In this phase of the Perception tutorial, you will start from downloading and installing Unity Editor and the Perception package. You will then use our sample assets and provided components to easily generate a synthetic dataset for training an object-detection model.
Through-out the tutorial, lines starting with bullet points followed by **":green_circle:Action:"** denote the individual actions you will need to perform in order to progress through the tutorial. This is while non-bulleted lines will provide additional context and explanation around the actions. If in a hurry, you can just follow the actions!
Through-out the tutorial, lines starting with bullet points followed by **":green_circle:Action:"** denote the individual actions you will need to perform in order to progress through the tutorial. This is while non-bulleted lines will provide additional context and explanation around the actions. If in a hurry, you can just follow the actions!
Steps included this phase of the tutorial:
- [Step 1: Download Unity Editor and Create a New Project](#step-1)
- [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.)
* **:green_circle: 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.)
* **Action**: Make sure the _**Linux Build Support (Mono)**_ and _**Visual Studio**_ installation options are checked when selecting modules during installation.
* **:green_circle: Action**: Make sure the _**Linux Build Support (Mono)**_ and _**Visual Studio**_ installation options are checked when selecting modules during installation.
* **Action**: Open Unity and create a new project using the Universal Render Pipeline. Name your new project _**Perception Tutorial**_, and specify a desired location as shown below.
* **:green_circle: Action**: Open Unity and create a new project using the Universal Render Pipeline. Name your new project _**Perception Tutorial**_, and specify a desired location as shown below.
Once your new project is created and loaded, you will be presented with the Unity Editor interface. From this point on, whenever we refer to _**the editor**_, we mean Unity Editor.
* **Action**: From the top menu bar, open _**Window**_ -> _**Package Manager**_.
* **:green_circle: Action**: From the top menu bar, open _**Window**_ -> _**Package Manager**_.
* **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**_.
* **:green_circle: 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...**_.
* **:green_circle: Action**: Enter the address `com.unity.perception` and click _**Add**_.
> :information_source: If you would like to install 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).
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 that the 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**_.
* **:green_circle: 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**_.
Once the sample files are imported, they will be placed inside the `Assets/Samples/Perception` folder in your Unity project. You can view your project's folder structure and access your files from the _**Project**_ tab of the editor, as seen in the image 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:
* **:green_circle: 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**: **(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:
* **:green_circle: 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 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**.
* **:green_circle: 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">
As seen above, the new Scene already contains a camera (`Main Camera`) and a light (`Directional Light`). We will now modify the camera's field of view and position to prepare it for the tutorial.
* **Action**: Click on `Main Camera` and in the _**Inspector**_ tab, modify the camera's `Position`, `Rotation`, `Projection` and `Size` to match the screenshot below. (Note that `Size` only becomes available once you set `Projection` to `Orthographic`)
* **:green_circle: Action**: Click on `Main Camera` and in the _**Inspector**_ tab, modify the camera's `Position`, `Rotation`, `Projection` and `Size` to match the screenshot below. (Note that `Size` only becomes available once you set `Projection` to `Orthographic`)
<palign="center">
<imgsrc="Images/camera_prep.png"/>
For this tutorial, we prefer our light to not cast any shadows, therefore:
* **Action**: Click on `Directional Light` and in the _**Inspector**_ tab, set `Shadow Type` to `No Shadows`.
* **:green_circle: Action**: Click on `Directional Light` and in the _**Inspector**_ tab, set `Shadow Type` to `No Shadows`.
* **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:
* **:green_circle: Action**: Select `Main Camera` again and in the _**Inspector**_ tab, click on the _**Add Component**_ button.
* **:green_circle: Action**: Start typing `Perception Camera` in the search bar that appears, until the `Perception Camera` script is found, with a **#** icon to the left:
* **Action**: Click on this script to add it as a component. Your camera is now a `Perception` camera.
* **:green_circle: Action**: Click on this script to add it as a component. Your camera is now a `Perception` camera.
> :information_source: 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**_.
To speed-up your perception workflow, the Perception package comes with five common labelers for object-detection tasks; however, if you are comfortable with code, you can also add your own custom labelers. The labelers that come with the Perception package cover **3D bounding boxes, 2D bounding boxes, object counts, object information (pixel counts and ids), and semantic segmentation images (each object rendered in a unique colour)**. We will use four of these in this tutorial.
* **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`.
* **:green_circle: Action**: Click on the _**+**_ button at the bottom right corner of the empty labeler list and select `BoundingBox2DLabeler`.
* **:green_circle: 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:
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**_.
* **:green_circle: Action**: In the _**Project**_ tab, right-click the `Assets` folder, then click _**Create -> Perception -> Id Label Config**_.
* **Action**: Rename the newly created `IdLabelConfig` asset to `TutorialIdLabelConfig`.
* **:green_circle: Action**: Rename the newly created `IdLabelConfig` asset to `TutorialIdLabelConfig`.
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.
* **Action**: In the _**Project**_ tab, right-click the `Assets` folder, then click _**Create -> Perception -> Semantic Segmentation Label Config**_. Name this asset `TutorialSemanticSegmentationLabelConfig`.
* **:green_circle: Action**: In the _**Project**_ tab, right-click the `Assets` folder, then click _**Create -> Perception -> Semantic Segmentation Label Config**_. Name this asset `TutorialSemanticSegmentationLabelConfig`.
* **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:
* **:green_circle: 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 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.
* **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.
* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`
* **:green_circle: Action**: Double click the file named `drink_whippingcream_lucerne.prefab` to open the Prefab asset.
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:
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.
* **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.
* **:green_circle: Action**: Select **all the files** inside the `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs` folder.
* **:green_circle: Action**: From the _**Inspector**_ tab, enable `Use Automatic Labeling for All Selected Items`, and then select `Use asset name` as the labeling scheme.
<palign="center">
<imgsrc="Images/autolabel.png"width="400"/>
* **Action**: Click _**Add Automatic Labels of All Selected Assets to Config...**_.
* **:green_circle: Action**: Click _**Add Automatic Labels of All Selected Assets to Config...**_.
* **Action**: Add the list of labels to `TutorialIdLabelConfig` and `TutorialSemanticSegmentationLabelConfig` by clicking the _**Add All Labels**_ button for both.
* **:green_circle: Action**: Add the list of labels to `TutorialIdLabelConfig` and `TutorialSemanticSegmentationLabelConfig` by clicking the _**Add All Labels**_ button for both.
<palign="center">
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/0.6.0-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.
* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
* **:green_circle: Action**: Drag and drop any of the Prefabs inside this folder into the Scene.
* **:green_circle: Action**: Click on the **▷** (play) button located at the top middle section of the editor to run your simulation.
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:
In this tutorial, you will learn how to use the provided Randomizers, as well as how to create new ones that are custom-fitted to your randomization needs.
* **Action**: Create a new GameObject in your Scene by right-clicking in the _**Hierarchy**_ tab and clicking `Create Empty`.
* **Action**: Rename your new GameObject to `Simulation Scenario`.
* **Action**: In the _**Inspector**_ view of this new object, add a new `Fixed Length Scenario` component.
* **:green_circle: Action**: Create a new GameObject in your Scene by right-clicking in the _**Hierarchy**_ tab and clicking `Create Empty`.
* **:green_circle: Action**: Rename your new GameObject to `Simulation Scenario`.
* **:green_circle: 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 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.
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 Randomizer**_, and from the list choose `BackgroundObjectPlacementRandomizer`.
* **:green_circle: Action**: Click _**Add Randomizer**_, and from the list choose `BackgroundObjectPlacementRandomizer`.
* **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`.
* **:green_circle: 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`.
* **Action**: Set the rest of the properties according to the image below. That is, `Depth = 0, Layer Count = 2, Separation Distance = 0.5, Placement Area = (6,6)`.
* **:green_circle: Action**: Set the rest of the properties according to the image below. That is, `Depth = 0, Layer Count = 2, Separation Distance = 0.5, Placement Area = (6,6)`.
* **Action**: Click on the **▷** (play) button located at the top middle section of the editor to run your simulation.
* **:green_circle: 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"/>
<imgsrc="Images/game_aspect.png"width ="400"/>
</p>
* **Action**: Repeat the previous steps to add `TextureRandomizer`, `HueOffsetRandomizer`, and `RotationRandomizer`.
* **:green_circle: Action**: Repeat the previous steps to add `TextureRandomizer`, `HueOffsetRandomizer`, and `RotationRandomizer`.
* **Action**: In the UI snippet for `TextureRandomizer`, click _**Add Folder**_ and choose `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Background Textures`.
* **:green_circle: 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`, verify that all the minimum values for the three ranges are `0` and that maximum values are `360`.
* **:green_circle: 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/0.6.0-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.
* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.6.0-preview.1/Tutorial Files/Background Objects/Prefabs`.
* **:green_circle: 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.
* **:green_circle: Action**: Repeat the above step to add `HueOffsetRandomizerTag` and `RotationRandomizerTag` to all selected Prefabs.
Once the above step is done, the _**Inspector**_ tab for a background Prefab should look like this:
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)`.
* **:green_circle: 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`.
* **:green_circle: Action**: Set these values for the above Randomizer: `Depth = -3, Separation Distance = 1.5, Placement Area = (5,5)`.
* **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.
* **:green_circle: 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.
* **Action**: Drag `ForegroundObjectPlacementRandomizer` using the striped handle bar (on its left side) and drop it above `RotationRandomizer`.
* **:green_circle: Action**: Drag `ForegroundObjectPlacementRandomizer` using the striped handle bar (on its left side) and drop it above `RotationRandomizer`.
Your full list of Randomizers should now look like the screenshot below:
You are now ready to generate your first dataset. Our current setup will produce 100 frames of annotated captures.
* **Action** Click **▷** (play) again and this time let the simulation finish. This should take only a few seconds.
* **:green_circle: Action** Click **▷** (play) again and this time let the simulation finish. This should take only a few seconds.
While the simulation is running, your _**Game**_ view will quickly generate frames similar to the gif below (note: visualization for `SemanticSegmentationLabeler` is disabled here):
<imgsrc="Images/dataset_written.png"/>
</p>
* **Action**: Navigate to the dataset path addressed in the _**Console**_.
* **:green_circle: Action**: Navigate to the dataset path addressed in the _**Console**_.
In this folder, you will find a few types of data, depending on your `Perception Camera` settings. These can include:
- Logs
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 visible object, the annotations include:
* **: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`
* `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.
* **:green_circle: 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>
* **Action**: Download and install [Docker Desktop](https://www.docker.com/products/docker-desktop)
* **Action**: Open a command line interface (Command Prompt on Windows, Terminal on Mac OS, etc.) and type the following command to run the Dataset Insights Docker image:
* **:green_circle: Action**: Download and install [Docker Desktop](https://www.docker.com/products/docker-desktop)
* **:green_circle: Action**: Open a command line interface (Command Prompt on Windows, Terminal on Mac OS, etc.) and type the following command to run the Dataset Insights Docker image:
* **Action**: The image is now running on your computer. Open a web browser and navigate to `http://localhost:8888` to open the Jupyter notebook:
* **:green_circle: Action**: The image is now running on your computer. Open a web browser and navigate to `http://localhost:8888` to open the Jupyter notebook:
* **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, 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.
* **:green_circle: 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.
* **:green_circle: Action**: Navigate to the `datasetinsights/notebooks` folder and open `Perception_Statistics.ipynb`.
* **:green_circle: 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"/>
</p>
* **Action**: Follow the instructions laid out in the notebook and run each code block to view its outputs.
* **:green_circle: 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 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)
Mohsen Kamalzadeh
4 年前