kazu
9947dced15
added new helper methods/classes (e.g. for heading) new test cases optimize the dijkstra cleanups/refactoring added timed-benchmarks to the log many more...
60 lines
1.5 KiB
C++
60 lines
1.5 KiB
C++
#ifndef KNNARRAY_H
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#define KNNARRAY_H
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/**
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* this wrapper class provides all methods needed for nanoflanns KNN-search.
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* in order for this wrapper class to work, your data-structure must provide
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* the following methods:
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*
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* PointList:
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* size() - return the number of contained points
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* operator [] - access points via their index
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* Point
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* operator [] - access each dimension via its index
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*
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* example:
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* std::vector<Point3> points;
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* KNNArray<std::vector<Point3>> arr(points);
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* KNN<KNNArray<std::vector<Point3>>, 3> knn(arr);
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*/
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template <typename T> class KNNArray {
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private:
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/** the underlying data structure */
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const T& elem;
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public:
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/** ctor with the underlying data structure */
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KNNArray(const T& elem) : elem(elem) {
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;
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}
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/** get the number of elements to search throrugh */
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inline int kdtree_get_point_count() const {
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return elem.size();
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}
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/** use nanoflanns default bbox */
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template <class BBOX> inline bool kdtree_get_bbox(BBOX& bb) const {
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(void) bb; return false;
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}
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/** get the idx-th element's dim-th coordinate */
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inline float kdtree_get_pt(const size_t idx, const int dim) const {
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return elem[idx][dim];
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}
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/** get the SQUARED distance between the given coordinates and the provided element */
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inline float kdtree_distance(const float* p1, const size_t idx_p2, size_t) const {
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const float d0 = p1[0] - elem[idx_p2][0];
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const float d1 = p1[1] - elem[idx_p2][1];
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const float d2 = p1[2] - elem[idx_p2][2];
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return (d0*d0) + (d1*d1) + (d2*d2);
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}
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};
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#endif // KNNARRAY_H
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