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Open Source Computer Vision Library
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114 lines
4.6 KiB
114 lines
4.6 KiB
#include "test_precomp.hpp" |
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using namespace cv; |
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struct CV_EXPORTS L2Fake : public L2<float> |
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{ |
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enum { normType = NORM_L2 }; |
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}; |
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class CV_BruteForceMatcherTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_BruteForceMatcherTest() {} |
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protected: |
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void run( int ) |
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{ |
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const int dimensions = 64; |
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const int descriptorsNumber = 5000; |
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Mat train = Mat( descriptorsNumber, dimensions, CV_32FC1); |
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Mat query = Mat( descriptorsNumber, dimensions, CV_32FC1); |
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Mat permutation( 1, descriptorsNumber, CV_32SC1 ); |
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for( int i=0;i<descriptorsNumber;i++ ) |
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permutation.at<int>( 0, i ) = i; |
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//RNG rng = RNG( cvGetTickCount() ); |
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RNG rng; |
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randShuffle( permutation, 1, &rng ); |
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float boundary = 500.f; |
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for( int row=0;row<descriptorsNumber;row++ ) |
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{ |
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for( int col=0;col<dimensions;col++ ) |
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{ |
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int bit = rng( 2 ); |
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train.at<float>( permutation.at<int>( 0, row ), col ) = bit*boundary + rng.uniform( 0.f, boundary ); |
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query.at<float>( row, col ) = bit*boundary + rng.uniform( 0.f, boundary ); |
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} |
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} |
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vector<DMatch> specMatches, genericMatches; |
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BruteForceMatcher<L2<float> > specMatcher; |
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BruteForceMatcher<L2Fake > genericMatcher; |
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int64 time0 = cvGetTickCount(); |
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specMatcher.match( query, train, specMatches ); |
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int64 time1 = cvGetTickCount(); |
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genericMatcher.match( query, train, genericMatches ); |
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int64 time2 = cvGetTickCount(); |
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float specMatcherTime = float(time1 - time0)/(float)cvGetTickFrequency(); |
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ts->printf( cvtest::TS::LOG, "Matching by matrix multiplication time s: %f, us per pair: %f\n", |
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specMatcherTime*1e-6, specMatcherTime/( descriptorsNumber*descriptorsNumber ) ); |
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float genericMatcherTime = float(time2 - time1)/(float)cvGetTickFrequency(); |
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ts->printf( cvtest::TS::LOG, "Matching without matrix multiplication time s: %f, us per pair: %f\n", |
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genericMatcherTime*1e-6, genericMatcherTime/( descriptorsNumber*descriptorsNumber ) ); |
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if( (int)specMatches.size() != descriptorsNumber || (int)genericMatches.size() != descriptorsNumber ) |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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for( int i=0;i<descriptorsNumber;i++ ) |
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{ |
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float epsilon = 0.01f; |
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bool isEquiv = fabs( specMatches[i].distance - genericMatches[i].distance ) < epsilon && |
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specMatches[i].queryIdx == genericMatches[i].queryIdx && |
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specMatches[i].trainIdx == genericMatches[i].trainIdx; |
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if( !isEquiv || specMatches[i].trainIdx != permutation.at<int>( 0, i ) ) |
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{ |
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ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH ); |
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break; |
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} |
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} |
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//Test mask |
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Mat mask( query.rows, train.rows, CV_8UC1 ); |
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rng.fill( mask, RNG::UNIFORM, 0, 2 ); |
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time0 = cvGetTickCount(); |
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specMatcher.match( query, train, specMatches, mask ); |
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time1 = cvGetTickCount(); |
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genericMatcher.match( query, train, genericMatches, mask ); |
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time2 = cvGetTickCount(); |
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specMatcherTime = float(time1 - time0)/(float)cvGetTickFrequency(); |
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ts->printf( cvtest::TS::LOG, "Matching by matrix multiplication time with mask s: %f, us per pair: %f\n", |
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specMatcherTime*1e-6, specMatcherTime/( descriptorsNumber*descriptorsNumber ) ); |
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genericMatcherTime = float(time2 - time1)/(float)cvGetTickFrequency(); |
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ts->printf( cvtest::TS::LOG, "Matching without matrix multiplication time with mask s: %f, us per pair: %f\n", |
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genericMatcherTime*1e-6, genericMatcherTime/( descriptorsNumber*descriptorsNumber ) ); |
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if( specMatches.size() != genericMatches.size() ) |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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for( size_t i=0;i<specMatches.size();i++ ) |
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{ |
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//float epsilon = 1e-2; |
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float epsilon = 10000000; |
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bool isEquiv = fabs( specMatches[i].distance - genericMatches[i].distance ) < epsilon && |
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specMatches[i].queryIdx == genericMatches[i].queryIdx && |
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specMatches[i].trainIdx == genericMatches[i].trainIdx; |
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if( !isEquiv ) |
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{ |
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ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH ); |
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break; |
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} |
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} |
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} |
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}; |
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TEST(Legacy_BruteForceMatcher, accuracy) { CV_BruteForceMatcherTest test; test.safe_run(); }
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