Open Source Image Processing Software - Page 2
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Image Processing Software
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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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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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Perspec
Scriptable desktop app to correct the perspective of images
App and workflow to perspectively correct images. For example whiteboards, document scans, or facades. Perspec currently only works on macOS and Linux. Any help to make it work on Microsoft (Ticket) would be greatly appreciated! Rescale image on viewport change. Handle JPEG rotation. Draw lines between corners to simplify guessing of clipped corners. Bundle Imagemagick. Better error if the wrong file format is dropped (images/error-message.jpg). Center Perspec window on screen. Drag'n'Drop for corner markers. "Submit" button. "Convert to Grayscale" button. Add support for custom output size (e.g. A4). Manual rotation buttons. Zoom view for corners. Label corner markers. -
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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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CSSgram
CSS library for Instagram filters
Simply put, CSSgram is a library for editing your images with Instagram-like filters directly using CSS. What we're doing is adding filters to the images, as well as applying color and/or gradient overlays via various blending techniques to mimic filter effects. This means less manual image processing and more fun filter effects on the web! We are using pseudo-elements (i.e. :before and :after) to create the filter effects, so you must apply these filters on a containing element (i.e. not a content-block like <img>. The recommendation is to wrap your images in a <figure> tag. If you use custom naming in your CSS architecture, you can add the .scss files for the provided styles within your project and then @extend the filter effects within your style definitions. Mixins allow for multiple filter arguments to be passed into your classes. This is useful for if you want to add filters in addition to the ones provided (i.e. add a blur). -
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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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A GUI interface to a tool for generating SSBumps (Self Shadowed Bump Maps). Includes a CUDA GPU rendering extension.
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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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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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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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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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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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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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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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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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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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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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DICOM Anonymizer replaces the patient names in all the DICOM files in a folder (and sub-folders) with other strings you assigned. Works as a batch and works fast. Supports number index. Anonymizing is required for medical image processing.
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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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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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Robean
Rodent Neurobehavioral Analysis Software
Robean is a standalone freeware application for automated rodent neurobehavioural analysis. The software provides an intuitive graphical interface for analysing Morris Water Maze, Elevated Plus Maze, and Open Field Test experiments from prerecorded videos or live camera feeds. Robean integrates arena definition, zone management, centroid tracking, trajectory visualization, occupancy heatmap generation, automated PDF reporting, and batch study summaries within a single desktop application requiring no programming expertise. -
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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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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.