mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
545 lines
20 KiB
545 lines
20 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
|
// |
|
// By downloading, copying, installing or using the software you agree to this license. |
|
// If you do not agree to this license, do not download, install, |
|
// copy or use the software. |
|
// |
|
// |
|
// Intel License Agreement |
|
// For Open Source Computer Vision Library |
|
// |
|
// Copyright (C) 2000, Intel Corporation, all rights reserved. |
|
// Third party copyrights are property of their respective owners. |
|
// |
|
// 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. |
|
// |
|
// * Redistribution's in binary form must reproduce the above copyright notice, |
|
// this list of conditions and the following disclaimer in the documentation |
|
// and/or other materials provided with the distribution. |
|
// |
|
// * The name of Intel Corporation may not be used to endorse or promote products |
|
// derived from this software without specific prior written permission. |
|
// |
|
// This software is provided by the copyright holders and contributors "as is" and |
|
// any express or implied warranties, including, but not limited to, the implied |
|
// warranties of merchantability and fitness for a particular purpose are disclaimed. |
|
// In no event shall the Intel Corporation or contributors be liable for any direct, |
|
// indirect, incidental, special, exemplary, or consequential damages |
|
// (including, but not limited to, procurement of substitute goods or services; |
|
// loss of use, data, or profits; or business interruption) however caused |
|
// and on any theory of liability, whether in contract, strict liability, |
|
// or tort (including negligence or otherwise) arising in any way out of |
|
// the use of this software, even if advised of the possibility of such damage. |
|
// |
|
//M*/ |
|
|
|
#include "test_precomp.hpp" |
|
#include "opencv2/highgui.hpp" |
|
|
|
using namespace std; |
|
using namespace cv; |
|
|
|
const string FEATURES2D_DIR = "features2d"; |
|
const string IMAGE_FILENAME = "tsukuba.png"; |
|
|
|
/****************************************************************************************\ |
|
* Algorithmic tests for descriptor matchers * |
|
\****************************************************************************************/ |
|
class CV_DescriptorMatcherTest : public cvtest::BaseTest |
|
{ |
|
public: |
|
CV_DescriptorMatcherTest( const string& _name, const Ptr<DescriptorMatcher>& _dmatcher, float _badPart ) : |
|
badPart(_badPart), name(_name), dmatcher(_dmatcher) |
|
{} |
|
protected: |
|
static const int dim = 500; |
|
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 ); |
|
|
|
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 ); |
|
|
|
string name; |
|
Ptr<DescriptorMatcher> dmatcher; |
|
|
|
private: |
|
CV_DescriptorMatcherTest& operator=(const CV_DescriptorMatcherTest&) { return *this; } |
|
}; |
|
|
|
void CV_DescriptorMatcherTest::emptyDataTest() |
|
{ |
|
assert( !dmatcher.empty() ); |
|
Mat queryDescriptors, trainDescriptors, mask; |
|
vector<Mat> trainDescriptorCollection, masks; |
|
vector<DMatch> matches; |
|
vector<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_DescriptorMatcherTest::generateData( Mat& query, Mat& train ) |
|
{ |
|
RNG& rng = theRNG(); |
|
|
|
// Generate query descriptors randomly. |
|
// Descriptor vector elements are integer values. |
|
Mat buf( queryDescCount, dim, CV_32SC1 ); |
|
rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) ); |
|
buf.convertTo( query, CV_32FC1 ); |
|
|
|
// Generate train decriptors as follows: |
|
// copy each query descriptor to train set countFactor times |
|
// and perturb some one element of the copied descriptors in |
|
// in ascending order. General boundaries of the perturbation |
|
// are (0.f, 1.f). |
|
train.create( query.rows*countFactor, query.cols, CV_32FC1 ); |
|
float step = 1.f / countFactor; |
|
for( int qIdx = 0; qIdx < query.rows; qIdx++ ) |
|
{ |
|
Mat queryDescriptor = query.row(qIdx); |
|
for( int c = 0; c < countFactor; c++ ) |
|
{ |
|
int tIdx = qIdx * countFactor + c; |
|
Mat trainDescriptor = train.row(tIdx); |
|
queryDescriptor.copyTo( trainDescriptor ); |
|
int elem = rng(dim); |
|
float diff = rng.uniform( step*c, step*(c+1) ); |
|
trainDescriptor.at<float>(0, elem) += diff; |
|
} |
|
} |
|
} |
|
|
|
void CV_DescriptorMatcherTest::matchTest( const Mat& query, const Mat& train ) |
|
{ |
|
dmatcher->clear(); |
|
|
|
// test const version of match() |
|
{ |
|
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 |
|
{ |
|
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]; |
|
//std::cout << match.distance << std::endl; |
|
|
|
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() |
|
{ |
|
vector<DMatch> matches; |
|
// make add() twice to test such case |
|
dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) ); |
|
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) ); |
|
// prepare masks (make first nearest match illegal) |
|
vector<Mat> masks(2); |
|
for(int mi = 0; mi < 2; mi++ ) |
|
{ |
|
masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1)); |
|
for( int di = 0; di < queryDescCount/2; di++ ) |
|
masks[mi].col(di*countFactor).setTo(Scalar::all(0)); |
|
} |
|
|
|
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]; |
|
int shift = dmatcher->isMaskSupported() ? 1 : 0; |
|
{ |
|
if( i < queryDescCount/2 ) |
|
{ |
|
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + shift) || (match.imgIdx != 0) ) |
|
badCount++; |
|
} |
|
else |
|
{ |
|
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*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_DescriptorMatcherTest::knnMatchTest( const Mat& query, const Mat& train ) |
|
{ |
|
dmatcher->clear(); |
|
|
|
// test const version of knnMatch() |
|
{ |
|
const int knn = 3; |
|
|
|
vector<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; |
|
vector<vector<DMatch> > matches; |
|
// make add() twice to test such case |
|
dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) ); |
|
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) ); |
|
// prepare masks (make first nearest match illegal) |
|
vector<Mat> masks(2); |
|
for(int mi = 0; mi < 2; mi++ ) |
|
{ |
|
masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1)); |
|
for( int di = 0; di < queryDescCount/2; di++ ) |
|
masks[mi].col(di*countFactor).setTo(Scalar::all(0)); |
|
} |
|
|
|
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; |
|
int shift = dmatcher->isMaskSupported() ? 1 : 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 + shift) || |
|
(match.imgIdx != 0) ) |
|
localBadCount++; |
|
} |
|
else |
|
{ |
|
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) || |
|
(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_DescriptorMatcherTest::radiusMatchTest( const Mat& query, const Mat& train ) |
|
{ |
|
dmatcher->clear(); |
|
// test const version of match() |
|
{ |
|
const float radius = 1.f/countFactor; |
|
vector<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 ); |
|
} |
|
} |
|
} |
|
|
|
// test version of match() with add() |
|
{ |
|
int n = 3; |
|
const float radius = 1.f/countFactor * n; |
|
vector<vector<DMatch> > matches; |
|
// make add() twice to test such case |
|
dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) ); |
|
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) ); |
|
// prepare masks (make first nearest match illegal) |
|
vector<Mat> masks(2); |
|
for(int mi = 0; mi < 2; mi++ ) |
|
{ |
|
masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1)); |
|
for( int di = 0; di < queryDescCount/2; di++ ) |
|
masks[mi].col(di*countFactor).setTo(Scalar::all(0)); |
|
} |
|
|
|
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; |
|
int shift = dmatcher->isMaskSupported() ? 1 : 0; |
|
int needMatchCount = dmatcher->isMaskSupported() ? n-1 : n; |
|
for( size_t i = 0; i < matches.size(); i++ ) |
|
{ |
|
if( (int)matches[i].size() != needMatchCount ) |
|
badCount++; |
|
else |
|
{ |
|
int localBadCount = 0; |
|
for( int k = 0; k < needMatchCount; k++ ) |
|
{ |
|
DMatch& match = matches[i][k]; |
|
{ |
|
if( i < queryDescCount/2 ) |
|
{ |
|
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + k + shift) || |
|
(match.imgIdx != 0) ) |
|
localBadCount++; |
|
} |
|
else |
|
{ |
|
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) || |
|
(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_DescriptorMatcherTest::run( int ) |
|
{ |
|
Mat query, train; |
|
generateData( query, train ); |
|
|
|
matchTest( query, train ); |
|
|
|
knnMatchTest( query, train ); |
|
|
|
radiusMatchTest( query, train ); |
|
} |
|
|
|
/****************************************************************************************\ |
|
* Tests registrations * |
|
\****************************************************************************************/ |
|
|
|
TEST( Features2d_DescriptorMatcher_BruteForce, regression ) |
|
{ |
|
CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force", |
|
DescriptorMatcher::create("BruteForce"), 0.01f ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_DescriptorMatcher_FlannBased, regression ) |
|
{ |
|
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based", |
|
DescriptorMatcher::create("FlannBased"), 0.04f ); |
|
test.safe_run(); |
|
}
|
|
|