Open Source Windows Image Processing Software - Page 2
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Image Processing Software for Windows
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Image Tools is a screen capture, file sharing and image processing tool. It features multi-threaded batch image resizing, conversion, cropping, flipping/rotating, watermarks, decolorizing (grayscale, negative, sepia), and optimizing. The BMP, GIF, TIFF, JPEG, PNG, and EMF image types are supported. It is compatible with Linux MONO (only for GNOME and Xfce). Multicore processing is supported to increase performance. The quality for output when optimizing is variable. Color channels can be filtered. An internal benchmarking tool is available. Indexed pixel format images can be processed. You can process your images, publish pieces of your screens, files and send links to everyone you want to share it.
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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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SMILI
Scientific Visualisation Made Easy
The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with graphical user interfaces and/or via the command-line. See our YouTube channel for tutorial videos via the homepage. The applications are all built out of a uniform user-interface framework that provides a very high level (Qt) interface to powerful image processing and scientific visualisation algorithms from the Insight Toolkit (ITK) and Visualisation Toolkit (VTK). The framework allows one to build stand-alone medical imaging applications quickly and easily. -
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TESTIMAGES
Testing images for scientific purposes
The TESTIMAGES archive is a huge and free collection of sample images designed for analysis and quality assessment of different kinds of displays and image processing techniques. The archive includes more than 2 million images originally acquired and divided in three different categories: SAMPLING and SAMPLING_PATTERNS (aimed at testing resampling algorithms), COLOR (aimed at testing color rendering on different displays) and PATTERNS (aimed at testing the rendering of standard geometrical patterns). Please cite the following papers when using any image in this archive: * ASUNI N, GIACHETTI A, "TESTIMAGES: A Large Data Archive For Display and Algorithm Testing", Journal of Graphics Tools, Volume 17, Issue 4, 2015, pages 113-125, DOI:10.1080/2165347X.2015.1024298 * ASUNI N, GIACHETTI A, "TESTIMAGES: a large-scale archive for testing visual devices and basic image processing algorithms", STAG - Smart Tools & Apps for Graphics Conference, 2014. -
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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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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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SimpleITK
A layer built on top of the Insight Toolkit (ITK)
SimpleITK is an image analysis toolkit with a large number of components supporting general filtering operations, image segmentation and registration. It is built on top of the Insight Segmentation and Registration Toolkit ITK with the intent of providing a simplified interface to ITK. SimpleITK itself is written in C++ but is available for a large number of programming languages. Wrapping of the C++ code is accomplished through SWIG, in principle, any language wrapped by SWIG should be applicable to SimpleITK. Unlike ITK's support of n-dimensional spatio-temporal images, SimpleITK supports 2D, 3D and 4D images. The dimensionality refers to spatiotemporal dimensions, the voxels can be n-dimensional vectors. Open-source multi-dimensional image analysis in Python, R, Java, C#, Lua, Ruby, TCL and C++. Developed by the Insight Toolkit community for the biomedical sciences and beyond. -
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A GUI interface to a tool for generating SSBumps (Self Shadowed Bump Maps). Includes a CUDA GPU rendering extension.
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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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The 'ImageJ Plugins' project is a source of custom plugins for the Image/J software. Image/J is a public domain image processing and analysis program developed in Java (http://rsb.info.nih.gov/ij/).
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plastimatch
Volumetric image processing software
Plastimatch is an open source software for image computation. Our main focus is high-performance volumetric registration of medical images, such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). -
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EPSILON is a powerful Open Source wavelet image compressor. The project is aimed on parallel and robust image processing.
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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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jbig2enc
JBIG2 Encoder
This is an encoder for JBIG2. JBIG2 encodes bi-level (1 bpp) images using a number of clever tricks to get better compression than G4. This encoder can: Generate JBIG2 files, or fragments for embedding in PDFs Generic region encoding Perform symbol extraction, classification and text region coding Perform refinement coding and, Compress multipage documents It uses the (Apache-ish licensed) Leptonica library: http://leptonica.com/ -
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Orfeo ToolBox
OTB is an open-source C++ library for remote sensing images processing
The Orfeo Toolbox is a C++ library for high resolution remote sensing image processing. It is developped by CNES in the frame of the ORFEO program. More information is available at www.orfeo-toolbox.org It is based on the medical image processing library ITK and offers particular functionalities for remote sensing image processing in general and for high spatial resolution images in particular. Targeted algorithms for high resolution optical images (SPOT, Quickbird, Worldview, Landsat, Ikonos), hyperspectral sensors (Hyperion) or SAR (TerraSarX, ERS, Palsar) are available. Orfeo ToolBox has three main access depending on the category of user: write processing chains in C++ using existing filters or creating new ones, use the OTB applications, which is a plugin-based framework allowing to extend high-level processing chains from various environment ( command-line, QT, QGis, Python....) or use Monteverdi, a software for everyday life image manipulation and analysis. -
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The purpose of this program is to teach a computer to classify plants via their leaves. You just need to input the image of a leaf(acquired from scanner or camera), then the computer can tell you what kind of plant it is.
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scantailor-experimental
Scan Tailor Experimental is an interactive post-processing tool
Scan Tailor Experimental is an interactive post-processing tool for scanned pages. You give it raw scans, and you get pages ready to be printed or assembled into a PDF or DJVU file. Scanning, optical character recognition, and assembling multi-page documents are out of scope of this project. -
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S2iLib includes Harpia, a visual way of programming image processing algorithms, with a growing selection of OpenCV functions. Harpia is portable, and features capture from video devices in GNU/Linux and MS-Windows, and retrieves a compact OpenCV C-code.
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disser
LaTeX classes and templates for typesetting dissertations in Russian
LaTeX classes, Makefiles and document templates for typesetting dissertations in Russian -
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PolyBench/C 4.2 Copyright (c) 2011-2016 the Ohio State University. Contact: Louis-Noel Pouchet <pouchet@cse.ohio-state.edu> Tomofumi Yuki <tomofumi.yuki@inria.fr> PolyBench is a benchmark suite of 30 numerical computations with static control flow, extracted from operations in various application domains (linear algebra computations, image processing, physics simulation, dynamic programming, statistics, etc.). PolyBench features include: - A single file, tunable at compile-time, used for the kernel instrumentation. It performs extra operations such as cache flushing before the kernel execution, and can set real-time scheduling to prevent OS interference. - Non-null data initialization, and live-out data dump. - Syntactic constructs to prevent any dead code elimination on the kernel. - Parametric loop bounds in the kernels, for general-purpose implementation. - Clear kernel marking, using pragma-based delimiters.
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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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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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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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Vibrant.js
Extract prominent colors from an image. JS port of Android's Palette
Vibrant.js is a JavaScript library for extracting prominent colors from images to generate aesthetically pleasing color palettes. It is inspired by the Android Palette API and allows developers to style interfaces dynamically based on image content. Common use cases include adapting UI elements to match album art, user avatars, or featured content images. -
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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.
