Open Source Mac Image Processing Software

Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCV functions to be called from .NET compatible languages such as C#, VB, VC++, IronPython etc. The wrapper can be compiled in Mono and run on Windows, Linux, Mac OS X, iPhone, iPad and Android devices.

MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. Please read getting_started for the basic usage of MMDeploy.

The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and extensive documentation and a wiki help fill in the details. The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile. After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible.

The application shows a 3D donut, that can be rotated with two sliders in both axis. It is a simple example of what can be done with frojasg1.com platform libraries: - Zoom option for components - Multi language - Dark mode option - Automatic Undo-Redo for text components, with popup menu included - Text Search/Replace window prepared to be used. - Base components for auto-completion windows. - Automatic component relocation after redimensioning a window. - Base classes for formating text on the fly in JTextPanes - Improved JFileChooser and other functions: - Basic functions of all kind. - Accurate image resizing. - Function to process images. - Classes to easy the xml file management (without validation) - JDK-17 compatibility You will find more about it at this web site: https://www.frojasg1.com:8443/downloads_web/web/html/donut.html?origin=sourceforge

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.

HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages.

Grayscale and binary image processing library for Qt.

C++ Software Framework based on wxWidgets for Developing Image Processing Algorithms

The Flexible Registration and Evaluation Engine (f.r.e.e.) allows the composition, evaluation and optimization of image processing/registration algorithms. It also aims to boost the exchangeability and comparability of data and algorithms in research.