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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
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#ifndef OPENCV_FLANN_INDEX_TESTING_H_
#define OPENCV_FLANN_INDEX_TESTING_H_
#include <cstring>
#include <cassert>
#include <cmath>
#include "matrix.h"
#include "nn_index.h"
#include "result_set.h"
#include "logger.h"
#include "timer.h"
namespace cvflann
{
inline int countCorrectMatches(int* neighbors, int* 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,
int* neighbors, int* 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 Distance>
float search_with_ground_truth(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches, int nn, int checks,
float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches)
{
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");
}
KNNResultSet<DistanceType> resultSet(nn+skipMatches);
SearchParams searchParams(checks);
std::vector<int> indices(nn+skipMatches);
std::vector<DistanceType> dists(nn+skipMatches);
int* 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++) {
resultSet.init(&indices[0], &dists[0]);
index.findNeighbors(resultSet, testData[i], searchParams);
correct += countCorrectMatches(neighbors,matches[i], nn);
distR += computeDistanceRaport<Distance>(inputData, testData[i], neighbors, matches[i], (int)testData.cols, nn, distance);
}
t.stop();
}
time = float(t.value/repeats);
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 Distance>
float test_index_checks(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& 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 Distance>
float test_index_precision(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& 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 Distance>
void test_index_precisions(NNIndex<Distance>& 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 //OPENCV_FLANN_INDEX_TESTING_H_
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