Browse free open source Swift Graphics Software and projects below. Use the toggles on the left to filter open source Swift Graphics Software by OS, license, language, programming language, and project status.

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  • 1
    KeyboardKit

    KeyboardKit

    KeyboardKit is a Swift library that helps you build custom keyboard

    KeyboardKit helps you build custom keyboard extensions with Swift and SwiftUI. KeyboardKit is a Swift-based SDK that helps you build custom keyboard extensions with Swift and SwiftUI. It extends the native keyboard APIs and provides you with a lot of functionality. The KeyboardKit project is open-source and completely free. It's hosted at GitHub, which is where you find information, documentation, sample code, demo apps etc. KeyboardKit Pro is a license-based extension to KeyboardKit. It unlocks a bunch of pro features, like more locales, localized system keyboards, local and remote autocomplete etc. KeyboardKit comes with support for 50+ locales, with more being added over time. KeyboardKit has tools for customizing everything from keys, key behavior, layout, callout actions, audio & haptic feedback, colors and styling, autocomplete etc. The KeyboardKit app let you create keyboards directly on your iPhone and iPad.
    Downloads: 3 This Week
    Last Update:
    See Project
  • 2
    GPUImage 2

    GPUImage 2

    Framework for GPU-accelerated video and image processing

    GPUImage 2 is the second generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac, iOS, and now Linux. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS.
    Downloads: 0 This Week
    Last Update:
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