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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

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 the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly Index

Processing images with PIL


Why use Python for image processing if we can use WIMP (http://en.wikipedia.org/wiki/WIMP_(computing)) or WYSIWYG (http://en.wikipedia.org/wiki/WYSIWYG) to achieve the same goal? Python 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 method for performing basic operations over a loaded image object (im):

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

  • im.crop(box): This crops the image inside the coordinates defined by box. box defines the left, upper, right, and lower pixel 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...

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