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ceres-solver / include /colmap /lib /FLANN /nn /index_testing.h
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/***********************************************************************
* 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 FLANN_INDEX_TESTING_H_
#define FLANN_INDEX_TESTING_H_
#include <cstring>
#include <cassert>
#include <cmath>
#include "FLANN/util/matrix.h"
#include "FLANN/algorithms/nn_index.h"
#include "FLANN/util/result_set.h"
#include "FLANN/util/logger.h"
#include "FLANN/util/timer.h"
namespace flann
{
inline int countCorrectMatches(size_t* neighbors, size_t* groundTruth, int n)
{
int count = 0;
for (int i=0; i<n; ++i) {
for (int k=0; k<n; ++k) {
if (neighbors[i]==groundTruth[k]) {
count++;
break;
}
}
}
return count;
}
template <typename Distance>
typename Distance::ResultType computeDistanceRaport(const Matrix<typename Distance::ElementType>& inputData, typename Distance::ElementType* target,
size_t* neighbors, size_t* groundTruth, int veclen, int n, const Distance& distance)
{
typedef typename Distance::ResultType DistanceType;
DistanceType ret = 0;
for (int i=0; i<n; ++i) {
DistanceType den = distance(inputData[groundTruth[i]], target, veclen);
DistanceType num = distance(inputData[neighbors[i]], target, veclen);
if ((den==0)&&(num==0)) {
ret += 1;
}
else {
ret += num/den;
}
}
return ret;
}
template <typename Index, typename Distance>
float search_with_ground_truth(Index& index, const Matrix<typename Distance::ElementType>& inputData,
const Matrix<typename Distance::ElementType>& testData, const Matrix<size_t>& matches, int nn, int checks,
float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches)
{
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
if (matches.cols<size_t(nn)) {
Logger::info("matches.cols=%d, nn=%d\n",matches.cols,nn);
throw FLANNException("Ground truth is not computed for as many neighbors as requested");
}
SearchParams searchParams(checks);
size_t* indices = new size_t[nn+skipMatches];
DistanceType* dists = new DistanceType[nn+skipMatches];
Matrix<size_t> indices_mat(indices, 1, nn+skipMatches);
Matrix<DistanceType> dists_mat(dists, 1, nn+skipMatches);
size_t* neighbors = indices + skipMatches;
int correct = 0;
DistanceType distR = 0;
StartStopTimer t;
int repeats = 0;
while (t.value<0.2) {
repeats++;
t.start();
correct = 0;
distR = 0;
for (size_t i = 0; i < testData.rows; i++) {
index.knnSearch(Matrix<ElementType>(testData[i], 1, testData.cols), indices_mat, dists_mat, nn+skipMatches, searchParams);
correct += countCorrectMatches(neighbors,matches[i], nn);
distR += computeDistanceRaport<Distance>(inputData, testData[i], neighbors, matches[i], testData.cols, nn, distance);
}
t.stop();
}
time = float(t.value/repeats);
delete[] indices;
delete[] dists;
float precicion = (float)correct/(nn*testData.rows);
dist = distR/(testData.rows*nn);
Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n",
checks, precicion, time, 1000.0 * time / testData.rows, dist);
return precicion;
}
template <typename Index, typename Distance>
float test_index_checks(Index& index, const Matrix<typename Distance::ElementType>& inputData,
const Matrix<typename Distance::ElementType>& testData, const Matrix<size_t>& matches,
int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0)
{
typedef typename Distance::ResultType DistanceType;
Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n");
Logger::info("---------------------------------------------------------\n");
float time = 0;
DistanceType dist = 0;
precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches);
return time;
}
template <typename Index, typename Distance>
float test_index_precision(Index& index, const Matrix<typename Distance::ElementType>& inputData,
const Matrix<typename Distance::ElementType>& testData, const Matrix<size_t>& matches,
float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0)
{
typedef typename Distance::ResultType DistanceType;
const float SEARCH_EPS = 0.001f;
Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n");
Logger::info("---------------------------------------------------------\n");
int c2 = 1;
float p2;
int c1 = 1;
// float p1;
float time;
DistanceType dist;
p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
if (p2>precision) {
Logger::info("Got as close as I can\n");
checks = c2;
return time;
}
while (p2<precision) {
c1 = c2;
// p1 = p2;
c2 *=2;
p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
}
int cx;
float realPrecision;
if (fabs(p2-precision)>SEARCH_EPS) {
Logger::info("Start linear estimation\n");
// after we got to values in the vecinity of the desired precision
// use linear approximation get a better estimation
cx = (c1+c2)/2;
realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
while (fabs(realPrecision-precision)>SEARCH_EPS) {
if (realPrecision<precision) {
c1 = cx;
}
else {
c2 = cx;
}
cx = (c1+c2)/2;
if (cx==c1) {
Logger::info("Got as close as I can\n");
break;
}
realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
}
c2 = cx;
p2 = realPrecision;
}
else {
Logger::info("No need for linear estimation\n");
cx = c2;
realPrecision = p2;
}
checks = cx;
return time;
}
template <typename Index, typename Distance>
void test_index_precisions(Index& index, const Matrix<typename Distance::ElementType>& inputData,
const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0)
{
typedef typename Distance::ResultType DistanceType;
const float SEARCH_EPS = 0.001;
// make sure precisions array is sorted
std::sort(precisions, precisions+precisions_length);
int pindex = 0;
float precision = precisions[pindex];
Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n");
Logger::info("---------------------------------------------------------\n");
int c2 = 1;
float p2;
int c1 = 1;
float p1;
float time;
DistanceType dist;
p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
// if precision for 1 run down the tree is already
// better then some of the requested precisions, then
// skip those
while (precisions[pindex]<p2 && pindex<precisions_length) {
pindex++;
}
if (pindex==precisions_length) {
Logger::info("Got as close as I can\n");
return;
}
for (int i=pindex; i<precisions_length; ++i) {
precision = precisions[i];
while (p2<precision) {
c1 = c2;
p1 = p2;
c2 *=2;
p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
if ((maxTime> 0)&&(time > maxTime)&&(p2<precision)) return;
}
int cx;
float realPrecision;
if (fabs(p2-precision)>SEARCH_EPS) {
Logger::info("Start linear estimation\n");
// after we got to values in the vecinity of the desired precision
// use linear approximation get a better estimation
cx = (c1+c2)/2;
realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
while (fabs(realPrecision-precision)>SEARCH_EPS) {
if (realPrecision<precision) {
c1 = cx;
}
else {
c2 = cx;
}
cx = (c1+c2)/2;
if (cx==c1) {
Logger::info("Got as close as I can\n");
break;
}
realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
}
c2 = cx;
p2 = realPrecision;
}
else {
Logger::info("No need for linear estimation\n");
cx = c2;
realPrecision = p2;
}
}
}
}
#endif //FLANN_INDEX_TESTING_H_