mirror of
https://github.com/opencv/opencv.git
synced 2024-12-28 03:48:17 +08:00
156 lines
5.8 KiB
C++
156 lines
5.8 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.
|
|
*
|
|
* 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_ALL_INDICES_H_
|
|
#define OPENCV_FLANN_ALL_INDICES_H_
|
|
|
|
#include "general.h"
|
|
|
|
#include "nn_index.h"
|
|
#include "kdtree_index.h"
|
|
#include "kdtree_single_index.h"
|
|
#include "kmeans_index.h"
|
|
#include "composite_index.h"
|
|
#include "linear_index.h"
|
|
#include "hierarchical_clustering_index.h"
|
|
#include "lsh_index.h"
|
|
#include "autotuned_index.h"
|
|
|
|
|
|
namespace cvflann
|
|
{
|
|
|
|
template<typename KDTreeCapability, typename VectorSpace, typename Distance>
|
|
struct index_creator
|
|
{
|
|
static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
|
|
{
|
|
flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
|
|
|
|
NNIndex<Distance>* nnIndex;
|
|
switch (index_type) {
|
|
case FLANN_INDEX_LINEAR:
|
|
nnIndex = new LinearIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_KDTREE_SINGLE:
|
|
nnIndex = new KDTreeSingleIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_KDTREE:
|
|
nnIndex = new KDTreeIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_KMEANS:
|
|
nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_COMPOSITE:
|
|
nnIndex = new CompositeIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_AUTOTUNED:
|
|
nnIndex = new AutotunedIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_HIERARCHICAL:
|
|
nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_LSH:
|
|
nnIndex = new LshIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
default:
|
|
throw FLANNException("Unknown index type");
|
|
}
|
|
|
|
return nnIndex;
|
|
}
|
|
};
|
|
|
|
template<typename VectorSpace, typename Distance>
|
|
struct index_creator<False,VectorSpace,Distance>
|
|
{
|
|
static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
|
|
{
|
|
flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
|
|
|
|
NNIndex<Distance>* nnIndex;
|
|
switch (index_type) {
|
|
case FLANN_INDEX_LINEAR:
|
|
nnIndex = new LinearIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_KMEANS:
|
|
nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_HIERARCHICAL:
|
|
nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_LSH:
|
|
nnIndex = new LshIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
default:
|
|
throw FLANNException("Unknown index type");
|
|
}
|
|
|
|
return nnIndex;
|
|
}
|
|
};
|
|
|
|
template<typename Distance>
|
|
struct index_creator<False,False,Distance>
|
|
{
|
|
static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
|
|
{
|
|
flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
|
|
|
|
NNIndex<Distance>* nnIndex;
|
|
switch (index_type) {
|
|
case FLANN_INDEX_LINEAR:
|
|
nnIndex = new LinearIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_HIERARCHICAL:
|
|
nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
case FLANN_INDEX_LSH:
|
|
nnIndex = new LshIndex<Distance>(dataset, params, distance);
|
|
break;
|
|
default:
|
|
throw FLANNException("Unknown index type");
|
|
}
|
|
|
|
return nnIndex;
|
|
}
|
|
};
|
|
|
|
template<typename Distance>
|
|
NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
|
|
{
|
|
return index_creator<typename Distance::is_kdtree_distance,
|
|
typename Distance::is_vector_space_distance,
|
|
Distance>::create(dataset, params,distance);
|
|
}
|
|
|
|
}
|
|
|
|
#endif /* OPENCV_FLANN_ALL_INDICES_H_ */
|