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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_NNINDEX_H
#define OPENCV_FLANN_NNINDEX_H
#include <string>
#include "general.h"
#include "matrix.h"
#include "result_set.h"
#include "params.h"
namespace cvflann
{
/**
* Nearest-neighbour index base class
*/
template <typename Distance>
class NNIndex
{
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
public:
virtual ~NNIndex() {}
/**
* \brief Builds the index
*/
virtual void buildIndex() = 0;
/**
* \brief implementation for algorithms of addable indexes after that.
*/
virtual void addIndex(const Matrix<ElementType>& wholeData, const Matrix<ElementType>& additionalData) = 0;
/**
* \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);
#if 0
KNNResultSet<DistanceType> resultSet(knn);
for (size_t i = 0; i < queries.rows; i++) {
resultSet.init(indices[i], dists[i]);
findNeighbors(resultSet, queries[i], params);
}
#else
KNNUniqueResultSet<DistanceType> resultSet(knn);
for (size_t i = 0; i < queries.rows; i++) {
resultSet.clear();
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);
}
#endif
}
/**
* \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
*/
virtual int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params)
{
if (query.rows != 1) {
fprintf(stderr, "I can only search one feature at a time for range search\n");
return -1;
}
assert(query.cols == veclen());
assert(indices.cols == dists.cols);
int n = 0;
int* indices_ptr = NULL;
DistanceType* dists_ptr = NULL;
if (indices.cols > 0) {
n = (int)indices.cols;
indices_ptr = indices[0];
dists_ptr = dists[0];
}
RadiusUniqueResultSet<DistanceType> resultSet((DistanceType)radius);
resultSet.clear();
findNeighbors(resultSet, query[0], params);
if (n>0) {
if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices_ptr, dists_ptr, n);
else resultSet.copy(indices_ptr, dists_ptr, n);
}
return (int)resultSet.size();
}
/**
* \brief Saves the index to a stream
* \param stream The stream to save the index to
*/
virtual void saveIndex(FILE* stream) = 0;
/**
* \brief Loads the index from a stream
* \param stream The stream from which the index is loaded
*/
virtual void loadIndex(FILE* stream) = 0;
/**
* \returns number of features in this index.
*/
virtual size_t size() const = 0;
/**
* \returns The dimensionality of the features in this index.
*/
virtual size_t veclen() const = 0;
/**
* \returns The amount of memory (in bytes) used by the index.
*/
virtual int usedMemory() const = 0;
/**
* \returns The index type (kdtree, kmeans,...)
*/
virtual flann_algorithm_t getType() const = 0;
/**
* \returns The index parameters
*/
virtual IndexParams getParameters() const = 0;
/**
* \brief Method that searches for nearest-neighbours
*/
virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) = 0;
};
}
#endif //OPENCV_FLANN_NNINDEX_H
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