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.
To speed-up your perception workflow, the Perception package comes with four common labelers for object-detection tasks; however, if you are comfortable with code, you can easily add your own custom labelers. The labelers that come with the Perception package cover **2D bounding boxes, object counts, object information (pixel counts and ids), and semantic segmentation images (each object rendered in a unique colour)**. In this tutorial, we will be working with the first three.
To speed-up your perception workflow, the Perception package comes with four 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 **2D bounding boxes, object counts, object information (pixel counts and ids), and semantic segmentation images (each object rendered in a unique colour)**.
* **Action**: Repeat the above step to add `ObjectCountLabeler` and `RenderedObjectInfoLabeler`.
* **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:
One of the useful features that comes with the `Perception Camera` is the ability to display real-time visualizations of the labelers when your simulation is running. For instance, the`BoundingBox2DLabeler` can display bounding boxes around the foreground objects that it tracks in real-time. 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 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.
It is now time to tell your each labeler added to the `Perception Camera` which objects it should label in the generated dataset. For instance, if your workflow is intended to generate 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 their intended 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 configuartions.
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 labeles within the scene and ignore the rest. To do that, we should first create a fitting label configuration.
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.
* **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.
In this tutorial, we will generate synthetic data intended for detecting 10 everyday grocery items. In this step, you will add labels for each of these 10 items to the list of labels for `TutorialIdLabelConfig`.
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:
1 `candy_minipralines_lindt`
<imgsrc="Images/idlabelconfig.png"width="400"/>
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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` .
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` .
Now that you have created your label configuration, we need to assign this configuration to labelers that you previously added to your `Perception Camera` component.
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.
* **Action**: Select the `Main Camera` object from the Scene _**Hierarchy**_, and in the _**Inspector**_ tab, assign the newly created `TutorialIdLabelConfig` to all 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. The `Perception Camera` component will now look like the image below:
* **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:
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:
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`. Inside this folder, there are four assets related to each of the 10 foreground objects. These four includes a .fbx model file, a material, a .jpg texture, and a Prefab asset. While the file extensions are not shown as part of the file names in the folder view, if you click on a file to select it, you can view its full name and path in the address bar at the bottom of the _**Project**_ tab.
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`.
In Unity, Prefabs are essentially reusable GameObjects that are stored to disk, along with all their child GameObjects, components, and property values. In our case here, the Prefab asset is what brings all the pieces of each foreground object together and will be the actual asset that you will use in your perception pipeline. Let's see what these prefabs include.
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/0.5.0-preview.1/Tutorial Files/ Foreground Objects/Phase 1`
* **Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/ Foreground Objects/Phase 1/Prefbas`
When you open the Prefab asset, you will see the object shown in the Scene view and its components shown on the right side of the editor, in the _**Inspector**_ view:
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:
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, which means you do not need to perform any additonal steps to label your foreground objects.
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 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 a label to the cream carton, you can click on the _**+**_ button to the bottom right corner of the label list, in the `Labeling` component.
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.
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, `Parameter`s can define the range, sampling distribution, and a seed for randomization. This is while another class of `Paramter`s let you randomly select one 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 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 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 `Iterations`, and each `Iteration` carries on for a number of frames. These are timing elements you can leverage in order to customize your `Scenario`s 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 most beginner 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 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.
Once the `Fixed Length Scenario` component is added, the inspector view for your `Simulation Scenario` object will look like below:
The _**Inspector**_ view of `Fixed Length Scenario` looks like below:
There are a number settings and properties you can modify here, such as `Quit On Complete` or `Constants`. Just like the `Perception Camera` component, if you hover your mouse cursor over each of these properties, you will see a tooltip containing a relevant explanation. For now, we will leave all of these fields as they are. This is currently an empty `Scneario`. Let's add some Randomizers.
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.
This Randomizer uses Poisson-Disk sampling to select random positions from a given area, and spawn copies of randomly selected Prefabs (from the given list) at the chosen positions. We will use this component to generate a background that will act as a distraction for our eventual object-detection machine learning model.
This Randomizer uses Poisson-Disk sampling to select random positions from a given area, and spawn copies of randomly selected Prefabs (from a given list) at the chosen positions. We will use this component to generate a background that will act as a distraction for our eventual object-detection machine learning model.
The beckground Prefabs are primitve shapes devoid of color or texture. Later Randomizers will take care of those aspects. 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.
The beckground Prefabs are primitve 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.
When the simulation starts running, Unity Editor will switch to the _**Game**_ tab to show you the output of the active camera, which carries the `Perception Camera` component, as seen below:
When the simulation starts running, Unity Editor will switch to the _**Game**_ tab to show you the output of the active camera, which carries the `Perception Camera` component:
In this view, you will also see the real-time visualizations we discussed before shown on top of the camera's view. In the top right corner of the window, you can see a visualization control panel, through which you can enable or disable visualizations for individual labelers. That said, we currently have no foreground objects in the Scene yet, so no bounding boxes will be displayed.
In this view, you will also see the real-time visualizations we discussed before shown on top of the camera's view. In the top right corner of the window, you can see a visualization control panel, through which you can enable or disable visualizations for individual labelers. That said, we currently have no foreground objects in the Scene yet, so no bounding boxes or semantic segmentations will be displayed.
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)
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. 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`.
`TextureRandomizer`and 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. Simlarly, `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 `RotationRandomizer`, change the all the maximum values for the three ranges to `360` and leave the minimums at `0`.
* **Action**: In the UI snippet for `RotationRandomizer`, change all the maximum values for the three ranges to `360` and leave the minimums at `0`.
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 the `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/Background Objects/Prefabs`.
* **Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.5.0-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.
* **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)`.
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 differentating between how background and foreground objects are treated.
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:
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:
You are now ready to generate your first dataset. The `FixedLengthScenario` component that we used has 1000 `Iteration`s by default, and each `Iteration` runs for one frame. Thus, the simulation will produce 1000 frames of annotated captures.
You are now ready to generate your first dataset. Our current set-up will produce 100 frames of annotated captures.
* **Action** Click **▷** (play) again and this time let the simulation finish. It should take about 1 minute, depending on your hardware.
* **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:
- Logs
- JSON data
- RGB images (raw camera output) (if the `Save Camera Output to Disk` checkmark is enabled on `Perception Camera`)
- Semantic segmentation images (if the `SemanticSegmentationLabeler` is added and active on `Perception Camera')
- Semantic segmentation images (if the `SemanticSegmentationLabeler` is added and active on `Perception Camera`)
The output dataset includes a large variety of information about various 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.
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.
* `x` and `y`: Pixel coordinates of the top-left corner of the object's bounding box. (measured from the top-left corner of the image)
* `width` and `height` of the object's bounding box.
* `x` and `y`: Pixel coordinates of the top-left corner of the object's bounding box (measured from the top-left corner of the image)
* `width` and `height` of the object's bounding box
* **Action**: Open a command line interface (Command Prompt, Terminal, etc.) and type the following command to run the Dataset Insights Docker image:
`docker run -p 8888:8888 -v <path to synthetic data>:/data -t unitytechnologies/datasetinsights:latest`, where the path is what we earlier found in Unity's console messages.
* **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:
`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.
<imgsrc="Images/jupyter1.png"/>
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* **Action**: To make sure your data is properly mounted, navigate to the `data` folder. If you see the dataset's folders here, we are good to go.
* **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**: Once in the notebook, replace the `<GUID>` in the `data_root = /data/<GUID>` with the name of the dataset folder inside your generated data. For example `Dataseta26351bc-1b72-46c5-9e0c-d7afd6df2974`.
* **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`.
This notebook contains a variety of functions for generating plots, tables, and bounding box images that help you analyze your generated dataset. Certain parts of this notebook are currently not of use to us, such as the code meant for downloading data generated through Unity Simulation (coming later in this tutorial), or parts that deal with output from the semantic segmentation labeler (which we did not include in our `PerceptionCamera`).
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. When you run a code block, an **asterisk (\*)** will be shonw next to the code block on the left side, until the code finishes executing.
This notebook contains a variety of functions for generating plots, tables, and bounding box images that help you analyze your generated dataset. Certain parts of this notebook are currently not of use to us, such as the code meant for downloading data generated through Unity Simulation (coming later in this tutorial).
* **Action**: Follow the instructions laid out in the notebook and run each code block to view its outputs. Not how parts of the code that are relevant to Unity Simulation are commented.
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.
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.
</p>
* **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)
mohsen.k
4 年前