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Open Source Computer Vision Library
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55 lines
1.2 KiB
55 lines
1.2 KiB
#!/usr/bin/env python |
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''' |
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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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# Python 2/3 compatibility |
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from __future__ import print_function |
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import numpy as np |
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import cv2 as cv |
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from gaussian_mix import make_gaussians |
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def main(): |
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cluster_n = 5 |
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img_size = 512 |
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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 = cv.cvtColor(colors, cv.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 = (cv.TERM_CRITERIA_EPS, 30, 0.1) |
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ret, labels, centers = cv.kmeans(points, cluster_n, None, 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 = list(map(int, colors[label])) |
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cv.circle(img, (x, y), 1, c, -1) |
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cv.imshow('kmeans', img) |
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ch = cv.waitKey(0) |
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if ch == 27: |
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break |
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print('Done') |
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if __name__ == '__main__': |
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print(__doc__) |
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main() |
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cv.destroyAllWindows()
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