Fixed a bug in instance segmentation labeler that erroneously logged that object ID 255 was not supported
## [0.6.0-preview.1] - 2020-12-03
### Added
Added support for labeling Terrain objects. Trees and details are not labeled but will occlude other objects.
Added instance segmentation labeler.
Added support for full screen visual overlays and overlay manager.
All-new editor interface for the Labeling component and Label Configuration assets. The new UI improves upon various parts of the label specification and configuration workflow, making it more efficient and less error-prone to setup a new Perception project.
Added Assets->Perception menu for current and future asset preparation and validation tools. Currently contains one function which lets the user create prefabs out of multiple selected models with one click, removing the need for going through all models individually.
### Changed
Updated dependencies to com.unity.simulation.capture:0.0.10-preview.14, com.unity.simulation.core:0.0.10-preview.20, and com.unity.burst:1.3.9.
Changed InstanceSegmentationImageReadback event to provide a NativeArray\<Color32\> instead of NativeArray\<uint\>.
Expanded all Unity Simulation references from USim to Unity Simulation.
Uniform and Normal samplers now serialize their random seeds.
The ScenarioBase's GenerateIterativeRandomSeed() method has been renamed to GenerateRandomSeedFromIndex().
### Deprecated
### Removed
### Fixed
UnitySimulationScenario now correctly deserializes app-params before offsetting the current scenario iteration when executing on Unity Simulation.
Fixed Unity Simulation nodes generating one extra empty image before generating their share of the randomization scenario iterations.
Fixed enumeration in the CategoricalParameter.categories property.
The GenerateRandomSeedFromIndex method now correctly hashes the current scenario iteration into the random seed it generates.
Corrupted .meta files have been rebuilt and replaced.
The Randomizer list inspector UI now updates appropriately when a user clicks undo.
The Labeling component associates a list of string-based labels with a GameObject and its descendants. A Labeling component on a descendant overrides its parent's labels.
### Limitations
Labeling is supported on MeshRenderers, SkinnedMeshRenderers, and partially supported on Terrains.
On terrains, the labels will be applied to the entire terrain. Trees and details can not be labeled. They will always render as black or zero in instance and segmentation images and will occlude other objects in ground truth.
## Label Config
Many labelers require a Label Config asset. This asset specifies a list of all labels to be captured in the dataset along with extra information used by the various labelers.
The SemanticSegmentationLabeler generates a 2D RGB image with the attached Camera. Unity draws objects in the color you associate with the label in the SemanticSegmentationLabelingConfiguration. If Unity can't find a label for an object, it draws it in black.
### InstanceSegmentationLabeler
The instance segmentation labeler generates a 2D RGB image with the attached camera. Unity draws each instance of a labeled
object with a unique color.
### BoundingBox2DLabeler
<br/>_Example bounding box visualization from SynthDet generated by the `SynthDet_Statistics` Jupyter notebook_
2. Make sure to include the [Serializable] attribute on a constant class. This will ensure that the constants can be manipulated from the Unity inspector.
3. By default, UnityEngine.Object class references cannot be serialized to JSON in a meaningful way. This includes Monobehaviors and SerializedObjects. For more information on what can and can't be serialized, take a look at the [Unity JsonUtility manual](https://docs.unity3d.com/ScriptReference/JsonUtility.html).
4. A scenario class's Serialize() and Deserialized() methods can be overriden to implement custom serialization strategies.
Follow the instructions below to generate a constants configuration file to modify your scenario constants in a built player:
1. Click the serialize constants button in the scenario's inspector window. This will generate a constants.json file and place it in the project's Assets/StreamingAssets folder.
2. Build your player. The new player will have a [ProjectName]_Data/StreamingAssets folder. A copy of the constants.json file previously constructed in the editor will be found in this folder.
3. Change the contents of the constants file. Any running player thereafter will utilize the newly authored constants values.
This page provides brief instructions on installing the Perception package. Head over to the [Perception Tutorial](Tutorial/TUTORIAL.md) for more detailed instructions and steps for building a sample project.
1. Install the latest version of 2020.1.x Unity Editor from [here](https://unity3d.com/get-unity/download/archive). (Perception has not been tested on Unity versions newer than 2020.1)
1. Install the latest version of 2019.4.x or 2020.1.x Unity Editor from [here](https://unity3d.com/get-unity/download/archive). (Perception has not been tested on Unity versions newer than 2020.1)
1. Create a new HDRP or URP project, or open an existing project.
1. Open `Window` -> `Package Manager`
1. In the Package Manager window find and click the ***+*** button in the upper lefthand corner of the window
- [Step 8: Verify Data Using Dataset Insights](#step-8)
### <aname="step-1">Step 1: Download Unity Editor and Create a New Project</a>
* **Action**: Navigate to [this](https://unity3d.com/get-unity/download/archive) page to download and install the latest version of **Unity Editor 2019.4.x**. (The tutorial has not yet been fully tested on newer versions.)
During the installation of Unity, you will be asked to choose which modules you would like to include. This will depend on the types of applications you eventually intend to build with your Unity installation; however, for the purposes of this tutorial, we need to make make sure _**Linux Build Support**_ is checked. In addition, if you do not already have _**Visual Studio**_ on your computer, the wizard will give you an option to install it. Go ahead and check this option, as we will need _**Visual Studio**_ for writing some simple scripts in Phase 2 of the tutorial.
During the installation of Unity, you will be asked to choose which modules you would like to include. This will depend on the types of applications you eventually intend to build with your Unity installation; however, for the purposes of this tutorial, we need to make sure _**Linux Build Support (Mono)**_ is checked (the IL2CPP option may be selected by default, but for this tutorial, we will need the Mono option). In addition, if you do not already have _**Visual Studio**_ on your computer, the wizard will give you an option to install it. Go ahead and check this option, as we will need _**Visual Studio**_ for writing some simple scripts in Phase 2 of the tutorial.
* **Action**: Make sure the _**Linux Build Support**_ and _**Visual Studio**_ installation options are checked when selecting modules during installation.
* **Action**: Make sure the _**Linux Build Support (Mono)**_ and _**Visual Studio**_ installation options are checked when selecting modules during installation.
When you first run Unity, you will be asked to open an existing project, or create a new one.
As the name suggests, the _**Package Manager**_ is where you can download new packages, update or remove existing ones, and access a variety of information and additional actions for each package.
* **Action**: Click on the _**+**_ sign at the top-left corner of the _**Package Manager**_ window and then choose the option _**Add package frim git URL...**_.
* **Action**: Click on the _**+**_ sign at the top-left corner of the _**Package Manager**_ window and then choose the option _**Add package from git URL...**_.
* **Action**: Enter the address `com.unity.perception` and click _**Add**_.
**Note:** If you would like a specific version of the package, you can append the version to the end of the url. For example `com.unity.perception@0.1.0-preview.5`. For this tutorial, **we do not need to add a version**. You can also install the package from a local clone of the Perception repository. More information on installing local packages is available [here](https://docs.unity3d.com/Manual/upm-ui-local.html).
</p>
Each package can come with a set of samples. As seen in the righthand panel, the Perception package includes a sample named _**Tutorial Files**_, which will be required for completing this tutorial. The sample files consist of example foreground and background objects, randomizers, shaders, and other useful elements to work with during this tutorial. **Foreground** objects are those thatthe eventual machine learning model will try to detect, and **background** objects will be placed in the background as distractors for the model.
Each package can come with a set of samples. As seen in the righthand panel, the Perception package includes a sample named _**Tutorial Files**_, which will be required for completing this tutorial. The sample files consist of example foreground and background objects, randomizers, shaders, and other useful elements to work with during this tutorial. **Foreground** objects are those thatthe eventual machine learning model will try to detect, and **background** objects will be placed in the background as distractors for the model.
* **Action**: In the _**Package Manager**_ window, from the list of _**Samples**_ for the Perception package, click on the _**Import into Project**_ button for the sample named _**Tutorial Files**_.
* **Action**: The _**Project**_ tab contains a search bar; use it to find the file named `ForwardRenderer.asset`, as shown below:
* **Action**: **(For URP projects only)**The _**Project**_ tab contains a search bar; use it to find the file named `ForwardRenderer.asset`, as shown below:
* **Action**: Click on the found file to select it. Then, from the _**Inspector**_ tab of the editor, click on the _**Add Renderer Feature**_ button, and select _**Ground Truth Renderer Feature**_ from the dropdown menu:
* **Action**: **(For URP projects only)**Click on the found file to select it. Then, from the _**Inspector**_ tab of the editor, click on the _**Add Renderer Feature**_ button, and select _**Ground Truth Renderer Feature**_ from the dropdown menu:
### <aname="step-3">Step 3: Setup a Scene for Your Perception Simulation</a>
Simply put, in Unity, Scenes contain any object that exists in the world. This world can be a game, or in this case, a perception-oriented simulation. Every new project contains a Scene named `SampleScene`, which is automatically openned when the project is created. This Scenes comes with several objects and settings that we do not need, so let's create a new one.
Simply put, in Unity, Scenes contain any object that exists in the world. This world can be a game, or in this case, a perception-oriented simulation. Every new project contains a Scene named `SampleScene`, which is automatically opened when the project is created. This Scene comes with several objects and settings that we do not need, so let's create a new one.
* **Action**: In the _**Project**_ tab, right-click on the `Assets/Scenes` folder and click _**Create -> Scene**_. Name this new Scene `TutorialScene` and double-click on it to open it.
* **Action**: In the _**Project**_ tab, right-click on the `Assets/Scenes` folder and click _**Create -> Scene**_. Name this new Scene `TutorialScene` and **double-click on it to open it**.
The _**Hierarchy**_ tab of the editor displays all the Scenes currently loaded, and all the objects currently present in each loaded Scene, as shown below:
<palign="center">
* **Action**: Click on `Directional Light` and in the _**Inspector**_ tab, set `Shadow Type` to `No Shadows`.
We will now add the necessary components to the camera in order to equip it for the perception workflow. To do this, we need to add a `PerceptionCamera` component to it, and then define which types of ground-truth we wish to generate using this camera.
We will now add the necessary components to the camera in order to equip it for the perception workflow. To do this, we need to add a `PerceptionCamera` component to it, and then define which types of ground-truth we wish to generate using this camera.
* **Action**: Select `Main Camera` again and in the _**Inspector**_ tab, click on the _**Add Component**_ button.
* **Action**: Start typing `Perception Camera` in the search bar that appears, until the `Perception Camera` script is found, with a **#** icon to the left:
</p>
* **Action**: Click on this script to add it as a component. Your camera is now a `Perception` camera.
**Note:** You may now see a warning regarding asynchronous shader compilation in the UI for the `Perception Camera` component. To fix this issue, from the top menu bar go to _**Edit -> Project Settings… -> Editor**_ and under _**Shader Compilation**_ settings, disable _**Asynchronous Shader Compilation**_.
Adding components is the standard way in which objects can have various kinds of logic and data attached to them in Unity. This includes objects placed within the Scene (called GameObjects), such as the camera above, or objects outside of a Scene, in your project folders (called Prefabs).
If you hover your mouse pointer over each of the fields shown (e.g. `Capture Interval`), you will see a tooltip popup with an explanation on what the item controls. You may see a warning at the bottom of this UI regarding asynchronous shader compilation. If so, follow the instructions in the warning message to disable this functionality and remove the warning.
As seen in the UI for `Perception Camera`, the list of `Camera Lebelers` is currently empty. For each type of ground-truth you wish to generate along-side your captured frames (e.g. 2D bounding boxes around objects), you will need to add a corresponding `Camera Labeler` to this list.
As seen in the UI for `Perception Camera`, the list of `Camera Labelers` is currently empty. For each type of ground-truth you wish to generate along-side your captured frames (e.g. 2D bounding boxes around objects), you will need to add a corresponding `Camera Labeler` to this list.
* **Action**: Click on the _**+**_ button at the bottom right corner of the empty labeler list, and select `BoundingBox2DLabeler`.
* **Action**: Click on the _**+**_ button at the bottom right corner of the empty labeler list and select `BoundingBox2DLabeler`.
* **Action**: Repeat the above step to add `ObjectCountLabeler`, `RenderedObjectInfoLabeler`, `SemanticSegmentationLabeler`.
Once you add the labelers, the _**Inspector**_ view of the `Perception Camera` component will look like this:
</p>
One of the useful features that comes with the `Perception Camera` component is the ability to display real-time visualizations of the labelers when your simulation is running. For instance, `BoundingBox2DLabeler` can display two-dimensional bounding boxes around the foreground objects that it tracks in real-time and `SemanticSegmentationLabeler` displays the semantic segmentation image overlaid on top of the camera's view. To enable this feature, make sure the `Show Labeler Visualizations` checkmark is enabled.
One of the useful features that comes with the `Perception Camera` component is the ability to display real-time visualizations of the labelers when your simulation is running. For instance, `BoundingBox2DLabeler` can display two-dimensional bounding boxes around the foreground objects that it tracks in real-time and `SemanticSegmentationLabeler` displays the semantic segmentation image overlaid on top of the camera's view. To enable this feature, make sure the `Show Labeler Visualizations` checkmark is enabled.
### <aname="step-4">Step 4: Specify Ground-Truth and Setup Object Labels</a>
### <aname="step-4">Step 4: Specify Ground-Truth and Set Up Object Labels</a>
It is now time to tell each labeler added to the `Perception Camera` which objects it should label in the generated dataset. For instance, if your workflow is intended for generating frames and ground-truth for detecting chairs, your labelers would need to know that they should look for objects labeled "chair" within the scene. The chairs should in turn also be labeled "chair" in order to make them visible to the labelers. We will now learn how to set-up these configuartions.
It is now time to tell each labeler added to the `Perception Camera` which objects it should label in the generated dataset. For instance, if your workflow is intended for generating frames and ground-truth for detecting chairs, your labelers would need to know that they should look for objects labeled "chair" within the scene. The chairs should in turn also be labeled "chair" in order to make them visible to the labelers. We will now learn how to set up these configurations.
You will notice each added labeler has a field named `Id Label Config`. By adding a label configuration here you can instruct the labeler to look for certain labels within the scene and ignore the rest. To do that, we should first create label configurations.
You will notice each added labeler has a `Label Config` field. By adding a label configuration here you can instruct the labeler to look for certain labels within the scene and ignore the rest. To do that, we should first create label configurations.
* **Action**: In the _**Project**_ tab, right-click the `Assets` folder, then click _**Create -> Perception -> Id Label Config**_.
Then, click on this asset to bring up its _**Inspector**_ view. In there, you can specify the labels that this config will keep track of. A new label config like this one contains an empty list of labels.
Click on this asset to bring up its _**Inspector**_ view. In there, you can specify the labels that this config will keep track of. You can type in labels, add any labels defined in the project (through being added to prefabs), and import/export this label config as a JSON file. A new label config like this one contains an empty list of labels.
In this tutorial, we will generate synthetic data intended for detecting 10 everyday grocery items. These grocery items were imported into your project when you imported the tutorial files from the _**Package Manager**_, and are located in the folder `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
The label configuration we have created (`TutorialIdLabelConfig`) is of type `IdLabelConfig`, and is compatible with three of the four labelers we have attached to our `Perception Camera`. This type of label configuration carries a unique numerical ID for each label. However, `SemanticSegmentationLabeler` requires a different kind of label configuration which includes unique colors for each label instead of numerical IDs. This is because the output of this labeler is a set of images in which each visible foreground object is painted in a unique color.
* **Action**: In the _**Project**_ tab, right-click the `Assets` folder, then click _**Create -> Perception -> Semantic Segmentation Label Config**_. Name this asset `TutorialSemanticSegmentationLabelConfig`.
Now that you have created your label configurations, we need to assign them to labelers that you previously added to your `Perception Camera` component.
* **Action**: Select the `Main Camera` object from the Scene _**Hierarchy**_, and in the _**Inspector**_ tab, assign the newly created `TutorialIdLabelConfig` to the first three labelers. To do so, you can either drag and drop the former into the corresponding fields for each labeler, or click on the small circular button in front of the `Id Label Config` field, which brings up an asset selection window filtered to only show compatible assets. Assign `TutorialSemanticSegmentationLabelConfig` to the fourth labeler. The `Perception Camera` component will now look like the image below:
In this tutorial, we will generate synthetic data intended for detecting 10 everyday grocery items. Thus, in this step, you will add labels for each of these 10 items to the list of labels for `TutorialIdLabelConfig`.
* **Action**: Select `TutorialIdLabelConfig` and in the _**Inspector**_ tab, click on the _**+**_ button to add 10 new label entries. Use the following exact names for these entries:
It is now time to assign labels to the objects that are supposed to be detected by an eventual object-detection model, and add those labels to both of the label configurations we have created. As mentioned above, these objects are located at `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
1 `candy_minipralines_lindt`
In Unity, Prefabs are essentially reusable GameObjects that are stored to disk, along with all their child GameObjects, components, and property values. Let's see what our sample prefabs include.
2 `cereal_cheerios_honeynut`
* **Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`
* **Action**: Double click the file named `drink_whippingcream_lucerne.prefab` to open the Prefab asset.
3 `cleaning_snuggle_henkel`
When you open the Prefab asset, you will see the object shown in the Scene tab and its components shown on the right side of the editor, in the _**Inspector**_ tab:
4 `craft_yarn_caron`
<palign="center">
<imgsrc="Images/exampleprefab.png"/>
</p>
5 `drink_greentea_itoen`
The Prefab contains a number of components, including a `Transform`, a `Mesh Filter`, a `Mesh Renderer` and a `Labeling` component (highlighted in the image above). While the first three of these are common Unity components, the fourth one is specific to the Perception package, and is used for assigning labels to objects. You can see here that the Prefab has one label already added, displayed in the list of `Added Labels`. The UI here provides a multitude of ways for you to assign labels to the object. You can either choose to have the asset automatically labeled (by enabling `Use Automatic Labeling`), or add labels manually. In case of automatic labeling, you can choose from a number of labeling schemes, e.g. the asset's name or folder name. If you go the manual route, you can type in labels, add labels from any of the label configurations included in the project, or add from lists of suggested labels based on the Prefab's name and path.
6 `drink_whippingcream_lucerne`
Note that each object can have multiple labels assigned, and thus appear as different objects to labelers with different label configurations. For instance, you may want your semantic segmentation labeler to detect all cream cartons as `dairy_product`, while your bounding box labeler still distinguishes between different types of dairy product. To achieve this, you can add a `dairy_product` label to all your dairy products, and then in your label configuration for semantic segmentation, only add the `dairy_product` label, and not any specific products or brand names.
7 `lotion_essentially_nivea`
For this tutorial, we have already added the `Labeling` component to all the foreground Prefabs; however, if you are making your own Prefabs, you can easily add a `Labeling` component to them using the _**Add Component**_ button in the screenshot above.
8 `pasta_lasagne_barilla`
**Note:** If you would like to start from `.fbx` models, the Perception package lets you quickly create Prefabs from multiple models. Just select all your models and from the top menu bar select _**Assets -> Perception -> Create Prefabs from Selected Models**_. The newly created Prefabs will be placed in the same folders as their corresponding models.
9 `snack_biscotti_ghiott`
Even though the sample Prefabs already have a label manually added, to learn more about how to use the Labeling component, we will now use automatic labeling to label all our foreground objects. This will overwrite their manually added labels.
10 `snack_granolabar_naturevalley`
Once done, the _**Inspector**_ window for `TutorialIdLabelConfig` will look like this:
* **Action**: Select **all the files** inside the `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs` folder.
* **Action**: From the _**Inspector**_ tab, enable `Use Automatic Labeling for All Selected Items`, and then select `Use asset name` as the labeling scheme.
<imgsrc="Images/idlabelconfig.png"width="400"/>
<imgsrc="Images/autolabel.png"width="400"/>
These are the names of the 10 grocery items that we will work with in this tutorial. Wonder were the actual objects are? They were imported into your project when you imported the tutorial files from the _**Package Manager**_, and are located in the folder `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs` .
This will assign each of the selected Prefabs its own name as a label.
Notice that each of the labels you entered automatically has a numerical ID assigned. These ids are required in order to use the generated data in machine learning models, which typically require numerical ids for classification of objects.
* **Action**: Click _**Add Automatic Labels of All Selected Assets to Config...**_.
The label configuration we have created is compatible with three of the four labelers we plan to attach to our `Perception Camera`. However, `SemanticSegmentationLabeler` requires a different kind of label configuration which includes unique colors for each label instead of numerical IDs. This is because the output of this labeler are images in which each visibile foreground object is painted in a unique color.
In the window that opens, you can add all the automatic labels you just added to your Prefabs, to the label configurations you created earlier. At the top, there is a list of all the labels you are about to add, and below that, a list of all label configurations currently present in the project.
* **Action**: In the _**Project**_ tab, right-click the `Assets` folder, then click _**Create -> Perception -> Semantic Segmentation Label Config**_. Name this asset `TutorialSemanticSegmentationLabelConfig`.
* **Action**: Add the same 10 labels from the above list to this new label configuration. Note how this time they each get a new unique color instead of a number:
* **Action**: Add the list of labels to `TutorialIdLabelConfig` and `TutorialSemanticSegmentationLabelConfig` by clicking the _**Add All Labels**_ button for both.
Now that you have created your label configurations, we need to assign them to labelers that you previously added to your `Perception Camera` component.
* **Action**: Select the `Main Camera` object from the Scene _**Hierarchy**_, and in the _**Inspector**_ tab, assign the newly created `TutorialIdLabelConfig` to the first three labelers. To do so, you can either drag and drop the former into the corresponding fields for each labeler, or click on the small circular button in front of the `Id Label Config` field, which brings up an asset selection window filtered to only show compatible assets. Assign `TutorialSemanticSegmentationLabelConfig` to the fourth labeler. The `Perception Camera` component will now look like the image below:
Here, you can also open either of the configurations by clicking the _**Open**_ buttons. Open both configurations to make sure the list of labels has been added to them. They should now look similar to the screenshots below:
The final piece of the label set-up workflow is to assign the same 10 labels to the objects that are supposed to be detected by an eventual object-detection model. As mentioned above, these are located at `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/ Foreground Objects/Phase 1/Prefabs`.
**Note:** Since we used automatic labels here and added them to our configurations, we are confident that the labels in the configurations match the labels of our objects. In cases where you decide to add manual labels to objects and configurations, make sure you use the exact same labels, otherwise, the objects for which a matching label is not found in your configurations will not be detected by the labelers that are using those configurations.
In Unity, Prefabs are essentially reusable GameObjects that are stored to disk, along with all their child GameObjects, components, and property values. Let's see what our sample prefabs include.
Now that we have labelled all our foreground objects and setup our label configurations, let's briefly test things.
* **Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/ Foreground Objects/Phase 1/Prefbas`
* **Action**: Double click the file named `drink_whippingcream_lucerne.prefab` to open the Prefab asset.
* **Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
* **Action**: Drag and drop any of the Prefabs inside this folder into the Scene.
* **Action**: Click on the **▷** (play) button located at the top middle section of the editor to run your simulation.
When you open the Prefab asset, you will see the object shown in the Scene tab and its components shown on the right side of the editor, in the _**Inspector**_ tab:
Since we have visualizations enabled on our `Perception Camera`, you should now see a bounding box being drawn around the object you put in the scene, and the object itself being colored according to its label's color in `TutorialSemanticSegmentationLabelConfig`, similar to the image below:
<imgsrc="Images/exampleprefab.png"/>
<imgsrc="Images/one_object_run.png"width="600"/>
The Prefab contains a number of components, including a `Transform`, a `Mesh Filter`, a `Mesh Renderer` and a `Labeling` component (highlighted in the image above). While the first three of these are common Unity components, the fourth one is specific to the Perception package, and is used for assigning labels to objects. You can see here that the cream carton is already labeled `drink_whippingcream_lucerner`. This is true for all the foreground objects supplied in the sample tutorial files in order to save time, which means you do not need to perform any additonal steps to label your foreground objects. However, adding a label to a prefab would be as simple as clicking _**Add Component**_ and adding the `Labeling` script, then typing the label in.
Note that each object can have multiple labels assigned, and thus appear as different objects to labelers with different label configurations. For instance, you may want your semantic segmentation labeler to detect all cream cartons as `dairy_product`, while your bounding box labeler still distinguishes between different types of dairy product. To achieve this, you can add a `dairy_product` label to all your dairy products, and then in your label configuration for semantic segmentation, only add the `dairy_product` label, and not any specific products or brand names. To add an additional label to the cream carton, you can click on the _**+**_ button to the bottom right corner of the label list, in the `Labeling` component.
### <aname="step-5">Step 5: Add and Set-up Randomizers</a>
### <aname="step-5">Step 5: Set Up Background Randomizers</a>
To start randomizing your simulation you will first need to add a `Scenario` to your scene. Scenarios control the execution flow of your simulation by coordinating all `Randomizer` components added to them. The Perception package comes with a useful set of Randomizers that let you quickly place your foreground objects in the Scene, generate varied backgrounds, as well as randomize various parameters of the simulation over time, including things such as positon, scale, and rotation of objects, number of objects within the camera's view, and so on. Randomizers achieve this through coordinating a number of `Parameter`s, which essentially define the most granular randomization behaviors. For instance, for continuous variable types such as floats, vectors, and colors, Parameters can define the range, sampling distribution, and seed for randomization. This is while another class of Paramters let you randomly select one out of a number of categorical options.
To start randomizing your simulation you will first need to add a `Scenario` to your scene. Scenarios control the execution flow of your simulation by coordinating all `Randomizer` components added to them. The Perception package comes with a useful set of Randomizers that let you quickly place your foreground objects in the Scene, generate varied backgrounds, as well as randomize various parameters of the simulation over time, including things such as position, scale, and rotation of objects, number of objects within the camera's view, and so on. Randomizers achieve this through coordinating a number of `Parameter`s, which essentially define the most granular randomization behaviors. For instance, for continuous variable types such as floats, vectors, and colors, Parameters can define the range, sampling distribution, and seed for randomization. This is while another class of Parameters let you randomly select one out of a number of categorical options.
To summarize, a sample `Scenario` could look like this:
* **Action**: Rename your new GameObject to `Simulation Scenario`.
* **Action**: In the _**Inspector**_ view of this new object, add a new `Fixed Length Scenario` component.
Each `Scenario` executes a number of `Iteration`s, and each Iteration carries on for a number of frames. These are timing elements you can leverage in order to customize your Scenarios and the timing of your randomizations. You will learn how to use Iteartions and frames in Phase 2 of this tutorial. For now, we will use the `Fixed Length Scenario`, which is a special kind of Scenario that runs for a fixed number of frames during each Iteration, and is sufficient for many common use-cases. Note that at any given time, you can have only one Scenario active in your Scene.
Each `Scenario` executes a number of `Iteration`s, and each Iteration carries on for a number of frames. These are timing elements you can leverage in order to customize your Scenarios and the timing of your randomizations. You will learn how to use Iterations and frames in Phase 2 of this tutorial. For now, we will use the `Fixed Length Scenario`, which is a special kind of Scenario that runs for a fixed number of frames during each Iteration, and is sufficient for many common use-cases. Note that at any given time, you can have only one Scenario active in your Scene.
The _**Inspector**_ view of `Fixed Length Scenario` looks like below:
There are a number settings and properties you can modify here. `Quit On Complete` instructs the simulation to quit once this Scenario has completed executing. We can see here that the Scenario has been set to run for 100 Iterations, and that each Iteration will run for one frame. But this is currently an empty `Scneario`, so let's add some Randomizers.
There are a number of settings and properties you can modify here. `Quit On Complete` instructs the simulation to quit once this Scenario has completed executing. We can see here that the Scenario has been set to run for 100 Iterations, and that each Iteration will run for one frame. But this is currently an empty `Scenario`, so let's add some Randomizers.
* **Action**: Click _**Add Folder**_, and from the file explorer window that opnes, choose the folder `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/Background Objects/Prefabs`.
* **Action**: Click _**Add Folder**_, and from the file explorer window that opens, choose the folder `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Background Objects/Prefabs`.
The beckground Prefabs are primitve shapes devoid of color or texture. Later Randomizers will take care of those aspects.
The background Prefabs are primitive shapes devoid of color or texture. Later Randomizers will take care of those aspects.
* **Action**: Set the rest of the properties (except for `Seed`) according to the image below. The `Seed` attribute is the seed used for the underlying random sampler, and does not need to match the image shown.
* **Action**: Set the rest of the properties (except for `Seed`) according to the image below. That is, `Depth = 0, Layer Count = 2, Separation Distance = 0.5, Placement Area = (6,6)`. The `Seed` attribute is the seed used for the underlying random sampler and does not need to match the image shown.
* **Action**: Click on the **▷** (play) button located at top middle section of the editor to run your simulation.
* **Action**: Click on the **▷** (play) button located at the top middle section of the editor to run your simulation.
<palign="center">
<imgsrc="Images/play.png"width ="500"/>
To generate data as fast as possible, the simulation utilizes asynchronous processing to churn through frames quickly, rearranging and randomizing the objects in each frame. To be able to check out individual frames and inspect the real-time visualizations, click on the pause button (next to play). You can also switch back to the Scene view to be able to inspect each object individually. For performance reasons, it is recommended to disable visualizations altogether (from the _**Inspector**_ view of `Perception Camera`) once you are ready to generate a large dataset.
As seen in the image above, what we have now is just a beige-colored wall of shapes. This is because so far we are only spawning them, and the beige color of our light is what gives them their current look. To make this background more useful, let's add a couple more `Randomizers`.
As seen in the image above, what we have now is just a beige-colored wall of shapes. This is because so far, we are only spawning them, and the beige color of our light is what gives them their current look. To make this background more useful, let's add a couple more `Randomizers`.
**Note:** If at this point you don't see any objects being displayed, make sure the Separation Distance for `BackgroundObjectPlacementRandomizer` is (6,6) and not (0,0).
**Note:** If your _**Game**_ tab has a different field of view than the one shown here, change the aspect ratio of your _**Game**_ tab to `4:3`, as shown below:
<palign="center">
<imgsrc="Images/game_aspect.png"width ="400"/>
</p>
`TextureRandomizer` will have the task of attaching random textures to our colorless background objects at each Iteration of the Scenario. Simlarly, `HueOffsetRandomizer` will alter the color of the objects, and `RotationRandomizer` will give the objects a new random rotation each Iteration.
`TextureRandomizer` will have the task of attaching random textures to our colorless background objects at each Iteration of the Scenario. Similarly, `HueOffsetRandomizer` will alter the color of the objects, and `RotationRandomizer` will give the objects a new random rotation each Iteration.
* **Action**: In the UI snippet for `TextureRandomizer`, click _**Add Folder**_ and choose `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/Background Textures`.
* **Action**: In the UI snippet for `TextureRandomizer`, click _**Add Folder**_ and choose `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Background Textures`.
* **Action**: In the UI snippet for `RotationRandomizer`, change all the maximum values for the three ranges to `360` and leave the minimums at `0`.
* **Action**: In the UI snippet for `RotationRandomizer`, verify that all the minimum values for the three ranges are `0` and that maximum values are `360`.
Your list of Randomizers should now look like the screenshot below:
To make sure each Randomizer knows which objects it should work with, we will use an object tagging and querying workflow that the bundled Randomizers already use. Each Randomizer can query the Scene for objects that carry certain types of `RandomizerTag` components. For instance, the `TextureRandomizer` queries the Scene for objects that have a `TextureRandomizerTag` component (you can change this in code!). Therefore, in order to make sure our background Prefabs are affected by the `TextureRandomizer` we need to make sure they have `TextureRandomizerTag` attached to them.
* **Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/Background Objects/Prefabs`.
* **Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Background Objects/Prefabs`.
* **Action**: Select all the files inside and from the _**Inspector**_ tab add a `TextureRandomizerTag` to them. This will add the component to all the selected files.
* **Action**: Repeat the above step to add `HueOffsetRandomizerTag` and `RotationRandomizerTag` to all selected Prefabs.
It is now time to spawn and randomize our foregournd objects. We are getting close to generating our first set of synthetic data!
### <aname="step-6">Step 6: Set Up Foreground Randomizers</a>
* **Action**: Add `ForegroundObjectPlacementRandomizer` to your list of Randomizers. Click _**Add Folder**_ and select `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
* **Action**: Set these values for the above Randomizer: `Depth = 3, Separation Distance = 1.5, Placement Area = (5,5)`.
It is now time to spawn and randomize our foreground objects.
* **Action**: Add `ForegroundObjectPlacementRandomizer` to your list of Randomizers. Click _**Add Folder**_ and select `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
* **Action**: Set these values for the above Randomizer: `Depth = -3, Separation Distance = 1.5, Placement Area = (5,5)`.
This Randomizer uses the same algorithm as the one we used for backgrounds; however, it is defined in a separate C# class because you can only have **one of each type of Randomizer added to your Scenario**. Therefore, this is our way of differentating between how background and foreground objects are treated.
This Randomizer uses the same algorithm as the one we used for backgrounds; however, it is defined in a separate C# class because you can only have **one of each type of Randomizer added to your Scenario**. Therefore, this is our way of differentiating between how background and foreground objects are treated.
* **Action**: From the _**Project**_ tab select all the foreground Prefabs located in `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`, and add a `RotationRandomizerTag` component to them.
* **Action**: From the _**Project**_ tab select all the foreground Prefabs located in `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`, and add a `RotationRandomizerTag` component to them.
The last step here is to make sure the order of randomizations is correct. Randomizers execute according to their order within the list of Randomizers added to your Scenario. If you look at the list now, you will notice that `ForegroundObjectPlacementRandomizer` is coming after `RotationRandomizer`, therefore, foreground objects will NOT be included in the rotation randomizations, even though they are carrying the proper tag. To fix that:
Randomizers execute according to their order within the list of Randomizers added to your Scenario. If you look at the list now, you will notice that `ForegroundObjectPlacementRandomizer` is coming after `RotationRandomizer`, therefore, foreground objects will NOT be included in the rotation randomizations, even though they are carrying the proper RandomizerTag. To fix that:
* **Action**: Drag `ForegroundObjectPlacementRandomizer` and drop it above `RotationRandomizer`.
* **Action**: Drag `ForegroundObjectPlacementRandomizer` using the striped handle bar (on its left side) and drop it above `RotationRandomizer`.
### <aname="step-6">Step 6: Generate and Verify Synthetic Data</a>
Your full list of Randomizers should now look like the screenshot below:
You are now ready to generate your first dataset. Our current set-up will produce 100 frames of annotated captures.
You are now ready to generate your first dataset. Our current setup will produce 100 frames of annotated captures.
While the simulation is running, your _**Game**_ view will quickly generate frames similar to the gif below (visualization for `SemanticSegmentationLabeler` is disabled here):
While the simulation is running, your _**Game**_ view will quickly generate frames similar to the gif below (note: visualization for `SemanticSegmentationLabeler` is disabled here):
Once the run is complete, you will see a message in the _**Console**_ tab of the editor, with information on where the generated data has been saved. An example is shown below (Mac OS):
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.
* **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 visibile object, the annotations include:
* **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`
* `instance_id`: Unique instance id of the object
* **Action**: Review the JSON meta-data and the images captured for the first annotated frame, and verify that the objects within them match.
### <aname="step-8">Step 8: Verify Data Using Dataset Insights</a>
`docker run -p 8888:8888 -v <path to synthetic data>:/data -t unitytechnologies/datasetinsights:latest`, where the path to data is what we earlier found in Unity's console messages.
`docker run -p 8888:8888 -v "<path to synthetic data>:/data" -t unitytechnologies/datasetinsights:latest`, where the path to data is what we earlier found in Unity's console messages.
This will download a Docker image from Unity. If you get an error regarding the path to your dataset, make sure you have not included the enclosing `<` and `>` in the path and that the spaces are properly escaped.
* **Action**: To make sure your data is properly mounted, navigate to the `data` folder. If you see the dataset's folders there, we are good to go.
* **Action**: Navigate to the `datasetinsights/notebooks` folder and open `Perception_Statistics.ipynb`.
* **Action**: Once in the notebook, replace the `<GUID>` in the `data_root = /data/<GUID>` line with the name of the dataset folder inside your generated data. For example `data_root = /data/Dataseta26351bc-1b72-46c5-9e0c-d7afd6df2974`.
* **Action**: Once in the notebook, remove the `/<GUID>` part of the `data_root = /data/<GUID>` path. Since the dataset root is already mapped to `/data`, you can use this path directly.
<palign="center">
<imgsrc="Images/jupyter2.png"/>
Each of the code blocks in this notebook can be executed by clicking on them to select them, and the clicking the _**Run**_ button at the top of the notebook. When you run a code block, an **asterisk (\*)** will be shown next to it on the left side, until the code finishes executing.
Each of the code blocks in this notebook can be executed by clicking on them to select them, and then clicking the _**Run**_ button at the top of the notebook. When you run a code block, an **asterisk (\*)** will be shown next to it on the left side, until the code finishes executing.
Below, you can see a sample plot generated by the Dataset Insights notebook, depicting the number of times each of the 10 foreground objects appeared in the dataset. As shown in the histogram, there is a high level of uniformity between the labels, which is a desirable outcome.
* **Action**: Follow the instructions laid out in the notebook and run each code block to view its outputs.
This concludes Phase 1 of the Perception tutoial. In the next phase, you will dive a little bit into randomization code and learn how to build your own custom Randomizer. [Click here to continue to Phase 2: Custom Randomizations](Phase2.md)
This concludes Phase 1 of the Perception tutorial. In the next phase, you will dive a little bit into randomization code and learn how to build your own custom Randomizer. [Click here to continue to Phase 2: Custom Randomizations](Phase2.md)
- [Step 2: Bundle Data and Logic Inside Randomization Tags](#step-2)
- [Step 2: Bundle Data and Logic Inside RandomizerTags](#step-2)
### <aname="step-1">Step 1: Build a Lighting Randomizer</a>
* **Action**: Create another script and name it `MyLightRandomizerTag.cs`.
* **Action**: Double-click `MyLightRandomizer.cs` to open it in _**Visual Studio**_.
Note that while _**Visual Studio**_ is the default option, you can choose any text editor of your choice. You can change the this setting in _**Preferences -> External Tools -> External Script Editor**_.
Note that while _**Visual Studio**_ is the default option, you can choose any text editor of your choice. You can change this setting in _**Preferences -> External Tools -> External Script Editor**_.
* **Action**: Remove the contents of the class and copy/paste the code below:
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.
**Note:** 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.
You will notice the 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`), `Seed`, and `Range` for this float Parameter:
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`), `Seed`, and `Range` for this float Parameter:
<palign="center">
<imgsrc="Images/light_rand_1.png"width="420"/>
This range of intensities is arbitrary but will give us a typically nice lighting without excessive darkness or burnt-out highlights.
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 Randomziers.
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.
The `OnIterationStart()` function is used for telling the Randomizer what actions to perform at the start of each Iteration of the Scenario. As seen in the code block, at the start of each Iteration, this class queries the `tagManager` object for all objects that carry the `MyLightRandomizerTag` component. Then, for each object inside the queried list, it first retrieves the `Light` component, and then sets its intensity to a new random float sampled from `lightIntensityParamter`.
The `OnIterationStart()` function is used for telling the Randomizer what actions to perform at the start of each Iteration of the Scenario. As seen in the code block, at the start of each Iteration, this class queries the `tagManager` object for all objects that carry the `MyLightRandomizerTag` component. Then, for each object inside the queried list, it first retrieves the `Light` component, and then sets its intensity to a new random float sampled from `lightIntensityParameter`.
* **Action**: Open `MyLightRandomizerTag.cs` and replace its contents with the code below:
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 objects have a `Light` component, and can thus be Randomized.
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 objects have a `Light` component and can thus be Randomized.
* **Action**: Select `Directional Light` in the Scene's _**Hierarchy**_, and in the _**Inspector**_ tab, add a `Light Randomizer Tag` component.
* **Action**: Select `Directional Light` in the Scene's _**Hierarchy**_, and in the _**Inspector**_ tab, add a `My Light Randomizer Tag` component.
* **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
Let's now add more variation to our light by randomizing its color as well.
}
```
If you now check the UI snippet for `MyLightRandomizer`, you will notice that `Color Parameter` is added. This Parameter includes four separate randomized values for `Red`, `Green`, `Blue` and `Alpha`. Note that the meaningful range for all of these values is 0-1 (and not 0-255). You can see that the sampling range for red, green, and blue is currently also set to 0-1, which means the parameter covers a full range of colors. A color with (0,0,0) RGB components essentially emits no light. So let's increase the minimum a bit to avoid such a scenario..
If you now check the UI snippet for `MyLightRandomizer`, you will notice that `Color Parameter` is added. This Parameter includes four separate randomized values for `Red`, `Green`, `Blue` and `Alpha`. Note that the meaningful range for all of these values is 0-1 (and not 0-255). You can see that the sampling range for red, green, and blue is currently also set to 0-1, which means the parameter covers a full range of colors. A color with (0,0,0) RGB components essentially emits no light. So, let's increase the minimum a bit to avoid such a scenario.
Each value should also already have a unique `Seed` specified. This is the seed which the sampler will use to produce a random value from the specifed distribution. If two random parameters have the same seed, range, and distribution, they will always have the same value. In the case of this color, this would lead to the red, green, and blue components having equal values, and thus the produced color always being a shade of grey. As such, in order to get varied colors and not just grey, we need to make sure the seed values are different for our red, green, and blue components.
Each value should also already have a unique `Seed` specified. This is the seed which the sampler will use to produce a random value from the specified distribution. If two random parameters have the same seed, range, and distribution, they will always have the same value. In the case of this color, this would lead to the red, green, and blue components having equal values, and thus the produced color always being a shade of grey. As such, in order to get varied colors and not just grey, we need to make sure the seed values are different for our red, green, and blue components.
* **Action**: In the UI snippet for `MyLightRandomizer`, make sure the red, green, and blue components have different `Seed` values. Set the distribution and value for Alpha to `Constant` and 1, as we do not want to randomize the alpha component of the color.
* **Action**: Run the simulation for a few frames to observe the lighting color changing on each iteration.
### <aname="step-2">Step 2: Bundle Data and Logic Inside Randomization Tags</a>
### <aname="step-2">Step 2: Bundle Data and Logic Inside RandomizerTags</a>
There are also cases were you may need to include certain logic within your object in order to make the Randomizer code more reusable and easy to maintain. For instance, you may want to build an office chair Prefab to use in various simulations. This chair is likely to support a range of customizations for its various parts (back angle, seat angle, seat height, etc.). Instead of directly mapping a Rotation Parameter from a Randomizer to the rotation of the back angle object within the chair, it might be more convenient to have the chair expose the range of possible angles in the form of a simple float between 0 and 1. With this approach, the Randomizer would only need to sample a float Parameter and assign it to the chair. The chair would in turn have a script attached that knows how to map this single float to a certain plausible back angle. You could even map this float to a more complex state of the chair. Your Randomizer would still only need one float Parameter.
There are also cases where you may need to include certain logic within your object in order to make the Randomizer code more reusable and easier to maintain. For instance, you may want to build an office chair Prefab to use in various simulations. This chair is likely to support a range of customizations for its various parts (back angle, seat angle, seat height, etc.). Instead of directly mapping a Rotation Parameter from a Randomizer to the rotation of the back angle object within the chair, it might be more convenient to have the chair expose the range of possible angles in the form of a simple float between 0 and 1. With this approach, the Randomizer would only need to sample a float Parameter and assign it to the chair. The chair would in turn have a script attached that knows how to map this single float to a certain plausible back angle. You could even map this float to a more complex state of the chair. Your Randomizer would still only need one float Parameter.
* **Action**: Right-click on `Directional Light` in the Scene _**Hierarchy**_, and select _**Duplicate**_. The new light will automatically be named `Directional Light (1)`.
* **Action**: Right-click on `Directional Light` in the Scene _**Hierarchy**_ and select _**Duplicate**_. The new light will automatically be named `Directional Light (1)`.
<imgsrc="Images/light_rand_2.png"width="420"/>
<imgsrc="Images/light_2.png"width="420"/>
This makes the two lights illuminate the scene from opposing sides, each having a 30 degree angle with the background and foreground planes.
This makes the two lights illuminate the scene from opposing angles, each having a 30-degree angle with the background and foreground planes. Note that the position of Directional Lights in Unity does not affect how they illuminate the scene, so you do not need to use the same position as the screenshot above.
* **Action**: Open `MyLightRandomizerTag.cs` and modify it to match the code below:
```
This component is already added to both our lights. We now need to set our desired minimum and maximum intensities, and this can be done through the _**Inspector**_ view.
* **Action**: Select `Directional Light` and from the _**Inspector** UI for the `MyLightRandomizerTag` component, set `Min Intensity` to 0.5 and `Max Intensity` to 3.
* **Action**: Select `Directional Light` and from the **Inspector** UI for the `MyLightRandomizerTag` component, set `Min Intensity` to 0.5 and `Max Intensity` to 3.
* **Action**: Repeat the above step for `Directional Light (1)` and set `Min Intensity` to 0 and `Max Intensity` to 0.4.
Note that with this change, we fully transfer the responsibility for the light's intensity range to `MyLightRandomizerTag.cs` and assume the intensity value coming from `My Light Randomizer` is always between 0 and 1. Therefore, we now need to change the range for the corresponding Parameter in `My Light Randomizer` to (0,1).
By this point in the tutorial, we have learned how to set-up a Perception Scene, randomize our simulation, and verify our generated datasets using Dataset Insights. That said, the size of the dataset we created was only 100 captures, which is not sufficient for model-training purposes. It is now time to generate a large-scale synthetic dataset with tens of thousands of frames using Unity Simulation.
[Click here to continue to Phase 3: Cloud](Phase3.md)
[Click here to continue to Phase 3: Cloud](Phase3.md)
In this phase of the tutorial, we will learn how to run our Scene on _**Unity Simulation (USim)**_ and analyze the generated dataset using _**Dataset Insights**_. USim will allow us to generate a much larger dataset than what is typically plausible on a workstation computer.
In this phase of the tutorial, we will learn how to run our Scene on _**Unity Simulation**_ and analyze the generated dataset using _**Dataset Insights**_. Unity Simulation will allow us to generate a much larger dataset than what is typically plausible on a workstation computer.
- [Step 1: Setup Unity Account, USim, and Cloud Project](#step-1)
- [Step 2: Run Project on USim](#step-2)
- [Step 3: Keep Track of USim Runs Using USim-CLI](#step-3)
In order to use Unity Simulation you need to first create a Unity account or login with your existing one. Once logged in, you will also need to sign-up for Unity Simulation.
In order to use Unity Simulation, you need to first create a Unity account or login with your existing one. Once logged in, you will also need to sign-up for Unity Simulation.
* **Action** Click on the _**Cloud**_ button at the top-right corner of Unity Editor to open the _**Services**_ tab.
* **Action**: Click _**Sign in...**_ and follow the steps in the window that opens to sign in or create an account.
* **Action**: Sign up for a free trial of Unity Simulation [here](https://unity.com/products/unity-simulation).
Unity Simulation is a cloud-based service that makes it possible for you run thousands of instances of Unity builds in order to generate massive amounts of data. The USim service is billed on a per-usage basis, and the free trial offers up to $100 of free credit per month. In order to access the free trial, you will need to provide credit card information. **This information will be used to charge your account if you exceed the $100 monthly credit.** A list of hourly and daily rates for various computational resources is available in the page where you first register for USim.
Unity Simulation is a cloud-based service that makes it possible for you to run hundreds of instances of Unity builds in order to generate massive amounts of data. The Unity Simulation service is billed on a per-usage basis, and the free trial offers up to $100 of free credit per month. In order to access the free trial, you will need to provide credit card information. **This information will be used to charge your account if you exceed the $100 monthly credit.** A list of hourly and daily rates for various computational resources is available in the page where you first register for Unity Simulation.
Once you have registered for a free trial, you will be taken to your USim dashboard, where you will be able to observe your usage and billing invoices.
Once you have registered for a free trial, you will be taken to your Unity Simulation dashboard, where you will be able to observe your usage and billing invoices.
It is now time to connect your local Unity project to a cloud project.
* **Action**: Click _**Create**_ to create a new cloud project and connect your local project to it.
### <aname="step-2">Step 2: Run Project on USim</a>
### <aname="step-2">Step 2: Run Project on Unity Simulation</a>
The process of running a project on Unity Simulation involves building it for Linux and then uploading this build, along with a set of parameters, to Unity Simulation. The Perception package simplifies this process by including a dedicated _**Run in USim**_ window that accepts a small number of required parameters and handles everything else automatically.
The process of running a project on Unity Simulation involves building it for Linux and then uploading this build, along with a set of parameters, to Unity Simulation. The Perception package simplifies this process by including a dedicated _**Run in Unity Simulation**_ window that accepts a small number of required parameters and handles everything else automatically.
For performance reasons, it is best to disable real-time visualizations before carrying on with the USim run.
For performance reasons, it is best to disable real-time visualizations before carrying on with the Unity Simulation run.
In order to make sure our builds are compatible with USim, we need to set our project's scripting backend to _**Mono**_ rather than _**IL2CPP**_. The latter is the default option for projects created with newer versions of Unity, so we need to change it. We will also need to switch to _**Windowed**_ mode.
In order to make sure our builds are compatible with Unity Simulation, we need to set our project's scripting backend to _**Mono**_ rather than _**IL2CPP**_ (if not already set). We will also need to switch to _**Windowed**_ mode.
* **Action**: From the top menu bar, open _**Edit -> Project Settings**_.
* **Action**: In the window that opens, navigate to the _**Player**_ tab, find the _**Scripting Backend**_ setting (under _**Other Settings**_), and change it to _**Mono**_:
</p>
* **Action**: Close _**Project Settings**_.
* **Action**: From the top menu bar, open _**Window -> Run in USim**_.
* **Action**: From the top menu bar, open _**Window -> Run in Unity Simulation**_.
<palign="center">
<imgsrc="Images/runinusim.png"width="600"/>
* **Action**: Name your run `FirstRun`, set the number of iterations to `1000`, and instances to `20`.
* **Action**: Click _**Build and Run**_.
Your project will now be built and then uploaded to USim. Depending on the upload speed of your internet connection, this might take anywhere from a few seconds to a couple of minutes.
Your project will now be built and then uploaded to Unity Simulation. Depending on the upload speed of your internet connection, this might take anywhere from a few seconds to a couple of minutes.
* **Action**: Once the operation is complete, you can find the **Build ID**, **Run Definition ID**, and **Execution ID** of this USim run in the _**Console**_ tab:
* **Action**: Once the operation is complete, you can find the **Build ID**, **Run Definition ID**, and **Execution ID** of this Unity Simulation run in the _**Console**_ tab:
<palign="center">
<imgsrc="Images/build_uploaded.png"/>
### <aname="step-3">Step 3: Keep Track of USim Runs Using USim-CLI</a>
### <aname="step-3">Step 3: Keep Track of Your Runs Using the Unity Simulation Command-Line Interface</a>
To keep track of the progress of your USim run, you will need to use USim's command-line interface (USim-CLI). Detailed instructions for USim-CLI are provided [here](https://github.com/Unity-Technologies/Unity-Simulation-Docs/blob/master/doc/quickstart.md#download-unity-simulation-quickstart-materials). For the purposes of this tutorial, we will only go through the most essential commands, which will help us know when our USim run is complete and where to find the produced dataset.
To keep track of the progress of your Unity Simulation run, you will need to use Unity Simulation's command-line interface (CLI). Detailed instructions for this CLI are provided [here](https://github.com/Unity-Technologies/Unity-Simulation-Docs/blob/master/doc/quickstart.md#download-unity-simulation-quickstart-materials). For the purposes of this tutorial, we will only go through the most essential commands, which will help us know when our Unity Simulation run is complete and where to find the produced dataset.
* **Action**: Download the latest version of `unity_simulation_bundle.zip` from [here](https://github.com/Unity-Technologies/Unity-Simulation-Docs/releases).
You will now be using the _**usim**_ executable to interact with Unity Simluation through commands.
You will now be using the _**usim**_ executable to interact with Unity Simulation through commands.
* **Action** To see a list of available commands, simply run `usim` once:
The first step is to login.
* **Action**: Login to USim using the `usim login auth` command.
* **Action**: Login to Unity Simulation using the `usim login auth` command.
MacOS:
`USimCLI/mac/usim login auth`
</p>
**Note**: On MacOS, you might get errors related to permissions. In these cases, try running your commands with the `sudo` qualifier. For example:
`sudo USimCLI/mac/usim login auth`. This will ask for your MacOS account's password, and should help overcome the persmission issues.
`sudo USimCLI/mac/usim login auth`. This will ask for your MacOS account's password and should help overcome the permission issues.
**Note : From this point on we will only include MacOS formatted commands in the tutorial, but all the USim commands we use will work in all supported operating systems.**
**Note : From this point on we will only include MacOS formatted commands in the tutorial, but all the `usim` commands we use will work in all supported operating systems.**
* **Action**: Return to your command-line interface. Get a list of cloud projects associated with your Unity account using the `usim get projects` command:
In case you have more than one cloud project, you will need to "activate" the one corresponding with your perception tutorial project. If there is only one project, it is already activated and you will not need to execute the command below (note: replace `<project-id>` with the id of your desired project).
In case you have more than one cloud project, you will need to "activate" the one corresponding with your perception tutorial project. If there is only one project, it is already activated, and you will not need to execute the command below (note: replace `<project-id>` with the id of your desired project).
* **Action**: Activate the relevant project:
xBv3arj Completed 2020-10-01 02:27:11
```
As seen above, each run has a name, an ID, a creation time, and a list of executions. Note that each "run" can have more than one "execution", as you can manually execute runs again using USim-CLI.
As seen above, each run has a name, an ID, a creation time, and a list of executions. Note that each "run" can have more than one "execution", as you can manually execute runs again using the CLI.
You can also obtain a list of all the builds you have uploaded to USim using the `usim get builds` command.
You can also obtain a list of all the builds you have uploaded to Unity Simulation using the `usim get builds` command.
USim runs executions on simulation nodes. If you enter a number larger than 1 for the number of instances in the _**Run in USim**_ window, your run will execute simultaneously on more than one node. You can view the status of each execution node using the `usim summarize run-execution <execution-id>` command. This command will tell you how many nodes 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 `yegz4WN`.
* **Action**: Use the `usim summarize run-execution <execution-id>` command to observe the status of your execution nodes:
Here is an example output of this command, indiciating that there is only one node, and that the node is still in progress:
Here is an example output of this command, indicating that there is only one node, and that the node is still in progress:
```
state count
### <aname="step-4">Step 4: Analyze the Dataset using Dataset Insights</a>
In order to to download the actual data from your run, we will now use Dataset Insights again. This time though, we will utilize some of the lines that were commented in our previous use with locally generated data.
In order to download the actual data from your run, we will now use Dataset Insights again. This time though, we will utilize some of the lines that were commented in our previous use with locally generated data.
* **Action**: Open the Dataset Insights Jupyter notebook again, using the command below:
Once the Docker image is running, the rest of the workflow is quite similar to what we did in Phase 1, with certain differences caused by the need to download the data from USim.
Once the Docker image is running, the rest of the workflow is quite similar to what we did in Phase 1, with certain differences caused by the need to download the data from Unity Simulation.
* **Action**: Open a web browser and navigate to `http://localhost:8888` to open the Jupyter notebook.
* **Action**: Navigate to the `datasetinsights/notebooks` folder and open `Perception_Statistics.ipynb`.
<imgsrc="Images/di_usim_1.png"/>
</p>
The next few lines of code pertain to setting up your notebook for downloading data from USim.
The next few lines of code pertain to setting up your notebook for downloading data from Unity Simulation.
* **Action**: In the block of code titled "Unity Simulation [Optional]", uncomment the lines that assign values to variables, and insert the correct values, based on information from your USim run.
* **Action**: In the block of code titled "Unity Simulation [Optional]", uncomment the lines that assign values to variables, and insert the correct values, based on information from your Unity Simulation run.
We have previoulsy learned how to obtain the `run_execution_id` and `project_id`. You can remove the value already present for `annotation_definition_id` and leave it blank. What's left is the `access_token`.
We have previously learned how to obtain the `run_execution_id` and `project_id`. You can remove the value already present for `annotation_definition_id` and leave it blank. What's left is the `access_token`.
* **Action**: Return to your command-line interface and run the `usim inspect auth` command.
If you receive errors regarding authentication, your token might have timed out. Repeat the login step (`usim login auth`) to login again and fix this issue.
A sample output from `usim inspect auth` will like like below:
A sample output from `usim inspect auth` will look like below:
```
Protect your credentials. They may be used to impersonate your requests.
updated: 2020-10-02 14:50:11.412979
```
The `access_token` you need for your Dataset Insights notebook is the access token shown by the above command, minus the `'Bearer '` part. So in this case, we should input `0CfQbhJ6gjYIHjC6BaP5gkYn1x5xtAp7ZA9I003fTNT1sFp` in the notebook.
The `access_token` you need for your Dataset Insights notebook is the access token shown by the above command, minus the `'Bearer '` part. So, in this case, we should input `0CfQbhJ6gjYIHjC6BaP5gkYn1x5xtAp7ZA9I003fTNT1sFp` in the notebook.
* **Action**: Copy the access token excluding the `'Bearer '` part to the corresponding field in the Dataset Inisghts notebook.
* **Action**: Copy the access token excluding the `'Bearer '` part to the corresponding field in the Dataset Insights notebook.
Once you have entered all the information, the block of code should look like the screenshot below (the actual values you input will be different):
* **Action**: Continue to the next code block and run it to download all the meta-data files from the generated dataset. This includes JSON files and logs, but does not include images (which will be downloaded later).
* **Action**: Continue to the next code block and run it to download all the metadata files from the generated dataset. This includes JSON files and logs but does not include images (which will be downloaded later).
You will see a progress bar while the data downloads:
The next couple of code blocks (under "Load dataset metadata") analyze the downloaded meta-data and display a table containing annotation-id's for the various metrics defined in the dataset.
The next couple of code blocks (under "Load dataset metadata") analyze the downloaded metadata and display a table containing annotation-definition-ids for the various metrics defined in the dataset.
* **Action**: Once you reach the code block titled "Built-in Statistics", make sure the value assigned to the field `rendered_object_info_definition_id` matches the id displayed for this metric in the table output by the code block immediately before it. The screenshot below demonstrates this (note that your ids might differ from the ones here):
Follow the rest of the steps inside the notebook to generate a variety of plots and stats. Keep in mind that this notebook is provided just as an example, and you can modify and extend it according to your own needs using the tools provided by the [Dataset Insights framework](https://datasetinsights.readthedocs.io/en/latest/).
This concludes the Perception tutorial. The next step in this workflow would be to train an object-detection model using a USim-generated dataset. It is important to note that the 1000 large dataset we generated here is probably not sufficiently large for training most models. We chose this number here so that the run would complete in a fairly short period of time, allowing us to move on to learning how to analyze the dataset's statistics. In order to generate data for training, we recommend a dataset of about 400,000 captures.
This concludes the Perception tutorial. The next step in this workflow would be to train an object-detection model using a dataset generated on Unity Simulation. It is important to note that the 1000 large dataset we generated here is probably not sufficiently large for training most models. We chose this number here so that the run would complete in a fairly short period of time, allowing us to move on to learning how to analyze the dataset's statistics. In order to generate data for training, we recommend a dataset of about 400,000 captures.
The grocery objects we used in the foreground are a subset of objects from the [SynthDet](https://github.com/Unity-Technologies/SynthDet) project, which is a custom project based on the Perception package. Instructions for training a [Faster-RCNN](https://arxiv.org/abs/1506.01497) object-detection model based on data generated with the SynthDet project are provided [here](https://github.com/Unity-Technologies/datasetinsights/blob/master/docs/source/Evaluation_Tutorial.md). Although the instructions are tailored to SynthDet, the principles will be the same for training a model.
In the near future, we will expand this tutorial to Phase 4, which will inclde model training instructions which are tailor-made for the project we built together here.
In the near future, we will expand this tutorial to Phase 4, which will include instructions on how to train a Faster R-CNN object-detection model using a dataset that can be generated by following this tutorial.
The Perception package offers a variety of tools for generating synthetic datasets intended for use in perception-based machine learning tasks, such as object detection, semantic segmentation, and so on. These datasets are in the form of **frames** captured using simulated sensors. These frames are **annotated** with **ground-truth**, and are thus ready to be used for training and validating machine learning models. While the type of ground-truth bundled with this data will depend on your intended machine learning task, the Perception package already comes with a number of common ground-truth labelers which will make it easier for you to generate synthetic data. This tutorial will guide you all the way from setting up Unity on your computer to generating a large-scale synthetic dataset for training an object-detection model.
The Perception package offers a variety of tools for generating synthetic datasets intended for use in perception-based machine learning tasks, such as object detection, semantic segmentation, and so on. These datasets are in the form of **frames** captured using simulated sensors. These frames are **annotated** with **ground-truth** and are thus ready to be used for training and validating machine learning models. While the type of ground-truth bundled with this data will depend on your intended machine learning task, the Perception package already comes with a number of common ground-truth labelers which will make it easier for you to generate synthetic data. This tutorial will guide you all the way from setting up Unity on your computer to generating a large-scale synthetic dataset for training an object-detection model.
While this process may sound complicated, **you do not need to have any prior experience with Unity or C# coding** in order to follow the first phase of this tutorial and generate a dataset using our provided samples and components. The tutorial will be divided into three high-level phases based on the complexity of the tasks involved. During these phases, you will be gradually introduced to more advanced tools and workflows that the Perception package enables you to perform.
While this process may sound complicated, **you do not need to have any prior experience with Unity or C#** in order to follow the first phase of this tutorial and generate a dataset using our provided samples and components. The tutorial will be divided into three high-level phases based on the complexity of the tasks involved. During these phases, you will be gradually introduced to more advanced tools and workflows that the Perception package enables you to perform.
## [Phase 1: Setup and Basic Randomizations](Phase1.md)
## [Phase 2: Custom Randomizations](Phase2.md)
In order to get the best out of comptuer vision models, the training data needs to contain a large-degree of variation. This is achieved through randomizing various aspects of your simulation between captured frames. While you will use basic randomizations in Phase 1, Phase 2 of the tutorial will help you learn how to randomize your simulations in more complex ways by guiding you through writing your first customized randomizer in C# code. Once you complete this phase, you will know how to:
In order to get the best out of computer vision models, the training data needs to contain a largedegree of variation. This is achieved through randomizing various aspects of your simulation between captured frames. While you will use basic randomizations in Phase 1, Phase 2 of the tutorial will help you learn how to randomize your simulations in more complex ways by guiding you through writing your first customized randomizer in C# code. Once you complete this phase, you will know how to:
* Create custom randomizers by extending our provided samples.
* Coordinate the operation of several randomizers by specifying their order of execution and the objects they affect.
* Have objects specify criteria (e.g. ranges, means, etc.) and logic (e.g. unique behaviors) for their randomizable attributes.
You will generally require a large amount of data to train your computer vision model. Generating data in these practical sizes will take incredible amounts of time to finish if performed on typical workstation computers. This is where the cloud comes in. In this phase, you will learn how to:
* Generate large-scale synthetic datasets containing hundreds of thousands of frames by leveraging the power of **Unity Simulation** (USim).
* Keep track of your USim runs using the USim command-line interface (USim-CLI).
* Generate large-scale synthetic datasets containing hundreds of thousands of frames by leveraging the power of **Unity Simulation**.
* Keep track of your Unity Simulation runs using the Unity Simulation command-line interface.
* Use Dataset Insights to download and analyze your cloud-generated data.
|[Labeling](GroundTruth-Labeling.md)|A component that marks a GameObject and its descendants with a set of labels|
|[LabelConfig](GroundTruth-Labeling.md#LabelConfig)|An asset that defines a taxonomy of labels for ground truth generation|
|[Labeling](GroundTruthLabeling.md)|A component that marks a GameObject and its descendants with a set of labels|
|[LabelConfig](GroundTruthLabeling.md#label-config)|An asset that defines a taxonomy of labels for ground truth generation|
|[Perception Camera](PerceptionCamera.md)|Captures RGB images and ground truth from a [Camera](https://docs.unity3d.com/Manual/class-Camera.html).|
|[DatasetCapture](DatasetCapture.md)|Ensures sensors are triggered at proper rates and accepts data for the JSON dataset.|
|[Randomization (Experimental)](Randomization/Index.md)|The Randomization tool set lets you integrate domain randomization principles into your simulation.|
//The listview recycles child visual elements and that causes the RegisterValueChangedCallback event to be called when scrolling.
//Therefore, we need to make sure we are not in this code block just because of scrolling, but because the user is actively changing one of the labels.
//The index check is for this purpose.
Debug.LogWarning("A label with the ID "+evt.newValue+" has already been added to this label configuration.");
}
}
else
{
Debug.LogError("Provided id is not a valid integer. Please provide integer values.");
serObj.FindProperty(nameof(Labeling.useAutoLabeling)).boolValue=true;//only set this flag once the user has actually chosen a scheme, otherwise, we will not touch the flag
//The listview recycles child visual elements and that causes the RegisterValueChangedCallback event to be called when scrolling.
//Therefore, we need to make sure we are not in this code block just because of scrolling, but because the user is actively changing one of the labels.
//The editor.CommonLabels.IndexOf(cEvent.newValue) != m_IndexInList check is for this purpose.
Debug.LogError("A label with the string "+cEvent.newValue+" has already been added to selected objects.");
//the value change event is called even when the listview recycles its child elements for re-use during scrolling, therefore, we should check to make sure there are modified properties, otherwise we would be doing the refresh for no reason (reduces scrolling performance)
continue;//Do not allow duplicate labels in one asset. Duplicate labels have no use and cause other operations (especially mutlt asset editing) to get messed up
//The listview recycles child visual elements and that causes the RegisterValueChangedCallback event to be called when scrolling.
//Therefore, we need to make sure we are not in this code block just because of scrolling, but because the user is actively changing one of the labels.
//The index check is for this purpose.
m_LabelsList.DoLayoutList();
this.serializedObject.ApplyModifiedProperties();
Debug.LogWarning("A label with the chosen color "+cEvent.newValue+" has already been added to this label configuration.");
/// Each 3D bounding box data record maps a tuple of (instance, label) to translation, size and rotation that draws a 3D bounding box,
/// as well as velocity and acceleration (optional) of the 3D bounding box. All location data is given with respect to the sensor coordinate system.
///
/// bounding_box_3d
/// label_id (int): Integer identifier of the label
/// label_name (str): String identifier of the label
/// instance_id (str): UUID of the instance.
/// translation (float, float, float): 3d bounding box's center location in meters as center_x, center_y, center_z with respect to global coordinate system.
/// size (float, float, float): 3d bounding box size in meters as width, length, height.
/// rotation (float, float, float, float): 3d bounding box orientation as quaternion: w, x, y, z.
/// velocity (float, float, float) [optional]: 3d bounding box velocity in meters per second as v_x, v_y, v_z.
/// acceleration (float, float, float) [optional]: 3d bounding box acceleration in meters per second^2 as a_x, a_y, a_z.
/// </summary>
/// <remarks>
/// Currently not supporting exporting velocity and acceleration. Both values will be null.
publicstructBoxData
{
/// <summary>
/// Integer identifier of the label
/// </summary>
/// <summary>
/// String identifier of the label
/// </summary>
/// <summary>
/// UUID of the instance
/// </summary>
/// <summary>
/// 3d bounding box's center location in meters as center_x, center_y, center_z with respect to global coordinate system
/// </summary>
/// <summary>
/// 3d bounding box size in meters as width, length, height
/// </summary>
/// <summary>
/// 3d bounding box orientation as quaternion: w, x, y, z
/// </summary>
/// <summary>
/// [optional]: 3d bounding box velocity in meters per second as v_x, v_y, v_z
/// </summary>
/// <summary>
/// [optional]: 3d bounding box acceleration in meters per second^2 as a_x, a_y, a_z
/// </summary>
intm_CurrentFrame;
/// <inheritdoc/>
"Bounding box for each labeled object visible to the sensor",id:newGuid(annotationId));
/// Name of the function that checks whether a given string matches any of the label entries in this label configuration, used for reflection purposes.
// /// A list for backing up the asset's manually added labels, so that if the user switches to auto labeling and back, the previously added labels can be revived
// /// </summary>
// public List<string> manualLabelsBackup = new List<string>();
/// <summary>
/// Whether this labeling component is currently using an automatic labeling scheme. When this is enabled, the asset can have only one label (the automatic one) and the user cannot add more labels.
/// </summary>
publicbooluseAutoLabeling;
/// <summary>
/// The specific subtype of AssetLabelingScheme that this component is using, if useAutoLabeling is enabled.
/// </summary>
publicstringautoLabelingSchemeType=string.Empty;
/// <summary>
/// The unique id of this labeling component instance
Jon Hogins
4 年前
