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Open Source Computer Vision Library
https://opencv.org/
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77 lines
2.1 KiB
77 lines
2.1 KiB
#!/usr/bin/env python |
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# Python 2/3 compatibility |
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from __future__ import print_function |
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import sys |
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PY3 = sys.version_info[0] == 3 |
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if PY3: |
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xrange = range |
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import numpy as np |
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import cv2 as cv |
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from numpy import random |
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def make_gaussians(cluster_n, img_size): |
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points = [] |
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ref_distrs = [] |
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for _i in xrange(cluster_n): |
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mean = (0.1 + 0.8*random.rand(2)) * img_size |
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a = (random.rand(2, 2)-0.5)*img_size*0.1 |
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cov = np.dot(a.T, a) + img_size*0.05*np.eye(2) |
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n = 100 + random.randint(900) |
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pts = random.multivariate_normal(mean, cov, n) |
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points.append( pts ) |
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ref_distrs.append( (mean, cov) ) |
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points = np.float32( np.vstack(points) ) |
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return points, ref_distrs |
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def draw_gaussain(img, mean, cov, color): |
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x, y = np.int32(mean) |
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w, u, _vt = cv.SVDecomp(cov) |
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ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi) |
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s1, s2 = np.sqrt(w)*3.0 |
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cv.ellipse(img, (int(x), int(y)), (int(s1), int(s2)), ang, 0, 360, color, 1, cv.LINE_AA) |
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def main(): |
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cluster_n = 5 |
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img_size = 512 |
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print('press any key to update distributions, ESC - exit\n') |
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while True: |
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print('sampling distributions...') |
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points, ref_distrs = make_gaussians(cluster_n, img_size) |
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print('EM (opencv) ...') |
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em = cv.ml.EM_create() |
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em.setClustersNumber(cluster_n) |
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em.setCovarianceMatrixType(cv.ml.EM_COV_MAT_GENERIC) |
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em.trainEM(points) |
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means = em.getMeans() |
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covs = em.getCovs() # Known bug: https://github.com/opencv/opencv/pull/4232 |
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found_distrs = zip(means, covs) |
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print('ready!\n') |
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img = np.zeros((img_size, img_size, 3), np.uint8) |
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for x, y in np.int32(points): |
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cv.circle(img, (x, y), 1, (255, 255, 255), -1) |
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for m, cov in ref_distrs: |
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draw_gaussain(img, m, cov, (0, 255, 0)) |
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for m, cov in found_distrs: |
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draw_gaussain(img, m, cov, (0, 0, 255)) |
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cv.imshow('gaussian mixture', 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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