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opencv_mv/QMainPro/QMacVisual/include/opencv2/flann/kdtree_index.h

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/***********************************************************************
* 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_KDTREE_INDEX_H_
#define OPENCV_FLANN_KDTREE_INDEX_H_
//! @cond IGNORED
#include <algorithm>
#include <map>
#include <cstring>
#include "nn_index.h"
#include "dynamic_bitset.h"
#include "matrix.h"
#include "result_set.h"
#include "heap.h"
#include "allocator.h"
#include "random.h"
#include "saving.h"
namespace cvflann
{
struct KDTreeIndexParams : public IndexParams
{
KDTreeIndexParams(int trees = 4)
{
(*this)["algorithm"] = FLANN_INDEX_KDTREE;
(*this)["trees"] = trees;
}
};
/**
* Randomized kd-tree index
*
* Contains the k-d trees and other information for indexing a set of points
* for nearest-neighbor matching.
*/
template <typename Distance>
class KDTreeIndex : public NNIndex<Distance>
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
/**
* KDTree constructor
*
* Params:
* inputData = dataset with the input features
* params = parameters passed to the kdtree algorithm
*/
KDTreeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeIndexParams(),
Distance d = Distance() ) :
dataset_(inputData), index_params_(params), distance_(d)
{
size_ = dataset_.rows;
veclen_ = dataset_.cols;
trees_ = get_param(index_params_,"trees",4);
tree_roots_ = new NodePtr[trees_];
// Create a permutable array of indices to the input vectors.
vind_.resize(size_);
for (size_t i = 0; i < size_; ++i) {
vind_[i] = int(i);
}
mean_ = new DistanceType[veclen_];
var_ = new DistanceType[veclen_];
}
KDTreeIndex(const KDTreeIndex&);
KDTreeIndex& operator=(const KDTreeIndex&);
/**
* Standard destructor
*/
~KDTreeIndex()
{
if (tree_roots_!=NULL) {
delete[] tree_roots_;
}
delete[] mean_;
delete[] var_;
}
/**
* Builds the index
*/
void buildIndex() CV_OVERRIDE
{
/* Construct the randomized trees. */
for (int i = 0; i < trees_; i++) {
/* Randomize the order of vectors to allow for unbiased sampling. */
#ifndef OPENCV_FLANN_USE_STD_RAND
cv::randShuffle(vind_);
#else
std::random_shuffle(vind_.begin(), vind_.end());
#endif
tree_roots_[i] = divideTree(&vind_[0], int(size_) );
}
}
flann_algorithm_t getType() const CV_OVERRIDE
{
return FLANN_INDEX_KDTREE;
}
void saveIndex(FILE* stream) CV_OVERRIDE
{
save_value(stream, trees_);
for (int i=0; i<trees_; ++i) {
save_tree(stream, tree_roots_[i]);
}
}
void loadIndex(FILE* stream) CV_OVERRIDE
{
load_value(stream, trees_);
if (tree_roots_!=NULL) {
delete[] tree_roots_;
}
tree_roots_ = new NodePtr[trees_];
for (int i=0; i<trees_; ++i) {
load_tree(stream,tree_roots_[i]);
}
index_params_["algorithm"] = getType();
index_params_["trees"] = tree_roots_;
}
/**
* Returns size of index.
*/
size_t size() const CV_OVERRIDE
{
return size_;
}
/**
* Returns the length of an index feature.
*/
size_t veclen() const CV_OVERRIDE
{
return veclen_;
}
/**
* Computes the inde memory usage
* Returns: memory used by the index
*/
int usedMemory() const CV_OVERRIDE
{
return int(pool_.usedMemory+pool_.wastedMemory+dataset_.rows*sizeof(int)); // pool memory and vind array memory
}
/**
* 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
* maxCheck = the maximum number of restarts (in a best-bin-first manner)
*/
void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
{
const int maxChecks = get_param(searchParams,"checks", 32);
const float epsError = 1+get_param(searchParams,"eps",0.0f);
const bool explore_all_trees = get_param(searchParams,"explore_all_trees",false);
if (maxChecks==FLANN_CHECKS_UNLIMITED) {
getExactNeighbors(result, vec, epsError);
}
else {
getNeighbors(result, vec, maxChecks, epsError, explore_all_trees);
}
}
IndexParams getParameters() const CV_OVERRIDE
{
return index_params_;
}
private:
/*--------------------- Internal Data Structures --------------------------*/
struct Node
{
/**
* Dimension used for subdivision.
*/
int divfeat;
/**
* The values used for subdivision.
*/
DistanceType divval;
/**
* The child nodes.
*/
Node* child1, * child2;
};
typedef Node* NodePtr;
typedef BranchStruct<NodePtr, DistanceType> BranchSt;
typedef BranchSt* Branch;
void save_tree(FILE* stream, NodePtr tree)
{
save_value(stream, *tree);
if (tree->child1!=NULL) {
save_tree(stream, tree->child1);
}
if (tree->child2!=NULL) {
save_tree(stream, tree->child2);
}
}
void load_tree(FILE* stream, NodePtr& tree)
{
tree = pool_.allocate<Node>();
load_value(stream, *tree);
if (tree->child1!=NULL) {
load_tree(stream, tree->child1);
}
if (tree->child2!=NULL) {
load_tree(stream, tree->child2);
}
}
/**
* Create a tree node that subdivides the list of vecs from vind[first]
* to vind[last]. The routine is called recursively on each sublist.
* Place a pointer to this new tree node in the location pTree.
*
* Params: pTree = the new node to create
* first = index of the first vector
* last = index of the last vector
*/
NodePtr divideTree(int* ind, int count)
{
NodePtr node = pool_.allocate<Node>(); // allocate memory
/* If too few exemplars remain, then make this a leaf node. */
if ( count == 1) {
node->child1 = node->child2 = NULL; /* Mark as leaf node. */
node->divfeat = *ind; /* Store index of this vec. */
}
else {
int idx;
int cutfeat;
DistanceType cutval;
meanSplit(ind, count, idx, cutfeat, cutval);
node->divfeat = cutfeat;
node->divval = cutval;
node->child1 = divideTree(ind, idx);
node->child2 = divideTree(ind+idx, count-idx);
}
return node;
}
/**
* Choose which feature to use in order to subdivide this set of vectors.
* Make a random choice among those with the highest variance, and use
* its variance as the threshold value.
*/
void meanSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval)
{
memset(mean_,0,veclen_*sizeof(DistanceType));
memset(var_,0,veclen_*sizeof(DistanceType));
/* Compute mean values. Only the first SAMPLE_MEAN values need to be
sampled to get a good estimate.
*/
int cnt = std::min((int)SAMPLE_MEAN+1, count);
for (int j = 0; j < cnt; ++j) {
ElementType* v = dataset_[ind[j]];
for (size_t k=0; k<veclen_; ++k) {
mean_[k] += v[k];
}
}
for (size_t k=0; k<veclen_; ++k) {
mean_[k] /= cnt;
}
/* Compute variances (no need to divide by count). */
for (int j = 0; j < cnt; ++j) {
ElementType* v = dataset_[ind[j]];
for (size_t k=0; k<veclen_; ++k) {
DistanceType dist = v[k] - mean_[k];
var_[k] += dist * dist;
}
}
/* Select one of the highest variance indices at random. */
cutfeat = selectDivision(var_);
cutval = mean_[cutfeat];
int lim1, lim2;
planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
if (lim1>count/2) index = lim1;
else if (lim2<count/2) index = lim2;
else index = count/2;
/* If either list is empty, it means that all remaining features
* are identical. Split in the middle to maintain a balanced tree.
*/
if ((lim1==count)||(lim2==0)) index = count/2;
}
/**
* Select the top RAND_DIM largest values from v and return the index of
* one of these selected at random.
*/
int selectDivision(DistanceType* v)
{
int num = 0;
size_t topind[RAND_DIM];
/* Create a list of the indices of the top RAND_DIM values. */
for (size_t i = 0; i < veclen_; ++i) {
if ((num < RAND_DIM)||(v[i] > v[topind[num-1]])) {
/* Put this element at end of topind. */
if (num < RAND_DIM) {
topind[num++] = i; /* Add to list. */
}
else {
topind[num-1] = i; /* Replace last element. */
}
/* Bubble end value down to right location by repeated swapping. */
int j = num - 1;
while (j > 0 && v[topind[j]] > v[topind[j-1]]) {
std::swap(topind[j], topind[j-1]);
--j;
}
}
}
/* Select a random integer in range [0,num-1], and return that index. */
int rnd = rand_int(num);
return (int)topind[rnd];
}
/**
* Subdivide the list of points by a plane perpendicular on axe corresponding
* to the 'cutfeat' dimension at 'cutval' position.
*
* On return:
* dataset[ind[0..lim1-1]][cutfeat]<cutval
* dataset[ind[lim1..lim2-1]][cutfeat]==cutval
* dataset[ind[lim2..count]][cutfeat]>cutval
*/
void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2)
{
/* Move vector indices for left subtree to front of list. */
int left = 0;
int right = count-1;
for (;; ) {
while (left<=right && dataset_[ind[left]][cutfeat]<cutval) ++left;
while (left<=right && dataset_[ind[right]][cutfeat]>=cutval) --right;
if (left>right) break;
std::swap(ind[left], ind[right]); ++left; --right;
}
lim1 = left;
right = count-1;
for (;; ) {
while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left;
while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right;
if (left>right) break;
std::swap(ind[left], ind[right]); ++left; --right;
}
lim2 = left;
}
/**
* Performs an exact nearest neighbor search. The exact search performs a full
* traversal of the tree.
*/
void getExactNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, float epsError)
{
// checkID -= 1; /* Set a different unique ID for each search. */
if (trees_ > 1) {
fprintf(stderr,"It doesn't make any sense to use more than one tree for exact search");
}
if (trees_>0) {
searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError);
}
CV_Assert(result.full());
}
/**
* Performs the approximate nearest-neighbor search. The search is approximate
* because the tree traversal is abandoned after a given number of descends in
* the tree.
*/
void getNeighbors(ResultSet<DistanceType>& result, const ElementType* vec,
int maxCheck, float epsError, bool explore_all_trees = false)
{
int i;
BranchSt branch;
int checkCount = 0;
DynamicBitset checked(size_);
// Priority queue storing intermediate branches in the best-bin-first search
const cv::Ptr<Heap<BranchSt>>& heap = Heap<BranchSt>::getPooledInstance(cv::utils::getThreadID(), (int)size_);
/* Search once through each tree down to root. */
for (i = 0; i < trees_; ++i) {
searchLevel(result, vec, tree_roots_[i], 0, checkCount, maxCheck,
epsError, heap, checked, explore_all_trees);
if (!explore_all_trees && (checkCount >= maxCheck) && result.full())
break;
}
/* Keep searching other branches from heap until finished. */
while ( heap->popMin(branch) && (checkCount < maxCheck || !result.full() )) {
searchLevel(result, vec, branch.node, branch.mindist, checkCount, maxCheck,
epsError, heap, checked, false);
}
CV_Assert(result.full());
}
/**
* Search starting from a given node of the tree. Based on any mismatches at
* higher levels, all exemplars below this level must have a distance of
* at least "mindistsq".
*/
void searchLevel(ResultSet<DistanceType>& result_set, const ElementType* vec, NodePtr node, DistanceType mindist, int& checkCount, int maxCheck,
float epsError, const cv::Ptr<Heap<BranchSt>>& heap, DynamicBitset& checked, bool explore_all_trees = false)
{
if (result_set.worstDist()<mindist) {
// printf("Ignoring branch, too far\n");
return;
}
/* If this is a leaf node, then do check and return. */
if ((node->child1 == NULL)&&(node->child2 == NULL)) {
/* Do not check same node more than once when searching multiple trees.
Once a vector is checked, we set its location in vind to the
current checkID.
*/
int index = node->divfeat;
if ( checked.test(index) ||
(!explore_all_trees && (checkCount>=maxCheck) && result_set.full()) ) {
return;
}
checked.set(index);
checkCount++;
DistanceType dist = distance_(dataset_[index], vec, veclen_);
result_set.addPoint(dist,index);
return;
}
/* Which child branch should be taken first? */
ElementType val = vec[node->divfeat];
DistanceType diff = val - node->divval;
NodePtr bestChild = (diff < 0) ? node->child1 : node->child2;
NodePtr otherChild = (diff < 0) ? node->child2 : node->child1;
/* Create a branch record for the branch not taken. Add distance
of this feature boundary (we don't attempt to correct for any
use of this feature in a parent node, which is unlikely to
happen and would have only a small effect). Don't bother
adding more branches to heap after halfway point, as cost of
adding exceeds their value.
*/
DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat);
// if (2 * checkCount < maxCheck || !result.full()) {
if ((new_distsq*epsError < result_set.worstDist())|| !result_set.full()) {
heap->insert( BranchSt(otherChild, new_distsq) );
}
/* Call recursively to search next level down. */
searchLevel(result_set, vec, bestChild, mindist, checkCount, maxCheck, epsError, heap, checked);
}
/**
* Performs an exact search in the tree starting from a node.
*/
void searchLevelExact(ResultSet<DistanceType>& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist, const float epsError)
{
/* If this is a leaf node, then do check and return. */
if ((node->child1 == NULL)&&(node->child2 == NULL)) {
int index = node->divfeat;
DistanceType dist = distance_(dataset_[index], vec, veclen_);
result_set.addPoint(dist,index);
return;
}
/* Which child branch should be taken first? */
ElementType val = vec[node->divfeat];
DistanceType diff = val - node->divval;
NodePtr bestChild = (diff < 0) ? node->child1 : node->child2;
NodePtr otherChild = (diff < 0) ? node->child2 : node->child1;
/* Create a branch record for the branch not taken. Add distance
of this feature boundary (we don't attempt to correct for any
use of this feature in a parent node, which is unlikely to
happen and would have only a small effect). Don't bother
adding more branches to heap after halfway point, as cost of
adding exceeds their value.
*/
DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat);
/* Call recursively to search next level down. */
searchLevelExact(result_set, vec, bestChild, mindist, epsError);
if (new_distsq*epsError<=result_set.worstDist()) {
searchLevelExact(result_set, vec, otherChild, new_distsq, epsError);
}
}
private:
enum
{
/**
* To improve efficiency, only SAMPLE_MEAN random values are used to
* compute the mean and variance at each level when building a tree.
* A value of 100 seems to perform as well as using all values.
*/
SAMPLE_MEAN = 100,
/**
* Top random dimensions to consider
*
* When creating random trees, the dimension on which to subdivide is
* selected at random from among the top RAND_DIM dimensions with the
* highest variance. A value of 5 works well.
*/
RAND_DIM=5
};
/**
* Number of randomized trees that are used
*/
int trees_;
/**
* Array of indices to vectors in the dataset.
*/
std::vector<int> vind_;
/**
* The dataset used by this index
*/
const Matrix<ElementType> dataset_;
IndexParams index_params_;
size_t size_;
size_t veclen_;
DistanceType* mean_;
DistanceType* var_;
/**
* Array of k-d trees used to find neighbours.
*/
NodePtr* tree_roots_;
/**
* Pooled memory allocator.
*
* Using a pooled memory allocator is more efficient
* than allocating memory directly when there is a large
* number small of memory allocations.
*/
PooledAllocator pool_;
Distance distance_;
}; // class KDTreeForest
}
//! @endcond
#endif //OPENCV_FLANN_KDTREE_INDEX_H_
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