Here is a list of image processing libraries for Python along with small code examples wherever possible.

This is a part of our Python Knowledge & Resources List

  1. PIL

    PIL (Python Imaging Library) supports opening, manipulating and saving the images in many file formats. It supports various image manipulations like filtering, enhancing, masking, handling transparency, additions and the like.

  2. Pillow
    Pillow is a “friendly fork” to the PIL. Development seems to have stalled on PILform 2011, so it is been adopted as a replacement for PIL in several linux distributions. Pillow and PIL cannot co-exist in the same environment. Before installing Pillow, please uninstall PIL.
  3. Mahotas

    Mahotas library provides fast computer vision algorithms like watershed, thinning, thresholding etc implemented in c++. The algorithms can be operated over numpy arrays. Mahotas currently has over 100 functions for image processing and computer vision and it keeps growing. The release schedule is roughly one release a month and each release brings new functionality and improved performance. The interface is very stable, though, and code written using a version of mahotas from years back will work just fine in the current version, except it will be faster (some interfaces are deprecated and will be removed after a few years, but in the meanwhile, you only get a warning). In a few unfortunate cases, there was a bug in the old code and your results will change for the better.

  4. scikit-image
    scikit-image library includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection in images. Its mostly written in python except for the parts written in Cython for the sake of performance. It can be interoperated with SciPy and NumPy. It 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.
  5. scipy
    scipy.ndimage packages provide a various image processing functions that can be operated with arrays of any dimensionality. The packages currently include functions for linear and non-linear filtering, binary morphology, B-spline interpolation, and object measurements.Since Scipy contains parts written in C, C++, and Fortran that need to be compiled before use, make sure you have the necessary compilers and Python development headers installed.
  6. PythonMagick
    PythonMagick is the Python binding of the ImageMagick which is a free software. It supports cropping, changing colors, applying various effects, adding text and geometrical figures etc. It supports reading, modifying and creating images in over 200 file formats.It is a boost.python (requires Python 2.2) based wrapper around GraphicsMagick( It wraps the C++ API of GraphicsMagick, which is much more pythonic that the original C API.
  7. pycairo
    pycairo is a set of python bindings for the 2D graphics library cairo.They provide an object oriented interface to cairo.The Cairo library can output data to consistently to X Window system, win32 image buffers, pdf, svg files etc. It is free software. py2cairo is available to be redistributed and/or modified under the terms of either the GNU Lesser General Public License (LGPL) version 2.1 or the Mozilla Public License (MPL) version 1.1.
  8. OpenCV-Python
    OpenCV-Python is a Python wrapper for the OpenCV C++ implementation. OpenCV-Python makes use of Numpy. All the OpenCV array structures are converted to and from Numpy arrays. This also makes it easier to integrate with other libraries that use Numpy such as SciPy and Matplotlib. OpenCV was started at Intel in 1999 by Gary Bradsky, and the first release came out in 2000. Vadim Pisarevsky joined Gary Bradsky to manage Intel’s Russian software OpenCV team. In 2005, OpenCV was used on Stanley, the vehicle that won the 2005 DARPA Grand Challenge. Later, its active development continued under the support of Willow Garage with Gary Bradsky and Vadim Pisarevsky leading the project. OpenCV now supports a multitude of algorithms related to Computer Vision and Machine Learning and is expanding day by day. OpenCV supports a wide variety of programming languages such as C++, Python, Java, etc., and is available on different platforms including Windows, Linux, OS X, Android, and iOS. Interfaces for high-speed GPU operations based on CUDA and OpenCL are also under active development.
  9. SimpleITK
    Insight Segmentation and Registration Toolkit (ITK) provides software tools for image analysis. ITK employs leading-edge algorithms for registering and segmenting multidimensional data. SimpleITK provides a simplified interface to ITK in languages like python. ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. Among them, SimpleITK is a simplified layer built on top of ITK, intended to facilitate its use in rapid prototyping, education, interpreted languages. SimpleITK has the following main characteristics: C++ library, Object-oriented, Provides a simplified, easy-to-use, procedural interface without templates. Is distributed under an open source Apache 2.0 License. Binary distributions for Python and Java
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