kdtree_cpu.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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modification, are permitted provided that the following conditions are met:
    * Redistributions of source code must retain the above copyright
      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.
    * Neither the name of the <organization> nor the
      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_private.h"
#include "index_heap.h"
#include <iostream>
#include <stdexcept>
#include <limits>
#include <queue>
#include <algorithm>
#include <utility>
#ifdef HAVE_OPENMP
#include <omp.h>
#endif

namespace Nabo
{

        
        using namespace std;
        

        template<typename T>
        T getStorageBitCount(T v)
        {
                for (T i = 0; i < 64; ++i)
                {
                        if (v == 0)
                                return i;
                        v >>= 1;
                }
                return 64;
        }
        

        template<typename T, typename CloudType>
        size_t argMax(const typename NearestNeighbourSearch<T, CloudType>::Vector& v)
        {
                T maxVal(0);
                size_t maxIdx(0);
                for (int i = 0; i < v.size(); ++i)
                {
                        if (v[i] > maxVal)
                        {
                                maxVal = v[i];
                                maxIdx = i;
                        }
                }
                return maxIdx;
        }
        
        // OPT
        template<typename T, typename Heap, typename CloudType>
        pair<T,T> KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, Heap, CloudType>::getBounds(const BuildPointsIt first, const BuildPointsIt last, const unsigned dim)
        {
                T minVal(std::numeric_limits<T>::max());
                T maxVal(std::numeric_limits<T>::lowest());
                
                for (BuildPointsCstIt it(first); it != last; ++it)
                {
                        const T val(cloud.coeff(dim, *it));
                        minVal = min(val, minVal);
                        maxVal = max(val, maxVal);
                }
                
                return make_pair(minVal, maxVal);
        }
        
        template<typename T, typename Heap, typename CloudType>
        unsigned KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, Heap, CloudType>::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues)
        {
                const int count(last - first);
                assert(count >= 1);
                const unsigned pos(nodes.size());
                
                //cerr << count << endl;
                if (count <= int(bucketSize))
                {
                        const uint32_t initBucketsSize(buckets.size());
                        //cerr << "creating bucket with " << count << " values" << endl;
                        for (int i = 0; i < count; ++i)
                        {
                                const Index index(*(first+i));
                                assert(index < cloud.cols());
                                buckets.push_back(BucketEntry(&cloud.coeff(0, index), index));
                                //cerr << "  " << &cloud.coeff(0, index) << ", " << index << endl;
                        }
                        //cerr << "at address " << bucketStart << endl;
                        nodes.push_back(Node(createDimChildBucketSize(dim, count),initBucketsSize));
                        return pos;
                }
                
                // find the largest dimension of the box
                const unsigned cutDim = argMax<T, CloudType>(maxValues - minValues);
                const T idealCutVal((maxValues(cutDim) + minValues(cutDim))/2);
                
                // get bounds from actual points
                const pair<T,T> minMaxVals(getBounds(first, last, cutDim));
                
                // correct cut following bounds
                T cutVal;
                if (idealCutVal < minMaxVals.first)
                        cutVal = minMaxVals.first;
                else if (idealCutVal > minMaxVals.second)
                        cutVal = minMaxVals.second;
                else
                        cutVal = idealCutVal;
                
                int l(0);
                int r(count-1);
                // partition points around cutVal
                while (1)
                {
                        while (l < count && cloud.coeff(cutDim, *(first+l)) < cutVal)
                                ++l;
                        while (r >= 0 && cloud.coeff(cutDim, *(first+r)) >= cutVal)
                                --r;
                        if (l > r)
                                break;
                        swap(*(first+l), *(first+r));
                        ++l; --r;
                }
                const int br1 = l;      // now: points[0..br1-1] < cutVal <= points[br1..count-1]
                r = count-1;
                // partition points[br1..count-1] around cutVal
                while (1)
                {
                        while (l < count && cloud.coeff(cutDim, *(first+l)) <= cutVal)
                                ++l;
                        while (r >= br1 && cloud.coeff(cutDim, *(first+r)) > cutVal)
                                --r;
                        if (l > r)
                                break;
                        swap(*(first+l), *(first+r));
                        ++l; --r;
                }
                const int br2 = l; // now: points[br1..br2-1] == cutVal < points[br2..count-1]
                
                // find best split index
                int leftCount;
                if (idealCutVal < minMaxVals.first)
                        leftCount = 1;
                else if (idealCutVal > minMaxVals.second)
                        leftCount = count-1;
                else if (br1 > count / 2)
                        leftCount = br1;
                else if (br2 < count / 2)
                        leftCount = br2;
                else
                        leftCount = count / 2;
                assert(leftCount > 0);
                /*if (leftCount >= count)
                {
                        cerr << "Error found in kdtree:" << endl;
                        cerr << "cloud size: " << cloud.cols() << endl;
                        cerr << "count:" << count << endl;
                        cerr << "leftCount: " << leftCount << endl;
                        cerr << "br1: " << br1 << endl;
                        cerr << "br2: " << br2 << endl;
                        cerr << "idealCutVal: " << idealCutVal << endl;
                        cerr << "cutVal: " << cutVal << endl;
                        cerr << "minMaxVals.first: " << minMaxVals.first << endl;
                        cerr << "minMaxVals.second: " << minMaxVals.second << endl;
                }*/
                assert(leftCount < count);
                
                // update bounds for left
                Vector leftMaxValues(maxValues);
                leftMaxValues[cutDim] = cutVal;
                // update bounds for right
                Vector rightMinValues(minValues);
                rightMinValues[cutDim] = cutVal;
                
                // add this
                nodes.push_back(Node(0, cutVal));
                
                // recurse
                const unsigned _UNUSED leftChild = buildNodes(first, first + leftCount, minValues, leftMaxValues);
                assert(leftChild == pos + 1);
                const unsigned rightChild = buildNodes(first + leftCount, last, rightMinValues, maxValues);
                
                // write right child index and return
                nodes[pos].dimChildBucketSize = createDimChildBucketSize(cutDim, rightChild);
                return pos;
        }

        template<typename T, typename Heap, typename CloudType>
        KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, Heap, CloudType>::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters):
                NearestNeighbourSearch<T, CloudType>::NearestNeighbourSearch(cloud, dim, creationOptionFlags),
                bucketSize(additionalParameters.get<unsigned>("bucketSize", 8)),
                dimBitCount(getStorageBitCount<uint32_t>(this->dim)),
                dimMask((1<<dimBitCount)-1)
        {
                if (bucketSize < 2)
                        throw runtime_error("Requested bucket size " + std::to_string(bucketSize) + ", but must be larger than 2");
                if (cloud.cols() <= bucketSize)
                {
                        // make a single-bucket tree
                        for (int i = 0; i < cloud.cols(); ++i)
                                buckets.push_back(BucketEntry(&cloud.coeff(0, i), i));
                        nodes.push_back(Node(createDimChildBucketSize(this->dim, cloud.cols()),uint32_t(0)));
                        return;
                }
                
                const uint64_t maxNodeCount((0x1ULL << (32-dimBitCount)) - 1);
                const uint64_t estimatedNodeCount(cloud.cols() / (bucketSize / 2));
                if (estimatedNodeCount > maxNodeCount)
                {
                        throw runtime_error("Cloud has a risk to have more nodes (" + std::to_string(estimatedNodeCount) + ") than the kd-tree allows (" + std::to_string(maxNodeCount) + "). "
                                        "The kd-tree has " + std::to_string(dimBitCount) + " bits for dimensions and " + std::to_string((32-dimBitCount)) + " bits for node indices");
                }
                
                // build point vector and compute bounds
                BuildPoints buildPoints;
                buildPoints.reserve(cloud.cols());
                for (int i = 0; i < cloud.cols(); ++i)
                {
                        const Vector& v(cloud.block(0,i,this->dim,1));
                        buildPoints.push_back(i);
#ifdef EIGEN3_API
                        const_cast<Vector&>(minBound) = minBound.array().min(v.array());
                        const_cast<Vector&>(maxBound) = maxBound.array().max(v.array());
#else // EIGEN3_API
                        const_cast<Vector&>(minBound) = minBound.cwise().min(v);
                        const_cast<Vector&>(maxBound) = maxBound.cwise().max(v);
#endif // EIGEN3_API
                }
                
                // create nodes
                buildNodes(buildPoints.begin(), buildPoints.end(), minBound, maxBound);
                buildPoints.clear();
        }
        
        template<typename T, typename Heap, typename CloudType>
        unsigned long KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, Heap, CloudType>::knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const
        {
                checkSizesKnn(query, indices, dists2, k, optionFlags);
                
                const bool allowSelfMatch(optionFlags & NearestNeighbourSearch<T>::ALLOW_SELF_MATCH);
                const bool sortResults(optionFlags & NearestNeighbourSearch<T>::SORT_RESULTS);
                const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch<T>::TOUCH_STATISTICS);
                const T maxRadius2(maxRadius * maxRadius);
                const T maxError2((1+epsilon)*(1+epsilon));
                const int colCount(query.cols());
                
                assert(nodes.size() > 0);

                IndexMatrix result(k, query.cols());
                unsigned long leafTouchedCount(0);

#pragma omp parallel 
                {               

                Heap heap(k);
                std::vector<T> off(dim, 0);

#pragma omp for reduction(+:leafTouchedCount) schedule(guided,32)
                for (int i = 0; i < colCount; ++i)
                {
                        leafTouchedCount += onePointKnn(query, indices, dists2, i, heap, off, maxError2, maxRadius2, allowSelfMatch, collectStatistics, sortResults);
                }
                }
                return leafTouchedCount;
        }
        
        template<typename T, typename Heap, typename CloudType>
        unsigned long KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, Heap, CloudType>::knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k, const T epsilon, const unsigned optionFlags) const
        {
                checkSizesKnn(query, indices, dists2, k, optionFlags, &maxRadii);
                
                const bool allowSelfMatch(optionFlags & NearestNeighbourSearch<T>::ALLOW_SELF_MATCH);
                const bool sortResults(optionFlags & NearestNeighbourSearch<T>::SORT_RESULTS);
                const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch<T>::TOUCH_STATISTICS);
                const T maxError2((1+epsilon)*(1+epsilon));
                const int colCount(query.cols());
                
                assert(nodes.size() > 0);
                IndexMatrix result(k, query.cols());
                unsigned long leafTouchedCount(0);

#pragma omp parallel 
                {               

                Heap heap(k);
                std::vector<T> off(dim, 0);
                
#pragma omp for reduction(+:leafTouchedCount) schedule(guided,32)
                for (int i = 0; i < colCount; ++i)
                {
                        const T maxRadius(maxRadii[i]);
                        const T maxRadius2(maxRadius * maxRadius);
                        leafTouchedCount += onePointKnn(query, indices, dists2, i, heap, off, maxError2, maxRadius2, allowSelfMatch, collectStatistics, sortResults);
                }
                }
                return leafTouchedCount;
        }
        
        template<typename T, typename Heap, typename CloudType>
        unsigned long KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, Heap, CloudType>::onePointKnn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, int i, Heap& heap, std::vector<T>& off, const T maxError2, const T maxRadius2, const bool allowSelfMatch, const bool collectStatistics, const bool sortResults) const
        {
                fill(off.begin(), off.end(), static_cast<T>(0));
                heap.reset();
                unsigned long leafTouchedCount(0);
                
                if (allowSelfMatch)
                {
                        if (collectStatistics)
                                leafTouchedCount += recurseKnn<true, true>(&query.coeff(0, i), 0, 0, heap, off, maxError2, maxRadius2);
                        else
                                recurseKnn<true, false>(&query.coeff(0, i), 0, 0, heap, off, maxError2, maxRadius2);
                }
                else
                {
                        if (collectStatistics)
                                leafTouchedCount += recurseKnn<false, true>(&query.coeff(0, i), 0, 0, heap, off, maxError2, maxRadius2);
                        else
                                recurseKnn<false, false>(&query.coeff(0, i), 0, 0, heap, off, maxError2, maxRadius2);
                }
                
                if (sortResults)
                        heap.sort();
                
                heap.getData(indices.col(i), dists2.col(i));
                return leafTouchedCount;
        }
        
        template<typename T, typename Heap, typename CloudType> template<bool allowSelfMatch, bool collectStatistics>
        unsigned long KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, Heap, CloudType>::recurseKnn(const T* query, const unsigned n, T rd, Heap& heap, std::vector<T>& off, const T maxError2, const T maxRadius2) const
        {
                const Node& node(nodes[n]);
                const uint32_t cd(getDim(node.dimChildBucketSize));
                
                if (cd == uint32_t(dim))
                {
                        //cerr << "entering bucket " << node.bucket << endl;
                        const BucketEntry* bucket(&buckets[node.bucketIndex]);
                        const uint32_t bucketSize(getChildBucketSize(node.dimChildBucketSize));
                        for (uint32_t i = 0; i < bucketSize; ++i)
                        {
                                //cerr << "  " << bucket-> pt << endl;
                                //const T dist(dist2<T>(query, cloud.col(index)));
                                //const T dist((query - cloud.col(index)).squaredNorm());
                                T dist(0);
                                const T* qPtr(query);
                                const T* dPtr(bucket->pt);
                                for (int i = 0; i < this->dim; ++i)
                                {
                                        const T diff(*qPtr - *dPtr);
                                        dist += diff*diff;
                                        qPtr++; dPtr++;
                                }
                                if ((dist <= maxRadius2) &&
                                        (dist < heap.headValue()) &&
                                        (allowSelfMatch || (dist > numeric_limits<T>::epsilon()))
                                )
                                        heap.replaceHead(bucket->index, dist);
                                ++bucket;
                        }
                        return (unsigned long)(bucketSize);
                }
                else
                {
                        const unsigned rightChild(getChildBucketSize(node.dimChildBucketSize));
                        unsigned long leafVisitedCount(0);
                        T& offcd(off[cd]);
                        //const T old_off(off.coeff(cd));
                        const T old_off(offcd);
                        const T new_off(query[cd] - node.cutVal);
                        if (new_off > 0)
                        {
                                if (collectStatistics)
                                        leafVisitedCount += recurseKnn<allowSelfMatch, true>(query, rightChild, rd, heap, off, maxError2, maxRadius2);
                                else
                                        recurseKnn<allowSelfMatch, false>(query, rightChild, rd, heap, off, maxError2, maxRadius2);
                                rd += - old_off*old_off + new_off*new_off;
                                if ((rd <= maxRadius2) &&
                                        (rd * maxError2 < heap.headValue()))
                                {
                                        offcd = new_off;
                                        if (collectStatistics)
                                                leafVisitedCount += recurseKnn<allowSelfMatch, true>(query, n + 1, rd, heap, off, maxError2, maxRadius2);
                                        else
                                                recurseKnn<allowSelfMatch, false>(query, n + 1, rd, heap, off, maxError2, maxRadius2);
                                        offcd = old_off;
                                }
                        }
                        else
                        {
                                if (collectStatistics)
                                        leafVisitedCount += recurseKnn<allowSelfMatch, true>(query, n+1, rd, heap, off, maxError2, maxRadius2);
                                else
                                        recurseKnn<allowSelfMatch, false>(query, n+1, rd, heap, off, maxError2, maxRadius2);
                                rd += - old_off*old_off + new_off*new_off;
                                if ((rd <= maxRadius2) &&
                                        (rd * maxError2 < heap.headValue()))
                                {
                                        offcd = new_off;
                                        if (collectStatistics)
                                                leafVisitedCount += recurseKnn<allowSelfMatch, true>(query, rightChild, rd, heap, off, maxError2, maxRadius2);
                                        else
                                                recurseKnn<allowSelfMatch, false>(query, rightChild, rd, heap, off, maxError2, maxRadius2);
                                        offcd = old_off;
                                }
                        }
                        return leafVisitedCount;
                }
        }
        
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<float,IndexHeapSTL<int,float> >;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<float,IndexHeapBruteForceVector<int,float> >;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<double,IndexHeapSTL<int,double> >;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<double,IndexHeapBruteForceVector<int,double> >;
        
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<float,IndexHeapSTL<int,float>,Eigen::Matrix3Xf>;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<float,IndexHeapBruteForceVector<int,float>,Eigen::Matrix3Xf>;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<double,IndexHeapSTL<int,double>,Eigen::Matrix3Xd>;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<double,IndexHeapBruteForceVector<int,double>,Eigen::Matrix3Xd>;

        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<float,IndexHeapSTL<int,float>,Eigen::Map<const Eigen::Matrix3Xf, Eigen::Aligned> >;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<float,IndexHeapBruteForceVector<int,float>,Eigen::Map<const Eigen::Matrix3Xf, Eigen::Aligned> >;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<double,IndexHeapSTL<int,double>,Eigen::Map<const Eigen::Matrix3Xd, Eigen::Aligned> >;
        template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<double,IndexHeapBruteForceVector<int,double>,Eigen::Map<const Eigen::Matrix3Xd, Eigen::Aligned> >;

}
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