94 lines
2.3 KiB
Python
94 lines
2.3 KiB
Python
"""
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======================================================================
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A demo of structured Ward hierarchical clustering on an image of coins
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======================================================================
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Compute the segmentation of a 2D image with Ward hierarchical
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clustering. The clustering is spatially constrained in order
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for each segmented region to be in one piece.
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"""
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# Author : Vincent Michel, 2010
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# Alexandre Gramfort, 2011
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# License: BSD 3 clause
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# %%
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# Generate data
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# -------------
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from skimage.data import coins
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orig_coins = coins()
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# %%
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# Resize it to 20% of the original size to speed up the processing
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# Applying a Gaussian filter for smoothing prior to down-scaling
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# reduces aliasing artifacts.
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import numpy as np
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from scipy.ndimage import gaussian_filter
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from skimage.transform import rescale
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smoothened_coins = gaussian_filter(orig_coins, sigma=2)
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rescaled_coins = rescale(
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smoothened_coins,
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0.2,
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mode="reflect",
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anti_aliasing=False,
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)
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X = np.reshape(rescaled_coins, (-1, 1))
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# %%
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# Define structure of the data
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# ----------------------------
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#
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# Pixels are connected to their neighbors.
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from sklearn.feature_extraction.image import grid_to_graph
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connectivity = grid_to_graph(*rescaled_coins.shape)
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# %%
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# Compute clustering
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# ------------------
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import time as time
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from sklearn.cluster import AgglomerativeClustering
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print("Compute structured hierarchical clustering...")
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st = time.time()
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n_clusters = 27 # number of regions
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ward = AgglomerativeClustering(
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n_clusters=n_clusters, linkage="ward", connectivity=connectivity
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)
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ward.fit(X)
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label = np.reshape(ward.labels_, rescaled_coins.shape)
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print(f"Elapsed time: {time.time() - st:.3f}s")
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print(f"Number of pixels: {label.size}")
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print(f"Number of clusters: {np.unique(label).size}")
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# %%
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# Plot the results on an image
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# ----------------------------
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#
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# Agglomerative clustering is able to segment each coin however, we have had to
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# use a ``n_cluster`` larger than the number of coins because the segmentation
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# is finding a large in the background.
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import matplotlib.pyplot as plt
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plt.figure(figsize=(5, 5))
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plt.imshow(rescaled_coins, cmap=plt.cm.gray)
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for l in range(n_clusters):
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plt.contour(
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label == l,
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colors=[
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plt.cm.nipy_spectral(l / float(n_clusters)),
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],
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)
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plt.axis("off")
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plt.show()
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