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.
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.
<imgsrc="Images/idlabelconfig.png"width="400"/>
</p>
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`.
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`.
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.
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 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`.
* **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:
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/Prefbas`
* **Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.5.0-preview.1/Tutorial Files/ Foreground Objects/Phase 1/Prefabs`
* **Action**: Double click the file named `drink_whippingcream_lucerne.prefab` to open the Prefab asset.
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:
</p>
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.
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 additional 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.
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/0.5.0-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. The `Seed` attribute is the seed used for the underlying random sampler and does not need to match the image shown.
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`.
`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`.
It is now time to spawn and randomize our foregournd objects. We are getting close to generating our first set of synthetic data!
It is now time to spawn and randomize our foreground objects. We are getting close to generating our first set of synthetic data!
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.
While the texture and color of the foreground objects will be constant during the simulation, we would like their rotation to be randomized similar to the background Prefabs. To achieve this:
* **Action**: Drag `ForegroundObjectPlacementRandomizer` and drop it above `RotationRandomizer`.
* **Action**: Drag `ForegroundObjectPlacementRandomizer` and drop it above `RotationRandomizer`.
### <aname="step-6">Step 6: Generate and Verify Synthetic Data</a>
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**: 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, 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`.
<palign="center">
<imgsrc="Images/jupyter2.png"/>
* **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)
* **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:
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**: 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
}
```
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.
You may sometimes need to bundle certain randomization-related data or logic within an object that are inherent to the object itself. For instance, you may have multiple lights in the Scene but would like each of them to have their own unique range of intensities. It would be quite tedious to add a new Parameter to your light Randomizer for each of your lights. Furthermore, this would make your light Randomizer excessively tailored to one use-case, limiting the Randomizer's reusability.
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)`.
* **Action**: Change the Y rotation of `Directional Light (1)` to 60, as shown below:
<palign="center">
* **Action**: Change the Y rotation of `Directional Light` to -60.
This makes the two lights illuminate the scene from opposing sides, each having a 30degree angle with the background and foreground planes.
This makes the two lights illuminate the scene from opposing sides, each having a 30-degree angle with the background and foreground planes.
* **Action**: Open `MyLightRandomizerTag.cs` and modify it to match the code below:
```
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 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.
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:
</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.**
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:
You may notice that the IDs seen above for the run named `FirstRun` match those we saw earlier in Unity Editor's _**Console**_. You can see here that the single execution for our recently uploaded build is `In_Progress` and that the execution ID is `yegz4WN`.
Unity Simulation utilizes the ability to run simulation instances in parallel. If you enter a number larger than 1 for the number of instances in the _**Run in Unity Simulation**_ window, your run will be parallelized and multiple simulation instances will simultaneously execute. You can view the status of all simulation instances using the `usim summarize run-execution <execution-id>` command. This command will tell you how many instances have succeeded, failed, have not run yet, or are in progress. Make sure to replace `<execution-id>` with the execution ID seen in your run list. In the above example, this ID would be `yegz4WN`.
Unity Simulation utilizes the ability to run simulation instances in parallel. If you enter a number larger than 1 for the number of instances in the _**Run in Unity Simulation**_ window, your run will be parallelized, and multiple simulation instances will simultaneously execute. You can view the status of all simulation instances using the `usim summarize run-execution <execution-id>` command. This command will tell you how many instances have succeeded, failed, have not run yet, or are in progress. Make sure to replace `<execution-id>` with the execution ID seen in your run list. In the above example, this ID would be `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
* **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):
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.
In the near future, we will expand this tutorial to Phase 4, which will inclde instructions on how to train a Faster R-CNN object-detection model using a dataset that can be generated by following this tutorial.
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.
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4 年前