Open Source Mac Image Processing Software

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.

Open Source Remote Sensing OSSIM project, pronounced "awesome", will leverage existing algorithms/tools/packages from the open source community in construction of the ultimate Remote Sensing/Image Processing/GIS package. osgPlanet extends OSSIM and Op

The JPedal JBIG2 Image Decoder is a 100% pure Java image decoder for the JBIG2 file format. The decoder takes the JBIG2 image processing technology developed for the JPedal PDF renderer in 2007 and makes it available as a generic library for more general usage. The JDeli Image Library https://www.idrsolutions.com/jdeli contains a much enhanced and faster version and also provides a complete ImageIO replacement (see https://www.idrsolutions.com/jdeli/imageio-replacement) for Tiff, JPEG, JPEG2000, Png as well as lots of other image formats including Dicom, WMF, PSD, SGI and WebP.

ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices. The library serves users in every technical computing market. Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware. The community of ArrayFire developers invites you to build with us if you're interested and able to write top performing tensor functions. Together we can fulfill The ArrayFire Mission under an excellent Code of Conduct that promotes a respectful and friendly building experience. Rigorous benchmarks and tests ensuring top performance and numerical accuracy. Cross-platform compatibility with support for CUDA, OpenCL, and native CPU on Windows, Mac, and Linux. Built-in visualization functions through Forge.

phpCollect aims to quickly and easily graph, record, and display statistical host data.