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File Content: `plot_face_ward_segmentation.py`

"""
=========================================================================
A demo of structured Ward hierarchical clustering on a raccoon face image
=========================================================================

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

print(__doc__)

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
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)

# 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',
                               connectivity=connectivity)
ward.fit(X)
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)

###############################################################################
# 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)), ])
plt.xticks(())
plt.yticks(())
plt.show()

Name	Size	Permission
README.txt	101 bytes	0644
plot_adjusted_for_chance_measures.py	4300 bytes	0644
plot_affinity_propagation.py	2304 bytes	0644
plot_agglomerative_clustering.py	2931 bytes	0644
plot_agglomerative_clustering_metrics.py	4492 bytes	0644
plot_birch_vs_minibatchkmeans.py	3694 bytes	0644
plot_cluster_comparison.py	4681 bytes	0644
plot_cluster_iris.py	2593 bytes	0644
plot_color_quantization.py	3444 bytes	0644
plot_dbscan.py	2479 bytes	0644
plot_dict_face_patches.py	2747 bytes	0644
plot_digits_agglomeration.py	1694 bytes	0644
plot_digits_linkage.py	2959 bytes	0644
plot_face_compress.py	2479 bytes	0644
plot_face_segmentation.py	2839 bytes	0644
plot_face_ward_segmentation.py	2460 bytes	0644
plot_feature_agglomeration_vs_univariate_selection.py	3903 bytes	0644
plot_kmeans_assumptions.py	2040 bytes	0644
plot_kmeans_digits.py	4524 bytes	0644
plot_kmeans_silhouette_analysis.py	5888 bytes	0644
plot_kmeans_stability_low_dim_dense.py	4324 bytes	0644
plot_mean_shift.py	1793 bytes	0644
plot_mini_batch_kmeans.py	4092 bytes	0644
plot_segmentation_toy.py	3522 bytes	0644
plot_ward_structured_vs_unstructured.py	3369 bytes	0644

File Content: plot_face_ward_segmentation.py

File Content: `plot_face_ward_segmentation.py`