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    <p>There are better segmentation algorithms included in <a href="http://scikits-image.org" rel="nofollow">http://scikits-image.org</a> , but if you want to build your own you can look at this example, based on clustering, called ICM segmentation. Specify N=4 to identify four regions.</p> <pre><code>import numpy as np from scipy.cluster.vq import kmeans2 def ICM(data, N, beta): print "Performing ICM segmentation..." # Initialise segmentation using kmeans print "K-means initialisation..." clusters, labels = kmeans2(np.ravel(data), N) print "Iterative segmentation..." f = data.copy() def _minimise_cluster_distance(data, labels, N, beta): data_flat = np.ravel(data) cluster_means = np.array( [np.mean(data_flat[labels == k]) for k in range(N)] ) variance = np.sum((data_flat - cluster_means[labels])**2) \ / data_flat.size # How many of the 8-connected neighbouring pixels are in the # same cluster? count = np.zeros(data.shape + (N,), dtype=int) count_inside = count[1:-1, 1:-1, :] labels_img = labels.reshape(data.shape) for k in range(N): count_inside[..., k] += (k == labels_img[1:-1:, 2:]) count_inside[..., k] += (k == labels_img[2:, 1:-1]) count_inside[..., k] += (k == labels_img[:-2, 1:-1]) count_inside[..., k] += (k == labels_img[1:-1, :-2]) count_inside[..., k] += (k == labels_img[:-2, :-2]) count_inside[..., k] += (k == labels_img[2:, 2:]) count_inside[..., k] += (k == labels_img[:-2, 2:]) count_inside[..., k] += (k == labels_img[2:, :-2]) count = count.reshape((len(labels), N)) cluster_measure = (data_flat[:, None] - cluster_means)**2 \ - beta * variance * count labels = np.argmin(cluster_measure, axis=1) return cluster_means, labels # Initialise segmentation cluster_means, labels = _minimise_cluster_distance(f, labels, N, 0) stable_counter = 0 old_label_diff = 0 i = 0 while stable_counter &lt; 3: i += 1 cluster_means, labels_ = \ _minimise_cluster_distance(f, labels, N, beta) new_label_diff = np.sum(labels_ != labels) if new_label_diff != old_label_diff: stable_counter = 0 else: stable_counter += 1 old_label_diff = new_label_diff labels = labels_ print "Clustering converged after %d steps." % i return labels.reshape(data.shape) </code></pre>
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    1. POSlow python image processing with PIL and numpy
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    1. USStefan van der Walt
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