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NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/omni/example/ui_scene/object_info/object_info_manipulator.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
__all__ = ["ObjectInfoManipulator"]
from omni.ui import color as cl
from omni.ui import scene as sc
import omni.ui as ui
LEADER_LINE_CIRCLE_RADIUS = 2
LEADER_LINE_THICKNESS = 2
LEADER_LINE_SEGMENT_LENGTH = 20
VERTICAL_MULT = 1.5
HORIZ_TEXT_OFFSET = 5
LINE1_OFFSET = 3
LINE2_OFFSET = 0
class ObjectInfoManipulator(sc.Manipulator):
"""Manipulator that displays the object path and material assignment
with a leader line to the top of the object's bounding box.
"""
def on_build(self):
"""Called when the model is changed and rebuilds the whole manipulator"""
if not self.model:
return
# If we don't have a selection then just return
if self.model.get_item("name") == "":
return
position = self.model.get_as_floats(self.model.get_item("position"))
# Move everything to where the object is
with sc.Transform(transform=sc.Matrix44.get_translation_matrix(*position)):
# Rotate everything to face the camera
with sc.Transform(look_at=sc.Transform.LookAt.CAMERA):
# Leader lines with a small circle on the end
sc.Arc(LEADER_LINE_CIRCLE_RADIUS, axis=2, color=cl.yellow)
sc.Line([0, 0, 0], [0, LEADER_LINE_SEGMENT_LENGTH, 0],
color=cl.yellow, thickness=LEADER_LINE_THICKNESS)
sc.Line([0, LEADER_LINE_SEGMENT_LENGTH, 0],
[LEADER_LINE_SEGMENT_LENGTH, LEADER_LINE_SEGMENT_LENGTH * VERTICAL_MULT, 0],
color=cl.yellow, thickness=LEADER_LINE_THICKNESS)
# Shift text to the end of the leader line with some offset
with sc.Transform(transform=sc.Matrix44.get_translation_matrix(
LEADER_LINE_SEGMENT_LENGTH + HORIZ_TEXT_OFFSET,
LEADER_LINE_SEGMENT_LENGTH * VERTICAL_MULT,
0)):
with sc.Transform(scale_to=sc.Space.SCREEN):
# Offset each Label vertically in screen space
with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, LINE1_OFFSET, 0)):
sc.Label(f"Path: {self.model.get_item('name')}",
alignment=ui.Alignment.LEFT_BOTTOM)
with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, LINE2_OFFSET, 0)):
sc.Label(f"Material: {self.model.get_item('material')}",
alignment=ui.Alignment.LEFT_TOP)
def on_model_updated(self, item):
# Regenerate the manipulator
self.invalidate()
| 3,114 | Python | 44.808823 | 108 | 0.621387 |
NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/docs/CHANGELOG.md | # Changelog
omni.example.ui_scene.object_info
## [1.0.0] - 2022-5-1
### Added
- The initial version
| 102 | Markdown | 11.874999 | 33 | 0.666667 |
NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.object_info/docs/README.md | # Object Info (omni.example.ui_scene.object_info)
## Overview
This Extension displays the selected prim's Path and Type.
## [Tutorial](../Tutorial/object_info.tutorial.md)
This extension sample also includes a step-by-step tutorial to accelerate your growth as you learn to build your own Omniverse Kit extensions.
Learn how to create an extension from the Extension Manager in Omniverse Code, set up your files, and use Omniverse's Library. Additionally, the tutorial has a `Final Scripts` folder to use as a reference as you go along.
[Get started with the tutorial here.](../Tutorial/object_info.tutorial.md)
## Usage
Once the extension is enabled in the *Extension Manager*, go to your *Viewport* and right-click to create a prim - such as a cube, sphere, cyclinder, etc. Then, left-click/select it to view the Object Info.
| 1,003 | Markdown | 51.842103 | 222 | 0.768694 |
NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/Tutorial/object.info.widget.tutorial.md | # How to make a Object Info Widget Extension
This guide will provide you with a starting point for displaying Object Info and nesting these modules into a Widget. A Widget is a useful utility in `Omniverse Kit` that can be used to add features such as buttons and sliders.
# Learning Objectives
In this guide you will learn how to:
- Create a Widget Extension
- Use Omniverse UI Framework
- Create a label
- (optional) Create a toggle button feature
- (optional) Create a slider
# Prerequisites
It is recommended that you have completed the following:
- [Extension Enviroment Tutorial](https://github.com/NVIDIA-Omniverse/ExtensionEnvironmentTutorial)
- [How to make an extension to display Object Info](../../omni.example.ui_scene.object_info/Tutorial/object_info.tutorial.md)
# Step 1: Create a Widget Module
In this series of steps, you will be setting up your Extension to create a module needed for a widget.
## Step 1.1: Clone Slider Tutorial Branch
Clone the `slider-tutorial-start` branch of the `kit-extension-sample-ui-scene` [github respositiory](https://github.com/NVIDIA-Omniverse/kit-extension-sample-ui-scene) to get the assets needed for this hands-on lab.
## Step 1.2: Add Extension Path to Extension Manager
Open the `Extensions Manager` in `Omniverse Code`
Select gear icon to display `Extension Search Paths`.
Use the <span style="color:green">green</span> :heavy_plus_sign: to add the path to `exts/slider-tutorial-start` from the cloned directory.
:memo: Check that the `UI Scene Object Info` Extension is enabled in the `Extensions Manager` and working by creating a new primitive in the `Viewport` and selecting it, the object's path and info should be displayed above the object.
## Step 1.3 Open VS Code with Shortcut
Open `VS Code` directly from the `Extension Manager`
:bulb:If you would like to know more about how to create the modules for displaying Object Info, [check out the guide here.](../../omni.example.ui_scene.object_info/Tutorial/object_info.tutorial.md)
## Step 1.4: Create the Module
Create a new module called `object_info_widget.py` in the `exts` hierarchy that our other modules are located in.
This will be our widget module.
`object_info_widget.py` will be building off the Object Info modules provided for you. You will see these modules as `object_info_manipulator.py`, `object_info_model.py`, `viewport_scene.py`, and an updated `extension.py`.
:memo: Visual Studio Code (VS Code) is our preferred IDE, hence forth referred to throughout this guide.
## Step 1.5: Set up Widget Class
Inside of the `object_info_widget.py`, import `omni.ui` then create the `WidgetInfoManipulator` class to nest our functions. After, initialize our methods, as so:
```python
from omni.ui import scene as sc
from omni.ui import color as cl
import omni.ui as ui
class WidgetInfoManipulator(sc.Manipulator):
def __init__(self, **kwargs) -> None:
super().__init__(**kwargs)
self.destroy()
def destroy(self):
self._root = None
self._name_label = None
```
This widget will house our widget info to make the information contrasted in the viewport and add other utilities later on.
You will accomplish this by creating a box for the label with a background color.
## Step 1.6: Build the widget
Let's define this as `on_build_widgets` and use the `Omniverse UI Framework` to create the label for this widget in a `ZStack`. [See here for more documentation on Omniverse UI Framework](https://docs.omniverse.nvidia.com/py/kit/source/extensions/omni.ui/docs/index.html).
```python
...
def on_build_widgets(self):
with ui.ZStack():
```
Once you have established the UI layout, you can create the background for the widget using `ui.Rectangle` and set the border attributes and background color. You can then create the `ui.Label` and set its alignment, as so:
```python
...
def on_build_widgets(self):
with ui.ZStack():
ui.Rectangle(style={
"background_color": cl(0.2),
"border_color": cl(0.7),
"border_width": 2,
"border_radius": 4,
})
self._name_label = ui.Label("", height=0, alignment=ui.Alignment.CENTER)
```
## Step 1.7: Create Manipulator Functions
With a Manipulator, you need to define an `on_build` function.
This function is called when the model is changed so that the widget is rebuilt. [You can find more information about the Manipulator here.](https://docs.omniverse.nvidia.com/py/kit/source/extensions/omni.ui.scene/docs/Manipulator.html)
```python
...
self.on_model_updated(None)
def on_build(self):
"""Called when the model is changed and rebuilds the whole slider"""
self._root = sc.Transform(visible=False)
with self._root:
with sc.Transform(scale_to=sc.Space.SCREEN):
with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, 100, 0)):
self._widget = sc.Widget(500, 150, update_policy=sc.Widget.UpdatePolicy.ON_MOUSE_HOVERED)
self._widget.frame.set_build_fn(self.on_build_widgets)
```
Now define `on_model_updated()` that was called above. In this function you need to establish what happens if nothing is selected, when to update the prims, and when to update the prim name, as so:
```python
...
def on_model_updated(self, _):
# if you don't have selection then show nothing
if not self.model or not self.model.get_item("name"):
self._root.visible = False
return
# Update the shapes
position = self.model.get_as_floats(self.model.get_item("position"))
if self._root:
self._root.transform = sc.Matrix44.get_translation_matrix(*position)
self._root.visible = True
# Update the shape name
if self._name_label:
self._name_label.text = f"Prim:{self.model.get_item('name')}"
```
<details>
<summary>Click here for the end code of <b>widget_info_manipulator.py</b></summary>
```python
from omni.ui import scene as sc
from omni.ui import color as cl
import omni.ui as ui
class WidgetInfoManipulator(sc.Manipulator):
def __init__(self, **kwargs) -> None:
super().__init__(**kwargs)
self.destroy()
def destroy(self):
self._root = None
self._name_label = None
def on_build_widgets(self):
with ui.ZStack():
ui.Rectangle(style={
"background_color": cl(0.2),
"border_color": cl(0.7),
"border_width": 2,
"border_radius": 4,
})
self._name_label = ui.Label("", height=0, alignment=ui.Alignment.CENTER)
self.on_model_updated(None)
def on_build(self):
"""Called when the model is changed and rebuilds the whole slider"""
self._root = sc.Transform(visible=False)
with self._root:
with sc.Transform(scale_to=sc.Space.SCREEN):
with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, 100, 0)):
self._widget = sc.Widget(500, 150, update_policy=sc.Widget.UpdatePolicy.ON_MOUSE_HOVERED)
self._widget.frame.set_build_fn(self.on_build_widgets)
def on_model_updated(self, _):
# if you don't have selection then show nothing
if not self.model or not self.model.get_item("name"):
self._root.visible = False
return
# Update the shapes
position = self.model.get_as_floats(self.model.get_item("position"))
if self._root:
self._root.transform = sc.Matrix44.get_translation_matrix(*position)
self._root.visible = True
# Update the shape name
if self._name_label:
self._name_label.text = f"Prim:{self.model.get_item('name')}"
```
</details>
# Step 2: Update Viewport and Extension
Now that you have created a new module, it is important for us to bring this information into `viewport_scene.py` and update `extension.py` to reflect these new changes.
## Step 2.1: Import Widget Info
Begin by updating `viewport_scene.py` and importing `WidgetInfoManipulator` at the top of the file with the other imports.
```python
from omni.ui import scene as sc
import omni.ui as ui
from .object_info_manipulator import ObjInfoManipulator
from .object_info_model import ObjInfoModel
# NEW
from .widget_info_manipulator import WidgetInfoManipulator
# END NEW
```
### Step 2.2: Add Display Widget
Inside the `ViewportSceneInfo` class, you will add a `display_widget` parameter to `__init__()`:
```python
...
class ViewportSceneInfo():
# NEW PARAMETER: display_widget
def __init__(self, viewport_window, ext_id, display_widget) -> None:
self.scene_view = None
self.viewport_window = viewport_window
...
```
### Step 2.3: Use `display_widget`
Use `display_widget` to control whether to show `WidgetInfoManipulator` or `ObjInfoManipulator` as so:
```python
...
class ViewportSceneInfo():
# NEW PARAMETER: display_widget
def __init__(self, viewport_window, ext_id, display_widget) -> None:
self.scene_view = None
self.viewport_window = viewport_window
with self.viewport_window.get_frame(ext_id):
self.scene_view = sc.SceneView()
with self.scene_view.scene:
# NEW
if display_widget:
WidgetInfoManipulator(model=ObjInfoModel())
else:
# END NEW
ObjInfoManipulator(model=ObjInfoModel())
...
```
<details>
<summary>Click here for the updated <b>viewport_scene.py</b></summary>
```python
from omni.ui import scene as sc
import omni.ui as ui
from .object_info_manipulator import ObjInfoManipulator
from .object_info_model import ObjInfoModel
from .widget_info_manipulator import WidgetInfoManipulator
class ViewportSceneInfo():
def __init__(self, viewport_window, ext_id, display_widget) -> None:
self.scene_view = None
self.viewport_window = viewport_window
with self.viewport_window.get_frame(ext_id):
self.scene_view = sc.SceneView()
with self.scene_view.scene:
if display_widget:
WidgetInfoManipulator(model=ObjInfoModel())
else:
ObjInfoManipulator(model=ObjInfoModel())
self.viewport_window.viewport_api.add_scene_view(self.scene_view)
def __del__(self):
self.destroy()
def destroy(self):
if self.scene_view:
self.scene_view.scene.clear()
if self.viewport_window:
self.viewport_window.viewport_api.remove_scene_view(self.scene_view)
self.viewport_window = None
self.scene_view = None
```
</details>
<br>
## Step 3: Update `extension.py`
Now that you have created the widget and passed it into the viewport, you need to call this in the `extension.py` module for it to function.
### Step 3.1: Edit the Class Name
Start by changing the class name of `extension.py` from `MyExtension` to something more descriptive, like `ObjectInfoWidget`:
```python
...
## Replace ##
class MyExtension(omni.ext.IExt):
## With ##
class ObjectInfoWidget(omni.ext.IExt):
## END ##
def __init__(self) -> None:
super().__init__()
self.viewportScene = None
```
### Step 3.2: Pass the Parameter
Pass the new parameter in `on_startup()` as follows:
```python
...
def on_startup(self, ext_id):
#Grab a reference to the viewport
viewport_window = get_active_viewport_window()
# NEW PARAMETER PASSED
self.viewportScene = ViewportSceneInfo(viewport_window, ext_id, True)
...
```
<details>
<summary>Click here for the updated <b>extension.py</b> module </summary>
```python
import omni.ext
import omni.ui as ui
from omni.ui import scene as sc
from omni.ui import color as cl
from omni.kit.viewport.utility import get_active_viewport_window
from .viewport_scene import ViewportSceneInfo
class ObjectInfoWidget(omni.ext.IExt):
def __init__(self) -> None:
super().__init__()
self.viewportScene = None
def on_startup(self, ext_id):
#Grab a reference to the viewport
viewport_window = get_active_viewport_window()
self.viewportScene = ViewportSceneInfo(viewport_window, ext_id, True)
def on_shutdown(self):
if self.viewportScene:
self.viewportScene.destroy()
self.viewportScene = None
```
</details>
Excellent, You should now see these updates in Omniverse Code at this point.
## Step 4: Create a Toggle Button
In this section you will create a button that enables us to turn the object info widget on and off in the viewport. This feature is built in `extension.py` and is an optional section. If you do not want the toggle button, feel free to skip this part.
## Step 4.1: Add the button to `extension.py`
First define new properties in `extension.py` for `viewport_scene`,`widget_view`, and `ext_id`, as follows:
```python
...
class ObjectInfoWidget(omni.ext.IExt):
def __init__(self) -> None:
super().__init__()
# NEW VALUES
self.viewport_scene = None
self.widget_view_on = False
self.ext_id = None
# END NEW
```
### Step 4.2 Update Startup
Update `on_startup()` to create a new window for the button.
### Step 4.3 Passs `self.widget_view_on` into ViewportSceneInfo
```python
...
def on_startup(self, ext_id):
# NEW: Window with a label and button to toggle the widget / info
self.window = ui.Window("Toggle Widget View", width=300, height=300)
self.ext_id = ext_id
with self.window.frame:
with ui.HStack(height=0):
ui.Label("Toggle Widget View", alignment=ui.Alignment.CENTER_TOP, style={"margin": 5})
ui.Button("Toggle Widget", clicked_fn=self.toggle_view)
# END NEW
#Grab a reference to the viewport
viewport_window = get_active_viewport_window()
# NEW: passed in our new value self.widget_view_on
self.viewport_scene = ViewportSceneInfo(viewport_window, ext_id, self.widget_view_on)
...
```
### Step 4.4 Create the Toggle
Define `toggle_view()`.
This function will be bound to the button's clicked function, thus requiring an `if` statement to check when the button is on/off:
```python
...
# NEW: New function that is binded to our button's clicked_fn
def toggle_view(self):
self.reset_viewport_scene()
self.widget_view_on = not self.widget_view_on
if self.widget_view_on:
self._toggle_button.text = "Toggle Widget Info Off"
else:
self._toggle_button.text = "Toggle Widget Info On"
viewport_window = get_active_viewport_window()
self.viewport_scene = ViewportSceneInfo(viewport_window, self.ext_id, self.widget_view_on)
# END NEW
...
```
### Step 4.5: Create Reset Viewport Scene Function
This button is used in more than one spot, therefore define `reset_viewport_scene()`.
This function will purge our viewport scene when the button is reset.
```python
...
# NEW: New function for resetting the viewport scene (since this will be used in more than one spot)
def reset_viewport_scene(self):
if self.viewport_scene:
self.viewport_scene.destroy()
self.viewport_scene = None
# END NEW
...
```
### Step 4.6: Reset Viewport Scene on Shutdown
Update `on_shutdown` to remove the `viewport_scene` parameters you moved into the reset function and then call that function.
```python
...
def on_shutdown(self):
# NEW: Moved code block to a function and call it
self.reset_viewport_scene()
# END NEW
```
<br>
<details>
<summary>Click here for the updated <b>extension.py</b> module </summary>
```python
import omni.ext
import omni.ui as ui
from omni.kit.viewport.utility import get_active_viewport_window
from .viewport_scene import ViewportSceneInfo
class ObjectInfoWidget(omni.ext.IExt):
def __init__(self) -> None:
super().__init__()
self.viewport_scene = None
self.widget_view_on = False
self.ext_id = None
def on_startup(self, ext_id):
self.window = ui.Window("Toggle Widget View", width=300, height=300)
self.ext_id = ext_id
with self.window.frame:
with ui.HStack(height=0):
ui.Label("Toggle Widget View", alignment=ui.Alignment.CENTER_TOP, style={"margin": 5})
ui.Button("Toggle Widget", clicked_fn=self.toggle_view)
#Grab a reference to the viewport
viewport_window = get_active_viewport_window()
self.viewport_scene = ViewportSceneInfo(viewport_window, ext_id, self.widget_view_on)
def toggle_view(self):
self.reset_viewport_scene()
self.widget_view_on = not self.widget_view_on
if self.widget_view_on:
self._toggle_button.text = "Toggle Widget Info Off"
else:
self._toggle_button.text = "Toggle Widget Info On"
viewport_window = get_active_viewport_window()
self.viewport_scene = ViewportSceneInfo(viewport_window, self.ext_id, self.widget_view_on)
def reset_viewport_scene(self):
if self.viewport_scene:
self.viewport_scene.destroy()
self.viewport_scene = None
def on_shutdown(self):
self.reset_viewport_scene()
```
</details>
<br>
Here is what you should see in the viewport at this point:
<br>
## Step 5: Add a Slider
In this step, you will be adding a slider to the widget. This slider will change the scale of the object. This is an optional step and may be skipped as it is just to showcase a simple addition of what a widget can do. For a more complex slider, [check out the guide to `Slider Manipulator` here.](https://github.com/NVIDIA-Omniverse/kit-extension-sample-ui-scene/blob/main/exts/omni.example.ui_scene.slider_manipulator/Tutorial/slider_Manipulator_Tutorial.md)
### Step 5.1: Add to `widget_info_manipulator.py`
Use `omni.ui` to build the framework for the slider in the function `on_build_widgets()`.
This slider is an optional feature to the widget but is a great way to add utility.
```python
...
def on_build_widgets(self):
with ui.ZStack():
ui.Rectangle(style={
"background_color": cl(0.2),
"border_color": cl(0.7),
"border_width": 2,
"border_radius": 4,
})
# NEW: Adding the Slider to the widget in the scene
with ui.VStack(style={"font_size": 24}):
self._name_label = ui.Label("", height=0, alignment=ui.Alignment.CENTER)
# setup some model, just for simple demonstration here
self._slider_model = ui.SimpleFloatModel()
ui.Spacer(height=5)
with ui.HStack():
ui.Spacer(width=10)
ui.Label("scale", height=0, width=0)
ui.Spacer(width=5)
ui.FloatSlider(self._slider_model)
ui.Spacer(width=5)
ui.Spacer(height=5)
# END NEW
self.on_model_updated(None)
...
```
### Step 5.2: Update the Scale with a Slider Function
Add a new function that will scale the model when the slider is dragged.
Define this function after `on_model_updated` and name it `update_scale`.
```python
...
def on_model_updated(self, _):
# if you don't have selection then show nothing
if not self.model or not self.model.get_item("name"):
self._root.visible = False
return
# Update the shapes
position = self.model.get_as_floats(self.model.get_item("position"))
if self._root:
self._root.transform = sc.Matrix44.get_translation_matrix(*position)
self._root.visible = True
# NEW
# Update the slider
def update_scale(prim_name, value):
print(f"changing scale of {prim_name}, {value}")
stage = self.model.usd_context.get_stage()
prim = stage.GetPrimAtPath(self.model.current_path)
scale = prim.GetAttribute("xformOp:scale")
scale.Set(Gf.Vec3d(value, value, value))
if self._slider_model:
self._slider_subscription = None
self._slider_model.as_float = 1.0
self._slider_subscription = self._slider_model.subscribe_value_changed_fn(
lambda m, p=self.model.get_item("name"): update_scale(p, m.as_float)
)
# END NEW
...
```
<details>
<summary>Click here for the updated <b>widget_info_manipulator.py</b> module </summary>
```python
from omni.ui import scene as sc
from omni.ui import color as cl
import omni.ui as ui
from pxr import Gf
class WidgetInfoManipulator(sc.Manipulator):
def __init__(self, **kwargs) -> None:
super().__init__(**kwargs)
self.destroy()
def destroy(self):
self._root = None
self._name_label = None
self._slider_model = None
def on_build_widgets(self):
with ui.ZStack():
ui.Rectangle(style={
"background_color": cl(0.2),
"border_color": cl(0.7),
"border_width": 2,
"border_radius": 4,
})
with ui.VStack():
self._name_label = ui.Label("", height=0, alignment=ui.Alignment.CENTER)
# setup some model, just for simple demonstration here
self._slider_model = ui.SimpleFloatModel()
ui.Spacer(height=5)
with ui.HStack():
ui.Spacer(width=10)
ui.Label("scale", height=0, width=0)
ui.Spacer(width=5)
ui.FloatSlider(self._slider_model)
ui.Spacer(width=5)
ui.Spacer(height=5)
self.on_model_updated(None)
def on_build(self):
"""Called when the model is changed and rebuilds the whole slider"""
self._root = sc.Transform(visibile=False)
with self._root:
with sc.Transform(scale_to=sc.Space.SCREEN):
with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, 100, 0)):
self._widget = sc.Widget(500, 150, update_policy=sc.Widget.UpdatePolicy.ON_MOUSE_HOVERED)
self._widget.frame.set_build_fn(self.on_build_widgets)
def on_model_updated(self, _):
# if you don't have selection then show nothing
if not self.model or not self.model.get_item("name"):
self._root.visible = False
return
# Update the shapes
position = self.model.get_as_floats(self.model.get_item("position"))
if self._root:
self._root.transform = sc.Matrix44.get_translation_matrix(*position)
self._root.visible = True
# Update the slider
def update_scale(prim_name, value):
print(f"changing scale of {prim_name}, {value}")
stage = self.model.usd_context.get_stage()
prim = stage.GetPrimAtPath(self.model.current_path)
scale = prim.GetAttribute("xformOp:scale")
scale.Set(Gf.Vec3d(value, value, value))
if self._slider_model:
self._slider_subscription = None
self._slider_model.as_float = 1.0
self._slider_subscription = self._slider_model.subscribe_value_changed_fn(
lambda m, p=self.model.get_item("name"): update_scale(p, m.as_float)
)
# Update the shape name
if self._name_label:
self._name_label.text = f"Prim:{self.model.get_item('name')}"
```
</details>
<br>
Here is what is created in the viewport of Omniverse Code:
<br>
# Congratulations!
You have successfully created a Widget Info Extension! | 24,282 | Markdown | 33.201408 | 460 | 0.640763 |
NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/omni/example/ui_scene/widget_info/widget_info_manipulator.py | ## Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
##
## NVIDIA CORPORATION and its licensors retain all intellectual property
## and proprietary rights in and to this software, related documentation
## and any modifications thereto. Any use, reproduction, disclosure or
## distribution of this software and related documentation without an express
## license agreement from NVIDIA CORPORATION is strictly prohibited.
##
__all__ = ["WidgetInfoManipulator"]
from omni.ui import color as cl
from omni.ui import scene as sc
import omni.ui as ui
class _ViewportLegacyDisableSelection:
"""Disables selection in the Viewport Legacy"""
def __init__(self):
self._focused_windows = None
focused_windows = []
try:
# For some reason is_focused may return False, when a Window is definitely in fact is the focused window!
# And there's no good solution to this when mutliple Viewport-1 instances are open; so we just have to
# operate on all Viewports for a given usd_context.
import omni.kit.viewport_legacy as vp
vpi = vp.acquire_viewport_interface()
for instance in vpi.get_instance_list():
window = vpi.get_viewport_window(instance)
if not window:
continue
focused_windows.append(window)
if focused_windows:
self._focused_windows = focused_windows
for window in self._focused_windows:
# Disable the selection_rect, but enable_picking for snapping
window.disable_selection_rect(True)
except Exception:
pass
class _DragPrioritize(sc.GestureManager):
"""Refuses preventing _DragGesture."""
def can_be_prevented(self, gesture):
# Never prevent in the middle of drag
return gesture.state != sc.GestureState.CHANGED
def should_prevent(self, gesture, preventer):
if preventer.state == sc.GestureState.BEGAN or preventer.state == sc.GestureState.CHANGED:
return True
class _DragGesture(sc.DragGesture):
""""Gesture to disable rectangle selection in the viewport legacy"""
def __init__(self):
super().__init__(manager=_DragPrioritize())
def on_began(self):
# When the user drags the slider, we don't want to see the selection
# rect. In Viewport Next, it works well automatically because the
# selection rect is a manipulator with its gesture, and we add the
# slider manipulator to the same SceneView.
# In Viewport Legacy, the selection rect is not a manipulator. Thus it's
# not disabled automatically, and we need to disable it with the code.
self.__disable_selection = _ViewportLegacyDisableSelection()
def on_ended(self):
# This re-enables the selection in the Viewport Legacy
self.__disable_selection = None
class WidgetInfoManipulator(sc.Manipulator):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.destroy()
self._radius = 2
self._distance_to_top = 5
self._thickness = 2
self._radius_hovered = 20
def destroy(self):
self._root = None
self._slider_subscription = None
self._slider_model = None
self._name_label = None
def _on_build_widgets(self):
with ui.ZStack():
ui.Rectangle(
style={
"background_color": cl(0.2),
"border_color": cl(0.7),
"border_width": 2,
"border_radius": 4,
}
)
with ui.VStack(style={"font_size": 24}):
ui.Spacer(height=4)
with ui.ZStack(style={"margin": 1}, height=30):
ui.Rectangle(
style={
"background_color": cl(0.0),
}
)
ui.Line(style={"color": cl(0.7), "border_width": 2}, alignment=ui.Alignment.BOTTOM)
ui.Label("Hello world, I am a scene.Widget!", height=0, alignment=ui.Alignment.CENTER)
ui.Spacer(height=4)
self._name_label = ui.Label("", height=0, alignment=ui.Alignment.CENTER)
# setup some model, just for simple demonstration here
self._slider_model = ui.SimpleFloatModel()
ui.Spacer(height=10)
with ui.HStack():
ui.Spacer(width=10)
ui.Label("scale", height=0, width=0)
ui.Spacer(width=5)
ui.FloatSlider(self._slider_model)
ui.Spacer(width=10)
ui.Spacer(height=4)
ui.Spacer()
self.on_model_updated(None)
# Additional gesture that prevents Viewport Legacy selection
self._widget.gestures += [_DragGesture()]
def on_build(self):
"""Called when the model is chenged and rebuilds the whole slider"""
self._root = sc.Transform(visible=False)
with self._root:
with sc.Transform(scale_to=sc.Space.SCREEN):
with sc.Transform(transform=sc.Matrix44.get_translation_matrix(0, 100, 0)):
# Label
with sc.Transform(look_at=sc.Transform.LookAt.CAMERA):
self._widget = sc.Widget(500, 150, update_policy=sc.Widget.UpdatePolicy.ON_MOUSE_HOVERED)
self._widget.frame.set_build_fn(self._on_build_widgets)
def on_model_updated(self, _):
# if we don't have selection then show nothing
if not self.model or not self.model.get_item("name"):
self._root.visible = False
return
# Update the shapes
position = self.model.get_as_floats(self.model.get_item("position"))
self._root.transform = sc.Matrix44.get_translation_matrix(*position)
self._root.visible = True
# Update the slider
def update_scale(prim_name, value):
print(f"changing scale of {prim_name}, {value}")
if self._slider_model:
self._slider_subscription = None
self._slider_model.as_float = 1.0
self._slider_subscription = self._slider_model.subscribe_value_changed_fn(
lambda m, p=self.model.get_item("name"): update_scale(p, m.as_float)
)
# Update the shape name
if self._name_label:
self._name_label.text = f"Prim:{self.model.get_item('name')}"
| 6,631 | Python | 38.011764 | 117 | 0.58513 |
NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/omni/example/ui_scene/widget_info/widget_info_extension.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
__all__ = ["WidgetInfoExtension"]
from .widget_info_scene import WidgetInfoScene
from omni.kit.viewport.utility import get_active_viewport_window
import carb
import omni.ext
class WidgetInfoExtension(omni.ext.IExt):
"""The entry point to the extension"""
def on_startup(self, ext_id):
# Get the active (which at startup is the default Viewport)
viewport_window = get_active_viewport_window()
# Issue an error if there is no Viewport
if not viewport_window:
carb.log_warn(f"No Viewport Window to add {ext_id} scene to")
self._widget_info_viewport = None
return
# Build out the scene
self._widget_info_viewport = WidgetInfoScene(viewport_window, ext_id)
def on_shutdown(self):
if self._widget_info_viewport:
self._widget_info_viewport.destroy()
self._widget_info_viewport = None
| 1,340 | Python | 35.243242 | 77 | 0.706716 |
NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/omni/example/ui_scene/widget_info/tests/test_info.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
__all__ = ["TestInfo"]
from omni.example.ui_scene.widget_info.widget_info_manipulator import WidgetInfoManipulator
from omni.ui import scene as sc
from omni.ui.tests.test_base import OmniUiTest
from pathlib import Path
import omni.kit.app
import omni.kit.test
EXTENSION_FOLDER_PATH = Path(omni.kit.app.get_app().get_extension_manager().get_extension_path_by_module(__name__))
TEST_DATA_PATH = EXTENSION_FOLDER_PATH.joinpath("data/tests")
class WidgetInfoTestModelItem(sc.AbstractManipulatorItem):
pass
class WidgetInfoTestModel(sc.AbstractManipulatorModel):
def __init__(self):
super().__init__()
self.position = WidgetInfoTestModelItem()
def get_item(self, identifier):
if identifier == "position":
return self.position
if identifier == "name":
return "Name"
if identifier == "material":
return "Material"
def get_as_floats(self, item):
if item == self.position:
return [0, 0, 0]
class TestInfo(OmniUiTest):
async def test_general(self):
"""Testing general look of the item"""
window = await self.create_test_window(width=256, height=256)
with window.frame:
# Camera matrices
projection = [1e-2, 0, 0, 0]
projection += [0, 1e-2, 0, 0]
projection += [0, 0, -2e-7, 0]
projection += [0, 0, 1, 1]
view = sc.Matrix44.get_translation_matrix(0, 0, 0)
scene_view = sc.SceneView(sc.CameraModel(projection, view))
with scene_view.scene:
# The manipulator
model = WidgetInfoTestModel()
WidgetInfoManipulator(model=model)
await omni.kit.app.get_app().next_update_async()
model._item_changed(None)
for _ in range(10):
await omni.kit.app.get_app().next_update_async()
await self.finalize_test(threshold=100, golden_img_dir=TEST_DATA_PATH, golden_img_name="general.png")
| 2,430 | Python | 32.763888 | 115 | 0.653909 |
NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/docs/CHANGELOG.md | # Changelog
omni.ui.scene.object_info
## [1.0.1] - 2022-06-01
### Changed
- It doesn't recreate sc.Widget to avoid crash
## [1.0.0] - 2022-5-1
### Added
- The initial version
| 178 | Markdown | 13.916666 | 46 | 0.646067 |
NVIDIA-Omniverse/kit-extension-sample-ui-scene/exts/omni.example.ui_scene.widget_info/docs/README.md | # Widget Info (omni.example.ui_scene.widget_info)
## Overview
In the example, we show how to leverage `ui.scene.Widget` item to create a
`ui.Widget` that is in 3D space. The Widget can have any type of `omni.ui` element,
including being interactive, as shown with the slider.
## [Tutorial](https://github.com/NVIDIA-Omniverse/kit-extension-sample-ui-scene/blob/main/exts/omni.example.ui_scene.widget_info/Tutorial/object.info.widget.tutorial.md)
This extension sample also includes a step-by-step tutorial to accelerate your growth as you learn to build your own Omniverse Kit extensions.
In the tutorial you will learn how to build from existing modules to create a Widget. A Widget is a useful utility in `Omniverse Kit` that can be used to add features such as buttons and sliders.
[Get started with the tutorial here.](https://github.com/NVIDIA-Omniverse/kit-extension-sample-ui-scene/blob/main/exts/omni.example.ui_scene.widget_info/Tutorial/object.info.widget.tutorial.md)
## Usage
Once the extension is enabled in the `Extension Manager`, go to your `Viewport` and right-click to create a primitive - such as a cube, sphere, cyclinder, etc. Then, left-click/select the primitive to view the Object Info. The Path and Type of the Object will be displayed inside of a Widget.
## The extension showcases view concepts
Similarly to the other `ui.scene` example it shows you how to set up the viewport
scene in `viewport_scene.py`. Then there is the Manipulator object that manages
the presentation of the item `widget_info_manipulator.py`. Finally, the
`widget_info_model.py` contains the model that connects the world with the
manipulator.
## Overlaying with the viewport
We use `sc.Manipulator` with `sc.Widget` to draw `omni.ui` widgets in 3D view.
To show it in the viewport, we overlay `sc.SceneView` with our `sc.Manipulator`
on top of the viewport window.
```python
from omni.kit.viewport.utility import get_active_viewport_window
viewport_window = get_active_viewport_window()
# Create a unique frame for our SceneView
with viewport_window.get_frame(ext_id):
# Create a default SceneView (it has a default camera-model)
self._scene_view = sc.SceneView()
# Add the manipulator into the SceneView's scene
with self._scene_view.scene:
WidgetInfoManipulator(model=WidgetInfoModel())
```
To synchronize the projection and view matrices, `omni.kit.viewport.utility` has
the method `add_scene_view`, which replaces the camera model, and the
manipulator visually looks like it's in the main viewport.
```python
# Register the SceneView with the Viewport to get projection and view updates
viewport_window.viewport_api.add_scene_view(self._scene_view)
```
| 2,840 | Markdown | 47.982758 | 292 | 0.772183 |
NVIDIA-Omniverse/IsaacSim-Automator/CONTRUBUTING.md |
# Isaac Automation OSS Contribution Rules
- [Development Tips](#development-tips)
- [Updating Pre-Built VM Images](#updating-pre-built-vm-images)
- [Azure](#azure)
- [Issue Tracking](#issue-tracking)
- [Coding Guidelines](#coding-guidelines)
- [Formatting and Linting](#formatting-and-linting)
- [General](#general)
- [Pull Requests](#pull-requests)
- [Signing Your Work](#signing-your-work)
## Development Tips
### Updating Pre-Built VM Images
Pre-built VM images are created using [Packer](https://www.packer.io/) and can be used to accelerate deployment of the app instances by skipping the time-consuming installation and configuration steps. To use pre-built images, add `--from-image` flag to the `deploy-*` commands.
```sh
#### AWS
Refer to [../src/packer/aws/README.md](src/packer/aws/README.md) for pre-requisites. Then:
```sh
packer build -force /app/src/packer/aws/isaac
...
```
#### Azure
Refer to [../src/packer/azure/README.md](src/packer/azure/README.md) for pre-requisites. Then:
```sh
packer build -force /app/src/packer/azure/isaac
...
```
## Issue Tracking
- All enhancement, bugfix, or change requests must begin with the creation of a [Isaac Automation Issue Request](https://github.com/nvidia/Isaac-Automation/issues).
- The issue request must be reviewed by Isaac Automation engineers and approved prior to code review.
## Coding Guidelines
Please follow the existing conventions in the relevant file, submodule, module, and project when you add new code or when you extend/fix existing functionality.
### Formatting and Linting
- Make sure your editor is using the included `.editorconfig` file for indentation, line endings, etc.
- Use the following formatters and linting tools for the respective languages:
- Python: [black](<https://github.com/psf/black>), [isort](<https://github.com/pycqa/isort/>), [flake8](https://github.com/pycqa/flake8)
- Terraform: [terraform fmt](<https://www.terraform.io/docs/commands/fmt.html>)
- Ansible: [ansible-lint](<https://github.com/ansible/ansible-lint>)
- Packer: [packer fmt](<https://www.packer.io/docs/commands/fmt.html>)
Project includes settings and recommended extensions for [Visual Studio Code](https://code.visualstudio.com/) which make it easier following the formatting linting and guidelines.
### General
- Avoid introducing unnecessary complexity into existing code so that maintainability and readability are preserved.
- Try to keep pull requests (PRs) as concise as possible:
- Avoid committing commented-out code.
- Wherever possible, each PR should address a single concern. If there are several otherwise-unrelated things that should be fixed to reach a desired endpoint, our recommendation is to open several PRs and indicate the dependencies in the description. The more complex the changes are in a single PR, the more time it will take to review those changes.
- Write commit titles using imperative mood and [these rules](https://chris.beams.io/posts/git-commit/), and reference the Issue number corresponding to the PR. Following is the recommended format for commit texts:
```text
#<Issue Number> - <Commit Title>
<Commit Body>
```
- All OSS components must contain accompanying documentation (READMEs) describing the functionality, dependencies, and known issues.
- Accompanying tests are highly desireable and recommended. If the test is not possible or not feasible to implement, please provide a sample usage information.
- Make sure that you can contribute your work to open source (no license and/or patent conflict is introduced by your code). You will need to [`sign`](#signing-your-work) your commit.
- Thanks in advance for your patience as we review your contributions; we do appreciate them!
## Pull Requests
Developer workflow for code contributions is as follows:
1. Developers must first [fork](https://help.github.com/en/articles/fork-a-repo) the [upstream](https://github.com/nvidia/Isaac-Automation) Isaac Automation OSS repository.
1. Git clone the forked repository and push changes to the personal fork.
```bash
git clone https://github.com/YOUR_USERNAME/YOUR_FORK.git Isaac-Automation
# Checkout the targeted branch and commit changes
# Push the commits to a branch on the fork (remote).
git push -u origin <local-branch>:<remote-branch>
```
1. Once the code changes are staged on the fork and ready for review, a [Pull Request](https://help.github.com/en/articles/about-pull-requests) (PR) can be [requested](https://help.github.com/en/articles/creating-a-pull-request) to merge the changes from a branch of the fork into a selected branch of upstream.
## Signing Your Work
- We require that all contributors "sign-off" on their commits. This certifies that the contribution is your original work, or you have rights to submit it under the same license, or a compatible license.
- Any contribution which contains commits that are not Signed-Off will not be accepted.
- To sign off on a commit you simply use the `--signoff` (or `-s`) option when committing your changes:
```bash
git commit -s -m "Add cool feature."
```
This will append the following to your commit message:
```text
Signed-off-by: Your Name <[email protected]>
```
- Full text of the DCO:
```text
Developer Certificate of Origin
Version 1.1
Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
1 Letterman Drive
Suite D4700
San Francisco, CA, 94129
Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
Developer's Certificate of Origin 1.1
By making a contribution to this project, I certify that:
(a) The contribution was created in whole or in part by me and I have the right to submit it under the open source license indicated in the file; or
(b) The contribution is based upon previous work that, to the best of my knowledge, is covered under an appropriate open source license and I have the right under that license to submit that work with modifications, whether created in whole or in part by me, under the same open source license (unless I am permitted to submit under a different license), as indicated in the file; or
(c) The contribution was provided directly to me by some other person who certified (a), (b) or (c) and I have not modified it.
(d) I understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information I submit with it, including my sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open source license(s) involved.
```
| 6,699 | Markdown | 44.578231 | 385 | 0.754292 |
NVIDIA-Omniverse/IsaacSim-Automator/README.md | # Isaac Sim Automator
- [Installation](#installation)
- [Installing Docker](#installing-docker)
- [Obtaining NGC API Key](#obtaining-ngc-api-key)
- [Building the Container](#building-the-container)
- [Usage](#usage)
- [Tip: Running the Automator Commands](#tip-running-the-automator-commands)
- [Deploying Isaac Sim](#deploying-isaac-sim)
- [AWS](#aws)
- [GCP](#gcp)
- [Azure](#azure)
- [Alibaba Cloud](#alibaba-cloud)
- [Connecting to Deployed Instances](#connecting-to-deployed-instances)
- [Running Applications](#running-applications)
- [Isaac Sim](#isaac-sim)
- [Shell in Isaac Sim Container](#shell-in-isaac-sim-container)
- [Omniverse Isaac Gym Environments](#omniverse-isaac-gym-environments)
- [Isaac Orbit](#isaac-orbit)
- [Mapped Folders](#mapped-folders)
- [Pausing and Resuming](#pausing-and-resuming)
- [Uploading Data](#uploading-data)
- [Downloading Data](#downloading-data)
- [Repairing](#repairing)
- [Destroying](#destroying)
This tool automates deployment of [Isaac Sim](https://developer.nvidia.com/isaac-sim) to public clouds.
## Installation
### Installing Docker
`docker` should be present on your system. Visit <https://docs.docker.com/engine/install/> for installation instructions.
### Obtaining NGC API Key
**NGC API Key** allows you to download docker images from <https://ngc.nvidia.com/>. Please prepare one or obtain it at <https://ngc.nvidia.com/setup/api-key>.
### Building the Container
Please enter the following command in the project root directory to build the container:
```sh
./build
```
This will build the Isaac Sim Automator container and tag it as `isa`.
## Usage
### Tip: Running the Automator Commands
There are two ways to run the automator commands:
1. First enter the automator container and then run the command inside the container:
```sh
# enter the automator container
./run
# inside container:
./someconnad
```
2. Simply prepend the command with `./run` like so:
```sh
./run ./somecommand <parameters>
```
for example:
```sh
./run ./deploy-aws
./run ./destroy my-deployment
```
### Deploying Isaac Sim
#### AWS
<details>
<a name="#aws-permissions"></a>
<summary>Enabling Access Permissions</summary>
You need _AmazonEC2FullAccess_ permissions enabled for your AWS user. You can enable those in [Identity and Access Management (IAM) Section](https://console.aws.amazon.com/iamv2/home#/home) in AWS console like so:
1. Go to <https://console.aws.amazon.com/iamv2/home#/home>
2. Click "Access Management" \> "Users" in the left menu
3. Search for your user name
4. Under "Permissions" tab click "Add permissions"
5. Choose "Attach existing policies directly"
6. Search for _AmazonEC2FullAccess_, check the box next to it, click "Next"
7. Click "Add permissions"
</details>
<details>
<a name="#aws-access-creds"></a>
<summary>Getting Access Credentials</summary>
You will need _AWS Access Key_ and _AWS Secret Key_ for an existing account. You can obtain those in <a href="https://console.aws.amazon.com/iamv2/home#/home">Identity and Access Management (IAM) Section</a> in the AWS console.
</details>
If yoou have completed the above steps or already have your permissions and credentials set up, run the following command in the project root directory:
```sh
# enter the automator container
./run
# inside container:
./deploy-aws
```
Tip: Run `./deploy-aws --help` to see more options.
#### GCP
```sh
# enter the automator container
./run
# inside container:
./deploy-gcp
```
Tip: Run `./deploy-gcp --help` to see more options.
#### Azure
If You Have Single Subscription:
```sh
# enter the automator container
./run
# inside container:
./deploy-azure
```
If You Have Multiple Subscriptions:
```sh
# enter the automator container
./run
# inside container:
az login # login
az account show --output table # list subscriptions
az account set --subscription "<subscription_name>"
./deploy-azure --no-login
```
Tip: Run `./deploy-azure --help` to see more options.
#### Alibaba Cloud
<details>
<a name="#alicloud-access-creds"></a>
<summary>Getting Access Credentials</summary>
You will need <i>Access Key</i> and <i>Secret Key</i> for an existing AliCloud account. You can obtain those in <a href="https://usercenter.console.aliyun.com/#/manage/ak">AccessKey Management</a> section in the Alibaba Cloud console.
</details>
Once you have prepared the access credentials, run the following command in the project root directory:
```sh
# enter the automator container
./run
# inside container:
./deploy-alicloud
```
Tip: Run `./deploy-alicloud --help` to see more options.
GPU-accelerated instances with NVIDIA A100, A10 and T4 GPUs are supported. You can find the complete list of instance types, availability and pricing at <https://www.alibabacloud.com/help/en/ecs/user-guide/gpu-accelerated-compute-optimized-and-vgpu-accelerated-instance-families-1>. Please note that vGPU instances are not supported.
### Connecting to Deployed Instances
Deployed Isaac Sim instances can be accessed via:
- SSH
- noVNC (browser-based VNC client)
- NoMachine (remote desktop client)
Look for the connection instructions at the end of the deploymnt command output. Additionally, this info is saved in `state/<deployment-name>/info.txt` file.
You can view available arguments with `--help` switch for the start scripts, in most cases you wouldn't need to change the defaults.
Tip: You can use `./connect <deployment-name>` helper command to connect to the deployed instance via ssh.
### Running Applications
To use installed applications, connect to the deployed instance using noVNC or NoMachine. You can find the connection instructions at the end of the deployment command output. Additionally, this info is saved in `state/<deployment-name>/info.txt` file.
#### Isaac Sim
Isaac Sim will be automatically started when cloud VM is deployed. Alternatively you can click "Isaac Sim" icon on the desktop or run the following command in the terminal on the deployed instance or launch it from the terminal as follows:
```sh
~/Desktop/isaacsim.sh
```
#### Shell in Isaac Sim Container
To get a shell inside Isaac Sim container, click "Isaac Sim Shell" icon on the desktop. Alternatively you can run the following command in the terminal on the deployed instance:
```sh
~/Desktop/isaacsim-shell.sh
```
#### Omniverse Isaac Gym Environments
[Omniverse Isaac Gym Reinforcement Learning Environments for Isaac Sim](https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs) ("Omni Isaac Gym Envs") can be pre-installed on the deployed Isaac instances.
To run Omniverse Isaac Gym Environments click "Omni Isaac Gym Envs" icon on the desktop or run the following command in the terminal:
```sh
~/Desktop/omni-isaac-gym-envs.sh
```
Default output directory (`/OmniIsaacGymEnvs/omniisaacgymenvs/runs`) in the OmniIsaacGymEnvs contaner will be linked to the default results directory (`/home/ubuntu/results`) on the deployed instance. You can download the contents of this directory to your local machine using `./download <deployment_name>` command.
Tip: To install a specific git reference of OmniIsaacGymEnvs, provide valid reference from <https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs> as a value of `--oige` parameter to the deployment command. For example, to install `devel` branch on an AWS instance, run the following command:
```sh
./deploy-aws --oige devel
```
#### Isaac Orbit
*Isaac Orbit is still experimental and intended for preview purposes only.*
[Isaac Orbit](https://isaac-orbit.github.io/orbit/index.html) can be pre-installed on the deployed Isaac instances.
To run Isaac Orbit click "Isaac Orbit" icon on the desktop or run the following command in the terminal:
```sh
~/Desktop/isaac-orbit.sh
```
Tip: To install a specific git reference of Isaac Orbit, provide valid git reference from <https://github.com/NVIDIA-Omniverse/Orbit> as a value of `--orbit` parameter to the deployment command. For example, to install `devel` branch on an AWS instance, run the following command:
```sh
./deploy-aws --orbit devel
```
### Mapped Folders
The following folders are mapped to the running Isaac Sim container by default (container paths may be different for specific applications):
- `/home/ubuntu/uploads` (host) --> `/uploads` (container) - user data uploaded to the deployment with `./upload` command or automatically from local `uploads/` folder
- `/home/ubuntu/results` (host) --> `/results` (container) - results of the applications run on the deployment, can be downloaded from the deployed machine with `./download` command
- `/home/ubuntu/workspace` (host) --> `/workspace` (container) - workspace folder, can be used to exchange data between the host and the container.
### Pausing and Resuming
You can stop and re-start instances to save on cloud costs. To do so, run the following commands:
```sh
# enter the automator container
./run
# inside container:
./stop <deployment-name>
./start <deployment-name>
```
### Uploading Data
You can upload user data from `uploads/` folder (in the project root) to the deployment by running the following command:
```sh
# enter the automator container
./run
# inside container:
./upload <deployment-name>
```
Data will be uploaded to `/home/ubuntu/uploads` directory by default to all deployed instances. You can change this by passing `--remote-dir` argument to the command. Run `./upload --help` to see more options.
### Downloading Data
You can download user data to `results/` folder (in the project root) from deployed instances by running the following command:
```sh
# enter the automator container
./run
# inside container:
./download <deployment-name>
```
Data will be downloaded from `/home/ubuntu/results` directory by default. You can change this by passing `--remote-dir` argument to the command. Run `./download --help` to see more options.
### Repairing
If for some reason the deployment cloud resouces or software configuration get corrupted, you can attempt to repair the deployment by running the following command:
```sh
# run both terraform and ansible
./repair <deployment-name>
# just run terraform to try fixing the cloud resources
./repair <deployment-name> --no-ansible
# just run ansible to try fixing the software configuration
./repair <deployment-name> --no-terraform
```
### Destroying
To destroy a deployment, run the following command:
```sh
# enter the automator container
./run
# inside container:
./destroy <deployment-name>
```
You will be prompted to enter the deployment name to destroy.
*Please note that information about the deployed cloud resources is stored in `state/` directory. Do not delete this directory ever.*
| 10,734 | Markdown | 33.187898 | 333 | 0.74483 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/isaac.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
- name: Wait for the instance to become available
hosts: isaac
gather_facts: false
tasks:
- wait_for_connection: timeout=300
tags:
# packer checks connectivity beforehand
- skip_in_image
- on_stop_start
- name: Deploy Isaac Sim
hosts: isaac
gather_facts: true
vars:
in_china: False
application_name: isaac
prompt_ansi_color: 36 # cyan
roles:
- isaac
handlers:
- include: roles/rdesktop/handlers/main.yml
- include: roles/system/handlers/main.yml
| 1,119 | YAML | 27.717948 | 74 | 0.726542 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/ovami.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
- name: Wait for the instance to become available
hosts: ovami
gather_facts: false
tasks:
- wait_for_connection: timeout=300
tags:
# packer checks connectivity beforehand
- skip_in_ovami
- name: OV AMI
hosts: ovami
gather_facts: true
vars:
in_china: False
application_name: ovami
prompt_ansi_color: 34 # bright blue
roles:
- ovami
handlers:
- include: roles/rdesktop/handlers/main.yml
- include: roles/system/handlers/main.yml
| 1,096 | YAML | 27.86842 | 74 | 0.729015 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/nvidia/tasks/ngc.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
- name: Install packages
apt: name={{ item }}
state=latest
update_cache=yes
with_items:
- unzip
- name: Download and extract NGC client
unarchive:
src: https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip
dest: /opt
remote_src: yes
- name: Add ngc cli to PATH
lineinfile:
dest: "/etc/profile.d/ngc-cli.sh"
line: export PATH="$PATH:/opt/ngc-cli"
create: yes
| 1,022 | YAML | 27.416666 | 74 | 0.716243 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/nvidia/tasks/nvidia-docker.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
- name: Add repository
shell: "rm -f /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg && rm -f /etc/apt/sources.list.d/nvidia-container-toolkit.list && distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list"
- name: Install nvidia-docker2
apt: name={{ item }}
state=latest
update_cache=yes
with_items:
- nvidia-docker2
- name: Restart docker service
service: >
name=docker
state=restarted
| 1,461 | YAML | 44.687499 | 610 | 0.742642 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/nvidia/tasks/nvidia-driver.azure.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
# @see https://learn.microsoft.com/en-us/azure/virtual-machines/linux/n-series-driver-setup#install-grid-drivers-on-nv-or-nvv3-series-vms
- name: Azure / Prerequisites (APT)
apt: name={{ item }}
state=latest
update_cache=yes
install_recommends=no
with_items:
- build-essential
- ubuntu-desktop
- linux-azure
# reqd by asible get_url
- python3-pip
# reqd by driver runfile
- pkg-config
- libglvnd-dev
- name: Azure /Prerequisites (PIP)
pip:
# older version is needed by ansible get_url for some reason
name: requests==2.20.1
executable: pip3
- name: Azure / Blacklist nouveau
kernel_blacklist: name={{ item }}
state=present
with_items:
- nouveau
- lbs-nouveau
- name: Azure / Fix 3818429
kernel_blacklist: name=hyperv_drm state=present
- name: Azure / Check if reboot required
stat:
path: /var/run/reboot-required
register: reboot_required_file
- name: Azure / Reboot and wait
reboot:
post_reboot_delay: 5
connect_timeout: 3
reboot_timeout: 600
when: reboot_required_file.stat.exists == true
# download driver, timeout 3 min, 5 retries
# @see https://github.com/Azure/azhpc-extensions/blob/master/NvidiaGPU/resources.json#L275
- name: Azure / Download GRID driver
get_url:
url: https://go.microsoft.com/fwlink/?linkid=874272
dest: /tmp/nvidia_grid_azure_driver.run
mode: "0755"
timeout: 180
retries: 5
- name: Azure / Install GRID driver
shell: "/tmp/nvidia_grid_azure_driver.run --run-nvidia-xconfig --disable-nouveau --no-questions --silent"
- name: Azure / Enable persistent mode for the driver
shell: nvidia-smi -pm ENABLED
- name: Azure / Copy grid.conf
copy: >
src=/etc/nvidia/gridd.conf.template
dest=/etc/nvidia/gridd.conf
remote_src=true
force=no
- name: Azure / Update GRID config [1]
lineinfile:
path: /etc/nvidia/gridd.conf
line: "{{ item }}"
state: present
with_items:
- "IgnoreSP=FALSE"
- "EnableUI=FALSE"
- name: Azure / Update GRID config [2]
lineinfile:
path: /etc/nvidia/gridd.conf
regexp: "^FeatureType=(.*)$"
line: '# FeatureType=\1'
state: present
backrefs: yes
| 2,805 | YAML | 26.509804 | 137 | 0.693761 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/nvidia/tasks/nvidia-driver.gcp.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
# @see https://cloud.google.com/compute/docs/gpus/install-grid-drivers
- name: GCP / Prerequisites (APT)
apt: name={{ item }}
state=latest
update_cache=yes
install_recommends=no
with_items:
- build-essential
- python3-pip # required by asible get_url
- name: GCP / Prerequisites (PIP)
pip:
# older version is needed by ansible get_url for some reason
name: requests==2.20.1
executable: pip3
# download driver
- name: GCP / Download GRID driver
get_url:
url: "{{ gcp_driver_url }}"
dest: /tmp/nvidia_driver.run
mode: 0755
- name: GCP / Install GRID driver
shell: "/tmp/nvidia_driver.run \
--x-module-path=/usr/lib/xorg/modules/drivers \
--run-nvidia-xconfig \
--disable-nouveau \
--no-questions \
--silent"
- name: GCP / Enable persistent mode for the driver
shell: nvidia-smi -pm ENABLED
- name: GCP / Copy gridd.conf
copy: >
src=/etc/nvidia/gridd.conf.template
dest=/etc/nvidia/gridd.conf
remote_src=true
force=no
- name: GCP / Update GRID config [1]
lineinfile:
path: /etc/nvidia/gridd.conf
line: "{{ item }}"
state: present
with_items:
- "IgnoreSP=FALSE"
- "EnableUI=TRUE"
- name: GCP / Update GRID config [2]
lineinfile:
path: /etc/nvidia/gridd.conf
regexp: "^FeatureType=(.*)$"
line: 'FeatureType=\2'
state: present
backrefs: yes
| 1,998 | YAML | 25.653333 | 74 | 0.680681 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/nvidia/tasks/main.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
# driver
- name: Detect if the driver is already installed
shell: lsmod | grep nvidia_drm | wc -l
register: driver_installed
- import_tasks: nvidia-driver.azure.yml
when: driver_installed.stdout == "0" and cloud == "azure"
- import_tasks: nvidia-driver.generic.yml
when: driver_installed.stdout == "0" and (cloud == "aws" or cloud == "alicloud")
- import_tasks: nvidia-driver.gcp.yml
when: driver_installed.stdout == "0" and cloud == "gcp"
# Disable ECC
# Needs to be done after restoring from image or from scratch
- name: Detect ECC status
shell: nvidia-smi --query-gpu="ecc.mode.current" --format="csv,noheader" -i 0
register: ecc_status
tags: on_stop_start
- debug:
msg: "ECC status: {{ ecc_status.stdout }}"
#
- name: Disable ECC
shell: nvidia-smi --ecc-config=0
when: ecc_status.stdout == 'Enabled'
tags: on_stop_start
#
- name: Reboot and wait
reboot: post_reboot_delay=5 connect_timeout=3 reboot_timeout=600
when: ecc_status.stdout == 'Enabled'
tags: on_stop_start
# nvidia-docker2
- package_facts: manager=apt
- import_tasks: nvidia-docker.yml
when: '"nvidia-docker2" not in ansible_facts.packages'
| 1,768 | YAML | 29.499999 | 82 | 0.721154 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/nvidia/tasks/nvidia-driver.generic.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
- name: NVIDIA GPU Driver / Add CUDA keyring
apt:
deb: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.1-1_all.deb
- name: NVIDIA GPU Driver / Blacklist the nouveau driver module
kernel_blacklist: name=nouveau
state=present
- name: NVIDIA GPU Driver / Install pre-requisites
apt: name={{ item }}
state=latest
update_cache=yes
with_items:
- xserver-xorg
- "{{ generic_driver_apt_package }}"
- name: NVIDIA GPU Driver / Enable persistent mode
shell: nvidia-smi -pm ENABLED
- name: NVIDIA GPU Driver / Check if reboot is needed
stat:
path: /var/run/reboot-required
register: reboot_required_file
- name: AWS / Reboot and wait
reboot:
post_reboot_delay: 5
connect_timeout: 3
reboot_timeout: 600
when: reboot_required_file.stat.exists == true
| 1,461 | YAML | 29.458333 | 110 | 0.726899 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/isaac/tasks/icon.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
- name: Make sure dirs exist
file:
path: "{{ item }}"
state: directory
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
mode: 0755
with_items:
- /home/{{ ansible_user }}/Pictures
- /home/{{ ansible_user }}/Desktop
- name: Upload icon
copy:
src: "{{ item }}"
dest: /home/{{ ansible_user }}/Pictures/isaacsim-{{ item }}
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
mode: 0644
with_items:
- icon.png
- icon-shell.png
- name: Create desktop icon
copy:
src: "{{ item }}"
dest: /home/{{ ansible_user }}/Desktop/{{ item }}
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
mode: 0644
with_items:
- isaacsim.desktop
- isaacsim-shell.desktop
- name: Allow execution of desktop icon
shell: gio set /home/{{ ansible_user }}/Desktop/{{ item }} metadata::trusted true
become_user: "{{ ansible_user }}"
with_items:
- isaacsim.desktop
- isaacsim-shell.desktop
- name: Set permissions for desktop icon
file:
path: /home/{{ ansible_user }}/Desktop/{{ item }}
mode: 0755
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
with_items:
- isaacsim.desktop
- isaacsim-shell.desktop
| 1,845 | YAML | 26.969697 | 83 | 0.645528 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/isaac/tasks/wallpaper.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
- name: Make sure wallpaper dir exists
file:
path: /home/{{ ansible_user }}/Pictures
state: directory
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
mode: 0755
- name: Upload wallpaper
copy:
src: wallpaper.png
dest: /home/{{ ansible_user }}/Pictures/wallpaper.png
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
mode: 0644
- name: Set wallpaper
shell: gsettings set org.gnome.desktop.background picture-uri file:///home/{{ ansible_user }}/Pictures/wallpaper.png
become_user: "{{ ansible_user }}"
| 1,177 | YAML | 31.722221 | 118 | 0.705183 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/isaac/tasks/isaac_app.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
- name: Create cache directory
file:
path: "{{ isaac_cache_dir }}"
state: directory
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
mode: 0777
- name: Launch scripts [1]
file:
path: /home/ubuntu/Desktop
state: directory
owner: ubuntu
group: ubuntu
- name: Launch scripts [2]
template: src={{ item }}
dest=/home/ubuntu/Desktop/{{ item }}
mode=755
owner=ubuntu
group=ubuntu
with_items:
- isaacsim.sh
- isaacsim-shell.sh
- name: Log into nvcr.io
shell: until docker login -u "\$oauthtoken" --password "{{ ngc_api_key }}" nvcr.io; do sleep 10; done
ignore_errors: true
become_user: "{{ item }}"
with_items:
- root
- ubuntu
timeout: 60 # for each item
when: ngc_api_key != "none"
tags:
- skip_in_ovami
- name: Pull Isaac Sim image
docker_image:
name: "{{ isaac_image }}"
repository: nvcr.io
source: pull
ignore_errors: true
when: ngc_api_key != "none"
tags:
- skip_in_ovami
- name: Log out from nvcr.io
shell: docker logout nvcr.io
become_user: "{{ item }}"
with_items:
- root
- ubuntu
when: ngc_api_key != "none"
tags:
- never
- cleanup
- name: Start Isaac Sim
shell: |
export DISPLAY=":0"
gnome-terminal -- bash -c "./isaacsim.sh; exec bash"
args:
chdir: /home/{{ ansible_user }}/Desktop
become_user: "{{ ansible_user }}"
when: ngc_api_key != "none"
tags:
- skip_in_image
- on_stop_start
| 2,087 | YAML | 23.279069 | 103 | 0.647341 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/isaac/tasks/main.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
- name: Prerequisites
apt:
name: "{{ item }}"
state: latest
with_items:
- libvulkan-dev
- name: Wallpaper
import_tasks: wallpaper.yml
- name: Desktop Icon
import_tasks: icon.yml
- name: Isaac App
import_tasks: isaac_app.yml
# https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs
- name: Omni Isaac Gym Envs
import_tasks: omni_isaac_gym_envs.yml
when: omni_isaac_gym_envs_git_checkpoint != 'no'
# https://isaac-orbit.github.io/orbit/index.html
- name: Orbit
import_tasks: orbit.yml
when: orbit_git_checkpoint != 'no'
tags: __orbit
- name: Restart NX server
meta: noop
notify: nx_restart
| 1,243 | YAML | 25.468085 | 74 | 0.726468 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/isaac/defaults/main.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
deployment_name:
isaac_image:
isaac_cache_dir: /home/{{ ansible_user }}/isaac
# OmniIsaacGymEnvs
omni_isaac_gym_envs_dir: /opt/omni-isaac-gym-envs
omni_isaac_gym_envs_git_checkpoint: main
# Orbit
orbit_dir: /opt/orbit
orbit_git_checkpoint: devel
# "none" skips login/pull
ngc_api_key:
# directory to output results to
results_dir: /home/{{ ansible_user }}/results
workspace_dir: /home/{{ ansible_user }}/results
uploads_dir: /home/{{ ansible_user }}/uploads
omniverse_user:
omniverse_password:
nucleus_uri:
| 1,128 | YAML | 25.880952 | 74 | 0.75266 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/ovami/tasks/nice_dcv.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
# install nice dcv
# @see https://docs.aws.amazon.com/dcv/latest/adminguide/setting-up-installing-linux-prereq.html
# @see https://docs.aws.amazon.com/dcv/latest/adminguide/setting-up-installing-linux-server.html
# prerequisites
- name: Install prerequisites for NICE DCV
apt:
name: "{{ item }}"
state: latest
with_items:
- rpm # needed for rpm_key ansible module
- mesa-utils
# import GPG key
- name: Import GPG key for NICE DCV
rpm_key:
key: https://d1uj6qtbmh3dt5.cloudfront.net/NICE-GPG-KEY
state: present
# download nice dcv
- name: Download NICE DCV
get_url:
url: https://d1uj6qtbmh3dt5.cloudfront.net/nice-dcv-ubuntu{{ ansible_distribution_version | replace('.', '') }}-{{ ansible_machine }}.tgz
dest: /tmp/nice-dcv.tgz
mode: 0644
# download and unzip nice dcv
- name: Unarchive NICE DCV
unarchive:
src: /tmp/nice-dcv.tgz
remote_src: yes
dest: /tmp
extra_opts: [--strip-components=1]
# find .deb for nice dcv installer
- name: Find .deb for NICE DCV Server installer
find:
paths: /tmp
patterns: "nice-dcv-server*.deb"
register: nice_dcv_deb
# install nice dcv .deb file
- name: Install NICE DCV Server
apt:
deb: "{{ nice_dcv_deb.files[0].path }}"
state: present
# find .deb for nice xdcv
- name: Find .deb for NICE XDCV
find:
paths: /tmp
patterns: "nice-xdcv*.deb"
register: nice_xdcv_deb
# install nice xdcv
- name: Install NICE XDCV
apt:
deb: "{{ nice_xdcv_deb.files[0].path }}"
state: present
# print nice_dcv_unarchive
- name: Print nice_dcv_unarchive
debug:
msg: "{{ nice_dcv_deb.files[0].path }}"
# add the dcv user to the video group
- name: Add "dcv" user to "video" group
user:
name: dcv
groups: video
append: yes
# find nice-dcv-gl .deb file
- name: Find .deb for NICE DCV GL
find:
paths: /tmp
patterns: "nice-dcv-gl*.deb"
register: nice_dcv_gl_deb
# print nice_dcv_gl_deb
- name: Print nice_dcv_gl_deb
debug:
msg: "{{ nice_dcv_gl_deb.files[0].path }}"
# install nice-dcv-gl from .deb file
- name: Install NICE DCV GL
apt:
deb: "{{ nice_dcv_gl_deb.files[0].path }}"
state: present
# post-install steps
# @see https://docs.aws.amazon.com/dcv/latest/adminguide/setting-up-installing-linux-checks.html
# configure dcvgladmin
- name: Post-install confgiuration of NICE DCV GL
shell: |
systemctl isolate multi-user.target
dcvgladmin disable
dcvgladmin enable
systemctl isolate graphical.target
# configure dcvserver
- name: Configure NICE DCV Server
ini_file:
section: "{{ item.section }}"
path: /etc/dcv/dcv.conf
option: "{{ item.key }}"
value: "{{ item.value }}"
no_extra_spaces: true
with_items:
- {
section: "security",
key: "authentication",
value: "{{ dcv_authentication_method }}",
}
- { section: "session-management", key: "create-session", value: "true" }
- {
section: "session-management/automatic-console-session",
key: "owner",
value: "{{ ansible_user }}",
}
- {
section: "display",
key: "default-layout",
value: "[{'w':<1920>, 'h':<1200>, 'x':<0>,'y':<0>}]",
}
# start dcvserver, reboot
- name: Start/Restart/Enable NICE DCV Server
systemd:
name: dcvserver
state: restarted
enabled: yes
- name: Reboot
reboot: post_reboot_delay=5 connect_timeout=3 reboot_timeout=600
| 4,042 | YAML | 25.424836 | 141 | 0.664028 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/ovami/tasks/vscode.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
# @see https://code.visualstudio.com/docs/setup/linux
- name: Prerequisites for VS Code
apt:
name: "{{ item }}"
state: latest
with_items:
- wget
- gpg
- apt-transport-https
- name: Install VSC sources
shell: |
wget -qO- https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > packages.microsoft.gpg
install -D -o root -g root -m 644 packages.microsoft.gpg /etc/apt/keyrings/packages.microsoft.gpg
sh -c 'echo "deb [arch=amd64,arm64,armhf signed-by=/etc/apt/keyrings/packages.microsoft.gpg] https://packages.microsoft.com/repos/code stable main" > /etc/apt/sources.list.d/vscode.list'
args:
creates: /etc/apt/sources.list.d/vscode.list
- name: Install VS Code
apt:
name: code
update_cache: yes
state: latest
- name: Add desktop icon
copy:
src: /usr/share/applications/code.desktop
remote_src: yes
dest: /home/{{ ansible_user }}/Desktop/code.desktop
mode: 0644
- name: Allow execution of VSC desktop icon
shell: gio set /home/{{ ansible_user }}/Desktop/{{ item }} metadata::trusted true
become_user: "{{ ansible_user }}"
with_items:
- code.desktop
- name: Set permissions for VSC desktop icon
file:
path: /home/{{ ansible_user }}/Desktop/{{ item }}
mode: 0755
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
with_items:
- code.desktop
| 1,983 | YAML | 30.492063 | 190 | 0.695411 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/ovami/tasks/ov_launcher.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
- name: Make sure dirs exist
file:
path: "{{ item }}"
state: directory
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
mode: 0755
with_items:
- /home/{{ ansible_user }}/Pictures
- /home/{{ ansible_user }}/Desktop
- name: Upload OV Icon
copy:
src: "{{ item }}"
dest: "/home/{{ ansible_user }}/Pictures/{{ item }}"
mode: 0644
with_items:
- ov-icon.png
- name: Download OV Launcher
get_url:
url: https://install.launcher.omniverse.nvidia.com/installers/omniverse-launcher-linux.AppImage
dest: "/home/{{ ansible_user }}/Omniverse.AppImage"
mode: 0755
become_user: "{{ ansible_user }}"
- name: Create desktop icon
copy:
content: |
[Desktop Entry]
Name=Omniverse Launcher
Comment=Omniverse Launcher
Exec=/home/{{ ansible_user }}/Omniverse.AppImage
Icon=/home/{{ ansible_user }}/Pictures/ov-icon.png
Terminal=false
Type=Application
Categories=Utility;
dest: /home/{{ ansible_user }}/Desktop/ovl.desktop
mode: 0644
- name: Allow execution of OVL desktop icon
shell: gio set /home/{{ ansible_user }}/Desktop/{{ item }} metadata::trusted true
become_user: "{{ ansible_user }}"
with_items:
- ovl.desktop
- name: Set permissions for OVL desktop icon
file:
path: /home/{{ ansible_user }}/Desktop/{{ item }}
mode: 0755
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
with_items:
- ovl.desktop
| 2,078 | YAML | 28.28169 | 99 | 0.662656 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/ovami/tasks/main.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
# this role is meant to be executed while creating
# the OV AMI, so no skip tags are present
# for NV-use only, don't try to make too much sense of it
- name: Prerequisites
apt:
name: "{{ item }}"
state: latest
with_items:
- libvulkan-dev
- firefox
- xdg-utils
- name: NICE DCV
import_tasks: nice_dcv.yml
- name: OV Launcher
import_tasks: ov_launcher.yml
- name: Install VS Code
import_tasks: vscode.yml
- name: Set wallpaper
import_tasks: wallpaper.yml
- name: Configure cloud-init
import_tasks: cloud_init.yml
| 1,167 | YAML | 24.955555 | 74 | 0.725793 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/docker/tasks/main.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
- name: Add Docker apt key
apt_key: url="https://download.docker.com/linux/ubuntu/gpg"
state=present
- name: Add Docker apt package repository
apt_repository:
repo="deb https://download.docker.com/linux/ubuntu {{ ansible_distribution_release }} stable"
state=present
mode=644
- name: Install docker packages
apt: name={{ item }}
state=latest
update_cache=yes
with_items:
- docker-ce
- docker-ce-cli
- docker-compose
- name: Create docker group
group: name=docker
state=present
- name: Add user ubuntu to docker group
user: name=ubuntu
groups=docker
append=yes
state=present
- name: Reset connection so docker group is picked up
meta: reset_connection
| 1,340 | YAML | 26.367346 | 97 | 0.724627 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/system/tasks/id.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
# change color of the bash prompt
- name: Enable color of the bash prompt
lineinfile:
path: /home/{{ ansible_user }}/.bashrc
line: "force_color_prompt=yes"
insertbefore: if \[ \-n \"\$force_color_prompt\"
backup: yes
- name: Change color of the bash prompt
lineinfile:
path: /home/{{ ansible_user }}/.bashrc
regexp: "^ PS1='\\${debian_chroot:\\+\\(\\$debian_chroot\\)}\\\\\\[\\\\033\\[01;32m\\\\\\]\\\\u@\\\\h\\\\\\[\\\\033\\[00m\\\\\\]:\\\\\\[\\\\033\\[01;34m\\\\\\]\\\\w\\\\\\[\\\\033\\[00m\\\\\\]\\\\\\$ '"
line: " PS1='${debian_chroot:+($debian_chroot)}\\[\\033[01;{{ prompt_ansi_color }}m\\]\\u\\[\\033[01;{{ prompt_ansi_color }}m\\]@\\h\\[\\033[00m\\]:\\[\\033[01;34m\\]\\w\\[\\033[00m\\]\\$ '"
backup: yes
# set hostname
- set_fact:
hostname: "{{ (application_name + '-' + deployment_name) | replace('_', '-') }}"
- debug:
msg: "hostname: {{ hostname }}"
- name: Set hostname [1]
hostname:
name: "{{ hostname }}"
- name: Set hostname [2]
lineinfile:
path: /etc/hosts
line: "127.0.0.1 {{ hostname }}"
| 1,698 | YAML | 32.979999 | 208 | 0.61013 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/system/tasks/swap.yml | - name: Disable swap and remove /swapfile
shell: "[[ -f /swapfile ]] && (swapoff -a && rm -f /swapfile) || echo 'no swap file found'"
- name: Create /swapfile
shell: "fallocate -l {{ swap_size }} /swapfile"
- name: Set permissions on /swapfile
shell: "chmod 600 /swapfile"
- name: Make swap in /swapfile
shell: "mkswap /swapfile"
- name: Add /swapfile to fstab
lineinfile:
path: /etc/fstab
line: "/swapfile none swap sw 0 0"
- name: Enable swap
shell: "swapon -a"
| 489 | YAML | 23.499999 | 93 | 0.648262 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/system/tasks/password.yml | # set ubuntu user password to "ubuntu"
# this is supposed to change with userdata
- name: Create password hash
shell: python3 -c "import crypt; print(crypt.crypt('{{ system_user_password }}'))"
register: system_user_password_hash
- name: Set password for "{{ ansible_user }}" user
user:
name: "{{ ansible_user }}"
password: "{{ system_user_password_hash.stdout }}"
| 381 | YAML | 30.833331 | 84 | 0.682415 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/system/tasks/main.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
- name: Check OS name and version
assert: that="ansible_distribution == 'Ubuntu'"
# add extra ssh ports
- name: Change SSH port to {{ ssh_port }}
include: custom_ssh.yml
when: ssh_port != 22
- name: Upgrade the OS (apt-get dist-upgrade)
apt: upgrade=dist
update_cache=yes
- name: Set OS user password
include: password.yml
- name: Disable IPv6 for apt-get
copy:
dest: /etc/apt/apt.conf.d/99force-ipv4
src: etc.apt.apt.conf.d.99force-ipv4
mode: 0644
- name: Disable unattended upgrades
copy:
src: etc.apt.apt.conf.d.20auto-upgrades
dest: /etc/apt/apt.conf.d/20auto-upgrades
mode: 0644
# add packages for convinience
- name: Install common apt packages
apt: name=htop
state=latest
- name: Add user ubuntu to sudo group
user: name=ubuntu
groups=sudo
append=yes
state=present
- name: Check if reboot required
stat:
path: /var/run/reboot-required
register: reboot_required_file
- name: Reboot and wait
reboot:
post_reboot_delay: 5
connect_timeout: 3
reboot_timeout: 600
when: reboot_required_file.stat.exists == true
# - set hostname
# - set prompt color and hostname
- include: id.yml
# swap
- name: Check if swap is enabled
shell: "swapon -s | wc -l"
register: swap_enabled
tags:
- skip_in_image
- import_tasks: swap.yml
when: swap_enabled.stdout | int == 0
tags:
- skip_in_image
| 2,012 | YAML | 22.964285 | 74 | 0.704274 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/system/tasks/custom_ssh.yml | # change ssh port to custom value
- name: Set SSH port to {{ ssh_port }}
lineinfile:
path: /etc/ssh/sshd_config
line: "Port {{ item }}"
state: present
mode: 0644
with_items:
- "22"
- "{{ ssh_port }}"
- name: Create ufw profile for the custom ssh port
template: src=custom_ssh.ufwprofile
dest=/etc/ufw/applications.d/custom_ssh
mode=644
- name: Allow incoming connections to the custom ssh port
ufw: rule=allow name=custom_ssh
- name: Restart sshd
service: name=sshd state=restarted
- name: Make Ansible to use new ssh port
set_fact:
ansible_port: "{{ ssh_port }}"
| 617 | YAML | 21.888888 | 57 | 0.659643 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/novnc.yml | # Install noVNC
- name: Prerequisites
apt:
name: snapd
state: latest
# Install noVNC via snap package
- name: Install noVNC
snap:
name: novnc
state: present
- name: Add noVNC systemd config
template: src=novnc.service
dest=/etc/systemd/system
mode=0444
owner=root
group=root
- name: Start noVNC
systemd: name=novnc
daemon_reload=yes
enabled=yes
state=restarted
| 417 | YAML | 15.076922 | 32 | 0.676259 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/busid.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
# update BusID in xorg.conf before GDM start
# executed from /etc/gdm3/PreSession/Default
- name: Create BusID updater
copy:
content: |
#!/bin/bash
BUS_ID=$(nvidia-xconfig --query-gpu-info | grep 'PCI BusID' | head -n 1 | cut -c15-)
sed -i "s/BusID.*$/BusID \"$BUS_ID\"/" /etc/X11/xorg.conf
dest: /opt/update-busid
mode: 0755
# add /opt/update-busid to /etc/gdm3/PreSession/Default
- name: Add BusID updater to /etc/gdm3/PreSession/Default
lineinfile:
path: /etc/gdm3/PreSession/Default
line: /opt/update-busid
insertafter: EOF
state: present
| 1,209 | YAML | 32.61111 | 90 | 0.715467 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/vnc.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
- name: Prerequisites (PIP packages)
pip:
name: "{{ item }}"
state: latest
with_items:
- pexpect
- name: Install x11vnc and helper util
apt: name={{ item }}
update_cache=yes
state=latest
with_items:
- x11vnc
- expect
- name: Add x11vnc systemd config
template: src=x11vnc-ubuntu.service
dest=/etc/systemd/system
mode=0444
owner=root
group=root
- name: Start x11vnc
systemd: name=x11vnc-ubuntu
daemon_reload=yes
enabled=yes
state=restarted
- name: Clear VNC password
file:
path: /home/ubuntu/.vnc/passwd
state: absent
- name: Set VNC password
expect:
command: /usr/bin/x11vnc -storepasswd
responses:
(?i).*password:.*: "{{ vnc_password }}\r"
(?i)write.*: "y\r"
creates: /home/ubuntu/.vnc/passwd
become_user: ubuntu
tags:
- skip_in_image
- name: Cleanup VNC password
file:
path: /home/ubuntu/.vnc/passwd
state: absent
tags:
- never
- cleanup
| 1,596 | YAML | 22.144927 | 74 | 0.679825 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/utils.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
# install extra packages
- name: Install extra packages
apt: name={{ item }}
state=latest
update_cache=yes
install_recommends=no
with_items:
- eog # EOG image viewer (https://help.gnome.org/users/eog/stable/)
| 843 | YAML | 32.759999 | 74 | 0.742586 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/vscode.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
- name: Prerequisites
apt:
name: snapd
state: latest
# install visual studio code
- name: Install Visual Studio Code
snap:
name: code
state: present
classic: yes
# install remote development extension pack
- name: Install Remote Development extension pack
shell: code --install-extension ms-vscode-remote.vscode-remote-extensionpack
become_user: "{{ ansible_user }}"
# make sure desktop directory exists
- name: Make sure desktop directory exists
file:
path: /home/{{ ansible_user }}/Desktop
state: directory
mode: 0755
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
# create a desktop shortcut for visual studio code
- name: Create desktop shortcut for Visual Studio Code
copy:
dest: /home/{{ ansible_user }}/Desktop/vscode.desktop
mode: 0755
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
content: |
[Desktop Entry]
Version=1.0
Type=Application
Name=Visual Studio Code
GenericName=Text Editor
Comment=Edit text files
Exec=/snap/bin/code --no-sandbox --unity-launch %F
Icon=/snap/code/current/meta/gui/vscode.png
StartupWMClass=Code
StartupNotify=true
Terminal=false
Categories=Utility;TextEditor;Development;IDE;
MimeType=text/plain;inode/directory;
Actions=new-empty-window;
Keywords=vscode;
become_user: "{{ ansible_user }}"
- name: Allow execution of desktop icon for Orbit
shell: gio set "/home/{{ ansible_user }}/Desktop/{{ item }}" metadata::trusted true
become_user: "{{ ansible_user }}"
with_items:
- vscode.desktop
| 2,237 | YAML | 29.657534 | 85 | 0.698257 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/nomachine.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
# @see https://downloads.nomachine.com/download/?id=2 for new versions
- name: Download NoMachine server
get_url:
url: https://download.nomachine.com/download/8.9/Linux/nomachine_8.9.1_1_amd64.deb
dest: /tmp/nomachine.deb
mode: 0644
timeout: 600 # 10 minutes
- name: Install NoMachine server
apt:
deb: /tmp/nomachine.deb
state: present
- name: Create NX config dir
file: >
path=/home/ubuntu/.nx/config
state=directory
owner=ubuntu
group=ubuntu
- name: Link authorized keys to NX config
file: >
src=/home/ubuntu/.ssh/authorized_keys
dest=/home/ubuntu/.nx/config/authorized.crt
state=link
owner=ubuntu
group=ubuntu
notify: nx_restart
# add env var DISPLAY to /usr/lib/systemd/system/nxserver.service
- name: Add DISPLAY env var to nxserver.service
lineinfile:
path: /usr/lib/systemd/system/nxserver.service
line: Environment="DISPLAY=:0"
insertafter: "\\[Service\\]"
state: present
# restart nxserver.service on GDM init (fix for "no display detected" error)
- name: Restart nxserver.service on GDM init
lineinfile:
path: /etc/gdm3/PreSession/Default
line: (/usr/bin/sleep 5 && /usr/bin/systemctl restart nxserver.service) &
insertafter: EOF
state: present
- name: Do daemon-reload
systemd:
daemon_reload: yes
| 1,946 | YAML | 28.059701 | 86 | 0.717883 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/virtual-display.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
- name: Copy EDID file
template: src=vdisplay.edid
dest=/etc/X11/vdisplay.edid
mode=644
notify: reboot
- name: Get PCI Bus ID of the first GPU
shell: nvidia-xconfig --query-gpu-info | grep 'PCI BusID' | head -n 1 | cut -c15-
register: GPU0_PCI_BUS_ID
- name: Write X11 config
template: src=xorg.conf
dest=/etc/X11/xorg.conf
mode=644
notify: reboot
- name: Create Xauthority file
file:
path: /home/{{ ansible_user }}/.Xauthority
state: touch
owner: "{{ ansible_user }}"
group: "{{ ansible_user }}"
mode: 0666
| 1,179 | YAML | 27.780487 | 83 | 0.706531 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/main.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
# check if we need to skip stuff
- name: Check installed services
service_facts:
- name: Prerequisites (1)
apt: |
name="{{ item }}"
state=latest
update_cache=yes
install_recommends=no
with_items:
- ubuntu-desktop
- python3-pip
# install only if ubuntu 20
- name: Prerequisites (2)
apt: name=yaru-theme-gtk state=latest
when: ansible_distribution_release == "focal"
- name: Configure desktop environment
import_tasks: desktop.yml
- name: Virtual display
import_tasks: virtual-display.yml
# updates bus id of the gpu in the xorg.conf file
# needed for starting from the image without ansible
- name: Bus ID updater
import_tasks: busid.yml
# install misc utils
- name: Misc utils
import_tasks: utils.yml
# install visual studio code
- name: Visual Studio Code
import_tasks: vscode.yml
tags:
- __vscode
# VNC
- name: VNC server
import_tasks: vnc.yml
# NoMachine
- name: NoMachine server
import_tasks: nomachine.yml
when: "'nxserver.service' not in ansible_facts.services"
tags:
- skip_in_ovami
# NoVNC
- name: NoVNC server
import_tasks: novnc.yml
# do reboots if needed
- name: Reboot if needed
meta: flush_handlers
| 1,808 | YAML | 22.493506 | 74 | 0.724004 |
NVIDIA-Omniverse/IsaacSim-Automator/src/ansible/roles/rdesktop/tasks/desktop.yml | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
---
#
# Good desktop experience
#
- name: Configure auto login [1]
lineinfile:
path: /etc/gdm3/custom.conf
state: present
line: "AutomaticLoginEnable=true"
insertafter: "\\[daemon\\]"
notify: reboot
- name: Configure auto login [2]
lineinfile:
path: /etc/gdm3/custom.conf
state: present
line: "AutomaticLogin=ubuntu"
insertafter: "\\[daemon\\]"
notify: reboot
# disable blank screen
- name: Mask sleep targets
shell: systemctl mask sleep.target suspend.target hibernate.target hybrid-sleep.target
notify: reboot
# disable screen lock
- name: Disable screen lock
shell: "{{ item }}"
with_items:
- gsettings set org.gnome.desktop.session idle-delay 0
- gsettings set org.gnome.desktop.screensaver lock-enabled 'false'
- gsettings set org.gnome.desktop.lockdown disable-lock-screen 'true'
- gsettings set org.gnome.desktop.screensaver idle-activation-enabled 'false'
- gsettings set org.gnome.settings-daemon.plugins.power sleep-inactive-battery-timeout 0
become_user: "{{ ansible_user }}"
notify: reboot
# increase font size
- name: Set font size to 125%
shell: gsettings set org.gnome.desktop.interface text-scaling-factor 1.25
become_user: "{{ ansible_user }}"
# enable dark theme
- name: Make it dark
shell: gsettings set org.gnome.desktop.interface gtk-theme 'Yaru-dark'
become_user: "{{ ansible_user }}"
# fix terminal font
- name: Fix terminal font
shell: "{{ item }}"
become_user: "{{ ansible_user }}"
with_items:
- gsettings set org.gnome.Terminal.Legacy.Profile:/org/gnome/terminal/legacy/profiles:/:$(gsettings get org.gnome.Terminal.ProfilesList default|tr -d \')/ use-system-font false
- gsettings set org.gnome.Terminal.Legacy.Profile:/org/gnome/terminal/legacy/profiles:/:$(gsettings get org.gnome.Terminal.ProfilesList default|tr -d \')/ font "Monospace 12"
# make terminal semi-transparent
- name: Make terminal semi-transparent
shell: "{{ item }}"
become_user: "{{ ansible_user }}"
with_items:
- gsettings set org.gnome.Terminal.Legacy.Profile:/org/gnome/terminal/legacy/profiles:/:$(gsettings get org.gnome.Terminal.ProfilesList default|tr -d \')/ background-transparency-percent 12
- gsettings set org.gnome.Terminal.Legacy.Profile:/org/gnome/terminal/legacy/profiles:/:$(gsettings get org.gnome.Terminal.ProfilesList default|tr -d \')/ use-transparent-background true
# disable new ubuntu version prompt
- name: Disable new ubuntu version prompt
lineinfile:
path: /etc/update-manager/release-upgrades
regexp: "Prompt=.*"
line: "Prompt=never"
notify: reboot
| 3,223 | YAML | 35.224719 | 193 | 0.730686 |
NVIDIA-Omniverse/IsaacSim-Automator/src/packer/aws/README.md | # Running packer for AWS
Set env vars with credentials:
```sh
export AWS_ACCESS_KEY_ID ="..."
export AWS_SECRET_ACCESS_KEY ="..."
export NGC_API_KEY="..." # optional
```
Alternatively you can pass them as variables in the packer command (`packer -var=varname=value...`).
Then launch image builds with:
```sh
packer build [-force] [-var=aws_region="us-east-1"] [-var=image_name="..."] [-var=system_user_password="..."] [-var=vnc_password="..."] <folder>/
```
For example:
```sh
packer build -force -var=isaac_image="nvcr.io/nvidia/isaac-sim:2023.1.0-hotfix.1" /app/src/packer/aws/isaac
```
```sh
packer build -force \
-var=aws_region="us-east-1" \
-var=image_name=ovami-test-1 \
-var=system_user_password="nvidia123" \
-var=vnc_password="nvidia123" \
/app/src/packer/aws/ovami
```
| 788 | Markdown | 22.90909 | 145 | 0.673858 |
NVIDIA-Omniverse/IsaacSim-Automator/src/packer/azure/README.md | # Running packer for Azure
See <https://learn.microsoft.com/en-us/azure/virtual-machines/linux/build-image-with-packer>
## 1. Create resource group for packer output
```sh
az group create -n isa.packer -l westus3
```
## 2. Create Azure service principal
```sh
export AZURE_SUBSCRIPTION_ID=`az account list | jq -r .[0].id`
az ad sp create-for-rbac \
--role Contributor \
--scopes /subscriptions/$AZURE_SUBSCRIPTION_ID \
--query "{ client_id: appId, client_secret: password, tenant_id: tenant }"
```
## 3. Build image
Set env vars with credentials:
```sh
export AZURE_SUBSCRIPTION_ID="..."
export AZURE_TENANT_ID="..."
export AZURE_SP_CLIENT_ID="..."
export AZURE_SP_CLIENT_SECRET="..."
export NGC_API_KEY="..."
```
Alternatively you can pass them as variables in the packer command (`packer -var=varname=value...`).
Then launch image builds with:
```sh
packer build [-var=image_name=...] <folder>/
```
For example:
```sh
packer build isaac/
packer build -var=image_name=my_image_1 isaac/
```
| 1,011 | Markdown | 20.531914 | 100 | 0.693373 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/config.py | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
from typing import Any, Dict
c: Dict[str, Any] = {}
# paths
c["app_dir"] = "/app"
c["state_dir"] = "/app/state"
c["results_dir"] = "/app/results"
c["uploads_dir"] = "/app/uploads"
c["tests_dir"] = "/app/src/tests"
c["ansible_dir"] = "/app/src/ansible"
c["terraform_dir"] = "/app/src/terraform"
# app image name
c["app_image_name"] = "isa"
# gcp driver
# @see https://cloud.google.com/compute/docs/gpus/grid-drivers-table
c[
"gcp_driver_url"
] = "https://storage.googleapis.com/nvidia-drivers-us-public/GRID/vGPU16.2/NVIDIA-Linux-x86_64-535.129.03-grid.run"
# aws/alicloud driver
c["generic_driver_apt_package"] = "nvidia-driver-535-server"
# default remote dirs
c["default_remote_uploads_dir"] = "/home/ubuntu/uploads"
c["default_remote_results_dir"] = "/home/ubuntu/results"
c["default_remote_workspace_dir"] = "/home/ubuntu/workspace"
# defaults
# --isaac-image
c["default_isaac_image"] = "nvcr.io/nvidia/isaac-sim:2023.1.1"
# --ssh-port
c["default_ssh_port"] = 22
# --from-image
c["azure_default_from_image"] = False
c["aws_default_from_image"] = False
# --omniverse-user
c["default_omniverse_user"] = "omniverse"
# --remote-dir
c["default_remote_uploads_dir"] = "/home/ubuntu/uploads"
c["default_remote_results_dir"] = "/home/ubuntu/results"
# --isaac-instance-type
c["aws_default_isaac_instance_type"] = "g5.2xlarge"
# str, 1-index in DeployAzureCommand.AZURE_OVKIT_INSTANCE_TYPES
c["azure_default_isaac_instance_type"] = "2"
c["gcp_default_isaac_instance_type"] = "g2-standard-8"
c["alicloud_default_isaac_instance_type"] = "ecs.gn7i-c16g1.4xlarge"
# --isaac-gpu-count
c["gcp_default_isaac_gpu_count"] = 1
# --region
c["alicloud_default_region"] = "us-east-1"
# --prefix for the created cloud resources
c["default_prefix"] = "isa"
# --oige
c["default_oige_git_checkpoint"] = "main"
# --orbit
c["default_orbit_git_checkpoint"] = "devel"
| 2,478 | Python | 27.494253 | 115 | 0.705408 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/aws.py | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
"""
Utils for AWS
"""
from src.python.utils import read_meta, shell_command
def aws_configure_cli(
deployment_name,
verbose=False,
):
"""
Configure AWS CLI for deployment
"""
meta = read_meta(deployment_name)
aws_access_key_id = meta["params"]["aws_access_key_id"]
aws_secret_access_key = meta["params"]["aws_secret_access_key"]
region = meta["params"]["region"]
shell_command(
f"aws configure set aws_access_key_id '{aws_access_key_id}'",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
shell_command(
f"aws configure set aws_secret_access_key '{aws_secret_access_key}'",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
shell_command(
f"aws configure set region '{region}'",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
def aws_stop_instance(instance_id, verbose=False):
shell_command(
f"aws ec2 stop-instances --instance-ids '{instance_id}'",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
def aws_start_instance(instance_id, verbose=False):
shell_command(
f"aws ec2 start-instances --instance-ids '{instance_id}'",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
def aws_get_instance_status(instance_id, verbose=False):
"""
Query instance status
Returns: "stopping" | "stopped" | "pending" | "running"
"""
status = (
shell_command(
f"aws ec2 describe-instances --instance-ids '{instance_id}'"
+ " | jq -r .Reservations[0].Instances[0].State.Name",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
.stdout.decode()
.strip()
)
return status
| 2,500 | Python | 25.606383 | 77 | 0.632 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/ngc.py | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
import pathlib
import subprocess
SELF_DIR = pathlib.Path(__file__).parent.resolve()
def check_ngc_access(ngc_api_key, org="", team="", verbose=False):
"""
Checks if NGC API key is valid and user has access to DRIVE Sim.
Returns:
- 0 - all is fine
- 100 - invalid api key
- 102 - user is not in the team
"""
proc = subprocess.run(
[f"{SELF_DIR}/ngc_check.expect", ngc_api_key, org, team],
capture_output=not verbose,
timeout=60,
)
if proc.returncode not in [0, 100, 101, 102]:
raise RuntimeError(
f"Error checking NGC API Key. Return code: {proc.returncode}"
)
return proc.returncode
| 1,301 | Python | 27.304347 | 74 | 0.680246 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/alicloud.py | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
"""
Utils for AliCloud
"""
from src.python.utils import read_meta, shell_command
def alicloud_configure_cli(
deployment_name,
verbose=False,
):
"""
Configure Aliyun CLI
"""
meta = read_meta(deployment_name)
aliyun_access_key = meta["params"]["aliyun_access_key"]
aliyun_secret_key = meta["params"]["aliyun_secret_key"]
region = meta["params"]["region"]
shell_command(
"aliyun configure set "
+ f"--access-key-id '{aliyun_access_key}'"
+ f" --access-key-secret '{aliyun_secret_key}'"
+ f" --region '{region}'",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
def alicloud_start_instance(vm_id, verbose=False):
"""
Start VM
"""
shell_command(
f"aliyun ecs StartInstance --InstanceId '{vm_id}'",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
def alicloud_stop_instance(vm_id, verbose=False):
"""
Stop VM
"""
shell_command(
f"aliyun ecs StopInstance --InstanceId '{vm_id}'",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
def alicloud_get_instance_status(vm_id, verbose=False):
"""
Query VM status
Returns: "Stopping" | "Stopped" | "Starting" | "Running"
"""
status = (
shell_command(
f"aliyun ecs DescribeInstances --InstanceIds '[\"{vm_id}\"]'"
+ " | jq -r .Instances.Instance[0].Status",
verbose=verbose,
exit_on_error=True,
capture_output=True,
)
.stdout.decode()
.strip()
)
return status
def alicloud_list_regions(
aliyun_access_key,
aliyun_secret_key,
verbose=False,
):
"""
List regions
"""
res = (
shell_command(
f"aliyun --access-key-id {aliyun_access_key}"
+ f" --access-key-secret {aliyun_secret_key}"
+ " --region cn-beijing ecs DescribeRegions"
+ " | jq -r '.Regions.Region[].RegionId'",
capture_output=True,
exit_on_error=True,
verbose=verbose,
)
.stdout.decode()
.strip()
)
valid_regions = res.split("\n")
return valid_regions
| 2,886 | Python | 23.675213 | 74 | 0.596674 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/deployer.py | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
import json
import os
import re
import shlex
import sys
from pathlib import Path
import click
from src.python.utils import (
colorize_error,
colorize_info,
colorize_prompt,
colorize_result,
read_meta,
shell_command,
)
from src.python.debug import debug_break # noqa
from src.python.ngc import check_ngc_access
class Deployer:
def __init__(self, params, config):
self.tf_outputs = {}
self.params = params
self.config = config
self.existing_behavior = None
# save original params so we can recreate command line
self.input_params = params.copy()
# convert "in_china"
self.params["in_china"] = {"yes": True, "no": False, "auto": False}[
self.params["in_china"]
]
# create state directory if it doesn't exist
os.makedirs(self.config["state_dir"], exist_ok=True)
# print complete command line
if self.params["debug"]:
click.echo(colorize_info("* Command:\n" + self.recreate_command_line()))
def __del__(self):
# update meta info
self.save_meta()
def save_meta(self):
"""
Save command parameters in json file, just in case
"""
meta_file = (
f"{self.config['state_dir']}/{self.params['deployment_name']}/meta.json"
)
data = {
"command": self.recreate_command_line(separator=" "),
"input_params": self.input_params,
"params": self.params,
"config": self.config,
}
Path(meta_file).parent.mkdir(parents=True, exist_ok=True)
Path(meta_file).write_text(json.dumps(data, indent=4))
if self.params["debug"]:
click.echo(colorize_info(f"* Meta info saved to '{meta_file}'"))
def read_meta(self):
return read_meta(
self.params["deployment_name"],
self.params["debug"],
)
def recreate_command_line(self, separator=" \\\n"):
"""
Recreate command line
"""
command_line = sys.argv[0]
for k, v in self.input_params.items():
k = k.replace("_", "-")
if isinstance(v, bool):
if v:
command_line += separator + "--" + k
else:
not_prefix = "--no-"
if k in ["from-image"]:
not_prefix = "--not-"
command_line += separator + not_prefix + k
else:
command_line += separator + "--" + k + " "
if isinstance(v, str):
command_line += "'" + shlex.quote(v) + "'"
else:
command_line += str(v)
return command_line
def ask_existing_behavior(self):
"""
Ask what to do if deployment already exists
"""
deployment_name = self.params["deployment_name"]
existing = self.params["existing"]
self.existing_behavior = existing
if existing == "ask" and os.path.isfile(
f"{self.config['state_dir']}/{deployment_name}/.tfvars"
):
self.existing_behavior = click.prompt(
text=colorize_prompt(
"* Deploymemnt exists, what would you like to do? See --help for details."
),
type=click.Choice(["repair", "modify", "replace", "run_ansible"]),
default="replace",
)
if (
self.existing_behavior == "repair"
or self.existing_behavior == "run_ansible"
):
# restore params from meta file
r = self.read_meta()
self.params = r["params"]
click.echo(
colorize_info(
f"* Repairing existing deployment \"{self.params['deployment_name']}\"..."
)
)
# update meta info (with new value for existing_behavior)
self.save_meta()
# destroy existing deployment``
if self.existing_behavior == "replace":
debug = self.params["debug"]
click.echo(colorize_info("* Deleting existing deployment..."))
shell_command(
command=f'{self.config["app_dir"]}/destroy "{deployment_name}" --yes'
+ f' {"--debug" if debug else ""}',
verbose=debug,
)
# update meta info if deployment was destroyed
self.save_meta()
def validate_ngc_api_key(self, image, restricted_image=False):
"""
Check if NGC API key allows to log in and has access to appropriate NGC image
@param image: NGC image to check access to
@param restricted_image: If image is restricted to specific org/team?
"""
debug = self.params["debug"]
ngc_api_key = self.params["ngc_api_key"]
ngc_api_key_check = self.params["ngc_api_key_check"]
# extract org and team from the image path
r = re.findall(
"^nvcr\\.io/([a-z0-9\\-_]+)/([a-z0-9\\-_]+/)?[a-z0-9\\-_]+:[a-z0-9\\-_.]+$",
image,
)
ngc_org, ngc_team = r[0]
ngc_team = ngc_team.rstrip("/")
if ngc_org == "nvidia":
click.echo(
colorize_info(
"* Access to docker image can't be checked for NVIDIA org. But you'll be fine. Fingers crossed."
)
)
return
if debug:
click.echo(colorize_info(f'* Will check access to NGC Org: "{ngc_org}"'))
click.echo(colorize_info(f'* Will check access to NGC Team: "{ngc_team}"'))
if ngc_api_key_check and ngc_api_key != "none":
click.echo(colorize_info("* Validating NGC API key... "))
r = check_ngc_access(
ngc_api_key=ngc_api_key, org=ngc_org, team=ngc_team, verbose=debug
)
if r == 100:
raise Exception(colorize_error("NGC API key is invalid."))
# only check access to org/team if restricted image is deployed
elif restricted_image and (r == 101 or r == 102):
raise Exception(
colorize_error(
f'NGC API key is valid but you don\'t have access to image "{image}".'
)
)
click.echo(colorize_info(("* NGC API Key is valid!")))
def create_tfvars(self, tfvars: dict = {}):
"""
- Check if deployment with this deployment_name exists and deal with it
- Create/update tfvars file
Expected values for "existing_behavior" arg:
- repair: keep tfvars/tfstate, don't ask for user input
- modify: keep tfstate file, update tfvars file with user input
- replace: delete tfvars/tfstate files
- run_ansible: keep tfvars/tfstate, don't ask for user input, skip terraform steps
"""
# default values common for all clouds
tfvars.update(
{
"isaac_enabled": self.params["isaac"]
if "isaac" in self.params
else False,
#
"isaac_instance_type": self.params["isaac_instance_type"]
if "isaac_instance_type" in self.params
else "none",
#
"prefix": self.params["prefix"],
"ssh_port": self.params["ssh_port"],
#
"from_image": self.params["from_image"]
if "from_image" in self.params
else False,
#
"deployment_name": self.params["deployment_name"],
}
)
debug = self.params["debug"]
deployment_name = self.params["deployment_name"]
# deal with existing deployment:
tfvars_file = f"{self.config['state_dir']}/{deployment_name}/.tfvars"
tfstate_file = f"{self.config['state_dir']}/{deployment_name}/.tfstate"
# tfvars
if os.path.exists(tfvars_file):
if (
self.existing_behavior == "modify"
or self.existing_behavior == "overwrite"
):
os.remove(tfvars_file)
if debug:
click.echo(colorize_info(f'* Deleted "{tfvars_file}"...'))
# tfstate
if os.path.exists(tfstate_file):
if self.existing_behavior == "overwrite":
os.remove(tfstate_file)
if debug:
click.echo(colorize_info(f'* Deleted "{tfstate_file}"...'))
# create tfvars file
if (
self.existing_behavior == "modify"
or self.existing_behavior == "overwrite"
or not os.path.exists(tfvars_file)
):
self._write_tfvars_file(path=tfvars_file, tfvars=tfvars)
def _write_tfvars_file(self, path: str, tfvars: dict):
"""
Write tfvars file
"""
debug = self.params["debug"]
if debug:
click.echo(colorize_info(f'* Created tfvars file "{path}"'))
# create <dn>/ directory if it doesn't exist
Path(path).parent.mkdir(parents=True, exist_ok=True)
with open(path, "w") as f:
for key, value in tfvars.items():
# convert booleans to strings
if isinstance(value, bool):
value = {
True: "true",
False: "false",
}[value]
# format key names
key = key.replace("-", "_")
# write values
if isinstance(value, str):
value = value.replace('"', '\\"')
f.write(f'{key} = "{value}"\n')
elif isinstance(value, list):
f.write(f"{key} = " + str(value).replace("'", '"') + "\n")
else:
f.write(f"{key} = {value}\n")
def create_ansible_inventory(self, write: bool = True):
"""
Create Ansible inventory, return it as text
Write to file if write=True
"""
debug = self.params["debug"]
deployment_name = self.params["deployment_name"]
ansible_vars = self.params.copy()
# add config
ansible_vars["config"] = self.config
# get missing values from terraform
for k in [
"isaac_ip",
"ovami_ip",
"cloud",
]:
if k not in self.params or ansible_vars[k] is None:
ansible_vars[k] = self.tf_output(k)
# convert booleans to ansible format
ansible_booleans = {True: "true", False: "false"}
for k, v in ansible_vars.items():
if isinstance(v, bool):
ansible_vars[k] = ansible_booleans[v]
template = Path(f"{self.config['ansible_dir']}/inventory.template").read_text()
res = template.format(**ansible_vars)
# write to file
if write:
inventory_file = f"{self.config['state_dir']}/{deployment_name}/.inventory"
Path(inventory_file).parent.mkdir(parents=True, exist_ok=True) # create dir
Path(inventory_file).write_text(res) # write file
if debug:
click.echo(
colorize_info(
f'* Created Ansible inventory file "{inventory_file}"'
)
)
return res
def initialize_terraform(self, cwd: str):
"""
Initialize Terraform via shell command
cwd: directory where terraform scripts are located
"""
debug = self.params["debug"]
shell_command(
f"terraform init -upgrade -no-color -input=false {' > /dev/null' if not debug else ''}",
verbose=debug,
cwd=cwd,
)
def run_terraform(self, cwd: str):
"""
Apply Terraform via shell command
cwd: directory where terraform scripts are located
"""
debug = self.params["debug"]
deployment_name = self.params["deployment_name"]
shell_command(
"terraform apply -auto-approve "
+ f"-state={self.config['state_dir']}/{deployment_name}/.tfstate "
+ f"-var-file={self.config['state_dir']}/{deployment_name}/.tfvars",
cwd=cwd,
verbose=debug,
)
def export_ssh_key(self):
"""
Export SSH key from Terraform state
"""
debug = self.params["debug"]
deployment_name = self.params["deployment_name"]
shell_command(
f"terraform output -state={self.config['state_dir']}/{deployment_name}/.tfstate -raw ssh_key"
+ f" > {self.config['state_dir']}/{deployment_name}/key.pem && "
+ f"chmod 0600 {self.config['state_dir']}/{deployment_name}/key.pem",
verbose=debug,
)
def run_ansible(self, playbook_name: str, cwd: str):
"""
Run Ansible playbook via shell command
"""
debug = self.params["debug"]
deployment_name = self.params["deployment_name"]
shell_command(
f"ansible-playbook -i {self.config['state_dir']}/{deployment_name}/.inventory "
+ f"{playbook_name}.yml {'-vv' if self.params['debug'] else ''}",
cwd=cwd,
verbose=debug,
)
def run_all_ansible(self):
# run ansible for isaac
if "isaac" in self.params and self.params["isaac"]:
click.echo(colorize_info("* Running Ansible for Isaac Sim..."))
self.run_ansible(playbook_name="isaac", cwd=f"{self.config['ansible_dir']}")
# run ansible for ovami
# todo: move to ./deploy-aws
if "ovami" in self.params and self.params["ovami"]:
click.echo(colorize_info("* Running Ansible for OV AMI..."))
self.run_ansible(playbook_name="ovami", cwd=f"{self.config['ansible_dir']}")
def tf_output(self, key: str, default: str = ""):
"""
Read Terraform output.
Cache read values in self._tf_outputs.
"""
if key not in self.tf_outputs:
debug = self.params["debug"]
deployment_name = self.params["deployment_name"]
r = shell_command(
f"terraform output -state='{self.config['state_dir']}/{deployment_name}/.tfstate' -raw '{key}'",
capture_output=True,
exit_on_error=False,
verbose=debug,
)
if r.returncode == 0:
self.tf_outputs[key] = r.stdout.decode()
else:
if self.params["debug"]:
click.echo(
colorize_error(
f"* Warning: Terraform output '{key}' cannot be read."
),
err=True,
)
self.tf_outputs[key] = default
# update meta file to reflect tf outputs
self.save_meta()
return self.tf_outputs[key]
def upload_user_data(self):
shell_command(
f'./upload "{self.params["deployment_name"]}" '
+ f'{"--debug" if self.params["debug"] else ""}',
cwd=self.config["app_dir"],
verbose=self.params["debug"],
exit_on_error=True,
capture_output=False,
)
# generate ssh connection command for the user
def ssh_connection_command(self, ip: str):
r = f"ssh -i state/{self.params['deployment_name']}/key.pem "
r += f"-o StrictHostKeyChecking=no ubuntu@{ip}"
if self.params["ssh_port"] != 22:
r += f" -p {self.params['ssh_port']}"
return r
def output_deployment_info(self, extra_text: str = "", print_text=True):
"""
Print connection info for the user
Save info to file (_state_dir_/_deployment_name_/info.txt)
"""
isaac = "isaac" in self.params and self.params["isaac"]
ovami = "ovami" in self.params and self.params["ovami"]
vnc_password = self.params["vnc_password"]
deployment_name = self.params["deployment_name"]
# templates
nomachine_instruction = f"""* To connect to __app__ via NoMachine:
0. Download NoMachine client at https://downloads.nomachine.com/, install and launch it.
1. Click "Add" button.
2. Enter Host: "__ip__".
3. In "Configuration" > "Use key-based authentication with a key you provide",
select file "state/{deployment_name}/key.pem".
4. Click "Connect" button.
5. Enter "ubuntu" as a username when prompted.
"""
vnc_instruction = f"""* To connect to __app__ via VNC:
- IP: __ip__
- Port: 5900
- Password: {vnc_password}"""
nonvc_instruction = f"""* To connect to __app__ via noVNC:
1. Open http://__ip__:6080/vnc.html?host=__ip__&port=6080 in your browser.
2. Click "Connect" and use password \"{vnc_password}\""""
# print connection info
instructions_file = f"{self.config['state_dir']}/{deployment_name}/info.txt"
instructions = ""
if isaac:
instructions += f"""{'*' * (29+len(self.tf_output('isaac_ip')))}
* Isaac Sim is deployed at {self.tf_output('isaac_ip')} *
{'*' * (29+len(self.tf_output('isaac_ip')))}
* To connect to Isaac Sim via SSH:
{self.ssh_connection_command(self.tf_output('isaac_ip'))}
{nonvc_instruction}
{nomachine_instruction}""".replace(
"__app__", "Isaac Sim"
).replace(
"__ip__", self.tf_output("isaac_ip")
)
# todo: move to ./deploy-aws
if ovami:
instructions += f"""\n
* OV AMI is deployed at {self.tf_output('ovami_ip')}
* To connect to OV AMI via SSH:
{self.ssh_connection_command(self.tf_output('ovami_ip'))}
* To connect to OV AMI via NICE DCV:
- IP: __ip__
{vnc_instruction}
{nomachine_instruction}
""".replace(
"__app__", "OV AMI"
).replace(
"__ip__", self.tf_output("ovami_ip")
)
# extra text
if len(extra_text) > 0:
instructions += extra_text + "\n"
# print instructions for the user
if print_text:
click.echo(colorize_result("\n" + instructions))
# create <dn>/ directory if it doesn't exist
Path(instructions_file).parent.mkdir(parents=True, exist_ok=True)
# write file
Path(instructions_file).write_text(instructions)
return instructions
| 19,262 | Python | 31.760204 | 116 | 0.53224 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/utils.py | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
"""
CLI Utils
"""
import json
import os
import subprocess
from glob import glob
from pathlib import Path
import click
from src.python.config import c as config
def colorize_prompt(text):
return click.style(text, fg="bright_cyan", italic=True)
def colorize_error(text):
return click.style(text, fg="bright_red", italic=True)
def colorize_info(text):
return click.style(text, fg="bright_magenta", italic=True)
def colorize_result(text):
return click.style(text, fg="bright_green", italic=True)
def shell_command(
command, verbose=False, cwd=None, exit_on_error=True, capture_output=False
):
"""
Execute shell command, print it if debug is enabled
"""
if verbose:
if cwd is not None:
click.echo(colorize_info(f"* Running `(cd {cwd} && {command})`..."))
else:
click.echo(colorize_info(f"* Running `{command}`..."))
res = subprocess.run(
command,
shell=True,
cwd=cwd,
capture_output=capture_output,
)
if res.returncode == 0:
if verbose and res.stdout is not None:
click.echo(res.stdout.decode())
elif exit_on_error:
if res.stderr is not None:
click.echo(
colorize_error(f"Error: {res.stderr.decode()}"),
err=True,
)
exit(1)
return res
def deployments():
"""List existing deployments by name"""
state_dir = config["state_dir"]
deployments = sorted(
[
os.path.basename(os.path.dirname(d))
for d in glob(os.path.join(state_dir, "*/"))
]
)
return deployments
def read_meta(deployment_name: str, verbose: bool = False):
"""
Read metadata from json file
"""
meta_file = f"{config['state_dir']}/{deployment_name}/meta.json"
if os.path.isfile(meta_file):
data = json.loads(Path(meta_file).read_text())
if verbose:
click.echo(colorize_info(f"* Meta info loaded from '{meta_file}'"))
return data
raise Exception(f"Meta file '{meta_file}' not found")
def read_tf_output(deployment_name, output, verbose=False):
"""
Read terraform output from tfstate file
"""
return (
shell_command(
f"terraform output -state={config['state_dir']}/{deployment_name}/.tfstate -raw {output}",
capture_output=True,
exit_on_error=False,
verbose=verbose,
)
.stdout.decode()
.strip()
)
def format_app_name(app_name):
"""
Format app name for user output
"""
formatted = {
"isaac": "Isaac Sim",
"ovami": "OV AMI",
}
if app_name in formatted:
return formatted[app_name]
return app_name
def format_cloud_name(cloud_name):
"""
Format cloud name for user output
"""
formatted = {
"aws": "AWS",
"azure": "Azure",
"gcp": "GCP",
"alicloud": "Alibaba Cloud",
}
if cloud_name in formatted:
return formatted[cloud_name]
return cloud_name
def gcp_login(verbose=False):
"""
Log into GCP
"""
# detect if we need to re-login
click.echo(colorize_info("* Checking GCP login status..."), nl=False)
res = shell_command(
"gcloud auth application-default print-access-token 2>&1 > /dev/null",
verbose=verbose,
exit_on_error=False,
capture_output=True,
)
logged_in = res.returncode == 0
if logged_in:
click.echo(colorize_info(" logged in!"))
if not logged_in:
click.echo(colorize_info(" not logged in"))
shell_command(
"gcloud auth application-default login --no-launch-browser --disable-quota-project --verbosity none",
verbose=verbose,
)
| 4,426 | Python | 22.42328 | 113 | 0.608676 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/azure.py | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
"""
Utils for Azure
"""
import click
from src.python.utils import colorize_info, read_meta, shell_command
def azure_login(verbose=False):
"""
Log into Azure
"""
# detect if we need to re-login
logged_in = (
'"Enabled"'
== shell_command(
"az account show --query state",
verbose=verbose,
exit_on_error=False,
capture_output=True,
)
.stdout.decode()
.strip()
)
if not logged_in:
click.echo(colorize_info("* Logging into Azure..."))
shell_command("az login --use-device-code", verbose=verbose)
def azure_stop_instance(vm_id, verbose=False):
shell_command(
f"az vm deallocate --ids {vm_id}",
verbose=verbose,
exit_on_error=True,
capture_output=False,
)
def azure_start_instance(vm_id, verbose=False):
shell_command(
f"az vm start --ids {vm_id}",
verbose=verbose,
exit_on_error=True,
capture_output=False,
)
| 1,639 | Python | 24.230769 | 74 | 0.640635 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/deploy_command.py | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
"""
Base deploy- command
"""
import os
import re
import click
import randomname
from pwgen import pwgen
from src.python.config import c as config
from src.python.debug import debug_break # noqa
from src.python.utils import colorize_error, colorize_prompt
class DeployCommand(click.core.Command):
"""
Defines common cli options for "deploy-*" commands.
"""
@staticmethod
def isaac_callback(ctx, param, value):
"""
Called after --isaac option is parsed
"""
# disable isaac instance type selection if isaac is disabled
if value is False:
for p in ctx.command.params:
if p.name.startswith("isaac"):
p.prompt = None
return value
@staticmethod
def deployment_name_callback(ctx, param, value):
# validate
if not re.match("^[a-z0-9\\-]{1,32}$", value):
raise click.BadParameter(
colorize_error(
"Only lower case letters, numbers and '-' are allowed."
+ f" Length should be between 1 and 32 characters ({len(value)} provided)."
)
)
return value
@staticmethod
def ngc_api_key_callback(ctx, param, value):
"""
Validate NGC API key
"""
# fix click bug
if value is None:
return value
# allow "none" as a special value
if "none" == value:
return value
# check if it contains what's allowed
if not re.match("^[A-Za-z0-9]{32,}$", value):
raise click.BadParameter(
colorize_error("Key contains invalid characters or too short.")
)
return value
@staticmethod
def ngc_image_callback(ctx, param, value):
"""
Called after parsing --isaac-image options are parsed
"""
# ignore case
value = value.lower()
if not re.match(
"^nvcr\\.io/[a-z0-9\\-_]+/([a-z0-9\\-_]+/)?[a-z0-9\\-_]+:[a-z0-9\\-_.]+$",
value,
):
raise click.BadParameter(
colorize_error(
"Invalid image name. "
+ "Expected: nvcr.io/<org>/[<team>/]<image>:<tag>"
)
)
return value
@staticmethod
def oige_callback(ctx, param, value):
"""
Called after parsing --oige option
"""
if "" == value:
return config["default_oige_git_checkpoint"]
return value
@staticmethod
def orbit_callback(ctx, param, value):
"""
Called after parsing --orbit option
"""
if "" == value:
return config["default_orbit_git_checkpoint"]
return value
def param_index(self, param_name):
"""
Return index of parameter with given name.
Useful for inserting new parameters at a specific position.
"""
return list(
filter(
lambda param: param[1].name == param_name,
enumerate(self.params),
)
)[0][0]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# add common options
self.params.insert(
len(self.params),
click.core.Option(
("--debug/--no-debug",),
default=False,
show_default=True,
help="Enable debug output.",
),
)
# --prefix
self.params.insert(
len(self.params),
click.core.Option(
("--prefix",),
default=config["default_prefix"],
show_default=True,
help="Prefix for all cloud resources.",
),
)
# --from-image/--not-from-image
self.params.insert(
len(self.params),
click.core.Option(
("--from-image/--not-from-image",),
default=False,
show_default=True,
help="Deploy from pre-built image, from bare OS otherwise.",
),
)
# --in-china
self.params.insert(
len(self.params),
click.core.Option(
("--in-china",),
type=click.Choice(["auto", "yes", "no"]),
prompt=False,
default="auto",
show_default=True,
help="Is deployment in China? (Local mirrors will be used.)",
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--deployment-name", "--dn"),
prompt=colorize_prompt(
'* Deployment Name (lower case letters, numbers and "-")'
),
default=randomname.get_name,
callback=DeployCommand.deployment_name_callback,
show_default="<randomly generated>",
help="Name of the deployment. Used to identify the created cloud resources and files.",
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--existing",),
type=click.Choice(
["ask", "repair", "modify", "replace", "run_ansible"]
),
default="ask",
show_default=True,
help="""What to do if deployment already exists:
\n* 'repair' will try to fix broken deployment without applying new user parameters.
\n* 'modify' will update user selected parameters and attempt to update existing cloud resources.
\n* 'replace' will attempt to delete old deployment's cloud resources first.
\n* 'run_ansible' will re-run Ansible playbooks.""",
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--isaac/--no-isaac",),
default=True,
show_default="yes",
prompt=colorize_prompt("* Deploy Isaac Sim?"),
callback=DeployCommand.isaac_callback,
help="Deploy Isaac Sim (BETA)?",
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--isaac-image",),
default=config["default_isaac_image"],
prompt=colorize_prompt("* Isaac Sim docker image"),
show_default=True,
callback=DeployCommand.ngc_image_callback,
help="Isaac Sim docker image to use.",
),
)
# --oige
help = (
"Install Omni Isaac Gym Envs? Valid values: 'no', "
+ "or <git ref in github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs>"
)
self.params.insert(
len(self.params),
click.core.Option(
("--oige",),
help=help,
default="main",
show_default=True,
prompt=colorize_prompt("* " + help),
callback=DeployCommand.oige_callback,
),
)
# --orbit
help = (
"[EXPERIMENTAL] Install Isaac Sim Orbit? Valid values: 'no', "
+ "or <git ref in github.com/NVIDIA-Omniverse/orbit>"
)
self.params.insert(
len(self.params),
click.core.Option(
("--orbit",),
help=help,
default="no",
show_default=True,
prompt=colorize_prompt("* " + help),
callback=DeployCommand.orbit_callback,
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--ngc-api-key",),
type=str,
prompt=colorize_prompt(
"* NGC API Key (can be obtained at https://ngc.nvidia.com/setup/api-key)"
),
default=os.environ.get("NGC_API_KEY", ""),
show_default='"NGC_API_KEY" environment variable',
help="NGC API Key (can be obtained at https://ngc.nvidia.com/setup/api-key)",
callback=DeployCommand.ngc_api_key_callback,
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--ngc-api-key-check/--no-ngc-api-key-check",),
default=True,
help="Skip NGC API key validity check.",
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--vnc-password",),
default=lambda: pwgen(10),
help="Password for VNC access to DRIVE Sim/Isaac Sim/etc.",
show_default="<randomly generated>",
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--system-user-password",),
default=lambda: pwgen(10),
help="System user password",
show_default="<randomly generated>",
),
)
self.params.insert(
len(self.params),
click.core.Option(
("--ssh-port",),
default=config["default_ssh_port"],
help="SSH port for connecting to the deployed machines.",
show_default=True,
),
)
# --upload/--no-upload
self.params.insert(
len(self.params),
click.core.Option(
("--upload/--no-upload",),
prompt=False,
default=True,
show_default=True,
help=f"Upload user data from \"{config['uploads_dir']}\" to cloud "
+ f"instances (to \"{config['default_remote_uploads_dir']}\")?",
),
)
default_nucleus_admin_password = pwgen(10)
# --omniverse-user
self.params.insert(
len(self.params),
click.core.Option(
("--omniverse-user",),
default=config["default_omniverse_user"],
help="Username for accessing content on the Nucleus server.",
show_default=True,
),
)
# --omniverse-password
self.params.insert(
len(self.params),
click.core.Option(
("--omniverse-password",),
default=default_nucleus_admin_password,
help="Password for accessing content on the Nucleus server.",
show_default="<randomly generated>",
),
)
| 11,460 | Python | 29.975676 | 113 | 0.494503 |
NVIDIA-Omniverse/IsaacSim-Automator/src/python/ngc.test.py | #!/usr/bin/env python3
# region copyright
# Copyright 2023 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# endregion
import os
import unittest
from src.python.ngc import check_ngc_access
class Test_NGC_Key_Validation(unittest.TestCase):
INVALID_KEY = "__invalid__"
VALID_KEY = os.environ.get("NGC_API_KEY", "__none__")
def test_invalid_key(self):
"""Test invalid key"""
r = check_ngc_access(self.INVALID_KEY)
self.assertEqual(r, 100)
def test_valid_key(self):
"""Test valid key (should be set in NGC_API_KEY env var)"""
if "__none__" == self.VALID_KEY:
self.skipTest("No NGC_API_KEY env var set")
return
r = check_ngc_access(self.VALID_KEY)
self.assertEqual(r, 0)
if __name__ == "__main__":
unittest.main()
| 1,337 | Python | 27.468085 | 74 | 0.672401 |
NVIDIA-Omniverse/IsaacSim-Automator/src/tests/deployer.test.py | #!/usr/bin/env python3
import unittest
from src.python.config import c
from src.python.deployer import Deployer
from pathlib import Path
class Test_Deployer(unittest.TestCase):
def setUp(self):
self.config = c
self.config["state_dir"] = f"{c['tests_dir']}/res/state"
self.deployer = Deployer(
params={
"debug": False,
"prefix": "isa",
"from_image": False,
"deployment_name": "test-1",
"existing": "ask",
"region": "us-east-1",
"isaac": True,
"isaac_instance_type": "g5.2xlarge",
"isaac_image": "nvcr.io/nvidia/isaac-sim:2022.2.0",
"ngc_api_key": "__ngc_api_key__",
"ngc_api_key_check": True,
"vnc_password": "__vnc_password__",
"omniverse_user": "ovuser",
"omniverse_password": "__omniverse_password__",
"ssh_port": 22,
"upload": True,
"aws_access_key_id": "__aws_access_key_id__",
"aws_secret_access_key": "__aws_secret_access_key__",
},
config=self.config,
)
def tearDown(self):
self.deployer = None
def test_output_deployment_info(self):
self.deployer.output_deployment_info(print_text=False)
file_generated = f"{self.config['state_dir']}/test-1/info.txt"
file_expected = f"{self.config['state_dir']}/test-1/info.expected.txt"
file_generated = Path(file_generated).read_text()
file_expected = Path(file_expected).read_text()
self.assertEqual(file_generated, file_expected)
if __name__ == "__main__":
unittest.main()
| 1,760 | Python | 29.894736 | 78 | 0.526136 |
NVIDIA-Omniverse/synthetic-data-examples/README.md | # Synthetic Data Examples
This public repository is for examples of the generation and/or use of synthetic data, primarily using tools like [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/), [Omniverse Replicator](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html), [NVIDIA Tao](https://developer.nvidia.com/tao-toolkit), and [NVIDIA NGC](https://www.nvidia.com/en-us/gpu-cloud/).
## Synthetic Data Blogs & Repositories
* [Blog - How to Train Autonomous Mobile Robots to Detect Warehouse Pallet Jacks](https://developer.nvidia.com/blog/how-to-train-autonomous-mobile-robots-to-detect-warehouse-pallet-jacks-using-synthetic-data/)
[GitHub](https://github.com/NVIDIA-AI-IOT/synthetic_data_generation_training_workflow)
* [Blog - Developing a Pallet Detection Model Using OpenUSD and Synthetic Data](https://developer.nvidia.com/blog/developing-a-pallet-detection-model-using-openusd-and-synthetic-data/)
[GitHub](https://github.com/NVIDIA-AI-IOT/sdg_pallet_model)
| 1,020 | Markdown | 67.066662 | 400 | 0.781373 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/README.md | # Omniverse Replicator Examples
Code here requires the installation of[NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/) and [Omniverse Replicator](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html).
| 246 | Markdown | 60.749985 | 212 | 0.808943 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_trigger_intervals.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))
# Create simple shapes to manipulate
plane = rep.create.plane(
semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100)
)
spheres = rep.create.sphere(
semantics=[("class", "sphere")], position=(0, 0, 100), count=6
)
# Modify the position every 5 frames
with rep.trigger.on_frame(num_frames=10, interval=5):
with spheres:
rep.modify.pose(
position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),
scale=rep.distribution.uniform(0.1, 2),
)
# Modify color every frame for 50 frames
with rep.trigger.on_frame(num_frames=50):
with spheres:
rep.randomizer.color(
colors=rep.distribution.normal((0.1, 0.1, 0.1), (1.0, 1.0, 1.0))
)
render_product = rep.create.render_product(camera, (512, 512))
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="trigger_intervals",
rgb=True,
)
writer.attach([render_product])
| 2,854 | Python | 41.61194 | 84 | 0.709881 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_multiple_semantic_classes.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 100, 100))
cube = rep.create.cube(semantics=[("class2", "cube")], position=(200, 200, 100))
plane = rep.create.plane(semantics=[("class3", "plane")], scale=10)
def get_shapes():
shapes = rep.get.prims(semantics=[("class", "cube"), ("class", "sphere")])
with shapes:
rep.modify.pose(
position=rep.distribution.uniform((-500, 50, -500), (500, 50, 500)),
rotation=rep.distribution.uniform((0, -180, 0), (0, 180, 0)),
scale=rep.distribution.normal(1, 0.5),
)
return shapes.node
with rep.trigger.on_frame(num_frames=2):
rep.randomizer.register(get_shapes)
# Setup Camera
camera = rep.create.camera(position=(500, 500, 500), look_at=(0, 0, 0))
render_product = rep.create.render_product(camera, (512, 512))
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="semantics_classes",
rgb=True,
semantic_segmentation=True,
colorize_semantic_segmentation=True,
semantic_types=["class", "class2", "class3"],
)
writer.attach([render_product])
rep.orchestrator.run()
| 2,956 | Python | 41.855072 | 87 | 0.715832 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_scatter_multi_trigger.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
"""
This snippet shows how to setup multiple independent triggers that happen
at different intervals in the simulation.
"""
import omni.graph.core as og
import omni.replicator.core as rep
# A light to see
distance_light = rep.create.light(rotation=(-45, 0, 0), light_type="distant")
# Create a plane to sample on
plane_samp = rep.create.plane(scale=4, rotation=(20, 0, 0))
# Create a larger sphere to sample on the surface of
sphere_samp = rep.create.sphere(scale=2.4, position=(0, 100, -180))
# Create a larger cylinder we do not want to collide with
cylinder = rep.create.cylinder(semantics=[("class", "cylinder")], scale=(2, 1, 2))
def randomize_spheres():
# create small spheres to sample inside the plane
spheres = rep.create.sphere(scale=0.4, count=60)
# scatter small spheres
with spheres:
rep.randomizer.scatter_2d(
surface_prims=[plane_samp, sphere_samp],
no_coll_prims=[cylinder],
check_for_collisions=True,
)
# Add color to small spheres
rep.randomizer.color(
colors=rep.distribution.uniform((0.2, 0.2, 0.2), (1, 1, 1))
)
return spheres.node
rep.randomizer.register(randomize_spheres)
# Trigger will execute 5 times, every-other-frame (interval=2)
with rep.trigger.on_frame(num_frames=5, interval=2):
rep.randomizer.randomize_spheres()
# Trigger will execute 10 times, once every frame
with rep.trigger.on_frame(num_frames=10):
with cylinder:
rep.modify.visibility(rep.distribution.sequence([True, False]))
og.Controller.evaluate_sync() # Only for snippet demonstration preview, not needed for production
rep.orchestrator.preview() # Only for snippet demonstration preview, not needed for production
rp = rep.create.render_product("/OmniverseKit_Persp", (1024, 768))
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(output_dir="scatter_example", rgb=True)
writer.attach([rp])
rep.orchestrator.run()
| 3,612 | Python | 40.056818 | 98 | 0.745847 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_writer_segmentation_colors.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""
A snippet showing to how create a custom writer to output specific colors
in the semantic annotator output image.
"""
import omni.replicator.core as rep
from omni.replicator.core import Writer, BackendDispatch, WriterRegistry
class MyWriter(Writer):
def __init__(self, output_dir: str):
self._frame_id = 0
self.backend = BackendDispatch({"paths": {"out_dir": output_dir}})
self.annotators = ["rgb", "semantic_segmentation"]
# Dictionary mapping of label to RGBA color
self.CUSTOM_LABELS = {
"unlabelled": (0, 0, 0, 0),
"sphere": (128, 64, 128, 255),
"cube": (244, 35, 232, 255),
"plane": (102, 102, 156, 255),
}
def write(self, data):
render_products = [k for k in data.keys() if k.startswith("rp_")]
self._write_rgb(data, "rgb")
self._write_segmentation(data, "semantic_segmentation")
self._frame_id += 1
def _write_rgb(self, data, annotator: str):
# Save the rgb data under the correct path
rgb_file_path = f"rgb_{self._frame_id}.png"
self.backend.write_image(rgb_file_path, data[annotator])
def _write_segmentation(self, data, annotator: str):
seg_filepath = f"seg_{self._frame_id}.png"
semantic_seg_data_colorized = rep.tools.colorize_segmentation(
data[annotator]["data"],
data[annotator]["info"]["idToLabels"],
mapping=self.CUSTOM_LABELS,
)
self.backend.write_image(seg_filepath, semantic_seg_data_colorized)
def on_final_frame(self):
self.backend.sync_pending_paths()
# Register new writer
WriterRegistry.register(MyWriter)
# Create a new layer for our work to be performed in.
# This is a good habit to develop for later when working on existing Usd scenes
with rep.new_layer():
light = rep.create.light(light_type="dome")
# Create a simple camera with a position and a point to look at
camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))
# Create some simple shapes to manipulate
plane = rep.create.plane(
semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100)
)
torus = rep.create.torus(position=(200, 0, 100)) # Torus will be unlabeled
sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 0, 100))
cube = rep.create.cube(semantics=[("class", "cube")], position=(-200, 0, 100))
# Randomize position and scale of each object on each frame
with rep.trigger.on_frame(num_frames=10):
# Creating a group so that our modify.pose operation works on all the shapes at once
with rep.create.group([torus, sphere, cube]):
rep.modify.pose(
position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),
scale=rep.distribution.uniform(0.1, 2),
)
# Initialize render product and attach a writer
render_product = rep.create.render_product(camera, (1024, 1024))
writer = rep.WriterRegistry.get("MyWriter")
writer.initialize(output_dir="myWriter_output")
writer.attach([render_product])
rep.orchestrator.run() # Run the simulation
| 4,866 | Python | 43.245454 | 92 | 0.690506 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_remove_semantics.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.graph.core as og
import omni.replicator.core as rep
from omni.usd._impl.utils import get_prim_at_path
from pxr import Semantics
from semantics.schema.editor import remove_prim_semantics
# Setup simple scene
with rep.new_layer():
# Simple scene setup
camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))
# Create simple shapes to manipulate
plane = rep.create.plane(
semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100)
)
cubes = rep.create.cube(
semantics=[("class", "cube")],
position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),
count=6,
)
spheres = rep.create.sphere(
semantics=[("class", "sphere")],
position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),
count=6,
)
# Get prims to remove semantics on - Execute this first by itself
my_spheres = rep.get.prims(semantics=[("class", "sphere")])
og.Controller.evaluate_sync() # Trigger an OmniGraph evaluation of the graph to set the values
get_targets = rep.utils.get_node_targets(my_spheres.node, "outputs_prims")
print(get_targets)
# [Sdf.Path('/Replicator/Sphere_Xform'), Sdf.Path('/Replicator/Sphere_Xform_01'), Sdf.Path('/Replicator/Sphere_Xform_02'), Sdf.Path('/Replicator/Sphere_Xform_03'), Sdf.Path('/Replicator/Sphere_Xform_04'), Sdf.Path('/Replicator/Sphere_Xform_05')]
# Loop through each prim_path and remove all semantic data
for prim_path in get_targets:
prim = get_prim_at_path(prim_path)
# print(prim.HasAPI(Semantics.SemanticsAPI))
result = remove_prim_semantics(prim) # To remove all semantics
# result = remove_prim_semantics(prim, label_type='class') # To remove only 'class' semantics
print(result)
| 3,457 | Python | 45.729729 | 245 | 0.731559 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replcator_clear_layer.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.usd
stage = omni.usd.get_context().get_stage()
for layer in stage.GetLayerStack():
if layer.GetDisplayName() == "test":
# del layer
layer.Clear()
| 1,868 | Python | 46.923076 | 84 | 0.770343 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_annotator_segmentation.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""
This is an example of how to view annotator data if needed.
"""
import asyncio
import omni.replicator.core as rep
import omni.syntheticdata as sd
async def test_semantics():
cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0))
# Setup semantic filter
# sd.SyntheticData.Get().set_instance_mapping_semantic_filter("prim:*")
cam = rep.create.camera(position=(500, 500, 500), look_at=(0, 0, 0))
rp = rep.create.render_product(cam, (1024, 512))
segmentation = rep.AnnotatorRegistry.get_annotator("semantic_segmentation")
segmentation.attach(rp)
# step_async() tells Omniverse to update, otherwise the annoation buffer could be empty
await rep.orchestrator.step_async()
data = segmentation.get_data()
print(data)
# Example Output:
# {
# "data": array(
# [
# [0, 0, 0, ..., 0, 0, 0],
# [0, 0, 0, ..., 0, 0, 0],
# [0, 0, 0, ..., 0, 0, 0],
# ...,
# [0, 0, 0, ..., 0, 0, 0],
# [0, 0, 0, ..., 0, 0, 0],
# [0, 0, 0, ..., 0, 0, 0],
# ],
# dtype=uint32,
# ),
# "info": {
# "_uniqueInstanceIDs": array([1, 1, 1], dtype=uint8),
# "idToLabels": {
# "0": {"class": "BACKGROUND"},
# "2": {"prim": "cone"},
# "3": {"prim": "sphere"},
# "4": {"shape": "boxy"},
# },
# },
# }
asyncio.ensure_future(test_semantics())
| 3,332 | Python | 38.211764 | 91 | 0.653962 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/replicator_multi_object_visibility_toggle.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""This will create a group from a list of objects and
1. Render all the objects together
2. Toggle sole visiblity for each object & render
3. Randomize pose for all objects, repeat
This can be useful for training on object occlusions.
"""
import omni.replicator.core as rep
NUM_POSE_RANDOMIZATIONS = 10
# Make a list-of-lists of True/False for each object
# In this example of 3 objects:
# [[True, True, True]
# [True, False, False]
# [False, True, False]
# [False, False, True]]
def make_visibility_lists(num_objects):
visib = []
# Make an all-visible first pass
visib.append(tuple([True for x in range(num_objects)]))
# List to toggle one object visible at a time
for x in range(num_objects):
sub_vis = []
for i in range(num_objects):
if x == i:
sub_vis.append(True)
else:
sub_vis.append(False)
visib.append(tuple(sub_vis))
return visib
with rep.new_layer():
# Setup camera and simple light
camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))
light = rep.create.light(rotation=(-45, 45, 0))
# Create simple shapes to manipulate
plane = rep.create.plane(
semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100)
)
torus = rep.create.torus(semantics=[("class", "torus")], position=(200, 0, 100))
sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 0, 100))
cube = rep.create.cube(semantics=[("class", "cube")], position=(-200, 0, 100))
# Create a group of the objects we will be manipulating
# Leaving-out camera, light, and plane from visibility toggling and pose randomization
object_group = rep.create.group([torus, sphere, cube])
# Get the number of objects to toggle, can work with any number of objects
num_objects_to_toggle = len(object_group.get_output_prims()["prims"])
# Create our lists-of-lists for visibility
visibility_sequence = make_visibility_lists(num_objects_to_toggle)
# Trigger to toggle visibility one at a time
with rep.trigger.on_frame(
max_execs=(num_objects_to_toggle + 1) * NUM_POSE_RANDOMIZATIONS
):
with object_group:
rep.modify.visibility(rep.distribution.sequence(visibility_sequence))
# Trigger to randomize position and scale, interval set to number of objects +1(1 extra for the "all visible" frame)
with rep.trigger.on_frame(
max_execs=NUM_POSE_RANDOMIZATIONS, interval=num_objects_to_toggle + 1
):
with object_group:
rep.modify.pose(
position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),
scale=rep.distribution.uniform(0.1, 2),
)
# Initialize render product and attach writer
render_product = rep.create.render_product(camera, (512, 512))
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="toggle_multi_visibility",
rgb=True,
semantic_segmentation=True,
)
writer.attach([render_product])
rep.orchestrator.run()
| 4,759 | Python | 40.391304 | 120 | 0.703929 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/surface_scratches/scratches_randomization.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
from pathlib import Path
import carb
import omni.replicator.core as rep
import omni.usd
from pxr import Sdf, UsdGeom
"""
Instructions:
Open the example scene file "scratches_randomization.usda",
located adjacent to this script, in Omniverse prior to using this script
"""
# Get the current Usd "stage". This is where all the scene objects live
stage = omni.usd.get_context().get_stage()
with rep.new_layer():
camera = rep.create.camera(position=(-30, 38, 60), look_at=(0, 0, 0))
render_product = rep.create.render_product(camera, (1280, 720))
# Get Scene cube
cube_prim = stage.GetPrimAtPath("/World/RoundedCube2/Cube/Cube")
# Set the primvars on the cubes once
primvars_api = UsdGeom.PrimvarsAPI(cube_prim)
primvars_api.CreatePrimvar("random_color", Sdf.ValueTypeNames.Float3).Set(
(1.0, 1.0, 1.0)
)
primvars_api.CreatePrimvar("random_intensity", Sdf.ValueTypeNames.Float3).Set(
(1.0, 1.0, 1.0)
)
def change_colors():
# Change color primvars
cubes = rep.get.prims(
path_pattern="/World/RoundedCube2/Cube/Cube", prim_types=["Mesh"]
)
with cubes:
rep.modify.attribute(
"primvars:random_color",
rep.distribution.uniform((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
attribute_type="float3",
)
rep.modify.attribute(
"primvars:random_intensity",
rep.distribution.uniform((0.0, 0.0, 0.0), (10.0, 10.0, 10.0)),
attribute_type="float3",
)
return cubes.node
rep.randomizer.register(change_colors)
# Setup randomization of colors, different each frame
with rep.trigger.on_frame(num_frames=10):
rep.randomizer.change_colors()
# (optional) Write output images to disk
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="~/replicator_examples/box_scratches",
rgb=True,
bounding_box_2d_tight=True,
semantic_segmentation=True,
distance_to_image_plane=True,
)
writer.attach([render_product])
carb.log_info("scratches randomization complete")
| 3,884 | Python | 37.465346 | 84 | 0.697477 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/22_Change_Textures.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
# create new objects to be used in the dataset
with rep.new_layer():
sphere = rep.create.sphere(
semantics=[("class", "sphere")], position=(0, 100, 100), count=5
)
cube = rep.create.cube(
semantics=[("class", "cube")], position=(200, 200, 100), count=5
)
cone = rep.create.cone(
semantics=[("class", "cone")], position=(200, 400, 200), count=10
)
cylinder = rep.create.cylinder(
semantics=[("class", "cylinder")], position=(200, 100, 200), count=5
)
# create new camera & render product and attach to camera
camera = rep.create.camera(position=(0, 0, 1000))
render_product = rep.create.render_product(camera, (1024, 1024))
# create plane if needed (but unused here)
plane = rep.create.plane(scale=10)
# function to get shapes that you've created above, via their semantic labels
def get_shapes():
shapes = rep.get.prims(
semantics=[
("class", "cube"),
("class", "sphere"),
("class", "cone"),
("class", "cylinder"),
]
)
with shapes:
# assign textures to the different objects
rep.randomizer.texture(
textures=[
"omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/aggregate_exposed_diff.jpg",
"omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_diff.jpg",
"omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_multi_R_rough_G_ao.jpg",
"omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/rough_gravel_rough.jpg",
]
)
# modify pose and distribution
rep.modify.pose(
position=rep.distribution.uniform((-500, 50, -500), (500, 50, 500)),
rotation=rep.distribution.uniform((0, -180, 0), (0, 180, 0)),
scale=rep.distribution.normal(1, 0.5),
)
return shapes.node
# register the get shapes function as a randomizer function
rep.randomizer.register(get_shapes)
# Setup randomization. 100 variations here from 'num_frames'
with rep.trigger.on_frame(num_frames=100):
rep.randomizer.get_shapes()
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(output_dir="~/replicator_examples/dli_example_22", rgb=True)
writer.attach([render_product])
rep.orchestrator.run()
| 4,321 | Python | 43.556701 | 134 | 0.672992 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/03_replicator_advanced.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
def dome_lights():
lights = rep.create.light(
light_type="Dome",
rotation=(270, 0, 0),
texture=rep.distribution.choice(
[
"omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCGcom_ExhibitionHall_Interior1.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCG_com_WarehouseInterior2b.hdr",
]
),
)
return lights.node
rep.randomizer.register(dome_lights)
conference_tables = (
"omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Conference/"
)
# create randomizer function conference table assets.
# This randomization includes placement and rotation of the assets on the surface.
def env_conference_table(size=5):
confTable = rep.randomizer.instantiate(
rep.utils.get_usd_files(conference_tables, recursive=False),
size=size,
mode="scene_instance",
)
with confTable:
rep.modify.pose(
position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),
rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),
)
return confTable.node
# Register randomization
rep.randomizer.register(env_conference_table)
# Setup camera and attach it to render product
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
surface = rep.create.disk(scale=100, visible=False)
# trigger on frame for an interval
with rep.trigger.on_frame(5):
rep.randomizer.env_conference_table(2)
rep.randomizer.dome_lights()
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)),
look_at=surface,
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(output_dir="~/replicator_examples/dli_example_3", rgb=True)
writer.attach([render_product])
| 3,860 | Python | 40.074468 | 110 | 0.688083 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/01_hello_replicator.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
# Create a new layer for our work to be performed in.
# This is a good habit to develop for later when working on existing Usd scenes
with rep.new_layer():
# Create a simple camera with a position and a point to look at
camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))
# Create some simple shapes to manipulate
plane = rep.create.plane(
semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100)
)
torus = rep.create.torus(semantics=[("class", "torus")], position=(200, 0, 100))
sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 0, 100))
cube = rep.create.cube(semantics=[("class", "cube")], position=(-200, 0, 100))
# Randomize position and scale of each object on each frame
with rep.trigger.on_frame(num_frames=10):
# Creating a group so that our modify.pose operation works on all the shapes at once
with rep.create.group([torus, sphere, cube]):
rep.modify.pose(
position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),
scale=rep.distribution.uniform(0.1, 2),
)
# Initialize render product and attach a writer
render_product = rep.create.render_product(camera, (1024, 1024))
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="~/replicator_examples/dli_hello_replicator/",
rgb=True,
semantic_segmentation=True,
bounding_box_2d_tight=True,
)
writer.attach([render_product])
rep.orchestrator.run()
| 3,261 | Python | 46.970588 | 92 | 0.726771 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/physics.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
# Define paths for the character, the props, the environment and the surface where the assets will be scattered in.
PROPS = "omniverse://localhost/NVIDIA/Assets/Isaac/2022.1/Isaac/Props/YCB/Axis_Aligned_Physics"
SURFACE = (
"omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Basic/display_riser.usd"
)
ENVS = "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Interior/ZetCG_ExhibitionHall.usd"
# Define randomizer function for Base assets. This randomization includes placement and rotation of the assets on the surface.
def env_props(size=50):
instances = rep.randomizer.instantiate(
rep.utils.get_usd_files(PROPS, recursive=True),
size=size,
mode="scene_instance",
)
with instances:
rep.modify.pose(
position=rep.distribution.uniform((-50, 5, -50), (50, 20, 50)),
rotation=rep.distribution.uniform((0, -180, 0), (0, 180, 0)),
scale=100,
)
rep.physics.rigid_body(
velocity=rep.distribution.uniform((-0, 0, -0), (0, 0, 0)),
angular_velocity=rep.distribution.uniform((-0, 0, -100), (0, 0, 0)),
)
return instances.node
# Register randomization
rep.randomizer.register(env_props)
# Setup the static elements
env = rep.create.from_usd(ENVS)
surface = rep.create.from_usd(SURFACE)
with surface:
rep.physics.collider()
# Setup camera and attach it to render product
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
# sphere lights for extra randomization
def sphere_lights(num):
lights = rep.create.light(
light_type="Sphere",
temperature=rep.distribution.normal(6500, 500),
intensity=rep.distribution.normal(35000, 5000),
position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)),
scale=rep.distribution.uniform(50, 100),
count=num,
)
return lights.node
rep.randomizer.register(sphere_lights)
# trigger on frame for an interval
with rep.trigger.on_time(interval=2, num=10):
rep.randomizer.env_props(10)
rep.randomizer.sphere_lights(10)
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-50, 20, 100), (50, 50, 150)),
look_at=surface,
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="~/replicator_examples/dli_physics",
rgb=True,
bounding_box_2d_tight=True,
)
writer.attach([render_product])
| 4,509 | Python | 41.952381 | 130 | 0.681082 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/02_background_randomization.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
def dome_lights():
lights = rep.create.light(
light_type="Dome",
rotation=(270, 0, 0),
texture=rep.distribution.choice(
[
"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/champagne_castle_1_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Clear/evening_road_01_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/kloofendal_48d_partly_cloudy_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Clear/qwantani_4k.hdr",
]
),
)
return lights.node
rep.randomizer.register(dome_lights)
torus = rep.create.torus(semantics=[("class", "torus")], position=(0, -200, 100))
# create surface
surface = rep.create.disk(scale=5, visible=False)
# create camera & render product for the scene
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
with rep.trigger.on_frame(num_frames=10, interval=10):
rep.randomizer.dome_lights()
with rep.create.group([torus]):
rep.modify.pose(
position=rep.distribution.uniform((-100, -100, -100), (200, 200, 200)),
scale=rep.distribution.uniform(0.1, 2),
)
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)),
look_at=surface,
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(output_dir="~/replicator_examples/dli_example_02", rgb=True)
writer.attach([render_product])
# Run Replicator
# rep.orchestrator.run()
| 3,520 | Python | 41.421686 | 107 | 0.685227 |
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_randomizer_materials.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Creating materials, and applying them to objects in a scene with a randomizer
# Create the materials to apply to the objects
mats:
create.material_omnipbr:
diffuse:
distribution.uniform:
lower: [0, 0, 0]
upper: [1, 1, 1]
count: 100
# Create the objects in the scene
spheres:
create.sphere:
scale: 0.2
position:
distribution.uniform:
lower: [-1, -1, -1]
upper: [1, 1, 1]
count: 100
plane:
create.plane:
semantics: [["class", "plane"]]
position: [0, 0, -1.5]
visible: true
scale: 100
# Create the camera and render product
camera:
create.camera:
position: [5, 0, 0]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialRandomizerMaterials/"
rgb: True
writer_attach:
writer.attach:
render_products: render_product
# Register a randomizer that sets the materials of the spheres
register_materials:
randomizer.register:
get_spheres:
inputs:
spheres: null
mats: null
with.spheres:
randomizer.materials:
materials: mats
# Set the trigger as on_frame, setting subframes to accumulate frames for a
# higher quality render
trigger:
trigger.on_frame:
max_execs: 20
rt_subframes: 3
# When the trigger executes, apply the randomizer
with_trigger:
with.trigger:
randomizer.get_spheres:
spheres: spheres
mats: mats
| 3,700 | YAML | 29.841666 | 84 | 0.721892 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/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. | 3,543 | Markdown | 60.103447 | 117 | 0.812024 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/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
| 2,771 | Markdown | 36.972602 | 294 | 0.738001 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/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.
| 1,167 | Markdown | 54.619045 | 97 | 0.796058 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/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 | 2,308 | Markdown | 66.911763 | 396 | 0.786395 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/README.md | # Requirements
- Install [Isaac Sim](https://docs.omniverse.nvidia.com/isaacsim/latest/install_workstation.html)
- Training via TAO Toolkit Docker container (TAO setup instructions in `local_train` notebook)
## Synthetic Data Generation
- Provide the path of your Isaac Sim installation folder in the `generate_data.sh` script
- Make the script an executable after adding the Isaac Sim Path (`chmod +x generate_data.sh`)
- Run the script (`./generate_data.sh`)
- We will generate data for the `palletjack` class of objects with annotations in KITTI format
- This generated data can be used to train your own model (framework and architecture of your choice)
## Training with TAO Toolkit
- The data generated in the previus step can be directly fed to TAO for training
- The `local_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 | 2,370 | Markdown | 66.742855 | 396 | 0.7827 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/palletjack_sdg/standalone_palletjack_sdg.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
from omni.isaac.kit import SimulationApp
import os
import argparse
parser = argparse.ArgumentParser("Dataset generator")
parser.add_argument("--headless", type=bool, default=False, help="Launch script headless, default is False")
parser.add_argument("--height", type=int, default=544, help="Height of image")
parser.add_argument("--width", type=int, default=960, help="Width of image")
parser.add_argument("--num_frames", type=int, default=1000, help="Number of frames to record")
parser.add_argument("--distractors", type=str, default="warehouse",
help="Options are 'warehouse' (default), 'additional' or None")
parser.add_argument("--data_dir", type=str, default=os.getcwd() + "/_palletjack_data",
help="Location where data will be output")
args, unknown_args = parser.parse_known_args()
# This is the config used to launch simulation.
CONFIG = {"renderer": "RayTracedLighting", "headless": args.headless,
"width": args.width, "height": args.height, "num_frames": args.num_frames}
simulation_app = SimulationApp(launch_config=CONFIG)
## This is the path which has the background scene in which objects will be added.
ENV_URL = "/Isaac/Environments/Simple_Warehouse/warehouse.usd"
import carb
import omni
import omni.usd
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.stage import get_current_stage, open_stage
from pxr import Semantics
import omni.replicator.core as rep
from omni.isaac.core.utils.semantics import get_semantics
# Increase subframes if shadows/ghosting appears of moving objects
# See known issues: https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html#known-issues
rep.settings.carb_settings("/omni/replicator/RTSubframes", 4)
# This is the location of the palletjacks in the simready asset library
PALLETJACKS = ["http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Scale_A/PalletTruckScale_A01_PR_NVD_01.usd",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Heavy_Duty_A/HeavyDutyPalletTruck_A01_PR_NVD_01.usd",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Low_Profile_A/LowProfilePalletTruck_A01_PR_NVD_01.usd"]
# The warehouse distractors which will be added to the scene and randomized
DISTRACTORS_WAREHOUSE = 2 * ["/Isaac/Environments/Simple_Warehouse/Props/S_TrafficCone.usd",
"/Isaac/Environments/Simple_Warehouse/Props/S_WetFloorSign.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_C_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticB_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticD_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticE_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BucketPlastic_B.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1262.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1268.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1482.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1683.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_291.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxD_01_1454.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxD_01_1513.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_A_04.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_B_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_B_05.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_C_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_E_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_PushcartA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_RackPile_04.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_RackPile_03.usd"]
## Additional distractors which can be added to the scene
DISTRACTORS_ADDITIONAL = ["/Isaac/Environments/Hospital/Props/Pharmacy_Low.usd",
"/Isaac/Environments/Hospital/Props/SM_BedSideTable_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_BooksSet_26.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleB.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleA.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleC.usd",
"/Isaac/Environments/Hospital/Props/SM_Cart_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Chair_02a.usd",
"/Isaac/Environments/Hospital/Props/SM_Chair_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Computer_02b.usd",
"/Isaac/Environments/Hospital/Props/SM_Desk_04a.usd",
"/Isaac/Environments/Hospital/Props/SM_DisposalStand_02.usd",
"/Isaac/Environments/Hospital/Props/SM_FirstAidKit_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_GasCart_01c.usd",
"/Isaac/Environments/Hospital/Props/SM_Gurney_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_HospitalBed_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_MedicalBag_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Mirror.usd",
"/Isaac/Environments/Hospital/Props/SM_MopSet_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_SideTable_02a.usd",
"/Isaac/Environments/Hospital/Props/SM_SupplyCabinet_01c.usd",
"/Isaac/Environments/Hospital/Props/SM_SupplyCart_01e.usd",
"/Isaac/Environments/Hospital/Props/SM_TrashCan.usd",
"/Isaac/Environments/Hospital/Props/SM_Washbasin.usd",
"/Isaac/Environments/Hospital/Props/SM_WheelChair_01a.usd",
"/Isaac/Environments/Office/Props/SM_WaterCooler.usd",
"/Isaac/Environments/Office/Props/SM_TV.usd",
"/Isaac/Environments/Office/Props/SM_TableC.usd",
"/Isaac/Environments/Office/Props/SM_Recliner.usd",
"/Isaac/Environments/Office/Props/SM_Personenleitsystem_Red1m.usd",
"/Isaac/Environments/Office/Props/SM_Lamp02_162.usd",
"/Isaac/Environments/Office/Props/SM_Lamp02.usd",
"/Isaac/Environments/Office/Props/SM_HandDryer.usd",
"/Isaac/Environments/Office/Props/SM_Extinguisher.usd"]
# The textures which will be randomized for the wall and floor
TEXTURES = ["/Isaac/Materials/Textures/Patterns/nv_asphalt_yellow_weathered.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_hexagonal_green_white.jpg",
"/Isaac/Materials/Textures/Patterns/nv_rubber_woven_charcoal.jpg",
"/Isaac/Materials/Textures/Patterns/nv_granite_tile.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_square_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_marble.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_reclaimed.jpg",
"/Isaac/Materials/Textures/Patterns/nv_concrete_aged_with_lines.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wooden_wall.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stone_painted_grey.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_shingles_brown.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_hexagonal_various.jpg",
"/Isaac/Materials/Textures/Patterns/nv_carpet_abstract_pattern.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_siding_weathered_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_animalfur_pattern_greys.jpg",
"/Isaac/Materials/Textures/Patterns/nv_artificialgrass_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_bamboo_desktop.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_reclaimed.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_red_stacked.jpg",
"/Isaac/Materials/Textures/Patterns/nv_fireplace_wall.jpg",
"/Isaac/Materials/Textures/Patterns/nv_fabric_square_grid.jpg",
"/Isaac/Materials/Textures/Patterns/nv_granite_tile.jpg",
"/Isaac/Materials/Textures/Patterns/nv_marble.jpg",
"/Isaac/Materials/Textures/Patterns/nv_gravel_grey_leaves.jpg",
"/Isaac/Materials/Textures/Patterns/nv_plastic_blue.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stone_red_hatch.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stucco_red_painted.jpg",
"/Isaac/Materials/Textures/Patterns/nv_rubber_woven_charcoal.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stucco_smooth_blue.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_shingles_brown.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wooden_wall.jpg"]
def update_semantics(stage, keep_semantics=[]):
""" Remove semantics from the stage except for keep_semantic classes"""
for prim in stage.Traverse():
if prim.HasAPI(Semantics.SemanticsAPI):
processed_instances = set()
for property in prim.GetProperties():
is_semantic = Semantics.SemanticsAPI.IsSemanticsAPIPath(property.GetPath())
if is_semantic:
instance_name = property.SplitName()[1]
if instance_name in processed_instances:
# Skip repeated instance, instances are iterated twice due to their two semantic properties (class, data)
continue
processed_instances.add(instance_name)
sem = Semantics.SemanticsAPI.Get(prim, instance_name)
type_attr = sem.GetSemanticTypeAttr()
data_attr = sem.GetSemanticDataAttr()
for semantic_class in keep_semantics:
# Check for our data classes needed for the model
if data_attr.Get() == semantic_class:
continue
else:
# remove semantics of all other prims
prim.RemoveProperty(type_attr.GetName())
prim.RemoveProperty(data_attr.GetName())
prim.RemoveAPI(Semantics.SemanticsAPI, instance_name)
# needed for loading textures correctly
def prefix_with_isaac_asset_server(relative_path):
assets_root_path = get_assets_root_path()
if assets_root_path is None:
raise Exception("Nucleus server not found, could not access Isaac Sim assets folder")
return assets_root_path + relative_path
def full_distractors_list(distractor_type="warehouse"):
"""Distractor type allowed are warehouse, additional or None. They load corresponding objects and add
them to the scene for DR"""
full_dist_list = []
if distractor_type == "warehouse":
for distractor in DISTRACTORS_WAREHOUSE:
full_dist_list.append(prefix_with_isaac_asset_server(distractor))
elif distractor_type == "additional":
for distractor in DISTRACTORS_ADDITIONAL:
full_dist_list.append(prefix_with_isaac_asset_server(distractor))
else:
print("No Distractors being added to the current scene for SDG")
return full_dist_list
def full_textures_list():
full_tex_list = []
for texture in TEXTURES:
full_tex_list.append(prefix_with_isaac_asset_server(texture))
return full_tex_list
def add_palletjacks():
rep_obj_list = [rep.create.from_usd(palletjack_path, semantics=[("class", "palletjack")], count=2) for palletjack_path in PALLETJACKS]
rep_palletjack_group = rep.create.group(rep_obj_list)
return rep_palletjack_group
def add_distractors(distractor_type="warehouse"):
full_distractors = full_distractors_list(distractor_type)
distractors = [rep.create.from_usd(distractor_path, count=1) for distractor_path in full_distractors]
distractor_group = rep.create.group(distractors)
return distractor_group
# This will handle replicator
def run_orchestrator():
rep.orchestrator.run()
# Wait until started
while not rep.orchestrator.get_is_started():
simulation_app.update()
# Wait until stopped
while rep.orchestrator.get_is_started():
simulation_app.update()
rep.BackendDispatch.wait_until_done()
rep.orchestrator.stop()
def main():
# Open the environment in a new stage
print(f"Loading Stage {ENV_URL}")
open_stage(prefix_with_isaac_asset_server(ENV_URL))
stage = get_current_stage()
# Run some app updates to make sure things are properly loaded
for i in range(100):
if i % 10 == 0:
print(f"App uppdate {i}..")
simulation_app.update()
textures = full_textures_list()
rep_palletjack_group = add_palletjacks()
rep_distractor_group = add_distractors(distractor_type=args.distractors)
# We only need labels for the palletjack objects
update_semantics(stage=stage, keep_semantics=["palletjack"])
# Create camera with Replicator API for gathering data
cam = rep.create.camera(clipping_range=(0.1, 1000000))
# trigger replicator pipeline
with rep.trigger.on_frame(num_frames=CONFIG["num_frames"]):
# Move the camera around in the scene, focus on the center of warehouse
with cam:
rep.modify.pose(position=rep.distribution.uniform((-9.2, -11.8, 0.4), (7.2, 15.8, 4)),
look_at=(0, 0, 0))
# Get the Palletjack body mesh and modify its color
with rep.get.prims(path_pattern="SteerAxles"):
rep.randomizer.color(colors=rep.distribution.uniform((0, 0, 0), (1, 1, 1)))
# Randomize the pose of all the added palletjacks
with rep_palletjack_group:
rep.modify.pose(position=rep.distribution.uniform((-6, -6, 0), (6, 12, 0)),
rotation=rep.distribution.uniform((0, 0, 0), (0, 0, 360)),
scale=rep.distribution.uniform((0.01, 0.01, 0.01), (0.01, 0.01, 0.01)))
# Modify the pose of all the distractors in the scene
with rep_distractor_group:
rep.modify.pose(position=rep.distribution.uniform((-6, -6, 0), (6, 12, 0)),
rotation=rep.distribution.uniform((0, 0, 0), (0, 0, 360)),
scale=rep.distribution.uniform(1, 1.5))
# Randomize the lighting of the scene
with rep.get.prims(path_pattern="RectLight"):
rep.modify.attribute("color", rep.distribution.uniform((0, 0, 0), (1, 1, 1)))
rep.modify.attribute("intensity", rep.distribution.normal(100000.0, 600000.0))
rep.modify.visibility(rep.distribution.choice([True, False, False, False, False, False, False]))
# select floor material
random_mat_floor = rep.create.material_omnipbr(diffuse_texture=rep.distribution.choice(textures),
roughness=rep.distribution.uniform(0, 1),
metallic=rep.distribution.choice([0, 1]),
emissive_texture=rep.distribution.choice(textures),
emissive_intensity=rep.distribution.uniform(0, 1000),)
with rep.get.prims(path_pattern="SM_Floor"):
rep.randomizer.materials(random_mat_floor)
# select random wall material
random_mat_wall = rep.create.material_omnipbr(diffuse_texture=rep.distribution.choice(textures),
roughness=rep.distribution.uniform(0, 1),
metallic=rep.distribution.choice([0, 1]),
emissive_texture=rep.distribution.choice(textures),
emissive_intensity=rep.distribution.uniform(0, 1000),)
with rep.get.prims(path_pattern="SM_Wall"):
rep.randomizer.materials(random_mat_wall)
# Set up the writer
writer = rep.WriterRegistry.get("KittiWriter")
# output directory of writer
output_directory = args.data_dir
print("Outputting data to ", output_directory)
# use writer for bounding boxes, rgb and segmentation
writer.initialize(output_dir=output_directory,
omit_semantic_type=True,)
# attach camera render products to wrieter so that data is outputted
RESOLUTION = (CONFIG["width"], CONFIG["height"])
render_product = rep.create.render_product(cam, RESOLUTION)
writer.attach(render_product)
# run rep pipeline
run_orchestrator()
simulation_app.update()
if __name__ == "__main__":
try:
main()
except Exception as e:
carb.log_error(f"Exception: {e}")
import traceback
traceback.print_exc()
finally:
simulation_app.close()
| 20,199 | Python | 52.439153 | 191 | 0.634388 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/docker-compose.yml | version: '3'
services:
deployment:
build: deployment
# Access the lab with: http://127.0.0.1:8884/lab?token=nvsecuretoken2
command: jupyter lab -y --allow-root --no-browser --ip=0.0.0.0 --port=8884 --notebook-dir=/opt/project/ --NotebookApp.token='nvsecuretoken2' --NotebookApp.password='nvsecurepassword'
shm_size: '2gb'
volumes:
- './deployment/code/:/opt/project/'
- './models:/opt/models/'
ports:
- "8884:8884"
training:
build: training
# Access the lab with: http://127.0.0.1:8883/lab?token=nvsecuretoken1
command: jupyter lab -y --allow-root --no-browser --ip=0.0.0.0 --port=8883 --notebook-dir=/opt/project/ --NotebookApp.token='nvsecuretoken1' --NotebookApp.password='nvsecurepassword'
volumes:
- './training/code/:/opt/project/'
ports:
- "8883:8883"
data-generation:
build: data_generation
# Access the lab with: http://127.0.0.1:8882/lab?token=nvsecuretoken0s
command: jupyter lab -y --allow-root --no-browser --ip=0.0.0.0 --port=8882 --notebook-dir=/opt/project/ --NotebookApp.token='nvsecuretoken0' --NotebookApp.password='nvsecurepassword'
volumes:
- './data_generation/code/:/opt/project/'
ports:
- "8882:8882"
| 1,238 | YAML | 40.299999 | 186 | 0.661551 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/README.md | ## Getting started
### Install Dependencies
- [`docker-compose`](https://docs.docker.com/compose/install/)
- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
### Run the labs
```
bash run.sh
```
### Access the labs
Part 1: Generate synthetic data with Omnvierse - [http://127.0.0.1:8882/lab?token=nvsecuretoken0](http://127.0.0.1:8882/lab?token=nvsecuretoken0) \
Part 2: Training a model with synthetic data - [http://127.0.0.1:8883/lab?token=nvsecuretoken1](http://127.0.0.1:8883/lab?token=nvsecuretoken1) \
Part 3: Deploy model to Triton - [http://127.0.0.1:8884/lab?token=nvsecuretoken2](http://127.0.0.1:8884/lab?token=nvsecuretoken2)
| 708 | Markdown | 38.388887 | 147 | 0.731638 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/visualize.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import os
import json
import hashlib
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from optparse import OptionParser
"""
Takes in the data from a specific label id and maps it to the proper color for the bounding box
"""
def data_to_colour(data):
if isinstance(data, str):
data = bytes(data, "utf-8")
else:
data = bytes(data)
m = hashlib.sha256()
m.update(data)
key = int(m.hexdigest()[:8], 16)
r = ((((key >> 0) & 0xFF) + 1) * 33) % 255
g = ((((key >> 8) & 0xFF) + 1) * 33) % 255
b = ((((key >> 16) & 0xFF) + 1) * 33) % 255
# illumination normalization to 128
inv_norm_i = 128 * (3.0 / (r + g + b))
return (
int(r * inv_norm_i) / 255,
int(g * inv_norm_i) / 255,
int(b * inv_norm_i) / 255,
)
"""
Takes in the path to the rgb image for the background, then it takes bounding box data, the labels and the place to store the visualization. It outputs a colorized bounding box.
"""
def colorize_bbox_2d(rgb_path, data, id_to_labels, file_path):
rgb_img = Image.open(rgb_path)
colors = [data_to_colour(bbox["semanticId"]) for bbox in data]
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(rgb_img)
for bbox_2d, color, index in zip(data, colors, range(len(data))):
labels = id_to_labels[str(index)]
rect = patches.Rectangle(
xy=(bbox_2d["x_min"], bbox_2d["y_min"]),
width=bbox_2d["x_max"] - bbox_2d["x_min"],
height=bbox_2d["y_max"] - bbox_2d["y_min"],
edgecolor=color,
linewidth=2,
label=labels,
fill=False,
)
ax.add_patch(rect)
plt.legend(loc="upper left")
plt.savefig(file_path)
"""
Parses command line options. Requires input directory, output directory, and number for image to use.
"""
def parse_input():
usage = "usage: visualize.py [options] arg1 arg2 arg3"
parser = OptionParser(usage)
parser.add_option(
"-d",
"--data_dir",
dest="data_dir",
help="Directory location for Omniverse synthetic data",
)
parser.add_option(
"-o", "--out_dir", dest="out_dir", help="Directory location for output image"
)
parser.add_option(
"-n", "--number", dest="number", help="Number of image to use for visualization"
)
(options, args) = parser.parse_args()
return options, args
def main():
options, args = parse_input()
out_dir = options.data_dir
rgb = "png/rgb_" + options.number + ".png"
rgb_path = os.path.join(out_dir, rgb)
bbox2d_tight_file_name = "npy/bounding_box_2d_tight_" + options.number + ".npy"
data = np.load(os.path.join(options.data_dir, bbox2d_tight_file_name))
# Check for labels
bbox2d_tight_labels_file_name = (
"json/bounding_box_2d_tight_labels_" + options.number + ".json"
)
with open(
os.path.join(options.data_dir, bbox2d_tight_labels_file_name), "r"
) as json_data:
bbox2d_tight_id_to_labels = json.load(json_data)
# colorize and save image
colorize_bbox_2d(
rgb_path,
data,
bbox2d_tight_id_to_labels,
os.path.join(options.out_dir, "bbox2d_tight.png"),
)
if __name__ == "__main__":
main()
| 5,013 | Python | 32.651006 | 177 | 0.658687 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/export.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import os
import torch
import torchvision
from optparse import OptionParser
def parse_input():
usage = "usage: export.py [options] arg1 "
parser = OptionParser(usage)
parser.add_option(
"-d",
"--pytorch_dir",
dest="pytorch_dir",
help="Location of output PyTorch model",
)
parser.add_option(
"-o",
"--output_dir",
dest="output_dir",
help="Export and save ONNX model to this path",
)
(options, args) = parser.parse_args()
return options, args
def main():
torch.manual_seed(0)
options, args = parse_input()
model = torch.load(options.pytorch_dir)
model.eval()
OUTPUT_DIR = options.output_dir
os.makedirs(OUTPUT_DIR, exist_ok=True)
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
weights="DEFAULT", num_classes=91
)
model.eval()
dummy_input = torch.rand(1, 3, 1024, 1024)
torch.onnx.export(
model,
dummy_input,
os.path.join(OUTPUT_DIR, "model.onnx"),
opset_version=11,
input_names=["input"],
output_names=["output"],
)
if __name__ == "__main__":
main()
| 2,865 | Python | 33.119047 | 84 | 0.704363 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/train.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
from PIL import Image
import os
import numpy as np
import torch
import torch.utils.data
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision import transforms as T
import json
import shutil
from optparse import OptionParser
from torch.utils.tensorboard import SummaryWriter
class FruitDataset(torch.utils.data.Dataset):
def __init__(self, root, transforms):
self.root = root
self.transforms = transforms
list_ = os.listdir(root)
for file_ in list_:
name, ext = os.path.splitext(file_)
ext = ext[1:]
if ext == "":
continue
if os.path.exists(root + "/" + ext):
shutil.move(root + "/" + file_, root + "/" + ext + "/" + file_)
else:
os.makedirs(root + "/" + ext)
shutil.move(root + "/" + file_, root + "/" + ext + "/" + file_)
self.imgs = list(sorted(os.listdir(os.path.join(root, "png"))))
self.label = list(sorted(os.listdir(os.path.join(root, "json"))))
self.box = list(sorted(os.listdir(os.path.join(root, "npy"))))
def __getitem__(self, idx):
img_path = os.path.join(self.root, "png", self.imgs[idx])
img = Image.open(img_path).convert("RGB")
label_path = os.path.join(self.root, "json", self.label[idx])
with open(os.path.join("root", label_path), "r") as json_data:
json_labels = json.load(json_data)
box_path = os.path.join(self.root, "npy", self.box[idx])
dat = np.load(str(box_path))
boxes = []
labels = []
for i in dat:
obj_val = i[0]
xmin = torch.as_tensor(np.min(i[1]), dtype=torch.float32)
xmax = torch.as_tensor(np.max(i[3]), dtype=torch.float32)
ymin = torch.as_tensor(np.min(i[2]), dtype=torch.float32)
ymax = torch.as_tensor(np.max(i[4]), dtype=torch.float32)
if (ymax > ymin) & (xmax > xmin):
boxes.append([xmin, ymin, xmax, ymax])
area = (xmax - xmin) * (ymax - ymin)
labels += [json_labels.get(str(obj_val)).get("class")]
label_dict = {}
static_labels = {
"apple": 0,
"avocado": 1,
"kiwi": 2,
"lime": 3,
"lychee": 4,
"pomegranate": 5,
"onion": 6,
"strawberry": 7,
"lemon": 8,
"orange": 9,
}
labels_out = []
for i in range(len(labels)):
label_dict[i] = labels[i]
for i in label_dict:
fruit = label_dict[i]
final_fruit_label = static_labels[fruit]
labels_out += [final_fruit_label]
target = {}
target["boxes"] = torch.as_tensor(boxes, dtype=torch.float32)
target["labels"] = torch.as_tensor(labels_out, dtype=torch.int64)
target["image_id"] = torch.tensor([idx])
target["area"] = area
if self.transforms is not None:
img = self.transforms(img)
return img, target
def __len__(self):
return len(self.imgs)
"""
Parses command line options. Requires input data directory, output torch file, and number epochs used to train.
"""
def parse_input():
usage = "usage: train.py [options] arg1 arg2 "
parser = OptionParser(usage)
parser.add_option(
"-d",
"--data_dir",
dest="data_dir",
help="Directory location for Omniverse synthetic data.",
)
parser.add_option(
"-o",
"--output_file",
dest="output_file",
help="Save torch model to this file and location (file ending in .pth)",
)
parser.add_option(
"-e",
"--epochs",
dest="epochs",
help="Give number of epochs to be used for training",
)
(options, args) = parser.parse_args()
return options, args
def get_transform(train):
transforms = []
transforms.append(T.PILToTensor())
transforms.append(T.ConvertImageDtype(torch.float))
return T.Compose(transforms)
def collate_fn(batch):
return tuple(zip(*batch))
def create_model(num_classes):
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT")
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
return model
def main():
writer = SummaryWriter()
options, args = parse_input()
dataset = FruitDataset(options.data_dir, get_transform(train=True))
train_size = int(len(dataset) * 0.7)
valid_size = int(len(dataset) * 0.2)
test_size = len(dataset) - valid_size - train_size
train, valid, test = torch.utils.data.random_split(
dataset, [train_size, valid_size, test_size]
)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=16, shuffle=True, num_workers=4, collate_fn=collate_fn
)
validloader = torch.utils.data.DataLoader(
valid, batch_size=16, shuffle=True, num_workers=4, collate_fn=collate_fn
)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
num_classes = 10
num_epochs = int(options.epochs)
model = create_model(num_classes)
model.to(device)
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.001)
len_dataloader = len(data_loader)
model.train()
for epoch in range(num_epochs):
optimizer.zero_grad()
i = 0
for imgs, annotations in data_loader:
i += 1
imgs = list(img.to(device) for img in imgs)
annotations = [{k: v.to(device) for k, v in t.items()} for t in annotations]
loss_dict = model(imgs, annotations)
losses = sum(loss for loss in loss_dict.values())
writer.add_scalar("Loss/train", losses, epoch)
losses.backward()
optimizer.step()
print(f"Iteration: {i}/{len_dataloader}, Loss: {losses}")
writer.close()
torch.save(model, options.output_file)
if __name__ == "__main__":
main()
| 7,902 | Python | 32.487288 | 111 | 0.621362 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/data_generation/code/generate_data_gui.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import datetime
now = datetime.datetime.now()
from functools import partial
import omni.replicator.core as rep
with rep.new_layer():
# Define paths for the character, the props, the environment and the surface where the assets will be scattered in.
CRATE = "omniverse://localhost/NVIDIA/Samples/Marbles/assets/standalone/SM_room_crate_3/SM_room_crate_3.usd"
SURFACE = (
"omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Basic/display_riser.usd"
)
ENVS = "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Interior/ZetCG_ExhibitionHall.usd"
FRUIT_PROPS = {
"apple": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Apple.usd",
"avocado": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Avocado01.usd",
"kiwi": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Kiwi01.usd",
"lime": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Lime01.usd",
"lychee": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Lychee01.usd",
"pomegranate": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Pomegranate01.usd",
"onion": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Vegetables/RedOnion.usd",
"strawberry": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Berries/strawberry.usd",
"lemon": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Decor/Tchotchkes/Lemon_01.usd",
"orange": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Decor/Tchotchkes/Orange_01.usd",
}
# Define randomizer function for Base assets. This randomization includes placement and rotation of the assets on the surface.
def random_props(file_name, class_name, max_number=1, one_in_n_chance=3):
instances = rep.randomizer.instantiate(
file_name, size=max_number, mode="scene_instance"
)
print(file_name)
with instances:
rep.modify.semantics([("class", class_name)])
rep.modify.pose(
position=rep.distribution.uniform((-8, 5, -25), (8, 30, 25)),
rotation=rep.distribution.uniform((-180, -180, -180), (180, 180, 180)),
scale=rep.distribution.uniform((0.8), (1.2)),
)
rep.modify.visibility(
rep.distribution.choice([True], [False] * (one_in_n_chance))
)
return instances.node
# Define randomizer function for sphere lights.
def sphere_lights(num):
lights = rep.create.light(
light_type="Sphere",
temperature=rep.distribution.normal(6500, 500),
intensity=rep.distribution.normal(30000, 5000),
position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)),
scale=rep.distribution.uniform(50, 100),
count=num,
)
return lights.node
rep.randomizer.register(random_props)
# Setup the static elements
env = rep.create.from_usd(ENVS)
surface = rep.create.from_usd(SURFACE)
with surface:
rep.physics.collider()
crate = rep.create.from_usd(CRATE)
with crate:
rep.physics.collider("none")
rep.physics.mass(mass=10000)
rep.modify.pose(position=(0, 20, 0), rotation=(0, 0, 90))
# Setup camera and attach it to render product
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
rep.randomizer.register(sphere_lights)
# trigger on frame for an interval
with rep.trigger.on_frame(num_frames=100):
for n, f in FRUIT_PROPS.items():
random_props(f, n)
rep.randomizer.sphere_lights(5)
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-3, 114, -17), (-1, 116, -15)),
look_at=(0, 20, 0),
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
now = now.strftime("%Y-%m-%d")
output_dir = "fruit_data_" + now
writer.initialize(output_dir=output_dir, rgb=True, bounding_box_2d_tight=True)
writer.attach([render_product])
| 5,923 | Python | 47.162601 | 130 | 0.690866 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/deployment/code/deploy.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import tritonclient.grpc as grpcclient
from optparse import OptionParser
# load image data
import cv2
import numpy as np
from matplotlib import pyplot as plt
import subprocess
def install(name):
subprocess.call(["pip", "install", name])
"""
Parses command line options. Requires input sample png
"""
def parse_input():
usage = "usage: deploy.py [options] arg1 "
parser = OptionParser(usage)
parser.add_option(
"-p", "--png", dest="png", help="Directory location for single sample image."
)
(options, args) = parser.parse_args()
return options, args
def main():
options, args = parse_input()
target_width, target_height = 1024, 1024
# add path to test image
image_sample = options.png
image_bgr = cv2.imread(image_sample)
image_bgr
image_bgr = cv2.resize(image_bgr, (target_width, target_height))
image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
image = np.float32(image_rgb)
# preprocessing
image = image / 255
image = np.moveaxis(image, -1, 0) # HWC to CHW
image = image[np.newaxis, :] # add batch dimension
image = np.float32(image)
plt.imshow(image_rgb)
inference_server_url = "0.0.0.0:9001"
triton_client = grpcclient.InferenceServerClient(url=inference_server_url)
# find out info about model
model_name = "fasterrcnn_resnet50"
triton_client.get_model_config(model_name)
# create input
input_name = "input"
inputs = [grpcclient.InferInput(input_name, image.shape, "FP32")]
inputs[0].set_data_from_numpy(image)
output_name = "output"
outputs = [grpcclient.InferRequestedOutput("output")]
results = triton_client.infer(model_name, inputs, outputs=outputs)
output = results.as_numpy("output")
# annotate
annotated_image = image_bgr.copy()
if output.size > 0: # ensure something is found
for box in output:
box_top_left = int(box[0]), int(box[1])
box_bottom_right = int(box[2]), int(box[3])
text_origin = int(box[0]), int(box[3])
border_color = (50, 0, 100)
text_color = (255, 255, 255)
font_scale = 0.9
thickness = 1
# bounding box
cv2.rectangle(
annotated_image,
box_top_left,
box_bottom_right,
border_color,
thickness=5,
lineType=cv2.LINE_8,
)
plt.imshow(cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB))
if __name__ == "__main__":
main()
| 4,261 | Python | 31.287879 | 85 | 0.680357 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/docs/part_2.md | # Part 2: Training a model with synthetic data
## Setup training
To use the training script you can see required parameters by running
-`python train.py --help`
- Example command:
- `python train.py -d /home/omni.replicator_out/fruit_data_$DATE/ -o /home/model.pth -e 10`
## Visualize training
We have included a visdualization script to run after your first training. This will show how Omniverse generates the labeled data. To see required parameters
- `python visualize.py --help`
- Example command:
- `python visualize.py -d /home/$USER/omni.replicator_out/fruit_data_$DATE -o /home/$USER -n 0`
## Export model
- To use the export script you can see required parameters by running
- `python export.py --help`
- Example command, make sure to dave to the `models/fasterrcnn_resnet50/1`
- `python export.py -d /home/out.pth -o /home/models/fasterrcnn_resnet50/1`
| 874 | Markdown | 38.772726 | 158 | 0.744851 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/docs/part_3.md | # Part 3: Deploy model to Triton
## Start triton server
When we start the server we want our model to be properly located in the `/models/fasterrcnn_resnet50/1` folder.
`sudo docker run --gpus=1 --rm -p9000:8000 -p9001:8001 -p9002:8002 -v /home/$USER/sdg_workflow/models/:/models nvcr.io/nvidia/tritonserver:23.01-py3 tritonserver --model-repository=/models`
Once started, you should see:
```
+---------------------+---------+--------+
| Model | Version | Status |
+---------------------+---------+--------+
| fasterrcnn_resnet50 | 1 | READY |
+---------------------+---------+--------+
```
## Start triton client
In another terminal window, with your server running start your client
- `sudo docker run -it --rm --net=host -v /home/zoe/Desktop/sdg_workflow:/workspace nvcr.io/nvidia/tritonserver:23.01-py3-sdk`
- To use the deploy script you can see required parameters by running
- `python deploy.py --help`
- Example command:
- ` python deploy.py -p /workspace/rgb_0.png`
| 1,006 | Markdown | 34.964284 | 189 | 0.622266 |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/docs/part_1.md | # Part 1: Generate synthetic data with Omnvierse
## Install Dependencies
In this section you can generate your synthetic data using the Omniverse GUI or as a headless version in your local terminal. Either option requires an Omniverse install.
- [Install Omniverse Launcher](https://docs.omniverse.nvidia.com/prod_install-guide/prod_install-guide/overview.html#omniverse-install-guide)
## Omniverse Launcher & Code
- [Install Omniverse Code](https://docs.omniverse.nvidia.com/prod_workflows/prod_workflows/extensions/environment_configuration.html#step-2-install-omniverse-code) from the `Exchange` tab within Omniverse Launcher
## Generate data in Omniverse GUI
Copy the contents of the generate_data.py script into the Script Editor tab in the bottom section of the Code window. Press the RUn1 button or ctrl + Enter on your keyboard to load the scene in the Viewport. From there you can preview a single scene in the Replciator tab at the top by clicking Preview or run the full script by clicking Run. If you make no changes to this script it will generate 100 frames.
- From inside the Code GUI using the [script editor](https://docs.omniverse.nvidia.com/app_code/prod_extensions/ext_script-editor.html)
- If using Linux, copy code from `generate_data_gui.py` into the Script Editor window
-Execute code by clicking the `Run` button or pressing `ctrl+Enter`
- To preview what the scene will look like click Replicator then `Preview` in the top bar of your Omniverse Code window
- When you are ready to generate all your data go ahead and click `Replicator` and then `Run`, this will generate the designated number of frames and drop the RGB, bounding box data, and labels into the desired folder
## Generate data headlessly
Follow the documentation guidelines to launch a terminal in the correct folder location. The correct script to pass to your --/omni/replicator.scrip is generate_data_headless.py. This will generate and save the synthetic data in the same way as before, without utilizing the Omniverse GUI.
- [How to run](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator/headless_example.html)
- Script location: `/FruitBasketOVEReplicatorDemo/data_generation/code/generate_data_headless.py`
- We need to locate `omni.code.replicator.sh`
To find look for where Omniverse ode is locally installed
- Run (script dictates where the output data is stored):
`./omni.code.replicator.sh --no-window --/omni/replicator/script= “/FruitBasketOVEReplicatorDemo/data_generation/code/generate_data_headless.py”`
| 2,562 | Markdown | 72.228569 | 411 | 0.79313 |
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/predict.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import utils
import cv2
import torch
if __name__ == "__main__":
# Parse command line arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"engine",
type=str,
help="The file path of the TensorRT engine."
)
parser.add_argument(
"image",
type=str,
help="The file path of the image provided as input for inference."
)
parser.add_argument(
"--output",
type=str,
default=None,
help="The path to output the inference visualization."
)
parser.add_argument(
"--inference-size",
type=str,
default="512x512",
help="The height and width that the image is resized to for inference."
" Denoted as (height)x(width)."
)
parser.add_argument(
"--peak-window",
type=str,
default="7x7",
help="The size of the window used when finding local peaks. Denoted as "
" (window_height)x(window_width)."
)
parser.add_argument(
'--peak-threshold',
type=float,
default=0.5,
help="The heatmap threshold to use when finding peaks. Values must be "
" larger than this value to be considered peaks."
)
parser.add_argument(
'--line-thickness',
type=int,
default=1,
help="The line thickness for drawn boxes"
)
args = parser.parse_args()
# Parse inference height, width from arguments
inference_size = tuple(int(x) for x in args.inference_size.split('x'))
peak_window = tuple(int(x) for x in args.peak_window.split('x'))
if args.output is None:
output_path = '.'.join(args.image.split('.')[:-1]) + "_output.jpg"
else:
output_path = args.output
# Create offset grid
offset_grid = utils.make_offset_grid(inference_size).to("cuda")
# Load model
model = utils.load_trt_engine_wrapper(
args.engine,
input_names=["input"],
output_names=["heatmap", "vectormap"]
)
# Load image
image = cv2.imread(args.image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Pad and resize image (aspect ratio preserving resize)
image, _, _ = utils.pad_resize(image, inference_size)
with torch.no_grad():
# Format image for inference
x = utils.format_bgr8_image(image)
x = x.to("cuda")
# Execute model
heatmap, vectormap = model(x)
# Scale and offset vectormap
keypointmap = utils.vectormap_to_keypointmap(
offset_grid,
vectormap
)
# Find local peaks
peak_mask = utils.find_heatmap_peak_mask(
heatmap,
peak_window,
args.peak_threshold
)
# Extract keypoints at local peak
keypoints = keypointmap[0][peak_mask[0, 0]]
# Draw
vis_image = utils.draw_box(
image,
keypoints,
color=(118, 186, 0),
thickness=args.line_thickness
)
vis_image = cv2.cvtColor(vis_image, cv2.COLOR_RGB2BGR)
cv2.imwrite(output_path, vis_image)
| 3,833 | Python | 26.191489 | 98 | 0.610749 |
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/utils.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn.functional as F
import numpy as np
import cv2
import einops
import tensorrt as trt
import torch2trt
from typing import Sequence
BOX_EDGES = [
[0, 1],
[1, 5],
[5, 4],
[4, 0],
[2, 3],
[3, 7],
[7, 6],
[6, 2],
[0, 2],
[1, 3],
[4, 6],
[5, 7]
]
def make_offset_grid(
size,
stride=(1, 1)
):
grid = torch.stack(
torch.meshgrid(
stride[0] * (torch.arange(size[0]) + 0.5),
stride[1] * (torch.arange(size[1]) + 0.5)
),
dim=-1
)
return grid
def vectormap_to_keypointmap(
offset_grid,
vector_map,
vector_scale: float = 1./256.
):
vector_map = vector_map / vector_scale
keypoint_map = einops.rearrange(vector_map, "b (k d) h w -> b h w k d", d=2)
keypoint_map = keypoint_map + offset_grid[:, :, None, :]
# yx -> xy
keypoint_map = keypoint_map[..., [1, 0]]
return keypoint_map
def find_heatmap_peak_mask(heatmap, window=3, threshold=0.5):
all_indices = torch.arange(
heatmap.numel(),
device=heatmap.device
)
all_indices = all_indices.reshape(heatmap.shape)
if isinstance(window, int):
window = (window, window)
values, max_indices = F.max_pool2d_with_indices(
heatmap,
kernel_size=window,
stride=1,
padding=(window[0] // 2, window[1] // 2)
)
is_above_threshold = heatmap >= threshold
is_max = max_indices == all_indices
is_peak = is_above_threshold & is_max
return is_peak
def draw_box(image_bgr, keypoints, color=(118, 186, 0), thickness=1):
num_objects = int(keypoints.shape[0])
for i in range(num_objects):
keypoints_i = keypoints[i]
kps_i = [(int(x), int(y)) for x, y in keypoints_i]
edges = BOX_EDGES
for e in edges:
cv2.line(
image_bgr,
kps_i[e[0]],
kps_i[e[1]],
(118, 186, 0),
thickness=thickness
)
return image_bgr
def pad_resize(image, output_shape):
ar_i = image.shape[1] / image.shape[0]
ar_o = output_shape[1] / output_shape[0]
# resize
if ar_i > ar_o:
w_i = output_shape[1]
h_i = min(int(w_i / ar_i), output_shape[0])
else:
h_i = output_shape[0]
w_i = min(int(h_i * ar_i), output_shape[1])
# paste
pad_left = (output_shape[1] - w_i) // 2
pad_top = (output_shape[0] - h_i) // 2
image_resize = cv2.resize(image, (w_i, h_i))
out = np.zeros_like(
image,
shape=(output_shape[0], output_shape[1], image.shape[2])
)
out[pad_top:pad_top + h_i, pad_left:pad_left + w_i] = image_resize
pad = (pad_top, pad_left)
scale = (image.shape[0] / h_i, image.shape[1] / w_i)
return out, pad, scale
def load_trt_engine(path: str):
with trt.Logger() as logger, trt.Runtime(logger) as runtime:
with open(path, 'rb') as f:
engine_bytes = f.read()
engine = runtime.deserialize_cuda_engine(engine_bytes)
return engine
def load_trt_engine_wrapper(
path: str,
input_names: Sequence,
output_names: Sequence
):
engine = load_trt_engine(path)
wrapper = torch2trt.TRTModule(
engine=engine,
input_names=input_names,
output_names=output_names
)
return wrapper
def format_bgr8_image(image, device="cuda"):
x = torch.from_numpy(image)
x = x.permute(2, 0, 1)[None, ...]
x = (x / 255 - 0.45) / 0.25
return x | 4,290 | Python | 22.194594 | 98 | 0.577156 |
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/README.md | # SDG Pallet Model
<img src="images/test_image_1_output.jpg" height="256"/>
This repository contains code for performing optimized TensorRT inference with a pre-trained
pallet detection model that was trained using synthetic data with [NVIDIA Omniverse Replicator](https://developer.nvidia.com/omniverse/replicator).
The model takes as input a monocular RGB image, and outputs the pallet box estimates. The box esimates
are defined for each pallet side face. So a single pallet may have multiple box
estimates.
If you have any questions, please feel free to reach out by opening an issue!
## Instructions
### Step 1 - Install dependencies
Assumes you've already set up your system with OpenCV, PyTorch and numpy.
Install einops for some utility functions.
```bash
pip3 install einops
```
Install [torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt). This is used
for the ``TRTModule`` class which simplifies engine inference.
```bash
git clone https://github.com/NVIDIA-AI-IOT/torch2trt
cd torch2trt
python3 setup.py develop
```
### Step 2 - Download the ONNX model
Download the pallet model ONNX file.
| Model | Notes | Links |
|-------|-------|-------|
| pallet_model_v1_all | Trained for wood and other pallets (metal, plastic). | [onnx](https://drive.google.com/file/d/1Vsl7s5YhBFxkTkd3UYYgPWFCLNRm_O_Q/view?usp=share_link) |
| pallet_model_v1_wood | Trained only for wood pallets. | [onnx](https://drive.google.com/file/d/1Fd1gS7NYkWHPhUn7iZLK43hLQ1qDkuvb/view?usp=share_link) |
### Step 3 - Build the TensorRT engine
#### Option 1 (*recommended*) - Build the FP16 engine
To build the FP16 engine, call the following:
```bash
./build_trt_fp16.sh <onnx_path> <engine_output_path>
```
#### Option 2 - Build the INT8 engine
> The INT8 model instructions do not yet include calibration. Please only use
> this model for throughput profiling. The accuracy is likely to vary from
> FP32/FP16 models. However, once calibration is included, this may become
> the recommended option given the improved throughput results.
To build the INT8 engine, call the following:
```bash
./build_trt_int8.sh <onnx_path> <engine_output_path>
```
We hope to provide instructions for using the Deep Learning Accelerator (DLA)
on Jetson AGX Orin, and INT8 calibration soon.
### Step 3 - Profile the engine
To profile the engine with the ``trtexec`` tool, call the following:
```bash
./profile_engine.sh <engine_path>
```
Here are the results for a model inference at 256x256 resolution,
profiled on Jetson AGX Orin.
<a id="throughput_results"/>
| Precision | Throughput (FPS) |
|-----------|------------------|
| FP16 | 465 |
| INT8 | 710 |
Notes:
- Called ``jetson_clocks`` before running
- Using MAXN power mode by calling ``sudo nvpmodel -m0``
- Batch size 1
- ``--useSpinWait`` flag enabled to stabilize timings
- ``--useCudaGraph`` flag enabled to use CUDA graph optimizations. Cuda graph
isn't yet used in the predict function.
### Step 4 - Run inference on an example image.
```bash
python3 predict.py <engine_path> <image_path> --output=<output_path>
```
For more options
```
python3 predict.py --help
```
### Next Steps
Try modifying the predict.py code to visualize inference on a live camera feed.
| 3,292 | Markdown | 27.634782 | 174 | 0.717801 |
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/LICENSE.md | SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: Apache-2.0
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
| 654 | Markdown | 42.666664 | 96 | 0.801223 |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/README.md | # Isaac Sim ROS & ROS2 Workspaces
This repository contains three workspaces: `noetic_ws` (ROS Noetic), `foxy_ws` (ROS2 Foxy) and `humble_ws` (ROS2 Humble).
[Click here for usage and installation instructions with Isaac Sim](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_ros.html)
When cloning this repository, all three workspaces are downloaded. Depending on which ROS distro you are using, follow the [setup instructions](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_ros.html#setting-up-workspaces) for building your specific workspace. | 593 | Markdown | 83.857131 | 284 | 0.797639 |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/scripts/ros2_publisher.py | #!/usr/bin/env python3
# Copyright (c) 2020-2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import JointState
import numpy as np
import time
class TestROS2Bridge(Node):
def __init__(self):
super().__init__("test_ros2bridge")
# Create the publisher. This publisher will publish a JointState message to the /joint_command topic.
self.publisher_ = self.create_publisher(JointState, "joint_command", 10)
# Create a JointState message
self.joint_state = JointState()
self.joint_state.name = [
"panda_joint1",
"panda_joint2",
"panda_joint3",
"panda_joint4",
"panda_joint5",
"panda_joint6",
"panda_joint7",
"panda_finger_joint1",
"panda_finger_joint2",
]
num_joints = len(self.joint_state.name)
# make sure kit's editor is playing for receiving messages
self.joint_state.position = np.array([0.0] * num_joints, dtype=np.float64).tolist()
self.default_joints = [0.0, -1.16, -0.0, -2.3, -0.0, 1.6, 1.1, 0.4, 0.4]
# limiting the movements to a smaller range (this is not the range of the robot, just the range of the movement
self.max_joints = np.array(self.default_joints) + 0.5
self.min_joints = np.array(self.default_joints) - 0.5
# position control the robot to wiggle around each joint
self.time_start = time.time()
timer_period = 0.05 # seconds
self.timer = self.create_timer(timer_period, self.timer_callback)
def timer_callback(self):
self.joint_state.header.stamp = self.get_clock().now().to_msg()
joint_position = (
np.sin(time.time() - self.time_start) * (self.max_joints - self.min_joints) * 0.5 + self.default_joints
)
self.joint_state.position = joint_position.tolist()
# Publish the message to the topic
self.publisher_.publish(self.joint_state)
def main(args=None):
rclpy.init(args=args)
ros2_publisher = TestROS2Bridge()
rclpy.spin(ros2_publisher)
# Destroy the node explicitly
ros2_publisher.destroy_node()
rclpy.shutdown()
if __name__ == "__main__":
main()
| 2,662 | Python | 31.084337 | 119 | 0.644252 |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/carter_navigation.launch.py | ## Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
## NVIDIA CORPORATION and its licensors retain all intellectual property
## and proprietary rights in and to this software, related documentation
## and any modifications thereto. Any use, reproduction, disclosure or
## distribution of this software and related documentation without an express
## license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch.actions import IncludeLaunchDescription
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration
from launch_ros.actions import Node
def generate_launch_description():
use_sim_time = LaunchConfiguration("use_sim_time", default="True")
map_dir = LaunchConfiguration(
"map",
default=os.path.join(
get_package_share_directory("carter_navigation"), "maps", "carter_warehouse_navigation.yaml"
),
)
param_dir = LaunchConfiguration(
"params_file",
default=os.path.join(
get_package_share_directory("carter_navigation"), "params", "carter_navigation_params.yaml"
),
)
nav2_bringup_launch_dir = os.path.join(get_package_share_directory("nav2_bringup"), "launch")
rviz_config_dir = os.path.join(get_package_share_directory("carter_navigation"), "rviz2", "carter_navigation.rviz")
return LaunchDescription(
[
DeclareLaunchArgument("map", default_value=map_dir, description="Full path to map file to load"),
DeclareLaunchArgument(
"params_file", default_value=param_dir, description="Full path to param file to load"
),
DeclareLaunchArgument(
"use_sim_time", default_value="true", description="Use simulation (Omniverse Isaac Sim) clock if true"
),
IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(nav2_bringup_launch_dir, "rviz_launch.py")),
launch_arguments={"namespace": "", "use_namespace": "False", "rviz_config": rviz_config_dir}.items(),
),
IncludeLaunchDescription(
PythonLaunchDescriptionSource([nav2_bringup_launch_dir, "/bringup_launch.py"]),
launch_arguments={"map": map_dir, "use_sim_time": use_sim_time, "params_file": param_dir}.items(),
),
Node(
package='pointcloud_to_laserscan', executable='pointcloud_to_laserscan_node',
remappings=[('cloud_in', ['/front_3d_lidar/point_cloud']),
('scan', ['/scan'])],
parameters=[{
'target_frame': 'front_3d_lidar',
'transform_tolerance': 0.01,
'min_height': -0.4,
'max_height': 1.5,
'angle_min': -1.5708, # -M_PI/2
'angle_max': 1.5708, # M_PI/2
'angle_increment': 0.0087, # M_PI/360.0
'scan_time': 0.3333,
'range_min': 0.05,
'range_max': 100.0,
'use_inf': True,
'inf_epsilon': 1.0,
# 'concurrency_level': 1,
}],
name='pointcloud_to_laserscan'
)
]
)
| 3,521 | Python | 42.481481 | 119 | 0.601534 |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/carter_navigation_individual.launch.py | ## Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
## NVIDIA CORPORATION and its licensors retain all intellectual property
## and proprietary rights in and to this software, related documentation
## and any modifications thereto. Any use, reproduction, disclosure or
## distribution of this software and related documentation without an express
## license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, ExecuteProcess, IncludeLaunchDescription
from launch.conditions import IfCondition
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration, PythonExpression, TextSubstitution
from launch_ros.actions import Node
def generate_launch_description():
# Get the launch directory
nav2_launch_dir = os.path.join(get_package_share_directory("nav2_bringup"), "launch")
# Create the launch configuration variables
slam = LaunchConfiguration("slam")
namespace = LaunchConfiguration("namespace")
use_namespace = LaunchConfiguration("use_namespace")
map_yaml_file = LaunchConfiguration("map")
use_sim_time = LaunchConfiguration("use_sim_time")
params_file = LaunchConfiguration("params_file")
default_bt_xml_filename = LaunchConfiguration("default_bt_xml_filename")
autostart = LaunchConfiguration("autostart")
# Declare the launch arguments
declare_namespace_cmd = DeclareLaunchArgument("namespace", default_value="", description="Top-level namespace")
declare_use_namespace_cmd = DeclareLaunchArgument(
"use_namespace", default_value="false", description="Whether to apply a namespace to the navigation stack"
)
declare_slam_cmd = DeclareLaunchArgument("slam", default_value="False", description="Whether run a SLAM")
declare_map_yaml_cmd = DeclareLaunchArgument(
"map",
default_value=os.path.join(nav2_launch_dir, "maps", "carter_warehouse_navigation.yaml"),
description="Full path to map file to load",
)
declare_use_sim_time_cmd = DeclareLaunchArgument(
"use_sim_time", default_value="True", description="Use simulation (Isaac Sim) clock if true"
)
declare_params_file_cmd = DeclareLaunchArgument(
"params_file",
default_value=os.path.join(nav2_launch_dir, "params", "nav2_params.yaml"),
description="Full path to the ROS2 parameters file to use for all launched nodes",
)
declare_bt_xml_cmd = DeclareLaunchArgument(
"default_bt_xml_filename",
default_value=os.path.join(
get_package_share_directory("nav2_bt_navigator"), "behavior_trees", "navigate_w_replanning_and_recovery.xml"
),
description="Full path to the behavior tree xml file to use",
)
declare_autostart_cmd = DeclareLaunchArgument(
"autostart", default_value="true", description="Automatically startup the nav2 stack"
)
bringup_cmd = IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(nav2_launch_dir, "bringup_launch.py")),
launch_arguments={
"namespace": namespace,
"use_namespace": use_namespace,
"slam": slam,
"map": map_yaml_file,
"use_sim_time": use_sim_time,
"params_file": params_file,
"default_bt_xml_filename": default_bt_xml_filename,
"autostart": autostart,
}.items(),
)
# Create the launch description and populate
ld = LaunchDescription()
# Declare the launch options
ld.add_action(declare_namespace_cmd)
ld.add_action(declare_use_namespace_cmd)
ld.add_action(declare_slam_cmd)
ld.add_action(declare_map_yaml_cmd)
ld.add_action(declare_use_sim_time_cmd)
ld.add_action(declare_params_file_cmd)
ld.add_action(declare_bt_xml_cmd)
ld.add_action(declare_autostart_cmd)
ld.add_action(bringup_cmd)
return ld
| 4,076 | Python | 39.366336 | 120 | 0.711237 |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/multiple_robot_carter_navigation_hospital.launch.py | ## Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
## NVIDIA CORPORATION and its licensors retain all intellectual property
## and proprietary rights in and to this software, related documentation
## and any modifications thereto. Any use, reproduction, disclosure or
## distribution of this software and related documentation without an express
## license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
Example for spawing multiple robots in Gazebo.
This is an example on how to create a launch file for spawning multiple robots into Gazebo
and launch multiple instances of the navigation stack, each controlling one robot.
The robots co-exist on a shared environment and are controlled by independent nav stacks
"""
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, ExecuteProcess, GroupAction, IncludeLaunchDescription, LogInfo
from launch.conditions import IfCondition
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration, TextSubstitution
from launch_ros.actions import Node
def generate_launch_description():
# Get the launch and rviz directories
carter_nav2_bringup_dir = get_package_share_directory("carter_navigation")
nav2_bringup_dir = get_package_share_directory("nav2_bringup")
nav2_bringup_launch_dir = os.path.join(nav2_bringup_dir, "launch")
rviz_config_dir = os.path.join(carter_nav2_bringup_dir, "rviz2", "carter_navigation_namespaced.rviz")
# Names and poses of the robots
robots = [{"name": "carter1"}, {"name": "carter2"}, {"name": "carter3"}]
# Common settings
ENV_MAP_FILE = "carter_hospital_navigation.yaml"
use_sim_time = LaunchConfiguration("use_sim_time", default="True")
map_yaml_file = LaunchConfiguration("map")
default_bt_xml_filename = LaunchConfiguration("default_bt_xml_filename")
autostart = LaunchConfiguration("autostart")
rviz_config_file = LaunchConfiguration("rviz_config")
use_rviz = LaunchConfiguration("use_rviz")
log_settings = LaunchConfiguration("log_settings", default="true")
# Declare the launch arguments
declare_map_yaml_cmd = DeclareLaunchArgument(
"map",
default_value=os.path.join(carter_nav2_bringup_dir, "maps", ENV_MAP_FILE),
description="Full path to map file to load",
)
declare_robot1_params_file_cmd = DeclareLaunchArgument(
"carter1_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_1.yaml"
),
description="Full path to the ROS2 parameters file to use for robot1 launched nodes",
)
declare_robot2_params_file_cmd = DeclareLaunchArgument(
"carter2_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_2.yaml"
),
description="Full path to the ROS2 parameters file to use for robot2 launched nodes",
)
declare_robot3_params_file_cmd = DeclareLaunchArgument(
"carter3_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_3.yaml"
),
description="Full path to the ROS2 parameters file to use for robot3 launched nodes",
)
declare_bt_xml_cmd = DeclareLaunchArgument(
"default_bt_xml_filename",
default_value=os.path.join(
get_package_share_directory("nav2_bt_navigator"), "behavior_trees", "navigate_w_replanning_and_recovery.xml"
),
description="Full path to the behavior tree xml file to use",
)
declare_autostart_cmd = DeclareLaunchArgument(
"autostart", default_value="True", description="Automatically startup the stacks"
)
declare_rviz_config_file_cmd = DeclareLaunchArgument(
"rviz_config", default_value=rviz_config_dir, description="Full path to the RVIZ config file to use."
)
declare_use_rviz_cmd = DeclareLaunchArgument("use_rviz", default_value="True", description="Whether to start RVIZ")
# Define commands for launching the navigation instances
nav_instances_cmds = []
for robot in robots:
params_file = LaunchConfiguration(robot["name"] + "_params_file")
group = GroupAction(
[
IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(nav2_bringup_launch_dir, "rviz_launch.py")),
condition=IfCondition(use_rviz),
launch_arguments={
"namespace": TextSubstitution(text=robot["name"]),
"use_namespace": "True",
"rviz_config": rviz_config_file,
}.items(),
),
IncludeLaunchDescription(
PythonLaunchDescriptionSource(
os.path.join(carter_nav2_bringup_dir, "launch", "carter_navigation_individual.launch.py")
),
launch_arguments={
"namespace": robot["name"],
"use_namespace": "True",
"map": map_yaml_file,
"use_sim_time": use_sim_time,
"params_file": params_file,
"default_bt_xml_filename": default_bt_xml_filename,
"autostart": autostart,
"use_rviz": "False",
"use_simulator": "False",
"headless": "False",
}.items(),
),
Node(
package='pointcloud_to_laserscan', executable='pointcloud_to_laserscan_node',
remappings=[('cloud_in', ['front_3d_lidar/point_cloud']),
('scan', ['scan'])],
parameters=[{
'target_frame': 'front_3d_lidar',
'transform_tolerance': 0.01,
'min_height': -0.4,
'max_height': 1.5,
'angle_min': -1.5708, # -M_PI/2
'angle_max': 1.5708, # M_PI/2
'angle_increment': 0.0087, # M_PI/360.0
'scan_time': 0.3333,
'range_min': 0.05,
'range_max': 100.0,
'use_inf': True,
'inf_epsilon': 1.0,
# 'concurrency_level': 1,
}],
name='pointcloud_to_laserscan',
namespace = robot["name"]
),
LogInfo(condition=IfCondition(log_settings), msg=["Launching ", robot["name"]]),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " map yaml: ", map_yaml_file]),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " params yaml: ", params_file]),
LogInfo(
condition=IfCondition(log_settings),
msg=[robot["name"], " behavior tree xml: ", default_bt_xml_filename],
),
LogInfo(
condition=IfCondition(log_settings), msg=[robot["name"], " rviz config file: ", rviz_config_file]
),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " autostart: ", autostart]),
]
)
nav_instances_cmds.append(group)
# Create the launch description and populate
ld = LaunchDescription()
# Declare the launch options
ld.add_action(declare_map_yaml_cmd)
ld.add_action(declare_robot1_params_file_cmd)
ld.add_action(declare_robot2_params_file_cmd)
ld.add_action(declare_robot3_params_file_cmd)
ld.add_action(declare_bt_xml_cmd)
ld.add_action(declare_use_rviz_cmd)
ld.add_action(declare_autostart_cmd)
ld.add_action(declare_rviz_config_file_cmd)
for simulation_instance_cmd in nav_instances_cmds:
ld.add_action(simulation_instance_cmd)
return ld
| 8,338 | Python | 42.432291 | 120 | 0.601823 |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/maps/carter_office_navigation.yaml | image: carter_office_navigation.png
resolution: 0.05
origin: [-29.975, -39.975, 0.0000]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.196
| 139 | YAML | 18.999997 | 35 | 0.733813 |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/maps/carter_hospital_navigation.yaml | image: carter_hospital_navigation.png
resolution: 0.05
origin: [-49.625, -4.675, 0.0000]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.196
| 140 | YAML | 19.142854 | 37 | 0.735714 |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/maps/carter_warehouse_navigation.yaml | image: carter_warehouse_navigation.png
resolution: 0.05
origin: [-11.975, -17.975, 0.0000]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.196
| 142 | YAML | 19.428569 | 38 | 0.739437 |
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