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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.
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#ifndef OPENCV_FLANN_BASE_HPP_
#define OPENCV_FLANN_BASE_HPP_
#include <vector>
#include <cassert>
#include <cstdio>
#include "general.h"
#include "matrix.h"
#include "params.h"
#include "saving.h"
#include "all_indices.h"
namespace cvflann
{
/**
* Sets the log level used for all flann functions
* @param level Verbosity level
*/
inline void log_verbosity(int level)
{
if (level >= 0) {
Logger::setLevel(level);
}
}
/**
* (Deprecated) Index parameters for creating a saved index.
*/
struct SavedIndexParams : public IndexParams
{
SavedIndexParams(cv::String filename)
{
(* this)["algorithm"] = FLANN_INDEX_SAVED;
(*this)["filename"] = filename;
}
};
template<typename Distance>
NNIndex<Distance>* load_saved_index(const Matrix<typename Distance::ElementType>& dataset, const cv::String& filename, Distance distance)
{
typedef typename Distance::ElementType ElementType;
FILE* fin = fopen(filename.c_str(), "rb");
if (fin == NULL) {
return NULL;
}
IndexHeader header = load_header(fin);
if (header.data_type != Datatype<ElementType>::type()) {
throw FLANNException("Datatype of saved index is different than of the one to be created.");
}
if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) {
throw FLANNException("The index saved belongs to a different dataset");
}
IndexParams params;
params["algorithm"] = header.index_type;
NNIndex<Distance>* nnIndex = create_index_by_type<Distance>(dataset, params, distance);
nnIndex->loadIndex(fin);
fclose(fin);
return nnIndex;
}
template<typename Distance>
class Index : public NNIndex<Distance>
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
Index(const Matrix<ElementType>& features, const IndexParams& params, Distance distance = Distance() )
: index_params_(params)
{
flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
loaded_ = false;
if (index_type == FLANN_INDEX_SAVED) {
nnIndex_ = load_saved_index<Distance>(features, get_param<cv::String>(params,"filename"), distance);
loaded_ = true;
}
else {
nnIndex_ = create_index_by_type<Distance>(features, params, distance);
}
}
~Index()
{
delete nnIndex_;
}
/**
* Builds the index.
*/
void buildIndex()
{
if (!loaded_) {
nnIndex_->buildIndex();
}
}
void save(cv::String filename)
{
FILE* fout = fopen(filename.c_str(), "wb");
if (fout == NULL) {
throw FLANNException("Cannot open file");
}
save_header(fout, *nnIndex_);
saveIndex(fout);
fclose(fout);
}
/**
* \brief Saves the index to a stream
* \param stream The stream to save the index to
*/
virtual void saveIndex(FILE* stream)
{
nnIndex_->saveIndex(stream);
}
/**
* \brief Loads the index from a stream
* \param stream The stream from which the index is loaded
*/
virtual void loadIndex(FILE* stream)
{
nnIndex_->loadIndex(stream);
}
/**
* \returns number of features in this index.
*/
size_t veclen() const
{
return nnIndex_->veclen();
}
/**
* \returns The dimensionality of the features in this index.
*/
size_t size() const
{
return nnIndex_->size();
}
/**
* \returns The index type (kdtree, kmeans,...)
*/
flann_algorithm_t getType() const
{
return nnIndex_->getType();
}
/**
* \returns The amount of memory (in bytes) used by the index.
*/
virtual int usedMemory() const
{
return nnIndex_->usedMemory();
}
/**
* \returns The index parameters
*/
IndexParams getParameters() const
{
return nnIndex_->getParameters();
}
/**
* \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
*/
void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
{
nnIndex_->knnSearch(queries, indices, dists, knn, params);
}
/**
* \brief Perform radius search
* \param[in] query The query point
* \param[out] indices The indinces of the neighbors found within the given radius
* \param[out] dists The distances to the nearest neighbors found
* \param[in] radius The radius used for search
* \param[in] params Search parameters
* \returns Number of neighbors found
*/
int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params)
{
return nnIndex_->radiusSearch(query, indices, dists, radius, params);
}
/**
* \brief Method that searches for nearest-neighbours
*/
void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
{
nnIndex_->findNeighbors(result, vec, searchParams);
}
/**
* \brief Returns actual index
*/
FLANN_DEPRECATED NNIndex<Distance>* getIndex()
{
return nnIndex_;
}
/**
* \brief Returns index parameters.
* \deprecated use getParameters() instead.
*/
FLANN_DEPRECATED const IndexParams* getIndexParameters()
{
return &index_params_;
}
private:
/** Pointer to actual index class */
NNIndex<Distance>* nnIndex_;
/** Indices if the index was loaded from a file */
bool loaded_;
/** Parameters passed to the index */
IndexParams index_params_;
};
/**
* Performs a hierarchical clustering of the points passed as argument and then takes a cut in the
* the clustering tree to return a flat clustering.
* @param[in] points Points to be clustered
* @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the
* number of clusters requested.
* @param params Clustering parameters (The same as for cvflann::KMeansIndex)
* @param d Distance to be used for clustering (eg: cvflann::L2)
* @return number of clusters computed (can be different than clusters.rows and is the highest number
* of the form (branching-1)*K+1 smaller than clusters.rows).
*/
template <typename Distance>
int hierarchicalClustering(const Matrix<typename Distance::ElementType>& points, Matrix<typename Distance::ResultType>& centers,
const KMeansIndexParams& params, Distance d = Distance())
{
KMeansIndex<Distance> kmeans(points, params, d);
kmeans.buildIndex();
int clusterNum = kmeans.getClusterCenters(centers);
return clusterNum;
}
}
#endif /* OPENCV_FLANN_BASE_HPP_ */