Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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// 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.
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#include "test_precomp.hpp"
#include <algorithm>
#include <vector>
#include <iostream>
using namespace cv;
using namespace cv::flann;
//--------------------------------------------------------------------------------
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 checkGetPoins( const Mat& data );
virtual int checkFindBoxed();
virtual int checkFind( const Mat& data );
virtual void releaseModel() = 0;
};
int NearestNeighborTest::checkGetPoins( 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;
if (correctPerc < .75)
{
ts->printf( cvtest::TS::LOG, "correct_perc = %d\n", correctPerc );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
}
return code;
}
void NearestNeighborTest::run( int /*start_from*/ ) {
int code = cvtest::TS::OK, tempCode;
Mat desc( featuresCount, dims, CV_32FC1 );
randu( desc, Scalar(minValue), Scalar(maxValue) );
createModel( desc );
tempCode = checkGetPoins( 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();
ts->set_failed_test_info( code );
}
//--------------------------------------------------------------------------------
class CV_LSHTest : public NearestNeighborTest
{
public:
CV_LSHTest() {}
protected:
virtual void createModel( const Mat& data );
virtual int findNeighbors( Mat& points, Mat& neighbors );
virtual void releaseModel();
struct CvLSH* lsh;
CvMat desc;
};
void CV_LSHTest::createModel( const Mat& data )
{
desc = data;
lsh = cvCreateMemoryLSH( data.cols, data.rows, 70, 20, CV_32FC1 );
cvLSHAdd( lsh, &desc );
}
int CV_LSHTest::findNeighbors( Mat& points, Mat& neighbors )
{
const int emax = 20;
Mat dist( points.rows, neighbors.cols, CV_64FC1);
CvMat _dist = dist, _points = points, _neighbors = neighbors;
cvLSHQuery( lsh, &_points, &_neighbors, &_dist, neighbors.cols, emax );
return cvtest::TS::OK;
}
void CV_LSHTest::releaseModel()
{
cvReleaseLSH( &lsh );
}
//--------------------------------------------------------------------------------
class CV_FeatureTreeTest_C : public NearestNeighborTest
{
public:
CV_FeatureTreeTest_C() {}
protected:
virtual int findNeighbors( Mat& points, Mat& neighbors );
virtual void releaseModel();
CvFeatureTree* tr;
CvMat desc;
};
int CV_FeatureTreeTest_C::findNeighbors( Mat& points, Mat& neighbors )
{
const int emax = 20;
Mat dist( points.rows, neighbors.cols, CV_64FC1);
CvMat _dist = dist, _points = points, _neighbors = neighbors;
cvFindFeatures( tr, &_points, &_neighbors, &_dist, neighbors.cols, emax );
return cvtest::TS::OK;
}
void CV_FeatureTreeTest_C::releaseModel()
{
cvReleaseFeatureTree( tr );
}
//--------------------------------------
class CV_SpillTreeTest_C : public CV_FeatureTreeTest_C
{
public:
CV_SpillTreeTest_C() {}
protected:
virtual void createModel( const Mat& data );
};
void CV_SpillTreeTest_C::createModel( const Mat& data )
{
desc = data;
tr = cvCreateSpillTree( &desc );
}
//--------------------------------------
class CV_KDTreeTest_C : public CV_FeatureTreeTest_C
{
public:
CV_KDTreeTest_C() {}
protected:
virtual void createModel( const Mat& data );
virtual int checkFindBoxed();
};
void CV_KDTreeTest_C::createModel( const Mat& data )
{
desc = data;
tr = cvCreateKDTree( &desc );
}
int CV_KDTreeTest_C::checkFindBoxed()
{
Mat min(1, dims, CV_32FC1 ), max(1, dims, CV_32FC1 ), indices( 1, 1, CV_32SC1 );
float l = minValue, r = maxValue;
min.setTo(Scalar(l)), max.setTo(Scalar(r));
CvMat _min = min, _max = max, _indices = indices;
// TODO check indices
if( cvFindFeaturesBoxed( tr, &_min, &_max, &_indices ) != featuresCount )
return cvtest::TS::FAIL_BAD_ACCURACY;
return cvtest::TS::OK;
}
TEST(Legacy_LSH, regression) { CV_LSHTest test; test.safe_run(); }
TEST(Legacy_SpillTree, regression) { CV_SpillTreeTest_C test; test.safe_run(); }
TEST(Legacy_KDTree_C, regression) { CV_KDTreeTest_C test; test.safe_run(); }