#!/usr/bin/env python # Python 2/3 compatibility from __future__ import print_function import sys PY3 = sys.version_info[0] == 3 if PY3: xrange = range import numpy as np import cv2 as cv from numpy import random def make_gaussians(cluster_n, img_size): points = [] ref_distrs = [] for _i in xrange(cluster_n): mean = (0.1 + 0.8*random.rand(2)) * img_size a = (random.rand(2, 2)-0.5)*img_size*0.1 cov = np.dot(a.T, a) + img_size*0.05*np.eye(2) n = 100 + random.randint(900) pts = random.multivariate_normal(mean, cov, n) points.append( pts ) ref_distrs.append( (mean, cov) ) points = np.float32( np.vstack(points) ) return points, ref_distrs def draw_gaussain(img, mean, cov, color): x, y = mean w, u, _vt = cv.SVDecomp(cov) ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi) s1, s2 = np.sqrt(w)*3.0 cv.ellipse(img, (int(x), int(y)), (int(s1), int(s2)), ang, 0, 360, color, 1, cv.LINE_AA) def main(): cluster_n = 5 img_size = 512 print('press any key to update distributions, ESC - exit\n') while True: print('sampling distributions...') points, ref_distrs = make_gaussians(cluster_n, img_size) print('EM (opencv) ...') em = cv.ml.EM_create() em.setClustersNumber(cluster_n) em.setCovarianceMatrixType(cv.ml.EM_COV_MAT_GENERIC) em.trainEM(points) means = em.getMeans() covs = em.getCovs() # Known bug: https://github.com/opencv/opencv/pull/4232 found_distrs = zip(means, covs) print('ready!\n') img = np.zeros((img_size, img_size, 3), np.uint8) for x, y in np.int32(points): cv.circle(img, (x, y), 1, (255, 255, 255), -1) for m, cov in ref_distrs: draw_gaussain(img, m, cov, (0, 255, 0)) for m, cov in found_distrs: draw_gaussain(img, m, cov, (0, 0, 255)) cv.imshow('gaussian mixture', img) ch = cv.waitKey(0) if ch == 27: break print('Done') if __name__ == '__main__': print(__doc__) main() cv.destroyAllWindows()