flann.hpp 23 KB
Newer Older
wester committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561
/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's 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.
//
//   * The name of the copyright holders may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "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 Intel Corporation or contributors 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.
//
//M*/

#ifndef _OPENCV_FLANN_HPP_
#define _OPENCV_FLANN_HPP_

#include "opencv2/core.hpp"
#include "opencv2/flann/miniflann.hpp"
#include "opencv2/flann/flann_base.hpp"

/**
@defgroup flann Clustering and Search in Multi-Dimensional Spaces

This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate
Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest
neighbor search in large datasets and for high dimensional features. More information about FLANN
can be found in @cite Muja2009 .
*/

namespace cvflann
{
    CV_EXPORTS flann_distance_t flann_distance_type();
    FLANN_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order);
}


namespace cv
{
namespace flann
{


//! @addtogroup flann
//! @{

template <typename T> struct CvType {};
template <> struct CvType<unsigned char> { static int type() { return CV_8U; } };
template <> struct CvType<char> { static int type() { return CV_8S; } };
template <> struct CvType<unsigned short> { static int type() { return CV_16U; } };
template <> struct CvType<short> { static int type() { return CV_16S; } };
template <> struct CvType<int> { static int type() { return CV_32S; } };
template <> struct CvType<float> { static int type() { return CV_32F; } };
template <> struct CvType<double> { static int type() { return CV_64F; } };


// bring the flann parameters into this namespace
using ::cvflann::get_param;
using ::cvflann::print_params;

// bring the flann distances into this namespace
using ::cvflann::L2_Simple;
using ::cvflann::L2;
using ::cvflann::L1;
using ::cvflann::MinkowskiDistance;
using ::cvflann::MaxDistance;
using ::cvflann::HammingLUT;
using ::cvflann::Hamming;
using ::cvflann::Hamming2;
using ::cvflann::HistIntersectionDistance;
using ::cvflann::HellingerDistance;
using ::cvflann::ChiSquareDistance;
using ::cvflann::KL_Divergence;


/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which
the index is built.
 */
template <typename Distance>
class GenericIndex
{
public:
        typedef typename Distance::ElementType ElementType;
        typedef typename Distance::ResultType DistanceType;

        /** @brief Constructs a nearest neighbor search index for a given dataset.

        @param features Matrix of containing the features(points) to index. The size of the matrix is
        num_features x feature_dimensionality and the data type of the elements in the matrix must
        coincide with the type of the index.
        @param params Structure containing the index parameters. The type of index that will be
        constructed depends on the type of this parameter. See the description.
        @param distance

        The method constructs a fast search structure from a set of features using the specified algorithm
        with specified parameters, as defined by params. params is a reference to one of the following class
        IndexParams descendants:

        - **LinearIndexParams** When passing an object of this type, the index will perform a linear,
        brute-force search. :
        @code
        struct LinearIndexParams : public IndexParams
        {
        };
        @endcode
        - **KDTreeIndexParams** When passing an object of this type the index constructed will consist of
        a set of randomized kd-trees which will be searched in parallel. :
        @code
        struct KDTreeIndexParams : public IndexParams
        {
            KDTreeIndexParams( int trees = 4 );
        };
        @endcode
        - **KMeansIndexParams** When passing an object of this type the index constructed will be a
        hierarchical k-means tree. :
        @code
        struct KMeansIndexParams : public IndexParams
        {
            KMeansIndexParams(
                int branching = 32,
                int iterations = 11,
                flann_centers_init_t centers_init = CENTERS_RANDOM,
                float cb_index = 0.2 );
        };
        @endcode
        - **CompositeIndexParams** When using a parameters object of this type the index created
        combines the randomized kd-trees and the hierarchical k-means tree. :
        @code
        struct CompositeIndexParams : public IndexParams
        {
            CompositeIndexParams(
                int trees = 4,
                int branching = 32,
                int iterations = 11,
                flann_centers_init_t centers_init = CENTERS_RANDOM,
                float cb_index = 0.2 );
        };
        @endcode
        - **LshIndexParams** When using a parameters object of this type the index created uses
        multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search
        by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd
        International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007) :
        @code
        struct LshIndexParams : public IndexParams
        {
            LshIndexParams(
                unsigned int table_number,
                unsigned int key_size,
                unsigned int multi_probe_level );
        };
        @endcode
        - **AutotunedIndexParams** When passing an object of this type the index created is
        automatically tuned to offer the best performance, by choosing the optimal index type
        (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. :
        @code
        struct AutotunedIndexParams : public IndexParams
        {
            AutotunedIndexParams(
                float target_precision = 0.9,
                float build_weight = 0.01,
                float memory_weight = 0,
                float sample_fraction = 0.1 );
        };
        @endcode
        - **SavedIndexParams** This object type is used for loading a previously saved index from the
        disk. :
        @code
        struct SavedIndexParams : public IndexParams
        {
            SavedIndexParams( String filename );
        };
        @endcode
         */
        GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance());

        ~GenericIndex();

        /** @brief Performs a K-nearest neighbor search for a given query point using the index.

        @param query The query point
        @param indices Vector that will contain the indices of the K-nearest neighbors found. It must have
        at least knn size.
        @param dists Vector that will contain the distances to the K-nearest neighbors found. It must have
        at least knn size.
        @param knn Number of nearest neighbors to search for.
        @param params SearchParams
         */
        void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices,
                       std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
        void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);

        int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices,
                         std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& params);
        int radiusSearch(const Mat& query, Mat& indices, Mat& dists,
                         DistanceType radius, const ::cvflann::SearchParams& params);

        void save(String filename) { nnIndex->save(filename); }

        int veclen() const { return nnIndex->veclen(); }

        int size() const { return nnIndex->size(); }

        ::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); }

        FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); }

private:
        ::cvflann::Index<Distance>* nnIndex;
};

//! @cond IGNORED

#define FLANN_DISTANCE_CHECK \
    if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \
        printf("[WARNING] You are using cv::flann::Index (or cv::flann::GenericIndex) and have also changed "\
        "the distance using cvflann::set_distance_type. This is no longer working as expected "\
        "(cv::flann::Index always uses L2). You should create the index templated on the distance, "\
        "for example for L1 distance use: GenericIndex< L1<float> > \n"); \
    }


template <typename Distance>
GenericIndex<Distance>::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Distance distance)
{
    CV_Assert(dataset.type() == CvType<ElementType>::type());
    CV_Assert(dataset.isContinuous());
    ::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);

    nnIndex = new ::cvflann::Index<Distance>(m_dataset, params, distance);

    FLANN_DISTANCE_CHECK

    nnIndex->buildIndex();
}

template <typename Distance>
GenericIndex<Distance>::~GenericIndex()
{
    delete nnIndex;
}

template <typename Distance>
void GenericIndex<Distance>::knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
    ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
    ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
    ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());

    FLANN_DISTANCE_CHECK

    nnIndex->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
}


template <typename Distance>
void GenericIndex<Distance>::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
    CV_Assert(queries.type() == CvType<ElementType>::type());
    CV_Assert(queries.isContinuous());
    ::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);

    CV_Assert(indices.type() == CV_32S);
    CV_Assert(indices.isContinuous());
    ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);

    CV_Assert(dists.type() == CvType<DistanceType>::type());
    CV_Assert(dists.isContinuous());
    ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);

    FLANN_DISTANCE_CHECK

    nnIndex->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
}

template <typename Distance>
int GenericIndex<Distance>::radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
    ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
    ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
    ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());

    FLANN_DISTANCE_CHECK

    return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}

template <typename Distance>
int GenericIndex<Distance>::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
    CV_Assert(query.type() == CvType<ElementType>::type());
    CV_Assert(query.isContinuous());
    ::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);

    CV_Assert(indices.type() == CV_32S);
    CV_Assert(indices.isContinuous());
    ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);

    CV_Assert(dists.type() == CvType<DistanceType>::type());
    CV_Assert(dists.isContinuous());
    ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);

    FLANN_DISTANCE_CHECK

    return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}

//! @endcond

/**
 * @deprecated Use GenericIndex class instead
 */
template <typename T>
class
#ifndef _MSC_VER
 FLANN_DEPRECATED
#endif
 Index_ {
public:
        typedef typename L2<T>::ElementType ElementType;
        typedef typename L2<T>::ResultType DistanceType;

    Index_(const Mat& features, const ::cvflann::IndexParams& params);

    ~Index_();

    void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
    void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);

    int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& params);
    int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& params);

    void save(String filename)
        {
            if (nnIndex_L1) nnIndex_L1->save(filename);
            if (nnIndex_L2) nnIndex_L2->save(filename);
        }

    int veclen() const
    {
            if (nnIndex_L1) return nnIndex_L1->veclen();
            if (nnIndex_L2) return nnIndex_L2->veclen();
        }

    int size() const
    {
            if (nnIndex_L1) return nnIndex_L1->size();
            if (nnIndex_L2) return nnIndex_L2->size();
        }

        ::cvflann::IndexParams getParameters()
        {
            if (nnIndex_L1) return nnIndex_L1->getParameters();
            if (nnIndex_L2) return nnIndex_L2->getParameters();

        }

        FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters()
        {
            if (nnIndex_L1) return nnIndex_L1->getIndexParameters();
            if (nnIndex_L2) return nnIndex_L2->getIndexParameters();
        }

private:
        // providing backwards compatibility for L2 and L1 distances (most common)
        ::cvflann::Index< L2<ElementType> >* nnIndex_L2;
        ::cvflann::Index< L1<ElementType> >* nnIndex_L1;
};

#ifdef _MSC_VER
template <typename T>
class FLANN_DEPRECATED Index_;
#endif

//! @cond IGNORED

template <typename T>
Index_<T>::Index_(const Mat& dataset, const ::cvflann::IndexParams& params)
{
    printf("[WARNING] The cv::flann::Index_<T> class is deperecated, use cv::flann::GenericIndex<Distance> instead\n");

    CV_Assert(dataset.type() == CvType<ElementType>::type());
    CV_Assert(dataset.isContinuous());
    ::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);

    if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
        nnIndex_L1 = NULL;
        nnIndex_L2 = new ::cvflann::Index< L2<ElementType> >(m_dataset, params);
    }
    else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
        nnIndex_L1 = new ::cvflann::Index< L1<ElementType> >(m_dataset, params);
        nnIndex_L2 = NULL;
    }
    else {
        printf("[ERROR] cv::flann::Index_<T> only provides backwards compatibility for the L1 and L2 distances. "
        "For other distance types you must use cv::flann::GenericIndex<Distance>\n");
        CV_Assert(0);
    }
    if (nnIndex_L1) nnIndex_L1->buildIndex();
    if (nnIndex_L2) nnIndex_L2->buildIndex();
}

template <typename T>
Index_<T>::~Index_()
{
    if (nnIndex_L1) delete nnIndex_L1;
    if (nnIndex_L2) delete nnIndex_L2;
}

template <typename T>
void Index_<T>::knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
    ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
    ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
    ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());

    if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
    if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
}


template <typename T>
void Index_<T>::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
    CV_Assert(queries.type() == CvType<ElementType>::type());
    CV_Assert(queries.isContinuous());
    ::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);

    CV_Assert(indices.type() == CV_32S);
    CV_Assert(indices.isContinuous());
    ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);

    CV_Assert(dists.type() == CvType<DistanceType>::type());
    CV_Assert(dists.isContinuous());
    ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);

    if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
    if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
}

template <typename T>
int Index_<T>::radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
    ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
    ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
    ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());

    if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
    if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}

template <typename T>
int Index_<T>::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
    CV_Assert(query.type() == CvType<ElementType>::type());
    CV_Assert(query.isContinuous());
    ::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);

    CV_Assert(indices.type() == CV_32S);
    CV_Assert(indices.isContinuous());
    ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);

    CV_Assert(dists.type() == CvType<DistanceType>::type());
    CV_Assert(dists.isContinuous());
    ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);

    if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
    if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}

//! @endcond

/** @brief Clusters features using hierarchical k-means algorithm.

@param features The points to be clustered. The matrix must have elements of type
Distance::ElementType.
@param centers The centers of the clusters obtained. The matrix must have type
Distance::ResultType. The number of rows in this matrix represents the number of clusters desired,
however, because of the way the cut in the hierarchical tree is chosen, the number of clusters
computed will be the highest number of the form (branching-1)\*k+1 that's lower than the number of
clusters desired, where branching is the tree's branching factor (see description of the
KMeansIndexParams).
@param params Parameters used in the construction of the hierarchical k-means tree.
@param d Distance to be used for clustering.

The method clusters the given feature vectors by constructing a hierarchical k-means tree and
choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters
found.
 */
template <typename Distance>
int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params,
                           Distance d = Distance())
{
    typedef typename Distance::ElementType ElementType;
    typedef typename Distance::ResultType DistanceType;

    CV_Assert(features.type() == CvType<ElementType>::type());
    CV_Assert(features.isContinuous());
    ::cvflann::Matrix<ElementType> m_features((ElementType*)features.ptr<ElementType>(0), features.rows, features.cols);

    CV_Assert(centers.type() == CvType<DistanceType>::type());
    CV_Assert(centers.isContinuous());
    ::cvflann::Matrix<DistanceType> m_centers((DistanceType*)centers.ptr<DistanceType>(0), centers.rows, centers.cols);

    return ::cvflann::hierarchicalClustering<Distance>(m_features, m_centers, params, d);
}

/** @deprecated
*/
template <typename ELEM_TYPE, typename DIST_TYPE>
FLANN_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params)
{
    printf("[WARNING] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> is deprecated, use "
        "cv::flann::hierarchicalClustering<Distance> instead\n");

    if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
        return hierarchicalClustering< L2<ELEM_TYPE> >(features, centers, params);
    }
    else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
        return hierarchicalClustering< L1<ELEM_TYPE> >(features, centers, params);
    }
    else {
        printf("[ERROR] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> only provides backwards "
        "compatibility for the L1 and L2 distances. "
        "For other distance types you must use cv::flann::hierarchicalClustering<Distance>\n");
        CV_Assert(0);
    }
}

//! @} flann

} } // namespace cv::flann

#endif