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Open League Displays

What is Open League Displays?

License: MIT

Open League Displays is an open source, cross platform application for downloading and setting high resolution wallpapers to your desktop.

Screencast.from.30-04-23.11.09.43.mp4

The whole idea and design came from the Riot Game's League Displays which is an application that runs only under Windows and isn't maintained anymore.

I was really enjoying Riot's League Displays but I was also getting frustrated over the fact that I couldn't set my wallpaper to newly released skins or on my Linux machine. So I decided to create this project.

How to download and install it?

Go to the releases page and download the latest version for your operating system. Because I don't own a mac, I haven't built it and test it there, if you known how to use a shell and are familiar with node, you can build it yourself (see below).

How it works?

  • It gets updated about new skins from Riot's DataDragon API automatically.
  • It downloads low resolution images from Riot's DataDragon API (CDN) used for thumbnails in the UI.
  • It downloads high resolution images from League of Legends Fandom Wiki used for the wallpapers.

How to run it / build it from source?

git clone [email protected]:KonstantinosPetrakis/open-league-displays.git
cd open-league-displays
npm install
npm run reset # This will delete all stored data if any, and push migrations to the database
# npm run reset-windows Equivalent to npm run reset but for windows
npm run dev 
npm run build 
# npm run build-windows Equivalent to npm run build but for windows

Disclaimers

This project is not affiliated with Riot Games in any way. All the images and assets used in this project are property of Riot Games.

Known issues

  • The application doesn't work for skins with repeated champion names (Draven Draven, Bard Bard, ...) the solution is trivial but I am too lazy to implement it right now.
  • Data loss on thumbnails or incomplete download/initial update could happen on extremely (<1mbps) slow connections. This most likely happens because the concurrent connections are getting timed out from the DataDragon API Server.