Vector Quantization Example

Face, a 1024 x 768 size image of a raccoon face, is used here to illustrate how k-means is used for vector quantization.

print(__doc__)
 
 
# Code source: Ga Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
 
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
 
from sklearn import cluster
from sklearn.utils.testing import SkipTest
from sklearn.utils.fixes import sp_version
 
if sp_version < (0, 12):
    raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
                   "thus does not include the scipy.misc.face() image.")
 
try:
    face = sp.face(gray=True)
except AttributeError:
    # Newer versions of scipy have face in misc
    from scipy import misc
    face = misc.face(gray=True)
 
n_clusters = 5
np.random.seed(0)
 
X = face.reshape((-1, 1))  # We need an (n_sample, n_feature) array
k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
k_means.fit(X)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
 
# create an array from labels and values
face_compressed = np.choose(labels, values)
face_compressed.shape = face.shape
 
vmin = face.min()
vmax = face.max()
 
# original face
plt.figure(1, figsize=(3, 2.2))
plt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256)
 
# compressed face
plt.figure(2, figsize=(3, 2.2))
plt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
 
# equal bins face
regular_values = np.linspace(0, 256, n_clusters + 1)
regular_labels = np.searchsorted(regular_values, face) - 1
regular_values = .5 * (regular_values[1:] + regular_values[:-1])  # mean
regular_face = np.choose(regular_labels.ravel(), regular_values, mode="clip")
regular_face.shape = face.shape
plt.figure(3, figsize=(3, 2.2))
plt.imshow(regular_face, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
 
# histogram
plt.figure(4, figsize=(3, 2.2))
plt.clf()
plt.axes([.01, .01, .98, .98])
plt.hist(X, bins=256, color='.5', edgecolor='.5')
plt.yticks(())
plt.xticks(regular_values)
values = np.sort(values)
for center_1, center_2 in zip(values[:-1], values[1:]):
    plt.axvline(.5 * (center_1 + center_2), color='b')
 
for center_1, center_2 in zip(regular_values[:-1], regular_values[1:]):
    plt.axvline(.5 * (center_1 + center_2), color='b', linestyle='--')
 
plt.show()

Total running time of the script: (0 minutes 3.968 seconds)

Download Python source code: plot_face_compress.py

Download IPython notebook: plot_face_compress.ipynb

Links:

http://scikit-learn.org/stable/auto_examples/cluster/plot_face_compress.html

doc_scikit_learn

2025-01-10 15:47:30

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