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OpenCV is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Adopted all around the world, OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 6 million.

dscho's picture


ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. Developed through extreme programming methodologies, ITK employs leading-edge algorithms for registering and segmenting multidimensional data. It is widely used and contributed in the medical imaging field.


dscho's picture


This library gives the numpy-based infrastructure functions for image processing with a focus on bioimage informatics. It provides image filtering and morphological processing as well as feature computation (both image-level features such as Haralick texture features and SURF local features). These can be used with other Python-based libraries for machine learning to build a complete analysis pipeline.

Mahotas is appropriate for users comfortable with programming or builders of end-user tools.

dscho's picture


Pink is an generic image processing library with inclination towards morphology, discrete topology, skeletonization and image segmentation. Operators support 2D, 3D and 4D images where there exist algorithms for such extensions. There is no practical limitation about the image site. It is suitable for general image processing or as a preprocessing / postprocessing toolset for specific image processing problems.
There are several successfull applications of PINK in the fields of Biology, Material Sciences, Education and Image Processing Research.

ukoethe's picture


VIGRA is a free C++ and Python library that provides fundamental image processing and analysis algorithms. Its generic architecture allows it to be used in many different application contexts and ecosystems. It is designed as an intelligent library (using the C++ template mechanism) which allows users to write code at a fairly high level of abstraction and optimizes away the abstraction overhead upon compilation. It can therefore work efficiently on very large data and forms the basis of ilastik and CellCognition.

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