srcnativelibs.Include.OpenCV.opencv2.flann.index_testing.h Maven / Gradle / Ivy
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* Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved.
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#ifndef OPENCV_FLANN_INDEX_TESTING_H_
#define OPENCV_FLANN_INDEX_TESTING_H_
#include
#include
#include
#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
typename Distance::ResultType computeDistanceRaport(const Matrix& 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
float search_with_ground_truth(NNIndex& index, const Matrix& inputData,
const Matrix& testData, const Matrix& matches, int nn, int checks,
float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches)
{
typedef typename Distance::ResultType DistanceType;
if (matches.cols resultSet(nn+skipMatches);
SearchParams searchParams(checks);
std::vector indices(nn+skipMatches);
std::vector 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(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
float test_index_checks(NNIndex& index, const Matrix& inputData,
const Matrix& testData, const Matrix& 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
float test_index_precision(NNIndex& index, const Matrix& inputData,
const Matrix& testData, const Matrix& 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 (p2SEARCH_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
void test_index_precisions(NNIndex& index, const Matrix& inputData,
const Matrix& testData, const Matrix& 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] 0)&&(time > maxTime)&&(p2SEARCH_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 © 2015 - 2025 Weber Informatics LLC | Privacy Policy