212 lines
6.2 KiB
C++
212 lines
6.2 KiB
C++
/***********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
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* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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*
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* THE BSD LICENSE
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*************************************************************************/
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#ifndef DIST_H
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#define DIST_H
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#include <cmath>
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using namespace std;
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#include "constants.h"
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namespace cvflann
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{
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/**
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* Distance function by default set to the custom distance
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* function. This can be set to a specific distance function
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* for further efficiency.
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*/
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#define flann_dist custom_dist
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//#define flann_dist euclidean_dist
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/**
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* Compute the squared Euclidean distance between two vectors.
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*
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* This is highly optimised, with loop unrolling, as it is one
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* of the most expensive inner loops.
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*
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* The computation of squared root at the end is omitted for
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* efficiency.
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*/
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template <typename Iterator1, typename Iterator2>
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double euclidean_dist(Iterator1 first1, Iterator1 last1, Iterator2 first2, double acc = 0)
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{
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double distsq = acc;
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double diff0, diff1, diff2, diff3;
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Iterator1 lastgroup = last1 - 3;
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/* Process 4 items with each loop for efficiency. */
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while (first1 < lastgroup) {
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diff0 = first1[0] - first2[0];
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diff1 = first1[1] - first2[1];
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diff2 = first1[2] - first2[2];
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diff3 = first1[3] - first2[3];
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distsq += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
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first1 += 4;
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first2 += 4;
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}
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/* Process last 0-3 pixels. Not needed for standard vector lengths. */
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while (first1 < last1) {
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diff0 = *first1++ - *first2++;
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distsq += diff0 * diff0;
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}
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return distsq;
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}
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/**
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* Compute the Manhattan (L_1) distance between two vectors.
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*
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* This is highly optimised, with loop unrolling, as it is one
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* of the most expensive inner loops.
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*/
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template <typename Iterator1, typename Iterator2>
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double manhattan_dist(Iterator1 first1, Iterator1 last1, Iterator2 first2, double acc = 0)
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{
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double distsq = acc;
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double diff0, diff1, diff2, diff3;
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Iterator1 lastgroup = last1 - 3;
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/* Process 4 items with each loop for efficiency. */
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while (first1 < lastgroup) {
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diff0 = fabs(first1[0] - first2[0]);
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diff1 = fabs(first1[1] - first2[1]);
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diff2 = fabs(first1[2] - first2[2]);
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diff3 = fabs(first1[3] - first2[3]);
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distsq += diff0 + diff1 + diff2 + diff3;
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first1 += 4;
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first2 += 4;
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}
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/* Process last 0-3 pixels. Not needed for standard vector lengths. */
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while (first1 < last1) {
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diff0 = fabs(*first1++ - *first2++);
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distsq += diff0;
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}
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return distsq;
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}
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extern int flann_minkowski_order;
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/**
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* Compute the Minkowski (L_p) distance between two vectors.
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*
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* This is highly optimised, with loop unrolling, as it is one
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* of the most expensive inner loops.
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*
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* The computation of squared root at the end is omitted for
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* efficiency.
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*/
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template <typename Iterator1, typename Iterator2>
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double minkowski_dist(Iterator1 first1, Iterator1 last1, Iterator2 first2, double acc = 0)
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{
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double distsq = acc;
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double diff0, diff1, diff2, diff3;
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Iterator1 lastgroup = last1 - 3;
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int p = flann_minkowski_order;
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/* Process 4 items with each loop for efficiency. */
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while (first1 < lastgroup) {
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diff0 = fabs(first1[0] - first2[0]);
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diff1 = fabs(first1[1] - first2[1]);
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diff2 = fabs(first1[2] - first2[2]);
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diff3 = fabs(first1[3] - first2[3]);
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distsq += pow(diff0,p) + pow(diff1,p) + pow(diff2,p) + pow(diff3,p);
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first1 += 4;
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first2 += 4;
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}
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/* Process last 0-3 pixels. Not needed for standard vector lengths. */
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while (first1 < last1) {
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diff0 = fabs(*first1++ - *first2++);
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distsq += pow(diff0,p);
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}
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return distsq;
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}
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extern flann_distance_t flann_distance_type;
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/**
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* Custom distance function. The distance computed is dependent on the value
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* of the 'flann_distance_type' global variable.
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*
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* If the last argument 'acc' is passed, the result is accumulated to the value
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* of this argument.
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*/
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template <typename Iterator1, typename Iterator2>
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float custom_dist(Iterator1 first1, Iterator1 last1, Iterator2 first2, double acc = 0)
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{
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switch (flann_distance_type) {
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case EUCLIDEAN:
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return (float)euclidean_dist(first1, last1, first2, acc);
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case MANHATTAN:
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return (float)manhattan_dist(first1, last1, first2, acc);
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case MINKOWSKI:
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return (float)minkowski_dist(first1, last1, first2, acc);
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default:
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return (float)euclidean_dist(first1, last1, first2, acc);
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}
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}
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/*
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* This is a "zero iterator". It basically behaves like a zero filled
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* array to all algorithms that use arrays as iterators (STL style).
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* It's useful when there's a need to compute the distance between feature
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* and origin it and allows for better compiler optimisation than using a
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* zero-filled array.
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*/
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template <typename T>
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struct ZeroIterator {
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T operator*() {
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return 0;
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}
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T operator[](int /*index*/) {
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return 0;
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}
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ZeroIterator<T>& operator ++(int) {
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return *this;
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}
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ZeroIterator<T>& operator+=(int) {
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return *this;
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}
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};
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extern ZeroIterator<float> zero;
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}
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#endif //DIST_H
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