Open Source Swift Realtime Processing Software

Swift Realtime Processing Software

View 201 business solutions

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

  • Red Hat Ansible Automation Platform on Microsoft Azure Icon
    Red Hat Ansible Automation Platform on Microsoft Azure

    Red Hat Ansible Automation Platform on Azure allows you to quickly deploy, automate, and manage resources securely and at scale.

    Deploy Red Hat Ansible Automation Platform on Microsoft Azure for a strategic automation solution that allows you to orchestrate, govern and operationalize your Azure environment.
    Learn More
  • Top-Rated Free CRM Software Icon
    Top-Rated Free CRM Software

    216,000+ customers in over 135 countries grow their businesses with HubSpot

    HubSpot is an AI-powered customer platform with all the software, integrations, and resources you need to connect your marketing, sales, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.
    Get started free
  • 1
    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:
    See Project
  • Previous
  • You're on page 1
  • Next