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45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
'''
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K-means clusterization sample.
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Usage:
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kmeans.py
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Keyboard shortcuts:
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ESC - exit
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space - generate new distribution
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'''
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import numpy as np
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import cv2
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from gaussian_mix import make_gaussians
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if __name__ == '__main__':
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cluster_n = 5
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img_size = 512
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print __doc__
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# generating bright palette
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colors = np.zeros((1, cluster_n, 3), np.uint8)
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colors[0,:] = 255
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colors[0,:,0] = np.arange(0, 180, 180.0/cluster_n)
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colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0]
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while True:
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print 'sampling distributions...'
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points, _ = make_gaussians(cluster_n, img_size)
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term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
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ret, labels, centers = cv2.kmeans(points, cluster_n, term_crit, 10, 0)
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img = np.zeros((img_size, img_size, 3), np.uint8)
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for (x, y), label in zip(np.int32(points), labels.ravel()):
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c = map(int, colors[label])
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cv2.circle(img, (x, y), 1, c, -1)
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cv2.imshow('gaussian mixture', img)
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ch = 0xFF & cv2.waitKey(0)
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if ch == 27:
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break
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cv2.destroyAllWindows()
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