Open Source Image Processing Software
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Image Processing Software
View 62 business solutionsBrowse free open source Image Processing software and projects for Windows and Mac below. Use the toggles on the left to filter open source Image Processing software by OS, license, language, programming language, and project status.
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Octave Forge
A collection of packages providing extra functionality for GNU Octave
Octave Forge is a central location for collaborative development of packages for GNU Octave. The Octave Forge packages expand Octave's core functionality by providing field specific features via Octave's package system. See https://octave.sourceforge.io/packages.php for a list of all available packages. GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The Octave language is quite similar to Matlab so that most programs are easily portable. Refer to https://www.octave.org for more information. -
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Pillow
The friendly Python Imaging Library fork
If you've ever wondered or worried about the future of Python's Imaging Library, it's time to stop. Pillow is here to answer your concerns, and offer you more. Pillow is the friendly fork of the Python Imaging Library or PIL, a library that adds image processing capabilities to your Python interpreter. Why turn to Pillow? Aside from offering extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities, Pillow is setuptools compatible. While PIL is not officially over yet, with Pillow you can be assured of continuous integration testing, publicized development activity, and regular releases to the Python Package Index. -
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SciPy
SciPy library main repository
This is the main repository for the SciPy library, one of the core packages that make up the SciPy stack. SciPy is an open source software used in the fields of mathematics, science, and engineering, with modules for statistics, optimization, integration, linear algebra, signal and image processing, and many more. The SciPy library contains many of the user-friendly and efficient numerical routines, including those for numerical integration, interpolation, and optimization. SciPy is built to work with NumPy, a software that provides convenient and fast N-dimensional array manipulation. Both SciPy and NumPy run on all popular operating systems, are fast and easy to install, and are powerful yet easy to use. They’re currently depended upon by numerous leading scientists and engineers all over the world. Try them for yourself! -
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ImageMagick
ImageMagick 7
ImageMagick® is a free, open-source software suite, used for editing and manipulating digital images. It can be used to create, edit, compose, or convert bitmap images, and supports a wide range of file formats, including JPEG, PNG, GIF, TIFF, and PDF. ImageMagick is widely used in industries such as web development, graphic design, and video editing, as well as in scientific research, medical imaging, and astronomy. Its versatile and customizable nature, along with its robust image processing capabilities, make it a popular choice for a wide range of image-related tasks. ImageMagick includes a command-line interface for executing complex image processing tasks, as well as APIs for integrating its features into software applications. It is written in C and can be used on a variety of operating systems, including Linux, Windows, and macOS. -
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Emgu CV
Emgu CV is a cross platform .Net wrapper for OpenCV
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. -
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Point Cloud Library
A standalone, large scale, open project for 2D/3D image processing
The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing. PCL is released under the terms of the BSD license, and thus free for commercial and research use. Whether you’ve just discovered PCL or you’re a long time veteran, this page contains links to a set of resources that will help consolidate your knowledge on PCL and 3D processing. An additional Wiki resource for developers is available too. To simplify both usage and development, we split PCL into a series of modular libraries. PCL is cross-platform, and has been successfully compiled and deployed on Linux, MacOS, Windows, and Android. To simplify development, PCL is split into a series of smaller code libraries, that can be compiled separately. This modularity is important for distributing PCL on platforms with reduced computational or size constraints. -
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C3D is a command-line tool for converting 3D images between common file formats. The tool also includes a growing list of commands for image manipulation, such as thresholding and resampling.
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MMDeploy
OpenMMLab Model Deployment Framework
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. -
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WebP Codec
Library to encode and decode images in WebP format
libwebp is the reference codec library for Google’s WebP image format, providing both encoding and decoding along with command-line tools. It supplies cwebp to compress images into WebP and dwebp to decompress them back, making it easy to test quality/size trade-offs across presets and tuning parameters. The GitHub repository is a mirror; the canonical source of truth lives on Chromium’s git, and developer docs are hosted on WebP’s portal. The project underpins WebP support across browsers, imaging libraries, and many native apps thanks to its stable C API. Additional companion repos host test data and demos, including JavaScript builds and timing tests for various platforms. As a widely deployed codec, libwebp balances compression efficiency, speed, and integration simplicity across platforms. -
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Accord.NET Framework
Machine learning, computer vision, statistics and computing for .NET
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. -
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Kingfisher
Lightweight, pure-Swift library for downloading images from the web
Kingfisher is a powerful, pure-Swift library for downloading and caching images from the web. It provides you a chance to use a pure-Swift way to work with remote images in your next app. Asynchronous image downloading and caching. Loading image from either URLSession-based networking or local provided data. Useful image processors and filters provided. Multiple-layer hybrid cache for both memory and disk. Fine control on cache behavior. Customizable expiration date and size limit. Cancelable downloading and auto-reusing previous downloaded content to improve performance. Independent components. Use the downloader, caching system, and image processors separately as you need. Prefetching images and showing them from the cache to boost your app. View extensions for UIImageView, NSImageView, NSButton and UIButton to directly set an image from a URL. Built-in transition animation when setting images. Customizable placeholder and indicator while loading images. -
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satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful please give it a star and consider sponsoring it. You can also follow me on Twitter and LinkedIn where I aim to post frequent updates on my new discoveries, and I have created a dedicated group on LinkedIn. I have also started a blog here and have published a post on the history of this repository called Dissecting the satellite-image-deep-learning repo. -
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ArrayFire
ArrayFire, a general purpose GPU library
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. -
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Jimp
An image processing library written entirely in JavaScript for Node
An image processing library for Node written entirely in JavaScript, with zero native dependencies. If you're using this library with TypeScript the method of importing slightly differs from JavaScript. Instead of using require, you must import it with ES6 default import scheme. If you're using a web bundles (webpack, rollup, parcel) you can benefit from using the module build of jimp. Using the module build will allow your bundler to understand your code better and exclude things you aren't using. If you're using webpack you can set process.browser to true and your build of jimp will exclude certain parts, making it load faster. The static Jimp.read method takes the path to a file, URL, dimensions, a Jimp instance or a buffer and returns a Promise. In some cases, you need to pass additional parameters with an image's URL. -
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sharp
High performance Node.js image processing module
The typical use case for this high speed Node.js module is to convert large images in common formats to smaller, web-friendly JPEG, PNG, AVIF and WebP images of varying dimensions. Resizing an image is typically 4x-5x faster than using the quickest ImageMagick and GraphicsMagick settings due to its use of libvips. Colour spaces, embedded ICC profiles and alpha transparency channels are all handled correctly. Lanczos resampling ensures quality is not sacrificed for speed. As well as image resizing, operations such as rotation, extraction, compositing and gamma correction are available. Most modern macOS, Windows and Linux systems running Node.js v10+ do not require any additional install or runtime dependencies. This module supports reading JPEG, PNG, WebP, AVIF, TIFF, GIF and SVG images. Output images can be in JPEG, PNG, WebP, AVIF and TIFF formats as well as uncompressed raw pixel data. Streams, Buffer objects and the filesystem can be used for input and output. -
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Imagine
PHP 5.3 object oriented image manipulation library
Imagine is a OOP library for image manipulation built in PHP 5.3 using the latest best practices and thoughtful design that should allow for decoupled and unit-testable code. ImagineInterface (Imagine\Image\ImagineInterface) and its implementations is the main entry point into Imagine. You may think of it as a factory for Imagine\Image\ImageInterface as it is responsible for creating and opening instances of it and also for instantiating Imagine\Image\FontInterface object. The main piece of image processing functionality is concentrated in the ImageInterface implementations (one per driver - e.g. Imagick\Image). The main idea of Imagine is to avoid driver specific methods spill outside of this class and couple of other internal interfaces (Draw\DrawerInterface), so that the filters and any other image manipulations can operate on ImageInterface through its public API. -
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Nuke
Image loading system
Nuke ILS provides an efficient way to download and display images in your app. It's easy to learn and use thanks to a clear and concise API. Its architecture enables many powerful features while offering virtually unlimited possibilities for customization. Despite the number of features, the framework is lean and compiles in just under 3 seconds¹. Nuke has an automated test suite 2x the size of the codebase itself, ensuring excellent reliability. Every feature is carefully designed and optimized for performance. Fast LRU memory cache, native HTTP disk cache, and custom aggressive LRU disk cache. Customize image pipeline using built-in Alamofire, Gifu, FLAnimatedImage, WebP plugins or create your own. Enable progressive decoding with a single line of code. Nuke supports progressive JPEG out of the box, and WebP via a plugin built by the community. Automatically prefetch images ahead of time using either Preheat or native table and collection view prefetching APIs. -
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tracking.js
A modern approach for Computer Vision on the web
The tracking.js library brings different computer vision algorithms and techniques into the browser environment. By using modern HTML5 specifications, we enable you to do real-time color tracking, face detection and much more, all that with a lightweight core (~7 KB) and intuitive interface. To get started, download the project. This project includes all of the tracking.js examples, source code dependencies you'll need to get started. Unzip the project somewhere on your local drive. The package includes an initial version of the project you'll be working with. While you're working, you'll need a basic HTTP server to serve your pages. Test out the web server by loading the finished version of the project. The main goal of tracking.js is to provide those complex techniques in a simple and intuitive way on the web. We believe computer vision is important to improve people's life, bringing it to the web will make this future a reality a lot faster. -
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ImageJ2x
Java Image Processing Program
ImageJ2x is medical image processing program inspired by NIH Image for the Macintosh. It can display, edit, analyze, process, save and print 8-bit, 16-bit and 32-bit images. There can be many image formats including TIFF, GIF, JPEG, BMP, DICOM, PNG, FITS and "raw" and ... Read and supports "stacks" (stack), a series of images that share a single window. The program supports simultaneous that number of windows (images), limited only by available memory. It is multithreaded, so that time-consuming operations such as reading lists in parallel with other operations are performed. It can calculate area and pixel value statistics of user-defined selection. It can measure distances and angles. It can record density histograms and line profiles. It supports standard image processing functions such as contrast manipulation, sharpening, smoothing, edge detection and filtering it through all kinds of geometric transformations such as Zoom in / out and rotation. Images can be enlarged up to 32 -
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An implementation of Bruhn et al.'s fast variational optical flow algorithm using the OpenCV image processing library. The code calculates dense flow fields with a user-specified level of precision.
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The Camellia library is an open source and cross-platform image processing library, written in plain C. It includes a lot of speed-optimized imaging functions (filtering, morpho, labeling, motion detection, warping, color conversion, project/backproject)
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QVision: Computer Vision Library for Qt
Computer vision and image processing library for Qt.
This library contains among other things a set of graphical widgets for video output, performance evaluation and augmented reality. The library also provides classes for several data types usually required by computer vision and image processing applications such as vectors, matrices, quaternions and images. Thanks to a large number of wrapper functions these objects can be used with highly efficient functionality from third party libraries such as OpenCV, GNU Scientific Library, Computational Geometry Algorithms Library, Intel's Math Kernel Library and Integrated Performance Primitives, the Octave library, etc... -
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Quantum Mechanical Toolkit And 3D Viewer for C++. Allows Data Visualisation via Images, Surfaces and Volume plots using OpenGL, as well as rapid development Quantum Mechanical Simulations. Uses the Blitz++, VTK Visualisation and Qt Libraries.
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AlgART Java Libraries
Open source library for processing arrays and matrices
AlgART Java libraries for processing arrays and matrices are open-source product, distributed under MIT license. So, anyone can use them for free without any restrictions. Main features: 63-bit addressing of array elements (64-bit long int indexes), memory model concept (allowing storing data in different schemes from RAM to mapped disk files), wide usage of lazy evaluations, built-in multithreading optimization for multi-core processors, wide set of image processing algorithms over matrices, etc. - please see at the site. Almost all classes and methods are thoroughly documented via JavaDoc (you may read full JavaDoc at the site). -
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Asido is a PHP (PHP4/PHP5) image processing solution, with "pluggable" drivers(adapters) for virtually any environment (GD2, Image Magick, etc). Asido does: various resize routines, watermark, rotation, copy images, cropping, grayscale, convert, etc.
