nabo.cpp

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/*

Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland
You can contact the author at <stephane at magnenat dot net>

All rights reserved.

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      notice, this list of conditions and the following disclaimer.
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      documentation and/or other materials provided with the distribution.
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      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

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*/

#include "nabo.h"
#include "nabo_private.h"
#include "index_heap.h"
#include <limits>
#include <algorithm>
#include <stdexcept>
#include <boost/format.hpp>

namespace Nabo
{
    using namespace std;
    
    template<typename T>
    NearestNeighbourSearch<T>::NearestNeighbourSearch(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags):
        cloud(cloud),
        dim(min(dim, int(cloud.rows()))),
        creationOptionFlags(creationOptionFlags),
        minBound(Vector::Constant(this->dim, numeric_limits<T>::max())),
        maxBound(Vector::Constant(this->dim, numeric_limits<T>::min()))
    {
        
    }
    
    template<typename T>
    unsigned long NearestNeighbourSearch<T>::knn(const Vector& query, IndexVector& indices, Vector& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const
    {
#ifdef EIGEN3_API
        const Eigen::Map<const Matrix> queryMatrix(&query.coeff(0,0), dim, 1);
#else // EIGEN3_API
        const Eigen::Map<Matrix> queryMatrix(&query.coeff(0,0), dim, 1);
#endif // EIGEN3_API
        // note: this is inefficient, because we copy memory, due to the template-
        // based abstraction of Eigen. High-performance implementation should
        // take care of knnM and then implement knn on top of it.
        // C++0x should solve this with rvalue
        IndexMatrix indexMatrix(k, 1);
        Matrix dists2Matrix(k, 1);
        const unsigned long stats = knn(queryMatrix, indexMatrix, dists2Matrix, k, epsilon, optionFlags, maxRadius);
        indices = indexMatrix.col(0);
        dists2 = dists2Matrix.col(0);
        return stats;
    }
    
    template<typename T>
    void NearestNeighbourSearch<T>::checkSizesKnn(const Matrix& query, const IndexMatrix& indices, const Matrix& dists2, const Index k, const Vector* maxRadii) const
    {
        if (query.rows() < dim)
            throw runtime_error((boost::format("Query has less dimensions (%1%) than requested for cloud (%2%)") % query.rows() % dim).str());
        if (indices.rows() != k)
            throw runtime_error((boost::format("Index matrix has a different number of rows (%1%) than k (%2%)") % indices.rows() % k).str());
        if (indices.cols() != query.cols())
            throw runtime_error((boost::format("Index matrix has a different number of columns (%1%) than query (%2%)") % indices.rows() % query.cols()).str());
        if (dists2.rows() != k)
            throw runtime_error((boost::format("Distance matrix has a different number of rows (%1%) than k (%2%)") % dists2.rows() % k).str());
        if (dists2.cols() != query.cols())
            throw runtime_error((boost::format("Distance matrix has a different number of columns (%1%) than query (%2%)") % dists2.rows() % query.cols()).str());
        if (maxRadii && (maxRadii->size() != query.cols()))
            throw runtime_error((boost::format("Maximum radii vector has not the same length (%1%) than query has columns (%2%)") % maxRadii->size() % k).str());
    }
    
    
    template<typename T>
    NearestNeighbourSearch<T>* NearestNeighbourSearch<T>::create(const Matrix& cloud, const Index dim, const SearchType preferedType, const unsigned creationOptionFlags, const Parameters& additionalParameters)
    {
        if (dim <= 0)
            throw runtime_error("Your space must have at least one dimension");
        switch (preferedType)
        {
            case BRUTE_FORCE: return new BruteForceSearch<T>(cloud, dim, creationOptionFlags);
            case KDTREE_LINEAR_HEAP: return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapBruteForceVector<int,T> >(cloud, dim, creationOptionFlags, additionalParameters);
            case KDTREE_TREE_HEAP: return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapSTL<int,T> >(cloud, dim, creationOptionFlags, additionalParameters);
            #ifdef HAVE_OPENCL
            case KDTREE_CL_PT_IN_NODES: return new KDTreeBalancedPtInNodesStackOpenCL<T>(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU);
            case KDTREE_CL_PT_IN_LEAVES: return new KDTreeBalancedPtInLeavesStackOpenCL<T>(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU);
            case BRUTE_FORCE_CL: return new BruteForceSearchOpenCL<T>(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU);
            #else // HAVE_OPENCL
            case KDTREE_CL_PT_IN_NODES: throw runtime_error("OpenCL not found during compilation");
            case KDTREE_CL_PT_IN_LEAVES: throw runtime_error("OpenCL not found during compilation");
            case BRUTE_FORCE_CL: throw runtime_error("OpenCL not found during compilation");
            #endif // HAVE_OPENCL
            default: throw runtime_error("Unknown search type");
        }
    }
    
    template<typename T>
    NearestNeighbourSearch<T>* NearestNeighbourSearch<T>::createBruteForce(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags)
    {
        if (dim <= 0)
            throw runtime_error("Your space must have at least one dimension");
        return new BruteForceSearch<T>(cloud, dim, creationOptionFlags);
    }
    
    template<typename T>
    NearestNeighbourSearch<T>* NearestNeighbourSearch<T>::createKDTreeLinearHeap(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters)
    {
        if (dim <= 0)
            throw runtime_error("Your space must have at least one dimension");
        return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapBruteForceVector<int,T> >(cloud, dim, creationOptionFlags, additionalParameters);
    }
    
    template<typename T>
    NearestNeighbourSearch<T>* NearestNeighbourSearch<T>::createKDTreeTreeHeap(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters)
    {
        if (dim <= 0)
            throw runtime_error("Your space must have at least one dimension");
        return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapSTL<int,T> >(cloud, dim, creationOptionFlags, additionalParameters);
    }
    
    template struct NearestNeighbourSearch<float>;
    template struct NearestNeighbourSearch<double>;
}