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414 lines
14 KiB
C++
414 lines
14 KiB
C++
/*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 std;
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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_KDTreeTest_CPP : public NearestNeighborTest
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{
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public:
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CV_KDTreeTest_CPP() {}
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protected:
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virtual void createModel( const Mat& data );
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virtual int checkGetPoins( const Mat& data );
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virtual int findNeighbors( Mat& points, Mat& neighbors );
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virtual int checkFindBoxed();
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virtual void releaseModel();
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KDTree* tr;
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};
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void CV_KDTreeTest_CPP::createModel( const Mat& data )
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{
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tr = new KDTree( data, false );
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}
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int CV_KDTreeTest_CPP::checkGetPoins( const Mat& data )
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{
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Mat res1( data.size(), data.type() ),
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res3( data.size(), data.type() );
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Mat idxs( 1, data.rows, CV_32SC1 );
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for( int pi = 0; pi < data.rows; pi++ )
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{
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idxs.at<int>(0, pi) = pi;
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// 1st way
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const float* point = tr->getPoint(pi);
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for( int di = 0; di < data.cols; di++ )
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res1.at<float>(pi, di) = point[di];
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}
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// 3d way
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tr->getPoints( idxs, res3 );
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if( cvtest::norm( res1, data, NORM_L1) != 0 ||
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cvtest::norm( res3, data, NORM_L1) != 0)
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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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int CV_KDTreeTest_CPP::checkFindBoxed()
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{
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vector<float> min( dims, static_cast<float>(minValue)), max(dims, static_cast<float>(maxValue));
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vector<int> indices;
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tr->findOrthoRange( min, max, indices );
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// TODO check indices
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if( (int)indices.size() != 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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int CV_KDTreeTest_CPP::findNeighbors( Mat& points, Mat& neighbors )
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{
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const int emax = 20;
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Mat neighbors2( neighbors.size(), CV_32SC1 );
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int j;
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vector<float> min(points.cols, static_cast<float>(minValue));
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vector<float> max(points.cols, static_cast<float>(maxValue));
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for( int pi = 0; pi < points.rows; pi++ )
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{
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// 1st way
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Mat nrow = neighbors.row(pi);
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tr->findNearest( points.row(pi), neighbors.cols, emax, nrow );
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// 2nd way
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vector<int> neighborsIdx2( neighbors2.cols, 0 );
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tr->findNearest( points.row(pi), neighbors2.cols, emax, neighborsIdx2 );
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vector<int>::const_iterator it2 = neighborsIdx2.begin();
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for( j = 0; it2 != neighborsIdx2.end(); ++it2, j++ )
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neighbors2.at<int>(pi,j) = *it2;
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}
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// compare results
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if( cvtest::norm( neighbors, neighbors2, NORM_L1 ) != 0 )
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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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void CV_KDTreeTest_CPP::releaseModel()
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{
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delete tr;
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}
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//--------------------------------------------------------------------------------
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class CV_FlannTest : public NearestNeighborTest
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{
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public:
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CV_FlannTest() {}
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protected:
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void createIndex( const Mat& data, const IndexParams& params );
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int knnSearch( Mat& points, Mat& neighbors );
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int radiusSearch( Mat& points, Mat& neighbors );
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virtual void releaseModel();
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Index* index;
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};
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void CV_FlannTest::createIndex( const Mat& data, const IndexParams& params )
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{
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index = new Index( data, params );
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}
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int CV_FlannTest::knnSearch( Mat& points, Mat& neighbors )
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{
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Mat dist( points.rows, neighbors.cols, CV_32FC1);
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int knn = 1, j;
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// 1st way
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index->knnSearch( points, neighbors, dist, knn, SearchParams() );
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// 2nd way
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Mat neighbors1( neighbors.size(), CV_32SC1 );
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for( int i = 0; i < points.rows; i++ )
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{
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float* fltPtr = points.ptr<float>(i);
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vector<float> query( fltPtr, fltPtr + points.cols );
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vector<int> indices( neighbors1.cols, 0 );
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vector<float> dists( dist.cols, 0 );
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index->knnSearch( query, indices, dists, knn, SearchParams() );
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vector<int>::const_iterator it = indices.begin();
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for( j = 0; it != indices.end(); ++it, j++ )
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neighbors1.at<int>(i,j) = *it;
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}
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// compare results
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if( cvtest::norm( neighbors, neighbors1, NORM_L1 ) != 0 )
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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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int CV_FlannTest::radiusSearch( Mat& points, Mat& neighbors )
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{
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Mat dist( 1, neighbors.cols, CV_32FC1);
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Mat neighbors1( neighbors.size(), CV_32SC1 );
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float radius = 10.0f;
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int j;
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// radiusSearch can only search one feature at a time for range search
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for( int i = 0; i < points.rows; i++ )
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{
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// 1st way
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Mat p( 1, points.cols, CV_32FC1, points.ptr<float>(i) ),
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n( 1, neighbors.cols, CV_32SC1, neighbors.ptr<int>(i) );
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index->radiusSearch( p, n, dist, radius, neighbors.cols, SearchParams() );
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// 2nd way
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float* fltPtr = points.ptr<float>(i);
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vector<float> query( fltPtr, fltPtr + points.cols );
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vector<int> indices( neighbors1.cols, 0 );
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vector<float> dists( dist.cols, 0 );
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index->radiusSearch( query, indices, dists, radius, neighbors.cols, SearchParams() );
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vector<int>::const_iterator it = indices.begin();
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for( j = 0; it != indices.end(); ++it, j++ )
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neighbors1.at<int>(i,j) = *it;
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}
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// compare results
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if( cvtest::norm( neighbors, neighbors1, NORM_L1 ) != 0 )
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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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void CV_FlannTest::releaseModel()
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{
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delete index;
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}
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//---------------------------------------
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class CV_FlannLinearIndexTest : public CV_FlannTest
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{
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public:
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CV_FlannLinearIndexTest() {}
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protected:
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virtual void createModel( const Mat& data ) { createIndex( data, LinearIndexParams() ); }
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virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
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};
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//---------------------------------------
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class CV_FlannKMeansIndexTest : public CV_FlannTest
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{
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public:
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CV_FlannKMeansIndexTest() {}
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protected:
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virtual void createModel( const Mat& data ) { createIndex( data, KMeansIndexParams() ); }
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virtual int findNeighbors( Mat& points, Mat& neighbors ) { return radiusSearch( points, neighbors ); }
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};
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//---------------------------------------
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class CV_FlannKDTreeIndexTest : public CV_FlannTest
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{
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public:
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CV_FlannKDTreeIndexTest() {}
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protected:
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virtual void createModel( const Mat& data ) { createIndex( data, KDTreeIndexParams() ); }
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virtual int findNeighbors( Mat& points, Mat& neighbors ) { return radiusSearch( points, neighbors ); }
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};
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//----------------------------------------
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class CV_FlannCompositeIndexTest : public CV_FlannTest
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{
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public:
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CV_FlannCompositeIndexTest() {}
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protected:
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virtual void createModel( const Mat& data ) { createIndex( data, CompositeIndexParams() ); }
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virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
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};
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//----------------------------------------
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class CV_FlannAutotunedIndexTest : public CV_FlannTest
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{
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public:
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CV_FlannAutotunedIndexTest() {}
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protected:
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virtual void createModel( const Mat& data ) { createIndex( data, AutotunedIndexParams() ); }
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virtual int findNeighbors( Mat& points, Mat& neighbors ) { return knnSearch( points, neighbors ); }
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};
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//----------------------------------------
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class CV_FlannSavedIndexTest : public CV_FlannTest
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{
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public:
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CV_FlannSavedIndexTest() {}
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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 ) { return knnSearch( points, neighbors ); }
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};
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void CV_FlannSavedIndexTest::createModel(const cv::Mat &data)
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{
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switch ( cvtest::randInt(ts->get_rng()) % 2 )
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{
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//case 0: createIndex( data, LinearIndexParams() ); break; // nothing to save for linear search
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case 0: createIndex( data, KMeansIndexParams() ); break;
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case 1: createIndex( data, KDTreeIndexParams() ); break;
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//case 2: createIndex( data, CompositeIndexParams() ); break; // nothing to save for linear search
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//case 2: createIndex( data, AutotunedIndexParams() ); break; // possible linear index !
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default: assert(0);
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}
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string filename = tempfile();
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index->save( filename );
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createIndex( data, SavedIndexParams(filename.c_str()));
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remove( filename.c_str() );
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}
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TEST(Features2d_KDTree_CPP, regression) { CV_KDTreeTest_CPP test; test.safe_run(); }
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TEST(Features2d_FLANN_Linear, regression) { CV_FlannLinearIndexTest test; test.safe_run(); }
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TEST(Features2d_FLANN_KMeans, regression) { CV_FlannKMeansIndexTest test; test.safe_run(); }
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TEST(Features2d_FLANN_KDTree, regression) { CV_FlannKDTreeIndexTest test; test.safe_run(); }
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TEST(Features2d_FLANN_Composite, regression) { CV_FlannCompositeIndexTest test; test.safe_run(); }
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TEST(Features2d_FLANN_Auto, regression) { CV_FlannAutotunedIndexTest test; test.safe_run(); }
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TEST(Features2d_FLANN_Saved, regression) { CV_FlannSavedIndexTest test; test.safe_run(); }
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