Open Source Android Image Processing Apps

ZXing or “Zebra Crossing” is an open source multi-format 1D/2D barcode image processing library that’s been implemented in Java, and also comes with ports to other languages. It currently supports the following formats: UPC-A and UPC-E EAN-8 and EAN-13 Code 39 Code 93 Code 128 ITF Codabar RSS-14 (all variants) RSS Expanded (most variants) QR Code Data Matrix Aztec ('beta' quality) PDF 417 ('alpha' quality) MaxiCode ZXing is made up of several modules, including a core image decoding library, JavaSE-specific client code, and Android client Barcode Scanner. It is the basis of many other third-party open source projects.

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.

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.

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.

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.

Fun Morph is a morphing app for Android and Windows Desktop, its able to morph human faces with animal faces for fun and a good brain teaser for image processing enthusiasts.

libcrn is document image processing library written in C++11 for Linux, Windows, Mac OsX and Google Android. It is a toolbox that allows to create easily software such as OCRs and layout analysis tools.

Image cropping is a common task in many web applications. Usually just cutting out the center of the image works out ok. It's often a compromise and sometimes it fails miserably. Smartcrop.js is the result of my experiments with content aware image cropping. It uses fairly simple image processing and a few rules to attempt to create better crops of images. This library is still in it's infancy but the early results look promising. So true to the open source mantra of release early, release often, I'm releasing version 0.0.0 of smartcrop.js. Smartcrop.js implements an algorithm to find good crops for images. It can be used in the browser, in node or via a CLI. Smarcrop requires support for Promises, use a polyfill for unsupported browsers or set smartcrop.Promise to your favorite promise implementation (I recommend bluebird).