libopencv-flann-dev 2.3.1-7 / usr / include / opencv2 / flann / dist.h

/***********************************************************************
 * 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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 * modification, are permitted provided that the following conditions
 * are met:
 *
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 *    notice, this list of conditions and the following disclaimer.
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 *    notice, this list of conditions and the following disclaimer in the
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 *************************************************************************/

#ifndef OPENCV_FLANN_DIST_H_
#define OPENCV_FLANN_DIST_H_

#include <cmath>
#include <cstdlib>
#include <string.h>
#ifdef _MSC_VER
typedef unsigned uint32_t;
typedef unsigned __int64 uint64_t;
#else
#include <stdint.h>
#endif

#include "defines.h"


namespace cvflann
{

template<typename T>
inline T abs(T x) { return (x<0) ? -x : x; }

template<>
inline int abs<int>(int x) { return ::abs(x); }

template<>
inline float abs<float>(float x) { return fabsf(x); }

template<>
inline double abs<double>(double x) { return fabs(x); }

template<>
inline long double abs<long double>(long double x) { return fabsl(x); }


template<typename T>
struct Accumulator { typedef T Type; };
template<>
struct Accumulator<unsigned char>  { typedef float Type; };
template<>
struct Accumulator<unsigned short> { typedef float Type; };
template<>
struct Accumulator<unsigned int> { typedef float Type; };
template<>
struct Accumulator<char>   { typedef float Type; };
template<>
struct Accumulator<short>  { typedef float Type; };
template<>
struct Accumulator<int> { typedef float Type; };


class True
{
};

class False
{
};


/**
 * Squared Euclidean distance functor.
 *
 * This is the simpler, unrolled version. This is preferable for
 * very low dimensionality data (eg 3D points)
 */
template<class T>
struct L2_Simple
{
    typedef True is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
    {
        ResultType result = ResultType();
        ResultType diff;
        for(size_t i = 0; i < size; ++i ) {
            diff = *a++ - *b++;
            result += diff*diff;
        }
        return result;
    }

    template <typename U, typename V>
    inline ResultType accum_dist(const U& a, const V& b, int) const
    {
        return (a-b)*(a-b);
    }
};



/**
 * Squared Euclidean distance functor, optimized version
 */
template<class T>
struct L2
{
    typedef True is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    /**
     *  Compute the squared Euclidean distance between two vectors.
     *
     *	This is highly optimised, with loop unrolling, as it is one
     *	of the most expensive inner loops.
     *
     *	The computation of squared root at the end is omitted for
     *	efficiency.
     */
    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
    {
        ResultType result = ResultType();
        ResultType diff0, diff1, diff2, diff3;
        Iterator1 last = a + size;
        Iterator1 lastgroup = last - 3;

        /* Process 4 items with each loop for efficiency. */
        while (a < lastgroup) {
            diff0 = (ResultType)(a[0] - b[0]);
            diff1 = (ResultType)(a[1] - b[1]);
            diff2 = (ResultType)(a[2] - b[2]);
            diff3 = (ResultType)(a[3] - b[3]);
            result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
            a += 4;
            b += 4;

            if ((worst_dist>0)&&(result>worst_dist)) {
                return result;
            }
        }
        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
        while (a < last) {
            diff0 = (ResultType)(*a++ - *b++);
            result += diff0 * diff0;
        }
        return result;
    }

    /**
     *	Partial euclidean distance, using just one dimension. This is used by the
     *	kd-tree when computing partial distances while traversing the tree.
     *
     *	Squared root is omitted for efficiency.
     */
    template <typename U, typename V>
    inline ResultType accum_dist(const U& a, const V& b, int) const
    {
        return (a-b)*(a-b);
    }
};


/*
 * Manhattan distance functor, optimized version
 */
template<class T>
struct L1
{
    typedef True is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    /**
     *  Compute the Manhattan (L_1) distance between two vectors.
     *
     *	This is highly optimised, with loop unrolling, as it is one
     *	of the most expensive inner loops.
     */
    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
    {
        ResultType result = ResultType();
        ResultType diff0, diff1, diff2, diff3;
        Iterator1 last = a + size;
        Iterator1 lastgroup = last - 3;

        /* Process 4 items with each loop for efficiency. */
        while (a < lastgroup) {
            diff0 = (ResultType)abs(a[0] - b[0]);
            diff1 = (ResultType)abs(a[1] - b[1]);
            diff2 = (ResultType)abs(a[2] - b[2]);
            diff3 = (ResultType)abs(a[3] - b[3]);
            result += diff0 + diff1 + diff2 + diff3;
            a += 4;
            b += 4;

            if ((worst_dist>0)&&(result>worst_dist)) {
                return result;
            }
        }
        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
        while (a < last) {
            diff0 = (ResultType)abs(*a++ - *b++);
            result += diff0;
        }
        return result;
    }

    /**
     * Partial distance, used by the kd-tree.
     */
    template <typename U, typename V>
    inline ResultType accum_dist(const U& a, const V& b, int) const
    {
        return abs(a-b);
    }
};



template<class T>
struct MinkowskiDistance
{
    typedef True is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    int order;

    MinkowskiDistance(int order_) : order(order_) {}

    /**
     *  Compute the Minkowsky (L_p) distance between two vectors.
     *
     *	This is highly optimised, with loop unrolling, as it is one
     *	of the most expensive inner loops.
     *
     *	The computation of squared root at the end is omitted for
     *	efficiency.
     */
    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
    {
        ResultType result = ResultType();
        ResultType diff0, diff1, diff2, diff3;
        Iterator1 last = a + size;
        Iterator1 lastgroup = last - 3;

        /* Process 4 items with each loop for efficiency. */
        while (a < lastgroup) {
            diff0 = (ResultType)abs(a[0] - b[0]);
            diff1 = (ResultType)abs(a[1] - b[1]);
            diff2 = (ResultType)abs(a[2] - b[2]);
            diff3 = (ResultType)abs(a[3] - b[3]);
            result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order);
            a += 4;
            b += 4;

            if ((worst_dist>0)&&(result>worst_dist)) {
                return result;
            }
        }
        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
        while (a < last) {
            diff0 = (ResultType)abs(*a++ - *b++);
            result += pow(diff0,order);
        }
        return result;
    }

    /**
     * Partial distance, used by the kd-tree.
     */
    template <typename U, typename V>
    inline ResultType accum_dist(const U& a, const V& b, int) const
    {
        return pow(static_cast<ResultType>(abs(a-b)),order);
    }
};



template<class T>
struct MaxDistance
{
    typedef False is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    /**
     *  Compute the max distance (L_infinity) between two vectors.
     *
     *  This distance is not a valid kdtree distance, it's not dimensionwise additive.
     */
    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
    {
        ResultType result = ResultType();
        ResultType diff0, diff1, diff2, diff3;
        Iterator1 last = a + size;
        Iterator1 lastgroup = last - 3;

        /* Process 4 items with each loop for efficiency. */
        while (a < lastgroup) {
            diff0 = abs(a[0] - b[0]);
            diff1 = abs(a[1] - b[1]);
            diff2 = abs(a[2] - b[2]);
            diff3 = abs(a[3] - b[3]);
            if (diff0>result) {result = diff0; }
            if (diff1>result) {result = diff1; }
            if (diff2>result) {result = diff2; }
            if (diff3>result) {result = diff3; }
            a += 4;
            b += 4;

            if ((worst_dist>0)&&(result>worst_dist)) {
                return result;
            }
        }
        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
        while (a < last) {
            diff0 = abs(*a++ - *b++);
            result = (diff0>result) ? diff0 : result;
        }
        return result;
    }

    /* This distance functor is not dimension-wise additive, which
     * makes it an invalid kd-tree distance, not implementing the accum_dist method */

};

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

/**
 * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
 * bit count of A exclusive XOR'ed with B
 */
struct HammingLUT
{
    typedef False is_kdtree_distance;
    typedef False is_vector_space_distance;

    typedef unsigned char ElementType;
    typedef int ResultType;

    /** this will count the bits in a ^ b
     */
    ResultType operator()(const unsigned char* a, const unsigned char* b, int size) const
    {
        ResultType result = 0;
        for (int i = 0; i < size; i++) {
            result += byteBitsLookUp(a[i] ^ b[i]);
        }
        return result;
    }


    /** \brief given a byte, count the bits using a look up table
     *  \param b the byte to count bits.  The look up table has an entry for all
     *  values of b, where that entry is the number of bits.
     *  \return the number of bits in byte b
     */
    static unsigned char byteBitsLookUp(unsigned char b)
    {
        static const unsigned char table[256]  = {
            /* 0 */ 0, /* 1 */ 1, /* 2 */ 1, /* 3 */ 2,
            /* 4 */ 1, /* 5 */ 2, /* 6 */ 2, /* 7 */ 3,
            /* 8 */ 1, /* 9 */ 2, /* a */ 2, /* b */ 3,
            /* c */ 2, /* d */ 3, /* e */ 3, /* f */ 4,
            /* 10 */ 1, /* 11 */ 2, /* 12 */ 2, /* 13 */ 3,
            /* 14 */ 2, /* 15 */ 3, /* 16 */ 3, /* 17 */ 4,
            /* 18 */ 2, /* 19 */ 3, /* 1a */ 3, /* 1b */ 4,
            /* 1c */ 3, /* 1d */ 4, /* 1e */ 4, /* 1f */ 5,
            /* 20 */ 1, /* 21 */ 2, /* 22 */ 2, /* 23 */ 3,
            /* 24 */ 2, /* 25 */ 3, /* 26 */ 3, /* 27 */ 4,
            /* 28 */ 2, /* 29 */ 3, /* 2a */ 3, /* 2b */ 4,
            /* 2c */ 3, /* 2d */ 4, /* 2e */ 4, /* 2f */ 5,
            /* 30 */ 2, /* 31 */ 3, /* 32 */ 3, /* 33 */ 4,
            /* 34 */ 3, /* 35 */ 4, /* 36 */ 4, /* 37 */ 5,
            /* 38 */ 3, /* 39 */ 4, /* 3a */ 4, /* 3b */ 5,
            /* 3c */ 4, /* 3d */ 5, /* 3e */ 5, /* 3f */ 6,
            /* 40 */ 1, /* 41 */ 2, /* 42 */ 2, /* 43 */ 3,
            /* 44 */ 2, /* 45 */ 3, /* 46 */ 3, /* 47 */ 4,
            /* 48 */ 2, /* 49 */ 3, /* 4a */ 3, /* 4b */ 4,
            /* 4c */ 3, /* 4d */ 4, /* 4e */ 4, /* 4f */ 5,
            /* 50 */ 2, /* 51 */ 3, /* 52 */ 3, /* 53 */ 4,
            /* 54 */ 3, /* 55 */ 4, /* 56 */ 4, /* 57 */ 5,
            /* 58 */ 3, /* 59 */ 4, /* 5a */ 4, /* 5b */ 5,
            /* 5c */ 4, /* 5d */ 5, /* 5e */ 5, /* 5f */ 6,
            /* 60 */ 2, /* 61 */ 3, /* 62 */ 3, /* 63 */ 4,
            /* 64 */ 3, /* 65 */ 4, /* 66 */ 4, /* 67 */ 5,
            /* 68 */ 3, /* 69 */ 4, /* 6a */ 4, /* 6b */ 5,
            /* 6c */ 4, /* 6d */ 5, /* 6e */ 5, /* 6f */ 6,
            /* 70 */ 3, /* 71 */ 4, /* 72 */ 4, /* 73 */ 5,
            /* 74 */ 4, /* 75 */ 5, /* 76 */ 5, /* 77 */ 6,
            /* 78 */ 4, /* 79 */ 5, /* 7a */ 5, /* 7b */ 6,
            /* 7c */ 5, /* 7d */ 6, /* 7e */ 6, /* 7f */ 7,
            /* 80 */ 1, /* 81 */ 2, /* 82 */ 2, /* 83 */ 3,
            /* 84 */ 2, /* 85 */ 3, /* 86 */ 3, /* 87 */ 4,
            /* 88 */ 2, /* 89 */ 3, /* 8a */ 3, /* 8b */ 4,
            /* 8c */ 3, /* 8d */ 4, /* 8e */ 4, /* 8f */ 5,
            /* 90 */ 2, /* 91 */ 3, /* 92 */ 3, /* 93 */ 4,
            /* 94 */ 3, /* 95 */ 4, /* 96 */ 4, /* 97 */ 5,
            /* 98 */ 3, /* 99 */ 4, /* 9a */ 4, /* 9b */ 5,
            /* 9c */ 4, /* 9d */ 5, /* 9e */ 5, /* 9f */ 6,
            /* a0 */ 2, /* a1 */ 3, /* a2 */ 3, /* a3 */ 4,
            /* a4 */ 3, /* a5 */ 4, /* a6 */ 4, /* a7 */ 5,
            /* a8 */ 3, /* a9 */ 4, /* aa */ 4, /* ab */ 5,
            /* ac */ 4, /* ad */ 5, /* ae */ 5, /* af */ 6,
            /* b0 */ 3, /* b1 */ 4, /* b2 */ 4, /* b3 */ 5,
            /* b4 */ 4, /* b5 */ 5, /* b6 */ 5, /* b7 */ 6,
            /* b8 */ 4, /* b9 */ 5, /* ba */ 5, /* bb */ 6,
            /* bc */ 5, /* bd */ 6, /* be */ 6, /* bf */ 7,
            /* c0 */ 2, /* c1 */ 3, /* c2 */ 3, /* c3 */ 4,
            /* c4 */ 3, /* c5 */ 4, /* c6 */ 4, /* c7 */ 5,
            /* c8 */ 3, /* c9 */ 4, /* ca */ 4, /* cb */ 5,
            /* cc */ 4, /* cd */ 5, /* ce */ 5, /* cf */ 6,
            /* d0 */ 3, /* d1 */ 4, /* d2 */ 4, /* d3 */ 5,
            /* d4 */ 4, /* d5 */ 5, /* d6 */ 5, /* d7 */ 6,
            /* d8 */ 4, /* d9 */ 5, /* da */ 5, /* db */ 6,
            /* dc */ 5, /* dd */ 6, /* de */ 6, /* df */ 7,
            /* e0 */ 3, /* e1 */ 4, /* e2 */ 4, /* e3 */ 5,
            /* e4 */ 4, /* e5 */ 5, /* e6 */ 5, /* e7 */ 6,
            /* e8 */ 4, /* e9 */ 5, /* ea */ 5, /* eb */ 6,
            /* ec */ 5, /* ed */ 6, /* ee */ 6, /* ef */ 7,
            /* f0 */ 4, /* f1 */ 5, /* f2 */ 5, /* f3 */ 6,
            /* f4 */ 5, /* f5 */ 6, /* f6 */ 6, /* f7 */ 7,
            /* f8 */ 5, /* f9 */ 6, /* fa */ 6, /* fb */ 7,
            /* fc */ 6, /* fd */ 7, /* fe */ 7, /* ff */ 8
        };
        return table[b];
    }
};

/**
 * Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set)
 * That code was taken from brief.cpp in OpenCV
 */
template<class T>
struct Hamming
{
    typedef False is_kdtree_distance;
    typedef False is_vector_space_distance;


    typedef T ElementType;
    typedef int ResultType;

    template<typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
    {
        ResultType result = 0;
#if __GNUC__
#if ANDROID && HAVE_NEON
        static uint64_t features = android_getCpuFeatures();
        if ((features& ANDROID_CPU_ARM_FEATURE_NEON)) {
            for (size_t i = 0; i < size; i += 16) {
                uint8x16_t A_vec = vld1q_u8 (a + i);
                uint8x16_t B_vec = vld1q_u8 (b + i);
                //uint8x16_t veorq_u8 (uint8x16_t, uint8x16_t)
                uint8x16_t AxorB = veorq_u8 (A_vec, B_vec);

                uint8x16_t bitsSet += vcntq_u8 (AxorB);
                //uint16x8_t vpadalq_u8 (uint16x8_t, uint8x16_t)
                uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
                uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);

                uint64x2_t bitSet2 = vpaddlq_u32 (bitSet4);
                result += vgetq_lane_u64 (bitSet2,0);
                result += vgetq_lane_u64 (bitSet2,1);
            }
        }
        else
#endif
        //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll)
        typedef unsigned long long pop_t;
        const size_t modulo = size % sizeof(pop_t);
        const pop_t* a2 = reinterpret_cast<const pop_t*> (a);
        const pop_t* b2 = reinterpret_cast<const pop_t*> (b);
        const pop_t* a2_end = a2 + (size / sizeof(pop_t));

        for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2));

        if (modulo) {
            //in the case where size is not dividable by sizeof(size_t)
            //need to mask off the bits at the end
            pop_t a_final = 0, b_final = 0;
            memcpy(&a_final, a2, modulo);
            memcpy(&b_final, b2, modulo);
            result += __builtin_popcountll(a_final ^ b_final);
        }
#else
        HammingLUT lut;
        result = lut(reinterpret_cast<const unsigned char*> (a),
                     reinterpret_cast<const unsigned char*> (b), size * sizeof(pop_t));
#endif
        return result;
    }
};

template<typename T>
struct Hamming2
{
    typedef False is_kdtree_distance;
    typedef False is_vector_space_distance;

    typedef T ElementType;
    typedef int ResultType;

    /** This is popcount_3() from:
     * http://en.wikipedia.org/wiki/Hamming_weight */
    unsigned int popcnt32(uint32_t n) const
    {
        n -= ((n >> 1) & 0x55555555);
        n = (n & 0x33333333) + ((n >> 2) & 0x33333333);
        return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24;
    }

#ifdef FLANN_PLATFORM_64_BIT
    unsigned int popcnt64(uint64_t n) const
    {
        n -= ((n >> 1) & 0x5555555555555555);
        n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333);
        return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56;
    }
#endif

    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
    {
#ifdef FLANN_PLATFORM_64_BIT
        const uint64_t* pa = reinterpret_cast<const uint64_t*>(a);
        const uint64_t* pb = reinterpret_cast<const uint64_t*>(b);
        ResultType result = 0;
        size /= (sizeof(uint64_t)/sizeof(unsigned char));
        for(size_t i = 0; i < size; ++i ) {
            result += popcnt64(*pa ^ *pb);
            ++pa;
            ++pb;
        }
#else
        const uint32_t* pa = reinterpret_cast<const uint32_t*>(a);
        const uint32_t* pb = reinterpret_cast<const uint32_t*>(b);
        ResultType result = 0;
        size /= (sizeof(uint32_t)/sizeof(unsigned char));
        for(size_t i = 0; i < size; ++i ) {
        	result += popcnt32(*pa ^ *pb);
        	++pa;
        	++pb;
        }
#endif
        return result;
    }
};



////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

template<class T>
struct HistIntersectionDistance
{
    typedef True is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    /**
     *  Compute the histogram intersection distance
     */
    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
    {
        ResultType result = ResultType();
        ResultType min0, min1, min2, min3;
        Iterator1 last = a + size;
        Iterator1 lastgroup = last - 3;

        /* Process 4 items with each loop for efficiency. */
        while (a < lastgroup) {
            min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]);
            min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]);
            min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]);
            min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]);
            result += min0 + min1 + min2 + min3;
            a += 4;
            b += 4;
            if ((worst_dist>0)&&(result>worst_dist)) {
                return result;
            }
        }
        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
        while (a < last) {
            min0 = (ResultType)(*a < *b ? *a : *b);
            result += min0;
        }
        return result;
    }

    /**
     * Partial distance, used by the kd-tree.
     */
    template <typename U, typename V>
    inline ResultType accum_dist(const U& a, const V& b, int) const
    {
        return a<b ? a : b;
    }
};



template<class T>
struct HellingerDistance
{
    typedef True is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    /**
     *  Compute the histogram intersection distance
     */
    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
    {
        ResultType result = ResultType();
        ResultType diff0, diff1, diff2, diff3;
        Iterator1 last = a + size;
        Iterator1 lastgroup = last - 3;

        /* Process 4 items with each loop for efficiency. */
        while (a < lastgroup) {
            diff0 = sqrt(static_cast<ResultType>(a[0])) - sqrt(static_cast<ResultType>(b[0]));
            diff1 = sqrt(static_cast<ResultType>(a[1])) - sqrt(static_cast<ResultType>(b[1]));
            diff2 = sqrt(static_cast<ResultType>(a[2])) - sqrt(static_cast<ResultType>(b[2]));
            diff3 = sqrt(static_cast<ResultType>(a[3])) - sqrt(static_cast<ResultType>(b[3]));
            result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
            a += 4;
            b += 4;
        }
        while (a < last) {
            diff0 = sqrt(static_cast<ResultType>(*a++)) - sqrt(static_cast<ResultType>(*b++));
            result += diff0 * diff0;
        }
        return result;
    }

    /**
     * Partial distance, used by the kd-tree.
     */
    template <typename U, typename V>
    inline ResultType accum_dist(const U& a, const V& b, int) const
    {
        return sqrt(static_cast<ResultType>(a)) - sqrt(static_cast<ResultType>(b));
    }
};


template<class T>
struct ChiSquareDistance
{
    typedef True is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    /**
     *  Compute the chi-square distance
     */
    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
    {
        ResultType result = ResultType();
        ResultType sum, diff;
        Iterator1 last = a + size;

        while (a < last) {
            sum = (ResultType)(*a + *b);
            if (sum>0) {
                diff = (ResultType)(*a - *b);
                result += diff*diff/sum;
            }
            ++a;
            ++b;

            if ((worst_dist>0)&&(result>worst_dist)) {
                return result;
            }
        }
        return result;
    }

    /**
     * Partial distance, used by the kd-tree.
     */
    template <typename U, typename V>
    inline ResultType accum_dist(const U& a, const V& b, int) const
    {
        ResultType result = ResultType();
        ResultType sum, diff;

        sum = (ResultType)(a+b);
        if (sum>0) {
            diff = (ResultType)(a-b);
            result = diff*diff/sum;
        }
        return result;
    }
};


template<class T>
struct KL_Divergence
{
    typedef True is_kdtree_distance;
    typedef True is_vector_space_distance;

    typedef T ElementType;
    typedef typename Accumulator<T>::Type ResultType;

    /**
     *  Compute the Kullback–Leibler divergence
     */
    template <typename Iterator1, typename Iterator2>
    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
    {
        ResultType result = ResultType();
        Iterator1 last = a + size;

        while (a < last) {
            if (* a != 0) {
                ResultType ratio = (ResultType)(*a / *b);
                if (ratio>0) {
                    result += *a * log(ratio);
                }
            }
            ++a;
            ++b;

            if ((worst_dist>0)&&(result>worst_dist)) {
                return result;
            }
        }
        return result;
    }

    /**
     * Partial distance, used by the kd-tree.
     */
    template <typename U, typename V>
    inline ResultType accum_dist(const U& a, const V& b, int) const
    {
        ResultType result = ResultType();
        ResultType ratio = (ResultType)(a / b);
        if (ratio>0) {
            result = a * log(ratio);
        }
        return result;
    }
};



/*
 * This is a "zero iterator". It basically behaves like a zero filled
 * array to all algorithms that use arrays as iterators (STL style).
 * It's useful when there's a need to compute the distance between feature
 * and origin it and allows for better compiler optimisation than using a
 * zero-filled array.
 */
template <typename T>
struct ZeroIterator
{

    T operator*()
    {
        return 0;
    }

    T operator[](int)
    {
        return 0;
    }

    const ZeroIterator<T>& operator ++()
    {
        return *this;
    }

    ZeroIterator<T> operator ++(int)
    {
        return *this;
    }

    ZeroIterator<T>& operator+=(int)
    {
        return *this;
    }

};

}

#endif //OPENCV_FLANN_DIST_H_