8 Best Python Libraries for Image Processing
Published on May 23,2020 by Maulik
Image processing has become ever more sought after field in today’s day and age, where the images and videos occupy most of our storage spaces in our smartphones and computers. As image capturing has been made as easy as it has, the images generated these days are, well, plentiful, and the graph is going up and up as the time proceeds.
As they say, “A picture speaks a thousand words,” indeed, it’s true. But for us programmers, it is a little different. Those “words” are often not apparent by just the sight of it. Sometimes, We might need to manipulate, perform image recognition, or extract features from the images to get the meaning out of it. That’s where Image processing comes in. Image processing is a technique to manipulate the pixels, which forms a fundamental component of an image. We can do image classification, image restoration, image recognition, and necessary manipulations like rotating, flipping, cropping, etc. to name a few using image processing libraries.
When it comes to Python, It has got several excellent libraries for such tasks as Python has developed more and more as a scientific programming language. Its libraries have got closer to state-of-art techniques, right out of the academic papers for image processing. Here is a list of the most popular Python libraries used in Image processing.
Numpy is a trendy library for working with a sizeable multi-dimensional array. Numpy is built over a C programming language, and it uses sophisticated algorithms to perform complex mathematical calculations. NumPy, at its core, has a multi-dimensional array object, which is called ndarray. Images are necessarily a multi-dimensional array and represent a ndarray object.
To manipulate the image pixels, we can change the pixel values by applying various NumPy functions for color reduction, gamma correction, negative inversion, etc. Moreover, other popular libraries for image processing from this list also use NumPy to represent the image in an array. So we can say it is the necessary foundation for image processing. NumPy is suitable for beginners, straightforward to use, and has excellent community support.
OpenCV is one of the most popular libraries when it comes to Image processing, especially computer vision. It is an open-source library developed with the focus mainly on real-time image processing. It boasts of over 2500 optimized algorithms, state-of-art as well as traditional ones. Today, from the big giants like Google, Microsoft, IBM to small startups, all are using OpenCV for their computer vision applications.
It is written in C++ and utilizes NumPy to store an array. It is a compelling library capable of identifying objects, detecting and recognizing a human face, extracting 3D model objects, and stitching together high-resolution images. Although OpenCV is a more robust library, it often has a steep learning curve. You need to spend some time reading the documentation and just how the library is structured to get yourself familiar with it.
SimpleCV is a framework for building computer vision applications for Python. As its name aptly suggests, Its goal is to simplify computer vision for you. It uses powerful libraries such as OpenCV and other such libraries at its core and makes it simple to get you started. It is far easier to learn SimpleCV as compared to OpenCV. So if you are beginning, you should check out this library because it has got powerful image manipulation techniques like hue effects, motion blur filters, image segmentation, and lots of other functionalities.
Because SimpleCV is built on top of OpenCV and to provide simplicity, It is not as powerful as OpenCV. However, I would suggest that to begin with SimpleCV and as you get familiar with it and require an advanced image processing tool, shift to OpenCV. This way, the steep learning curve for OpenCV will be considerably flattened.
Pillow is a simple, secure, and fast basic image manipulation library. Unlike some other libraries on this list, this is not built for its use in computer vision applications. It performs basic image processing functionalities like rotating, resizing, cropping, color transform, etc. It is perfect if your application needs to retrieve multiple images from some URLs and to perform batch processing.
Pillow is mostly useful for Image archival applications. It supports a wide variety of image formats for reading the images and the most common image formats for writing images. If your application needs scientific computation, computer vision, or any advanced image processing functionality, you should go with OpenCV or other similar libraries.
Scikit-image(formerly known as scikits.image) uses scipy.ndimage for image processing to provide a set of state-of-art image processing algorithms with also some popular classic algorithms. It uses the underlying scipy and NumPy library for its array storage and mathematical calculations. It is written mostly in Python and some core algorithms in Cython to achieve performance.
It includes algorithms for image segmentation, color space manipulation, filtering, geometric transformation feature detection, and more. It has several state-of-art algorithms that require you to know about computer vision to use those algorithms. If you know about computer vision, you would like it. So again, I would start with something like SimpleCV and OpenCV for computer vision and later move to scikit-image if need be.
Mahotas provides you with advanced computer vision functions like local binary patterns, Haralick, but at the same time, it doesn’t confuse you with a lot of details. It is written in C/C++ to give faster computation, utilizes NumPy array just like OpenCV but provides you with a clean and easy-to-read Python interface.
It has tons of image processing utility functions that can be found in OpenCV or SimpleCV, but sometimes, Mahotas provides a comprehensive interface than its counterparts. Another feature of Mahotas is it has minimal dependencies. It only requires a Numpy array for executing the mahotas code.
Like NumPy on this list, Scipy is not precisely meant for image processing as it is primarily used for mathematical computations. It is built over Numpy array as a fundamental array structure to contain images. It provides various functions like binary morphology, B-spline interpolation, linear and non-linear filtering, distance functions used for finding the correlation between the two images.
Scipy, just like scikit-image, uses scipy.ndimage for multi-dimensional image processing over NumPy array and also uses matplotlib, a very mature and popular image plotting library. Scipy is a powerful library for image arithmetic operations where you perform mathematical operations on an array. Scipy is widely used and one of the most popular libraries on this list.
SimpleITK is a cross-language programming interface to open source ITK(Insight and Segmentation and Registration Toolkit) but with limitations for simplification as its name aptly suggests. SimpleITK is written in C++, but it comes with interfaces for multiple languages like Python.
It is an image analysis toolkit utilizing state-of-art algorithms for registering and segmentation of multi-dimensional data. SimpleITK supports 2D, 3D, and 4D dimensions. It expresses an image as a set of points rather than an array. Hence the image segments are defined as origin, spacing, and direction cosine matrix. It is useful the most for the medical image processing community. But it has got a very steep learning curve, more than OpenCV. That is why people outside of medical applications prefer OpenCV.
Python being a rich language it has plenty of libraries for image processing for different applications ranging from essential reading and saving files to complicated feature extraction and computer vision applications. All the libraries listed above are good at what they are meant to do. There is no one particular library best for all applications, so its for you to decide which library you would choose for your application.
If your application is storing, retrieving, creating thumbnails and resizing images, or some other necessary manipulations, then Pillow is a better choice. There are many libraries listed here that would be of use if you are using some machine learning algorithm or computer vision on an image. See for yourself which suits best as per your preference and requirements. This list does not contain all of the image processing libraries but includes the major popular image processing libraries. I hope you enjoyed it. Thank you for reading this article.