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