In the 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. This example requires the mahotas image processing library, which can be installed using pip install mahotas:
>>> import numpy as np >>> import matplotlib.pyplot as plt >>> from sklearn.cluster import KMeans >>> from sklearn.utils import shuffle >>> import mahotas as mh
First we read and flatten the image:
>>> original_img = np.array(mh.imread('img/tree.jpg'), dtype=np.float64) / 255
>>> original_dimensions = tuple(original_img.shape)
>>> width, height, depth = tuple(original_img.shape)
>>> image_flattened = np.reshape(original_img, (width * height, depth))We then use K-Means to create 64 clusters from a sample of 1,000 randomly selected colors. Each of the clusters will be a color in the compressed palette. The code is as follows:
>>> image_array_sample = shuffle(image_flattened, random_state=0)[:1000] >>> estimator = KMeans(n_clusters=64, random_state=0) >>> estimator.fit(image_array_sample)
Next, we predict the cluster assignment for each of the pixels in the original image:
>>> cluster_assignments = estimator.predict(image_flattened)
Finally, we create the compressed image from the compressed palette and cluster assignments:
>>> compressed_palette = estimator.cluster_centers_
>>> compressed_img = np.zeros((width, height, compressed_palette.shape[1]))
>>> label_idx = 0
>>> for i in range(width):
>>> for j in range(height):
>>> compressed_img[i][j] = compressed_palette[cluster_assignments[label_idx]]
>>> label_idx += 1
>>> plt.subplot(122)
>>> plt.title('Original Image')
>>> plt.imshow(original_img)
>>> plt.axis('off')
>>> plt.subplot(121)
>>> plt.title('Compressed Image')
>>> plt.imshow(compressed_img)
>>> plt.axis('off')
>>> plt.show()The original and compressed versions of the image are show in the following figure:
