In a kind of world that we live in today and that is so rich in data, Images are known to represent a significant subset of all measurements that are made. Examples not only include microscopy slides, astronomical observations, satellite maps, and High-dimensional images such as 3-D magnetic resonance but a lot more if we just try to take a look at them. And to explore sources that are so Rich in data, we require software tools that are sophisticated, easy to use, free of charge and restrictions, and should also be capable of addressing all the challenges that are posed by such a diverse field of Analysis. And these very principles act as the foundation for any development practices that take place in the scikit-image community.

Scikit-image, is an Image processing library that implements algorithms and utilities for use in research, education, industry and is developed by a community of active, International Team of Collaborators. This Library is available under the BSD Open Source license and provides a well-documented API in the Python programming language. It can also be used in closed-source, commercial environments. The Scikit-image project started in the August 2009 and since then, has received numerous contributions and the number continues to grow significantly. This package can be installed on all major platforms like OSD, OS X, Windows etc.

The rise in the popularity of Python as a programming language over time, along with its increasing availability of numerous complementary tools, makes it an environment that is ideal to produce an image processing toolkit. This Image-processing follows an “Anything In, Anything Out” approach, where all functions are not only expected to allow input of an arbitrary data-type but, also get to choose their own output format.

One of the main goals of the Scikit-image processing library is to make it easier for any user to get started —especially when users are already familiar with the scientific tools of Python. A new user can now simply load an image from the disk or use any one of sample images from the SciKit-images, process that image with image filters that can be one or more, and display the results very quickly. It represents images as NumPy arrays because its use as the fundamental data structure maximizes its compatibility with the rest of the Python ecosystem.

This project aims at providing a high quality, well-documented and easy-to-use implementation of image processing algorithms and to also facilitate education in image processing along with facing the upcoming challenges in the Industry as High-quality implementations of trusted algorithms provide the industry with a reliable way of attacking problems without having to spend a significant amount of energy in re-implementing algorithms that already available in commercial packages. These Industrial Companies can use this library free of cost and also have the option of contributing the changes back if they wished to do so.

Over the past few years, this Library has seen significant growth in both its adoption and contribution and the developing team is ready to collaborate with others to see it grow even further and to establish the de facto library for image processing in Python.

GitHub: Scikit Image