/***********************************************************************
* 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
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/***********************************************************************
* Author: Vincent Rabaud
*************************************************************************/
#ifndef OPENCV_FLANN_LSH_INDEX_H_
#define OPENCV_FLANN_LSH_INDEX_H_
#include <algorithm>
#include <cassert>
#include <cstring>
#include <map>
#include <vector>
#include "general.h"
#include "nn_index.h"
#include "matrix.h"
#include "result_set.h"
#include "heap.h"
#include "lsh_table.h"
#include "allocator.h"
#include "random.h"
#include "saving.h"
namespace cvflann
{
struct LshIndexParams : public IndexParams
{
LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2)
{
(* this)["algorithm"] = FLANN_INDEX_LSH;
// The number of hash tables to use
(*this)["table_number"] = table_number;
// The length of the key in the hash tables
(*this)["key_size"] = key_size;
// Number of levels to use in multi-probe (0 for standard LSH)
(*this)["multi_probe_level"] = multi_probe_level;
}
};
/**
* 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 LshIndex : public NNIndex<Distance>
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
/** Constructor
* @param input_data dataset with the input features
* @param params parameters passed to the LSH algorithm
* @param d the distance used
*/
LshIndex(const Matrix<ElementType>& input_data, const IndexParams& params = LshIndexParams(),
Distance d = Distance()) :
dataset_(input_data), index_params_(params), distance_(d)
{
// cv::flann::IndexParams sets integer params as 'int', so it is used with get_param
// in place of 'unsigned int'
table_number_ = (unsigned int)get_param<int>(index_params_,"table_number",12);
key_size_ = (unsigned int)get_param<int>(index_params_,"key_size",20);
multi_probe_level_ = (unsigned int)get_param<int>(index_params_,"multi_probe_level",2);
feature_size_ = (unsigned)dataset_.cols;
fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
}
LshIndex(const LshIndex&);
LshIndex& operator=(const LshIndex&);
/**
* Builds the index
*/
void buildIndex()
{
tables_.resize(table_number_);
for (unsigned int i = 0; i < table_number_; ++i) {
lsh::LshTable<ElementType>& table = tables_[i];
table = lsh::LshTable<ElementType>(feature_size_, key_size_);
// Add the features to the table
table.add(dataset_);
}
}
flann_algorithm_t getType() const
{
return FLANN_INDEX_LSH;
}
void saveIndex(FILE* stream)
{
save_value(stream,table_number_);
save_value(stream,key_size_);
save_value(stream,multi_probe_level_);
save_value(stream, dataset_);
}
void loadIndex(FILE* stream)
{
load_value(stream, table_number_);
load_value(stream, key_size_);
load_value(stream, multi_probe_level_);
load_value(stream, dataset_);
// Building the index is so fast we can afford not storing it
buildIndex();
index_params_["algorithm"] = getType();
index_params_["table_number"] = table_number_;
index_params_["key_size"] = key_size_;
index_params_["multi_probe_level"] = multi_probe_level_;
}
/**
* Returns size of index.
*/
size_t size() const
{
return dataset_.rows;
}
/**
* Returns the length of an index feature.
*/
size_t veclen() const
{
return feature_size_;
}
/**
* Computes the index memory usage
* Returns: memory used by the index
*/
int usedMemory() const
{
return (int)(dataset_.rows * sizeof(int));
}
IndexParams getParameters() const
{
return index_params_;
}
/**
* \brief Perform k-nearest neighbor search
* \param[in] queries The query points for which to find the nearest neighbors
* \param[out] indices The indices of the nearest neighbors found
* \param[out] dists Distances to the nearest neighbors found
* \param[in] knn Number of nearest neighbors to return
* \param[in] params Search parameters
*/
virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
{
assert(queries.cols == veclen());
assert(indices.rows >= queries.rows);
assert(dists.rows >= queries.rows);
assert(int(indices.cols) >= knn);
assert(int(dists.cols) >= knn);
KNNUniqueResultSet<DistanceType> resultSet(knn);
for (size_t i = 0; i < queries.rows; i++) {
resultSet.clear();
std::fill_n(indices[i], knn, -1);
std::fill_n(dists[i], knn, std::numeric_limits<DistanceType>::max());
findNeighbors(resultSet, queries[i], params);
if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn);
else resultSet.copy(indices[i], dists[i], knn);
}
}
/**
* 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*/)
{
getNeighbors(vec, result);
}
private:
/** Defines the comparator on score and index
*/
typedef std::pair<float, unsigned int> ScoreIndexPair;
struct SortScoreIndexPairOnSecond
{
bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const
{
return left.second < right.second;
}
};
/** Fills the different xor masks to use when getting the neighbors in multi-probe LSH
* @param key the key we build neighbors from
* @param lowest_index the lowest index of the bit set
* @param level the multi-probe level we are at
* @param xor_masks all the xor mask
*/
void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level,
std::vector<lsh::BucketKey>& xor_masks)
{
xor_masks.push_back(key);
if (level == 0) return;
for (int index = lowest_index - 1; index >= 0; --index) {
// Create a new key
lsh::BucketKey new_key = key | (1 << index);
fill_xor_mask(new_key, index, level - 1, xor_masks);
}
}
/** Performs the approximate nearest-neighbor search.
* @param vec the feature to analyze
* @param do_radius flag indicating if we check the radius too
* @param radius the radius if it is a radius search
* @param do_k flag indicating if we limit the number of nn
* @param k_nn the number of nearest neighbors
* @param checked_average used for debugging
*/
void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn,
float& /*checked_average*/)
{
static std::vector<ScoreIndexPair> score_index_heap;
if (do_k) {
unsigned int worst_score = std::numeric_limits<unsigned int>::max();
typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
for (; table != table_end; ++table) {
size_t key = table->getKey(vec);
std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
for (; xor_mask != xor_mask_end; ++xor_mask) {
size_t sub_key = key ^ (*xor_mask);
const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
if (bucket == 0) continue;
// Go over each descriptor index
std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
DistanceType hamming_distance;
// Process the rest of the candidates
for (; training_index < last_training_index; ++training_index) {
hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
if (hamming_distance < worst_score) {
// Insert the new element
score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
std::push_heap(score_index_heap.begin(), score_index_heap.end());
if (score_index_heap.size() > (unsigned int)k_nn) {
// Remove the highest distance value as we have too many elements
std::pop_heap(score_index_heap.begin(), score_index_heap.end());
score_index_heap.pop_back();
// Keep track of the worst score
worst_score = score_index_heap.front().first;
}
}
}
}
}
}
else {
typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
for (; table != table_end; ++table) {
size_t key = table->getKey(vec);
std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
for (; xor_mask != xor_mask_end; ++xor_mask) {
size_t sub_key = key ^ (*xor_mask);
const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
if (bucket == 0) continue;
// Go over each descriptor index
std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
DistanceType hamming_distance;
// Process the rest of the candidates
for (; training_index < last_training_index; ++training_index) {
// Compute the Hamming distance
hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
}
}
}
}
}
/** Performs the approximate nearest-neighbor search.
* This is a slower version than the above as it uses the ResultSet
* @param vec the feature to analyze
*/
void getNeighbors(const ElementType* vec, ResultSet<DistanceType>& result)
{
typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
for (; table != table_end; ++table) {
size_t key = table->getKey(vec);
std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
for (; xor_mask != xor_mask_end; ++xor_mask) {
size_t sub_key = key ^ (*xor_mask);
const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key);
if (bucket == 0) continue;
// Go over each descriptor index
std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
DistanceType hamming_distance;
// Process the rest of the candidates
for (; training_index < last_training_index; ++training_index) {
// Compute the Hamming distance
hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols);
result.addPoint(hamming_distance, *training_index);
}
}
}
}
/** The different hash tables */
std::vector<lsh::LshTable<ElementType> > tables_;
/** The data the LSH tables where built from */
Matrix<ElementType> dataset_;
/** The size of the features (as ElementType[]) */
unsigned int feature_size_;
IndexParams index_params_;
/** table number */
unsigned int table_number_;
/** key size */
unsigned int key_size_;
/** How far should we look for neighbors in multi-probe LSH */
unsigned int multi_probe_level_;
/** The XOR masks to apply to a key to get the neighboring buckets */
std::vector<lsh::BucketKey> xor_masks_;
Distance distance_;
};
}
#endif //OPENCV_FLANN_LSH_INDEX_H_
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