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Master commit of OpenFace.
2016-04-28 21:40:36 +02:00
/***********************************************************************
* 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_DIST_H_
#define OPENCV_FLANN_DIST_H_
#include <cmath>
#include <cstdlib>
#include <string.h>
#ifdef _MSC_VER
typedef unsigned __int32 uint32_t;
typedef unsigned __int64 uint64_t;
#else
#include <stdint.h>
#endif
#include "defines.h"
#if (defined WIN32 || defined _WIN32) && defined(_M_ARM)
# include <Intrin.h>
#endif
#ifdef __ARM_NEON__
# include "arm_neon.h"
#endif
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<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; };
#undef True
#undef False
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, size_t size) const
{
static const uchar popCountTable[] =
{
0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
};
ResultType result = 0;
for (size_t i = 0; i < size; i++) {
result += popCountTable[a[i] ^ b[i]];
}
return result;
}
};
/**
* 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;
#ifdef __ARM_NEON__
{
uint32x4_t bits = vmovq_n_u32(0);
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 AxorB = veorq_u8 (A_vec, B_vec);
uint8x16_t bitsSet = vcntq_u8 (AxorB);
uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
bits = vaddq_u32(bits, bitSet4);
}
uint64x2_t bitSet2 = vpaddlq_u32 (bits);
result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0);
result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2);
}
#elif __GNUC__
{
//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 // NO NEON and NOT GNUC
typedef unsigned long long pop_t;
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;
++a;
++b;
}
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 Hellinger 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
{
ResultType diff = sqrt(static_cast<ResultType>(a)) - sqrt(static_cast<ResultType>(b));
return diff * diff;
}
};
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 (* b != 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();
if( *b != 0 ) {
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;
}
};
/*
* Depending on processed distances, some of them are already squared (e.g. L2)
* and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure
* we are working on ^2 distances, thus following templates to ensure that.
*/
template <typename Distance, typename ElementType>
struct squareDistance
{
typedef typename Distance::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist*dist; }
};
template <typename ElementType>
struct squareDistance<L2_Simple<ElementType>, ElementType>
{
typedef typename L2_Simple<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<L2<ElementType>, ElementType>
{
typedef typename L2<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<MinkowskiDistance<ElementType>, ElementType>
{
typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<HellingerDistance<ElementType>, ElementType>
{
typedef typename HellingerDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<ChiSquareDistance<ElementType>, ElementType>
{
typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename Distance>
typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist )
{
typedef typename Distance::ElementType ElementType;
squareDistance<Distance, ElementType> dummy;
return dummy( dist );
}
/*
* ...and a template to ensure the user that he will process the normal distance,
* and not squared distance, without loosing processing time calling sqrt(ensureSquareDistance)
* that will result in doing actually sqrt(dist*dist) for L1 distance for instance.
*/
template <typename Distance, typename ElementType>
struct simpleDistance
{
typedef typename Distance::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct simpleDistance<L2_Simple<ElementType>, ElementType>
{
typedef typename L2_Simple<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return sqrt(dist); }
};
template <typename ElementType>
struct simpleDistance<L2<ElementType>, ElementType>
{
typedef typename L2<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return sqrt(dist); }
};
template <typename ElementType>
struct simpleDistance<MinkowskiDistance<ElementType>, ElementType>
{
typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return sqrt(dist); }
};
template <typename ElementType>
struct simpleDistance<HellingerDistance<ElementType>, ElementType>
{
typedef typename HellingerDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return sqrt(dist); }
};
template <typename ElementType>
struct simpleDistance<ChiSquareDistance<ElementType>, ElementType>
{
typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return sqrt(dist); }
};
template <typename Distance>
typename Distance::ResultType ensureSimpleDistance( typename Distance::ResultType dist )
{
typedef typename Distance::ElementType ElementType;
simpleDistance<Distance, ElementType> dummy;
return dummy( dist );
}
}
#endif //OPENCV_FLANN_DIST_H_
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