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2ae2672ee9
Fixing dep build script on Windows and removing some warnings. Use bundled igl by default. Not building with the dependency scripts if not explicitly stated. This way, it will stay in Fix the libigl patch to include C source files in header only mode.
106 lines
3.8 KiB
C++
106 lines
3.8 KiB
C++
#include "knn.h"
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#include "parallel_for.h"
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#include <cmath>
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#include <queue>
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namespace igl {
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template <typename DerivedP, typename KType, typename IndexType,
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typename DerivedCH, typename DerivedCN, typename DerivedW,
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typename DerivedI>
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IGL_INLINE void knn(const Eigen::MatrixBase<DerivedP>& P,
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const KType & k,
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const std::vector<std::vector<IndexType> > & point_indices,
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const Eigen::MatrixBase<DerivedCH>& CH,
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const Eigen::MatrixBase<DerivedCN>& CN,
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const Eigen::MatrixBase<DerivedW>& W,
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Eigen::PlainObjectBase<DerivedI> & I)
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{
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typedef typename DerivedCN::Scalar CentersType;
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typedef typename DerivedW::Scalar WidthsType;
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typedef Eigen::Matrix<typename DerivedP::Scalar, 1, 3> RowVector3PType;
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int n = P.rows();
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const KType real_k = std::min(n,k);
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auto distance_to_width_one_cube = [](RowVector3PType point){
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return std::sqrt(std::pow(std::max(std::abs(point(0))-1,0.0),2)
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+ std::pow(std::max(std::abs(point(1))-1,0.0),2)
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+ std::pow(std::max(std::abs(point(2))-1,0.0),2));
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};
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auto distance_to_cube = [&distance_to_width_one_cube]
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(RowVector3PType point,
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Eigen::Matrix<CentersType,1,3> cube_center,
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WidthsType cube_width){
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RowVector3PType transformed_point = (point-cube_center)/cube_width;
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return cube_width*distance_to_width_one_cube(transformed_point);
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};
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I.resize(n,real_k);
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igl::parallel_for(n,[&](int i)
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{
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int points_found = 0;
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RowVector3PType point_of_interest = P.row(i);
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//To make my priority queue take both points and octree cells,
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//I use the indices 0 to n-1 for the n points,
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// and the indices n to n+m-1 for the m octree cells
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// Using lambda to compare elements.
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auto cmp = [&point_of_interest, &P, &CN, &W,
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&n, &distance_to_cube](int left, int right) {
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double leftdistance, rightdistance;
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if(left < n){ //left is a point index
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leftdistance = (P.row(left) - point_of_interest).norm();
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} else { //left is an octree cell
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leftdistance = distance_to_cube(point_of_interest,
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CN.row(left-n),
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W(left-n));
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}
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if(right < n){ //left is a point index
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rightdistance = (P.row(right) - point_of_interest).norm();
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} else { //left is an octree cell
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rightdistance = distance_to_cube(point_of_interest,
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CN.row(right-n),
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W(right-n));
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}
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return leftdistance >= rightdistance;
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};
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std::priority_queue<IndexType, std::vector<IndexType>,
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decltype(cmp)> queue(cmp);
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queue.push(n); //This is the 0th octree cell (ie the root)
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while(points_found < real_k){
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IndexType curr_cell_or_point = queue.top();
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queue.pop();
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if(curr_cell_or_point < n){ //current index is for is a point
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I(i,points_found) = curr_cell_or_point;
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points_found++;
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} else {
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IndexType curr_cell = curr_cell_or_point - n;
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if(CH(curr_cell,0) == -1){ //In the case of a leaf
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if(point_indices.at(curr_cell).size() > 0){
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//Assumption: Leaves either have one point, or none
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queue.push(point_indices.at(curr_cell).at(0));
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}
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} else { //Not a leaf
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for(int j = 0; j < 8; j++){
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//+n to adjust for the octree cells
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queue.push(CH(curr_cell,j)+n);
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}
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}
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}
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}
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},1000);
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}
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}
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