Open Source Windows Image Processing Libraries
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Image Processing Libraries for Windows
View 11 business solutionsBrowse free open source Image Processing Libraries and projects for Windows below. Use the toggles on the left to filter open source Image Processing Libraries by OS, license, language, programming language, and project status.
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GLM
OpenGL Mathematics (GLM)
OpenGL Mathematics (GLM) is a header only C++ mathematics library for graphics software based on the OpenGL Shading Language (GLSL) specifications. GLM provides classes and functions designed and implemented with the same naming conventions and functionality than GLSL so that anyone who knows GLSL, can use GLM as well in C++. This project isn't limited to GLSL features. An extension system, based on the GLSL extension conventions, provides extended capabilities: matrix transformations, quaternions, data packing, random numbers, noise, etc. This library works perfectly with OpenGL but it also ensures interoperability with other third party libraries and SDK. It is a good candidate for software rendering (raytracing / rasterisation), image processing, physics simulations and any development context that requires a simple and convenient mathematics library. GLM is written in C++98 but can take advantage of C++11 when supported by the compiler. -
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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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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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AwesomeBump
Open Source graphic tool and alternative to Insane Bump
AwesomeBump is a free and open source graphic app written using Qt library. It was made as an alternative to known gimp plugin Insane Bump or the commercial tool Crazy Bump. It is designed to generate normal, height, specular or ambient occlusion, metallic, roughness and other textures from a single image. Most of the image processing is done on GPU so the program runs very fast and all the parameters can be changed in real time. -
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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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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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libvips
A fast image processing library with low memory needs
libvips is a demand-driven, horizontally threaded image processing library. Compared to similar libraries, libvips runs quickly and uses little memory. libvips is licensed under the LGPL 2.1+. It has around 300 operations covering arithmetic, histograms, convolution, morphological operations, frequency filtering, colour, resampling, statistics and others. It supports a large range of numeric types, from 8-bit int to 128-bit complex. Images can have any number of bands. It supports a good range of image formats, including JPEG, JPEG2000, JPEG-XL, TIFF, PNG, WebP, HEIC, AVIF, FITS, Matlab, OpenEXR, PDF, SVG, HDR, PPM / PGM / PFM, CSV, GIF, Analyze, NIfTI, DeepZoom, and OpenSlide. It can also load images via ImageMagick or GraphicsMagick, letting it work with formats like DICOM. It comes with bindings for C, C++, and the command-line. Full bindings are available for Ruby, Python, PHP, C# / .NET, Go, and Lua. -
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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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ImageProcessor
Wrapper around System.Drawing for the processing of image files
A .NET library for on-the-fly processing of images. ImageProcessor is a collection of lightweight libraries written in C# that allows you to manipulate images on-the-fly using .NET 4.5+. It consists of two main libraries ImageProcessor, for desktop and application use and ImageProcessor.Web, a dynamic image processing extension built for ASP.NET. It’s lighting fast, extensible, easy to use, comes bundled with some great features and is fully open source. ImageProcessor is free and open source but developing it has taken thousands of hours of my time and a large part of my sanity. If you feel like dropping me a few precious pounds (there’s no obligation) it would go a great way to ensure that I can afford to take the time to continue to improve the library. It's fast, extensible, easy to use, comes bundled with some great features and is fully open source. -
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CxImage is a C++ image processing library. It can load, save, display, transform images in a very simple and fast way, with transparency, multiple layers and selections, support for BMP GIF JPG PNG MNG TIF ICO TGA PCX J2K JBG RAS PNM RAW PSD
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Simd
High performance image processing library in C++
The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. The library supports dynamic and static linking, 32-bit and 64-bit Windows, Android and Linux, MSVS, G++ and Clang compilers, MSVS project and CMake build systems. -
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ImageSharp
A modern, cross-platform, 2D Graphics library for .NET
ImageSharp carries no native dependencies and can be installed anywhere that supports .NET Standard 2.0+, ImageSharp can be used in device, cloud, and embedded/IoT scenarios. ImageSharp's API is unparalleled for it's ease of use. Designed from the ground up to be flexible and extensible, the library provides API endpoints for common image processing operations and the building blocks to allow for the development of additional operations. Complete support for jpeg, bmp, gif, pbm, png, tga, tiff, and webp with more formats on the way. Supports over 25 different pixel formats. Metadata editing support (IPTC EXIF). Colorspace transforms (RGB/CMYK/Grayscale/CIELab and many more). Over 40 common processing operations. Great performance and memory friendly; work with any dimensions (even hundreds of megapixels). ImageSharp is brimming with features. Common operations are only a few keystrokes away. -
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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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Mozilla JPEG Encoder Project
Improved JPEG encoder
MozJPEG improves JPEG compression efficiency achieving higher visual quality and smaller file sizes at the same time. It is compatible with the JPEG standard, and the vast majority of the world's deployed JPEG decoders. MozJPEG is compatible with the libjpeg API and ABI. It is intended to be a drop-in replacement for libjpeg. MozJPEG is a strict superset of libjpeg-turbo's functionality. All MozJPEG's improvements can be disabled at run time, and in that case it behaves exactly like libjpeg-turbo. MozJPEG is meant to be used as a library in graphics programs and image processing tools. We include a demo cjpeg command-line tool, but it's not intended for serious use. We encourage authors of graphics programs to use libjpeg's C API and link with MozJPEG library instead. Progressive encoding with "jpegrescan" optimization. It can be applied to any JPEG file (with jpegtran) to losslessly reduce file size. -
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JACo Watermark
Add watermark to any image or photo (batch processing available).
A free open source Java application created to help you apply watermarks to your pictures in order to protect them from unauthorized distribution. Different font, color, size and transparency texts or images can be added as a watermark. Batch processing is also provided. -
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NOTE: the project has moved to https://scipy.org/scipylib/ --- go there to find latest versions. This sourceforge project contains only old historical versions of the software. SciPy is package of tools for science and engineering for Python. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.
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BoofCV
BoofCV is an open source Java library for real-time computer vision.
BoofCV is an open source Java library for real-time computer vision and robotics applications. Written from scratch for ease of use and high performance, it provides both basic and advanced features needed for creating a computer vision system. Functionality include optimized low level image processing routines (e.g. convolution, interpolation, gradient) to high level functionality such as image stabilization. Released under an Apache 2.0 license for both academic and commercial use. -
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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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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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scikit-image
Image processing in Python
scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. scikit-image builds on scipy.ndimage to provide a versatile set of image processing routines in Python. This library is developed by its community, and contributions are most welcome! Read about our mission, vision, and values and how we govern the project. Major proposals to the project are documented in SKIPs. The scikit-image community consists of anyone using or working with the project in any way. A community member can become a contributor by interacting directly with the project in concrete ways. -
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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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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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Kornia
Open Source Differentiable Computer Vision Library
Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. With Kornia we fill the gap between classical and deep computer vision that implements standard and advanced vision algorithms for AI. Our libraries and initiatives are always according to the community needs. -
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bild
Image processing algorithms in pure Go
A collection of parallel image processing algorithms in pure Go. The aim of this project is simplicity in use and development over absolute high performance, but most algorithms are designed to be efficient and make use of parallelism when available. It uses packages from the standard library whenever possible to reduce dependency use and development abstractions. All operations return image types from the standard library. Package convolution provides the functionality to create and apply a kernel to an image. Package effect provides the functionality to manipulate images to achieve various looks. Package histogram provides basic histogram types and functions to analyze RGBA images. Package paint provides functions to edit a group of pixels on an image. Package parallel provides helper functions for the dispatching of parallel jobs. -
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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.
