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NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.usd/python/tests/test_usd_example.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.test import omni.kit.app import omni.usd import omni.example.cpp.usd class TestUsdExample(omni.kit.test.AsyncTestCase): async def setUp(self): # Cache the example usd interface. self.example_usd_interface = omni.example.cpp.usd.get_example_usd_interface() # Open a new USD stage. omni.usd.get_context().new_stage() self.usd_stage = omni.usd.get_context().get_stage() async def tearDown(self): # Close the USD stage. await omni.usd.get_context().close_stage_async() self.usd_stage = None # Clear the example usd interface. self.example_usd_interface = None async def test_create_prims(self): self.example_usd_interface.create_prims() self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_0")) self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_1")) self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_2")) self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_3")) self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_4")) self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_5")) self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_6")) self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_7")) self.assertTrue(self.usd_stage.GetPrimAtPath("/World/example_prim_8")) self.assertFalse(self.usd_stage.GetPrimAtPath("/World/a_random_prim"))

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.usd/python/impl/example_usd_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. ## import omni.ext import omni.usd from .._example_usd_bindings import * # Global public interface object. _example_usd_interface = None # Public API. def get_example_usd_interface() -> IExampleUsdInterface: return _example_usd_interface # Use the extension entry points to acquire and release the interface, # and to subscribe to usd stage events. class ExampleUsdExtension(omni.ext.IExt): def on_startup(self): # Acquire the example USD interface. global _example_usd_interface _example_usd_interface = acquire_example_usd_interface() # Inform the C++ plugin if a USD stage is already open. usd_context = omni.usd.get_context() if usd_context.get_stage_state() == omni.usd.StageState.OPENED: _example_usd_interface.on_default_usd_stage_changed(usd_context.get_stage_id()) # Subscribe to omni.usd stage events so we can inform the C++ plugin when a new stage opens. self._stage_event_sub = usd_context.get_stage_event_stream().create_subscription_to_pop( self._on_stage_event, name="omni.example.cpp.usd" ) # Print some info about the stage from C++. _example_usd_interface.print_stage_info() # Create some example prims from C++. _example_usd_interface.create_prims() # Print some info about the stage from C++. _example_usd_interface.print_stage_info() # Animate the example prims from C++. _example_usd_interface.start_timeline_animation() def on_shutdown(self): global _example_usd_interface # Stop animating the example prims from C++. _example_usd_interface.stop_timeline_animation() # Remove the example prims from C++. _example_usd_interface.remove_prims() # Unsubscribe from omni.usd stage events. self._stage_event_sub = None # Release the example USD interface. release_example_usd_interface(_example_usd_interface) _example_usd_interface = None def _on_stage_event(self, event): if event.type == int(omni.usd.StageEventType.OPENED): _example_usd_interface.on_default_usd_stage_changed(omni.usd.get_context().get_stage_id()) elif event.type == int(omni.usd.StageEventType.CLOSED): _example_usd_interface.on_default_usd_stage_changed(0)

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.python.usdrt/omni/example/python/usdrt/example_python_usdrt_extension.py

## Copyright (c) 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 math import random from ctypes import alignment import omni.ext import omni.ui as ui import omni.usd from usdrt import Gf, Rt, Sdf, Usd, Vt try: wp = None import warp as wp wp.init() @wp.kernel def deform(positions: wp.array(dtype=wp.vec3), t: float): tid = wp.tid() x = positions[tid] offset = -wp.sin(x[0]) scale = wp.sin(t) * 10.0 x = x + wp.vec3(0.0, offset * scale, 0.0) positions[tid] = x except ImportError: pass def get_selected_prim_path(): """Return the path of the first selected prim""" context = omni.usd.get_context() selection = context.get_selection() paths = selection.get_selected_prim_paths() return None if not paths else paths[0] def get_stage_id(): """Return the stage Id of the current stage""" context = omni.usd.get_context() return context.get_stage_id() def is_vtarray(obj): """Check if this is a VtArray type In Python, each data type gets its own VtArray class i.e. Vt.Float3Array etc. so this helper identifies any of them. """ return hasattr(obj, "IsFabricData") def condensed_vtarray_str(data): """Return a string representing VtArray data Include at most 6 values, and the total items in the array """ size = len(data) if size > 6: datastr = "[{}, {}, {}, .. {}, {}, {}] (size: {})".format( data[0], data[1], data[2], data[-3], data[-2], data[-1], size ) else: datastr = "[" for i in range(size - 1): datastr += str(data[i]) + ", " datastr += str(data[-1]) + "]" return datastr def get_fabric_data_for_prim(stage_id, path): """Get the Fabric data for a path as a string""" if path is None: return "Nothing selected" stage = Usd.Stage.Attach(stage_id) # If a prim does not already exist in Fabric, # it will be fetched from USD by simply creating the # Usd.Prim object. At this time, only the attributes that have # authored opinions will be fetch into Fabric. prim = stage.GetPrimAtPath(Sdf.Path(path)) if not prim: return f"Prim at path {path} is not in Fabric" # This diverges a bit from USD - only attributes # that exist in Fabric are returned by this API attrs = prim.GetAttributes() result = f"Fabric data for prim at path {path}\n\n\n" for attr in attrs: try: data = attr.Get() datastr = str(data) if data is None: datastr = "<no value>" elif is_vtarray(data): datastr = condensed_vtarray_str(data) except TypeError: # Some data types not yet supported in Python datastr = "<no Python conversion>" result += "{} ({}): {}\n".format(attr.GetName(), str(attr.GetTypeName().GetAsToken()), datastr) return result def apply_random_rotation(stage_id, path): """Apply a random world space rotation to a prim in Fabric""" if path is None: return "Nothing selected" stage = Usd.Stage.Attach(stage_id) prim = stage.GetPrimAtPath(Sdf.Path(path)) if not prim: return f"Prim at path {path} is not in Fabric" rtxformable = Rt.Xformable(prim) if not rtxformable.HasWorldXform(): rtxformable.SetWorldXformFromUsd() angle = random.random() * math.pi * 2 axis = Gf.Vec3f(random.random(), random.random(), random.random()).GetNormalized() halfangle = angle / 2.0 shalfangle = math.sin(halfangle) rotation = Gf.Quatf(math.cos(halfangle), axis[0] * shalfangle, axis[1] * shalfangle, axis[2] * shalfangle) rtxformable.GetWorldOrientationAttr().Set(rotation) return f"Set new world orientation on {path} to {rotation}" def deform_mesh_with_warp(stage_id, path, time): """Use Warp to deform a Mesh prim""" if path is None: return "Nothing selected" stage = Usd.Stage.Attach(stage_id) prim = stage.GetPrimAtPath(Sdf.Path(path)) if not prim: return f"Prim at path {path} is not in Fabric" if not prim.HasAttribute("points"): return f"Prim at path {path} does not have points attribute" if not wp: return "Warp failed to initialize. Install/Load the warp extension." # Tell OmniHydra to render points from Fabric if not prim.HasAttribute("Deformable"): prim.CreateAttribute("Deformable", Sdf.ValueTypeNames.PrimTypeTag, True) points = prim.GetAttribute("points") pointsarray = points.Get() warparray = wp.array(pointsarray, dtype=wp.vec3, device="cuda") wp.launch(kernel=deform, dim=len(pointsarray), inputs=[warparray, time], device="cuda") points.Set(Vt.Vec3fArray(warparray.numpy())) return f"Deformed points on prim {path}" # 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 UsdrtExamplePythonExtension(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): print("[omni.example.python.usdrt] startup") self._window = ui.Window( "What's in Fabric?", width=300, height=300, dockPreference=ui.DockPreference.RIGHT_BOTTOM ) self._t = 0 with self._window.frame: with ui.VStack(): frame = ui.ScrollingFrame() with frame: label = ui.Label("Select a prim and push a button", alignment=ui.Alignment.LEFT_TOP) def get_fabric_data(): label.text = get_fabric_data_for_prim(get_stage_id(), get_selected_prim_path()) def rotate_prim(): label.text = apply_random_rotation(get_stage_id(), get_selected_prim_path()) def deform_prim(): label.text = deform_mesh_with_warp(get_stage_id(), get_selected_prim_path(), self._t) self._t += 1 ui.Button("What's in Fabric?", clicked_fn=get_fabric_data, height=0) ui.Button("Rotate it in Fabric!", clicked_fn=rotate_prim, height=0) ui.Button("Deform it with Warp!", clicked_fn=deform_prim, height=0) def on_shutdown(self): print("[omni.example.python.usdrt] shutdown")

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.python.usdrt/omni/example/python/usdrt/__init__.py

from .example_python_usdrt_extension import *

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.python.usdrt/omni/example/python/usdrt/tests/test_whats_in_fabric.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.example.python.usdrt # omni.kit.test is primarily Python's standard unittest module # with additional wrapping to add suport for async/await tests. # Please see: https://docs.python.org/3/library/unittest.html import omni.kit.test # The Python module we are testing, imported with an absolute # path to simulate using it from a different Python extension. import omni.usd import usdrt # Any class that dervives from 'omni.kit.test.AsyncTestCase' # declared at the root of the module will be auto-discovered, class ExamplePythonUsdrtTest(omni.kit.test.AsyncTestCase): async def setUp(self): # Open a new USD stage. omni.usd.get_context().new_stage() self.usd_stage = omni.usd.get_context().get_stage() self.stage_id = omni.usd.get_context().get_stage_id() # create a torus (success, pathString) = omni.kit.commands.execute("CreateMeshPrimWithDefaultXformCommand", prim_type="Torus") self.assertTrue(success) self.prim_path = pathString async def tearDown(self): # Close the USD stage. await omni.usd.get_context().close_stage_async() self.usd_stage = None async def test_get_fabric_data_for_prim(self): result = omni.example.python.usdrt.get_fabric_data_for_prim(self.stage_id, self.prim_path) self.assertTrue("Fabric data for prim at path %s\n\n\n" % self.prim_path in result) for attr in ["points", "normals", "primvars:st", "extent"]: self.assertTrue(attr in result) # test invalid prim result = omni.example.python.usdrt.get_fabric_data_for_prim(self.stage_id, "/invalidPrim") self.assertTrue(result == "Prim at path /invalidPrim is not in Fabric") # test empty path result = omni.example.python.usdrt.get_fabric_data_for_prim(self.stage_id, None) self.assertTrue(result == "Nothing selected") async def test_apply_random_rotation(self): result = omni.example.python.usdrt.apply_random_rotation(self.stage_id, self.prim_path) self.assertTrue("Set new world orientation on %s to (" % self.prim_path in result) # test invalid prim result = omni.example.python.usdrt.apply_random_rotation(self.stage_id, "/invalidPrim") self.assertTrue(result == "Prim at path /invalidPrim is not in Fabric") # test empty path result = omni.example.python.usdrt.apply_random_rotation(self.stage_id, None) self.assertTrue(result == "Nothing selected") async def test_deform_mesh_with_warp(self): try: import warp t = 0 result = omni.example.python.usdrt.deform_mesh_with_warp(self.stage_id, self.prim_path, t) self.assertTrue(result == f"Deformed points on prim {self.prim_path}") # test invalid prim result = omni.example.python.usdrt.deform_mesh_with_warp(self.stage_id, "/invalidPrim", t) self.assertTrue(result == "Prim at path /invalidPrim is not in Fabric") # test empty path result = omni.example.python.usdrt.deform_mesh_with_warp(self.stage_id, None, t) self.assertTrue(result == "Nothing selected") except ImportError: pass

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.pybind/python/tests/test_pybind_example.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.test import omni.example.cpp.pybind class TestPybindExample(omni.kit.test.AsyncTestCase): async def setUp(self): # Cache the pybind interface. self.bound_interface = omni.example.cpp.pybind.get_bound_interface() # Create and register a bound object. self.bound_object = omni.example.cpp.pybind.BoundObject("test_bound_object") self.bound_object.property_int = 9 self.bound_object.property_bool = True self.bound_object.property_string = "Ninety-Nine" self.bound_interface.register_bound_object(self.bound_object) async def tearDown(self): # Deregister and clear the bound object. self.bound_interface.deregister_bound_object(self.bound_object) self.bound_object = None # Clear the pybind interface. self.bound_interface = None async def test_find_bound_object(self): found_object = self.bound_interface.find_bound_object("test_bound_object") self.assertIsNotNone(found_object) async def test_find_unregistered_bound_object(self): found_object = self.bound_interface.find_bound_object("unregistered_object") self.assertIsNone(found_object) async def test_access_bound_object_properties(self): self.assertEqual(self.bound_object.id, "test_bound_object") self.assertEqual(self.bound_object.property_int, 9) self.assertEqual(self.bound_object.property_bool, True) self.assertEqual(self.bound_object.property_string, "Ninety-Nine") async def test_call_bound_object_functions(self): # Test calling a bound function that accepts an argument. self.bound_object.multiply_int_property(9) self.assertEqual(self.bound_object.property_int, 81) # Test calling a bound function that returns a value. result = self.bound_object.toggle_bool_property() self.assertEqual(result, False) self.assertEqual(self.bound_object.property_bool, False) # Test calling a bound function that accepts an argument and returns a value. result = self.bound_object.append_string_property(" Red Balloons") self.assertEqual(result, "Ninety-Nine Red Balloons") self.assertEqual(self.bound_object.property_string, "Ninety-Nine Red Balloons")

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.pybind/python/impl/example_pybind_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. ## import omni.ext from .._example_pybind_bindings import * # Global public interface object. _bound_interface = None # Public API. def get_bound_interface() -> IExampleBoundInterface: return _bound_interface # Use the extension entry points to acquire and release the interface. class ExamplePybindExtension(omni.ext.IExt): def __init__(self): super().__init__() global _bound_interface _bound_interface = acquire_bound_interface() def on_shutdown(self): global _bound_interface release_bound_interface(_bound_interface) _bound_interface = None

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.commands/omni/example/cpp/commands/tests/test_commands_example.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.test import omni.kit.commands import omni.kit.undo class TestCommandsExample(omni.kit.test.AsyncTestCase): async def setUp(self): omni.kit.undo.clear_stack() async def tearDown(self): omni.kit.undo.clear_stack() async def test_cpp_commands(self): # Execute res = omni.kit.commands.execute("ExampleCpp") self.assertEqual(res, (True, None)) # Undo res = omni.kit.undo.undo() self.assertTrue(res) # Redo res = omni.kit.undo.redo() self.assertTrue(res) # Repeat res = omni.kit.undo.repeat() self.assertTrue(res) # Undo res = omni.kit.undo.undo() self.assertTrue(res) # Undo res = omni.kit.undo.undo() self.assertTrue(res) # Undo (empty command stack) res = omni.kit.undo.undo() self.assertFalse(res)

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.ui_widget/python/tests/test_cpp_widget.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. ## __all__ = ["TestCppWidget"] from omni.example.cpp.ui_widget import CppWidget from omni.ui.tests.test_base import OmniUiTest from pathlib import Path import omni.kit.app import omni.ui as ui EXTENSION_PATH = Path(omni.kit.app.get_app().get_extension_manager().get_extension_path_by_module(__name__)) GOLDEN_PATH = EXTENSION_PATH.joinpath("data/golden") STYLE = {"CppWidget": {"color": ui.color.red}} class TestCppWidget(OmniUiTest): async def test_general(self): """Testing general look of CppWidget""" window = await self.create_test_window() with window.frame: CppWidget(thickness=2, style=STYLE) await self.finalize_test(golden_img_dir=GOLDEN_PATH, golden_img_name=f"test_general.png")

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.usdrt/python/tests/test_usdrt_example.py

## Copyright (c) 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 omni.example.cpp.usdrt import omni.kit.app import omni.kit.test import omni.usd from usdrt import Sdf class TestUsdrtExample(omni.kit.test.AsyncTestCase): async def setUp(self): # Cache the example usdrt interface. self.example_usdrt_interface = omni.example.cpp.usdrt.get_example_usdrt_interface() # Open a new USD stage. omni.usd.get_context().new_stage() self.usd_stage = omni.usd.get_context().get_stage() self.stage_id = omni.usd.get_context().get_stage_id() # Create a torus (success, pathString) = omni.kit.commands.execute("CreateMeshPrimWithDefaultXformCommand", prim_type="Torus") self.assertTrue(success) self.prim_path = Sdf.Path(pathString) async def tearDown(self): # Close the USD stage. await omni.usd.get_context().close_stage_async() self.usd_stage = None # Clear the example usdrt interface. self.example_usdrt_interface = None async def test_get_attributes_for_prim(self): (err, attrs) = self.example_usdrt_interface.get_attributes_for_prim(self.stage_id, self.prim_path) self.assertTrue(err == "") self.assertTrue(attrs) # test invalid prim (err, attrs) = self.example_usdrt_interface.get_attributes_for_prim(self.stage_id, Sdf.Path("/invalidPrim")) self.assertTrue(err == "Prim at path /invalidPrim is not in Fabric") self.assertFalse(attrs) # test empty path (err, attrs) = self.example_usdrt_interface.get_attributes_for_prim(self.stage_id, Sdf.Path("")) self.assertTrue(err == "Nothing selected") self.assertFalse(attrs) async def test_apply_random_rotation(self): (err, rotation) = self.example_usdrt_interface.apply_random_rotation(self.stage_id, self.prim_path) self.assertTrue(err == "") self.assertTrue(rotation) # test invalid prim (err, rotation) = self.example_usdrt_interface.apply_random_rotation(self.stage_id, Sdf.Path("/invalidPrim")) self.assertTrue(err == "Prim at path /invalidPrim is not in Fabric") # test empty path (err, rotation) = self.example_usdrt_interface.apply_random_rotation(self.stage_id, Sdf.Path("")) self.assertTrue(err == "Nothing selected") async def test_deform_mesh(self): t = 0 result = self.example_usdrt_interface.deform_mesh(self.stage_id, self.prim_path, t) self.assertTrue(result == f"Deformed points on prim {self.prim_path}") # test invalid prim result = self.example_usdrt_interface.deform_mesh(self.stage_id, Sdf.Path("/invalidPrim"), t) self.assertTrue(result == "Prim at path /invalidPrim is not in Fabric") # test empty path result =self.example_usdrt_interface.deform_mesh(self.stage_id, Sdf.Path(""), t) self.assertTrue(result == "Nothing selected")

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.usdrt/python/impl/example_usdrt_extension.py

## Copyright (c) 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 math import random from ctypes import alignment import omni.ext import omni.ui as ui import omni.usd from usdrt import Gf, Rt, Sdf, Usd, Vt from .._example_usdrt_bindings import * # Global public interface object. _example_usdrt_interface = None # Public API. def get_example_usdrt_interface() -> IExampleUsdrtInterface: return _example_usdrt_interface def get_selected_prim_path(): """Return the path of the first selected prim""" context = omni.usd.get_context() selection = context.get_selection() paths = selection.get_selected_prim_paths() return None if not paths else paths[0] def get_stage_id(): """Return the stage Id of the current stage""" context = omni.usd.get_context() return context.get_stage_id() # 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 ExampleUsdrtExtension(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): # Acquire the example USDRT interface. global _example_usdrt_interface _example_usdrt_interface = acquire_example_usdrt_interface() print("[omni.example.cpp.usdrt] startup") self._window = ui.Window( "What's in Fabric?", width=300, height=300, dockPreference=ui.DockPreference.RIGHT_BOTTOM ) self._t = 0 with self._window.frame: with ui.VStack(): frame = ui.ScrollingFrame() with frame: label = ui.Label("Select a prim and push a button", alignment=ui.Alignment.LEFT_TOP) def get_fabric_data(): selected_prim_path = get_selected_prim_path() (err, data) = _example_usdrt_interface.get_attributes_for_prim( get_stage_id(), selected_prim_path ) if err: label.text = err else: result = f"Fabric data for prim at path {selected_prim_path}\n\n\n" for attr in data: try: data = attr.Get() datastr = str(data) if data is None: datastr = "<no value>" except TypeError: # Some data types not yet supported in Python datastr = "<no Python conversion>" result += "{} ({}): {}\n".format( attr.GetName(), str(attr.GetTypeName().GetAsToken()), datastr ) label.text = result def rotate_prim(): selected_prim_path = get_selected_prim_path() (err, rotation) = _example_usdrt_interface.apply_random_rotation( get_stage_id(), selected_prim_path ) label.text = err if err else f"Set new world orientation on {selected_prim_path} to {rotation}" def deform_prim(): label.text = _example_usdrt_interface.deform_mesh( get_stage_id(), get_selected_prim_path(), self._t ) self._t += 1 ui.Button("What's in Fabric?", clicked_fn=get_fabric_data, height=0) ui.Button("Rotate it in Fabric!", clicked_fn=rotate_prim, height=0) ui.Button("Deform it!", clicked_fn=deform_prim, height=0) def on_shutdown(self): global _example_usdrt_interface # Release the example USDRT interface. release_example_usdrt_interface(_example_usdrt_interface) _example_usdrt_interface = None print("[omni.example.cpp.usdrt] shutdown")

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.python.ui/omni/example/python/ui/example_python_ui_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. ## import omni.ext import omni.ui as ui from omni.example.cpp.ui_widget import CppWidget class ExamplePythonUIExtension(omni.ext.IExt): def on_startup(self, ext_id): print(f"ExamplePythonUIExtension starting up (ext_id: {ext_id}).") self._count = 0 self._window = ui.Window("Example Window", width=300, height=300) with self._window.frame: with ui.VStack(): label = ui.Label("") def on_click(): self._count += 1 label.text = f"count: {self._count}" def on_reset(): self._count = 0 label.text = "empty" on_reset() with ui.HStack(): ui.Button("Add", clicked_fn=on_click) ui.Button("Reset", clicked_fn=on_reset) # Use a widget that was defined in C++ STYLE = {"CppWidget": {"color": ui.color.red}} CppWidget(thickness=2, style=STYLE) def on_shutdown(self): print(f"ExamplePythonUIExtension shutting down.") self._count = 0

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.python.ui/omni/example/python/ui/tests/test_example_python_ui.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. ## # omni.kit.test is primarily Python's standard unittest module # with additional wrapping to add suport for async/await tests. # Please see: https://docs.python.org/3/library/unittest.html import omni.kit.test # omni.kit.ui_test is for simulating UI interactions in tests. import omni.kit.ui_test as ui_test # The Python module we are testing, imported with an absolute # path to simulate using it from a different Python extension. import omni.example.python.ui # Any class that dervives from 'omni.kit.test.AsyncTestCase' # declared at the root of the module will be auto-discovered, class Test(omni.kit.test.AsyncTestCase): # Called before running each test. async def setUp(self): pass # Called after running each test. async def tearDown(self): pass # Example test case that simulates UI interactions. async def test_window_button(self): # Find a label in the window. label = ui_test.find("Example Window//Frame/**/Label[*]") # Find buttons in the window. add_button = ui_test.find("Example Window//Frame/**/Button[*].text=='Add'") reset_button = ui_test.find("Example Window//Frame/**/Button[*].text=='Reset'") # Click the add button. await add_button.click() self.assertEqual(label.widget.text, "count: 1") # Click the add button. await add_button.click() self.assertEqual(label.widget.text, "count: 2") # Click the reset button. await reset_button.click() self.assertEqual(label.widget.text, "empty")

Python

NVIDIA-Omniverse/kit-extension-template-cpp/source/extensions/omni.example.cpp.actions/omni/example/cpp/actions/tests/test_actions_example.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.test import omni.kit.actions.core class TestActionsExample(omni.kit.test.AsyncTestCase): async def setUp(self): self.action_registry = omni.kit.actions.core.get_action_registry() self.extension_id = "omni.example.cpp.actions" async def tearDown(self): self.extension_id = None self.action_registry = None async def test_find_and_execute_custom_action(self): action = self.action_registry.get_action(self.extension_id, "example_custom_action_id") self.assertIsNotNone(action) result = action.execute() self.assertEqual(result, 3) # 3 because this was already executed twice in the C++ tests result = action.execute() self.assertEqual(result, 4) async def test_find_and_execute_lambda_action(self): action = self.action_registry.get_action(self.extension_id, "example_lambda_action_id") self.assertIsNotNone(action) result = action.execute() self.assertIsNone(result)

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.slider_manipulator/Tutorial/Final Scripts/slider_manipulator.py

from omni.ui import scene as sc from omni.ui import color as cl import omni.ui as ui class SliderManipulator(sc.Manipulator): class SliderDragGesturePayload(sc.AbstractGesture.GesturePayload): """ Public payload. The user will access it to get the current value of the slider. """ def __init__(self, base): super().__init__(base.item_closest_point, base.ray_closest_point, base.ray_distance) self.slider_value = 0 class SliderChangedGesture(sc.ManipulatorGesture): """ Public Gesture. The user will reimplement it to process the manipulator's callbacks. """ def __init__(self, **kwargs): super().__init__(**kwargs) def process(self): # Redirection to methods if self.state == sc.GestureState.BEGAN: self.on_began() elif self.state == sc.GestureState.CHANGED: self.on_changed() elif self.state == sc.GestureState.ENDED: self.on_ended() # Public API: def on_began(self): pass def on_changed(self): pass def on_ended(self): pass class _ArcGesturePrioritize(sc.GestureManager): """ Manager makes _ArcGesture the priority gesture """ def can_be_prevented(self, gesture): # Never prevent in the middle of drag return gesture.state != sc.GestureState.CHANGED def should_prevent(self, gesture, preventer): if isinstance(preventer, SliderManipulator._ArcGesture): if preventer.state == sc.GestureState.BEGAN or preventer.state == sc.GestureState.CHANGED: return True class _ArcGesture(sc.DragGesture): """ Internal gesture that sets the new slider value and redirects to public SliderChangedGesture. """ def __init__(self, manipulator): super().__init__(manager=SliderManipulator._ArcGesturePrioritize()) self._manipulator = manipulator def __repr__(self): return f"<_ArcGesture at {hex(id(self))}>" def process(self): if self.state in [sc.GestureState.BEGAN, sc.GestureState.CHANGED, sc.GestureState.ENDED]: # Form new gesture_payload object new_gesture_payload = SliderManipulator.SliderDragGesturePayload(self.gesture_payload) # Save the new slider position in the gesture_payload object object_ray_point = self._manipulator.transform_space( sc.Space.WORLD, sc.Space.OBJECT, self.gesture_payload.ray_closest_point ) center = self._manipulator.model.get_as_floats(self._manipulator.model.get_item("position")) slider_value = (object_ray_point[0] - center[0]) / self._manipulator.width + 0.5 _min = self._manipulator.model.get_as_floats(self._manipulator.model.get_item("min"))[0] _max = self._manipulator.model.get_as_floats(self._manipulator.model.get_item("max"))[0] new_gesture_payload.slider_value = _min + slider_value * (_max - _min) # Call the public gesture self._manipulator._process_gesture( SliderManipulator.SliderChangedGesture, self.state, new_gesture_payload ) # Base process of the gesture super().process() def __init__(self, **kwargs): super().__init__(**kwargs) self.width = 100 self.thickness = 5 self._radius = 5 self._radius_hovered = 7 def set_radius(circle, radius): circle.radius = radius # We don't recreate the gesture to make sure it's active when the # underlying object is recreated self._arc_gesture = self._ArcGesture(self) if hasattr(sc, "HoverGesture"): self._hover_gesture = sc.HoverGesture( on_began_fn=lambda sender: set_radius(sender, self._radius_hovered), on_ended_fn=lambda sender: set_radius(sender, self._radius), ) else: self._hover_gesture = None def destroy(self): pass @property def width(self): return self._width @width.setter def width(self, value): self._width = value # Regenerate the mesh self.invalidate() @property def thickness(self): return self._thickness @thickness.setter def thickness(self, value): self._thickness = value # Regenerate the mesh self.invalidate() def on_build(self): """Called when the model is changed and rebuilds the whole slider""" if not self.model: return # If we don't have a selection then just return if self.model.get_item("name") == "": return _min = self.model.get_as_floats(self.model.get_item("min"))[0] _max = self.model.get_as_floats(self.model.get_item("max"))[0] value = float(self.model.get_as_floats(self.model.get_item("value"))[0]) value_normalized = (value - _min) / (_max - _min) value_normalized = max(min(value_normalized, 1.0), 0.0) position = self.model.get_as_floats(self.model.get_item("position")) with sc.Transform(transform=sc.Matrix44.get_translation_matrix(*position)): # Left line line_from = -self.width * 0.5 line_to = -self.width * 0.5 + self.width * value_normalized - self._radius if line_to > line_from: sc.Line([line_from, 0, 0], [line_to, 0, 0], color=cl.darkgray, thickness=self.thickness) # NEW: same as left line but flipped # Right line line_from = -self.width * 0.5 + self.width * value_normalized + self._radius line_to = self.width * 0.5 if line_to > line_from: sc.Line([line_from, 0, 0], [line_to, 0, 0], color=cl.darkgray, thickness=self.thickness) # Circle circle_position = -self.width * 0.5 + self.width * value_normalized with sc.Transform(transform=sc.Matrix44.get_translation_matrix(circle_position, 0, 0)): radius = self._radius # NEW: Added Gesture when hovering over the circle it will increase in size gestures = [self._arc_gesture] if self._hover_gesture: gestures.append(self._hover_gesture) if self._hover_gesture.state == sc.GestureState.CHANGED: radius = self._radius_hovered sc.Arc(radius, axis=2, color=cl.gray, gestures=gestures) # END NEW # Label with sc.Transform(look_at=sc.Transform.LookAt.CAMERA): # NEW: Added more space between the slider and the label # Move it to the top with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, self._radius_hovered, 0)): # END NEW with sc.Transform(scale_to=sc.Space.SCREEN): # Move it 5 points more to the top in the screen space with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, 5, 0)): sc.Label(f"{value:.1f}", alignment=ui.Alignment.CENTER_BOTTOM) def on_model_updated(self, item): # Regenerate the manipulator self.invalidate()

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.slider_manipulator/Tutorial/Final Scripts/slider_registry.py

from .slider_model import SliderModel from .slider_manipulator import SliderManipulator from typing import Any from typing import Dict from typing import Optional class ViewportLegacyDisableSelection: """Disables selection in the Viewport Legacy""" def __init__(self): self._focused_windows = None focused_windows = [] try: # For some reason is_focused may return False, when a Window is definitely in fact the focused window! # And there's no good solution to this when mutliple Viewport-1 instances are open; so we just have to # operate on all Viewports for a given usd_context. import omni.kit.viewport_legacy as vp vpi = vp.acquire_viewport_interface() for instance in vpi.get_instance_list(): window = vpi.get_viewport_window(instance) if not window: continue focused_windows.append(window) if focused_windows: self._focused_windows = focused_windows for window in self._focused_windows: # Disable the selection_rect, but enable_picking for snapping window.disable_selection_rect(True) except Exception: pass class SliderChangedGesture(SliderManipulator.SliderChangedGesture): """User part. Called when slider is changed.""" def __init__(self, **kwargs): super().__init__(**kwargs) def on_began(self): # When the user drags the slider, we don't want to see the selection rect self.__disable_selection = ViewportLegacyDisableSelection() def on_changed(self): """Called when the user moved the slider""" if not hasattr(self.gesture_payload, "slider_value"): return # The current slider value is in the payload. slider_value = self.gesture_payload.slider_value # Change the model. Slider watches it and it will update the mesh. self.sender.model.set_floats(self.sender.model.get_item("value"), [slider_value]) def on_ended(self): # This re-enables the selection in the Viewport Legacy self.__disable_selection = None class SliderRegistry: """ Created by omni.kit.viewport.registry or omni.kit.manipulator.viewport per viewport. Keeps the manipulator and some properties that are needed to the viewport. """ def __init__(self, description: Optional[Dict[str, Any]] = None): self.__slider_manipulator = SliderManipulator(model=SliderModel(), gesture=SliderChangedGesture()) def destroy(self): if self.__slider_manipulator: self.__slider_manipulator.destroy() self.__slider_manipulator = None # PrimTransformManipulator & TransformManipulator don't have their own visibility @property def visible(self): return True @visible.setter def visible(self, value): pass @property def categories(self): return ("manipulator",) @property def name(self): return "Example Slider Manipulator"

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.slider_manipulator/Tutorial/Final Scripts/extension.py

import omni.ext from omni.kit.manipulator.viewport import ManipulatorFactory from omni.kit.viewport.registry import RegisterScene from .slider_registry import SliderRegistry 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): self.slider_registry = RegisterScene(SliderRegistry, "omni.example.slider") self.slider_factory = ManipulatorFactory.create_manipulator(SliderRegistry) def on_shutdown(self): ManipulatorFactory.destroy_manipulator(self.slider_factory) self.slider_factory = None self.slider_registry.destroy() self.slider_registry = None

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.slider_manipulator/Tutorial/Final Scripts/slider_model.py

from omni.ui import scene as sc from pxr import Tf from pxr import Usd from pxr import UsdGeom import omni.usd import omni.kit.commands from pxr import Gf class SliderModel(sc.AbstractManipulatorModel): """ User part. The model tracks the position and scale of the selected object. """ class PositionItem(sc.AbstractManipulatorItem): """ The Model Item represents the position. It doesn't contain anything because because we take the position directly from USD when requesting. """ def __init__(self): super().__init__() self.value = [0, 0, 0] class ValueItem(sc.AbstractManipulatorItem): """The Model Item contains a single float value""" def __init__(self, value=0): super().__init__() self.value = [value] def __init__(self) -> None: super().__init__() self.scale = SliderModel.ValueItem() self.min = SliderModel.ValueItem() self.max = SliderModel.ValueItem(1) self.position = SliderModel.PositionItem() # Current selection self.current_path = "" self.stage_listener = None self.usd_context = omni.usd.get_context() self.stage: Usd.Stage = self.usd_context.get_stage() # Track selection self.selection = self.usd_context.get_selection() self.events = self.usd_context.get_stage_event_stream() self.stage_event_delegate = self.events.create_subscription_to_pop( self.on_stage_event, name="Slider Selection Update" ) def on_stage_event(self, event): """Called by stage_event_stream""" if event.type == int(omni.usd.StageEventType.SELECTION_CHANGED): prim_paths = self.selection.get_selected_prim_paths() if not prim_paths: self._item_changed(self.position) # Revoke the Tf.Notice listener, we don't need to update anything if self.stage_listener: self.stage_listener.Revoke() self.stage_listener = None return prim = self.stage.GetPrimAtPath(prim_paths[0]) if not prim.IsA(UsdGeom.Imageable): return self.current_path = prim_paths[0] (old_scale, old_rotation_euler, old_rotation_order, old_translation) = omni.usd.get_local_transform_SRT(prim) scale = old_scale[0] _min = scale * 0.1 _max = scale * 2.0 self.set_floats(self.min, [_min]) self.set_floats(self.max, [_max]) self.set_floats(self.scale, [scale]) # Add a Tf.Notice listener to update the position if not self.stage_listener: self.stage_listener = Tf.Notice.Register(Usd.Notice.ObjectsChanged, self._notice_changed, self.stage) # Position is changed self._item_changed(self.position) def _notice_changed(self, notice, stage): """Called by Tf.Notice""" for p in notice.GetChangedInfoOnlyPaths(): if self.current_path in str(p.GetPrimPath()): self._item_changed(self.position) def get_item(self, identifier): if identifier == "position": return self.position if identifier == "value": return self.scale if identifier == "min": return self.min if identifier == "max": return self.max def get_as_floats(self, item): if item == self.position: # Requesting position return self.get_position() if item: # Get the value directly from the item return item.value return [] def set_floats(self, item, value): if not self.current_path: return if not value or not item or item.value == value: return if item == self.scale: # Set the scale when setting the value. value[0] = min(max(value[0], self.min.value[0]), self.max.value[0]) (old_scale, old_rotation_euler, old_rotation_order, old_translation) = omni.usd.get_local_transform_SRT( self.stage.GetPrimAtPath(self.current_path) ) omni.kit.commands.execute( "TransformPrimSRTCommand", path=self.current_path, new_translation=old_translation, new_rotation_euler=old_rotation_euler, new_scale=Gf.Vec3d(value[0], value[0], value[0]), ) # Set directly to the item item.value = value # This makes the manipulator updated self._item_changed(item) def get_position(self): """Returns position of currently selected object""" if not self.current_path: return [0, 0, 0] # Get position directly from USD prim = self.stage.GetPrimAtPath(self.current_path) box_cache = UsdGeom.BBoxCache(Usd.TimeCode.Default(), includedPurposes=[UsdGeom.Tokens.default_]) bound = box_cache.ComputeWorldBound(prim) range = bound.ComputeAlignedBox() bboxMin = range.GetMin() bboxMax = range.GetMax() x_Pos = (bboxMin[0] + bboxMax[0]) * 0.5 y_Pos = (bboxMax[1] + 10) z_Pos = (bboxMin[2] + bboxMax[2]) * 0.5 position = [x_Pos, y_Pos, z_Pos] return position

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.light_manipulator/omni/example/ui_scene/light_manipulator/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. # __all__ = ["LightManipulatorExtension"] import carb import omni.ext from omni.kit.viewport.utility import get_active_viewport_window from .viewport_scene import ViewportScene class LightManipulatorExtension(omni.ext.IExt): def __init__(self): self._viewport_scene = None def on_startup(self, ext_id): # Get the active (which at startup is the default Viewport) viewport_window = get_active_viewport_window() # Issue an error if there is no Viewport if not viewport_window: carb.log_error(f"No Viewport Window to add {ext_id} scene to") return # Build out the scene self._viewport_scene = ViewportScene(viewport_window, ext_id) def on_shutdown(self): if self._viewport_scene: self._viewport_scene.destroy() self._viewport_scene = None

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.light_manipulator/omni/example/ui_scene/light_manipulator/light_model.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. # __all__ = ["LightModel"] import carb from omni.ui import scene as sc import omni.usd from pxr import Usd, UsdGeom, UsdLux, Tf, Gf def _flatten_matrix(matrix: Gf.Matrix4d): m0, m1, m2, m3 = matrix[0], matrix[1], matrix[2], matrix[3] return [ m0[0], m0[1], m0[2], m0[3], m1[0], m1[1], m1[2], m1[3], m2[0], m2[1], m2[2], m2[3], m3[0], m3[1], m3[2], m3[3], ] class LightModel(sc.AbstractManipulatorModel): """ User part. The model tracks the attributes of the selected light. """ class MatrixItem(sc.AbstractManipulatorItem): """ The Model Item represents the tranformation. It doesn't contain anything because we take the tranformation directly from USD when requesting. """ identity = [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1] def __init__(self): super().__init__() self.value = self.identity.copy() class FloatItem(sc.AbstractManipulatorItem): """The Model Item contains a single float value about some attibute""" def __init__(self, value=0.0): super().__init__() self.value = value class StringItem(sc.AbstractManipulatorItem): """The Model Item contains a single string value about some attibute""" def __init__(self, value=""): super().__init__() self.value = value def __init__(self): super().__init__() self.prim_path = LightModel.StringItem() self.transform = LightModel.MatrixItem() self.intensity = LightModel.FloatItem() self.width = LightModel.FloatItem() self.height = LightModel.FloatItem() # Save the UsdContext name (we currently only work with single Context) self._usd_context_name = "" # Current selection self._light = None self._stage_listener = None # Track selection change self._events = self._usd_context.get_stage_event_stream() self._stage_event_sub = self._events.create_subscription_to_pop( self._on_stage_event, name="Light Manipulator Selection Change" ) def __del__(self): self._invalidate_object() @property def _usd_context(self) -> Usd.Stage: # Get the UsdContext we are attached to return omni.usd.get_context(self._usd_context_name) @property def _current_path(self): return self.prim_path.value @property def _time(self): return Usd.TimeCode.Default() def _notice_changed(self, notice, stage): """Called by Tf.Notice. When USD data changes, we update the ui""" light_path = self.prim_path.value if not light_path: return changed_items = set() for p in notice.GetChangedInfoOnlyPaths(): prim_path = p.GetPrimPath().pathString if prim_path != light_path: # Update on any parent transformation changes too if light_path.startswith(prim_path): if UsdGeom.Xformable.IsTransformationAffectedByAttrNamed(p.name): changed_items.add(self.transform) continue if UsdGeom.Xformable.IsTransformationAffectedByAttrNamed(p.name): changed_items.add(self.transform) elif self.width and p.name == "width": changed_items.add(self.width) elif self.height and p.name == "height": changed_items.add(self.height) elif self.intensity and p.name == "intensity": changed_items.add(self.intensity) for item in changed_items: self._item_changed(item) def get_as_floats(self, item): """get the item value directly from USD""" if item == self.transform: return self._get_transform(self._time) if item == self.intensity: return self._get_intensity(self._time) if item == self.width: return self._get_width(self._time) if item == self.height: return self._get_height(self._time) if item: # Get the value directly from the item return item.value return None def set_floats_commands(self, item, value): """set the item value to USD using commands, this is useful because it supports undo/redo""" if not self._current_path: return if not value or not item: return # we get the previous value from the model instead of USD if item == self.height: prev_value = self.height.value if prev_value == value: return height_attr = self._light.GetHeightAttr() omni.kit.commands.execute('ChangeProperty', prop_path=height_attr.GetPath(), value=value, prev=prev_value) elif item == self.width: prev_value = self.width.value if prev_value == value: return width_attr = self._light.GetWidthAttr() omni.kit.commands.execute('ChangeProperty', prop_path=width_attr.GetPath(), value=value, prev=prev_value) elif item == self.intensity: prev_value = self.intensity.value if prev_value == value: return intensity_attr = self._light.GetIntensityAttr() omni.kit.commands.execute('ChangeProperty', prop_path=intensity_attr.GetPath(), value=value, prev=prev_value) # This makes the manipulator updated self._item_changed(item) def set_item_value(self, item, value): """ This is used to set the model value instead of the usd. This is used to record previous value for omni.kit.commands """ item.value = value def set_floats(self, item, value): """set the item value directly to USD. This is useful when we want to update the usd but not record it in commands""" if not self._current_path: return if not value or not item: return pre_value = self.get_as_floats(item) # no need to update if the updated value is the same if pre_value == value: return if item == self.height: self._set_height(self._time, value) elif item == self.width: self._set_width(self._time, value) elif item == self.intensity: self._set_intensity(self._time, value) def _on_stage_event(self, event): """Called by stage_event_stream""" if event.type == int(omni.usd.StageEventType.SELECTION_CHANGED): self._on_kit_selection_changed() def _invalidate_object(self, settings): # Revoke the Tf.Notice listener, we don't need to update anything if self._stage_listener: self._stage_listener.Revoke() self._stage_listener = None # Reset original Viewport gizmo line width settings.set("/persistent/app/viewport/gizmo/lineWidth", 0) # Clear any cached UsdLux.Light object self._light = None # Set the prim_path to empty self.prim_path.value = "" self._item_changed(self.prim_path) def _on_kit_selection_changed(self): # selection change, reset it for now self._light = None # Turn off any native selected light drawing settings = carb.settings.get_settings() settings.set("/persistent/app/viewport/gizmo/lineWidth", 0) usd_context = self._usd_context if not usd_context: return self._invalidate_object(settings) stage = usd_context.get_stage() if not stage: return self._invalidate_object(settings) prim_paths = usd_context.get_selection().get_selected_prim_paths() if usd_context else None if not prim_paths: return self._invalidate_object(settings) prim = stage.GetPrimAtPath(prim_paths[0]) if prim and prim.IsA(UsdLux.RectLight): self._light = UsdLux.RectLight(prim) if not self._light: return self._invalidate_object(settings) selected_path = self._light.GetPrim().GetPath().pathString if selected_path != self.prim_path.value: self.prim_path.value = selected_path self._item_changed(self.prim_path) # Add a Tf.Notice listener to update the light attributes if not self._stage_listener: self._stage_listener = Tf.Notice.Register(Usd.Notice.ObjectsChanged, self._notice_changed, stage) def _get_transform(self, time: Usd.TimeCode): """Returns world transform of currently selected object""" if not self._light: return LightModel.MatrixItem.identity.copy() # Compute matrix from world-transform in USD world_xform = self._light.ComputeLocalToWorldTransform(time) # Flatten Gf.Matrix4d to list return _flatten_matrix(world_xform) def _get_intensity(self, time: Usd.TimeCode): """Returns intensity of currently selected light""" if not self._light: return 0.0 # Get intensity directly from USD return self._light.GetIntensityAttr().Get(time) def _set_intensity(self, time: Usd.TimeCode, value): """set intensity of currently selected light""" if not self._light: return # set height dirctly to USD self._light.GetIntensityAttr().Set(value, time=time) def _get_width(self, time: Usd.TimeCode): """Returns width of currently selected light""" if not self._light: return 0.0 # Get radius directly from USD return self._light.GetWidthAttr().Get(time) def _set_width(self, time: Usd.TimeCode, value): """set width of currently selected light""" if not self._light: return # set height dirctly to USD self._light.GetWidthAttr().Set(value, time=time) def _get_height(self, time: Usd.TimeCode): """Returns height of currently selected light""" if not self._light: return 0.0 # Get height directly from USD return self._light.GetHeightAttr().Get(time) def _set_height(self, time: Usd.TimeCode, value): """set height of currently selected light""" if not self._light: return # set height dirctly to USD self._light.GetHeightAttr().Set(value, time=time)

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.light_manipulator/omni/example/ui_scene/light_manipulator/light_manipulator.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. __all__ = ["LightManipulator"] from omni.ui import scene as sc from omni.ui import color as cl import omni.kit import omni.kit.commands INTENSITY_SCALE = 500.0 ARROW_WIDTH = 0.015 ARROW_HEIGHT = 0.1 ARROW_P = [ [ARROW_WIDTH, ARROW_WIDTH, 0], [-ARROW_WIDTH, ARROW_WIDTH, 0], [0, 0, ARROW_HEIGHT], # [ARROW_WIDTH, -ARROW_WIDTH, 0], [-ARROW_WIDTH, -ARROW_WIDTH, 0], [0, 0, ARROW_HEIGHT], # [ARROW_WIDTH, ARROW_WIDTH, 0], [ARROW_WIDTH, -ARROW_WIDTH, 0], [0, 0, ARROW_HEIGHT], # [-ARROW_WIDTH, ARROW_WIDTH, 0], [-ARROW_WIDTH, -ARROW_WIDTH, 0], [0, 0, ARROW_HEIGHT], # [ARROW_WIDTH, ARROW_WIDTH, 0], [-ARROW_WIDTH, ARROW_WIDTH, 0], [-ARROW_WIDTH, -ARROW_WIDTH, 0], [ARROW_WIDTH, -ARROW_WIDTH, 0], ] ARROW_VC = [3, 3, 3, 3, 4] ARROW_VI = [i for i in range(sum(ARROW_VC))] class _ViewportLegacyDisableSelection: """Disables selection in the Viewport Legacy""" def __init__(self): self._focused_windows = None focused_windows = [] try: # For some reason is_focused may return False, when a Window is definitely in fact is the focused window! # And there's no good solution to this when mutliple Viewport-1 instances are open; so we just have to # operate on all Viewports for a given usd_context. import omni.kit.viewport_legacy as vp vpi = vp.acquire_viewport_interface() for instance in vpi.get_instance_list(): window = vpi.get_viewport_window(instance) if not window: continue focused_windows.append(window) if focused_windows: self._focused_windows = focused_windows for window in self._focused_windows: # Disable the selection_rect, but enable_picking for snapping window.disable_selection_rect(True) except Exception: pass class _DragGesture(sc.DragGesture): """"Gesture to disable rectangle selection in the viewport legacy""" def __init__(self, manipulator, orientation, flag): super().__init__() self._manipulator = manipulator # record this _previous_ray_point to get the mouse moved vector self._previous_ray_point = None # this defines the orientation of the move, 0 means x, 1 means y, 2 means z. It's a list so that we can move a selection self.orientations = orientation # global flag to indicate if the manipulator changes all the width, height and intensity, rectangle manipulator # in the example self.is_global = len(self.orientations) > 1 # this defines the negative or positive of the move. E.g. when we move the positive x line to the right, it # enlarges the width, and when we move the negative line to the left, it also enlarges the width # 1 means positive and -1 means negative. It's a list so that we can reflect list orientation self.flag = flag def on_began(self): # When the user drags the slider, we don't want to see the selection # rect. In Viewport Next, it works well automatically because the # selection rect is a manipulator with its gesture, and we add the # slider manipulator to the same SceneView. # In Viewport Legacy, the selection rect is not a manipulator. Thus it's # not disabled automatically, and we need to disable it with the code. self.__disable_selection = _ViewportLegacyDisableSelection() # initialize the self._previous_ray_point self._previous_ray_point = self.gesture_payload.ray_closest_point # record the previous value for the model self.model = self._manipulator.model if 0 in self.orientations: self.width_item = self.model.width self._manipulator.model.set_item_value(self.width_item, self.model.get_as_floats(self.width_item)) if 1 in self.orientations: self.height_item = self.model.height self._manipulator.model.set_item_value(self.height_item, self.model.get_as_floats(self.height_item)) if 2 in self.orientations or self.is_global: self.intensity_item = self.model.intensity self._manipulator.model.set_item_value(self.intensity_item, self.model.get_as_floats(self.intensity_item)) def on_changed(self): object_ray_point = self.gesture_payload.ray_closest_point # calculate the ray moved vector moved = [a - b for a, b in zip(object_ray_point, self._previous_ray_point)] # transfer moved from world to object space, [0] to make it a normal, not point moved = self._manipulator._x_xform.transform_space(sc.Space.WORLD, sc.Space.OBJECT, moved + [0]) # 2.0 because `_shape_xform.transform` is a scale matrix and it means # the width of the rectangle is twice the scale matrix. moved_x = moved[0] * 2.0 * self.flag[0] moved_y = moved[1] * 2.0 * (self.flag[1] if self.is_global else self.flag[0]) moved_z = moved[2] * self.flag[0] # update the self._previous_ray_point self._previous_ray_point = object_ray_point # since self._shape_xform.transform = [x, 0, 0, 0, 0, y, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1] # when we want to update the manipulator, we are actually updating self._manipulator._shape_xform.transform[0] # for width and self._manipulator._shape_xform.transform[5] for height and # self._manipulator._shape_xform.transform[10] for intensity width = self._manipulator._shape_xform.transform[0] height = self._manipulator._shape_xform.transform[5] intensity = self._manipulator._shape_xform.transform[10] self.width_new = width + moved_x self.height_new = height + moved_y # update the USD as well as update the ui if 0 in self.orientations: # update the data in the model self.model.set_floats(self.width_item, self.width_new) self._manipulator._shape_xform.transform[0] = self.width_new if 1 in self.orientations: # update the data in the model self.model.set_floats(self.height_item, self.height_new) self._manipulator._shape_xform.transform[5] = self.height_new if 2 in self.orientations: self._manipulator._shape_xform.transform[10] += moved_z self.intensity_new = self._manipulator._shape_xform.transform[10] * INTENSITY_SCALE self.model.set_floats(self.intensity_item, self.intensity_new) if self.is_global: # need to update the intensity in a different way intensity_new = intensity * width * height / (self.width_new * self.height_new) self._manipulator._shape_xform.transform[10] = intensity_new self.intensity_new = intensity_new * INTENSITY_SCALE self.model.set_floats(self.intensity_item, self.intensity_new) def on_ended(self): # This re-enables the selection in the Viewport Legacy self.__disable_selection = None if self.is_global: # start group command omni.kit.undo.begin_group() if 0 in self.orientations: self.model.set_floats_commands(self.width_item, self.width_new) if 1 in self.orientations: self.model.set_floats_commands(self.height_item, self.height_new) if 2 in self.orientations or self.is_global: self.model.set_floats_commands(self.intensity_item, self.intensity_new) if self.is_global: # end group command omni.kit.undo.end_group() class LightManipulator(sc.Manipulator): def __init__(self, **kwargs): super().__init__(**kwargs) self._shape_xform = None def __del__(self): self.model = None def _build_shape(self): if not self.model: return if self.model.width and self.model.height and self.model.intensity: x = self.model.get_as_floats(self.model.width) y = self.model.get_as_floats(self.model.height) # this INTENSITY_SCALE is too make the transform a reasonable length with large intensity number z = self.model.get_as_floats(self.model.intensity) / INTENSITY_SCALE self._shape_xform.transform = [x, 0, 0, 0, 0, y, 0, 0, 0, 0, z, 0, 0, 0, 0, 1] def on_build(self): """Called when the model is changed and rebuilds the whole slider""" model = self.model if not model: return # if we don't have selection then just return prim_path_item = model.prim_path prim_path = prim_path_item.value if prim_path_item else None if not prim_path: return # Style settings, as kwargs thickness = 1 hover_thickness = 3 color = cl.yellow shape_style = {"thickness": thickness, "color": color} def set_thickness(sender, shapes, thickness): for shape in shapes: shape.thickness = thickness self.__root_xf = sc.Transform(model.get_as_floats(model.transform)) with self.__root_xf: self._x_xform = sc.Transform() with self._x_xform: self._shape_xform = sc.Transform() # Build the shape's transform self._build_shape() with self._shape_xform: # Build the shape geomtery as unit-sized h = 0.5 z = -1.0 # the rectangle shape1 = sc.Line((-h, h, 0), (h, h, 0), **shape_style) shape2 = sc.Line((-h, -h, 0), (h, -h, 0), **shape_style) shape3 = sc.Line((h, h, 0), (h, -h, 0), **shape_style) shape4 = sc.Line((-h, h, 0), (-h, -h, 0), **shape_style) # add gesture to the lines of the rectangle to update width or height of the light vertical_hover_gesture = sc.HoverGesture( on_began_fn=lambda sender: set_thickness(sender, [shape1, shape2], hover_thickness), on_ended_fn=lambda sender: set_thickness(sender, [shape1, shape2], thickness), ) shape1.gestures = [_DragGesture(self, [1], [1]), vertical_hover_gesture] shape2.gestures = [_DragGesture(self, [1], [-1]), vertical_hover_gesture] horizontal_hover_gesture = sc.HoverGesture( on_began_fn=lambda sender: set_thickness(sender, [shape3, shape4], hover_thickness), on_ended_fn=lambda sender: set_thickness(sender, [shape3, shape4], thickness), ) shape3.gestures = [_DragGesture(self, [0], [1]), horizontal_hover_gesture] shape4.gestures = [_DragGesture(self, [0], [-1]), horizontal_hover_gesture] # create z-axis to indicate the intensity z1 = sc.Line((h, h, 0), (h, h, z), **shape_style) z2 = sc.Line((-h, -h, 0), (-h, -h, z), **shape_style) z3 = sc.Line((h, -h, 0), (h, -h, z), **shape_style) z4 = sc.Line((-h, h, 0), (-h, h, z), **shape_style) def make_arrow(translate): vert_count = len(ARROW_VI) with sc.Transform( transform=sc.Matrix44.get_translation_matrix(translate[0], translate[1], translate[2]) * sc.Matrix44.get_rotation_matrix(0, -180, 0, True) ): return sc.PolygonMesh(ARROW_P, [color] * vert_count, ARROW_VC, ARROW_VI, visible=False) # arrows on the z-axis arrow_1 = make_arrow((h, h, z)) arrow_2 = make_arrow((-h, -h, z)) arrow_3 = make_arrow((h, -h, z)) arrow_4 = make_arrow((-h, h, z)) # the line underneath the arrow which is where the gesture applies z1_arrow = sc.Line((h, h, z), (h, h, z - ARROW_HEIGHT), **shape_style) z2_arrow = sc.Line((-h, -h, z), (-h, -h, z - ARROW_HEIGHT), **shape_style) z3_arrow = sc.Line((h, -h, z), (h, -h, z - ARROW_HEIGHT), **shape_style) z4_arrow = sc.Line((-h, h, z), (-h, h, z - ARROW_HEIGHT), **shape_style) def set_visible(sender, shapes, thickness, arrows, visible): set_thickness(sender, shapes, thickness) for arrow in arrows: arrow.visible = visible thickness_group = [z1, z1_arrow, z2, z2_arrow, z3, z3_arrow, z4, z4_arrow] visible_group = [arrow_1, arrow_2, arrow_3, arrow_4] visible_arrow_gesture = sc.HoverGesture( on_began_fn=lambda sender: set_visible(sender, thickness_group, hover_thickness, visible_group, True), on_ended_fn=lambda sender: set_visible(sender, thickness_group, thickness, visible_group, False), ) gestures = [_DragGesture(self, [2], [-1]), visible_arrow_gesture] z1_arrow.gestures = gestures z2_arrow.gestures = gestures z3_arrow.gestures = gestures z4_arrow.gestures = gestures # create 4 rectangles at the corner, and add gesture to update width, height and intensity at the same time s = 0.03 def make_corner_rect(translate): with sc.Transform(transform=sc.Matrix44.get_translation_matrix(translate[0], translate[1], translate[2])): return sc.Rectangle(s, s, color=0x0) r1 = make_corner_rect((h - 0.5 * s, -h + 0.5 * s, 0)) r2 = make_corner_rect((h - 0.5 * s, h - 0.5 * s, 0)) r3 = make_corner_rect((-h + 0.5 * s, h - 0.5 * s, 0)) r4 = make_corner_rect((-h + 0.5 * s, -h + 0.5 * s, 0)) def set_color_and_visible(sender, shapes, thickness, arrows, visible, rects, color): set_visible(sender, shapes, thickness, arrows, visible) for rect in rects: rect.color = color highlight_group = [shape1, shape2, shape3, shape4] + thickness_group color_group = [r1, r2, r3, r4] hight_all_gesture = sc.HoverGesture( on_began_fn=lambda sender: set_color_and_visible(sender, highlight_group, hover_thickness, visible_group, True, color_group, color), on_ended_fn=lambda sender: set_color_and_visible(sender, highlight_group, thickness, visible_group, False, color_group, 0x0), ) r1.gestures = [_DragGesture(self, [0, 1], [1, -1]), hight_all_gesture] r2.gestures = [_DragGesture(self, [0, 1], [1, 1]), hight_all_gesture] r3.gestures = [_DragGesture(self, [0, 1], [-1, 1]), hight_all_gesture] r4.gestures = [_DragGesture(self, [0, 1], [-1, -1]), hight_all_gesture] def on_model_updated(self, item): # Regenerate the mesh if not self.model: return if item == self.model.transform: # If transform changed, update the root transform self.__root_xf.transform = self.model.get_as_floats(item) elif item == self.model.prim_path: # If prim_path or width or height or intensity changed, redraw everything self.invalidate() elif item == self.model.width or item == self.model.height or item == self.model.intensity: # Interpret None as changing multiple light shape settings self._build_shape()

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.light_manipulator/omni/example/ui_scene/light_manipulator/tests/test_manipulator.py

## Copyright (c) 2021, 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.ui.tests.test_base import OmniUiTest from pathlib import Path import carb import omni.kit import omni.kit.app import omni.kit.test from omni.example.ui_scene.light_manipulator import LightManipulator, LightModel import omni.usd from omni.ui import scene as sc from pxr import UsdLux, UsdGeom from omni.kit.viewport.utility import next_viewport_frame_async from omni.kit.viewport.utility.tests import setup_vieport_test_window CURRENT_PATH = Path(carb.tokens.get_tokens_interface().resolve("${omni.example.ui_scene.light_manipulator}/data")) OUTPUTS_DIR = Path(omni.kit.test.get_test_output_path()) class TestLightManipulator(OmniUiTest): # Before running each test async def setUp(self): await super().setUp() self._golden_img_dir = CURRENT_PATH.absolute().resolve().joinpath("tests") # After running each test async def tearDown(self): self._golden_img_dir = None await super().tearDown() async def setup_viewport(self, resolution_x: int = 800, resolution_y: int = 600): await self.create_test_area(resolution_x, resolution_y) return await setup_vieport_test_window(resolution_x, resolution_y) async def test_manipulator_transform(self): viewport_window = await self.setup_viewport() viewport = viewport_window.viewport_api await omni.usd.get_context().new_stage_async() stage = omni.usd.get_context().get_stage() # Wait until the Viewport has delivered some frames await next_viewport_frame_async(viewport, 2) with viewport_window.get_frame(0): # Create a default SceneView (it has a default camera-model) scene_view = sc.SceneView() # Add the manipulator into the SceneView's scene with scene_view.scene: LightManipulator(model=LightModel()) omni.kit.commands.execute( "CreatePrim", prim_path="/RectLight", prim_type="RectLight", select_new_prim=True, attributes={}, ) rect_light = UsdLux.RectLight(stage.GetPrimAtPath("/RectLight")) # change light attribute rect_light.GetHeightAttr().Set(100) rect_light.GetWidthAttr().Set(200) rect_light.GetIntensityAttr().Set(10000) # rotate the light to have a better angle rect_light_x = UsdGeom.Xformable(rect_light) rect_light_x.ClearXformOpOrder() rect_light_x.AddRotateXOp().Set(30) rect_light_x.AddRotateYOp().Set(45) for _ in range(10): await omni.kit.app.get_app().next_update_async() await self.finalize_test(golden_img_dir=self._golden_img_dir)

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.light_manipulator/omni/example/ui_scene/light_manipulator/tests/__init__.py

from .test_manipulator import TestLightManipulator

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/Tutorial/Final Scripts/extension.py

import omni.ext import omni.ui as ui from omni.kit.viewport.utility import get_active_viewport_window from .viewport_scene import ViewportSceneInfo class MyExtension(omni.ext.IExt): """Creates an extension which will display object info in 3D over any object in a UI Scene. """ # 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 __init__(self) -> None: super().__init__() self.viewport_scene = None def on_startup(self, ext_id): viewport_window = get_active_viewport_window() self.viewport_scene = ViewportSceneInfo(viewport_window, ext_id) def on_shutdown(self): """Called when the extension is shutting down.""" if self.viewport_scene: self.viewport_scene.destroy() self.viewport_scene = None

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/Tutorial/Final Scripts/viewport_scene.py

from omni.ui import scene as sc import omni.ui as ui from .object_info_manipulator import ObjInfoManipulator from .object_info_model import ObjInfoModel class ViewportSceneInfo(): def __init__(self, viewportWindow, ext_id) -> None: self.sceneView = None self.viewportWindow = viewportWindow with self.viewportWindow.get_frame(ext_id): self.sceneView = sc.SceneView() with self.sceneView.scene: ObjInfoManipulator(model=ObjInfoModel()) self.viewportWindow.viewport_api.add_scene_view(self.sceneView) def __del__(self): self.destroy() def destroy(self): if self.sceneView: self.sceneView.scene.clear() if self.viewportWindow: self.viewportWindow.viewport_api.remove_scene_view(self.sceneView) self.viewportWindow = None self.sceneView = None

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/Tutorial/Final Scripts/object_info_model.py

from pxr import Tf from pxr import Usd from pxr import UsdGeom from omni.ui import scene as sc import omni.usd class ObjInfoModel(sc.AbstractManipulatorModel): """ The model tracks the position and info of the selected object. """ class PositionItem(sc.AbstractManipulatorItem): """ The Model Item represents the position. It doesn't contain anything because we take the position directly from USD when requesting. """ def __init__(self) -> None: super().__init__() self.value = [0, 0, 0] def __init__(self) -> None: super().__init__() # Current selected prim self.prim = None self.current_path = "" self.stage_listener = None self.position = ObjInfoModel.PositionItem() # Save the UsdContext name (we currently only work with a single Context) self.usd_context = omni.usd.get_context() # Track selection changes self.events = self.usd_context.get_stage_event_stream() self.stage_event_delegate = self.events.create_subscription_to_pop( self.on_stage_event, name="Object Info Selection Update" ) def on_stage_event(self, event): """Called by stage_event_stream. We only care about selection changes.""" if event.type == int(omni.usd.StageEventType.SELECTION_CHANGED): prim_path = self.usd_context.get_selection().get_selected_prim_paths() if not prim_path: self.current_path = "" self._item_changed(self.position) return stage = self.usd_context.get_stage() prim = stage.GetPrimAtPath(prim_path[0]) if not prim.IsA(UsdGeom.Imageable): self.prim = None if self.stage_listener: self.stage_listener.Revoke() self.stage_listener = None return if not self.stage_listener: self.stage_listener = Tf.Notice.Register(Usd.Notice.ObjectsChanged, self.notice_changed, stage) self.prim = prim self.current_path = prim_path[0] # Position is changed because new selected object has a different position self._item_changed(self.position) def get_item(self, identifier): if identifier == "name": return self.current_path elif identifier == "position": return self.position def get_as_floats(self, item): if item == self.position: # Requesting position return self.get_position() if item: # Get the value directly from the item return item.value return [] def get_position(self): """Returns position of currently selected object""" stage = self.usd_context.get_stage() if not stage or self.current_path == "": return [0, 0, 0] # Get position directly from USD prim = stage.GetPrimAtPath(self.current_path) box_cache = UsdGeom.BBoxCache(Usd.TimeCode.Default(), includedPurposes=[UsdGeom.Tokens.default_]) bound = box_cache.ComputeWorldBound(prim) range = bound.ComputeAlignedBox() bboxMin = range.GetMin() bboxMax = range.GetMax() # Find the top center of the bounding box and add a small offset upward. x_Pos = (bboxMin[0] + bboxMax[0]) * 0.5 y_Pos = bboxMax[1] + 5 z_Pos = (bboxMin[2] + bboxMax[2]) * 0.5 position = [x_Pos, y_Pos, z_Pos] return position # loop through all notices that get passed along until we find selected def notice_changed(self, notice: Usd.Notice, stage: Usd.Stage) -> None: """Called by Tf.Notice. Used when the current selected object changes in some way.""" for p in notice.GetChangedInfoOnlyPaths(): if self.current_path in str(p.GetPrimPath()): self._item_changed(self.position) def destroy(self): self.events = None self.stage_event_delegate.unsubscribe()

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/Tutorial/Final Scripts/object_info_manipulator.py

from omni.ui import scene as sc import omni.ui as ui class ObjInfoManipulator(sc.Manipulator): def on_build(self): """Called when the model is changed and rebuilds the whole manipulator""" if not self.model: return # If we don't have a selection then just return if self.model.get_item("name") == "": return # NEW: update to position value and added transform functions to position the Label at the object's origin and +5 in the up direction # we also want to make sure it is scaled properly position = self.model.get_as_floats(self.model.get_item("position")) with sc.Transform(transform=sc.Matrix44.get_translation_matrix(*position)): with sc.Transform(scale_to=sc.Space.SCREEN): # END NEW sc.Label(f"Path: {self.model.get_item('name')}") sc.Label(f"Path: {self.model.get_item('name')}") def on_model_updated(self, item): # Regenerate the manipulator self.invalidate()

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/omni/example/ui_scene/object_info/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. # __all__ = ["ObjectInfoExtension"] import carb import omni.ext from omni.kit.viewport.utility import get_active_viewport_window from .viewport_scene import ViewportScene class ObjectInfoExtension(omni.ext.IExt): """Creates an extension which will display object info in 3D over any object in a UI Scene. """ def __init__(self): self._viewport_scene = None def on_startup(self, ext_id: str) -> None: """Called when the extension is starting up. Args: ext_id: Extension ID provided by Kit. """ # Get the active Viewport (which at startup is the default Viewport) viewport_window = get_active_viewport_window() # Issue an error if there is no Viewport if not viewport_window: carb.log_error(f"No Viewport Window to add {ext_id} scene to") return # Build out the scene self._viewport_scene = ViewportScene(viewport_window, ext_id) def on_shutdown(self) -> None: """Called when the extension is shutting down.""" if self._viewport_scene: self._viewport_scene.destroy() self._viewport_scene = None

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/omni/example/ui_scene/object_info/object_info_manipulator.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. __all__ = ["ObjectInfoManipulator"] from omni.ui import color as cl from omni.ui import scene as sc import omni.ui as ui LEADER_LINE_CIRCLE_RADIUS = 2 LEADER_LINE_THICKNESS = 2 LEADER_LINE_SEGMENT_LENGTH = 20 VERTICAL_MULT = 1.5 HORIZ_TEXT_OFFSET = 5 LINE1_OFFSET = 3 LINE2_OFFSET = 0 class ObjectInfoManipulator(sc.Manipulator): """Manipulator that displays the object path and material assignment with a leader line to the top of the object's bounding box. """ def on_build(self): """Called when the model is changed and rebuilds the whole manipulator""" if not self.model: return # If we don't have a selection then just return if self.model.get_item("name") == "": return position = self.model.get_as_floats(self.model.get_item("position")) # Move everything to where the object is with sc.Transform(transform=sc.Matrix44.get_translation_matrix(*position)): # Rotate everything to face the camera with sc.Transform(look_at=sc.Transform.LookAt.CAMERA): # Leader lines with a small circle on the end sc.Arc(LEADER_LINE_CIRCLE_RADIUS, axis=2, color=cl.yellow) sc.Line([0, 0, 0], [0, LEADER_LINE_SEGMENT_LENGTH, 0], color=cl.yellow, thickness=LEADER_LINE_THICKNESS) sc.Line([0, LEADER_LINE_SEGMENT_LENGTH, 0], [LEADER_LINE_SEGMENT_LENGTH, LEADER_LINE_SEGMENT_LENGTH * VERTICAL_MULT, 0], color=cl.yellow, thickness=LEADER_LINE_THICKNESS) # Shift text to the end of the leader line with some offset with sc.Transform(transform=sc.Matrix44.get_translation_matrix( LEADER_LINE_SEGMENT_LENGTH + HORIZ_TEXT_OFFSET, LEADER_LINE_SEGMENT_LENGTH * VERTICAL_MULT, 0)): with sc.Transform(scale_to=sc.Space.SCREEN): # Offset each Label vertically in screen space with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, LINE1_OFFSET, 0)): sc.Label(f"Path: {self.model.get_item('name')}", alignment=ui.Alignment.LEFT_BOTTOM) with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, LINE2_OFFSET, 0)): sc.Label(f"Material: {self.model.get_item('material')}", alignment=ui.Alignment.LEFT_TOP) def on_model_updated(self, item): # Regenerate the manipulator self.invalidate()

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/omni/example/ui_scene/widget_info/widget_info_manipulator.py

## Copyright (c) 2018-2021, 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. ## __all__ = ["WidgetInfoManipulator"] from omni.ui import color as cl from omni.ui import scene as sc import omni.ui as ui class _ViewportLegacyDisableSelection: """Disables selection in the Viewport Legacy""" def __init__(self): self._focused_windows = None focused_windows = [] try: # For some reason is_focused may return False, when a Window is definitely in fact is the focused window! # And there's no good solution to this when mutliple Viewport-1 instances are open; so we just have to # operate on all Viewports for a given usd_context. import omni.kit.viewport_legacy as vp vpi = vp.acquire_viewport_interface() for instance in vpi.get_instance_list(): window = vpi.get_viewport_window(instance) if not window: continue focused_windows.append(window) if focused_windows: self._focused_windows = focused_windows for window in self._focused_windows: # Disable the selection_rect, but enable_picking for snapping window.disable_selection_rect(True) except Exception: pass class _DragPrioritize(sc.GestureManager): """Refuses preventing _DragGesture.""" def can_be_prevented(self, gesture): # Never prevent in the middle of drag return gesture.state != sc.GestureState.CHANGED def should_prevent(self, gesture, preventer): if preventer.state == sc.GestureState.BEGAN or preventer.state == sc.GestureState.CHANGED: return True class _DragGesture(sc.DragGesture): """"Gesture to disable rectangle selection in the viewport legacy""" def __init__(self): super().__init__(manager=_DragPrioritize()) def on_began(self): # When the user drags the slider, we don't want to see the selection # rect. In Viewport Next, it works well automatically because the # selection rect is a manipulator with its gesture, and we add the # slider manipulator to the same SceneView. # In Viewport Legacy, the selection rect is not a manipulator. Thus it's # not disabled automatically, and we need to disable it with the code. self.__disable_selection = _ViewportLegacyDisableSelection() def on_ended(self): # This re-enables the selection in the Viewport Legacy self.__disable_selection = None class WidgetInfoManipulator(sc.Manipulator): def __init__(self, **kwargs): super().__init__(**kwargs) self.destroy() self._radius = 2 self._distance_to_top = 5 self._thickness = 2 self._radius_hovered = 20 def destroy(self): self._root = None self._slider_subscription = None self._slider_model = None self._name_label = None def _on_build_widgets(self): with ui.ZStack(): ui.Rectangle( style={ "background_color": cl(0.2), "border_color": cl(0.7), "border_width": 2, "border_radius": 4, } ) with ui.VStack(style={"font_size": 24}): ui.Spacer(height=4) with ui.ZStack(style={"margin": 1}, height=30): ui.Rectangle( style={ "background_color": cl(0.0), } ) ui.Line(style={"color": cl(0.7), "border_width": 2}, alignment=ui.Alignment.BOTTOM) ui.Label("Hello world, I am a scene.Widget!", height=0, alignment=ui.Alignment.CENTER) ui.Spacer(height=4) self._name_label = ui.Label("", height=0, alignment=ui.Alignment.CENTER) # setup some model, just for simple demonstration here self._slider_model = ui.SimpleFloatModel() ui.Spacer(height=10) with ui.HStack(): ui.Spacer(width=10) ui.Label("scale", height=0, width=0) ui.Spacer(width=5) ui.FloatSlider(self._slider_model) ui.Spacer(width=10) ui.Spacer(height=4) ui.Spacer() self.on_model_updated(None) # Additional gesture that prevents Viewport Legacy selection self._widget.gestures += [_DragGesture()] def on_build(self): """Called when the model is chenged and rebuilds the whole slider""" self._root = sc.Transform(visible=False) with self._root: with sc.Transform(scale_to=sc.Space.SCREEN): with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, 100, 0)): # Label with sc.Transform(look_at=sc.Transform.LookAt.CAMERA): self._widget = sc.Widget(500, 150, update_policy=sc.Widget.UpdatePolicy.ON_MOUSE_HOVERED) self._widget.frame.set_build_fn(self._on_build_widgets) def on_model_updated(self, _): # if we don't have selection then show nothing if not self.model or not self.model.get_item("name"): self._root.visible = False return # Update the shapes position = self.model.get_as_floats(self.model.get_item("position")) self._root.transform = sc.Matrix44.get_translation_matrix(*position) self._root.visible = True # Update the slider def update_scale(prim_name, value): print(f"changing scale of {prim_name}, {value}") if self._slider_model: self._slider_subscription = None self._slider_model.as_float = 1.0 self._slider_subscription = self._slider_model.subscribe_value_changed_fn( lambda m, p=self.model.get_item("name"): update_scale(p, m.as_float) ) # Update the shape name if self._name_label: self._name_label.text = f"Prim:{self.model.get_item('name')}"

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/omni/example/ui_scene/widget_info/widget_info_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. # __all__ = ["WidgetInfoExtension"] from .widget_info_scene import WidgetInfoScene from omni.kit.viewport.utility import get_active_viewport_window import carb import omni.ext class WidgetInfoExtension(omni.ext.IExt): """The entry point to the extension""" def on_startup(self, ext_id): # Get the active (which at startup is the default Viewport) viewport_window = get_active_viewport_window() # Issue an error if there is no Viewport if not viewport_window: carb.log_warn(f"No Viewport Window to add {ext_id} scene to") self._widget_info_viewport = None return # Build out the scene self._widget_info_viewport = WidgetInfoScene(viewport_window, ext_id) def on_shutdown(self): if self._widget_info_viewport: self._widget_info_viewport.destroy() self._widget_info_viewport = None

Python

NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/omni/example/ui_scene/widget_info/tests/test_info.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. # __all__ = ["TestInfo"] from omni.example.ui_scene.widget_info.widget_info_manipulator import WidgetInfoManipulator from omni.ui import scene as sc from omni.ui.tests.test_base import OmniUiTest from pathlib import Path import omni.kit.app import omni.kit.test EXTENSION_FOLDER_PATH = Path(omni.kit.app.get_app().get_extension_manager().get_extension_path_by_module(__name__)) TEST_DATA_PATH = EXTENSION_FOLDER_PATH.joinpath("data/tests") class WidgetInfoTestModelItem(sc.AbstractManipulatorItem): pass class WidgetInfoTestModel(sc.AbstractManipulatorModel): def __init__(self): super().__init__() self.position = WidgetInfoTestModelItem() def get_item(self, identifier): if identifier == "position": return self.position if identifier == "name": return "Name" if identifier == "material": return "Material" def get_as_floats(self, item): if item == self.position: return [0, 0, 0] class TestInfo(OmniUiTest): async def test_general(self): """Testing general look of the item""" window = await self.create_test_window(width=256, height=256) with window.frame: # Camera matrices projection = [1e-2, 0, 0, 0] projection += [0, 1e-2, 0, 0] projection += [0, 0, -2e-7, 0] projection += [0, 0, 1, 1] view = sc.Matrix44.get_translation_matrix(0, 0, 0) scene_view = sc.SceneView(sc.CameraModel(projection, view)) with scene_view.scene: # The manipulator model = WidgetInfoTestModel() WidgetInfoManipulator(model=model) await omni.kit.app.get_app().next_update_async() model._item_changed(None) for _ in range(10): await omni.kit.app.get_app().next_update_async() await self.finalize_test(threshold=100, golden_img_dir=TEST_DATA_PATH, golden_img_name="general.png")

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/config.py

# region copyright # 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. # endregion from typing import Any, Dict c: Dict[str, Any] = {} # paths c["app_dir"] = "/app" c["state_dir"] = "/app/state" c["results_dir"] = "/app/results" c["uploads_dir"] = "/app/uploads" c["tests_dir"] = "/app/src/tests" c["ansible_dir"] = "/app/src/ansible" c["terraform_dir"] = "/app/src/terraform" # app image name c["app_image_name"] = "isa" # gcp driver # @see https://cloud.google.com/compute/docs/gpus/grid-drivers-table c[ "gcp_driver_url" ] = "https://storage.googleapis.com/nvidia-drivers-us-public/GRID/vGPU16.2/NVIDIA-Linux-x86_64-535.129.03-grid.run" # aws/alicloud driver c["generic_driver_apt_package"] = "nvidia-driver-535-server" # default remote dirs c["default_remote_uploads_dir"] = "/home/ubuntu/uploads" c["default_remote_results_dir"] = "/home/ubuntu/results" c["default_remote_workspace_dir"] = "/home/ubuntu/workspace" # defaults # --isaac-image c["default_isaac_image"] = "nvcr.io/nvidia/isaac-sim:2023.1.1" # --ssh-port c["default_ssh_port"] = 22 # --from-image c["azure_default_from_image"] = False c["aws_default_from_image"] = False # --omniverse-user c["default_omniverse_user"] = "omniverse" # --remote-dir c["default_remote_uploads_dir"] = "/home/ubuntu/uploads" c["default_remote_results_dir"] = "/home/ubuntu/results" # --isaac-instance-type c["aws_default_isaac_instance_type"] = "g5.2xlarge" # str, 1-index in DeployAzureCommand.AZURE_OVKIT_INSTANCE_TYPES c["azure_default_isaac_instance_type"] = "2" c["gcp_default_isaac_instance_type"] = "g2-standard-8" c["alicloud_default_isaac_instance_type"] = "ecs.gn7i-c16g1.4xlarge" # --isaac-gpu-count c["gcp_default_isaac_gpu_count"] = 1 # --region c["alicloud_default_region"] = "us-east-1" # --prefix for the created cloud resources c["default_prefix"] = "isa" # --oige c["default_oige_git_checkpoint"] = "main" # --orbit c["default_orbit_git_checkpoint"] = "devel"

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/aws.py

# region copyright # 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. # endregion """ Utils for AWS """ from src.python.utils import read_meta, shell_command def aws_configure_cli( deployment_name, verbose=False, ): """ Configure AWS CLI for deployment """ meta = read_meta(deployment_name) aws_access_key_id = meta["params"]["aws_access_key_id"] aws_secret_access_key = meta["params"]["aws_secret_access_key"] region = meta["params"]["region"] shell_command( f"aws configure set aws_access_key_id '{aws_access_key_id}'", verbose=verbose, exit_on_error=True, capture_output=True, ) shell_command( f"aws configure set aws_secret_access_key '{aws_secret_access_key}'", verbose=verbose, exit_on_error=True, capture_output=True, ) shell_command( f"aws configure set region '{region}'", verbose=verbose, exit_on_error=True, capture_output=True, ) def aws_stop_instance(instance_id, verbose=False): shell_command( f"aws ec2 stop-instances --instance-ids '{instance_id}'", verbose=verbose, exit_on_error=True, capture_output=True, ) def aws_start_instance(instance_id, verbose=False): shell_command( f"aws ec2 start-instances --instance-ids '{instance_id}'", verbose=verbose, exit_on_error=True, capture_output=True, ) def aws_get_instance_status(instance_id, verbose=False): """ Query instance status Returns: "stopping" | "stopped" | "pending" | "running" """ status = ( shell_command( f"aws ec2 describe-instances --instance-ids '{instance_id}'" + " | jq -r .Reservations[0].Instances[0].State.Name", verbose=verbose, exit_on_error=True, capture_output=True, ) .stdout.decode() .strip() ) return status

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/ngc.py

# region copyright # 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. # endregion import pathlib import subprocess SELF_DIR = pathlib.Path(__file__).parent.resolve() def check_ngc_access(ngc_api_key, org="", team="", verbose=False): """ Checks if NGC API key is valid and user has access to DRIVE Sim. Returns: - 0 - all is fine - 100 - invalid api key - 102 - user is not in the team """ proc = subprocess.run( [f"{SELF_DIR}/ngc_check.expect", ngc_api_key, org, team], capture_output=not verbose, timeout=60, ) if proc.returncode not in [0, 100, 101, 102]: raise RuntimeError( f"Error checking NGC API Key. Return code: {proc.returncode}" ) return proc.returncode

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/alicloud.py

# region copyright # 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. # endregion """ Utils for AliCloud """ from src.python.utils import read_meta, shell_command def alicloud_configure_cli( deployment_name, verbose=False, ): """ Configure Aliyun CLI """ meta = read_meta(deployment_name) aliyun_access_key = meta["params"]["aliyun_access_key"] aliyun_secret_key = meta["params"]["aliyun_secret_key"] region = meta["params"]["region"] shell_command( "aliyun configure set " + f"--access-key-id '{aliyun_access_key}'" + f" --access-key-secret '{aliyun_secret_key}'" + f" --region '{region}'", verbose=verbose, exit_on_error=True, capture_output=True, ) def alicloud_start_instance(vm_id, verbose=False): """ Start VM """ shell_command( f"aliyun ecs StartInstance --InstanceId '{vm_id}'", verbose=verbose, exit_on_error=True, capture_output=True, ) def alicloud_stop_instance(vm_id, verbose=False): """ Stop VM """ shell_command( f"aliyun ecs StopInstance --InstanceId '{vm_id}'", verbose=verbose, exit_on_error=True, capture_output=True, ) def alicloud_get_instance_status(vm_id, verbose=False): """ Query VM status Returns: "Stopping" | "Stopped" | "Starting" | "Running" """ status = ( shell_command( f"aliyun ecs DescribeInstances --InstanceIds '[\"{vm_id}\"]'" + " | jq -r .Instances.Instance[0].Status", verbose=verbose, exit_on_error=True, capture_output=True, ) .stdout.decode() .strip() ) return status def alicloud_list_regions( aliyun_access_key, aliyun_secret_key, verbose=False, ): """ List regions """ res = ( shell_command( f"aliyun --access-key-id {aliyun_access_key}" + f" --access-key-secret {aliyun_secret_key}" + " --region cn-beijing ecs DescribeRegions" + " | jq -r '.Regions.Region[].RegionId'", capture_output=True, exit_on_error=True, verbose=verbose, ) .stdout.decode() .strip() ) valid_regions = res.split("\n") return valid_regions

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/deployer.py

# region copyright # 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. # endregion import json import os import re import shlex import sys from pathlib import Path import click from src.python.utils import ( colorize_error, colorize_info, colorize_prompt, colorize_result, read_meta, shell_command, ) from src.python.debug import debug_break # noqa from src.python.ngc import check_ngc_access class Deployer: def __init__(self, params, config): self.tf_outputs = {} self.params = params self.config = config self.existing_behavior = None # save original params so we can recreate command line self.input_params = params.copy() # convert "in_china" self.params["in_china"] = {"yes": True, "no": False, "auto": False}[ self.params["in_china"] ] # create state directory if it doesn't exist os.makedirs(self.config["state_dir"], exist_ok=True) # print complete command line if self.params["debug"]: click.echo(colorize_info("* Command:\n" + self.recreate_command_line())) def __del__(self): # update meta info self.save_meta() def save_meta(self): """ Save command parameters in json file, just in case """ meta_file = ( f"{self.config['state_dir']}/{self.params['deployment_name']}/meta.json" ) data = { "command": self.recreate_command_line(separator=" "), "input_params": self.input_params, "params": self.params, "config": self.config, } Path(meta_file).parent.mkdir(parents=True, exist_ok=True) Path(meta_file).write_text(json.dumps(data, indent=4)) if self.params["debug"]: click.echo(colorize_info(f"* Meta info saved to '{meta_file}'")) def read_meta(self): return read_meta( self.params["deployment_name"], self.params["debug"], ) def recreate_command_line(self, separator=" \\\n"): """ Recreate command line """ command_line = sys.argv[0] for k, v in self.input_params.items(): k = k.replace("_", "-") if isinstance(v, bool): if v: command_line += separator + "--" + k else: not_prefix = "--no-" if k in ["from-image"]: not_prefix = "--not-" command_line += separator + not_prefix + k else: command_line += separator + "--" + k + " " if isinstance(v, str): command_line += "'" + shlex.quote(v) + "'" else: command_line += str(v) return command_line def ask_existing_behavior(self): """ Ask what to do if deployment already exists """ deployment_name = self.params["deployment_name"] existing = self.params["existing"] self.existing_behavior = existing if existing == "ask" and os.path.isfile( f"{self.config['state_dir']}/{deployment_name}/.tfvars" ): self.existing_behavior = click.prompt( text=colorize_prompt( "* Deploymemnt exists, what would you like to do? See --help for details." ), type=click.Choice(["repair", "modify", "replace", "run_ansible"]), default="replace", ) if ( self.existing_behavior == "repair" or self.existing_behavior == "run_ansible" ): # restore params from meta file r = self.read_meta() self.params = r["params"] click.echo( colorize_info( f"* Repairing existing deployment \"{self.params['deployment_name']}\"..." ) ) # update meta info (with new value for existing_behavior) self.save_meta() # destroy existing deployment`` if self.existing_behavior == "replace": debug = self.params["debug"] click.echo(colorize_info("* Deleting existing deployment...")) shell_command( command=f'{self.config["app_dir"]}/destroy "{deployment_name}" --yes' + f' {"--debug" if debug else ""}', verbose=debug, ) # update meta info if deployment was destroyed self.save_meta() def validate_ngc_api_key(self, image, restricted_image=False): """ Check if NGC API key allows to log in and has access to appropriate NGC image @param image: NGC image to check access to @param restricted_image: If image is restricted to specific org/team? """ debug = self.params["debug"] ngc_api_key = self.params["ngc_api_key"] ngc_api_key_check = self.params["ngc_api_key_check"] # extract org and team from the image path r = re.findall( "^nvcr\\.io/([a-z0-9\\-_]+)/([a-z0-9\\-_]+/)?[a-z0-9\\-_]+:[a-z0-9\\-_.]+$", image, ) ngc_org, ngc_team = r[0] ngc_team = ngc_team.rstrip("/") if ngc_org == "nvidia": click.echo( colorize_info( "* Access to docker image can't be checked for NVIDIA org. But you'll be fine. Fingers crossed." ) ) return if debug: click.echo(colorize_info(f'* Will check access to NGC Org: "{ngc_org}"')) click.echo(colorize_info(f'* Will check access to NGC Team: "{ngc_team}"')) if ngc_api_key_check and ngc_api_key != "none": click.echo(colorize_info("* Validating NGC API key... ")) r = check_ngc_access( ngc_api_key=ngc_api_key, org=ngc_org, team=ngc_team, verbose=debug ) if r == 100: raise Exception(colorize_error("NGC API key is invalid.")) # only check access to org/team if restricted image is deployed elif restricted_image and (r == 101 or r == 102): raise Exception( colorize_error( f'NGC API key is valid but you don\'t have access to image "{image}".' ) ) click.echo(colorize_info(("* NGC API Key is valid!"))) def create_tfvars(self, tfvars: dict = {}): """ - Check if deployment with this deployment_name exists and deal with it - Create/update tfvars file Expected values for "existing_behavior" arg: - repair: keep tfvars/tfstate, don't ask for user input - modify: keep tfstate file, update tfvars file with user input - replace: delete tfvars/tfstate files - run_ansible: keep tfvars/tfstate, don't ask for user input, skip terraform steps """ # default values common for all clouds tfvars.update( { "isaac_enabled": self.params["isaac"] if "isaac" in self.params else False, # "isaac_instance_type": self.params["isaac_instance_type"] if "isaac_instance_type" in self.params else "none", # "prefix": self.params["prefix"], "ssh_port": self.params["ssh_port"], # "from_image": self.params["from_image"] if "from_image" in self.params else False, # "deployment_name": self.params["deployment_name"], } ) debug = self.params["debug"] deployment_name = self.params["deployment_name"] # deal with existing deployment: tfvars_file = f"{self.config['state_dir']}/{deployment_name}/.tfvars" tfstate_file = f"{self.config['state_dir']}/{deployment_name}/.tfstate" # tfvars if os.path.exists(tfvars_file): if ( self.existing_behavior == "modify" or self.existing_behavior == "overwrite" ): os.remove(tfvars_file) if debug: click.echo(colorize_info(f'* Deleted "{tfvars_file}"...')) # tfstate if os.path.exists(tfstate_file): if self.existing_behavior == "overwrite": os.remove(tfstate_file) if debug: click.echo(colorize_info(f'* Deleted "{tfstate_file}"...')) # create tfvars file if ( self.existing_behavior == "modify" or self.existing_behavior == "overwrite" or not os.path.exists(tfvars_file) ): self._write_tfvars_file(path=tfvars_file, tfvars=tfvars) def _write_tfvars_file(self, path: str, tfvars: dict): """ Write tfvars file """ debug = self.params["debug"] if debug: click.echo(colorize_info(f'* Created tfvars file "{path}"')) # create <dn>/ directory if it doesn't exist Path(path).parent.mkdir(parents=True, exist_ok=True) with open(path, "w") as f: for key, value in tfvars.items(): # convert booleans to strings if isinstance(value, bool): value = { True: "true", False: "false", }[value] # format key names key = key.replace("-", "_") # write values if isinstance(value, str): value = value.replace('"', '\\"') f.write(f'{key} = "{value}"\n') elif isinstance(value, list): f.write(f"{key} = " + str(value).replace("'", '"') + "\n") else: f.write(f"{key} = {value}\n") def create_ansible_inventory(self, write: bool = True): """ Create Ansible inventory, return it as text Write to file if write=True """ debug = self.params["debug"] deployment_name = self.params["deployment_name"] ansible_vars = self.params.copy() # add config ansible_vars["config"] = self.config # get missing values from terraform for k in [ "isaac_ip", "ovami_ip", "cloud", ]: if k not in self.params or ansible_vars[k] is None: ansible_vars[k] = self.tf_output(k) # convert booleans to ansible format ansible_booleans = {True: "true", False: "false"} for k, v in ansible_vars.items(): if isinstance(v, bool): ansible_vars[k] = ansible_booleans[v] template = Path(f"{self.config['ansible_dir']}/inventory.template").read_text() res = template.format(**ansible_vars) # write to file if write: inventory_file = f"{self.config['state_dir']}/{deployment_name}/.inventory" Path(inventory_file).parent.mkdir(parents=True, exist_ok=True) # create dir Path(inventory_file).write_text(res) # write file if debug: click.echo( colorize_info( f'* Created Ansible inventory file "{inventory_file}"' ) ) return res def initialize_terraform(self, cwd: str): """ Initialize Terraform via shell command cwd: directory where terraform scripts are located """ debug = self.params["debug"] shell_command( f"terraform init -upgrade -no-color -input=false {' > /dev/null' if not debug else ''}", verbose=debug, cwd=cwd, ) def run_terraform(self, cwd: str): """ Apply Terraform via shell command cwd: directory where terraform scripts are located """ debug = self.params["debug"] deployment_name = self.params["deployment_name"] shell_command( "terraform apply -auto-approve " + f"-state={self.config['state_dir']}/{deployment_name}/.tfstate " + f"-var-file={self.config['state_dir']}/{deployment_name}/.tfvars", cwd=cwd, verbose=debug, ) def export_ssh_key(self): """ Export SSH key from Terraform state """ debug = self.params["debug"] deployment_name = self.params["deployment_name"] shell_command( f"terraform output -state={self.config['state_dir']}/{deployment_name}/.tfstate -raw ssh_key" + f" > {self.config['state_dir']}/{deployment_name}/key.pem && " + f"chmod 0600 {self.config['state_dir']}/{deployment_name}/key.pem", verbose=debug, ) def run_ansible(self, playbook_name: str, cwd: str): """ Run Ansible playbook via shell command """ debug = self.params["debug"] deployment_name = self.params["deployment_name"] shell_command( f"ansible-playbook -i {self.config['state_dir']}/{deployment_name}/.inventory " + f"{playbook_name}.yml {'-vv' if self.params['debug'] else ''}", cwd=cwd, verbose=debug, ) def run_all_ansible(self): # run ansible for isaac if "isaac" in self.params and self.params["isaac"]: click.echo(colorize_info("* Running Ansible for Isaac Sim...")) self.run_ansible(playbook_name="isaac", cwd=f"{self.config['ansible_dir']}") # run ansible for ovami # todo: move to ./deploy-aws if "ovami" in self.params and self.params["ovami"]: click.echo(colorize_info("* Running Ansible for OV AMI...")) self.run_ansible(playbook_name="ovami", cwd=f"{self.config['ansible_dir']}") def tf_output(self, key: str, default: str = ""): """ Read Terraform output. Cache read values in self._tf_outputs. """ if key not in self.tf_outputs: debug = self.params["debug"] deployment_name = self.params["deployment_name"] r = shell_command( f"terraform output -state='{self.config['state_dir']}/{deployment_name}/.tfstate' -raw '{key}'", capture_output=True, exit_on_error=False, verbose=debug, ) if r.returncode == 0: self.tf_outputs[key] = r.stdout.decode() else: if self.params["debug"]: click.echo( colorize_error( f"* Warning: Terraform output '{key}' cannot be read." ), err=True, ) self.tf_outputs[key] = default # update meta file to reflect tf outputs self.save_meta() return self.tf_outputs[key] def upload_user_data(self): shell_command( f'./upload "{self.params["deployment_name"]}" ' + f'{"--debug" if self.params["debug"] else ""}', cwd=self.config["app_dir"], verbose=self.params["debug"], exit_on_error=True, capture_output=False, ) # generate ssh connection command for the user def ssh_connection_command(self, ip: str): r = f"ssh -i state/{self.params['deployment_name']}/key.pem " r += f"-o StrictHostKeyChecking=no ubuntu@{ip}" if self.params["ssh_port"] != 22: r += f" -p {self.params['ssh_port']}" return r def output_deployment_info(self, extra_text: str = "", print_text=True): """ Print connection info for the user Save info to file (_state_dir_/_deployment_name_/info.txt) """ isaac = "isaac" in self.params and self.params["isaac"] ovami = "ovami" in self.params and self.params["ovami"] vnc_password = self.params["vnc_password"] deployment_name = self.params["deployment_name"] # templates nomachine_instruction = f"""* To connect to __app__ via NoMachine: 0. Download NoMachine client at https://downloads.nomachine.com/, install and launch it. 1. Click "Add" button. 2. Enter Host: "__ip__". 3. In "Configuration" > "Use key-based authentication with a key you provide", select file "state/{deployment_name}/key.pem". 4. Click "Connect" button. 5. Enter "ubuntu" as a username when prompted. """ vnc_instruction = f"""* To connect to __app__ via VNC: - IP: __ip__ - Port: 5900 - Password: {vnc_password}""" nonvc_instruction = f"""* To connect to __app__ via noVNC: 1. Open http://__ip__:6080/vnc.html?host=__ip__&port=6080 in your browser. 2. Click "Connect" and use password \"{vnc_password}\"""" # print connection info instructions_file = f"{self.config['state_dir']}/{deployment_name}/info.txt" instructions = "" if isaac: instructions += f"""{'*' * (29+len(self.tf_output('isaac_ip')))} * Isaac Sim is deployed at {self.tf_output('isaac_ip')} * {'*' * (29+len(self.tf_output('isaac_ip')))} * To connect to Isaac Sim via SSH: {self.ssh_connection_command(self.tf_output('isaac_ip'))} {nonvc_instruction} {nomachine_instruction}""".replace( "__app__", "Isaac Sim" ).replace( "__ip__", self.tf_output("isaac_ip") ) # todo: move to ./deploy-aws if ovami: instructions += f"""\n * OV AMI is deployed at {self.tf_output('ovami_ip')} * To connect to OV AMI via SSH: {self.ssh_connection_command(self.tf_output('ovami_ip'))} * To connect to OV AMI via NICE DCV: - IP: __ip__ {vnc_instruction} {nomachine_instruction} """.replace( "__app__", "OV AMI" ).replace( "__ip__", self.tf_output("ovami_ip") ) # extra text if len(extra_text) > 0: instructions += extra_text + "\n" # print instructions for the user if print_text: click.echo(colorize_result("\n" + instructions)) # create <dn>/ directory if it doesn't exist Path(instructions_file).parent.mkdir(parents=True, exist_ok=True) # write file Path(instructions_file).write_text(instructions) return instructions

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/utils.py

# region copyright # 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. # endregion """ CLI Utils """ import json import os import subprocess from glob import glob from pathlib import Path import click from src.python.config import c as config def colorize_prompt(text): return click.style(text, fg="bright_cyan", italic=True) def colorize_error(text): return click.style(text, fg="bright_red", italic=True) def colorize_info(text): return click.style(text, fg="bright_magenta", italic=True) def colorize_result(text): return click.style(text, fg="bright_green", italic=True) def shell_command( command, verbose=False, cwd=None, exit_on_error=True, capture_output=False ): """ Execute shell command, print it if debug is enabled """ if verbose: if cwd is not None: click.echo(colorize_info(f"* Running `(cd {cwd} && {command})`...")) else: click.echo(colorize_info(f"* Running `{command}`...")) res = subprocess.run( command, shell=True, cwd=cwd, capture_output=capture_output, ) if res.returncode == 0: if verbose and res.stdout is not None: click.echo(res.stdout.decode()) elif exit_on_error: if res.stderr is not None: click.echo( colorize_error(f"Error: {res.stderr.decode()}"), err=True, ) exit(1) return res def deployments(): """List existing deployments by name""" state_dir = config["state_dir"] deployments = sorted( [ os.path.basename(os.path.dirname(d)) for d in glob(os.path.join(state_dir, "*/")) ] ) return deployments def read_meta(deployment_name: str, verbose: bool = False): """ Read metadata from json file """ meta_file = f"{config['state_dir']}/{deployment_name}/meta.json" if os.path.isfile(meta_file): data = json.loads(Path(meta_file).read_text()) if verbose: click.echo(colorize_info(f"* Meta info loaded from '{meta_file}'")) return data raise Exception(f"Meta file '{meta_file}' not found") def read_tf_output(deployment_name, output, verbose=False): """ Read terraform output from tfstate file """ return ( shell_command( f"terraform output -state={config['state_dir']}/{deployment_name}/.tfstate -raw {output}", capture_output=True, exit_on_error=False, verbose=verbose, ) .stdout.decode() .strip() ) def format_app_name(app_name): """ Format app name for user output """ formatted = { "isaac": "Isaac Sim", "ovami": "OV AMI", } if app_name in formatted: return formatted[app_name] return app_name def format_cloud_name(cloud_name): """ Format cloud name for user output """ formatted = { "aws": "AWS", "azure": "Azure", "gcp": "GCP", "alicloud": "Alibaba Cloud", } if cloud_name in formatted: return formatted[cloud_name] return cloud_name def gcp_login(verbose=False): """ Log into GCP """ # detect if we need to re-login click.echo(colorize_info("* Checking GCP login status..."), nl=False) res = shell_command( "gcloud auth application-default print-access-token 2>&1 > /dev/null", verbose=verbose, exit_on_error=False, capture_output=True, ) logged_in = res.returncode == 0 if logged_in: click.echo(colorize_info(" logged in!")) if not logged_in: click.echo(colorize_info(" not logged in")) shell_command( "gcloud auth application-default login --no-launch-browser --disable-quota-project --verbosity none", verbose=verbose, )

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/azure.py

# region copyright # 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. # endregion """ Utils for Azure """ import click from src.python.utils import colorize_info, read_meta, shell_command def azure_login(verbose=False): """ Log into Azure """ # detect if we need to re-login logged_in = ( '"Enabled"' == shell_command( "az account show --query state", verbose=verbose, exit_on_error=False, capture_output=True, ) .stdout.decode() .strip() ) if not logged_in: click.echo(colorize_info("* Logging into Azure...")) shell_command("az login --use-device-code", verbose=verbose) def azure_stop_instance(vm_id, verbose=False): shell_command( f"az vm deallocate --ids {vm_id}", verbose=verbose, exit_on_error=True, capture_output=False, ) def azure_start_instance(vm_id, verbose=False): shell_command( f"az vm start --ids {vm_id}", verbose=verbose, exit_on_error=True, capture_output=False, )

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/deploy_command.py

# region copyright # 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. # endregion """ Base deploy- command """ import os import re import click import randomname from pwgen import pwgen from src.python.config import c as config from src.python.debug import debug_break # noqa from src.python.utils import colorize_error, colorize_prompt class DeployCommand(click.core.Command): """ Defines common cli options for "deploy-*" commands. """ @staticmethod def isaac_callback(ctx, param, value): """ Called after --isaac option is parsed """ # disable isaac instance type selection if isaac is disabled if value is False: for p in ctx.command.params: if p.name.startswith("isaac"): p.prompt = None return value @staticmethod def deployment_name_callback(ctx, param, value): # validate if not re.match("^[a-z0-9\\-]{1,32}$", value): raise click.BadParameter( colorize_error( "Only lower case letters, numbers and '-' are allowed." + f" Length should be between 1 and 32 characters ({len(value)} provided)." ) ) return value @staticmethod def ngc_api_key_callback(ctx, param, value): """ Validate NGC API key """ # fix click bug if value is None: return value # allow "none" as a special value if "none" == value: return value # check if it contains what's allowed if not re.match("^[A-Za-z0-9]{32,}$", value): raise click.BadParameter( colorize_error("Key contains invalid characters or too short.") ) return value @staticmethod def ngc_image_callback(ctx, param, value): """ Called after parsing --isaac-image options are parsed """ # ignore case value = value.lower() if not re.match( "^nvcr\\.io/[a-z0-9\\-_]+/([a-z0-9\\-_]+/)?[a-z0-9\\-_]+:[a-z0-9\\-_.]+$", value, ): raise click.BadParameter( colorize_error( "Invalid image name. " + "Expected: nvcr.io/<org>/[<team>/]<image>:<tag>" ) ) return value @staticmethod def oige_callback(ctx, param, value): """ Called after parsing --oige option """ if "" == value: return config["default_oige_git_checkpoint"] return value @staticmethod def orbit_callback(ctx, param, value): """ Called after parsing --orbit option """ if "" == value: return config["default_orbit_git_checkpoint"] return value def param_index(self, param_name): """ Return index of parameter with given name. Useful for inserting new parameters at a specific position. """ return list( filter( lambda param: param[1].name == param_name, enumerate(self.params), ) )[0][0] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # add common options self.params.insert( len(self.params), click.core.Option( ("--debug/--no-debug",), default=False, show_default=True, help="Enable debug output.", ), ) # --prefix self.params.insert( len(self.params), click.core.Option( ("--prefix",), default=config["default_prefix"], show_default=True, help="Prefix for all cloud resources.", ), ) # --from-image/--not-from-image self.params.insert( len(self.params), click.core.Option( ("--from-image/--not-from-image",), default=False, show_default=True, help="Deploy from pre-built image, from bare OS otherwise.", ), ) # --in-china self.params.insert( len(self.params), click.core.Option( ("--in-china",), type=click.Choice(["auto", "yes", "no"]), prompt=False, default="auto", show_default=True, help="Is deployment in China? (Local mirrors will be used.)", ), ) self.params.insert( len(self.params), click.core.Option( ("--deployment-name", "--dn"), prompt=colorize_prompt( '* Deployment Name (lower case letters, numbers and "-")' ), default=randomname.get_name, callback=DeployCommand.deployment_name_callback, show_default="<randomly generated>", help="Name of the deployment. Used to identify the created cloud resources and files.", ), ) self.params.insert( len(self.params), click.core.Option( ("--existing",), type=click.Choice( ["ask", "repair", "modify", "replace", "run_ansible"] ), default="ask", show_default=True, help="""What to do if deployment already exists: \n* 'repair' will try to fix broken deployment without applying new user parameters. \n* 'modify' will update user selected parameters and attempt to update existing cloud resources. \n* 'replace' will attempt to delete old deployment's cloud resources first. \n* 'run_ansible' will re-run Ansible playbooks.""", ), ) self.params.insert( len(self.params), click.core.Option( ("--isaac/--no-isaac",), default=True, show_default="yes", prompt=colorize_prompt("* Deploy Isaac Sim?"), callback=DeployCommand.isaac_callback, help="Deploy Isaac Sim (BETA)?", ), ) self.params.insert( len(self.params), click.core.Option( ("--isaac-image",), default=config["default_isaac_image"], prompt=colorize_prompt("* Isaac Sim docker image"), show_default=True, callback=DeployCommand.ngc_image_callback, help="Isaac Sim docker image to use.", ), ) # --oige help = ( "Install Omni Isaac Gym Envs? Valid values: 'no', " + "or <git ref in github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs>" ) self.params.insert( len(self.params), click.core.Option( ("--oige",), help=help, default="main", show_default=True, prompt=colorize_prompt("* " + help), callback=DeployCommand.oige_callback, ), ) # --orbit help = ( "[EXPERIMENTAL] Install Isaac Sim Orbit? Valid values: 'no', " + "or <git ref in github.com/NVIDIA-Omniverse/orbit>" ) self.params.insert( len(self.params), click.core.Option( ("--orbit",), help=help, default="no", show_default=True, prompt=colorize_prompt("* " + help), callback=DeployCommand.orbit_callback, ), ) self.params.insert( len(self.params), click.core.Option( ("--ngc-api-key",), type=str, prompt=colorize_prompt( "* NGC API Key (can be obtained at https://ngc.nvidia.com/setup/api-key)" ), default=os.environ.get("NGC_API_KEY", ""), show_default='"NGC_API_KEY" environment variable', help="NGC API Key (can be obtained at https://ngc.nvidia.com/setup/api-key)", callback=DeployCommand.ngc_api_key_callback, ), ) self.params.insert( len(self.params), click.core.Option( ("--ngc-api-key-check/--no-ngc-api-key-check",), default=True, help="Skip NGC API key validity check.", ), ) self.params.insert( len(self.params), click.core.Option( ("--vnc-password",), default=lambda: pwgen(10), help="Password for VNC access to DRIVE Sim/Isaac Sim/etc.", show_default="<randomly generated>", ), ) self.params.insert( len(self.params), click.core.Option( ("--system-user-password",), default=lambda: pwgen(10), help="System user password", show_default="<randomly generated>", ), ) self.params.insert( len(self.params), click.core.Option( ("--ssh-port",), default=config["default_ssh_port"], help="SSH port for connecting to the deployed machines.", show_default=True, ), ) # --upload/--no-upload self.params.insert( len(self.params), click.core.Option( ("--upload/--no-upload",), prompt=False, default=True, show_default=True, help=f"Upload user data from \"{config['uploads_dir']}\" to cloud " + f"instances (to \"{config['default_remote_uploads_dir']}\")?", ), ) default_nucleus_admin_password = pwgen(10) # --omniverse-user self.params.insert( len(self.params), click.core.Option( ("--omniverse-user",), default=config["default_omniverse_user"], help="Username for accessing content on the Nucleus server.", show_default=True, ), ) # --omniverse-password self.params.insert( len(self.params), click.core.Option( ("--omniverse-password",), default=default_nucleus_admin_password, help="Password for accessing content on the Nucleus server.", show_default="<randomly generated>", ), )

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/python/ngc.test.py

#!/usr/bin/env python3 # region copyright # 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. # endregion import os import unittest from src.python.ngc import check_ngc_access class Test_NGC_Key_Validation(unittest.TestCase): INVALID_KEY = "__invalid__" VALID_KEY = os.environ.get("NGC_API_KEY", "__none__") def test_invalid_key(self): """Test invalid key""" r = check_ngc_access(self.INVALID_KEY) self.assertEqual(r, 100) def test_valid_key(self): """Test valid key (should be set in NGC_API_KEY env var)""" if "__none__" == self.VALID_KEY: self.skipTest("No NGC_API_KEY env var set") return r = check_ngc_access(self.VALID_KEY) self.assertEqual(r, 0) if __name__ == "__main__": unittest.main()

Python

NVIDIA-Omniverse/IsaacSim-Automator/src/tests/deployer.test.py

#!/usr/bin/env python3 import unittest from src.python.config import c from src.python.deployer import Deployer from pathlib import Path class Test_Deployer(unittest.TestCase): def setUp(self): self.config = c self.config["state_dir"] = f"{c['tests_dir']}/res/state" self.deployer = Deployer( params={ "debug": False, "prefix": "isa", "from_image": False, "deployment_name": "test-1", "existing": "ask", "region": "us-east-1", "isaac": True, "isaac_instance_type": "g5.2xlarge", "isaac_image": "nvcr.io/nvidia/isaac-sim:2022.2.0", "ngc_api_key": "__ngc_api_key__", "ngc_api_key_check": True, "vnc_password": "__vnc_password__", "omniverse_user": "ovuser", "omniverse_password": "__omniverse_password__", "ssh_port": 22, "upload": True, "aws_access_key_id": "__aws_access_key_id__", "aws_secret_access_key": "__aws_secret_access_key__", }, config=self.config, ) def tearDown(self): self.deployer = None def test_output_deployment_info(self): self.deployer.output_deployment_info(print_text=False) file_generated = f"{self.config['state_dir']}/test-1/info.txt" file_expected = f"{self.config['state_dir']}/test-1/info.expected.txt" file_generated = Path(file_generated).read_text() file_expected = Path(file_expected).read_text() self.assertEqual(file_generated, file_expected) if __name__ == "__main__": unittest.main()

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_trigger_intervals.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.replicator.core as rep with rep.new_layer(): camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0)) # Create simple shapes to manipulate plane = rep.create.plane( semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100) ) spheres = rep.create.sphere( semantics=[("class", "sphere")], position=(0, 0, 100), count=6 ) # Modify the position every 5 frames with rep.trigger.on_frame(num_frames=10, interval=5): with spheres: rep.modify.pose( position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)), scale=rep.distribution.uniform(0.1, 2), ) # Modify color every frame for 50 frames with rep.trigger.on_frame(num_frames=50): with spheres: rep.randomizer.color( colors=rep.distribution.normal((0.1, 0.1, 0.1), (1.0, 1.0, 1.0)) ) render_product = rep.create.render_product(camera, (512, 512)) writer = rep.WriterRegistry.get("BasicWriter") writer.initialize( output_dir="trigger_intervals", rgb=True, ) writer.attach([render_product])

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_multiple_semantic_classes.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.replicator.core as rep with rep.new_layer(): sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 100, 100)) cube = rep.create.cube(semantics=[("class2", "cube")], position=(200, 200, 100)) plane = rep.create.plane(semantics=[("class3", "plane")], scale=10) def get_shapes(): shapes = rep.get.prims(semantics=[("class", "cube"), ("class", "sphere")]) with shapes: rep.modify.pose( position=rep.distribution.uniform((-500, 50, -500), (500, 50, 500)), rotation=rep.distribution.uniform((0, -180, 0), (0, 180, 0)), scale=rep.distribution.normal(1, 0.5), ) return shapes.node with rep.trigger.on_frame(num_frames=2): rep.randomizer.register(get_shapes) # Setup Camera camera = rep.create.camera(position=(500, 500, 500), look_at=(0, 0, 0)) render_product = rep.create.render_product(camera, (512, 512)) writer = rep.WriterRegistry.get("BasicWriter") writer.initialize( output_dir="semantics_classes", rgb=True, semantic_segmentation=True, colorize_semantic_segmentation=True, semantic_types=["class", "class2", "class3"], ) writer.attach([render_product]) rep.orchestrator.run()

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_scatter_multi_trigger.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: BSD-3-Clause # # 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. # """ This snippet shows how to setup multiple independent triggers that happen at different intervals in the simulation. """ import omni.graph.core as og import omni.replicator.core as rep # A light to see distance_light = rep.create.light(rotation=(-45, 0, 0), light_type="distant") # Create a plane to sample on plane_samp = rep.create.plane(scale=4, rotation=(20, 0, 0)) # Create a larger sphere to sample on the surface of sphere_samp = rep.create.sphere(scale=2.4, position=(0, 100, -180)) # Create a larger cylinder we do not want to collide with cylinder = rep.create.cylinder(semantics=[("class", "cylinder")], scale=(2, 1, 2)) def randomize_spheres(): # create small spheres to sample inside the plane spheres = rep.create.sphere(scale=0.4, count=60) # scatter small spheres with spheres: rep.randomizer.scatter_2d( surface_prims=[plane_samp, sphere_samp], no_coll_prims=[cylinder], check_for_collisions=True, ) # Add color to small spheres rep.randomizer.color( colors=rep.distribution.uniform((0.2, 0.2, 0.2), (1, 1, 1)) ) return spheres.node rep.randomizer.register(randomize_spheres) # Trigger will execute 5 times, every-other-frame (interval=2) with rep.trigger.on_frame(num_frames=5, interval=2): rep.randomizer.randomize_spheres() # Trigger will execute 10 times, once every frame with rep.trigger.on_frame(num_frames=10): with cylinder: rep.modify.visibility(rep.distribution.sequence([True, False])) og.Controller.evaluate_sync() # Only for snippet demonstration preview, not needed for production rep.orchestrator.preview() # Only for snippet demonstration preview, not needed for production rp = rep.create.render_product("/OmniverseKit_Persp", (1024, 768)) # Initialize and attach writer writer = rep.WriterRegistry.get("BasicWriter") writer.initialize(output_dir="scatter_example", rgb=True) writer.attach([rp]) rep.orchestrator.run()

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_writer_segmentation_colors.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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. """ A snippet showing to how create a custom writer to output specific colors in the semantic annotator output image. """ import omni.replicator.core as rep from omni.replicator.core import Writer, BackendDispatch, WriterRegistry class MyWriter(Writer): def __init__(self, output_dir: str): self._frame_id = 0 self.backend = BackendDispatch({"paths": {"out_dir": output_dir}}) self.annotators = ["rgb", "semantic_segmentation"] # Dictionary mapping of label to RGBA color self.CUSTOM_LABELS = { "unlabelled": (0, 0, 0, 0), "sphere": (128, 64, 128, 255), "cube": (244, 35, 232, 255), "plane": (102, 102, 156, 255), } def write(self, data): render_products = [k for k in data.keys() if k.startswith("rp_")] self._write_rgb(data, "rgb") self._write_segmentation(data, "semantic_segmentation") self._frame_id += 1 def _write_rgb(self, data, annotator: str): # Save the rgb data under the correct path rgb_file_path = f"rgb_{self._frame_id}.png" self.backend.write_image(rgb_file_path, data[annotator]) def _write_segmentation(self, data, annotator: str): seg_filepath = f"seg_{self._frame_id}.png" semantic_seg_data_colorized = rep.tools.colorize_segmentation( data[annotator]["data"], data[annotator]["info"]["idToLabels"], mapping=self.CUSTOM_LABELS, ) self.backend.write_image(seg_filepath, semantic_seg_data_colorized) def on_final_frame(self): self.backend.sync_pending_paths() # Register new writer WriterRegistry.register(MyWriter) # Create a new layer for our work to be performed in. # This is a good habit to develop for later when working on existing Usd scenes with rep.new_layer(): light = rep.create.light(light_type="dome") # Create a simple camera with a position and a point to look at camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0)) # Create some simple shapes to manipulate plane = rep.create.plane( semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100) ) torus = rep.create.torus(position=(200, 0, 100)) # Torus will be unlabeled sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 0, 100)) cube = rep.create.cube(semantics=[("class", "cube")], position=(-200, 0, 100)) # Randomize position and scale of each object on each frame with rep.trigger.on_frame(num_frames=10): # Creating a group so that our modify.pose operation works on all the shapes at once with rep.create.group([torus, sphere, cube]): rep.modify.pose( position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)), scale=rep.distribution.uniform(0.1, 2), ) # Initialize render product and attach a writer render_product = rep.create.render_product(camera, (1024, 1024)) writer = rep.WriterRegistry.get("MyWriter") writer.initialize(output_dir="myWriter_output") writer.attach([render_product]) rep.orchestrator.run() # Run the simulation

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_remove_semantics.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.graph.core as og import omni.replicator.core as rep from omni.usd._impl.utils import get_prim_at_path from pxr import Semantics from semantics.schema.editor import remove_prim_semantics # Setup simple scene with rep.new_layer(): # Simple scene setup camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0)) # Create simple shapes to manipulate plane = rep.create.plane( semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100) ) cubes = rep.create.cube( semantics=[("class", "cube")], position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)), count=6, ) spheres = rep.create.sphere( semantics=[("class", "sphere")], position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)), count=6, ) # Get prims to remove semantics on - Execute this first by itself my_spheres = rep.get.prims(semantics=[("class", "sphere")]) og.Controller.evaluate_sync() # Trigger an OmniGraph evaluation of the graph to set the values get_targets = rep.utils.get_node_targets(my_spheres.node, "outputs_prims") print(get_targets) # [Sdf.Path('/Replicator/Sphere_Xform'), Sdf.Path('/Replicator/Sphere_Xform_01'), Sdf.Path('/Replicator/Sphere_Xform_02'), Sdf.Path('/Replicator/Sphere_Xform_03'), Sdf.Path('/Replicator/Sphere_Xform_04'), Sdf.Path('/Replicator/Sphere_Xform_05')] # Loop through each prim_path and remove all semantic data for prim_path in get_targets: prim = get_prim_at_path(prim_path) # print(prim.HasAPI(Semantics.SemanticsAPI)) result = remove_prim_semantics(prim) # To remove all semantics # result = remove_prim_semantics(prim, label_type='class') # To remove only 'class' semantics print(result)

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replcator_clear_layer.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.usd stage = omni.usd.get_context().get_stage() for layer in stage.GetLayerStack(): if layer.GetDisplayName() == "test": # del layer layer.Clear()

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_annotator_segmentation.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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. """ This is an example of how to view annotator data if needed. """ import asyncio import omni.replicator.core as rep import omni.syntheticdata as sd async def test_semantics(): cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0)) sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0)) invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0)) # Setup semantic filter # sd.SyntheticData.Get().set_instance_mapping_semantic_filter("prim:*") cam = rep.create.camera(position=(500, 500, 500), look_at=(0, 0, 0)) rp = rep.create.render_product(cam, (1024, 512)) segmentation = rep.AnnotatorRegistry.get_annotator("semantic_segmentation") segmentation.attach(rp) # step_async() tells Omniverse to update, otherwise the annoation buffer could be empty await rep.orchestrator.step_async() data = segmentation.get_data() print(data) # Example Output: # { # "data": array( # [ # [0, 0, 0, ..., 0, 0, 0], # [0, 0, 0, ..., 0, 0, 0], # [0, 0, 0, ..., 0, 0, 0], # ..., # [0, 0, 0, ..., 0, 0, 0], # [0, 0, 0, ..., 0, 0, 0], # [0, 0, 0, ..., 0, 0, 0], # ], # dtype=uint32, # ), # "info": { # "_uniqueInstanceIDs": array([1, 1, 1], dtype=uint8), # "idToLabels": { # "0": {"class": "BACKGROUND"}, # "2": {"prim": "cone"}, # "3": {"prim": "sphere"}, # "4": {"shape": "boxy"}, # }, # }, # } asyncio.ensure_future(test_semantics())

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_multi_object_visibility_toggle.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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. """This will create a group from a list of objects and 1. Render all the objects together 2. Toggle sole visiblity for each object & render 3. Randomize pose for all objects, repeat This can be useful for training on object occlusions. """ import omni.replicator.core as rep NUM_POSE_RANDOMIZATIONS = 10 # Make a list-of-lists of True/False for each object # In this example of 3 objects: # [[True, True, True] # [True, False, False] # [False, True, False] # [False, False, True]] def make_visibility_lists(num_objects): visib = [] # Make an all-visible first pass visib.append(tuple([True for x in range(num_objects)])) # List to toggle one object visible at a time for x in range(num_objects): sub_vis = [] for i in range(num_objects): if x == i: sub_vis.append(True) else: sub_vis.append(False) visib.append(tuple(sub_vis)) return visib with rep.new_layer(): # Setup camera and simple light camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0)) light = rep.create.light(rotation=(-45, 45, 0)) # Create simple shapes to manipulate plane = rep.create.plane( semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100) ) torus = rep.create.torus(semantics=[("class", "torus")], position=(200, 0, 100)) sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 0, 100)) cube = rep.create.cube(semantics=[("class", "cube")], position=(-200, 0, 100)) # Create a group of the objects we will be manipulating # Leaving-out camera, light, and plane from visibility toggling and pose randomization object_group = rep.create.group([torus, sphere, cube]) # Get the number of objects to toggle, can work with any number of objects num_objects_to_toggle = len(object_group.get_output_prims()["prims"]) # Create our lists-of-lists for visibility visibility_sequence = make_visibility_lists(num_objects_to_toggle) # Trigger to toggle visibility one at a time with rep.trigger.on_frame( max_execs=(num_objects_to_toggle + 1) * NUM_POSE_RANDOMIZATIONS ): with object_group: rep.modify.visibility(rep.distribution.sequence(visibility_sequence)) # Trigger to randomize position and scale, interval set to number of objects +1(1 extra for the "all visible" frame) with rep.trigger.on_frame( max_execs=NUM_POSE_RANDOMIZATIONS, interval=num_objects_to_toggle + 1 ): with object_group: rep.modify.pose( position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)), scale=rep.distribution.uniform(0.1, 2), ) # Initialize render product and attach writer render_product = rep.create.render_product(camera, (512, 512)) writer = rep.WriterRegistry.get("BasicWriter") writer.initialize( output_dir="toggle_multi_visibility", rgb=True, semantic_segmentation=True, ) writer.attach([render_product]) rep.orchestrator.run()

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/surface_scratches/scratches_randomization.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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. from pathlib import Path import carb import omni.replicator.core as rep import omni.usd from pxr import Sdf, UsdGeom """ Instructions: Open the example scene file "scratches_randomization.usda", located adjacent to this script, in Omniverse prior to using this script """ # Get the current Usd "stage". This is where all the scene objects live stage = omni.usd.get_context().get_stage() with rep.new_layer(): camera = rep.create.camera(position=(-30, 38, 60), look_at=(0, 0, 0)) render_product = rep.create.render_product(camera, (1280, 720)) # Get Scene cube cube_prim = stage.GetPrimAtPath("/World/RoundedCube2/Cube/Cube") # Set the primvars on the cubes once primvars_api = UsdGeom.PrimvarsAPI(cube_prim) primvars_api.CreatePrimvar("random_color", Sdf.ValueTypeNames.Float3).Set( (1.0, 1.0, 1.0) ) primvars_api.CreatePrimvar("random_intensity", Sdf.ValueTypeNames.Float3).Set( (1.0, 1.0, 1.0) ) def change_colors(): # Change color primvars cubes = rep.get.prims( path_pattern="/World/RoundedCube2/Cube/Cube", prim_types=["Mesh"] ) with cubes: rep.modify.attribute( "primvars:random_color", rep.distribution.uniform((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)), attribute_type="float3", ) rep.modify.attribute( "primvars:random_intensity", rep.distribution.uniform((0.0, 0.0, 0.0), (10.0, 10.0, 10.0)), attribute_type="float3", ) return cubes.node rep.randomizer.register(change_colors) # Setup randomization of colors, different each frame with rep.trigger.on_frame(num_frames=10): rep.randomizer.change_colors() # (optional) Write output images to disk writer = rep.WriterRegistry.get("BasicWriter") writer.initialize( output_dir="~/replicator_examples/box_scratches", rgb=True, bounding_box_2d_tight=True, semantic_segmentation=True, distance_to_image_plane=True, ) writer.attach([render_product]) carb.log_info("scratches randomization complete")

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/22_Change_Textures.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.replicator.core as rep # create new objects to be used in the dataset with rep.new_layer(): sphere = rep.create.sphere( semantics=[("class", "sphere")], position=(0, 100, 100), count=5 ) cube = rep.create.cube( semantics=[("class", "cube")], position=(200, 200, 100), count=5 ) cone = rep.create.cone( semantics=[("class", "cone")], position=(200, 400, 200), count=10 ) cylinder = rep.create.cylinder( semantics=[("class", "cylinder")], position=(200, 100, 200), count=5 ) # create new camera & render product and attach to camera camera = rep.create.camera(position=(0, 0, 1000)) render_product = rep.create.render_product(camera, (1024, 1024)) # create plane if needed (but unused here) plane = rep.create.plane(scale=10) # function to get shapes that you've created above, via their semantic labels def get_shapes(): shapes = rep.get.prims( semantics=[ ("class", "cube"), ("class", "sphere"), ("class", "cone"), ("class", "cylinder"), ] ) with shapes: # assign textures to the different objects rep.randomizer.texture( textures=[ "omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/aggregate_exposed_diff.jpg", "omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_diff.jpg", "omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_multi_R_rough_G_ao.jpg", "omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/rough_gravel_rough.jpg", ] ) # modify pose and distribution rep.modify.pose( position=rep.distribution.uniform((-500, 50, -500), (500, 50, 500)), rotation=rep.distribution.uniform((0, -180, 0), (0, 180, 0)), scale=rep.distribution.normal(1, 0.5), ) return shapes.node # register the get shapes function as a randomizer function rep.randomizer.register(get_shapes) # Setup randomization. 100 variations here from 'num_frames' with rep.trigger.on_frame(num_frames=100): rep.randomizer.get_shapes() # Initialize and attach writer writer = rep.WriterRegistry.get("BasicWriter") writer.initialize(output_dir="~/replicator_examples/dli_example_22", rgb=True) writer.attach([render_product]) rep.orchestrator.run()

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/03_replicator_advanced.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.replicator.core as rep with rep.new_layer(): def dome_lights(): lights = rep.create.light( light_type="Dome", rotation=(270, 0, 0), texture=rep.distribution.choice( [ "omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCGcom_ExhibitionHall_Interior1.hdr", "omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCG_com_WarehouseInterior2b.hdr", ] ), ) return lights.node rep.randomizer.register(dome_lights) conference_tables = ( "omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Conference/" ) # create randomizer function conference table assets. # This randomization includes placement and rotation of the assets on the surface. def env_conference_table(size=5): confTable = rep.randomizer.instantiate( rep.utils.get_usd_files(conference_tables, recursive=False), size=size, mode="scene_instance", ) with confTable: rep.modify.pose( position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)), rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)), ) return confTable.node # Register randomization rep.randomizer.register(env_conference_table) # Setup camera and attach it to render product camera = rep.create.camera() render_product = rep.create.render_product(camera, resolution=(1024, 1024)) surface = rep.create.disk(scale=100, visible=False) # trigger on frame for an interval with rep.trigger.on_frame(5): rep.randomizer.env_conference_table(2) rep.randomizer.dome_lights() with camera: rep.modify.pose( position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)), look_at=surface, ) # Initialize and attach writer writer = rep.WriterRegistry.get("BasicWriter") writer.initialize(output_dir="~/replicator_examples/dli_example_3", rgb=True) writer.attach([render_product])

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/01_hello_replicator.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.replicator.core as rep # Create a new layer for our work to be performed in. # This is a good habit to develop for later when working on existing Usd scenes with rep.new_layer(): # Create a simple camera with a position and a point to look at camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0)) # Create some simple shapes to manipulate plane = rep.create.plane( semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100) ) torus = rep.create.torus(semantics=[("class", "torus")], position=(200, 0, 100)) sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 0, 100)) cube = rep.create.cube(semantics=[("class", "cube")], position=(-200, 0, 100)) # Randomize position and scale of each object on each frame with rep.trigger.on_frame(num_frames=10): # Creating a group so that our modify.pose operation works on all the shapes at once with rep.create.group([torus, sphere, cube]): rep.modify.pose( position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)), scale=rep.distribution.uniform(0.1, 2), ) # Initialize render product and attach a writer render_product = rep.create.render_product(camera, (1024, 1024)) writer = rep.WriterRegistry.get("BasicWriter") writer.initialize( output_dir="~/replicator_examples/dli_hello_replicator/", rgb=True, semantic_segmentation=True, bounding_box_2d_tight=True, ) writer.attach([render_product]) rep.orchestrator.run()

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/physics.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.replicator.core as rep with rep.new_layer(): # Define paths for the character, the props, the environment and the surface where the assets will be scattered in. PROPS = "omniverse://localhost/NVIDIA/Assets/Isaac/2022.1/Isaac/Props/YCB/Axis_Aligned_Physics" SURFACE = ( "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Basic/display_riser.usd" ) ENVS = "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Interior/ZetCG_ExhibitionHall.usd" # Define randomizer function for Base assets. This randomization includes placement and rotation of the assets on the surface. def env_props(size=50): instances = rep.randomizer.instantiate( rep.utils.get_usd_files(PROPS, recursive=True), size=size, mode="scene_instance", ) with instances: rep.modify.pose( position=rep.distribution.uniform((-50, 5, -50), (50, 20, 50)), rotation=rep.distribution.uniform((0, -180, 0), (0, 180, 0)), scale=100, ) rep.physics.rigid_body( velocity=rep.distribution.uniform((-0, 0, -0), (0, 0, 0)), angular_velocity=rep.distribution.uniform((-0, 0, -100), (0, 0, 0)), ) return instances.node # Register randomization rep.randomizer.register(env_props) # Setup the static elements env = rep.create.from_usd(ENVS) surface = rep.create.from_usd(SURFACE) with surface: rep.physics.collider() # Setup camera and attach it to render product camera = rep.create.camera() render_product = rep.create.render_product(camera, resolution=(1024, 1024)) # sphere lights for extra randomization def sphere_lights(num): lights = rep.create.light( light_type="Sphere", temperature=rep.distribution.normal(6500, 500), intensity=rep.distribution.normal(35000, 5000), position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)), scale=rep.distribution.uniform(50, 100), count=num, ) return lights.node rep.randomizer.register(sphere_lights) # trigger on frame for an interval with rep.trigger.on_time(interval=2, num=10): rep.randomizer.env_props(10) rep.randomizer.sphere_lights(10) with camera: rep.modify.pose( position=rep.distribution.uniform((-50, 20, 100), (50, 50, 150)), look_at=surface, ) # Initialize and attach writer writer = rep.WriterRegistry.get("BasicWriter") writer.initialize( output_dir="~/replicator_examples/dli_physics", rgb=True, bounding_box_2d_tight=True, ) writer.attach([render_product])

Python

NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/02_background_randomization.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 omni.replicator.core as rep with rep.new_layer(): def dome_lights(): lights = rep.create.light( light_type="Dome", rotation=(270, 0, 0), texture=rep.distribution.choice( [ "omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/champagne_castle_1_4k.hdr", "omniverse://localhost/NVIDIA/Assets/Skies/Clear/evening_road_01_4k.hdr", "omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/kloofendal_48d_partly_cloudy_4k.hdr", "omniverse://localhost/NVIDIA/Assets/Skies/Clear/qwantani_4k.hdr", ] ), ) return lights.node rep.randomizer.register(dome_lights) torus = rep.create.torus(semantics=[("class", "torus")], position=(0, -200, 100)) # create surface surface = rep.create.disk(scale=5, visible=False) # create camera & render product for the scene camera = rep.create.camera() render_product = rep.create.render_product(camera, resolution=(1024, 1024)) with rep.trigger.on_frame(num_frames=10, interval=10): rep.randomizer.dome_lights() with rep.create.group([torus]): rep.modify.pose( position=rep.distribution.uniform((-100, -100, -100), (200, 200, 200)), scale=rep.distribution.uniform(0.1, 2), ) with camera: rep.modify.pose( position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)), look_at=surface, ) # Initialize and attach writer writer = rep.WriterRegistry.get("BasicWriter") writer.initialize(output_dir="~/replicator_examples/dli_example_02", rgb=True) writer.attach([render_product]) # Run Replicator # rep.orchestrator.run()

Python

NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/palletjack_sdg/standalone_palletjack_sdg.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: MIT # 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. from omni.isaac.kit import SimulationApp import os import argparse parser = argparse.ArgumentParser("Dataset generator") parser.add_argument("--headless", type=bool, default=False, help="Launch script headless, default is False") parser.add_argument("--height", type=int, default=544, help="Height of image") parser.add_argument("--width", type=int, default=960, help="Width of image") parser.add_argument("--num_frames", type=int, default=1000, help="Number of frames to record") parser.add_argument("--distractors", type=str, default="warehouse", help="Options are 'warehouse' (default), 'additional' or None") parser.add_argument("--data_dir", type=str, default=os.getcwd() + "/_palletjack_data", help="Location where data will be output") args, unknown_args = parser.parse_known_args() # This is the config used to launch simulation. CONFIG = {"renderer": "RayTracedLighting", "headless": args.headless, "width": args.width, "height": args.height, "num_frames": args.num_frames} simulation_app = SimulationApp(launch_config=CONFIG) ## This is the path which has the background scene in which objects will be added. ENV_URL = "/Isaac/Environments/Simple_Warehouse/warehouse.usd" import carb import omni import omni.usd from omni.isaac.core.utils.nucleus import get_assets_root_path from omni.isaac.core.utils.stage import get_current_stage, open_stage from pxr import Semantics import omni.replicator.core as rep from omni.isaac.core.utils.semantics import get_semantics # Increase subframes if shadows/ghosting appears of moving objects # See known issues: https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html#known-issues rep.settings.carb_settings("/omni/replicator/RTSubframes", 4) # This is the location of the palletjacks in the simready asset library PALLETJACKS = ["http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Scale_A/PalletTruckScale_A01_PR_NVD_01.usd", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Heavy_Duty_A/HeavyDutyPalletTruck_A01_PR_NVD_01.usd", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Low_Profile_A/LowProfilePalletTruck_A01_PR_NVD_01.usd"] # The warehouse distractors which will be added to the scene and randomized DISTRACTORS_WAREHOUSE = 2 * ["/Isaac/Environments/Simple_Warehouse/Props/S_TrafficCone.usd", "/Isaac/Environments/Simple_Warehouse/Props/S_WetFloorSign.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_01.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_02.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_03.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_01.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_01.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_03.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_C_02.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticB_01.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticD_01.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticE_01.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_BucketPlastic_B.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1262.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1268.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1482.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1683.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_291.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxD_01_1454.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxD_01_1513.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_A_04.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_B_03.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_B_05.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_C_02.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_E_02.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_PushcartA_02.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_RackPile_04.usd", "/Isaac/Environments/Simple_Warehouse/Props/SM_RackPile_03.usd"] ## Additional distractors which can be added to the scene DISTRACTORS_ADDITIONAL = ["/Isaac/Environments/Hospital/Props/Pharmacy_Low.usd", "/Isaac/Environments/Hospital/Props/SM_BedSideTable_01b.usd", "/Isaac/Environments/Hospital/Props/SM_BooksSet_26.usd", "/Isaac/Environments/Hospital/Props/SM_BottleB.usd", "/Isaac/Environments/Hospital/Props/SM_BottleA.usd", "/Isaac/Environments/Hospital/Props/SM_BottleC.usd", "/Isaac/Environments/Hospital/Props/SM_Cart_01a.usd", "/Isaac/Environments/Hospital/Props/SM_Chair_02a.usd", "/Isaac/Environments/Hospital/Props/SM_Chair_01a.usd", "/Isaac/Environments/Hospital/Props/SM_Computer_02b.usd", "/Isaac/Environments/Hospital/Props/SM_Desk_04a.usd", "/Isaac/Environments/Hospital/Props/SM_DisposalStand_02.usd", "/Isaac/Environments/Hospital/Props/SM_FirstAidKit_01a.usd", "/Isaac/Environments/Hospital/Props/SM_GasCart_01c.usd", "/Isaac/Environments/Hospital/Props/SM_Gurney_01b.usd", "/Isaac/Environments/Hospital/Props/SM_HospitalBed_01b.usd", "/Isaac/Environments/Hospital/Props/SM_MedicalBag_01a.usd", "/Isaac/Environments/Hospital/Props/SM_Mirror.usd", "/Isaac/Environments/Hospital/Props/SM_MopSet_01b.usd", "/Isaac/Environments/Hospital/Props/SM_SideTable_02a.usd", "/Isaac/Environments/Hospital/Props/SM_SupplyCabinet_01c.usd", "/Isaac/Environments/Hospital/Props/SM_SupplyCart_01e.usd", "/Isaac/Environments/Hospital/Props/SM_TrashCan.usd", "/Isaac/Environments/Hospital/Props/SM_Washbasin.usd", "/Isaac/Environments/Hospital/Props/SM_WheelChair_01a.usd", "/Isaac/Environments/Office/Props/SM_WaterCooler.usd", "/Isaac/Environments/Office/Props/SM_TV.usd", "/Isaac/Environments/Office/Props/SM_TableC.usd", "/Isaac/Environments/Office/Props/SM_Recliner.usd", "/Isaac/Environments/Office/Props/SM_Personenleitsystem_Red1m.usd", "/Isaac/Environments/Office/Props/SM_Lamp02_162.usd", "/Isaac/Environments/Office/Props/SM_Lamp02.usd", "/Isaac/Environments/Office/Props/SM_HandDryer.usd", "/Isaac/Environments/Office/Props/SM_Extinguisher.usd"] # The textures which will be randomized for the wall and floor TEXTURES = ["/Isaac/Materials/Textures/Patterns/nv_asphalt_yellow_weathered.jpg", "/Isaac/Materials/Textures/Patterns/nv_tile_hexagonal_green_white.jpg", "/Isaac/Materials/Textures/Patterns/nv_rubber_woven_charcoal.jpg", "/Isaac/Materials/Textures/Patterns/nv_granite_tile.jpg", "/Isaac/Materials/Textures/Patterns/nv_tile_square_green.jpg", "/Isaac/Materials/Textures/Patterns/nv_marble.jpg", "/Isaac/Materials/Textures/Patterns/nv_brick_reclaimed.jpg", "/Isaac/Materials/Textures/Patterns/nv_concrete_aged_with_lines.jpg", "/Isaac/Materials/Textures/Patterns/nv_wooden_wall.jpg", "/Isaac/Materials/Textures/Patterns/nv_stone_painted_grey.jpg", "/Isaac/Materials/Textures/Patterns/nv_wood_shingles_brown.jpg", "/Isaac/Materials/Textures/Patterns/nv_tile_hexagonal_various.jpg", "/Isaac/Materials/Textures/Patterns/nv_carpet_abstract_pattern.jpg", "/Isaac/Materials/Textures/Patterns/nv_wood_siding_weathered_green.jpg", "/Isaac/Materials/Textures/Patterns/nv_animalfur_pattern_greys.jpg", "/Isaac/Materials/Textures/Patterns/nv_artificialgrass_green.jpg", "/Isaac/Materials/Textures/Patterns/nv_bamboo_desktop.jpg", "/Isaac/Materials/Textures/Patterns/nv_brick_reclaimed.jpg", "/Isaac/Materials/Textures/Patterns/nv_brick_red_stacked.jpg", "/Isaac/Materials/Textures/Patterns/nv_fireplace_wall.jpg", "/Isaac/Materials/Textures/Patterns/nv_fabric_square_grid.jpg", "/Isaac/Materials/Textures/Patterns/nv_granite_tile.jpg", "/Isaac/Materials/Textures/Patterns/nv_marble.jpg", "/Isaac/Materials/Textures/Patterns/nv_gravel_grey_leaves.jpg", "/Isaac/Materials/Textures/Patterns/nv_plastic_blue.jpg", "/Isaac/Materials/Textures/Patterns/nv_stone_red_hatch.jpg", "/Isaac/Materials/Textures/Patterns/nv_stucco_red_painted.jpg", "/Isaac/Materials/Textures/Patterns/nv_rubber_woven_charcoal.jpg", "/Isaac/Materials/Textures/Patterns/nv_stucco_smooth_blue.jpg", "/Isaac/Materials/Textures/Patterns/nv_wood_shingles_brown.jpg", "/Isaac/Materials/Textures/Patterns/nv_wooden_wall.jpg"] def update_semantics(stage, keep_semantics=[]): """ Remove semantics from the stage except for keep_semantic classes""" for prim in stage.Traverse(): if prim.HasAPI(Semantics.SemanticsAPI): processed_instances = set() for property in prim.GetProperties(): is_semantic = Semantics.SemanticsAPI.IsSemanticsAPIPath(property.GetPath()) if is_semantic: instance_name = property.SplitName()[1] if instance_name in processed_instances: # Skip repeated instance, instances are iterated twice due to their two semantic properties (class, data) continue processed_instances.add(instance_name) sem = Semantics.SemanticsAPI.Get(prim, instance_name) type_attr = sem.GetSemanticTypeAttr() data_attr = sem.GetSemanticDataAttr() for semantic_class in keep_semantics: # Check for our data classes needed for the model if data_attr.Get() == semantic_class: continue else: # remove semantics of all other prims prim.RemoveProperty(type_attr.GetName()) prim.RemoveProperty(data_attr.GetName()) prim.RemoveAPI(Semantics.SemanticsAPI, instance_name) # needed for loading textures correctly def prefix_with_isaac_asset_server(relative_path): assets_root_path = get_assets_root_path() if assets_root_path is None: raise Exception("Nucleus server not found, could not access Isaac Sim assets folder") return assets_root_path + relative_path def full_distractors_list(distractor_type="warehouse"): """Distractor type allowed are warehouse, additional or None. They load corresponding objects and add them to the scene for DR""" full_dist_list = [] if distractor_type == "warehouse": for distractor in DISTRACTORS_WAREHOUSE: full_dist_list.append(prefix_with_isaac_asset_server(distractor)) elif distractor_type == "additional": for distractor in DISTRACTORS_ADDITIONAL: full_dist_list.append(prefix_with_isaac_asset_server(distractor)) else: print("No Distractors being added to the current scene for SDG") return full_dist_list def full_textures_list(): full_tex_list = [] for texture in TEXTURES: full_tex_list.append(prefix_with_isaac_asset_server(texture)) return full_tex_list def add_palletjacks(): rep_obj_list = [rep.create.from_usd(palletjack_path, semantics=[("class", "palletjack")], count=2) for palletjack_path in PALLETJACKS] rep_palletjack_group = rep.create.group(rep_obj_list) return rep_palletjack_group def add_distractors(distractor_type="warehouse"): full_distractors = full_distractors_list(distractor_type) distractors = [rep.create.from_usd(distractor_path, count=1) for distractor_path in full_distractors] distractor_group = rep.create.group(distractors) return distractor_group # This will handle replicator def run_orchestrator(): rep.orchestrator.run() # Wait until started while not rep.orchestrator.get_is_started(): simulation_app.update() # Wait until stopped while rep.orchestrator.get_is_started(): simulation_app.update() rep.BackendDispatch.wait_until_done() rep.orchestrator.stop() def main(): # Open the environment in a new stage print(f"Loading Stage {ENV_URL}") open_stage(prefix_with_isaac_asset_server(ENV_URL)) stage = get_current_stage() # Run some app updates to make sure things are properly loaded for i in range(100): if i % 10 == 0: print(f"App uppdate {i}..") simulation_app.update() textures = full_textures_list() rep_palletjack_group = add_palletjacks() rep_distractor_group = add_distractors(distractor_type=args.distractors) # We only need labels for the palletjack objects update_semantics(stage=stage, keep_semantics=["palletjack"]) # Create camera with Replicator API for gathering data cam = rep.create.camera(clipping_range=(0.1, 1000000)) # trigger replicator pipeline with rep.trigger.on_frame(num_frames=CONFIG["num_frames"]): # Move the camera around in the scene, focus on the center of warehouse with cam: rep.modify.pose(position=rep.distribution.uniform((-9.2, -11.8, 0.4), (7.2, 15.8, 4)), look_at=(0, 0, 0)) # Get the Palletjack body mesh and modify its color with rep.get.prims(path_pattern="SteerAxles"): rep.randomizer.color(colors=rep.distribution.uniform((0, 0, 0), (1, 1, 1))) # Randomize the pose of all the added palletjacks with rep_palletjack_group: rep.modify.pose(position=rep.distribution.uniform((-6, -6, 0), (6, 12, 0)), rotation=rep.distribution.uniform((0, 0, 0), (0, 0, 360)), scale=rep.distribution.uniform((0.01, 0.01, 0.01), (0.01, 0.01, 0.01))) # Modify the pose of all the distractors in the scene with rep_distractor_group: rep.modify.pose(position=rep.distribution.uniform((-6, -6, 0), (6, 12, 0)), rotation=rep.distribution.uniform((0, 0, 0), (0, 0, 360)), scale=rep.distribution.uniform(1, 1.5)) # Randomize the lighting of the scene with rep.get.prims(path_pattern="RectLight"): rep.modify.attribute("color", rep.distribution.uniform((0, 0, 0), (1, 1, 1))) rep.modify.attribute("intensity", rep.distribution.normal(100000.0, 600000.0)) rep.modify.visibility(rep.distribution.choice([True, False, False, False, False, False, False])) # select floor material random_mat_floor = rep.create.material_omnipbr(diffuse_texture=rep.distribution.choice(textures), roughness=rep.distribution.uniform(0, 1), metallic=rep.distribution.choice([0, 1]), emissive_texture=rep.distribution.choice(textures), emissive_intensity=rep.distribution.uniform(0, 1000),) with rep.get.prims(path_pattern="SM_Floor"): rep.randomizer.materials(random_mat_floor) # select random wall material random_mat_wall = rep.create.material_omnipbr(diffuse_texture=rep.distribution.choice(textures), roughness=rep.distribution.uniform(0, 1), metallic=rep.distribution.choice([0, 1]), emissive_texture=rep.distribution.choice(textures), emissive_intensity=rep.distribution.uniform(0, 1000),) with rep.get.prims(path_pattern="SM_Wall"): rep.randomizer.materials(random_mat_wall) # Set up the writer writer = rep.WriterRegistry.get("KittiWriter") # output directory of writer output_directory = args.data_dir print("Outputting data to ", output_directory) # use writer for bounding boxes, rgb and segmentation writer.initialize(output_dir=output_directory, omit_semantic_type=True,) # attach camera render products to wrieter so that data is outputted RESOLUTION = (CONFIG["width"], CONFIG["height"]) render_product = rep.create.render_product(cam, RESOLUTION) writer.attach(render_product) # run rep pipeline run_orchestrator() simulation_app.update() if __name__ == "__main__": try: main() except Exception as e: carb.log_error(f"Exception: {e}") import traceback traceback.print_exc() finally: simulation_app.close()

Python

NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/visualize.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 import json import hashlib from PIL import Image import numpy as np import matplotlib.pyplot as plt import matplotlib.patches as patches from optparse import OptionParser """ Takes in the data from a specific label id and maps it to the proper color for the bounding box """ def data_to_colour(data): if isinstance(data, str): data = bytes(data, "utf-8") else: data = bytes(data) m = hashlib.sha256() m.update(data) key = int(m.hexdigest()[:8], 16) r = ((((key >> 0) & 0xFF) + 1) * 33) % 255 g = ((((key >> 8) & 0xFF) + 1) * 33) % 255 b = ((((key >> 16) & 0xFF) + 1) * 33) % 255 # illumination normalization to 128 inv_norm_i = 128 * (3.0 / (r + g + b)) return ( int(r * inv_norm_i) / 255, int(g * inv_norm_i) / 255, int(b * inv_norm_i) / 255, ) """ Takes in the path to the rgb image for the background, then it takes bounding box data, the labels and the place to store the visualization. It outputs a colorized bounding box. """ def colorize_bbox_2d(rgb_path, data, id_to_labels, file_path): rgb_img = Image.open(rgb_path) colors = [data_to_colour(bbox["semanticId"]) for bbox in data] fig, ax = plt.subplots(figsize=(10, 10)) ax.imshow(rgb_img) for bbox_2d, color, index in zip(data, colors, range(len(data))): labels = id_to_labels[str(index)] rect = patches.Rectangle( xy=(bbox_2d["x_min"], bbox_2d["y_min"]), width=bbox_2d["x_max"] - bbox_2d["x_min"], height=bbox_2d["y_max"] - bbox_2d["y_min"], edgecolor=color, linewidth=2, label=labels, fill=False, ) ax.add_patch(rect) plt.legend(loc="upper left") plt.savefig(file_path) """ Parses command line options. Requires input directory, output directory, and number for image to use. """ def parse_input(): usage = "usage: visualize.py [options] arg1 arg2 arg3" parser = OptionParser(usage) parser.add_option( "-d", "--data_dir", dest="data_dir", help="Directory location for Omniverse synthetic data", ) parser.add_option( "-o", "--out_dir", dest="out_dir", help="Directory location for output image" ) parser.add_option( "-n", "--number", dest="number", help="Number of image to use for visualization" ) (options, args) = parser.parse_args() return options, args def main(): options, args = parse_input() out_dir = options.data_dir rgb = "png/rgb_" + options.number + ".png" rgb_path = os.path.join(out_dir, rgb) bbox2d_tight_file_name = "npy/bounding_box_2d_tight_" + options.number + ".npy" data = np.load(os.path.join(options.data_dir, bbox2d_tight_file_name)) # Check for labels bbox2d_tight_labels_file_name = ( "json/bounding_box_2d_tight_labels_" + options.number + ".json" ) with open( os.path.join(options.data_dir, bbox2d_tight_labels_file_name), "r" ) as json_data: bbox2d_tight_id_to_labels = json.load(json_data) # colorize and save image colorize_bbox_2d( rgb_path, data, bbox2d_tight_id_to_labels, os.path.join(options.out_dir, "bbox2d_tight.png"), ) if __name__ == "__main__": main()

Python

NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/export.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 import torch import torchvision from optparse import OptionParser def parse_input(): usage = "usage: export.py [options] arg1 " parser = OptionParser(usage) parser.add_option( "-d", "--pytorch_dir", dest="pytorch_dir", help="Location of output PyTorch model", ) parser.add_option( "-o", "--output_dir", dest="output_dir", help="Export and save ONNX model to this path", ) (options, args) = parser.parse_args() return options, args def main(): torch.manual_seed(0) options, args = parse_input() model = torch.load(options.pytorch_dir) model.eval() OUTPUT_DIR = options.output_dir os.makedirs(OUTPUT_DIR, exist_ok=True) model = torchvision.models.detection.fasterrcnn_resnet50_fpn( weights="DEFAULT", num_classes=91 ) model.eval() dummy_input = torch.rand(1, 3, 1024, 1024) torch.onnx.export( model, dummy_input, os.path.join(OUTPUT_DIR, "model.onnx"), opset_version=11, input_names=["input"], output_names=["output"], ) if __name__ == "__main__": main()

Python

NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/train.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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. from PIL import Image import os import numpy as np import torch import torch.utils.data import torchvision from torchvision.models.detection.faster_rcnn import FastRCNNPredictor from torchvision import transforms as T import json import shutil from optparse import OptionParser from torch.utils.tensorboard import SummaryWriter class FruitDataset(torch.utils.data.Dataset): def __init__(self, root, transforms): self.root = root self.transforms = transforms list_ = os.listdir(root) for file_ in list_: name, ext = os.path.splitext(file_) ext = ext[1:] if ext == "": continue if os.path.exists(root + "/" + ext): shutil.move(root + "/" + file_, root + "/" + ext + "/" + file_) else: os.makedirs(root + "/" + ext) shutil.move(root + "/" + file_, root + "/" + ext + "/" + file_) self.imgs = list(sorted(os.listdir(os.path.join(root, "png")))) self.label = list(sorted(os.listdir(os.path.join(root, "json")))) self.box = list(sorted(os.listdir(os.path.join(root, "npy")))) def __getitem__(self, idx): img_path = os.path.join(self.root, "png", self.imgs[idx]) img = Image.open(img_path).convert("RGB") label_path = os.path.join(self.root, "json", self.label[idx]) with open(os.path.join("root", label_path), "r") as json_data: json_labels = json.load(json_data) box_path = os.path.join(self.root, "npy", self.box[idx]) dat = np.load(str(box_path)) boxes = [] labels = [] for i in dat: obj_val = i[0] xmin = torch.as_tensor(np.min(i[1]), dtype=torch.float32) xmax = torch.as_tensor(np.max(i[3]), dtype=torch.float32) ymin = torch.as_tensor(np.min(i[2]), dtype=torch.float32) ymax = torch.as_tensor(np.max(i[4]), dtype=torch.float32) if (ymax > ymin) & (xmax > xmin): boxes.append([xmin, ymin, xmax, ymax]) area = (xmax - xmin) * (ymax - ymin) labels += [json_labels.get(str(obj_val)).get("class")] label_dict = {} static_labels = { "apple": 0, "avocado": 1, "kiwi": 2, "lime": 3, "lychee": 4, "pomegranate": 5, "onion": 6, "strawberry": 7, "lemon": 8, "orange": 9, } labels_out = [] for i in range(len(labels)): label_dict[i] = labels[i] for i in label_dict: fruit = label_dict[i] final_fruit_label = static_labels[fruit] labels_out += [final_fruit_label] target = {} target["boxes"] = torch.as_tensor(boxes, dtype=torch.float32) target["labels"] = torch.as_tensor(labels_out, dtype=torch.int64) target["image_id"] = torch.tensor([idx]) target["area"] = area if self.transforms is not None: img = self.transforms(img) return img, target def __len__(self): return len(self.imgs) """ Parses command line options. Requires input data directory, output torch file, and number epochs used to train. """ def parse_input(): usage = "usage: train.py [options] arg1 arg2 " parser = OptionParser(usage) parser.add_option( "-d", "--data_dir", dest="data_dir", help="Directory location for Omniverse synthetic data.", ) parser.add_option( "-o", "--output_file", dest="output_file", help="Save torch model to this file and location (file ending in .pth)", ) parser.add_option( "-e", "--epochs", dest="epochs", help="Give number of epochs to be used for training", ) (options, args) = parser.parse_args() return options, args def get_transform(train): transforms = [] transforms.append(T.PILToTensor()) transforms.append(T.ConvertImageDtype(torch.float)) return T.Compose(transforms) def collate_fn(batch): return tuple(zip(*batch)) def create_model(num_classes): model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT") in_features = model.roi_heads.box_predictor.cls_score.in_features model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) return model def main(): writer = SummaryWriter() options, args = parse_input() dataset = FruitDataset(options.data_dir, get_transform(train=True)) train_size = int(len(dataset) * 0.7) valid_size = int(len(dataset) * 0.2) test_size = len(dataset) - valid_size - train_size train, valid, test = torch.utils.data.random_split( dataset, [train_size, valid_size, test_size] ) data_loader = torch.utils.data.DataLoader( dataset, batch_size=16, shuffle=True, num_workers=4, collate_fn=collate_fn ) validloader = torch.utils.data.DataLoader( valid, batch_size=16, shuffle=True, num_workers=4, collate_fn=collate_fn ) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") num_classes = 10 num_epochs = int(options.epochs) model = create_model(num_classes) model.to(device) params = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.SGD(params, lr=0.001) len_dataloader = len(data_loader) model.train() for epoch in range(num_epochs): optimizer.zero_grad() i = 0 for imgs, annotations in data_loader: i += 1 imgs = list(img.to(device) for img in imgs) annotations = [{k: v.to(device) for k, v in t.items()} for t in annotations] loss_dict = model(imgs, annotations) losses = sum(loss for loss in loss_dict.values()) writer.add_scalar("Loss/train", losses, epoch) losses.backward() optimizer.step() print(f"Iteration: {i}/{len_dataloader}, Loss: {losses}") writer.close() torch.save(model, options.output_file) if __name__ == "__main__": main()

Python

NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/data_generation/code/generate_data_gui.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 datetime now = datetime.datetime.now() from functools import partial import omni.replicator.core as rep with rep.new_layer(): # Define paths for the character, the props, the environment and the surface where the assets will be scattered in. CRATE = "omniverse://localhost/NVIDIA/Samples/Marbles/assets/standalone/SM_room_crate_3/SM_room_crate_3.usd" SURFACE = ( "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Basic/display_riser.usd" ) ENVS = "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Interior/ZetCG_ExhibitionHall.usd" FRUIT_PROPS = { "apple": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Apple.usd", "avocado": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Avocado01.usd", "kiwi": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Kiwi01.usd", "lime": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Lime01.usd", "lychee": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Lychee01.usd", "pomegranate": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Pomegranate01.usd", "onion": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Vegetables/RedOnion.usd", "strawberry": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Berries/strawberry.usd", "lemon": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Decor/Tchotchkes/Lemon_01.usd", "orange": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Decor/Tchotchkes/Orange_01.usd", } # Define randomizer function for Base assets. This randomization includes placement and rotation of the assets on the surface. def random_props(file_name, class_name, max_number=1, one_in_n_chance=3): instances = rep.randomizer.instantiate( file_name, size=max_number, mode="scene_instance" ) print(file_name) with instances: rep.modify.semantics([("class", class_name)]) rep.modify.pose( position=rep.distribution.uniform((-8, 5, -25), (8, 30, 25)), rotation=rep.distribution.uniform((-180, -180, -180), (180, 180, 180)), scale=rep.distribution.uniform((0.8), (1.2)), ) rep.modify.visibility( rep.distribution.choice([True], [False] * (one_in_n_chance)) ) return instances.node # Define randomizer function for sphere lights. def sphere_lights(num): lights = rep.create.light( light_type="Sphere", temperature=rep.distribution.normal(6500, 500), intensity=rep.distribution.normal(30000, 5000), position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)), scale=rep.distribution.uniform(50, 100), count=num, ) return lights.node rep.randomizer.register(random_props) # Setup the static elements env = rep.create.from_usd(ENVS) surface = rep.create.from_usd(SURFACE) with surface: rep.physics.collider() crate = rep.create.from_usd(CRATE) with crate: rep.physics.collider("none") rep.physics.mass(mass=10000) rep.modify.pose(position=(0, 20, 0), rotation=(0, 0, 90)) # Setup camera and attach it to render product camera = rep.create.camera() render_product = rep.create.render_product(camera, resolution=(1024, 1024)) rep.randomizer.register(sphere_lights) # trigger on frame for an interval with rep.trigger.on_frame(num_frames=100): for n, f in FRUIT_PROPS.items(): random_props(f, n) rep.randomizer.sphere_lights(5) with camera: rep.modify.pose( position=rep.distribution.uniform((-3, 114, -17), (-1, 116, -15)), look_at=(0, 20, 0), ) # Initialize and attach writer writer = rep.WriterRegistry.get("BasicWriter") now = now.strftime("%Y-%m-%d") output_dir = "fruit_data_" + now writer.initialize(output_dir=output_dir, rgb=True, bounding_box_2d_tight=True) writer.attach([render_product])

Python

NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/deployment/code/deploy.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. # SPDX-License-Identifier: BSD-3-Clause # # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. 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 tritonclient.grpc as grpcclient from optparse import OptionParser # load image data import cv2 import numpy as np from matplotlib import pyplot as plt import subprocess def install(name): subprocess.call(["pip", "install", name]) """ Parses command line options. Requires input sample png """ def parse_input(): usage = "usage: deploy.py [options] arg1 " parser = OptionParser(usage) parser.add_option( "-p", "--png", dest="png", help="Directory location for single sample image." ) (options, args) = parser.parse_args() return options, args def main(): options, args = parse_input() target_width, target_height = 1024, 1024 # add path to test image image_sample = options.png image_bgr = cv2.imread(image_sample) image_bgr image_bgr = cv2.resize(image_bgr, (target_width, target_height)) image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) image = np.float32(image_rgb) # preprocessing image = image / 255 image = np.moveaxis(image, -1, 0) # HWC to CHW image = image[np.newaxis, :] # add batch dimension image = np.float32(image) plt.imshow(image_rgb) inference_server_url = "0.0.0.0:9001" triton_client = grpcclient.InferenceServerClient(url=inference_server_url) # find out info about model model_name = "fasterrcnn_resnet50" triton_client.get_model_config(model_name) # create input input_name = "input" inputs = [grpcclient.InferInput(input_name, image.shape, "FP32")] inputs[0].set_data_from_numpy(image) output_name = "output" outputs = [grpcclient.InferRequestedOutput("output")] results = triton_client.infer(model_name, inputs, outputs=outputs) output = results.as_numpy("output") # annotate annotated_image = image_bgr.copy() if output.size > 0: # ensure something is found for box in output: box_top_left = int(box[0]), int(box[1]) box_bottom_right = int(box[2]), int(box[3]) text_origin = int(box[0]), int(box[3]) border_color = (50, 0, 100) text_color = (255, 255, 255) font_scale = 0.9 thickness = 1 # bounding box cv2.rectangle( annotated_image, box_top_left, box_bottom_right, border_color, thickness=5, lineType=cv2.LINE_8, ) plt.imshow(cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)) if __name__ == "__main__": main()

Python

NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/predict.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 argparse import utils import cv2 import torch if __name__ == "__main__": # Parse command line arguments parser = argparse.ArgumentParser() parser.add_argument( "engine", type=str, help="The file path of the TensorRT engine." ) parser.add_argument( "image", type=str, help="The file path of the image provided as input for inference." ) parser.add_argument( "--output", type=str, default=None, help="The path to output the inference visualization." ) parser.add_argument( "--inference-size", type=str, default="512x512", help="The height and width that the image is resized to for inference." " Denoted as (height)x(width)." ) parser.add_argument( "--peak-window", type=str, default="7x7", help="The size of the window used when finding local peaks. Denoted as " " (window_height)x(window_width)." ) parser.add_argument( '--peak-threshold', type=float, default=0.5, help="The heatmap threshold to use when finding peaks. Values must be " " larger than this value to be considered peaks." ) parser.add_argument( '--line-thickness', type=int, default=1, help="The line thickness for drawn boxes" ) args = parser.parse_args() # Parse inference height, width from arguments inference_size = tuple(int(x) for x in args.inference_size.split('x')) peak_window = tuple(int(x) for x in args.peak_window.split('x')) if args.output is None: output_path = '.'.join(args.image.split('.')[:-1]) + "_output.jpg" else: output_path = args.output # Create offset grid offset_grid = utils.make_offset_grid(inference_size).to("cuda") # Load model model = utils.load_trt_engine_wrapper( args.engine, input_names=["input"], output_names=["heatmap", "vectormap"] ) # Load image image = cv2.imread(args.image) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Pad and resize image (aspect ratio preserving resize) image, _, _ = utils.pad_resize(image, inference_size) with torch.no_grad(): # Format image for inference x = utils.format_bgr8_image(image) x = x.to("cuda") # Execute model heatmap, vectormap = model(x) # Scale and offset vectormap keypointmap = utils.vectormap_to_keypointmap( offset_grid, vectormap ) # Find local peaks peak_mask = utils.find_heatmap_peak_mask( heatmap, peak_window, args.peak_threshold ) # Extract keypoints at local peak keypoints = keypointmap[0][peak_mask[0, 0]] # Draw vis_image = utils.draw_box( image, keypoints, color=(118, 186, 0), thickness=args.line_thickness ) vis_image = cv2.cvtColor(vis_image, cv2.COLOR_RGB2BGR) cv2.imwrite(output_path, vis_image)

Python

NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/utils.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 torch import torch.nn.functional as F import numpy as np import cv2 import einops import tensorrt as trt import torch2trt from typing import Sequence BOX_EDGES = [ [0, 1], [1, 5], [5, 4], [4, 0], [2, 3], [3, 7], [7, 6], [6, 2], [0, 2], [1, 3], [4, 6], [5, 7] ] def make_offset_grid( size, stride=(1, 1) ): grid = torch.stack( torch.meshgrid( stride[0] * (torch.arange(size[0]) + 0.5), stride[1] * (torch.arange(size[1]) + 0.5) ), dim=-1 ) return grid def vectormap_to_keypointmap( offset_grid, vector_map, vector_scale: float = 1./256. ): vector_map = vector_map / vector_scale keypoint_map = einops.rearrange(vector_map, "b (k d) h w -> b h w k d", d=2) keypoint_map = keypoint_map + offset_grid[:, :, None, :] # yx -> xy keypoint_map = keypoint_map[..., [1, 0]] return keypoint_map def find_heatmap_peak_mask(heatmap, window=3, threshold=0.5): all_indices = torch.arange( heatmap.numel(), device=heatmap.device ) all_indices = all_indices.reshape(heatmap.shape) if isinstance(window, int): window = (window, window) values, max_indices = F.max_pool2d_with_indices( heatmap, kernel_size=window, stride=1, padding=(window[0] // 2, window[1] // 2) ) is_above_threshold = heatmap >= threshold is_max = max_indices == all_indices is_peak = is_above_threshold & is_max return is_peak def draw_box(image_bgr, keypoints, color=(118, 186, 0), thickness=1): num_objects = int(keypoints.shape[0]) for i in range(num_objects): keypoints_i = keypoints[i] kps_i = [(int(x), int(y)) for x, y in keypoints_i] edges = BOX_EDGES for e in edges: cv2.line( image_bgr, kps_i[e[0]], kps_i[e[1]], (118, 186, 0), thickness=thickness ) return image_bgr def pad_resize(image, output_shape): ar_i = image.shape[1] / image.shape[0] ar_o = output_shape[1] / output_shape[0] # resize if ar_i > ar_o: w_i = output_shape[1] h_i = min(int(w_i / ar_i), output_shape[0]) else: h_i = output_shape[0] w_i = min(int(h_i * ar_i), output_shape[1]) # paste pad_left = (output_shape[1] - w_i) // 2 pad_top = (output_shape[0] - h_i) // 2 image_resize = cv2.resize(image, (w_i, h_i)) out = np.zeros_like( image, shape=(output_shape[0], output_shape[1], image.shape[2]) ) out[pad_top:pad_top + h_i, pad_left:pad_left + w_i] = image_resize pad = (pad_top, pad_left) scale = (image.shape[0] / h_i, image.shape[1] / w_i) return out, pad, scale def load_trt_engine(path: str): with trt.Logger() as logger, trt.Runtime(logger) as runtime: with open(path, 'rb') as f: engine_bytes = f.read() engine = runtime.deserialize_cuda_engine(engine_bytes) return engine def load_trt_engine_wrapper( path: str, input_names: Sequence, output_names: Sequence ): engine = load_trt_engine(path) wrapper = torch2trt.TRTModule( engine=engine, input_names=input_names, output_names=output_names ) return wrapper def format_bgr8_image(image, device="cuda"): x = torch.from_numpy(image) x = x.permute(2, 0, 1)[None, ...] x = (x / 255 - 0.45) / 0.25 return x

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/scripts/ros2_publisher.py

#!/usr/bin/env python3 # Copyright (c) 2020-2022, 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 rclpy from rclpy.node import Node from sensor_msgs.msg import JointState import numpy as np import time class TestROS2Bridge(Node): def __init__(self): super().__init__("test_ros2bridge") # Create the publisher. This publisher will publish a JointState message to the /joint_command topic. self.publisher_ = self.create_publisher(JointState, "joint_command", 10) # Create a JointState message self.joint_state = JointState() self.joint_state.name = [ "panda_joint1", "panda_joint2", "panda_joint3", "panda_joint4", "panda_joint5", "panda_joint6", "panda_joint7", "panda_finger_joint1", "panda_finger_joint2", ] num_joints = len(self.joint_state.name) # make sure kit's editor is playing for receiving messages self.joint_state.position = np.array([0.0] * num_joints, dtype=np.float64).tolist() self.default_joints = [0.0, -1.16, -0.0, -2.3, -0.0, 1.6, 1.1, 0.4, 0.4] # limiting the movements to a smaller range (this is not the range of the robot, just the range of the movement self.max_joints = np.array(self.default_joints) + 0.5 self.min_joints = np.array(self.default_joints) - 0.5 # position control the robot to wiggle around each joint self.time_start = time.time() timer_period = 0.05 # seconds self.timer = self.create_timer(timer_period, self.timer_callback) def timer_callback(self): self.joint_state.header.stamp = self.get_clock().now().to_msg() joint_position = ( np.sin(time.time() - self.time_start) * (self.max_joints - self.min_joints) * 0.5 + self.default_joints ) self.joint_state.position = joint_position.tolist() # Publish the message to the topic self.publisher_.publish(self.joint_state) def main(args=None): rclpy.init(args=args) ros2_publisher = TestROS2Bridge() rclpy.spin(ros2_publisher) # Destroy the node explicitly ros2_publisher.destroy_node() rclpy.shutdown() if __name__ == "__main__": main()

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/carter_navigation.launch.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 os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.actions import DeclareLaunchArgument from launch.actions import IncludeLaunchDescription from launch.launch_description_sources import PythonLaunchDescriptionSource from launch.substitutions import LaunchConfiguration from launch_ros.actions import Node def generate_launch_description(): use_sim_time = LaunchConfiguration("use_sim_time", default="True") map_dir = LaunchConfiguration( "map", default=os.path.join( get_package_share_directory("carter_navigation"), "maps", "carter_warehouse_navigation.yaml" ), ) param_dir = LaunchConfiguration( "params_file", default=os.path.join( get_package_share_directory("carter_navigation"), "params", "carter_navigation_params.yaml" ), ) nav2_bringup_launch_dir = os.path.join(get_package_share_directory("nav2_bringup"), "launch") rviz_config_dir = os.path.join(get_package_share_directory("carter_navigation"), "rviz2", "carter_navigation.rviz") return LaunchDescription( [ DeclareLaunchArgument("map", default_value=map_dir, description="Full path to map file to load"), DeclareLaunchArgument( "params_file", default_value=param_dir, description="Full path to param file to load" ), DeclareLaunchArgument( "use_sim_time", default_value="true", description="Use simulation (Omniverse Isaac Sim) clock if true" ), IncludeLaunchDescription( PythonLaunchDescriptionSource(os.path.join(nav2_bringup_launch_dir, "rviz_launch.py")), launch_arguments={"namespace": "", "use_namespace": "False", "rviz_config": rviz_config_dir}.items(), ), IncludeLaunchDescription( PythonLaunchDescriptionSource([nav2_bringup_launch_dir, "/bringup_launch.py"]), launch_arguments={"map": map_dir, "use_sim_time": use_sim_time, "params_file": param_dir}.items(), ), Node( package='pointcloud_to_laserscan', executable='pointcloud_to_laserscan_node', remappings=[('cloud_in', ['/front_3d_lidar/point_cloud']), ('scan', ['/scan'])], parameters=[{ 'target_frame': 'front_3d_lidar', 'transform_tolerance': 0.01, 'min_height': -0.4, 'max_height': 1.5, 'angle_min': -1.5708, # -M_PI/2 'angle_max': 1.5708, # M_PI/2 'angle_increment': 0.0087, # M_PI/360.0 'scan_time': 0.3333, 'range_min': 0.05, 'range_max': 100.0, 'use_inf': True, 'inf_epsilon': 1.0, # 'concurrency_level': 1, }], name='pointcloud_to_laserscan' ) ] )

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/carter_navigation_individual.launch.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 os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.actions import DeclareLaunchArgument, ExecuteProcess, IncludeLaunchDescription from launch.conditions import IfCondition from launch.launch_description_sources import PythonLaunchDescriptionSource from launch.substitutions import LaunchConfiguration, PythonExpression, TextSubstitution from launch_ros.actions import Node def generate_launch_description(): # Get the launch directory nav2_launch_dir = os.path.join(get_package_share_directory("nav2_bringup"), "launch") # Create the launch configuration variables slam = LaunchConfiguration("slam") namespace = LaunchConfiguration("namespace") use_namespace = LaunchConfiguration("use_namespace") map_yaml_file = LaunchConfiguration("map") use_sim_time = LaunchConfiguration("use_sim_time") params_file = LaunchConfiguration("params_file") default_bt_xml_filename = LaunchConfiguration("default_bt_xml_filename") autostart = LaunchConfiguration("autostart") # Declare the launch arguments declare_namespace_cmd = DeclareLaunchArgument("namespace", default_value="", description="Top-level namespace") declare_use_namespace_cmd = DeclareLaunchArgument( "use_namespace", default_value="false", description="Whether to apply a namespace to the navigation stack" ) declare_slam_cmd = DeclareLaunchArgument("slam", default_value="False", description="Whether run a SLAM") declare_map_yaml_cmd = DeclareLaunchArgument( "map", default_value=os.path.join(nav2_launch_dir, "maps", "carter_warehouse_navigation.yaml"), description="Full path to map file to load", ) declare_use_sim_time_cmd = DeclareLaunchArgument( "use_sim_time", default_value="True", description="Use simulation (Isaac Sim) clock if true" ) declare_params_file_cmd = DeclareLaunchArgument( "params_file", default_value=os.path.join(nav2_launch_dir, "params", "nav2_params.yaml"), description="Full path to the ROS2 parameters file to use for all launched nodes", ) declare_bt_xml_cmd = DeclareLaunchArgument( "default_bt_xml_filename", default_value=os.path.join( get_package_share_directory("nav2_bt_navigator"), "behavior_trees", "navigate_w_replanning_and_recovery.xml" ), description="Full path to the behavior tree xml file to use", ) declare_autostart_cmd = DeclareLaunchArgument( "autostart", default_value="true", description="Automatically startup the nav2 stack" ) bringup_cmd = IncludeLaunchDescription( PythonLaunchDescriptionSource(os.path.join(nav2_launch_dir, "bringup_launch.py")), launch_arguments={ "namespace": namespace, "use_namespace": use_namespace, "slam": slam, "map": map_yaml_file, "use_sim_time": use_sim_time, "params_file": params_file, "default_bt_xml_filename": default_bt_xml_filename, "autostart": autostart, }.items(), ) # Create the launch description and populate ld = LaunchDescription() # Declare the launch options ld.add_action(declare_namespace_cmd) ld.add_action(declare_use_namespace_cmd) ld.add_action(declare_slam_cmd) ld.add_action(declare_map_yaml_cmd) ld.add_action(declare_use_sim_time_cmd) ld.add_action(declare_params_file_cmd) ld.add_action(declare_bt_xml_cmd) ld.add_action(declare_autostart_cmd) ld.add_action(bringup_cmd) return ld

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/multiple_robot_carter_navigation_hospital.launch.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 for spawing multiple robots in Gazebo. This is an example on how to create a launch file for spawning multiple robots into Gazebo and launch multiple instances of the navigation stack, each controlling one robot. The robots co-exist on a shared environment and are controlled by independent nav stacks """ import os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.actions import DeclareLaunchArgument, ExecuteProcess, GroupAction, IncludeLaunchDescription, LogInfo from launch.conditions import IfCondition from launch.launch_description_sources import PythonLaunchDescriptionSource from launch.substitutions import LaunchConfiguration, TextSubstitution from launch_ros.actions import Node def generate_launch_description(): # Get the launch and rviz directories carter_nav2_bringup_dir = get_package_share_directory("carter_navigation") nav2_bringup_dir = get_package_share_directory("nav2_bringup") nav2_bringup_launch_dir = os.path.join(nav2_bringup_dir, "launch") rviz_config_dir = os.path.join(carter_nav2_bringup_dir, "rviz2", "carter_navigation_namespaced.rviz") # Names and poses of the robots robots = [{"name": "carter1"}, {"name": "carter2"}, {"name": "carter3"}] # Common settings ENV_MAP_FILE = "carter_hospital_navigation.yaml" use_sim_time = LaunchConfiguration("use_sim_time", default="True") map_yaml_file = LaunchConfiguration("map") default_bt_xml_filename = LaunchConfiguration("default_bt_xml_filename") autostart = LaunchConfiguration("autostart") rviz_config_file = LaunchConfiguration("rviz_config") use_rviz = LaunchConfiguration("use_rviz") log_settings = LaunchConfiguration("log_settings", default="true") # Declare the launch arguments declare_map_yaml_cmd = DeclareLaunchArgument( "map", default_value=os.path.join(carter_nav2_bringup_dir, "maps", ENV_MAP_FILE), description="Full path to map file to load", ) declare_robot1_params_file_cmd = DeclareLaunchArgument( "carter1_params_file", default_value=os.path.join( carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_1.yaml" ), description="Full path to the ROS2 parameters file to use for robot1 launched nodes", ) declare_robot2_params_file_cmd = DeclareLaunchArgument( "carter2_params_file", default_value=os.path.join( carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_2.yaml" ), description="Full path to the ROS2 parameters file to use for robot2 launched nodes", ) declare_robot3_params_file_cmd = DeclareLaunchArgument( "carter3_params_file", default_value=os.path.join( carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_3.yaml" ), description="Full path to the ROS2 parameters file to use for robot3 launched nodes", ) declare_bt_xml_cmd = DeclareLaunchArgument( "default_bt_xml_filename", default_value=os.path.join( get_package_share_directory("nav2_bt_navigator"), "behavior_trees", "navigate_w_replanning_and_recovery.xml" ), description="Full path to the behavior tree xml file to use", ) declare_autostart_cmd = DeclareLaunchArgument( "autostart", default_value="True", description="Automatically startup the stacks" ) declare_rviz_config_file_cmd = DeclareLaunchArgument( "rviz_config", default_value=rviz_config_dir, description="Full path to the RVIZ config file to use." ) declare_use_rviz_cmd = DeclareLaunchArgument("use_rviz", default_value="True", description="Whether to start RVIZ") # Define commands for launching the navigation instances nav_instances_cmds = [] for robot in robots: params_file = LaunchConfiguration(robot["name"] + "_params_file") group = GroupAction( [ IncludeLaunchDescription( PythonLaunchDescriptionSource(os.path.join(nav2_bringup_launch_dir, "rviz_launch.py")), condition=IfCondition(use_rviz), launch_arguments={ "namespace": TextSubstitution(text=robot["name"]), "use_namespace": "True", "rviz_config": rviz_config_file, }.items(), ), IncludeLaunchDescription( PythonLaunchDescriptionSource( os.path.join(carter_nav2_bringup_dir, "launch", "carter_navigation_individual.launch.py") ), launch_arguments={ "namespace": robot["name"], "use_namespace": "True", "map": map_yaml_file, "use_sim_time": use_sim_time, "params_file": params_file, "default_bt_xml_filename": default_bt_xml_filename, "autostart": autostart, "use_rviz": "False", "use_simulator": "False", "headless": "False", }.items(), ), Node( package='pointcloud_to_laserscan', executable='pointcloud_to_laserscan_node', remappings=[('cloud_in', ['front_3d_lidar/point_cloud']), ('scan', ['scan'])], parameters=[{ 'target_frame': 'front_3d_lidar', 'transform_tolerance': 0.01, 'min_height': -0.4, 'max_height': 1.5, 'angle_min': -1.5708, # -M_PI/2 'angle_max': 1.5708, # M_PI/2 'angle_increment': 0.0087, # M_PI/360.0 'scan_time': 0.3333, 'range_min': 0.05, 'range_max': 100.0, 'use_inf': True, 'inf_epsilon': 1.0, # 'concurrency_level': 1, }], name='pointcloud_to_laserscan', namespace = robot["name"] ), LogInfo(condition=IfCondition(log_settings), msg=["Launching ", robot["name"]]), LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " map yaml: ", map_yaml_file]), LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " params yaml: ", params_file]), LogInfo( condition=IfCondition(log_settings), msg=[robot["name"], " behavior tree xml: ", default_bt_xml_filename], ), LogInfo( condition=IfCondition(log_settings), msg=[robot["name"], " rviz config file: ", rviz_config_file] ), LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " autostart: ", autostart]), ] ) nav_instances_cmds.append(group) # Create the launch description and populate ld = LaunchDescription() # Declare the launch options ld.add_action(declare_map_yaml_cmd) ld.add_action(declare_robot1_params_file_cmd) ld.add_action(declare_robot2_params_file_cmd) ld.add_action(declare_robot3_params_file_cmd) ld.add_action(declare_bt_xml_cmd) ld.add_action(declare_use_rviz_cmd) ld.add_action(declare_autostart_cmd) ld.add_action(declare_rviz_config_file_cmd) for simulation_instance_cmd in nav_instances_cmds: ld.add_action(simulation_instance_cmd) return ld

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/setup.py

from setuptools import setup from glob import glob import os package_name = "isaac_ros_navigation_goal" setup( name=package_name, version="0.0.1", packages=[package_name, package_name + "/goal_generators"], data_files=[ ("share/ament_index/resource_index/packages", ["resource/" + package_name]), ("share/" + package_name, ["package.xml"]), (os.path.join("share", package_name, "launch"), glob("launch/*.launch.py")), ("share/" + package_name + "/assets", glob("assets/*")), ], install_requires=["setuptools"], zip_safe=True, maintainer="isaac sim", maintainer_email="isaac-sim@todo.todo", description="Package to set goals for navigation stack.", license="NVIDIA Isaac ROS Software License", tests_require=["pytest"], entry_points={"console_scripts": ["SetNavigationGoal = isaac_ros_navigation_goal.set_goal:main"]}, )

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/test/test_flake8.py

# Copyright 2017 Open Source Robotics Foundation, Inc. # # 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. from ament_flake8.main import main_with_errors import pytest @pytest.mark.flake8 @pytest.mark.linter def test_flake8(): rc, errors = main_with_errors(argv=[]) assert rc == 0, "Found %d code style errors / warnings:\n" % len(errors) + "\n".join(errors)

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/launch/isaac_ros_navigation_goal.launch.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 os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.substitutions import LaunchConfiguration from launch_ros.actions import Node def generate_launch_description(): map_yaml_file = LaunchConfiguration( "map_yaml_path", default=os.path.join( get_package_share_directory("isaac_ros_navigation_goal"), "assets", "carter_warehouse_navigation.yaml" ), ) goal_text_file = LaunchConfiguration( "goal_text_file_path", default=os.path.join(get_package_share_directory("isaac_ros_navigation_goal"), "assets", "goals.txt"), ) navigation_goal_node = Node( name="set_navigation_goal", package="isaac_ros_navigation_goal", executable="SetNavigationGoal", parameters=[ { "map_yaml_path": map_yaml_file, "iteration_count": 3, "goal_generator_type": "RandomGoalGenerator", "action_server_name": "navigate_to_pose", "obstacle_search_distance_in_meters": 0.2, "goal_text_file_path": goal_text_file, "initial_pose": [-6.4, -1.04, 0.0, 0.0, 0.0, 0.99, 0.02], } ], output="screen", ) return LaunchDescription([navigation_goal_node])

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/isaac_ros_navigation_goal/obstacle_map.py

import numpy as np import yaml import os import math from PIL import Image class GridMap: def __init__(self, yaml_file_path): self.__get_meta_from_yaml(yaml_file_path) self.__get_raw_map() self.__add_max_range_to_meta() # print(self.__map_meta) def __get_meta_from_yaml(self, yaml_file_path): """ Reads map meta from the yaml file. Parameters ---------- yaml_file_path: path of the yaml file. """ with open(yaml_file_path, "r") as f: file_content = f.read() self.__map_meta = yaml.safe_load(file_content) self.__map_meta["image"] = os.path.join(os.path.dirname(yaml_file_path), self.__map_meta["image"]) def __get_raw_map(self): """ Reads the map image and generates the grid map.\n Grid map is a 2D boolean matrix where True=>occupied space & False=>Free space. """ img = Image.open(self.__map_meta.get("image")) img = np.array(img) # Anything greater than free_thresh is considered as occupied if self.__map_meta["negate"]: res = np.where((img / 255)[:, :, 0] > self.__map_meta["free_thresh"]) else: res = np.where(((255 - img) / 255)[:, :, 0] > self.__map_meta["free_thresh"]) self.__grid_map = np.zeros(shape=(img.shape[:2]), dtype=bool) for i in range(res[0].shape[0]): self.__grid_map[res[0][i], res[1][i]] = 1 def __add_max_range_to_meta(self): """ Calculates and adds the max value of pose in x & y direction to the meta. """ max_x = self.__grid_map.shape[1] * self.__map_meta["resolution"] + self.__map_meta["origin"][0] max_y = self.__grid_map.shape[0] * self.__map_meta["resolution"] + self.__map_meta["origin"][1] self.__map_meta["max_x"] = round(max_x, 2) self.__map_meta["max_y"] = round(max_y, 2) def __pad_obstacles(self, distance): pass def get_range(self): """ Returns the bounds of pose values in x & y direction.\n Returns ------- [List]:\n Where list[0][0]: min value in x direction list[0][1]: max value in x direction list[1][0]: min value in y direction list[1][1]: max value in y direction """ return [ [self.__map_meta["origin"][0], self.__map_meta["max_x"]], [self.__map_meta["origin"][1], self.__map_meta["max_y"]], ] def __transform_to_image_coordinates(self, point): """ Transforms a pose in meters to image pixel coordinates. Parameters ---------- Point: A point as list. where list[0]=>pose.x and list[1]=pose.y Returns ------- [Tuple]: tuple[0]=>pixel value in x direction. i.e column index. tuple[1]=> pixel vlaue in y direction. i.e row index. """ p_x, p_y = point i_x = math.floor((p_x - self.__map_meta["origin"][0]) / self.__map_meta["resolution"]) i_y = math.floor((p_y - self.__map_meta["origin"][1]) / self.__map_meta["resolution"]) # because origin in yaml is at bottom left of image i_y = self.__grid_map.shape[0] - i_y return i_x, i_y def __transform_distance_to_pixels(self, distance): """ Converts the distance in meters to number of pixels based on the resolution. Parameters ---------- distance: value in meters Returns ------- [Integer]: number of pixel which represent the same distance. """ return math.ceil(distance / self.__map_meta["resolution"]) def __is_obstacle_in_distance(self, img_point, distance): """ Checks if any obstacle is in vicinity of the given image point. Parameters ---------- img_point: pixel values of the point distance: distnace in pixels in which there shouldn't be any obstacle. Returns ------- [Bool]: True if any obstacle found else False. """ # need to make sure that patch xmin & ymin are >=0, # because of python's negative indexing capability row_start_idx = 0 if img_point[1] - distance < 0 else img_point[1] - distance col_start_idx = 0 if img_point[0] - distance < 0 else img_point[0] - distance # image point acts as the center of the square, where each side of square is of size # 2xdistance patch = self.__grid_map[row_start_idx : img_point[1] + distance, col_start_idx : img_point[0] + distance] obstacles = np.where(patch == True) return len(obstacles[0]) > 0 def is_valid_pose(self, point, distance=0.2): """ Checks if a given pose is "distance" away from a obstacle. Parameters ---------- point: pose in 2D space. where point[0]=pose.x and point[1]=pose.y distance: distance in meters. Returns ------- [Bool]: True if pose is valid else False """ assert len(point) == 2 img_point = self.__transform_to_image_coordinates(point) img_pixel_distance = self.__transform_distance_to_pixels(distance) # Pose is not valid if there is obstacle in the vicinity return not self.__is_obstacle_in_distance(img_point, img_pixel_distance)

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/isaac_ros_navigation_goal/set_goal.py

import rclpy from rclpy.action import ActionClient from rclpy.node import Node from nav2_msgs.action import NavigateToPose from .obstacle_map import GridMap from .goal_generators import RandomGoalGenerator, GoalReader import sys from geometry_msgs.msg import PoseWithCovarianceStamped import time class SetNavigationGoal(Node): def __init__(self): super().__init__("set_navigation_goal") self.declare_parameters( namespace="", parameters=[ ("iteration_count", 1), ("goal_generator_type", "RandomGoalGenerator"), ("action_server_name", "navigate_to_pose"), ("obstacle_search_distance_in_meters", 0.2), ("frame_id", "map"), ("map_yaml_path", None), ("goal_text_file_path", None), ("initial_pose", None), ], ) self.__goal_generator = self.__create_goal_generator() action_server_name = self.get_parameter("action_server_name").value self._action_client = ActionClient(self, NavigateToPose, action_server_name) self.MAX_ITERATION_COUNT = self.get_parameter("iteration_count").value assert self.MAX_ITERATION_COUNT > 0 self.curr_iteration_count = 1 self.__initial_goal_publisher = self.create_publisher(PoseWithCovarianceStamped, "/initialpose", 1) self.__initial_pose = self.get_parameter("initial_pose").value self.__is_initial_pose_sent = True if self.__initial_pose is None else False def __send_initial_pose(self): """ Publishes the initial pose. This function is only called once that too before sending any goal pose to the mission server. """ goal = PoseWithCovarianceStamped() goal.header.frame_id = self.get_parameter("frame_id").value goal.header.stamp = self.get_clock().now().to_msg() goal.pose.pose.position.x = self.__initial_pose[0] goal.pose.pose.position.y = self.__initial_pose[1] goal.pose.pose.position.z = self.__initial_pose[2] goal.pose.pose.orientation.x = self.__initial_pose[3] goal.pose.pose.orientation.y = self.__initial_pose[4] goal.pose.pose.orientation.z = self.__initial_pose[5] goal.pose.pose.orientation.w = self.__initial_pose[6] self.__initial_goal_publisher.publish(goal) def send_goal(self): """ Sends the goal to the action server. """ if not self.__is_initial_pose_sent: self.get_logger().info("Sending initial pose") self.__send_initial_pose() self.__is_initial_pose_sent = True # Assumption is that initial pose is set after publishing first time in this duration. # Can be changed to more sophisticated way. e.g. /particlecloud topic has no msg until # the initial pose is set. time.sleep(10) self.get_logger().info("Sending first goal") self._action_client.wait_for_server() goal_msg = self.__get_goal() if goal_msg is None: rclpy.shutdown() sys.exit(1) self._send_goal_future = self._action_client.send_goal_async( goal_msg, feedback_callback=self.__feedback_callback ) self._send_goal_future.add_done_callback(self.__goal_response_callback) def __goal_response_callback(self, future): """ Callback function to check the response(goal accpted/rejected) from the server.\n If the Goal is rejected it stops the execution for now.(We can change to resample the pose if rejected.) """ goal_handle = future.result() if not goal_handle.accepted: self.get_logger().info("Goal rejected :(") rclpy.shutdown() return self.get_logger().info("Goal accepted :)") self._get_result_future = goal_handle.get_result_async() self._get_result_future.add_done_callback(self.__get_result_callback) def __get_goal(self): """ Get the next goal from the goal generator. Returns ------- [NavigateToPose][goal] or None if the next goal couldn't be generated. """ goal_msg = NavigateToPose.Goal() goal_msg.pose.header.frame_id = self.get_parameter("frame_id").value goal_msg.pose.header.stamp = self.get_clock().now().to_msg() pose = self.__goal_generator.generate_goal() # couldn't sample a pose which is not close to obstacles. Rare but might happen in dense maps. if pose is None: self.get_logger().error( "Could not generate next goal. Returning. Possible reasons for this error could be:" ) self.get_logger().error( "1. If you are using GoalReader then please make sure iteration count <= number of goals avaiable in file." ) self.get_logger().error( "2. If RandomGoalGenerator is being used then it was not able to sample a pose which is given distance away from the obstacles." ) return self.get_logger().info("Generated goal pose: {0}".format(pose)) goal_msg.pose.pose.position.x = pose[0] goal_msg.pose.pose.position.y = pose[1] goal_msg.pose.pose.orientation.x = pose[2] goal_msg.pose.pose.orientation.y = pose[3] goal_msg.pose.pose.orientation.z = pose[4] goal_msg.pose.pose.orientation.w = pose[5] return goal_msg def __get_result_callback(self, future): """ Callback to check result.\n It calls the send_goal() function in case current goal sent count < required goals count. """ # Nav2 is sending empty message for success as well as for failure. result = future.result().result self.get_logger().info("Result: {0}".format(result.result)) if self.curr_iteration_count < self.MAX_ITERATION_COUNT: self.curr_iteration_count += 1 self.send_goal() else: rclpy.shutdown() def __feedback_callback(self, feedback_msg): """ This is feeback callback. We can compare/compute/log while the robot is on its way to goal. """ # self.get_logger().info('FEEDBACK: {}\n'.format(feedback_msg)) pass def __create_goal_generator(self): """ Creates the GoalGenerator object based on the specified ros param value. """ goal_generator_type = self.get_parameter("goal_generator_type").value goal_generator = None if goal_generator_type == "RandomGoalGenerator": if self.get_parameter("map_yaml_path").value is None: self.get_logger().info("Yaml file path is not given. Returning..") sys.exit(1) yaml_file_path = self.get_parameter("map_yaml_path").value grid_map = GridMap(yaml_file_path) obstacle_search_distance_in_meters = self.get_parameter("obstacle_search_distance_in_meters").value assert obstacle_search_distance_in_meters > 0 goal_generator = RandomGoalGenerator(grid_map, obstacle_search_distance_in_meters) elif goal_generator_type == "GoalReader": if self.get_parameter("goal_text_file_path").value is None: self.get_logger().info("Goal text file path is not given. Returning..") sys.exit(1) file_path = self.get_parameter("goal_text_file_path").value goal_generator = GoalReader(file_path) else: self.get_logger().info("Invalid goal generator specified. Returning...") sys.exit(1) return goal_generator def main(): rclpy.init() set_goal = SetNavigationGoal() result = set_goal.send_goal() rclpy.spin(set_goal) if __name__ == "__main__": main()

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/isaac_ros_navigation_goal/goal_generators/goal_reader.py

from .goal_generator import GoalGenerator class GoalReader(GoalGenerator): def __init__(self, file_path): self.__file_path = file_path self.__generator = self.__get_goal() def generate_goal(self, max_num_of_trials=1000): try: return next(self.__generator) except StopIteration: return def __get_goal(self): for row in open(self.__file_path, "r"): yield list(map(float, row.strip().split(" ")))

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/isaac_ros_navigation_goal/goal_generators/random_goal_generator.py

import numpy as np from .goal_generator import GoalGenerator class RandomGoalGenerator(GoalGenerator): """ Random goal generator. parameters ---------- grid_map: GridMap Object distance: distance in meters to check vicinity for obstacles. """ def __init__(self, grid_map, distance): self.__grid_map = grid_map self.__distance = distance def generate_goal(self, max_num_of_trials=1000): """ Generate the goal. Parameters ---------- max_num_of_trials: maximum number of pose generations when generated pose keep is not a valid pose. Returns ------- [List][Pose]: Pose in format [pose.x,pose.y,orientaion.x,orientaion.y,orientaion.z,orientaion.w] """ range_ = self.__grid_map.get_range() trial_count = 0 while trial_count < max_num_of_trials: x = np.random.uniform(range_[0][0], range_[0][1]) y = np.random.uniform(range_[1][0], range_[1][1]) orient_x = np.random.uniform(0, 1) orient_y = np.random.uniform(0, 1) orient_z = np.random.uniform(0, 1) orient_w = np.random.uniform(0, 1) if self.__grid_map.is_valid_pose([x, y], self.__distance): goal = [x, y, orient_x, orient_y, orient_z, orient_w] return goal trial_count += 1

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/isaac_ros_navigation_goal/goal_generators/__init__.py

from .random_goal_generator import RandomGoalGenerator from .goal_reader import GoalReader

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/isaac_ros_navigation_goal/isaac_ros_navigation_goal/goal_generators/goal_generator.py

from abc import ABC, abstractmethod class GoalGenerator(ABC): """ Parent class for the Goal generators """ def __init__(self): pass @abstractmethod def generate_goal(self, max_num_of_trials=2000): """ Generate the goal. Parameters ---------- max_num_of_trials: maximum number of pose generations when generated pose keep is not a valid pose. Returns ------- [List][Pose]: Pose in format [pose.x,pose.y,orientaion.x,orientaion.y,orientaion.z,orientaion.w] """ pass

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/noetic_ws/src/isaac_tutorials/scripts/ros_publisher.py

#!/usr/bin/env python # Copyright (c) 2021-2022, 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 rospy from sensor_msgs.msg import JointState import numpy as np import time rospy.init_node("test_rosbridge", anonymous=True) pub = rospy.Publisher("/joint_command", JointState, queue_size=10) joint_state = JointState() joint_state.name = [ "panda_joint1", "panda_joint2", "panda_joint3", "panda_joint4", "panda_joint5", "panda_joint6", "panda_joint7", "panda_finger_joint1", "panda_finger_joint2", ] num_joints = len(joint_state.name) # make sure kit's editor is playing for receiving messages ## joint_state.position = np.array([0.0] * num_joints) default_joints = [0.0, -1.16, -0.0, -2.3, -0.0, 1.6, 1.1, 0.4, 0.4] # limiting the movements to a smaller range (this is not the range of the robot, just the range of the movement max_joints = np.array(default_joints) + 0.5 min_joints = np.array(default_joints) - 0.5 # position control the robot to wiggle around each joint time_start = time.time() rate = rospy.Rate(20) while not rospy.is_shutdown(): joint_state.position = np.sin(time.time() - time_start) * (max_joints - min_joints) * 0.5 + default_joints pub.publish(joint_state) rate.sleep()

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/noetic_ws/src/isaac_tutorials/scripts/ros_service_client.py

#!/usr/bin/env python # Copyright (c) 2021-2022, 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 rospy import numpy as np from isaac_ros_messages.srv import IsaacPose from isaac_ros_messages.srv import IsaacPoseRequest from geometry_msgs.msg import Pose def teleport_client(msg): rospy.wait_for_service("teleport") try: teleport = rospy.ServiceProxy("teleport", IsaacPose) teleport(msg) return except rospy.ServiceException as e: print("Service call failed: %s" % e) # compose teleport messages cube_pose = Pose() cube_pose.position.x = np.random.uniform(-2, 2) cube_pose.position.y = 0 cube_pose.position.z = 0 cube_pose.orientation.w = 1 cube_pose.orientation.x = 0 cube_pose.orientation.y = 0 cube_pose.orientation.z = 0 cone_pose = Pose() cone_pose.position.x = 0 cone_pose.position.y = np.random.uniform(-2, 2) cone_pose.position.z = 0 cone_pose.orientation.w = 1 cone_pose.orientation.x = 0 cone_pose.orientation.y = 0 cone_pose.orientation.z = 0 teleport_msg = IsaacPoseRequest() teleport_msg.names = ["/World/Cube", "/World/Cone"] teleport_msg.poses = [cube_pose, cone_pose] teleport_client(teleport_msg)

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/noetic_ws/src/isaac_moveit/scripts/panda_combined_joints_publisher.py

#!/usr/bin/env python # Copyright (c) 2021-2022, 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 rospy from sensor_msgs.msg import JointState joints_dict = {} def joint_states_callback(message): joint_commands = JointState() joint_commands.header = message.header for i, name in enumerate(message.name): # Storing arm joint names and positions joints_dict[name] = message.position[i] if name == "panda_finger_joint1": # Adding additional panda_finger_joint2 state info (extra joint used in isaac sim) # panda_finger_joint2 mirrors panda_finger_joint1 joints_dict["panda_finger_joint2"] = message.position[i] joint_commands.name = joints_dict.keys() joint_commands.position = joints_dict.values() # Publishing combined message containing all arm and finger joints pub.publish(joint_commands) return if __name__ == "__main__": rospy.init_node("panda_combined_joints_publisher") pub = rospy.Publisher("/joint_command", JointState, queue_size=1) rospy.Subscriber("/joint_command_desired", JointState, joint_states_callback, queue_size=1) rospy.spin()

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/noetic_ws/src/cortex_control_franka/src/python/franka_gripper_commander.py

# Copyright (c) 2019-2022, 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. # Simple action client interface to the gripper action server. from __future__ import absolute_import, division, print_function, unicode_literals from franka_gripper.msg import GraspAction, GraspGoal, GraspEpsilon, MoveAction, MoveGoal import numpy as np import rospy import actionlib import argparse # A gripper opening width of 0.8 appears full open, but Franka claims it will cause issues. # The nominal maximum opening width is 0.7. Here we compromise between the two. open_pos = 0.75 class FrankaGripperCommander(object): def __init__(self, verbose=False): self.verbose = verbose self.grasp_client = actionlib.SimpleActionClient("/franka_gripper/grasp", GraspAction) self.move_client = actionlib.SimpleActionClient("/franka_gripper/move", MoveAction) if self.verbose: print("Waiting for grasp client...") self.grasp_client.wait_for_server() if self.verbose: print("Waiting for move client...") self.move_client.wait_for_server() def close(self, width=0.0, speed=0.03, force=40.0, grasp_eps=(0.2, 0.2), wait=True): grasp_goal = GraspGoal() grasp_goal.width = width grasp_goal.speed = speed grasp_goal.force = force grasp_goal.epsilon = GraspEpsilon(inner=grasp_eps[0], outer=grasp_eps[1]) self.grasp_client.send_goal(grasp_goal) if wait: self.grasp_client.wait_for_result() if self.verbose: print("result:", self.grasp_client.get_result()) def move(self, width, speed=0.03, wait=True): move_goal = MoveGoal() move_goal.width = width move_goal.speed = speed print("sending goal") self.move_client.send_goal(move_goal) if wait: print("waiting for finish") self.move_client.wait_for_result() if self.verbose: print("result:", self.move_client.get_result()) print("move complete") def open(self, speed=0.03, wait=True): self.move(open_pos, speed=speed, wait=wait) if __name__ == "__main__": def Grasp(args): print("Grasping...") client = actionlib.SimpleActionClient("/franka_gripper/grasp", GraspAction) # Waits until the action server has started up and started # listening for goals. client.wait_for_server() # Creates a goal to send to the action server. grasp_goal = GraspGoal() grasp_goal.width = args.grasp_width grasp_goal.speed = args.speed grasp_goal.force = args.force grasp_goal.epsilon = GraspEpsilon(inner=args.eps_inner, outer=args.eps_outer) # Sends the goal to the action server. print(">>>>", grasp_goal) client.send_goal(grasp_goal) # Waits for the server to finish performing the action. client.wait_for_result() # Prints out the result of executing the action print("result:", client.get_result()) def Move(args): print("Moving...") client = actionlib.SimpleActionClient("/franka_gripper/move", MoveAction) # Waits until the action server has started up and started # listening for goals. client.wait_for_server() # Creates a goal to send to the action server. move_goal = GraspGoal() move_goal.width = args.width move_goal.speed = args.speed # Sends the goal to the action server. client.send_goal(move_goal) # Waits for the server to finish performing the action. client.wait_for_result() # Prints out the result of executing the action print("result:", client.get_result()) def FrankaGripperCommanderTest(args): print("Creating gripper commander...") gripper_commander = FrankaGripperCommander() print("Closing...") gripper_commander.close() print("Opening to all the way...") gripper_commander.move(0.08) print("Opening to .2...") gripper_commander.move(0.02) print("Opening to .5...") gripper_commander.move(0.05) print("Closing...") gripper_commander.close() print("Opening to all the way...") gripper_commander.move(0.08) def RobustnessTest(args): commander = FrankaGripperCommander() mode = "open" while not rospy.is_shutdown(): if mode == "open": commander.open(speed=0.2, wait=False) print("opening...") mode = "close" elif mode == "close": commander.close(speed=0.2, wait=False) print("closing...") mode = "open" else: raise RuntimeError("Invalid mode:", mode) wait_time = abs(np.random.normal(loc=0.5, scale=0.75)) print(" wait:", wait_time) rospy.sleep(wait_time) parser = argparse.ArgumentParser("gripper_test") parser.add_argument( "--mode", type=str, required=True, help="Which mode: close, move, gripper_commander_test, robustness_test." ) parser.add_argument( "--width", type=float, default=None, help="How wide in meters. Note that the gripper can open to about .8m wide.", ) parser.add_argument("--speed", type=float, default=0.03, help="How fast to go in meter per second.") parser.add_argument("--force", type=float, default=0.03, help="How strongly to grip.") parser.add_argument( "--grasp_width", type=float, default=0.0, help="Width of the grasp. Defaults to closing all the way. " "In conjunction with the default error (set wide) the default " "behavior is to just close until it feels something.", ) parser.add_argument( "--eps_inner", type=float, default=0.2, help="Inner epsilon threshold. Defaults to enabling any error." ) parser.add_argument( "--eps_outer", type=float, default=0.2, help="Outer epsilon threshold. Defaults to enabling any error." ) args = parser.parse_args() rospy.init_node("gripper_test") if args.mode == "move": Move(args) elif args.mode == "close": Grasp(args) elif args.mode == "gripper_commander_test": FrankaGripperCommanderTest(args) elif args.mode == "robustness_test": RobustnessTest(args) else: print("ERROR -- unrecognized mode:", args.mode)

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/noetic_ws/src/cortex_control_franka/src/python/franka_gripper_command_relay.py

#!/usr/bin/python # Copyright (c) 2019-2022, 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. # Simple action client interface to the gripper action server. from __future__ import print_function import argparse import json import threading import rospy from sensor_msgs.msg import JointState from std_msgs.msg import String from franka_gripper_commander import FrankaGripperCommander pinch_width = 0.0265 speed = 0.2 class SimGripperCommander(object): def __init__(self): pass def move(self, width, speed, wait=True): print("[move] width: %.4f, speed %.2f" % (width, speed)) def close(self, width=0.0, speed=0.03, force=40.0, grasp_eps=(0.2, 0.2), wait=True): print("[close] width: %.4f, speed: %.2f, force: %.2f" % (width, speed, force)) class FrankaGripperCommandRelay(object): def __init__(self, is_sim=False): print("Setting up gripper commander") self.is_sim = is_sim if self.is_sim: print("<is sim>") self.gripper_commander = SimGripperCommander() else: print("<is real>") self.gripper_commander = FrankaGripperCommander(verbose=True) self.start_time = rospy.Time.now() self.last_tick_time = self.start_time self.seconds_between_tick_prints = 0.1 self.command_queue = [] self.command_queue_lock = threading.Lock() print("Starting subscriber...") self.command_sub = rospy.Subscriber("/cortex/gripper/command", String, self.command_callback) print("<ready and listening>") def command_callback(self, msg): try: command = json.loads(msg.data) try: self.command_queue_lock.acquire() self.command_queue.append(command) finally: self.command_queue_lock.release() except ValueError as ve: print("Jsg parse error -- could not parse command:\n", msg.data) except Exception as e: print("Exception in processing command:", e) print("message data:\n", msg.data) def process_latest_commands(self): now = rospy.Time.now() if (now - self.last_tick_time).to_sec() >= self.seconds_between_tick_prints: self.last_tick_time = now try: self.command_queue_lock.acquire() command_queue = self.command_queue self.command_queue = [] finally: self.command_queue_lock.release() for command in command_queue: self.process_latest_command(command) def process_latest_command(self, cmd): try: print("\nprocessing command:", cmd["command"]) if cmd["command"] == "move_to": print("moving to:", cmd["width"]) self.gripper_commander.move(cmd["width"], speed=speed, wait=True) elif cmd["command"] == "close_to_grasp": print("closing to grasp") self.gripper_commander.close(speed=speed) else: print("WARNING -- unrecognized gripper command:", cmd["command"]) except Exception as e: print("ERROR processing command:\n", cmd) print("exception:", e) def run(self): rate = rospy.Rate(60.0) while not rospy.is_shutdown(): self.process_latest_commands() rate.sleep() if __name__ == "__main__": node_name = "franka_gripper_commander_relay" rospy.init_node(node_name) parser = argparse.ArgumentParser(node_name) parser.add_argument("--is_sim", action="store_true", help="Set to start in simulated env.") parser.add_argument("--open", action="store_true", help="Open the gripper then exit.") parser.add_argument("--close", action="store_true", help="Close the gripper then exit.") parser.add_argument("--close_pinch", action="store_true", help="Close the gripper then exit.") args = parser.parse_args() if args.open: gripper_commander = FrankaGripperCommander(verbose=True) gripper_commander.open(speed=speed) elif args.close: gripper_commander = FrankaGripperCommander(verbose=True) gripper_commander.close(speed=speed) elif args.close_pinch: gripper_commander = FrankaGripperCommander(verbose=True) gripper_commander.move(pinch_width, speed=speed, wait=True) else: listener = FrankaGripperCommandRelay(args.is_sim) listener.run()

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/noetic_ws/src/cortex_control_franka/src/python/set_high_collision_thresholds.py

#!/usr/bin/env python # Copyright (c) 2021-2022, 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. # Simple action client interface to the gripper action server. import rospy from franka_control.srv import SetJointImpedance from franka_control.srv import SetJointImpedanceRequest from franka_control.srv import SetForceTorqueCollisionBehavior from franka_control.srv import SetForceTorqueCollisionBehaviorRequest rospy.init_node("set_control_parameters") force_torque_srv = "/franka_control/set_force_torque_collision_behavior" lower_torque_thresholds_nominal = [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0] upper_torque_thresholds_nominal = [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0] lower_force_thresholds_nominal = [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0] upper_force_thresholds_nominal = [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0] ft_req = SetForceTorqueCollisionBehaviorRequest() ft_req.lower_torque_thresholds_nominal = lower_torque_thresholds_nominal ft_req.upper_torque_thresholds_nominal = upper_torque_thresholds_nominal ft_req.lower_force_thresholds_nominal = lower_force_thresholds_nominal ft_req.upper_force_thresholds_nominal = upper_force_thresholds_nominal print(ft_req) rospy.loginfo("Waiting for services...") rospy.wait_for_service(force_torque_srv) rospy.loginfo("Services ready.") ft_srv = rospy.ServiceProxy(force_torque_srv, SetForceTorqueCollisionBehavior) resp = ft_srv(ft_req) failed = False if not resp.success: rospy.logerr("Could not set force torque collision behavior!") failed = True else: rospy.loginfo("Set force torque collision behavior!") if failed: raise RuntimeError("Failed to set control parameters")

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/noetic_ws/src/navigation/isaac_ros_navigation_goal/setup.py

from setuptools import setup from catkin_pkg.python_setup import generate_distutils_setup d = generate_distutils_setup( packages=["goal_generators", "obstacle_map"], package_dir={"": "isaac_ros_navigation_goal"} ) setup(**d)

Python

NVIDIA-Omniverse/IsaacSim-ros_workspaces/noetic_ws/src/navigation/isaac_ros_navigation_goal/isaac_ros_navigation_goal/set_goal.py

#!/usr/bin/env python from __future__ import absolute_import import rospy import actionlib import sys from move_base_msgs.msg import MoveBaseAction, MoveBaseGoal from obstacle_map import GridMap from goal_generators import RandomGoalGenerator, GoalReader from geometry_msgs.msg import PoseWithCovarianceStamped class SetNavigationGoal: def __init__(self): self.__goal_generator = self.__create_goal_generator() action_server_name = rospy.get_param("action_server_name", "move_base") self._action_client = actionlib.SimpleActionClient(action_server_name, MoveBaseAction) self.MAX_ITERATION_COUNT = rospy.get_param("iteration_count", 1) assert self.MAX_ITERATION_COUNT > 0 self.curr_iteration_count = 1 self.__initial_goal_publisher = rospy.Publisher("initialpose", PoseWithCovarianceStamped, queue_size=1) self.__initial_pose = rospy.get_param("initial_pose", None) self.__is_initial_pose_sent = True if self.__initial_pose is None else False def __send_initial_pose(self): """ Publishes the initial pose. This function is only called once that too before sending any goal pose to the mission server. """ goal = PoseWithCovarianceStamped() goal.header.frame_id = rospy.get_param("frame_id", "map") goal.header.stamp = rospy.get_rostime() goal.pose.pose.position.x = self.__initial_pose[0] goal.pose.pose.position.y = self.__initial_pose[1] goal.pose.pose.position.z = self.__initial_pose[2] goal.pose.pose.orientation.x = self.__initial_pose[3] goal.pose.pose.orientation.y = self.__initial_pose[4] goal.pose.pose.orientation.z = self.__initial_pose[5] goal.pose.pose.orientation.w = self.__initial_pose[6] rospy.sleep(1) self.__initial_goal_publisher.publish(goal) def send_goal(self): """ Sends the goal to the action server. """ if not self.__is_initial_pose_sent: rospy.loginfo("Sending initial pose") self.__send_initial_pose() self.__is_initial_pose_sent = True # Assumption is that initial pose is set after publishing first time in this duration. # Can be changed to more sophisticated way. e.g. /particlecloud topic has no msg until # the initial pose is set. rospy.sleep(10) rospy.loginfo("Sending first goal") self._action_client.wait_for_server() goal_msg = self.__get_goal() if goal_msg is None: rospy.signal_shutdown("Goal message not generated.") sys.exit(1) self._action_client.send_goal(goal_msg, feedback_cb=self.__goal_response_callback) def __goal_response_callback(self, feedback): """ Callback function to check the response(goal accpted/rejected) from the server.\n If the Goal is rejected it stops the execution for now.(We can change to resample the pose if rejected.) """ if self.verify_goal_state(): rospy.loginfo("Waiting to reach goal") wait = self._action_client.wait_for_result() if self.verify_goal_state(): self.__get_result_callback(True) def verify_goal_state(self): print("Action Client State:", self._action_client.get_state(), self._action_client.get_goal_status_text()) if self._action_client.get_state() not in [0, 1, 3]: rospy.signal_shutdown("Goal Rejected :(") return False return True def __get_goal(self): goal_msg = MoveBaseGoal() goal_msg.target_pose.header.frame_id = rospy.get_param("frame_id", "map") goal_msg.target_pose.header.stamp = rospy.get_rostime() pose = self.__goal_generator.generate_goal() # couldn't sample a pose which is not close to obstacles. Rare but might happen in dense maps. if pose is None: rospy.logerr("Could not generate next goal. Returning. Possible reasons for this error could be:") rospy.logerr( "1. If you are using GoalReader then please make sure iteration count <= no of goals avaiable in file." ) rospy.logerr( "2. If RandomGoalGenerator is being used then it was not able to sample a pose which is given distance away from the obstacles." ) return rospy.loginfo("Generated goal pose: {0}".format(pose)) goal_msg.target_pose.pose.position.x = pose[0] goal_msg.target_pose.pose.position.y = pose[1] goal_msg.target_pose.pose.orientation.x = pose[2] goal_msg.target_pose.pose.orientation.y = pose[3] goal_msg.target_pose.pose.orientation.z = pose[4] goal_msg.target_pose.pose.orientation.w = pose[5] return goal_msg def __get_result_callback(self, wait): if wait and self.curr_iteration_count < self.MAX_ITERATION_COUNT: self.curr_iteration_count += 1 self.send_goal() else: rospy.signal_shutdown("Iteration done or Goal not reached.") # in this callback func we can compare/compute/log something while the robot is on its way to goal. def __feedback_callback(self, feedback_msg): pass def __create_goal_generator(self): goal_generator_type = rospy.get_param("goal_generator_type", "RandomGoalGenerator") goal_generator = None if goal_generator_type == "RandomGoalGenerator": if rospy.get_param("map_yaml_path", None) is None: rospy.loginfo("Yaml file path is not given. Returning..") sys.exit(1) yaml_file_path = rospy.get_param("map_yaml_path", None) grid_map = GridMap(yaml_file_path) obstacle_search_distance_in_meters = rospy.get_param("obstacle_search_distance_in_meters", 0.2) assert obstacle_search_distance_in_meters > 0 goal_generator = RandomGoalGenerator(grid_map, obstacle_search_distance_in_meters) elif goal_generator_type == "GoalReader": if rospy.get_param("goal_text_file_path", None) is None: rospy.loginfo("Goal text file path is not given. Returning..") sys.exit(1) file_path = rospy.get_param("goal_text_file_path", None) goal_generator = GoalReader(file_path) else: rospy.loginfo("Invalid goal generator specified. Returning...") sys.exit(1) return goal_generator def main(): rospy.init_node("set_goal_py") set_goal = SetNavigationGoal() result = set_goal.send_goal() rospy.spin() if __name__ == "__main__": main()

Python

NVIDIA-Omniverse/usd_scene_construction_utils/setup.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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. from setuptools import setup, find_packages setup( name="usd_scene_construction_utils", version="0.0.1", description="", py_modules=["usd_scene_construction_utils"] )

Python

NVIDIA-Omniverse/usd_scene_construction_utils/usd_scene_construction_utils.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 numpy as np import math from typing import Optional, Sequence, Tuple from typing_extensions import Literal from pxr import Gf, Sdf, Usd, UsdGeom, UsdLux, UsdShade def new_in_memory_stage() -> Usd.Stage: """Creates a new in memory USD stage. Returns: Usd.Stage: The USD stage. """ stage = Usd.Stage.CreateInMemory() UsdGeom.SetStageUpAxis(stage, UsdGeom.Tokens.z) return stage def new_omniverse_stage() -> Usd.Stage: """Creates a new Omniverse USD stage. This method creates a new Omniverse USD stage. This will clear the active omniverse stage, replacing it with a new one. Returns: Usd.Stage: The Omniverse USD stage. """ try: import omni.usd except ImportError: raise ImportError("Omniverse not found. This method is unavailable.") omni.usd.get_context().new_stage() stage = omni.usd.get_context().get_stage() UsdGeom.SetStageUpAxis(stage, UsdGeom.Tokens.z) return stage def get_omniverse_stage() -> Usd.Stage: """Returns the current Omniverse USD stage. Returns: Usd.Stage: The currently active Omniverse USD stage. """ try: import omni.usd except ImportError: raise ImportError("Omniverse not found. This method is unavailable.") stage = omni.usd.get_context().get_stage() return stage def add_usd_ref(stage: Usd.Stage, path: str, usd_path: str) -> Usd.Prim: """Adds an external USD reference to a USD stage. Args: stage (:class:`Usd.Stage`): The USD stage to modify. path (str): The path to add the USD reference. usd_path (str): The filepath or URL of the USD reference (ie: a Nucleus server URL). Returns: Usd.Prim: The created USD prim. """ stage.DefinePrim(path, "Xform") prim_ref = stage.DefinePrim(os.path.join(path, "ref")) prim_ref.GetReferences().AddReference(usd_path) return get_prim(stage, path) def _make_box_mesh(size: Tuple[float, float, float]): # private utility function used by make_box numFaces = 6 numVertexPerFace = 4 # Generate vertices on box vertices = [] for i in [-1, 1]: for j in [-1, 1]: for k in [-1, 1]: vertices.append((i * size[0], j * size[1], k * size[2])) # Make faces for box (ccw convention) faceVertexCounts = [numVertexPerFace] * numFaces faceVertexIndices = [ 2, 0, 1, 3, 4, 6, 7, 5, 0, 4, 5, 1, 6, 2, 3, 7, 0, 2, 6, 4, 5, 7, 3, 1, ] # Make normals for face vertices _faceVertexNormals = [ (-1, 0, 0), (1, 0, 0), (0, -1, 0), (0, 1, 0), (0, 0, -1), (0, 0, 1), ] faceVertexNormals = [] for n in _faceVertexNormals: for i in range(numVertexPerFace): faceVertexNormals.append(n) # Assign uv-mapping for face vertices _faceUvMaps = [ (0, 0), (1, 0), (1, 1), (0, 1) ] faceUvMaps = [] for i in range(numFaces): for uvm in _faceUvMaps: faceUvMaps.append(uvm) return (vertices, faceVertexCounts, faceVertexIndices, faceVertexNormals, faceUvMaps) def add_box(stage: Usd.Stage, path: str, size: Tuple[float, float, float]) -> Usd.Prim: """Adds a 3D box to a USD stage. This adds a 3D box to the USD stage. The box is created with it's center at (x, y, z) = (0, 0, 0). Args: stage (:class:`Usd.Stage`): The USD stage to modify. path (str): The path to add the USD prim. size (Tuple[float, float, float]): The size of the box (x, y, z sizes). Returns: Usd.Prim: The created USD prim. """ half_size = (size[0] / 2, size[1] / 2, size[2] / 2) stage.DefinePrim(path, "Xform") (vertices, faceVertexCounts, faceVertexIndices, faceVertexNormals, faceUvMaps) = _make_box_mesh(half_size) # create mesh at {path}/mesh, but return prim at {path} prim: UsdGeom.Mesh = UsdGeom.Mesh.Define(stage, os.path.join(path, "mesh")) prim.CreateExtentAttr().Set([ (-half_size[0], -half_size[1], -half_size[2]), (half_size[0], half_size[1], half_size[2]) ]) prim.CreateFaceVertexCountsAttr().Set(faceVertexCounts) prim.CreateFaceVertexIndicesAttr().Set(faceVertexIndices) var = UsdGeom.Primvar(prim.CreateNormalsAttr()) var.Set(faceVertexNormals) var.SetInterpolation(UsdGeom.Tokens.faceVarying) var = UsdGeom.PrimvarsAPI(prim).CreatePrimvar("primvars:st", Sdf.ValueTypeNames.Float2Array) var.Set(faceUvMaps) var.SetInterpolation(UsdGeom.Tokens.faceVarying) prim.CreatePointsAttr().Set(vertices) prim.CreateSubdivisionSchemeAttr().Set(UsdGeom.Tokens.none) return get_prim(stage, path) def add_xform(stage: Usd.Stage, path: str): """Adds a USD transform (Xform) to a USD stage. This method adds a USD Xform to the USD stage at a given path. This is helpful when you want to add hierarchy to a scene. After you create a transform, any USD prims located under the transform path will be children of the transform and can be moved as a group. Args: stage (:class:`Usd.Stage`): The USD stage to modify. path (str): The path to add the USD prim. Returns: Usd.Prim: The created USD prim. """ stage.DefinePrim(path, "Xform") return get_prim(stage, path) def add_plane( stage: Usd.Stage, path: str, size: Tuple[float, float], uv: Tuple[float, float]=(1, 1)): """Adds a 2D plane to a USD stage. Args: stage (Usd.Stage): The USD stage to modify. path (str): The path to add the USD prim. size (Tuple[float, float]): The size of the 2D plane (x, y). uv (Tuple[float, float]): The UV mapping for textures applied to the plane. For example, uv=(1, 1), means the texture will be spread to fit the full size of the plane. uv=(10, 10) means the texture will repeat 10 times along each dimension. uv=(5, 10) means the texture will be scaled to repeat 5 times along the x dimension and 10 times along the y direction. Returns: Usd.Prim: The created USD prim. """ stage.DefinePrim(path, "Xform") # create mesh at {path}/mesh, but return prim at {path} prim: UsdGeom.Mesh = UsdGeom.Mesh.Define(stage, os.path.join(path, "mesh")) prim.CreateExtentAttr().Set([ (-size[0], -size[1], 0), (size[0], size[1], 0) ]) prim.CreateFaceVertexCountsAttr().Set([4]) prim.CreateFaceVertexIndicesAttr().Set([0, 1, 3, 2]) var = UsdGeom.Primvar(prim.CreateNormalsAttr()) var.Set([(0, 0, 1)] * 4) var.SetInterpolation(UsdGeom.Tokens.faceVarying) var = UsdGeom.PrimvarsAPI(prim).CreatePrimvar("primvars:st", Sdf.ValueTypeNames.Float2Array) var.Set( [(0, 0), (uv[0], 0), (uv[0], uv[1]), (0, uv[1])] ) var.SetInterpolation(UsdGeom.Tokens.faceVarying) prim.CreatePointsAttr().Set([ (-size[0], -size[1], 0), (size[0], -size[1], 0), (-size[0], size[1], 0), (size[0], size[1], 0), ]) prim.CreateSubdivisionSchemeAttr().Set(UsdGeom.Tokens.none) return get_prim(stage, path) def add_dome_light(stage: Usd.Stage, path: str, intensity: float = 1000, angle: float = 180, exposure: float=0.) -> UsdLux.DomeLight: """Adds a dome light to a USD stage. Args: stage (Usd.Stage): The USD stage to modify. path (str): The path to add the USD prim. intensity (float): The intensity of the dome light (default 1000). angle (float): The angle of the dome light (default 180) exposure (float): THe exposure of the dome light (default 0) Returns: UsdLux.DomeLight: The created Dome light. """ light = UsdLux.DomeLight.Define(stage, path) # intensity light.CreateIntensityAttr().Set(intensity) light.CreateTextureFormatAttr().Set(UsdLux.Tokens.latlong) light.CreateExposureAttr().Set(exposure) # cone angle shaping = UsdLux.ShapingAPI(light) shaping.Apply(light.GetPrim()) shaping.CreateShapingConeAngleAttr().Set(angle) shaping.CreateShapingConeSoftnessAttr() shaping.CreateShapingFocusAttr() shaping.CreateShapingFocusTintAttr() shaping.CreateShapingIesFileAttr() return light def add_sphere_light(stage: Usd.Stage, path: str, intensity=30000, radius=50, angle=180, exposure=0.): """Adds a sphere light to a USD stage. Args: stage (Usd.Stage): The USD stage to modify. path (str): The path to add the USD prim. radius (float): The radius of the sphere light intensity (float): The intensity of the sphere light (default 1000). angle (float): The angle of the sphere light (default 180) exposure (float): THe exposure of the sphere light (default 0) Returns: UsdLux.SphereLight: The created sphere light. """ light = UsdLux.SphereLight.Define(stage, path) # intensity light.CreateIntensityAttr().Set(intensity) light.CreateRadiusAttr().Set(radius) light.CreateExposureAttr().Set(exposure) # cone angle shaping = UsdLux.ShapingAPI(light) shaping.Apply(light.GetPrim()) shaping.CreateShapingConeAngleAttr().Set(angle) shaping.CreateShapingConeSoftnessAttr() shaping.CreateShapingFocusAttr() shaping.CreateShapingFocusTintAttr() shaping.CreateShapingIesFileAttr() return light def add_mdl_material(stage: Usd.Stage, path: str, material_url: str, material_name: Optional[str] = None) -> UsdShade.Material: """Adds an Omniverse MDL material to a USD stage. *Omniverse only* Args: stage (Usd.Stage): The USD stage to modify. path (str): The path to add the USD prim. material_url (str): The URL of the material, such as on a Nucelus server. material_name (Optional[str]): An optional name to give the material. If one is not provided, it will default to the filename of the material URL (excluding the extension). returns: UsdShade.Material: The created USD material. """ try: import omni.usd except ImportError: raise ImportError("Omniverse not found. This method is unavailable.") # Set default mtl_name if material_name is None: material_name = os.path.basename(material_url).split('.')[0] # Create material using omniverse kit if not stage.GetPrimAtPath(path): success, result = omni.kit.commands.execute( "CreateMdlMaterialPrimCommand", mtl_url=material_url, mtl_name=material_name, mtl_path=path ) # Get material from stage material = UsdShade.Material(stage.GetPrimAtPath(path)) return material def add_camera( stage: Usd.Stage, path: str, focal_length: float = 35, horizontal_aperature: float = 20.955, vertical_aperature: float = 20.955, clipping_range: Tuple[float, float] = (0.1, 100000) ) -> UsdGeom.Camera: """Adds a camera to a USD stage. Args: stage (Usd.Stage): The USD stage to modify. path (str): The path to add the USD prim. focal_length (float): The focal length of the camera (default 35). horizontal_aperature (float): The horizontal aperature of the camera (default 20.955). vertical_aperature (float): The vertical aperature of the camera (default 20.955). clipping_range (Tuple[float, float]): The clipping range of the camera. returns: UsdGeom.Camera: The created USD camera. """ camera = UsdGeom.Camera.Define(stage, path) camera.CreateFocalLengthAttr().Set(focal_length) camera.CreateHorizontalApertureAttr().Set(horizontal_aperature) camera.CreateVerticalApertureAttr().Set(vertical_aperature) camera.CreateClippingRangeAttr().Set(clipping_range) return camera def get_prim(stage: Usd.Stage, path: str) -> Usd.Prim: """Returns a prim at the specified path in a USD stage. Args: stage (Usd.Stage): The USD stage to query. path (str): The path of the prim. Returns: Usd.Prim: The USD prim at the specified path. """ return stage.GetPrimAtPath(path) def get_material(stage: Usd.Stage, path: str) -> UsdShade.Material: """Returns a material at the specified path in a USD stage. Args: stage (Usd.Stage): The USD stage to query. path (str): The path of the material. Returns: UsdShade.Material: The USD material at the specified path. """ prim = get_prim(stage, path) return UsdShade.Material(prim) def export_stage(stage: Usd.Stage, filepath: str, default_prim=None): """Exports a USD stage to a given filepath. Args: stage (Usd.Stage): The USD stage to export. path (str): The filepath to export the USD stage to. default_prim (Optional[str]): The path of the USD prim in the stage to set as the default prim. This is useful when you want to use the exported USD as a reference, or when you want to place the USD in Omniverse. """ if default_prim is not None: stage.SetDefaultPrim(get_prim(stage, default_prim)) stage.Export(filepath) def add_semantics(prim: Usd.Prim, type: str, name: str): """Adds semantics to a USD prim. This function adds semantics to a USD prim. This is useful for assigning classes to objects when generating synthetic data with Omniverse Replicator. For example: add_semantics(dog_prim, "class", "dog") add_semantics(cat_prim, "class", "cat") Args: prim (Usd.Prim): The USD prim to modify. type (str): The semantics type. This depends on how the data is ingested. Typically, when using Omniverse replicator you will set this to "class". name (str): The value of the semantic type. Typically, this would correspond to the class label. Returns: Usd.Prim: The USD prim with added semantics. """ prim.AddAppliedSchema(f"SemanticsAPI:{type}_{name}") prim.CreateAttribute(f"semantic:{type}_{name}:params:semanticType", Sdf.ValueTypeNames.String).Set(type) prim.CreateAttribute(f"semantic:{type}_{name}:params:semanticData", Sdf.ValueTypeNames.String).Set(name) return prim def bind_material(prim: Usd.Prim, material: UsdShade.Material): """Binds a USD material to a USD prim. Args: prim (Usd.Prim): The USD prim to modify. material (UsdShade.Material): The USD material to bind to the USD prim. Returns: Usd.Prim: The modified USD prim with the specified material bound to it. """ prim.ApplyAPI(UsdShade.MaterialBindingAPI) UsdShade.MaterialBindingAPI(prim).Bind(material, UsdShade.Tokens.strongerThanDescendants) return prim def collapse_xform(prim: Usd.Prim): """Collapses all xforms on a given USD prim. This method collapses all Xforms on a given prim. For example, a series of rotations, translations would be combined into a single matrix operation. Args: prim (Usd.Prim): The Usd.Prim to collapse the transforms of. Returns: Usd.Prim: The Usd.Prim. """ x = UsdGeom.Xformable(prim) local = x.GetLocalTransformation() prim.RemoveProperty("xformOp:translate") prim.RemoveProperty("xformOp:transform") prim.RemoveProperty("xformOp:rotateX") prim.RemoveProperty("xformOp:rotateY") prim.RemoveProperty("xformOp:rotateZ") var = x.MakeMatrixXform() var.Set(local) return prim def get_xform_op_order(prim: Usd.Prim): """Returns the order of Xform ops on a given prim.""" x = UsdGeom.Xformable(prim) op_order = x.GetXformOpOrderAttr().Get() if op_order is not None: op_order = list(op_order) return op_order else: return [] def set_xform_op_order(prim: Usd.Prim, op_order: Sequence[str]): """Sets the order of Xform ops on a given prim""" x = UsdGeom.Xformable(prim) x.GetXformOpOrderAttr().Set(op_order) return prim def xform_op_move_end_to_front(prim: Usd.Prim): """Pops the last xform op on a given prim and adds it to the front.""" order = get_xform_op_order(prim) end = order.pop(-1) order.insert(0, end) set_xform_op_order(prim, order) return prim def get_num_xform_ops(prim: Usd.Prim) -> int: """Returns the number of xform ops on a given prim.""" return len(get_xform_op_order(prim)) def apply_xform_matrix(prim: Usd.Prim, transform: np.ndarray): """Applies a homogeneous transformation matrix to the current prim's xform list. Args: prim (Usd.Prim): The USD prim to transform. transform (np.ndarray): The 4x4 homogeneous transform matrix to apply. Returns: Usd.Prim: The modified USD prim with the provided transform applied after current transforms. """ x = UsdGeom.Xformable(prim) x.AddTransformOp(opSuffix=f"num_{get_num_xform_ops(prim)}").Set( Gf.Matrix4d(transform) ) xform_op_move_end_to_front(prim) return prim def scale(prim: Usd.Prim, scale: Tuple[float, float, float]): """Scales a prim along the (x, y, z) dimensions. Args: prim (Usd.Prim): The USD prim to scale. scale (Tuple[float, float, float]): The scaling factors for the (x, y, z) dimensions. Returns: Usd.Prim: The scaled prim. """ x = UsdGeom.Xformable(prim) x.AddScaleOp(opSuffix=f"num_{get_num_xform_ops(prim)}").Set(scale) xform_op_move_end_to_front(prim) return prim def translate(prim: Usd.Prim, offset: Tuple[float, float, float]): """Translates a prim along the (x, y, z) dimensions. Args: prim (Usd.Prim): The USD prim to translate. offset (Tuple[float, float, float]): The offsets for the (x, y, z) dimensions. Returns: Usd.Prim: The translated prim. """ x = UsdGeom.Xformable(prim) x.AddTranslateOp(opSuffix=f"num_{get_num_xform_ops(prim)}").Set(offset) xform_op_move_end_to_front(prim) return prim def rotate_x(prim: Usd.Prim, angle: float): """Rotates a prim around the X axis. Args: prim (Usd.Prim): The USD prim to rotate. angle (float): The rotation angle in degrees. Returns: Usd.Prim: The rotated prim. """ x = UsdGeom.Xformable(prim) x.AddRotateXOp(opSuffix=f"num_{get_num_xform_ops(prim)}").Set(angle) xform_op_move_end_to_front(prim) return prim def rotate_y(prim: Usd.Prim, angle: float): """Rotates a prim around the Y axis. Args: prim (Usd.Prim): The USD prim to rotate. angle (float): The rotation angle in degrees. Returns: Usd.Prim: The rotated prim. """ x = UsdGeom.Xformable(prim) x.AddRotateYOp(opSuffix=f"num_{get_num_xform_ops(prim)}").Set(angle) xform_op_move_end_to_front(prim) return prim def rotate_z(prim: Usd.Prim, angle: float): """Rotates a prim around the Z axis. Args: prim (Usd.Prim): The USD prim to rotate. angle (float): The rotation angle in degrees. Returns: Usd.Prim: The rotated prim. """ x = UsdGeom.Xformable(prim) x.AddRotateZOp(opSuffix=f"num_{get_num_xform_ops(prim)}").Set(angle) xform_op_move_end_to_front(prim) return prim def stack_prims(prims: Sequence[Usd.Prim], axis: int = 2, gap: float = 0, align_center=False): """Stacks prims on top of each other (or side-by-side). This function stacks prims by placing them so their bounding boxes are adjacent along a given axis. Args: prim (Usd.Prim): The USD prims to stack. axis (int): The axis along which to stack the prims. x=0, y=1, z=2. Default 2. gap (float): The spacing to add between stacked elements. Returns: Sequence[Usd.Prim]: The stacked prims. """ for i in range(1, len(prims)): prev = prims[i - 1] cur = prims[i] bb_cur_min, bb_cur_max = compute_bbox(cur) bb_prev_min, bb_prev_max = compute_bbox(prev) if align_center: offset = [ (bb_cur_max[0] + bb_cur_min[0]) / 2. - (bb_prev_max[0] + bb_prev_min[0]) / 2., (bb_cur_max[1] + bb_cur_min[1]) / 2. - (bb_prev_max[1] + bb_prev_min[1]) / 2., (bb_cur_max[2] + bb_cur_min[2]) / 2. - (bb_prev_max[2] + bb_prev_min[2]) / 2. ] else: offset = [0, 0, 0] offset[axis] = bb_prev_max[axis] - bb_cur_min[axis] if isinstance(gap, list): offset[axis] = offset[axis] + gap[i] else: offset[axis] = offset[axis] + gap translate(cur, tuple(offset)) return prims def compute_bbox(prim: Usd.Prim) -> \ Tuple[Tuple[float, float, float], Tuple[float, float, float]]: """Computes the axis-aligned bounding box for a USD prim. Args: prim (Usd.Prim): The USD prim to compute the bounding box of. Returns: Tuple[Tuple[float, float, float], Tuple[float, float, float]] The ((min_x, min_y, min_z), (max_x, max_y, max_z)) values of the bounding box. """ bbox_cache: UsdGeom.BBoxCache = UsdGeom.BBoxCache( time=Usd.TimeCode.Default(), includedPurposes=[UsdGeom.Tokens.default_], useExtentsHint=True ) total_bounds = Gf.BBox3d() for p in Usd.PrimRange(prim): total_bounds = Gf.BBox3d.Combine( total_bounds, Gf.BBox3d(bbox_cache.ComputeWorldBound(p).ComputeAlignedRange()) ) box = total_bounds.ComputeAlignedBox() return (box.GetMin(), box.GetMax()) def compute_bbox_size(prim: Usd.Prim) -> Tuple[float, float, float]: """Computes the (x, y, z) size of the axis-aligned bounding box for a prim.""" bbox_min, bbox_max = compute_bbox(prim) size = ( bbox_max[0] - bbox_min[0], bbox_max[1] - bbox_min[1], bbox_max[2] - bbox_min[2] ) return size def compute_bbox_center(prim: Usd.Prim) -> Tuple[float, float, float]: """Computes the (x, y, z) center of the axis-aligned bounding box for a prim.""" bbox_min, bbox_max = compute_bbox(prim) center = ( (bbox_max[0] + bbox_min[0]) / 2, (bbox_max[1] + bbox_min[1]) / 2, (bbox_max[2] + bbox_min[2]) / 2 ) return center def set_visibility(prim: Usd.Prim, visibility: Literal["inherited", "invisible"] = "inherited"): """Sets the visibility of a prim. Args: prim (Usd.Prim): The prim to control the visibility of. visibility (str): The visibility of the prim. "inherited" if the prim is visibile as long as it's parent is visible, or invisible if it's parent is invisible. Otherwise, "invisible" if the prim is invisible regardless of it's parent's visibility. Returns: Usd.Prim: The USD prim. """ attr = prim.GetAttribute("visibility") if attr is None: prim.CreateAttribute("visibility") attr.Set(visibility) return prim def get_visibility(prim: Usd.Prim): """Returns the visibility of a given prim. See set_visibility for details. """ return prim.GetAttribute("visibility").Get() def rad2deg(x): """Convert radians to degrees.""" return 180. * x / math.pi def deg2rad(x): """Convert degrees to radians.""" return math.pi * x / 180. def compute_sphere_point( elevation: float, azimuth: float, distance: float ) -> Tuple[float, float, float]: """Compute a sphere point given an elevation, azimuth and distance. Args: elevation (float): The elevation in degrees. azimuth (float): The azimuth in degrees. distance (float): The distance. Returns: Tuple[float, float, float]: The sphere coordinate. """ elevation = rad2deg(elevation) azimuth = rad2deg(azimuth) elevation = elevation camera_xy_distance = math.cos(elevation) * distance camera_x = math.cos(azimuth) * camera_xy_distance camera_y = math.sin(azimuth) * camera_xy_distance camera_z = math.sin(elevation) * distance eye = ( float(camera_x), float(camera_y), float(camera_z) ) return eye def compute_look_at_matrix( at: Tuple[float, float, float], up: Tuple[float, float, float], eye: Tuple[float, float, float] ) -> np.ndarray: """Computes a 4x4 homogeneous "look at" transformation matrix. Args: at (Tuple[float, float, float]): The (x, y, z) location that the transform should be facing. For example (0, 0, 0) if the transformation should face the origin. up (Tuple[float, float, float]): The up axis fot the transform. ie: (0, 0, 1) for the up-axis to correspond to the z-axis. eye (Tuple[float, float]): The (x, y, z) location of the transform. For example, (100, 100, 100) if we want to place a camera at (x=100,y=100,z=100) Returns: np.ndarray: The 4x4 homogeneous transformation matrix. """ at = np.array(at) up = np.array(up) up = up / np.linalg.norm(up) eye = np.array(eye) # forward axis (z) z_axis = np.array(eye) - np.array(at) z_axis = z_axis / np.linalg.norm(z_axis) # right axis (x) x_axis = np.cross(up, z_axis) x_axis = x_axis / np.linalg.norm(x_axis) # up axis y_axis = np.cross(z_axis, x_axis) y_axis = y_axis / np.linalg.norm(y_axis) matrix = np.array([ [x_axis[0], x_axis[1], x_axis[2], 0.0], [y_axis[0], y_axis[1], y_axis[2], 0.0], [z_axis[0], z_axis[1], z_axis[2], 0.0], [eye[0], eye[1], eye[2], 1.0] ]) return matrix

Python

NVIDIA-Omniverse/usd_scene_construction_utils/examples/bind_mdl_material/main.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 sys import os from pathlib import Path sys.path.append(f"{Path.home()}/usd_scene_construction_utils") # use your install path from usd_scene_construction_utils import ( add_mdl_material, new_omniverse_stage, add_plane, add_box, stack_prims, bind_material, add_dome_light ) stage = new_omniverse_stage() # Add cardboard material cardboard = add_mdl_material( stage, "/scene/cardboard", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wall_Board/Cardboard.mdl" ) # Add concrete material concrete = add_mdl_material( stage, "/scene/concrete", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Masonry/Concrete_Smooth.mdl" ) # Add floor plane floor = add_plane(stage, "/scene/floor", size=(500, 500)) # Add box box = add_box(stage, "/scene/box", size=(100, 100, 100)) # Stack box on floor stack_prims([floor, box], axis=2) # Bind materials to objects bind_material(floor, concrete) bind_material(box, cardboard) # Add dome light light = add_dome_light(stage, "/scene/dome_light")

Python

NVIDIA-Omniverse/usd_scene_construction_utils/examples/hand_truck_w_boxes/main.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 sys import os from pathlib import Path sys.path.append(f"{Path.home()}/usd_scene_construction_utils") # use your install path from usd_scene_construction_utils import ( add_usd_ref, rotate_x, rotate_y, rotate_z, scale, compute_bbox, add_xform, compute_bbox_center, translate, set_visibility, new_omniverse_stage, add_dome_light, add_plane, add_mdl_material, bind_material ) import random from typing import Tuple box_asset_url = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping/Cardboard_Boxes/Flat_A/FlatBox_A02_15x21x8cm_PR_NVD_01.usd" hand_truck_asset_url = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Hand_Trucks/Convertible_Aluminum_A/ConvertableAlumHandTruck_A02_PR_NVD_01.usd" def add_box_of_size( stage, path: str, size: Tuple[float, float, float] ): """Adds a box and re-scales it to match the specified dimensions """ # Add USD box prim = add_usd_ref(stage, path, usd_path=box_asset_url) rotate_x(prim, random.choice([-90, 0, 90, 180])) rotate_y(prim, random.choice([-90, 0, 90, 180])) # Scale USD box to fit dimensions usd_min, usd_max = compute_bbox(prim) usd_size = ( usd_max[0] - usd_min[0], usd_max[1] - usd_min[1], usd_max[2] - usd_min[2] ) required_scale = ( size[0] / usd_size[0], size[1] / usd_size[1], size[2] / usd_size[2] ) scale(prim, required_scale) return prim def add_random_box_stack( stage, path: str, count_range=(1, 5), size_range=((30, 30, 10), (50, 50, 25)), angle_range=(-5, 5), jitter_range=(-3,3) ): container = add_xform(stage, path) count = random.randint(*count_range) # get sizes and sort sizes = [ ( random.uniform(size_range[0][0], size_range[1][0]), random.uniform(size_range[0][1], size_range[1][1]), random.uniform(size_range[0][2], size_range[1][2]) ) for i in range(count) ] sizes = sorted(sizes, key=lambda x: x[0]**2 + x[1]**2, reverse=True) boxes = [] for i in range(count): box_i = add_box_of_size(stage, os.path.join(path, f"box_{i}"), sizes[i]) boxes.append(box_i) if count > 0: center = compute_bbox_center(boxes[0]) for i in range(1, count): prev_box, cur_box = boxes[i - 1], boxes[i] cur_bbox = compute_bbox(cur_box) cur_center = compute_bbox_center(cur_box) prev_bbox = compute_bbox(prev_box) offset = ( center[0] - cur_center[0], center[1] - cur_center[1], prev_bbox[1][2] - cur_bbox[0][2] ) translate(cur_box, offset) # add some noise for i in range(count): rotate_z(boxes[i], random.uniform(*angle_range)) translate(boxes[i], ( random.uniform(*jitter_range), random.uniform(*jitter_range), 0 )) return container, boxes def add_random_box_stacks( stage, path: str, count_range=(0, 3), ): container = add_xform(stage, path) stacks = [] count = random.randint(*count_range) for i in range(count): stack, items = add_random_box_stack(stage, os.path.join(path, f"stack_{i}")) stacks.append(stack) for i in range(count): cur_stack = stacks[i] cur_bbox = compute_bbox(cur_stack) cur_center = compute_bbox_center(cur_stack) translate(cur_stack, (0, -cur_center[1], -cur_bbox[0][2])) if i > 0: prev_bbox = compute_bbox(stacks[i - 1]) translate(cur_stack, (prev_bbox[1][0] - cur_bbox[0][0], 0, 0)) return container, stacks def add_hand_truck_with_boxes(stage, path: str): container = add_xform(stage, path) hand_truck_path = f"{path}/truck" box_stacks_path = f"{path}/box_stacks" add_usd_ref( stage, hand_truck_path, hand_truck_asset_url ) box_stacks_container, box_stacks = add_random_box_stacks(stage, box_stacks_path, count_range=(1,4)) rotate_z(box_stacks_container, 90) translate( box_stacks_container, offset=(0, random.uniform(8, 12), 28) ) # remove out of bounds stacks last_visible = box_stacks[0] for i in range(len(box_stacks)): _, stack_bbox_max = compute_bbox(box_stacks[i]) print(stack_bbox_max) if stack_bbox_max[1] > 74: set_visibility(box_stacks[i], "invisible") else: last_visible = box_stacks[i] # wiggle inide bounds boxes_bbox = compute_bbox(last_visible) wiggle = (82 - boxes_bbox[1][1]) translate(box_stacks_container, (0, random.uniform(0, wiggle), 1)) return container stage = new_omniverse_stage() light = add_dome_light(stage, "/scene/dome_light") floor = add_plane(stage, "/scene/floor", size=(1000, 1000)) concrete = add_mdl_material( stage, "/scene/materials/concrete", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Masonry/Concrete_Polished.mdl" ) bind_material(floor, concrete) all_objects_container = add_xform(stage, "/scene/objects") for i in range(5): for j in range(5): path = f"/scene/objects/hand_truck_{i}_{j}" current_object = add_hand_truck_with_boxes(stage, path) rotate_z(current_object, random.uniform(-15, 15)) translate(current_object, (100*i, 150*j, 0)) objects_center = compute_bbox_center(all_objects_container) translate(all_objects_container, (-objects_center[0], -objects_center[1], 0))

Python

NVIDIA-Omniverse/usd_scene_construction_utils/examples/pallet_with_boxes/main.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 sys import os import random from pathlib import Path sys.path.append(f"{Path.home()}/usd_scene_construction_utils") # use your install path sys.path.append(f"{Path.home()}/usd_scene_construction_utils/examples/pallet_with_boxes") # use your install path from usd_scene_construction_utils import * PALLET_URIS = [ "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping/Pallets/Wood/Block_A/BlockPallet_A01_PR_NVD_01.usd", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping/Pallets/Wood/Block_B/BlockPallet_B01_PR_NVD_01.usd", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping/Pallets/Wood/Wing_A/WingPallet_A01_PR_NVD_01.usd" ] CARDBOARD_BOX_URIS = [ "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping/Cardboard_Boxes/Cube_A/CubeBox_A02_16cm_PR_NVD_01.usd", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping/Cardboard_Boxes/Flat_A/FlatBox_A05_26x26x11cm_PR_NVD_01.usd", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping/Cardboard_Boxes/Printer_A/PrintersBox_A05_23x28x25cm_PR_NVD_01.usd" ] def add_pallet(stage, path: str): prim = add_usd_ref(stage, path, random.choice(PALLET_URIS)) add_semantics(prim, "class", "pallet") return prim def add_cardboard_box(stage, path: str): prim = add_usd_ref(stage, path, random.choice(CARDBOARD_BOX_URIS)) add_semantics(prim, "class", "box") return prim def add_pallet_with_box(stage, path: str): container = add_xform(stage, path) pallet = add_pallet(stage, os.path.join(path, "pallet")) box = add_cardboard_box(stage, os.path.join(path, "box")) pallet_bbox = compute_bbox(pallet) box_bbox = compute_bbox(box) translate(box,(0, 0, pallet_bbox[1][2] - box_bbox[0][2])) rotate_z(pallet, random.uniform(-25, 25)) return container def add_tree(stage, path: str): url = "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Vegetation/Trees/American_Beech.usd" return add_usd_ref(stage, path, url) stage = new_omniverse_stage() brick = add_mdl_material(stage, "/scene/brick", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Masonry/Brick_Pavers.mdl") pallet_box = add_pallet_with_box(stage, "/scene/pallet") floor = add_plane(stage, "/scene/floor", size=(1000, 1000), uv=(20., 20.)) tree = add_tree(stage, "/scene/tree") translate(tree, (100, -150, 0)) bind_material(floor, brick) light = add_dome_light(stage, "/scene/dome_light")

Python

NVIDIA-Omniverse/usd_scene_construction_utils/examples/add_camera/main.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 sys from pathlib import Path sys.path.append(f"{Path.home()}/usd_scene_construction_utils") # use your install path from usd_scene_construction_utils import ( new_in_memory_stage, add_box, add_camera, compute_look_at_matrix, apply_xform_matrix, export_stage ) stage = new_in_memory_stage() box = add_box(stage, "/scene/box", size=(100, 100, 100)) camera = add_camera(stage, "/scene/camera") matrix = compute_look_at_matrix( at=(0, 0, 0), up=(0, 0, 1), eye=(500, 500, 500) ) apply_xform_matrix(camera, matrix) export_stage(stage, "add_camera.usda", default_prim="/scene")

Python

NVIDIA-Omniverse/usd_scene_construction_utils/examples/render_with_replicator/main.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 sys import os import random from pathlib import Path sys.path.append(f"{Path.home()}/usd_scene_construction_utils") # use your install path from usd_scene_construction_utils import * # set to your output dir OUTPUT_DIR = f"{Path.home()}/usd_scene_construction_utils/examples/render_with_replicator/output" def add_box_stack(stage, path: str, box_material): container = add_xform(stage, path) boxes = [] for i in range(3): box_path = f"{path}/box_{i}" box = add_box(stage, box_path, (random.uniform(20, 30), random.uniform(20, 30), 10)) add_semantics(box, "class", "box_stack") bind_material(box, box_material) rotate_z(box, random.uniform(-10, 10)) boxes.append(box) stack_prims(boxes, axis=2) return container def build_scene(stage): # Add cardboard material cardboard = add_mdl_material( stage, "/scene/cardboard", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wall_Board/Cardboard.mdl" ) # Add concrete material concrete = add_mdl_material( stage, "/scene/concrete", "http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Masonry/Concrete_Smooth.mdl" ) # Add floor plane floor = add_plane(stage, "/scene/floor", size=(500, 500)) bind_material(floor, concrete) # Add box box_stack = add_box_stack(stage, "/scene/box_stack", box_material=cardboard) # Stack box on floor stack_prims([floor, box_stack], axis=2) # Add dome light add_dome_light(stage, "/scene/dome_light") import omni.replicator.core as rep with rep.new_layer(): stage = new_omniverse_stage() build_scene(stage) camera = rep.create.camera() render_product = rep.create.render_product(camera, (1024, 1024)) box_stack = rep.get.prims(path_pattern="^/scene/box_stack$") # Setup randomization with rep.trigger.on_frame(num_frames=100): with box_stack: rep.modify.pose(position=rep.distribution.uniform((-100, -100, 0), (100, 100, 0))) with camera: rep.modify.pose(position=rep.distribution.uniform((0, 0, 0), (400, 400, 400)), look_at=(0, 0, 0)) writer = rep.WriterRegistry.get("BasicWriter") writer.initialize( output_dir=OUTPUT_DIR, rgb=True, bounding_box_2d_tight=True, distance_to_camera=True, bounding_box_3d=True, camera_params=True, instance_id_segmentation=True, colorize_instance_id_segmentation=False ) writer.attach([render_product])

Python

NVIDIA-Omniverse/usd_scene_construction_utils/examples/hello_box/main.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 sys from pathlib import Path sys.path.append(f"{Path.home()}/usd_scene_construction_utils") # use your install path import random from usd_scene_construction_utils import * stage = new_omniverse_stage() # Create floor floor = add_plane(stage, "/scene/floor", (1000, 1000)) # Add a dome light light = add_dome_light(stage, "/scene/dome_light") # Create a grid of boxes all_boxes = add_xform(stage, "/scene/boxes") for i in range(5): for j in range(5): path = f"/scene/boxes/box_{i}_{j}" # Add box of random size size = ( random.uniform(20, 50), random.uniform(20, 50), random.uniform(20, 50), ) box = add_box(stage, path, size=size) # Set position in xy grid translate(box, (100*i, 100*j, 0)) # Align z with floor box_min, _ = compute_bbox(box) translate(box, (0, 0, -box_min[2])) # Translate all boxes to have xy center at (0, 0) boxes_center = compute_bbox_center(all_boxes) translate("/scene/boxes", (-boxes_center[0], -boxes_center[1], 0))

Python

NVIDIA-Omniverse/usd_scene_construction_utils/docs/conf.py

# Configuration file for the Sphinx documentation builder. # # For the full list of built-in configuration values, see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = 'USD Scene Construction Utilities' copyright = '2023, NVIDIA' author = 'NVIDIA' # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode', 'sphinxcontrib.katex', 'sphinx.ext.autosectionlabel', 'sphinx_copybutton', 'sphinx_panels', 'myst_parser', ] templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = 'sphinx_rtd_theme' html_static_path = ['_static']

Python

NVIDIA-Omniverse/kit-extension-sample-spawn-prims/exts/omni.example.spawn_prims/omni/example/spawn_prims/extension.py

# Copyright (c) 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 omni.ext import omni.ui as ui import omni.kit.commands # 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): """ Called when MyExtension starts. Args: ext_id : id of the extension that is """ print("[omni.example.spawn_prims] MyExtension startup") self._window = ui.Window("Spawn Primitives", width=300, height=300) with self._window.frame: # VStack which will layout UI elements vertically with ui.VStack(): def on_click(prim_type): """ Creates a mesh primitive of the given type. Args: prim_type : The type of primitive to """ # omni.kit.commands.execute will execute the given command that is passed followed by the commands arguments omni.kit.commands.execute('CreateMeshPrimWithDefaultXform', prim_type=prim_type, above_ground=True) # Button UI Elements ui.Button("Spawn Cube", clicked_fn=lambda: on_click("Cube")) ui.Button("Spawn Cone", clicked_fn=lambda: on_click("Cone")) ui.Button("Spawn Cylinder", clicked_fn=lambda: on_click("Cylinder")) ui.Button("Spawn Disk", clicked_fn=lambda: on_click("Disk")) ui.Button("Spawn Plane", clicked_fn=lambda: on_click("Plane")) ui.Button("Spawn Sphere", clicked_fn=lambda: on_click("Sphere")) ui.Button("Spawn Torus", clicked_fn=lambda: on_click("Torus")) def on_shutdown(self): """ Called when the extension is shutting down. """ print("[omni.example.spawn_prims] MyExtension shutdown")

Python

NVIDIA-Omniverse/mjcf-importer-extension/source/extensions/omni.importer.mjcf/python/scripts/style.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 carb.settings import omni.ui as ui from omni.kit.window.extensions.common import get_icons_path # Pilaged from omni.kit.widnow.property style.py LABEL_WIDTH = 120 BUTTON_WIDTH = 120 HORIZONTAL_SPACING = 4 VERTICAL_SPACING = 5 COLOR_X = 0xFF5555AA COLOR_Y = 0xFF76A371 COLOR_Z = 0xFFA07D4F COLOR_W = 0xFFAA5555 def get_style(): icons_path = get_icons_path() KIT_GREEN = 0xFF8A8777 KIT_GREEN_CHECKBOX = 0xFF9A9A9A BORDER_RADIUS = 1.5 FONT_SIZE = 14.0 TOOLTIP_STYLE = ( { "background_color": 0xFFD1F7FF, "color": 0xFF333333, "margin_width": 0, "margin_height": 0, "padding": 0, "border_width": 0, "border_radius": 1.5, "border_color": 0x0, }, ) style_settings = carb.settings.get_settings().get("/persistent/app/window/uiStyle") if not style_settings: style_settings = "NvidiaDark" if style_settings == "NvidiaLight": WINDOW_BACKGROUND_COLOR = 0xFF444444 BUTTON_BACKGROUND_COLOR = 0xFF545454 BUTTON_BACKGROUND_HOVERED_COLOR = 0xFF9E9E9E BUTTON_BACKGROUND_PRESSED_COLOR = 0xC22A8778 BUTTON_LABEL_DISABLED_COLOR = 0xFF606060 FRAME_TEXT_COLOR = 0xFF545454 FIELD_BACKGROUND = 0xFF545454 FIELD_SECONDARY = 0xFFABABAB FIELD_TEXT_COLOR = 0xFFD6D6D6 FIELD_TEXT_COLOR_READ_ONLY = 0xFF9C9C9C FIELD_TEXT_COLOR_HIDDEN = 0x01000000 COLLAPSABLEFRAME_BORDER_COLOR = 0x0 COLLAPSABLEFRAME_BACKGROUND_COLOR = 0x7FD6D6D6 COLLAPSABLEFRAME_TEXT_COLOR = 0xFF545454 COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR = 0xFFC9C9C9 COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR = 0xFFD6D6D6 COLLAPSABLEFRAME_HOVERED_BACKGROUND_COLOR = 0xFFCCCFBF COLLAPSABLEFRAME_PRESSED_BACKGROUND_COLOR = 0xFF2E2E2B COLLAPSABLEFRAME_HOVERED_SECONDARY_COLOR = 0xFFD6D6D6 COLLAPSABLEFRAME_PRESSED_SECONDARY_COLOR = 0xFFE6E6E6 LABEL_VECTORLABEL_COLOR = 0xFFDDDDDD LABEL_MIXED_COLOR = 0xFFD6D6D6 LIGHT_FONT_SIZE = 14.0 LIGHT_BORDER_RADIUS = 3 style = { "Window": {"background_color": WINDOW_BACKGROUND_COLOR}, "Button": {"background_color": BUTTON_BACKGROUND_COLOR, "margin": 0, "padding": 3, "border_radius": 2}, "Button:hovered": {"background_color": BUTTON_BACKGROUND_HOVERED_COLOR}, "Button:pressed": {"background_color": BUTTON_BACKGROUND_PRESSED_COLOR}, "Button.Label:disabled": {"color": 0xFFD6D6D6}, "Button.Label": {"color": 0xFFD6D6D6}, "Field::models": { "background_color": FIELD_BACKGROUND, "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR, "border_radius": LIGHT_BORDER_RADIUS, "secondary_color": FIELD_SECONDARY, }, "Field::models_mixed": { "background_color": FIELD_BACKGROUND, "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": LIGHT_BORDER_RADIUS, }, "Field::models_readonly": { "background_color": FIELD_BACKGROUND, "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR_READ_ONLY, "border_radius": LIGHT_BORDER_RADIUS, "secondary_color": FIELD_SECONDARY, }, "Field::models_readonly_mixed": { "background_color": FIELD_BACKGROUND, "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": LIGHT_BORDER_RADIUS, }, "Field::models:pressed": {"background_color": 0xFFCECECE}, "Field": {"background_color": 0xFF535354, "color": 0xFFCCCCCC}, "Label": {"font_size": 12, "color": FRAME_TEXT_COLOR}, "Label::label:disabled": {"color": BUTTON_LABEL_DISABLED_COLOR}, "Label::label": { "font_size": LIGHT_FONT_SIZE, "background_color": FIELD_BACKGROUND, "color": FRAME_TEXT_COLOR, }, "Label::title": { "font_size": LIGHT_FONT_SIZE, "background_color": FIELD_BACKGROUND, "color": FRAME_TEXT_COLOR, }, "Label::mixed_overlay": { "font_size": LIGHT_FONT_SIZE, "background_color": FIELD_BACKGROUND, "color": FRAME_TEXT_COLOR, }, "Label::mixed_overlay_normal": { "font_size": LIGHT_FONT_SIZE, "background_color": FIELD_BACKGROUND, "color": FRAME_TEXT_COLOR, }, "ComboBox::choices": { "font_size": 12, "color": 0xFFD6D6D6, "background_color": FIELD_BACKGROUND, "secondary_color": FIELD_BACKGROUND, "border_radius": LIGHT_BORDER_RADIUS * 2, }, "ComboBox::xform_op": { "font_size": 10, "color": 0xFF333333, "background_color": 0xFF9C9C9C, "secondary_color": 0x0, "selected_color": 0xFFACACAF, "border_radius": LIGHT_BORDER_RADIUS * 2, }, "ComboBox::xform_op:hovered": {"background_color": 0x0}, "ComboBox::xform_op:selected": {"background_color": 0xFF545454}, "ComboBox": { "font_size": 10, "color": 0xFFE6E6E6, "background_color": 0xFF545454, "secondary_color": 0xFF545454, "selected_color": 0xFFACACAF, "border_radius": LIGHT_BORDER_RADIUS * 2, }, # "ComboBox": {"background_color": 0xFF535354, "selected_color": 0xFFACACAF, "color": 0xFFD6D6D6}, "ComboBox:hovered": {"background_color": 0xFF545454}, "ComboBox:selected": {"background_color": 0xFF545454}, "ComboBox::choices_mixed": { "font_size": LIGHT_FONT_SIZE, "color": 0xFFD6D6D6, "background_color": FIELD_BACKGROUND, "secondary_color": FIELD_BACKGROUND, "secondary_selected_color": FIELD_TEXT_COLOR, "border_radius": LIGHT_BORDER_RADIUS * 2, }, "ComboBox:hovered:choices": {"background_color": FIELD_BACKGROUND, "secondary_color": FIELD_BACKGROUND}, "Slider": { "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR, "border_radius": LIGHT_BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": WINDOW_BACKGROUND_COLOR, "draw_mode": ui.SliderDrawMode.FILLED, }, "Slider::value": { "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR, # COLLAPSABLEFRAME_TEXT_COLOR "border_radius": LIGHT_BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": KIT_GREEN, }, "Slider::value_mixed": { "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": LIGHT_BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": KIT_GREEN, }, "Slider::multivalue": { "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR, "border_radius": LIGHT_BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": KIT_GREEN, "draw_mode": ui.SliderDrawMode.HANDLE, }, "Slider::multivalue_mixed": { "font_size": LIGHT_FONT_SIZE, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": LIGHT_BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": KIT_GREEN, "draw_mode": ui.SliderDrawMode.HANDLE, }, "Checkbox": { "margin": 0, "padding": 0, "radius": 0, "font_size": 10, "background_color": 0xFFA8A8A8, "background_color": 0xFFA8A8A8, }, "CheckBox::greenCheck": {"font_size": 10, "background_color": KIT_GREEN, "color": 0xFF23211F}, "CheckBox::greenCheck_mixed": { "font_size": 10, "background_color": KIT_GREEN, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": LIGHT_BORDER_RADIUS, }, "CollapsableFrame": { "background_color": COLLAPSABLEFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_BACKGROUND_COLOR, "color": COLLAPSABLEFRAME_TEXT_COLOR, "border_radius": LIGHT_BORDER_RADIUS, "border_color": 0x0, "border_width": 1, "font_size": LIGHT_FONT_SIZE, "padding": 6, "Tooltip": TOOLTIP_STYLE, }, "CollapsableFrame::groupFrame": { "background_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, "border_radius": BORDER_RADIUS * 2, "padding": 6, }, "CollapsableFrame::groupFrame:hovered": { "background_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, }, "CollapsableFrame::groupFrame:pressed": { "background_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, }, "CollapsableFrame::subFrame": { "background_color": COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR, }, "CollapsableFrame::subFrame:hovered": { "background_color": COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_HOVERED_BACKGROUND_COLOR, }, "CollapsableFrame::subFrame:pressed": { "background_color": COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_PRESSED_BACKGROUND_COLOR, }, "CollapsableFrame.Header": { "font_size": LIGHT_FONT_SIZE, "background_color": FRAME_TEXT_COLOR, "color": FRAME_TEXT_COLOR, }, "CollapsableFrame:hovered": {"secondary_color": COLLAPSABLEFRAME_HOVERED_SECONDARY_COLOR}, "CollapsableFrame:pressed": {"secondary_color": COLLAPSABLEFRAME_PRESSED_SECONDARY_COLOR}, "ScrollingFrame": {"margin": 0, "padding": 3, "border_radius": LIGHT_BORDER_RADIUS}, "TreeView": { "background_color": 0xFFE0E0E0, "background_selected_color": 0x109D905C, "secondary_color": 0xFFACACAC, }, "TreeView.ScrollingFrame": {"background_color": 0xFFE0E0E0}, "TreeView.Header": {"color": 0xFFCCCCCC}, "TreeView.Header::background": { "background_color": 0xFF535354, "border_color": 0xFF707070, "border_width": 0.5, }, "TreeView.Header::columnname": {"margin": 3}, "TreeView.Image::object_icon_grey": {"color": 0x80FFFFFF}, "TreeView.Item": {"color": 0xFF535354, "font_size": 16}, "TreeView.Item::object_name": {"margin": 3}, "TreeView.Item::object_name_grey": {"color": 0xFFACACAC}, "TreeView.Item::object_name_missing": {"color": 0xFF6F72FF}, "TreeView.Item:selected": {"color": 0xFF2A2825}, "TreeView:selected": {"background_color": 0x409D905C}, "Label::vector_label": {"font_size": 14, "color": LABEL_VECTORLABEL_COLOR}, "Rectangle::vector_label": {"border_radius": BORDER_RADIUS * 2, "corner_flag": ui.CornerFlag.LEFT}, "Rectangle::mixed_overlay": { "border_radius": LIGHT_BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "border_width": 3, }, "Rectangle": { "border_radius": LIGHT_BORDER_RADIUS, "color": 0xFFC2C2C2, "background_color": 0xFFC2C2C2, }, # FIELD_BACKGROUND}, "Rectangle::xform_op:hovered": {"background_color": 0x0}, "Rectangle::xform_op": {"background_color": 0x0}, # text remove "Button::remove": {"background_color": FIELD_BACKGROUND, "margin": 0}, "Button::remove:hovered": {"background_color": FIELD_BACKGROUND}, "Button::options": {"background_color": 0x0, "margin": 0}, "Button.Image::options": {"image_url": f"{icons_path}/options.svg", "color": 0xFF989898}, "Button.Image::options:hovered": {"color": 0xFFC2C2C2}, "IconButton": {"margin": 0, "padding": 0, "background_color": 0x0}, "IconButton:hovered": {"background_color": 0x0}, "IconButton:checked": {"background_color": 0x0}, "IconButton:pressed": {"background_color": 0x0}, "IconButton.Image": {"color": 0xFFA8A8A8}, "IconButton.Image:hovered": {"color": 0xFF929292}, "IconButton.Image:pressed": {"color": 0xFFA4A4A4}, "IconButton.Image:checked": {"color": 0xFFFFFFFF}, "IconButton.Tooltip": {"color": 0xFF9E9E9E}, "IconButton.Image::OpenFolder": { "image_url": f"{icons_path}/open-folder.svg", "background_color": 0x0, "color": 0xFFA8A8A8, "tooltip": TOOLTIP_STYLE, }, "IconButton.Image::OpenConfig": { "image_url": f"{icons_path}/open-config.svg", "background_color": 0x0, "color": 0xFFA8A8A8, "tooltip": TOOLTIP_STYLE, }, "IconButton.Image::OpenLink": { "image_url": "resources/glyphs/link.svg", "background_color": 0x0, "color": 0xFFA8A8A8, "tooltip": TOOLTIP_STYLE, }, "IconButton.Image::OpenDocs": { "image_url": "resources/glyphs/docs.svg", "background_color": 0x0, "color": 0xFFA8A8A8, "tooltip": TOOLTIP_STYLE, }, "IconButton.Image::CopyToClipboard": { "image_url": "resources/glyphs/copy.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::Export": { "image_url": f"{icons_path}/export.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::Sync": { "image_url": "resources/glyphs/sync.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::Upload": { "image_url": "resources/glyphs/upload.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::FolderPicker": { "image_url": "resources/glyphs/folder.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "ItemButton": {"padding": 2, "background_color": 0xFF444444, "border_radius": 4}, "ItemButton.Image::add": {"image_url": f"{icons_path}/plus.svg", "color": 0xFF06C66B}, "ItemButton.Image::remove": {"image_url": f"{icons_path}/trash.svg", "color": 0xFF1010C6}, "ItemButton:hovered": {"background_color": 0xFF333333}, "ItemButton:pressed": {"background_color": 0xFF222222}, "Tooltip": TOOLTIP_STYLE, } else: LABEL_COLOR = 0xFF8F8E86 FIELD_BACKGROUND = 0xFF23211F FIELD_TEXT_COLOR = 0xFFD5D5D5 FIELD_TEXT_COLOR_READ_ONLY = 0xFF5C5C5C FIELD_TEXT_COLOR_HIDDEN = 0x01000000 FRAME_TEXT_COLOR = 0xFFCCCCCC WINDOW_BACKGROUND_COLOR = 0xFF444444 BUTTON_BACKGROUND_COLOR = 0xFF292929 BUTTON_BACKGROUND_HOVERED_COLOR = 0xFF9E9E9E BUTTON_BACKGROUND_PRESSED_COLOR = 0xC22A8778 BUTTON_LABEL_DISABLED_COLOR = 0xFF606060 LABEL_LABEL_COLOR = 0xFF9E9E9E LABEL_TITLE_COLOR = 0xFFAAAAAA LABEL_MIXED_COLOR = 0xFFE6B067 LABEL_VECTORLABEL_COLOR = 0xFFDDDDDD COLORWIDGET_BORDER_COLOR = 0xFF1E1E1E COMBOBOX_HOVERED_BACKGROUND_COLOR = 0xFF33312F COLLAPSABLEFRAME_BORDER_COLOR = 0x0 COLLAPSABLEFRAME_BACKGROUND_COLOR = 0xFF343432 COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR = 0xFF23211F COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR = 0xFF343432 COLLAPSABLEFRAME_HOVERED_BACKGROUND_COLOR = 0xFF2E2E2B COLLAPSABLEFRAME_PRESSED_BACKGROUND_COLOR = 0xFF2E2E2B style = { "Window": {"background_color": WINDOW_BACKGROUND_COLOR}, "Button": {"background_color": BUTTON_BACKGROUND_COLOR, "margin": 0, "padding": 3, "border_radius": 2}, "Button:hovered": {"background_color": BUTTON_BACKGROUND_HOVERED_COLOR}, "Button:pressed": {"background_color": BUTTON_BACKGROUND_PRESSED_COLOR}, "Button.Label:disabled": {"color": BUTTON_LABEL_DISABLED_COLOR}, "Field::models": { "background_color": FIELD_BACKGROUND, "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR, "border_radius": BORDER_RADIUS, }, "Field::models_mixed": { "background_color": FIELD_BACKGROUND, "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": BORDER_RADIUS, }, "Field::models_readonly": { "background_color": FIELD_BACKGROUND, "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR_READ_ONLY, "border_radius": BORDER_RADIUS, }, "Field::models_readonly_mixed": { "background_color": FIELD_BACKGROUND, "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": BORDER_RADIUS, }, "Label": {"font_size": FONT_SIZE, "color": LABEL_COLOR}, "Label::label": {"font_size": FONT_SIZE, "color": LABEL_LABEL_COLOR}, "Label::label:disabled": {"color": BUTTON_LABEL_DISABLED_COLOR}, "Label::title": {"font_size": FONT_SIZE, "color": LABEL_TITLE_COLOR}, "Label::mixed_overlay": {"font_size": FONT_SIZE, "color": LABEL_MIXED_COLOR}, "Label::mixed_overlay_normal": {"font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR}, "Label::path_label": {"font_size": FONT_SIZE, "color": LABEL_LABEL_COLOR}, "Label::stage_label": {"font_size": FONT_SIZE, "color": LABEL_LABEL_COLOR}, "ComboBox::choices": { "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR, "background_color": FIELD_BACKGROUND, "secondary_color": FIELD_BACKGROUND, "secondary_selected_color": FIELD_TEXT_COLOR, "border_radius": BORDER_RADIUS, }, "ComboBox::choices_mixed": { "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR_HIDDEN, "background_color": FIELD_BACKGROUND, "secondary_color": FIELD_BACKGROUND, "secondary_selected_color": FIELD_TEXT_COLOR, "border_radius": BORDER_RADIUS, }, "ComboBox:hovered:choices": { "background_color": COMBOBOX_HOVERED_BACKGROUND_COLOR, "secondary_color": COMBOBOX_HOVERED_BACKGROUND_COLOR, }, "Slider": { "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR, "border_radius": BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": WINDOW_BACKGROUND_COLOR, "draw_mode": ui.SliderDrawMode.FILLED, }, "Slider::value": { "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR, "border_radius": BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": WINDOW_BACKGROUND_COLOR, }, "Slider::value_mixed": { "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": WINDOW_BACKGROUND_COLOR, }, "Slider::multivalue": { "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR, "border_radius": BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": WINDOW_BACKGROUND_COLOR, "draw_mode": ui.SliderDrawMode.HANDLE, }, "Slider::multivalue_mixed": { "font_size": FONT_SIZE, "color": FIELD_TEXT_COLOR, "border_radius": BORDER_RADIUS, "background_color": FIELD_BACKGROUND, "secondary_color": WINDOW_BACKGROUND_COLOR, "draw_mode": ui.SliderDrawMode.HANDLE, }, "CheckBox::greenCheck": { "font_size": 12, "background_color": KIT_GREEN_CHECKBOX, "color": FIELD_BACKGROUND, "border_radius": BORDER_RADIUS, }, "CheckBox::greenCheck_mixed": { "font_size": 12, "background_color": KIT_GREEN_CHECKBOX, "color": FIELD_TEXT_COLOR_HIDDEN, "border_radius": BORDER_RADIUS, }, "CollapsableFrame": { "background_color": COLLAPSABLEFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_BACKGROUND_COLOR, "border_radius": BORDER_RADIUS * 2, "border_color": COLLAPSABLEFRAME_BORDER_COLOR, "border_width": 1, "padding": 6, }, "CollapsableFrame::groupFrame": { "background_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, "border_radius": BORDER_RADIUS * 2, "padding": 6, }, "CollapsableFrame::groupFrame:hovered": { "background_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, }, "CollapsableFrame::groupFrame:pressed": { "background_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_GROUPFRAME_BACKGROUND_COLOR, }, "CollapsableFrame::subFrame": { "background_color": COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR, }, "CollapsableFrame::subFrame:hovered": { "background_color": COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_HOVERED_BACKGROUND_COLOR, }, "CollapsableFrame::subFrame:pressed": { "background_color": COLLAPSABLEFRAME_SUBFRAME_BACKGROUND_COLOR, "secondary_color": COLLAPSABLEFRAME_PRESSED_BACKGROUND_COLOR, }, "CollapsableFrame.Header": { "font_size": FONT_SIZE, "background_color": FRAME_TEXT_COLOR, "color": FRAME_TEXT_COLOR, }, "CollapsableFrame:hovered": {"secondary_color": COLLAPSABLEFRAME_HOVERED_BACKGROUND_COLOR}, "CollapsableFrame:pressed": {"secondary_color": COLLAPSABLEFRAME_PRESSED_BACKGROUND_COLOR}, "ScrollingFrame": {"margin": 0, "padding": 3, "border_radius": BORDER_RADIUS}, "TreeView": { "background_color": 0xFF23211F, "background_selected_color": 0x664F4D43, "secondary_color": 0xFF403B3B, }, "TreeView.ScrollingFrame": {"background_color": 0xFF23211F}, "TreeView.Header": {"background_color": 0xFF343432, "color": 0xFFCCCCCC, "font_size": 12}, "TreeView.Image::object_icon_grey": {"color": 0x80FFFFFF}, "TreeView.Image:disabled": {"color": 0x60FFFFFF}, "TreeView.Item": {"color": 0xFF8A8777}, "TreeView.Item:disabled": {"color": 0x608A8777}, "TreeView.Item::object_name_grey": {"color": 0xFF4D4B42}, "TreeView.Item::object_name_missing": {"color": 0xFF6F72FF}, "TreeView.Item:selected": {"color": 0xFF23211F}, "TreeView:selected": {"background_color": 0xFF8A8777}, "ColorWidget": { "border_radius": BORDER_RADIUS, "border_color": COLORWIDGET_BORDER_COLOR, "border_width": 0.5, }, "Label::vector_label": {"font_size": 16, "color": LABEL_VECTORLABEL_COLOR}, "PlotLabel::X": {"color": 0xFF1515EA, "background_color": 0x0}, "PlotLabel::Y": {"color": 0xFF5FC054, "background_color": 0x0}, "PlotLabel::Z": {"color": 0xFFC5822A, "background_color": 0x0}, "PlotLabel::W": {"color": 0xFFAA5555, "background_color": 0x0}, "Rectangle::vector_label": {"border_radius": BORDER_RADIUS * 2, "corner_flag": ui.CornerFlag.LEFT}, "Rectangle::mixed_overlay": { "border_radius": BORDER_RADIUS, "background_color": LABEL_MIXED_COLOR, "border_width": 3, }, "Rectangle": { "border_radius": BORDER_RADIUS, "background_color": FIELD_TEXT_COLOR_READ_ONLY, }, # FIELD_BACKGROUND}, "Rectangle::xform_op:hovered": {"background_color": 0xFF444444}, "Rectangle::xform_op": {"background_color": 0xFF333333}, # text remove "Button::remove": {"background_color": FIELD_BACKGROUND, "margin": 0}, "Button::remove:hovered": {"background_color": FIELD_BACKGROUND}, "Button::options": {"background_color": 0x0, "margin": 0}, "Button.Image::options": {"image_url": f"{icons_path}/options.svg", "color": 0xFF989898}, "Button.Image::options:hovered": {"color": 0xFFC2C2C2}, "IconButton": {"margin": 0, "padding": 0, "background_color": 0x0}, "IconButton:hovered": {"background_color": 0x0}, "IconButton:checked": {"background_color": 0x0}, "IconButton:pressed": {"background_color": 0x0}, "IconButton.Image": {"color": 0xFFA8A8A8}, "IconButton.Image:hovered": {"color": 0xFFC2C2C2}, "IconButton.Image:pressed": {"color": 0xFFA4A4A4}, "IconButton.Image:checked": {"color": 0xFFFFFFFF}, "IconButton.Tooltip": {"color": 0xFF9E9E9E}, "IconButton.Image::OpenFolder": { "image_url": f"{icons_path}/open-folder.svg", "background_color": 0x0, "color": 0xFFA8A8A8, "tooltip": TOOLTIP_STYLE, }, "IconButton.Image::OpenConfig": { "tooltip": TOOLTIP_STYLE, "image_url": f"{icons_path}/open-config.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::OpenLink": { "image_url": "resources/glyphs/link.svg", "background_color": 0x0, "color": 0xFFA8A8A8, "tooltip": TOOLTIP_STYLE, }, "IconButton.Image::OpenDocs": { "image_url": "resources/glyphs/docs.svg", "background_color": 0x0, "color": 0xFFA8A8A8, "tooltip": TOOLTIP_STYLE, }, "IconButton.Image::CopyToClipboard": { "image_url": "resources/glyphs/copy.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::Export": { "image_url": f"{icons_path}/export.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::Sync": { "image_url": "resources/glyphs/sync.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::Upload": { "image_url": "resources/glyphs/upload.svg", "background_color": 0x0, "color": 0xFFA8A8A8, }, "IconButton.Image::FolderPicker": { "image_url": "resources/glyphs/folder.svg", "background_color": 0x0, "color": 0xFF929292, }, "ItemButton": {"padding": 2, "background_color": 0xFF444444, "border_radius": 4}, "ItemButton.Image::add": {"image_url": f"{icons_path}/plus.svg", "color": 0xFF06C66B}, "ItemButton.Image::remove": {"image_url": f"{icons_path}/trash.svg", "color": 0xFF1010C6}, "ItemButton:hovered": {"background_color": 0xFF333333}, "ItemButton:pressed": {"background_color": 0xFF222222}, "Tooltip": TOOLTIP_STYLE, } return style

Python

NVIDIA-Omniverse/mjcf-importer-extension/source/extensions/omni.importer.mjcf/python/scripts/commands.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 omni.client import omni.kit.commands # import omni.kit.utils from omni.client._omniclient import Result from omni.importer.mjcf import _mjcf from pxr import Usd class MJCFCreateImportConfig(omni.kit.commands.Command): """ Returns an ImportConfig object that can be used while parsing and importing. Should be used with the `MJCFCreateAsset` command Returns: :obj:`omni.importer.mjcf._mjcf.ImportConfig`: Parsed MJCF stored in an internal structure. """ def __init__(self) -> None: pass def do(self) -> _mjcf.ImportConfig: return _mjcf.ImportConfig() def undo(self) -> None: pass class MJCFCreateAsset(omni.kit.commands.Command): """ This command parses and imports a given mjcf file. Args: arg0 (:obj:`str`): The absolute path the mjcf file arg1 (:obj:`omni.importer.mjcf._mjcf.ImportConfig`): Import configuration arg2 (:obj:`str`): Path to the robot on the USD stage arg3 (:obj:`str`): destination path for robot usd. Default is "" which will load the robot in-memory on the open stage. """ def __init__( self, mjcf_path: str = "", import_config=_mjcf.ImportConfig(), prim_path: str = "", dest_path: str = "" ) -> None: self.prim_path = prim_path self.dest_path = dest_path self._mjcf_path = mjcf_path self._root_path, self._filename = os.path.split(os.path.abspath(self._mjcf_path)) self._import_config = import_config self._mjcf_interface = _mjcf.acquire_mjcf_interface() pass def do(self) -> str: # if self.prim_path: # self.prim_path = self.prim_path.replace( # "\\", "/" # ) # Omni client works with both slashes cross platform, making it standard to make it easier later on if self.dest_path: self.dest_path = self.dest_path.replace( "\\", "/" ) # Omni client works with both slashes cross platform, making it standard to make it easier later on result = omni.client.read_file(self.dest_path) if result[0] != Result.OK: stage = Usd.Stage.CreateNew(self.dest_path) stage.Save() return self._mjcf_interface.create_asset_mjcf( self._mjcf_path, self.prim_path, self._import_config, self.dest_path ) def undo(self) -> None: pass omni.kit.commands.register_all_commands_in_module(__name__)

Python

NVIDIA-Omniverse/mjcf-importer-extension/source/extensions/omni.importer.mjcf/python/scripts/extension.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 asyncio import gc import os import weakref import carb import omni.client import omni.ext import omni.ui as ui from omni.client._omniclient import Result from omni.importer.mjcf import _mjcf from omni.kit.menu.utils import MenuItemDescription, add_menu_items, remove_menu_items from omni.kit.window.filepicker import FilePickerDialog from pxr import Sdf, Usd, UsdGeom, UsdPhysics # from omni.isaac.ui.menu import make_menu_item_description from .ui_utils import ( btn_builder, cb_builder, dropdown_builder, float_builder, str_builder, ) EXTENSION_NAME = "MJCF Importer" import omni.ext from omni.kit.menu.utils import MenuItemDescription def make_menu_item_description(ext_id: str, name: str, onclick_fun, action_name: str = "") -> None: """Easily replace the onclick_fn with onclick_action when creating a menu description Args: ext_id (str): The extension you are adding the menu item to. name (str): Name of the menu item displayed in UI. onclick_fun (Function): The function to run when clicking the menu item. action_name (str): name for the action, in case ext_id+name don't make a unique string Note: ext_id + name + action_name must concatenate to a unique identifier. """ # TODO, fix errors when reloading extensions # action_unique = f'{ext_id.replace(" ", "_")}{name.replace(" ", "_")}{action_name.replace(" ", "_")}' # action_registry = omni.kit.actions.core.get_action_registry() # action_registry.register_action(ext_id, action_unique, onclick_fun) return MenuItemDescription(name=name, onclick_fn=onclick_fun) def is_mjcf_file(path: str): _, ext = os.path.splitext(path.lower()) return ext == ".xml" def on_filter_item(item) -> bool: if not item or item.is_folder: return not (item.name == "Omniverse" or item.path.startswith("omniverse:")) return is_mjcf_file(item.path) class Extension(omni.ext.IExt): def on_startup(self, ext_id): self._mjcf_interface = _mjcf.acquire_mjcf_interface() self._usd_context = omni.usd.get_context() self._window = omni.ui.Window( EXTENSION_NAME, width=600, height=400, visible=False, dockPreference=ui.DockPreference.LEFT_BOTTOM ) self._window.deferred_dock_in("Console", omni.ui.DockPolicy.DO_NOTHING) self._window.set_visibility_changed_fn(self._on_window) menu_items = [ make_menu_item_description(ext_id, EXTENSION_NAME, lambda a=weakref.proxy(self): a._menu_callback()) ] self._menu_items = [MenuItemDescription(name="Workflows", sub_menu=menu_items)] add_menu_items(self._menu_items, "Isaac Utils") self._models = {} result, self._config = omni.kit.commands.execute("MJCFCreateImportConfig") self._filepicker = None self._last_folder = None self._content_browser = None self._extension_path = omni.kit.app.get_app().get_extension_manager().get_extension_path(ext_id) self._imported_robot = None # Set defaults # self._config.set_merge_fixed_joints(False) # self._config.set_convex_decomp(False) self._config.set_fix_base(False) self._config.set_import_inertia_tensor(False) self._config.set_distance_scale(1.0) self._config.set_density(0.0) # self._config.set_default_drive_type(1) # self._config.set_default_drive_strength(1e7) # self._config.set_default_position_drive_damping(1e5) self._config.set_self_collision(False) self._config.set_make_default_prim(True) self._config.set_create_physics_scene(True) self._config.set_import_sites(True) self._config.set_visualize_collision_geoms(True) def build_ui(self): with self._window.frame: with ui.VStack(spacing=20, height=0): with ui.HStack(spacing=10): with ui.VStack(spacing=2, height=0): # cb_builder( # label="Merge Fixed Joints", # tooltip="Check this box to skip adding articulation on fixed joints", # on_clicked_fn=lambda m, config=self._config: config.set_merge_fixed_joints(m), # ) cb_builder( "Fix Base Link", tooltip="If true, enables the fix base property on the root of the articulation.", default_val=False, on_clicked_fn=lambda m, config=self._config: config.set_fix_base(m), ) cb_builder( "Import Inertia Tensor", tooltip="If True, inertia will be loaded from mjcf, if the mjcf does not specify inertia tensor, identity will be used and scaled by the scaling factor. If false physx will compute automatically", on_clicked_fn=lambda m, config=self._config: config.set_import_inertia_tensor(m), ) cb_builder( "Import Sites", tooltip="If True, sites will be imported from mjcf.", default_val=True, on_clicked_fn=lambda m, config=self._config: config.set_import_sites(m), ) cb_builder( "Visualize Collision Geoms", tooltip="If True, collision geoms will also be imported as visual geoms", default_val=True, on_clicked_fn=lambda m, config=self._config: config.set_visualize_collision_geoms(m), ) self._models["scale"] = float_builder( "Stage Units Per Meter", default_val=1.0, tooltip="[1.0 / stage_units] Set the distance units the robot is imported as, default is 1.0 corresponding to m", ) self._models["scale"].add_value_changed_fn( lambda m, config=self._config: config.set_distance_scale(m.get_value_as_float()) ) self._models["density"] = float_builder( "Link Density", default_val=0.0, tooltip="[kg/stage_units^3] If a link doesn't have mass, use this density as backup, A density of 0.0 results in the physics engine automatically computing a default density", ) self._models["density"].add_value_changed_fn( lambda m, config=self._config: config.set_density(m.get_value_as_float()) ) # dropdown_builder( # "Joint Drive Type", # items=["None", "Position", "Velocity"], # default_val=1, # on_clicked_fn=lambda i, config=self._config: i, # #config.set_default_drive_type(0 if i == "None" else (1 if i == "Position" else 2) # tooltip="Set the default drive configuration, None: stiffness and damping are zero, Position/Velocity: use default specified below.", # ) # self._models["drive_strength"] = float_builder( # "Joint Drive Strength", # default_val=1e7, # tooltip="Corresponds to stiffness for position or damping for velocity, set to -1 to prevent this value from getting used", # ) # self._models["drive_strength"].add_value_changed_fn( # lambda m, config=self._config: m # # config.set_default_drive_strength(m.get_value_as_float()) # ) # self._models["position_drive_damping"] = float_builder( # "Joint Position Drive Damping", # default_val=1e5, # tooltip="If the drive type is set to position, this will be used as a default damping for the drive, set to -1 to prevent this from getting used", # ) # self._models["position_drive_damping"].add_value_changed_fn( # lambda m, config=self._config: m # #config.set_default_position_drive_damping(m.get_value_as_float() # ) with ui.VStack(spacing=2, height=0): self._models["clean_stage"] = cb_builder( label="Clean Stage", tooltip="Check this box to load MJCF on a clean stage" ) # cb_builder( # "Convex Decomposition", # tooltip="If true, non-convex meshes will be decomposed into convex collision shapes, if false a convex hull will be used.", # on_clicked_fn=lambda m, config=self._config: config.set_convex_decomp(m), # ) cb_builder( "Self Collision", tooltip="If true, allows self intersection between links in the robot, can cause instability if collision meshes between links are self intersecting", on_clicked_fn=lambda m, config=self._config: config.set_self_collision(m), ) cb_builder( "Create Physics Scene", tooltip="If true, creates a default physics scene if one does not already exist in the stage", default_val=True, on_clicked_fn=lambda m, config=self._config: config.set_create_physics_scene(m), ), cb_builder( "Make Default Prim", tooltip="If true, makes imported robot the default prim for the stage", default_val=True, on_clicked_fn=lambda m, config=self._config: config.set_make_default_prim(m), ) cb_builder( "Create Instanceable Asset", tooltip="If true, creates an instanceable version of the asset. Meshes will be saved in a separate USD file", default_val=False, on_clicked_fn=lambda m, config=self._config: config.set_make_instanceable(m), ) self._models["instanceable_usd_path"] = str_builder( "Instanceable USD Path", tooltip="USD file to store instanceable meshes in", default_val="./instanceable_meshes.usd", use_folder_picker=True, folder_dialog_title="Select Output File", folder_button_title="Select File", ) self._models["instanceable_usd_path"].add_value_changed_fn( lambda m, config=self._config: config.set_instanceable_usd_path(m.get_value_as_string()) ) with ui.VStack(height=0): with ui.HStack(spacing=20): btn_builder("Import MJCF", text="Select and Import", on_clicked_fn=self._parse_mjcf) def _menu_callback(self): self._window.visible = not self._window.visible def _on_window(self, visible): if self._window.visible: self.build_ui() self._events = self._usd_context.get_stage_event_stream() else: self._events = None self._stage_event_sub = None def _refresh_filebrowser(self): parent = None selection_name = None if len(self._filebrowser.get_selections()): parent = self._filebrowser.get_selections()[0].parent selection_name = self._filebrowser.get_selections()[0].name self._filebrowser.refresh_ui(parent) if selection_name: selection = [child for child in parent.children.values() if child.name == selection_name] if len(selection): self._filebrowser.select_and_center(selection[0]) def _parse_mjcf(self): self._filepicker = FilePickerDialog( "Import MJCF", allow_multi_selection=False, apply_button_label="Import", click_apply_handler=lambda filename, path, c=weakref.proxy(self): c._select_picked_file_callback( self._filepicker, filename, path ), click_cancel_handler=lambda a, b, c=weakref.proxy(self): c._filepicker.hide(), item_filter_fn=on_filter_item, enable_versioning_pane=True, ) if self._last_folder: self._filepicker.set_current_directory(self._last_folder) self._filepicker.navigate_to(self._last_folder) self._filepicker.refresh_current_directory() self._filepicker.toggle_bookmark_from_path("Built In MJCF Files", (self._extension_path + "/data/mjcf"), True) self._filepicker.show() def _load_robot(self, path=None): if path: base_path = path[: path.rfind("/")] basename = path[path.rfind("/") + 1 :] basename = basename[: basename.rfind(".")] if path.rfind("/") < 0: base_path = path[: path.rfind("\\")] basename = path[path.rfind("\\") + 1] # sanitize basename if basename[0].isdigit(): basename = "_" + basename full_path = os.path.abspath(os.path.join(self.root_path, self.filename)) dest_path = "{}/{}/{}.usd".format(base_path, basename, basename) current_stage = omni.usd.get_context().get_stage() prim_path = omni.usd.get_stage_next_free_path(current_stage, "/" + basename, False) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=full_path, import_config=self._config, prim_path=prim_path, dest_path=dest_path, ) stage = Usd.Stage.Open(dest_path) prim_name = str(stage.GetDefaultPrim().GetName()) def add_reference_to_stage(): current_stage = omni.usd.get_context().get_stage() if current_stage: prim_path = omni.usd.get_stage_next_free_path( current_stage, str(current_stage.GetDefaultPrim().GetPath()) + "/" + prim_name, False ) robot_prim = current_stage.OverridePrim(prim_path) if "anon:" in current_stage.GetRootLayer().identifier: robot_prim.GetReferences().AddReference(dest_path) else: robot_prim.GetReferences().AddReference( omni.client.make_relative_url(current_stage.GetRootLayer().identifier, dest_path) ) if self._config.create_physics_scene: UsdPhysics.Scene.Define(current_stage, Sdf.Path("/physicsScene")) async def import_with_clean_stage(): await omni.usd.get_context().new_stage_async() await omni.kit.app.get_app().next_update_async() current_stage = omni.usd.get_context().get_stage() UsdGeom.SetStageUpAxis(current_stage, UsdGeom.Tokens.z) UsdGeom.SetStageMetersPerUnit(stage, 1) add_reference_to_stage() await omni.kit.app.get_app().next_update_async() if self._models["clean_stage"].get_value_as_bool(): asyncio.ensure_future(import_with_clean_stage()) else: upAxis = UsdGeom.GetStageUpAxis(current_stage) if upAxis == "Y": carb.log_error("The stage Up-Axis must be Z to use the MJCF importer") add_reference_to_stage() def _select_picked_file_callback(self, dialog: FilePickerDialog, filename=None, path=None): if not path.startswith("omniverse://"): self.root_path = path self.filename = filename if path and filename: self._last_folder = path self._load_robot(path + "/" + filename) else: carb.log_error("path and filename not specified") else: carb.log_error("Only Local Paths supported") dialog.hide() def on_shutdown(self): _mjcf.release_mjcf_interface(self._mjcf_interface) if self._filepicker: self._filepicker.toggle_bookmark_from_path( "Built In MJCF Files", (self._extension_path + "/data/mjcf"), False ) self._filepicker.destroy() self._filepicker = None remove_menu_items(self._menu_items, "Isaac Utils") if self._window: self._window = None gc.collect()

Python

NVIDIA-Omniverse/mjcf-importer-extension/source/extensions/omni.importer.mjcf/python/scripts/ui_utils.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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. # str_builder import asyncio import os import subprocess import sys from cmath import inf import carb.settings import omni.appwindow import omni.ext import omni.ui as ui from omni.kit.window.extensions import SimpleCheckBox from omni.kit.window.filepicker import FilePickerDialog from omni.kit.window.property.templates import LABEL_HEIGHT, LABEL_WIDTH # from .callbacks import on_copy_to_clipboard, on_docs_link_clicked, on_open_folder_clicked, on_open_IDE_clicked from .style import BUTTON_WIDTH, COLOR_W, COLOR_X, COLOR_Y, COLOR_Z, get_style def add_line_rect_flourish(draw_line=True): """Aesthetic element that adds a Line + Rectangle after all UI elements in the row. Args: draw_line (bool, optional): Set false to only draw rectangle. Defaults to True. """ if draw_line: ui.Line(style={"color": 0x338A8777}, width=ui.Fraction(1), alignment=ui.Alignment.CENTER) ui.Spacer(width=10) with ui.Frame(width=0): with ui.VStack(): with ui.Placer(offset_x=0, offset_y=7): ui.Rectangle(height=5, width=5, alignment=ui.Alignment.CENTER) ui.Spacer(width=5) def format_tt(tt): import string formated = "" i = 0 for w in tt.split(): if w.isupper(): formated += w + " " elif len(w) > 3 or i == 0: formated += string.capwords(w) + " " else: formated += w.lower() + " " i += 1 return formated def add_folder_picker_icon( on_click_fn, item_filter_fn=None, bookmark_label=None, bookmark_path=None, dialog_title="Select Output Folder", button_title="Select Folder", ): def open_file_picker(): def on_selected(filename, path): on_click_fn(filename, path) file_picker.hide() def on_canceled(a, b): file_picker.hide() file_picker = FilePickerDialog( dialog_title, allow_multi_selection=False, apply_button_label=button_title, click_apply_handler=lambda a, b: on_selected(a, b), click_cancel_handler=lambda a, b: on_canceled(a, b), item_filter_fn=item_filter_fn, enable_versioning_pane=True, ) if bookmark_label and bookmark_path: file_picker.toggle_bookmark_from_path(bookmark_label, bookmark_path, True) with ui.Frame(width=0, tooltip=button_title): ui.Button( name="IconButton", width=24, height=24, clicked_fn=open_file_picker, style=get_style()["IconButton.Image::FolderPicker"], alignment=ui.Alignment.RIGHT_TOP, ) def btn_builder(label="", type="button", text="button", tooltip="", on_clicked_fn=None): """Creates a stylized button. Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "button". text (str, optional): Text rendered on the button. Defaults to "button". tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (Callable, optional): Call-back function when clicked. Defaults to None. Returns: ui.Button: Button """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) btn = ui.Button( text.upper(), name="Button", width=BUTTON_WIDTH, clicked_fn=on_clicked_fn, style=get_style(), alignment=ui.Alignment.LEFT_CENTER, ) ui.Spacer(width=5) add_line_rect_flourish(True) # ui.Spacer(width=ui.Fraction(1)) # ui.Spacer(width=10) # with ui.Frame(width=0): # with ui.VStack(): # with ui.Placer(offset_x=0, offset_y=7): # ui.Rectangle(height=5, width=5, alignment=ui.Alignment.CENTER) # ui.Spacer(width=5) return btn def cb_builder(label="", type="checkbox", default_val=False, tooltip="", on_clicked_fn=None): """Creates a Stylized Checkbox Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "checkbox". default_val (bool, optional): Checked is True, Unchecked is False. Defaults to False. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (Callable, optional): Call-back function when clicked. Defaults to None. Returns: ui.SimpleBoolModel: model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH - 12, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) model = ui.SimpleBoolModel() callable = on_clicked_fn if callable is None: callable = lambda x: None SimpleCheckBox(default_val, callable, model=model) add_line_rect_flourish() return model def dropdown_builder( label="", type="dropdown", default_val=0, items=["Option 1", "Option 2", "Option 3"], tooltip="", on_clicked_fn=None ): """Creates a Stylized Dropdown Combobox Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "dropdown". default_val (int, optional): Default index of dropdown items. Defaults to 0. items (list, optional): List of items for dropdown box. Defaults to ["Option 1", "Option 2", "Option 3"]. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (Callable, optional): Call-back function when clicked. Defaults to None. Returns: AbstractItemModel: model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) combo_box = ui.ComboBox( default_val, *items, name="ComboBox", width=ui.Fraction(1), alignment=ui.Alignment.LEFT_CENTER ).model add_line_rect_flourish(False) def on_clicked_wrapper(model, val): on_clicked_fn(items[model.get_item_value_model().as_int]) if on_clicked_fn is not None: combo_box.add_item_changed_fn(on_clicked_wrapper) return combo_box def float_builder(label="", type="floatfield", default_val=0, tooltip="", min=-inf, max=inf, step=0.1, format="%.2f"): """Creates a Stylized Floatfield Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "floatfield". default_val (int, optional): Default Value of UI element. Defaults to 0. tooltip (str, optional): Tooltip to display over the UI elements. Defaults to "". Returns: AbstractValueModel: model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) float_field = ui.FloatDrag( name="FloatField", width=ui.Fraction(1), height=0, alignment=ui.Alignment.LEFT_CENTER, min=min, max=max, step=step, format=format, ).model float_field.set_value(default_val) add_line_rect_flourish(False) return float_field def str_builder( label="", type="stringfield", default_val=" ", tooltip="", on_clicked_fn=None, use_folder_picker=False, read_only=False, item_filter_fn=None, bookmark_label=None, bookmark_path=None, folder_dialog_title="Select Output Folder", folder_button_title="Select Folder", ): """Creates a Stylized Stringfield Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "stringfield". default_val (str, optional): Text to initialize in Stringfield. Defaults to " ". tooltip (str, optional): Tooltip to display over the UI elements. Defaults to "". use_folder_picker (bool, optional): Add a folder picker button to the right. Defaults to False. read_only (bool, optional): Prevents editing. Defaults to False. item_filter_fn (Callable, optional): filter function to pass to the FilePicker bookmark_label (str, optional): bookmark label to pass to the FilePicker bookmark_path (str, optional): bookmark path to pass to the FilePicker Returns: AbstractValueModel: model of Stringfield """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) str_field = ui.StringField( name="StringField", width=ui.Fraction(1), height=0, alignment=ui.Alignment.LEFT_CENTER, read_only=read_only ).model str_field.set_value(default_val) if use_folder_picker: def update_field(filename, path): if filename == "": val = path elif filename[0] != "/" and path[-1] != "/": val = path + "/" + filename elif filename[0] == "/" and path[-1] == "/": val = path + filename[1:] else: val = path + filename str_field.set_value(val) add_folder_picker_icon( update_field, item_filter_fn, bookmark_label, bookmark_path, dialog_title=folder_dialog_title, button_title=folder_button_title, ) else: add_line_rect_flourish(False) return str_field

Python

NVIDIA-Omniverse/mjcf-importer-extension/source/extensions/omni.importer.mjcf/python/tests/test_mjcf.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 asyncio import filecmp import os import carb import numpy as np import omni.kit.commands # NOTE: # omni.kit.test - std python's unittest module with additional wrapping to add suport for async/await tests # For most things refer to unittest docs: https://docs.python.org/3/library/unittest.html import omni.kit.test import pxr from pxr import Gf, PhysicsSchemaTools, Sdf, UsdGeom, UsdPhysics, UsdShade # Having a test class dervived from omni.kit.test.AsyncTestCase declared on the root of module will make it auto-discoverable by omni.kit.test class TestMJCF(omni.kit.test.AsyncTestCase): # Before running each test async def setUp(self): self._timeline = omni.timeline.get_timeline_interface() ext_manager = omni.kit.app.get_app().get_extension_manager() ext_id = ext_manager.get_enabled_extension_id("omni.importer.mjcf") self._extension_path = ext_manager.get_extension_path(ext_id) await omni.usd.get_context().new_stage_async() await omni.kit.app.get_app().next_update_async() # After running each test async def tearDown(self): while omni.usd.get_context().get_stage_loading_status()[2] > 0: print("tearDown, assets still loading, waiting to finish...") await asyncio.sleep(1.0) await omni.kit.app.get_app().next_update_async() await omni.usd.get_context().new_stage_async() async def test_mjcf_ant(self): stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_fix_base(True) import_config.set_import_inertia_tensor(True) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/nv_ant.xml", import_config=import_config, prim_path="/ant", ) await omni.kit.app.get_app().next_update_async() # check if object is there prim = stage.GetPrimAtPath("/ant") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # make sure the joints and links exist front_left_leg_joint = stage.GetPrimAtPath("/ant/torso/joints/hip_1") self.assertNotEqual(front_left_leg_joint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(front_left_leg_joint.GetTypeName(), "PhysicsRevoluteJoint") self.assertAlmostEqual(front_left_leg_joint.GetAttribute("physics:upperLimit").Get(), 40) self.assertAlmostEqual(front_left_leg_joint.GetAttribute("physics:lowerLimit").Get(), -40) front_left_leg = stage.GetPrimAtPath("/ant/torso/front_left_leg") self.assertAlmostEqual(front_left_leg.GetAttribute("physics:diagonalInertia").Get()[0], 0.0) self.assertAlmostEqual(front_left_leg.GetAttribute("physics:mass").Get(), 0.0) front_left_foot_joint = stage.GetPrimAtPath("/ant/torso/joints/ankle_1") self.assertNotEqual(front_left_foot_joint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(front_left_foot_joint.GetTypeName(), "PhysicsRevoluteJoint") self.assertAlmostEqual(front_left_foot_joint.GetAttribute("physics:upperLimit").Get(), 100) self.assertAlmostEqual(front_left_foot_joint.GetAttribute("physics:lowerLimit").Get(), 30) front_left_foot = stage.GetPrimAtPath("/ant/torso/front_left_foot") self.assertAlmostEqual(front_left_foot.GetAttribute("physics:diagonalInertia").Get()[0], 0.0) self.assertAlmostEqual(front_left_foot.GetAttribute("physics:mass").Get(), 0.0) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() self.assertAlmostEqual(UsdGeom.GetStageMetersPerUnit(stage), 1.0) async def test_mjcf_humanoid(self): stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_fix_base(True) import_config.set_import_inertia_tensor(True) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/nv_humanoid.xml", import_config=import_config, prim_path="/humanoid", ) await omni.kit.app.get_app().next_update_async() # check if object is there prim = stage.GetPrimAtPath("/humanoid") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # make sure the joints and link exist root_joint = stage.GetPrimAtPath("/humanoid/torso/joints/rootJoint_torso") self.assertNotEqual(root_joint.GetPath(), Sdf.Path.emptyPath) pelvis_joint = stage.GetPrimAtPath("/humanoid/torso/joints/abdomen_x") self.assertNotEqual(pelvis_joint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(pelvis_joint.GetTypeName(), "PhysicsRevoluteJoint") self.assertAlmostEqual(pelvis_joint.GetAttribute("physics:upperLimit").Get(), 35) self.assertAlmostEqual(pelvis_joint.GetAttribute("physics:lowerLimit").Get(), -35) lower_waist_joint = stage.GetPrimAtPath("/humanoid/torso/joints/lower_waist") self.assertNotEqual(lower_waist_joint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(lower_waist_joint.GetTypeName(), "PhysicsJoint") self.assertAlmostEqual(lower_waist_joint.GetAttribute("limit:rotX:physics:high").Get(), 45) self.assertAlmostEqual(lower_waist_joint.GetAttribute("limit:rotX:physics:low").Get(), -45) self.assertAlmostEqual(lower_waist_joint.GetAttribute("limit:rotY:physics:high").Get(), 30) self.assertAlmostEqual(lower_waist_joint.GetAttribute("limit:rotY:physics:low").Get(), -75) self.assertAlmostEqual(lower_waist_joint.GetAttribute("limit:rotZ:physics:high").Get(), -1) self.assertAlmostEqual(lower_waist_joint.GetAttribute("limit:rotZ:physics:low").Get(), 1) left_foot = stage.GetPrimAtPath("/humanoid/torso/left_foot") self.assertAlmostEqual(left_foot.GetAttribute("physics:diagonalInertia").Get()[0], 0.0) self.assertAlmostEqual(left_foot.GetAttribute("physics:mass").Get(), 0.0) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() self.assertAlmostEqual(UsdGeom.GetStageMetersPerUnit(stage), 1.0) # This sample corresponds to the example in the docs, keep this and the version in the docs in sync async def test_doc_sample(self): import omni.kit.commands from pxr import Gf, PhysicsSchemaTools, Sdf, UsdLux, UsdPhysics # setting up import configuration: status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_fix_base(True) import_config.set_import_inertia_tensor(True) # Get path to extension data: ext_manager = omni.kit.app.get_app().get_extension_manager() ext_id = ext_manager.get_enabled_extension_id("omni.importer.mjcf") extension_path = ext_manager.get_extension_path(ext_id) # import MJCF omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=extension_path + "/data/mjcf/nv_ant.xml", import_config=import_config, prim_path="/ant", ) # get stage handle stage = omni.usd.get_context().get_stage() # enable physics scene = UsdPhysics.Scene.Define(stage, Sdf.Path("/physicsScene")) # set gravity scene.CreateGravityDirectionAttr().Set(Gf.Vec3f(0.0, 0.0, -1.0)) scene.CreateGravityMagnitudeAttr().Set(9.81) # add lighting distantLight = UsdLux.DistantLight.Define(stage, Sdf.Path("/DistantLight")) distantLight.CreateIntensityAttr(500) async def test_mjcf_scale(self): stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_distance_scale(100.0) import_config.set_fix_base(True) import_config.set_import_inertia_tensor(True) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/nv_ant.xml", import_config=import_config, prim_path="/ant", ) await omni.kit.app.get_app().next_update_async() # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() self.assertAlmostEqual(UsdGeom.GetStageMetersPerUnit(stage), 0.01) async def test_mjcf_self_collision(self): stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_self_collision(True) import_config.set_fix_base(True) import_config.set_import_inertia_tensor(True) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/nv_ant.xml", import_config=import_config, prim_path="/ant", ) await omni.kit.app.get_app().next_update_async() prim = stage.GetPrimAtPath("/ant/torso") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) self.assertEqual(prim.GetAttribute("physxArticulation:enabledSelfCollisions").Get(), True) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() async def test_mjcf_default_prim(self): stage = omni.usd.get_context().get_stage() mjcf_path = os.path.abspath(self._extension_path + "/data/mjcf/nv_ant.xml") status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_fix_base(True) import_config.set_import_inertia_tensor(True) import_config.set_make_default_prim(True) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/nv_ant.xml", import_config=import_config, prim_path="/ant_1", ) await omni.kit.app.get_app().next_update_async() omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/nv_ant.xml", import_config=import_config, prim_path="/ant_2", ) await omni.kit.app.get_app().next_update_async() default_prim = stage.GetDefaultPrim() self.assertNotEqual(default_prim.GetPath(), Sdf.Path.emptyPath) prim_2 = stage.GetPrimAtPath("/ant_2") self.assertNotEqual(prim_2.GetPath(), Sdf.Path.emptyPath) self.assertEqual(default_prim.GetPath(), prim_2.GetPath()) async def test_mjcf_visualize_collision_geom(self): stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_self_collision(True) import_config.set_fix_base(True) import_config.set_import_inertia_tensor(True) import_config.set_visualize_collision_geoms(False) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/open_ai_assets/hand/manipulate_block.xml", import_config=import_config, prim_path="/shadow_hand", ) await omni.kit.app.get_app().next_update_async() prim = stage.GetPrimAtPath("/shadow_hand/robot0_hand_mount/robot0_forearm/visuals/robot0_C_forearm") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) imageable = UsdGeom.Imageable(prim) visibility_attr = imageable.GetVisibilityAttr().Get() self.assertEqual(visibility_attr, "invisible") # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() async def test_mjcf_import_shadow_hand_egg(self): stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_self_collision(True) import_config.set_import_inertia_tensor(True) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/open_ai_assets/hand/manipulate_egg_touch_sensors.xml", import_config=import_config, prim_path="/shadow_hand", ) await omni.kit.app.get_app().next_update_async() prim = stage.GetPrimAtPath("/shadow_hand/robot0_hand_mount") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) prim = stage.GetPrimAtPath("/shadow_hand/object") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) prim = stage.GetPrimAtPath("/shadow_hand/worldBody") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() async def test_mjcf_import_humanoid_100(self): stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("MJCFCreateImportConfig") import_config.set_self_collision(False) import_config.set_import_inertia_tensor(True) omni.kit.commands.execute( "MJCFCreateAsset", mjcf_path=self._extension_path + "/data/mjcf/mujoco_sim_assets/humanoid100.xml", import_config=import_config, prim_path="/humanoid_100", ) await omni.kit.app.get_app().next_update_async() # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop()

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