In an era where data is booming and is omnipresent, technology is put to test and bet against its own capabilities to process the ever-growing data. What adds to this is the variety of data. Data can be text, images, videos, audios or anything. This puts a lot of programming minds under an urge to come up with solutions to process the varieties of data that would make the job of millions and millions of developers easier.
Java is a very popular language. Even when many other languages dominate over it in the AI and ML space Java still has solutions that can help it stand firm. In this listicle, we will mention some of the popular Java libraries that can be used for tasks that involve the processing of images.
Here is a list of popular libraries used for image processing in java. The list is not sorted under any criteria.
A rather popular Computer Vision library, the main objective of this library is real-time computer vision. Originally developed by Intel the library is licensed for free use under the open-source BSD license. Officially launched way back in 1999 the library is still popular and is favoured by many programmers around the world.
OpenCV also includes a range of statistical Machine Learning libraries that includes algorithms like Boosting, Random Forest, Decision Trees and many more. Written in C++, OpenCV has primary interfaces for C++, Python and Java and is also expanding to cover and reach out developers in many other languages such as C#, Perl, Ch, Haskell and Ruby.
Click here to learn more about OpenCV
BoofCV is an open-source java library licensed under Apache 2.0 license for real-time computer vision and robotics applications. It is a complete package for carrying out image and vision-related tasks. It is organized into several packages based on application such as the Image Processing package which consists of common functions to directly operate on pixels and the Features package that consists of algorithms that are specific to feature extraction tasks.
BoofCV can be used for low-level image processing routines such as convolution and interpolation to high-level functionality such as image stabilization.
Click here to learn more about BoofCV.
An exclusive Deep Learning toolkit for java, Deeplearning4j is a complete set of packages and libraries for deep learning in Java. Deeplearning4j is open-source, released under Apache 2.0 license. The library also has APIs for other languages like Scala, Python, Clojure and Kotlin. Deeplearning4j is not limited to any specific ML application but is made to help Java programmers carry out a wide range of Deep learning tasks which also includes Computer Vision.
Deeplearning4j has been growing in popularity among Java programmers and was also contributed to the Eclipse Foundation in late 2017.
Find more about Deeplearning4j here.
There are lots of libraries available today that are built on top of existing libraries. Such libraries offer the features of the base libraries adding to it improved usability and maybe some additional features.
JavaCV is not a dedicated computer vision library but is a wrapper for popular CV packages. It wraps up under its hood a group of libraries that are used by programmers and researchers worldwide. The libraries include OpenCV, FFmpeg, libdc1394, PGR FlyCapture, OpenKinect, librealsense, CL PS3 Eye Driver, videoInput, ARToolKitPlus, flandmark, Leptonica, and Tesseract. JavaCV makes it easier to use these libraries in a Java environment including Android development.
The official Github repo for JavaCV can be found here.
AlgART is an open-source library for array-based computations and image processing distributed under the MIT license making it free to use without any restrictions. The algorithms are designed for any number of matrix dimensions, thus allowing users to easily deal with 2D, 3D or other multidimensional image processing.
The official documentation and tutorials can be found here.
Libraries and packages pop up almost every now and then, but only a few grabs the attention of the huge developers’ community. With the amount of data growing every second, more advanced set of tools will be needed to process them faster especially the multimedia data which are best known for its greed for resources.