many changes :P

misc/KNN.h

@@ -0,0 +1,76 @@
+#ifndef KNN_H
+#define KNN_H
+
+#include "../lib/nanoflann/nanoflann.hpp"
+
+/**
+ * helper class to extract k-nearest-neighbors
+ * from a given input data structure.
+ * uses nanoflann
+ *
+ * usage:
+ *	KNN<float, Grid<20, T>, T, 3> knn(theGrid);
+ *	float search[] = {0,0,0};
+ *	std::vector<T> elems = knn.get(search, 3);
+ */
+template <typename Scalar, typename DataStructure, typename Element, int dim> class KNN {
+
+private:
+
+	/** type-definition for the nanoflann KD-Tree used for searching */
+	typedef nanoflann::KDTreeSingleIndexAdaptor<nanoflann::L2_Simple_Adaptor<Scalar, DataStructure>, DataStructure, dim> Tree;
+
+	/** the maximum depth of the tree */
+	static constexpr int maxLeafs = 10;
+
+	/** the constructed tree used for searching */
+	Tree tree;
+
+	/** the underlying data-structure we want to search within */
+	DataStructure& data;
+
+public:
+
+	/** ctor */
+	KNN(DataStructure& data) : tree(dim, data, nanoflann::KDTreeSingleIndexAdaptorParams(maxLeafs)), data(data) {
+		tree.buildIndex();
+	}
+
+	/** get the k-nearest-neighbors for the given input point */
+	std::vector<Element> get(const Scalar* point, const int numNeighbors, const float maxDistSquared = 99999) const {
+
+		// buffer for to-be-fetched neighbors
+		size_t indices[numNeighbors];
+		float distances[numNeighbors];
+
+		// find k-nearest-neighbors
+		tree.knnSearch(point, numNeighbors, indices, distances);
+
+		// construct output
+		std::vector<Element> elements;
+		for (int i = 0; i < numNeighbors; ++i) {
+			if (distances[i] > maxDistSquared) {continue;}		// too far away?
+			elements.push_back(data[indices[i]]);
+		}
+		return elements;
+
+	}
+
+	/** get the nearest neighbor and its distance */
+	void getNearest(const Scalar* point, size_t& idx, float& distSquared) {
+
+		// find 1-nearest-neighbors
+		tree.knnSearch(point, 1, &idx, &distSquared);
+
+	}
+
+	void get(const Scalar* point, const int numNeighbors, size_t* indices, float* squaredDist) {
+
+		// find k-nearest-neighbors
+		tree.knnSearch(point, numNeighbors, indices, squaredDist);
+
+	}
+
+};
+
+#endif // KNN_H