Open Source Computer Vision Library https://opencv.org/
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#include "test_precomp.hpp"
#if 0
using namespace cv;
class BruteForceMatcherTest : public cvtest::BaseTest
{
public:
BruteForceMatcherTest();
protected:
void run( int );
};
struct CV_EXPORTS L2Fake : public L2<float>
{
};
BruteForceMatcherTest::BruteForceMatcherTest() : cvtest::BaseTest( "BruteForceMatcher", "BruteForceMatcher::matchImpl")
{
support_testing_modes = cvtest::TS::TIMING_MODE;
}
void BruteForceMatcherTest::run( int )
{
const int dimensions = 64;
const int descriptorsNumber = 5000;
Mat train = Mat( descriptorsNumber, dimensions, CV_32FC1);
Mat query = Mat( descriptorsNumber, dimensions, CV_32FC1);
Mat permutation( 1, descriptorsNumber, CV_32SC1 );
for( int i=0;i<descriptorsNumber;i++ )
permutation.at<int>( 0, i ) = i;
//RNG rng = RNG( cvGetTickCount() );
RNG rng = RNG( *ts->get_rng() );
randShuffle( permutation, 1, &rng );
float boundary = 500.f;
for( int row=0;row<descriptorsNumber;row++ )
{
for( int col=0;col<dimensions;col++ )
{
int bit = rng( 2 );
train.at<float>( permutation.at<int>( 0, row ), col ) = bit*boundary + rng.uniform( 0.f, boundary );
query.at<float>( row, col ) = bit*boundary + rng.uniform( 0.f, boundary );
}
}
vector<DMatch> specMatches, genericMatches;
BruteForceMatcher<L2<float> > specMatcher;
BruteForceMatcher<L2Fake > genericMatcher;
int64 time0 = cvGetTickCount();
specMatcher.match( query, train, specMatches );
int64 time1 = cvGetTickCount();
genericMatcher.match( query, train, genericMatches );
int64 time2 = cvGetTickCount();
float specMatcherTime = float(time1 - time0)/(float)cvGetTickFrequency();
ts->printf( cvtest::TS::LOG, "Matching by matrix multiplication time s: %f, us per pair: %f\n",
specMatcherTime*1e-6, specMatcherTime/( descriptorsNumber*descriptorsNumber ) );
float genericMatcherTime = float(time2 - time1)/(float)cvGetTickFrequency();
ts->printf( cvtest::TS::LOG, "Matching without matrix multiplication time s: %f, us per pair: %f\n",
genericMatcherTime*1e-6, genericMatcherTime/( descriptorsNumber*descriptorsNumber ) );
if( (int)specMatches.size() != descriptorsNumber || (int)genericMatches.size() != descriptorsNumber )
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
for( int i=0;i<descriptorsNumber;i++ )
{
float epsilon = 0.01f;
bool isEquiv = fabs( specMatches[i].distance - genericMatches[i].distance ) < epsilon &&
specMatches[i].queryIdx == genericMatches[i].queryIdx &&
specMatches[i].trainIdx == genericMatches[i].trainIdx;
if( !isEquiv || specMatches[i].trainIdx != permutation.at<int>( 0, i ) )
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
break;
}
}
//Test mask
Mat mask( query.rows, train.rows, CV_8UC1 );
rng.fill( mask, RNG::UNIFORM, 0, 2 );
time0 = cvGetTickCount();
specMatcher.match( query, train, specMatches, mask );
time1 = cvGetTickCount();
genericMatcher.match( query, train, genericMatches, mask );
time2 = cvGetTickCount();
specMatcherTime = float(time1 - time0)/(float)cvGetTickFrequency();
ts->printf( cvtest::TS::LOG, "Matching by matrix multiplication time with mask s: %f, us per pair: %f\n",
specMatcherTime*1e-6, specMatcherTime/( descriptorsNumber*descriptorsNumber ) );
genericMatcherTime = float(time2 - time1)/(float)cvGetTickFrequency();
ts->printf( cvtest::TS::LOG, "Matching without matrix multiplication time with mask s: %f, us per pair: %f\n",
genericMatcherTime*1e-6, genericMatcherTime/( descriptorsNumber*descriptorsNumber ) );
if( specMatches.size() != genericMatches.size() )
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
for( size_t i=0;i<specMatches.size();i++ )
{
//float epsilon = 1e-2;
float epsilon = 10000000;
bool isEquiv = fabs( specMatches[i].distance - genericMatches[i].distance ) < epsilon &&
specMatches[i].queryIdx == genericMatches[i].queryIdx &&
specMatches[i].trainIdx == genericMatches[i].trainIdx;
if( !isEquiv )
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
break;
}
}
}
BruteForceMatcherTest taBruteForceMatcherTest;
#endif