A demo of structured Ward hierarchical clustering on an image of coins

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.

Out:

/home/circleci/project/examples/cluster/plot_coin_ward_segmentation.py:19: DeprecationWarning: Please use `gaussian_filter` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.
  from scipy.ndimage.filters import gaussian_filter
/home/circleci/project/examples/cluster/plot_coin_ward_segmentation.py:45: FutureWarning: `multichannel` is a deprecated argument name for `rescale`. It will be removed in version 1.0. Please use `channel_axis` instead.
  rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect", **rescale_params)
Compute structured hierarchical clustering...
Elapsed time:  0.1521284580230713
Number of pixels:  4697
Number of clusters:  27

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

import time as time

import numpy as np
from scipy.ndimage.filters import gaussian_filter

import matplotlib.pyplot as plt

import skimage
from skimage.data import coins
from skimage.transform import rescale

from sklearn.feature_extraction.image import grid_to_graph
from sklearn.cluster import AgglomerativeClustering
from sklearn.utils.fixes import parse_version

# these were introduced in skimage-0.14
if parse_version(skimage.__version__) >= parse_version("0.14"):
    rescale_params = {"anti_aliasing": False, "multichannel": False}
else:
    rescale_params = {}

# #############################################################################
# Generate data
orig_coins = coins()

# Resize it to 20% of the original size to speed up the processing
# Applying a Gaussian filter for smoothing prior to down-scaling
# reduces aliasing artifacts.
smoothened_coins = gaussian_filter(orig_coins, sigma=2)
rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect", **rescale_params)

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

# #############################################################################
# Define the structure A of the data. Pixels connected to their neighbors.
connectivity = grid_to_graph(*rescaled_coins.shape)

# #############################################################################
# Compute clustering
print("Compute structured hierarchical clustering...")
st = time.time()
n_clusters = 27  # number of regions
ward = AgglomerativeClustering(
    n_clusters=n_clusters, linkage="ward", connectivity=connectivity
)
ward.fit(X)
label = np.reshape(ward.labels_, rescaled_coins.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(rescaled_coins, cmap=plt.cm.gray)
for l in range(n_clusters):
    plt.contour(
        label == l,
        colors=[
            plt.cm.nipy_spectral(l / float(n_clusters)),
        ],
    )
plt.xticks(())
plt.yticks(())
plt.show()