A demo of structured Ward hierarchical clustering on a raccoon face imageΒΆ

Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spatially constrained in order for each segmented region to be in one piece.

# Author : Vincent Michel, 2010
#          Alexandre Gramfort, 2011
# License: BSD 3 clause


import time as time

import numpy as np
import scipy as sp

import matplotlib.pyplot as plt

from sklearn.feature_extraction.image import grid_to_graph
from sklearn.cluster import AgglomerativeClustering
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.")

Generate data

    face = sp.face(gray=True)
except AttributeError:
    # Newer versions of scipy have face in misc
    from scipy import misc
    face = misc.face(gray=True)

# Resize it to 10% of the original size to speed up the processing
face = sp.misc.imresize(face, 0.10) / 255.

X = np.reshape(face, (-1, 1))

Define the structure A of the data. Pixels connected to their neighbors.

connectivity = grid_to_graph(*face.shape)

Compute clustering

print("Compute structured hierarchical clustering...")
st = time.time()
n_clusters = 15  # number of regions
ward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward',
label = np.reshape(ward.labels_, face.shape)
print("Elapsed time: ", time.time() - st)
print("Number of pixels: ", label.size)
print("Number of clusters: ", np.unique(label).size)


Compute structured hierarchical clustering...
Elapsed time:  0.675858974457
Number of pixels:  7752
Number of clusters:  15

Plot the results on an image

plt.figure(figsize=(5, 5))
plt.imshow(face, cmap=plt.cm.gray)
for l in range(n_clusters):
    plt.contour(label == l, contours=1,
                colors=[plt.cm.spectral(l / float(n_clusters)), ])

Total running time of the script: ( 0 minutes 1.138 seconds)

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