Working on improved interconnection strategy

1 files changed, 89 insertions, 0 deletions

diff --git a/src/libslic3r/SLA/SLASupportTreeIGL.cpp b/src/libslic3r/SLA/SLASupportTreeIGL.cpp
index da2bb1b79..bd2d16215 100644
--- a/src/libslic3r/SLA/SLASupportTreeIGL.cpp
+++ b/src/libslic3r/SLA/SLASupportTreeIGL.cpp
@@ -347,6 +347,95 @@ PointSet normals(const PointSet& points,
     return ret;
 }
 
+//template<class Vec> double distance(const Vec& p) {
+//    return std::sqrt(p.transpose() * p);
+//}
+
+//template<class Vec> double distance(const Vec& pp1, const Vec& pp2) {
+//    auto p = pp2 - pp1;
+//    return distance(p);
+//}
+
+// Clustering a set of points by the given criteria
+ClusteredPoints cluster_nearest2d(
+        const sla::PointSet& points, const std::vector<unsigned>& indices,
+        double dist,
+        unsigned max_points = 0)
+{
+
+    namespace bgi = boost::geometry::index;
+    using Index3D = bgi::rtree< SpatElement, bgi::rstar<16, 4> /* ? */ >;
+
+    // A spatial index for querying the nearest points
+    Index3D sindex;
+
+    // Build the index
+    for(unsigned idx : indices) {
+        Vec3d p = points.row(idx); p(Z) = 0;
+        sindex.insert( std::make_pair(points.row(idx), idx));
+    }
+
+    using Elems = std::vector<SpatElement>;
+
+    // Recursive function for visiting all the points in a given distance to
+    // each other
+    std::function<void(Elems&, Elems&)> group =
+    [&sindex, &group, max_points, dist](Elems& pts, Elems& cluster)
+    {
+        for(auto& p : pts) {
+            std::vector<SpatElement> tmp;
+
+            sindex.query(
+                bgi::nearest(p.first, max_points),
+                std::back_inserter(tmp)
+            );
+
+            for(auto it = tmp.begin(); it < tmp.end(); ++it)
+                if(distance(p.first, it->first) > dist) it = tmp.erase(it);
+
+            auto cmp = [](const SpatElement& e1, const SpatElement& e2){
+                return e1.second < e2.second;
+            };
+
+            std::sort(tmp.begin(), tmp.end(), cmp);
+
+            Elems newpts;
+            std::set_difference(tmp.begin(), tmp.end(),
+                                cluster.begin(), cluster.end(),
+                                std::back_inserter(newpts), cmp);
+
+            int c = max_points && newpts.size() + cluster.size() > max_points?
+                        int(max_points - cluster.size()) : int(newpts.size());
+
+            cluster.insert(cluster.end(), newpts.begin(), newpts.begin() + c);
+            std::sort(cluster.begin(), cluster.end(), cmp);
+
+            if(!newpts.empty() && (!max_points || cluster.size() < max_points))
+                group(newpts, cluster);
+        }
+    };
+
+    std::vector<Elems> clusters;
+    for(auto it = sindex.begin(); it != sindex.end();) {
+        Elems cluster = {};
+        Elems pts = {*it};
+        group(pts, cluster);
+
+        for(auto& c : cluster) sindex.remove(c);
+        it = sindex.begin();
+
+        clusters.emplace_back(cluster);
+    }
+
+    ClusteredPoints result;
+    for(auto& cluster : clusters) {
+        result.emplace_back();
+        for(auto c : cluster) result.back().emplace_back(c.second);
+    }
+
+    return result;
+}
+
 // Clustering a set of points by the given criteria
 ClusteredPoints cluster(
         const sla::PointSet& points, const std::vector<unsigned>& indices,