__name__,
"get",
exception,
LogType.EXCEP,
throw_when_excep = True)
if env_indxs is None:
return self.contact_geom_prim_views[index].get_net_contact_forces(clone = clone,
dt = dt).view(self.n_envs, 3)
else:
if self._enable_debug:
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
msg = "Provided env_indxs should be a torch tensor of indexes!"
Journal.log(self.__class__.__name__,
"get",
msg,
LogType.EXCEP,
throw_when_excep = True)
if not len(env_indxs.shape) == 1:
msg = "Provided robot_indxs should be a 1D torch tensor!"
Journal.log(self.__class__.__name__,
"get",
msg,
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
return self.contact_geom_prim_views[index].get_net_contact_forces(clone = clone,
dt = dt).view(self.n_envs, 3)[env_indxs, :]
| 10,792 |
Python
| 43.415638 | 133 | 0.47424 |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/math_utils.py
|
import torch
import time
import torch.nn.functional as F
def normalize_quaternion(q):
# Normalizes the quaternion
return q / torch.norm(q, dim=-1, keepdim=True)
def quaternion_difference(q1, q2):
""" Compute the quaternion difference needed to rotate from q1 to q2 """
def quat_conjugate(q):
# Computes the conjugate of a quaternion
w, x, y, z = q.unbind(-1)
return torch.stack([w, -x, -y, -z], dim=-1)
q1_conj = quat_conjugate(q1)
return quaternion_multiply(q2, q1_conj)
def quaternion_multiply(q1, q2):
""" Multiply two quaternions. """
w1, x1, y1, z1 = q1.unbind(-1)
w2, x2, y2, z2 = q2.unbind(-1)
return torch.stack([
w1*w2 - x1*x2 - y1*y2 - z1*z2,
w1*x2 + x1*w2 + y1*z2 - z1*y2,
w1*y2 - x1*z2 + y1*w2 + z1*x2,
w1*z2 + x1*y2 - y1*x2 + z1*w2
], dim=-1)
def quaternion_to_angular_velocity(q_diff, dt):
""" Convert a quaternion difference to an angular velocity vector. """
angle = 2 * torch.arccos(q_diff[..., 0].clamp(-1.0, 1.0)) # Clamping for numerical stability
axis = q_diff[..., 1:]
norm = axis.norm(dim=-1, keepdim=True)
norm = torch.where(norm > 0, norm, torch.ones_like(norm))
axis = axis / norm
angle = angle.unsqueeze(-1) # Add an extra dimension for broadcasting
return (angle / dt) * axis
def quat_to_omega(q0, q1, dt):
""" Convert quaternion pairs to angular velocities """
if q0.shape != q1.shape:
raise ValueError("Tensor shapes do not match in quat_to_omega.")
# Normalize quaternions and compute differences
q0_normalized = normalize_quaternion(q0)
q1_normalized = normalize_quaternion(q1)
q_diff = quaternion_difference(q0_normalized, q1_normalized)
return quaternion_to_angular_velocity(q_diff, dt)
def rel_vel(offset_q0_q1,
v0):
# Calculate relative linear velocity in frame q1 from linear velocity in frame q0 using quaternions.
# Ensure the quaternion is normalized
offset_q0_q1 = F.normalize(offset_q0_q1, p=2, dim=0)
# Convert the linear velocity vector to a quaternion
v0_q = torch.cat([torch.tensor([0]), v0])
# Rotate the linear velocity quaternion using the orientation offset quaternion
rotated_velocity_quaternion = quaternion_multiply(offset_q0_q1, v0_q)
offset_q0_q1_inverse = torch.cat([offset_q0_q1[0:1], -offset_q0_q1[1:]])
# Multiply by the conjugate of the orientation offset quaternion to obtain the result in frame f1
v1_q = quaternion_multiply(rotated_velocity_quaternion, offset_q0_q1_inverse)
# Extract the linear velocity vector from the quaternion result
v1 = v1_q[1:]
return v1
# Example usage
n_envs = 100 # Number of environments
dt = 0.1 # Time step
# Random example tensors for initial and final orientations
q_initial = torch.randn(n_envs, 4)
q_final = torch.randn(n_envs, 4)
start_time = time.perf_counter()
# Convert to angular velocities
omega = quat_to_omega(q_initial, q_final, dt)
end_time = time.perf_counter()
elapsed_time = end_time - start_time
print(f"Time taken to compute angular velocities: {elapsed_time:.6f} seconds")
| 3,149 |
Python
| 32.870967 | 104 | 0.668466 |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/rt_factor.py
|
import time
class RtFactor():
def __init__(self,
dt_nom: float,
window_size: int):
self._it_counter = 0
self._dt_nom = dt_nom
self._start_time = time.perf_counter()
self._current_rt_factor = 0.0
self._window_size = window_size
self._real_time = 0
self._nom_time = 0
def update(self):
self._real_time = time.perf_counter() - self._start_time
self._it_counter += 1
self._nom_time += self._dt_nom
self._current_rt_factor = self._nom_time / self._real_time
def reset_due(self):
return (self._it_counter+1) % self._window_size == 0
def get_avrg_step_time(self):
return self._real_time / self._window_size
def get_dt_nom(self):
return self._dt_nom
def get_nom_time(self):
return self._now_time
def get(self):
return self._current_rt_factor
def reset(self):
self._it_counter = 0
self._nom_time = 0
self._start_time = time.perf_counter()
| 1,096 |
Python
| 17.913793 | 66 | 0.530109 |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/urdf_helpers.py
|
import xml.etree.ElementTree as ET
class UrdfLimitsParser:
def __init__(self, urdf_path, joint_names,
backend = "numpy",
device = "cpu"):
self.urdf_path = urdf_path
self.joint_names = joint_names
self.limits_matrix = None
self.backend = backend
self.device = device
if self.backend == "numpy" and \
self.device != "cpu":
raise Exception("When using numpy backend, only cpu device is supported!")
self.parse_urdf()
def parse_urdf(self):
tree = ET.parse(self.urdf_path)
root = tree.getroot()
num_joints = len(self.joint_names)
self.limits_matrix = None
self.inf = None
if self.backend == "numpy":
import numpy as np
self.limits_matrix = np.full((num_joints, 6), np.nan)
self.inf = np.inf
elif self.backend == "torch":
import torch
self.limits_matrix = torch.full((num_joints, 6), torch.nan, device=self.device)
self.inf = torch.inf
else:
raise Exception("Backend not supported")
for joint_name in self.joint_names:
joint_element = root.find(".//joint[@name='{}']".format(joint_name))
if joint_element is not None:
limit_element = joint_element.find('limit')
jnt_index = self.joint_names.index(joint_name)
# position limits
q_lower = float(limit_element.get('lower', - self.inf))
q_upper = float(limit_element.get('upper', self.inf))
# effort limits
effort_limit = float(limit_element.get('effort', self.inf))
# vel limits
velocity_limit = float(limit_element.get('velocity', self.inf))
self.limits_matrix[jnt_index, 0] = q_lower
self.limits_matrix[jnt_index, 3] = q_upper
self.limits_matrix[jnt_index, 1] = - abs(velocity_limit)
self.limits_matrix[jnt_index, 4] = abs(velocity_limit)
self.limits_matrix[jnt_index, 2] = - abs(effort_limit)
self.limits_matrix[jnt_index, 5] = abs(effort_limit)
def get_limits_matrix(self):
return self.limits_matrix
| 2,425 |
Python
| 28.228915 | 91 | 0.524536 |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/homing.py
|
# Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
from omni.isaac.core.articulations.articulation_view import ArticulationView
import torch
import xml.etree.ElementTree as ET
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
class OmniRobotHomer:
def __init__(self,
articulation: ArticulationView,
srdf_path: str,
backend = "torch",
device: torch.device = torch.device("cpu"),
dtype = torch.float64):
self.torch_dtype = dtype
if not articulation.initialized:
exception = f"the provided articulation is not initialized properly!"
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
self._articulation = articulation
self.srdf_path = srdf_path
self._device = device
self.num_robots = self._articulation.count
self.n_dofs = self._articulation.num_dof
self.jnts_names = self._articulation.dof_names
self.joint_idx_map = {}
for joint in range(0, self.n_dofs):
self.joint_idx_map[self.jnts_names[joint]] = joint
if (backend != "torch"):
print(f"[{self.__class__.__name__}]" + f"[{self.journal.info}]" + ": forcing torch backend. Other backends are not yet supported.")
self._backend = "torch"
self._homing = torch.full((self.num_robots, self.n_dofs),
0.0,
device = self._device,
dtype=self.torch_dtype) # homing configuration
# open srdf and parse the homing field
with open(srdf_path, 'r') as file:
self._srdf_content = file.read()
try:
self._srdf_root = ET.fromstring(self._srdf_content)
# Now 'root' holds the root element of the XML tree.
# You can navigate through the XML tree to extract the tags and their values.
# Example: To find all elements with a specific tag, you can use:
# elements = root.findall('.//your_tag_name')
# Example: If you know the specific structure of your .SRDF file, you can extract
# the data accordingly, for instance:
# for child in root:
# if child.tag == 'some_tag_name':
# tag_value = child.text
# # Do something with the tag value.
# elif child.tag == 'another_tag_name':
# # Handle another tag.
except ET.ParseError as e:
print(f"[{self.__class__.__name__}]" + f"[{self.journal.warning}]" + ": could not read SRDF properly!!")
# Find all the 'joint' elements within 'group_state' with the name attribute and their values
joints = self._srdf_root.findall(".//group_state[@name='home']/joint")
self._homing_map = {}
for joint in joints:
joint_name = joint.attrib['name']
joint_value = joint.attrib['value']
self._homing_map[joint_name] = float(joint_value)
self._assign2homing()
def _assign2homing(self):
for joint in list(self._homing_map.keys()):
if joint in self.joint_idx_map:
self._homing[:, self.joint_idx_map[joint]] = torch.full((self.num_robots, 1),
self._homing_map[joint],
device = self._device,
dtype=self.torch_dtype).flatten()
else:
print(f"[{self.__class__.__name__}]" + f"[{self.journal.warning}]" + f"[{self._assign2homing.__name__}]" \
+ ": joint " + f"{joint}" + " is not present in the articulation. It will be ignored.")
def get_homing(self,
clone: bool = False):
if not clone:
return self._homing
else:
return self._homing.clone()
| 5,070 |
Python
| 36.286764 | 144 | 0.554438 |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/jnt_imp_cntrl.py
|
# Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
import torch
from typing import List
from enum import Enum
from omni.isaac.core.articulations.articulation_view import ArticulationView
from omni_robo_gym.utils.urdf_helpers import UrdfLimitsParser
import time
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
class FirstOrderFilter:
# a class implementing a simple first order filter
def __init__(self,
dt: float,
filter_BW: float = 0.1,
rows: int = 1,
cols: int = 1,
device: torch.device = torch.device("cpu"),
dtype = torch.double):
self._torch_dtype = dtype
self._torch_device = device
self._dt = dt
self._rows = rows
self._cols = cols
self._filter_BW = filter_BW
import math
self._gain = 2 * math.pi * self._filter_BW
self.yk = torch.zeros((self._rows, self._cols), device = self._torch_device,
dtype=self._torch_dtype)
self.ykm1 = torch.zeros((self._rows, self._cols), device = self._torch_device,
dtype=self._torch_dtype)
self.refk = torch.zeros((self._rows, self._cols), device = self._torch_device,
dtype=self._torch_dtype)
self.refkm1 = torch.zeros((self._rows, self._cols), device = self._torch_device,
dtype=self._torch_dtype)
self._kh2 = self._gain * self._dt / 2.0
self._coeff_ref = self._kh2 * 1/ (1 + self._kh2)
self._coeff_km1 = (1 - self._kh2) / (1 + self._kh2)
def update(self,
refk: torch.Tensor = None):
if refk is not None:
self.refk[:, :] = refk
self.yk[:, :] = torch.add(torch.mul(self.ykm1, self._coeff_km1),
torch.mul(torch.add(self.refk, self.refkm1),
self._coeff_ref))
self.refkm1[:, :] = self.refk
self.ykm1[:, :] = self.yk
def reset(self,
idxs: torch.Tensor = None):
if idxs is not None:
self.yk[:, :] = torch.zeros((self._rows, self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.ykm1[:, :] = torch.zeros((self._rows, self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.refk[:, :] = torch.zeros((self._rows, self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.refkm1[:, :] = torch.zeros((self._rows, self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
else:
self.yk[idxs, :] = torch.zeros((idxs.shape[0], self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.ykm1[idxs, :] = torch.zeros((idxs.shape[0], self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.refk[idxs, :] = torch.zeros((idxs.shape[0], self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.refkm1[idxs, :] = torch.zeros((idxs.shape[0], self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
def get(self):
return self.yk
class JntSafety:
def __init__(self,
urdf_parser: UrdfLimitsParser):
self.limits_parser = urdf_parser
self.limit_matrix = self.limits_parser.get_limits_matrix()
def apply(self, q_cmd=None, v_cmd=None, eff_cmd=None):
if q_cmd is not None:
self.saturate_tensor(q_cmd, position=True)
if v_cmd is not None:
self.saturate_tensor(v_cmd, velocity=True)
if eff_cmd is not None:
self.saturate_tensor(eff_cmd, effort=True)
def has_nan(self,
tensor):
return torch.any(torch.isnan(tensor))
def saturate_tensor(self, tensor, position=False, velocity=False, effort=False):
if self.has_nan(tensor):
exception = f"Found nan elements in provided tensor!!"
Journal.log(self.__class__.__name__,
"saturate_tensor",
exception,
LogType.EXCEP,
throw_when_excep = False)
# Replace NaN values with infinity, so that we can clamp it
tensor[:, :] = torch.nan_to_num(tensor, nan=torch.inf)
if position:
tensor[:, :] = torch.clamp(tensor[:, :], min=self.limit_matrix[:, 0], max=self.limit_matrix[:, 3])
elif velocity:
tensor[:, :] = torch.clamp(tensor[:, :], min=self.limit_matrix[:, 1], max=self.limit_matrix[:, 4])
elif effort:
tensor[:, :] = torch.clamp(tensor[:, :], min=self.limit_matrix[:, 2], max=self.limit_matrix[:, 5])
class OmniJntImpCntrl:
class IndxState(Enum):
NONE = -1
VALID = 1
INVALID = 0
def __init__(self,
articulation: ArticulationView,
default_pgain = 300.0,
default_vgain = 30.0,
backend = "torch",
device: torch.device = torch.device("cpu"),
filter_BW = 50.0, # [Hz]
filter_dt = None, # should correspond to the dt between samples
override_art_controller = False,
init_on_creation = False,
dtype = torch.double,
enable_safety = True,
urdf_path: str = None,
enable_profiling: bool = False,
debug_checks: bool = False): # [s]
self._torch_dtype = dtype
self._torch_device = device
self.enable_profiling = enable_profiling
self._debug_checks = debug_checks
# debug data
self.profiling_data = {}
self.profiling_data["time_to_update_state"] = -1.0
self.profiling_data["time_to_set_refs"] = -1.0
self.profiling_data["time_to_apply_cmds"] = -1.0
self.start_time = None
if self.enable_profiling:
self.start_time = time.perf_counter()
self.enable_safety = enable_safety
self.limiter = None
self.robot_limits = None
self.urdf_path = urdf_path
self.override_art_controller = override_art_controller # whether to override Isaac's internal joint
# articulation PD controller or not
self.init_art_on_creation = init_on_creation # init. articulation's gains and refs as soon as the controller
# is created
self.gains_initialized = False
self.refs_initialized = False
self._default_pgain = default_pgain
self._default_vgain = default_vgain
self._filter_BW = filter_BW
self._filter_dt = filter_dt
self._articulation_view = articulation # used to actually apply control
# signals to the robot
if not self._articulation_view.initialized:
exception = f"the provided articulation_view is not initialized properly!"
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
self._valid_signal_types = ["pos_ref", "vel_ref", "eff_ref", # references
"pos", "vel", "eff", # measurements (necessary if overriding Isaac's art. controller)
"pgain", "vgain"]
self.num_robots = self._articulation_view.count
self.n_dofs = self._articulation_view.num_dof
self.jnts_names = self._articulation_view.dof_names
if (backend != "torch"):
warning = f"Only supported backend is torch!!!"
Journal.log(self.__class__.__name__,
"__init__",
warning,
LogType.WARN,
throw_when_excep = True)
self._backend = "torch"
if self.enable_safety:
if self.urdf_path is None:
exception = "If enable_safety is set to True, a urdf_path should be provided too!"
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
self.robot_limits = UrdfLimitsParser(urdf_path=self.urdf_path,
joint_names=self.jnts_names,
backend=self._backend,
device=self._torch_device)
self.limiter = JntSafety(urdf_parser=self.robot_limits)
self._pos_err = None
self._vel_err = None
self._pos = None
self._vel = None
self._eff = None
self._imp_eff = None
self._filter_available = False
if filter_dt is not None:
self._filter_BW = filter_BW
self._filter_dt = filter_dt
self._pos_ref_filter = FirstOrderFilter(dt=self._filter_dt,
filter_BW=self._filter_BW,
rows=self.num_robots,
cols=self.n_dofs,
device=self._torch_device,
dtype=self._torch_dtype)
self._vel_ref_filter = FirstOrderFilter(dt=self._filter_dt,
filter_BW=self._filter_BW,
rows=self.num_robots,
cols=self.n_dofs,
device=self._torch_device,
dtype=self._torch_dtype)
self._eff_ref_filter = FirstOrderFilter(dt=self._filter_dt,
filter_BW=self._filter_BW,
rows=self.num_robots,
cols=self.n_dofs,
device=self._torch_device,
dtype=self._torch_dtype)
self._filter_available = True
else:
warning = f"No filter dt provided -> reference filter will not be used!"
Journal.log(self.__class__.__name__,
"__init__",
warning,
LogType.WARN,
throw_when_excep = True)
self.reset() # initialize data
def update_state(self,
pos: torch.Tensor = None,
vel: torch.Tensor = None,
eff: torch.Tensor = None,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
if self.enable_profiling:
self.start_time = time.perf_counter()
selector = self._gen_selector(robot_indxs=robot_indxs,
jnt_indxs=jnt_indxs) # only checks and throws
# if debug_checks
if pos is not None:
self._validate_signal(signal = pos,
selector = selector,
name="pos") # does nothing if not debug_checks
self._pos[selector] = pos
if vel is not None:
self._validate_signal(signal = vel,
selector = selector,
name="vel")
self._vel[selector] = vel
if eff is not None:
self._validate_signal(signal = eff,
selector = selector,
name="eff")
self._eff[selector] = eff
if self.enable_profiling:
self.profiling_data["time_to_update_state"] = \
time.perf_counter() - self.start_time
def set_gains(self,
pos_gains: torch.Tensor = None,
vel_gains: torch.Tensor = None,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
selector = self._gen_selector(robot_indxs=robot_indxs,
jnt_indxs=jnt_indxs) # only checks and throws
# if debug_checks
if pos_gains is not None:
self._validate_signal(signal = pos_gains,
selector = selector,
name="pos_gains")
self._pos_gains[selector] = pos_gains
if not self.override_art_controller:
self._articulation_view.set_gains(kps = self._pos_gains)
if vel_gains is not None:
self._validate_signal(signal = vel_gains,
selector = selector,
name="vel_gains")
self._vel_gains[selector] = vel_gains
if not self.override_art_controller:
self._articulation_view.set_gains(kds = self._vel_gains)
def set_refs(self,
eff_ref: torch.Tensor = None,
pos_ref: torch.Tensor = None,
vel_ref: torch.Tensor = None,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
if self.enable_profiling:
self.start_time = time.perf_counter()
selector = self._gen_selector(robot_indxs=robot_indxs,
jnt_indxs=jnt_indxs) # only checks and throws
# if debug_checks
if eff_ref is not None:
self._validate_signal(signal = eff_ref,
selector = selector,
name="eff_ref")
self._eff_ref[selector] = eff_ref
if pos_ref is not None:
self._validate_signal(signal = pos_ref,
selector = selector,
name="pos_ref")
self._pos_ref[selector] = pos_ref
if vel_ref is not None:
self._validate_signal(signal = vel_ref,
selector = selector,
name="vel_ref")
self._vel_ref[selector] = vel_ref
if self.enable_profiling:
self.profiling_data["time_to_set_refs"] = time.perf_counter() - self.start_time
def apply_cmds(self,
filter = False):
# initialize gains and refs if not done previously
if self.enable_profiling:
self.start_time = time.perf_counter()
if not self.gains_initialized:
self._apply_init_gains_to_art()
if not self.refs_initialized:
self._apply_init_refs_to_art()
if filter and self._filter_available:
self._pos_ref_filter.update(self._pos_ref)
self._vel_ref_filter.update(self._vel_ref)
self._eff_ref_filter.update(self._eff_ref)
# we first filter, then apply safety
eff_ref_filt = self._eff_ref_filter.get()
pos_ref_filt = self._pos_ref_filter.get()
vel_ref_filt = self._vel_ref_filter.get()
if self.limiter is not None:
# saturating ref cmds
self.limiter.apply(q_cmd=pos_ref_filt,
v_cmd=vel_ref_filt,
eff_cmd=eff_ref_filt)
if not self.override_art_controller:
# using omniverse's articulation PD controller
self._articulation_view.set_joint_efforts(eff_ref_filt)
self._articulation_view.set_joint_position_targets(pos_ref_filt)
self._articulation_view.set_joint_velocity_targets(vel_ref_filt)
else:
# impedance torque computed explicitly
self._pos_err = torch.sub(self._pos_ref_filter.get(), self._pos)
self._vel_err = torch.sub(self._vel_ref_filter.get(), self._vel)
self._imp_eff = torch.add(self._eff_ref_filter.get(),
torch.add(
torch.mul(self._pos_gains,
self._pos_err),
torch.mul(self._vel_gains,
self._vel_err)))
# torch.cuda.synchronize()
# we also make the resulting imp eff safe
if self.limiter is not None:
self.limiter.apply(eff_cmd=eff_ref_filt)
# apply only effort (comprehensive of all imp. terms)
self._articulation_view.set_joint_efforts(self._imp_eff)
else:
# we first apply safety to reference joint cmds
if self.limiter is not None:
self.limiter.apply(q_cmd=self._pos_ref,
v_cmd=self._vel_ref,
eff_cmd=self._eff_ref)
if not self.override_art_controller:
# using omniverse's articulation PD controller
self._articulation_view.set_joint_efforts(self._eff_ref)
self._articulation_view.set_joint_position_targets(self._pos_ref)
self._articulation_view.set_joint_velocity_targets(self._vel_ref)
else:
# impedance torque computed explicitly
self._pos_err = torch.sub(self._pos_ref, self._pos)
self._vel_err = torch.sub(self._vel_ref, self._vel)
self._imp_eff = torch.add(self._eff_ref,
torch.add(
torch.mul(self._pos_gains,
self._pos_err),
torch.mul(self._vel_gains,
self._vel_err)))
# torch.cuda.synchronize()
# we also make the resulting imp eff safe
if self.limiter is not None:
self.limiter.apply(eff_cmd=self._imp_eff)
# apply only effort (comprehensive of all imp. terms)
self._articulation_view.set_joint_efforts(self._imp_eff)
if self.enable_profiling:
self.profiling_data["time_to_apply_cmds"] = \
time.perf_counter() - self.start_time
def get_jnt_names_matching(self,
name_pattern: str):
return [jnt for jnt in self.jnts_names if name_pattern in jnt]
def get_jnt_idxs_matching(self,
name_pattern: str):
jnts_names = self.get_jnt_names_matching(name_pattern)
jnt_idxs = [self.jnts_names.index(jnt) for jnt in jnts_names]
if not len(jnt_idxs) == 0:
return torch.tensor(jnt_idxs,
dtype=torch.int64,
device=self._torch_device)
else:
return None
def pos_gains(self):
return self._pos_gains
def vel_gains(self):
return self._vel_gains
def eff_ref(self):
return self._eff_ref
def pos_ref(self):
return self._pos_ref
def vel_ref(self):
return self._vel_ref
def pos_err(self):
return self._pos_err
def vel_err(self):
return self._vel_err
def pos(self):
return self._pos
def vel(self):
return self._vel
def eff(self):
return self._eff
def imp_eff(self):
return self._imp_eff
def reset(self,
robot_indxs: torch.Tensor = None):
self.gains_initialized = False
self.refs_initialized = False
self._all_dofs_idxs = torch.tensor([i for i in range(0, self.n_dofs)],
dtype=torch.int64,
device=self._torch_device)
self._all_robots_idxs = torch.tensor([i for i in range(0, self.num_robots)],
dtype=torch.int64,
device=self._torch_device)
if robot_indxs is None: # reset all data
# we assume diagonal joint impedance gain matrices, so we can save on memory and only store the diagonal
self._pos_gains = torch.full((self.num_robots, self.n_dofs),
self._default_pgain,
device = self._torch_device,
dtype=self._torch_dtype)
self._vel_gains = torch.full((self.num_robots, self.n_dofs),
self._default_vgain,
device = self._torch_device,
dtype=self._torch_dtype)
self._eff_ref = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._pos_ref = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel_ref = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._pos_err = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel_err = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._pos = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._eff = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._imp_eff = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
if self._filter_available:
self._pos_ref_filter.reset()
self._vel_ref_filter.reset()
self._eff_ref_filter.reset()
else: # only reset some robots
if self._debug_checks:
self._validate_selectors(robot_indxs=robot_indxs) # throws if checks not satisfied
n_envs = robot_indxs.shape[0]
# we assume diagonal joint impedance gain matrices, so we can save on memory and only store the diagonal
self._pos_gains[robot_indxs, :] = torch.full((n_envs, self.n_dofs),
self._default_pgain,
device = self._torch_device,
dtype=self._torch_dtype)
self._vel_gains[robot_indxs, :] = torch.full((n_envs, self.n_dofs),
self._default_vgain,
device = self._torch_device,
dtype=self._torch_dtype)
self._eff_ref[robot_indxs, :] = 0
self._pos_ref[robot_indxs, :] = 0
self._vel_ref[robot_indxs, :] = 0
# if self.override_art_controller:
# saving memory (these are not necessary if not overriding Isaac's art. controller)
self._pos_err[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel_err[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._pos[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._eff[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._imp_eff[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
if self._filter_available:
self._pos_ref_filter.reset(idxs = robot_indxs)
self._vel_ref_filter.reset(idxs = robot_indxs)
self._eff_ref_filter.reset(idxs = robot_indxs)
if self.init_art_on_creation:
# will use updated gains/refs based on reset (non updated gains/refs will be the same)
self._apply_init_gains_to_art()
self._apply_init_refs_to_art()
def _apply_init_gains_to_art(self):
if not self.gains_initialized:
if not self.override_art_controller:
self._articulation_view.set_gains(kps = self._pos_gains,
kds = self._vel_gains)
else:
# settings Isaac's PD controller gains to 0
no_gains = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._articulation_view.set_gains(kps = no_gains,
kds = no_gains)
self.gains_initialized = True
def _apply_init_refs_to_art(self):
if not self.refs_initialized:
if not self.override_art_controller:
self._articulation_view.set_joint_efforts(self._eff_ref)
self._articulation_view.set_joint_position_targets(self._pos_ref)
self._articulation_view.set_joint_velocity_targets(self._vel_ref)
else:
self._articulation_view.set_joint_efforts(self._eff_ref)
self.refs_initialized = True
def _validate_selectors(self,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
if robot_indxs is not None:
robot_indxs_shape = robot_indxs.shape
if (not (len(robot_indxs_shape) == 1 and \
robot_indxs.dtype == torch.int64 and \
bool(torch.min(robot_indxs) >= 0) and \
bool(torch.max(robot_indxs) < self.num_robots)) and \
robot_indxs.device.type == self._torch_device.type): # sanity checks
error = "Mismatch in provided selector \n" + \
"robot_indxs_shape -> " + f"{len(robot_indxs_shape)}" + " VS" + " expected -> " + f"{1}" + "\n" + \
"robot_indxs.dtype -> " + f"{robot_indxs.dtype}" + " VS" + " expected -> " + f"{torch.int64}" + "\n" + \
"torch.min(robot_indxs) >= 0) -> " + f"{bool(torch.min(robot_indxs) >= 0)}" + " VS" + f" {True}" + "\n" + \
"torch.max(robot_indxs) < self.n_dofs -> " + f"{torch.max(robot_indxs)}" + " VS" + f" {self.num_robots}\n" + \
"robot_indxs.device -> " + f"{robot_indxs.device.type}" + " VS" + " expected -> " + f"{self._torch_device.type}" + "\n"
Journal.log(self.__class__.__name__,
"_validate_selectors",
error,
LogType.EXCEP,
throw_when_excep = True)
if jnt_indxs is not None:
jnt_indxs_shape = jnt_indxs.shape
if (not (len(jnt_indxs_shape) == 1 and \
jnt_indxs.dtype == torch.int64 and \
bool(torch.min(jnt_indxs) >= 0) and \
bool(torch.max(jnt_indxs) < self.n_dofs)) and \
jnt_indxs.device.type == self._torch_device.type): # sanity checks
error = "Mismatch in provided selector \n" + \
"jnt_indxs_shape -> " + f"{len(jnt_indxs_shape)}" + " VS" + " expected -> " + f"{1}" + "\n" + \
"jnt_indxs.dtype -> " + f"{jnt_indxs.dtype}" + " VS" + " expected -> " + f"{torch.int64}" + "\n" + \
"torch.min(jnt_indxs) >= 0) -> " + f"{bool(torch.min(jnt_indxs) >= 0)}" + " VS" + f" {True}" + "\n" + \
"torch.max(jnt_indxs) < self.n_dofs -> " + f"{torch.max(jnt_indxs)}" + " VS" + f" {self.num_robots}" + \
"robot_indxs.device -> " + f"{jnt_indxs.device.type}" + " VS" + " expected -> " + f"{self._torch_device.type}" + "\n"
Journal.log(self.__class__.__name__,
"_validate_selectors",
error,
LogType.EXCEP,
throw_when_excep = True)
def _validate_signal(self,
signal: torch.Tensor,
selector: torch.Tensor = None,
name: str = "signal"):
if self._debug_checks:
signal_shape = signal.shape
selector_shape = selector[0].shape
if not (signal_shape[0] == selector_shape[0] and \
signal_shape[1] == selector_shape[1] and \
signal.device.type == self._torch_device.type and \
signal.dtype == self._torch_dtype):
big_error = f"Mismatch in provided signal [{name}" + "] and/or selector \n" + \
"signal rows -> " + f"{signal_shape[0]}" + " VS" + " expected rows -> " + f"{selector_shape[0]}" + "\n" + \
"signal cols -> " + f"{signal_shape[1]}" + " VS" + " expected cols -> " + f"{selector_shape[1]}" + "\n" + \
"signal dtype -> " + f"{signal.dtype}" + " VS" + " expected -> " + f"{self._torch_dtype}" + "\n" + \
"signal device -> " + f"{signal.device.type}" + " VS" + " expected type -> " + f"{self._torch_device.type}"
Journal.log(self.__class__.__name__,
"_validate_signal",
big_error,
LogType.EXCEP,
throw_when_excep = True)
def _gen_selector(self,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
if self._debug_checks:
self._validate_selectors(robot_indxs=robot_indxs,
jnt_indxs=jnt_indxs) # throws if not valid
if robot_indxs is None:
robot_indxs = self._all_robots_idxs
if jnt_indxs is None:
jnt_indxs = self._all_dofs_idxs
return torch.meshgrid((robot_indxs, jnt_indxs),
indexing="ij")
| 32,884 |
Python
| 40.157697 | 139 | 0.485282 |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/terrains.py
|
# Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
import os, sys
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(SCRIPT_DIR)
import numpy as np
from omni_robo_gym.utils.terrain_utils import *
from pxr import Usd
class RlTerrains():
def __init__(self,
stage: Usd.Stage):
self._stage = stage
def get_wave_terrain(self,
terrain_size = 40,
num_waves = 10,
amplitude = 1,
position = np.array([0.0, 0.0, 0.0])):
# creates a terrain
num_terrains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terrains * num_rows,
num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows,
length=num_cols,
vertical_scale=vertical_scale,
horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = wave_terrain(new_sub_terrain(), num_waves=num_waves,
amplitude=amplitude).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield,
horizontal_scale=horizontal_scale,
vertical_scale=vertical_scale,
slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage,
vertices=vertices,
triangles=triangles,
position=position,
orientation=orientation)
def get_sloped_terrain(self,
terrain_size = 40,
slope = -0.5,
position = np.array([0.0, 0.0, 0.0])):
# creates a terrain
num_terrains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terrains * num_rows,
num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows,
length=num_cols,
vertical_scale=vertical_scale,
horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = pyramid_sloped_terrain(new_sub_terrain(),
slope=slope).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield,
horizontal_scale=horizontal_scale,
vertical_scale=vertical_scale,
slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage,
vertices=vertices,
triangles=triangles,
position=position,
orientation=orientation)
def get_stairs_terrain(self,
terrain_size = 40,
step_width = 0.75,
step_height = -0.5,
position = np.array([0.0, 0.0, 0.0])):
# creates a terrain
num_terrains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terrains * num_rows,
num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows,
length=num_cols,
vertical_scale=vertical_scale,
horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = stairs_terrain(new_sub_terrain(), step_width=step_width,
step_height=step_height).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield,
horizontal_scale=horizontal_scale,
vertical_scale=vertical_scale,
slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage,
vertices=vertices,
triangles=triangles,
position=position,
orientation=orientation)
def get_random_terrain(self,
terrain_size = 40,
min_height = -0.2,
max_height = 0.2,
step = 0.2,
downsampled_scale=0.5,
position = np.array([0.0, 0.0, 0.0])):
# creates a terrain
num_terrains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terrains * num_rows,
num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows,
length=num_cols,
vertical_scale=vertical_scale,
horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = random_uniform_terrain(new_sub_terrain(),
min_height=min_height, max_height=max_height,
step=step,
downsampled_scale=downsampled_scale).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield,
horizontal_scale=horizontal_scale,
vertical_scale=vertical_scale,
slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage,
vertices=vertices,
triangles=triangles,
position=position,
orientation=orientation)
def get_obstacles_terrain(self,
terrain_size = 40.0,
num_obs = 50,
max_height = 0.5,
min_size = 0.5,
max_size = 5.0,
position = np.array([0.0, 0.0, 0.0])):
# create all available terrain types
num_terains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terains*num_rows, num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows, length=num_cols, vertical_scale=vertical_scale, horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = discrete_obstacles_terrain(new_sub_terrain(),
max_height=max_height,
min_size=min_size,
max_size=max_size,
num_rects=num_obs).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield, horizontal_scale=horizontal_scale, vertical_scale=vertical_scale, slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage, vertices=vertices, triangles=triangles, position=position, orientation=orientation)
def post_reset(self):
a = 1
def get_observations(self):
pass
def calculate_metrics(self) -> None:
pass
def is_done(self) -> None:
pass
| 9,922 |
Python
| 36.730038 | 160 | 0.528926 |
AndrePatri/OmniRoboGym/docs/isaac2023.1.0_issues.md
|
### Some bugs of Isaac2023.1.0 which can be easily fixed
#### 1.0 Nucleus blocking function makes startup super slow
Easy temporary fix: modify /home/username/.local/share/ov/pkg/isaac_sim-2023.1.0/exts/omni.isaac.core/omni/isaac/core/utils/nucleus.py .
Change lines 178 to 198 which is the check server function to below:
```python
def check_server(server: str, path: str, timeout: float = 10.0) -> bool:
"""Check a specific server for a path
Args:
server (str): Name of Nucleus server
path (str): Path to search
Returns:
bool: True if folder is found
"""
carb.log_info("Checking path: {}{}".format(server, path))
# Increase hang detection timeout
if "localhost" not in server:
omni.client.set_hang_detection_time_ms(10000)
result, _ = omni.client.stat("{}{}".format(server, path))
if result == Result.OK:
carb.log_info("Success: {}{}".format(server, path))
return True
carb.log_info("Failure: {}{} not accessible".format(server, path))
return False
```
#### 2.0 Grid Cloner bug
See `docs/grid_cloner_bugfix.py` for more details
#### 3.0 Contact sensor bug
When cloning environments, it's not possible to create contact sensors on the cloned environments because of a failed collision_API enabled flag option. Removing the check seems to recolve the problem without any major or noticeable issues.
| 1,413 |
Markdown
| 39.399999 | 240 | 0.683652 |
AndrePatri/OmniRoboGym/docs/grid_cloner_bugfix/grid_cloner.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
from typing import List, Union
import numpy as np
import omni.usd
import torch
from omni.isaac.cloner import Cloner
from pxr import Gf, UsdGeom
class GridCloner(Cloner):
""" This is a specialized Cloner class that will automatically generate clones in a grid fashion. """
def __init__(self, spacing: float, num_per_row: int = -1):
"""
Args:
spacing (float): Spacing between clones.
num_per_row (int): Number of clones to place in a row. Defaults to sqrt(num_clones).
"""
self._spacing = spacing
self._num_per_row = num_per_row
Cloner.__init__(self)
def clone(
self,
source_prim_path: str,
prim_paths: List[str],
position_offsets: np.ndarray = None,
orientation_offsets: np.ndarray = None,
replicate_physics: bool = False,
base_env_path: str = None,
root_path: str = None,
copy_from_source: bool = False
):
""" Creates clones in a grid fashion. Positions of clones are computed automatically.
Args:
source_prim_path (str): Path of source object.
prim_paths (List[str]): List of destination paths.
position_offsets (np.ndarray): Positions to be applied as local translations on top of computed clone position.
Defaults to None, no offset will be applied.
orientation_offsets (np.ndarray): Orientations to be applied as local rotations for each clone.
Defaults to None, no offset will be applied.
replicate_physics (bool): Uses omni.physics replication. This will replicate physics properties directly for paths beginning with root_path and skip physics parsing for anything under the base_env_path.
base_env_path (str): Path to namespace for all environments. Required if replicate_physics=True and define_base_env() not called.
root_path (str): Prefix path for each environment. Required if replicate_physics=True and generate_paths() not called.
copy_from_source: (bool): Setting this to False will inherit all clones from the source prim; any changes made to the source prim will be reflected in the clones.
Setting this to True will make copies of the source prim when creating new clones; changes to the source prim will not be reflected in clones. Defaults to False. Note that setting this to True will take longer to execute.
Returns:
positions (List): Computed positions of all clones.
"""
num_clones = len(prim_paths)
self._num_per_row = int(np.sqrt(num_clones)) if self._num_per_row == -1 else self._num_per_row
num_rows = np.ceil(num_clones / self._num_per_row)
num_cols = np.ceil(num_clones / num_rows)
row_offset = 0.5 * self._spacing * (num_rows - 1)
col_offset = 0.5 * self._spacing * (num_cols - 1)
stage = omni.usd.get_context().get_stage()
positions = []
orientations = []
for i in range(num_clones):
# compute transform
row = i // num_cols
col = i % num_cols
x = row_offset - row * self._spacing
y = col * self._spacing - col_offset
up_axis = UsdGeom.GetStageUpAxis(stage)
position = [x, y, 0] if up_axis == UsdGeom.Tokens.z else [x, 0, y]
orientation = Gf.Quatd.GetIdentity()
if position_offsets is not None:
translation = position_offsets[i] + position
else:
translation = position
if orientation_offsets is not None:
orientation = (
Gf.Quatd(orientation_offsets[i][0].item(), Gf.Vec3d(orientation_offsets[i][1:].tolist()))
* orientation
)
else:
orientation = [
orientation.GetReal(),
orientation.GetImaginary()[0],
orientation.GetImaginary()[1],
orientation.GetImaginary()[2],
]
positions.append(translation)
orientations.append(orientation)
super().clone(
source_prim_path=source_prim_path,
prim_paths=prim_paths,
positions=positions,
orientations=orientations,
replicate_physics=replicate_physics,
base_env_path=base_env_path,
root_path=root_path,
copy_from_source=copy_from_source,
)
return positions
| 5,073 |
Python
| 40.590164 | 246 | 0.606742 |
AndrePatri/OmniRoboGym/docs/contact_sensor_bugfix/contact_sensor.py
|
# Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
from omni.isaac.kit import SimulationApp
simulation_app = SimulationApp({"headless": False})
import argparse
import sys
import carb
import numpy as np
from omni.isaac.core import World
from omni.isaac.core.articulations import Articulation
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.stage import add_reference_to_stage
from omni.isaac.sensor import ContactSensor
from omni.isaac.cloner import GridCloner
import omni.isaac.core.utils.prims as prim_utils
parser = argparse.ArgumentParser()
parser.add_argument("--test", default=False, action="store_true", help="Run in test mode")
args, unknown = parser.parse_known_args()
assets_root_path = get_assets_root_path()
if assets_root_path is None:
carb.log_error("Could not find Isaac Sim assets folder")
simulation_app.close()
sys.exit()
my_world = World(stage_units_in_meters=1.0)
my_world.scene.add_default_ground_plane()
asset_path = assets_root_path + "/Isaac/Robots/Ant/ant.usd"
add_reference_to_stage(usd_path=asset_path, prim_path="/World/envs/env_0/Ant")
ant = my_world.scene.add(Articulation(prim_path="/World/envs/env_0/Ant/torso", name="ant", translation=np.array([0, 0, 1.5])))
ant_foot_prim_names = ["right_back_foot", "left_back_foot", "front_right_foot", "front_left_foot"]
translations = np.array(
[[0.38202, -0.40354, -0.0887], [-0.4, -0.40354, -0.0887], [-0.4, 0.4, -0.0887], [0.4, 0.4, -0.0887]]
)
# moving def prim
# move_prim(robot_prim_path_default, # from
# robot_base_prim_path) # to
num_envs = 3
env_ns = "/World/envs"
env_spacing = 15 # [m]
template_env_ns = env_ns + "/env_0"
cloner = GridCloner(spacing=env_spacing)
cloner.define_base_env(env_ns)
envs_prim_paths = cloner.generate_paths(env_ns + "/env",
num_envs)
cloner.clone(
source_prim_path=template_env_ns,
prim_paths=envs_prim_paths,
replicate_physics=True,
position_offsets = None
)
ant_sensors = []
for i in range(4):
ant_sensors.append(
my_world.scene.add(
ContactSensor(
prim_path="/World/envs/env_0/Ant/" + ant_foot_prim_names[i] + "/contact_sensor",
name="ant_contact_sensor_{}".format(i),
min_threshold=0,
max_threshold=10000000,
radius=0.1,
translation=translations[i],
)
)
)
ant_sensors[0].add_raw_contact_data_to_frame()
ant_sensors2 = []
for i in range(4):
ant_sensors2.append(
my_world.scene.add(
ContactSensor(
prim_path="/World/envs/env_1/Ant/" + ant_foot_prim_names[i] + "/contact_sensor",
name="ant_contact_sensor2_{}".format(i),
min_threshold=0,
max_threshold=10000000,
radius=0.1,
translation=translations[i],
)
)
)
ant_sensors2[0].add_raw_contact_data_to_frame()
my_world.reset()
while simulation_app.is_running():
my_world.step(render=True)
if my_world.is_playing():
print(ant_sensors2[0].get_current_frame())
if my_world.current_time_step_index == 0:
my_world.reset()
simulation_app.close()
| 3,638 |
Python
| 30.370689 | 126 | 0.657779 |
AndrePatri/OmniRoboGym/docs/sim_substepping_reset_issue/test_substepping_when_reset.py
|
# Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
import numpy as np
import torch
def get_device(sim_params):
if "sim_device" in sim_params:
device = sim_params["sim_device"]
else:
device = "cpu"
physics_device_id = carb.settings.get_settings().get_as_int("/physics/cudaDevice")
gpu_id = 0 if physics_device_id < 0 else physics_device_id
if sim_params and "use_gpu_pipeline" in sim_params:
# GPU pipeline must use GPU simulation
if sim_params["use_gpu_pipeline"]:
device = "cuda:" + str(gpu_id)
elif sim_params and "use_gpu" in sim_params:
if sim_params["use_gpu"]:
device = "cuda:" + str(gpu_id)
return device
def sim_parameters():
# simulation parameters
sim_params = {}
# device settings
sim_params["use_gpu_pipeline"] = True # disabling gpu pipeline is necessary to be able
# to retrieve some quantities from the simulator which, otherwise, would have random values
sim_params["use_gpu"] = True # does this actually do anything?
if sim_params["use_gpu_pipeline"]:
sim_params["device"] = "cuda"
else:
sim_params["device"] = "cpu"
device = sim_params["device"]
# sim_params["dt"] = 1.0/100.0 # physics_dt?
sim_params["physics_dt"] = 1.0/400.0 # physics_dt?
sim_params["rendering_dt"] = sim_params["physics_dt"]
sim_params["substeps"] = 1 # number of physics steps to be taken for for each rendering step
sim_params["gravity"] = np.array([0.0, 0.0, -9.81])
sim_params["enable_scene_query_support"] = False
sim_params["use_fabric"] = True # Enable/disable reading of physics buffers directly. Default is True.
sim_params["replicate_physics"] = True
# sim_params["worker_thread_count"] = 4
sim_params["solver_type"] = 1 # 0: PGS, 1:TGS, defaults to TGS. PGS faster but TGS more stable
sim_params["enable_stabilization"] = True
# sim_params["bounce_threshold_velocity"] = 0.2
# sim_params["friction_offset_threshold"] = 0.04
# sim_params["friction_correlation_distance"] = 0.025
# sim_params["enable_sleeping"] = True
# Per-actor settings ( can override in actor_options )
sim_params["solver_position_iteration_count"] = 4 # defaults to 4
sim_params["solver_velocity_iteration_count"] = 1 # defaults to 1
sim_params["sleep_threshold"] = 0.0 # Mass-normalized kinetic energy threshold below which an actor may go to sleep.
# Allowed range [0, max_float).
sim_params["stabilization_threshold"] = 1e-5
# Per-body settings ( can override in actor_options )
# sim_params["enable_gyroscopic_forces"] = True
# sim_params["density"] = 1000 # density to be used for bodies that do not specify mass or density
# sim_params["max_depenetration_velocity"] = 100.0
# sim_params["solver_velocity_iteration_count"] = 1
# GPU buffers settings
# sim_params["gpu_max_rigid_contact_count"] = 512 * 1024
# sim_params["gpu_max_rigid_patch_count"] = 80 * 1024
# sim_params["gpu_found_lost_pairs_capacity"] = 1024
# sim_params["gpu_found_lost_aggregate_pairs_capacity"] = 1024
# sim_params["gpu_total_aggregate_pairs_capacity"] = 1024
# sim_params["gpu_max_soft_body_contacts"] = 1024 * 1024
# sim_params["gpu_max_particle_contacts"] = 1024 * 1024
# sim_params["gpu_heap_capacity"] = 64 * 1024 * 1024
# sim_params["gpu_temp_buffer_capacity"] = 16 * 1024 * 1024
# sim_params["gpu_max_num_partitions"] = 8
return sim_params
def reset_state(art_view,
idxs: torch.Tensor):
# root q
art_view.set_world_poses(positions = root_p_default[idxs, :],
orientations=root_q_default[idxs, :],
indices = idxs)
# jnts q
art_view.set_joint_positions(positions = jnts_q_default[idxs, :],
indices = idxs)
# root v and omega
art_view.set_joint_velocities(velocities = jnts_v_default[idxs, :],
indices = idxs)
# jnts v
concatenated_vel = torch.cat((root_v_default[idxs, :],
root_omega_default[idxs, :]), dim=1)
art_view.set_velocities(velocities = concatenated_vel,
indices = idxs)
# jnts eff
art_view.set_joint_efforts(efforts = jnts_eff_default[idxs, :],
indices = idxs)
def get_robot_state(
art_view):
pose = art_view.get_world_poses(
clone = True) # tuple: (pos, quat)
# root p (measured, previous, default)
root_p = pose[0]
# root q (measured, previous, default)
root_q = pose[1] # root orientation
# jnt q (measured, previous, default)
jnts_q = art_view.get_joint_positions(
clone = True) # joint positions
# root v (measured, default)
root_v= art_view.get_linear_velocities(
clone = True) # root lin. velocity
# root omega (measured, default)
root_omega = art_view.get_angular_velocities(
clone = True) # root ang. velocity
# joints v (measured, default)
jnts_v = art_view.get_joint_velocities(
clone = True) # joint velocities
jnts_eff = art_view.get_measured_joint_efforts(clone = True)
return root_p, root_q, jnts_q, root_v, root_omega, jnts_v, jnts_eff
from omni.isaac.kit import SimulationApp
import carb
import os
experience = f'{os.environ["EXP_PATH"]}/omni.isaac.sim.python.omnirobogym.headless.kit'
sim_params = sim_parameters()
num_envs = 2
headless = True
simulation_app = SimulationApp({"headless": headless,
"physics_gpu": 0},
experience=experience)
from omni.isaac.core import World
from omni.isaac.core.articulations import ArticulationView
from omni.importer.urdf import _urdf
# urdf import config
import_config = _urdf.ImportConfig()
import_config.merge_fixed_joints = True
import_config.import_inertia_tensor = True
import_config.fix_base = False
import_config.self_collision = False
my_world = World(stage_units_in_meters=1.0,
physics_dt=sim_params["physics_dt"],
rendering_dt=sim_params["rendering_dt"],
backend="torch",
device=str(get_device(sim_params=sim_params)),
physics_prim_path="/physicsScene",
set_defaults = False,
sim_params=sim_params)
# create initial robot
import omni.isaac.core.utils.prims as prim_utils
# create GridCloner instance
env_ns = "/World/envs"
template_env_ns = env_ns + "/env" # a single env. may contain multiple robots
base_env = template_env_ns + "_0"
base_robot_path = base_env + "/panda"
# get path to resource
from omni.isaac.core.utils.extensions import get_extension_path_from_name
extension_path = get_extension_path_from_name("omni.importer.urdf")
# import URDF at default prim path
import omni.kit
success, robot_prim_path_default = omni.kit.commands.execute(
"URDFParseAndImportFile",
urdf_path=extension_path + "/data/urdf/robots/franka_description/robots/panda_arm.urdf",
import_config=import_config,
)
# moving default prim to base prim path (for potential cloning)
from omni.isaac.core.utils.prims import move_prim
prim_utils.define_prim(base_env)
move_prim(robot_prim_path_default, # from
base_robot_path) # to
# cloning
from omni.isaac.cloner import GridCloner
cloner = GridCloner(spacing=6)
_envs_prim_paths = cloner.generate_paths(template_env_ns, num_envs)
position_offsets = np.array([[0.0, 0.0, 0.6]] * num_envs)
cloner.clone(
source_prim_path=base_env,
prim_paths=_envs_prim_paths,
base_env_path=base_env,
position_offsets=position_offsets,
replicate_physics=True
)
# Prim paths structure:
# World/envs/env_0/panda/panda_link0/...
# this only in 2023.1.0
art_view = ArticulationView(name = "Panda" + "ArtView",
prim_paths_expr = env_ns + "/env_.*"+ "/panda/panda_link0",
reset_xform_properties=False # required as per doc. when cloning
)
# moreover, robots are not cloned at different locations
my_world.scene.add(art_view)
ground_plane_prim_path = "/World/terrain"
my_world.scene.add_default_ground_plane(z_position=0,
name="terrain",
prim_path= ground_plane_prim_path,
static_friction=0.5,
dynamic_friction=0.5,
restitution=0.8)
cloner.filter_collisions(physicsscene_path = my_world.get_physics_context().prim_path,
collision_root_path = "/World/collisions",
prim_paths=_envs_prim_paths,
global_paths=[ground_plane_prim_path] # can collide with these prims
)
my_world.reset()
# init default state from measurements
root_p, root_q, jnts_q, root_v, \
root_omega, jnts_v, jnts_eff = get_robot_state(art_view)
root_p_default = torch.clone(root_p)
root_q_default = torch.clone(root_q)
jnts_q_default = torch.clone(jnts_q)
jnts_v_default = torch.clone(jnts_v)
root_omega_default = torch.clone(root_omega)
root_v_default = torch.clone(root_v)
jnts_eff_default = torch.clone(jnts_eff).zero_()
# default values
root_p_default[:, 0] = 0
root_p_default[:, 1] = 0
root_p_default[:, 2] = 0.5
root_q_default[:, 0] = 0.0
root_q_default[:, 1] = 0.0
root_q_default[:, 2] = 0.0
root_q_default[:, 3] = 1.0
jnts_q_default[:, :] = 1.0
jnts_v_default[:, :] = 0.0
root_omega_default[:, :] = 0.0
root_v_default[:, :] = 0.0
no_gains = torch.zeros((num_envs, jnts_eff_default.shape[1]), device = get_device(sim_params),
dtype=torch.float32)
art_view.set_gains(kps = no_gains,
kds = no_gains)
print("Extension path: " + str(extension_path))
print("Prim paths: " + str(art_view.prim_paths))
reset_ever_n_steps = 100
just_reset = False
for i in range(0, 1000):
if ((i + 1) % reset_ever_n_steps) == 0:
print("resetting to default")
reset_state(art_view,
torch.tensor([0], dtype=torch.int))
just_reset = True
my_world.step()
# retrieve state
root_p, root_q, jnts_q, root_v, \
root_omega, jnts_v, jnts_eff = get_robot_state(art_view)
# if just_reset:
# check we hace reset correcty
print("measured")
print(jnts_q)
print("default")
print(jnts_q_default)
simulation_app.close()
| 11,081 |
Python
| 34.06962 | 120 | 0.624222 |
abizovnuralem/go2_omniverse/terrain_cfg.py
|
# Copyright (c) 2024, RoboVerse community
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from terrain_generator_cfg import TerrainGeneratorCfg
import omni.isaac.orbit.terrains as terrain_gen
ROUGH_TERRAINS_CFG = TerrainGeneratorCfg(
size=(8.0, 8.0),
border_width=0.0,
num_rows=1,
num_cols=2,
horizontal_scale=0.1,
vertical_scale=0.005,
slope_threshold=0.75,
use_cache=False,
sub_terrains={
"pyramid_stairs": terrain_gen.MeshPyramidStairsTerrainCfg(
proportion=0.2,
step_height_range=(0.05, 0.23),
step_width=0.3,
platform_width=3.0,
border_width=1.0,
holes=False,
),
"pyramid_stairs_inv": terrain_gen.MeshInvertedPyramidStairsTerrainCfg(
proportion=0.2,
step_height_range=(0.05, 0.23),
step_width=0.3,
platform_width=3.0,
border_width=1.0,
holes=False,
),
},
)
| 2,217 |
Python
| 38.607142 | 80 | 0.700947 |
abizovnuralem/go2_omniverse/agent_cfg.py
|
# Copyright (c) 2024, RoboVerse community
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
unitree_go2_agent_cfg = {
'seed': 42,
'device': 'cuda',
'num_steps_per_env': 24,
'max_iterations': 15000,
'empirical_normalization': False,
'policy': {
'class_name': 'ActorCritic',
'init_noise_std': 1.0,
'actor_hidden_dims': [512, 256, 128],
'critic_hidden_dims': [512, 256, 128],
'activation': 'elu'
},
'algorithm': {
'class_name': 'PPO',
'value_loss_coef': 1.0,
'use_clipped_value_loss': True,
'clip_param': 0.2,
'entropy_coef': 0.01,
'num_learning_epochs': 5,
'num_mini_batches': 4,
'learning_rate': 0.001,
'schedule': 'adaptive',
'gamma': 0.99,
'lam': 0.95,
'desired_kl': 0.01,
'max_grad_norm': 1.0
},
'save_interval': 50,
'experiment_name': 'unitree_go2_rough',
'run_name': '',
'logger': 'tensorboard',
'neptune_project': 'orbit',
'wandb_project': 'orbit',
'resume': False,
'load_run': '.*',
'load_checkpoint': 'model_.*.pt'
}
| 2,562 |
Python
| 40.338709 | 80 | 0.613193 |
abizovnuralem/go2_omniverse/main.py
|
# Copyright (c) 2024, RoboVerse community
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Script to play a checkpoint if an RL agent from RSL-RL."""
from __future__ import annotations
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.orbit.app import AppLauncher
# local imports
import cli_args # isort: skip
# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
parser.add_argument("--cpu", action="store_true", default=False, help="Use CPU pipeline.")
parser.add_argument(
"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
)
parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default="Isaac-Velocity-Rough-Unitree-Go2-v0", help="Name of the task.")
parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
# append RSL-RL cli arguments
cli_args.add_rsl_rl_args(parser)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
import omni
ext_manager = omni.kit.app.get_app().get_extension_manager()
ext_manager.set_extension_enabled_immediate("omni.isaac.ros2_bridge", True)
"""Rest everything follows."""
import os
import math
import gymnasium as gym
import torch
import carb
import usdrt.Sdf
from omni.isaac.orbit_tasks.utils import get_checkpoint_path
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlVecEnvWrapper
)
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_assets.unitree import UNITREE_GO2_CFG
from omni.isaac.orbit.envs import RLTaskEnvCfg
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg
from omni.isaac.orbit.managers import CurriculumTermCfg as CurrTerm
from omni.isaac.orbit.managers import EventTermCfg as EventTerm
from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup
from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm
from omni.isaac.orbit.managers import RewardTermCfg as RewTerm
from omni.isaac.orbit.managers import SceneEntityCfg
from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm
from omni.isaac.orbit.scene import InteractiveSceneCfg
from omni.isaac.orbit.sensors import ContactSensorCfg, RayCasterCfg, patterns, CameraCfg
from omni.isaac.orbit.terrains import TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit.utils.noise import AdditiveUniformNoiseCfg as Unoise
import omni.isaac.orbit_tasks.locomotion.velocity.mdp as mdp
import omni.appwindow # Contains handle to keyboard
from rsl_rl.runners import OnPolicyRunner
from typing import Literal
from dataclasses import MISSING
from omnigraph import create_front_cam_omnigraph
from agent_cfg import unitree_go2_agent_cfg
from terrain_cfg import ROUGH_TERRAINS_CFG
base_command = [0, 0, 0]
@configclass
class MySceneCfg(InteractiveSceneCfg):
"""Configuration for the terrain scene with a legged robot."""
# ground terrain
terrain = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="generator",
terrain_generator=ROUGH_TERRAINS_CFG,
max_init_terrain_level=5,
collision_group=-1,
physics_material=sim_utils.RigidBodyMaterialCfg(
friction_combine_mode="multiply",
restitution_combine_mode="multiply",
static_friction=1.0,
dynamic_friction=1.0,
),
visual_material=sim_utils.MdlFileCfg(
mdl_path="{NVIDIA_NUCLEUS_DIR}/Materials/Base/Architecture/Shingles_01.mdl",
project_uvw=True,
),
debug_vis=False,
)
# robots
robot: ArticulationCfg = MISSING
# sensors
camera = CameraCfg(
prim_path="{ENV_REGEX_NS}/Robot/base/front_cam",
update_period=0.1,
height=480,
width=640,
data_types=["rgb", "distance_to_image_plane"],
spawn=sim_utils.PinholeCameraCfg(
focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5)
),
offset=CameraCfg.OffsetCfg(pos=(0.510, 0.0, 0.015), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"),
)
height_scanner = RayCasterCfg(
prim_path="{ENV_REGEX_NS}/Robot/base",
offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
attach_yaw_only=True,
pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]),
debug_vis=False,
mesh_prim_paths=["/World/ground"],
)
contact_forces = ContactSensorCfg(prim_path="{ENV_REGEX_NS}/Robot/.*", history_length=3, track_air_time=True)
# lights
light = AssetBaseCfg(
prim_path="/World/light",
spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
)
sky_light = AssetBaseCfg(
prim_path="/World/skyLight",
spawn=sim_utils.DomeLightCfg(color=(0.13, 0.13, 0.13), intensity=1000.0),
)
def constant_commands(env: RLTaskEnvCfg) -> torch.Tensor:
global base_command
"""The generated command from the command generator."""
return torch.tensor([base_command], device=env.device).repeat(env.num_envs, 1)
@configclass
class ObservationsCfg:
"""Observation specifications for the MDP."""
@configclass
class PolicyCfg(ObsGroup):
"""Observations for policy group."""
# observation terms (order preserved)
base_lin_vel = ObsTerm(func=mdp.base_lin_vel)
base_ang_vel = ObsTerm(func=mdp.base_ang_vel)
projected_gravity = ObsTerm(
func=mdp.projected_gravity,
noise=Unoise(n_min=-0.05, n_max=0.05),
)
velocity_commands = ObsTerm(func=constant_commands)
joint_pos = ObsTerm(func=mdp.joint_pos_rel)
joint_vel = ObsTerm(func=mdp.joint_vel_rel)
actions = ObsTerm(func=mdp.last_action)
height_scan = ObsTerm(
func=mdp.height_scan,
params={"sensor_cfg": SceneEntityCfg("height_scanner")},
clip=(-1.0, 1.0),
)
def __post_init__(self):
self.enable_corruption = True
self.concatenate_terms = True
# observation groups
policy: PolicyCfg = PolicyCfg()
@configclass
class ActionsCfg:
"""Action specifications for the MDP."""
joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True)
@configclass
class CommandsCfg:
"""Command specifications for the MDP."""
base_velocity = mdp.UniformVelocityCommandCfg(
asset_name="robot",
resampling_time_range=(0.0, 0.0),
rel_standing_envs=0.02,
rel_heading_envs=1.0,
heading_command=True,
heading_control_stiffness=0.5,
debug_vis=True,
ranges=mdp.UniformVelocityCommandCfg.Ranges(
lin_vel_x=(0.0, 0.0), lin_vel_y=(0.0, 0.0), ang_vel_z=(0.0, 0.0), heading=(0, 0)
),
)
@configclass
class RewardsCfg:
"""Reward terms for the MDP."""
# -- task
track_lin_vel_xy_exp = RewTerm(
func=mdp.track_lin_vel_xy_exp, weight=1.0, params={"command_name": "base_velocity", "std": math.sqrt(0.25)}
)
track_ang_vel_z_exp = RewTerm(
func=mdp.track_ang_vel_z_exp, weight=0.5, params={"command_name": "base_velocity", "std": math.sqrt(0.25)}
)
# -- penalties
lin_vel_z_l2 = RewTerm(func=mdp.lin_vel_z_l2, weight=-2.0)
ang_vel_xy_l2 = RewTerm(func=mdp.ang_vel_xy_l2, weight=-0.05)
dof_torques_l2 = RewTerm(func=mdp.joint_torques_l2, weight=-1.0e-5)
dof_acc_l2 = RewTerm(func=mdp.joint_acc_l2, weight=-2.5e-7)
action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-0.01)
feet_air_time = RewTerm(
func=mdp.feet_air_time,
weight=0.125,
params={
"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*FOOT"),
"command_name": "base_velocity",
"threshold": 0.5,
},
)
undesired_contacts = RewTerm(
func=mdp.undesired_contacts,
weight=-1.0,
params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*THIGH"), "threshold": 1.0},
)
# -- optional penalties
flat_orientation_l2 = RewTerm(func=mdp.flat_orientation_l2, weight=0.0)
dof_pos_limits = RewTerm(func=mdp.joint_pos_limits, weight=0.0)
@configclass
class TerminationsCfg:
"""Termination terms for the MDP."""
time_out = DoneTerm(func=mdp.time_out, time_out=True)
base_contact = DoneTerm(
func=mdp.illegal_contact,
params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names="base"), "threshold": 1.0},
)
@configclass
class EventCfg:
"""Configuration for events."""
# startup
physics_material = EventTerm(
func=mdp.randomize_rigid_body_material,
mode="startup",
params={
"asset_cfg": SceneEntityCfg("robot", body_names=".*"),
"static_friction_range": (0.8, 0.8),
"dynamic_friction_range": (0.6, 0.6),
"restitution_range": (0.0, 0.0),
"num_buckets": 64,
},
)
@configclass
class CurriculumCfg:
"""Curriculum terms for the MDP."""
terrain_levels = CurrTerm(func=mdp.terrain_levels_vel)
@configclass
class ViewerCfg:
"""Configuration of the scene viewport camera."""
eye: tuple[float, float, float] = (7.5, 7.5, 7.5)
lookat: tuple[float, float, float] = (0.0, 0.0, 0.0)
cam_prim_path: str = "/OmniverseKit_Persp"
resolution: tuple[int, int] = (1920, 1080)
origin_type: Literal["world", "env", "asset_root"] = "world"
env_index: int = 0
asset_name: str | None = None
@configclass
class LocomotionVelocityRoughEnvCfg(RLTaskEnvCfg):
"""Configuration for the locomotion velocity-tracking environment."""
# Scene settings
scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=2.5)
viewer: ViewerCfg = ViewerCfg()
# Basic settings
observations: ObservationsCfg = ObservationsCfg()
actions: ActionsCfg = ActionsCfg()
commands: CommandsCfg = CommandsCfg()
# MDP settings
rewards: RewardsCfg = RewardsCfg()
terminations: TerminationsCfg = TerminationsCfg()
events: EventCfg = EventCfg()
curriculum: CurriculumCfg = CurriculumCfg()
def __post_init__(self):
"""Post initialization."""
# general settings
self.decimation = 4
self.episode_length_s = 20.0
# simulation settings
self.sim.dt = 0.005
self.sim.disable_contact_processing = True
self.sim.physics_material = self.scene.terrain.physics_material
# update sensor update periods
# we tick all the sensors based on the smallest update period (physics update period)
if self.scene.height_scanner is not None:
self.scene.height_scanner.update_period = self.decimation * self.sim.dt
if self.scene.contact_forces is not None:
self.scene.contact_forces.update_period = self.sim.dt
# check if terrain levels curriculum is enabled - if so, enable curriculum for terrain generator
# this generates terrains with increasing difficulty and is useful for training
if getattr(self.curriculum, "terrain_levels", None) is not None:
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.curriculum = True
else:
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.curriculum = False
@configclass
class UnitreeGo2RoughEnvCfg(LocomotionVelocityRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
self.scene.robot = UNITREE_GO2_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/base"
# reduce action scale
self.actions.joint_pos.scale = 0.25
# rewards
self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot"
self.rewards.feet_air_time.weight = 0.01
self.rewards.undesired_contacts = None
self.rewards.dof_torques_l2.weight = -0.0002
self.rewards.track_lin_vel_xy_exp.weight = 1.5
self.rewards.track_ang_vel_z_exp.weight = 0.75
self.rewards.dof_acc_l2.weight = -2.5e-7
# terminations
self.terminations.base_contact.params["sensor_cfg"].body_names = "base"
#create ros2 camera stream omnigraph
create_front_cam_omnigraph()
def sub_keyboard_event(event, *args, **kwargs) -> bool:
global base_command
if event.type == carb.input.KeyboardEventType.KEY_PRESS:
if event.input.name == 'W':
base_command = [1, 0, 0]
if event.input.name == 'S':
base_command = [-1, 0, 0]
if event.input.name == 'A':
base_command = [0, 1, 0]
if event.input.name == 'D':
base_command = [0, -1, 0]
if event.input.name == 'Q':
base_command = [0, 0, 1]
if event.input.name == 'E':
base_command = [0, 0, -1]
elif event.type == carb.input.KeyboardEventType.KEY_RELEASE:
base_command = [0, 0, 0]
return True
def main():
# acquire input interface
_input = carb.input.acquire_input_interface()
_appwindow = omni.appwindow.get_default_app_window()
_keyboard = _appwindow.get_keyboard()
_sub_keyboard = _input.subscribe_to_keyboard_events(_keyboard, sub_keyboard_event)
"""Play with RSL-RL agent."""
# parse configuration
env_cfg = UnitreeGo2RoughEnvCfg()
env_cfg.scene.num_envs = 1
agent_cfg: RslRlOnPolicyRunnerCfg = unitree_go2_agent_cfg
# create isaac environment
env = gym.make(args_cli.task, cfg=env_cfg)
# wrap around environment for rsl-rl
env = RslRlVecEnvWrapper(env)
# specify directory for logging experiments
log_root_path = os.path.join("logs", "rsl_rl", agent_cfg["experiment_name"])
log_root_path = os.path.abspath(log_root_path)
print(f"[INFO] Loading experiment from directory: {log_root_path}")
resume_path = get_checkpoint_path(log_root_path, agent_cfg["load_run"], agent_cfg["load_checkpoint"])
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
# load previously trained model
ppo_runner = OnPolicyRunner(env, agent_cfg, log_dir=None, device=agent_cfg["device"])
ppo_runner.load(resume_path)
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
# obtain the trained policy for inference
policy = ppo_runner.get_inference_policy(device=env.unwrapped.device)
# reset environment
obs, _ = env.get_observations()
# simulate environment
while simulation_app.is_running():
# run everything in inference mode
with torch.inference_mode():
# agent stepping
actions = policy(obs)
# env stepping
obs, _, _, _ = env.step(actions)
# close the simulator
env.close()
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 16,627 |
Python
| 34.529914 | 118 | 0.669333 |
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