Open Source Linux Image Processing Software - Page 2
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Image Processing Software for Linux
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RawTherapee
A powerful cross-platform raw photo processing program
RawTherapee is a powerful, cross-platform raw photo processing application targeted at enthusiast and semi-professional photographers who want deep control over image development. It’s written primarily in C++ with a GTK+ front-end, providing a high-precision 32-bit floating-point processing engine along with multiple demosaicing algorithms and advanced color-management features. The program supports a very wide range of camera raw formats (via a patched version of dcraw) as well as non-raw formats such as JPEG, TIFF, and PNG. Users can work non-destructively: adjustments are previewed but stored separately (sidecar files) and only applied during export, which allows experimentation without altering originals. The interface includes a file browser, processing queue, editing pane with full-image previews, history stack, and batch export functionality. -
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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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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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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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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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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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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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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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SuperEmbed.js
Fluid width for YouTube, Vimeo, Vine, VideoPress, DailyMotion, etc.
SuperEmbed.js detects embedded videos from YouTube, Vimeo, Vine, VideoPress, DailyMotion, and more on webpages and makes them responsive. Essentially, this means they stretch to fill their container while still maintaining the content's original aspect ratio. I created SuperEmbed to fix all my issues with existing solutions, including (but not limited to) unnecessary reliance on other libraries, bloated code, and poor fallback support. -
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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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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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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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BioImageXD - free open source software for analysis, processing and 3D rendering of multi dimensional, multi data channel, time series image data from microscopy and other sources.
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itom
itom - an Open Source Measurement, Automation and Evaluation Software
itom is an open source software suite for operating measurement systems, laboratory automation and data evaluation. One main application of itom is the development and operation of sensor and measurement system for instance in a laboratory environment. Therefore, the software has to be able to communicate with a wide range of different hardware systems, such as cameras or actuators and should provide a diversified and as complete as possible set of evaluation and data processing methods. Additionally, the rapid prototyping of modern measurement and inspection setups requires a system, where parameters or components can easily be changed at runtime, necessitating the availability of an embedded scripting language. Finally, when operating a measurement system, it is also desirable to extend the graphical user interface by system adapted dialogs and windows. The project has been moved mid 2023 to github: https://itom-project.github.io https://github.com/itom-project -
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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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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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GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. -
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Intervention Image
PHP Image Processing
Intervention Image is a PHP image handling and manipulation library. It provides an easy-to-use interface for performing common image operations such as resizing, cropping, and applying filters. It supports a variety of image formats and can be integrated into Laravel projects or used independently in other PHP applications. The library is highly customizable, allowing for simple image manipulation tasks, or more advanced image processing workflows. -
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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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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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Surface Defect Detection Dataset Papers
Constantly summarizing open source dataset and critical papers
At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. -
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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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imagor
Fast, secure image processing server and Go library, using libvips
Imagor is a fast, Docker-ready image processing server built in Go, ideal for on-the-fly image resizing, cropping, filtering, and optimization. It supports a wide variety of image formats and integrates seamlessly with cloud storage backends like AWS S3 and Google Cloud Storage. With support for HTTP and gRPC APIs, Imagor can be used in production environments to serve optimized images dynamically with high performance and low latency. -
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react-imgpro
Image Processing Component for React
react-imgpro is an image-processing component for React. This component process an image with filters supplied as props and returns a base64 image. I was working on a project last month which involved a lot of image processing and I'd to rely on third party libraries. But before using them directly, I'd to learn different concepts in gl (shaders) and then try to implement them in React. The difficult part was not learning but it was the verbosity, boilerplate code and redundancy introduced by the libraries in the codebase. It was getting difficult to organize all the things. And React's component-based model was perfect for hiding all the implementation details in a component. -
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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.
