a LOT of obsolete stuff has been moved to the legacy module.
This commit is contained in:
Vadim Pisarevsky
264
modules/legacy/test/test_nearestneighbors.cpp
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264
modules/legacy/test/test_nearestneighbors.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#include <algorithm>
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#include <vector>
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#include <iostream>
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using namespace cv;
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using namespace cv::flann;
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//--------------------------------------------------------------------------------
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class NearestNeighborTest : public cvtest::BaseTest
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{
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public:
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NearestNeighborTest() {}
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protected:
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static const int minValue = 0;
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static const int maxValue = 1;
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static const int dims = 30;
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static const int featuresCount = 2000;
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static const int K = 1; // * should also test 2nd nn etc.?
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virtual void run( int start_from );
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virtual void createModel( const Mat& data ) = 0;
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virtual int findNeighbors( Mat& points, Mat& neighbors ) = 0;
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virtual int checkGetPoins( const Mat& data );
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virtual int checkFindBoxed();
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virtual int checkFind( const Mat& data );
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virtual void releaseModel() = 0;
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};
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int NearestNeighborTest::checkGetPoins( const Mat& )
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{
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return cvtest::TS::OK;
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}
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int NearestNeighborTest::checkFindBoxed()
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{
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return cvtest::TS::OK;
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}
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int NearestNeighborTest::checkFind( const Mat& data )
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{
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int code = cvtest::TS::OK;
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int pointsCount = 1000;
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float noise = 0.2f;
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RNG rng;
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Mat points( pointsCount, dims, CV_32FC1 );
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Mat results( pointsCount, K, CV_32SC1 );
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std::vector<int> fmap( pointsCount );
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for( int pi = 0; pi < pointsCount; pi++ )
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{
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int fi = rng.next() % featuresCount;
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fmap[pi] = fi;
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for( int d = 0; d < dims; d++ )
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points.at<float>(pi, d) = data.at<float>(fi, d) + rng.uniform(0.0f, 1.0f) * noise;
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}
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code = findNeighbors( points, results );
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if( code == cvtest::TS::OK )
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{
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int correctMatches = 0;
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for( int pi = 0; pi < pointsCount; pi++ )
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{
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if( fmap[pi] == results.at<int>(pi, 0) )
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correctMatches++;
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}
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double correctPerc = correctMatches / (double)pointsCount;
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if (correctPerc < .75)
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{
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ts->printf( cvtest::TS::LOG, "correct_perc = %d\n", correctPerc );
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code = cvtest::TS::FAIL_BAD_ACCURACY;
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}
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}
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return code;
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}
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void NearestNeighborTest::run( int /*start_from*/ ) {
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int code = cvtest::TS::OK, tempCode;
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Mat desc( featuresCount, dims, CV_32FC1 );
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randu( desc, Scalar(minValue), Scalar(maxValue) );
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createModel( desc );
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tempCode = checkGetPoins( desc );
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if( tempCode != cvtest::TS::OK )
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{
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ts->printf( cvtest::TS::LOG, "bad accuracy of GetPoints \n" );
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code = tempCode;
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}
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tempCode = checkFindBoxed();
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if( tempCode != cvtest::TS::OK )
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{
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ts->printf( cvtest::TS::LOG, "bad accuracy of FindBoxed \n" );
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code = tempCode;
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}
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tempCode = checkFind( desc );
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if( tempCode != cvtest::TS::OK )
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{
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ts->printf( cvtest::TS::LOG, "bad accuracy of Find \n" );
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code = tempCode;
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}
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releaseModel();
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ts->set_failed_test_info( code );
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}
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//--------------------------------------------------------------------------------
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class CV_LSHTest : public NearestNeighborTest
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{
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public:
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CV_LSHTest() {}
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protected:
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virtual void createModel( const Mat& data );
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virtual int findNeighbors( Mat& points, Mat& neighbors );
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virtual void releaseModel();
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struct CvLSH* lsh;
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CvMat desc;
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};
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void CV_LSHTest::createModel( const Mat& data )
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{
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desc = data;
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lsh = cvCreateMemoryLSH( data.cols, data.rows, 70, 20, CV_32FC1 );
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cvLSHAdd( lsh, &desc );
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}
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int CV_LSHTest::findNeighbors( Mat& points, Mat& neighbors )
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{
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const int emax = 20;
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Mat dist( points.rows, neighbors.cols, CV_64FC1);
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CvMat _dist = dist, _points = points, _neighbors = neighbors;
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cvLSHQuery( lsh, &_points, &_neighbors, &_dist, neighbors.cols, emax );
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return cvtest::TS::OK;
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}
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void CV_LSHTest::releaseModel()
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{
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cvReleaseLSH( &lsh );
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}
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//--------------------------------------------------------------------------------
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class CV_FeatureTreeTest_C : public NearestNeighborTest
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{
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public:
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CV_FeatureTreeTest_C() {}
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protected:
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virtual int findNeighbors( Mat& points, Mat& neighbors );
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virtual void releaseModel();
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CvFeatureTree* tr;
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CvMat desc;
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};
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int CV_FeatureTreeTest_C::findNeighbors( Mat& points, Mat& neighbors )
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{
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const int emax = 20;
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Mat dist( points.rows, neighbors.cols, CV_64FC1);
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CvMat _dist = dist, _points = points, _neighbors = neighbors;
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cvFindFeatures( tr, &_points, &_neighbors, &_dist, neighbors.cols, emax );
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return cvtest::TS::OK;
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}
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void CV_FeatureTreeTest_C::releaseModel()
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{
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cvReleaseFeatureTree( tr );
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}
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//--------------------------------------
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class CV_SpillTreeTest_C : public CV_FeatureTreeTest_C
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{
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public:
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CV_SpillTreeTest_C() {}
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protected:
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virtual void createModel( const Mat& data );
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};
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void CV_SpillTreeTest_C::createModel( const Mat& data )
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{
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desc = data;
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tr = cvCreateSpillTree( &desc );
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}
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//--------------------------------------
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class CV_KDTreeTest_C : public CV_FeatureTreeTest_C
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{
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public:
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CV_KDTreeTest_C() {}
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protected:
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virtual void createModel( const Mat& data );
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virtual int checkFindBoxed();
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};
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void CV_KDTreeTest_C::createModel( const Mat& data )
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{
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desc = data;
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tr = cvCreateKDTree( &desc );
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}
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int CV_KDTreeTest_C::checkFindBoxed()
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{
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Mat min(1, dims, CV_32FC1 ), max(1, dims, CV_32FC1 ), indices( 1, 1, CV_32SC1 );
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float l = minValue, r = maxValue;
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min.setTo(Scalar(l)), max.setTo(Scalar(r));
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CvMat _min = min, _max = max, _indices = indices;
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// TODO check indices
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if( cvFindFeaturesBoxed( tr, &_min, &_max, &_indices ) != featuresCount )
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return cvtest::TS::FAIL_BAD_ACCURACY;
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return cvtest::TS::OK;
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}
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TEST(Features2d_LSH, regression) { CV_LSHTest test; test.safe_run(); }
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TEST(Features2d_SpillTree, regression) { CV_SpillTreeTest_C test; test.safe_run(); }
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TEST(Features2d_KDTree_C, regression) { CV_KDTreeTest_C test; test.safe_run(); }
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