212 lines
6.2 KiB
C
212 lines
6.2 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.
|
||
|
|
*
|
||
|
|
* 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 DIST_H
|
||
|
|
#define DIST_H
|
||
|
|
|
||
|
|
#include <cmath>
|
||
|
|
using namespace std;
|
||
|
|
|
||
|
|
#include "constants.h"
|
||
|
|
|
||
|
|
namespace flann
|
||
|
|
{
|
||
|
|
|
||
|
|
/**
|
||
|
|
* Distance function by default set to the custom distance
|
||
|
|
* function. This can be set to a specific distance function
|
||
|
|
* for further efficiency.
|
||
|
|
*/
|
||
|
|
#define flann_dist custom_dist
|
||
|
|
//#define flann_dist euclidean_dist
|
||
|
|
|
||
|
|
|
||
|
|
/**
|
||
|
|
* 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>
|
||
|
|
double euclidean_dist(Iterator1 first1, Iterator1 last1, Iterator2 first2, double acc = 0)
|
||
|
|
{
|
||
|
|
double distsq = acc;
|
||
|
|
double diff0, diff1, diff2, diff3;
|
||
|
|
Iterator1 lastgroup = last1 - 3;
|
||
|
|
|
||
|
|
/* Process 4 items with each loop for efficiency. */
|
||
|
|
while (first1 < lastgroup) {
|
||
|
|
diff0 = first1[0] - first2[0];
|
||
|
|
diff1 = first1[1] - first2[1];
|
||
|
|
diff2 = first1[2] - first2[2];
|
||
|
|
diff3 = first1[3] - first2[3];
|
||
|
|
distsq += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
|
||
|
|
first1 += 4;
|
||
|
|
first2 += 4;
|
||
|
|
}
|
||
|
|
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
|
||
|
|
while (first1 < last1) {
|
||
|
|
diff0 = *first1++ - *first2++;
|
||
|
|
distsq += diff0 * diff0;
|
||
|
|
}
|
||
|
|
return distsq;
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* 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>
|
||
|
|
double manhattan_dist(Iterator1 first1, Iterator1 last1, Iterator2 first2, double acc = 0)
|
||
|
|
{
|
||
|
|
double distsq = acc;
|
||
|
|
double diff0, diff1, diff2, diff3;
|
||
|
|
Iterator1 lastgroup = last1 - 3;
|
||
|
|
|
||
|
|
/* Process 4 items with each loop for efficiency. */
|
||
|
|
while (first1 < lastgroup) {
|
||
|
|
diff0 = fabs(first1[0] - first2[0]);
|
||
|
|
diff1 = fabs(first1[1] - first2[1]);
|
||
|
|
diff2 = fabs(first1[2] - first2[2]);
|
||
|
|
diff3 = fabs(first1[3] - first2[3]);
|
||
|
|
distsq += diff0 + diff1 + diff2 + diff3;
|
||
|
|
first1 += 4;
|
||
|
|
first2 += 4;
|
||
|
|
}
|
||
|
|
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
|
||
|
|
while (first1 < last1) {
|
||
|
|
diff0 = fabs(*first1++ - *first2++);
|
||
|
|
distsq += diff0;
|
||
|
|
}
|
||
|
|
return distsq;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
extern int flann_minkowski_order;
|
||
|
|
/**
|
||
|
|
* Compute the Minkowski (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>
|
||
|
|
double minkowski_dist(Iterator1 first1, Iterator1 last1, Iterator2 first2, double acc = 0)
|
||
|
|
{
|
||
|
|
double distsq = acc;
|
||
|
|
double diff0, diff1, diff2, diff3;
|
||
|
|
Iterator1 lastgroup = last1 - 3;
|
||
|
|
|
||
|
|
int p = flann_minkowski_order;
|
||
|
|
|
||
|
|
/* Process 4 items with each loop for efficiency. */
|
||
|
|
while (first1 < lastgroup) {
|
||
|
|
diff0 = fabs(first1[0] - first2[0]);
|
||
|
|
diff1 = fabs(first1[1] - first2[1]);
|
||
|
|
diff2 = fabs(first1[2] - first2[2]);
|
||
|
|
diff3 = fabs(first1[3] - first2[3]);
|
||
|
|
distsq += pow(diff0,p) + pow(diff1,p) + pow(diff2,p) + pow(diff3,p);
|
||
|
|
first1 += 4;
|
||
|
|
first2 += 4;
|
||
|
|
}
|
||
|
|
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
|
||
|
|
while (first1 < last1) {
|
||
|
|
diff0 = fabs(*first1++ - *first2++);
|
||
|
|
distsq += pow(diff0,p);
|
||
|
|
}
|
||
|
|
return distsq;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
extern flann_distance_t flann_distance_type;
|
||
|
|
/**
|
||
|
|
* Custom distance function. The distance computed is dependent on the value
|
||
|
|
* of the 'flann_distance_type' global variable.
|
||
|
|
*
|
||
|
|
* If the last argument 'acc' is passed, the result is accumulated to the value
|
||
|
|
* of this argument.
|
||
|
|
*/
|
||
|
|
template <typename Iterator1, typename Iterator2>
|
||
|
|
float custom_dist(Iterator1 first1, Iterator1 last1, Iterator2 first2, double acc = 0)
|
||
|
|
{
|
||
|
|
switch (flann_distance_type) {
|
||
|
|
case EUCLIDEAN:
|
||
|
|
return (float)euclidean_dist(first1, last1, first2, acc);
|
||
|
|
case MANHATTAN:
|
||
|
|
return (float)manhattan_dist(first1, last1, first2, acc);
|
||
|
|
case MINKOWSKI:
|
||
|
|
return (float)minkowski_dist(first1, last1, first2, acc);
|
||
|
|
default:
|
||
|
|
return (float)euclidean_dist(first1, last1, first2, acc);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/*
|
||
|
|
* 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 /*index*/) {
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
ZeroIterator<T>& operator ++(int) {
|
||
|
|
return *this;
|
||
|
|
}
|
||
|
|
|
||
|
|
ZeroIterator<T>& operator+=(int) {
|
||
|
|
return *this;
|
||
|
|
}
|
||
|
|
|
||
|
|
};
|
||
|
|
extern ZeroIterator<float> zero;
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
#endif //DIST_H
|