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NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/window.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from typing import Callable
import omni.ui as ui
OnStartCallback = Callable[[str, int], bool]
OnStopCallback = Callable[[], bool]
class OscWindow(ui.Window):
def __init__(
self, default_addr: str, default_port: int, on_start: OnStartCallback, on_stop: OnStopCallback
) -> None:
super().__init__("OSC UDP Server", width=300, height=300)
def start() -> None:
"""
Callback when the user presses the start button
"""
is_running = on_start(addr.as_string, port.as_int)
running.set_value(is_running)
def stop() -> None:
"""
Callback when the user presses the stop button
"""
is_running = on_stop()
running.set_value(is_running)
def update_running_label(label: ui.Label, running: bool) -> None:
"""
Keep the UI label up to date with the state of the server
"""
if running:
label.text = f"Running UDP server @ {addr.as_string}:{port.as_int}"
label.set_style({"color": "green"})
else:
label.text = "Stopped"
label.set_style({"color": "red"})
def toggle_enabled(field: ui.AbstractField, running: bool) -> None:
"""
Enable or disable the input field based on the state of the server
"""
field.enabled = not running
color = "gray" if running else "white"
field.set_style({"color": color})
# Settings
addr = ui.SimpleStringModel(default_addr)
port = ui.SimpleIntModel(default_port)
running = ui.SimpleBoolModel(False)
with self.frame:
with ui.VStack():
label = ui.Label("", height=20)
update_running_label(label, running.get_value_as_bool())
running.add_value_changed_fn(lambda m: update_running_label(label, m.get_value_as_bool()))
with ui.VStack(height=20):
with ui.HStack():
ui.Label("Address:")
addr_field = ui.StringField(addr)
toggle_enabled(addr_field, running.get_value_as_bool())
running.add_value_changed_fn(lambda m: toggle_enabled(addr_field, m.get_value_as_bool()))
ui.Spacer(height=2)
with ui.HStack():
ui.Label("Port:")
port_field = ui.IntField(port)
toggle_enabled(port_field, running.get_value_as_bool())
running.add_value_changed_fn(lambda m: toggle_enabled(port_field, m.get_value_as_bool()))
with ui.VStack():
ui.Button("Start", clicked_fn=start)
ui.Button("Stop", clicked_fn=stop)
|
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/ogn/__init__.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
Dynamically import every file in a directory tree that looks like a Python Ogn Node.
This includes linked directories, which is the mechanism by which nodes can be hot-reloaded from the source tree.
"""
# Required to register nodes in Kit 104
try:
import omni.graph.core as og
og.register_ogn_nodes(__file__, "omni.osc")
except Exception:
# Swallow any exceptions
pass
|
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/ogn/nodes/OgnOnOscEvent.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
This is the implementation of the OGN node defined in OgnOnOscEvent.ogn
This implementation is inspired by the OgnOnCustomEvent node
See https://gitlab-master.nvidia.com/omniverse/kit/-/blob/master/kit/source/extensions/omni.graph.action/nodes/OgnOnCustomEvent.py # noqa E501
"""
import re
from typing import Any, List, Union
import carb
import carb.events
import carb.profiler
import omni.graph.core as og
import omni.osc
from omni.osc.core import OSC_MESSAGE_ADDRESS_STR, OSC_MESSAGE_ARGUMENTS_STR
from .. import OgnOnOscEventDatabase
class OgnOnOscEventInternalState:
"""Convenience class for maintaining per-node state information"""
def __init__(self):
"""Instantiate the per-node state information."""
# This subscription object controls the lifetime of our callback, it will be
# cleaned up automatically when our node is destroyed
self.sub = None
# Set when the callback has triggered
self.is_set = False
# The last event received
self.event: Union[None, carb.events.IEvent] = None
# The node instance handle
self.node = None
# The regex used to match the OSC address path
self.osc_path_regex = ""
# The compiled regex pattern
self.osc_path_regex_pattern = None
@carb.profiler.profile
def on_event(self, event: carb.events.IEvent):
"""The event callback"""
if event is None:
return
# Only handle messages with a path that matches the OSC address path regex
osc_addr, _ = omni.osc.osc_message_from_carb_event(event)
if self.osc_path_regex_pattern is None or not self.osc_path_regex_pattern.match(osc_addr):
return
self.is_set = True
self.event = event
# Tell the evaluator we need to be computed
if self.node.is_valid():
self.node.request_compute()
@carb.profiler.profile
def first_time_subscribe(self, node: og.Node, osc_path_regex: str) -> bool:
"""Checked call to set up carb subscription
Args:
node: The node instance
event_name: The name of the carb event
Returns:
True if we subscribed, False if we are already subscribed
"""
if self.osc_path_regex != osc_path_regex:
# osc path regex changed since we last subscribed, re-compile
try:
self.osc_path_regex_pattern = re.compile(osc_path_regex)
self.osc_path_regex = osc_path_regex
except Exception as e:
carb.log_error(f"Error compiling OSC Address Path Regex '{osc_path_regex}': {e}")
if self.sub is None:
self.sub = omni.osc.subscribe_to_osc_event_stream(self.on_event)
self.node = node
return True
return False
def try_pop_event(self) -> Union[None, carb.events.IEvent]:
"""Pop the last event received, or None if there is no event to pop"""
if self.is_set:
self.is_set = False
event = self.event
self.event = None
return event
return None
# ======================================================================
class OgnOnOscEvent:
"""
This node triggers when an OSC event is received that matches the OSC address path regex.
"""
@staticmethod
def internal_state():
"""Returns an object that will contain per-node state information"""
return OgnOnOscEventInternalState()
@staticmethod
def release(node):
state = OgnOnOscEventDatabase.OgnOnOscEventDatabase.per_node_internal_state(node)
if state.sub:
state.sub.unsubscribe()
state.sub = None
@staticmethod
def check_all_args_are_floats(args: List[Any]) -> bool:
"""
Returns true if the OSC message arguments has the shape of List[float]
"""
all_args_are_float = all(isinstance(arg, float) for arg in args)
return all_args_are_float
@staticmethod
@carb.profiler.profile
def compute(db: og.Database) -> bool:
state: OgnOnOscEventInternalState = db.internal_state
osc_path_regex = db.inputs.path
state.first_time_subscribe(db.node, osc_path_regex)
event = state.try_pop_event()
if event is None:
return False
try:
addr, args = omni.osc.osc_message_from_carb_event(event)
# Populate the output bundle
bundle: og._impl.bundles.BundleContents = db.outputs.message
bundle.clear()
# Update the address attribute
addr_attribute = bundle.insert((og.Type(og.BaseDataType.TOKEN), OSC_MESSAGE_ADDRESS_STR))
addr_attribute.value = addr
# Update the arguments attribute
all_args_are_floats = OgnOnOscEvent.check_all_args_are_floats(args)
# NOTE(jshrake): This node currently only supports OSC arguments shaped like a List[Float]
if all_args_are_floats:
if len(args) == 1:
# Argument list contains a single element, write it as a double
args_attribute = bundle.insert((og.Type(og.BaseDataType.DOUBLE), OSC_MESSAGE_ARGUMENTS_STR))
args_attribute.value = args[0]
elif len(args) > 1:
# Argument list contains multiple element, write it as a list
args_attribute = bundle.insert((og.Type(og.BaseDataType.DOUBLE, tuple_count=len(args), array_depth=0), OSC_MESSAGE_ARGUMENTS_STR))
args_attribute.value = args
else:
carb.log_warn(f"OnOscMessage node expected OSC message arguments to be of type List[Float], instead got {args}")
return False
db.outputs.execOut = og.ExecutionAttributeState.ENABLED
except Exception as e:
carb.log_error(f"Error in OgnOnOscEvent::compute: {e}")
return False
return True
|
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/ogn/nodes/OgnOnOscEvent.ogn | {
"OnOscEvent": {
"description": [
"Receive OSC Messages"
],
"version": 1,
"uiName": "On OSC Message",
"categories": [],
"scheduling": [
"compute-on-request",
"global-read"
],
"language": "Python",
"state": {},
"inputs": {
"path": {
"type": "string",
"description": "A regex to match an OSC Address",
"uiName": "OSC Address",
"default": "/.*"
}
},
"outputs": {
"message": {
"type": "bundle",
"description": "The OSC message output as an OmniGraph Bundle with attributes \"address\" and \"arguments\"",
"uiName": "OSC Message"
},
"execOut": {
"type": "execution",
"description": "Executes when the OSC message is received",
"uiName": "Received"
}
}
}
} |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/ogn/nodes/__init__.py | |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/tests/tests.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import asyncio
import omni.kit.test
import omni.osc
class Test(omni.kit.test.AsyncTestCase):
# Before running each test
async def setUp(self):
pass
# After running each test
async def tearDown(self):
pass
async def test_can_start_and_stop_server(self):
server = omni.osc.DaemonOSCUDPServer(None)
is_running = server.start("localhost", 12345)
self.assertTrue(is_running)
await asyncio.sleep(0.1)
is_running = server.running()
self.assertTrue(is_running)
is_running = server.stop()
self.assertFalse(is_running)
async def test_server_can_receive_messages(self):
server = omni.osc.OmniOscExt.create_server()
is_running = server.start("localhost", 3337)
self.assertTrue(is_running)
self.count = 0
def on_event(e) -> None:
addr, _ = omni.osc.osc_message_from_carb_event(e)
self.assertEqual(e.type, omni.osc.core.OSC_EVENT_TYPE)
self.assertEqual(addr, "/filter")
self.count += 1
sub = omni.osc.subscribe_to_osc_event_stream(on_event)
total_msg_count = 10
def send_messages():
import random
from pythonosc import udp_client
client = udp_client.SimpleUDPClient(address="127.0.0.1", port=3337)
self.assertTrue(client is not None)
for _ in range(total_msg_count):
client.send_message("/filter", random.random())
send_messages()
# Wait a few seconds for the server to receive the messages
await asyncio.sleep(3)
# Manually pump the stream so our subscription callback executes
omni.osc.get_osc_event_stream().pump()
self.assertEqual(self.count, total_msg_count)
|
NVIDIA-Omniverse/kit-osc/exts/omni.osc/omni/osc/tests/__init__.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from .tests import * # noqa: F401,F403
|
NVIDIA-Omniverse/kit-osc/exts/omni.osc/docs/CHANGELOG.md | # Changelog
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
## [0.3.1] - 2023-09-28
### Changed
- Update CHANGELOG
## [0.3.0] - 2023-09-26
### Changed
- Fix OGN node registration for Kit 105.1
## [0.2.0] - 2022-09-12
### Changed
- The `On OSC Message` OmniGraph node now outputs a Bundle typed value rather than an Unknown typed value.
- Users can extract the "address" and the "arguments" of the OSC message with the `Extract Attribute` node.
## [0.1.1] - 2022-09-12
### Changed
- Updated documentation.
## [0.1.0] - 2022-09-02
### Added
- Initial release.
|
NVIDIA-Omniverse/kit-osc/exts/omni.osc/docs/README.md | # omni.osc
Omniverse Kit extension for sending and receiving OSC (Open Sound Control) messages. |
NVIDIA-Omniverse/kit-osc/exts/omni.osc/data/examples/cube-with-osc-graph.usda | #usda 1.0
(
customLayerData = {
dictionary cameraSettings = {
dictionary Front = {
double3 position = (0, 0, 50000)
double radius = 500
}
dictionary Perspective = {
double3 position = (500, 500, 500)
double3 target = (2.7041300728820943, -2.6285389091291904, -0.06136586173619207)
}
dictionary Right = {
double3 position = (-50000, 0, 0)
double radius = 500
}
dictionary Top = {
double3 position = (0, 50000, 0)
double radius = 500
}
string boundCamera = "/OmniverseKit_Persp"
}
dictionary omni_layer = {
dictionary muteness = {
}
}
dictionary renderSettings = {
float3 "rtx:debugView:pixelDebug:textColor" = (0, 1e18, 0)
float3 "rtx:dynamicDiffuseGI:probeCounts" = (6, 6, 6)
float3 "rtx:dynamicDiffuseGI:probeGridOrigin" = (-210, -250, -10)
float3 "rtx:dynamicDiffuseGI:volumeSize" = (600, 440, 300)
float3 "rtx:fog:fogColor" = (0.75, 0.75, 0.75)
float3 "rtx:lightspeed:material:overrideAlbedo" = (0.5, 0.5, 0.5)
float3 "rtx:lightspeed:material:overrideEmissiveColor" = (0.5, 0.5, 0.5)
float3 "rtx:post:backgroundZeroAlpha:backgroundDefaultColor" = (0, 0, 0)
float3 "rtx:post:colorcorr:contrast" = (1, 1, 1)
float3 "rtx:post:colorcorr:gain" = (1, 1, 1)
float3 "rtx:post:colorcorr:gamma" = (1, 1, 1)
float3 "rtx:post:colorcorr:offset" = (0, 0, 0)
float3 "rtx:post:colorcorr:saturation" = (1, 1, 1)
float3 "rtx:post:colorgrad:blackpoint" = (0, 0, 0)
float3 "rtx:post:colorgrad:contrast" = (1, 1, 1)
float3 "rtx:post:colorgrad:gain" = (1, 1, 1)
float3 "rtx:post:colorgrad:gamma" = (1, 1, 1)
float3 "rtx:post:colorgrad:lift" = (0, 0, 0)
float3 "rtx:post:colorgrad:multiply" = (1, 1, 1)
float3 "rtx:post:colorgrad:offset" = (0, 0, 0)
float3 "rtx:post:colorgrad:whitepoint" = (1, 1, 1)
float3 "rtx:post:lensDistortion:lensFocalLengthArray" = (10, 30, 50)
float3 "rtx:post:lensFlares:anisoFlareFalloffX" = (450, 475, 500)
float3 "rtx:post:lensFlares:anisoFlareFalloffY" = (10, 10, 10)
float3 "rtx:post:lensFlares:cutoffPoint" = (2, 2, 2)
float3 "rtx:post:lensFlares:haloFlareFalloff" = (10, 10, 10)
float3 "rtx:post:lensFlares:haloFlareRadius" = (75, 75, 75)
float3 "rtx:post:lensFlares:isotropicFlareFalloff" = (50, 50, 50)
float3 "rtx:post:tonemap:whitepoint" = (1, 1, 1)
float3 "rtx:raytracing:inscattering:singleScatteringAlbedo" = (0.9, 0.9, 0.9)
float3 "rtx:raytracing:inscattering:transmittanceColor" = (0.5, 0.5, 0.5)
float3 "rtx:sceneDb:ambientLightColor" = (0.1, 0.1, 0.1)
}
}
defaultPrim = "World"
endTimeCode = 100
metersPerUnit = 0.01
startTimeCode = 0
timeCodesPerSecond = 24
upAxis = "Y"
)
def Xform "World"
{
def Mesh "Cube"
{
float3[] extent = [(-50, -50, -50), (50, 50, 50)]
int[] faceVertexCounts = [4, 4, 4, 4, 4, 4]
int[] faceVertexIndices = [0, 1, 3, 2, 0, 4, 5, 1, 1, 5, 6, 3, 2, 3, 6, 7, 0, 2, 7, 4, 4, 7, 6, 5]
normal3f[] normals = [(0, -1, 0), (0, -1, 0), (0, -1, 0), (0, -1, 0), (0, 0, -1), (0, 0, -1), (0, 0, -1), (0, 0, -1), (1, 0, 0), (1, 0, 0), (1, 0, 0), (1, 0, 0), (0, 0, 1), (0, 0, 1), (0, 0, 1), (0, 0, 1), (-1, 0, 0), (-1, 0, 0), (-1, 0, 0), (-1, 0, 0), (0, 1, 0), (0, 1, 0), (0, 1, 0), (0, 1, 0)] (
interpolation = "faceVarying"
)
point3f[] points = [(-50, -50, -50), (50, -50, -50), (-50, -50, 50), (50, -50, 50), (-50, 50, -50), (50, 50, -50), (50, 50, 50), (-50, 50, 50)]
float2[] primvars:st = [(1, 0), (0, 0), (0, 1), (1, 1), (1, 0), (1, 1), (0, 1), (0, 0), (1, 0), (0, 0), (0, 1), (1, 1), (1, 0), (0, 0), (0, 1), (1, 1), (1, 0), (1, 1), (0, 1), (0, 0), (1, 0), (1, 1), (0, 1), (0, 0)] (
interpolation = "faceVarying"
)
uniform token subdivisionScheme = "none"
double3 xformOp:rotateXYZ = (0, 0, 0)
double3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (0, 0, 0)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
def OmniGraph "ActionGraph"
{
token evaluationMode = "Automatic"
token evaluator:type = "execution"
token fabricCacheBacking = "Shared"
int2 fileFormatVersion = (1, 5)
token pipelineStage = "pipelineStageSimulation"
def OmniGraphNode "on_osc_message" (
prepend apiSchemas = ["NodeGraphNodeAPI"]
)
{
custom string inputs:path = "/.*"
token node:type = "omni.osc.OnOscEvent"
int node:typeVersion = 1
custom uint outputs:execOut (
customData = {
bool isExecution = 1
}
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (146, 104)
def Output "outputs_message"
{
}
}
def OmniGraphNode "write_prim_attribute" (
prepend apiSchemas = ["NodeGraphNodeAPI"]
)
{
custom uint inputs:execIn
prepend uint inputs:execIn.connect = </World/ActionGraph/on_osc_message.outputs:execOut>
custom token inputs:name = "xformOp:scale"
custom rel inputs:prim
prepend rel inputs:prim = </World/Cube>
custom token inputs:primPath
custom bool inputs:usdWriteBack = 1
custom bool inputs:usePath = 0
custom token inputs:value
prepend token inputs:value.connect = </World/ActionGraph/multiply.outputs:product>
token node:type = "omni.graph.nodes.WritePrimAttribute"
int node:typeVersion = 1
custom uint outputs:execOut (
customData = {
bool isExecution = 1
}
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (972, 91)
}
def OmniGraphNode "multiply" (
prepend apiSchemas = ["NodeGraphNodeAPI"]
)
{
custom token inputs:a
prepend token inputs:a.connect = </World/ActionGraph/extract_attribute.outputs:output>
custom token inputs:b
prepend token inputs:b.connect = </World/ActionGraph/constant_double.inputs:value>
token node:type = "omni.graph.nodes.Multiply"
int node:typeVersion = 1
custom token outputs:product
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (671, 277)
}
def OmniGraphNode "constant_double" (
prepend apiSchemas = ["NodeGraphNodeAPI"]
)
{
custom double inputs:value = 100
token node:type = "omni.graph.nodes.ConstantDouble"
int node:typeVersion = 1
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (386, 410)
}
def OmniGraphNode "extract_attribute" (
prepend apiSchemas = ["NodeGraphNodeAPI"]
)
{
custom token inputs:attrName = "arguments"
custom rel inputs:data
prepend rel inputs:data = </World/ActionGraph/on_osc_message/outputs_message>
token node:type = "omni.graph.nodes.ExtractAttribute"
int node:typeVersion = 1
custom token outputs:output
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (366, 211)
}
}
}
def Xform "Environment"
{
double3 xformOp:rotateXYZ = (0, 0, 0)
double3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (0, 0, 0)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
def DistantLight "defaultLight" (
prepend apiSchemas = ["ShapingAPI"]
)
{
float angle = 1
float intensity = 3000
float shaping:cone:angle = 180
float shaping:cone:softness
float shaping:focus
color3f shaping:focusTint
asset shaping:ies:file
double3 xformOp:rotateXYZ = (315, 0, 0)
double3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (0, 0, 0)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
}
|
AccelerationAgency/omniverse-extensions/exts/taa.google.spreadsheet.api/taa/google/spreadsheet/api/extension.py | import omni.ext
import omni.ui as ui
import omni.kit.commands
from typing import List
from pxr import Gf
omni.kit.pipapi.install('google-api-python-client')
omni.kit.pipapi.install('google-auth-httplib2')
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
SPACING = 4
LABEL_WIDTH = 120
class MyExtension(omni.ext.IExt):
data = {'translate_x': 0, 'translate_y': 0, 'translate_z': 0, 'rotate_x': 0, 'rotate_y': 0, 'rotate_z': 0, 'scale_x': 0, 'scale_y': 0, 'scale_z': 0}
subscription = None
stage = None
google_sheet = None
label_width = 50
_source_prim_model = ui.SimpleStringModel()
# lifecycle
def on_startup(self, ext_id):
print("[taa.google.spreadsheet.api] Extension starting up")
self.stage = omni.usd.get_context().get_stage()
self._window = ui.Window("TAA Google Spreadsheet API", width=400, height=270)
with self._window.frame:
with ui.VStack(height=0, spacing=SPACING):
with ui.CollapsableFrame("Source", name="group"):
with ui.VStack(height=0, spacing=SPACING):
with ui.HStack():
ui.Label("Prim", name="attribute_name", width=LABEL_WIDTH)
ui.StringField(model=self._source_prim_model)
ui.Button(" S ", width=0, height=0, style={"margin": 0}, clicked_fn=self._on_get_selection, tooltip="Get From Selection")
ui.Spacer(height= 12)
with ui.CollapsableFrame("Settings", name="group"):
with ui.VStack(height=0, spacing=SPACING):
ui.Label('Spreadsheet ID', height=20)
self.spreadsheet_id_field = ui.StringField(height=20)
ui.Label('Range', height=20)
self.range_field = ui.StringField(height=20)
ui.Label('API Key', height=20)
self.api_key_field = ui.StringField(height=20)
ui.Spacer(height= 12)
self.startButton = ui.Button("Start", height=54, clicked_fn=lambda: self.start(), style={"background_color": "green"})
self.stopButton = ui.Button("Stop", height=54, clicked_fn=lambda: self.stop(), style={"color": "red"})
ui.Spacer(height= 12)
self.statusLabel = ui.Label('Click start to begin', height=14, style={"font_size": 12})
self.stopButton.visible = False
print("[taa.google.spreadsheet.api] Extension start up complete")
def on_shutdown(self):
print("Extension shutting down")
self.stop()
print("Extension shutdown complete")
# custom methods
def _on_get_selection(self):
print('_on_get_selection', self.get_selection())
self._source_prim_model.as_string = ", ".join(self.get_selection())
def get_selection(self) -> List[str]:
return omni.usd.get_context().get_selection().get_selected_prim_paths()
def apply_changes(self, frame):
try:
# load the data from Google Spreadsheet ever few seconds; this API is rate limited
frameNumber = int(frame.payload["SWHFrameNumber"])
if(frameNumber % 180 != 0): return
print('applying changes')
self.read_data()
# act on all selected prims
paths = self.list_paths_of_selected_prims()
for path in paths:
# get reference to the prim on stage, making sure that it's valid
prim = self.stage.GetPrimAtPath(path)
if prim.IsValid() == False: continue
# transform the prim based on the settings in the Google Spreadsheet
self.move_prim(prim)
self.rotate_prim(prim)
self.scale_prim(prim)
print('changes applied successfully')
except Exception as err:
print(err)
def read_config(self):
try:
spreadsheetId = self.spreadsheet_id_field.model.get_value_as_string()
range = self.range_field.model.get_value_as_string()
api_key = self.api_key_field.model.get_value_as_string()
return (spreadsheetId, range, api_key)
except Exception as err:
print(err)
def read_data(self):
try:
spreadsheetId, range, api_key = self.read_config()
if self.google_sheet == None:
service = build('sheets', 'v4', developerKey=api_key)
self.google_sheet = service.spreadsheets()
result = self.google_sheet.values().get(spreadsheetId=spreadsheetId, range=range).execute()
values = result.get('values', [])
data = toJSON(values)
# normalize and clean data
self.data["shape"] = data.setdefault('shape', 'Cube')
self.data["size"] = float(data.setdefault('size', 100))
self.data["radius"] = float(data.setdefault('radius', 100))
self.data["translate_x"] = float(data.setdefault('translate_x', 0))
self.data["translate_y"] = float(data.setdefault('translate_y', 0))
self.data["translate_z"] = float(data.setdefault('translate_z', 0))
self.data["rotate_x"] = float(data.setdefault('rotate_x', 0))
self.data["rotate_y"] = float(data.setdefault('rotate_y', 0))
self.data["rotate_z"] = float(data.setdefault('rotate_z', 0))
self.data["scale_x"] = float(data.setdefault('scale_x', 1))
self.data["scale_y"] = float(data.setdefault('scale_y', 1))
self.data["scale_z"] = float(data.setdefault('scale_z', 1))
except HttpError as err:
print(err)
def move_prim(self, prim):
try:
x = self.data.get('translate_x')
y = self.data.get('translate_y')
z = self.data.get('translate_z')
omni.kit.commands.execute('TransformPrimSRT',
path=prim.GetPath(),
new_translation=Gf.Vec3d(x, y, z),
)
except Exception as err:
print("Failed to move prim", err)
def rotate_prim(self, prim):
try:
x = self.data.get('rotate_x')
y = self.data.get('rotate_y')
z = self.data.get('rotate_z')
omni.kit.commands.execute('TransformPrimSRT',
path=prim.GetPath(),
new_rotation_euler=Gf.Vec3d(x, y, z),
)
except Exception as err:
print("Failed to rotate prime", err)
def scale_prim(self, prim):
try:
x = self.data.get('scale_x')
y = self.data.get('scale_y')
z = self.data.get('scale_z')
omni.kit.commands.execute('TransformPrimSRT',
path=prim.GetPath(),
new_scale=Gf.Vec3d(x, y, z),
)
except Exception as err:
print("Failed to scale prim", err)
def list_paths_of_selected_prims(self):
try:
paths = [i.strip() for i in self._source_prim_model.as_string.split(",")]
if not paths:
paths = self.get_selection()
if not paths:
pass
return paths
except Exception as err:
print(err)
def start(self):
self.read_data()
def on_update_apply(frame): self.apply_changes(frame)
self.subscription = omni.kit.app.get_app().get_update_event_stream().create_subscription_to_pop(on_update_apply)
self.startButton.visible = False
self.stopButton.visible = True
self.statusLabel.text = "Status: started"
def stop(self):
if self.subscription: del self.subscription
self.startButton.visible = True
self.stopButton.visible = False
self.statusLabel.text = "Status: stopped"
"""
Utility functions
"""
def toJSON(values):
json = {}
if not values:
return json
for row in values:
key = row[0]
value = row[1]
if not key or not value:
continue
json[row[0]] = row[1]
return json
|
AccelerationAgency/omniverse-extensions/exts/taa.google.spreadsheet.api/taa/google/spreadsheet/api/__init__.py | from .extension import *
|
AccelerationAgency/omniverse-extensions/exts/taa.google.spreadsheet.api/config/extension.toml | [package]
version = "1.0.0"
title = "TAA - Google Spreadsheet API"
description="An exploration into using Google Spreadsheet data to objects on the stage"
readme = "docs/README.md"
repository = ""
category = "Other"
keywords = ["taa", "google", "spreadsheet", "api", "example"]
icon = "data/taa-logo.png"
[dependencies]
"omni.kit.uiapp" = {}
[[python.module]]
name = "taa.google.spreadsheet.api" |
AccelerationAgency/omniverse-extensions/exts/taa.google.spreadsheet.api/docs/README.md | |
AccelerationAgency/omniverse-extensions/exts/taa.omniverse.cameracreator/taa/omniverse/cameracreator/extension.py | import omni.ext
import omni.ui as ui
import omni.kit.commands as commands
class MyExtension(omni.ext.IExt):
# Lifecycle
def on_startup(self, ext_id):
print("[taa.omniverse.viewport] Extension starting up")
self._window = ui.Window("TAA Quick Camera", width=200, height = 200)
with self._window.frame:
with ui.VStack(height = 0, spacing = 4):
self.perspectiveButton = ui.Button("Perspective", height=40, clicked_fn=lambda: self.create_perspective_camera(), style={"background_color":"black"})
self.topButton = ui.Button("Top", height=40, clicked_fn=lambda: self.create_top_camera(), style={"background_color":"black"})
self.frontButton = ui.Button("Front", height=40, clicked_fn=lambda: self.create_front_camera(), style={"background_color":"black"})
self.rightButton = ui.Button("Right", height=40, clicked_fn=lambda: self.create_right_camera(), style={"background_color":"black"})
print("[taa.omniverse.viewport] Extension start up complete")
def on_shutdown(self):
print("[taa.omniverse.viewport] Extension shutting down")
self.stop()
print("[taa.omniverse.viewport] Extension shutdown complete")
# Custom methods
def set_camera(self, path):
omni.kit.viewport_legacy.get_viewport_interface().get_viewport_window().set_active_camera(path)
def rename_camera(self, name):
cameraPath = omni.kit.viewport_legacy.get_viewport_interface().get_viewport_window().get_active_camera()
omni.kit.commands.execute('MovePrims', paths_to_move={cameraPath: f'/World/Camera_{name}'})
def create_perspective_camera(self):
print("[taa.omniverse.viewport] Creating new perspective camera")
self.set_camera("/OmniverseKit_Persp")
commands.execute('DuplicateFromActiveViewportCameraCommand', viewport_name='Viewport')
self.rename_camera("Perspective")
def create_top_camera(self):
print("[taa.omniverse.viewport] Creating new top-down camera")
self.set_camera("/OmniverseKit_Top")
commands.execute('DuplicateFromActiveViewportCameraCommand', viewport_name='Viewport')
self.rename_camera("Top")
def create_front_camera(self):
print("[taa.omniverse.viewport] Creating new front view camera")
self.set_camera("/OmniverseKit_Front")
commands.execute('DuplicateFromActiveViewportCameraCommand', viewport_name='Viewport')
self.rename_camera("Front")
def create_right_camera(self):
print("[taa.omniverse.viewport] Creating new right view camera")
self.set_camera("/OmniverseKit_Right")
commands.execute('DuplicateFromActiveViewportCameraCommand', viewport_name='Viewport')
self.rename_camera("Right")
def start(self):
print("[taa.omniverse.viewport] Starting...")
def stop(self):
print("[taa.omniverse.viewport] Stopping...")
|
AccelerationAgency/omniverse-extensions/exts/taa.omniverse.cameracreator/taa/omniverse/cameracreator/__init__.py | from .extension import *
|
AccelerationAgency/omniverse-extensions/exts/taa.omniverse.cameracreator/config/extension.toml | [package]
version = "1.0.0"
title = "TAA - Omniverse Camera Creator"
description = "An simple extension that lets you quickly create cameras with a single click."
readme = "docs/README.md"
repository = ""
category = "Other"
keywords = ["taa", "viewport", "create", "camera", "view"]
icon = "data/taa-logo.png"
[dependencies]
"omni.kit.uiapp" = {}
[[python.module]]
name = "taa.omniverse.cameracreator" |
AccelerationAgency/omniverse-extensions/exts/taa.omniverse.cameracreator/docs/README.md | |
ilanhuang/audio2face-streamgpt-public/link_app.sh | #!/bin/bash
set -e
SCRIPT_DIR=$(dirname ${BASH_SOURCE})
cd "$SCRIPT_DIR"
exec "tools/packman/python.sh" tools/scripts/link_app.py $@
|
ilanhuang/audio2face-streamgpt-public/link_app.bat | @echo off
call "%~dp0tools\packman\python.bat" %~dp0tools\scripts\link_app.py %*
if %errorlevel% neq 0 ( goto Error )
:Success
exit /b 0
:Error
exit /b %errorlevel%
|
ilanhuang/audio2face-streamgpt-public/README.md | # Stream-GPT
Stream-GPT is an Omniverse Extension that uses OpenAI's GPT-3 model to create a virtual assistant. It allows users to interact with the assistant through both text and voice, and the assistant responds in kind. The extension uses OpenAI's Whisper ASR system to transcribe audio input and Eleven Labs' API to convert the assistant's text responses into audio.
## Getting Started
### Prerequisites
- Python 3.6 or higher
- Omniverse Kit
- Omniverse Audio2Face
- OpenAI API key
- Eleven Labs API key
### Installation
1. Clone the repository:
```bash
git clone https://github.com/ilanhuang/audio2face-stream-chatgpt.git
```
2. Install the required Python packages:
```bash
pip install -r requirements.txt
```
3. Update the `sys.path.append` in `extension.py` with the correct path to the `streaming_server` directory in your local clone of the repository.
```python
sys.path.append("C:\\Users\\YourUsername\\path\\to\\stream-gpt\\pkg\\audio2face-2022.2.1\\exts\\omni.audio2face.player\omni\\audio2face\\player\\scripts\\streaming_server")
```
4. Add the custom extension to Omniverse:
- Go to the "Windows" tab on the top of the screen.
- Scroll down to "Extensions".
- Click on the gear icon to open the Extensions settings.
- Click on the "+" button to add a new path to the custom extension.
- A window will pop up when you turn on the extension.
5. Set your OpenAI and Eleven Labs API keys, as well as the voice_id, model_id, and the Audio2Face's audioplayer's prim path (instance_name) in the extension's settings:
- Open the extension and click on the "Settings" button.
- Enter your OpenAI API key, Eleven Labs API key, voice_id, model_id and instance name in the corresponding fields. (A text file in the repository lists the available voice ids.)
## Usage
Once the application is running, you can interact with the virtual assistant through the UI. You can type your prompts into the text field and click on the "Send" button or use the "Record Audio" button to speak your prompts. The assistant will respond in the chat log and through your speakers.
You can also add a system to the GPT virtual assistant by typing it in the "System" field in the UI.
All interactions made with the extension are saved in a folder named "chat_logs" for future reference. |
ilanhuang/audio2face-streamgpt-public/tools/scripts/link_app.py | import argparse
import json
import os
import sys
import packmanapi
import urllib3
def find_omniverse_apps():
http = urllib3.PoolManager()
try:
r = http.request("GET", "http://127.0.0.1:33480/components")
except Exception as e:
print(f"Failed retrieving apps from an Omniverse Launcher, maybe it is not installed?\nError: {e}")
sys.exit(1)
apps = {}
for x in json.loads(r.data.decode("utf-8")):
latest = x.get("installedVersions", {}).get("latest", "")
if latest:
for s in x.get("settings", []):
if s.get("version", "") == latest:
root = s.get("launch", {}).get("root", "")
apps[x["slug"]] = (x["name"], root)
break
return apps
def create_link(src, dst):
print(f"Creating a link '{src}' -> '{dst}'")
packmanapi.link(src, dst)
APP_PRIORITIES = ["code", "create", "view"]
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Create folder link to Kit App installed from Omniverse Launcher")
parser.add_argument(
"--path",
help="Path to Kit App installed from Omniverse Launcher, e.g.: 'C:/Users/bob/AppData/Local/ov/pkg/create-2021.3.4'",
required=False,
)
parser.add_argument(
"--app", help="Name of Kit App installed from Omniverse Launcher, e.g.: 'code', 'create'", required=False
)
args = parser.parse_args()
path = args.path
if not path:
print("Path is not specified, looking for Omniverse Apps...")
apps = find_omniverse_apps()
if len(apps) == 0:
print(
"Can't find any Omniverse Apps. Use Omniverse Launcher to install one. 'Code' is the recommended app for developers."
)
sys.exit(0)
print("\nFound following Omniverse Apps:")
for i, slug in enumerate(apps):
name, root = apps[slug]
print(f"{i}: {name} ({slug}) at: '{root}'")
if args.app:
selected_app = args.app.lower()
if selected_app not in apps:
choices = ", ".join(apps.keys())
print(f"Passed app: '{selected_app}' is not found. Specify one of the following found Apps: {choices}")
sys.exit(0)
else:
selected_app = next((x for x in APP_PRIORITIES if x in apps), None)
if not selected_app:
selected_app = next(iter(apps))
print(f"\nSelected app: {selected_app}")
_, path = apps[selected_app]
if not os.path.exists(path):
print(f"Provided path doesn't exist: {path}")
else:
SCRIPT_ROOT = os.path.dirname(os.path.realpath(__file__))
create_link(f"{SCRIPT_ROOT}/../../app", path)
print("Success!")
|
ilanhuang/audio2face-streamgpt-public/tools/packman/python.sh | #!/bin/bash
# Copyright 2019-2020 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.
set -e
PACKMAN_CMD="$(dirname "${BASH_SOURCE}")/packman"
if [ ! -f "$PACKMAN_CMD" ]; then
PACKMAN_CMD="${PACKMAN_CMD}.sh"
fi
source "$PACKMAN_CMD" init
export PYTHONPATH="${PM_MODULE_DIR}:${PYTHONPATH}"
export PYTHONNOUSERSITE=1
# workaround for our python not shipping with certs
if [[ -z ${SSL_CERT_DIR:-} ]]; then
export SSL_CERT_DIR=/etc/ssl/certs/
fi
"${PM_PYTHON}" -u "$@"
|
ilanhuang/audio2face-streamgpt-public/tools/packman/python.bat | :: Copyright 2019-2020 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.
@echo off
setlocal
call "%~dp0\packman" init
set "PYTHONPATH=%PM_MODULE_DIR%;%PYTHONPATH%"
set PYTHONNOUSERSITE=1
"%PM_PYTHON%" -u %*
|
ilanhuang/audio2face-streamgpt-public/tools/packman/packman.cmd | :: Reset errorlevel status (don't inherit from caller) [xxxxxxxxxxx]
@call :ECHO_AND_RESET_ERROR
:: You can remove the call below if you do your own manual configuration of the dev machines
call "%~dp0\bootstrap\configure.bat"
if %errorlevel% neq 0 ( exit /b %errorlevel% )
:: Everything below is mandatory
if not defined PM_PYTHON goto :PYTHON_ENV_ERROR
if not defined PM_MODULE goto :MODULE_ENV_ERROR
:: Generate temporary path for variable file
for /f "delims=" %%a in ('powershell -ExecutionPolicy ByPass -NoLogo -NoProfile ^
-File "%~dp0bootstrap\generate_temp_file_name.ps1"') do set PM_VAR_PATH=%%a
if %1.==. (
set PM_VAR_PATH_ARG=
) else (
set PM_VAR_PATH_ARG=--var-path="%PM_VAR_PATH%"
)
"%PM_PYTHON%" -S -s -u -E "%PM_MODULE%" %* %PM_VAR_PATH_ARG%
if %errorlevel% neq 0 ( exit /b %errorlevel% )
:: Marshall environment variables into the current environment if they have been generated and remove temporary file
if exist "%PM_VAR_PATH%" (
for /F "usebackq tokens=*" %%A in ("%PM_VAR_PATH%") do set "%%A"
)
if %errorlevel% neq 0 ( goto :VAR_ERROR )
if exist "%PM_VAR_PATH%" (
del /F "%PM_VAR_PATH%"
)
if %errorlevel% neq 0 ( goto :VAR_ERROR )
set PM_VAR_PATH=
goto :eof
:: Subroutines below
:PYTHON_ENV_ERROR
@echo User environment variable PM_PYTHON is not set! Please configure machine for packman or call configure.bat.
exit /b 1
:MODULE_ENV_ERROR
@echo User environment variable PM_MODULE is not set! Please configure machine for packman or call configure.bat.
exit /b 1
:VAR_ERROR
@echo Error while processing and setting environment variables!
exit /b 1
:ECHO_AND_RESET_ERROR
@echo off
if /I "%PM_VERBOSITY%"=="debug" (
@echo on
)
exit /b 0
|
ilanhuang/audio2face-streamgpt-public/tools/packman/config.packman.xml | <config remotes="cloudfront">
<remote2 name="cloudfront">
<transport actions="download" protocol="https" packageLocation="d4i3qtqj3r0z5.cloudfront.net/${name}@${version}" />
</remote2>
</config>
|
ilanhuang/audio2face-streamgpt-public/tools/packman/bootstrap/generate_temp_file_name.ps1 | <#
Copyright 2019 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.
#>
$out = [System.IO.Path]::GetTempFileName()
Write-Host $out
# SIG # Begin signature block
# MIIaVwYJKoZIhvcNAQcCoIIaSDCCGkQCAQExDzANBglghkgBZQMEAgEFADB5Bgor
# BgEEAYI3AgEEoGswaTA0BgorBgEEAYI3AgEeMCYCAwEAAAQQH8w7YFlLCE63JNLG
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# SIG # End signature block
|
ilanhuang/audio2face-streamgpt-public/tools/packman/bootstrap/configure.bat | :: Copyright 2019 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.
set PM_PACKMAN_VERSION=6.33.2
:: Specify where packman command is rooted
set PM_INSTALL_PATH=%~dp0..
:: The external root may already be configured and we should do minimal work in that case
if defined PM_PACKAGES_ROOT goto ENSURE_DIR
:: If the folder isn't set we assume that the best place for it is on the drive that we are currently
:: running from
set PM_DRIVE=%CD:~0,2%
set PM_PACKAGES_ROOT=%PM_DRIVE%\packman-repo
:: We use *setx* here so that the variable is persisted in the user environment
echo Setting user environment variable PM_PACKAGES_ROOT to %PM_PACKAGES_ROOT%
setx PM_PACKAGES_ROOT %PM_PACKAGES_ROOT%
if %errorlevel% neq 0 ( goto ERROR )
:: The above doesn't work properly from a build step in VisualStudio because a separate process is
:: spawned for it so it will be lost for subsequent compilation steps - VisualStudio must
:: be launched from a new process. We catch this odd-ball case here:
if defined PM_DISABLE_VS_WARNING goto ENSURE_DIR
if not defined VSLANG goto ENSURE_DIR
echo The above is a once-per-computer operation. Unfortunately VisualStudio cannot pick up environment change
echo unless *VisualStudio is RELAUNCHED*.
echo If you are launching VisualStudio from command line or command line utility make sure
echo you have a fresh launch environment (relaunch the command line or utility).
echo If you are using 'linkPath' and referring to packages via local folder links you can safely ignore this warning.
echo You can disable this warning by setting the environment variable PM_DISABLE_VS_WARNING.
echo.
:: Check for the directory that we need. Note that mkdir will create any directories
:: that may be needed in the path
:ENSURE_DIR
if not exist "%PM_PACKAGES_ROOT%" (
echo Creating directory %PM_PACKAGES_ROOT%
mkdir "%PM_PACKAGES_ROOT%"
)
if %errorlevel% neq 0 ( goto ERROR_MKDIR_PACKAGES_ROOT )
:: The Python interpreter may already be externally configured
if defined PM_PYTHON_EXT (
set PM_PYTHON=%PM_PYTHON_EXT%
goto PACKMAN
)
set PM_PYTHON_VERSION=3.7.9-windows-x86_64
set PM_PYTHON_BASE_DIR=%PM_PACKAGES_ROOT%\python
set PM_PYTHON_DIR=%PM_PYTHON_BASE_DIR%\%PM_PYTHON_VERSION%
set PM_PYTHON=%PM_PYTHON_DIR%\python.exe
if exist "%PM_PYTHON%" goto PACKMAN
if not exist "%PM_PYTHON_BASE_DIR%" call :CREATE_PYTHON_BASE_DIR
set PM_PYTHON_PACKAGE=python@%PM_PYTHON_VERSION%.cab
for /f "delims=" %%a in ('powershell -ExecutionPolicy ByPass -NoLogo -NoProfile -File "%~dp0\generate_temp_file_name.ps1"') do set TEMP_FILE_NAME=%%a
set TARGET=%TEMP_FILE_NAME%.zip
call "%~dp0fetch_file_from_packman_bootstrap.cmd" %PM_PYTHON_PACKAGE% "%TARGET%"
if %errorlevel% neq 0 (
echo !!! Error fetching python from CDN !!!
goto ERROR
)
for /f "delims=" %%a in ('powershell -ExecutionPolicy ByPass -NoLogo -NoProfile -File "%~dp0\generate_temp_folder.ps1" -parentPath "%PM_PYTHON_BASE_DIR%"') do set TEMP_FOLDER_NAME=%%a
echo Unpacking Python interpreter ...
"%SystemRoot%\system32\expand.exe" -F:* "%TARGET%" "%TEMP_FOLDER_NAME%" 1> nul
del "%TARGET%"
:: Failure during extraction to temp folder name, need to clean up and abort
if %errorlevel% neq 0 (
echo !!! Error unpacking python !!!
call :CLEAN_UP_TEMP_FOLDER
goto ERROR
)
:: If python has now been installed by a concurrent process we need to clean up and then continue
if exist "%PM_PYTHON%" (
call :CLEAN_UP_TEMP_FOLDER
goto PACKMAN
) else (
if exist "%PM_PYTHON_DIR%" ( rd /s /q "%PM_PYTHON_DIR%" > nul )
)
:: Perform atomic rename
rename "%TEMP_FOLDER_NAME%" "%PM_PYTHON_VERSION%" 1> nul
:: Failure during move, need to clean up and abort
if %errorlevel% neq 0 (
echo !!! Error renaming python !!!
call :CLEAN_UP_TEMP_FOLDER
goto ERROR
)
:PACKMAN
:: The packman module may already be externally configured
if defined PM_MODULE_DIR_EXT (
set PM_MODULE_DIR=%PM_MODULE_DIR_EXT%
) else (
set PM_MODULE_DIR=%PM_PACKAGES_ROOT%\packman-common\%PM_PACKMAN_VERSION%
)
set PM_MODULE=%PM_MODULE_DIR%\packman.py
if exist "%PM_MODULE%" goto ENSURE_7ZA
set PM_MODULE_PACKAGE=packman-common@%PM_PACKMAN_VERSION%.zip
for /f "delims=" %%a in ('powershell -ExecutionPolicy ByPass -NoLogo -NoProfile -File "%~dp0\generate_temp_file_name.ps1"') do set TEMP_FILE_NAME=%%a
set TARGET=%TEMP_FILE_NAME%
call "%~dp0fetch_file_from_packman_bootstrap.cmd" %PM_MODULE_PACKAGE% "%TARGET%"
if %errorlevel% neq 0 (
echo !!! Error fetching packman from CDN !!!
goto ERROR
)
echo Unpacking ...
"%PM_PYTHON%" -S -s -u -E "%~dp0\install_package.py" "%TARGET%" "%PM_MODULE_DIR%"
if %errorlevel% neq 0 (
echo !!! Error unpacking packman !!!
goto ERROR
)
del "%TARGET%"
:ENSURE_7ZA
set PM_7Za_VERSION=16.02.4
set PM_7Za_PATH=%PM_PACKAGES_ROOT%\7za\%PM_7ZA_VERSION%
if exist "%PM_7Za_PATH%" goto END
set PM_7Za_PATH=%PM_PACKAGES_ROOT%\chk\7za\%PM_7ZA_VERSION%
if exist "%PM_7Za_PATH%" goto END
"%PM_PYTHON%" -S -s -u -E "%PM_MODULE%" pull "%PM_MODULE_DIR%\deps.packman.xml"
if %errorlevel% neq 0 (
echo !!! Error fetching packman dependencies !!!
goto ERROR
)
goto END
:ERROR_MKDIR_PACKAGES_ROOT
echo Failed to automatically create packman packages repo at %PM_PACKAGES_ROOT%.
echo Please set a location explicitly that packman has permission to write to, by issuing:
echo.
echo setx PM_PACKAGES_ROOT {path-you-choose-for-storing-packman-packages-locally}
echo.
echo Then launch a new command console for the changes to take effect and run packman command again.
exit /B %errorlevel%
:ERROR
echo !!! Failure while configuring local machine :( !!!
exit /B %errorlevel%
:CLEAN_UP_TEMP_FOLDER
rd /S /Q "%TEMP_FOLDER_NAME%"
exit /B
:CREATE_PYTHON_BASE_DIR
:: We ignore errors and clean error state - if two processes create the directory one will fail which is fine
md "%PM_PYTHON_BASE_DIR%" > nul 2>&1
exit /B 0
:END
|
ilanhuang/audio2face-streamgpt-public/tools/packman/bootstrap/fetch_file_from_packman_bootstrap.cmd | :: Copyright 2019 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.
:: You need to specify <package-name> <target-path> as input to this command
@setlocal
@set PACKAGE_NAME=%1
@set TARGET_PATH=%2
@echo Fetching %PACKAGE_NAME% ...
@powershell -ExecutionPolicy ByPass -NoLogo -NoProfile -File "%~dp0download_file_from_url.ps1" ^
-source "http://bootstrap.packman.nvidia.com/%PACKAGE_NAME%" -output %TARGET_PATH%
:: A bug in powershell prevents the errorlevel code from being set when using the -File execution option
:: We must therefore do our own failure analysis, basically make sure the file exists and is larger than 0 bytes:
@if not exist %TARGET_PATH% goto ERROR_DOWNLOAD_FAILED
@if %~z2==0 goto ERROR_DOWNLOAD_FAILED
@endlocal
@exit /b 0
:ERROR_DOWNLOAD_FAILED
@echo Failed to download file from S3
@echo Most likely because endpoint cannot be reached or file %PACKAGE_NAME% doesn't exist
@endlocal
@exit /b 1 |
ilanhuang/audio2face-streamgpt-public/tools/packman/bootstrap/download_file_from_url.ps1 | <#
Copyright 2019 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.
#>
param(
[Parameter(Mandatory=$true)][string]$source=$null,
[string]$output="out.exe"
)
$filename = $output
$triesLeft = 3
do
{
$triesLeft -= 1
try
{
Write-Host "Downloading from bootstrap.packman.nvidia.com ..."
$wc = New-Object net.webclient
$wc.Downloadfile($source, $fileName)
$triesLeft = 0
}
catch
{
Write-Host "Error downloading $source!"
Write-Host $_.Exception|format-list -force
}
} while ($triesLeft -gt 0)
|
ilanhuang/audio2face-streamgpt-public/tools/packman/bootstrap/generate_temp_folder.ps1 | <#
Copyright 2019 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.
#>
param(
[Parameter(Mandatory=$true)][string]$parentPath=$null
)
[string] $name = [System.Guid]::NewGuid()
$out = Join-Path $parentPath $name
New-Item -ItemType Directory -Path ($out) | Out-Null
Write-Host $out
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# SIG # End signature block
|
ilanhuang/audio2face-streamgpt-public/tools/packman/bootstrap/install_package.py | # Copyright 2019 NVIDIA CORPORATION
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import shutil
import sys
import tempfile
import zipfile
__author__ = "hfannar"
logging.basicConfig(level=logging.WARNING, format="%(message)s")
logger = logging.getLogger("install_package")
class TemporaryDirectory:
def __init__(self):
self.path = None
def __enter__(self):
self.path = tempfile.mkdtemp()
return self.path
def __exit__(self, type, value, traceback):
# Remove temporary data created
shutil.rmtree(self.path)
def install_package(package_src_path, package_dst_path):
with zipfile.ZipFile(package_src_path, allowZip64=True) as zip_file, TemporaryDirectory() as temp_dir:
zip_file.extractall(temp_dir)
# Recursively copy (temp_dir will be automatically cleaned up on exit)
try:
# Recursive copy is needed because both package name and version folder could be missing in
# target directory:
shutil.copytree(temp_dir, package_dst_path)
except OSError as exc:
logger.warning("Directory %s already present, packaged installation aborted" % package_dst_path)
else:
logger.info("Package successfully installed to %s" % package_dst_path)
install_package(sys.argv[1], sys.argv[2])
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/recording_transcription.py | #Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import os
import pyaudio
import wave
import keyboard
import time
from time import sleep
import openai
import datetime
def open_file(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return infile.read()
def save_file(filepath, content):
with open(filepath, 'w', encoding='utf-8') as outfile:
outfile.write(content)
def timestamp_to_datetime(unix_time):
return datetime.datetime.fromtimestamp(unix_time).strftime("%A, %B %d, %Y at %I:%M%p %Z")
def record_client_voice(output_filename, recording_status):
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
frames = []
p = pyaudio.PyAudio()
stream = None
try:
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
start_time = time.time()
min_duration = 0.1
while recording_status() or time.time() - start_time < min_duration:
data = stream.read(CHUNK)
frames.append(data)
except Exception as e:
print(f"Error while recording audio: {e}")
finally:
if stream is not None:
stream.stop_stream()
stream.close()
p.terminate()
wf = wave.open(output_filename, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
wf.close()
return output_filename
def transcribe_audio_to_text(file_path):
with open(file_path, 'rb') as audio_file:
transcript_response = openai.Audio.transcribe("whisper-1", audio_file)
return transcript_response["text"] |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/transmission.py | #Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import grpc
import os
import soundfile
import numpy as np
import audio2face_pb2
import audio2face_pb2_grpc
import sounddevice as sd
import time
from typing import Iterator
import requests
import queue
import threading
import carb
def generate_stream(text: str, voice_id: str, model_id: str, api_key: str, stream_chunk_size: int = 2048) -> Iterator[bytes]:
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream"
data = dict(text=text, model_id=model_id, voice_settings=None)
headers = {"xi-api-key": api_key}
response = requests.post(url, json=data, headers=headers, stream=True)
for chunk in response.iter_content(chunk_size=stream_chunk_size):
if chunk:
yield chunk
def read_api_key_from_file(file_path: str) -> str:
with open(file_path, 'r') as f:
return f.read().strip()
def text_to_audio_stream(text, instance_name, api_key):
print("text_to_audio_stream: start")
settings = carb.settings.get_settings()
voice_id = settings.get_as_string("/persistent/exts/omni.example.streamgpt/VOICE_ID")
model_id = settings.get_as_string("/persistent/exts/omni.example.streamgpt/MODEL_ID")
audio_stream = generate_stream(text, voice_id, model_id, api_key)
current_dir = os.path.dirname(os.path.realpath(__file__))
audio_filename = os.path.join(current_dir, "temp_audio_response.mp3")
with open(audio_filename, 'wb') as f:
for chunk in audio_stream:
f.write(chunk)
audio_data, samplerate = soundfile.read(audio_filename, dtype="float32")
if len(audio_data.shape) > 1:
audio_data = np.average(audio_data, axis=1)
url = "localhost:50051"
audio_queue = queue.Queue()
audio_queue.put(audio_data)
def audio_streamer():
while not audio_queue.empty():
audio_chunk = audio_queue.get()
push_audio_track_stream(url, audio_chunk, samplerate, instance_name)
audio_thread = threading.Thread(target=audio_streamer)
audio_thread.start()
os.remove(audio_filename)
print("text_to_audio_stream: end")
def push_audio_track_stream(url, audio_data, samplerate, instance_name):
print("push_audio_track_stream: start")
chunk_size = samplerate // 10
sleep_between_chunks = 0.04
with grpc.insecure_channel(url) as channel:
print("Channel created")
stub = audio2face_pb2_grpc.Audio2FaceStub(channel)
def make_generator():
start_marker = audio2face_pb2.PushAudioRequestStart(
samplerate=samplerate,
instance_name=instance_name,
block_until_playback_is_finished=False,
)
yield audio2face_pb2.PushAudioStreamRequest(start_marker=start_marker)
for i in range(len(audio_data) // chunk_size + 1):
try:
time.sleep(sleep_between_chunks)
chunk = audio_data[i * chunk_size : i * chunk_size + chunk_size]
yield audio2face_pb2.PushAudioStreamRequest(audio_data=chunk.astype(np.float32).tobytes())
except Exception as e:
print(f"Error in generator function: {e}")
break
request_generator = make_generator()
print("Sending audio data...")
response = stub.PushAudioStream(request_generator)
if response.success:
print("SUCCESS")
else:
print(f"ERROR: {response.message}")
print("Channel closed") |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/extension.py | #Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import omni.ext
import sys
sys.path.append("C:\\Users\\ERKS 2\\Documents\\Omniverse\\ov\\pkg\\audio2face-2022.2.1\\exts\\omni.audio2face.player\omni\\audio2face\\player\\scripts\\streaming_server")
import openai
import carb
from .window import AudioChatWindow
def open_file(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return infile.read()
# Any class derived from `omni.ext.IExt` in top level module (defined in `python.modules` of `extension.toml`) will be
# instantiated when extension gets enabled and `on_startup(ext_id)` will be called. Later when extension gets disabled
# on_shutdown() is called.
class MyExtension(omni.ext.IExt):
# ext_id is current extension id. It can be used with extension manager to query additional information, like where
# this extension is located on filesystem.
def on_startup(self, ext_id):
openai.api_key = AudioChatWindow.get_openai_api_key()
self._window = AudioChatWindow("VIRTUAL ASSISTANT", width=400, height=525)
def on_shutdown(self):
self._window.destroy()
self._window = None
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/__init__.py | from .extension import *
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/chatbot.py | #Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import os
import openai
import json
import numpy as np
from numpy.linalg import norm
import re
from time import time,sleep
from uuid import uuid4
import datetime
def open_file(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return infile.read()
def save_file(filepath, content):
with open(filepath, 'w', encoding='utf-8') as outfile:
outfile.write(content)
def load_json(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return json.load(infile)
def save_json(filepath, payload):
with open(filepath, 'w', encoding='utf-8') as outfile:
json.dump(payload, outfile, ensure_ascii=False, sort_keys=True, indent=2)
def timestamp_to_datetime(unix_time):
return datetime.datetime.fromtimestamp(unix_time).strftime("%A, %B %d, %Y at %I:%M%p %Z")
def gpt3_embedding(content, engine='text-embedding-ada-002'):
content = content.encode(encoding='ASCII',errors='ignore').decode() # fix any UNICODE errors
response = openai.Embedding.create(input=content,engine=engine)
vector = response['data'][0]['embedding'] # this is a normal list
return vector
def chatgpt_completion(messages, model="gpt-4", temp=0.0, top_p=1.0, tokens=400, freq_pen=0.0, pres_pen=0.0):
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temp,
max_tokens=tokens,
top_p=top_p,
frequency_penalty=freq_pen,
presence_penalty=pres_pen,)
text = response['choices'][0]['message']['content']
tokens_used = response['usage']['total_tokens']
filename = 'chat_%s_aibot.json' % time()
script_dir = os.path.dirname(os.path.realpath(__file__))
chat_logs_path = os.path.join(script_dir, 'chat_logs')
if not os.path.exists(chat_logs_path):
os.makedirs(chat_logs_path)
input_message = messages[-1]['content']
log_content = f"User:\n{input_message}\n\nAi_Bot:\n{text}\n\nTokens used: {tokens_used}"
save_file(os.path.join(chat_logs_path, filename), log_content)
return text
def flatten_convo(conversation):
convo = ''
for i in conversation:
convo += '%s: %s\n' % (i['role'].upper(), i['content'])
return convo.strip()
def set_openai_api_key(api_key):
openai.api_key = api_key
def set_system_content(content):
global system_content
system_content = content
if __name__ == '__main__':
convo_length = 30
set_openai_api_key(api_key)
conversation = list()
conversation.append({'role': 'system', 'content': system_content})
counter = 0
while True:
# get user input, save to file
a = input('\n\nCLIENT: ')
conversation.append({'role': 'user', 'content': a})
filename = 'chat_%s_client.txt' % time()
if not os.path.exists('chat_logs'):
os.makedirs('chat_logs')
save_file('chat_logs/%s' % filename, a)
flat = flatten_convo(conversation)
# generate a response
response = chatgpt_completion(conversation)
conversation.append({'role': 'assistant', 'content': response})
print('\n\nAI_Bot: %s' % response)
# increment counter and consolidate memories
counter += 2
if counter >= 10:
# reset conversation
conversation = list()
conversation.append({'role': 'system', 'content': system_content})
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/window.py | #Stream-GPT
#GNU - GLP Licence
#Copyright (C) <year> <Huang I Lan & Erks - Virtual Studio>
#This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
import os
import omni.ui as ui
import omni.kit.commands
from omni.kit.window.popup_dialog.form_dialog import FormDialog
from time import time
from .recording_transcription import record_client_voice, transcribe_audio_to_text
from .chatbot import chatgpt_completion, set_system_content
from .transmission import text_to_audio_stream
import threading
import time
import tempfile
import datetime
import carb
def save_file(filepath, content):
with open(filepath, 'w', encoding='utf-8') as outfile:
outfile.write(content)
def timestamp_to_datetime(unix_time):
return datetime.datetime.fromtimestamp(unix_time).strftime("%A, %B %d, %Y at %I:%M%p %Z")
class AudioChatWindow(ui.Window):
def _build_fn(self):
with self.frame:
with ui.VStack():
with ui.ScrollingFrame(height=ui.Percent(75)):
self.chat_log = ui.Label("", word_wrap=True)
with ui.HStack(height=ui.Percent(10)):
ui.StringField(model=self._prompt_model, multiline=True)
with ui.HStack(height=ui.Percent(10)):
self.record_audio_button = ui.Button("Record Audio", height=40, clicked_fn=lambda *_args, **_kwargs: self._toggle_record_audio())
ui.Button("Send", height=40, clicked_fn=lambda: self._send_text_prompt())
with ui.HStack():
ui.Button("Settings", tooltip="Configure API Key, Instance name and Default System", width=0, height=0, clicked_fn=lambda: self._open_settings())
system_settings_button = ui.Button("System", height=0, width=0)
system_settings_button.set_clicked_fn(lambda: self.show_system_settings_menu())
def __init__(self, title: str, **kwargs) -> None:
self.conversation = [{"role": "system", "content": ""}]
self.system_content_model = ui.SimpleStringModel()
self.lock = threading.Lock()
super().__init__(title, **kwargs)
self._prompt_model = ui.SimpleStringModel()
self.frame.set_build_fn(self._build_fn)
def show_system_settings_menu(self):
self.system_settings_menu = ui.Menu("")
with self.system_settings_menu:
ui.StringField(model=self.system_content_model, multiline=True)
self.system_settings_menu.show()
def _toggle_record_audio(self):
if not hasattr(self, "recording"):
self.recording = False
if not self.recording:
self.recording = True
threading.Thread(target=self._record_and_transcribe_audio).start()
else:
self.recording = False
def _process_conversation(self, user_content):
current_system_content = self.system_content_model.get_value_as_string().strip()
if current_system_content != self.conversation[0]['content']:
self.reset_chat()
set_system_content(current_system_content)
self.conversation.append({"role": "user", "content": user_content})
response = chatgpt_completion(self.conversation)
self.chat_log.text += f"\nUser: {user_content}\nAssistant: {response}"
settings = carb.settings.get_settings()
instance_name = settings.get_as_string("/persistent/exts/omni.example.streamgpt/INSTANCE_NAME")
threading.Thread(target=text_to_audio_stream, args=(response, instance_name, self.get_elevenlabs_api_key())).start()
def _record_and_transcribe_audio(self):
output_filename = "recorded_audio.wav"
record_client_voice(output_filename)
transcript = transcribe_audio_to_text(output_filename)
self._send_audio_transcript(transcript)
def _send_audio_transcript(self, transcript):
self.chat_log.text += "\nThinking..."
threading.Thread(target=self._process_conversation, args=(transcript,)).start()
def reset_chat(self):
self.chat_log.text = ""
self.conversation = [{"role": "system", "content": self.system_content_model.get_value_as_string().strip()}]
def _save_settings(self, dialog):
values = dialog.get_values()
settings = carb.settings.get_settings()
settings.set_string("/persistent/exts/omni.example.streamgpt/APIKey_OPEN_AI", values["APIKey_OPEN_AI"])
settings.set_string("/persistent/exts/omni.example.streamgpt/APIKey_ELEVEN_LABS", values["APIKey_ELEVEN_LABS"])
settings.set_string("/persistent/exts/omni.example.streamgpt/VOICE_ID", values["ELEVEN_LABS_VOICE_ID"])
settings.set_string("/persistent/exts/omni.example.streamgpt/MODEL_ID", values["ELEVEN_LABS_MODEL_ID"])
settings.set_string("/persistent/exts/omni.example.streamgpt/INSTANCE_NAME", values["INSTANCE_NAME"])
dialog.hide()
def _open_settings(self):
settings = carb.settings.get_settings()
apikey_open_ai = settings.get_as_string("/persistent/exts/omni.example.streamgpt/APIKey_OPEN_AI")
apikey_eleven_labs = settings.get_as_string("/persistent/exts/omni.example.streamgpt/APIKey_ELEVEN_LABS")
voice_id = settings.get_as_string("/persistent/exts/omni.example.streamgpt/VOICE_ID")
model_id = settings.get_as_string("/persistent/exts/omni.example.streamgpt/MODEL_ID")
instance_name = settings.get_as_string("/persistent/exts/omni.example.streamgpt/INSTANCE_NAME")
if apikey_open_ai == "":
apikey_open_ai = "Enter OPEN-AI API Key Here"
if apikey_eleven_labs == "":
apikey_eleven_labs = "Enter ELEVEN-LABS API Key Here"
if instance_name == "":
instance_name = "Enter Instance Name Here"
if voice_id == "":
voice_id = "Enter Eleven Labs Voice ID Here"
if model_id == "":
model_id = "Enter Eleven Labs Model ID Here"
field_defs = [
FormDialog.FieldDef("APIKey_OPEN_AI", "OPEN-AI API Key: ", ui.StringField, apikey_open_ai),
FormDialog.FieldDef("APIKey_ELEVEN_LABS", "ELEVEN-LABS API Key: ", ui.StringField, apikey_eleven_labs),
FormDialog.FieldDef("ELEVEN_LABS_VOICE_ID", "Voice ID: ", ui.StringField, voice_id),
FormDialog.FieldDef("ELEVEN_LABS_MODEL_ID", "Model ID: ", ui.StringField, model_id),
FormDialog.FieldDef("INSTANCE_NAME", "Instance Name: ", ui.StringField, instance_name),
]
dialog = FormDialog(
title="Settings",
message="Your Settings: ",
field_defs=field_defs,
ok_handler=lambda dialog: self._save_settings(dialog))
dialog.show()
@staticmethod
def get_openai_api_key():
settings = carb.settings.get_settings()
return settings.get_as_string("/persistent/exts/omni.example.streamgpt/APIKey_OPEN_AI")
def get_elevenlabs_api_key(self):
settings = carb.settings.get_settings()
return settings.get_as_string("/persistent/exts/omni.example.streamgpt/APIKey_ELEVEN_LABS")
def _send_text_prompt(self):
prompt = self._prompt_model.get_value_as_string()
self.chat_log.text += "\nThinking..."
threading.Thread(target=self._process_conversation, args=(prompt,)).start()
self._prompt_model.set_value("")
def _toggle_record_audio(self):
if not hasattr(self, "recording"):
self.recording = False
self.recording = not self.recording
if self.recording:
self.record_audio_button.text = "Stop Recording"
else:
self.record_audio_button.text = "Record Audio"
threading.Thread(target=self._record_and_transcribe_audio_alternative).start()
def recording_status(self):
return self.recording
def _record_and_transcribe_audio_alternative(self):
with self.lock:
temp_audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
temp_audio_filename = temp_audio_file.name
temp_audio_file.close()
recorded_audio_filename = record_client_voice(temp_audio_filename, self.recording_status)
transcript = transcribe_audio_to_text(recorded_audio_filename)
os.remove(temp_audio_filename)
if transcript.strip():
self._send_audio_transcript(transcript)
def destroy(self):
super().destroy()
self._prompt_model = None |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/pytransform/__init__.py | # These module alos are used by protection code, so that protection
# code needn't import anything
import os
import platform
import sys
import struct
# Because ctypes is new from Python 2.5, so pytransform doesn't work
# before Python 2.5
#
from ctypes import cdll, c_char, c_char_p, c_int, c_void_p, \
pythonapi, py_object, PYFUNCTYPE, CFUNCTYPE
from fnmatch import fnmatch
#
# Support Platforms
#
plat_path = 'platforms'
plat_table = (
('windows', ('windows', 'cygwin*')),
('darwin', ('darwin',)),
('ios', ('ios',)),
('linux', ('linux*',)),
('freebsd', ('freebsd*', 'openbsd*', 'isilon onefs')),
('poky', ('poky',)),
)
arch_table = (
('x86', ('i?86', )),
('x86_64', ('x64', 'x86_64', 'amd64', 'intel')),
('arm', ('armv5',)),
('armv6', ('armv6l',)),
('armv7', ('armv7l',)),
('ppc64', ('ppc64le',)),
('mips32', ('mips',)),
('aarch32', ('aarch32',)),
('aarch64', ('aarch64', 'arm64'))
)
#
# Hardware type
#
HT_HARDDISK, HT_IFMAC, HT_IPV4, HT_IPV6, HT_DOMAIN = range(5)
#
# Global
#
_pytransform = None
class PytransformError(Exception):
pass
def dllmethod(func):
def wrap(*args, **kwargs):
return func(*args, **kwargs)
return wrap
@dllmethod
def version_info():
prototype = PYFUNCTYPE(py_object)
dlfunc = prototype(('version_info', _pytransform))
return dlfunc()
@dllmethod
def init_pytransform():
major, minor = sys.version_info[0:2]
# Python2.5 no sys.maxsize but sys.maxint
# bitness = 64 if sys.maxsize > 2**32 else 32
prototype = PYFUNCTYPE(c_int, c_int, c_int, c_void_p)
init_module = prototype(('init_module', _pytransform))
ret = init_module(major, minor, pythonapi._handle)
if (ret & 0xF000) == 0x1000:
raise PytransformError('Initialize python wrapper failed (%d)'
% (ret & 0xFFF))
return ret
@dllmethod
def init_runtime():
prototype = PYFUNCTYPE(c_int, c_int, c_int, c_int, c_int)
_init_runtime = prototype(('init_runtime', _pytransform))
return _init_runtime(0, 0, 0, 0)
@dllmethod
def encrypt_code_object(pubkey, co, flags, suffix=''):
_pytransform.set_option(6, suffix.encode())
prototype = PYFUNCTYPE(py_object, py_object, py_object, c_int)
dlfunc = prototype(('encrypt_code_object', _pytransform))
return dlfunc(pubkey, co, flags)
@dllmethod
def generate_license_key(prikey, keysize, rcode):
prototype = PYFUNCTYPE(py_object, c_char_p, c_int, c_char_p)
dlfunc = prototype(('generate_license_key', _pytransform))
return dlfunc(prikey, keysize, rcode) if sys.version_info[0] == 2 \
else dlfunc(prikey, keysize, rcode.encode())
@dllmethod
def get_registration_code():
prototype = PYFUNCTYPE(py_object)
dlfunc = prototype(('get_registration_code', _pytransform))
return dlfunc()
@dllmethod
def get_expired_days():
prototype = PYFUNCTYPE(py_object)
dlfunc = prototype(('get_expired_days', _pytransform))
return dlfunc()
@dllmethod
def clean_obj(obj, kind):
prototype = PYFUNCTYPE(c_int, py_object, c_int)
dlfunc = prototype(('clean_obj', _pytransform))
return dlfunc(obj, kind)
def clean_str(*args):
tdict = {
'str': 0,
'bytearray': 1,
'unicode': 2
}
for obj in args:
k = tdict.get(type(obj).__name__)
if k is None:
raise RuntimeError('Can not clean object: %s' % obj)
clean_obj(obj, k)
def get_hd_info(hdtype, name=None):
if hdtype not in range(HT_DOMAIN + 1):
raise RuntimeError('Invalid parameter hdtype: %s' % hdtype)
size = 256
t_buf = c_char * size
buf = t_buf()
cname = c_char_p(0 if name is None
else name.encode('utf-8') if hasattr('name', 'encode')
else name)
if (_pytransform.get_hd_info(hdtype, buf, size, cname) == -1):
raise PytransformError('Get hardware information failed')
return buf.value.decode()
def show_hd_info():
return _pytransform.show_hd_info()
def assert_armored(*names):
prototype = PYFUNCTYPE(py_object, py_object)
dlfunc = prototype(('assert_armored', _pytransform))
def wrapper(func):
def wrap_execute(*args, **kwargs):
dlfunc(names)
return func(*args, **kwargs)
return wrap_execute
return wrapper
def check_armored(*names):
try:
prototype = PYFUNCTYPE(py_object, py_object)
prototype(('assert_armored', _pytransform))(names)
return True
except RuntimeError:
return False
def get_license_info():
info = {
'ISSUER': None,
'EXPIRED': None,
'HARDDISK': None,
'IFMAC': None,
'IFIPV4': None,
'DOMAIN': None,
'DATA': None,
'CODE': None,
}
rcode = get_registration_code().decode()
if rcode.startswith('*VERSION:'):
index = rcode.find('\n')
info['ISSUER'] = rcode[9:index].split('.')[0].replace('-sn-1.txt', '')
rcode = rcode[index+1:]
index = 0
if rcode.startswith('*TIME:'):
from time import ctime
index = rcode.find('\n')
info['EXPIRED'] = ctime(float(rcode[6:index]))
index += 1
if rcode[index:].startswith('*FLAGS:'):
index += len('*FLAGS:') + 1
info['FLAGS'] = ord(rcode[index - 1])
prev = None
start = index
for k in ['HARDDISK', 'IFMAC', 'IFIPV4', 'DOMAIN', 'FIXKEY', 'CODE']:
index = rcode.find('*%s:' % k)
if index > -1:
if prev is not None:
info[prev] = rcode[start:index]
prev = k
start = index + len(k) + 2
info['CODE'] = rcode[start:]
i = info['CODE'].find(';')
if i > 0:
info['DATA'] = info['CODE'][i+1:]
info['CODE'] = info['CODE'][:i]
return info
def get_license_code():
return get_license_info()['CODE']
def get_user_data():
return get_license_info()['DATA']
def _match_features(patterns, s):
for pat in patterns:
if fnmatch(s, pat):
return True
def _gnu_get_libc_version():
try:
prototype = CFUNCTYPE(c_char_p)
ver = prototype(('gnu_get_libc_version', cdll.LoadLibrary('')))()
return ver.decode().split('.')
except Exception:
pass
def format_platform(platid=None):
if platid:
return os.path.normpath(platid)
plat = platform.system().lower()
mach = platform.machine().lower()
for alias, platlist in plat_table:
if _match_features(platlist, plat):
plat = alias
break
if plat == 'linux':
cname, cver = platform.libc_ver()
if cname == 'musl':
plat = 'musl'
elif cname == 'libc':
plat = 'android'
elif cname == 'glibc':
v = _gnu_get_libc_version()
if v and len(v) >= 2 and (int(v[0]) * 100 + int(v[1])) < 214:
plat = 'centos6'
for alias, archlist in arch_table:
if _match_features(archlist, mach):
mach = alias
break
if plat == 'windows' and mach == 'x86_64':
bitness = struct.calcsize('P'.encode()) * 8
if bitness == 32:
mach = 'x86'
return os.path.join(plat, mach)
# Load _pytransform library
def _load_library(path=None, is_runtime=0, platid=None, suffix='', advanced=0):
path = os.path.dirname(__file__) if path is None \
else os.path.normpath(path)
plat = platform.system().lower()
for alias, platlist in plat_table:
if _match_features(platlist, plat):
plat = alias
break
name = '_pytransform' + suffix
if plat == 'linux':
filename = os.path.abspath(os.path.join(path, name + '.so'))
elif plat in ('darwin', 'ios'):
filename = os.path.join(path, name + '.dylib')
elif plat == 'windows':
filename = os.path.join(path, name + '.dll')
elif plat in ('freebsd', 'poky'):
filename = os.path.join(path, name + '.so')
else:
filename = None
if platid is not None and os.path.isfile(platid):
filename = platid
elif platid is not None or not os.path.exists(filename) or not is_runtime:
libpath = platid if platid is not None and os.path.isabs(platid) else \
os.path.join(path, plat_path, format_platform(platid))
filename = os.path.join(libpath, os.path.basename(filename))
if filename is None:
raise PytransformError('Platform %s not supported' % plat)
if not os.path.exists(filename):
raise PytransformError('Could not find "%s"' % filename)
try:
m = cdll.LoadLibrary(filename)
except Exception as e:
if sys.flags.debug:
print('Load %s failed:\n%s' % (filename, e))
raise
# Removed from v4.6.1
# if plat == 'linux':
# m.set_option(-1, find_library('c').encode())
if not os.path.abspath('.') == os.path.abspath(path):
m.set_option(1, path.encode() if sys.version_info[0] == 3 else path)
elif (not is_runtime) and sys.platform.startswith('cygwin'):
path = os.environ['PYARMOR_CYGHOME']
m.set_option(1, path.encode() if sys.version_info[0] == 3 else path)
# Required from Python3.6
m.set_option(2, sys.byteorder.encode())
if sys.flags.debug:
m.set_option(3, c_char_p(1))
m.set_option(4, c_char_p(not is_runtime))
# Disable advanced mode by default
m.set_option(5, c_char_p(not advanced))
# Set suffix for private package
if suffix:
m.set_option(6, suffix.encode())
return m
def pyarmor_init(path=None, is_runtime=0, platid=None, suffix='', advanced=0):
global _pytransform
_pytransform = _load_library(path, is_runtime, platid, suffix, advanced)
return init_pytransform()
def pyarmor_runtime(path=None, suffix='', advanced=0):
if _pytransform is not None:
return
try:
pyarmor_init(path, is_runtime=1, suffix=suffix, advanced=advanced)
init_runtime()
except Exception as e:
if sys.flags.debug or hasattr(sys, '_catch_pyarmor'):
raise
sys.stderr.write("%s\n" % str(e))
sys.exit(1)
# ----------------------------------------------------------
# End of pytransform
# ----------------------------------------------------------
#
# Unused
#
@dllmethod
def generate_license_file(filename, priname, rcode, start=-1, count=1):
prototype = PYFUNCTYPE(c_int, c_char_p, c_char_p, c_char_p, c_int, c_int)
dlfunc = prototype(('generate_project_license_files', _pytransform))
return dlfunc(filename.encode(), priname.encode(), rcode.encode(),
start, count) if sys.version_info[0] == 3 \
else dlfunc(filename, priname, rcode, start, count)
#
# Not available from v5.6
#
def generate_capsule(licfile):
prikey, pubkey, prolic = _generate_project_capsule()
capkey, newkey = _generate_pytransform_key(licfile, pubkey)
return prikey, pubkey, capkey, newkey, prolic
@dllmethod
def _generate_project_capsule():
prototype = PYFUNCTYPE(py_object)
dlfunc = prototype(('generate_project_capsule', _pytransform))
return dlfunc()
@dllmethod
def _generate_pytransform_key(licfile, pubkey):
prototype = PYFUNCTYPE(py_object, c_char_p, py_object)
dlfunc = prototype(('generate_pytransform_key', _pytransform))
return dlfunc(licfile.encode() if sys.version_info[0] == 3 else licfile,
pubkey)
#
# Deprecated functions from v5.1
#
@dllmethod
def encrypt_project_files(proname, filelist, mode=0):
prototype = PYFUNCTYPE(c_int, c_char_p, py_object, c_int)
dlfunc = prototype(('encrypt_project_files', _pytransform))
return dlfunc(proname.encode(), filelist, mode)
def generate_project_capsule(licfile):
prikey, pubkey, prolic = _generate_project_capsule()
capkey = _encode_capsule_key_file(licfile)
return prikey, pubkey, capkey, prolic
@dllmethod
def _encode_capsule_key_file(licfile):
prototype = PYFUNCTYPE(py_object, c_char_p, c_char_p)
dlfunc = prototype(('encode_capsule_key_file', _pytransform))
return dlfunc(licfile.encode(), None)
@dllmethod
def encrypt_files(key, filelist, mode=0):
t_key = c_char * 32
prototype = PYFUNCTYPE(c_int, t_key, py_object, c_int)
dlfunc = prototype(('encrypt_files', _pytransform))
return dlfunc(t_key(*key), filelist, mode)
@dllmethod
def generate_module_key(pubname, key):
t_key = c_char * 32
prototype = PYFUNCTYPE(py_object, c_char_p, t_key, c_char_p)
dlfunc = prototype(('generate_module_key', _pytransform))
return dlfunc(pubname.encode(), t_key(*key), None)
#
# Compatible for PyArmor v3.0
#
@dllmethod
def old_init_runtime(systrace=0, sysprofile=1, threadtrace=0, threadprofile=1):
'''Only for old version, before PyArmor 3'''
pyarmor_init(is_runtime=1)
prototype = PYFUNCTYPE(c_int, c_int, c_int, c_int, c_int)
_init_runtime = prototype(('init_runtime', _pytransform))
return _init_runtime(systrace, sysprofile, threadtrace, threadprofile)
@dllmethod
def import_module(modname, filename):
'''Only for old version, before PyArmor 3'''
prototype = PYFUNCTYPE(py_object, c_char_p, c_char_p)
_import_module = prototype(('import_module', _pytransform))
return _import_module(modname.encode(), filename.encode())
@dllmethod
def exec_file(filename):
'''Only for old version, before PyArmor 3'''
prototype = PYFUNCTYPE(c_int, c_char_p)
_exec_file = prototype(('exec_file', _pytransform))
return _exec_file(filename.encode())
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/tests/__init__.py | from .test_hello_world import * |
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/stream/gptchat/tests/test_hello_world.py | # 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
# Extnsion for writing UI tests (simulate UI interaction)
import omni.kit.ui_test as ui_test
# Import extension python module we are testing with absolute import path, as if we are external user (other extension)
import stream.gptchat
# 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 Test(omni.kit.test.AsyncTestCase):
# Before running each test
async def setUp(self):
pass
# After running each test
async def tearDown(self):
pass
# Actual test, notice it is "async" function, so "await" can be used if needed
async def test_hello_public_function(self):
result = stream.gptchat.some_public_function(4)
self.assertEqual(result, 256)
async def test_window_button(self):
# Find a label in our window
label = ui_test.find("My Window//Frame/**/Label[*]")
# Find buttons in our window
add_button = ui_test.find("My Window//Frame/**/Button[*].text=='Add'")
reset_button = ui_test.find("My Window//Frame/**/Button[*].text=='Reset'")
# Click reset button
await reset_button.click()
self.assertEqual(label.widget.text, "empty")
await add_button.click()
self.assertEqual(label.widget.text, "count: 1")
await add_button.click()
self.assertEqual(label.widget.text, "count: 2")
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/config/extension.toml | [package]
# Semantic Versioning is used: https://semver.org/
version = "1.0.2"
# Lists people or organizations that are considered the "authors" of the package.
authors = ["Huang I Lan - Erks Virtual Studio"]
# The title and description fields are primarily for displaying extension info in UI
title = "stream-gpt"
description="Extension for NVIDIA Omniverse that provides a simple chatbot UI to record audio inputs, transcribe them, use transcriptions as chat GPT prompts, generate responses, convert responses to audio, and transmit them to Audio2Face via gRPC, while maintaining your original scripting style and modular system.."
# Path (relative to the root) or content of readme markdown file for UI.
readme = "docs/README.md"
# URL of the extension source repository.
repository = ""
# One of categories for UI.
category = "Chatbot"
# Keywords for the extension
keywords = ["Chat_GPT", "AI_assistant"]
# Location of change log file in target (final) folder of extension, relative to the root.
# More info on writing changelog: https://keepachangelog.com/en/1.0.0/
changelog="docs/CHANGELOG.md"
# Preview image and icon. Folder named "data" automatically goes in git lfs (see .gitattributes file).
# Preview image is shown in "Overview" of Extensions window. Screenshot of an extension might be a good preview image.
preview_image = "data/preview.png"
# Icon is shown in Extensions window, it is recommended to be square, of size 256x256.
icon = "data/icon.png"
# Use omni.ui to build simple UI
[dependencies]
"omni.kit.uiapp" = {}
[python.pipapi]
requirements = [
"pyaudio",
"openai",
"keyboard",
"soundfile",
"elevenlabs",
"pydub",
"gtts",
]
# Allow going to online index if package can't be found locally (not recommended)
use_online_index = true
# Main python module this extension provides, it will be publicly available as "import stream.gptchat".
[[python.module]]
name = "stream.gptchat"
[[test]]
# Extra dependencies only to be used during test run
dependencies = [
"omni.kit.ui_test" # UI testing extension
]
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/docs/CHANGELOG.md | # Changelog
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
## [1.0.2] - 2023-07-06
- Upgraded the UI to allow users to add API keys, Voice_ID, Voice_Models, and Instance Name directly from the UI, eliminating the need for hardcoding.
## [1.0.0] - 2023-04-13
- Initial version of extension UI template with a window.
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/docs/README.md | # Stream-GPT
Stream-GPT is an Omniverse Extension that uses OpenAI's GPT-3 model to create a virtual assistant. It allows users to interact with the assistant through both text and voice, and the assistant responds in kind. The extension uses OpenAI's Whisper ASR system to transcribe audio input and Eleven Labs' API to convert the assistant's text responses into audio.
## Getting Started
### Prerequisites
- OpenAI API key
- Eleven Labs API key
### SET UP
1. Set your OpenAI and Eleven Labs API keys, as well as the voice_id, model_id, and the Audio2Face's audioplayer's prim path (instance_name) in the extension's settings:
- Open the extension and click on the "Settings" button.
- Enter your OpenAI API key, Eleven Labs API key, voice_id, model_id and instance name in the corresponding fields. (A text file in the repository lists the available voice ids.)
## Usage
Once the application is running, you can interact with the virtual assistant through the UI. You can type your prompts into the text field and click on the "Send" button or use the "Record Audio" button to speak your prompts. The assistant will respond in the chat log and through your speakers.
You can also add a system to the GPT virtual assistant by typing it in the "System" field in the UI.
All interactions made with the extension are saved in a folder named "chat_logs" for future reference.
|
ilanhuang/audio2face-streamgpt-public/exts/stream.gptchat/docs/index.rst | stream.gpt
#############################
Example of Python only extension
.. toctree::
:maxdepth: 1
README
CHANGELOG
.. automodule::"stream-gpt"
:platform: Windows-x86_64, Linux-x86_64
:members:
:undoc-members:
:show-inheritance:
:imported-members:
:exclude-members: contextmanager
|
ilanhuang/audio2face-streamgpt-public/elevenlabs_ID/Voice_ID.json | {"voices":[{"voice_id":"21m00Tcm4TlvDq8ikWAM","name":"Rachel","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/21m00Tcm4TlvDq8ikWAM/6edb9076-c3e4-420c-b6ab-11d43fe341c8.mp3","available_for_tiers":[],"settings":null,"sharing":null},{"voice_id":"AZnzlk1XvdvUeBnXmlld","name":"Domi","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/AZnzlk1XvdvUeBnXmlld/69c5373f-0dc2-4efd-9232-a0140182c0a9.mp3","available_for_tiers":[],"settings":null,"sharing":null},{"voice_id":"EXAVITQu4vr4xnSDxMaL","name":"Bella","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/EXAVITQu4vr4xnSDxMaL/04365bce-98cc-4e99-9f10-56b60680cda9.mp3","available_for_tiers":[],"settings":null,"sharing":null},{"voice_id":"ErXwobaYiN019PkySvjV","name":"Antoni","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/ErXwobaYiN019PkySvjV/38d8f8f0-1122-4333-b323-0b87478d506a.mp3","available_for_tiers":[],"settings":null,"sharing":null},{"voice_id":"MF3mGyEYCl7XYWbV9V6O","name":"Elli","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/MF3mGyEYCl7XYWbV9V6O/f9fd64c3-5d62-45cd-b0dc-ad722ee3284e.mp3","available_for_tiers":[],"settings":null,"sharing":null},{"voice_id":"TxGEqnHWrfWFTfGW9XjX","name":"Josh","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/TxGEqnHWrfWFTfGW9XjX/c6c80dcd-5fe5-4a4c-a74c-b3fec4c62c67.mp3","available_for_tiers":[],"settings":null,"sharing":null},{"voice_id":"VR6AewLTigWG4xSOukaG","name":"Arnold","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/VR6AewLTigWG4xSOukaG/66e83dc2-6543-4897-9283-e028ac5ae4aa.mp3","available_for_tiers":[],"settings":null,"sharing":null},{"voice_id":"pNInz6obpgDQGcFmaJgB","name":"Adam","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/pNInz6obpgDQGcFmaJgB/e0b45450-78db-49b9-aaa4-d5358a6871bd.mp3","available_for_tiers":[],"settings":null,"sharing":null},{"voice_id":"yoZ06aMxZJJ28mfd3POQ","name":"Sam","samples":null,"category":"premade","fine_tuning":{"model_id":null,"language":null,"is_allowed_to_fine_tune":false,"fine_tuning_requested":false,"finetuning_state":"not_started","verification_attempts":null,"verification_failures":[],"verification_attempts_count":0,"slice_ids":null,"manual_verification":null,"manual_verification_requested":false},"labels":{},"description":null,"preview_url":"https://storage.googleapis.com/eleven-public-prod/premade/voices/yoZ06aMxZJJ28mfd3POQ/1c4d417c-ba80-4de8-874a-a1c57987ea63.mp3","available_for_tiers":[],"settings":null,"sharing":null}]} |
ilanhuang/audio2face-streamgpt-public/elevenlabs_ID/Model_ID.json | [{"model_id":"eleven_monolingual_v1","name":"Eleven English v1","can_be_finetuned":true,"can_do_text_to_speech":true,"can_do_voice_conversion":false,"can_use_style":false,"can_use_speaker_boost":false,"serves_pro_voices":false,"token_cost_factor":1.0,"description":"Use our standard English language model to generate speech in a variety of voices, styles and moods.","requires_alpha_access":false,"max_characters_request_free_user":2500,"max_characters_request_subscribed_user":5000,"languages":[{"language_id":"en","name":"English"}]},
{"model_id":"eleven_multilingual_v1","name":"Eleven Multilingual v1","can_be_finetuned":true,"can_do_text_to_speech":true,"can_do_voice_conversion":true,"can_use_style":false,"can_use_speaker_boost":false,"serves_pro_voices":false,"token_cost_factor":1.0,"description":"Generate lifelike speech in multiple languages and create content that resonates with a broader audience. ","requires_alpha_access":false,"max_characters_request_free_user":2500,"max_characters_request_subscribed_user":5000,"languages":[{"language_id":"en","name":"English"},{"language_id":"de","name":"German"},{"language_id":"pl","name":"Polish"},{"language_id":"es","name":"Spanish"},{"language_id":"it","name":"Italian"},{"language_id":"fr","name":"French"},{"language_id":"pt","name":"Portuguese"},{"language_id":"hi","name":"Hindi"}]}] |
ilanhuang/audio2face-streamgpt-public/UE5_install_files/extension.toml | [package]
version = "104.10.8"
title = "Audio2Face Exporter"
authors = ["NVIDIA"]
description="Custom Kit exporter for audio2face"
repository = ""
keywords = ["audio2face"]
category = "Animation"
readme = "docs/README.md"
changelog = "docs/CHANGELOG.md"
preview_image = "data/preview.png"
icon = "data/icon.png"
[dependencies]
"omni.ui" = {optional = true}
"omni.kit.window.filepicker" = {optional = true}
"omni.graph" = {}
"omni.graph.tools" = {}
"omni.kit.menu.utils" = {optional = true}
"omni.kit.window.viewport" = {optional = true}
"omni.kit.viewport.utility" = {optional = true}
"omni.client" = {}
"omni.anim.shared" = {}
"omni.deform.shared" = {}
"omni.audio2face.common" = {}
"omni.audio2face.ui.common" = {optional = true}
"omni.audio2face.tool" = {}
"omni.services.core"={}
[[python.module]]
name = "omni.audio2face.exporter"
[[test]]
dependencies = [
"omni.kit.renderer.core",
"omni.ui",
"omni.kit.window.filepicker",
"omni.kit.menu.utils",
"omni.kit.window.viewport",
"omni.kit.viewport.utility",
"omni.audio2face.ui.common"
]
timeout = 900
stdoutFailPatterns.exclude = [
"*failed to upload minidump*", # Exclude grahics leaks until fixed
]
[package.writeTarget]
kit = true
platform = true
[python.pipapi]
requirements = ['python-osc']
use_online_index = true |
ilanhuang/audio2face-streamgpt-public/UE5_install_files/from pythonosc import udp_client.py | from pythonosc import udp_client
blend = ["eyeBlinkLeft", "eyeLookDownLeft", "eyeLookInLeft", "eyeLookOutLeft", "eyeLookUpLeft", "eyeSquintLeft", "eyeWideLeft", "eyeBlinkRight", "eyeLookDownRight", "eyeLookInRight", "eyeLookOutRight", "eyeLookUpRight", "eyeSquintRight", "eyeWideRight", "jawForward", "jawLeft", "jawRight", "jawOpen", "mouthClose", "mouthFunnel", "mouthPucker", "mouthLeft", "mouthRight", "mouthSmileLeft", "mouthSmileRight", "mouthFrownLeft", "mouthFrownRight", "mouthDimpleLeft", "mouthDimpleRight", "mouthStretchLeft", "mouthStretchRight", "mouthRollLower", "mouthRollUpper", "mouthShrugLower", "mouthShrugUpper", "mouthPressLeft", "mouthPressRight", "mouthLowerDownLeft", "mouthLowerDownRight", "mouthUpperUpLeft", "mouthUpperUpRight", "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft", "browOuterUpRight", "cheekPuff", "cheekSquintLeft", "cheekSquintRight", "noseSneerLeft", "noseSneerRight", "tongueOut"]
client = udp_client.SimpleUDPClient('127.0.0.1', 5008)
osc_array = outWeight.tolist()
count = 0
for i in osc_array:
client.send_message('/' + str(blend[count]), i)
count += 1
[python.pipapi]
requirements = ['python-osc']
use_online_index = true |
ilanhuang/audio2face-streamgpt-public/UE5_install_files/README.txt | # Audio2Face to Unreal Engine Metahuman Animation Redirector
This repository contains the necessary extension and scripts to redirect animations from Audio2Face to a Metahuman in Unreal Engine 5.1 or higher.
## Installation
Follow these steps to install and verify the extension:
1. Copy the `extension` file from this repository.
2. Paste the `extension` file into the following directory (or similar, depending on your system setup):
```
C:\Users\Username\AppData\Local\ov\pkg\audio2face-2022.2.1\exts\omni.audio2face.exporter\config
```
3. Open Audio2Face, navigate to the 'Windows' tab and enable 'Script Editor'.
4. In the script editor, enter the following lines:
```python
import pythonosc
print(pythonosc)
```
5. Click 'Run'. If no errors appear, the extension is installed correctly.
## FacsSolver Setup
1. Copy the `FacsSolver` file from this repository.
2. Paste the `FacsSolver` file into the following directory (or similar, depending on your system setup):
```
C:\Users\Username\AppData\Local\ov\pkg\audio2face-2022.2.1\exts\omni.audio2face.exporter\omni\audio2face\exporter\scripts
```
## INSIDE UE5 - Metahuman Blueprints
Follow this [tutorial video](https://www.youtube.com/watch?v=y1wVykdmJNM) to set up the corresponding blueprint for your metahuman. |
ilanhuang/audio2face-streamgpt-public/UE5_install_files/facsSolver.py | import numpy as np
from omni.audio2face.common import log_error, log_info, log_warn
from scipy.optimize import lsq_linear
from pythonosc import udp_client
class FacsSolver:
def __init__(self, neutral_mat, delta_mat):
self.weightRegulCoeff = 3.5
self.weightRegulCoeff_scale = 10.0
self.prevRegulCoeff = 3.5
self.prevRegulCoeff_scale = 100.0
self.sparseRegulCoeff = 1.0
self.sparseRegulCoeff_scale = 0.25
self.symmetryRegulCoeff = 1.0
self.symmetryRegulCoeff_scale = 10.0
self.neutral_mat = neutral_mat
self.delta_mat_orig = delta_mat
self.delta_mat = delta_mat
self.numPoses_orig = self.delta_mat_orig.shape[1]
self.numPoses = self.numPoses_orig
self.lb_orig = np.zeros(self.numPoses_orig)
self.ub_orig = self.lb_orig + 1.0
self.lb = self.lb_orig.copy()
self.ub = self.ub_orig.copy()
self.activeIdxMap = range(self.numPoses_orig)
self.activePosesBool = np.array([True for pi in range(self.numPoses_orig)], dtype=bool)
self.cancelPoseIndices = np.array([-1 for pi in range(self.numPoses_orig)], dtype=int)
self.symmetryPoseIndices = np.array([-1 for pi in range(self.numPoses_orig)], dtype=int)
self.cancelList = []
self.symmetryList = []
self.symShapeMat = np.zeros((self.numPoses_orig, self.numPoses_orig))
self.prevWeights = np.zeros(self.numPoses_orig)
# TODO L1 implementation
l1RegulMat = np.ones((1, self.numPoses))
self.l1RegulMat = np.dot(l1RegulMat.T, l1RegulMat)
self.compute_A_mat()
def compute_A_mat(self):
self.A = (
np.dot(self.delta_mat.T, self.delta_mat)
+ self.weightRegulCoeff * self.weightRegulCoeff_scale * np.eye(self.numPoses)
+ self.prevRegulCoeff * self.prevRegulCoeff_scale * np.eye(self.numPoses)
+ self.sparseRegulCoeff ** 2 * self.sparseRegulCoeff_scale * self.l1RegulMat
+ self.symmetryRegulCoeff * self.symmetryRegulCoeff_scale * self.symShapeMat
)
self.A = self.A.astype(np.float64)
def set_activePoses(self, activePosesBool):
self.activePosesBool = activePosesBool
# 1 - simple approach
# self.ub *= np.array(self.activePosesBool)
# 2- less computation way
self.delta_mat = self.delta_mat_orig[:, self.activePosesBool]
self.numPoses = self.delta_mat.shape[1]
self.lb = self.lb_orig[self.activePosesBool]
self.ub = self.ub_orig[self.activePosesBool]
self.prevWeights = np.zeros(self.numPoses)
self.activeIdxMap = []
cnt = 0
for idx in range(self.numPoses_orig):
if self.activePosesBool[idx]:
self.activeIdxMap.append(cnt)
cnt += 1
else:
self.activeIdxMap.append(-1)
# update L1 regularization mat
l1RegulMat = np.ones((1, self.numPoses))
self.l1RegulMat = np.dot(l1RegulMat.T, l1RegulMat)
# update cancel pair index
self.set_cancelPoses(self.cancelPoseIndices)
# update symmetry pair index
self.set_symmetryPoses(self.symmetryPoseIndices) # update self.A here
def set_cancelPoses(self, cancelPoseIndices):
self.cancelPoseIndices = cancelPoseIndices
# filter out cancel shapes
self.cancelList = []
maxIdx = np.max(self.cancelPoseIndices)
if maxIdx < 0:
return
for ci in range(maxIdx + 1):
cancelIndices = np.where(self.cancelPoseIndices == ci)[0]
if len(cancelIndices) > 2:
log_warn("There is more than 2 poses for a cancel index %d" % ci)
break
elif len(cancelIndices) < 2:
log_warn("There is less than 2 poses for a cancel index %d" % ci)
break
self.cancelList.append(cancelIndices)
# print ('cancel shape list', self.cancelList)
activeCancelList = []
for pIdx1, pIdx2 in self.cancelList:
if self.activePosesBool[pIdx1] and self.activePosesBool[pIdx2]:
activeCancelList.append([self.activeIdxMap[pIdx1], self.activeIdxMap[pIdx2]])
# print (activeCancelList)
self.cancelList = activeCancelList
def set_symmetryPoses(self, symmetryPoseIndices):
self.symmetryPoseIndices = symmetryPoseIndices
self.symmetryList = []
maxIdx = np.max(self.symmetryPoseIndices)
if maxIdx < 0:
self.symShapeMat = np.zeros((self.numPoses, self.numPoses))
else:
for ci in range(maxIdx + 1):
symmetryIndices = np.where(self.symmetryPoseIndices == ci)[0]
if len(symmetryIndices) > 2:
log_warn("There is more than 2 poses for a cancel index %d" % ci)
break
elif len(symmetryIndices) < 2:
log_warn("There is less than 2 poses for a cancel index %d" % ci)
break
self.symmetryList.append(symmetryIndices)
activeSymmetryList = []
for pIdx1, pIdx2 in self.symmetryList:
if self.activePosesBool[pIdx1] and self.activePosesBool[pIdx2]:
activeSymmetryList.append([self.activeIdxMap[pIdx1], self.activeIdxMap[pIdx2]])
self.symmetryList = activeSymmetryList
symShapeMat = np.zeros((len(self.symmetryList), self.numPoses))
for si, [pose1Idx, pose2Idx] in enumerate(self.symmetryList):
symShapeMat[si, pose1Idx] = 1.0
symShapeMat[si, pose2Idx] = -1.0
self.symShapeMat = np.dot(symShapeMat.T, symShapeMat)
self.compute_A_mat()
def set_l2_regularization(self, L2=3.5):
self.weightRegulCoeff = L2
self.compute_A_mat()
def set_tempo_regularization(self, temporal=3.5):
self.prevRegulCoeff = temporal
self.compute_A_mat()
def set_l1_regularization(self, L1=1.0):
self.sparseRegulCoeff = L1
self.compute_A_mat()
def set_symmetry_regularization(self, value=1.0):
self.symmetryRegulCoeff = value
self.compute_A_mat()
def computeFacsWeights(self, point_mat):
target_delta_mat = point_mat - self.neutral_mat
B = (
np.dot(self.delta_mat.T, target_delta_mat).flatten()
+ self.prevRegulCoeff * self.prevRegulCoeff_scale * self.prevWeights
)
B = B.astype(np.float64)
res = lsq_linear(self.A, B, bounds=(self.lb, self.ub), lsmr_tol="auto", verbose=0, method="bvls")
# print ('first pass:', res.x)
if len(self.cancelList) > 0:
# check cancelling poses -
ub = self.ub.copy()
lb = self.lb.copy()
for pose1Idx, pose2Idx in self.cancelList:
if res.x[pose1Idx] >= res.x[pose2Idx]:
ub[pose2Idx] = 1e-10
else:
ub[pose1Idx] = 1e-10
res = lsq_linear(self.A, B, bounds=(lb, ub), lsmr_tol="auto", verbose=0, method="bvls")
self.prevWeights = res.x
# print ('second pass:', res.x)
outWeight = np.zeros(self.numPoses_orig)
outWeight[self.activePosesBool] = res.x
outWeight = outWeight * (outWeight > 1.0e-9)
# print (outWeight)
blend = ["eyeBlinkLeft", "eyeLookDownLeft", "eyeLookInLeft", "eyeLookOutLeft", "eyeLookUpLeft", "eyeSquintLeft", "eyeWideLeft", "eyeBlinkRight", "eyeLookDownRight", "eyeLookInRight", "eyeLookOutRight", "eyeLookUpRight", "eyeSquintRight", "eyeWideRight", "jawForward", "jawLeft", "jawRight", "jawOpen", "mouthClose", "mouthFunnel", "mouthPucker", "mouthLeft", "mouthRight", "mouthSmileLeft", "mouthSmileRight", "mouthFrownLeft", "mouthFrownRight", "mouthDimpleLeft", "mouthDimpleRight", "mouthStretchLeft", "mouthStretchRight", "mouthRollLower", "mouthRollUpper", "mouthShrugLower", "mouthShrugUpper", "mouthPressLeft", "mouthPressRight", "mouthLowerDownLeft", "mouthLowerDownRight", "mouthUpperUpLeft", "mouthUpperUpRight", "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft", "browOuterUpRight", "cheekPuff", "cheekSquintLeft", "cheekSquintRight", "noseSneerLeft", "noseSneerRight", "tongueOut"]
try:
client = udp_client.SimpleUDPClient('127.0.0.1', 27008)
osc_array = outWeight.tolist()
count = 0
for i in osc_array:
client.send_message('/' + str(blend[count]), i)
count += 1
except Exception as e:
log_error(f"Error in OSC communication: {e}") |
matthias-research/omni.fun/README.md | # omni.fun
A simple plugin for nvidia's omniverse
|
matthias-research/omni.fun/exts/omni.fun/config/extension.toml |
[package]
# Semantic Versioning is used: https://semver.org/
version = "0.1.0"
authors = ["Ten Minute Physics"]
title = "Fun"
description="Ten Minute Physics domniverse extension"
readme = "docs/README.md"
repository="https://github.com/matthias-research/omni.fun"
category = "sim"
keywords = ["simulation"]
changelog="docs/CHANGELOG.md"
preview_image = "data/preview.png"
icon = "data/icon.png"
# Watch the .ogn files for hot reloading (only works for Python files)
[fswatcher.patterns]
include = ["*.ogn", "*.py"]
exclude = ["Ogn*Database.py", "*/ogn*"]
[dependencies]
"omni.kit.test" = {}
"omni.kit.menu.utils" = {}
"omni.timeline" = {}
"omni.usd" = {}
# Main python module this extension provides, it will be publicly available as "import omni.play".
[[python.module]]
name = "omni.fun"
|
matthias-research/omni.fun/exts/omni.fun/config/extension.gen.toml | [package]
[package.target]
python = ["cp37"]
[package.publish]
date = 1635811509
kitVersion = "103.0+master.0.75457a67.gitlab"
|
matthias-research/omni.fun/exts/omni.fun/omni/fun/__init__.py | from .scripts.extension import *
|
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/sim.py | # Copyright 2022 Matthias Müller - Ten Minute Physics,
# https://www.youtube.com/c/TenMinutePhysics
# www.matthiasMueller.info/tenMinutePhysics
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import numpy as np
import math
import warp as wp
from pxr import Usd, UsdGeom, Gf, Sdf
from .usdutils import *
gravity = -9.81
@wp.struct
class SimData:
sphere_radius: wp.array(dtype=float)
sphere_mass: wp.array(dtype=float)
sphere_pos: wp.array(dtype=wp.vec3)
sphere_rot: wp.array(dtype=wp.quat)
sphere_lin_vel: wp.array(dtype=wp.vec3)
sphere_ang_vel: wp.array(dtype=wp.vec3)
sphere_pos_corr: wp.array(dtype=wp.vec3)
sphere_lin_corr: wp.array(dtype=wp.vec3)
sphere_ang_corr: wp.array(dtype=wp.vec3)
sphere_num_corr: wp.array(dtype=int)
sphere_lower_bounds: wp.array(dtype=wp.vec3)
sphere_upper_bounds: wp.array(dtype=wp.vec3)
sphere_bvh_id: wp.uint64
obj_mesh_id: wp.uint64
obj_tri_ids: wp.array(dtype=int)
obj_orig_pos: wp.array(dtype=wp.vec3)
obj_pos: wp.array(dtype=wp.vec3)
obj_prev_pos: wp.array(dtype=wp.vec3)
obj_transforms: wp.array(dtype=wp.mat44)
obj_pos_transform_nr: wp.array(dtype=int)
@wp.kernel
def dev_integrate(
dt: float,
gravity: wp.vec3,
bounds_margin: float,
sim: SimData):
sphere_nr = wp.tid()
pos = sim.sphere_pos[sphere_nr]
lin_vel = sim.sphere_lin_vel[sphere_nr]
rot = sim.sphere_rot[sphere_nr]
ang_vel = sim.sphere_ang_vel[sphere_nr]
# move state forward in time
lin_vel = lin_vel + gravity * dt
pos = pos + lin_vel * dt
qt = wp.quat(ang_vel[0], ang_vel[1], ang_vel[2], 0.0) * (dt * 0.5)
rot = wp.normalize(rot + qt * rot)
sim.sphere_pos[sphere_nr] = pos
sim.sphere_lin_vel[sphere_nr] = lin_vel
sim.sphere_rot[sphere_nr] = rot
# compute bounding box for bvh
pred_pos = pos + lin_vel * dt
lower = wp.vec3(wp.min(pos[0], pred_pos[0]), wp.min(pos[1], pred_pos[1]), wp.min(pos[2], pred_pos[2]))
upper = wp.vec3(wp.max(pos[0], pred_pos[0]), wp.max(pos[1], pred_pos[1]), wp.max(pos[2], pred_pos[2]))
m = bounds_margin + sim.sphere_radius[sphere_nr]
sim.sphere_lower_bounds[sphere_nr] = lower - wp.vec3(m, m, m)
sim.sphere_upper_bounds[sphere_nr] = upper + wp.vec3(m, m, m)
@wp.kernel
def dev_handle_sphere_sphere_collisions(
restitution: float,
sim: SimData):
sphere0 = wp.tid()
eps = 0.00001
pos0 = sim.sphere_pos[sphere0]
radius0 = sim.sphere_radius[sphere0]
m0 = sim.sphere_mass[sphere0]
w0 = 1.0 / (m0 + eps)
vel0 = sim.lin_vel[sphere0]
ang0 = sim.ang_vel[sphere0]
lower = sim.sphere_lower_bounds[sphere0]
upper = sim.sphere_upper_bounds[sphere0]
query = wp.bvh_query_aabb(sim.spheres_bvh_id, lower, upper)
sphere1 = int(0)
while (wp.bvh_query_next(query, sphere1)):
if sphere1 < sphere0: # handle each pair only once!
pos1 = sim.sphere_pos[sphere1]
radius1 = sim.sphere_radius[sphere1]
m1 = sim.sphere_mass[sphere1]
w1 = 1.0 / (m1 + eps)
vel1 = sim.lin_vel[sphere1]
ang1 = sim.ang_vel[sphere1]
min_dist = radius0 + radius1
pos_normal = wp.normalize(pos1 - pos0)
dist = wp.dot(pos_normal, pos1 - pos0)
if dist < min_dist:
# bounce
wp.atomic_add(sim.sphere_num_corr, sphere0, 1)
wp.atomic_add(sim.sphere_num_corr, sphere1, 1)
pos_corr = pos_normal / (w0 + w1) * (min_dist - dist + eps)
wp.atomic_add(sim.pos_corr, sphere0, -w0 * pos_corr)
wp.atomic_add(sim.pos_corr, sphere1, +w1 * pos_corr)
vn0 = wp.dot(vel0, pos_normal)
vn1 = wp.dot(vel1, pos_normal)
new_vn0 = (m0 * vn0 + m1 * vn1 - m1 * (vn0 - vn1) * restitution) / (m0 + m1)
new_vn1 = (m0 * vn0 + m1 * vn1 - m0 * (vn1 - vn0) * restitution) / (m0 + m1)
new_vn0 = wp.min(0.0, new_vn0)
new_vn1 = wp.max(0.0, new_vn1)
lin_corr0 = pos_normal * (new_vn0 - vn0)
lin_corr1 = pos_normal * (new_vn1 - vn1)
wp.atomic_add(sim.sphere_lin_corr, sphere0, lin_corr0)
wp.atomic_add(sim.sphere_lin_corr, sphere1, lin_corr1)
vel0 = vel0 + lin_corr0
vel1 = vel1 + lin_corr1
# friction
ang_normal = wp.normalize(ang0 * m0 + ang1 * m1)
ang_normal = wp.nomralize(ang_normal - pos_normal * wp.dot(pos_normal, ang_normal))
vt0 = wp.dot(vel0, wp.cross(ang_normal, pos_normal))
vt1 = wp.dot(vel1, wp.cross(ang_normal, pos_normal))
omega0 = wp.dot(ang0, ang_normal)
omega1 = wp.dot(ang1, ang_normal)
# v0 + (o0 - do*w0) * r0 = v1 + (o1 + do*w1) * r1
domega = (vt1 + omega1 * radius1 - vt0 - omega0 * radius0) / (radius0 * w0 + radius1 * w1)
ang_corr0 = ang_normal * (omega0 - domega * w0) - ang0
ang_corr1 = ang_normal * (omega1 + domega * w1) - ang1
ang0 = ang0 + ang_corr0
ang1 = ang1 + ang_corr1
wp.atomic_add(sim.sphere_ang_corr, sphere0, ang_corr0)
wp.atomic_add(sim.sphere_ang_corr, sphere1, ang_corr1)
@wp.kernel
def dev_update_obj_pos(sim: SimData):
id = wp.tid()
trans_nr = sim.pos_transform_nr[id]
pos = sim.obj_transforms[trans_nr] * sim.orig_pos[id]
sim.prev_pos[id] = sim.pos[id]
sim.pos[id] = pos
@wp.kernel
def dev_handle_sphere_obj_collisions(
dt: float,
restitution: float,
sim: SimData):
sphere_nr = wp.tid()
pos = sim.sphere_pos[sphere_nr]
radius = sim.sphere_radius[sphere_nr]
vel = sim.lin_vel[sphere_nr]
ang = sim.ang_vel[sphere_nr]
inside = float(0.0)
face_nr = int(0)
u = float(0.0)
v = float(0.0)
found = wp.mesh_query_point(sim.obj_mesh_id, pos, radius, inside, face_nr, u, v)
if not found:
return
id0 = sim.obj_tri_ids[3 * face_nr]
id1 = sim.obj_tri_ids[3 * face_nr + 1]
id2 = sim.obj_tri_ids[3 * face_nr + 2]
p0 = sim.obj_pos[id0]
p1 = sim.obj_pos[id1]
p2 = sim.obj_pos[id2]
closest = u * p0 + v * p1 + (1.0 - u - v) * p2
pos_normal = wp.normalize(pos - closest)
dist = wp.dot(pos_normal, pos - closest)
if dist >= radius:
return
sim.sphere_pos[sphere_nr] = pos - pos_normal * (radius - dist)
v0 = (p0 - sim.mesh_prev_points[id0]) / dt
v1 = (p1 - sim.mesh_prev_points[id1]) / dt
v2 = (p2 - sim.mesh_prev_points[id2]) / dt
v_mesh = v0 + u * (v1 - v0) + v * (v2 - v0)
v_mesh = u * v0 + v * v1 + (1.0 - u - v) * v2
vn_sphere = wp.dot(sim.sphere_lin_vel[sphere_nr], pos_normal)
vn_mesh = wp.dot(v_mesh, pos_normal)
new_vn = wp.min(vn_mesh - (vn_sphere - vn_mesh) * restitution, 0.0)
sim.sphere_lin_vel[sphere_nr] = v + pos_normal * (new_vn - vn_sphere)
# friction
ang_normal = wp.normalize(ang)
ang_normal = wp.nomralize(ang - pos_normal * wp.dot(pos_normal, ang_normal))
vt = wp.dot(vel, wp.cross(ang_normal, pos_normal))
omega = wp.dot(ang, ang_normal)
# vel + (omega + do) * r = v_mesh
domega = (vt + omega * radius - v_mesh) / radius
ang = ang + ang_normal * (omega - domega)
sim.sphere_ang_vel[sphere_nr] = ang
@wp.kernel
def dev_apply_corrections(
sim: SimData):
sphere_nr = wp.tid()
num = sim.sphere_num_corr[sphere_nr]
if num > 0:
s = 1.0 / float(num)
sim.sphere_pos[sphere_nr] += sim.sphere_pos_corr[sphere_nr] * s
sim.sphere_lin_vel[sphere_nr] += sim.sphere_lin_corr[sphere_nr] * s
sim.sphere_ang_vel[sphere_nr] += sim.sphere_ang_corr[sphere_nr] * s
class Sim():
def __init__(self, stage):
self.paused = True
self.stage = stage
self.device = 'cuda'
self.prim_cache = UsdGeom.XformCache()
self.dev_sim_data = SimData()
self.host_sim_data = SimData()
self.spheres_bvh = None
self.obj_mesh = None
self.sphere_usd_meshes = []
self.obj_usd_prims = []
self.obj_usd_transforms = []
self.initialized = False
self.time_step = 1.0 / 30.0
self.num_substeps = 5
self.restitution = 0.1
self.jacobi_scale = 0.25
self.num_spheres = 0
self.frame_nr = 0
def init(self):
if not self.stage:
return
obj_pos = []
obj_pos_transform_nr = []
obj_tri_ids = []
sphere_pos = []
sphere_radius = []
sphere_inv_mass = []
self.sphere_usd_meshes = []
self.sphere_usd_transforms = []
s = 4.0 / 3.0 * 3.141592
print("traversing stage")
for prim in self.stage.Traverse():
if prim.GetTypeName() == "Mesh":
mesh = UsdGeom.Mesh(prim)
name = mesh.GetName()
points = mesh.GetPointsAttr().Get(0.0)
if name.find("sphere") != 0 or name.find("Sphere") != 0:
# create a sphere
trans_mat, trans_t = get_global_transform(prim, 0.0, False)
trans_points = points @ trans_mat
min = np.min(trans_points, axis = 0)
max = np.max(trans_points, axis = 0)
radius = np.max(max - min) * 0.5
sphere_radius.append(radius)
sphere_pos.append(trans_t)
mass = s * radius * radius * radius
sphere_inv_mass.append(1.0 / mass)
clone = clone_prim(self.stage, prim)
self.sphere_usd_meshes.append(UsdGeom.Mesh(clone))
self.sphere_usd_transforms.append(clone.Get)
else:
obj_nr = len(self.obj_usd_prims)
self.object_usd_prims.append(prim)
# create obstacle points
first_pos = len(obj_pos)
for i in range(len(mesh_points)):
p = mesh_points[i]
obj_pos.append(wp.vec3(*p))
obj_pos_transform_nr.append(obj_nr)
# create obstacle triangles
mesh_poly_indices = mesh.GetFaceVertexIndicesAttr().Get(0.0)
mesh_face_sizes = mesh.GetFaceVertexCountsAttr().Get(0.0)
mesh_points = np.array(points)
first_index = 0
for i in range(len(mesh_face_sizes)):
face_size = mesh_face_sizes[i]
for j in range(1, face_size-1):
obj_tri_ids.append(first_pos + mesh_poly_indices[first_index])
obj_tri_ids.append(first_pos + mesh_poly_indices[first_index + j])
obj_tri_ids.append(first_pos + mesh_poly_indices[first_index + j + 1])
first_index += face_size
# create objects warp buffers
if len(obj_pos) > 0:
self.dev_sim_data.obj_pos = wp.array(obj_pos, dtype=wp.vec3, device=self.device)
self.dev_sim_data.pbj_prev_pos = wp.array(obj_pos, dtype=wp.vec3, device=self.device)
self.dev_sim_data.obj_tri_ids = wp.array(obj_tri_ids, dtype=int, device=self.device)
self.obj_mesh = wp.Mesh(self.dev_sim_data.obj_pos, self.dev_sim_data.obj_tri_ids)
self.dev_sim_data.obj_mesh_id = self.obj_mesh.id
num_objs = len(self.object_usd_prims)
mat = wp.mat44()
self.obj_transforms = np.array([mat] * num_objs)
self.dev_sim_data.obj_transforms = wp.zeros(shape=(num_objs), dtype=wp.mat44, device=self.device)
# create sphere warp buffers
self.num_spheres = len(sphere_pos)
if self.num_spheres > 0:
self.dev_sim_data.sphere_radius = wp.array(sphere_radius, dtype=float, device=self.device)
self.dev_sim_data.sphere_pos = wp.array(sphere_pos, dtype=wp.vec3, device=self.device)
self.dev_sim_data.sphere_quat = wp.zeros(shape=(self.num_spheres), dtype=wp.quat, device=self.device)
self.dev_sim_data.sphere_vel = wp.zeros(shape=(self.num_spheres), dtype=wp.vec3, device=self.device)
self.dev_sim_data.sphere_omega = wp.zeros(shape=(self.num_spheres), dtype=wp.vec3, device=self.device)
self.dev_sim_data.sphere_lower_bounds = wp.zeros(shape=(self.num_spheres), dtype=wp.vec3, device=self.device)
self.dev_sim_data.sphere_upper_bounds = wp.zeros(shape=(self.num_spheres), dtype=wp.vec3, device=self.device)
self.host_sim_data.sphere_pos = wp.array(sphere_pos, dtype=wp.vec3, device="cpu")
self.host_sim_data.sphere_quat = wp.zeros(shape=(self.num_spheres), dtype=wp.quat, device="cpu")
# zero time step to initialize sphere bounds
wp.launch(kernel = self.dev_integrate,
inputs = [0.0, wp.vec3(0.0, 0.0, 0.0), self.dev_sim_data],
dim = self.num_spheres, device=self.device)
self.sphere_bvh = wp.Bvh(self.dev_sim_data.sphere_lower_bounds, self.dev_sim_data.sphere_upper_bounds)
self.dev_sim_data.sphere_bvh_id = self.sphere_bvh.id
def simulate(self):
if self.paused:
return
self.frame_nr += 1
print("simulating", self.frame_nr)
return
# update objects
for i in range(len(self.object_usd_prims)):
self.obj_transforms[i] = get_global_transform(self.object_usd_prims[i], 0.0, True)
wp.copy(self.dev_sim_data.obj_transforms, wp.array(self.obj_transforms, dtype=wp.array(wp.mat44), copy=False, device="cpu"))
wp.launch(kernel = dev_update_obj_pos,
inputs = [self.dev_sim_data],
dim = len(self.dev_sim_data.obj_pos), device=self.device)
self.obj_mesh.refit()
#simulate spheres
wp.launch(kernel = dev_integrate,
inputs = [self.time_step, self.gravity, self.dev_sim_data],
dim = self.num_spheres, device=self.device)
self.sphere_bvh.refit()
self.dev_sim_data.sphere_pos_corr.zero_()
self.dev_sim_data.sphere_lin_corr.zero_()
self.dev_sim_data.sphere_ang_corr.zero_()
self.dev_sim_data.sphere_num_corr.zero_()
wp.launch(kernel = dev_handle_sphere_sphere_collisions,
inputs = [self.restitution, self.dev_sim_data],
dim = self.num_spheres, device=self.device)
wp.launch(kernel = dev_apply_corrections,
inputs = [self.dev_sim_data],
dim = self.num_spheres, device=self.device)
wp.launch(kernel = dev_handle_sphere_obj_collisions,
inputs = [self.time_step, self.restitution, self.dev_sim_data],
dim = self.num_spheres, device=self.device)
# update stage
wp.copy(self.host_sim_data.sphere_pos, self.dev_sim_data.sphere_pos)
wp.copy(self.host_sim_data.sphere_quat, self.dev_sim_data.sphere_quat)
pos = self.host_sim_data.numpy()
quat = self.host_sim_data.numpy()
for i in range(self.num_spheres):
set_transform(self.sphere_usd_meshes, pos[i], quat[i])
def reset(self):
hide_clones(self.stage)
self.paused = True
|
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/extension.py | # Copyright 2022 Matthias Müller - Ten Minute Physics,
# https://www.youtube.com/c/TenMinutePhysics
# www.matthiasMueller.info/tenMinutePhysics
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import omni.ext
import os
import omni.usd
from omni import ui
from pxr import Usd
from .controls import ControlsWindow
from .sim import Sim
EXAMPLES_PATH = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../../data/scenes"))
class OmniFunExtension(omni.ext.IExt):
def on_startup(self, ext_id):
print("fun on_startup")
setattr(self, "controls", None)
setattr(self, "sim", None)
stage = omni.usd.get_context().get_stage()
self.sim = Sim(stage)
self.sim.init()
editor_menu = omni.kit.ui.get_editor_menu()
self.menu_items = []
if editor_menu:
self.controls_menu = editor_menu.add_item(
f"Window/Fun/Controls",
lambda _, value: self.show_controls(value),
toggle=True, value=False
)
self.menu_items.append(editor_menu.add_item(
f"Window/Fun/SimpleScene",
lambda _, value: self.load_example("simple.usd"),
toggle=False, value=False
))
# self.show_controls(True)
# set callbacks
self.update_event_stream = omni.kit.app.get_app_interface().get_update_event_stream()
self.stage_event_sub = omni.usd.get_context().get_stage_event_stream().create_subscription_to_pop(self.on_event)
def on_shutdown(self):
print("fun on_shutdown")
self.menu_items = None
self.update_event_stream = None
self.stage_event_sub = None
if self.sim:
self.sim.reset()
self.show_controls(False)
def init_callback(self, state):
if state:
stage = omni.usd.get_context().get_stage()
if self.sim:
self.sim = Sim(stage)
self.update_event_sub = self.update_event_stream.create_subscription_to_pop(self.on_update)
else:
if self.sim:
self.sim.reset()
self.sim = None
def play_callback(self, state):
if self.sim:
self.sim.paused = not state
def on_update(self, dt):
if self.sim:
self.sim.simulate()
def set_controls_menu(self, visible):
omni.kit.ui.get_editor_menu().set_value(f"Window/Fun/Controls", visible)
def show_controls(self, is_visible):
if is_visible:
if not hasattr(self, "controls"):
setattr(self, "controls", None)
if self.controls is None:
self.controls = ControlsWindow(
init_callback=self.init_callback,
play_callback=self.play_callback)
self.controls.create_window(lambda visible: self.set_controls_menu(visible))
self.controls.show_window()
else:
self.controls.show_window()
elif self.controls:
self.controls.destroy_window()
self.controls = None
def on_event(self, event):
if event.type == int(omni.usd.StageEventType.CLOSED):
if self.sim:
self.sim.reset()
if event.type == int(omni.usd.StageEventType.OPENED):
if self.sim:
self.sim.init()
def load_example(self, scene_name):
def new_stage():
stage_path = os.path.normpath(os.path.join(EXAMPLES_PATH, scene_name))
omni.usd.get_context().open_stage(stage_path)
if self.sim:
self.sim.init()
omni.kit.window.file.prompt_if_unsaved_stage(new_stage)
|
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/gpu.py | # Copyright 2022 Matthias Müller - Ten Minute Physics,
# https://www.youtube.com/c/TenMinutePhysics
# www.matthiasMueller.info/tenMinutePhysics
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import numpy as np
import warp as wp
@wp.struct
class SimData:
spheres_pos: wp.array(dtype=wp.vec3)
spheres_prev_pos: wp.array(dtype=wp.vec3)
spheres_pos_corr: wp.array(dtype=wp.vec3)
spheres_vel: wp.array(dtype=wp.vec3)
spheres_radius: wp.array(dtype=float)
spheres_inv_mass: wp.array(dtype=float)
mesh_id: wp.uint64
mesh_verts: wp.array(dtype=wp.vec3)
mesh_tri_ids: wp.array(dtype=int)
@wp.func
def closest_point_on_triangle(
p: wp.vec3, p0: wp.vec3, p1: wp.vec3, p2: wp.vec3):
e0 = p1 - p0
e1 = p2 - p0
tmp = p0 - p
a = wp.dot(e0, e0)
b = wp.dot(e0, e1)
c = wp.dot(e1, e1)
d = wp.dot(e0, tmp)
e = wp.dot(e1, tmp)
coords = wp.vec3(b*e - c*d, b*d - a*e, a*c - b*b)
x = 0.0
y = 0.0
z = 0.0
if coords[0] <= 0.0:
if c != 0.0:
y = -e / c
elif coords[1] <= 0.0:
if a != 0.0:
x = -d / a
elif coords[0] + coords[1] > coords[2]:
den = a + c - b - b
num = c + e - b - d
if den != 0.0:
x = num / den
y = 1.0 - x
else:
if coords[2] != 0.0:
x = coords[0] / coords[2]
y = coords[1] / coords[2]
x = wp.clamp(x, 0.0, 1.0)
y = wp.clamp(y, 0.0, 1.0)
bary = wp.vec3(1.0 - x - y, x, y)
return bary
@wp.kernel
def dev_integrate_spheres(
dt: float,
gravity: wp.vec3,
data: SimData):
sphere_nr = wp.tid()
w = data.spheres_inv_mass[sphere_nr]
if w > 0.0:
data.spheres_vel[sphere_nr] += gravity * dt
data.spheres_prev_pos[sphere_nr] = data.spheres_pos[sphere_nr]
data.spheres_pos[sphere_nr] += data.spheres_vel[sphere_nr] * dt
def integrate_spheres(num_spheres: int, dt: float, gravity: wp.vec3, data: SimData, device):
wp.launch(kernel = dev_integrate_spheres,
inputs = [dt, gravity, data], dim=num_spheres, device=device)
@wp.kernel
def dev_update_spheres(
dt: float,
jacobi_scale: float,
data: SimData):
sphere_nr = wp.tid()
w = data.spheres_inv_mass[sphere_nr]
if w > 0.0:
data.spheres_pos[sphere_nr] = data.spheres_pos[sphere_nr] + jacobi_scale * data.spheres_pos_corr
data.spheres_vel[sphere_nr] = (data.spheres_pos[sphere_nr] - data.spheres_prev_pos[sphere_nr]) / dt
def update_spheres(num_spheres: int, dt: float, jacobi_scale: float, data: SimData, device):
wp.launch(kernel = dev_update_spheres,
inputs = [dt, jacobi_scale, data], dim=num_spheres, device=device)
@wp.kernel
def dev_solve_mesh_collisions(
data: SimData):
sphere_nr = wp.tid()
w = data.spheres_inv_mass[sphere_nr]
if w > 0.0:
pos = data.spheres_pos[sphere_nr]
r = data.spheres_radius[sphere_nr]
# query bounding volume hierarchy
bounds_lower = pos - wp.vec3(r, r, r)
bounds_upper = pos + wp.vec3(r, r, r)
query = wp.mesh_query_aabb(data.mesh_id, bounds_lower, bounds_upper)
tri_nr = int(0)
while (wp.mesh_query_aabb_next(query, tri_nr)):
p0 = data.mesh_verts[data.mesh_tri_ids[3*tri_nr]]
p1 = data.mesh_verts[data.mesh_tri_ids[3*tri_nr + 1]]
p2 = data.mesh_verts[data.mesh_tri_ids[3*tri_nr + 2]]
hit = closest_point_on_triangle(pos, p0, p1, p2)
n = pos - hit
d = wp.length(n)
if d < r:
n = wp.normalize(n)
data.spheres_pos[sphere_nr] = data.spheres_pos[sphere_nr] + n * (r - d)
def solve_mesh_collisions(num_spheres: int, data: SimData, device):
wp.launch(kernel = dev_solve_mesh_collisions,
inputs = [data], dim=num_spheres, device=device)
@wp.kernel
def dev_solve_sphere_collisions(
num_spheres: int,
data: SimData):
i0 = wp.tid()
p0 = data.spheres_pos[i0]
r0 = data.spheres_radius[i0]
w0 = data.spheres_inv_mass[i0]
# simpe O(n^2) collision detection
for i1 in range(num_spheres):
if i1 > i0:
p1 = data.spheres_pos[i1]
r1 = data.spheres_radius[i1]
w1 = data.spheres_inv_mass[i1]
w = w0 + w1
if w > 0.0:
n = p1 - p0
d = wp.length(n)
n = wp.noramlize(n)
if d < r0 + r1:
corr = n * (r0 + r1 - d) / w
data.spheres_corr[i0] = data.spheres_corr[i0] - corr * w0
data.spheres_corr[i1] = data.spheres_corr[i1] - corr * w0
def solve_sphere_collisions(num_spheres: int, data: SimData, device):
wp.launch(kernel = dev_solve_sphere_collisions,
inputs = [num_spheres, data], dim=num_spheres, device=device)
|
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/controls.py | import carb
import omni.ui
import omni.usd
import omni.kit.app
from pxr import Usd, Sdf
from .sim import gravity
class ControlsWindow:
def __init__(self, init_callback=None, play_callback=None):
self._window = None
self.buttons = [
[None, init_callback, False, "Init", "Reset"],
[None, play_callback, False, "Play", "Pause"]]
def __bool__(self):
return self._window is not None
def create_window(self, visibility_changed_fn):
window_flags = omni.ui.WINDOW_FLAGS_NO_SCROLLBAR
self._window = omni.ui.Window("Fun Controls", flags=window_flags, width=400, height=400, dockPreference=omni.ui.DockPreference.RIGHT_TOP)
self._window.set_visibility_changed_fn(visibility_changed_fn)
self.rebuild_ui()
def show_window(self):
self._window.visible = True
def hide_window(self):
self._window.visible = False
def destroy_window(self):
if self._window:
self._window.visible = False
self._window.destroy()
self._window = None
def button_pressed(self, button):
state = not button[2]
button[2] = state
button[0].text = button[4] if state else button[3]
button[1](state)
def set_parameter(self, param_name, val):
if param_name == "gravity":
gravity = val
def rebuild_ui(self):
ui = omni.ui
row_height = 20
v_spacing = 10
h_spacing = 20
if self._window and self._window.visible:
with self._window.frame:
with ui.VStack(spacing=v_spacing, padding=50):
with ui.HStack(spacing=h_spacing, height=row_height):
for button in self.buttons:
button[0] = ui.Button(
button[3], width=100, height=15, margin=10,
clicked_fn=lambda button=button: self.button_pressed(button))
with ui.HStack(spacing=h_spacing, height=row_height):
ui.Label("Gravity", width=ui.Percent(50), height=10, name="Gravity")
slider = ui.FloatSlider(min=0.0,max=10.0, width=ui.Percent(50))
slider.model.add_value_changed_fn(
lambda val, param_name="gravity": self.set_parameter(param_name, val.get_value_as_float()))
|
matthias-research/omni.fun/exts/omni.fun/omni/fun/scripts/usdutils.py | from pxr import Usd, UsdGeom, Gf, UsdShade
import numpy as np
import warp as wp
prim_cache = None
def get_global_transform(prim, time, return_mat44):
if prim_cache is None:
prim_cache = UsdGeom.XformCache()
prim_cache.SetTime(time)
m = prim_cache.GetLocalToWorldTransform(prim)
if return_mat44:
return wp.mat44(
m[0][0], m[1][0], m[2][0], m[3][0],
m[0][1], m[1][1], m[2][1], m[3][1],
m[0][2], m[1][2], m[2][2], m[3][2],
m[0][3], m[1][3], m[2][3], m[3][3])
else:
A = np.array([[m[0][0], m[0][1], m[0][2]], [m[1][0], m[1][1], m[1][2]], [m[2][0], m[2][1], m[2][2]]])
b = np.array([m[3][0], m[3][1], m[3][2]])
return A, b
def set_transform(mesh, trans, quat):
usd_mat = Gf.Matrix4d()
usd_mat.SetRotateOnly(Gf.Quatd(*quat))
usd_mat.SetTranslateOnly(Gf.Vec3d(*trans))
xform = UsdGeom.Xform(mesh)
xform.GetOrderedXformOps()[0].Set(usd_mat)
def clone_primvar(self, prim, prim_clone, name, time=0.0):
try:
attr = UsdGeom.Primvar(prim.GetAttribute(name))
prim_clone.CreatePrimvar(name, attr.GetTypeName(), attr.GetInterpolation()).Set(attr.Get(time))
except:
pass
def clone_prim(stage, prim):
vis = prim.GetAttribute("visibility")
if vis:
vis.Set("invisible")
mesh = UsdGeom.Mesh(prim)
clone_prim_path = '/' + str(prim.GetPath()).replace("/", "_") + '_clone'
UsdGeom.Mesh.Define(stage, clone_prim_path)
prim_clone = UsdGeom.Mesh(stage.GetPrimAtPath(clone_prim_path))
mesh_clone = UsdGeom.Mesh(prim_clone)
stage.GetPrimAtPath(clone_prim_path).SetActive(True)
xform = UsdGeom.Xform(mesh_clone)
xform.ClearXformOpOrder()
xform.AddXformOp(UsdGeom.XformOp.TypeTransform)
trans_mat, trans_t = get_global_transform(prim, 0.0, True)
trans_points = mesh.GetPointsAttr().Get(0.0) @ trans_mat + trans_t
normal_mat = np.array([\
trans_mat[0,:] / np.linalg.norm(trans_mat[0,:]), \
trans_mat[1,:] / np.linalg.norm(trans_mat[1,:]), \
trans_mat[2,:] / np.linalg.norm(trans_mat[2,:])])
trans_normals = mesh.GetNormalsAttr().Get(0.0) @ normal_mat
mesh_clone.GetPointsAttr().Set(trans_points)
mesh_clone.GetNormalsAttr().Set(trans_normals)
mesh_clone.SetNormalsInterpolation(mesh.GetNormalsInterpolation())
mesh_clone.GetFaceVertexIndicesAttr().Set(mesh.GetFaceVertexIndicesAttr().Get(0.0))
mesh_clone.GetFaceVertexCountsAttr().Set(mesh.GetFaceVertexCountsAttr().Get(0.0))
mesh_clone.GetCornerIndicesAttr().Set(mesh.GetCornerIndicesAttr().Get(0.0))
mesh_clone.GetCornerSharpnessesAttr().Set(mesh.GetCornerSharpnessesAttr().Get(0.0))
mesh_clone.GetCreaseIndicesAttr().Set(mesh.GetCreaseIndicesAttr().Get(0.0))
mesh_clone.GetCreaseLengthsAttr().Set(mesh.GetCreaseLengthsAttr().Get(0.0))
mesh_clone.GetCreaseSharpnessesAttr().Set(mesh.GetCreaseSharpnessesAttr().Get(0.0))
mesh_clone.GetSubdivisionSchemeAttr().Set(mesh.GetSubdivisionSchemeAttr().Get(0.0))
mesh_clone.GetInterpolateBoundaryAttr().Set(mesh.GetInterpolateBoundaryAttr().Get(0.0))
mesh_clone.GetFaceVaryingLinearInterpolationAttr().Set(mesh.GetFaceVaryingLinearInterpolationAttr().Get(0.0))
mesh_clone.GetTriangleSubdivisionRuleAttr().Set(mesh.GetTriangleSubdivisionRuleAttr().Get(0.0))
mesh_clone.GetHoleIndicesAttr().Set(mesh.GetHoleIndicesAttr().Get(0.0))
for attr in prim.GetAttributes():
type = str(attr.GetTypeName())
if type.find("texCoord") >= 0:
clone_primvar(prim, prim_clone, attr.GetName())
try:
mat = UsdShade.MaterialBindingAPI(prim).GetDirectBinding().GetMaterial()
UsdShade.MaterialBindingAPI(prim_clone).Bind(mat)
except:
pass
return prim_clone
def hide_clones(stage):
if stage is None:
return
for prim in stage.Traverse():
if str(prim.GetName()).find("_clone") >= 0:
prim.SetActive(False)
else:
vis = prim.GetAttribute("visibility")
if vis:
vis.Set("inherited")
|
matthias-research/omni.fun/exts/omni.fun/docs/CHANGELOG.md | # CHANGELOG
## [0.1.0] - 2022-08-15
- Initial publish for alpha testing
|
matthias-research/omni.fun/exts/omni.fun/docs/README.md | # Play [omni.ten]
A simple plugin from ten minute physics.
## Documentation
None
## Source Code
None
|
qcr/benchbot_sim_omni/pip_package_fix.py | import subprocess
import sys
print("HACK FIX FOR BROKEN PACKAGES")
def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])
def uninstall(package):
subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "--yes", package])
uninstall("click")
install("click")
uninstall("typing-extensions")
install("typing-extensions")
|
qcr/benchbot_sim_omni/LICENSE.txt | Copyright (c) 2020, Queensland University of Technology (Ben Talbot, David Hall, and Niko Sünderhauf)
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.
|
qcr/benchbot_sim_omni/run.py | import flask
import numpy as np
import os
import signal
from builtins import print as bprint
from gevent import event, pywsgi, signal
from pathlib import Path
from spatialmath import SE3, UnitQuaternion
print("STARTING RUN.PY IN BENCHBOT_SIM_OMNI")
DEFAULT_POSE = np.array([1, 0, 0, 0, 0, 0, 0])
DIRTY_EPSILON_DIST = 1
DIRTY_EPSILON_YAW = 2
DIRTY_FILE = '/tmp/benchbot_dirty'
MAP_PRIM_PATH = '/env'
ROBOT_NAME = 'robot'
ROBOT_PRIM_PATH = '/%s' % ROBOT_NAME
ROBOT_COMPONENTS = {
'clock': '/ROS_Clock',
'diff_base': '%s/ROS_DifferentialBase' % ROBOT_PRIM_PATH,
'lidar': '%s/ROS_Lidar' % ROBOT_PRIM_PATH,
'rgbd': '%s/ROS_Camera_Stereo_Left' % ROBOT_PRIM_PATH,
'tf_sensors': '%s/ROS_Carter_Sensors_Broadcaster' % ROBOT_PRIM_PATH,
'tf': '%s/ROS_Carter_Broadcaster' % ROBOT_PRIM_PATH
}
UPDATE_DELAY_SECS = 3.0
def _dc_tf_to_SE3(tf):
r = np.array(tf.r)
return SE3(np.array(tf.p)) * UnitQuaternion(r[3], r[0:3]).SE3()
def _to_SE3(pose):
return SE3(pose[4::]) * UnitQuaternion(pose[0], pose[1:4]).SE3()
def disable_component(prop_path):
from omni.kit.commands import execute
from pxr import Sdf
print("DISABLING '%s.enabled'" % prop_path)
execute("ChangeProperty",
prop_path=Sdf.Path("%s.enabled" % prop_path),
value=False,
prev=None)
def print(*args, **kwargs):
bprint(*args, **kwargs, flush=True)
class SimulatorDaemon:
def __init__(self, port):
self.address = 'localhost:%s' % port
self.inst = None
self.sim = None
self.sim_i = 0
self.sim_collided = False
self.sim_dirty = False
self.map_usd = None
self.robot_usd = None
self.start_pose = None
self._map_usd = None
self._robot_usd = None
self._start_pose = None
self._dc = None
self._robot = None
self._robot_dc = None
def check_dirty(self):
delta = (_to_SE3(self.start_pose).inv() *
_dc_tf_to_SE3(self._dc.get_rigid_body_pose(self._robot_dc)))
return (np.linalg.norm(delta.t[0:2]) > DIRTY_EPSILON_DIST or
np.abs(delta.rpy(unit='deg')[2]) > DIRTY_EPSILON_YAW)
def check_collided(self):
return False
def open_usd(self):
# Bail early if we can't act
if self.inst is None:
print("No simulator running. "
"Stored environment USD, but not opening.")
return
if self.map_usd is None:
print("No environment USD selected. Returning.")
return
# Imports must go after bail early checks pass as they throw errors
# when called in an "inappropriate state" (no idea what that
# corresponds to...)
from omni.isaac.core.utils.stage import open_stage, update_stage
# Stop simulation if running
self.stop_simulation()
# Update the map
if self.map_usd != self._map_usd:
self._dc = None
self._start_pose = None
self._robot = None
self._robot_dc = None
self._robot_usd = None
open_stage(usd_path=self.map_usd)
update_stage()
self._map_usd = self.map_usd
else:
print("Skipping map load; already loaded.")
# Attempt to replace the robot
self.place_robot()
def place_robot(self):
# Bail early if we can't act
if self.inst is None:
print("No simulator running. "
"Stored robot USD & pose, but not opening.")
return
if self.robot_usd is None:
print("No robot USD selected. Returning.")
return
# Imports must go after bail early checks pass as they throw errors
# when called in an "inappropriate state" (no idea what that
# corresponds to...)
from omni.isaac.core.robots import Robot
from omni.isaac.core.utils.stage import (add_reference_to_stage,
update_stage)
# Stop simulation if running
self.stop_simulation()
# Add robot to the environment at the requested pose
p = DEFAULT_POSE if self.start_pose is None else self.start_pose
if self.robot_usd != self._robot_usd:
add_reference_to_stage(usd_path=self.robot_usd,
prim_path=ROBOT_PRIM_PATH)
self._robot = Robot(prim_path=ROBOT_PRIM_PATH, name=ROBOT_NAME)
update_stage()
self._robot_usd = self.robot_usd
else:
print("Skipping robot load; already loaded.")
if (p != self._start_pose).any():
self._robot.set_world_pose(position=p[4::],
orientation=p[:4])
update_stage()
self._start_pose = p
else:
print("Skipping robot move; already at requested pose.")
# Disable auto-publishing of all robot components (we'll manually
# publish at varying frequencies instead)
for p in ROBOT_COMPONENTS.values():
disable_component(p)
# Attempt to start the simulation
self.start_simulation()
def run(self):
f = flask.Flask('benchbot_sim_omni')
@f.route('/', methods=['GET'])
def __hello():
return flask.jsonify("Hello, I am the Omniverse Sim Daemon")
@f.route('/open_environment', methods=['POST'])
def __open_env():
r = flask.request.json
if 'environment' in r:
self.map_usd = r['environment']
self.open_usd()
return flask.jsonify({})
@f.route('/place_robot', methods=['POST'])
def __place_robot():
r = flask.request.json
if 'robot' in r:
self.robot_usd = r['robot']
if 'start_pose' in r:
# Probably should be regexing...
self.start_pose = np.array([
float(x.strip()) for x in r['start_pose'].replace(
'[', '').replace(']', '').split(',')
])
self.place_robot()
return flask.jsonify({})
@f.route('/restart_sim', methods=['POST'])
def __restart_sim():
self.stop_simulation()
self.start_simulation()
return flask.jsonify({})
@f.route('/start', methods=['POST'])
def __start_inst():
self.start_instance()
return flask.jsonify({})
@f.route('/start_sim', methods=['POST'])
def __start_sim():
self.start_simulation()
return flask.jsonify({})
@f.route('/started', methods=['GET'])
def __started():
# TODO note there is a race condition (returns true before a /start
# job finishes)
return flask.jsonify({'started': self.inst is not None})
@f.route('/stop_sim', methods=['POST'])
def __stop_sim():
self.stop_simulation()
return flask.jsonify({})
# Start long-running server
server = pywsgi.WSGIServer(self.address, f)
evt = event.Event()
for s in [signal.SIGINT, signal.SIGQUIT, signal.SIGTERM]:
signal.signal(s, lambda n, frame: evt.set())
server.start()
while not evt.is_set():
evt.wait(0.001)
self.tick_simulator()
# Cleanup
self.stop_instance()
def start_instance(self):
print("STARTING INSTANCE!!")
if not self.inst is None:
print("Instance already running. Please /stop first.")
return
env = {} if self.map_usd is None else {"open_usd": self.map_usd}
from omni.isaac.kit import SimulationApp
# Start the simulator
self.inst = SimulationApp({
"renderer": "RayTracedLighting",
"headless": False,
**env
})
# Import all required modules, and configure application
from omni.isaac.core.utils.extensions import enable_extension
enable_extension("omni.isaac.ros_bridge")
# Attempt to place the robot if we had a map
if env:
self.place_robot()
def start_simulation(self):
if self.sim is not None:
self.stop_simulation()
if self.inst is None or self.map_usd is None or self.robot_usd is None:
print("Can't start simulation. Missing some required state.")
return
from omni.isaac.core import SimulationContext
self.sim_i = 0
self.sim_collided = False
self.sim_dirty = False
self.sim = SimulationContext()
self.sim.play()
from omni.isaac.dynamic_control import _dynamic_control
self._dc = _dynamic_control.acquire_dynamic_control_interface()
self._robot_dc = self._dc.get_articulation_root_body(
self._dc.get_object(ROBOT_PRIM_PATH))
def stop_instance(self):
if self.inst is None:
print("No instance is running to stop.")
return
self.stop_simulation()
self.inst.close()
self.inst = None
def stop_simulation(self):
if self.sim is None:
print("Skipping. No running simulation to stop")
return
if self.inst is None:
print("Skipping. No running simulator found.")
return
self.sim.stop()
self.sim = None # TODO maybe could reuse with more guarding logic?
def tick_simulator(self):
# Tick simulator steps. Does less now than in 2021.2.1 due to new action graph
if self.inst is None:
return
if self.sim is None:
self.inst.update()
return
self.sim.step()
# Tick at 10Hz CHECK DIRTY
if self.sim_i % 6 == 0:
if not self.sim_dirty:
self.sim_dirty = self.check_dirty()
if self.sim_dirty:
Path(DIRTY_FILE).touch()
# Tick at 1Hz CHECK COLLIDED
if self.sim_i % 60 == 0:
self.sim_collided = self.check_collided()
self.sim_i += 1
if __name__ == '__main__':
print("inside run.py __main__")
sd = SimulatorDaemon(port=os.environ.get('PORT'))
sd.run()
|
qcr/benchbot_sim_omni/README.md | **NOTE: this software is part of the BenchBot software stack. For a complete working BenchBot system, please install the BenchBot software stack by following the instructions [here](https://github.com/qcr/benchbot).**
# BenchBot Simulator for Omniverse-powered Isaac Sim
[](http://benchbot.org)
[](https://qcr.github.io)
[](./LICENSE.txt)
The BenchBot Simulator bindings for Omniverse-powered Isaac Sim provide a simple `run` script that makes powerful photorealistic simulations available in ROS, and controllable through a basic HTTP API.
Through a single script, this package provides:
- creation of, and management of, a running [Omniverse-powered Isaac Sim](https://developer.nvidia.com/isaac-sim) instance
- a simple HTTP API for programmatically loading environments, placing robots, and controlling simulations
- ROS topics for common mobile robot topics: transforms, odometry, command velocity, RGB images, depth images, laser scans
The configuration is currently Carter specific, but could easily be extended in the future to target other robots. Also all simulator interactions come from a simple Python script that could be used as a starting point for more complex projects.
## Installation
**Please see the note at the top of the page; the BenchBot ecosystem contains much more than just these bindings**
There is no physical installation step for these bindings, simply install Isaac Sim, clone this repository, and install Python dependencies:
1. Follow the instructions on the [NVIDIA Isaac Sim documentation site](https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html) for [installing Isaac Sim](https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/install_basic.html)
2. Clone this repository:
```
git clone https://github.com/qcr/benchbot_sim_omni
```
3. Install declared Python dependencies:
```
pip install -r ./.custom_deps
```
## Running and using the simulator bindings
Simulator bindings are run through the `run` script, which will start a blank instance of the simulator with the HTTP API bound on port 10001 by default:
```
./run
```
A simulation in environment `my_env.usd`, with robot `my_robot.usd` at position `(0,0,0)` and quaternion (w,x,y,z) `(1,0,0,0)` can then be started by the following two CURL commands:
```
curl localhost:10001/open_environment \
-H "Content-Type: application/json" \
-d '{"environment": "my_env.usd"}'
curl localhost:10001/place_robot \
-H "Content-Type: application/json" \
-d '{"robot": "my_robot.usd", "start_pose": "1,0,0,0,0,0,0"}'
```
Full documentation of configuration options and HTTP API routes is available through the script's `--help` flag:
```
user@pc:~/benchbot_sim_omni/$ ./run --help
run -- BenchBot simulator daemon for Omniverse-powered Isaac Sim
USAGE:
Start the daemon:
run
run -p /path/to/python.sh -P 8080
Print this help information:
run [-h|--help]
OPTION DETAILS:
-h, --help
Show this help menu.
-P,--port
Port the daemon will bind to. Default port of 10001 will
be used if not provided.
-p,--python-sh-path
Path to the 'python.sh' environment script included with your Isaac
Sim installation. Will recursively search for the script in the
current directory if this flag is not provided.
INTERACTING WITH THE DAEMON:
The daemon responds to HTTP requests.
Following routes are supported:
/
Returns a greeting message
/open_environment
Opens a new environment, with USD path specified via 'environment'
data field
/place_robot
Places a robot at a specified pose. Robot USD is specified via
'robot' data field, and start pose via a comma-separated 7-tuple in
the 'pose' field. Format for pose is:
quat_w,quat_x,quat_y,quat_z,pos_x,pos_y,pos_z
/start
Starts a simulator instance (happens by default when first opened)
/stop
Stops a currently running simulator instance if it exists
/restart
Restarts the entire simulator (generally not needed)
FURTHER DETAILS:
Please contact the authors of BenchBot for support or to report bugs:
[email protected]
```
## Using this simulator with the BenchBot Robot Controller
The [BenchBot Robot Controller](https://github.com/qcr/benchbot_robot_controller) is a wrapping ROS / HTTP hybrid script that manages running robots and their required subprocesses. It is ultimately fed configurations from [BenchBot add-ons](https://github.com/qcr/benchbot_addons) through our [BenchBot supervisor](https://github.com/qcr/benchbot_supervisor) service.
These details are superfluous to these BenchBot simulator bindings, but are provided here for context. This context may be helpful if looking for examples of more complex interactions with the simulator bindings. For example, the `carter_sim_omni.yaml` file in the [robots_sim_omni](https://github.com/benchbot-addons/robots_sim_omni) BenchBot add-on may be of interest.
|
AndrePatri/OmniRoboGym/pyproject.toml | [build-system]
requires = ["flit_core >=2,<4"]
build-backend = "flit_core.buildapi"
[project]
name = "omni_robo_gym"
version = "0.1.0"
description = ""
authors = [{name = "AndrePatri", email = "[email protected]"}]
readme = "README.md"
license = {file = "LICENSE"} |
AndrePatri/OmniRoboGym/omnirobogym_mamba_env.yml | name: omni_robo_gym_isaac2023.1.1
channels:
- defaults
- pytorch
- nvidia
- conda-forge
- omnia
- robostack-staging
- AndrePatri
dependencies:
- python=3.10
- pip
- pytorch == 2.0.1
- torchvision
- torchaudio
- cuda-toolkit=11.7
- compilers
- cmake
- make
- quaternion
- anaconda-client
- yaml-cpp
- pybind11
- gtest
- eigen3
- posix_ipc=1.0.4
- rospkg=1.5.0
- ros-humble-xacro
- empy
- python-devtools
- perf_sleep
- pyqt
- pyqtgraph
- pip:
- flit
- nvidia-cublas-cu11==11.11.3.6
- gym==0.26.2
- gymnasium==0.28.1
- stable_baselines3[extra]==2.0.0a10
- box2d-py
- tensorboard
- tensorboard-plugin-wit
- protobuf
- matplotlib
- scipy
- urdf-parser-py
- multiprocess
|
AndrePatri/OmniRoboGym/meta.yaml | package:
name: omni_robo_gym
version: 0.1.0
source:
path: . # Path to the directory containing your built distribution artifacts
requirements:
build:
- python=3.7
- flit
run:
- python=3.7
about:
home: https://github.com/AndrePatri/CoClusterBridge
license: MIT
summary: Some custom implementations of Tasks and Gyms for Omniverse Isaac Sim based on Gymnasium. Easy URDF and SRDF import/cloning and simulation configuration exploiting Omniverse API
extra:
recipe-maintainers:
- AndrePatri
|
AndrePatri/OmniRoboGym/build.sh | #!/bin/bash
$PYTHON -m pip install . |
AndrePatri/OmniRoboGym/create_mamba_env.sh | #!/bin/bash
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
# should match env name from YAML
ENV_NAME=omni_robo_gym_isaac2023.1.1
pushd "${ROOT_DIR}/"
# setup mamba
MAMBA_DIR="$(mamba info --base)"
source "${MAMBA_DIR}/etc/profile.d/mamba.sh"
# !!! this removes existing version of the env
mamba remove -y -n "${ENV_NAME}" --all
# create the env from YAML
mamba env create -f ./omnirobogym_mamba_env.yml
# activate env
# mamba activate "${ENV_NAME}"
# # install omni_robo_gym package in editable mode
# pip install -e .
popd
|
AndrePatri/OmniRoboGym/README.md | # OmniRoboGym
Wrapper on top of [Omniverse Isaac Sim](https://developer.nvidia.com/isaac-sim), a photo-realistic GPU accelerated simulator from NVIDIA.
The aim of the package is to a easy interface for loading floating-base robots and their configuration from URDF and SRDF into IsaacSim, cloning them with Isaac Sim API and, in general, simplify simulation setup for RL-based robotics applications. |
AndrePatri/OmniRoboGym/LICENSE.md | GNU GENERAL PUBLIC LICENSE
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may add an explicit geographical distribution limitation excluding
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END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
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convey the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
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This is free software, and you are welcome to redistribute it
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`Gnomovision' (which makes passes at compilers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
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|
AndrePatri/OmniRoboGym/omni_robo_gym/__init__.py | |
AndrePatri/OmniRoboGym/omni_robo_gym/envs/isaac_env.py | # Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
from omni.isaac.kit import SimulationApp
import os
import signal
import carb
import torch
from abc import ABC, abstractmethod
from typing import Union, Tuple, Dict
from SharsorIPCpp.PySharsorIPC import VLevel
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
import numpy as np
# import gymnasium as gym
# class IsaacSimEnv(gym.Env):
class IsaacSimEnv():
def __init__(
self,
headless: bool,
sim_device: int = 0,
enable_livestream: bool = False,
enable_viewport: bool = False,
debug = False
) -> None:
""" Initializes RL and task parameters.
Args:
headless (bool): Whether to run training headless.
sim_device (int): GPU device ID for running physics simulation. Defaults to 0.
enable_livestream (bool): Whether to enable running with livestream.
enable_viewport (bool): Whether to enable rendering in headless mode.
"""
self.debug = debug
experience = f'{os.environ["EXP_PATH"]}/omni.isaac.sim.python.omnirobogym.kit'
# experience = ""
if headless:
info = f"Will run in headless mode."
Journal.log(self.__class__.__name__,
"__init__",
info,
LogType.STAT,
throw_when_excep = True)
if enable_livestream:
experience = ""
elif enable_viewport:
exception = f"Using viewport is not supported yet."
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
else:
experience = f'{os.environ["EXP_PATH"]}/omni.isaac.sim.python.omnirobogym.headless.kit'
# experience = f'{os.environ["EXP_PATH"]}/omni.isaac.sim.python.gym.headless.kit'
self._simulation_app = SimulationApp({"headless": headless,
"physics_gpu": sim_device},
experience=experience)
info = "Using IsaacSim experience file @ " + experience
Journal.log(self.__class__.__name__,
"__init__",
info,
LogType.STAT,
throw_when_excep = True)
# carb.settings.get_settings().set("/persistent/omnihydra/useSceneGraphInstancing", True)
if enable_livestream:
info = "Livestream enabled"
Journal.log(self.__class__.__name__,
"__init__",
info,
LogType.STAT,
throw_when_excep = True)
from omni.isaac.core.utils.extensions import enable_extension
self._simulation_app.set_setting("/app/livestream/enabled", True)
self._simulation_app.set_setting("/app/window/drawMouse", True)
self._simulation_app.set_setting("/app/livestream/proto", "ws")
self._simulation_app.set_setting("/app/livestream/websocket/framerate_limit", 120)
self._simulation_app.set_setting("/ngx/enabled", False)
enable_extension("omni.kit.livestream.native")
enable_extension("omni.services.streaming.manager")
# handle ctrl+c event
signal.signal(signal.SIGINT, self.signal_handler)
self._render = not headless or enable_livestream or enable_viewport
self._record = False
self.step_counter = 0 # step counter
self._world = None
self.metadata = None
self.gpu_pipeline_enabled = False
def signal_handler(self, sig, frame):
self.close()
def set_task(self,
task,
backend="torch",
sim_params=None,
init_sim=True) -> None:
""" Creates a World object and adds Task to World.
Initializes and registers task to the environment interface.
Triggers task start-up.
Args:
task (RLTask): The task to register to the env.
backend (str): Backend to use for task. Can be "numpy" or "torch". Defaults to "numpy".
sim_params (dict): Simulation parameters for physics settings. Defaults to None.
init_sim (Optional[bool]): Automatically starts simulation. Defaults to True.
"""
from omni.isaac.core.world import World
# parse device based on sim_param settings
if sim_params and "sim_device" in sim_params:
device = sim_params["sim_device"]
else:
device = "cpu"
physics_device_id = carb.settings.get_settings().get_as_int("/physics/cudaDevice")
gpu_id = 0 if physics_device_id < 0 else physics_device_id
if sim_params and "use_gpu_pipeline" in sim_params:
# GPU pipeline must use GPU simulation
if sim_params["use_gpu_pipeline"]:
device = "cuda:" + str(gpu_id)
elif sim_params and "use_gpu" in sim_params:
if sim_params["use_gpu"]:
device = "cuda:" + str(gpu_id)
self.gpu_pipeline_enabled = sim_params["use_gpu_pipeline"]
info = "Using device: " + str(device)
Journal.log(self.__class__.__name__,
"__init__",
info,
LogType.STAT,
throw_when_excep = True)
if (sim_params is None):
info = f"No sim params provided -> defaults will be used."
Journal.log(self.__class__.__name__,
"set_task",
info,
LogType.STAT,
throw_when_excep = True)
sim_params = {}
# defaults for integration and rendering dt
if not("physics_dt" in sim_params):
sim_params["physics_dt"] = 1.0/60.0
dt = sim_params["physics_dt"]
info = f"Using default integration_dt of {dt} s."
Journal.log(self.__class__.__name__,
"set_task",
info,
LogType.STAT,
throw_when_excep = True)
if not("rendering_dt" in sim_params):
sim_params["rendering_dt"] = sim_params["physics_dt"]
dt = sim_params["rendering_dt"]
info = f"Using default rendering_dt of {dt} s."
Journal.log(self.__class__.__name__,
"set_task",
info,
LogType.STAT,
throw_when_excep = True)
self._world = World(
stage_units_in_meters=1.0,
physics_dt=sim_params["physics_dt"],
rendering_dt=sim_params["rendering_dt"], # dt between rendering steps. Note: rendering means rendering a frame of
# the current application and not only rendering a frame to the viewports/ cameras.
# So UI elements of Isaac Sim will be refereshed with this dt as well if running non-headless
backend=backend,
device=str(device),
physics_prim_path="/physicsScene",
set_defaults = False, # set to True to use the defaults settings [physics_dt = 1.0/ 60.0,
# stage units in meters = 0.01 (i.e in cms), rendering_dt = 1.0 / 60.0, gravity = -9.81 m / s
# ccd_enabled, stabilization_enabled, gpu dynamics turned off,
# broadcast type is MBP, solver type is TGS]
sim_params=sim_params
)
self._sim_params = sim_params
big_info = "[World] Creating task " + task.name + "\n" + \
"use_gpu_pipeline: " + str(sim_params["use_gpu_pipeline"]) + "\n" + \
"device: " + str(device) + "\n" +\
"backend: " + str(backend) + "\n" +\
"integration_dt: " + str(sim_params["physics_dt"]) + "\n" + \
"rendering_dt: " + str(sim_params["rendering_dt"]) + "\n" \
Journal.log(self.__class__.__name__,
"set_task",
big_info,
LogType.STAT,
throw_when_excep = True)
## we get the physics context to expose additional low-level ##
# settings of the simulation
self._physics_context = self._world.get_physics_context()
self._physics_scene_path = self._physics_context.prim_path
self._physics_context.enable_gpu_dynamics(True)
self._physics_context.enable_stablization(True)
self._physics_scene_prim = self._physics_context.get_current_physics_scene_prim()
self._solver_type = self._physics_context.get_solver_type()
# we set parameters, depending on sim_params dict
if "gpu_max_rigid_contact_count" in sim_params:
self._physics_context.set_gpu_max_rigid_contact_count(sim_params["gpu_max_rigid_contact_count"])
if "gpu_max_rigid_patch_count" in sim_params:
self._physics_context.set_gpu_max_rigid_patch_count(sim_params["gpu_max_rigid_patch_count"])
if "gpu_found_lost_pairs_capacity" in sim_params:
self._physics_context.set_gpu_found_lost_pairs_capacity(sim_params["gpu_found_lost_pairs_capacity"])
if "gpu_found_lost_aggregate_pairs_capacity" in sim_params:
self._physics_context.set_gpu_found_lost_aggregate_pairs_capacity(sim_params["gpu_found_lost_aggregate_pairs_capacity"])
if "gpu_total_aggregate_pairs_capacity" in sim_params:
self._physics_context.set_gpu_total_aggregate_pairs_capacity(sim_params["gpu_total_aggregate_pairs_capacity"])
if "gpu_max_soft_body_contacts" in sim_params:
self._physics_context.set_gpu_max_soft_body_contacts(sim_params["gpu_max_soft_body_contacts"])
if "gpu_max_particle_contacts" in sim_params:
self._physics_context.set_gpu_max_particle_contacts(sim_params["gpu_max_particle_contacts"])
if "gpu_heap_capacity" in sim_params:
self._physics_context.set_gpu_heap_capacity(sim_params["gpu_heap_capacity"])
if "gpu_temp_buffer_capacity" in sim_params:
self._physics_context.set_gpu_temp_buffer_capacity(sim_params["gpu_temp_buffer_capacity"])
if "gpu_max_num_partitions" in sim_params:
self._physics_context.set_gpu_max_num_partitions(sim_params["gpu_max_num_partitions"])
# overwriting defaults
# self._physics_context.set_gpu_max_rigid_contact_count(2 * self._physics_context.get_gpu_max_rigid_contact_count())
# self._physics_context.set_gpu_max_rigid_patch_count(2 * self._physics_context.get_gpu_max_rigid_patch_count())
# self._physics_context.set_gpu_found_lost_pairs_capacity(2 * self._physics_context.get_gpu_found_lost_pairs_capacity())
# self._physics_context.set_gpu_found_lost_aggregate_pairs_capacity(20 * self._physics_context.get_gpu_found_lost_aggregate_pairs_capacity())
# self._physics_context.set_gpu_total_aggregate_pairs_capacity(20 * self._physics_context.get_gpu_total_aggregate_pairs_capacity())
# self._physics_context.set_gpu_heap_capacity(2 * self._physics_context.get_gpu_heap_capacity())
# self._physics_context.set_gpu_temp_buffer_capacity(20 * self._physics_context.get_gpu_heap_capacity())
# self._physics_context.set_gpu_max_num_partitions(20 * self._physics_context.get_gpu_temp_buffer_capacity())
# GPU buffers
self._gpu_max_rigid_contact_count = self._physics_context.get_gpu_max_rigid_contact_count()
self._gpu_max_rigid_patch_count = self._physics_context.get_gpu_max_rigid_patch_count()
self._gpu_found_lost_pairs_capacity = self._physics_context.get_gpu_found_lost_pairs_capacity()
self._gpu_found_lost_aggregate_pairs_capacity = self._physics_context.get_gpu_found_lost_aggregate_pairs_capacity()
self._gpu_total_aggregate_pairs_capacity = self._physics_context.get_gpu_total_aggregate_pairs_capacity()
self._gpu_max_soft_body_contacts = self._physics_context.get_gpu_max_soft_body_contacts()
self._gpu_max_particle_contacts = self._physics_context.get_gpu_max_particle_contacts()
self._gpu_heap_capacity = self._physics_context.get_gpu_heap_capacity()
self._gpu_temp_buffer_capacity = self._physics_context.get_gpu_temp_buffer_capacity()
# self._gpu_max_num_partitions = physics_context.get_gpu_max_num_partitions() # BROKEN->method does not exist
big_info2 = "[physics context]:" + "\n" + \
"gpu_max_rigid_contact_count: " + str(self._gpu_max_rigid_contact_count) + "\n" + \
"gpu_max_rigid_patch_count: " + str(self._gpu_max_rigid_patch_count) + "\n" + \
"gpu_found_lost_pairs_capacity: " + str(self._gpu_found_lost_pairs_capacity) + "\n" + \
"gpu_found_lost_aggregate_pairs_capacity: " + str(self._gpu_found_lost_aggregate_pairs_capacity) + "\n" + \
"gpu_total_aggregate_pairs_capacity: " + str(self._gpu_total_aggregate_pairs_capacity) + "\n" + \
"gpu_max_soft_body_contacts: " + str(self._gpu_max_soft_body_contacts) + "\n" + \
"gpu_max_particle_contacts: " + str(self._gpu_max_particle_contacts) + "\n" + \
"gpu_heap_capacity: " + str(self._gpu_heap_capacity) + "\n" + \
"gpu_temp_buffer_capacity: " + str(self._gpu_temp_buffer_capacity)
Journal.log(self.__class__.__name__,
"set_task",
big_info2,
LogType.STAT,
throw_when_excep = True)
self._scene = self._world.scene
from omni.usd import get_context
self._stage = get_context().get_stage()
from pxr import UsdLux, Sdf, Gf, UsdPhysics, PhysicsSchemaTools
# add lighting
distantLight = UsdLux.DistantLight.Define(self._stage, Sdf.Path("/World/DistantLight"))
distantLight.CreateIntensityAttr(500)
self._world._current_tasks = dict() # resets registered tasks
self._task = task
self._task.set_world(self._world)
self._task.configure_scene()
self._world.add_task(self._task)
self._num_envs = self._task.num_envs
if sim_params and "enable_viewport" in sim_params:
self._render = sim_params["enable_viewport"]
Journal.log(self.__class__.__name__,
"set_task",
"[render]: " + str(self._render),
LogType.STAT,
throw_when_excep = True)
# if init_sim:
# self._world.reset() # after the first reset we get get all quantities
# # from the scene
# self._task.post_initialization_steps() # performs initializations
# # steps after the fisrt world reset was called
def render(self, mode="human") -> None:
""" Step the renderer.
Args:
mode (str): Select mode of rendering based on OpenAI environments.
"""
if mode == "human":
self._world.render()
return None
elif mode == "rgb_array":
# check if viewport is enabled -- if not, then complain because we won't get any data
if not self._render or not self._record:
exception = f"Cannot render '{mode}' when rendering is not enabled. Please check the provided" + \
"arguments to the environment class at initialization."
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
# obtain the rgb data
rgb_data = self._rgb_annotator.get_data()
# convert to numpy array
rgb_data = np.frombuffer(rgb_data, dtype=np.uint8).reshape(*rgb_data.shape)
# return the rgb data
return rgb_data[:, :, :3]
else:
# gym.Env.render(self, mode=mode)
return None
def create_viewport_render_product(self, resolution=(1280, 720)):
"""Create a render product of the viewport for rendering."""
try:
import omni.replicator.core as rep
# create render product
self._render_product = rep.create.render_product("/OmniverseKit_Persp", resolution)
# create rgb annotator -- used to read data from the render product
self._rgb_annotator = rep.AnnotatorRegistry.get_annotator("rgb", device="cpu")
self._rgb_annotator.attach([self._render_product])
self._record = True
except Exception as e:
carb.log_info("omni.replicator.core could not be imported. Skipping creation of render product.")
carb.log_info(str(e))
def close(self) -> None:
""" Closes simulation.
"""
if self._simulation_app.is_running():
self._simulation_app.close()
return
@abstractmethod
def step(self,
actions = None):
""" Basic implementation for stepping simulation"""
pass
@abstractmethod
def reset(self):
""" Usually resets the task and updates observations +
# other custom operations. """
pass
@property
def num_envs(self):
""" Retrieves number of environments.
Returns:
num_envs(int): Number of environments.
"""
return self._num_envs
@property
def simulation_app(self):
"""Retrieves the SimulationApp object.
Returns:
simulation_app(SimulationApp): SimulationApp.
"""
return self._simulation_app
@property
def get_world(self):
"""Retrieves the World object for simulation.
Returns:
world(World): Simulation World.
"""
return self._world
@property
def task(self):
"""Retrieves the task.
Returns:
task(BaseTask): Task.
"""
return self._task
@property
def render_enabled(self):
"""Whether rendering is enabled.
Returns:
render(bool): is render enabled.
"""
return self._render
|
AndrePatri/OmniRoboGym/omni_robo_gym/tasks/isaac_task.py | # Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
from omni.isaac.core.tasks.base_task import BaseTask
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.viewports import set_camera_view
from omni.isaac.core.world import World
import omni.kit
import numpy as np
import torch
from omni.importer.urdf import _urdf
from omni.isaac.core.utils.prims import move_prim
from omni.isaac.cloner import GridCloner
import omni.isaac.core.utils.prims as prim_utils
# from omni.isaac.sensor import ContactSensor
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.scenes.scene import Scene
from omni_robo_gym.utils.jnt_imp_cntrl import OmniJntImpCntrl
from omni_robo_gym.utils.homing import OmniRobotHomer
from omni_robo_gym.utils.contact_sensor import OmniContactSensors
from omni_robo_gym.utils.terrains import RlTerrains
from omni_robo_gym.utils.math_utils import quat_to_omega, quaternion_difference, rel_vel
from abc import abstractmethod
from typing import List, Dict
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
class IsaacTask(BaseTask):
def __init__(self,
name: str,
integration_dt: float,
robot_names: List[str],
robot_pkg_names: List[str] = None,
contact_prims: Dict[str, List] = None,
contact_offsets: Dict[str, Dict[str, np.ndarray]] = None,
sensor_radii: Dict[str, Dict[str, np.ndarray]] = None,
num_envs = 1,
device = "cuda",
cloning_offset: np.array = None,
fix_base: List[bool] = None,
self_collide: List[bool] = None,
merge_fixed: List[bool] = None,
replicate_physics: bool = True,
solver_position_iteration_count: int = 4,
solver_velocity_iteration_count: int = 1,
solver_stabilization_thresh: float = 1e-5,
offset=None,
env_spacing = 5.0,
spawning_radius = 1.0,
use_flat_ground = True,
default_jnt_stiffness = 300.0,
default_jnt_damping = 20.0,
default_wheel_stiffness = 0.0,
default_wheel_damping = 10.0,
override_art_controller = False,
dtype = torch.float64,
debug_enabled: bool = False,
verbose = False,
use_diff_velocities = False) -> None:
self.torch_dtype = dtype
self._debug_enabled = debug_enabled
self._verbose = verbose
self.use_diff_velocities = use_diff_velocities
self.num_envs = num_envs
self._override_art_controller = override_art_controller
self._integration_dt = integration_dt # just used for contact reporting
self.torch_device = torch.device(device) # defaults to "cuda" ("cpu" also valid)
self.using_gpu = False
if self.torch_device == torch.device("cuda"):
self.using_gpu = True
self.robot_names = robot_names # these are (potentially) custom names to
self.robot_pkg_names = robot_pkg_names # will be used to search for URDF and SRDF packages
self.scene_setup_completed = False
if self.robot_pkg_names is None:
self.robot_pkg_names = self.robot_names # if not provided, robot_names are the same as robot_pkg_names
else:
# check dimension consistency
if len(robot_names) != len(robot_pkg_names):
exception = "The provided robot names list must match the length " + \
"of the provided robot package names"
raise Exception(exception)
if fix_base is None:
self._fix_base = [False] * len(self.robot_names)
else:
# check dimension consistency
if len(fix_base) != len(robot_pkg_names):
exception = "The provided fix_base list of boolean must match the length " + \
"of the provided robot package names"
raise Exception(exception)
self._fix_base = fix_base
if self_collide is None:
self._self_collide = [False] * len(self.robot_names)
else:
# check dimension consistency
if len(self_collide) != len(robot_pkg_names):
exception = "The provided self_collide list of boolean must match the length " + \
"of the provided robot package names"
raise Exception(exception)
self._self_collide = self_collide
if merge_fixed is None:
self._merge_fixed = [False] * len(self.robot_names)
else:
# check dimension consistency
if len(merge_fixed) != len(robot_pkg_names):
exception = "The provided merge_fixed list of boolean must match the length " + \
"of the provided robot package names"
raise Exception(exception)
self._merge_fixed = merge_fixed
self._urdf_paths = {}
self._srdf_paths = {}
self._robots_art_views = {}
self._robots_articulations = {}
self._robots_geom_prim_views = {}
self._solver_position_iteration_count = solver_position_iteration_count # solver position iteration count
# -> higher number makes simulation more accurate
self._solver_velocity_iteration_count = solver_velocity_iteration_count
self._solver_stabilization_thresh = solver_stabilization_thresh # threshold for kin. energy below which an articulatiion
# "goes to sleep", i.e. it's not simulated anymore until some action wakes him up
# potentially, each robot could have its own setting for the solver (not supported yet)
self._solver_position_iteration_counts = {}
self._solver_velocity_iteration_counts = {}
self._solver_stabilization_threshs = {}
self.robot_bodynames = {}
self.robot_n_links = {}
self.robot_n_dofs = {}
self.robot_dof_names = {}
self._root_p = {}
self._root_q = {}
self._jnts_q = {}
self._root_p_prev = {} # used for num differentiation
self._root_q_prev = {} # used for num differentiation
self._jnts_q_prev = {} # used for num differentiation
self._root_p_default = {}
self._root_q_default = {}
self._jnts_q_default = {}
self._root_v = {}
self._root_v_default = {}
self._root_omega = {}
self._root_omega_default = {}
self._jnts_v = {}
self._jnts_v_default = {}
self._jnts_eff_default = {}
self._root_pos_offsets = {}
self._root_q_offsets = {}
self.distr_offset = {} # decribed how robots within each env are distributed
self.jnt_imp_controllers = {}
self.homers = {}
# default jnt impedance settings
self.default_jnt_stiffness = default_jnt_stiffness
self.default_jnt_damping = default_jnt_damping
self.default_wheel_stiffness = default_wheel_stiffness
self.default_wheel_damping = default_wheel_damping
self.use_flat_ground = use_flat_ground
self.spawning_radius = spawning_radius # [m] -> default distance between roots of robots in a single
# environment
self._calc_robot_distrib() # computes the offsets of robots withing each env.
self._env_ns = "/World/envs"
self._env_spacing = env_spacing # [m]
self._template_env_ns = self._env_ns + "/env_0"
self._cloner = GridCloner(spacing=self._env_spacing)
self._cloner.define_base_env(self._env_ns)
prim_utils.define_prim(self._template_env_ns)
self._envs_prim_paths = self._cloner.generate_paths(self._env_ns + "/env",
self.num_envs)
self._cloning_offset = cloning_offset
if self._cloning_offset is None:
self._cloning_offset = np.array([[0, 0, 0]] * self.num_envs)
self._replicate_physics = replicate_physics
self._world_initialized = False
self._ground_plane_prim_path = "/World/terrain"
self._world = None
self._world_scene = None
self._world_physics_context = None
self.omni_contact_sensors = {}
self.contact_prims = contact_prims
for robot_name in contact_prims:
self.omni_contact_sensors[robot_name] = OmniContactSensors(
name = robot_name,
n_envs = self.num_envs,
contact_prims = contact_prims,
contact_offsets = contact_offsets,
sensor_radii = sensor_radii,
device = self.torch_device,
dtype = self.torch_dtype,
enable_debug=self._debug_enabled)
# trigger __init__ of parent class
BaseTask.__init__(self,
name=name,
offset=offset)
self.xrdf_cmd_vals = [] # by default empty, needs to be overriden by
# child class
def update_jnt_imp_control_gains(self,
robot_name: str,
jnt_stiffness: float,
jnt_damping: float,
wheel_stiffness: float,
wheel_damping: float,
env_indxs: torch.Tensor = None):
# updates joint imp. controller with new impedance values
if self._debug_enabled:
for_robots = ""
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
msg = "Provided env_indxs should be a torch tensor of indexes!"
Journal.log(self.__class__.__name__,
"update_jnt_imp_control_gains",
msg,
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for_robots = f"for robot {robot_name}, indexes: " + str(env_indxs.tolist())
if self._verbose:
Journal.log(self.__class__.__name__,
"update_jnt_imp_control_gains",
f"updating joint impedances " + for_robots,
LogType.STAT,
throw_when_excep = True)
# set jnt imp gains for the whole robot
if env_indxs is None:
gains_pos = torch.full((self.num_envs, \
self.jnt_imp_controllers[robot_name].n_dofs),
jnt_stiffness,
device = self.torch_device,
dtype=self.torch_dtype)
gains_vel = torch.full((self.num_envs, \
self.jnt_imp_controllers[robot_name].n_dofs),
jnt_damping,
device = self.torch_device,
dtype=self.torch_dtype)
else:
gains_pos = torch.full((env_indxs.shape[0], \
self.jnt_imp_controllers[robot_name].n_dofs),
jnt_stiffness,
device = self.torch_device,
dtype=self.torch_dtype)
gains_vel = torch.full((env_indxs.shape[0], \
self.jnt_imp_controllers[robot_name].n_dofs),
jnt_damping,
device = self.torch_device,
dtype=self.torch_dtype)
self.jnt_imp_controllers[robot_name].set_gains(
pos_gains = gains_pos,
vel_gains = gains_vel,
robot_indxs = env_indxs)
# in case of wheels
wheels_indxs = self.jnt_imp_controllers[robot_name].get_jnt_idxs_matching(
name_pattern="wheel")
if wheels_indxs is not None:
if env_indxs is None:
# wheels are velocity-controlled
wheels_pos_gains = torch.full((self.num_envs, len(wheels_indxs)),
wheel_stiffness,
device = self.torch_device,
dtype=self.torch_dtype)
wheels_vel_gains = torch.full((self.num_envs, len(wheels_indxs)),
wheel_damping,
device = self.torch_device,
dtype=self.torch_dtype)
else:
# wheels are velocity-controlled
wheels_pos_gains = torch.full((env_indxs.shape[0], len(wheels_indxs)),
wheel_stiffness,
device = self.torch_device,
dtype=self.torch_dtype)
wheels_vel_gains = torch.full((env_indxs.shape[0], len(wheels_indxs)),
wheel_damping,
device = self.torch_device,
dtype=self.torch_dtype)
self.jnt_imp_controllers[robot_name].set_gains(
pos_gains = wheels_pos_gains,
vel_gains = wheels_vel_gains,
jnt_indxs=wheels_indxs,
robot_indxs = env_indxs)
def update_root_offsets(self,
robot_name: str,
env_indxs: torch.Tensor = None):
if self._debug_enabled:
for_robots = ""
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
msg = "Provided env_indxs should be a torch tensor of indexes!"
Journal.log(self.__class__.__name__,
"update_root_offsets",
msg,
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for_robots = f"for robot {robot_name}, indexes: " + str(env_indxs.tolist())
if self._verbose:
Journal.log(self.__class__.__name__,
"update_root_offsets",
f"updating root offsets " + for_robots,
LogType.STAT,
throw_when_excep = True)
# only planar position used
if env_indxs is None:
self._root_pos_offsets[robot_name][:, 0:2] = self._root_p[robot_name][:, 0:2]
self._root_q_offsets[robot_name][:, :] = self._root_q[robot_name]
else:
self._root_pos_offsets[robot_name][env_indxs, 0:2] = self._root_p[robot_name][env_indxs, 0:2]
self._root_q_offsets[robot_name][env_indxs, :] = self._root_q[robot_name][env_indxs, :]
def synch_default_root_states(self,
robot_name: str = None,
env_indxs: torch.Tensor = None):
if self._debug_enabled:
for_robots = ""
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
msg = "Provided env_indxs should be a torch tensor of indexes!"
Journal.log(self.__class__.__name__,
"synch_default_root_states",
msg,
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for_robots = f"for robot {robot_name}, indexes: " + str(env_indxs.tolist())
if self._verbose:
Journal.log(self.__class__.__name__,
"synch_default_root_states",
f"updating default root states " + for_robots,
LogType.STAT,
throw_when_excep = True)
if env_indxs is None:
self._root_p_default[robot_name][:, :] = self._root_p[robot_name]
self._root_q_default[robot_name][:, :] = self._root_q[robot_name]
else:
self._root_p_default[robot_name][env_indxs, :] = self._root_p[robot_name][env_indxs, :]
self._root_q_default[robot_name][env_indxs, :] = self._root_q[robot_name][env_indxs, :]
def post_initialization_steps(self):
print("Performing post-initialization steps")
self._world_initialized = True # used by other methods which nees to run
# only when the world was initialized
# populates robot info fields
self._fill_robot_info_from_world()
# initializes homing managers
self._init_homing_managers()
# initializes robot state data
self._init_robots_state()
# default robot state
self._set_robots_default_jnt_config()
self._set_robots_root_default_config()
# initializes joint impedance controllers
self._init_jnt_imp_control()
# update solver options
self._update_art_solver_options()
self.reset()
self._custom_post_init()
self._get_solver_info() # get again solver option before printing everything
self._print_envs_info() # debug prints
def apply_collision_filters(self,
physicscene_path: str,
coll_root_path: str):
self._cloner.filter_collisions(physicsscene_path = physicscene_path,
collision_root_path = coll_root_path,
prim_paths=self._envs_prim_paths,
global_paths=[self._ground_plane_prim_path] # can collide with these prims
)
def reset_jnt_imp_control(self,
robot_name: str,
env_indxs: torch.Tensor = None):
if self._debug_enabled:
for_robots = ""
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
Journal.log(self.__class__.__name__,
"reset_jnt_imp_control",
"Provided env_indxs should be a torch tensor of indexes!",
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for_robots = f"for robot {robot_name}, indexes: " + str(env_indxs)
if self._verbose:
Journal.log(self.__class__.__name__,
"reset_jnt_imp_control",
f"resetting joint impedances " + for_robots,
LogType.STAT,
throw_when_excep = True)
# resets all internal data, refs to defaults
self.jnt_imp_controllers[robot_name].reset(robot_indxs = env_indxs)
# restore current state
if env_indxs is None:
self.jnt_imp_controllers[robot_name].update_state(pos = self._jnts_q[robot_name][:, :],
vel = self._jnts_v[robot_name][:, :],
eff = None,
robot_indxs = None)
else:
self.jnt_imp_controllers[robot_name].update_state(pos = self._jnts_q[robot_name][env_indxs, :],
vel = self._jnts_v[robot_name][env_indxs, :],
eff = None,
robot_indxs = env_indxs)
# restore default gains
self.update_jnt_imp_control_gains(robot_name = robot_name,
jnt_stiffness = self.default_jnt_stiffness,
jnt_damping = self.default_jnt_damping,
wheel_stiffness = self.default_wheel_stiffness,
wheel_damping = self.default_wheel_damping,
env_indxs = env_indxs)
#restore jnt imp refs to homing
if env_indxs is None:
self.jnt_imp_controllers[robot_name].set_refs(pos_ref=self.homers[robot_name].get_homing()[:, :],
robot_indxs = None)
else:
self.jnt_imp_controllers[robot_name].set_refs(pos_ref=self.homers[robot_name].get_homing()[env_indxs, :],
robot_indxs = env_indxs)
# actually applies reset commands to the articulation
# self.jnt_imp_controllers[robot_name].apply_cmds()
def set_world(self,
world: World):
if not isinstance(world, World):
Journal.log(self.__class__.__name__,
"configure_scene",
"world should be an instance of omni.isaac.core.world.World!",
LogType.EXCEP,
throw_when_excep = True)
self._world = world
self._world_scene = self._world.scene
self._world_physics_context = self._world.get_physics_context()
def set_up_scene(self,
scene: Scene):
super().set_up_scene(scene)
def configure_scene(self) -> None:
# this is called automatically by the environment BEFORE
# initializing the simulation
if self._world is None:
Journal.log(self.__class__.__name__,
"configure_scene",
"Did you call the set_world() method??",
LogType.EXCEP,
throw_when_excep = True)
if not self.scene_setup_completed:
for i in range(len(self.robot_names)):
robot_name = self.robot_names[i]
robot_pkg_name = self.robot_pkg_names[i]
fix_base = self._fix_base[i]
self_collide = self._self_collide[i]
merge_fixed = self._merge_fixed[i]
self._generate_rob_descriptions(robot_name=robot_name,
robot_pkg_name=robot_pkg_name)
self._import_urdf(robot_name,
fix_base=fix_base,
self_collide=self_collide,
merge_fixed=merge_fixed)
Journal.log(self.__class__.__name__,
"set_up_scene",
"cloning environments...",
LogType.STAT,
throw_when_excep = True)
self._cloner.clone(
source_prim_path=self._template_env_ns,
prim_paths=self._envs_prim_paths,
replicate_physics=self._replicate_physics,
position_offsets = self._cloning_offset
) # we can clone the environment in which all the robos are
Journal.log(self.__class__.__name__,
"set_up_scene",
"finishing scene setup...",
LogType.STAT,
throw_when_excep = True)
for i in range(len(self.robot_names)):
robot_name = self.robot_names[i]
self._robots_art_views[robot_name] = ArticulationView(name = robot_name + "ArtView",
prim_paths_expr = self._env_ns + "/env_.*"+ "/" + robot_name + "/base_link",
reset_xform_properties=False)
self._robots_articulations[robot_name] = self._world_scene.add(self._robots_art_views[robot_name])
# self._robots_geom_prim_views[robot_name] = GeometryPrimView(name = robot_name + "GeomView",
# prim_paths_expr = self._env_ns + "/env*"+ "/" + robot_name,
# # prepare_contact_sensors = True
# )
# self._robots_geom_prim_views[robot_name].apply_collision_apis() # to be able to apply contact sensors
if self.use_flat_ground:
self._world_scene.add_default_ground_plane(z_position=0,
name="terrain",
prim_path= self._ground_plane_prim_path,
static_friction=1.0,
dynamic_friction=1.0,
restitution=0.2)
else:
self.terrains = RlTerrains(get_current_stage())
self.terrains.get_obstacles_terrain(terrain_size=40,
num_obs=100,
max_height=0.4,
min_size=0.5,
max_size=5.0)
# delete_prim(self._ground_plane_prim_path + "/SphereLight") # we remove the default spherical light
# set default camera viewport position and target
self._set_initial_camera_params()
self.apply_collision_filters(self._world_physics_context.prim_path,
"/World/collisions")
# init contact sensors
self._init_contact_sensors() # IMPORTANT: this has to be called
# after calling the clone() method and initializing articulation views!!!
self._world.reset() # reset world to make art views available
self.post_initialization_steps()
self.scene_setup_completed = True
def post_reset(self):
pass
def reset(self,
env_indxs: torch.Tensor = None,
robot_names: List[str] =None):
# we first reset all target articulations to their default state
rob_names = robot_names if (robot_names is not None) else self.robot_names
# resets the state of target robot and env to the defaults
self.reset_state(env_indxs=env_indxs,
robot_names=rob_names)
# and jnt imp. controllers
for i in range(len(rob_names)):
self.reset_jnt_imp_control(robot_name=rob_names[i],
env_indxs=env_indxs)
def reset_state(self,
env_indxs: torch.Tensor = None,
robot_names: List[str] =None):
rob_names = robot_names if (robot_names is not None) else self.robot_names
if env_indxs is not None:
if self._debug_enabled:
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
for i in range(len(rob_names)):
robot_name = rob_names[i]
# root q
self._robots_art_views[robot_name].set_world_poses(positions = self._root_p_default[robot_name][env_indxs, :],
orientations=self._root_q_default[robot_name][env_indxs, :],
indices = env_indxs)
# jnts q
self._robots_art_views[robot_name].set_joint_positions(positions = self._jnts_q_default[robot_name][env_indxs, :],
indices = env_indxs)
# root v and omega
self._robots_art_views[robot_name].set_joint_velocities(velocities = self._jnts_v_default[robot_name][env_indxs, :],
indices = env_indxs)
# jnts v
concatenated_vel = torch.cat((self._root_v_default[robot_name][env_indxs, :],
self._root_omega_default[robot_name][env_indxs, :]), dim=1)
self._robots_art_views[robot_name].set_velocities(velocities = concatenated_vel,
indices = env_indxs)
# jnts eff
self._robots_art_views[robot_name].set_joint_efforts(efforts = self._jnts_eff_default[robot_name][env_indxs, :],
indices = env_indxs)
else:
for i in range(len(rob_names)):
robot_name = rob_names[i]
# root q
self._robots_art_views[robot_name].set_world_poses(positions = self._root_p_default[robot_name][:, :],
orientations=self._root_q_default[robot_name][:, :],
indices = None)
# jnts q
self._robots_art_views[robot_name].set_joint_positions(positions = self._jnts_q_default[robot_name][:, :],
indices = None)
# root v and omega
self._robots_art_views[robot_name].set_joint_velocities(velocities = self._jnts_v_default[robot_name][:, :],
indices = None)
# jnts v
concatenated_vel = torch.cat((self._root_v_default[robot_name][:, :],
self._root_omega_default[robot_name][:, :]), dim=1)
self._robots_art_views[robot_name].set_velocities(velocities = concatenated_vel,
indices = None)
# jnts eff
self._robots_art_views[robot_name].set_joint_efforts(efforts = self._jnts_eff_default[robot_name][:, :],
indices = None)
# we update the robots state
self.get_states(env_indxs=env_indxs,
robot_names=rob_names)
def close(self):
pass
def root_pos_offsets(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_pos_offsets[robot_name]
else:
return self._root_pos_offsets[robot_name][env_idxs, :]
def root_q_offsets(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_q_offsets[robot_name]
else:
return self._root_q_offsets[robot_name][env_idxs, :]
def root_p(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_p[robot_name]
else:
return self._root_p[robot_name][env_idxs, :]
def root_p_rel(self,
robot_name: str,
env_idxs: torch.Tensor = None):
rel_pos = torch.sub(self.root_p(robot_name=robot_name,
env_idxs=env_idxs),
self.root_pos_offsets(robot_name=robot_name,
env_idxs=env_idxs))
return rel_pos
def root_q(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_q[robot_name]
else:
return self._root_q[robot_name][env_idxs, :]
def root_q_rel(self,
robot_name: str,
env_idxs: torch.Tensor = None):
rel_q = quaternion_difference(self.root_q_offsets(robot_name=robot_name,
env_idxs=env_idxs),
self.root_q(robot_name=robot_name,
env_idxs=env_idxs))
return rel_q
def root_v(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_v[robot_name]
else:
return self._root_v[robot_name][env_idxs, :]
def root_v_rel(self,
robot_name: str,
env_idxs: torch.Tensor = None):
v_rel = rel_vel(offset_q0_q1=self.root_q_offsets(robot_name=robot_name,
env_idxs=env_idxs),
v0=self.root_v(robot_name=robot_name, env_idxs=env_idxs))
return v_rel
def root_omega(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._root_omega[robot_name]
else:
return self._root_omega[robot_name][env_idxs, :]
def root_omega_rel(self,
robot_name: str,
env_idxs: torch.Tensor = None):
omega_rel = rel_vel(offset_q0_q1=self.root_q_offsets(robot_name=robot_name,
env_idxs=env_idxs),
v0=self.root_omega(robot_name=robot_name, env_idxs=env_idxs))
return omega_rel
def jnts_q(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._jnts_q[robot_name]
else:
return self._jnts_q[robot_name][env_idxs, :]
def jnts_v(self,
robot_name: str,
env_idxs: torch.Tensor = None):
if env_idxs is None:
return self._jnts_v[robot_name]
else:
return self._jnts_v[robot_name][env_idxs, :]
def integration_dt(self):
return self._integration_dt
@abstractmethod
def _xrdf_cmds(self) -> Dict:
# this has to be implemented by the user depending on the arguments
# the xacro description of the robot takes. The output is a list
# of xacro commands.
# Example implementation:
# def _xrdf_cmds():
# cmds = {}
# cmds{self.robot_names[0]} = []
# xrdf_cmd_vals = [True, True, True, False, False, True]
# legs = "true" if xrdf_cmd_vals[0] else "false"
# big_wheel = "true" if xrdf_cmd_vals[1] else "false"
# upper_body ="true" if xrdf_cmd_vals[2] else "false"
# velodyne = "true" if xrdf_cmd_vals[3] else "false"
# realsense = "true" if xrdf_cmd_vals[4] else "false"
# floating_joint = "true" if xrdf_cmd_vals[5] else "false" # horizon needs a floating joint
# cmds.append("legs:=" + legs)
# cmds.append("big_wheel:=" + big_wheel)
# cmds.append("upper_body:=" + upper_body)
# cmds.append("velodyne:=" + velodyne)
# cmds.append("realsense:=" + realsense)
# cmds.append("floating_joint:=" + floating_joint)
# return cmds
pass
@abstractmethod
def pre_physics_step(self,
actions,
robot_name: str) -> None:
# apply actions to simulated robot
# to be overriden by child class depending
# on specific needs
pass
def _generate_srdf(self,
robot_name: str,
robot_pkg_name: str):
# we generate the URDF where the description package is located
import rospkg
rospackage = rospkg.RosPack()
descr_path = rospackage.get_path(robot_pkg_name + "_srdf")
srdf_path = descr_path + "/srdf"
xacro_name = robot_pkg_name
xacro_path = srdf_path + "/" + xacro_name + ".srdf.xacro"
self._srdf_paths[robot_name] = self._descr_dump_path + "/" + robot_name + ".srdf"
if self._xrdf_cmds() is not None:
cmds = self._xrdf_cmds()[robot_name]
if cmds is None:
xacro_cmd = ["xacro"] + [xacro_path] + ["-o"] + [self._srdf_paths[robot_name]]
else:
xacro_cmd = ["xacro"] + [xacro_path] + cmds + ["-o"] + [self._srdf_paths[robot_name]]
if self._xrdf_cmds() is None:
xacro_cmd = ["xacro"] + [xacro_path] + ["-o"] + [self._srdf_paths[robot_name]]
import subprocess
try:
xacro_gen = subprocess.check_call(xacro_cmd)
except:
Journal.log(self.__class__.__name__,
"_generate_urdf",
"failed to generate " + robot_name + "\'S SRDF!!!",
LogType.EXCEP,
throw_when_excep = True)
def _generate_urdf(self,
robot_name: str,
robot_pkg_name: str):
# we generate the URDF where the description package is located
import rospkg
rospackage = rospkg.RosPack()
descr_path = rospackage.get_path(robot_pkg_name + "_urdf")
urdf_path = descr_path + "/urdf"
xacro_name = robot_pkg_name
xacro_path = urdf_path + "/" + xacro_name + ".urdf.xacro"
self._urdf_paths[robot_name] = self._descr_dump_path + "/" + robot_name + ".urdf"
if self._xrdf_cmds() is not None:
cmds = self._xrdf_cmds()[robot_name]
if cmds is None:
xacro_cmd = ["xacro"] + [xacro_path] + ["-o"] + [self._urdf_paths[robot_name]]
else:
xacro_cmd = ["xacro"] + [xacro_path] + cmds + ["-o"] + [self._urdf_paths[robot_name]]
if self._xrdf_cmds() is None:
xacro_cmd = ["xacro"] + [xacro_path] + ["-o"] + [self._urdf_paths[robot_name]]
import subprocess
try:
xacro_gen = subprocess.check_call(xacro_cmd)
# we also generate an updated SRDF
except:
Journal.log(self.__class__.__name__,
"_generate_urdf",
"Failed to generate " + robot_name + "\'s URDF!!!",
LogType.EXCEP,
throw_when_excep = True)
def _generate_rob_descriptions(self,
robot_name: str,
robot_pkg_name: str):
self._descr_dump_path = "/tmp/" + f"{self.__class__.__name__}"
Journal.log(self.__class__.__name__,
"update_root_offsets",
"generating URDF for robot "+ f"{robot_name}, of type {robot_pkg_name}...",
LogType.STAT,
throw_when_excep = True)
self._generate_urdf(robot_name=robot_name,
robot_pkg_name=robot_pkg_name)
Journal.log(self.__class__.__name__,
"update_root_offsets",
"generating SRDF for robot "+ f"{robot_name}, of type {robot_pkg_name}...",
LogType.STAT,
throw_when_excep = True)
# we also generate SRDF files, which are useful for control
self._generate_srdf(robot_name=robot_name,
robot_pkg_name=robot_pkg_name)
def _import_urdf(self,
robot_name: str,
import_config: omni.importer.urdf._urdf.ImportConfig = _urdf.ImportConfig(),
fix_base = False,
self_collide = False,
merge_fixed = True):
Journal.log(self.__class__.__name__,
"update_root_offsets",
"importing robot URDF",
LogType.STAT,
throw_when_excep = True)
_urdf.acquire_urdf_interface()
# we overwrite some settings which are bound to be fixed
import_config.merge_fixed_joints = merge_fixed # makes sim more stable
# in case of fixed joints with light objects
import_config.import_inertia_tensor = True
# import_config.convex_decomp = False
import_config.fix_base = fix_base
import_config.self_collision = self_collide
# import_config.distance_scale = 1
# import_config.make_default_prim = True
# import_config.create_physics_scene = True
# import_config.default_drive_strength = 1047.19751
# import_config.default_position_drive_damping = 52.35988
# import_config.default_drive_type = _urdf.UrdfJointTargetType.JOINT_DRIVE_POSITION
# import URDF
success, robot_prim_path_default = omni.kit.commands.execute(
"URDFParseAndImportFile",
urdf_path=self._urdf_paths[robot_name],
import_config=import_config,
)
robot_base_prim_path = self._template_env_ns + "/" + robot_name
# moving default prim to base prim path for cloning
move_prim(robot_prim_path_default, # from
robot_base_prim_path) # to
return success
def _init_contact_sensors(self):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
# creates base contact sensor (which is then cloned)
self.omni_contact_sensors[robot_name].create_contact_sensors(
self._world,
self._env_ns
)
def _init_robots_state(self):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
pose = self._robots_art_views[robot_name].get_world_poses(
clone = True) # tuple: (pos, quat)
# root p (measured, previous, default)
self._root_p[robot_name] = pose[0]
self._root_p_prev[robot_name] = torch.clone(pose[0])
self._root_p_default[robot_name] = torch.clone(pose[0]) + self.distr_offset[robot_name]
# root q (measured, previous, default)
self._root_q[robot_name] = pose[1] # root orientation
self._root_q_prev[robot_name] = torch.clone(pose[1])
self._root_q_default[robot_name] = torch.clone(pose[1])
# jnt q (measured, previous, default)
self._jnts_q[robot_name] = self._robots_art_views[robot_name].get_joint_positions(
clone = True) # joint positions
self._jnts_q_prev[robot_name] = self._robots_art_views[robot_name].get_joint_positions(
clone = True)
self._jnts_q_default[robot_name] = self.homers[robot_name].get_homing(clone=True)
# root v (measured, default)
self._root_v[robot_name] = self._robots_art_views[robot_name].get_linear_velocities(
clone = True) # root lin. velocity
self._root_v_default[robot_name] = torch.full((self._root_v[robot_name].shape[0], self._root_v[robot_name].shape[1]),
0.0,
dtype=self.torch_dtype,
device=self.torch_device)
# root omega (measured, default)
self._root_omega[robot_name] = self._robots_art_views[robot_name].get_angular_velocities(
clone = True) # root ang. velocity
self._root_omega_default[robot_name] = torch.full((self._root_omega[robot_name].shape[0], self._root_omega[robot_name].shape[1]),
0.0,
dtype=self.torch_dtype,
device=self.torch_device)
# joints v (measured, default)
self._jnts_v[robot_name] = self._robots_art_views[robot_name].get_joint_velocities(
clone = True) # joint velocities
self._jnts_v_default[robot_name] = torch.full((self._jnts_v[robot_name].shape[0], self._jnts_v[robot_name].shape[1]),
0.0,
dtype=self.torch_dtype,
device=self.torch_device)
self._jnts_eff_default[robot_name] = torch.full((self._jnts_v[robot_name].shape[0], self._jnts_v[robot_name].shape[1]),
0.0,
dtype=self.torch_dtype,
device=self.torch_device)
self._root_pos_offsets[robot_name] = torch.zeros((self.num_envs, 3),
device=self.torch_device) # reference position offses
self._root_q_offsets[robot_name] = torch.zeros((self.num_envs, 4),
device=self.torch_device)
self._root_q_offsets[robot_name][:, 0] = 1.0 # init to valid identity quaternion
self.update_root_offsets(robot_name)
def _calc_robot_distrib(self):
import math
# we distribute robots in a single env. along the
# circumference of a circle of given radius
n_robots = len(self.robot_names)
offset_baseangle = 2 * math.pi / n_robots
for i in range(n_robots):
offset_angle = offset_baseangle * (i + 1)
robot_offset_wrt_center = torch.tensor([self.spawning_radius * math.cos(offset_angle),
self.spawning_radius * math.sin(offset_angle), 0],
device=self.torch_device,
dtype=self.torch_dtype)
# list with n references to the original tensor
tensor_list = [robot_offset_wrt_center] * self.num_envs
self.distr_offset[self.robot_names[i]] = torch.stack(tensor_list, dim=0)
def _get_robots_state(self,
env_indxs: torch.Tensor = None,
robot_names: List[str] = None,
dt: float = None,
reset: bool = False):
rob_names = robot_names if (robot_names is not None) else self.robot_names
if env_indxs is not None:
for i in range(0, len(rob_names)):
robot_name = rob_names[i]
pose = self._robots_art_views[robot_name].get_world_poses(
clone = True,
indices=env_indxs) # tuple: (pos, quat)
self._root_p[robot_name][env_indxs, :] = pose[0]
self._root_q[robot_name][env_indxs, :] = pose[1] # root orientation
self._jnts_q[robot_name][env_indxs, :] = self._robots_art_views[robot_name].get_joint_positions(
clone = True,
indices=env_indxs) # joint positions
if dt is None:
# we get velocities from the simulation. This is not good since
# these can actually represent artifacts which do not have physical meaning.
# It's better to obtain them by differentiation to avoid issues with controllers, etc...
self._root_v[robot_name][env_indxs, :] = self._robots_art_views[robot_name].get_linear_velocities(
clone = True,
indices=env_indxs) # root lin. velocity
self._root_omega[robot_name][env_indxs, :] = self._robots_art_views[robot_name].get_angular_velocities(
clone = True,
indices=env_indxs) # root ang. velocity
self._jnts_v[robot_name][env_indxs, :] = self._robots_art_views[robot_name].get_joint_velocities(
clone = True,
indices=env_indxs) # joint velocities
else:
# differentiate numerically
if not reset:
self._root_v[robot_name][env_indxs, :] = (self._root_p[robot_name][env_indxs, :] - \
self._root_p_prev[robot_name][env_indxs, :]) / dt
self._root_omega[robot_name][env_indxs, :] = quat_to_omega(self._root_q[robot_name][env_indxs, :],
self._root_q_prev[robot_name][env_indxs, :],
dt)
self._jnts_v[robot_name][env_indxs, :] = (self._jnts_q[robot_name][env_indxs, :] - \
self._jnts_q_prev[robot_name][env_indxs, :]) / dt
else:
# to avoid issues when differentiating numerically
self._root_v[robot_name][env_indxs, :].zero_()
self._root_omega[robot_name][env_indxs, :].zero_()
self._jnts_v[robot_name][env_indxs, :].zero_()
# update "previous" data for numerical differentiation
self._root_p_prev[robot_name][env_indxs, :] = self._root_p[robot_name][env_indxs, :]
self._root_q_prev[robot_name][env_indxs, :] = self._root_q[robot_name][env_indxs, :]
self._jnts_q_prev[robot_name][env_indxs, :] = self._jnts_q[robot_name][env_indxs, :]
else:
# updating data for all environments
for i in range(0, len(rob_names)):
robot_name = rob_names[i]
pose = self._robots_art_views[robot_name].get_world_poses(
clone = True) # tuple: (pos, quat)
self._root_p[robot_name][:, :] = pose[0]
self._root_q[robot_name][:, :] = pose[1] # root orientation
self._jnts_q[robot_name][:, :] = self._robots_art_views[robot_name].get_joint_positions(
clone = True) # joint positions
if dt is None:
# we get velocities from the simulation. This is not good since
# these can actually represent artifacts which do not have physical meaning.
# It's better to obtain them by differentiation to avoid issues with controllers, etc...
self._root_v[robot_name][:, :] = self._robots_art_views[robot_name].get_linear_velocities(
clone = True) # root lin. velocity
self._root_omega[robot_name][:, :] = self._robots_art_views[robot_name].get_angular_velocities(
clone = True) # root ang. velocity
self._jnts_v[robot_name][:, :] = self._robots_art_views[robot_name].get_joint_velocities(
clone = True) # joint velocities
else:
# differentiate numerically
if not reset:
self._root_v[robot_name][:, :] = (self._root_p[robot_name][:, :] - \
self._root_p_prev[robot_name][:, :]) / dt
self._root_omega[robot_name][:, :] = quat_to_omega(self._root_q[robot_name][:, :],
self._root_q_prev[robot_name][:, :],
dt)
self._jnts_v[robot_name][:, :] = (self._jnts_q[robot_name][:, :] - \
self._jnts_q_prev[robot_name][:, :]) / dt
# self._jnts_v[robot_name][:, :].zero_()
else:
# to avoid issues when differentiating numerically
self._root_v[robot_name][:, :].zero_()
self._root_omega[robot_name][:, :].zero_()
self._jnts_v[robot_name][:, :].zero_()
# update "previous" data for numerical differentiation
self._root_p_prev[robot_name][:, :] = self._root_p[robot_name][:, :]
self._root_q_prev[robot_name][:, :] = self._root_q[robot_name][:, :]
self._jnts_q_prev[robot_name][:, :] = self._jnts_q[robot_name][:, :]
def get_states(self,
env_indxs: torch.Tensor = None,
robot_names: List[str] = None):
if self.use_diff_velocities:
self._get_robots_state(dt = self.integration_dt(),
env_indxs = env_indxs,
robot_names = robot_names) # updates robot states
# but velocities are obtained via num. differentiation
else:
self._get_robots_state(env_indxs = env_indxs,
robot_names = robot_names) # velocities directly from simulator (can
# introduce relevant artifacts, making them unrealistic)
def _custom_post_init(self):
# can be overridden by child class
pass
def _set_robots_default_jnt_config(self):
# setting Isaac's internal defaults. Useful is resetting
# whole scenes or views, but single env reset has to be implemented
# manueally
# we use the homing of the robots
if (self._world_initialized):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
homing = self.homers[robot_name].get_homing()
self._robots_art_views[robot_name].set_joints_default_state(positions= homing,
velocities = torch.zeros((homing.shape[0], homing.shape[1]), \
dtype=self.torch_dtype, device=self.torch_device),
efforts = torch.zeros((homing.shape[0], homing.shape[1]), \
dtype=self.torch_dtype, device=self.torch_device))
else:
Journal.log(self.__class__.__name__,
"_set_robots_default_jnt_config",
"Before calling __set_robots_default_jnt_config(), you need to reset the World" + \
" at least once and call post_initialization_steps()",
LogType.EXCEP,
throw_when_excep = True)
def _set_robots_root_default_config(self):
if (self._world_initialized):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
self._robots_art_views[robot_name].set_default_state(positions = self._root_p_default[robot_name],
orientations = self._root_q_default[robot_name])
else:
Journal.log(self.__class__.__name__,
"_generate_urdf",
"Before calling _set_robots_root_default_config(), you need to reset the World" + \
" at least once and call post_initialization_steps()",
LogType.EXCEP,
throw_when_excep = True)
return True
def _get_solver_info(self):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
self._solver_position_iteration_counts[robot_name] = self._robots_art_views[robot_name].get_solver_position_iteration_counts()
self._solver_velocity_iteration_counts[robot_name] = self._robots_art_views[robot_name].get_solver_velocity_iteration_counts()
self._solver_stabilization_threshs[robot_name] = self._robots_art_views[robot_name].get_stabilization_thresholds()
def _update_art_solver_options(self):
# sets new solver iteration options for specifc articulations
self._get_solver_info() # gets current solver info for the articulations of the
# environments, so that dictionaries are filled properly
if (self._world_initialized):
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
# increase by a factor
self._solver_position_iteration_counts[robot_name] = torch.full((self.num_envs,), self._solver_position_iteration_count)
self._solver_velocity_iteration_counts[robot_name] = torch.full((self.num_envs,), self._solver_velocity_iteration_count)
self._solver_stabilization_threshs[robot_name] = torch.full((self.num_envs,), self._solver_stabilization_thresh)
self._robots_art_views[robot_name].set_solver_position_iteration_counts(self._solver_position_iteration_counts[robot_name])
self._robots_art_views[robot_name].set_solver_velocity_iteration_counts(self._solver_velocity_iteration_counts[robot_name])
self._robots_art_views[robot_name].set_stabilization_thresholds(self._solver_stabilization_threshs[robot_name])
self._get_solver_info() # gets again solver info for articulation, so that it's possible to debug if
# the operation was successful
else:
Journal.log(self.__class__.__name__,
"_set_robots_default_jnt_config",
"Before calling update_art_solver_options(), you need to reset the World at least once!",
LogType.EXCEP,
throw_when_excep = True)
def _print_envs_info(self):
if (self._world_initialized):
print("TASK INFO:")
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
task_info = f"[{robot_name}]" + "\n" + \
"bodies: " + str(self._robots_art_views[robot_name].body_names) + "\n" + \
"n. prims: " + str(self._robots_art_views[robot_name].count) + "\n" + \
"prims names: " + str(self._robots_art_views[robot_name].prim_paths) + "\n" + \
"n. bodies: " + str(self._robots_art_views[robot_name].num_bodies) + "\n" + \
"n. dofs: " + str(self._robots_art_views[robot_name].num_dof) + "\n" + \
"dof names: " + str(self._robots_art_views[robot_name].dof_names) + "\n" + \
"solver_position_iteration_counts: " + str(self._solver_position_iteration_counts[robot_name]) + "\n" + \
"solver_velocity_iteration_counts: " + str(self._solver_velocity_iteration_counts[robot_name]) + "\n" + \
"stabiliz. thresholds: " + str(self._solver_stabilization_threshs[robot_name])
# print("dof limits: " + str(self._robots_art_views[robot_name].get_dof_limits()))
# print("effort modes: " + str(self._robots_art_views[robot_name].get_effort_modes()))
# print("dof gains: " + str(self._robots_art_views[robot_name].get_gains()))
# print("dof max efforts: " + str(self._robots_art_views[robot_name].get_max_efforts()))
# print("dof gains: " + str(self._robots_art_views[robot_name].get_gains()))
# print("physics handle valid: " + str(self._robots_art_views[robot_name].is_physics_handle_valid())
Journal.log(self.__class__.__name__,
"_print_envs_info",
task_info,
LogType.STAT,
throw_when_excep = True)
else:
Journal.log(self.__class__.__name__,
"_set_robots_default_jnt_config",
"Before calling __print_envs_info(), you need to reset the World at least once!",
LogType.EXCEP,
throw_when_excep = True)
def _fill_robot_info_from_world(self):
if self._world_initialized:
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
self.robot_bodynames[robot_name] = self._robots_art_views[robot_name].body_names
self.robot_n_links[robot_name] = self._robots_art_views[robot_name].num_bodies
self.robot_n_dofs[robot_name] = self._robots_art_views[robot_name].num_dof
self.robot_dof_names[robot_name] = self._robots_art_views[robot_name].dof_names
else:
Journal.log(self.__class__.__name__,
"_fill_robot_info_from_world",
"Before calling _fill_robot_info_from_world(), you need to reset the World at least once!",
LogType.EXCEP,
throw_when_excep = True)
def _init_homing_managers(self):
if self._world_initialized:
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
self.homers[robot_name] = OmniRobotHomer(articulation=self._robots_art_views[robot_name],
srdf_path=self._srdf_paths[robot_name],
device=self.torch_device,
dtype=self.torch_dtype)
else:
exception = "you should reset the World at least once and call the " + \
"post_initialization_steps() method before initializing the " + \
"homing manager."
Journal.log(self.__class__.__name__,
"_init_homing_managers",
exception,
LogType.EXCEP,
throw_when_excep = True)
def _init_jnt_imp_control(self):
if self._world_initialized:
for i in range(0, len(self.robot_names)):
robot_name = self.robot_names[i]
# creates impedance controller
self.jnt_imp_controllers[robot_name] = OmniJntImpCntrl(articulation=self._robots_art_views[robot_name],
default_pgain = self.default_jnt_stiffness, # defaults
default_vgain = self.default_jnt_damping,
override_art_controller=self._override_art_controller,
filter_dt = None,
filter_BW = 50,
device= self.torch_device,
dtype=self.torch_dtype,
enable_safety=True,
enable_profiling=self._debug_enabled,
urdf_path=self._urdf_paths[robot_name],
debug_checks = self._debug_enabled)
self.reset_jnt_imp_control(robot_name)
else:
exception = "you should reset the World at least once and call the " + \
"post_initialization_steps() method before initializing the " + \
"joint impedance controller."
Journal.log(self.__class__.__name__,
"_init_homing_managers",
exception,
LogType.EXCEP,
throw_when_excep = True)
def _set_initial_camera_params(self,
camera_position=[10, 10, 3],
camera_target=[0, 0, 0]):
set_camera_view(eye=camera_position,
target=camera_target,
camera_prim_path="/OmniverseKit_Persp")
|
AndrePatri/OmniRoboGym/omni_robo_gym/tasks/__init__.py | |
AndrePatri/OmniRoboGym/omni_robo_gym/cfg/omni_kits/omni.isaac.sim.python.omnirobogym.headless.kit | [package]
title = "Isaac Sim Python - Headless Gym"
description = "A simplifed app for running Gym examples headlessly from Python"
version = "2023.1.1"
# That makes it browsable in UI with "experience" filter
keywords = ["experience", "app", "usd"]
[settings]
app.name = "Isaac-Sim"
app.version = "2023.1.1"
[dependencies]
"omni.kit.window.title" = {}
"omni.physx" = {}
"omni.physx.tensors" = {}
"omni.physx.fabric" = {}
"omni.warp.core" = {}
"usdrt.scenegraph" = {}
"omni.kit.mainwindow" = {}
"omni.kit.telemetry" = {}
[settings]
renderer.active = "rtx"
exts."omni.kit.viewport.menubar.camera".expand = true # Expand the extra-camera settings by default
exts."omni.kit.window.file".useNewFilePicker = true
exts."omni.kit.tool.asset_importer".useNewFilePicker = true
exts."omni.kit.tool.collect".useNewFilePicker = true
exts."omni.kit.widget.layers".useNewFilePicker = true
exts."omni.kit.renderer.core".imgui.enableMips = true
exts."omni.kit.browser.material".enabled = false
exts."omni.kit.browser.asset".visible_after_startup = false
exts."omni.kit.window.material".load_after_startup = true
exts."omni.kit.widget.cloud_share".require_access_code = false
exts."omni.kit.viewport.window".startup.windowName="Viewport" # Rename from Viewport Next
exts."omni.kit.menu.utils".logDeprecated = false
app.content.emptyStageOnStart = false
# deprecate support for old kit.ui.menu
app.menu.legacy_mode = false
# use omni.ui.Menu for the MenuBar
app.menu.compatibility_mode = false
# Setting the port for the embedded http server
exts."omni.services.transport.server.http".port = 8211
# default viewport is fill
app.runLoops.rendering_0.fillResolution = false
exts."omni.kit.window.viewport".blockingGetViewportDrawable = false
exts."omni.kit.test".includeTests.1 = "*isaac*"
# Fix PlayButtonGroup error
exts."omni.kit.widget.toolbar".PlayButton.enabled = false
[settings.app.settings]
persistent = true
dev_build = false
fabricDefaultStageFrameHistoryCount = 3 # needed for omni.syntheticdata TODO105 still true?
[settings.app.window]
title = "Isaac Sim"
hideUi = false
_iconSize = 256
iconPath = "${app}/../exts/omni.isaac.app.setup/data/nvidia-omniverse-isaacsim.ico"
# width = 1700
# height = 900
# x = -1
# y = -1
# Fonts
[setting.app.font]
file = "${fonts}/OpenSans-SemiBold.ttf"
size = 16
# [setting.app.runLoops]
# main.rateLimitEnabled = false
# main.rateLimitFrequency = 60
# main.rateLimitUseBusyLoop = false
# rendering_0.rateLimitEnabled = false
[settings.exts.'omni.kit.window.extensions']
# List extensions here we want to show as featured when extension manager is opened
featuredExts = []
[settings]
# MGPU is always on, you can turn it from the settings, and force this off to save even more resource if you
# only want to use a single GPU on your MGPU system
# False for Isaac Sim
renderer.multiGpu.enabled = true
renderer.multiGpu.autoEnable = true
'rtx-transient'.resourcemanager.enableTextureStreaming = true
app.asyncRendering = false
app.asyncRenderingLowLatency = false
app.hydraEngine.waitIdle = true
# app.hydra.aperture.conform = 4 # in 105.1 pixels are square by default
omni.replicator.asyncRendering = false
# Enable Iray and pxr by setting this to "rtx,iray,pxr"
renderer.enabled = "rtx"
# Disable IOMMU Enabled pop-up message on warmup (OM-100381)
persistent.renderer.startupMessageDisplayed = true
# Basic Kit App
################################
app.versionFile = "${app}/../VERSION"
app.name = "Isaac-Sim"
app.version = "2023.1.0"
# hide NonToggleable Exts
exts."omni.kit.window.extensions".hideNonToggleableExts = true
exts."omni.kit.window.extensions".showFeatureOnly = false
# Hang Detector
################################
# app.hangDetector.enabled = false
# app.hangDetector.timeout = 120
# Browsers
exts."omni.kit.browser.material".folders = [
"Base::http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base",
"vMaterials::http://omniverse-content-production.s3.us-west-2.amazonaws.com/Materials/vMaterials_2/",
"Twinbru Fabrics::https://twinbru.s3.eu-west-1.amazonaws.com/omniverse/Twinbru Fabrics/"
]
exts."omni.kit.window.environment".folders = [
"https://omniverse-content-production.s3.us-west-2.amazonaws.com/Assets/Skies/2022_1/Skies",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Scenes/Templates",
]
exts."omni.kit.browser.sample".folders = [ "http://omniverse-content-production.s3-us-west-2.amazonaws.com//Samples" ]
exts."omni.kit.browser.asset".folders = [
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Vegetation",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/ArchVis/Commercial",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/ArchVis/Industrial",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/ArchVis/Residential",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Safety",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Storage",
]
exts."omni.kit.browser.texture".folders = [
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Vegetation",
]
# set the default ros bridge to disable on startup
isaac.startup.ros_bridge_extension = ""
# Extensions
###############################
[settings.exts."omni.kit.registry.nucleus"]
registries = [
{ name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/shared" },
{ name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" },
{ name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" },
]
[settings.app.extensions]
skipPublishVerification = false
registryEnabled = true
[settings.exts."omni.kit.window.modifier.titlebar"]
titleFormatString = " Isaac Sim {version:${app}/../SHORT_VERSION,font_color=0x909090,font_size=16} {separator} {file, board=true}"
showFileFullPath = true
icon.file = "${app}/../exts/omni.isaac.app.setup/data/nvidia-omniverse-isaacsim.ico"
icon.size = 256
defaultFont.name = "Arial"
defaultFont.size = 16
defaultFont.color = 0xD0D0D0
separator.color = 0x00B976
separator.width = 1
windowBorder.color = 0x0F0F0F
windowBorder.width = 2
colors.caption = 0x0F0F0F
colors.client = 0x0F0F0F
respondOnMouseUp = true
changeWindowRegion = true
[settings.crashreporter.data]
experience = "Isaac Sim"
# Isaac Sim Settings
###############################
[settings.app.renderer]
skipWhileMinimized = false
sleepMsOnFocus = 0
sleepMsOutOfFocus = 0
resolution.width=1280
resolution.height=720
# default camera position in meters
[settings.app.viewport]
defaultCamPos.x = 5
defaultCamPos.y = 5
defaultCamPos.z = 5
[settings.rtx]
raytracing.fractionalCutoutOpacity = false
hydra.enableSemanticSchema = true
# descriptorSets=60000
# reservedDescriptors=500000
# sceneDb.maxInstances=1000000
# Enable this for static scenes, improves visual quality
# directLighting.sampledLighting.enabled = true
[settings.persistent]
app.file.recentFiles = []
app.stage.upAxis = "Z"
app.stage.movePrimInPlace = false
app.stage.instanceableOnCreatingReference = false
app.stage.materialStrength = "weakerThanDescendants"
app.transform.gizmoUseSRT = true
app.viewport.grid.scale = 1.0
app.viewport.pickingMode = "kind:model.ALL"
app.viewport.camMoveVelocity = 0.05 # 5 m/s
app.viewport.gizmo.scale = 0.01 # scaled to meters
app.viewport.previewOnPeek = false
app.viewport.snapToSurface = false
app.viewport.displayOptions = 31951 # Disable Frame Rate and Resolution by default
app.window.uiStyle = "NvidiaDark"
app.primCreation.DefaultXformOpType = "Scale, Orient, Translate"
app.primCreation.DefaultXformOpOrder="xformOp:translate, xformOp:orient, xformOp:scale"
app.primCreation.typedDefaults.camera.clippingRange = [0.01, 10000000.0]
simulation.minFrameRate = 15
simulation.defaultMetersPerUnit = 1.0
omnigraph.updateToUsd = false
omnigraph.useSchemaPrims = true
omnigraph.disablePrimNodes = true
physics.updateToUsd = false
physics.updateVelocitiesToUsd = false
physics.useFastCache = false
physics.visualizationDisplayJoints = false
omni.replicator.captureOnPlay = true
omnihydra.useSceneGraphInstancing = true
renderer.startupMessageDisplayed = true # hides the IOMMU popup window
# Make Detail panel visible by default
app.omniverse.content_browser.options_menu.show_details = true
app.omniverse.filepicker.options_menu.show_details = true
[settings.physics]
updateToUsd = false
updateVelocitiesToUsd = false
updateForceSensorsToUsd = false
outputVelocitiesLocalSpace = false
# Register extension folder from this repo in kit
[settings.app.exts]
folders = ["${app}/../exts", "${app}/../extscache", "${app}/../extsPhysics", "${app}/../kit/exts"]
[settings.ngx]
enabled=true # Enable this for DLSS
# Isaac Sim Extensions
###############################
[dependencies]
"omni.isaac.core" = {}
"omni.isaac.core_archive" = {}
"omni.pip.compute" = {}
"omni.pip.cloud" = {}
"omni.isaac.cloner" = {}
"omni.isaac.gym" = {}
"omni.importer.urdf" = {}
"omni.isaac.sensor" = {}
"omni.isaac.kit" = {}
"omni.isaac.ml_archive" = {}
"omni.kit.loop-isaac" = {}
|
AndrePatri/OmniRoboGym/omni_robo_gym/cfg/omni_kits/omni.isaac.sim.python.omnirobogym.kit | [package]
title = "Isaac Sim Python"
description = "A trimmed down app for use with python samples"
version = "2023.1.1"
# That makes it browsable in UI with "experience" filter
keywords = ["experience", "app", "usd"]
[dependencies]
# The Main UI App
"omni.kit.uiapp" = {}
"omni.kit.renderer.core" = {}
# Status Bar
"omni.kit.window.status_bar" = {}
"omni.stats" = {}
"omni.kit.telemetry" = {}
"omni.kit.menu.utils" = {}
"omni.kit.menu.file" = {}
"omni.kit.menu.edit" = {}
"omni.kit.menu.create" = {}
"omni.kit.menu.common" = {}
"omni.kit.menu.stage" = {}
"omni.kit.window.file" = {}
"omni.kit.context_menu" = {}
"omni.kit.selection" = {}
"omni.kit.stage_templates" = {}
# "omni.kit.stage.mdl_converter" = {}
# Animation
# "omni.anim.skelvis" = {}
# PhysX
"omni.physx.bundle" = {}
"omni.physx.tensors" = {}
# "omni.physx.fabric" = {}
# "omni.physx.zerogravity" = {}
# "omni.kit.search.service" = {}
"omni.kit.primitive.mesh" = {}
# Create Windows
"omni.kit.window.title" = {}
"omni.kit.widget.live" = {}
"omni.kit.window.stage" = {}
"omni.kit.widget.layers" = {}
"omni.kit.window.cursor" = {}
"omni.kit.window.toolbar" = {}
"omni.kit.window.commands" = {}
# New Viewport, load the default bundle of extensions
"omni.kit.viewport.bundle" = {}
"omni.kit.viewport.menubar.lighting" = {}
# Load the rendering extensions
# "omni.renderer" = { tag = "rtx" }
# Load the RTX rendering bundle
"omni.kit.viewport.rtx" = {}
# Load the Storm rendering bundle
"omni.kit.viewport.pxr" = {}
# Needed for Fabric delegate
"omni.resourcemonitor" = {}
# Additional Viewport features (legacy grid etc, HUD GPU stats)
"omni.kit.viewport.legacy_gizmos" = {}
"omni.kit.viewport.ready" = {}
"omni.hydra.engine.stats" = {}
"omni.rtx.settings.core" = {} # this is the new Render Settings 2.0
# "omni.kit.window.movie_capture" = { }
"omni.kit.profiler.window" = {}
"omni.kit.stage_column.variant" = {}
"omni.kit.stage_column.payload" = {}
# Viewport Widgets and Collaboration
# "omni.kit.viewport_widgets_manager" = {}
# "omni.kit.collaboration.channel_manager" = {}
# "omni.kit.widgets.custom" = {}
# utils window
# "omni.kit.window.about" = {} # Isaac Sim: disable this and replace with our own
# "omni.kit.window.privacy" = {}
# "omni.kit.window.provide_feedback" = {} # Isaac Sim: disable this and replace with our own
# "omni.kit.material.library" = {}
# "omni.kit.window.imageviewer" = {}
"omni.kit.widget.filebrowser" = {}
"omni.kit.window.filepicker" = {}
"omni.kit.window.content_browser" = {}
"omni.kit.window.stats" = { order = 1000 }
"omni.kit.window.script_editor" = {}
"omni.kit.window.console" = {}
"omni.kit.window.extensions" = {}
# browsers
"omni.kit.browser.sample" = {}
# "omni.kit.browser.asset" = {}
# "omni.kit.browser.asset_store" = {}
# "omni.kit.browser.asset_provider.local" = {}
# "omni.kit.browser.asset_provider.sketchfab" = {}
# "omni.kit.browser.asset_provider.turbosquid" = {}
# "omni.kit.browser.asset_provider.actorcore" = {}
# "omni.kit.window.environment" = {} # potentially increases startup times
# Material
# "omni.kit.window.material" = { }
# "omni.kit.graph.delegate.default" = { }
# "omni.kit.window.material_graph" = { }
# "omni.kit.window.usd_paths" = {}
# "omni.kit.window.preferences" = { order = 1000 } # so the menu is in the correct place
# "omni.kit.renderer.capture" = {}
# "omni.kit.thumbnails.usd" = {}
# "omni.kit.thumbnails.images" = {}
# bring all the property Widgets and Window
"omni.kit.window.property" = {}
"omni.kit.property.bundle" = {}
"omni.kit.property.layer" = {}
# tool
# "omni.kit.asset_converter" = {}
# "omni.kit.tool.asset_importer" = {}
# "omni.kit.tool.asset_exporter" = {}
# "omni.kit.tool.collect" = {}
# "omni.kit.tool.remove_unused.core" = {}
# "omni.kit.tool.remove_unused.controller" = {}
# Iray
# "omni.iray.settings.core" = {}
# "omni.hydra.iray" = { order = -1000 }
#Particle/PointCloud FileFormat
# "omni.usd.fileformat.e57" = { }
# "omni.kit.pointclouds" = {}
# External Scene
# "omni.geo.streaming.bundle" = {}
# All QuickSearch
# "omni.kit.window.quicksearch" = {}
# "omni.kit.quicksearch.actions" = {}
# "omni.kit.quicksearch.settings" = {}
# "omni.kit.quicksearch.select" = {}
# "omni.kit.quicksearch.commands" = {}
# "omni.kit.quicksearch.menu" = {}
# "omni.kit.quicksearch.material" = {}
# "omni.kit.quicksearch.hdri" = {}
# "omni.kit.quicksearch.props" = {}
# "omni.kit.search.files" = {}
# Compatibility Checker
# "omni.kit.compatibility_checker" = {}
# VERSIONING
# "omni.kit.widget.versioning" = {}
# Paint Default now
# "omni.paint.system.bundle" = {}
# Manipulator
"omni.kit.manipulator.prim" = {}
"omni.kit.manipulator.transform" = {}
"omni.kit.manipulator.viewport" = {}
# "omni.kit.manipulator.tool.mesh_snap" = {}
# Destruction schema
# "omni.usd.schema.destruction" = {}
# Animation
# "omni.anim.skelJoint" = { }
# "omni.anim.curve" = { }
# "omni.kit.widget.timeline" = { }
# "omni.anim.curve_editor" = { }
# "omni.anim.window.timeline" = { }
# "omni.anim.shared.core" = {}
# "omni.anim.timeline" = { }
# "omni.anim.graph.bundle" = {}
# "omni.anim.graph.core" = {}
# "omni.anim.graph.ui" = {}
# "omni.anim.retarget.bundle" = {}
# "omni.anim.retarget.core" = {}
# "omni.anim.retarget.ui" = {}
#"omni.anim.camera_tool" = {}
# Needed to properly load navigation mesh
"omni.anim.navigation.schema" = {}
# OmniGraph
"omni.graph.bundle.action" = {}
"omni.graph.window.action" = {}
"omni.graph.window.generic" = {}
"omni.graph.visualization.nodes" = {}
# Python Scripting Component
# "omni.kit.scripting" = {}
# kit-testing
# "omni.kit.tests.usd_stress" = {}
# Curves
# "omni.curve.manipulator" = {}
# General Proceduralism
# "omni.genproc.bundle" = {}
# Sequencer
# "omni.kit.window.sequencer" = {}
# "omni.services.usd" = {}
# SBSAR
# "omni.kit.property.sbsar" = {}
# "omni.usd.fileformat.sbsar" = {}
# Thumbnails
# "omni.kit.thumbnails.mdl" = {}
# Quicklayout
# "omni.kit.quicklayout" = {}
# AOV
# "omni.kit.menu.aov" = {}
# "omni.graph.examples.cpp" = {}
# Collections
# "omni.kit.window.collection" = {}
# "omni.kit.widget.collection" = {}
# "omni.kit.property.collection" = {}
# Extended Searchfield
# "omni.kit.widget.extended_searchfield" = {}
# Particle
# "omni.particle.system.bundle" = {}
# Scene Visualization
"omni.usd.schema.scene.visualization" = {}
# "omni.scene.visualization.bundle" = {}
#Section Tool
# "omni.kit.window.section" = {}
# startfleet auth enabled for cloud_share to work on the receiver
# "omni.services.starfleet.auth" = {}
# Array Tool
# "omni.tools.array" = {}
# "omni.tools.pivot" = {}
# Randomizer
# "omni.tools.randomizer" = {}
# Deepsearch
# "omni.kit.browser.deepsearch" = {}
# Actions
# "omni.kit.actions.window" = {}
# "omni.kit.viewport.actions" = {}
# Scene Optimizer (formerly Data Adapter)
# "omni.scene.optimizer.bundle" = {}
# Hotkeys
"omni.kit.hotkeys.window" = {}
# USDA
# "omni.kit.usda_edit" = {}
# "omni.rakis" = {}
"omni.warp" = {}
# needed for omni.kit.viewport.ready.viewport_ready
"omni.activity.profiler" = {}
"omni.activity.pump" = {}
"omni.kit.widget.cache_indicator" = {}
[settings]
renderer.active = "rtx"
exts."omni.kit.viewport.menubar.camera".expand = true # Expand the extra-camera settings by default
exts."omni.kit.window.file".useNewFilePicker = true
exts."omni.kit.tool.asset_importer".useNewFilePicker = true
exts."omni.kit.tool.collect".useNewFilePicker = true
exts."omni.kit.widget.layers".useNewFilePicker = true
exts."omni.kit.renderer.core".imgui.enableMips = true
exts."omni.kit.browser.material".enabled = false
exts."omni.kit.browser.asset".visible_after_startup = false
exts."omni.kit.window.material".load_after_startup = true
exts."omni.kit.widget.cloud_share".require_access_code = false
exts."omni.kit.pipapi".installCheckIgnoreVersion = true
exts."omni.kit.viewport.window".startup.windowName="Viewport" # Rename from Viewport Next
exts."omni.kit.menu.utils".logDeprecated = false
# app.content.emptyStageOnStart = false
app.file.ignoreUnsavedOnExit = true # prevents save dialog when exiting
# deprecate support for old kit.ui.menu
app.menu.legacy_mode = false
# use omni.ui.Menu for the MenuBar
app.menu.compatibility_mode = false
# Setting the port for the embedded http server
exts."omni.services.transport.server.http".port = 8211
# default viewport is fill
app.runLoops.rendering_0.fillResolution = false
exts."omni.kit.window.viewport".blockingGetViewportDrawable = false
exts."omni.kit.test".includeTests.1 = "*isaac*"
[settings.app.python]
# These disable the kit app from also printing out python output, which gets confusing
interceptSysStdOutput = false
logSysStdOutput = false
[settings.app.settings]
persistent = false
dev_build = false
fabricDefaultStageFrameHistoryCount = 3 # needed for omni.syntheticdata TODO105 Still True?
[settings.app.window]
title = "Isaac Sim Python"
hideUi = false
_iconSize = 256
iconPath = "${app}/../exts/omni.isaac.app.setup/data/nvidia-omniverse-isaacsim.ico"
# width = 1700
# height = 900
# x = -1
# y = -1
# Fonts
[setting.app.font]
file = "${fonts}/OpenSans-SemiBold.ttf"
size = 16
# [setting.app.runLoops]
# main.rateLimitEnabled = false
# main.rateLimitFrequency = 60
# main.rateLimitUseBusyLoop = false
# rendering_0.rateLimitEnabled = false
[settings.exts.'omni.kit.window.extensions']
# List extensions here we want to show as featured when extension manager is opened
featuredExts = []
[settings]
# MGPU is always on, you can turn it from the settings, and force this off to save even more resource if you
# only want to use a single GPU on your MGPU system
# False for Isaac Sim
renderer.multiGpu.enabled = true
renderer.multiGpu.autoEnable = true
'rtx-transient'.resourcemanager.enableTextureStreaming = true
# app.hydra.aperture.conform = 4 # in 105.1 pixels are square by default
app.hydraEngine.waitIdle = true
rtx.newDenoiser.enabled = true
# Enable Iray and pxr by setting this to "rtx,iray,pxr"
renderer.enabled = "rtx"
physics.autoPopupSimulationOutputWindow=false
### async rendering settings
omni.replicator.asyncRendering = false
app.asyncRendering = false
app.asyncRenderingLowLatency = false
### Render thread settings
app.runLoops.main.rateLimitEnabled = false
app.runLoops.main.rateLimitFrequency = 120
app.runLoops.main.rateLimitUsePrecisionSleep = true
app.runLoops.main.syncToPresent = false
app.runLoops.present.rateLimitFrequency = 120
app.runLoops.present.rateLimitUsePrecisionSleep = true
app.runLoops.rendering_0.rateLimitFrequency = 120
app.runLoops.rendering_0.rateLimitUsePrecisionSleep = true
app.runLoops.rendering_0.syncToPresent = false
app.runLoops.rendering_1.rateLimitFrequency = 120
app.runLoops.rendering_1.rateLimitUsePrecisionSleep = true
app.runLoops.rendering_1.syncToPresent = false
app.runLoopsGlobal.syncToPresent = false
app.vsync = false
exts.omni.kit.renderer.core.present.enabled = false
exts.omni.kit.renderer.core.present.presentAfterRendering = false
persistent.app.viewport.defaults.tickRate = 120
rtx-transient.dlssg.enabled = false
privacy.externalBuild = true
# Basic Kit App
################################
app.versionFile = "${app}/../VERSION"
app.name = "Isaac-Sim"
app.version = "2023.1.1"
# hide NonToggleable Exts
exts."omni.kit.window.extensions".hideNonToggleableExts = true
exts."omni.kit.window.extensions".showFeatureOnly = false
# Hang Detector
################################
# app.hangDetector.enabled = false
# app.hangDetector.timeout = 120
# Browsers
exts."omni.kit.browser.material".folders = [
"Base::http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base",
"vMaterials::http://omniverse-content-production.s3.us-west-2.amazonaws.com/Materials/vMaterials_2/",
"Twinbru Fabrics::https://twinbru.s3.eu-west-1.amazonaws.com/omniverse/Twinbru Fabrics/"
]
exts."omni.kit.window.environment".folders = [
"https://omniverse-content-production.s3.us-west-2.amazonaws.com/Assets/Skies/2022_1/Skies",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Scenes/Templates",
]
exts."omni.kit.browser.sample".folders = [ "http://omniverse-content-production.s3-us-west-2.amazonaws.com//Samples" ]
exts."omni.kit.browser.asset".folders = [
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Vegetation",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/ArchVis/Commercial",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/ArchVis/Industrial",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/ArchVis/Residential",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Safety",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Shipping",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Storage",
]
exts."omni.kit.browser.texture".folders = [
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Vegetation",
]
# Content Browser
###############################
[settings.exts."omni.kit.window.content_browser"]
enable_thumbnail_generation_images = false # temp fix to avoid leaking python processes
# Extensions
###############################
[settings.exts."omni.kit.registry.nucleus"]
registries = [
{ name = "kit/default", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/shared" },
{ name = "kit/sdk", url = "https://ovextensionsprod.blob.core.windows.net/exts/kit/prod/sdk/${kit_version_short}/${kit_git_hash}" },
{ name = "kit/community", url = "https://dw290v42wisod.cloudfront.net/exts/kit/community" },
]
[settings.app.extensions]
skipPublishVerification = false
registryEnabled = true
[settings.exts."omni.kit.window.modifier.titlebar"]
titleFormatString = " Isaac Sim {version:${app}/../SHORT_VERSION,font_color=0x909090,font_size=16} {separator} {file, board=true}"
showFileFullPath = true
icon.file = "${app}/../exts/omni.isaac.app.setup/data/nvidia-omniverse-isaacsim.ico"
icon.size = 256
defaultFont.name = "Arial"
defaultFont.size = 16
defaultFont.color = 0xD0D0D0
separator.color = 0x00B976
separator.width = 1
windowBorder.color = 0x0F0F0F
windowBorder.width = 2
colors.caption = 0x0F0F0F
colors.client = 0x0F0F0F
respondOnMouseUp = true
changeWindowRegion = true
# Register extension folder from this repo in kit
[settings.app.exts]
folders = ["${app}/../exts", "${app}/../extscache", "${app}/../extsPhysics"]
[settings.crashreporter.data]
experience = "Isaac Sim Python"
# Isaac Sim Settings
###############################
[settings.app.renderer]
skipWhileMinimized = false
sleepMsOnFocus = 0
sleepMsOutOfFocus = 0
resolution.width=1280
resolution.height=720
# default camera position in meters
[settings.app.viewport]
defaultCamPos.x = 5
defaultCamPos.y = 5
defaultCamPos.z = 5
[settings.rtx]
raytracing.fractionalCutoutOpacity = false
hydra.enableSemanticSchema = true
# descriptorSets=60000
# reservedDescriptors=500000
# sceneDb.maxInstances=1000000
# Enable this for static scenes, improves visual quality
# directLighting.sampledLighting.enabled = true
[settings.persistent]
app.file.recentFiles = []
app.stage.upAxis = "Z"
app.stage.movePrimInPlace = false
app.stage.instanceableOnCreatingReference = false
app.stage.materialStrength = "weakerThanDescendants"
app.transform.gizmoUseSRT = true
app.viewport.grid.scale = 1.0
app.viewport.pickingMode = "kind:model.ALL"
app.viewport.camMoveVelocity = 0.05 # 5 m/s
app.viewport.gizmo.scale = 0.01 # scaled to meters
app.viewport.previewOnPeek = false
app.viewport.snapToSurface = false
app.viewport.displayOptions = 31887 # Disable Frame Rate and Resolution by default
app.window.uiStyle = "NvidiaDark"
app.primCreation.DefaultXformOpType = "Scale, Orient, Translate"
app.primCreation.DefaultXformOpOrder="xformOp:translate, xformOp:orient, xformOp:scale"
app.primCreation.typedDefaults.camera.clippingRange = [0.01, 10000000.0]
simulation.minFrameRate = 15
simulation.defaultMetersPerUnit = 1.0
omnigraph.updateToUsd = false
omnigraph.useSchemaPrims = true
omnigraph.disablePrimNodes = true
physics.updateToUsd = true
physics.updateVelocitiesToUsd = true
physics.useFastCache = false
physics.visualizationDisplayJoints = false
physics.visualizationSimulationOutput = false
omni.replicator.captureOnPlay = true
exts."omni.anim.navigation.core".navMesh.viewNavMesh = false
renderer.startupMessageDisplayed = true # hides the IOMMU popup window
# Make Detail panel visible by default
app.omniverse.content_browser.options_menu.show_details = true
app.omniverse.filepicker.options_menu.show_details = true
[settings.ngx]
enabled=true # Enable this for DLSS
# Isaac Sim Extensions
###############################
[dependencies]
"omni.isaac.core_archive" = {}
"omni.pip.compute" = {}
"omni.pip.cloud" = {}
"omni.isaac.ml_archive" = {}
"omni.importer.urdf" = {}
"omni.isaac.mjcf" = {}
"omni.isaac.utils" = {}
"omni.isaac.range_sensor" = {}
"omni.isaac.dynamic_control" = {}
"omni.isaac.kit" = {}
"omni.isaac.core" = {}
"omni.isaac.core_nodes" = {}
"omni.isaac.cloner" = {}
"omni.isaac.cortex" = {}
"omni.isaac.cortex.sample_behaviors" = {}
"omni.isaac.dofbot" = {}
"omni.isaac.surface_gripper" = {}
"omni.kit.property.isaac" = {}
"omni.isaac.scene_blox" = {}
"omni.isaac.sensor" = {}
"omni.isaac.debug_draw" = {}
"omni.isaac.gym" = {}
"omni.isaac.franka" = {}
"omni.isaac.manipulators" = {}
"omni.isaac.quadruped" = {}
"omni.isaac.wheeled_robots" = {}
"omni.isaac.lula" = {}
"omni.isaac.motion_generation" = {}
"omni.isaac.universal_robots" = {}
"omni.isaac.occupancy_map" = {}
"omni.replicator.isaac" = {}
"omni.kit.loop-isaac" = {}
#linux only extensions
[dependencies."filter:platform"."linux-x86_64"]
# "omni.isaac.ocs2" = {}
# Non Isaac Sim Extensions
######################
[dependencies]
"omni.syntheticdata" = {}
"semantics.schema.editor" = {}
"omni.replicator.core" = {}
"omni.replicator.replicator_yaml" = {}
"omni.replicator.composer" = {}
"omni.importer.mjcf" = {}
"omni.importer.urdf" = {} |
AndrePatri/OmniRoboGym/omni_robo_gym/cfg/omni_kits/copy2isaac_folder.sh | # cp ./omni.isaac.sim.python.omnirobogym.headless.kit ${HOME}/.local/share/ov/pkg/isaac_sim-2023.1.0-hotfix.1/apps/
# cp ./omni.isaac.sim.python.omnirobogym.kit ${HOME}/.local/share/ov/pkg/isaac_sim-2023.1.0-hotfix.1/apps/
cp ./omni.isaac.sim.python.omnirobogym.headless.kit ${HOME}/.local/share/ov/pkg/isaac_sim-2023.1.1/apps/
cp ./omni.isaac.sim.python.omnirobogym.kit ${HOME}/.local/share/ov/pkg/isaac_sim-2023.1.1/apps/
|
AndrePatri/OmniRoboGym/omni_robo_gym/tests/test_lunar_lander_stable_bs3.py | # Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
import gymnasium as gym
from stable_baselines3 import DQN
from stable_baselines3.common.evaluation import evaluate_policy
# Create environment
env = gym.make("LunarLander-v2", render_mode="rgb_array")
# Instantiate the agent
model = DQN("MlpPolicy", env, verbose=1)
# Train the agent and display a progress bar
model.learn(total_timesteps=int(2e5), progress_bar=True)
# Save the agent
model.save("dqn_lunar")
del model # delete trained model to demonstrate loading
# Load the trained agent
# NOTE: if you have loading issue, you can pass `print_system_info=True`
# to compare the system on which the model was trained vs the current one
# model = DQN.load("dqn_lunar", env=env, print_system_info=True)
model = DQN.load("dqn_lunar", env=env)
# Evaluate the agent
# NOTE: If you use wrappers with your environment that modify rewards,
# this will be reflected here. To evaluate with original rewards,
# wrap environment in a "Monitor" wrapper before other wrappers.
mean_reward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=10)
# Enjoy trained agent
vec_env = model.get_env()
obs = vec_env.reset()
n_pred_iterations = 100000
for i in range(n_pred_iterations):
action, _states = model.predict(obs, deterministic=True)
obs, rewards, dones, info = vec_env.step(action)
vec_env.render("human")
|
AndrePatri/OmniRoboGym/omni_robo_gym/tests/create_terrain_demo.py | # Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os, sys
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(SCRIPT_DIR)
import omni
from omni.isaac.kit import SimulationApp
import numpy as np
simulation_app = SimulationApp({"headless": False})
from omni.isaac.core.tasks import BaseTask
from omni.isaac.core import World
from omni.isaac.core.objects import DynamicSphere
from omni.isaac.core.utils.prims import define_prim
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.materials import PreviewSurface
from omni.isaac.cloner import GridCloner
from pxr import UsdLux, UsdShade, Sdf
from omni_robo_gym.utils.terrain_utils import *
from omni_robo_gym.utils.terrains import RlTerrains
class TerrainsTest(BaseTask):
def __init__(self,
name) -> None:
BaseTask.__init__(self, name=name)
self._device = "cpu"
def set_up_scene(self,
scene) -> None:
self._stage = get_current_stage()
distantLight = UsdLux.DistantLight.Define(self._stage, Sdf.Path("/World/DistantLight"))
distantLight.CreateIntensityAttr(2000)
self.terrains = RlTerrains(self._stage)
self.terrains.get_obstacles_terrain(
terrain_size = 40.0,
num_obs = 200,
max_height = 0.5,
min_size = 0.5,
max_size = 5.0,)
super().set_up_scene(scene)
return
def post_reset(self):
a = 1
def get_observations(self):
pass
def calculate_metrics(self) -> None:
pass
def is_done(self) -> None:
pass
if __name__ == "__main__":
world = World(
stage_units_in_meters=1.0,
rendering_dt=1.0/60.0,
backend="torch",
device="cpu",
)
terrain_creation_task = TerrainsTest(name="CustomTerrain",
)
world.add_task(terrain_creation_task)
world.reset()
while simulation_app.is_running():
if world.is_playing():
if world.current_time_step_index == 0:
world.reset(soft=True)
world.step(render=True)
else:
world.step(render=True)
simulation_app.close() |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/contact_sensor.py | import torch
import numpy as np
from omni.isaac.sensor import ContactSensor
from typing import List, Dict
from omni.isaac.core.world import World
from omni.isaac.core.prims import RigidPrimView, RigidContactView
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
class OmniContactSensors:
def __init__(self,
name: str, # robot name for which contact sensors are to be created
n_envs: int, # number of environments
contact_prims: Dict[str, List] = None,
contact_offsets: Dict[str, Dict[str, np.ndarray]] = None,
sensor_radii: Dict[str, Dict[str, np.ndarray]] = None,
device = "cuda",
dtype = torch.float64,
enable_debug: bool = False,
filter_paths: List[str] = ["/World/terrain/GroundPlane/CollisionPlane"]):
# contact sensors abstraction for a single robot
# over multiple environments
self._filter_paths = filter_paths
self._enable_debug = enable_debug
self.n_envs = n_envs
self.device = device
if self.device == "cuda":
self.using_gpu = True
else:
self.using_gpu = False
self.dtype = dtype
self.name = name
self.contact_radius_default = 0.003
# parses contact dictionaries and checks for issues
self._parse_contact_dicts(self.name,
contact_prims,
contact_offsets,
sensor_radii)
self.n_sensors = len(self.contact_prims)
self.in_contact = torch.full((n_envs, self.n_sensors),
False,
device = self.device,
dtype=torch.bool)
self.force_norm = torch.full((n_envs, self.n_sensors),
-1.0,
device = self.device,
dtype=self.dtype)
self.n_contacts = torch.full((n_envs, self.n_sensors),
0,
device = self.device,
dtype=torch.int)
self.contact_sensors = [[None] * self.n_sensors] * n_envs # outer: environment,
# inner: contact sensor, ordered as in contact_prims
self.contact_geom_prim_views = [None] * self.n_sensors
# self.contact_views = [None] * self.n_sensors
def _parse_contact_dicts(self,
name: str,
contact_prims: Dict[str, List],
contact_offsets: Dict[str, Dict[str, np.ndarray]],
sensor_radii: Dict[str, Dict[str, np.ndarray]]):
try:
self.contact_prims = contact_prims[name]
except:
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
f"Could not find key {name} in contact_prims dictionary.",
LogType.EXCEP,
throw_when_excep = True)
try:
self.contact_offsets = contact_offsets[name]
except:
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
f"Could not find key {name} in contact_offsets dictionary.",
LogType.EXCEP,
throw_when_excep = True)
try:
self.sensor_radii = sensor_radii[name]
except:
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
f"Could not find key {name} in sensor_radii dictionary.",
LogType.EXCEP,
throw_when_excep = True)
contact_offsets_ok = all(item in self.contact_offsets for item in self.contact_prims)
sensor_radii_ok = all(item in self.sensor_radii for item in self.contact_prims)
if not contact_offsets_ok:
warning = f"Provided contact_offsets dictionary does not posses all the necessary keys. " + \
f"It should contain all of [{' '.join(self.contact_prims)}]. \n" + \
f"Resetting all offsets to zero..."
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
warning,
LogType.WARN,
throw_when_excep = True)
for i in range(0, len(self.contact_prims)):
self.contact_offsets[self.contact_prims[i]] = np.array([0.0, 0.0, 0.0])
if not sensor_radii_ok:
warning = f"Provided sensor_radii dictionary does not posses all the necessary keys. " + \
f"It should contain all of [{' '.join(self.contact_prims)}]. \n" + \
f"Resetting all radii to {self.contact_radius_default} ..."
Journal.log(self.__class__.__name__,
"_parse_contact_dicts",
warning,
LogType.WARN,
throw_when_excep = True)
for i in range(0, len(self.contact_prims)):
self.sensor_radii[self.contact_prims[i]] = self.contact_radius_default
def create_contact_sensors(self,
world: World,
envs_namespace: str):
robot_name = self.name
contact_link_names = self.contact_prims
for sensor_idx in range(0, self.n_sensors):
# we create views of the contact links for all envs
if self.contact_geom_prim_views[sensor_idx] is None:
self.contact_geom_prim_views[sensor_idx] = RigidPrimView(prim_paths_expr=envs_namespace + "/env_.*/" + robot_name + \
"/" + contact_link_names[sensor_idx],
name= self.name + "RigidPrimView" + contact_link_names[sensor_idx],
contact_filter_prim_paths_expr= self._filter_paths,
prepare_contact_sensors=True,
track_contact_forces = True,
disable_stablization = False,
reset_xform_properties=False,
max_contact_count = self.n_envs
)
world.scene.add(self.contact_geom_prim_views[sensor_idx])
# for env_idx in range(0, self.n_envs):
# # env_idx = 0 # create contact sensors for base env only
# for sensor_idx in range(0, self.n_sensors):
# contact_link_prim_path = envs_namespace + f"/env_{env_idx}" + \
# "/" + robot_name + \
# "/" + contact_link_names[sensor_idx]
# sensor_prim_path = contact_link_prim_path + \
# "/contact_sensor" # contact sensor prim path
# print(f"[{self.__class__.__name__}]" + f"[{self.journal.status}]" + ": creating contact sensor at " +
# f"{sensor_prim_path}...")
# contact_sensor = ContactSensor(
# prim_path=sensor_prim_path,
# name=f"{robot_name}{env_idx}_{contact_link_names[sensor_idx]}_contact_sensor",
# min_threshold=0,
# max_threshold=10000000,
# radius=self.sensor_radii[contact_link_names[sensor_idx]],
# translation=self.contact_offsets[contact_link_names[sensor_idx]],
# position=None
# )
# self.contact_sensors[env_idx][sensor_idx] = world.scene.add(contact_sensor)
# self.contact_sensors[env_idx][sensor_idx].add_raw_contact_data_to_frame()
# print(f"[{self.__class__.__name__}]" + f"[{self.journal.status}]" + ": contact sensor at " +
# f"{sensor_prim_path} created.")
def get(self,
dt: float,
contact_link: str,
env_indxs: torch.Tensor = None,
clone = False):
index = -1
try:
index = self.contact_prims.index(contact_link)
except:
exception = f"[{self.__class__.__name__}]" + f"[{self.journal.exception}]" + \
f"could not find contact link {contact_link} in contact list {' '.join(self.contact_prims)}."
Journal.log(self.__class__.__name__,
"get",
exception,
LogType.EXCEP,
throw_when_excep = True)
if env_indxs is None:
return self.contact_geom_prim_views[index].get_net_contact_forces(clone = clone,
dt = dt).view(self.n_envs, 3)
else:
if self._enable_debug:
if env_indxs is not None:
if not isinstance(env_indxs, torch.Tensor):
msg = "Provided env_indxs should be a torch tensor of indexes!"
Journal.log(self.__class__.__name__,
"get",
msg,
LogType.EXCEP,
throw_when_excep = True)
if not len(env_indxs.shape) == 1:
msg = "Provided robot_indxs should be a 1D torch tensor!"
Journal.log(self.__class__.__name__,
"get",
msg,
LogType.EXCEP,
throw_when_excep = True)
if self.using_gpu:
if not env_indxs.device.type == "cuda":
error = "Provided env_indxs should be on GPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
else:
if not env_indxs.device.type == "cpu":
error = "Provided env_indxs should be on CPU!"
Journal.log(self.__class__.__name__,
"_step_jnt_imp_control",
error,
LogType.EXCEP,
True)
return self.contact_geom_prim_views[index].get_net_contact_forces(clone = clone,
dt = dt).view(self.n_envs, 3)[env_indxs, :] |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/math_utils.py | import torch
import time
import torch.nn.functional as F
def normalize_quaternion(q):
# Normalizes the quaternion
return q / torch.norm(q, dim=-1, keepdim=True)
def quaternion_difference(q1, q2):
""" Compute the quaternion difference needed to rotate from q1 to q2 """
def quat_conjugate(q):
# Computes the conjugate of a quaternion
w, x, y, z = q.unbind(-1)
return torch.stack([w, -x, -y, -z], dim=-1)
q1_conj = quat_conjugate(q1)
return quaternion_multiply(q2, q1_conj)
def quaternion_multiply(q1, q2):
""" Multiply two quaternions. """
w1, x1, y1, z1 = q1.unbind(-1)
w2, x2, y2, z2 = q2.unbind(-1)
return torch.stack([
w1*w2 - x1*x2 - y1*y2 - z1*z2,
w1*x2 + x1*w2 + y1*z2 - z1*y2,
w1*y2 - x1*z2 + y1*w2 + z1*x2,
w1*z2 + x1*y2 - y1*x2 + z1*w2
], dim=-1)
def quaternion_to_angular_velocity(q_diff, dt):
""" Convert a quaternion difference to an angular velocity vector. """
angle = 2 * torch.arccos(q_diff[..., 0].clamp(-1.0, 1.0)) # Clamping for numerical stability
axis = q_diff[..., 1:]
norm = axis.norm(dim=-1, keepdim=True)
norm = torch.where(norm > 0, norm, torch.ones_like(norm))
axis = axis / norm
angle = angle.unsqueeze(-1) # Add an extra dimension for broadcasting
return (angle / dt) * axis
def quat_to_omega(q0, q1, dt):
""" Convert quaternion pairs to angular velocities """
if q0.shape != q1.shape:
raise ValueError("Tensor shapes do not match in quat_to_omega.")
# Normalize quaternions and compute differences
q0_normalized = normalize_quaternion(q0)
q1_normalized = normalize_quaternion(q1)
q_diff = quaternion_difference(q0_normalized, q1_normalized)
return quaternion_to_angular_velocity(q_diff, dt)
def rel_vel(offset_q0_q1,
v0):
# Calculate relative linear velocity in frame q1 from linear velocity in frame q0 using quaternions.
# Ensure the quaternion is normalized
offset_q0_q1 = F.normalize(offset_q0_q1, p=2, dim=0)
# Convert the linear velocity vector to a quaternion
v0_q = torch.cat([torch.tensor([0]), v0])
# Rotate the linear velocity quaternion using the orientation offset quaternion
rotated_velocity_quaternion = quaternion_multiply(offset_q0_q1, v0_q)
offset_q0_q1_inverse = torch.cat([offset_q0_q1[0:1], -offset_q0_q1[1:]])
# Multiply by the conjugate of the orientation offset quaternion to obtain the result in frame f1
v1_q = quaternion_multiply(rotated_velocity_quaternion, offset_q0_q1_inverse)
# Extract the linear velocity vector from the quaternion result
v1 = v1_q[1:]
return v1
# Example usage
n_envs = 100 # Number of environments
dt = 0.1 # Time step
# Random example tensors for initial and final orientations
q_initial = torch.randn(n_envs, 4)
q_final = torch.randn(n_envs, 4)
start_time = time.perf_counter()
# Convert to angular velocities
omega = quat_to_omega(q_initial, q_final, dt)
end_time = time.perf_counter()
elapsed_time = end_time - start_time
print(f"Time taken to compute angular velocities: {elapsed_time:.6f} seconds")
|
AndrePatri/OmniRoboGym/omni_robo_gym/utils/terrain_utils.py | # Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
import numpy as np
from numpy.random import choice
from scipy import interpolate
from math import sqrt
from omni.isaac.core.prims import XFormPrim
from pxr import UsdPhysics, Sdf, Gf, PhysxSchema
def random_uniform_terrain(terrain, min_height, max_height, step=1, downsampled_scale=None,):
"""
Generate a uniform noise terrain
Parameters
terrain (SubTerrain): the terrain
min_height (float): the minimum height of the terrain [meters]
max_height (float): the maximum height of the terrain [meters]
step (float): minimum height change between two points [meters]
downsampled_scale (float): distance between two randomly sampled points ( musty be larger or equal to terrain.horizontal_scale)
"""
if downsampled_scale is None:
downsampled_scale = terrain.horizontal_scale
# switch parameters to discrete units
min_height = int(min_height / terrain.vertical_scale)
max_height = int(max_height / terrain.vertical_scale)
step = int(step / terrain.vertical_scale)
heights_range = np.arange(min_height, max_height + step, step)
height_field_downsampled = np.random.choice(heights_range, (int(terrain.width * terrain.horizontal_scale / downsampled_scale), int(
terrain.length * terrain.horizontal_scale / downsampled_scale)))
x = np.linspace(0, terrain.width * terrain.horizontal_scale, height_field_downsampled.shape[0])
y = np.linspace(0, terrain.length * terrain.horizontal_scale, height_field_downsampled.shape[1])
f = interpolate.interp2d(y, x, height_field_downsampled, kind='linear')
x_upsampled = np.linspace(0, terrain.width * terrain.horizontal_scale, terrain.width)
y_upsampled = np.linspace(0, terrain.length * terrain.horizontal_scale, terrain.length)
z_upsampled = np.rint(f(y_upsampled, x_upsampled))
terrain.height_field_raw += z_upsampled.astype(np.int16)
return terrain
def sloped_terrain(terrain, slope=1):
"""
Generate a sloped terrain
Parameters:
terrain (SubTerrain): the terrain
slope (int): positive or negative slope
Returns:
terrain (SubTerrain): update terrain
"""
x = np.arange(0, terrain.width)
y = np.arange(0, terrain.length)
xx, yy = np.meshgrid(x, y, sparse=True)
xx = xx.reshape(terrain.width, 1)
max_height = int(slope * (terrain.horizontal_scale / terrain.vertical_scale) * terrain.width)
terrain.height_field_raw[:, np.arange(terrain.length)] += (max_height * xx / terrain.width).astype(terrain.height_field_raw.dtype)
return terrain
def pyramid_sloped_terrain(terrain, slope=1, platform_size=1.):
"""
Generate a sloped terrain
Parameters:
terrain (terrain): the terrain
slope (int): positive or negative slope
platform_size (float): size of the flat platform at the center of the terrain [meters]
Returns:
terrain (SubTerrain): update terrain
"""
x = np.arange(0, terrain.width)
y = np.arange(0, terrain.length)
center_x = int(terrain.width / 2)
center_y = int(terrain.length / 2)
xx, yy = np.meshgrid(x, y, sparse=True)
xx = (center_x - np.abs(center_x-xx)) / center_x
yy = (center_y - np.abs(center_y-yy)) / center_y
xx = xx.reshape(terrain.width, 1)
yy = yy.reshape(1, terrain.length)
max_height = int(slope * (terrain.horizontal_scale / terrain.vertical_scale) * (terrain.width / 2))
terrain.height_field_raw += (max_height * xx * yy).astype(terrain.height_field_raw.dtype)
platform_size = int(platform_size / terrain.horizontal_scale / 2)
x1 = terrain.width // 2 - platform_size
x2 = terrain.width // 2 + platform_size
y1 = terrain.length // 2 - platform_size
y2 = terrain.length // 2 + platform_size
min_h = min(terrain.height_field_raw[x1, y1], 0)
max_h = max(terrain.height_field_raw[x1, y1], 0)
terrain.height_field_raw = np.clip(terrain.height_field_raw, min_h, max_h)
return terrain
def discrete_obstacles_terrain(terrain, max_height, min_size, max_size, num_rects, platform_size=1.):
"""
Generate a terrain with gaps
Parameters:
terrain (terrain): the terrain
max_height (float): maximum height of the obstacles (range=[-max, -max/2, max/2, max]) [meters]
min_size (float): minimum size of a rectangle obstacle [meters]
max_size (float): maximum size of a rectangle obstacle [meters]
num_rects (int): number of randomly generated obstacles
platform_size (float): size of the flat platform at the center of the terrain [meters]
Returns:
terrain (SubTerrain): update terrain
"""
# switch parameters to discrete units
max_height = int(max_height / terrain.vertical_scale)
min_size = int(min_size / terrain.horizontal_scale)
max_size = int(max_size / terrain.horizontal_scale)
platform_size = int(platform_size / terrain.horizontal_scale)
(i, j) = terrain.height_field_raw.shape
height_range = [-max_height, -max_height // 2, max_height // 2, max_height]
width_range = range(min_size, max_size, 4)
length_range = range(min_size, max_size, 4)
for _ in range(num_rects):
width = np.random.choice(width_range)
length = np.random.choice(length_range)
start_i = np.random.choice(range(0, i-width, 4))
start_j = np.random.choice(range(0, j-length, 4))
terrain.height_field_raw[start_i:start_i+width, start_j:start_j+length] = np.random.choice(height_range)
x1 = (terrain.width - platform_size) // 2
x2 = (terrain.width + platform_size) // 2
y1 = (terrain.length - platform_size) // 2
y2 = (terrain.length + platform_size) // 2
terrain.height_field_raw[x1:x2, y1:y2] = 0
return terrain
def wave_terrain(terrain, num_waves=1, amplitude=1.):
"""
Generate a wavy terrain
Parameters:
terrain (terrain): the terrain
num_waves (int): number of sine waves across the terrain length
Returns:
terrain (SubTerrain): update terrain
"""
amplitude = int(0.5*amplitude / terrain.vertical_scale)
if num_waves > 0:
div = terrain.length / (num_waves * np.pi * 2)
x = np.arange(0, terrain.width)
y = np.arange(0, terrain.length)
xx, yy = np.meshgrid(x, y, sparse=True)
xx = xx.reshape(terrain.width, 1)
yy = yy.reshape(1, terrain.length)
terrain.height_field_raw += (amplitude*np.cos(yy / div) + amplitude*np.sin(xx / div)).astype(
terrain.height_field_raw.dtype)
return terrain
def stairs_terrain(terrain, step_width, step_height):
"""
Generate a stairs
Parameters:
terrain (terrain): the terrain
step_width (float): the width of the step [meters]
step_height (float): the height of the step [meters]
Returns:
terrain (SubTerrain): update terrain
"""
# switch parameters to discrete units
step_width = int(step_width / terrain.horizontal_scale)
step_height = int(step_height / terrain.vertical_scale)
num_steps = terrain.width // step_width
height = step_height
for i in range(num_steps):
terrain.height_field_raw[i * step_width: (i + 1) * step_width, :] += height
height += step_height
return terrain
def pyramid_stairs_terrain(terrain, step_width, step_height, platform_size=1.):
"""
Generate stairs
Parameters:
terrain (terrain): the terrain
step_width (float): the width of the step [meters]
step_height (float): the step_height [meters]
platform_size (float): size of the flat platform at the center of the terrain [meters]
Returns:
terrain (SubTerrain): update terrain
"""
# switch parameters to discrete units
step_width = int(step_width / terrain.horizontal_scale)
step_height = int(step_height / terrain.vertical_scale)
platform_size = int(platform_size / terrain.horizontal_scale)
height = 0
start_x = 0
stop_x = terrain.width
start_y = 0
stop_y = terrain.length
while (stop_x - start_x) > platform_size and (stop_y - start_y) > platform_size:
start_x += step_width
stop_x -= step_width
start_y += step_width
stop_y -= step_width
height += step_height
terrain.height_field_raw[start_x: stop_x, start_y: stop_y] = height
return terrain
def stepping_stones_terrain(terrain, stone_size, stone_distance, max_height, platform_size=1., depth=-10):
"""
Generate a stepping stones terrain
Parameters:
terrain (terrain): the terrain
stone_size (float): horizontal size of the stepping stones [meters]
stone_distance (float): distance between stones (i.e size of the holes) [meters]
max_height (float): maximum height of the stones (positive and negative) [meters]
platform_size (float): size of the flat platform at the center of the terrain [meters]
depth (float): depth of the holes (default=-10.) [meters]
Returns:
terrain (SubTerrain): update terrain
"""
# switch parameters to discrete units
stone_size = int(stone_size / terrain.horizontal_scale)
stone_distance = int(stone_distance / terrain.horizontal_scale)
max_height = int(max_height / terrain.vertical_scale)
platform_size = int(platform_size / terrain.horizontal_scale)
height_range = np.arange(-max_height-1, max_height, step=1)
start_x = 0
start_y = 0
terrain.height_field_raw[:, :] = int(depth / terrain.vertical_scale)
if terrain.length >= terrain.width:
while start_y < terrain.length:
stop_y = min(terrain.length, start_y + stone_size)
start_x = np.random.randint(0, stone_size)
# fill first hole
stop_x = max(0, start_x - stone_distance)
terrain.height_field_raw[0: stop_x, start_y: stop_y] = np.random.choice(height_range)
# fill row
while start_x < terrain.width:
stop_x = min(terrain.width, start_x + stone_size)
terrain.height_field_raw[start_x: stop_x, start_y: stop_y] = np.random.choice(height_range)
start_x += stone_size + stone_distance
start_y += stone_size + stone_distance
elif terrain.width > terrain.length:
while start_x < terrain.width:
stop_x = min(terrain.width, start_x + stone_size)
start_y = np.random.randint(0, stone_size)
# fill first hole
stop_y = max(0, start_y - stone_distance)
terrain.height_field_raw[start_x: stop_x, 0: stop_y] = np.random.choice(height_range)
# fill column
while start_y < terrain.length:
stop_y = min(terrain.length, start_y + stone_size)
terrain.height_field_raw[start_x: stop_x, start_y: stop_y] = np.random.choice(height_range)
start_y += stone_size + stone_distance
start_x += stone_size + stone_distance
x1 = (terrain.width - platform_size) // 2
x2 = (terrain.width + platform_size) // 2
y1 = (terrain.length - platform_size) // 2
y2 = (terrain.length + platform_size) // 2
terrain.height_field_raw[x1:x2, y1:y2] = 0
return terrain
def convert_heightfield_to_trimesh(height_field_raw, horizontal_scale, vertical_scale, slope_threshold=None):
hf = height_field_raw
num_rows = hf.shape[0]
num_cols = hf.shape[1]
y = np.linspace(0, (num_cols-1)*horizontal_scale, num_cols)
x = np.linspace(0, (num_rows-1)*horizontal_scale, num_rows)
yy, xx = np.meshgrid(y, x)
if slope_threshold is not None:
slope_threshold *= horizontal_scale / vertical_scale
move_x = np.zeros((num_rows, num_cols))
move_y = np.zeros((num_rows, num_cols))
move_corners = np.zeros((num_rows, num_cols))
move_x[:num_rows-1, :] += (hf[1:num_rows, :] - hf[:num_rows-1, :] > slope_threshold)
move_x[1:num_rows, :] -= (hf[:num_rows-1, :] - hf[1:num_rows, :] > slope_threshold)
move_y[:, :num_cols-1] += (hf[:, 1:num_cols] - hf[:, :num_cols-1] > slope_threshold)
move_y[:, 1:num_cols] -= (hf[:, :num_cols-1] - hf[:, 1:num_cols] > slope_threshold)
move_corners[:num_rows-1, :num_cols-1] += (hf[1:num_rows, 1:num_cols] - hf[:num_rows-1, :num_cols-1] > slope_threshold)
move_corners[1:num_rows, 1:num_cols] -= (hf[:num_rows-1, :num_cols-1] - hf[1:num_rows, 1:num_cols] > slope_threshold)
xx += (move_x + move_corners*(move_x == 0)) * horizontal_scale
yy += (move_y + move_corners*(move_y == 0)) * horizontal_scale
# create triangle mesh vertices and triangles from the heightfield grid
vertices = np.zeros((num_rows*num_cols, 3), dtype=np.float32)
vertices[:, 0] = xx.flatten()
vertices[:, 1] = yy.flatten()
vertices[:, 2] = hf.flatten() * vertical_scale
triangles = -np.ones((2*(num_rows-1)*(num_cols-1), 3), dtype=np.uint32)
for i in range(num_rows - 1):
ind0 = np.arange(0, num_cols-1) + i*num_cols
ind1 = ind0 + 1
ind2 = ind0 + num_cols
ind3 = ind2 + 1
start = 2*i*(num_cols-1)
stop = start + 2*(num_cols-1)
triangles[start:stop:2, 0] = ind0
triangles[start:stop:2, 1] = ind3
triangles[start:stop:2, 2] = ind1
triangles[start+1:stop:2, 0] = ind0
triangles[start+1:stop:2, 1] = ind2
triangles[start+1:stop:2, 2] = ind3
return vertices, triangles
def add_terrain_to_stage(stage, vertices, triangles, position=None, orientation=None):
num_faces = triangles.shape[0]
terrain_mesh = stage.DefinePrim("/World/terrain",
"Mesh")
terrain_mesh.GetAttribute("points").Set(vertices)
terrain_mesh.GetAttribute("faceVertexIndices").Set(triangles.flatten())
terrain_mesh.GetAttribute("faceVertexCounts").Set(np.asarray([3]*num_faces))
terrain = XFormPrim(prim_path="/World/terrain",
name="terrain",
position=position,
orientation=orientation)
UsdPhysics.CollisionAPI.Apply(terrain.prim)
# collision_api = UsdPhysics.MeshCollisionAPI.Apply(terrain.prim)
# collision_api.CreateApproximationAttr().Set("meshSimplification")
physx_collision_api = PhysxSchema.PhysxCollisionAPI.Apply(terrain.prim)
physx_collision_api.GetContactOffsetAttr().Set(0.02)
physx_collision_api.GetRestOffsetAttr().Set(0.00)
class SubTerrain:
def __init__(self, terrain_name="terrain", width=256, length=256, vertical_scale=1.0, horizontal_scale=1.0):
self.terrain_name = terrain_name
self.vertical_scale = vertical_scale
self.horizontal_scale = horizontal_scale
self.width = width
self.length = length
self.height_field_raw = np.zeros((self.width, self.length), dtype=np.int16)
|
AndrePatri/OmniRoboGym/omni_robo_gym/utils/__init__.py | |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/rt_factor.py | import time
class RtFactor():
def __init__(self,
dt_nom: float,
window_size: int):
self._it_counter = 0
self._dt_nom = dt_nom
self._start_time = time.perf_counter()
self._current_rt_factor = 0.0
self._window_size = window_size
self._real_time = 0
self._nom_time = 0
def update(self):
self._real_time = time.perf_counter() - self._start_time
self._it_counter += 1
self._nom_time += self._dt_nom
self._current_rt_factor = self._nom_time / self._real_time
def reset_due(self):
return (self._it_counter+1) % self._window_size == 0
def get_avrg_step_time(self):
return self._real_time / self._window_size
def get_dt_nom(self):
return self._dt_nom
def get_nom_time(self):
return self._now_time
def get(self):
return self._current_rt_factor
def reset(self):
self._it_counter = 0
self._nom_time = 0
self._start_time = time.perf_counter() |
AndrePatri/OmniRoboGym/omni_robo_gym/utils/urdf_helpers.py | import xml.etree.ElementTree as ET
class UrdfLimitsParser:
def __init__(self, urdf_path, joint_names,
backend = "numpy",
device = "cpu"):
self.urdf_path = urdf_path
self.joint_names = joint_names
self.limits_matrix = None
self.backend = backend
self.device = device
if self.backend == "numpy" and \
self.device != "cpu":
raise Exception("When using numpy backend, only cpu device is supported!")
self.parse_urdf()
def parse_urdf(self):
tree = ET.parse(self.urdf_path)
root = tree.getroot()
num_joints = len(self.joint_names)
self.limits_matrix = None
self.inf = None
if self.backend == "numpy":
import numpy as np
self.limits_matrix = np.full((num_joints, 6), np.nan)
self.inf = np.inf
elif self.backend == "torch":
import torch
self.limits_matrix = torch.full((num_joints, 6), torch.nan, device=self.device)
self.inf = torch.inf
else:
raise Exception("Backend not supported")
for joint_name in self.joint_names:
joint_element = root.find(".//joint[@name='{}']".format(joint_name))
if joint_element is not None:
limit_element = joint_element.find('limit')
jnt_index = self.joint_names.index(joint_name)
# position limits
q_lower = float(limit_element.get('lower', - self.inf))
q_upper = float(limit_element.get('upper', self.inf))
# effort limits
effort_limit = float(limit_element.get('effort', self.inf))
# vel limits
velocity_limit = float(limit_element.get('velocity', self.inf))
self.limits_matrix[jnt_index, 0] = q_lower
self.limits_matrix[jnt_index, 3] = q_upper
self.limits_matrix[jnt_index, 1] = - abs(velocity_limit)
self.limits_matrix[jnt_index, 4] = abs(velocity_limit)
self.limits_matrix[jnt_index, 2] = - abs(effort_limit)
self.limits_matrix[jnt_index, 5] = abs(effort_limit)
def get_limits_matrix(self):
return self.limits_matrix
|
AndrePatri/OmniRoboGym/omni_robo_gym/utils/homing.py | # Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
from omni.isaac.core.articulations.articulation_view import ArticulationView
import torch
import xml.etree.ElementTree as ET
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
class OmniRobotHomer:
def __init__(self,
articulation: ArticulationView,
srdf_path: str,
backend = "torch",
device: torch.device = torch.device("cpu"),
dtype = torch.float64):
self.torch_dtype = dtype
if not articulation.initialized:
exception = f"the provided articulation is not initialized properly!"
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
self._articulation = articulation
self.srdf_path = srdf_path
self._device = device
self.num_robots = self._articulation.count
self.n_dofs = self._articulation.num_dof
self.jnts_names = self._articulation.dof_names
self.joint_idx_map = {}
for joint in range(0, self.n_dofs):
self.joint_idx_map[self.jnts_names[joint]] = joint
if (backend != "torch"):
print(f"[{self.__class__.__name__}]" + f"[{self.journal.info}]" + ": forcing torch backend. Other backends are not yet supported.")
self._backend = "torch"
self._homing = torch.full((self.num_robots, self.n_dofs),
0.0,
device = self._device,
dtype=self.torch_dtype) # homing configuration
# open srdf and parse the homing field
with open(srdf_path, 'r') as file:
self._srdf_content = file.read()
try:
self._srdf_root = ET.fromstring(self._srdf_content)
# Now 'root' holds the root element of the XML tree.
# You can navigate through the XML tree to extract the tags and their values.
# Example: To find all elements with a specific tag, you can use:
# elements = root.findall('.//your_tag_name')
# Example: If you know the specific structure of your .SRDF file, you can extract
# the data accordingly, for instance:
# for child in root:
# if child.tag == 'some_tag_name':
# tag_value = child.text
# # Do something with the tag value.
# elif child.tag == 'another_tag_name':
# # Handle another tag.
except ET.ParseError as e:
print(f"[{self.__class__.__name__}]" + f"[{self.journal.warning}]" + ": could not read SRDF properly!!")
# Find all the 'joint' elements within 'group_state' with the name attribute and their values
joints = self._srdf_root.findall(".//group_state[@name='home']/joint")
self._homing_map = {}
for joint in joints:
joint_name = joint.attrib['name']
joint_value = joint.attrib['value']
self._homing_map[joint_name] = float(joint_value)
self._assign2homing()
def _assign2homing(self):
for joint in list(self._homing_map.keys()):
if joint in self.joint_idx_map:
self._homing[:, self.joint_idx_map[joint]] = torch.full((self.num_robots, 1),
self._homing_map[joint],
device = self._device,
dtype=self.torch_dtype).flatten()
else:
print(f"[{self.__class__.__name__}]" + f"[{self.journal.warning}]" + f"[{self._assign2homing.__name__}]" \
+ ": joint " + f"{joint}" + " is not present in the articulation. It will be ignored.")
def get_homing(self,
clone: bool = False):
if not clone:
return self._homing
else:
return self._homing.clone()
|
AndrePatri/OmniRoboGym/omni_robo_gym/utils/jnt_imp_cntrl.py | # Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
import torch
from typing import List
from enum import Enum
from omni.isaac.core.articulations.articulation_view import ArticulationView
from omni_robo_gym.utils.urdf_helpers import UrdfLimitsParser
import time
from SharsorIPCpp.PySharsorIPC import LogType
from SharsorIPCpp.PySharsorIPC import Journal
class FirstOrderFilter:
# a class implementing a simple first order filter
def __init__(self,
dt: float,
filter_BW: float = 0.1,
rows: int = 1,
cols: int = 1,
device: torch.device = torch.device("cpu"),
dtype = torch.double):
self._torch_dtype = dtype
self._torch_device = device
self._dt = dt
self._rows = rows
self._cols = cols
self._filter_BW = filter_BW
import math
self._gain = 2 * math.pi * self._filter_BW
self.yk = torch.zeros((self._rows, self._cols), device = self._torch_device,
dtype=self._torch_dtype)
self.ykm1 = torch.zeros((self._rows, self._cols), device = self._torch_device,
dtype=self._torch_dtype)
self.refk = torch.zeros((self._rows, self._cols), device = self._torch_device,
dtype=self._torch_dtype)
self.refkm1 = torch.zeros((self._rows, self._cols), device = self._torch_device,
dtype=self._torch_dtype)
self._kh2 = self._gain * self._dt / 2.0
self._coeff_ref = self._kh2 * 1/ (1 + self._kh2)
self._coeff_km1 = (1 - self._kh2) / (1 + self._kh2)
def update(self,
refk: torch.Tensor = None):
if refk is not None:
self.refk[:, :] = refk
self.yk[:, :] = torch.add(torch.mul(self.ykm1, self._coeff_km1),
torch.mul(torch.add(self.refk, self.refkm1),
self._coeff_ref))
self.refkm1[:, :] = self.refk
self.ykm1[:, :] = self.yk
def reset(self,
idxs: torch.Tensor = None):
if idxs is not None:
self.yk[:, :] = torch.zeros((self._rows, self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.ykm1[:, :] = torch.zeros((self._rows, self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.refk[:, :] = torch.zeros((self._rows, self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.refkm1[:, :] = torch.zeros((self._rows, self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
else:
self.yk[idxs, :] = torch.zeros((idxs.shape[0], self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.ykm1[idxs, :] = torch.zeros((idxs.shape[0], self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.refk[idxs, :] = torch.zeros((idxs.shape[0], self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
self.refkm1[idxs, :] = torch.zeros((idxs.shape[0], self._cols),
device = self._torch_device,
dtype=self._torch_dtype)
def get(self):
return self.yk
class JntSafety:
def __init__(self,
urdf_parser: UrdfLimitsParser):
self.limits_parser = urdf_parser
self.limit_matrix = self.limits_parser.get_limits_matrix()
def apply(self, q_cmd=None, v_cmd=None, eff_cmd=None):
if q_cmd is not None:
self.saturate_tensor(q_cmd, position=True)
if v_cmd is not None:
self.saturate_tensor(v_cmd, velocity=True)
if eff_cmd is not None:
self.saturate_tensor(eff_cmd, effort=True)
def has_nan(self,
tensor):
return torch.any(torch.isnan(tensor))
def saturate_tensor(self, tensor, position=False, velocity=False, effort=False):
if self.has_nan(tensor):
exception = f"Found nan elements in provided tensor!!"
Journal.log(self.__class__.__name__,
"saturate_tensor",
exception,
LogType.EXCEP,
throw_when_excep = False)
# Replace NaN values with infinity, so that we can clamp it
tensor[:, :] = torch.nan_to_num(tensor, nan=torch.inf)
if position:
tensor[:, :] = torch.clamp(tensor[:, :], min=self.limit_matrix[:, 0], max=self.limit_matrix[:, 3])
elif velocity:
tensor[:, :] = torch.clamp(tensor[:, :], min=self.limit_matrix[:, 1], max=self.limit_matrix[:, 4])
elif effort:
tensor[:, :] = torch.clamp(tensor[:, :], min=self.limit_matrix[:, 2], max=self.limit_matrix[:, 5])
class OmniJntImpCntrl:
class IndxState(Enum):
NONE = -1
VALID = 1
INVALID = 0
def __init__(self,
articulation: ArticulationView,
default_pgain = 300.0,
default_vgain = 30.0,
backend = "torch",
device: torch.device = torch.device("cpu"),
filter_BW = 50.0, # [Hz]
filter_dt = None, # should correspond to the dt between samples
override_art_controller = False,
init_on_creation = False,
dtype = torch.double,
enable_safety = True,
urdf_path: str = None,
enable_profiling: bool = False,
debug_checks: bool = False): # [s]
self._torch_dtype = dtype
self._torch_device = device
self.enable_profiling = enable_profiling
self._debug_checks = debug_checks
# debug data
self.profiling_data = {}
self.profiling_data["time_to_update_state"] = -1.0
self.profiling_data["time_to_set_refs"] = -1.0
self.profiling_data["time_to_apply_cmds"] = -1.0
self.start_time = None
if self.enable_profiling:
self.start_time = time.perf_counter()
self.enable_safety = enable_safety
self.limiter = None
self.robot_limits = None
self.urdf_path = urdf_path
self.override_art_controller = override_art_controller # whether to override Isaac's internal joint
# articulation PD controller or not
self.init_art_on_creation = init_on_creation # init. articulation's gains and refs as soon as the controller
# is created
self.gains_initialized = False
self.refs_initialized = False
self._default_pgain = default_pgain
self._default_vgain = default_vgain
self._filter_BW = filter_BW
self._filter_dt = filter_dt
self._articulation_view = articulation # used to actually apply control
# signals to the robot
if not self._articulation_view.initialized:
exception = f"the provided articulation_view is not initialized properly!"
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
self._valid_signal_types = ["pos_ref", "vel_ref", "eff_ref", # references
"pos", "vel", "eff", # measurements (necessary if overriding Isaac's art. controller)
"pgain", "vgain"]
self.num_robots = self._articulation_view.count
self.n_dofs = self._articulation_view.num_dof
self.jnts_names = self._articulation_view.dof_names
if (backend != "torch"):
warning = f"Only supported backend is torch!!!"
Journal.log(self.__class__.__name__,
"__init__",
warning,
LogType.WARN,
throw_when_excep = True)
self._backend = "torch"
if self.enable_safety:
if self.urdf_path is None:
exception = "If enable_safety is set to True, a urdf_path should be provided too!"
Journal.log(self.__class__.__name__,
"__init__",
exception,
LogType.EXCEP,
throw_when_excep = True)
self.robot_limits = UrdfLimitsParser(urdf_path=self.urdf_path,
joint_names=self.jnts_names,
backend=self._backend,
device=self._torch_device)
self.limiter = JntSafety(urdf_parser=self.robot_limits)
self._pos_err = None
self._vel_err = None
self._pos = None
self._vel = None
self._eff = None
self._imp_eff = None
self._filter_available = False
if filter_dt is not None:
self._filter_BW = filter_BW
self._filter_dt = filter_dt
self._pos_ref_filter = FirstOrderFilter(dt=self._filter_dt,
filter_BW=self._filter_BW,
rows=self.num_robots,
cols=self.n_dofs,
device=self._torch_device,
dtype=self._torch_dtype)
self._vel_ref_filter = FirstOrderFilter(dt=self._filter_dt,
filter_BW=self._filter_BW,
rows=self.num_robots,
cols=self.n_dofs,
device=self._torch_device,
dtype=self._torch_dtype)
self._eff_ref_filter = FirstOrderFilter(dt=self._filter_dt,
filter_BW=self._filter_BW,
rows=self.num_robots,
cols=self.n_dofs,
device=self._torch_device,
dtype=self._torch_dtype)
self._filter_available = True
else:
warning = f"No filter dt provided -> reference filter will not be used!"
Journal.log(self.__class__.__name__,
"__init__",
warning,
LogType.WARN,
throw_when_excep = True)
self.reset() # initialize data
def update_state(self,
pos: torch.Tensor = None,
vel: torch.Tensor = None,
eff: torch.Tensor = None,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
if self.enable_profiling:
self.start_time = time.perf_counter()
selector = self._gen_selector(robot_indxs=robot_indxs,
jnt_indxs=jnt_indxs) # only checks and throws
# if debug_checks
if pos is not None:
self._validate_signal(signal = pos,
selector = selector,
name="pos") # does nothing if not debug_checks
self._pos[selector] = pos
if vel is not None:
self._validate_signal(signal = vel,
selector = selector,
name="vel")
self._vel[selector] = vel
if eff is not None:
self._validate_signal(signal = eff,
selector = selector,
name="eff")
self._eff[selector] = eff
if self.enable_profiling:
self.profiling_data["time_to_update_state"] = \
time.perf_counter() - self.start_time
def set_gains(self,
pos_gains: torch.Tensor = None,
vel_gains: torch.Tensor = None,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
selector = self._gen_selector(robot_indxs=robot_indxs,
jnt_indxs=jnt_indxs) # only checks and throws
# if debug_checks
if pos_gains is not None:
self._validate_signal(signal = pos_gains,
selector = selector,
name="pos_gains")
self._pos_gains[selector] = pos_gains
if not self.override_art_controller:
self._articulation_view.set_gains(kps = self._pos_gains)
if vel_gains is not None:
self._validate_signal(signal = vel_gains,
selector = selector,
name="vel_gains")
self._vel_gains[selector] = vel_gains
if not self.override_art_controller:
self._articulation_view.set_gains(kds = self._vel_gains)
def set_refs(self,
eff_ref: torch.Tensor = None,
pos_ref: torch.Tensor = None,
vel_ref: torch.Tensor = None,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
if self.enable_profiling:
self.start_time = time.perf_counter()
selector = self._gen_selector(robot_indxs=robot_indxs,
jnt_indxs=jnt_indxs) # only checks and throws
# if debug_checks
if eff_ref is not None:
self._validate_signal(signal = eff_ref,
selector = selector,
name="eff_ref")
self._eff_ref[selector] = eff_ref
if pos_ref is not None:
self._validate_signal(signal = pos_ref,
selector = selector,
name="pos_ref")
self._pos_ref[selector] = pos_ref
if vel_ref is not None:
self._validate_signal(signal = vel_ref,
selector = selector,
name="vel_ref")
self._vel_ref[selector] = vel_ref
if self.enable_profiling:
self.profiling_data["time_to_set_refs"] = time.perf_counter() - self.start_time
def apply_cmds(self,
filter = False):
# initialize gains and refs if not done previously
if self.enable_profiling:
self.start_time = time.perf_counter()
if not self.gains_initialized:
self._apply_init_gains_to_art()
if not self.refs_initialized:
self._apply_init_refs_to_art()
if filter and self._filter_available:
self._pos_ref_filter.update(self._pos_ref)
self._vel_ref_filter.update(self._vel_ref)
self._eff_ref_filter.update(self._eff_ref)
# we first filter, then apply safety
eff_ref_filt = self._eff_ref_filter.get()
pos_ref_filt = self._pos_ref_filter.get()
vel_ref_filt = self._vel_ref_filter.get()
if self.limiter is not None:
# saturating ref cmds
self.limiter.apply(q_cmd=pos_ref_filt,
v_cmd=vel_ref_filt,
eff_cmd=eff_ref_filt)
if not self.override_art_controller:
# using omniverse's articulation PD controller
self._articulation_view.set_joint_efforts(eff_ref_filt)
self._articulation_view.set_joint_position_targets(pos_ref_filt)
self._articulation_view.set_joint_velocity_targets(vel_ref_filt)
else:
# impedance torque computed explicitly
self._pos_err = torch.sub(self._pos_ref_filter.get(), self._pos)
self._vel_err = torch.sub(self._vel_ref_filter.get(), self._vel)
self._imp_eff = torch.add(self._eff_ref_filter.get(),
torch.add(
torch.mul(self._pos_gains,
self._pos_err),
torch.mul(self._vel_gains,
self._vel_err)))
# torch.cuda.synchronize()
# we also make the resulting imp eff safe
if self.limiter is not None:
self.limiter.apply(eff_cmd=eff_ref_filt)
# apply only effort (comprehensive of all imp. terms)
self._articulation_view.set_joint_efforts(self._imp_eff)
else:
# we first apply safety to reference joint cmds
if self.limiter is not None:
self.limiter.apply(q_cmd=self._pos_ref,
v_cmd=self._vel_ref,
eff_cmd=self._eff_ref)
if not self.override_art_controller:
# using omniverse's articulation PD controller
self._articulation_view.set_joint_efforts(self._eff_ref)
self._articulation_view.set_joint_position_targets(self._pos_ref)
self._articulation_view.set_joint_velocity_targets(self._vel_ref)
else:
# impedance torque computed explicitly
self._pos_err = torch.sub(self._pos_ref, self._pos)
self._vel_err = torch.sub(self._vel_ref, self._vel)
self._imp_eff = torch.add(self._eff_ref,
torch.add(
torch.mul(self._pos_gains,
self._pos_err),
torch.mul(self._vel_gains,
self._vel_err)))
# torch.cuda.synchronize()
# we also make the resulting imp eff safe
if self.limiter is not None:
self.limiter.apply(eff_cmd=self._imp_eff)
# apply only effort (comprehensive of all imp. terms)
self._articulation_view.set_joint_efforts(self._imp_eff)
if self.enable_profiling:
self.profiling_data["time_to_apply_cmds"] = \
time.perf_counter() - self.start_time
def get_jnt_names_matching(self,
name_pattern: str):
return [jnt for jnt in self.jnts_names if name_pattern in jnt]
def get_jnt_idxs_matching(self,
name_pattern: str):
jnts_names = self.get_jnt_names_matching(name_pattern)
jnt_idxs = [self.jnts_names.index(jnt) for jnt in jnts_names]
if not len(jnt_idxs) == 0:
return torch.tensor(jnt_idxs,
dtype=torch.int64,
device=self._torch_device)
else:
return None
def pos_gains(self):
return self._pos_gains
def vel_gains(self):
return self._vel_gains
def eff_ref(self):
return self._eff_ref
def pos_ref(self):
return self._pos_ref
def vel_ref(self):
return self._vel_ref
def pos_err(self):
return self._pos_err
def vel_err(self):
return self._vel_err
def pos(self):
return self._pos
def vel(self):
return self._vel
def eff(self):
return self._eff
def imp_eff(self):
return self._imp_eff
def reset(self,
robot_indxs: torch.Tensor = None):
self.gains_initialized = False
self.refs_initialized = False
self._all_dofs_idxs = torch.tensor([i for i in range(0, self.n_dofs)],
dtype=torch.int64,
device=self._torch_device)
self._all_robots_idxs = torch.tensor([i for i in range(0, self.num_robots)],
dtype=torch.int64,
device=self._torch_device)
if robot_indxs is None: # reset all data
# we assume diagonal joint impedance gain matrices, so we can save on memory and only store the diagonal
self._pos_gains = torch.full((self.num_robots, self.n_dofs),
self._default_pgain,
device = self._torch_device,
dtype=self._torch_dtype)
self._vel_gains = torch.full((self.num_robots, self.n_dofs),
self._default_vgain,
device = self._torch_device,
dtype=self._torch_dtype)
self._eff_ref = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._pos_ref = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel_ref = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._pos_err = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel_err = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._pos = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._eff = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._imp_eff = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
if self._filter_available:
self._pos_ref_filter.reset()
self._vel_ref_filter.reset()
self._eff_ref_filter.reset()
else: # only reset some robots
if self._debug_checks:
self._validate_selectors(robot_indxs=robot_indxs) # throws if checks not satisfied
n_envs = robot_indxs.shape[0]
# we assume diagonal joint impedance gain matrices, so we can save on memory and only store the diagonal
self._pos_gains[robot_indxs, :] = torch.full((n_envs, self.n_dofs),
self._default_pgain,
device = self._torch_device,
dtype=self._torch_dtype)
self._vel_gains[robot_indxs, :] = torch.full((n_envs, self.n_dofs),
self._default_vgain,
device = self._torch_device,
dtype=self._torch_dtype)
self._eff_ref[robot_indxs, :] = 0
self._pos_ref[robot_indxs, :] = 0
self._vel_ref[robot_indxs, :] = 0
# if self.override_art_controller:
# saving memory (these are not necessary if not overriding Isaac's art. controller)
self._pos_err[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel_err[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._pos[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._vel[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._eff[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._imp_eff[robot_indxs, :] = torch.zeros((n_envs, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
if self._filter_available:
self._pos_ref_filter.reset(idxs = robot_indxs)
self._vel_ref_filter.reset(idxs = robot_indxs)
self._eff_ref_filter.reset(idxs = robot_indxs)
if self.init_art_on_creation:
# will use updated gains/refs based on reset (non updated gains/refs will be the same)
self._apply_init_gains_to_art()
self._apply_init_refs_to_art()
def _apply_init_gains_to_art(self):
if not self.gains_initialized:
if not self.override_art_controller:
self._articulation_view.set_gains(kps = self._pos_gains,
kds = self._vel_gains)
else:
# settings Isaac's PD controller gains to 0
no_gains = torch.zeros((self.num_robots, self.n_dofs), device = self._torch_device,
dtype=self._torch_dtype)
self._articulation_view.set_gains(kps = no_gains,
kds = no_gains)
self.gains_initialized = True
def _apply_init_refs_to_art(self):
if not self.refs_initialized:
if not self.override_art_controller:
self._articulation_view.set_joint_efforts(self._eff_ref)
self._articulation_view.set_joint_position_targets(self._pos_ref)
self._articulation_view.set_joint_velocity_targets(self._vel_ref)
else:
self._articulation_view.set_joint_efforts(self._eff_ref)
self.refs_initialized = True
def _validate_selectors(self,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
if robot_indxs is not None:
robot_indxs_shape = robot_indxs.shape
if (not (len(robot_indxs_shape) == 1 and \
robot_indxs.dtype == torch.int64 and \
bool(torch.min(robot_indxs) >= 0) and \
bool(torch.max(robot_indxs) < self.num_robots)) and \
robot_indxs.device.type == self._torch_device.type): # sanity checks
error = "Mismatch in provided selector \n" + \
"robot_indxs_shape -> " + f"{len(robot_indxs_shape)}" + " VS" + " expected -> " + f"{1}" + "\n" + \
"robot_indxs.dtype -> " + f"{robot_indxs.dtype}" + " VS" + " expected -> " + f"{torch.int64}" + "\n" + \
"torch.min(robot_indxs) >= 0) -> " + f"{bool(torch.min(robot_indxs) >= 0)}" + " VS" + f" {True}" + "\n" + \
"torch.max(robot_indxs) < self.n_dofs -> " + f"{torch.max(robot_indxs)}" + " VS" + f" {self.num_robots}\n" + \
"robot_indxs.device -> " + f"{robot_indxs.device.type}" + " VS" + " expected -> " + f"{self._torch_device.type}" + "\n"
Journal.log(self.__class__.__name__,
"_validate_selectors",
error,
LogType.EXCEP,
throw_when_excep = True)
if jnt_indxs is not None:
jnt_indxs_shape = jnt_indxs.shape
if (not (len(jnt_indxs_shape) == 1 and \
jnt_indxs.dtype == torch.int64 and \
bool(torch.min(jnt_indxs) >= 0) and \
bool(torch.max(jnt_indxs) < self.n_dofs)) and \
jnt_indxs.device.type == self._torch_device.type): # sanity checks
error = "Mismatch in provided selector \n" + \
"jnt_indxs_shape -> " + f"{len(jnt_indxs_shape)}" + " VS" + " expected -> " + f"{1}" + "\n" + \
"jnt_indxs.dtype -> " + f"{jnt_indxs.dtype}" + " VS" + " expected -> " + f"{torch.int64}" + "\n" + \
"torch.min(jnt_indxs) >= 0) -> " + f"{bool(torch.min(jnt_indxs) >= 0)}" + " VS" + f" {True}" + "\n" + \
"torch.max(jnt_indxs) < self.n_dofs -> " + f"{torch.max(jnt_indxs)}" + " VS" + f" {self.num_robots}" + \
"robot_indxs.device -> " + f"{jnt_indxs.device.type}" + " VS" + " expected -> " + f"{self._torch_device.type}" + "\n"
Journal.log(self.__class__.__name__,
"_validate_selectors",
error,
LogType.EXCEP,
throw_when_excep = True)
def _validate_signal(self,
signal: torch.Tensor,
selector: torch.Tensor = None,
name: str = "signal"):
if self._debug_checks:
signal_shape = signal.shape
selector_shape = selector[0].shape
if not (signal_shape[0] == selector_shape[0] and \
signal_shape[1] == selector_shape[1] and \
signal.device.type == self._torch_device.type and \
signal.dtype == self._torch_dtype):
big_error = f"Mismatch in provided signal [{name}" + "] and/or selector \n" + \
"signal rows -> " + f"{signal_shape[0]}" + " VS" + " expected rows -> " + f"{selector_shape[0]}" + "\n" + \
"signal cols -> " + f"{signal_shape[1]}" + " VS" + " expected cols -> " + f"{selector_shape[1]}" + "\n" + \
"signal dtype -> " + f"{signal.dtype}" + " VS" + " expected -> " + f"{self._torch_dtype}" + "\n" + \
"signal device -> " + f"{signal.device.type}" + " VS" + " expected type -> " + f"{self._torch_device.type}"
Journal.log(self.__class__.__name__,
"_validate_signal",
big_error,
LogType.EXCEP,
throw_when_excep = True)
def _gen_selector(self,
robot_indxs: torch.Tensor = None,
jnt_indxs: torch.Tensor = None):
if self._debug_checks:
self._validate_selectors(robot_indxs=robot_indxs,
jnt_indxs=jnt_indxs) # throws if not valid
if robot_indxs is None:
robot_indxs = self._all_robots_idxs
if jnt_indxs is None:
jnt_indxs = self._all_dofs_idxs
return torch.meshgrid((robot_indxs, jnt_indxs),
indexing="ij")
|
AndrePatri/OmniRoboGym/omni_robo_gym/utils/terrains.py | # Copyright (C) 2023 Andrea Patrizi (AndrePatri, [email protected])
#
# This file is part of OmniRoboGym and distributed under the General Public License version 2 license.
#
# OmniRoboGym is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# OmniRoboGym is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OmniRoboGym. If not, see <http://www.gnu.org/licenses/>.
#
import os, sys
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(SCRIPT_DIR)
import numpy as np
from omni_robo_gym.utils.terrain_utils import *
from pxr import Usd
class RlTerrains():
def __init__(self,
stage: Usd.Stage):
self._stage = stage
def get_wave_terrain(self,
terrain_size = 40,
num_waves = 10,
amplitude = 1,
position = np.array([0.0, 0.0, 0.0])):
# creates a terrain
num_terrains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terrains * num_rows,
num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows,
length=num_cols,
vertical_scale=vertical_scale,
horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = wave_terrain(new_sub_terrain(), num_waves=num_waves,
amplitude=amplitude).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield,
horizontal_scale=horizontal_scale,
vertical_scale=vertical_scale,
slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage,
vertices=vertices,
triangles=triangles,
position=position,
orientation=orientation)
def get_sloped_terrain(self,
terrain_size = 40,
slope = -0.5,
position = np.array([0.0, 0.0, 0.0])):
# creates a terrain
num_terrains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terrains * num_rows,
num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows,
length=num_cols,
vertical_scale=vertical_scale,
horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = pyramid_sloped_terrain(new_sub_terrain(),
slope=slope).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield,
horizontal_scale=horizontal_scale,
vertical_scale=vertical_scale,
slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage,
vertices=vertices,
triangles=triangles,
position=position,
orientation=orientation)
def get_stairs_terrain(self,
terrain_size = 40,
step_width = 0.75,
step_height = -0.5,
position = np.array([0.0, 0.0, 0.0])):
# creates a terrain
num_terrains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terrains * num_rows,
num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows,
length=num_cols,
vertical_scale=vertical_scale,
horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = stairs_terrain(new_sub_terrain(), step_width=step_width,
step_height=step_height).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield,
horizontal_scale=horizontal_scale,
vertical_scale=vertical_scale,
slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage,
vertices=vertices,
triangles=triangles,
position=position,
orientation=orientation)
def get_random_terrain(self,
terrain_size = 40,
min_height = -0.2,
max_height = 0.2,
step = 0.2,
downsampled_scale=0.5,
position = np.array([0.0, 0.0, 0.0])):
# creates a terrain
num_terrains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terrains * num_rows,
num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows,
length=num_cols,
vertical_scale=vertical_scale,
horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = random_uniform_terrain(new_sub_terrain(),
min_height=min_height, max_height=max_height,
step=step,
downsampled_scale=downsampled_scale).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield,
horizontal_scale=horizontal_scale,
vertical_scale=vertical_scale,
slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage,
vertices=vertices,
triangles=triangles,
position=position,
orientation=orientation)
def get_obstacles_terrain(self,
terrain_size = 40.0,
num_obs = 50,
max_height = 0.5,
min_size = 0.5,
max_size = 5.0,
position = np.array([0.0, 0.0, 0.0])):
# create all available terrain types
num_terains = 1
terrain_width = terrain_size
terrain_length = terrain_size
horizontal_scale = 0.25 # [m]
vertical_scale = 0.005 # [m]
num_rows = int(terrain_width/horizontal_scale)
num_cols = int(terrain_length/horizontal_scale)
heightfield = np.zeros((num_terains*num_rows, num_cols), dtype=np.int16)
def new_sub_terrain():
return SubTerrain(width=num_rows, length=num_cols, vertical_scale=vertical_scale, horizontal_scale=horizontal_scale)
heightfield[0:num_rows, :] = discrete_obstacles_terrain(new_sub_terrain(),
max_height=max_height,
min_size=min_size,
max_size=max_size,
num_rects=num_obs).height_field_raw
vertices, triangles = convert_heightfield_to_trimesh(heightfield, horizontal_scale=horizontal_scale, vertical_scale=vertical_scale, slope_threshold=1.5)
position = np.array([-terrain_width/2.0, terrain_length/2.0, 0]) + position
orientation = np.array([0.70711, 0.0, 0.0, -0.70711])
add_terrain_to_stage(stage=self._stage, vertices=vertices, triangles=triangles, position=position, orientation=orientation)
def post_reset(self):
a = 1
def get_observations(self):
pass
def calculate_metrics(self) -> None:
pass
def is_done(self) -> None:
pass
|
AndrePatri/OmniRoboGym/docs/isaac2023.1.0_issues.md | ### Some bugs of Isaac2023.1.0 which can be easily fixed
#### 1.0 Nucleus blocking function makes startup super slow
Easy temporary fix: modify /home/username/.local/share/ov/pkg/isaac_sim-2023.1.0/exts/omni.isaac.core/omni/isaac/core/utils/nucleus.py .
Change lines 178 to 198 which is the check server function to below:
```python
def check_server(server: str, path: str, timeout: float = 10.0) -> bool:
"""Check a specific server for a path
Args:
server (str): Name of Nucleus server
path (str): Path to search
Returns:
bool: True if folder is found
"""
carb.log_info("Checking path: {}{}".format(server, path))
# Increase hang detection timeout
if "localhost" not in server:
omni.client.set_hang_detection_time_ms(10000)
result, _ = omni.client.stat("{}{}".format(server, path))
if result == Result.OK:
carb.log_info("Success: {}{}".format(server, path))
return True
carb.log_info("Failure: {}{} not accessible".format(server, path))
return False
```
#### 2.0 Grid Cloner bug
See `docs/grid_cloner_bugfix.py` for more details
#### 3.0 Contact sensor bug
When cloning environments, it's not possible to create contact sensors on the cloned environments because of a failed collision_API enabled flag option. Removing the check seems to recolve the problem without any major or noticeable issues.
|
AndrePatri/OmniRoboGym/docs/grid_cloner_bugfix/copy_to_isaac.sh | cp ./grid_cloner.py ${HOME}/.local/share/ov/pkg/isaac_sim-2023.1.0-hotfix.1/exts/omni.isaac.cloner/omni/isaac/cloner/
|
AndrePatri/OmniRoboGym/docs/grid_cloner_bugfix/grid_cloner.py | # Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
from typing import List, Union
import numpy as np
import omni.usd
import torch
from omni.isaac.cloner import Cloner
from pxr import Gf, UsdGeom
class GridCloner(Cloner):
""" This is a specialized Cloner class that will automatically generate clones in a grid fashion. """
def __init__(self, spacing: float, num_per_row: int = -1):
"""
Args:
spacing (float): Spacing between clones.
num_per_row (int): Number of clones to place in a row. Defaults to sqrt(num_clones).
"""
self._spacing = spacing
self._num_per_row = num_per_row
Cloner.__init__(self)
def clone(
self,
source_prim_path: str,
prim_paths: List[str],
position_offsets: np.ndarray = None,
orientation_offsets: np.ndarray = None,
replicate_physics: bool = False,
base_env_path: str = None,
root_path: str = None,
copy_from_source: bool = False
):
""" Creates clones in a grid fashion. Positions of clones are computed automatically.
Args:
source_prim_path (str): Path of source object.
prim_paths (List[str]): List of destination paths.
position_offsets (np.ndarray): Positions to be applied as local translations on top of computed clone position.
Defaults to None, no offset will be applied.
orientation_offsets (np.ndarray): Orientations to be applied as local rotations for each clone.
Defaults to None, no offset will be applied.
replicate_physics (bool): Uses omni.physics replication. This will replicate physics properties directly for paths beginning with root_path and skip physics parsing for anything under the base_env_path.
base_env_path (str): Path to namespace for all environments. Required if replicate_physics=True and define_base_env() not called.
root_path (str): Prefix path for each environment. Required if replicate_physics=True and generate_paths() not called.
copy_from_source: (bool): Setting this to False will inherit all clones from the source prim; any changes made to the source prim will be reflected in the clones.
Setting this to True will make copies of the source prim when creating new clones; changes to the source prim will not be reflected in clones. Defaults to False. Note that setting this to True will take longer to execute.
Returns:
positions (List): Computed positions of all clones.
"""
num_clones = len(prim_paths)
self._num_per_row = int(np.sqrt(num_clones)) if self._num_per_row == -1 else self._num_per_row
num_rows = np.ceil(num_clones / self._num_per_row)
num_cols = np.ceil(num_clones / num_rows)
row_offset = 0.5 * self._spacing * (num_rows - 1)
col_offset = 0.5 * self._spacing * (num_cols - 1)
stage = omni.usd.get_context().get_stage()
positions = []
orientations = []
for i in range(num_clones):
# compute transform
row = i // num_cols
col = i % num_cols
x = row_offset - row * self._spacing
y = col * self._spacing - col_offset
up_axis = UsdGeom.GetStageUpAxis(stage)
position = [x, y, 0] if up_axis == UsdGeom.Tokens.z else [x, 0, y]
orientation = Gf.Quatd.GetIdentity()
if position_offsets is not None:
translation = position_offsets[i] + position
else:
translation = position
if orientation_offsets is not None:
orientation = (
Gf.Quatd(orientation_offsets[i][0].item(), Gf.Vec3d(orientation_offsets[i][1:].tolist()))
* orientation
)
else:
orientation = [
orientation.GetReal(),
orientation.GetImaginary()[0],
orientation.GetImaginary()[1],
orientation.GetImaginary()[2],
]
positions.append(translation)
orientations.append(orientation)
super().clone(
source_prim_path=source_prim_path,
prim_paths=prim_paths,
positions=positions,
orientations=orientations,
replicate_physics=replicate_physics,
base_env_path=base_env_path,
root_path=root_path,
copy_from_source=copy_from_source,
)
return positions
|
AndrePatri/OmniRoboGym/docs/contact_sensor_bugfix/contact_sensor.py | # Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
from omni.isaac.kit import SimulationApp
simulation_app = SimulationApp({"headless": False})
import argparse
import sys
import carb
import numpy as np
from omni.isaac.core import World
from omni.isaac.core.articulations import Articulation
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.stage import add_reference_to_stage
from omni.isaac.sensor import ContactSensor
from omni.isaac.cloner import GridCloner
import omni.isaac.core.utils.prims as prim_utils
parser = argparse.ArgumentParser()
parser.add_argument("--test", default=False, action="store_true", help="Run in test mode")
args, unknown = parser.parse_known_args()
assets_root_path = get_assets_root_path()
if assets_root_path is None:
carb.log_error("Could not find Isaac Sim assets folder")
simulation_app.close()
sys.exit()
my_world = World(stage_units_in_meters=1.0)
my_world.scene.add_default_ground_plane()
asset_path = assets_root_path + "/Isaac/Robots/Ant/ant.usd"
add_reference_to_stage(usd_path=asset_path, prim_path="/World/envs/env_0/Ant")
ant = my_world.scene.add(Articulation(prim_path="/World/envs/env_0/Ant/torso", name="ant", translation=np.array([0, 0, 1.5])))
ant_foot_prim_names = ["right_back_foot", "left_back_foot", "front_right_foot", "front_left_foot"]
translations = np.array(
[[0.38202, -0.40354, -0.0887], [-0.4, -0.40354, -0.0887], [-0.4, 0.4, -0.0887], [0.4, 0.4, -0.0887]]
)
# moving def prim
# move_prim(robot_prim_path_default, # from
# robot_base_prim_path) # to
num_envs = 3
env_ns = "/World/envs"
env_spacing = 15 # [m]
template_env_ns = env_ns + "/env_0"
cloner = GridCloner(spacing=env_spacing)
cloner.define_base_env(env_ns)
envs_prim_paths = cloner.generate_paths(env_ns + "/env",
num_envs)
cloner.clone(
source_prim_path=template_env_ns,
prim_paths=envs_prim_paths,
replicate_physics=True,
position_offsets = None
)
ant_sensors = []
for i in range(4):
ant_sensors.append(
my_world.scene.add(
ContactSensor(
prim_path="/World/envs/env_0/Ant/" + ant_foot_prim_names[i] + "/contact_sensor",
name="ant_contact_sensor_{}".format(i),
min_threshold=0,
max_threshold=10000000,
radius=0.1,
translation=translations[i],
)
)
)
ant_sensors[0].add_raw_contact_data_to_frame()
ant_sensors2 = []
for i in range(4):
ant_sensors2.append(
my_world.scene.add(
ContactSensor(
prim_path="/World/envs/env_1/Ant/" + ant_foot_prim_names[i] + "/contact_sensor",
name="ant_contact_sensor2_{}".format(i),
min_threshold=0,
max_threshold=10000000,
radius=0.1,
translation=translations[i],
)
)
)
ant_sensors2[0].add_raw_contact_data_to_frame()
my_world.reset()
while simulation_app.is_running():
my_world.step(render=True)
if my_world.is_playing():
print(ant_sensors2[0].get_current_frame())
if my_world.current_time_step_index == 0:
my_world.reset()
simulation_app.close()
|
AndrePatri/OmniRoboGym/docs/sim_substepping_reset_issue/test_substepping_when_reset.py | # Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
import numpy as np
import torch
def get_device(sim_params):
if "sim_device" in sim_params:
device = sim_params["sim_device"]
else:
device = "cpu"
physics_device_id = carb.settings.get_settings().get_as_int("/physics/cudaDevice")
gpu_id = 0 if physics_device_id < 0 else physics_device_id
if sim_params and "use_gpu_pipeline" in sim_params:
# GPU pipeline must use GPU simulation
if sim_params["use_gpu_pipeline"]:
device = "cuda:" + str(gpu_id)
elif sim_params and "use_gpu" in sim_params:
if sim_params["use_gpu"]:
device = "cuda:" + str(gpu_id)
return device
def sim_parameters():
# simulation parameters
sim_params = {}
# device settings
sim_params["use_gpu_pipeline"] = True # disabling gpu pipeline is necessary to be able
# to retrieve some quantities from the simulator which, otherwise, would have random values
sim_params["use_gpu"] = True # does this actually do anything?
if sim_params["use_gpu_pipeline"]:
sim_params["device"] = "cuda"
else:
sim_params["device"] = "cpu"
device = sim_params["device"]
# sim_params["dt"] = 1.0/100.0 # physics_dt?
sim_params["physics_dt"] = 1.0/400.0 # physics_dt?
sim_params["rendering_dt"] = sim_params["physics_dt"]
sim_params["substeps"] = 1 # number of physics steps to be taken for for each rendering step
sim_params["gravity"] = np.array([0.0, 0.0, -9.81])
sim_params["enable_scene_query_support"] = False
sim_params["use_fabric"] = True # Enable/disable reading of physics buffers directly. Default is True.
sim_params["replicate_physics"] = True
# sim_params["worker_thread_count"] = 4
sim_params["solver_type"] = 1 # 0: PGS, 1:TGS, defaults to TGS. PGS faster but TGS more stable
sim_params["enable_stabilization"] = True
# sim_params["bounce_threshold_velocity"] = 0.2
# sim_params["friction_offset_threshold"] = 0.04
# sim_params["friction_correlation_distance"] = 0.025
# sim_params["enable_sleeping"] = True
# Per-actor settings ( can override in actor_options )
sim_params["solver_position_iteration_count"] = 4 # defaults to 4
sim_params["solver_velocity_iteration_count"] = 1 # defaults to 1
sim_params["sleep_threshold"] = 0.0 # Mass-normalized kinetic energy threshold below which an actor may go to sleep.
# Allowed range [0, max_float).
sim_params["stabilization_threshold"] = 1e-5
# Per-body settings ( can override in actor_options )
# sim_params["enable_gyroscopic_forces"] = True
# sim_params["density"] = 1000 # density to be used for bodies that do not specify mass or density
# sim_params["max_depenetration_velocity"] = 100.0
# sim_params["solver_velocity_iteration_count"] = 1
# GPU buffers settings
# sim_params["gpu_max_rigid_contact_count"] = 512 * 1024
# sim_params["gpu_max_rigid_patch_count"] = 80 * 1024
# sim_params["gpu_found_lost_pairs_capacity"] = 1024
# sim_params["gpu_found_lost_aggregate_pairs_capacity"] = 1024
# sim_params["gpu_total_aggregate_pairs_capacity"] = 1024
# sim_params["gpu_max_soft_body_contacts"] = 1024 * 1024
# sim_params["gpu_max_particle_contacts"] = 1024 * 1024
# sim_params["gpu_heap_capacity"] = 64 * 1024 * 1024
# sim_params["gpu_temp_buffer_capacity"] = 16 * 1024 * 1024
# sim_params["gpu_max_num_partitions"] = 8
return sim_params
def reset_state(art_view,
idxs: torch.Tensor):
# root q
art_view.set_world_poses(positions = root_p_default[idxs, :],
orientations=root_q_default[idxs, :],
indices = idxs)
# jnts q
art_view.set_joint_positions(positions = jnts_q_default[idxs, :],
indices = idxs)
# root v and omega
art_view.set_joint_velocities(velocities = jnts_v_default[idxs, :],
indices = idxs)
# jnts v
concatenated_vel = torch.cat((root_v_default[idxs, :],
root_omega_default[idxs, :]), dim=1)
art_view.set_velocities(velocities = concatenated_vel,
indices = idxs)
# jnts eff
art_view.set_joint_efforts(efforts = jnts_eff_default[idxs, :],
indices = idxs)
def get_robot_state(
art_view):
pose = art_view.get_world_poses(
clone = True) # tuple: (pos, quat)
# root p (measured, previous, default)
root_p = pose[0]
# root q (measured, previous, default)
root_q = pose[1] # root orientation
# jnt q (measured, previous, default)
jnts_q = art_view.get_joint_positions(
clone = True) # joint positions
# root v (measured, default)
root_v= art_view.get_linear_velocities(
clone = True) # root lin. velocity
# root omega (measured, default)
root_omega = art_view.get_angular_velocities(
clone = True) # root ang. velocity
# joints v (measured, default)
jnts_v = art_view.get_joint_velocities(
clone = True) # joint velocities
jnts_eff = art_view.get_measured_joint_efforts(clone = True)
return root_p, root_q, jnts_q, root_v, root_omega, jnts_v, jnts_eff
from omni.isaac.kit import SimulationApp
import carb
import os
experience = f'{os.environ["EXP_PATH"]}/omni.isaac.sim.python.omnirobogym.headless.kit'
sim_params = sim_parameters()
num_envs = 2
headless = True
simulation_app = SimulationApp({"headless": headless,
"physics_gpu": 0},
experience=experience)
from omni.isaac.core import World
from omni.isaac.core.articulations import ArticulationView
from omni.importer.urdf import _urdf
# urdf import config
import_config = _urdf.ImportConfig()
import_config.merge_fixed_joints = True
import_config.import_inertia_tensor = True
import_config.fix_base = False
import_config.self_collision = False
my_world = World(stage_units_in_meters=1.0,
physics_dt=sim_params["physics_dt"],
rendering_dt=sim_params["rendering_dt"],
backend="torch",
device=str(get_device(sim_params=sim_params)),
physics_prim_path="/physicsScene",
set_defaults = False,
sim_params=sim_params)
# create initial robot
import omni.isaac.core.utils.prims as prim_utils
# create GridCloner instance
env_ns = "/World/envs"
template_env_ns = env_ns + "/env" # a single env. may contain multiple robots
base_env = template_env_ns + "_0"
base_robot_path = base_env + "/panda"
# get path to resource
from omni.isaac.core.utils.extensions import get_extension_path_from_name
extension_path = get_extension_path_from_name("omni.importer.urdf")
# import URDF at default prim path
import omni.kit
success, robot_prim_path_default = omni.kit.commands.execute(
"URDFParseAndImportFile",
urdf_path=extension_path + "/data/urdf/robots/franka_description/robots/panda_arm.urdf",
import_config=import_config,
)
# moving default prim to base prim path (for potential cloning)
from omni.isaac.core.utils.prims import move_prim
prim_utils.define_prim(base_env)
move_prim(robot_prim_path_default, # from
base_robot_path) # to
# cloning
from omni.isaac.cloner import GridCloner
cloner = GridCloner(spacing=6)
_envs_prim_paths = cloner.generate_paths(template_env_ns, num_envs)
position_offsets = np.array([[0.0, 0.0, 0.6]] * num_envs)
cloner.clone(
source_prim_path=base_env,
prim_paths=_envs_prim_paths,
base_env_path=base_env,
position_offsets=position_offsets,
replicate_physics=True
)
# Prim paths structure:
# World/envs/env_0/panda/panda_link0/...
# this only in 2023.1.0
art_view = ArticulationView(name = "Panda" + "ArtView",
prim_paths_expr = env_ns + "/env_.*"+ "/panda/panda_link0",
reset_xform_properties=False # required as per doc. when cloning
)
# moreover, robots are not cloned at different locations
my_world.scene.add(art_view)
ground_plane_prim_path = "/World/terrain"
my_world.scene.add_default_ground_plane(z_position=0,
name="terrain",
prim_path= ground_plane_prim_path,
static_friction=0.5,
dynamic_friction=0.5,
restitution=0.8)
cloner.filter_collisions(physicsscene_path = my_world.get_physics_context().prim_path,
collision_root_path = "/World/collisions",
prim_paths=_envs_prim_paths,
global_paths=[ground_plane_prim_path] # can collide with these prims
)
my_world.reset()
# init default state from measurements
root_p, root_q, jnts_q, root_v, \
root_omega, jnts_v, jnts_eff = get_robot_state(art_view)
root_p_default = torch.clone(root_p)
root_q_default = torch.clone(root_q)
jnts_q_default = torch.clone(jnts_q)
jnts_v_default = torch.clone(jnts_v)
root_omega_default = torch.clone(root_omega)
root_v_default = torch.clone(root_v)
jnts_eff_default = torch.clone(jnts_eff).zero_()
# default values
root_p_default[:, 0] = 0
root_p_default[:, 1] = 0
root_p_default[:, 2] = 0.5
root_q_default[:, 0] = 0.0
root_q_default[:, 1] = 0.0
root_q_default[:, 2] = 0.0
root_q_default[:, 3] = 1.0
jnts_q_default[:, :] = 1.0
jnts_v_default[:, :] = 0.0
root_omega_default[:, :] = 0.0
root_v_default[:, :] = 0.0
no_gains = torch.zeros((num_envs, jnts_eff_default.shape[1]), device = get_device(sim_params),
dtype=torch.float32)
art_view.set_gains(kps = no_gains,
kds = no_gains)
print("Extension path: " + str(extension_path))
print("Prim paths: " + str(art_view.prim_paths))
reset_ever_n_steps = 100
just_reset = False
for i in range(0, 1000):
if ((i + 1) % reset_ever_n_steps) == 0:
print("resetting to default")
reset_state(art_view,
torch.tensor([0], dtype=torch.int))
just_reset = True
my_world.step()
# retrieve state
root_p, root_q, jnts_q, root_v, \
root_omega, jnts_v, jnts_eff = get_robot_state(art_view)
# if just_reset:
# check we hace reset correcty
print("measured")
print(jnts_q)
print("default")
print(jnts_q_default)
simulation_app.close() |
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/CLA.md | ## Individual Contributor License Agreement (CLA)
**Thank you for submitting your contributions to this project.**
By signing this CLA, you agree that the following terms apply to all of your past, present and future contributions
to the project.
### License.
You hereby represent that all present, past and future contributions are governed by the
[MIT License](https://opensource.org/licenses/MIT)
copyright statement.
This entails that to the extent possible under law, you transfer all copyright and related or neighboring rights
of the code or documents you contribute to the project itself or its maintainers.
Furthermore you also represent that you have the authority to perform the above waiver
with respect to the entirety of you contributions.
### Moral Rights.
To the fullest extent permitted under applicable law, you hereby waive, and agree not to
assert, all of your “moral rights” in or relating to your contributions for the benefit of the project.
### Third Party Content.
If your Contribution includes or is based on any source code, object code, bug fixes, configuration changes, tools,
specifications, documentation, data, materials, feedback, information or other works of authorship that were not
authored by you (“Third Party Content”) or if you are aware of any third party intellectual property or proprietary
rights associated with your Contribution (“Third Party Rights”),
then you agree to include with the submission of your Contribution full details respecting such Third Party
Content and Third Party Rights, including, without limitation, identification of which aspects of your
Contribution contain Third Party Content or are associated with Third Party Rights, the owner/author of the
Third Party Content and Third Party Rights, where you obtained the Third Party Content, and any applicable
third party license terms or restrictions respecting the Third Party Content and Third Party Rights. For greater
certainty, the foregoing obligations respecting the identification of Third Party Content and Third Party Rights
do not apply to any portion of a Project that is incorporated into your Contribution to that same Project.
### Representations.
You represent that, other than the Third Party Content and Third Party Rights identified by
you in accordance with this Agreement, you are the sole author of your Contributions and are legally entitled
to grant the foregoing licenses and waivers in respect of your Contributions. If your Contributions were
created in the course of your employment with your past or present employer(s), you represent that such
employer(s) has authorized you to make your Contributions on behalf of such employer(s) or such employer
(s) has waived all of their right, title or interest in or to your Contributions.
### Disclaimer.
To the fullest extent permitted under applicable law, your Contributions are provided on an "as is"
basis, without any warranties or conditions, express or implied, including, without limitation, any implied
warranties or conditions of non-infringement, merchantability or fitness for a particular purpose. You are not
required to provide support for your Contributions, except to the extent you desire to provide support.
### No Obligation.
You acknowledge that the maintainers of this project are under no obligation to use or incorporate your contributions
into the project. The decision to use or incorporate your contributions into the project will be made at the
sole discretion of the maintainers or their authorized delegates. |
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/README.md | # Synthetic Data Generation and Training with Sim Ready Assets
This project provides a workflow for Training Computer Vision models with Synthetic Data. We will use Isaac Sim with Omniverse Replicator to generate data for our use case and objects of interest. To ensure seamless compatibility with model training, the data generated is in the KITTI format.
These steps can be followed on a Cloud/remote GPU instance or locally
## How to use this repository
- [Guide](local/README.md) for running the workflow locally
- [Guide](cloud/README.md) for running on a cloud/remote instance
## Workflow Components:
* Generating Data: Use Isaac Sim to generate data
* Training: We will use TAO toolkit, however users can train a model in a framework of their choice with data generated
### SDG
- Using the `palletjack` assets from the Warehouse Sim Ready Asset collection
- Carry out Domain Randomization in the scene with Replicator:
- Various attributes of the scene like lighting, textures, object pose and materials can be modified
- Important to generate a good quality dataset to ensure model detects objects in the real world
- Data output KITTI format
- We will use the KITTI Writer for generating annotations
- Possible to implement a custom writer (can be useful when data is expected in a certain format for your model)
- Sample generated images:
<p>
<img src="images/sample_synthetic/21.png" height="256"/>
<img src="images/sample_synthetic/653.png" height="256"/>
</p>
<p>
<img src="images/sample_synthetic/896.png" height="256"/>
<img src="images/sample_synthetic/1545.png" height="256"/>
</p>
### Training
- TAO: Outline of steps
- Generating Tfrecords
- Model training and evaluation
- Model backbone selction
- Hyperparameters specified via `spec` file (provided with repo)
- Running inference with trained model
- Sample real world detections on LOCO dataset images:
<p>
<img src="images/real_world_results/1564562568.298206.jpg" height="256"/>
<img src="images/real_world_results/1564562843.0618184.jpg" height="256"/>
</p>
<p>
<img src="images/real_world_results/593768,3659.jpg" height="256"/>
<img src="images/real_world_results/510196244,1362.jpg" height="256"/>
</p>
<p>
<img src="images/real_world_results/1574675156.7667925.jpg" height="256"/>
<img src="images/real_world_results/426023,9672.jpg" height="256"/>
</p>
### Deployment
- Perform Optimizations: Pruning and QAT with TAO to reduce model size and improve performance
- Deploy on NVIDIA Jetson powered Robot with Isaac ROS or Deepstream
## References:
- Real world images from the [LOCO dataset](https://github.com/tum-fml/loco) are used for visualizing model performance
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/LICENSE.md | 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.
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/cloud/README.md | # Requirements
- Access to a cloud/remote GPU instance (workflow tested on a `g4dn` AWS EC2 instance with T4 GPU)
- Docker setup instructions are provided in the notebooks
- Entire workflow can be run in `headless` mode (SDG script and training)
## Synthetic Data Generation
- Use the Isaac Sim docker container for running the Data Generation [script](../palletjack_sdg/palletjack_datagen.sh)
- We will generate data for warehouse `palletjack` objects in KITTI format
- Follow the steps in the `cloud_sdg` notebook
- This generated data can be used to train your own model (framework and architecture of your choice), in this workflow we demonstrate using TAO for training
## Training with TAO Toolkit
- The `training/cloud_train` notebook provides a walkthrough of the steps:
- Setting up TAO docker container
- Downloading pre-trained model, we will use the `DetectNet_v2` model with a `resnet_18` backbone
- Running TAO training with `spec` files provided
- Visualizing model performance on real world data
- Visualize model metric with Tensorboard
<img src="../images/tensorboard/tensorboard_resized_palletjack.png"/>
## Next steps
### Generating Synthetic Data for your use case
- Make changes in the Domain Randomization under the Synthetic Data Generation [script](../palletjack_sdg/standalone_palletjack_sdg.py)
- Add additional objects of interest in the scene (similar to how palletjacks are added, you can add forklifts, ladders etc.) to generate dataUse different models for training with TAO (for object detection, you can use YOLO, SSD, EfficientDet)
- Replicator provides Semantic Segmentation, Instance Segmentation, Depth and various other ground truth annotations along with RGB. You can also write your own ground truth annotator (eg: Pose Estimation: Refer to [sample](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_replicator_offline_pose_estimation.html) These can be used for training a model of your own framework and choice)
- Exploring the option of using Synthetic + Real data for training a network. Can be particularly useful for generating more data around particular corner cases
### Deploying Trained Models
- The trained model can be pruned and optimized for inference with TAO
- This can then be deployed on a robot with NVIDIA Jetson |
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