Top Image Processing Python Libraries

Computer vision is a branch of artificial intelligence (AI) that allows computers and systems to extract useful information from digital photos, videos, and other visual inputs and initiate actions or make recommendations based on that data. Image processing, which is the phenomenon of manipulating or editing, or performing some operations on an image to extract features from it, is required to extract this information. We’ll go over some of the cool image processing libraries in Python in this article.

1. OpenCV

OpenCV is one of the fastest and most widely used libraries for image processing and computer vision applications. It is supported by Github, with over a thousand contributors contributing to the development of the library. Created by Intel in 1999, it supports many languages like C, C++, Java, and the most popular Python. OpenCV offers around 2500 algorithms to help build models for face recognition, object detection, image segmentation, etc.

2. Mahotas

Mahotas is an advanced python library for image processing and computer vision that offers advanced functionalities like thresholding, convolution, morphological processing, and much more. It was written in C++, which makes it fast.


SimpleCV can be considered as a less complicated version of OpenCV. It is a python framework. It does not require many image processing prerequisites and concepts like color spaces, buffer management, eigenvalues, etc. Therefore, it is beginner-friendly.

4. Pillow

Pillow is based on the Python Imaging Library (PIL). This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. It encompasses several image processing activities, including point operations, filtering, manipulating, etc.

5. Scikit-Image

Scikit-Image is an open-source python library for image processing. By transforming the original pictures, it uses NumPy arrays as image objects. As NumPy is built in C programming, it is a very fast & effective library for image processing. It includes algorithms for Filtering, Morphology,

Feature detection, Segmentation, Geometric transformations, Color space manipulation, etc.

6. SimplelTK

SimpleITK is an open-source library that offers multi-dimensional image analysis. Unlike most image processing and computer vision libraries that consider images as arrays, it treats images as a set of points in space. It supports languages like Python, R, Java, C#, Lua, Ruby, TCL, and C++.

7. SciPy

SciPy is mainly used for scientific and mathematical computations, but it can also be used for image processing and computer vision by importing relevant modules of the library. It can offer image processing functions such as Convolution, Face Detection, Feature Extraction, Image Segmentation, etc.

8. Pgmagick

Pgmagick is a GraphicsMagick python binding for image manipulation. It aids in image processing functions such as scaling, rotation, sharpening, gradient images, and so on. It can handle over 88 different image formats.

9. Seaborn

Seaborn is one of the most popular python libraries among data scientists because it helps understand the correlation between various data points. This is because it offers excellent visualizations that make the model understandable and attractive.

10. Matplotlib

Matplotlib is a python library known for creating visualizations, but it can also be used for image processing. It can be used to extract information out of the image. It is not supportive of all file formats.

11. Numpy

Numpy is a widely used library for machine learning models. It can be used in image processing to help manipulate pixels, mask pixel values, and image cropping.


Consultant Intern: Currently in her third year of B.Tech from Indian Institute of Technology(IIT), Goa. She is an ML enthusiast and has a keen interest in Data Science. She is a very good learner and tries to be well versed with the latest developments in Artificial Intelligence.

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