Top 7 Image Processing Libraries of Python that will Dominate in 2021

Ever thought which Image Processing Libraries of Python will Dominate in 2021?

Images play a significant role in providing crucial information. For business, images and videos act as an important source of data.

This is the reason, it is essential for businesses to translate and process images effectively & obtain valuable insights.

To feed images as an input to various deep learning and machine learning models, it is vital to pre-process the images.

So, here’s presenting the top 7 image processing libraries of Python that will make your career shine like stars.

Python Image Processing Libraries that will trend in 2021

1. Matplotlib

Primarily, Matplotlib is used for the purpose of 2D visualizations, but it can also be used for image processing.

While, Matplotlib is not supportive of all the file formats, but is the most effective in altering images for extracting information out of it.

2. SciPy

The primary use of SciPy includes mathematics and scientific computations, but it can also be used to perform basic image manipulation and processing tasks.

To execute this, you need to implement algorithms by importing the scipy.ndimage module.

You can carry out linear and non-linear filtering, object measurements, and binary morphology.

Along with this, one can adjust interpolation, effects, filter, draw contour lines, and other similar extraction & segmentation on images.

3. OpenCV

OpenCV is one of the most widely used libraries focused on image processing, face detection, object detection, and many more.

It is written in C++ and easy to use and read. It is supported by more than one thousand contributors on GitHub, working towards enhancing the library for effortless image processing.

OpenCV consists of more than 2500 algorithms and is used to build computer vision and machine learning applications.

Such algorithms are used to perform various tasks such as identification of objects, face recognition & detection, etc.

4. Scikit- Image

Scikit-Image is one of the most simple and straightforward libraries and can be used even by those having less or zero knowledge of the Python ecosystem.

By transforming the original pictures, it uses NumPy arrays as image objects.

Such ndarrays can either be integers or floats. As NumPy is built in C programming, it is a very fast & effective library for image processing.

It implements algorithms and utilities that are for use in education, research and industry-based applications. 

5. SimpleITK

While other libraries that treat images as arrays, SimpleITK considers images as sets of points on a physical region in space.

The region occupied by images is defined as origin, spacing size, and cosine matrix.

SimpleITK allows users to effectively process images and supports a wide range of dimensions including 2D, 3D, and 4D.

6. Pillow

Various functionalities, which are generally not provided by other libraries, such as opening, filtering, manipulating, and saving images are supported by Pillow.

It is an open-source library and is an advanced version of PIL, which is supported by Tidelift.

Wide range of image formats are also supported by Pillow, thus making it the most efficient and must-have library for handling images.

7. Mahotas

Mahotas is a computer vision and image processing library that has numerous functionalities & capabilities to carry out processes like morphological processing, watershed, thresholding, convolution & much more.

It executes the task of image processing faster and consists of various algorithms built using C++.

Haralick, local binary patterns are some of its advanced features which a developer can use and perform advanced image processing by extracting information from pictures.

Summary

So we have discussed some of the top Python image processing libraries that will be in trend in 2021.

Image processing libraries are significantly used by companies all over the world due to the advantages that it offers.

Before using any of the libraries, it is important that you identify your needs and then choose the best-fit image processing library.