334 lines
11 KiB
C++
334 lines
11 KiB
C++
|
/*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.
|
||
|
|
//
|
||
|
|
//
|
||
|
|
// License Agreement
|
||
|
|
// For Open Source Computer Vision Library
|
||
|
|
//
|
||
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||
|
|
// Copyright (C) 2014, Itseez Inc, 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 the copyright holders 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"
|
||
|
|
|
||
|
|
namespace opencv_test { namespace {
|
||
|
|
|
||
|
|
#ifdef HAVE_OPENCV_FLANN
|
||
|
|
using namespace cv::flann;
|
||
|
|
#endif
|
||
|
|
|
||
|
|
//--------------------------------------------------------------------------------
|
||
|
|
class NearestNeighborTest : public cvtest::BaseTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
NearestNeighborTest() {}
|
||
|
|
protected:
|
||
|
|
static const int minValue = 0;
|
||
|
|
static const int maxValue = 1;
|
||
|
|
static const int dims = 30;
|
||
|
|
static const int featuresCount = 2000;
|
||
|
|
static const int K = 1; // * should also test 2nd nn etc.?
|
||
|
|
|
||
|
|
|
||
|
|
virtual void run( int start_from );
|
||
|
|
virtual void createModel( const Mat& data ) = 0;
|
||
|
|
virtual int findNeighbors( Mat& points, Mat& neighbors ) = 0;
|
||
|
|
virtual int checkGetPoints( const Mat& data );
|
||
|
|
virtual int checkFindBoxed();
|
||
|
|
virtual int checkFind( const Mat& data );
|
||
|
|
virtual void releaseModel() = 0;
|
||
|
|
};
|
||
|
|
|
||
|
|
int NearestNeighborTest::checkGetPoints( const Mat& )
|
||
|
|
{
|
||
|
|
return cvtest::TS::OK;
|
||
|
|
}
|
||
|
|
|
||
|
|
int NearestNeighborTest::checkFindBoxed()
|
||
|
|
{
|
||
|
|
return cvtest::TS::OK;
|
||
|
|
}
|
||
|
|
|
||
|
|
int NearestNeighborTest::checkFind( const Mat& data )
|
||
|
|
{
|
||
|
|
int code = cvtest::TS::OK;
|
||
|
|
int pointsCount = 1000;
|
||
|
|
float noise = 0.2f;
|
||
|
|
|
||
|
|
RNG rng;
|
||
|
|
Mat points( pointsCount, dims, CV_32FC1 );
|
||
|
|
Mat results( pointsCount, K, CV_32SC1 );
|
||
|
|
|
||
|
|
std::vector<int> fmap( pointsCount );
|
||
|
|
for( int pi = 0; pi < pointsCount; pi++ )
|
||
|
|
{
|
||
|
|
int fi = rng.next() % featuresCount;
|
||
|
|
fmap[pi] = fi;
|
||
|
|
for( int d = 0; d < dims; d++ )
|
||
|
|
points.at<float>(pi, d) = data.at<float>(fi, d) + rng.uniform(0.0f, 1.0f) * noise;
|
||
|
|
}
|
||
|
|
|
||
|
|
code = findNeighbors( points, results );
|
||
|
|
|
||
|
|
if( code == cvtest::TS::OK )
|
||
|
|
{
|
||
|
|
int correctMatches = 0;
|
||
|
|
for( int pi = 0; pi < pointsCount; pi++ )
|
||
|
|
{
|
||
|
|
if( fmap[pi] == results.at<int>(pi, 0) )
|
||
|
|
correctMatches++;
|
||
|
|
}
|
||
|
|
|
||
|
|
double correctPerc = correctMatches / (double)pointsCount;
|
||
|
|
EXPECT_GE(correctPerc, .75) << "correctMatches=" << correctMatches << " pointsCount=" << pointsCount;
|
||
|
|
}
|
||
|
|
|
||
|
|
return code;
|
||
|
|
}
|
||
|
|
|
||
|
|
void NearestNeighborTest::run( int /*start_from*/ ) {
|
||
|
|
int code = cvtest::TS::OK, tempCode;
|
||
|
|
Mat desc( featuresCount, dims, CV_32FC1 );
|
||
|
|
ts->get_rng().fill( desc, RNG::UNIFORM, minValue, maxValue );
|
||
|
|
|
||
|
|
createModel( desc.clone() ); // .clone() is used to simulate dangling pointers problem: https://github.com/opencv/opencv/issues/17553
|
||
|
|
|
||
|
|
tempCode = checkGetPoints( desc );
|
||
|
|
if( tempCode != cvtest::TS::OK )
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "bad accuracy of GetPoints \n" );
|
||
|
|
code = tempCode;
|
||
|
|
}
|
||
|
|
|
||
|
|
tempCode = checkFindBoxed();
|
||
|
|
if( tempCode != cvtest::TS::OK )
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "bad accuracy of FindBoxed \n" );
|
||
|
|
code = tempCode;
|
||
|
|
}
|
||
|
|
|
||
|
|
tempCode = checkFind( desc );
|
||
|
|
if( tempCode != cvtest::TS::OK )
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "bad accuracy of Find \n" );
|
||
|
|
code = tempCode;
|
||
|
|
}
|
||
|
|
|
||
|
|
releaseModel();
|
||
|
|
|
||
|
|
if (::testing::Test::HasFailure()) code = cvtest::TS::FAIL_BAD_ACCURACY;
|
||
|
|
ts->set_failed_test_info( code );
|
||
|
|
}
|
||
|
|
|
||
|
|
//--------------------------------------------------------------------------------
|
||
|
|
#ifdef HAVE_OPENCV_FLANN
|
||
|
|
|
||
|
|
class CV_FlannTest : public NearestNeighborTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_FlannTest() : NearestNeighborTest(), index(NULL) { }
|
||
|
|
protected:
|
||
|
|
void createIndex( const Mat& data, const IndexParams& params );
|
||
|
|
int knnSearch( Mat& points, Mat& neighbors );
|
||
|
|
int radiusSearch( Mat& points, Mat& neighbors );
|
||
|
|
virtual void releaseModel();
|
||
|
|
Index* index;
|
||
|
|
};
|
||
|
|
|
||
|
|
void CV_FlannTest::createIndex( const Mat& data, const IndexParams& params )
|
||
|
|
{
|
||
|
|
// release previously allocated index
|
||
|
|
releaseModel();
|
||
|
|
|
||
|
|
index = new Index( data, params );
|
||
|
|
}
|
||
|
|
|
||
|
|
int CV_FlannTest::knnSearch( Mat& points, Mat& neighbors )
|
||
|
|
{
|
||
|
|
Mat dist( points.rows, neighbors.cols, CV_32FC1);
|
||
|
|
int knn = 1, j;
|
||
|
|
|
||
|
|
// 1st way
|
||
|
|
index->knnSearch( points, neighbors, dist, knn, SearchParams() );
|
||
|
|
|
||
|
|
// 2nd way
|
||
|
|
Mat neighbors1( neighbors.size(), CV_32SC1 );
|
||
|
|
for( int i = 0; i < points.rows; i++ )
|
||
|
|
{
|
||
|
|
float* fltPtr = points.ptr<float>(i);
|
||
|
|
vector<float> query( fltPtr, fltPtr + points.cols );
|
||
|
|
vector<int> indices( neighbors1.cols, 0 );
|
||
|
|
vector<float> dists( dist.cols, 0 );
|
||
|
|
index->knnSearch( query, indices, dists, knn, SearchParams() );
|
||
|
|
vector<int>::const_iterator it = indices.begin();
|
||
|
|
for( j = 0; it != indices.end(); ++it, j++ )
|
||
|
|
neighbors1.at<int>(i,j) = *it;
|
||
|
|
}
|
||
|
|
|
||
|
|
// compare results
|
||
|
|
EXPECT_LE(cvtest::norm(neighbors, neighbors1, NORM_L1), 0);
|
||
|
|
|
||
|
|
return ::testing::Test::HasFailure() ? cvtest::TS::FAIL_BAD_ACCURACY : cvtest::TS::OK;
|
||
|
|
}
|
||
|
|
|
||
|
|
int CV_FlannTest::radiusSearch( Mat& points, Mat& neighbors )
|
||
|
|
{
|
||
|
|
Mat dist( 1, neighbors.cols, CV_32FC1);
|
||
|
|
Mat neighbors1( neighbors.size(), CV_32SC1 );
|
||
|
|
float radius = 10.0f;
|
||
|
|
int j;
|
||
|
|
|
||
|
|
// radiusSearch can only search one feature at a time for range search
|
||
|
|
for( int i = 0; i < points.rows; i++ )
|
||
|
|
{
|
||
|
|
// 1st way
|
||
|
|
Mat p( 1, points.cols, CV_32FC1, points.ptr<float>(i) ),
|
||
|
|
n( 1, neighbors.cols, CV_32SC1, neighbors.ptr<int>(i) );
|
||
|
|
index->radiusSearch( p, n, dist, radius, neighbors.cols, SearchParams() );
|
||
|
|
|
||
|
|
// 2nd way
|
||
|
|
float* fltPtr = points.ptr<float>(i);
|
||
|
|
vector<float> query( fltPtr, fltPtr + points.cols );
|
||
|
|
vector<int> indices( neighbors1.cols, 0 );
|
||
|
|
vector<float> dists( dist.cols, 0 );
|
||
|
|
index->radiusSearch( query, indices, dists, radius, neighbors.cols, SearchParams() );
|
||
|
|
vector<int>::const_iterator it = indices.begin();
|
||
|
|
for( j = 0; it != indices.end(); ++it, j++ )
|
||
|
|
neighbors1.at<int>(i,j) = *it;
|
||
|
|
}
|
||
|
|
|
||
|
|
// compare results
|
||
|
|
EXPECT_LE(cvtest::norm(neighbors, neighbors1, NORM_L1), 0);
|
||
|
|
|
||
|
|
return ::testing::Test::HasFailure() ? cvtest::TS::FAIL_BAD_ACCURACY : cvtest::TS::OK;
|
||
|
|
}
|
||
|
|
|
||
|
|
void CV_FlannTest::releaseModel()
|
||
|
|
{
|
||
|
|
if (index)
|
||
|
|
{
|
||
|
|
delete index;
|
||
|
|
index = NULL;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
//---------------------------------------
|
||
|
|
class CV_FlannLinearIndexTest : public CV_FlannTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_FlannLinearIndexTest() {}
|
||
|
|
protected:
|
||
|
|
virtual void createModel( const Mat& data ) { createIndex( data, LinearIndexParams() ); }
|
||
|
|
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
|
||
|
|
};
|
||
|
|
|
||
|
|
//---------------------------------------
|
||
|
|
class CV_FlannKMeansIndexTest : public CV_FlannTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_FlannKMeansIndexTest() {}
|
||
|
|
protected:
|
||
|
|
virtual void createModel( const Mat& data ) { createIndex( data, KMeansIndexParams() ); }
|
||
|
|
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return radiusSearch( points, neighbors ); }
|
||
|
|
};
|
||
|
|
|
||
|
|
//---------------------------------------
|
||
|
|
class CV_FlannKDTreeIndexTest : public CV_FlannTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_FlannKDTreeIndexTest() {}
|
||
|
|
protected:
|
||
|
|
virtual void createModel( const Mat& data ) { createIndex( data, KDTreeIndexParams() ); }
|
||
|
|
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return radiusSearch( points, neighbors ); }
|
||
|
|
};
|
||
|
|
|
||
|
|
//----------------------------------------
|
||
|
|
class CV_FlannCompositeIndexTest : public CV_FlannTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_FlannCompositeIndexTest() {}
|
||
|
|
protected:
|
||
|
|
virtual void createModel( const Mat& data ) { createIndex( data, CompositeIndexParams() ); }
|
||
|
|
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
|
||
|
|
};
|
||
|
|
|
||
|
|
//----------------------------------------
|
||
|
|
class CV_FlannAutotunedIndexTest : public CV_FlannTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_FlannAutotunedIndexTest() {}
|
||
|
|
protected:
|
||
|
|
virtual void createModel( const Mat& data ) { createIndex( data, AutotunedIndexParams() ); }
|
||
|
|
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
|
||
|
|
};
|
||
|
|
//----------------------------------------
|
||
|
|
class CV_FlannSavedIndexTest : public CV_FlannTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
CV_FlannSavedIndexTest() {}
|
||
|
|
protected:
|
||
|
|
virtual void createModel( const Mat& data );
|
||
|
|
virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
|
||
|
|
};
|
||
|
|
|
||
|
|
void CV_FlannSavedIndexTest::createModel(const cv::Mat &data)
|
||
|
|
{
|
||
|
|
switch ( cvtest::randInt(ts->get_rng()) % 2 )
|
||
|
|
{
|
||
|
|
//case 0: createIndex( data, LinearIndexParams() ); break; // nothing to save for linear search
|
||
|
|
case 0: createIndex( data, KMeansIndexParams() ); break;
|
||
|
|
case 1: createIndex( data, KDTreeIndexParams() ); break;
|
||
|
|
//case 2: createIndex( data, CompositeIndexParams() ); break; // nothing to save for linear search
|
||
|
|
//case 2: createIndex( data, AutotunedIndexParams() ); break; // possible linear index !
|
||
|
|
default: CV_Assert(0);
|
||
|
|
}
|
||
|
|
string filename = tempfile();
|
||
|
|
index->save( filename );
|
||
|
|
|
||
|
|
createIndex( data, SavedIndexParams(filename.c_str()));
|
||
|
|
remove( filename.c_str() );
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Features2d_FLANN_Linear, regression) { CV_FlannLinearIndexTest test; test.safe_run(); }
|
||
|
|
TEST(Features2d_FLANN_KMeans, regression) { CV_FlannKMeansIndexTest test; test.safe_run(); }
|
||
|
|
TEST(Features2d_FLANN_KDTree, regression) { CV_FlannKDTreeIndexTest test; test.safe_run(); }
|
||
|
|
TEST(Features2d_FLANN_Composite, regression) { CV_FlannCompositeIndexTest test; test.safe_run(); }
|
||
|
|
TEST(Features2d_FLANN_Auto, regression) { CV_FlannAutotunedIndexTest test; test.safe_run(); }
|
||
|
|
TEST(Features2d_FLANN_Saved, regression) { CV_FlannSavedIndexTest test; test.safe_run(); }
|
||
|
|
|
||
|
|
#endif
|
||
|
|
|
||
|
|
}} // namespace
|