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opencv_contrib/modules/line_descriptor/test/test_matcher_regression.cpp
Alexander Alekhin edfdf12c31 tests: refactor test files
2018-02-02 19:15:28 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, Biagio Montesano, all rights reserved.
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//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
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// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
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#include "test_precomp.hpp"
namespace opencv_test { namespace {
class CV_BinaryDescriptorMatcherTest : public cvtest::BaseTest
{
public:
CV_BinaryDescriptorMatcherTest( float _badPart ) :
badPart( _badPart )
{
dmatcher = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();
}
protected:
static const int dim = 32;
static const int queryDescCount = 300; // must be even number because we split train data in some cases in two
static const int countFactor = 4; // do not change it
const float badPart;
virtual void run( int );
void generateData( Mat& query, Mat& train );
uchar invertSingleBits( uchar dividend_char, int numBits );
void emptyDataTest();
void matchTest( const Mat& query, const Mat& train );
void knnMatchTest( const Mat& query, const Mat& train );
void radiusMatchTest( const Mat& query, const Mat& train );
std::string name;
Ptr<BinaryDescriptorMatcher> dmatcher;
private:
CV_BinaryDescriptorMatcherTest& operator=( const CV_BinaryDescriptorMatcherTest& )
{
return *this;
}
};
/* invert numBits bits in input char */
uchar CV_BinaryDescriptorMatcherTest::invertSingleBits( uchar dividend_char, int numBits )
{
std::vector<int> bin_vector;
long dividend;
long bin_num;
/* convert input char to a long */
dividend = (long) dividend_char;
/*if a 0 has been obtained, just generate a 8-bit long vector of zeros */
if( dividend == 0 )
bin_vector = std::vector<int>( 8, 0 );
/* else, apply classic decimal to binary conversion */
else
{
while ( dividend >= 1 )
{
bin_num = dividend % 2;
dividend /= 2;
bin_vector.push_back( bin_num );
}
}
/* ensure that binary vector always has length 8 */
if( bin_vector.size() < 8 )
{
std::vector<int> zeros( 8 - bin_vector.size(), 0 );
bin_vector.insert( bin_vector.end(), zeros.begin(), zeros.end() );
}
/* invert numBits bits */
for ( int index = 0; index < numBits; index++ )
{
if( bin_vector[index] == 0 )
bin_vector[index] = 1;
else
bin_vector[index] = 0;
}
/* reconvert to decimal */
uchar result = 0;
for ( int i = (int) bin_vector.size() - 1; i >= 0; i-- )
result += (uchar) ( bin_vector[i] * ( 1 << i ) );
return result;
}
void CV_BinaryDescriptorMatcherTest::emptyDataTest()
{
Mat queryDescriptors, trainDescriptors, mask;
std::vector<Mat> trainDescriptorCollection, masks;
std::vector<DMatch> matches;
std::vector<std::vector<DMatch> > vmatches;
try
{
dmatcher->match( queryDescriptors, trainDescriptors, matches, mask );
}
catch ( ... )
{
ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->knnMatch( queryDescriptors, trainDescriptors, vmatches, 2, mask );
}
catch ( ... )
{
ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->radiusMatch( queryDescriptors, trainDescriptors, vmatches, 10.f, mask );
}
catch ( ... )
{
ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->add( trainDescriptorCollection );
}
catch ( ... )
{
ts->printf( cvtest::TS::LOG, "add() on empty descriptors must not generate exception.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->match( queryDescriptors, matches, masks );
}
catch ( ... )
{
ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->knnMatch( queryDescriptors, vmatches, 2, masks );
}
catch ( ... )
{
ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->radiusMatch( queryDescriptors, vmatches, 10.f, masks );
}
catch ( ... )
{
ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
void CV_BinaryDescriptorMatcherTest::generateData( Mat& query, Mat& train )
{
RNG& rng = theRNG();
/* Generate query descriptors randomly.
Descriptor vector elements are binary values. */
Mat buf( queryDescCount, dim, CV_8UC1 );
rng.fill( buf, RNG::UNIFORM, Scalar( 0 ), Scalar( 255 ) );
buf.convertTo( query, CV_8UC1 );
for ( int i = 0; i < query.rows; i++ )
{
for ( int j = 0; j < countFactor; j++ )
{
train.push_back( query.row( i ) );
int randCol = rand() % 32;
uchar u = query.at<uchar>( i, randCol );
uchar modified_u = invertSingleBits( u, j + 1 );
train.at<uchar>( i * countFactor + j, randCol ) = modified_u;
}
}
}
void CV_BinaryDescriptorMatcherTest::matchTest( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of match()
{
std::vector<DMatch> matches;
dmatcher->match( query, train, matches );
if( (int) matches.size() != queryDescCount )
{
ts->printf( cvtest::TS::LOG, "Incorrect matches count while test match() function (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for ( size_t i = 0; i < matches.size(); i++ )
{
DMatch& match = matches[i];
if( ( match.queryIdx != (int) i ) || ( match.trainIdx != (int) i * countFactor ) || ( match.imgIdx != 0 ) )
badCount++;
}
if( (float) badCount > (float) queryDescCount * badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test match() function (1).\n",
(float) badCount / (float) queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
// test const version of match() for the same query and test descriptors
{
std::vector<DMatch> matches;
dmatcher->match( query, query, matches );
if( (int) matches.size() != query.rows )
{
ts->printf( cvtest::TS::LOG, "Incorrect matches count while test match() function for the same query and test descriptors (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
for ( size_t i = 0; i < matches.size(); i++ )
{
DMatch& match = matches[i];
if( match.queryIdx != (int) i || match.trainIdx != (int) i || std::abs( match.distance ) > FLT_EPSILON )
{
ts->printf(
cvtest::TS::LOG,
"Bad match (i=%d, queryIdx=%d, trainIdx=%d, distance=%f) while test match() function for the same query and test descriptors (1).\n", i,
match.queryIdx, match.trainIdx, match.distance );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
}
// test version of match() with add()
{
dmatcher->clear();
std::vector<DMatch> matches;
// make add() twice to test such case
dmatcher->add( std::vector<Mat>( 1, train.rowRange( 0, train.rows / 2 ) ) );
dmatcher->add( std::vector<Mat>( 1, train.rowRange( train.rows / 2, train.rows ) ) );
// prepare masks (make first nearest match illegal)
std::vector<Mat> masks( 2 );
for ( int mi = 0; mi < 2; mi++ )
masks[mi] = Mat::ones( query.rows, 1/*train.rows / 2*/, CV_8UC1 );
dmatcher->match( query, matches, masks );
if( (int) matches.size() != queryDescCount )
{
ts->printf( cvtest::TS::LOG, "Incorrect matches count while test match() function (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for ( size_t i = 0; i < matches.size(); i++ )
{
DMatch& match = matches[i];
if( ( match.queryIdx != (int) i ) || ( match.trainIdx != (int) i * countFactor /*+ shift*/) || ( match.imgIdx > 1 ) )
badCount++;
}
if( (float) badCount > (float) queryDescCount * badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test match() function (2).\n",
(float) badCount / (float) queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
}
}
void CV_BinaryDescriptorMatcherTest::knnMatchTest( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of knnMatch()
{
const int knn = 3;
std::vector<std::vector<DMatch> > matches;
dmatcher->knnMatch( query, train, matches, knn );
if( (int) matches.size() != queryDescCount )
{
ts->printf( cvtest::TS::LOG, "Incorrect matches count while test knnMatch() function (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for ( size_t i = 0; i < matches.size(); i++ )
{
if( (int) matches[i].size() != knn )
badCount++;
else
{
int localBadCount = 0;
for ( int k = 0; k < knn; k++ )
{
DMatch& match = matches[i][k];
if( ( match.queryIdx != (int) i ) || ( match.trainIdx != (int) i * countFactor + k ) || ( match.imgIdx != 0 ) )
localBadCount++;
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if( (float) badCount > (float) queryDescCount * badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test knnMatch() function (1).\n",
(float) badCount / (float) queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
// // test version of knnMatch() with add()
{
const int knn = 2;
std::vector<std::vector<DMatch> > matches;
// make add() twice to test such case
dmatcher->add( std::vector<Mat>( 1, train.rowRange( 0, train.rows / 2 ) ) );
dmatcher->add( std::vector<Mat>( 1, train.rowRange( train.rows / 2, train.rows ) ) );
// prepare masks (make first nearest match illegal)
std::vector<Mat> masks( 2 );
for ( int mi = 0; mi < 2; mi++ )
{
masks[mi] = Mat::ones( query.rows, 1, CV_8UC1 );
}
dmatcher->knnMatch( query, matches, knn, masks );
if( (int) matches.size() != queryDescCount )
{
ts->printf( cvtest::TS::LOG, "Incorrect matches count while test knnMatch() function (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for ( size_t i = 0; i < matches.size(); i++ )
{
if( (int) matches[i].size() != knn )
badCount++;
else
{
int localBadCount = 0;
for ( int k = 0; k < knn; k++ )
{
DMatch& match = matches[i][k];
{
if( i < queryDescCount / 2 )
{
if( ( match.queryIdx != (int) i ) || ( match.trainIdx != (int) i * countFactor + k ) || ( match.imgIdx != 0 ) )
localBadCount++;
}
else
{
if( ( match.queryIdx != (int) i ) || ( match.trainIdx != (int) i * countFactor + k ) || ( match.imgIdx != 1 ) )
localBadCount++;
}
}
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if( (float) badCount > (float) queryDescCount * badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test knnMatch() function (2).\n",
(float) badCount / (float) queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
}
}
void CV_BinaryDescriptorMatcherTest::radiusMatchTest( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of match()
{
const float radius = 1;
std::vector<std::vector<DMatch> > matches;
dmatcher->radiusMatch( query, train, matches, radius );
if( (int) matches.size() != queryDescCount )
{
ts->printf( cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for ( size_t i = 0; i < matches.size(); i++ )
{
if( (int) matches[i].size() != 1 )
{
badCount++;
}
else
{
DMatch& match = matches[i][0];
if( ( match.queryIdx != (int) i ) || ( match.trainIdx != (int) i * countFactor ) || ( match.imgIdx != 0 ) )
badCount++;
}
}
if( (float) badCount > (float) queryDescCount * badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (1).\n",
(float) badCount / (float) queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
{
const float radius = 3;
std::vector<std::vector<DMatch> > matches;
// make add() twice to test such case
dmatcher->add( std::vector<Mat>( 1, train.rowRange( 0, train.rows / 2 ) ) );
dmatcher->add( std::vector<Mat>( 1, train.rowRange( train.rows / 2, train.rows ) ) );
// prepare masks
std::vector<Mat> masks( 2 );
for ( int mi = 0; mi < 2; mi++ )
masks[mi] = Mat::ones( query.rows, 1, CV_8UC1 );
dmatcher->radiusMatch( query, matches, radius, masks );
//int curRes = cvtest::TS::OK;
if( (int) matches.size() != queryDescCount )
{
ts->printf( cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
int badCount = 0;
for ( size_t i = 0; i < matches.size(); i++ )
{
if( (int) matches[i].size() != radius )
badCount++;
else
{
int localBadCount = 0;
for ( int k = 0; k < radius; k++ )
{
DMatch& match = matches[i][k];
{
if( i < queryDescCount / 2 )
{
if( ( match.queryIdx != (int) i ) || ( match.trainIdx != (int) i * countFactor + k ) || ( match.imgIdx != 0 ) )
localBadCount++;
}
else
{
if( ( match.queryIdx != (int) i ) || ( match.trainIdx != (int) i * countFactor + k ) || ( match.imgIdx != 1 ) )
localBadCount++;
}
}
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if( (float) badCount > (float) queryDescCount * badPart )
{
//curRes = cvtest::TS::FAIL_INVALID_OUTPUT;
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (2).\n",
(float) badCount / (float) queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
}
void CV_BinaryDescriptorMatcherTest::run( int )
{
Mat query, train;
emptyDataTest();
generateData( query, train );
matchTest( query, train );
knnMatchTest( query, train );
radiusMatchTest( query, train );
}
/****************************************************************************************\
* Tests registrations *
\****************************************************************************************/
TEST( BinaryDescriptor_Matcher, regression)
{
CV_BinaryDescriptorMatcherTest test( 0.01f );
test.safe_run();
}
}} // namespace
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