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.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      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, typename CloudType>
        NearestNeighbourSearch<T, CloudType>::NearestNeighbourSearch(const CloudType& 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()))
        {
                if (cloud.cols() == 0)
                        throw runtime_error("Cloud has no points");
                if (cloud.rows() == 0)
                        throw runtime_error("Cloud has 0 dimensions");
        }
        
        template<typename T, typename CloudType>
        unsigned long NearestNeighbourSearch<T, CloudType>::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, typename CloudType>
        void NearestNeighbourSearch<T, CloudType>::checkSizesKnn(const Matrix& query, const IndexMatrix& indices, const Matrix& dists2, const Index k, const unsigned optionFlags, const Vector* maxRadii) const
        {
                const bool allowSelfMatch(optionFlags & NearestNeighbourSearch<T, CloudType>::ALLOW_SELF_MATCH);
                if (allowSelfMatch)
                {
                        if (k > cloud.cols())
                                throw runtime_error((boost::format("Requesting more points (%1%) than available in cloud (%2%)") % k % cloud.cols()).str());
                }
                else
                {
                        if (k > cloud.cols()-1)
                                throw runtime_error((boost::format("Requesting more points (%1%) than available in cloud minus 1 (%2%) (as self match is forbidden)") % k % (cloud.cols()-1)).str());
                }
                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());
                const unsigned maxOptionFlagsValue(ALLOW_SELF_MATCH|SORT_RESULTS);
                if (optionFlags > maxOptionFlagsValue)
                        throw runtime_error((boost::format("OR-ed value of option flags (%1%) is larger than maximal valid value (%2%)") % optionFlags % maxOptionFlagsValue).str());
        }
        

        template<typename T, typename CloudType>
        NearestNeighbourSearch<T, CloudType>* NearestNeighbourSearch<T, CloudType>::create(const CloudType& 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, CloudType>(cloud, dim, creationOptionFlags);
                        case KDTREE_LINEAR_HEAP: return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapBruteForceVector<int,T>, CloudType>(cloud, dim, creationOptionFlags, additionalParameters);
                        case KDTREE_TREE_HEAP: return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapSTL<int,T>, CloudType>(cloud, dim, creationOptionFlags, additionalParameters);
                        #ifdef HAVE_OPENCL
                        case KDTREE_CL_PT_IN_NODES: return new KDTreeBalancedPtInNodesStackOpenCL<T, CloudType>(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU);
                        case KDTREE_CL_PT_IN_LEAVES: return new KDTreeBalancedPtInLeavesStackOpenCL<T, CloudType>(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU);
                        case BRUTE_FORCE_CL: return new BruteForceSearchOpenCL<T, CloudType>(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, typename CloudType>
        NearestNeighbourSearch<T, CloudType>* NearestNeighbourSearch<T, CloudType>::createBruteForce(const CloudType& 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, CloudType>(cloud, dim, creationOptionFlags);
        }

        template<typename T, typename CloudType>
        NearestNeighbourSearch<T, CloudType>* NearestNeighbourSearch<T, CloudType>::createKDTreeLinearHeap(const CloudType& 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>, CloudType>(cloud, dim, creationOptionFlags, additionalParameters);
        }

        template<typename T, typename CloudType>
        NearestNeighbourSearch<T, CloudType>* NearestNeighbourSearch<T, CloudType>::createKDTreeTreeHeap(const CloudType& 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>, CloudType>(cloud, dim, creationOptionFlags, additionalParameters);
        }
        
        template struct NearestNeighbourSearch<float>;
        template struct NearestNeighbourSearch<double>;
        template struct NearestNeighbourSearch<float, Eigen::Matrix3Xf>;
        template struct NearestNeighbourSearch<double, Eigen::Matrix3Xd>;
        template struct NearestNeighbourSearch<float, Eigen::Map<const Eigen::Matrix3Xf, Eigen::Aligned> >;
        template struct NearestNeighbourSearch<double, Eigen::Map<const Eigen::Matrix3Xd, Eigen::Aligned> >;
}