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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes

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Product type Paperback
Published in Sep 2014
Publisher
ISBN-13 9781783284818
Length 512 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Toc

Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Notebook 4. Profiling and Optimization 5. High-performance Computing 6. Advanced Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

Computing connected components in an image

In this recipe, we will show an application of graph theory in image processing. We will compute connected components in an image. This method will allow us to label contiguous regions of an image, similar to the bucket fill tool of paint programs.

Finding connected components is also useful in many puzzle video games such as Minesweeper, bubble shooters, and others. In these games, contiguous sets of items with the same color need to be automatically detected.

How to do it…

  1. Let's import the packages:
    In [1]: import itertools
            import numpy as np
            import networkx as nx
            import matplotlib.colors as col
            import matplotlib.pyplot as plt
            %matplotlib inline
  2. We create a 10 x 10 image where each pixel can take one of three possible labels (or colors):
    In [2]: n = 10
    In [3]: img = np.random.randint(size=(n, n), 
                                    low=0, high=3)
  3. Now, we create the underlying 2D grid graph encoding the...
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