Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

Note

Go to the end to download the full example code or to run this example in your browser via Binder

# Color Quantization using K-Means

Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace (China), reducing the number of colors required to show the image from 96,615 unique colors to 64, while preserving the overall appearance quality.

In this example, pixels are represented in a 3D-space and K-means is used to find 64 color clusters. In the image processing literature, the codebook obtained from K-means (the cluster centers) is called the color palette. Using a single byte, up to 256 colors can be addressed, whereas an RGB encoding requires 3 bytes per pixel. The GIF file format, for example, uses such a palette.

For comparison, a quantized image using a random codebook (colors picked up randomly) is also shown.

    Fitting model on a small sub-sample of the data
    done in 0.012s.
    Predicting color indices on the full image (k-means)
    done in 0.011s.
    Predicting color indices on the full image (random)
    done in 0.015s.

    # Authors: Robert Layton <robertlayton@gmail.com>
    #          Olivier Grisel <olivier.grisel@ensta.org>
    #          Mathieu Blondel <mathieu@mblondel.org>
    #
    # License: BSD 3 clause

    from time import time

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.cluster import KMeans
    from sklearn.datasets import load_sample_image
    from sklearn.metrics import pairwise_distances_argmin
    from sklearn.utils import shuffle

    n_colors = 64

    # Load the Summer Palace photo
    china = load_sample_image("china.jpg")

    # Convert to floats instead of the default 8 bits integer coding. Dividing by
    # 255 is important so that plt.imshow behaves works well on float data (need to
    # be in the range [0-1])
    china = np.array(china, dtype=np.float64) / 255

    # Load Image and transform to a 2D numpy array.
    w, h, d = original_shape = tuple(china.shape)
    assert d == 3
    image_array = np.reshape(china, (w * h, d))

    print("Fitting model on a small sub-sample of the data")
    t0 = time()
    image_array_sample = shuffle(image_array, random_state=0, n_samples=1_000)
    kmeans = KMeans(n_clusters=n_colors, n_init="auto", random_state=0).fit(
        image_array_sample
    )
    print(f"done in {time() - t0:0.3f}s.")

    # Get labels for all points
    print("Predicting color indices on the full image (k-means)")
    t0 = time()
    labels = kmeans.predict(image_array)
    print(f"done in {time() - t0:0.3f}s.")

    codebook_random = shuffle(image_array, random_state=0, n_samples=n_colors)
    print("Predicting color indices on the full image (random)")
    t0 = time()
    labels_random = pairwise_distances_argmin(codebook_random, image_array, axis=0)
    print(f"done in {time() - t0:0.3f}s.")

    def recreate_image(codebook, labels, w, h):
        """Recreate the (compressed) image from the code book & labels"""
        return codebook[labels].reshape(w, h, -1)

    # Display all results, alongside original image
    plt.figure(1)
    plt.clf()
    plt.axis("off")
    plt.title("Original image (96,615 colors)")
    plt.imshow(china)

    plt.figure(2)
    plt.clf()
    plt.axis("off")
    plt.title(f"Quantized image ({n_colors} colors, K-Means)")
    plt.imshow(recreate_image(kmeans.cluster_centers_, labels, w, h))

    plt.figure(3)
    plt.clf()
    plt.axis("off")
    plt.title(f"Quantized image ({n_colors} colors, Random)")
    plt.imshow(recreate_image(codebook_random, labels_random, w, h))
    plt.show()

Total running time of the script: (0 minutes 0.306 seconds)

Gallery generated by Sphinx-Gallery