79 lines
2.1 KiB
Python
79 lines
2.1 KiB
Python
# -*- coding: utf-8 -*-
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"""
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=========================================================
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Vector Quantization Example
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=========================================================
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Face, a 1024 x 768 size image of a raccoon face,
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is used here to illustrate how `k`-means is
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used for vector quantization.
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"""
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import numpy as np
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import scipy as sp
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import matplotlib.pyplot as plt
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from sklearn import cluster
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try: # SciPy >= 0.16 have face in misc
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from scipy.misc import face
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face = face(gray=True)
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except ImportError:
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face = sp.face(gray=True)
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n_clusters = 5
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np.random.seed(0)
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X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array
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k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
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k_means.fit(X)
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values = k_means.cluster_centers_.squeeze()
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labels = k_means.labels_
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# create an array from labels and values
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face_compressed = np.choose(labels, values)
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face_compressed.shape = face.shape
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vmin = face.min()
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vmax = face.max()
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# original face
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plt.figure(1, figsize=(3, 2.2))
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plt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256)
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# compressed face
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plt.figure(2, figsize=(3, 2.2))
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plt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
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# equal bins face
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regular_values = np.linspace(0, 256, n_clusters + 1)
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regular_labels = np.searchsorted(regular_values, face) - 1
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regular_values = 0.5 * (regular_values[1:] + regular_values[:-1]) # mean
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regular_face = np.choose(regular_labels.ravel(), regular_values, mode="clip")
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regular_face.shape = face.shape
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plt.figure(3, figsize=(3, 2.2))
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plt.imshow(regular_face, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
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# histogram
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plt.figure(4, figsize=(3, 2.2))
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plt.clf()
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plt.axes([0.01, 0.01, 0.98, 0.98])
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plt.hist(X, bins=256, color=".5", edgecolor=".5")
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plt.yticks(())
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plt.xticks(regular_values)
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values = np.sort(values)
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for center_1, center_2 in zip(values[:-1], values[1:]):
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plt.axvline(0.5 * (center_1 + center_2), color="b")
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for center_1, center_2 in zip(regular_values[:-1], regular_values[1:]):
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plt.axvline(0.5 * (center_1 + center_2), color="b", linestyle="--")
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plt.show()
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