Building Machine Learning Systems with Python - Richert, Coelho

Chapter 10
However, some specialized equipment (mostly in scientific fields) can take images
with more bit resolution. 12 or 16 bits are common. Mahotas can deal with all these
types, including floating point images (not all operations make sense with
floating point numbers, but when they do, mahotas supports them). In many
computations, even if the original data is composed of unsigned integers, it is
advantageous to convert to floating point numbers in order to simplify handling
of rounding and overflow issues.
Mahotas can use a variety of different input/output backends.
Unfortunately, none of them can load all existing image formats
(there are hundreds, with several variations of each). However,
loading PNG and JPEG images is supported by all of them. We
will focus on these common formats and refer you to the mahotas
documentation on how to read uncommon formats.
The return value of mh.imread is a NumPy array. This means that you can use
standard NumPy functionalities to work with images. For example, it is often useful
to subtract the mean value of the image from it. This can help to normalize images
taken under different lighting conditions and can be accomplished with the standard
mean method:
image = image – image.mean()
We can display the image on screen using maplotlib, the plotting library we have
already used several times:
from matplotlib import pyplot as plt
plt.imshow(image)
plt.show()
This shows the image using the convention that the first dimension is the height
and the second the width. It correctly handles color images as well. When using
Python for numerical computation, we benefit from the whole ecosystem working
well together.
Basic image processing
We will start with a small dataset that was collected especially for this book. It has
three classes: buildings, natural scenes (landscapes), and pictures of texts. There are
30 images in each category, and they were all taken using a cell phone camera with
minimal composition, so the images are similar to those that would be uploaded
to a modern website. This dataset is available from the book's website. Later in the
chapter, we will look at a harder dataset with more images and more categories.
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