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Learning IPython for Interactive Computing and Data Visualization, Second Edition

You're reading from   Learning IPython for Interactive Computing and Data Visualization, Second Edition Get started with Python for data analysis and numerical computing in the Jupyter notebook

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783986989
Length 200 pages
Edition 1st Edition
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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

Image processing

Several libraries bring image processing capabilities to Python. SciPy, the main scientific Python library, contains a few image processing routines. scikit-image is another library dedicated to image processing. We will show an example in this section, inspired by the one at http://scikit-image.org/docs/dev/auto_examples/plot_equalize.html.

When using the Anaconda distribution, scikit-image can be installed with conda install scikit-image.

Let's import some packages.

In [1]: import numpy as np
        import skimage
        from skimage import img_as_float
        import skimage.filters as skif
        from skimage.color import rgb2gray
        import skimage.data as skid
        import skimage.exposure as skie
        from ipywidgets import interact
        import matplotlib.pyplot as plt
        import seaborn
        %matplotlib inline

There are a few test images in scikit-image. Here is one:

In [2]: chelsea = skid.chelsea()
In [3]: chelsea.shape, chelsea.dtype
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