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Not in stock What does this mean? No, Python is not the first language many people think of when picturing large data analysis projects.
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On the other hand, Python is reasonably easy to learn, and it has strong and widespread support within the scientific and academic communities, where a lot of data must get crunched at a reasonable clip, if not at blinding speed. His book makes a good case for using Python in at least some Big Data situations. If you do scientific , academic, or business computing and need to crunch and visualize a lot of data, definitely check out Python for Data Analysis. You may be pleasantly surprised at how well and how easily Python and its data-analysis libraries can do the job.
Why learn computer vision? Indeed, cameras and computer vision now are being used in everything from facial recognition systems and video games to automobile safety, industrial automation, medicine, planetary exploration, and even agriculture. Practical Computer Vision with SimpleCV shows how to use the framework and simple application examples to get started toward building your own computer vision applications.
The page book has 10 chapters:. The book also has three appendices: Advanced Shell Tips, Cameras and Lenses; and Advanced Features deals with advanced segmentation and feature extraction tools. Think Python Allen B.
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It literally does throw you head-first into Python programming. By page 10, it has you working with nested lists. And, as the book continues, you keep improving and expanding the functionality of one project that stays in development from chapter to chapter. Downey is my new co -favorite book for learning Python. I intend to keep it handy right alongside Head First Python. It is an excellent reference book, as well as a clear, concise and calm how-to guide for beginning programmers. Think Python takes a gentle yet effective approach to introducing and exploring the language one step at a time.
First you learn some basic programming concepts. The page book has 19 chapters that carefully explain and illustrate each key point, without overkill. And he has gotten plenty of proofreading help from more than enthusiastic followers of his writings and teaching.
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What I like most about Think Python are its short, concise, clear explanations of each new concept and its use of very short code examples. Head First Python , on the other hand, suits me best when I have an hour or two to stay focused on reading, keying in a lot more code and making the required changes to the ongoing project. One minor caution: There are differences — sometimes significant and sometimes merely irritating — between Python 3 and Python 2.
But the opportunity to print or print something does come up a lot in the text.
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Skip to content Home Python programming. Blog at WordPress. Post to Cancel. By continuing to use this website, you agree to their use. PIL Python Imaging Library is a free library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats.
However, its development has stagnated, with its last release in Fortunately, there is Pillow , an actively developed fork of PIL, that is easier to install, runs on all major operating systems, and supports Python 3. The library contains basic image processing functionality, including point operations, filtering with a set of built-in convolution kernels, and color-space conversions.
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The documentation has instructions for installation as well as examples covering every module of the library. Image source code. This makes it a great choice to perform computationally intensive computer vision programs. SimpleCV is another open source framework for building computer vision applications. It offers access to several high-powered computer vision libraries such as OpenCV, but without having to know about bit depths, file formats, color spaces, etc. Its learning curve is substantially smaller than OpenCV's, and as its tagline says , "it's computer vision made easy.
The official documentation is very easy to follow and has tons of examples and use cases to follow.
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Mahotas is another computer vision and image processing library for Python. It contains traditional image processing functions such as filtering and morphological operations, as well as more modern computer vision functions for feature computation, including interest point detection and local descriptors. Mahotas' library is fast with minimalistic code and even minimum dependencies. Read its official paper for more insights. The documentation contains installation instructions, examples, and even some tutorials to help you get started using Mahotas easily.
The Mahotas library relies on simple code to get things done. For example, it does a good job with the Finding Wally problem with a minimum amount of code. ITK Insight Segmentation and Registration Toolkit is an "open source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. There are a large number of Jupyter Notebooks illustrating the use of SimpleITK for educational and research activities. The notebooks demonstrate using SimpleITK for interactive image analysis using the Python and R programming languages.
The GraphicsMagick image processing system is sometimes called the Swiss Army Knife of image processing.
There is also a detailed user guide. Pycairo is a set of Python bindings for the Cairo graphics library. Cairo is a 2D graphics library for drawing vector graphics. Vector graphics are interesting because they don't lose clarity when resized or transformed.
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