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Python Data Visualization Cookbook

You're reading from   Python Data Visualization Cookbook As a developer with knowledge of Python you are already in a great position to start using data visualization. This superb cookbook shows you how in plain language and practical recipes, culminating with 3D animations.

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Product type Paperback
Published in Nov 2013
Publisher Packt
ISBN-13 9781782163367
Length 280 pages
Edition 1st Edition
Languages
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Author (1):
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Igor Milovanovic Igor Milovanovic
Author Profile Icon Igor Milovanovic
Igor Milovanovic
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Table of Contents (15) Chapters Close

Python Data Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using Right Plots to Understand Data 8. More on matplotlib Gems Index

Processing images with PIL


Why use Python for image processing, if we could use WIMP (http://en.wikipedia.org/wiki/WIMP_(computing)) or WYSIWYG (http://en.wikipedia.org/wiki/WYSIWYG) to achieve the same goal? This is used because we want to create an automated system to process images in real time without human support, thus, optimizing the image pipeline.

Getting ready

Note that the PIL coordinate system assumes that the (0,0) coordinate is in the upper-left corner.

The Image module has a useful class and instance methods to perform basic operations over a loaded image object (im):

  • im = Image.open(filename): This opens a file and loads the image into im object.

  • im.crop(box): This crops the image inside the coordinates defined by box. box defines left, upper, right, lower pixels coordinates (for example: box = (0, 100, 100,100)).

  • im.filter(filter): This applies a filter on the image and returns a filtered image.

  • im.histogram(): This returns a histogram list for this image, where each item represents...

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