These Python libraries provide a simple and intuitive way to transform images and understand the underlying data.
Today’s world is full of data, and images are an important part of that data. However, before they can be used, these digital images must be processed – analyzed and manipulated to improve their quality or extract some information that can be used.
Common image processing tasks include display; Basic operations such as cutting, turning, rotation, etc. ; Image segmentation, classification and feature extraction; Image restoration; Image recognition. Python is an excellent choice for these image processing tasks because of its growing popularity as a scientific programming language and the free availability of many of the most advanced image processing tools in its ecosystem.
This article looks at the 10 most commonly used Python libraries for image processing tasks. These libraries provide a simple and intuitive way to transform images and understand the underlying data.
1. scikit-image
Scikit-image is an open source Python package used with NumPy arrays. It implements algorithms and utilities for research, education, and industry applications. It’s a fairly straightforward library, even for those unfamiliar with the Python ecosystem. The code is high-quality, peer-reviewed and written by an active community of volunteers.
resources
Scikit-image is well documented with many examples and practical uses.
usage
The package is imported via Skimage, and most of the functionality can be found in submodules.
Image filtering:
Use the match_template function for template matching:
You can find more examples in the gallery.
2. NumPy
NumPy is one of the core libraries in Python programming and provides support for arrays. The image is essentially a standard NumPy array containing data point pixels. Therefore, you can modify the pixel values of an image by using basic NumPy operations such as Slicing, masking, and Fancy indexing. Images can be loaded using Skimage and displayed using Matplotlib.
resources
NumPy’s official documentation page provides a complete list of resources and documentation.
usage
Use Numpy to mask images:
3. SciPy
SciPy is another core Python science module (such as NumPy) that can be used for basic image manipulation and processing tasks. In particular, the submodule scipy.ndimage (in Scipy V1.1.0) provides functions to run on n-dimensional NumPy arrays. The package currently includes linear and nonlinear filtering, binary morphology, B-spline interpolation, and object measurement.
resources
See the documentation for a complete list of features provided by the scipy.ndimage package.
usage
SciPy is used to blur by gaussian filter
4. PIL/Pillow
PIL (Python Imaging Library) is a free Python programming language Library that adds support for opening, manipulating and saving many different image formats. However, its growth stagnated and its last version was released in 2009. Fortunately, Pillow is an actively developed PIL branch that is easier to install, runs on all major operating systems, and supports Python 3. The library contains basic image processing capabilities, including point manipulation, filtering using a set of built-in convolution kernels, and color space conversion.
resources
The documentation contains installation instructions and examples covering each module of the library.
usage
Enhance the image in the Pillow using ImageFilter:
5. OpenCV-Python
OpenCV (Open Source Computer Vision Library) is one of the most widely used libraries in Computer Vision applications. OpenCV-Python is the Python API of OpenCV. Because the backend consists of code written in C/C ++, Opencv-Python is fast and fast, but it is also easy to code and deploy (thanks to the Python wrapper on the front end). This makes it an excellent choice for performing computationally intensive computer vision programs.
resources
Opencv-python is easy to get started with with opencv2-python-Guide
usage
Create an “Orapple” using the Image Blending theory in Opencv-Python using Pyramids:
6. SimpleCV
SimpleCV is another open source framework for building computer vision applications. It provides access to several high-performance computer vision libraries, such as OpenCV, without understanding bit depth, file format, color space, and so on. It has a much smaller learning curve than OpenCV, and (as its tagline says), “it makes computer vision simple.” Some arguments in support of SimpleCV are:
Even beginners can write simple machine vision tests
Cameras, video files, images and video streams are interoperable
resources
It’s easy to follow the instructions in the official documentation, and there are plenty of examples and use cases to follow.
usage
7. Mahotas
Mahotas is another computer vision and image processing library for Python. It includes traditional image processing functions, such as filtering and morphological manipulation, as well as more modern computer vision functions for feature computation, including point of interest detection and local descriptors. Written in Python, the interface is suitable for rapid development, but the algorithm is implemented in C ++ and optimized for speed. The Mahotas library is fast, has minimal code, and has few dependencies. Read its official article to learn more.
resources
The documentation includes installation instructions, examples, and even some tutorials to help you easily get started with Mahotas.
usage
The Mahotas library relies on simple code to get the job done. For example, the Finding Wally problem is best solved with minimal code.
Solving the Finding Wally problem:
8. SimpleITK
Insight Segmentation and Registration Toolkit (ITK) is an “open source, cross-platform system that provides developers with a wide range of software tools for image analysis. SimpleITK is a simplification layer built on ITK to facilitate its use in rapid prototyping, trading, and interpretation languages.” It is also an image analysis kit with a large number of components that support general filtering operations, image segmentation and registration. SimpleITK is written in C ++, but it operates in a number of programming languages, including Python.
resources
There are numerous Jupyter Notebooks that illustrate the use of SimpleITK in educational and research activities. Notebooks uses Python and R programming languages to demonstrate interactive image analysis using SimpleITK.
usage
Create a visual strict CT/MR registration process using SimpleITK and Python:
9. pgmagick
Pgmagick is a wrapper around the GraphicsMagick library based on Python. GraphicsMagick Image Processing system, sometimes referred to as the Swiss Army Knife of image processing. Its powerful and efficient collection of tools and libraries supports reading, writing and manipulating images in over 88 major formats including DPX, GIF, JPEG, JPEG-2000, PNG, PDF, PNM and TIFF.
resources
Pgmagick’s GitHub Respository has installation instructions and requirements. There is also a detailed user guide.
usage
Image scaling:
Edge extraction:
10. Pycairo
Pycairo is a set of Python bindings for the Cairo graphics library. Cairo is a 2D graphics library for drawing vector graphics. Vector shapes are interesting because they do not lose sharpness when resized or transformed. Pycairo calls Cairo commands from Python.
resources
Pycairo GitHub Respository is a good resource with detailed instructions on installation and use. There is also a how-to guide with a short tutorial on Pycairo.
usage
Draw lines, basic shapes, and radial gradients with Pycairo:
conclusion
These are some useful and freely available image processing libraries in Python. Some are well known and some may be new to you. Try them out to learn more about them!
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