Renesas
openCV library for Renesas RZ/A
Dependents: RZ_A2M_Mbed_samples
Diff: include/opencv2/flann/kmeans_index.h
- Revision:
- 0:0e0631af0305
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/include/opencv2/flann/kmeans_index.h Fri Jan 29 04:53:38 2021 +0000
@@ -0,0 +1,1171 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.
+ * 2. 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.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_KMEANS_INDEX_H_
+#define OPENCV_FLANN_KMEANS_INDEX_H_
+
+#include <algorithm>
+#include <map>
+#include <cassert>
+#include <limits>
+#include <cmath>
+
+#include "general.h"
+#include "nn_index.h"
+#include "dist.h"
+#include "matrix.h"
+#include "result_set.h"
+#include "heap.h"
+#include "allocator.h"
+#include "random.h"
+#include "saving.h"
+#include "logger.h"
+
+
+namespace cvflann
+{
+
+struct KMeansIndexParams : public IndexParams
+{
+ KMeansIndexParams(int branching = 32, int iterations = 11,
+ flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
+ {
+ (*this)["algorithm"] = FLANN_INDEX_KMEANS;
+ // branching factor
+ (*this)["branching"] = branching;
+ // max iterations to perform in one kmeans clustering (kmeans tree)
+ (*this)["iterations"] = iterations;
+ // algorithm used for picking the initial cluster centers for kmeans tree
+ (*this)["centers_init"] = centers_init;
+ // cluster boundary index. Used when searching the kmeans tree
+ (*this)["cb_index"] = cb_index;
+ }
+};
+
+
+/**
+ * Hierarchical kmeans index
+ *
+ * Contains a tree constructed through a hierarchical kmeans clustering
+ * and other information for indexing a set of points for nearest-neighbour matching.
+ */
+template <typename Distance>
+class KMeansIndex : public NNIndex<Distance>
+{
+public:
+ typedef typename Distance::ElementType ElementType;
+ typedef typename Distance::ResultType DistanceType;
+
+
+
+ typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&);
+
+ /**
+ * The function used for choosing the cluster centers.
+ */
+ centersAlgFunction chooseCenters;
+
+
+
+ /**
+ * Chooses the initial centers in the k-means clustering in a random manner.
+ *
+ * Params:
+ * k = number of centers
+ * vecs = the dataset of points
+ * indices = indices in the dataset
+ * indices_length = length of indices vector
+ *
+ */
+ void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
+ {
+ UniqueRandom r(indices_length);
+
+ int index;
+ for (index=0; index<k; ++index) {
+ bool duplicate = true;
+ int rnd;
+ while (duplicate) {
+ duplicate = false;
+ rnd = r.next();
+ if (rnd<0) {
+ centers_length = index;
+ return;
+ }
+
+ centers[index] = indices[rnd];
+
+ for (int j=0; j<index; ++j) {
+ DistanceType sq = distance_(dataset_[centers[index]], dataset_[centers[j]], dataset_.cols);
+ if (sq<1e-16) {
+ duplicate = true;
+ }
+ }
+ }
+ }
+
+ centers_length = index;
+ }
+
+
+ /**
+ * Chooses the initial centers in the k-means using Gonzales' algorithm
+ * so that the centers are spaced apart from each other.
+ *
+ * Params:
+ * k = number of centers
+ * vecs = the dataset of points
+ * indices = indices in the dataset
+ * Returns:
+ */
+ void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
+ {
+ int n = indices_length;
+
+ int rnd = rand_int(n);
+ assert(rnd >=0 && rnd < n);
+
+ centers[0] = indices[rnd];
+
+ int index;
+ for (index=1; index<k; ++index) {
+
+ int best_index = -1;
+ DistanceType best_val = 0;
+ for (int j=0; j<n; ++j) {
+ DistanceType dist = distance_(dataset_[centers[0]],dataset_[indices[j]],dataset_.cols);
+ for (int i=1; i<index; ++i) {
+ DistanceType tmp_dist = distance_(dataset_[centers[i]],dataset_[indices[j]],dataset_.cols);
+ if (tmp_dist<dist) {
+ dist = tmp_dist;
+ }
+ }
+ if (dist>best_val) {
+ best_val = dist;
+ best_index = j;
+ }
+ }
+ if (best_index!=-1) {
+ centers[index] = indices[best_index];
+ }
+ else {
+ break;
+ }
+ }
+ centers_length = index;
+ }
+
+
+ /**
+ * Chooses the initial centers in the k-means using the algorithm
+ * proposed in the KMeans++ paper:
+ * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
+ *
+ * Implementation of this function was converted from the one provided in Arthur's code.
+ *
+ * Params:
+ * k = number of centers
+ * vecs = the dataset of points
+ * indices = indices in the dataset
+ * Returns:
+ */
+ void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
+ {
+ int n = indices_length;
+
+ double currentPot = 0;
+ DistanceType* closestDistSq = new DistanceType[n];
+
+ // Choose one random center and set the closestDistSq values
+ int index = rand_int(n);
+ assert(index >=0 && index < n);
+ centers[0] = indices[index];
+
+ for (int i = 0; i < n; i++) {
+ closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+ closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
+ currentPot += closestDistSq[i];
+ }
+
+
+ const int numLocalTries = 1;
+
+ // Choose each center
+ int centerCount;
+ for (centerCount = 1; centerCount < k; centerCount++) {
+
+ // Repeat several trials
+ double bestNewPot = -1;
+ int bestNewIndex = -1;
+ for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
+
+ // Choose our center - have to be slightly careful to return a valid answer even accounting
+ // for possible rounding errors
+ double randVal = rand_double(currentPot);
+ for (index = 0; index < n-1; index++) {
+ if (randVal <= closestDistSq[index]) break;
+ else randVal -= closestDistSq[index];
+ }
+
+ // Compute the new potential
+ double newPot = 0;
+ for (int i = 0; i < n; i++) {
+ DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+ newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
+ }
+
+ // Store the best result
+ if ((bestNewPot < 0)||(newPot < bestNewPot)) {
+ bestNewPot = newPot;
+ bestNewIndex = index;
+ }
+ }
+
+ // Add the appropriate center
+ centers[centerCount] = indices[bestNewIndex];
+ currentPot = bestNewPot;
+ for (int i = 0; i < n; i++) {
+ DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
+ closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
+ }
+ }
+
+ centers_length = centerCount;
+
+ delete[] closestDistSq;
+ }
+
+
+
+public:
+
+ flann_algorithm_t getType() const
+ {
+ return FLANN_INDEX_KMEANS;
+ }
+
+ class KMeansDistanceComputer : public cv::ParallelLoopBody
+ {
+ public:
+ KMeansDistanceComputer(Distance _distance, const Matrix<ElementType>& _dataset,
+ const int _branching, const int* _indices, const Matrix<double>& _dcenters, const size_t _veclen,
+ int* _count, int* _belongs_to, std::vector<DistanceType>& _radiuses, bool& _converged, cv::Mutex& _mtx)
+ : distance(_distance)
+ , dataset(_dataset)
+ , branching(_branching)
+ , indices(_indices)
+ , dcenters(_dcenters)
+ , veclen(_veclen)
+ , count(_count)
+ , belongs_to(_belongs_to)
+ , radiuses(_radiuses)
+ , converged(_converged)
+ , mtx(_mtx)
+ {
+ }
+
+ void operator()(const cv::Range& range) const
+ {
+ const int begin = range.start;
+ const int end = range.end;
+
+ for( int i = begin; i<end; ++i)
+ {
+ DistanceType sq_dist = distance(dataset[indices[i]], dcenters[0], veclen);
+ int new_centroid = 0;
+ for (int j=1; j<branching; ++j) {
+ DistanceType new_sq_dist = distance(dataset[indices[i]], dcenters[j], veclen);
+ if (sq_dist>new_sq_dist) {
+ new_centroid = j;
+ sq_dist = new_sq_dist;
+ }
+ }
+ if (sq_dist > radiuses[new_centroid]) {
+ radiuses[new_centroid] = sq_dist;
+ }
+ if (new_centroid != belongs_to[i]) {
+ count[belongs_to[i]]--;
+ count[new_centroid]++;
+ belongs_to[i] = new_centroid;
+ mtx.lock();
+ converged = false;
+ mtx.unlock();
+ }
+ }
+ }
+
+ private:
+ Distance distance;
+ const Matrix<ElementType>& dataset;
+ const int branching;
+ const int* indices;
+ const Matrix<double>& dcenters;
+ const size_t veclen;
+ int* count;
+ int* belongs_to;
+ std::vector<DistanceType>& radiuses;
+ bool& converged;
+ cv::Mutex& mtx;
+ KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; }
+ };
+
+ /**
+ * Index constructor
+ *
+ * Params:
+ * inputData = dataset with the input features
+ * params = parameters passed to the hierarchical k-means algorithm
+ */
+ KMeansIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KMeansIndexParams(),
+ Distance d = Distance())
+ : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d)
+ {
+ memoryCounter_ = 0;
+
+ size_ = dataset_.rows;
+ veclen_ = dataset_.cols;
+
+ branching_ = get_param(params,"branching",32);
+ iterations_ = get_param(params,"iterations",11);
+ if (iterations_<0) {
+ iterations_ = (std::numeric_limits<int>::max)();
+ }
+ centers_init_ = get_param(params,"centers_init",FLANN_CENTERS_RANDOM);
+
+ if (centers_init_==FLANN_CENTERS_RANDOM) {
+ chooseCenters = &KMeansIndex::chooseCentersRandom;
+ }
+ else if (centers_init_==FLANN_CENTERS_GONZALES) {
+ chooseCenters = &KMeansIndex::chooseCentersGonzales;
+ }
+ else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
+ chooseCenters = &KMeansIndex::chooseCentersKMeanspp;
+ }
+ else {
+ throw FLANNException("Unknown algorithm for choosing initial centers.");
+ }
+ cb_index_ = 0.4f;
+
+ }
+
+
+ KMeansIndex(const KMeansIndex&);
+ KMeansIndex& operator=(const KMeansIndex&);
+
+
+ /**
+ * Index destructor.
+ *
+ * Release the memory used by the index.
+ */
+ virtual ~KMeansIndex()
+ {
+ if (root_ != NULL) {
+ free_centers(root_);
+ }
+ if (indices_!=NULL) {
+ delete[] indices_;
+ }
+ }
+
+ /**
+ * Returns size of index.
+ */
+ size_t size() const
+ {
+ return size_;
+ }
+
+ /**
+ * Returns the length of an index feature.
+ */
+ size_t veclen() const
+ {
+ return veclen_;
+ }
+
+
+ void set_cb_index( float index)
+ {
+ cb_index_ = index;
+ }
+
+ /**
+ * Computes the inde memory usage
+ * Returns: memory used by the index
+ */
+ int usedMemory() const
+ {
+ return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
+ }
+
+ /**
+ * Builds the index
+ */
+ void buildIndex()
+ {
+ if (branching_<2) {
+ throw FLANNException("Branching factor must be at least 2");
+ }
+
+ indices_ = new int[size_];
+ for (size_t i=0; i<size_; ++i) {
+ indices_[i] = int(i);
+ }
+
+ root_ = pool_.allocate<KMeansNode>();
+ std::memset(root_, 0, sizeof(KMeansNode));
+
+ computeNodeStatistics(root_, indices_, (int)size_);
+ computeClustering(root_, indices_, (int)size_, branching_,0);
+ }
+
+
+ void saveIndex(FILE* stream)
+ {
+ save_value(stream, branching_);
+ save_value(stream, iterations_);
+ save_value(stream, memoryCounter_);
+ save_value(stream, cb_index_);
+ save_value(stream, *indices_, (int)size_);
+
+ save_tree(stream, root_);
+ }
+
+
+ void loadIndex(FILE* stream)
+ {
+ load_value(stream, branching_);
+ load_value(stream, iterations_);
+ load_value(stream, memoryCounter_);
+ load_value(stream, cb_index_);
+ if (indices_!=NULL) {
+ delete[] indices_;
+ }
+ indices_ = new int[size_];
+ load_value(stream, *indices_, size_);
+
+ if (root_!=NULL) {
+ free_centers(root_);
+ }
+ load_tree(stream, root_);
+
+ index_params_["algorithm"] = getType();
+ index_params_["branching"] = branching_;
+ index_params_["iterations"] = iterations_;
+ index_params_["centers_init"] = centers_init_;
+ index_params_["cb_index"] = cb_index_;
+
+ }
+
+
+ /**
+ * Find set of nearest neighbors to vec. Their indices are stored inside
+ * the result object.
+ *
+ * Params:
+ * result = the result object in which the indices of the nearest-neighbors are stored
+ * vec = the vector for which to search the nearest neighbors
+ * searchParams = parameters that influence the search algorithm (checks, cb_index)
+ */
+ void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+ {
+
+ int maxChecks = get_param(searchParams,"checks",32);
+
+ if (maxChecks==FLANN_CHECKS_UNLIMITED) {
+ findExactNN(root_, result, vec);
+ }
+ else {
+ // Priority queue storing intermediate branches in the best-bin-first search
+ Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
+
+ int checks = 0;
+ findNN(root_, result, vec, checks, maxChecks, heap);
+
+ BranchSt branch;
+ while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
+ KMeansNodePtr node = branch.node;
+ findNN(node, result, vec, checks, maxChecks, heap);
+ }
+ assert(result.full());
+
+ delete heap;
+ }
+
+ }
+
+ /**
+ * Clustering function that takes a cut in the hierarchical k-means
+ * tree and return the clusters centers of that clustering.
+ * Params:
+ * numClusters = number of clusters to have in the clustering computed
+ * Returns: number of cluster centers
+ */
+ int getClusterCenters(Matrix<DistanceType>& centers)
+ {
+ int numClusters = centers.rows;
+ if (numClusters<1) {
+ throw FLANNException("Number of clusters must be at least 1");
+ }
+
+ DistanceType variance;
+ KMeansNodePtr* clusters = new KMeansNodePtr[numClusters];
+
+ int clusterCount = getMinVarianceClusters(root_, clusters, numClusters, variance);
+
+ Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount);
+
+ for (int i=0; i<clusterCount; ++i) {
+ DistanceType* center = clusters[i]->pivot;
+ for (size_t j=0; j<veclen_; ++j) {
+ centers[i][j] = center[j];
+ }
+ }
+ delete[] clusters;
+
+ return clusterCount;
+ }
+
+ IndexParams getParameters() const
+ {
+ return index_params_;
+ }
+
+
+private:
+ /**
+ * Struture representing a node in the hierarchical k-means tree.
+ */
+ struct KMeansNode
+ {
+ /**
+ * The cluster center.
+ */
+ DistanceType* pivot;
+ /**
+ * The cluster radius.
+ */
+ DistanceType radius;
+ /**
+ * The cluster mean radius.
+ */
+ DistanceType mean_radius;
+ /**
+ * The cluster variance.
+ */
+ DistanceType variance;
+ /**
+ * The cluster size (number of points in the cluster)
+ */
+ int size;
+ /**
+ * Child nodes (only for non-terminal nodes)
+ */
+ KMeansNode** childs;
+ /**
+ * Node points (only for terminal nodes)
+ */
+ int* indices;
+ /**
+ * Level
+ */
+ int level;
+ };
+ typedef KMeansNode* KMeansNodePtr;
+
+ /**
+ * Alias definition for a nicer syntax.
+ */
+ typedef BranchStruct<KMeansNodePtr, DistanceType> BranchSt;
+
+
+
+
+ void save_tree(FILE* stream, KMeansNodePtr node)
+ {
+ save_value(stream, *node);
+ save_value(stream, *(node->pivot), (int)veclen_);
+ if (node->childs==NULL) {
+ int indices_offset = (int)(node->indices - indices_);
+ save_value(stream, indices_offset);
+ }
+ else {
+ for(int i=0; i<branching_; ++i) {
+ save_tree(stream, node->childs[i]);
+ }
+ }
+ }
+
+
+ void load_tree(FILE* stream, KMeansNodePtr& node)
+ {
+ node = pool_.allocate<KMeansNode>();
+ load_value(stream, *node);
+ node->pivot = new DistanceType[veclen_];
+ load_value(stream, *(node->pivot), (int)veclen_);
+ if (node->childs==NULL) {
+ int indices_offset;
+ load_value(stream, indices_offset);
+ node->indices = indices_ + indices_offset;
+ }
+ else {
+ node->childs = pool_.allocate<KMeansNodePtr>(branching_);
+ for(int i=0; i<branching_; ++i) {
+ load_tree(stream, node->childs[i]);
+ }
+ }
+ }
+
+
+ /**
+ * Helper function
+ */
+ void free_centers(KMeansNodePtr node)
+ {
+ delete[] node->pivot;
+ if (node->childs!=NULL) {
+ for (int k=0; k<branching_; ++k) {
+ free_centers(node->childs[k]);
+ }
+ }
+ }
+
+ /**
+ * Computes the statistics of a node (mean, radius, variance).
+ *
+ * Params:
+ * node = the node to use
+ * indices = the indices of the points belonging to the node
+ */
+ void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length)
+ {
+
+ DistanceType radius = 0;
+ DistanceType variance = 0;
+ DistanceType* mean = new DistanceType[veclen_];
+ memoryCounter_ += int(veclen_*sizeof(DistanceType));
+
+ memset(mean,0,veclen_*sizeof(DistanceType));
+
+ for (size_t i=0; i<size_; ++i) {
+ ElementType* vec = dataset_[indices[i]];
+ for (size_t j=0; j<veclen_; ++j) {
+ mean[j] += vec[j];
+ }
+ variance += distance_(vec, ZeroIterator<ElementType>(), veclen_);
+ }
+ for (size_t j=0; j<veclen_; ++j) {
+ mean[j] /= size_;
+ }
+ variance /= size_;
+ variance -= distance_(mean, ZeroIterator<ElementType>(), veclen_);
+
+ DistanceType tmp = 0;
+ for (int i=0; i<indices_length; ++i) {
+ tmp = distance_(mean, dataset_[indices[i]], veclen_);
+ if (tmp>radius) {
+ radius = tmp;
+ }
+ }
+
+ node->variance = variance;
+ node->radius = radius;
+ node->pivot = mean;
+ }
+
+
+ /**
+ * The method responsible with actually doing the recursive hierarchical
+ * clustering
+ *
+ * Params:
+ * node = the node to cluster
+ * indices = indices of the points belonging to the current node
+ * branching = the branching factor to use in the clustering
+ *
+ * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
+ */
+ void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level)
+ {
+ node->size = indices_length;
+ node->level = level;
+
+ if (indices_length < branching) {
+ node->indices = indices;
+ std::sort(node->indices,node->indices+indices_length);
+ node->childs = NULL;
+ return;
+ }
+
+ cv::AutoBuffer<int> centers_idx_buf(branching);
+ int* centers_idx = (int*)centers_idx_buf;
+ int centers_length;
+ (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length);
+
+ if (centers_length<branching) {
+ node->indices = indices;
+ std::sort(node->indices,node->indices+indices_length);
+ node->childs = NULL;
+ return;
+ }
+
+
+ cv::AutoBuffer<double> dcenters_buf(branching*veclen_);
+ Matrix<double> dcenters((double*)dcenters_buf,branching,veclen_);
+ for (int i=0; i<centers_length; ++i) {
+ ElementType* vec = dataset_[centers_idx[i]];
+ for (size_t k=0; k<veclen_; ++k) {
+ dcenters[i][k] = double(vec[k]);
+ }
+ }
+
+ std::vector<DistanceType> radiuses(branching);
+ cv::AutoBuffer<int> count_buf(branching);
+ int* count = (int*)count_buf;
+ for (int i=0; i<branching; ++i) {
+ radiuses[i] = 0;
+ count[i] = 0;
+ }
+
+ // assign points to clusters
+ cv::AutoBuffer<int> belongs_to_buf(indices_length);
+ int* belongs_to = (int*)belongs_to_buf;
+ for (int i=0; i<indices_length; ++i) {
+
+ DistanceType sq_dist = distance_(dataset_[indices[i]], dcenters[0], veclen_);
+ belongs_to[i] = 0;
+ for (int j=1; j<branching; ++j) {
+ DistanceType new_sq_dist = distance_(dataset_[indices[i]], dcenters[j], veclen_);
+ if (sq_dist>new_sq_dist) {
+ belongs_to[i] = j;
+ sq_dist = new_sq_dist;
+ }
+ }
+ if (sq_dist>radiuses[belongs_to[i]]) {
+ radiuses[belongs_to[i]] = sq_dist;
+ }
+ count[belongs_to[i]]++;
+ }
+
+ bool converged = false;
+ int iteration = 0;
+ while (!converged && iteration<iterations_) {
+ converged = true;
+ iteration++;
+
+ // compute the new cluster centers
+ for (int i=0; i<branching; ++i) {
+ memset(dcenters[i],0,sizeof(double)*veclen_);
+ radiuses[i] = 0;
+ }
+ for (int i=0; i<indices_length; ++i) {
+ ElementType* vec = dataset_[indices[i]];
+ double* center = dcenters[belongs_to[i]];
+ for (size_t k=0; k<veclen_; ++k) {
+ center[k] += vec[k];
+ }
+ }
+ for (int i=0; i<branching; ++i) {
+ int cnt = count[i];
+ for (size_t k=0; k<veclen_; ++k) {
+ dcenters[i][k] /= cnt;
+ }
+ }
+
+ // reassign points to clusters
+ cv::Mutex mtx;
+ KMeansDistanceComputer invoker(distance_, dataset_, branching, indices, dcenters, veclen_, count, belongs_to, radiuses, converged, mtx);
+ parallel_for_(cv::Range(0, (int)indices_length), invoker);
+
+ for (int i=0; i<branching; ++i) {
+ // if one cluster converges to an empty cluster,
+ // move an element into that cluster
+ if (count[i]==0) {
+ int j = (i+1)%branching;
+ while (count[j]<=1) {
+ j = (j+1)%branching;
+ }
+
+ for (int k=0; k<indices_length; ++k) {
+ if (belongs_to[k]==j) {
+ // for cluster j, we move the furthest element from the center to the empty cluster i
+ if ( distance_(dataset_[indices[k]], dcenters[j], veclen_) == radiuses[j] ) {
+ belongs_to[k] = i;
+ count[j]--;
+ count[i]++;
+ break;
+ }
+ }
+ }
+ converged = false;
+ }
+ }
+
+ }
+
+ DistanceType** centers = new DistanceType*[branching];
+
+ for (int i=0; i<branching; ++i) {
+ centers[i] = new DistanceType[veclen_];
+ memoryCounter_ += (int)(veclen_*sizeof(DistanceType));
+ for (size_t k=0; k<veclen_; ++k) {
+ centers[i][k] = (DistanceType)dcenters[i][k];
+ }
+ }
+
+
+ // compute kmeans clustering for each of the resulting clusters
+ node->childs = pool_.allocate<KMeansNodePtr>(branching);
+ int start = 0;
+ int end = start;
+ for (int c=0; c<branching; ++c) {
+ int s = count[c];
+
+ DistanceType variance = 0;
+ DistanceType mean_radius =0;
+ for (int i=0; i<indices_length; ++i) {
+ if (belongs_to[i]==c) {
+ DistanceType d = distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_);
+ variance += d;
+ mean_radius += sqrt(d);
+ std::swap(indices[i],indices[end]);
+ std::swap(belongs_to[i],belongs_to[end]);
+ end++;
+ }
+ }
+ variance /= s;
+ mean_radius /= s;
+ variance -= distance_(centers[c], ZeroIterator<ElementType>(), veclen_);
+
+ node->childs[c] = pool_.allocate<KMeansNode>();
+ std::memset(node->childs[c], 0, sizeof(KMeansNode));
+ node->childs[c]->radius = radiuses[c];
+ node->childs[c]->pivot = centers[c];
+ node->childs[c]->variance = variance;
+ node->childs[c]->mean_radius = mean_radius;
+ computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
+ start=end;
+ }
+
+ delete[] centers;
+ }
+
+
+
+ /**
+ * Performs one descent in the hierarchical k-means tree. The branches not
+ * visited are stored in a priority queue.
+ *
+ * Params:
+ * node = node to explore
+ * result = container for the k-nearest neighbors found
+ * vec = query points
+ * checks = how many points in the dataset have been checked so far
+ * maxChecks = maximum dataset points to checks
+ */
+
+
+ void findNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
+ Heap<BranchSt>* heap)
+ {
+ // Ignore those clusters that are too far away
+ {
+ DistanceType bsq = distance_(vec, node->pivot, veclen_);
+ DistanceType rsq = node->radius;
+ DistanceType wsq = result.worstDist();
+
+ DistanceType val = bsq-rsq-wsq;
+ DistanceType val2 = val*val-4*rsq*wsq;
+
+ //if (val>0) {
+ if ((val>0)&&(val2>0)) {
+ return;
+ }
+ }
+
+ if (node->childs==NULL) {
+ if (checks>=maxChecks) {
+ if (result.full()) return;
+ }
+ checks += node->size;
+ for (int i=0; i<node->size; ++i) {
+ int index = node->indices[i];
+ DistanceType dist = distance_(dataset_[index], vec, veclen_);
+ result.addPoint(dist, index);
+ }
+ }
+ else {
+ DistanceType* domain_distances = new DistanceType[branching_];
+ int closest_center = exploreNodeBranches(node, vec, domain_distances, heap);
+ delete[] domain_distances;
+ findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap);
+ }
+ }
+
+ /**
+ * Helper function that computes the nearest childs of a node to a given query point.
+ * Params:
+ * node = the node
+ * q = the query point
+ * distances = array with the distances to each child node.
+ * Returns:
+ */
+ int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, Heap<BranchSt>* heap)
+ {
+
+ int best_index = 0;
+ domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_);
+ for (int i=1; i<branching_; ++i) {
+ domain_distances[i] = distance_(q, node->childs[i]->pivot, veclen_);
+ if (domain_distances[i]<domain_distances[best_index]) {
+ best_index = i;
+ }
+ }
+
+ // float* best_center = node->childs[best_index]->pivot;
+ for (int i=0; i<branching_; ++i) {
+ if (i != best_index) {
+ domain_distances[i] -= cb_index_*node->childs[i]->variance;
+
+ // float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q);
+ // if (domain_distances[i]<dist_to_border) {
+ // domain_distances[i] = dist_to_border;
+ // }
+ heap->insert(BranchSt(node->childs[i],domain_distances[i]));
+ }
+ }
+
+ return best_index;
+ }
+
+
+ /**
+ * Function the performs exact nearest neighbor search by traversing the entire tree.
+ */
+ void findExactNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec)
+ {
+ // Ignore those clusters that are too far away
+ {
+ DistanceType bsq = distance_(vec, node->pivot, veclen_);
+ DistanceType rsq = node->radius;
+ DistanceType wsq = result.worstDist();
+
+ DistanceType val = bsq-rsq-wsq;
+ DistanceType val2 = val*val-4*rsq*wsq;
+
+ // if (val>0) {
+ if ((val>0)&&(val2>0)) {
+ return;
+ }
+ }
+
+
+ if (node->childs==NULL) {
+ for (int i=0; i<node->size; ++i) {
+ int index = node->indices[i];
+ DistanceType dist = distance_(dataset_[index], vec, veclen_);
+ result.addPoint(dist, index);
+ }
+ }
+ else {
+ int* sort_indices = new int[branching_];
+
+ getCenterOrdering(node, vec, sort_indices);
+
+ for (int i=0; i<branching_; ++i) {
+ findExactNN(node->childs[sort_indices[i]],result,vec);
+ }
+
+ delete[] sort_indices;
+ }
+ }
+
+
+ /**
+ * Helper function.
+ *
+ * I computes the order in which to traverse the child nodes of a particular node.
+ */
+ void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices)
+ {
+ DistanceType* domain_distances = new DistanceType[branching_];
+ for (int i=0; i<branching_; ++i) {
+ DistanceType dist = distance_(q, node->childs[i]->pivot, veclen_);
+
+ int j=0;
+ while (domain_distances[j]<dist && j<i) j++;
+ for (int k=i; k>j; --k) {
+ domain_distances[k] = domain_distances[k-1];
+ sort_indices[k] = sort_indices[k-1];
+ }
+ domain_distances[j] = dist;
+ sort_indices[j] = i;
+ }
+ delete[] domain_distances;
+ }
+
+ /**
+ * Method that computes the squared distance from the query point q
+ * from inside region with center c to the border between this
+ * region and the region with center p
+ */
+ DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q)
+ {
+ DistanceType sum = 0;
+ DistanceType sum2 = 0;
+
+ for (int i=0; i<veclen_; ++i) {
+ DistanceType t = c[i]-p[i];
+ sum += t*(q[i]-(c[i]+p[i])/2);
+ sum2 += t*t;
+ }
+
+ return sum*sum/sum2;
+ }
+
+
+ /**
+ * Helper function the descends in the hierarchical k-means tree by spliting those clusters that minimize
+ * the overall variance of the clustering.
+ * Params:
+ * root = root node
+ * clusters = array with clusters centers (return value)
+ * varianceValue = variance of the clustering (return value)
+ * Returns:
+ */
+ int getMinVarianceClusters(KMeansNodePtr root, KMeansNodePtr* clusters, int clusters_length, DistanceType& varianceValue)
+ {
+ int clusterCount = 1;
+ clusters[0] = root;
+
+ DistanceType meanVariance = root->variance*root->size;
+
+ while (clusterCount<clusters_length) {
+ DistanceType minVariance = (std::numeric_limits<DistanceType>::max)();
+ int splitIndex = -1;
+
+ for (int i=0; i<clusterCount; ++i) {
+ if (clusters[i]->childs != NULL) {
+
+ DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size;
+
+ for (int j=0; j<branching_; ++j) {
+ variance += clusters[i]->childs[j]->variance*clusters[i]->childs[j]->size;
+ }
+ if (variance<minVariance) {
+ minVariance = variance;
+ splitIndex = i;
+ }
+ }
+ }
+
+ if (splitIndex==-1) break;
+ if ( (branching_+clusterCount-1) > clusters_length) break;
+
+ meanVariance = minVariance;
+
+ // split node
+ KMeansNodePtr toSplit = clusters[splitIndex];
+ clusters[splitIndex] = toSplit->childs[0];
+ for (int i=1; i<branching_; ++i) {
+ clusters[clusterCount++] = toSplit->childs[i];
+ }
+ }
+
+ varianceValue = meanVariance/root->size;
+ return clusterCount;
+ }
+
+private:
+ /** The branching factor used in the hierarchical k-means clustering */
+ int branching_;
+
+ /** Maximum number of iterations to use when performing k-means clustering */
+ int iterations_;
+
+ /** Algorithm for choosing the cluster centers */
+ flann_centers_init_t centers_init_;
+
+ /**
+ * Cluster border index. This is used in the tree search phase when determining
+ * the closest cluster to explore next. A zero value takes into account only
+ * the cluster centres, a value greater then zero also take into account the size
+ * of the cluster.
+ */
+ float cb_index_;
+
+ /**
+ * The dataset used by this index
+ */
+ const Matrix<ElementType> dataset_;
+
+ /** Index parameters */
+ IndexParams index_params_;
+
+ /**
+ * Number of features in the dataset.
+ */
+ size_t size_;
+
+ /**
+ * Length of each feature.
+ */
+ size_t veclen_;
+
+ /**
+ * The root node in the tree.
+ */
+ KMeansNodePtr root_;
+
+ /**
+ * Array of indices to vectors in the dataset.
+ */
+ int* indices_;
+
+ /**
+ * The distance
+ */
+ Distance distance_;
+
+ /**
+ * Pooled memory allocator.
+ */
+ PooledAllocator pool_;
+
+ /**
+ * Memory occupied by the index.
+ */
+ int memoryCounter_;
+};
+
+}
+
+#endif //OPENCV_FLANN_KMEANS_INDEX_H_