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This article mainly introduces the image processing tools in Python, which have a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, let the editor take you to understand it.

1.scikit-image

Scikit-image is an open source Python package for numpy arrays. It implements algorithms and utilities for research, education, and industry applications. Even for those new to the Python ecosystem, it is a fairly simple and straightforward library.

Resources

Many examples and actual use cases are well documented. User Guide-skimage v0.15.0 docs

Usage

The package is imported as skimage, and most of the functionality is found in sub-modules. Some examples of skimage include:

Image filtering

Template matching using match_template function

You can find more examples in the gallery.

2.Numpy

Numpy is one of the core libraries of Python programming and provides support for arrays. An image is essentially a standard Numpy array containing pixels of data points. Therefore, by using basic NumPy operations, such as slicing, masking, and fancy indexing, we can modify the pixel values of the image. You can use skimage to load images and matplotlib to display images.

Resources

The official documentation page of Numpy provides a complete list of resources and documents. NumPy-NumPy NumPy-NumPy

Usage

Use Numpy to mask the image.

3.Scipy

Scipy is another core scientific module of Python, such as Numpy, which can be used for basic image processing and processing tasks. In particular, the submodule scipy.ndimage provides functions that run on the n-dimensional NumPy array. At present, the software package includes linear and nonlinear filtering, binary morphology, B-spline interpolation and object measurement.

Resources

For a complete list of features provided by the scipy.ndimage package, see the documentation here. NumPy-NumPy uses SciPy for blurring and 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 file formats. However, its development has stagnated, and the last release was in 2009. Fortunately, Pillow is an actively developed branch of PIL that is easier to install; it runs on all major operating systems and supports Python 3. The library contains basic image processing functions, including point manipulation, filtering using a set of built-in convolution kernels, and color space conversion.

Resources

This document contains installation instructions and examples of each module that covers the library.

Usage

Use ImageFilter to enhance the image in Pillow:

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. OpenCV-Python is not only fast because the background consists of code written in C / C + +, but it is also easy to code and deploy (due to the front-end Python wrapper). This makes it an excellent choice for executing computing-intensive computer vision programs.

Resources

The OpenCV2-Python- guide makes it easy to get started with OpenCV-Python.

Usage

The following is an example of OpenCV-Python 's ability to create an image mix of a new fruit called "Orapple" using a pyramid.

6.SimpleCV

SimpleCV is also an open source framework for building computer vision applications. With it, you can access several high-performance computer vision libraries, such as OpenCV, without first understanding bit depth, file format, color space, and so on. The learning curve is much smaller than OpenCV, and as their slogan says, "computer vision becomes easier." Some of the arguments in favor of SimpleCV are:

Even beginners can write simple machine vision tests.

Cameras, video files, images and video streams are all interoperable

Resources

The official documentation is easy to understand and has a large number 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 operations, as well as more modern computer vision functions for feature computing, including point of interest detection and local descriptors. The interface uses Python, which is suitable for rapid development, but the algorithm is implemented in C + +, and the speed is adjusted. The Mahotas library is fast, concise, and has the least dependency. You can read their official article "Link" here.

Resources

This document contains installation instructions, examples, and even tutorials to help you get started with mahotas easily.

Usage

The Mahotas library relies on using simple code to get the job done. Mahotas does a good job of "finding Wally", and the code is simple. Here is the source code. Finding Wally-mahotas 1.4.3+git documentation

8.SimpleITK

ITK or Insight Segmentation and Registration Toolkit is an open source cross-platform system that provides developers with a complete set of software tools for image analysis. Among them, SimpleITK is a simplification layer based on ITK, which aims to promote its use in rapid prototyping, education and interpretation language. SimpleITK is an image analysis toolkit with a large number of components that support general filtering operations, image segmentation, and registration. SimpleITK itself is written in C + +, but it can be used in a number of programming languages, including Python.

Resources

A large number of Jupyter notebooks have been provided to show how to use SimpleITK for educational and research activities. The notebook demonstrates how to use SimpleITK for interactive image analysis using the Python and R programming languages.

Usage

The following animation is a visualization of the strict CT / MR registration process created using SimpleITK and Python. Read the source code here. Finding Wally-mahotas 1.4.3+git documentation

9.pgmagick

Pgmagick is a Python-based wrapper for the GraphicsMagick library. The GraphicsMagick image processing system is sometimes called the Swiss Army knife for image processing. It provides a powerful and efficient collection of tools and libraries that support reading, writing, and manipulating images in more than 88 major formats, including important formats such as DPX,GIF,JPEG,JPEG-2000,PNG,PDF,PNM and TIFF.

Resources

There is a Github repository dedicated to PgMagick with instructions for installation and requirements. There is also a detailed user guide on this topic.

Usage

Few image manipulation activities are performed using pgmagick:

Image zooming:

Edge extraction:

10.Pycairo

Pycairo is a set of python bindings for the graphics library cairo. Cairo is a 2D graphics library for drawing vector graphics. Vector graphics are interesting because they do not lose clarity when resized or transformed. Pycairo is a set of bindings for cairo that can be used to invoke cairo commands from Python.

Resources

The Pycairo GitHub repository is a good resource with detailed instructions on installation and use. There is also a getting started guide with a brief tutorial on Pycairo.

Usage

Pycairo draws lines, basic shapes, and radial gradients

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