10 Best Image Processing Libraries in Python (2022)

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Data is the most valuable resource businesses have in today’s digital age, and much of that data is images. Data scientists can process these images and feed them into machine learning (ML) models to gain deep insights for a business.

Image processing is the process of transforming images into digital forms before performing special operations on them, which yields valuable information.

There are a few main types of image processing:

  • Visualization: Objects not visible in the image are detected
  • Recognition: Detect objects present in the image
  • Sharpening and restoration: Original images are enhanced
  • Pattern recognition: Image patterns are measured
  • Recovery: Find images similar to the original by searching a large database

Once a business decides to use image processing, there are many potential applications. For example, image processing is often used in medical research and to develop precise treatment plans. It can also be used to recover and reconstruct corrupted parts of an image, or to perform face detection.

In order to quickly and efficiently process this large amount of data, data scientists must rely on image processing tools for machine learning and deep learning tasks. Most major image processing libraries are used in Python.

Let’s take a look at the top 10 image processing libraries in Python:

1. OpenCV

At the top of our list is OpenCV, which is an open source library developed and released by Intel in 2000. OpenCV is often deployed for computer vision tasks such as face detection, object detection, face recognition face, image segmentation, and much more.

Written in C++, OpenCV also comes with a Python wrapper and can be used with NumPy, SciPy, and Matplotlib. One of the best things about OpenCV is that the computer vision library is constantly evolving thanks to its many contributors on Github.

The image processing library provides access to over 2,500 state-of-the-art and classic algorithms. Users can use OpenCV to perform several specific tasks such as red eye removal and eye movement tracking.

Here are some of the main highlights of OpenCV:

  • Used by big companies like IBM, Google and Toyota
  • Algorithmic efficiency
  • Extensive access to algorithms
  • Multiple Interfaces

2. Scikit-Image

Another top image processing library in the market is Scikit-Image, which is used for almost all computer vision tasks. Scikit-Image is partly written in Cython, which is a programming language that is a superset of Python. This unique structure allows it to achieve good performance.

Scikit-Image, which uses NumPy arrays as image objects, offers many different algorithms for segmentation, color space manipulation, geometric transformation, parsing, morphology, feature detection, and much more .

Here are some of the main highlights of Scikit-Image:

  • Open source and easy to use
  • Free with minimal legal and licensing restrictions
  • Versatile
  • Real-world applications such as consumer behavior prediction

3. SciPy

Originally designed for mathematical and scientific computations, SciPy is also a premier library for performing multidimensional image processing by importing the scipy.ndimage submodule. SciPy provides functions for operating on n-dimensional Numpy arrays.

This image processing library is another great option if you are looking for a wide range of applications such as image segmentation, convolution, image reading, face detection, feature extraction, etc. .

Here are some of the main highlights of Scipy:

  • High-level commands and classes for visualizing and manipulating data
  • open-source
  • Interactive sessions with Python
  • Courses, web routines and database for parallel programming

4. Mahotas

Mahotas is another prominent image processing library in Python, which was originally designed for bio-image computing. Mahotas allows developers to take advantage of advanced features such as local and haralick binary models. It can compute 2D and 3D images through its mahotas.features.haralick module, and it extracts information from images to perform advanced image processing.

Mahotas has many popular functions such as watersheds, convex point calculations, morphological processing and pattern matching. There are over 100 features for computer vision capabilities.

Here are some of the main highlights of Mahotas:

  • More than 100 features for computer vision
  • Advanced features
  • Calculates 2D and 3D images
  • Constant addition of new features

5. Pillow/PIL

Another open source library for image processing tasks, Pillow is an advanced version of PIL (Python Imaging Library). With Pillow, you can perform many image processing processes such as point operations, filtering, and manipulation.

Pillow is one of the best libraries for managing images thanks to its support for a wide range of image formats. The image processing library is easy to use, making it one of the most common tools for data scientists working with images.

Here are some of the main highlights of Pillow:

  • Support for various image formats such as JPEG and PNG
  • Easy to use
  • Different image processing methods
  • Useful for augmenting training data for computer vision problems

6. SimpleITK

SimpleITK works a bit differently than the other image processing libraries on this list. Instead of viewing images as arrays, SimpleITK views them as a collection of points on a physical region of space. In other words, it defines the region occupied by the images as the origin, size, spacing and direction cosine matrix. This allows SimpleITK to process images efficiently and support 2D, 3D, and 4D dimensions.

SimpleITK is often used for image segmentation and image registration, which involves overlaying two or more images.

Here are some of the main highlights of SimpleITK:

  • Support for 2D and 3D images
  • Advanced programming features providing performance, flexibility and efficiency
  • Image segmentation and image registration
  • Considers images as a collection of points over a physical region in space

7. matplotlib

Matplotlib is another great option for an image processing library. It is particularly useful as an image module for working with images in Python, and it includes two specific methods for reading and displaying images. Matplotlib specializes in 2D array plots as a cross-platform data visualization library on Numpy arrays.

The image processing library is typically used for 2D visualizations such as scatterplots, histograms, and bar charts, but has proven useful for image processing by efficiently extracting information from ‘a picture. It is important to note that Matplotlib does not support all file formats.

Here are some of the main highlights of Matplotlib:

  • Simple and easy to use
  • Provides high quality images and plots in various formats
  • open-source
  • Highly customizable

8. Numpy

Although NumPy is an open-source Python library used for numerical analysis, it can also be used for image processing tasks such as cropping images, manipulating pixels, masking pixel values, etc. . NumPy contains matrix and multidimensional arrays as data structures.

NumPy can also be used to help with color reduction, binarization, slicing, positive or negative inversion, and many other features. Images can also be thought of as arrays, which allows NumPy to perform different image processing tasks.

Here are some of the main highlights of NumPy:

  • Compact data storage
  • High-speed array processing
  • Help with many features
  • Data compatibility with other libraries

9. Pgmagick

Towards the end of our list is Pgmagick, which is another top Python image processing library for the GraphicMagick library. The image processing tool has an impressive collection of tools and libraries that provide assistance in editing and manipulating images.

Here are some of the main strengths of Pgmagick:

  • Large collection of tools and libraries
  • Image editing and image manipulation
  • Supports many image formats
  • open-source

ten. SimpleCV

The last Python image processing library on our list is SimpleCV, which is a popular open-source framework for building computer vision applications with image processing. SimpleCV has a readable interface for cameras, format conversion, image manipulation, feature extraction, etc.

The image processing library is popular among those looking to easily create computer vision tasks. It allows users to access very powerful computer vision libraries like OpenCV without needing to learn about file formats, bit depths, color spaces, buffer management, etc.

Here are some of the main highlights of SimpleCV:

  • open-source
  • Readable interface
  • Easily create computer vision tasks
  • Access to high-powered computer vision libraries
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