void generate_random_clusters( const std::vector<sample_type>& samples)
{
typedef boost::variate_generator<boost::mt19937&, boost::uniform_int<> > random_type;
random_type random_( random_generator_, boost::uniform_int<>(0, samples.size() - 1));
for( int i = 0; i < K_; ++i)
{
while( 1)
{
int indx = random_();
if( labels_[indx] == 0)
{
clusters_.push_back( cluster_t( samples[indx]));
labels_[indx] = &(clusters_.back());
break;
}
}
}
}
void clusterHistograms(const std::vector<Mat> &hists, std::vector<cluster_t> &clusters, const cluster_vars &vars) {
// calculate hist distances
Mat dists = Mat::zeros((int)hists.size(), (int)hists.size(), CV_64FC1);
tbb::parallel_for(0, (int)hists.size()-1, 1,
[&hists, &dists](int i) {
tbb::parallel_for(i, (int)hists.size(), 1,
[&hists, &dists, &i](int j) {
double dist = compareHist(hists.at(i), hists.at(j), COMP_ALG);
dists.at<double>(i, j) = dist;
dists.at<double>(j, i) = dist;
}
);
}
);
// find optimal clusters
std::vector<idx_cluster_t> idxClusters;
find_optimal_clustering(dists, idxClusters, vars);
// calculate the cluster average for each cluster
for(size_t i = 0; i < idxClusters.size(); i++) {
idx_cluster_t cluster = idxClusters.at((int)i);
std::vector<Mat> clusterHists;
Mat clusterAvg;
for(size_t i = 0; i < cluster.size(); i++) {
//for(idx_cluster_t::const_iterator it = cluster.begin(); it != cluster.end(); it++) {
clusterHists.push_back(hists.at(cluster.at(i)));
}
averageHistograms(clusterHists, clusterAvg);
clusters.push_back(cluster_t(clusterAvg, cluster));
}
}