Image Processing Libraries

Top Python


OpenCV is arguably the best image processing library in the world due to its wide range of use cases in computer vision. Written in C++ and C programming, OpenCV delivers the necessary speed for real-time computer vision. Originally developed by Intel and later supported by Willow Garage and Itseez, the library has been helping machine learning practitioners since 2000.


Scikit-image is another widely used Python library for almost every image processing workflow. It is a collection of numerous algorithms for tasks like feature detection, color space manipulation, segmentation, transformations, and more. Created in 2009, scikit-image has gained traction from the developer community to simplify image processing workflows.


Built on top of Python Image Library (PIL), Pillow is among the top three libraries for image processing. Especially used in batch processing, Pillow is commonly used within organizations. Another advantage of Pillow is that it supports a wide range of file format support, making it a one-stop-shop for all your image processing needs.


Mahotas is an open-source library for computer vision in Python, which handles all the image data types. Similar to scikit-image, Mohotas also represents images as NumPy array structures. With Mohotas image processing library, you can expect speed as it is implemented in C++. With Mohotas, you can use over 100 functions for image processing and computer vision.


Although popular for scientific computation, SciPy is also used as image processing with scipy.ndimage submodule. Similar to scikit-image, SciPy works in tandem with NumPy to process images effortlessly. Due to the speed it offers, you can build several moderate-level workflows like feature extraction, face detection, image sharpening, denoising, geometrical transformations and more.


Matplotlib, along with visualization, can be used for manipulating images. The library uses Pillow library to load images data and can handle float32 and uint8, but is limited to uint8 for PNG files. While working with Matplotlib, you can use plt.imshow() to display the NumPy array representation of images. Matplotlib allows you to apply pseudocolor, display color scale reference, perform interpolation, and more.


Also commonly known as ITK–Insight Segmentation and Registration Toolkit–is a widely used image processing library. ITK is a powerful library to use but is very large and complex. Built to handle advanced projects, the library keeps evolving with the help of contributors on GitHub, which has 756 stars and 441 forks on the platform.


SimpleCV is a very easy to use computer vision and image processing library, but it is not used for intensive projects. If you are new, you can leverage SimpleCV for computer vision tasks but will have to eventually move towards OpenCV. Although it has 2.4k stars and 769 forks on GitHub, there is no further development in the open-source project.


Only officially supported on macOS and Linux, Pgmagic is another image processing library that is most common among enthusiasts. However, a Windows user can rely on unofficial binary packages to play with images. Pgmagick is a very simple library that works with over 88 image formats for processing basic manipulations like resizing, sharpening, blur filtering, rotation, and more.



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