626 lines
19 KiB
C++
626 lines
19 KiB
C++
/***********************************************************************
|
|
* 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_
|
|
|
|
#include <algorithm>
|
|
#include <map>
|
|
#include <cassert>
|
|
#include <cstring>
|
|
|
|
#include "general.h"
|
|
#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()
|
|
{
|
|
/* 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
|
|
{
|
|
return FLANN_INDEX_KDTREE;
|
|
}
|
|
|
|
|
|
void saveIndex(FILE* stream)
|
|
{
|
|
save_value(stream, trees_);
|
|
for (int i=0; i<trees_; ++i) {
|
|
save_tree(stream, tree_roots_[i]);
|
|
}
|
|
}
|
|
|
|
|
|
|
|
void loadIndex(FILE* stream)
|
|
{
|
|
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
|
|
{
|
|
return size_;
|
|
}
|
|
|
|
/**
|
|
* Returns the length of an index feature.
|
|
*/
|
|
size_t veclen() const
|
|
{
|
|
return veclen_;
|
|
}
|
|
|
|
/**
|
|
* Computes the inde memory usage
|
|
* Returns: memory used by the index
|
|
*/
|
|
int usedMemory() const
|
|
{
|
|
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)
|
|
{
|
|
int maxChecks = get_param(searchParams,"checks", 32);
|
|
float epsError = 1+get_param(searchParams,"eps",0.0f);
|
|
|
|
if (maxChecks==FLANN_CHECKS_UNLIMITED) {
|
|
getExactNeighbors(result, vec, epsError);
|
|
}
|
|
else {
|
|
getNeighbors(result, vec, maxChecks, epsError);
|
|
}
|
|
}
|
|
|
|
IndexParams getParameters() const
|
|
{
|
|
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);
|
|
}
|
|
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)
|
|
{
|
|
int i;
|
|
BranchSt branch;
|
|
|
|
int checkCount = 0;
|
|
Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
|
|
DynamicBitset checked(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);
|
|
}
|
|
|
|
/* 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);
|
|
}
|
|
|
|
delete heap;
|
|
|
|
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, Heap<BranchSt>* heap, DynamicBitset& checked)
|
|
{
|
|
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) || ((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
|
|
|
|
}
|
|
|
|
#endif //OPENCV_FLANN_KDTREE_INDEX_H_
|