/Toolkit-for-Collaborative-XR

Try out the ToolkitForCollaborativeXR

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Toolkit for Collaborative XR

This is an experimental toolkit designed for the workshop at 未來智慧工場 (Atelier Future) of National Cheng Kung University (NCKU) in 2023. The toolkit consists of an API server and examples, and we assume that XR scenes are created on the STYLY platform using Unity, PlayMaker, and STYLY Plugin for Unity. This toolkit allows you to build a common reference base between scenes, bringing consistency to the XR experience.

Philosophy

  • No Black Boxes: Remember the first time you wrote a "Hello, World!" in Python? That sense of achievement you felt? We believe in keeping things transparent. If you have the basics of Python under your belt, you'll be able to dive into our code, understand it, and even modify it to your heart's content. No mysteries here!
  • Minimalism at its Best: Our API server is like a blank canvas. We provide just the essential functions to get you started. But guess what? You're the artist here! Add features, modify existing ones, and paint your masterpiece. Our tool grows as you grow.
  • Abundant Documentation: Ever felt lost while using a tool? We've been there. That's why we've put together a comprehensive guide to help you out. Check out api_documentation.md for detailed insights and instructions. It's like having a friendly guide by your side!

Essential Concepts

Role

  • In our system, every user has a specific role. The roles are:
    • Designer: Responsible for creating and managing locations, items, and tags.
    • Player: Interacts with items, acquires them, and communicates using message boards.
    • Sensor: Updates the status of items based on physical-world inputs.
    • Actuator: Acts upon or responds to item statuses.
  • Each role has a unique API key, ensuring secure and role-specific interactions.

Location

  • Every item is associated with a location. Locations are of two types:
    • INDOOR: For items inside buildings or confined spaces.
    • OUTDOOR: For items in open areas or natural environments.
  • For items in an INDOOR location, we specify their position using three coordinates: x, y, and z.
  • For items in an OUTDOOR location, we use geographical coordinates: latitude (north-south position) and longitude (east-west position).

Item

  • Items are objects or entities you can interact with. Each item has:
    • Name: An identifier or label for the item.
    • Owner: Indicates who currently possesses the item. By default, items belong to PUBLIC_DOMAIN, meaning they are available for all. Once a player acquires an item, its ownership changes to A_PLAYER.
    • Type: Describes the category of the item.
    • Coordinates: Specifies the item's position in its location.
    • Tags (optional): Keywords to help categorize and filter items.
    • Attributes (optional): Additional properties or details about the item.

Tag

  • Tags are like labels. They help in organizing and finding items based on specific categories or properties. For example, if you're searching for a specific type of item, you can use tags to filter and find them quickly.

Essential Components

In the development and deployment of this API, we utilized a combination of modern technologies and platforms to ensure robustness, scalability, and ease of use. Here's an overview:

Flask

  • What it is: Flask is a lightweight web framework written in Python. It's designed for quick and easy development of web applications.
  • Role in this API: Flask serves as the backbone of our API server. We use it to define endpoints, handle requests, and send responses to users.

Cloud Firestore

  • What it is: Cloud Firestore is a flexible, scalable, and NoSQL cloud database from Google Firebase. It allows for real-time data synchronization and offers robust querying capabilities.
  • Role in this API: Cloud Firestore acts as our primary database. We store details like locations, items, and tags here. Its real-time capabilities ensure that users always interact with the most up-to-date data.

Render

  • What it is: Render is a Platform as a Service (PaaS) that offers cloud computing services. It simplifies the process of deploying, scaling, and managing applications and databases.
  • Role in this API: We use Render to host our Flask-based API server. It ensures that the API is always available and can handle multiple requests simultaneously. Render's infrastructure also ensures smooth scaling as the number of users grows.

How to launch your own API server

This sequence diagram provides a step-by-step representation of setting up a server using Cloud Firestore as a database and Render as a Platform-as-a-Service (PaaS). The flow captures interactions between the developer, user, Render, Cloud Firestore, and GitHub repository, highlighting the key actions from project creation to deploying updates.

sequenceDiagram
    actor User
    actor Developer
    participant Render
    participant Firestore as Cloud Firestore
    participant GitHub as GitHub Repository

    Developer->>Firestore: Create new project
    Developer->>Firestore: Set up new database
    Firestore-->>Developer: Provide private key
    Developer->>+GitHub: Request to fork repository
    GitHub->>GitHub: Execute fork
    GitHub-->>-Developer: Share forked repository details
    Developer->>Render: Initiate new project
    Developer->>Render: Configure using repository details, private key, etc.
    Developer->>Render: Click "Create Web Service" button
    Render->>GitHub: Fetch repository content
    GitHub-->>Render: Send repository content
    Render->>Render: Execute build

loop For every endpoint call
    User->>+Render: Send request
    Render->>+Firestore: Forward request
    Firestore-->>-Render: Return response
    Render-->>-User: Send response
end

loop For every push to the GitHub repository
    Developer->>GitHub: Push updates
    GitHub->>GitHub: Commit changes
    GitHub-->>Render: Notify about update
    Render->>GitHub: Fetch updated content
    GitHub-->>Render: Send updated content
    Render->>Render: Execute build
end
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Create a database

  1. Navigate to the Firebase console: https://console.firebase.google.com/.
  2. Click Add project and follow follow the on-screen instructions to create a Firebase project (you may uncheck the toggle button for Enable Google Analytics for this project to disable Google Analytics).
  3. Once your project is ready, click the Continue button, then navigate to the Cloud Firestore section.
  4. Look for Cloud Firestore and follow the database creation workflow.
    • Choose Start in production mode.
    • Choose a suitable Cloud Firestore location (e.g., asia-southeast1: Singapore).
  5. Once finished, navigate to the ⚙️ button on the top left → Project settings → Service accounts.
  6. Click the Generate new private key button, read the warning, and then click the Generate key button to generate a new key (a JSON file) and download the key to your local machine.
  7. IMPORTANT: Keep the JSON file in a safe place. You can’t reissue the key; if lost, you'll need to generate a new one.

Fork the GitHub project and deploy

  1. Navigate to https://github.com/kotobuki/Toolkit-for-Collaborative-XR/.
  2. Fork the project.
  3. Navigate to https://render.com/ and create an account (recommended to use a GitHub account).
  4. Click on the New + button on the top-right and choose Web Service.
  5. Click the Connect account button under the GitHub icon.
  6. Choose Only select repositories for the Repository access and select the forked repository.
  7. Choose Standard for the Instance Type.
  8. Click on the Advanced button.
    • Navigate to the Start Command section and set gunicorn server:app as the start command.
    • Navigate to the Environment Variables section and set the following environment variable keys with their respective values:
      • API_KEY_DESIGNER: abcdefgh00000000
      • API_KEY_PLAYER: abcdefgh00000001
      • API_KEY_SENSOR: abcdefgh00000002
      • API_KEY_ACTUATOR: abcdefgh00000003
      • SERVICE_ACCOUNT_KEY_JSON: Copy and paste the content of the generated key file. Ensure this content remains confidential.
  9. Click on the Create Web Service button to start deploying.

How to test the API server locally

This guide provides a step-by-step procedure for setting up and testing the API server on your local machine. Whether you are a developer or just someone curious to run the API server, by following these steps, you will have a locally running instance of the server. This is especially useful for debugging, feature development, or simply understanding the inner workings of the API.

  1. Clone the repository.

  2. Open a terminal in the repository directory.

  3. Create a virtual environment and activate the environment.

    python3 -m venv .venv
. .venv/bin/activate

  4. Install the required dependencies. Additionally, if you don't have jq installed and you're on macOS with Homebrew, you can install it as shown below. For other platforms, please consult the jq official documentation for installation instructions.

    pip install -r requirements.txt
brew install jq

  5. Export the environment variables. Remember to replace service-account-key.json with the path to your previously downloaded Firebase key file.

    export API_KEY_DESIGNER=abcdefgh00000000
export API_KEY_PLAYER=abcdefgh00000001
export API_KEY_SENSOR=abcdefgh00000002
export API_KEY_ACTUATOR=abcdefgh00000003
SERVICE_ACCOUNT_KEY_JSON=$(jq -c . service-account-key.json)
export SERVICE_ACCOUNT_KEY_JSON

  6. Run the server app in the environment.

    gunicorn server:app

  7. After running the server, check the terminal for a message indicating the listening address. This address (for example, http://127.0.0.1:8000) is the base URL you'll use to access the API.

    [INFO] Listening at: http://127.0.0.1:8000

How to use the utilities

Generate api_documentation.md from server.py

python docstring_to_md.py server.py

Generate test_client.html from server.py

python docstring_to_test_client.py server.py

Test the API with the test_client.html

  1. Open test_client.html in your web browser.
  2. Fill in the Base URL and API Key fields.
  3. Choose a role from radio buttons (e.g., Designer).
  4. Choose an endpoint, fill in the parameters, and press the Submit button.

Measure response time of each endpoint

Install necessary packages and run the test script.

pip install numpy tqdm
python api_test.py {full_url_with_parameters}

Tuning the server

If you choose the Starter plan (512MB of RAM, 0.5 CPU core), use the following configuration. This configuration allows for two simultaneous connections.

gunicorn --workers 1 --threads 2 server:app

If you choose the Standard plan (2 GB of RAM, 1 CPU core), use the following configuration. This configuration supports 6 simultaneous connections. You can experiment by increasing the number of threads to 3, which would allow for 9 simultaneous connections. However, always monitor the memory and CPU usage via the dashboard.

gunicorn --workers 3 --threads 2 server:app