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#ifndef SLA_CLUSTERING_HPP
#define SLA_CLUSTERING_HPP
#include <vector>
#include <libslic3r/Point.hpp>
#include <libslic3r/SLA/SpatIndex.hpp>
namespace Slic3r { namespace sla {
using ClusterEl = std::vector<unsigned>;
using ClusteredPoints = std::vector<ClusterEl>;
// Clustering a set of points by the given distance.
ClusteredPoints cluster(const std::vector<unsigned>& indices,
std::function<Vec3d(unsigned)> pointfn,
double dist,
unsigned max_points);
ClusteredPoints cluster(const Eigen::MatrixXd& points,
double dist,
unsigned max_points);
ClusteredPoints cluster(
const std::vector<unsigned>& indices,
std::function<Vec3d(unsigned)> pointfn,
std::function<bool(const PointIndexEl&, const PointIndexEl&)> predicate,
unsigned max_points);
// This function returns the position of the centroid in the input 'clust'
// vector of point indices.
template<class DistFn, class PointFn>
long cluster_centroid(const ClusterEl &clust, PointFn pointfn, DistFn df)
{
switch(clust.size()) {
case 0: /* empty cluster */ return -1;
case 1: /* only one element */ return 0;
case 2: /* if two elements, there is no center */ return 0;
default: ;
}
// The function works by calculating for each point the average distance
// from all the other points in the cluster. We create a selector bitmask of
// the same size as the cluster. The bitmask will have two true bits and
// false bits for the rest of items and we will loop through all the
// permutations of the bitmask (combinations of two points). Get the
// distance for the two points and add the distance to the averages.
// The point with the smallest average than wins.
// The complexity should be O(n^2) but we will mostly apply this function
// for small clusters only (cca 3 elements)
std::vector<bool> sel(clust.size(), false); // create full zero bitmask
std::fill(sel.end() - 2, sel.end(), true); // insert the two ones
std::vector<double> avgs(clust.size(), 0.0); // store the average distances
do {
std::array<size_t, 2> idx;
for(size_t i = 0, j = 0; i < clust.size(); i++)
if(sel[i]) idx[j++] = i;
double d = df(pointfn(clust[idx[0]]),
pointfn(clust[idx[1]]));
// add the distance to the sums for both associated points
for(auto i : idx) avgs[i] += d;
// now continue with the next permutation of the bitmask with two 1s
} while(std::next_permutation(sel.begin(), sel.end()));
// Divide by point size in the cluster to get the average (may be redundant)
for(auto& a : avgs) a /= clust.size();
// get the lowest average distance and return the index
auto minit = std::min_element(avgs.begin(), avgs.end());
return long(minit - avgs.begin());
}
}} // namespace Slic3r::sla
#endif // CLUSTERING_HPP
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