Mastering Machine Learning with scikit-learn - Second Edition

Image quantization

In previous sections, we used clustering to explore the structure of a dataset. Now let's apply it to a different problem. Image quantization is a lossy compression method that replaces a range of similar colors in an image with a single color. Quantization reduces the size of the image file since fewer bits are required to represent the colors. In the following example, we will use clustering to discover a compressed palette for an image that contains its most important colors. We will then rebuild the image using the compressed palette. First we read and flatten the image:

# In[1]:import numpy as npimport matplotlib.pyplot as pltfrom sklearn.cluster import KMeansfrom sklearn.utils import shufflefrom PIL import Image