This notebook contains an excerpt from the book Machine Learning for OpenCV by Michael Beyeler. The code is released under the MIT license, and is available on GitHub.

Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations. If you find this content useful, please consider supporting the work by buying the book!

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Visualizing Data from an External Dataset

As a final test for this chapter, let's visualize some data from an external dataset, such as the digits dataset from scikit-learn.

We will need three tools in specific:

scikit-learn for the actual data
NumPy for data munging
Matplotlib for visualization.

So let's start by importing all of these:



In [1]:

    
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
% matplotlib inline

The first step is to actually load the data:



In [2]:

    
digits = datasets.load_digits()

If we remember correctly, digits is supposed to have two different fields: a data field containing the actual image data, and a target field containing the image labels. Rather than trust our memory, we should simply investigate the digits object. We do this by typing out its name, adding a period, and then hitting the TAB key: digits.<TAB>. This will reveal that the digits object also contains some other fields, such as one called images. The two fields, images and data, seem to simply differ by shape:



In [3]:

    
print(digits.data.shape)
print(digits.images.shape)









    



(1797, 64)
(1797, 8, 8)

In both cases, the first dimension corresponds to the number of images in the dataset. However, data has all the pixels lined up in one big vector, whereas images preserves the 8 x 8 spatial arrangement of each image.

Thus, if we wanted to plot a single image, the images field would be more appropriate. First, we grab a single image from the dataset using NumPy's array slicing:



In [4]:

    
img = digits.images[0, :, :]

Here, we are saying that we want to grab the first row in the 1,797 items-long array and all the corresponding $8 \times 8=64$ pixels. We can then plot the image using plt's imshow function:



In [5]:

    
plt.imshow(img, cmap='gray')
plt.savefig('figures/02.04-digit0.png')

In addition, I also specified a color map with the cmap argument. By default, Matplotlib uses MATLAB's default colormap jet. However, in the case of grayscale images, the gray colormap makes more sense.

Finally, we can plot a whole number of digit samples using plt's subplot function. The subplot function is the same as in MATLAB, where we specify the number of rows, number of columns, and current subplot index (starts counting at 1). We will use for loop to iterate over the first ten images in the dataset and every image gets assigned its own subplot:



In [6]:

    
plt.figure(figsize=(14, 4))

for image_index in range(10):
    # images are 0-indexed, subplots are 1-indexed
    subplot_index = image_index + 1
    plt.subplot(2, 5, subplot_index)
    plt.imshow(digits.images[image_index, :, :], cmap='gray')

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