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Merge branch 'master' into h-i

/h-i
Mohsen Kamalzadeh 3 年前
当前提交
7598ce57
共有 63 个文件被更改,包括 9934 次插入2021 次删除
  1. 4
      .yamato/upm-ci-testprojects.yml
  2. 7
      README.md
  3. 14
      TestProjects/PerceptionHDRP/Packages/packages-lock.json
  4. 1
      TestProjects/PerceptionURP/Assets/Tests/Editor/PerceptionCameraEditorUrpTests.cs
  5. 14
      TestProjects/PerceptionURP/Packages/packages-lock.json
  6. 45
      com.unity.perception/CHANGELOG.md
  7. 1
      com.unity.perception/Documentation~/Schema/Synthetic_Dataset_Schema.md
  8. 999
      com.unity.perception/Documentation~/Schema/image_0.png
  9. 999
      com.unity.perception/Documentation~/Tutorial/Images/di_usim_2.png
  10. 589
      com.unity.perception/Documentation~/Tutorial/Images/build_uploaded.png
  11. 597
      com.unity.perception/Documentation~/Tutorial/Images/runinusim.png
  12. 13
      com.unity.perception/Documentation~/Tutorial/Phase3.md
  13. 2
      com.unity.perception/Documentation~/Tutorial/Phase1.md
  14. 34
      com.unity.perception/Documentation~/Tutorial/Phase2.md
  15. 2
      com.unity.perception/Editor/Randomization/Utilities/StaticData.cs
  16. 22
      com.unity.perception/Editor/Randomization/VisualElements/AssetSource/AssetSourceElement.cs
  17. 41
      com.unity.perception/Editor/Randomization/Editors/RunInUnitySimulationWindow.cs
  18. 9
      com.unity.perception/Editor/Randomization/Editors/ScenarioBaseEditor.cs
  19. 12
      com.unity.perception/Editor/GroundTruth/IdLabelConfigEditor.cs
  20. 27
      com.unity.perception/Editor/GroundTruth/PerceptionCameraEditor.cs
  21. 1
      com.unity.perception/Runtime/GroundTruth/PerceptionCamera.cs
  22. 12
      com.unity.perception/Runtime/GroundTruth/Labelers/CameraLabeler.cs
  23. 2
      com.unity.perception/Runtime/GroundTruth/Labelers/BoundingBox3DLabeler.cs
  24. 8
      com.unity.perception/Runtime/GroundTruth/Labelers/KeypointLabeler.cs
  25. 2
      com.unity.perception/Runtime/GroundTruth/SimulationState.cs
  26. 8
      com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/AssetRole.cs
  27. 2
      com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/AssetSource.cs
  28. 6
      com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/IAssetRoleBase.cs
  29. 2
      com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/IAssetRoleBase.cs.meta
  30. 7
      com.unity.perception/Runtime/Randomization/Scenarios/ScenarioBase.cs
  31. 1
      com.unity.perception/Runtime/Randomization/Scenarios/Serialization/SerializationStructures.cs
  32. 2
      com.unity.perception/Runtime/Randomization/Scenarios/UnitySimulationScenario.cs
  33. 4
      com.unity.perception/Tests/Editor/PerceptionCameraEditorTests.cs
  34. 2
      com.unity.perception/Tests/Runtime/GroundTruthTests/VisualizationTests.cs
  35. 1
      com.unity.perception/Tests/Runtime/Randomization/AssetSourceTests/LocalAssetSourceTests.cs
  36. 2
      com.unity.perception/Tests/Runtime/Randomization/ScenarioTests/ScenarioTests.cs
  37. 15
      com.unity.perception/Tests/Runtime/Randomization/ScenarioTests/Resources/SampleScenarioConfiguration.json
  38. 4
      com.unity.perception/package.json
  39. 15
      TestProjects/PerceptionURP/ProjectSettings/BurstAotSettings_StandaloneOSX.json
  40. 8
      com.unity.perception/Editor/Visualizer.meta
  41. 8
      com.unity.perception/Editor/Pyrception.meta
  42. 1001
      com.unity.perception/Documentation~/FAQ/images/inner_objects.png
  43. 1001
      com.unity.perception/Documentation~/FAQ/images/inner_labels.gif
  44. 229
      com.unity.perception/Documentation~/FAQ/images/cluster_randomizer.png
  45. 199
      com.unity.perception/Documentation~/FAQ/images/prefab_cluster.png
  46. 1001
      com.unity.perception/Documentation~/FAQ/images/hdrp.png
  47. 1001
      com.unity.perception/Documentation~/FAQ/images/hdrp_pt_128_samples.png
  48. 1001
      com.unity.perception/Documentation~/FAQ/images/hdrp_pt_4096_samples.png
  49. 1001
      com.unity.perception/Documentation~/FAQ/images/hdrp_rt_gi.png
  50. 419
      com.unity.perception/Documentation~/FAQ/images/volume.png
  51. 609
      com.unity.perception/Documentation~/FAQ/FAQ.md
  52. 11
      com.unity.perception/Editor/Visualizer/VisualizerInstaller.cs.meta
  53. 73
      com.unity.perception/Editor/Visualizer/PipAPI.cs
  54. 11
      com.unity.perception/Editor/Visualizer/PipAPI.cs.meta
  55. 58
      com.unity.perception/Editor/Visualizer/PythonForUnityInstaller.cs
  56. 11
      com.unity.perception/Editor/Visualizer/PythonForUnityInstaller.cs.meta
  57. 785
      com.unity.perception/Editor/Visualizer/VisualizerInstaller.cs
  58. 0
      /com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/IAssetRoleBase.cs
  59. 0
      /com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/IAssetRoleBase.cs.meta

4
.yamato/upm-ci-testprojects.yml


commands:
- git submodule update --init --recursive
- unity-downloader-cli -u {{ editor.version }} -c editor -c StandaloneSupport-IL2CPP -c Linux --wait --published
- .\.Editor\Unity.exe -projectPath ./TestProjects/PerceptionHDRP -testPlatform editmode -runTests -automated -testResults test-results -debugCodeOptimization -enableCodeCoverage -coverageResultsPath test-results -coverageOptions enableCyclomaticComplexity;generateHtmlReport;generateBadgeReport; assemblyFilters:Unity.Perception.Editor;Unity.Perception.Runtime;Unity.Perception.Sensors;
- .\.Editor\Unity.exe -projectPath ./TestProjects/PerceptionHDRP -testPlatform playmode -runTests -automated -testResults test-results -debugCodeOptimization -enableCodeCoverage -coverageResultsPath test-results -coverageOptions enableCyclomaticComplexity;generateHtmlReport;generateBadgeReport; assemblyFilters:Unity.Perception.Editor;Unity.Perception.Runtime;Unity.Perception.Sensors;
- .\.Editor\Unity.exe -projectPath ./TestProjects/PerceptionHDRP -testPlatform editmode -runTests -automated -testResults test-results -debugCodeOptimization -enableCodeCoverage -coverageResultsPath test-results -coverageOptions enableCyclomaticComplexity;generateHtmlReport;generateBadgeReport;assemblyFilters:+Unity.Perception.Runtime
- .\.Editor\Unity.exe -projectPath ./TestProjects/PerceptionHDRP -testPlatform playmode -runTests -automated -testResults test-results -debugCodeOptimization -enableCodeCoverage -coverageResultsPath test-results -coverageOptions enableCyclomaticComplexity;generateHtmlReport;generateBadgeReport;assemblyFilters:+Unity.Perception.Runtime
# - .\.Editor\Unity.exe -projectPath ./TestProjects/PerceptionHDRP -debugCodeOptimization -enableCodeCoverage -coverageResultsPath test-results -coverageOptions enableCyclomaticComplexity;generateHtmlReport;generateBadgeReport;
artifacts:
logs:

7
README.md


[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE.md)
<img src="https://img.shields.io/badge/unity-2019.4-green.svg?style=flat-square" alt="unity 2019.4">
<img src="https://img.shields.io/badge/unity-2020.2-green.svg?style=flat-square" alt="unity 2020.2">
<img src="https://img.shields.io/badge/unity-2020.2-green.svg?style=flat-square" alt="unity 2020.3">
> com.unity.perception is in active development. Its features and API are subject to significant change as development progresses.
> `com.unity.perception` is in active development. Its features and API are subject to significant change as development progresses.
# Perception Package ([Unity Computer Vision](https://unity.com/computer-vision))

**[Human Pose Labeling and Randomization Tutorial](com.unity.perception/Documentation~/HPTutorial/TUTORIAL.md)**
Step by step instructions for using the keypoint, pose, and animation randomization tools included in the Perception package. It is recommended that you finish Phase 1 of the Perception Tutorial above before starting this tutorial.
**[FAQ](com.unity.perception/Documentation~/FAQ/FAQ.md)**
Check out our FAQ for a list of common questions, tips, tricks, and some sample code.
## Documentation
In-depth documentation on individual components of the package.

14
TestProjects/PerceptionHDRP/Packages/packages-lock.json


"com.unity.collections": "0.9.0-preview.6",
"com.unity.nuget.newtonsoft-json": "1.1.2",
"com.unity.render-pipelines.core": "7.1.6",
"com.unity.simulation.capture": "0.0.10-preview.20",
"com.unity.simulation.capture": "0.0.10-preview.23",
"com.unity.simulation.client": "0.0.10-preview.10",
"com.unity.simulation.core": "0.0.10-preview.22"
}

"url": "https://packages.unity.com"
},
"com.unity.simulation.capture": {
"version": "0.0.10-preview.20",
"version": "0.0.10-preview.23",
"com.unity.simulation.core": "0.0.10-preview.22"
"com.unity.simulation.core": "0.0.10-preview.25"
},
"url": "https://packages.unity.com"
},

"url": "https://packages.unity.com"
},
"com.unity.simulation.core": {
"version": "0.0.10-preview.22",
"depth": 1,
"version": "0.0.10-preview.25",
"depth": 2,
"dependencies": {},
"dependencies": {
"com.unity.nuget.newtonsoft-json": "2.0.0-preview"
},
"url": "https://packages.unity.com"
},
"com.unity.test-framework": {

1
TestProjects/PerceptionURP/Assets/Tests/Editor/PerceptionCameraEditorUrpTests.cs


var gameObject = new GameObject();
gameObject.SetActive(false);
gameObject.AddComponent<Camera>();
gameObject.AddComponent<UniversalAdditionalCameraData>();
var perceptionCamera = gameObject.AddComponent<PerceptionCamera>();
gameObject.SetActive(true);
LogAssert.Expect(LogType.Error, "GroundTruthRendererFeature must be present on the ScriptableRenderer associated with the camera. The ScriptableRenderer can be accessed through Edit -> Project Settings... -> Graphics -> Scriptable Render Pipeline Settings -> Renderer List.");

14
TestProjects/PerceptionURP/Packages/packages-lock.json


"com.unity.collections": "0.9.0-preview.6",
"com.unity.nuget.newtonsoft-json": "1.1.2",
"com.unity.render-pipelines.core": "7.1.6",
"com.unity.simulation.capture": "0.0.10-preview.20",
"com.unity.simulation.capture": "0.0.10-preview.23",
"com.unity.simulation.client": "0.0.10-preview.10",
"com.unity.simulation.core": "0.0.10-preview.22"
}

"url": "https://packages.unity.com"
},
"com.unity.simulation.capture": {
"version": "0.0.10-preview.20",
"version": "0.0.10-preview.23",
"com.unity.simulation.core": "0.0.10-preview.22"
"com.unity.simulation.core": "0.0.10-preview.25"
},
"url": "https://packages.unity.com"
},

"url": "https://packages.unity.com"
},
"com.unity.simulation.core": {
"version": "0.0.10-preview.22",
"depth": 1,
"version": "0.0.10-preview.25",
"depth": 2,
"dependencies": {},
"dependencies": {
"com.unity.nuget.newtonsoft-json": "2.0.0-preview"
},
"url": "https://packages.unity.com"
},
"com.unity.test-framework": {

45
com.unity.perception/CHANGELOG.md


### Known Issues
### Added
Added support for 'step' button in editor.
The user can now choose the base folder location to store their generated datasets.
Added the AssetSource class for loading assets from generic sources inside randomizers.
Users can now choose the base folder location to store their generated datasets.
Added a `projection` field in the capture.sensor metadata. Values are either "perspective" or "orthographic".
### Changed
Changed the JSON serialization key of Normal Sampler's standard deviation property from "standardDeviation" to "stddev". Scneario JSON configurations that were generated using previous versions will need to be manually updated to reflect this change.
### Deprecated
### Removed
### Fixed
Fixed an indexing issue with the IdLabelConfig editor. When a new label was added to an empty Id Label Config with Auto Assign IDs enabled, the starting id (0 or 1) was ignored and the new label would always have an id of 0.
## [0.8.0-preview.4] - 2021-07-05
### Upgrade Notes
Added random seed field to the Run in Unity Simulation Window
### Known Issues
When using URP in OSX, having MSAA enabled on the camera while the post-processing option is disabled may cause the output RGB images to be blank. As a workaround, you can disable MSAA and use FXAA instead, until the issue is fixed.
Added AssetSource class for loading assets from generic sources inside randomizers
### Added
User can now choose the base folder location to store their generated data.
Added support for 'step' button in editor.
Added random seed field to the Run in Unity Simulation Window.
Increased color variety in instance segmentation images
Increased color variety in instance segmentation images.
Upgraded capture package dependency to 0.0.10-preview.22 to fix an issue with URP where post processing effects were not included when capturing images.
Changed the JSON serialization key of Normal Sampler's standard deviation property from "standardDeviation" to "stddev". Scneario JSON configurations that were generated using previous versions will need to be manually updated to reflect this change.
Upgraded capture package dependency to 0.0.10-preview.23 to fix two issues: (1) Post processing effects were not included when capturing images in URP (2) RGB images were upside-down when post processing effects were enabled and FXAA disabled.
### Deprecated

Fixed an issue where Simulation Delta Time values larger than 100 seconds (in Perception Camera) would cause incorrect capture scheduling behavior.
Fixed an issue where Simulation Delta Time values larger than 100 seconds in Perception Camera would cause incorrect capture scheduling behavior.
Fixed an issue where Categorical Parameters sometimes tried to fetch items at `i = categories.Count`, which caused an exception.

1
com.unity.perception/Documentation~/Schema/Synthetic_Dataset_Schema.md


translation: <float, float, float> -- Position in meters: (x, y, z) with respect to the ego coordinate system. This is typically fixed during the simulation, but we can allow small variation for domain randomization.
rotation: <float, float, float, float> -- Orientation as quaternion: (w, x, y, z) with respect to ego coordinate system. This is typically fixed during the simulation, but we can allow small variation for domain randomization.
camera_intrinsic: <3x3 float matrix> [optional] -- Intrinsic camera calibration. Empty for sensors that are not cameras.
projection: <string> -- holds the type of projection the camera used for that capture: Options: "perspective" or "orthographic"
# add arbitrary optional key-value pairs for sensor attributes
}

999
com.unity.perception/Documentation~/Schema/image_0.png
文件差异内容过多而无法显示
查看文件

999
com.unity.perception/Documentation~/Tutorial/Images/di_usim_2.png
文件差异内容过多而无法显示
查看文件

589
com.unity.perception/Documentation~/Tutorial/Images/build_uploaded.png

之前 之后
宽度: 1382  |  高度: 841  |  大小: 68 KiB

597
com.unity.perception/Documentation~/Tutorial/Images/runinusim.png

之前 之后
宽度: 1212  |  高度: 812  |  大小: 61 KiB

13
com.unity.perception/Documentation~/Tutorial/Phase3.md


Here, you can specify a name for the run, the number of Iterations the Scenario will execute for, and the number of Instances (number of nodes the work will be distributed across) for the run. This window automatically picks the currently active Scene and Scenario to run in Unity Simulation.
* **:green_circle: Action**: Name your run `FirstRun`, set the number of Iterations to `1000`, and Instances to `20`.
* **:green_circle: Action**: Name your run `FirstRun`, set the number of Iterations to `1000`, and Instances to `20`.
* **:green_circle: Action**: If you'd like to use a new random seed for this run of your Scenario, click `Randomize` to generate a new seed.
* **:green_circle: Action**: Click _**Build and Run**_.
> :information_source: You can ignore the ***Optional Configuration*** section for now. This is useful if you plan to specify a configuration for your Scenario (including the Randomizers) that will override the values set in the Scenario UI, in Unity Simulation. To generate a configuration, you can click on the ***Generate JSON Config*** button provided in the ***Inspector*** view of Scenario components.

```
name id creation time
--------------------- ---------------------------------------- ---------------------------
Perception Tutorial acd31956-582b-4138-bec8-6670be150f09 * 2020-09-30T00:33:41+00:00
Perception Tutorial 38baa0d0-a2cd-4ee1-801b-39ca3fc5cbc6 * 2020-09-30T00:33:41+00:00
SynthDet 9ec23417-73cd-becd-9dd6-556183946153 2020-08-12T19:46:20+00:00
```

An example output with 3 runs would look like this:
```
Active Project ID: acd31956-582b-4138-bec8-6670be150f09
Active Project ID: 38baa0d0-a2cd-4ee1-801b-39ca3fc5cbc6
yegz4WN In_Progress 2020-10-01 23:17:54
ojE8Z20 In_Progress 2020-10-01 23:17:54
Run2 klvfxgT 2020-10-01 21:46:39 id status created_at
--------- ------------- ---------------------
kML3i50 In_Progress 2020-10-01 21:46:42

You can also obtain a list of all the builds you have uploaded to Unity Simulation using the `usim get builds` command.
You may notice that the IDs seen above for the run named `FirstRun` match those we saw earlier in Unity Editor's _**Console**_. You can see here that the single execution for our recently uploaded build is `In_Progress` and that the execution ID is `yegz4WN`.
You may notice that the IDs seen above for the run named `FirstRun` match those we saw earlier in Unity Editor's _**Console**_. You can see here that the single execution for our recently uploaded build is `In_Progress` and that the execution ID is `ojE8Z20`.
Unity Simulation utilizes the ability to run simulation Instances in parallel. If you enter a number larger than 1 for the number of Instances in the _**Run in Unity Simulation**_ window, your run will be parallelized, and multiple simulation Instances will simultaneously execute. You can view the status of all simulation Instances using the `usim summarize run-execution <execution-id>` command. This command will tell you how many Instances have succeeded, failed, have not run yet, or are in progress. Make sure to replace `<execution-id>` with the execution ID seen in your run list. In the above example, this ID would be `yegz4WN`.
Unity Simulation utilizes the ability to run simulation Instances in parallel. If you enter a number larger than 1 for the number of Instances in the _**Run in Unity Simulation**_ window, your run will be parallelized, and multiple simulation Instances will simultaneously execute. You can view the status of all simulation Instances using the `usim summarize run-execution <execution-id>` command. This command will tell you how many Instances have succeeded, failed, have not run yet, or are in progress. Make sure to replace `<execution-id>` with the execution ID seen in your run list. In the above example, this ID would be `ojE8Z20`.
* **:green_circle: Action**: Use the `usim summarize run-execution <execution-id>` command to observe the status of your execution nodes:

2
com.unity.perception/Documentation~/Tutorial/Phase1.md


The output dataset includes a variety of information about different aspects of the active sensors in the Scene (currently only one), as well as the ground-truth generated by all active Labelers. [This page](https://github.com/Unity-Technologies/com.unity.perception/blob/master/com.unity.perception/Documentation%7E/Schema/Synthetic_Dataset_Schema.md) provides a comprehensive explanation on the schema of this dataset. We strongly recommend having a look at the page once you have completed this tutorial.
> :information_source: Are the RGB images blank? This may be a bug. When using URP in OSX, having MSAA enabled on the camera may cause the output RGB images to be blank. As a workaround, you can disable MSAA and use FXAA instead, until the issue is fixed. To do this, select `Main Camera`, and in the ***Inspector*** view of the Camera component, in the ***Output*** section, set MSAA to `Off`. If you would like to use FXAA, in the ***Rendering*** section, set the Anti-aliasing option to `Fast Approximate Anti-aliasing (FXAA)`.
* **:green_circle: Action**: To get a quick feel of how the data is stored, open the folder whose name starts with `Dataset`, then open the file named `captures_000.json`. This file contains the output from `BoundingBox2DLabeler`. The `captures` array contains the position and rotation of the sensor (camera), the position and rotation of the ego (sensor group, currently only one), and the annotations made by `BoundingBox2DLabeler` for all visible objects defined in its label configuration. For each visible object, the annotations include:
* `label_id`: The numerical id assigned to this object's label in the Labeler's label configuration
* `label_name`: The object's label, e.g. `candy_minipralines_lindt`

34
com.unity.perception/Documentation~/Tutorial/Phase2.md


The purpose of this piece of code is to obtain a random float Parameter and assign it to the light's `Intensity` field on the start of every Iteration. Let's go through the code above and understand each part. The `FloatParameter` field makes it possible for us to define a randomized float Parameter and modify its properties from the editor UI, similar to how we already modified the properties for the previous Randomizers we used.
> :information_source: If you look at the _**Console**_ tab of the editor now, you will see an error regarding `MyLightRandomizerTag` not being found. This is to be expected, since we have not yet created this class; the error will go away once we create the class later.
If you return to your list of Randomizers in the _**Inspector**_ view of `SimulationScenario`, you can now add this new Randomizer.
* **:green_circle: Action**: Add `MyLightRandomizer` to the list of Randomizers in `SimulationScenario`.
You will notice that the Randomizer's UI snippet contains one Parameter named `Light Intensity Parameter`. This is the same Parameter we added in the code block above. Here, you can set the sampling distribution (`Value`) and `Range` for this float Parameter:
<p align="center">
<img src="Images/light_rand_1.png" width="420"/>
</p>
* **:green_circle: Action**: In the UI snippet for `MyLightRandomzier`, set the minimum and maximum for range to **0.5** and **3**.
This range of intensities is arbitrary but will give us a typically nice lighting without excessive darkness or burnt-out highlights.
> :information_source: If you look at the _**Console**_ tab of the editor now, you will see an error regarding `MyLightRandomizerTag` not being found. This is normal, because we have not yet created this class; the error will go away once we create the class later.
The `MyLightRandomizer` class extends `Randomizer`, which is the base class for all Randomizers that can be added to a Scenario. This base class provides a plethora of useful functions and properties that can help catalyze the process of creating new Randomizers.

Yes, a RandomizerTag can be this simple if you just need it for helping Randomizers query for target objects. Later, you will learn how to add code here to encapsulate more data and logic within the randomized objects.
Notice there is a `RequireComponent(typeof(Light))` line at the top. This line makes it so that you can only add the `MyLightRandomizerTag` component to an object that already has a `Light` component attached. This way, the Randomizers that query for this tag can be confident that the found tags are attached to GameObjects that also have a `Light` component, and can thus be Randomized.
If you return to your list of Randomizers in the _**Inspector**_ view of `SimulationScenario`, you can now add the new Randomizer we created here.
* **:green_circle: Action**: Add `MyLightRandomizer` to the list of Randomizers in `SimulationScenario`.
You will notice that the Randomizer's UI snippet contains one Parameter named `Light Intensity Parameter`. This is the same Parameter we added in the code block above. Here, you can set the sampling distribution (`Value`) and `Range` for this float Parameter:
<p align="center">
<img src="Images/light_rand_1.png" width="420"/>
</p>
* **:green_circle: Action**: In the UI snippet for `MyLightRandomzier`, set the minimum and maximum for range to **0.5** and **3**.
This range of intensities is arbitrary but will give us a typically nice lighting without excessive darkness or burnt-out highlights.
It is now time to add the tag we created earlier to the light in our Scene, so that the Randomizer can find it.
* **:green_circle: Action**: Select `Directional Light` in the Scene's _**Hierarchy**_, and in the _**Inspector**_ tab, add a `My Light Randomizer Tag` component.
* **:green_circle: Action**: Run the simulation again and inspect how `Directional Light` now switches between different intensities. You can pause the simulation and then use the step button (to the right of the pause button) to move the simulation one frame forward and clearly see the varying light intensity

2
com.unity.perception/Editor/Randomization/Utilities/StaticData.cs


randomizerTypes = GetConstructableDerivedTypes<Randomizer>();
samplerTypes = GetConstructableDerivedTypes<ISampler>();
assetSourceLocationTypes = GetConstructableDerivedTypes<AssetSourceLocation>();
assetRoleTypes = GetConstructableDerivedTypes<AssetRoleBase>();
assetRoleTypes = GetConstructableDerivedTypes<IAssetRoleBase>();
}
static Type[] GetConstructableDerivedTypes<T>()

22
com.unity.perception/Editor/Randomization/VisualElements/AssetSource/AssetSourceElement.cs


using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Reflection;
using UnityEditor;

VisualElement m_FieldsContainer;
TextElement m_LocationNotes;
Type m_AssetType;
Dictionary<string, Type> m_AssetRoleLabelsToTypes = new Dictionary<string, Type>();
AssetRoleBase assetRole =>
(AssetRoleBase)StaticData.GetManagedReferenceValue(m_AssetRoleProperty);
IAssetRoleBase assetRole =>
(IAssetRoleBase)StaticData.GetManagedReferenceValue(m_AssetRoleProperty);
AssetSourceLocation assetSourceLocation =>
(AssetSourceLocation)StaticData.GetManagedReferenceValue(m_LocationProperty);

"None",
a => ReplaceAssetRole(null),
a => DropdownMenuAction.Status.Normal);
m_AssetRoleLabelsToTypes.Clear();
var label = GetAssetRoleDisplayName(type);
if (m_AssetRoleLabelsToTypes.ContainsKey(label))
Debug.LogError($"The asset role classes {type.Name} && {m_AssetRoleLabelsToTypes[label].Name} have an identical label: \"{label}\". Asset role labels should be unique.");
else
m_AssetRoleLabelsToTypes.Add(label, type);
GetAssetRoleDisplayName(type),
label,
a => ReplaceAssetRole(type),
a => DropdownMenuAction.Status.Normal);
}

}
else
{
m_AssetRoleToolbarMenu.text = GetDisplayName(type);
var newAssetRole = (AssetRoleBase)Activator.CreateInstance(type);
m_AssetRoleToolbarMenu.text = GetAssetRoleDisplayName(type);
var newAssetRole = (IAssetRoleBase)Activator.CreateInstance(type);
m_AssetRoleProperty.managedReferenceValue = newAssetRole;
}
m_AssetRoleProperty.serializedObject.ApplyModifiedProperties();

static string GetAssetRoleDisplayName(Type type)
{
return type.Name.Replace("AssetRole", string.Empty);
return $"{((IAssetRoleBase)Activator.CreateInstance(type)).label} ({type.Name})";
}
}
}

41
com.unity.perception/Editor/Randomization/Editors/RunInUnitySimulationWindow.cs


m_SelectedBuildPathTextField = root.Q<TextField>("selected-build-path");
m_SelectedBuildPathTextField.isReadOnly = true;
m_BuildPathField = root.Q<TextField>("selected-build-path");
m_BuildIdField = root.Q<TextField>("build-id");
UpdateBuildIdElements((BuildIdKind) m_BuildTypeMenu.value);

switch (buildIdKind)
{
case BuildIdKind.BuildPlayer:
CreateLinuxBuildAndZip();
buildId = await UploadBuild(cancellationTokenSource, token);
if (CreateLinuxBuildAndZip())
{
buildId = await UploadBuild(cancellationTokenSource, token);
}
break;
case BuildIdKind.ExistingBuildZip:
m_BuildZipPath = m_BuildPathField.value;

if (buildId == null)
return;
await StartUnitySimulationRun(runGuid, buildId);
StartUnitySimulationRun(runGuid, buildId);
}
catch (Exception e)
{

}
}
void CreateLinuxBuildAndZip()
bool CreateLinuxBuildAndZip()
var projectBuildDirectory = $"{m_BuildDirectory}/{m_RunParameters.runName}";
if (!Directory.Exists(projectBuildDirectory))
Directory.CreateDirectory(projectBuildDirectory);
List<string> scenes = new List<string>();
foreach(var scene in EditorBuildSettings.scenes)
{

if (scenes.Count == 0)
{
if (EditorUtility.DisplayDialog("No Scenes Found", "Could not find any enabled Scenes in build settings. Open File -> Build Settings and add all your required Scenes.", "Use Currently Open Scene", "Cancel Build"))
{
var currentOpenScenePath = SceneManager.GetSceneAt(0).path;
if (string.IsNullOrEmpty(currentOpenScenePath))
{
EditorUtility.DisplayDialog("Build Failed", "Could not find an active Scene.", "OK");
throw new Exception($"Could not find an active Scene.");
}
scenes.Add(currentOpenScenePath);
}
else
{
return false;
}
}
var projectBuildDirectory = $"{m_BuildDirectory}/{m_RunParameters.runName}";
if (!Directory.Exists(projectBuildDirectory))
Directory.CreateDirectory(projectBuildDirectory);
var buildPlayerOptions = new BuildPlayerOptions
{
scenes = scenes.ToArray(),

EditorUtility.DisplayProgressBar("Unity Simulation Run", "Zipping Linux build...", 0f);
Zip.DirectoryContents(projectBuildDirectory, m_RunParameters.runName);
m_BuildZipPath = projectBuildDirectory + ".zip";
return true;
}
List<AppParam> UploadAppParam()

return appParamIds;
}
async Task StartUnitySimulationRun(Guid runGuid, string buildId)
void StartUnitySimulationRun(Guid runGuid, string buildId)
{
// Generate and upload app-params
EditorUtility.DisplayProgressBar("Unity Simulation Run", "Uploading app-params...", 0.90f);

9
com.unity.perception/Editor/Randomization/Editors/ScenarioBaseEditor.cs


m_Root = AssetDatabase.LoadAssetAtPath<VisualTreeAsset>(
$"{StaticData.uxmlDir}/ScenarioBaseElement.uxml").CloneTree();
#if !ENABLE_SCENARIO_APP_PARAM_CONFIG
var configuration = m_Root.Q<PropertyField>("configuration-asset");
configuration.style.display = DisplayStyle.None;
m_Scenario.configuration = null;
EditorUtility.SetDirty(m_Scenario);
#endif
m_RandomizerListPlaceholder = m_Root.Q<VisualElement>("randomizer-list-placeholder");
CreatePropertyFields();

Undo.RecordObject(m_Scenario, "Deserialized scenario configuration");
var originalConfig = m_Scenario.configuration;
m_Scenario.LoadConfigurationFromFile(filePath);
m_Scenario.DeserializeConfiguration();
m_Scenario.DeserializeConfigurationInternal();
m_Scenario.configuration = originalConfig;
Debug.Log($"Deserialized scenario configuration from {Path.GetFullPath(filePath)}. " +
"Using undo in the editor will revert these changes to your scenario.");

12
com.unity.perception/Editor/GroundTruth/IdLabelConfigEditor.cs


protected override IdLabelEntry CreateLabelEntryFromLabelString(SerializedProperty serializedArray,
string labelToAdd)
{
int maxLabel = Int32.MinValue;
var maxLabel = int.MinValue;
for (int i = 0; i < serializedArray.arraySize; i++)
for (var i = 0; i < serializedArray.arraySize; i++)
}
if (maxLabel == -1)
{
var startingLabelId =
(StartingLabelId) serializedObject.FindProperty(nameof(IdLabelConfig.startingLabelId)).enumValueIndex;
if (startingLabelId == StartingLabelId.One)
maxLabel = 0;
}
return new IdLabelEntry

27
com.unity.perception/Editor/GroundTruth/PerceptionCameraEditor.cs


using System;
using System;
#if UNITY_EDITOR_WIN || UNITY_EDITOR_OSX
using UnityEditor.Perception.Visualizer;
#endif
namespace UnityEditor.Perception.GroundTruth
{

GUILayout.EndHorizontal();
GUILayout.EndVertical();
#if UNITY_EDITOR_WIN || UNITY_EDITOR_OSX
GUILayout.Space(10);
EditorGUILayout.LabelField("Visualizer Tool");
GUILayout.BeginHorizontal("TextArea");
if (GUILayout.Button("Open Visualizer"))
{
var project = Application.dataPath;
_=VisualizerInstaller.RunVisualizer(project);
}
if (GUILayout.Button("Check For Updates"))
{
var project = Application.dataPath;
_=VisualizerInstaller.CheckForUpdates();
}
GUILayout.EndHorizontal();
GUILayout.Space(10);
#endif
if (EditorSettings.asyncShaderCompilation)

1
com.unity.perception/Runtime/GroundTruth/PerceptionCamera.cs


#else
CaptureCamera.Capture(cam, colorFunctor, flipY: flipY);
#endif
Profiler.EndSample();
}

12
com.unity.perception/Runtime/GroundTruth/Labelers/CameraLabeler.cs


/// Initializes labeler with the target perception camera
/// </summary>
/// <param name="camera">The target perception camera</param>
public void Init(PerceptionCamera camera)
internal void Init(PerceptionCamera camera)
{
try
{

internal void InternalCleanup() => Cleanup();
internal void InternalVisualize() => OnVisualize();
bool m_ShowVisualizations;
bool m_ShowVisualizationsForLabeler;
/// <summary>
/// Turns on/off the labeler's realtime visualization capability. If a labeler does not support realtime

if (!supportsVisualization)
return false;
return perceptionCamera && perceptionCamera.showVisualizations;
return perceptionCamera && perceptionCamera.showVisualizations && m_ShowVisualizationsForLabeler;
if (value != m_ShowVisualizations)
if (value != m_ShowVisualizationsForLabeler)
m_ShowVisualizations = value;
m_ShowVisualizationsForLabeler = value;
OnVisualizerEnabledChanged(m_ShowVisualizations);
OnVisualizerEnabledChanged(m_ShowVisualizationsForLabeler);
}
}
}

2
com.unity.perception/Runtime/GroundTruth/Labelers/BoundingBox3DLabeler.cs


protected override void Setup()
{
if (idLabelConfig == null)
throw new InvalidOperationException("BoundingBox2DLabeler's idLabelConfig field must be assigned");
throw new InvalidOperationException("BoundingBox3DLabeler's idLabelConfig field must be assigned");
m_AnnotationDefinition = DatasetCapture.RegisterAnnotationDefinition("bounding box 3D", idLabelConfig.GetAnnotationSpecification(),
"Bounding box for each labeled object visible to the sensor", id: new Guid(annotationId));

8
com.unity.perception/Runtime/GroundTruth/Labelers/KeypointLabeler.cs


{
include = true;
break;
}
}
}
}
}
}
if (include)
m_KeypointEntriesToReport.Add(entry);
}

return json;
}
}
}
}

2
com.unity.perception/Runtime/GroundTruth/SimulationState.cs


m_Ids.Add(egoHandle.Id);
}
public bool IsEnabled(SensorHandle sensorHandle) => m_ActiveSensors.Contains(sensorHandle);
public void SetEnabled(SensorHandle sensorHandle, bool value)

Debug.LogError($"Simulation ended with pending metrics: {string.Join(", ", m_PendingMetrics.Select(c => $"id:{c.MetricId} step:{c.Step}"))}");
WriteReferences();
Time.captureDeltaTime = 0;
IsRunning = false;
}

8
com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/AssetRole.cs


/// Typed asset roles are used to apply preprocessing steps to assets loaded from an <see cref="AssetSource{T}"/>.
/// </summary>
/// <typeparam name="T">The type of asset to preprocess</typeparam>
public abstract class AssetRole<T> : AssetRoleBase where T : Object
public abstract class AssetRole<T> : IAssetRoleBase where T : Object
/// <inheritdoc/>
public abstract string label { get; }
/// <inheritdoc/>
public abstract string description { get; }
/// <summary>
/// Perform preprocessing operations on an asset loaded from an <see cref="AssetSource{T}"/>.
/// </summary>

2
com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/AssetSource.cs


[Serializable]
public sealed class AssetSource<T> where T : Object
{
[SerializeReference] AssetRoleBase m_AssetRoleBase;
[SerializeReference] IAssetRoleBase m_AssetRoleBase;
/// <summary>
/// The location to load assets from

6
com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/IAssetRoleBase.cs


/// <summary>
/// The base asset role class. Derive from <see cref="AssetRole{T}"/> instead to create a new asset role.
/// </summary>
public abstract class AssetRoleBase
interface IAssetRoleBase
public abstract string label { get; }
string label { get; }
public abstract string description { get; }
string description { get; }
}
}

2
com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/IAssetRoleBase.cs.meta


fileFormatVersion: 2
guid: 1af66eeef6ffa514a8307a228b8e85f9
guid: 7c335d3150918dc49be9cf2eddb144d2
MonoImporter:
externalObjects: {}
serializedVersion: 2

7
com.unity.perception/Runtime/Randomization/Scenarios/ScenarioBase.cs


/// <summary>
/// Overwrites this scenario's randomizer settings and scenario constants from a JSON serialized configuration
/// </summary>
public virtual void DeserializeConfiguration()
protected virtual void DeserializeConfiguration()
}
internal void DeserializeConfigurationInternal()
{
DeserializeConfiguration();
}
/// <summary>

1
com.unity.perception/Runtime/Randomization/Scenarios/Serialization/SerializationStructures.cs


{
public string name = string.Empty;
public string description = string.Empty;
public string imageLink = string.Empty;
}
class Group

2
com.unity.perception/Runtime/Randomization/Scenarios/UnitySimulationScenario.cs


}
/// <inheritdoc/>
public override void DeserializeConfiguration()
protected override void DeserializeConfiguration()
{
base.DeserializeConfiguration();
if (Configuration.Instance.IsSimulationRunningInCloud())

4
com.unity.perception/Tests/Editor/PerceptionCameraEditorTests.cs


using Moq;
using Moq.Protected;
using UnityEngine.Rendering;
#endif
namespace EditorTests

var camera = cameraObject.AddComponent<Camera>();
camera.orthographic = true;
camera.orthographicSize = 1;
#elif URP_PRESENT
cameraObject.AddComponent<UnityEngine.Rendering.Universal.UniversalAdditionalCameraData>();
#endif
var perceptionCamera = cameraObject.AddComponent<PerceptionCamera>();

2
com.unity.perception/Tests/Runtime/GroundTruthTests/VisualizationTests.cs


var labeler1 = new ObjectCountLabeler(cfg);
labeler1.objectCountMetricId = "a1da3c27-369d-4929-aea6-d01614635ce2";
var labeler2 = new ObjectCountLabeler(cfg);
labeler1.objectCountMetricId = "b1da3c27-369d-4929-aea6-d01614635ce2";
labeler2.objectCountMetricId = "b1da3c27-369d-4929-aea6-d01614635ce2";
perceptionCamera.AddLabeler(labeler1);
perceptionCamera.AddLabeler(labeler2);

1
com.unity.perception/Tests/Runtime/Randomization/AssetSourceTests/LocalAssetSourceTests.cs


class TestAssetRole : AssetRole<GameObject>
{
public override string label => "test";
public override string description => "";
public override void Preprocess(GameObject asset)
{

2
com.unity.perception/Tests/Runtime/Randomization/ScenarioTests/ScenarioTests.cs


// Change the values
m_Scenario.constants = changedConstants;
m_Scenario.DeserializeConfiguration();
m_Scenario.DeserializeConfigurationInternal();
// Check if the values reverted correctly
Assert.AreEqual(m_Scenario.constants.framesPerIteration, constants.framesPerIteration);

15
com.unity.perception/Tests/Runtime/Randomization/ScenarioTests/Resources/SampleScenarioConfiguration.json


"RotationRandomizer": {
"metadata": {
"name": "",
"description": ""
"description": "",
"imageLink": ""
},
"items": {
"rotation": {

"description": ""
"description": "",
"imageLink": ""
},
"items": {
"x": {

"description": ""
"description": "",
"imageLink": ""
},
"uniform": {
"min": 0.0,

"samplerOptions": {
"metadata": {
"name": "",
"description": ""
"description": "",
"imageLink": ""
},
"uniform": {
"min": 0.0,

"samplerOptions": {
"metadata": {
"name": "",
"description": ""
"description": "",
"imageLink": ""
},
"uniform": {
"min": 0.0,

4
com.unity.perception/package.json


"com.unity.collections": "0.9.0-preview.6",
"com.unity.nuget.newtonsoft-json": "1.1.2",
"com.unity.render-pipelines.core": "7.1.6",
"com.unity.simulation.capture": "0.0.10-preview.22",
"com.unity.simulation.capture": "0.0.10-preview.23",
"com.unity.simulation.client": "0.0.10-preview.10",
"com.unity.simulation.core": "0.0.10-preview.22"
},

"unity": "2019.4",
"version": "0.8.0-preview.3",
"version": "0.8.0-preview.4",
"samples": [
{
"displayName": "Tutorial Files",

15
TestProjects/PerceptionURP/ProjectSettings/BurstAotSettings_StandaloneOSX.json


{
"MonoBehaviour": {
"Version": 3,
"EnableBurstCompilation": true,
"EnableOptimisations": true,
"EnableSafetyChecks": false,
"EnableDebugInAllBuilds": false,
"UsePlatformSDKLinker": false,
"CpuMinTargetX32": 0,
"CpuMaxTargetX32": 0,
"CpuMinTargetX64": 0,
"CpuMaxTargetX64": 0,
"CpuTargetsX64": 72
}
}

8
com.unity.perception/Editor/Visualizer.meta


fileFormatVersion: 2
guid: 938fc660bd314604f888bf1c644b0c29
folderAsset: yes
DefaultImporter:
externalObjects: {}
userData:
assetBundleName:
assetBundleVariant:

8
com.unity.perception/Editor/Pyrception.meta


fileFormatVersion: 2
guid: 586f94aeebe844666bdf7cfa6bdc9cb1
folderAsset: yes
DefaultImporter:
externalObjects: {}
userData:
assetBundleName:
assetBundleVariant:

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com.unity.perception/Documentation~/FAQ/images/inner_labels.gif
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609
com.unity.perception/Documentation~/FAQ/FAQ.md


# FAQ and Code Samples
This page covers a variety of topics, including common questions and issues that may arise while using the Perception package, as well as code samples and recommendations for a number of popular workflows and use-cases.
## <a name="labeling">Labeling</a>
<details>
<summary><strong>Q: How can I disable or enable labeling on an object at runtime?</strong></summary>
<br>
You can turn labeling on and off on a GameObject by switching the enabled state of its `Labeling` component. For example:
```C#
gameObject.GetComponent<Labeling>().enabled = false;
```
---
</details>
<details>
<summary><strong>Q: How can I remove or add new labels to objects at runtime?</strong></summary><br>
This can be achieved through modifying the `labels` list of the `Labeling` component. The key is to call `RefreshLabeling()` on the component after making any changes to the labels. Example:
```C#
var labeling = gameObject.GetComponent<Labeling>();
labeling.labels.Clear();
labeling.labels.Add("new-label");
labeling.RefreshLabeling();
```
Keep in mind that any new label added with this method should already be present in the Label Config attached to the Labeler that is supposed to label this object.
---
</details>
<details>
<summary><strong>Q: Is it possible to label only parts of an object or assign different labels to different parts of objects?</strong></summary><br>
Labeling works on the GameObject level, so to achieve the scenarios described here, you will need to break down your main object into multiple GameObjects parented to the same root object, and add `Labeling` components to each of the inner objects, as shown below.
<p align="center">
<img src="images/inner_objects.png" width="800"/>
</p>
Alternatively, in cases where parts of the surface of the object need to be labeled (e.g. decals on objects), you can add labeled invisible surfaces on top of these sections. These invisible surfaces need to have a fully transparent material. To create an invisible material:
* Create a new material (***Assets -> Create -> Material***) and name it `TransparentMaterial`
* Set the **Surface Type** for the material to **Transparent**, and set the alpha channel of the **Base Map** color to 0.
* For HDRP: In addition to the above, disable **Preserve specular lighting**
An example labeled output for an object with separate labels on inner objects and decals is shown below:
<p align="center">
<img src="images/inner_labels.gif" width="600"/>
</p>
---
</details>
<details>
<summary><strong>Q: When visible surfaces of two objects are fully aligned, the bounding boxes seem to blink in and out of existence from one frame to another. Why is that?</strong></summary><br>
This is due to a common graphics problem called *z-fighting*. This occurs when the shader can't decide which of the two surfaces to draw on top of the other, since they both have the exact same distance from the camera. To fix this, simply move one of the objects slightly so that the two problematic surfaces do not fully align.
---
</details>
## <a name="randomization">Randomization</a>
<details>
<summary><strong>Q: How can I have multiple sets of prefabs in a foreground placement Randomizer, and on every Iteration select one from each set?</strong>
</summary><br>
This question is an example of more complex functionality that can be achieved by applying slight modifications to the provided sample Randomizers, or by creating completely custom ones by extending the `Randomizer` class.
Here, we have a variety of options toward achieving the described outcome. One simple method could be to add several more `GameObjectParameter` fields inside of the provided sample `ForegroundObjectPlacementRandomizer`. Each of these Parameters could hold one of our object lists. Then, on each iteration, we would fetch one prefab from each of the lists using the `Sample()` function of each Parameter.
The above solution can work but it is not modular enough, with the lists of prefabs not being reusable in other Randomizers.
A better approach can be to define each prefab list separately as a scriptable object asset, and then just reference those scriptable objects inside of our foreground Randomizer. To do this, we first define a `PrefabCluster` class to hold a list of prefabs.
```C#
using UnityEngine;
using UnityEngine.Perception.Randomization.Parameters;
[CreateAssetMenu(fileName="NewPrefabCluster", menuName="Test/PrefabCluster")]
public class PrefabCluster : ScriptableObject
{
public GameObjectParameter clusterPrefabs;
}
```
We can now create a cluster asset using the ***Assets -> Create -> Test -> PrefabCluster*** menu option and populate its list of prefabs. Each cluster contains one `GameObjectParameter`, which will hold the list of prefabs and provide us with a `Sample()` function.
To be able to edit these clusters with the same editor UI available for Randomizers, you will also need to add an empty custom editor for the `PrefabCluster` class that extends our bespoke `ParameterUIElementsEditor` class:
```C#
using UnityEditor;
using UnityEditor.Perception.Randomization;
[CustomEditor(typeof(PrefabCluster))]
public class PrefabClusterEditor : ParameterUIElementsEditor { }
```
Note that any editor scripts must be placed inside a folder named "Editor" within your project. "Editor" is a special folder name in Unity that prevents editor code from compiling into a player during the build process. For example, the file path for the `PrefabClusterEditor` script above could be `.../Assets/Scripts/Editor/PrefabClusterEditor`.
The ***Inspector*** view of a prefab cluster asset looks like below:
<p align="center">
<img src="images/prefab_cluster.png" width="400"/>
</p>
Now all that is left is to use our prefab clusters inside a Randomizer. Here is some sample code:
```C#
using System;
using UnityEngine;
[Serializable]
[UnityEngine.Perception.Randomization.Randomizers.AddRandomizerMenu("My Randomizers/Cluster Randomizer")]
public class ClusterRandomizer : UnityEngine.Perception.Randomization.Randomizers.Randomizer
{
public PrefabCluster[] clusters;
protected override void OnIterationStart()
{
//select a random prefab from each cluster
foreach (var cluster in clusters)
{
var prefab = cluster.clusterPrefabs.Sample();
//do things with this prefab, e.g. create instances of it, etc.
}
}
}
```
This Randomizer takes a list of `PrefabCluster` assets, then, on each Iteration, it goes through all the provided clusters and samples one prefab from each. The ***Inspector*** view for this Randomizer looks like this:
<p align="center">
<img src="images/cluster_randomizer.png" width="400"/>
</p>
---
</details>
<details>
<summary><strong>Q: How can I specify an exact number of objects to place using the sample foreground object placement Randomizer?</strong> </summary><br>
The provided `ForegroundObjectPlacementRandomizer` uses Poisson Disk sampling to find randomly positioned points in the space denoted by the provided `Width` and `Height` values. The lower bound on the distance between the sampled points will be `Separation Distance`. The number of sampled points will be the maximum number of points in the given area that match these criteria.
Thus, to limit the number of spawned objects, you can simply introduce a hard limit in the `for` loop that iterates over the Poisson Disk samples, to break out of the loop if the limit is reached. Additionally, we will need to shuffle the list of points retrieved from the Poisson Disk sampling in order to remove any selection bias when building our subset of points. This is because Poisson Disk points are sampled in sequence and relative to the points already sampled, therefore, the initial points in the list are likely to be closer to each other. We will use a `FloatParameter` to perform this shuffle, so that we can guarantee that our simulation is deterministic and reproducible.
```C#
FloatParameter m_IndexShuffleParameter = new FloatParameter { value = new UniformSampler(0, 1) };
protected override void OnIterationStart()
{
var seed = SamplerState.NextRandomState();
var placementSamples = PoissonDiskSampling.GenerateSamples(
placementArea.x, placementArea.y, separationDistance, seed);
var offset = new Vector3(placementArea.x, placementArea.y, 0f) * -0.5f;
//shuffle retrieved points
var indexes = Enumerable.Range(0, placementSamples.Length).ToList();
indexes = indexes.OrderBy(item => m_IndexShuffleParameter.Sample()).ToList();
//maximum number of objects to place
var limit = 50;
var instantiatedCount = 0;
//iterate over all points
foreach (var index in indexes)
{
instantiatedCount ++;
if (instantiatedCount == limit)
break;
var instance = m_GameObjectOneWayCache.GetOrInstantiate(prefabs.Sample());
instance.transform.position = new Vector3(placementSamples[index].x, placementSamples[index].y, depth) + offset;
}
placementSamples.Dispose();
}
```
This will guarantee an upper limit of 50 on the number of objects. To have exactly 50 objects, we need to make sure the `Separation Distance` is small enough for the given area, so that there are always at least 50 point samples found. Experiment with different values for the distance until you find one that produces the minimum number of points required.
---
</details>
<details>
<summary><strong>Q: How can I avoid object overlap with the sample foreground object placement Randomizer?</strong></summary><br>
There are a number of ways for procedurally placing objects while avoiding any overlap between them, and most of these methods can be rather complex and need to place objects in a sequence. All the modifications to the objects (like scale, rotation, etc.) would also need to happen before the next object is placed, so that the state of the world is fully known before each placement.
Here, we are going to introduce a rather simple modification in the sample foreground placement code provided with the package. In each Iteration, a random scale factor is chosen, and then a desirable separation distance is calculated based on this scale factor and the list of given prefabs. We also scale the objects here to introduce additional randomization, due to the fact that once we have placed the objects we can no longer scale them.
Based on the value given for `Non Overlap Guarantee`, this Randomizer can either reduce the amount of overlap or completely remove overlap.
```C#
using System;
using System.Collections.Generic;
using System.Linq;
using UnityEngine;
using UnityEngine.Perception.Randomization.Parameters;
using UnityEngine.Perception.Randomization.Randomizers;
using UnityEngine.Perception.Randomization.Randomizers.Utilities;
using UnityEngine.Perception.Randomization.Samplers;
[Serializable]
[AddRandomizerMenu("Example/No Overlap Foreground Object Placement Randomizer")]
public class NoOverlapForegroundObjectPlacementRandomizer : Randomizer
{
public float depth;
[Tooltip("Range of scales used for objects. All objects in each frame will use the same scale.")]
public FloatParameter scaleParameter = new FloatParameter { value = new UniformSampler(4, 8) };
public Vector2 placementArea;
public GameObjectParameter prefabs;
[Tooltip("Degree to which we can guarantee that no objects will overlap. Use 1 for no overlap and smaller values (down to 0) for more dense placement with a possibility of some overlap.")]
public float nonOverlapGuarantee = 1;
float m_ScaleFactor = 1f;
GameObject m_Container;
GameObjectOneWayCache m_GameObjectOneWayCache;
Dictionary<GameObject, float> m_GameObjectBoundsSizeCache;
List<GameObject> m_SelectedPrefabs;
int m_SelectionPoolSizePerFrame = 1;
FloatParameter m_IndexSelector = new FloatParameter { value = new UniformSampler(0, 1) };
protected override void OnAwake()
{
m_Container = new GameObject("Foreground Objects");
m_Container.transform.parent = scenario.transform;
m_GameObjectOneWayCache = new GameObjectOneWayCache(
m_Container.transform, prefabs.categories.Select(element => element.Item1).ToArray());
m_GameObjectBoundsSizeCache = new Dictionary<GameObject, float>();
m_SelectedPrefabs = new List<GameObject>();
//Calculate the average bounds size for the prefabs included in this categorical parameter
var averageBoundsSize = CalculateAverageBoundsSize();
//Calculate average scale based on the scale range given
var averageScale = 1f;
var sampler = (UniformSampler)scaleParameter.value;
if (sampler != null)
{
averageScale = (sampler.range.minimum + sampler.range.maximum) / 2;
}
//Use average bounds size and average scale to guess the maximum number of objects that can be placed without having them overlap.
//This is a heuristic to help us start the placement process. The actual number of items placed will usually be much smaller.
m_SelectionPoolSizePerFrame = (int)(placementArea.x * placementArea.y / (averageBoundsSize * averageScale));
}
protected override void OnIterationStart()
{
m_ScaleFactor = scaleParameter.Sample();
m_SelectedPrefabs.Clear();
//Select a random number of prefabs for this frame. Placement calculations will be done based on this subset.
for (var i = 0; i < m_SelectionPoolSizePerFrame; i++)
{
var randIndex = (int)Mathf.Round((m_IndexSelector.Sample() * prefabs.categories.Count) - 0.5f);
m_SelectedPrefabs.Add(prefabs.categories[randIndex].Item1);
}
//Calculate the minimum separation distance needed for the selected prefabs to not overlap.
var separationDistance = CalculateMaxSeparationDistance(m_SelectedPrefabs);
var seed = SamplerState.NextRandomState();
var placementSamples = PoissonDiskSampling.GenerateSamples(
placementArea.x, placementArea.y, separationDistance, seed);
var offset = new Vector3(placementArea.x, placementArea.y, 0f) * -0.5f;
foreach (var sample in placementSamples)
{
//Pick a random prefab from the selected subset and instantiate it.
var randIndex = (int)Mathf.Round((m_IndexSelector.Sample() * m_SelectedPrefabs.Count) - 0.5f);
var instance = m_GameObjectOneWayCache.GetOrInstantiate(m_SelectedPrefabs[randIndex]);
instance.transform.position = new Vector3(sample.x, sample.y, depth) + offset;
instance.transform.localScale = Vector3.one * m_ScaleFactor;
}
placementSamples.Dispose();
}
protected override void OnIterationEnd()
{
m_GameObjectOneWayCache.ResetAllObjects();
}
/// <summary>
/// Calculates the separation distance needed between placed objects to be sure that no two objects will overlap
/// </summary><br>
/// <returns>The max separation distance</returns>
float CalculateMaxSeparationDistance(ICollection<GameObject> categories)
{
var maxBoundsSize = m_GameObjectBoundsSizeCache.Where(item => categories.Contains(item.Key)).Max(pair => pair.Value);
return maxBoundsSize * m_ScaleFactor * nonOverlapGuarantee;
}
float CalculateAverageBoundsSize()
{
foreach (var category in prefabs.categories)
{
var prefab = category.Item1;
prefab.transform.localScale = Vector3.one;
var renderers = prefab.GetComponentsInChildren<Renderer>();
var totalBounds = new Bounds();
foreach (var renderer in renderers)
{
totalBounds.Encapsulate(renderer.bounds);
}
var boundsSize = totalBounds.size.magnitude;
m_GameObjectBoundsSizeCache.Add(prefab, boundsSize);
}
return m_GameObjectBoundsSizeCache.Values.Average();
}
}
```
---
</details>
<details>
<summary><strong>Q: What if I don't want randomized object placement? Can I move my objects in a non-randomized deterministic manner on each frame?</strong> </summary><br>
Even though we call them Randomizers, you can use a Randomizer to perform any task through-out the execution lifecycle of your Scenario. The power of the Randomizers comes from the lifecycle hooks that they have into the Iteration and the Scenario, making it easy to know and guarantee when and in which order in the life of your simulation each piece of code runs. These functions include:
* `OnEnable`
* `OnAwake`
* `OnUpdate`
* `OnIterationStart`
* `OnIterationEnd`
* `OnScenarioStart`
* `OnScenarioComplete`
* `OnDisable`
So, in order to have deliberate non-random object movement, you will just need to put your object movement code inside of one of the recurrent lifecycle functions. `OnUpdate()` runs on every frame of the simulation, and `OnIterationStart()` runs every Iteration (which can be the same as each frame if you have only 1 frame per Iteration of your Scenario). For example, the code below moves all objects tagged with the component `ForwardMoverTag` along their forward axis by 1 unit, on every Iteration.
```C#
protected override void OnIterationStart()
{
var tags = tagManager.Query<ForwardMoverTag>();
foreach (var tag in tags)
{
tag.transform.Translate(Vector3.forward);
}
}
```
Additionally, keep in mind that you can use Perception Samplers (and therefore Parameters) to generate constant values, not just random ones. The `ConstantSampler` class provides this functionality.
---
</details>
<details>
<summary><strong>Q: The objects instantiated using the sample foreground placement Randomizer are floating in the air. How can I use this Randomizer to place objects on a horizontal surface instead?</strong> </summary><br>
The objects instantiated by the sample foreground Randomizer are all parented to an object named `Foreground Objects` at the root of the Scene Hierarchy. To modify the orientation of the objects, you can simply rotate this parent object at the beginning of the Scenario.
Alternatively, you could also place `Foreground Objects` inside another GameObject in the Scene using the `Transform.SetParent()` method, and then modify the local position and rotation of `Foreground Objects` in such a way that makes the objects appear on the surface of the parent GameObject.
In addition, if you'd like to have horizontal placement without touching the parent object, you can modify the Randomizer's code to place objects horizontally instead of vertically. The lines responsible for placement are:
```C#
var offset = new Vector3(placementArea.x, placementArea.y, 0) * -0.5f;
```
```C#
instance.transform.position = new Vector3(sample.x, sample.y, depth) + offset;
```
The first line builds an offset vector that is later used to center the points retrieved from Poisson Disk sampling around the center of the coordinate system. The second line is executed in a loop, and each time, places one object at one of the sampled points at the depth (along Z axis) provided by the user.
To make this placement horizontal, you would just need to change these two lines to swap Y for Z. The resulting lines would be:
```C#
var offset = new Vector3(placementArea.x, 0, placementArea.y) * -0.5f;
```
```C#
instance.transform.position = new Vector3(sample.x, depth, sample.y) + offset;
```
Note that the variable `depth` is in fact playing the role of a height variable now.
Finally, to achieve more natural placement, you could also use Unity's physics engine to drop the objects on a surface, let them settle, and then capture an image. To achieve this, you would just need to have sufficient frames in each Iteration of the Scenario (instead of the default 1 frame per iteration), and set your Perception Camera's capture interval to a large enough number that would make it capture each Iteration once after the objects have settled. This example is explained in more detail in the [Perception Camera](#perception-camera) section of this FAQ.
---
</details>
<details>
<summary><strong>Q: Does using the same Random Seed value in two runs of the same Scenario guarantee that the generated datasets are identical?</strong></summary><br>
If you only use the Samplers (and Parameters, which internally use Samplers) provided in the Perception package to generate random values throughout the Scenario's lifecycle and keep the `Random Seed` value unchanged, an identical sequence of random numbers will be generated every time the Scenario is run. This is because the Samplers obtain their seeds through continually mutating the provided global `Random Seed` in the Scenario.
Keep in mind that any change in the order of sampling or the number of samples obtained can lead to different outcomes. For example, if you change the order of Randomizers in the Scenario, the Samplers inside of these Randomizers will now execute in the new order, and thus, they will operate based on different seeds than before and generate different numbers. The same can happen if you add additional calls to a Sampler inside a Randomizer, causing the Samplers in later Randomizers to now use different seeds, since the global seed has been mutated more times than before.
One more thing to keep in mind is that certain systems and components of Unity are not deterministic and can produce different outcomes in consecutive runs. Examples of this are the physics engine and the film grain post processing. Hence, if you need to guarantee that your simulation always produces the exact same dataset, make sure to research the various systems that you use to make sure they behave deterministically.
---
</details>
## <a name="perception-camera">Perception Camera</a>
<details>
<summary><strong>Q: What is the relationship between the Scenario's lifecycle properties (Iterations and Frames per Iteration) and the Perception Camera's timing properties (Simulation Delta Time, Start at Frame, and Frames Between Captures)?</strong> </summary><br>
Each Iteration of the Scenario resets the Perception Camera's timing variables. Thus, you can think of each Iteration of the Scenario as one separate Perception Camera sequence, in which the camera's internal timing properties come into play. For instance, if you have 10 `Frames Per Iteration` on your Scenario, and your Perception Camera's `Start at Frame` value is set to 8, you will get two captures from the camera at the 9th and 10th frames of each Iteration (note that `Start at Frame` starts from 0). Similarly, you can use the `Frames Between Captures` to introduce intervals between captures. A value of 0 leads to all frames being captured.
---
</details>
<details>
<summary><strong>Q: I want to simulate physics (or other accumulative behaviors such as auto-exposure) for a number of frames and let things settle before capturing the Scene. Is this possible with the Perception package?</strong></summary><br>
Yes. The Perception Camera can be set to capture at specific frame intervals, rather than every frame. The `Frames Between Captures` value is set to 0 by default, which causes the camera to capture all frames; however, you can change this to 1 to capture every other frame, or larger numbers to allow more time between captures. You can also have the camera start capturing at a certain frame rather than the first frame, by setting the `Start at Frame` value to a value other than 0. All of this timing happens within each Iteration of the Scenario, and gets reset when you advance to the next Iteration. Therefore, the combination of these properties and the Scenario's `Frames Per Iteration` property allows you to randomize the state of your Scene at the start of each Iteration, let things run for a number of frames, and then capture the Scene at the end of the Iteration.
Suppose we need to drop a few objects into the Scene, let them interact physically and settle after a number of frames, and then capture their final state once. Afterwards, we want to repeat this cycle by randomizing the initial positions of the objects, dropping them, and capturing the final state again. We will set the Scenario's `Frames Per Iteration` to 300, which should be sufficient for the objects to get close to a settled position (this depends on the value you use for `Simulation Delta Time` in Perception Camera and the physical properties of the engine and objects, and can be found through experimentation). We also set the `Start at Frame` value of the Perception Camera to 290, and the `Frames Between Captures` to a sufficiently large number (like 100), so that we only get one capture per Iteration of the Scenario. The results look like below. Note how the bounding boxes only appear after the objects are fairly settled. These are the timestamps at which captures are happening.
<p align="center">
<img src="images/object_drop.gif" width="700"/>
</p>
---
</details>
<details>
<summary><strong>Q: I do not want the Perception Camera to control the delta time of my simulation or capture on a scheduled basis. Can I have a Perception Camera in my Scene that does not affect timings and trigger captures manually from other scripts?</strong></summary><br>
Yes. The Perception Camera offers two trigger modes, `Scheduled` and `Manual`, and these can be chosen in the editor UI for the Perception Camera component. If you select the `Manual` mode, you will be able to trigger captures by calling the `RequestCapture()` method of `PerceptionCamera`. In this mode, you have the option to not have this camera dictate your simulation delta time. This is controlled using the `Affect Simulation Timing` checkbox.
---
</details>
<details>
<summary><strong>Q: Can I have multiple Perception Cameras active in my Scene simultaneously?</strong></summary><br>
No, at this time the Perception package only supports one active Perception Camera. This is something that is on our roadmap and we hope to support soon.
However, the package does support having more than one Perception Camera in the Scene, as long as only one is active when the simulation starts. Therefore, one possible workaround, if your simulation is fully deterministic from one run to the next, would be to run the simulation more than once, each time with one of the cameras active. While not ideal, this will at least let you generate matching datasets.
---
</details>
<details>
<summary><strong>Q: My RGB images look darker than what I see in Unity Editor, when I render the Perception Camera to a texture. How can I fix this?</strong>
</summary><br>
This issue is caused by the color format of the texture. In the ***Inspector*** view of the render texture, set color format to `R8G8B8A8_SRGB`.
---
</details>
<details>
<summary><strong>Q: How do I report additional custom information in my output dataset for each frame or the whole simulation (e.g. 3D position of objects at the start of each Iteration, intensity of lights, etc.)?</strong>
</summary><br>
This can be done by adding custom annotations to your dataset. Have a look at [this](https://github.com/Unity-Technologies/com.unity.perception/blob/master/com.unity.perception/Documentation%7E/DatasetCapture.md) page for an explanation, as well as an example for how to do this.
---
</details>
## <a name="miscellaneous">Miscellaneous</a>
<details>
<summary><strong>Q: Objects in my captured images have jagged edges, how can I fix this?</strong>
</summary><br>
This is a common issue with rendering graphics into raster images (digital images), when the resolution of the raster is not high enough to perfectly display slanting lines. The common solution to this issue is the use of anti-aliasing methods, and Unity offers a number of these in both URP and HDRP. To experiment with anti-aliasing, go to the ***Inspector*** view of your Perception Camera object and in the Camera component, change `Anti-aliasing` from `None` to another option.
---
</details>
<details>
<summary><strong>Q: I am using an HDRP Unity project with Perception and my images have significant blurring around most objects. How can I remove this blur?</strong>
</summary><br>
The effect you are observing here is motion blur, which happens because the placement Randomizers used in the Perception tutorial cache their instantiated objects from one Iteration to the next, and move them to new locations on each Iteration instead of destroying them and creating new ones. This "motion" of the objects causes the motion blur effect to kick in.
HDRP projects have motion blur and a number of other post processing effects enabled by default. To disable motion blur or any other effect, follow these steps:
1. Create an empty GameObject in your Scene and add a Volume component to it.
2. Set the Volume's profile to the **Volume Global** asset.
3. Uncheck the **Motion Blur** option.
<p align="center">
<img src="images/volume.png" width="500"/>
</p>
---
</details>
<details>
<summary><strong>Q: Are all post processing effects provided by Unity safe to use in synthetic datasets?</strong>
</summary><br>
No. When using post processing effects, you need to be careful about issues regarding randomness and determinism:
* There are certain post processing effects that need randomization internally, e.g. film grain. The film grain effect provided by Unity is not sufficiently randomized for model training and can thus mislead your CV model to look for a specific noise pattern during prediction.
* Even if such an effect is properly randomized, using a randomized effect would make your overall randomization strategy non-deterministic, meaning you would not be able to reproduce your datasets. This is because the effect would internally use random number generators outside of the Samplers provided by the Perception package. If you have access to the source code for a randomized effect, you can modify it to only use Perception Samplers for random number generation, which would make its behavior deterministic and thus appropriate for use in synthetic datasets that need to be reproducible.
To make sure you do not run into insufficient randomization or non-determinism, it would be best to implement effects such as film grain yourself or modify existing code to make sure no random number generators are used except for the Samplers provided in the Perception package.
---
</details>
<details>
<summary><strong>Q: What post processing effects can help improve model performance?</strong>
</summary><br>
Based on our experiments, randomizing contrast, saturation, lens blur, and lens distortion can help significantly improve the performance of your CV model. We recommend experimenting with these as well as other effects to determine those that work best for your use-case.
---
</details>
<details>
<summary><strong>Q: Can I debug my C# code?</strong>
</summary><br>
Unity projects can be debugged using external editors such as Visual Studio or JetBrains Rider. For local development and debugging, you will first need to clone the Perception repository to disk and add the Perception package from this cloned repository to your Unity project. Then, in Unity Editor, go to ***Edit (or "Unity" on OSX) -> Preferences -> External Tools***. Select your preferred editor as the External Script Editor, and enable
**General .csproj files** for at least **Embedded packages** and **Local packages**. This will allow you to quickly navigate through the code-base for the Perception package and internal Unity Editor packages.
All you need to do now is to double click any of the Perception package's C# script files from inside Unity Editor's **Project** window. The files are located in `Assets/Perception`. Double clicking will open them in your external editor of choice, and you will be able to attach the debugger to Unity.
---
</details>
<details>
<summary><strong>Q: What kind of synthetic environment will be best for my use-case?</strong>
</summary><br>
It is difficult to say what type of synthetic environment would lead to the best model performance. It is best to carry out small and quick experiments with both random unstructured environments (such as the [SynthDet](https://github.com/Unity-Technologies/SynthDet) project) and more structured ones that may resemble real environments in which prediction will need to happen. This will help identify the types of environments and randomizations that work best for each specific use-case. The beauty of synthetic data is that you can try these experiments fairly quickly.
Here are a few blog posts to give you some ideas: [1](https://blog.unity.com/technology/synthetic-data-simulating-myriad-possibilities-to-train-robust-machine-learning-models), [2](https://blog.unity.com/technology/use-unitys-perception-tools-to-generate-and-analyze-synthetic-data-at-scale-to-train), [3](https://blog.unity.com/technology/training-a-performant-object-detection-ml-model-on-synthetic-data-using-unity-perception), [4](https://blog.unity.com/technology/supercharge-your-computer-vision-models-with-synthetic-datasets-built-by-unity), [5](https://blog.unity.com/technology/boosting-computer-vision-performance-with-synthetic-data).
---
</details>
<details>
<summary><strong>Q: Can I have more realistic rendering in my Scene?</strong>
</summary><br>
A project's lighting configuration typically has the greatest influence over the final rendered output over any other simulation property. Unity has many lighting options, each of which is designed as a different trade-off between performance and realism/capability. The 3 most pertinent options that you will likely be interested in are:
* URP baked lighting: The Universal Render Pipeline offers the most performant lighting configurations by using an offline baking process to generate realistic bounce lighting within a static scene and then using simple shadow mapped dynamic lights in conjunctions with light probes to make dynamic (or randomized) objects "fit" into the baked scene. This option provides high performance, but lacks the visual fidelity needed for interior environments and is geared toward more outdoor-like settings. Also, depending on scene randomization complexity, light baking might not be the best option. Randomly generated scenes will often place objects and adjust lighting in ways that make the new scene incompatible with the original baked lighting configuration.
* HDRP lighting: A default HDRP scene offers a step toward more realistic environments with a much larger array of lighting settings (soft shadows, multiple dynamic lights, etc.) and a host of additional real-time effects like camera exposure and screen space ambient occlusion. A warning though: real time screen space effects may make your scene "look better", but the way these effects are calculated is not based on how light works in the real world, so realism may vary. Another huge advantage of HDRP is the potential to have moderately realistic lighting without baking your lighting configuration (though you can integrate light baking if you want to). However, there is no real-time global illumination option in default HDRP, meaning that your scene will not simulate complex real world light behavior such as light bouncing, light bleeding, or realistic shadows for dynamic scenes. This can result in unrealistically dark scenes when only using directional lights and windows (without extra interior lights to brighten things up). Overall though, HDRP offers a good compromise between performance and realism for some use cases.
* HDRP DXR (DirectX Raytracing): Unity offers some preview ray tracing features in its latest editor versions that can be used to drastically improve the realism of your scene. Here are the pros and cons of DXR:
* Pros:
* Can simulate more realistic light behaviors (light bouncing, light color bleeding, and realistic shadows)
* No light baking required
* Cons:
* Requires special hardware to run (Nvidia RTX graphics cards)
* Time consuming to render (relative to default HDRP). Some lighting options (Global Illumination) are less expensive than others (Path Tracing).
* More complicated to configure
* These features are still in preview and subject to change
A visual comparison of the different lighting configurations in HDRP is shown below. The Scene includes one directional light and one dim point light on the ceiling.
Default HDRP:
<p align="center">
<img src="images/hdrp.png" width="700"/>
</p>
HDRP with Global Illumination (notice how much brighter the scene is with ray traced light bouncing):
<p align="center">
<img src="images/hdrp_rt_gi.png" width="700"/>
</p>
HDRP with Path Tracing (128 samples) (notice the red light bleeding from the cube onto the floor and the increased shadow quality):
<p align="center">
<img src="images/hdrp_pt_128_samples.png" width="700"/>
</p>
HDRP with Path Tracing (4096 samples) (more samples leads to less ray tracing noise but also a longer time to render):
<p align="center">
<img src="images/hdrp_pt_4096_samples.png" width="700"/>
</p>
---
</details>
<details>
<summary><strong>Q: I am randomizing my Scene every frame and using ray casting to detect the position of objects, but my ray casts are returning incorrect results. What is the issue here?</strong>
</summary><br>
The physics engine needs to catch up with the position and rotation of your objects and is typically a frame behind. When you randomize things every frame, the physics engine can never catch up. To fix this, call `Physics.SyncTransforms` just before calling any ray casting methods.
---
</details>
<details>
<summary><strong>Q: Where can I get humanoid models and animations?</strong>
</summary><br>
One useful resource for humanoid characters and animations is [Mixamo](https://www.mixamo.com/#/?page=1&type=Motion%2CMotionPack).
---
</details>

11
com.unity.perception/Editor/Visualizer/VisualizerInstaller.cs.meta


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com.unity.perception/Editor/Visualizer/PipAPI.cs


#if UNITY_EDITOR_WIN || UNITY_EDITOR_OSX
using System;
using System.Net.Http;
using System.Threading.Tasks;
using Newtonsoft.Json;
using UnityEngine;
namespace UnityEditor.Perception.Visualizer
{
static class PipAPI
{
static readonly HttpClient k_HttpClient;
const string k_PypiServer = "https://pypi.org";
const string k_PackageName = "unity-cv-datasetvisualizer";
static PipAPI()
{
k_HttpClient = new HttpClient();
}
internal static async Task<string> GetLatestVersionNumber()
{
var requestUri = new Uri($"{k_PypiServer}/pypi/{k_PackageName}/json?Accept=application/json");
try
{
var httpResponse = await k_HttpClient.GetAsync(requestUri);
if (httpResponse.IsSuccessStatusCode)
{
var responseString = httpResponse.Content.ReadAsStringAsync().Result;
dynamic responseJson = JsonConvert.DeserializeObject(responseString);
return responseJson.info.version;
}
HandleApiErrors(httpResponse);
}
catch (HttpRequestException e)
{
Debug.LogException(e);
}
return null;
}
static void HandleApiErrors(HttpResponseMessage responseMessage)
{
Debug.LogError("A request to PyPI.org did not successfully complete: " + responseMessage.ReasonPhrase);
}
internal static int CompareVersions(string version1, string version2)
{
var split1 = version1.Split('.');
var split2 = version2.Split('.');
int i;
for(i = 0; i < Math.Min(split1.Length, split2.Length); i++)
{
var compare = Int32.Parse(split1[i]) - Int32.Parse(split2[i]);
if (compare != 0)
{
return compare;
}
}
if (i < split1.Length)
return 1;
if (i < split2.Length)
return -1;
return 0;
}
}
}
#endif

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com.unity.perception/Editor/Visualizer/PythonForUnityInstaller.cs


#if UNITY_EDITOR_WIN || UNITY_EDITOR_OSX
using System;
using UnityEditor.PackageManager.Requests;
using UnityEditor.PackageManager;
using UnityEngine;
using System.Threading;
namespace UnityEditor.Perception.Visualizer
{
[InitializeOnLoad]
static class PythonForUnityInstaller
{
static PythonForUnityInstaller()
{
Add();
}
static void Add()
{
if (!CheckIfPackageInstalled())
{
var request = Client.Add("com.unity.scripting.python@4.0.0-exp.5");
while (!request.IsCompleted)
{
Thread.Sleep(100);
}
if (request.Status == StatusCode.Success)
Debug.Log("Installed: " + request.Result.packageId);
else if (request.Status >= StatusCode.Failure)
Debug.Log(request.Error.message);
}
}
static bool CheckIfPackageInstalled()
{
var request = Client.List();
while (!request.IsCompleted)
{
Thread.Sleep(100);
}
if (request.Status == StatusCode.Success)
{
foreach (var package in request.Result)
{
if (package.packageId.Contains("com.unity.scripting.python"))
{
return true;
}
}
}
else if (request.Status >= StatusCode.Failure)
Debug.LogError(request.Error.message);
return false;
}
}
}
#endif

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785
com.unity.perception/Editor/Visualizer/VisualizerInstaller.cs


#if UNITY_EDITOR_WIN || UNITY_EDITOR_OSX
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Text.RegularExpressions;
using System.Threading.Tasks;
using UnityEngine;
using UnityEngine.Perception.GroundTruth;
using Debug = UnityEngine.Debug;
namespace UnityEditor.Perception.Visualizer
{
public class VisualizerInstaller : EditorWindow
{
//This files stores entries as ProjectDataPath,PythonPID,Port,VisualizerPID
//It keeps a record of the instances of visualizer opened so that we don't open a new one everytime
const string k_FilenameStreamlitInstances = "Unity/streamlit_instances.csv";
static string PathToStreamlitInstances
{
get
{
#if UNITY_EDITOR_WIN
return Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), k_FilenameStreamlitInstances);
#elif UNITY_EDITOR_OSX
return Path.Combine(
Path.Combine(
Environment.GetFolderPath(Environment.SpecialFolder.Personal), "Library"
),
k_FilenameStreamlitInstances);
#endif
}
}
const string k_NameOfVisualizerProcess
#if UNITY_EDITOR_OSX
= "bash";
#elif UNITY_EDITOR_WIN
= "datasetvisualizer";
#endif
static Task InstallationCommand(ref int exitCode, string packagesPath)
{
var exitCodeCopy = exitCode;
#if UNITY_EDITOR_WIN
var task = Task.Run(() => ExecuteCmd($"\"{packagesPath}\"\\pip3.bat install --upgrade --no-warn-script-location unity-cv-datasetvisualizer", ref exitCodeCopy));
#elif UNITY_EDITOR_OSX
var task = Task.Run(() => ExecuteCmd($"cd \'{packagesPath}\'; ./python3.7 -m pip install --upgrade unity-cv-datasetvisualizer", ref exitCodeCopy));
#endif
exitCode = exitCodeCopy;
return task;
}
// ReSharper disable Unity.PerformanceAnalysis
/// <summary>
/// Install visualizer (Uses the python3.7 installation that comes with python for unity)
/// </summary>
static async Task SetupVisualizer()
{
var project = Application.dataPath;
var (pythonPid, port, visualizerPid) = ReadEntry(project);
//If there is a python instance for this project AND it is alive then setup will fail (must kill instance)
if (pythonPid != -1 && ProcessAlive(pythonPid, port, visualizerPid))
{
if (EditorUtility.DisplayDialog("Shutdown visualizer?",
"The visualizer tool can't be running while you re-installs, would you like to shutdown the current instance?",
"Yes",
"No"))
{
Process.GetProcessById(pythonPid + 1).Kill();
}
else
{
return;
}
}
const int steps = 3;
var exitCode = 0;
//==============================SETUP PATHS======================================
#if UNITY_EDITOR_WIN
var packagesPath = Path.GetFullPath(Application.dataPath.Replace("/Assets", "/Library/PythonInstall/Scripts"));
#elif UNITY_EDITOR_OSX
var packagesPath = Path.GetFullPath(Application.dataPath.Replace("/Assets","/Library/PythonInstall/bin"));
#endif
#if UNITY_EDITOR_WIN
packagesPath = packagesPath.Replace("/", "\\");
#endif
//==============================INSTALL VISUALIZER IN PYTHON FOR UNITY======================================
EditorUtility.DisplayProgressBar("Setting up the Visualizer", "Installing Visualizer (This may take a few minutes)", 2f / steps);
await InstallationCommand(ref exitCode, packagesPath);
if (exitCode != 0)
{
EditorUtility.ClearProgressBar();
return;
}
EditorUtility.ClearProgressBar();
Debug.Log("Successfully installed visualizer tool!");
}
/// <summary>
/// Executes command in cmd or console depending on system
/// </summary>
/// <param name="command">The command to execute</param>
/// <param name="exitCode">int reference to get exit code</param>
/// <param name="waitForExit">Whether this method should wait for the command to exist</param>
static void ExecuteCmd(string command, ref int exitCode, bool waitForExit = true)
{
#if UNITY_EDITOR_WIN
const string shell = "cmd.exe";
var argument = $"/c \"{command}\"";
#elif UNITY_EDITOR_OSX
const string shell = "/bin/bash";
var argument = $"-c \"{command}\"";
#endif
var info = new ProcessStartInfo(shell, argument);
info.CreateNoWindow = true;
info.UseShellExecute = false;
info.RedirectStandardOutput = false;
info.RedirectStandardError = false;
var cmd = Process.Start(info);
if (cmd == null)
{
Debug.LogError($"Could not create process using command {command}");
return;
}
if (waitForExit)
{
cmd.WaitForExit();
exitCode = cmd.ExitCode;
}
}
/// <summary>
/// Executes command in cmd or console depending on system and waits a specified amount for the execution to finish
/// </summary>
/// <param name="command">The command to execute</param>
/// <param name="exitCode">int reference to get exit code</param>
/// <param name="output">string reference to save output</param>
/// <param name="waitForExit">Time to wait for the command to exit before returning to the editor</param>
static void ExecuteCmd(string command, ref int exitCode, ref string output, int waitForExit)
{
#if UNITY_EDITOR_WIN
const string shell = "cmd.exe";
var argument = $"/c \"{command}\"";
#elif UNITY_EDITOR_OSX
const string shell = "/bin/bash";
var argument = $"-c \"{command}\"";
#endif
var info = new ProcessStartInfo(shell, argument);
info.CreateNoWindow = true;
info.UseShellExecute = false;
info.RedirectStandardOutput = true;
info.RedirectStandardError = true;
var cmd = Process.Start(info);
if (cmd == null)
{
Debug.LogError($"Could not create process using command {command}");
return;
}
cmd.WaitForExit(waitForExit);
output = cmd.StandardOutput.ReadToEnd();
if (cmd.HasExited)
{
exitCode = cmd.ExitCode;
if (exitCode != 0)
{
Debug.LogError($"Error - {exitCode} - Failed to execute: {command} - {cmd.StandardError.ReadToEnd()}");
}
}
cmd.Close();
}
[MenuItem("Window/Dataset Visualizer/Open")]
public static async Task RunVisualizerButton()
{
var project = Application.dataPath;
await RunVisualizer(project);
}
// ReSharper disable Unity.PerformanceAnalysis
/// <summary>
/// If an instance is already running for this project it opens the browser at the correct port
/// If no instance is found it launches a new process
/// </summary>
public static async Task RunVisualizer(string project)
{
try
{
if (!CheckIfVisualizerInstalled(project))
{
await SetupVisualizer();
}
var (pythonPid, port, visualizerPid) = ReadEntry(project);
EditorUtility.DisplayProgressBar("Opening Visualizer", "Checking if instance exists", 0.5f / 4);
//If there is a python instance for this project AND it is alive then just run browser
if (pythonPid != -1 && ProcessAlive(pythonPid, port, visualizerPid))
{
EditorUtility.DisplayProgressBar("Opening Visualizer", "Opening", 4f / 4);
LaunchBrowser(port);
EditorUtility.ClearProgressBar();
}
//Otherwise delete any previous entry for this project and launch a new process
else
{
DeleteEntry(project);
var before = Process.GetProcesses();
EditorUtility.DisplayProgressBar("Opening Visualizer", "Running executable", 1f / 4);
ExecuteVisualizer(project);
Process[] after;
const int maxAttempts = 10;
//Poll for new processes until the visualizer process is launched
EditorUtility.DisplayProgressBar("Opening Visualizer", "Finding Visualizer instance", 2f / 4);
var newVisualizerPid = -1;
var attempts = 0;
while (newVisualizerPid == -1)
{
await Task.Delay(1000);
after = Process.GetProcesses();
newVisualizerPid = GetNewProcessID(before, after, k_NameOfVisualizerProcess);
if (attempts == maxAttempts)
{
Debug.LogWarning("Failed to get visualizer process ID after launch. Note that this does not necessarily mean the tool did not launch successfully. However, running the visualizer again will now create a new instance of the process.");
EditorUtility.ClearProgressBar();
return;
}
attempts++;
}
//Poll for new processes until the streamlit python script is launched
EditorUtility.DisplayProgressBar("Opening Visualizer", "Finding Streamlit instance", 3f / 4);
var newPythonPid = -1;
attempts = 0;
while (newPythonPid == -1)
{
await Task.Delay(1000);
after = Process.GetProcesses();
newPythonPid = GetNewProcessID(before, after, "python");
if (attempts == maxAttempts)
{
Debug.LogWarning("Failed to get python process ID after launch. Note that this does not necessarily mean the tool did not launch successfully. However, running the visualizer again will now create a new instance of the process.");
EditorUtility.ClearProgressBar();
return;
}
attempts++;
}
//Poll until the python script starts using the port
EditorUtility.DisplayProgressBar("Opening Visualizer", "Finding Port", 3.5f / 4);
var newPort = -1;
attempts = 0;
while (newPort == -1)
{
await Task.Delay(1000);
newPort = GetPortForPid(newPythonPid);
if (attempts == maxAttempts)
{
Debug.LogWarning("Failed to get port number used by the visualizer tool after launch. Note that this does not necessarily mean the tool did not launch successfully. However, running the visualizer again will now create a new instance of the process.");
EditorUtility.ClearProgressBar();
return;
}
attempts++;
}
//Save this into the streamlit_instances.csv file
WriteEntry(project, newPythonPid, newPort, newVisualizerPid);
EditorUtility.DisplayProgressBar("Opening Visualizer", "Opening", 4f / 4);
//When launching the process it will try to open a new tab in the default browser, however if a tab for it already exists it will not
//For convenience if the user wants to force a new one to open they can press on "manually open"
if (EditorUtility.DisplayDialog("Opening Visualizer Tool",
$"The visualizer tool should open shortly in your default browser at http://localhost:{newPort}.\n\nIf this is not the case after a few seconds you may open it manually",
"Manually Open",
"Cancel"))
{
LaunchBrowser(newPort);
}
EditorUtility.ClearProgressBar();
}
}
catch (Exception e)
{
Debug.Log(e.Message);
EditorUtility.ClearProgressBar();
}
}
/// <summary>
/// Runs visualizer instance (streamlit) from the python for unity install
/// </summary>
static void ExecuteVisualizer(string project)
{
#if UNITY_EDITOR_WIN
var packagesPath = Path.GetFullPath(project.Replace("/Assets", "/Library/PythonInstall/Scripts"));
#elif UNITY_EDITOR_OSX
var packagesPath = project.Replace("/Assets","/Library/PythonInstall/bin");
#endif
var pathToData = PlayerPrefs.GetString(SimulationState.latestOutputDirectoryKey);
#if UNITY_EDITOR_WIN
packagesPath = packagesPath.Replace("/", "\\");
pathToData = pathToData.Replace("/", "\\");
#endif
#if UNITY_EDITOR_WIN
var command = $"cd \"{pathToData}\" && \"{packagesPath}\\datasetvisualizer.exe\" --data=\".\"";
#elif UNITY_EDITOR_OSX
var command = $"cd \'{pathToData}\'; \'{packagesPath}/datasetvisualizer\' --data=\'.\'";
#endif
var exitCode = 0;
ExecuteCmd(command, ref exitCode, false);
if (exitCode != 0)
{
Debug.LogError("Failed launching the visualizer - Exit Code: " + exitCode);
}
}
static (int pythonPID, int port, int visualizerPID) ReadEntry(string project)
{
if (!File.Exists(PathToStreamlitInstances))
return (-1, -1, -1);
using (var sr = File.OpenText(PathToStreamlitInstances))
{
string line;
while ((line = sr.ReadLine()) != null)
{
var entry = line.TrimEnd().Split(',');
if (entry[0] == project)
{
//The -1 on ports is because the System.Diagnosis.Process API starts at 0 where as the PID in Windows and Mac start at 1
return (int.Parse(entry[1]) - 1, int.Parse(entry[2]), int.Parse(entry[3]) - 1);
}
}
}
return (-1, -1, -1);
}
static void WriteEntry(string project, int pythonId, int port, int visualizerId)
{
var path = PathToStreamlitInstances;
using (var sw = File.AppendText(path))
{
sw.WriteLine($"{project},{pythonId},{port},{visualizerId}");
}
}
static void DeleteEntry(string project)
{
var path = PathToStreamlitInstances;
if (!File.Exists(path))
return;
var entries = new List<string>(File.ReadAllLines(path));
entries = entries.FindAll(x => !x.StartsWith(project));
using (var sw = File.CreateText(path))
{
foreach (var entry in entries)
{
sw.WriteLine(entry.TrimEnd());
}
}
}
/// <summary>
/// Finds the process id of the first process that is different between the before and after array
/// and that contains name
/// </summary>
/// <param name="before"></param>
/// <param name="after"></param>
/// <param name="name"></param>
/// <returns></returns>
static int GetNewProcessID(Process[] before, Process[] after, string name)
{
foreach (var p in after)
{
var isNew = true;
// try/catch to skip any process that may:
// not exist anymore/may be on another computer/are not associated with a living process
try
{
if (p.ProcessName.ToLower().Contains(name.ToLower()))
{
foreach (var q in before)
{
if (p.Id == q.Id)
{
isNew = false;
break;
}
}
if (isNew)
{
return p.Id;
}
}
}
catch(Exception ex)
{
if (!(ex is InvalidOperationException || ex is NotSupportedException))
{
throw;
}
}
}
return -1;
}
/// <summary>
/// Finds which port the process PID is using
/// </summary>
/// <param name="pid"></param>
/// <returns></returns>
static int GetPortForPid(int pid)
{
var processPortMap = PortProcessor.ProcessPortMap;
if (processPortMap == null)
{
return -1;
}
foreach (var p in processPortMap.Where(p => p.ProcessId == pid))
{
return p.PortNumber;
}
return -1;
}
/// <summary>
/// Launches browser at localhost:port
/// </summary>
/// <param name="port"></param>
static void LaunchBrowser(int port)
{
Process.Start($"http://localhost:{port}");
}
/// <summary>
/// Check if streamlit process is alive
/// </summary>
/// <param name="pythonPid"></param>
/// <param name="port"></param>
/// <param name="visualizerPid"></param>
/// <returns></returns>
static bool ProcessAlive(int pythonPid, int port, int visualizerPid)
{
return PidExists(pythonPid) &&
CheckProcessName(pythonPid, "python") &&
ProcessListensToPort(pythonPid, port) &&
PidExists(visualizerPid) &&
CheckProcessName(visualizerPid, k_NameOfVisualizerProcess);
}
/// <summary>
/// Check if a process with ProcessId = PID is alive
/// </summary>
/// <param name="pid"></param>
/// <returns></returns>
static bool PidExists(int pid)
{
try
{
var proc = Process.GetProcessById(pid + 1);
return !proc.HasExited;
}
catch (ArgumentException)
{
return false;
}
}
/// <summary>
/// Check if process with PID has a name that contains "name"
/// </summary>
/// <param name="pid"></param>
/// <param name="name"></param>
/// <returns></returns>
static bool CheckProcessName(int pid, string name)
{
var proc = Process.GetProcessById(pid + 1);
return proc.ProcessName.ToLower().Contains(name);
}
/// <summary>
/// Check if the given PID listens to given port
/// </summary>
/// <param name="pid"></param>
/// <param name="port"></param>
/// <returns></returns>
static bool ProcessListensToPort(int pid, int port)
{
var processes = PortProcessor.ProcessPortMap.FindAll(
x => x.ProcessId == pid + 1 && x.PortNumber == port
);
return processes.Count >= 1;
}
/// <summary>
/// Static class that returns the list of processes and the ports those processes use.
/// </summary>
static class PortProcessor
{
/// <summary>
/// A list of ProcessPorts that contain the mapping of processes and the ports that the process uses.
/// </summary>
public static List<ProcessPort> ProcessPortMap => GetNetStatPorts();
/// <summary>
/// This method distills the output from Windows: netstat -a -n -o or OSX: netstat -n -v -a into a list of ProcessPorts that provide a mapping between
/// the process (name and id) and the ports that the process is using.
/// </summary>
/// <returns></returns>
static List<ProcessPort> GetNetStatPorts()
{
var processPorts = new List<ProcessPort>();
var startInfo = new ProcessStartInfo();
#if UNITY_EDITOR_WIN
startInfo.FileName = "netstat.exe";
startInfo.Arguments = "-a -n -o";
#elif UNITY_EDITOR_OSX
startInfo.FileName = "netstat";
startInfo.Arguments = "-n -v -a";
#endif
startInfo.WindowStyle = ProcessWindowStyle.Hidden;
startInfo.UseShellExecute = false;
startInfo.RedirectStandardInput = true;
startInfo.RedirectStandardOutput = true;
startInfo.RedirectStandardError = true;
using (var proc = new Process())
{
proc.StartInfo = startInfo;
proc.Start();
#if UNITY_EDITOR_OSX
proc.WaitForExit(2500);
#endif
var standardOutput = proc.StandardOutput;
var standardError = proc.StandardError;
var netStatContent = standardOutput.ReadToEnd() + standardError.ReadToEnd();
var netStatExitStatus = proc.ExitCode.ToString();
if (netStatExitStatus != "0")
{
Debug.LogError($"NetStat command failed. Try running '{startInfo.FileName} {startInfo.Arguments}' manually to verify that you have permissions to execute this command.");
return null;
}
#if UNITY_EDITOR_WIN
var netStatRows = Regex.Split(netStatContent, "\r\n");
#elif UNITY_EDITOR_OSX
var netStatRows = Regex.Split(netStatContent, "\n");
#endif
foreach (var netStatRow in netStatRows)
{
var tokens = Regex.Split(netStatRow, "\\s+");
#if UNITY_EDITOR_WIN
if (tokens.Length > 4 && (tokens[1].Equals("UDP") || tokens[1].Equals("TCP")))
{
var ipAddress = Regex.Replace(tokens[2], @"\[(.*?)\]", "1.1.1.1");
try
{
processPorts.Add(new ProcessPort(
tokens[1] == "UDP" ? Convert.ToInt32(tokens[4]) : Convert.ToInt32(tokens[5]),
Convert.ToInt32(ipAddress.Split(':')[1])
));
}
catch (Exception ex)
{
if (ex is IndexOutOfRangeException || ex is FormatException || ex is OverflowException)
{
Debug.LogError("Could not convert the following NetStat row to a process to port mapping.");
Debug.LogError(netStatRow);
}
else
{
throw;
}
}
}
else
{
if (!netStatRow.Trim().StartsWith("Proto") && !netStatRow.Trim().StartsWith("Active") && !String.IsNullOrWhiteSpace(netStatRow))
{
Debug.LogError("Could not convert the following NetStat row to a process to port mapping.");
Debug.LogError(netStatRow);
}
}
#elif UNITY_EDITOR_OSX
if (tokens.Length == 12 && tokens[0].Equals("tcp4") & (tokens[3].Contains("localhost") || tokens[3].Contains("*.")))
{
try
{
if(tokens[5] != "CLOSED")
{
processPorts.Add(new ProcessPort(
Convert.ToInt32(tokens[8]),
Convert.ToInt32(tokens[3].Split('.')[1])
));
}
}
catch (FormatException)
{
//On mac rows show up in a difficult to predict order of formats (this skips all rows that we don't care about)
}
catch (Exception ex)
{
if (ex is IndexOutOfRangeException || ex is OverflowException)
{
Debug.LogError("Could not convert the following NetStat row to a process to port mapping.");
Debug.LogError(netStatRow);
}
else
{
throw;
}
}
}
#endif
}
}
return processPorts;
}
}
/// <summary>
/// A mapping for processes to ports and ports to processes that are being used in the system.
/// </summary>
readonly struct ProcessPort
{
/// <summary>
/// Internal constructor to initialize the mapping of process to port.
/// </summary>
/// <param name="processId"></param>
/// <param name="portNumber"></param>
internal ProcessPort(int processId, int portNumber)
{
ProcessId = processId;
PortNumber = portNumber;
}
public int ProcessId { get; }
public int PortNumber { get; }
}
[MenuItem("Window/Dataset Visualizer/Check For Updates")]
internal static async Task CheckForUpdates()
{
var project = Application.dataPath;
if (!CheckIfVisualizerInstalled(project))
{
if (EditorUtility.DisplayDialog("Visualizer Not Installed",
$"The visualizer is not yet installed, do you wish to install it?",
"Install",
"Cancel"))
{
await SetupVisualizer();
}
return;
}
var latestVersion = await PipAPI.GetLatestVersionNumber();
#if UNITY_EDITOR_WIN
var packagesPath = Path.GetFullPath(Application.dataPath.Replace("/Assets", "/Library/PythonInstall/Scripts"));
#elif UNITY_EDITOR_OSX
var packagesPath = Path.GetFullPath(Application.dataPath.Replace("/Assets","/Library/PythonInstall/bin"));
#endif
#if UNITY_EDITOR_WIN
packagesPath = packagesPath.Replace("/", "\\");
#endif
var exitCode = -1;
string output = null;
#if UNITY_EDITOR_WIN
ExecuteCmd($"\"{packagesPath}\"\\pip3.bat show unity-cv-datasetvisualizer", ref exitCode, ref output, 1500);
#elif UNITY_EDITOR_OSX
ExecuteCmd($"cd \'{packagesPath}\'; ./python3.7 -m pip show unity-cv-datasetvisualizer", ref exitCode, ref output, 1500);
#endif
if (exitCode != 0)
{
Debug.LogError("Could not get the version of the current install of the visualizer tool");
return;
}
var outputLines = output.Split(new[] { Environment.NewLine }, StringSplitOptions.None);
var currentVersion = (from t in outputLines where t.StartsWith("Version: ") select t.Substring("Version: ".Length)).FirstOrDefault();
if (currentVersion == null)
{
Debug.LogError("Could not parse the version of the currently installed visualizer tool. Cancelling update check.");
return;
}
if (PipAPI.CompareVersions(latestVersion, currentVersion) > 0)
{
if (EditorUtility.DisplayDialog("New Version Found!",
$"An update was found for the Visualizer",
"Install",
"Cancel"))
{
await SetupVisualizer();
}
}
else
{
EditorUtility.DisplayDialog("Visualizer", "No new updates found", "OK");
}
}
static bool CheckIfVisualizerInstalled(string project)
{
#if UNITY_EDITOR_WIN
var packagesPath = Path.GetFullPath(project.Replace("/Assets", "/Library/PythonInstall/Scripts"));
#elif UNITY_EDITOR_OSX
var packagesPath = Path.GetFullPath(project.Replace("/Assets","/Library/PythonInstall/bin"));
#endif
#if UNITY_EDITOR_WIN
packagesPath = packagesPath.Replace("/", "\\");
#endif
var exitCode = 0;
string output = null;
#if UNITY_EDITOR_WIN
ExecuteCmd($"\"{packagesPath}\"\\pip3.bat list", ref exitCode, ref output, waitForExit: 1500);
#elif UNITY_EDITOR_OSX
ExecuteCmd($"cd \'{packagesPath}\'; ./python3.7 -m pip list", ref exitCode, ref output, waitForExit: 1500);
#endif
if (exitCode != 0)
{
Debug.LogError("Could not list pip packages to check whether the visualizer is installed. Cancelling update check");
return false;
}
var outputLines = output.Split(new[] { Environment.NewLine }, StringSplitOptions.None);
return outputLines.Any(t => t.StartsWith("unity-cv-datasetvisualizer"));
}
}
}
#endif

/com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/AssetRoleBase.cs → /com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/IAssetRoleBase.cs

/com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/AssetRoleBase.cs.meta → /com.unity.perception/Runtime/Randomization/Randomizers/AssetSources/IAssetRoleBase.cs.meta

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