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This article introduces the knowledge of "what are the image processing tools commonly used in Python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. 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, we can modify the pixel values of the image by using basic NumPy operations, such as slicing, masking, and fancy indexing. You can use skimage to load images and matplotlib to display images. Generally, Numpy is used to desensitize the image.

II. Scikit-image

Scikit-image is an open source Python package for numpy arrays. It implements algorithms and practical tools for research, education and industrial applications. Even for those new to the Python ecosystem, it is a fairly simple and straightforward library. This code is written by an active volunteer community and is of high quality and peer-reviewed nature.

Scikit-image is imported as skimage, and most of the features are found in sub-modules. It is mainly used in image filtering, using match_template function for template matching.

III. Scipy

Scipy is another core scientific module of Python similar to Numpy, which can be used for basic image manipulation and processing tasks. In particular, the submodule scipy.ndimage provides functions that operate on the n-dimensional NumPy array. The package currently includes functions such as linear and nonlinear filtering, binary morphology, B-spline interpolation and object measurement. Scipy is blurred by Gaussian filter.

IV. PIL

PIL (Python Imaging Library) is a free Python programming language library that adds support for opening, processing, and saving many different image file formats. However, its development has stagnated, and its last update was in 2009. Fortunately, PIL has a branch, Pillow, which is under active development, which is very easy to install. Pillow 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. Use ImageFilter to enhance the image in Pillow.

5. OpenCV

OpenCV (Open Source computer Vision Library) is one of the most widely used libraries in computer vision applications. OpenCV-Python is the python version of API for OpenCV. The advantages of OpenCV-Python are not only efficient, because its internal composition is written in C _ Python +, but it is also easy to write and deploy (because the front end is wrapped in Python). This makes it a good choice for executing computing-intensive computer vision programs.

VI. 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 learning bit depth, file format, color space, and so on. Its learning curve is much smaller than that of OpenCV, as their slogan says, "computer vision becomes easier." Even beginners can write simple machine vision tests, and cameras, video files, images, and video streams are interoperable resources.

7. Mahotas

Mahotas is another Python library for computer vision and image processing. 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 is Python language, which is suitable for rapid development, but the algorithm is implemented in C language and tuned according to speed. The Mahotas library is fast, concise, and even has minimal dependencies. The Mahotas library relies on using simple code to accomplish the task. With regard to the question of 'Finding Wally', Mahotas does a good job and has very little code.

VIII. ITK

ITK or Insight Segmentation and Registration Toolkit is an open source cross-platform system that provides developers with a wide range of image analysis software tools. Among them, SimpleITK is a simplification layer based on ITK, which aims to promote its application in rapid prototyping, education and interpretive language. SimpleITK is an image analysis toolkit that contains a large number of components that support general filtering operations, image segmentation, and matching. SimpleITK itself is written in C++, but is available for most programming languages, including Python. A large number of Jupyter Notebooks shows that SimpleITK has been used in the field of education and research. Notebook demonstrates interactive image analysis using SimpleITK in Python and R programming languages.

IX. 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 88 major formats, including important formats such as DPX, GIF, JPEG, JPEG-2000, PNG, PDF, PNM, and TIFF. There are not many image processing activities that can be carried out by Pgmagick, which are generally used for image scaling or edge extraction.

10. Pycairo

Pycairo is a set of Python bundles of the image processing 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 converted. Pycairo is a set of bindings to cairo that can be used to invoke cairo commands from Python. Pycairo is mainly used to draw lines, basic shapes and radial gradients.

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