2010-08-05 20:19:26 +08:00
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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 "precomp.hpp"
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#include <limits>
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using namespace cv;
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using namespace std;
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2010-08-05 21:29:43 +08:00
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static inline Point2f applyHomography( const Mat_<double>& H, const Point2f& pt )
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2010-08-05 20:19:26 +08:00
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{
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double z = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2);
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if( z )
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{
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double w = 1./z;
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2010-08-05 21:29:43 +08:00
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return Point2f( (float)((H(0,0)*pt.x + H(0,1)*pt.y + H(0,2))*w), (float)((H(1,0)*pt.x + H(1,1)*pt.y + H(1,2))*w) );
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2010-08-05 20:19:26 +08:00
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}
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2010-08-05 21:29:43 +08:00
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return Point2f( numeric_limits<float>::max(), numeric_limits<float>::max() );
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2010-08-05 20:19:26 +08:00
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}
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2010-08-05 21:29:43 +08:00
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static inline void linearizeHomographyAt( const Mat_<double>& H, const Point2f& pt, Mat_<double>& A )
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2010-08-05 20:19:26 +08:00
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{
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A.create(2,2);
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double p1 = H(0,0)*pt.x + H(0,1)*pt.y + H(0,2),
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p2 = H(1,0)*pt.x + H(1,1)*pt.y + H(1,2),
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p3 = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2),
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p3_2 = p3*p3;
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if( p3 )
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{
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A(0,0) = H(0,0)/p3 - p1*H(2,0)/p3_2; // fxdx
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A(0,1) = H(0,1)/p3 - p1*H(2,1)/p3_2; // fxdy
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A(1,0) = H(1,0)/p3 - p2*H(2,0)/p3_2; // fydx
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A(1,1) = H(1,1)/p3 - p2*H(2,1)/p3_2; // fydx
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}
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else
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A.setTo(Scalar::all(numeric_limits<double>::max()));
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}
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class EllipticKeyPoint
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{
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public:
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EllipticKeyPoint();
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EllipticKeyPoint( const Point2f& _center, const Scalar& _ellipse );
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static void convert( const vector<KeyPoint>& src, vector<EllipticKeyPoint>& dst );
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static void convert( const vector<EllipticKeyPoint>& src, vector<KeyPoint>& dst );
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static Mat_<double> getSecondMomentsMatrix( const Scalar& _ellipse );
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Mat_<double> getSecondMomentsMatrix() const;
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void calcProjection( const Mat_<double>& H, EllipticKeyPoint& projection ) const;
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static void calcProjection( const vector<EllipticKeyPoint>& src, const Mat_<double>& H, vector<EllipticKeyPoint>& dst );
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Point2f center;
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Scalar ellipse; // 3 elements a, b, c: ax^2+2bxy+cy^2=1
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Size_<float> axes; // half lenght of elipse axes
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Size_<float> boundingBox; // half sizes of bounding box which sides are parallel to the coordinate axes
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};
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EllipticKeyPoint::EllipticKeyPoint()
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{
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*this = EllipticKeyPoint(Point2f(0,0), Scalar(1, 0, 1) );
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}
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EllipticKeyPoint::EllipticKeyPoint( const Point2f& _center, const Scalar& _ellipse )
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{
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center = _center;
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ellipse = _ellipse;
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Mat_<double> M = getSecondMomentsMatrix(_ellipse), eval;
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eigen( M, eval );
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assert( eval.rows == 2 && eval.cols == 1 );
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2010-08-05 21:29:43 +08:00
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axes.width = 1.f / (float)sqrt(eval(0,0));
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axes.height = 1.f / (float)sqrt(eval(1,0));
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2010-08-05 20:19:26 +08:00
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2010-08-05 21:29:43 +08:00
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double ac_b2 = ellipse[0]*ellipse[2] - ellipse[1]*ellipse[1];
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boundingBox.width = (float)sqrt(ellipse[2]/ac_b2);
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boundingBox.height = (float)sqrt(ellipse[0]/ac_b2);
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2010-08-05 20:19:26 +08:00
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}
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Mat_<double> EllipticKeyPoint::getSecondMomentsMatrix( const Scalar& _ellipse )
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{
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Mat_<double> M(2, 2);
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M(0,0) = _ellipse[0];
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M(1,0) = M(0,1) = _ellipse[1];
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M(1,1) = _ellipse[2];
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return M;
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}
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Mat_<double> EllipticKeyPoint::getSecondMomentsMatrix() const
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{
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return getSecondMomentsMatrix(ellipse);
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}
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void EllipticKeyPoint::calcProjection( const Mat_<double>& H, EllipticKeyPoint& projection ) const
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{
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Point2f dstCenter = applyHomography(H, center);
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Mat_<double> invM; invert(getSecondMomentsMatrix(), invM);
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Mat_<double> Aff; linearizeHomographyAt(H, center, Aff);
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Mat_<double> dstM; invert(Aff*invM*Aff.t(), dstM);
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projection = EllipticKeyPoint( dstCenter, Scalar(dstM(0,0), dstM(0,1), dstM(1,1)) );
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}
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void EllipticKeyPoint::convert( const vector<KeyPoint>& src, vector<EllipticKeyPoint>& dst )
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{
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if( !src.empty() )
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{
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dst.resize(src.size());
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for( size_t i = 0; i < src.size(); i++ )
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{
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float rad = src[i].size/2;
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assert( rad );
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float fac = 1.f/(rad*rad);
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dst[i] = EllipticKeyPoint( src[i].pt, Scalar(fac, 0, fac) );
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}
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}
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}
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void EllipticKeyPoint::convert( const vector<EllipticKeyPoint>& src, vector<KeyPoint>& dst )
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{
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if( !src.empty() )
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{
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dst.resize(src.size());
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for( size_t i = 0; i < src.size(); i++ )
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{
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Size_<float> axes = src[i].axes;
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float rad = sqrt(axes.height*axes.width);
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dst[i] = KeyPoint(src[i].center, 2*rad );
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}
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}
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}
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void EllipticKeyPoint::calcProjection( const vector<EllipticKeyPoint>& src, const Mat_<double>& H, vector<EllipticKeyPoint>& dst )
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{
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if( !src.empty() )
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{
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assert( !H.empty() && H.cols == 3 && H.rows == 3);
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dst.resize(src.size());
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vector<EllipticKeyPoint>::const_iterator srcIt = src.begin();
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vector<EllipticKeyPoint>::iterator dstIt = dst.begin();
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for( ; srcIt != src.end(); ++srcIt, ++dstIt )
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srcIt->calcProjection(H, *dstIt);
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}
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}
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static void filterEllipticKeyPointsByImageSize( vector<EllipticKeyPoint>& keypoints, const Size& imgSize )
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{
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if( !keypoints.empty() )
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{
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vector<EllipticKeyPoint> filtered;
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filtered.reserve(keypoints.size());
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vector<EllipticKeyPoint>::const_iterator it = keypoints.begin();
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for( int i = 0; it != keypoints.end(); ++it, i++ )
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{
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if( it->center.x + it->boundingBox.width < imgSize.width &&
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it->center.x - it->boundingBox.width > 0 &&
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it->center.y + it->boundingBox.height < imgSize.height &&
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it->center.y - it->boundingBox.height > 0 )
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filtered.push_back(*it);
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}
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keypoints.assign(filtered.begin(), filtered.end());
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}
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}
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static void overlap( const vector<EllipticKeyPoint>& keypoints1, const vector<EllipticKeyPoint>& keypoints2t, bool commonPart,
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SparseMat_<float>& overlaps )
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{
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overlaps.clear();
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if( keypoints1.empty() || keypoints2t.empty() )
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return;
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int size[] = { keypoints1.size(), keypoints2t.size() };
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overlaps.create( 2, size );
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for( size_t i1 = 0; i1 < keypoints1.size(); i1++ )
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{
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EllipticKeyPoint kp1 = keypoints1[i1];
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float maxDist = sqrt(kp1.axes.width*kp1.axes.height),
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fac = 30.f/maxDist;
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if( !commonPart )
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fac=3;
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maxDist = maxDist*4;
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2010-08-05 21:29:43 +08:00
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fac = 1.f/(fac*fac);
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2010-08-05 20:19:26 +08:00
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EllipticKeyPoint keypoint1a = EllipticKeyPoint( kp1.center, Scalar(fac*kp1.ellipse[0], fac*kp1.ellipse[1], fac*kp1.ellipse[2]) );
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for( size_t i2 = 0; i2 < keypoints2t.size(); i2++ )
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{
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EllipticKeyPoint kp2 = keypoints2t[i2];
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Point2f diff = kp2.center - kp1.center;
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if( norm(diff) < maxDist )
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{
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EllipticKeyPoint keypoint2a = EllipticKeyPoint( kp2.center, Scalar(fac*kp2.ellipse[0], fac*kp2.ellipse[1], fac*kp2.ellipse[2]) );
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//find the largest eigenvalue
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float maxx = ceil(( keypoint1a.boundingBox.width > (diff.x+keypoint2a.boundingBox.width)) ?
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keypoint1a.boundingBox.width : (diff.x+keypoint2a.boundingBox.width));
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float minx = floor((-keypoint1a.boundingBox.width < (diff.x-keypoint2a.boundingBox.width)) ?
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-keypoint1a.boundingBox.width : (diff.x-keypoint2a.boundingBox.width));
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float maxy = ceil(( keypoint1a.boundingBox.height > (diff.y+keypoint2a.boundingBox.height)) ?
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keypoint1a.boundingBox.height : (diff.y+keypoint2a.boundingBox.height));
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float miny = floor((-keypoint1a.boundingBox.height < (diff.y-keypoint2a.boundingBox.height)) ?
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-keypoint1a.boundingBox.height : (diff.y-keypoint2a.boundingBox.height));
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float mina = (maxx-minx) < (maxy-miny) ? (maxx-minx) : (maxy-miny) ;
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2010-08-05 21:29:43 +08:00
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float dr = mina/50.f;
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float bua = 0.f, bna = 0.f;
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2010-08-05 20:19:26 +08:00
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//compute the area
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for( float rx1 = minx; rx1 <= maxx; rx1+=dr )
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{
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float rx2 = rx1-diff.x;
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for( float ry1=miny; ry1<=maxy; ry1+=dr )
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{
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float ry2=ry1-diff.y;
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//compute the distance from the ellipse center
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2010-08-05 21:29:43 +08:00
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float e1 = (float)(keypoint1a.ellipse[0]*rx1*rx1+2*keypoint1a.ellipse[1]*rx1*ry1+keypoint1a.ellipse[2]*ry1*ry1);
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float e2 = (float)(keypoint2a.ellipse[0]*rx2*rx2+2*keypoint2a.ellipse[1]*rx2*ry2+keypoint2a.ellipse[2]*ry2*ry2);
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2010-08-05 20:19:26 +08:00
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//compute the area
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if( e1<1 && e2<1 ) bna++;
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if( e1<1 || e2<1 ) bua++;
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}
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}
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if( bna > 0)
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overlaps.ref(i1,i2) = bna/bua;
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}
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}
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}
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}
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static void calculateRepeatability( const Mat& img1, const Mat& img2, const Mat& H1to2,
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const vector<KeyPoint>& _keypoints1, const vector<KeyPoint>& _keypoints2,
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float& repeatability, int& correspondencesCount,
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SparseMat_<uchar>* thresholdedOverlapMask=0 )
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{
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vector<EllipticKeyPoint> keypoints1, keypoints2, keypoints1t, keypoints2t;
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EllipticKeyPoint::convert( _keypoints1, keypoints1 );
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EllipticKeyPoint::convert( _keypoints2, keypoints2 );
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// calculate projections of key points
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EllipticKeyPoint::calcProjection( keypoints1, H1to2, keypoints1t );
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Mat H2to1; invert(H1to2, H2to1);
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EllipticKeyPoint::calcProjection( keypoints2, H2to1, keypoints2t );
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bool ifEvaluateDetectors = !thresholdedOverlapMask; // == commonPart
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float overlapThreshold;
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if( ifEvaluateDetectors )
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{
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overlapThreshold = 1.f - 0.4f;
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// remove key points from outside of the common image part
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Size sz1 = img1.size(), sz2 = img2.size();
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filterEllipticKeyPointsByImageSize( keypoints1, sz1 );
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filterEllipticKeyPointsByImageSize( keypoints1t, sz2 );
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filterEllipticKeyPointsByImageSize( keypoints2, sz2 );
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filterEllipticKeyPointsByImageSize( keypoints2t, sz1 );
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}
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else
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{
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overlapThreshold = 1.f - 0.5f;
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}
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int minCount = min( keypoints1.size(), keypoints2t.size() );
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// calculate overlap errors
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SparseMat_<float> overlaps;
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overlap( keypoints1, keypoints2t, ifEvaluateDetectors, overlaps );
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correspondencesCount = -1;
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repeatability = -1.f;
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|
|
|
const int* size = overlaps.size();
|
|
|
|
if( !size || overlaps.nzcount() == 0 )
|
|
|
|
return;
|
|
|
|
|
|
|
|
if( ifEvaluateDetectors )
|
|
|
|
{
|
|
|
|
// threshold the overlaps
|
|
|
|
for( int y = 0; y < size[0]; y++ )
|
|
|
|
{
|
|
|
|
for( int x = 0; x < size[1]; x++ )
|
|
|
|
{
|
|
|
|
if ( overlaps(y,x) < overlapThreshold )
|
|
|
|
overlaps.erase(y,x);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// regions one-to-one matching
|
|
|
|
correspondencesCount = 0;
|
|
|
|
while( overlaps.nzcount() > 0 )
|
|
|
|
{
|
|
|
|
double maxOverlap = 0;
|
|
|
|
int maxIdx[2];
|
|
|
|
minMaxLoc( overlaps, 0, &maxOverlap, 0, maxIdx );
|
|
|
|
for( size_t i1 = 0; i1 < keypoints1.size(); i1++ )
|
|
|
|
overlaps.erase(i1, maxIdx[1]);
|
|
|
|
for( size_t i2 = 0; i2 < keypoints2t.size(); i2++ )
|
|
|
|
overlaps.erase(maxIdx[0], i2);
|
|
|
|
correspondencesCount++;
|
|
|
|
}
|
|
|
|
repeatability = minCount ? (float)correspondencesCount/minCount : -1;
|
|
|
|
}
|
|
|
|
else
|
|
|
|
{
|
|
|
|
thresholdedOverlapMask->create( 2, size );
|
|
|
|
for( int y = 0; y < size[0]; y++ )
|
|
|
|
{
|
|
|
|
for( int x = 0; x < size[1]; x++ )
|
|
|
|
{
|
|
|
|
float val = overlaps(y,x);
|
|
|
|
if ( val >= overlapThreshold )
|
|
|
|
thresholdedOverlapMask->ref(y,x) = 1;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
void cv::evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
|
|
|
|
vector<KeyPoint>* _keypoints1, vector<KeyPoint>* _keypoints2,
|
|
|
|
float& repeatability, int& correspCount,
|
|
|
|
const Ptr<FeatureDetector>& _fdetector )
|
|
|
|
{
|
|
|
|
Ptr<FeatureDetector> fdetector(_fdetector);
|
|
|
|
vector<KeyPoint> *keypoints1, *keypoints2, buf1, buf2;
|
|
|
|
keypoints1 = _keypoints1 != 0 ? _keypoints1 : &buf1;
|
|
|
|
keypoints2 = _keypoints2 != 0 ? _keypoints2 : &buf2;
|
|
|
|
|
|
|
|
if( (keypoints1->empty() || keypoints2->empty()) && fdetector.empty() )
|
|
|
|
CV_Error( CV_StsBadArg, "fdetector must be no empty when keypoints1 or keypoints2 is empty" );
|
|
|
|
|
|
|
|
if( keypoints1->empty() )
|
|
|
|
fdetector->detect( img1, *keypoints1 );
|
|
|
|
if( keypoints2->empty() )
|
2010-10-01 17:02:54 +08:00
|
|
|
fdetector->detect( img2, *keypoints2 );
|
2010-08-05 20:19:26 +08:00
|
|
|
|
|
|
|
calculateRepeatability( img1, img2, H1to2, *keypoints1, *keypoints2, repeatability, correspCount );
|
|
|
|
}
|
|
|
|
|
|
|
|
struct DMatchForEvaluation : public DMatch
|
|
|
|
{
|
|
|
|
uchar isCorrect;
|
|
|
|
DMatchForEvaluation( const DMatch &dm ) : DMatch( dm ) {}
|
|
|
|
};
|
|
|
|
|
|
|
|
static inline float recall( int correctMatchCount, int correspondenceCount )
|
|
|
|
{
|
|
|
|
return correspondenceCount ? (float)correctMatchCount / (float)correspondenceCount : -1;
|
|
|
|
}
|
|
|
|
|
|
|
|
static inline float precision( int correctMatchCount, int falseMatchCount )
|
|
|
|
{
|
|
|
|
return correctMatchCount + falseMatchCount ? (float)correctMatchCount / (float)(correctMatchCount + falseMatchCount) : -1;
|
|
|
|
}
|
|
|
|
|
|
|
|
void cv::computeRecallPrecisionCurve( const vector<vector<DMatch> >& matches1to2,
|
|
|
|
const vector<vector<uchar> >& correctMatches1to2Mask,
|
|
|
|
vector<Point2f>& recallPrecisionCurve )
|
|
|
|
{
|
|
|
|
CV_Assert( matches1to2.size() == correctMatches1to2Mask.size() );
|
|
|
|
|
|
|
|
vector<DMatchForEvaluation> allMatches;
|
|
|
|
int correspondenceCount = 0;
|
|
|
|
for( size_t i = 0; i < matches1to2.size(); i++ )
|
|
|
|
{
|
|
|
|
for( size_t j = 0; j < matches1to2[i].size(); j++ )
|
|
|
|
{
|
|
|
|
DMatchForEvaluation match = matches1to2[i][j];
|
|
|
|
match.isCorrect = correctMatches1to2Mask[i][j] ;
|
|
|
|
allMatches.push_back( match );
|
|
|
|
correspondenceCount += match.isCorrect != 0 ? 1 : 0;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
std::sort( allMatches.begin(), allMatches.end() );
|
|
|
|
|
|
|
|
int correctMatchCount = 0, falseMatchCount = 0;
|
|
|
|
recallPrecisionCurve.resize( allMatches.size() );
|
|
|
|
for( size_t i = 0; i < allMatches.size(); i++ )
|
|
|
|
{
|
|
|
|
if( allMatches[i].isCorrect )
|
|
|
|
correctMatchCount++;
|
|
|
|
else
|
|
|
|
falseMatchCount++;
|
|
|
|
|
|
|
|
float r = recall( correctMatchCount, correspondenceCount );
|
|
|
|
float p = precision( correctMatchCount, falseMatchCount );
|
|
|
|
recallPrecisionCurve[i] = Point2f(1-p, r);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
float cv::getRecall( const vector<Point2f>& recallPrecisionCurve, float l_precision )
|
|
|
|
{
|
|
|
|
float recall = -1;
|
|
|
|
|
|
|
|
if( l_precision >= 0 && l_precision <= 1 )
|
|
|
|
{
|
|
|
|
int bestIdx = -1;
|
|
|
|
float minDiff = FLT_MAX;
|
|
|
|
for( size_t i = 0; i < recallPrecisionCurve.size(); i++ )
|
|
|
|
{
|
|
|
|
float curDiff = std::fabs(l_precision - recallPrecisionCurve[i].x);
|
|
|
|
if( curDiff <= minDiff )
|
|
|
|
{
|
|
|
|
bestIdx = i;
|
|
|
|
minDiff = curDiff;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
recall = recallPrecisionCurve[bestIdx].y;
|
|
|
|
}
|
|
|
|
|
|
|
|
return recall;
|
|
|
|
}
|
|
|
|
|
2010-09-24 00:17:48 +08:00
|
|
|
void cv::evaluateGenericDescriptorMatcher( const Mat& img1, const Mat& img2, const Mat& H1to2,
|
|
|
|
vector<KeyPoint>& keypoints1, vector<KeyPoint>& keypoints2,
|
|
|
|
vector<vector<DMatch> >* _matches1to2, vector<vector<uchar> >* _correctMatches1to2Mask,
|
|
|
|
vector<Point2f>& recallPrecisionCurve,
|
|
|
|
const Ptr<GenericDescriptorMatch>& _dmatch )
|
2010-08-05 20:19:26 +08:00
|
|
|
{
|
|
|
|
Ptr<GenericDescriptorMatch> dmatch = _dmatch;
|
|
|
|
dmatch->clear();
|
|
|
|
|
|
|
|
vector<vector<DMatch> > *matches1to2, buf1;
|
|
|
|
vector<vector<uchar> > *correctMatches1to2Mask, buf2;
|
|
|
|
matches1to2 = _matches1to2 != 0 ? _matches1to2 : &buf1;
|
|
|
|
correctMatches1to2Mask = _correctMatches1to2Mask != 0 ? _correctMatches1to2Mask : &buf2;
|
|
|
|
|
2010-08-10 00:33:44 +08:00
|
|
|
if( keypoints1.empty() )
|
|
|
|
CV_Error( CV_StsBadArg, "keypoints1 must be no empty" );
|
|
|
|
|
2010-08-05 20:19:26 +08:00
|
|
|
if( matches1to2->empty() && dmatch.empty() )
|
|
|
|
CV_Error( CV_StsBadArg, "dmatch must be no empty when matches1to2 is empty" );
|
2010-08-10 00:33:44 +08:00
|
|
|
|
|
|
|
bool computeKeypoints2ByPrj = keypoints2.empty();
|
|
|
|
if( computeKeypoints2ByPrj )
|
|
|
|
{
|
|
|
|
assert(0);
|
|
|
|
// TODO: add computing keypoints2 from keypoints1 using H1to2
|
|
|
|
}
|
|
|
|
|
|
|
|
if( matches1to2->empty() || computeKeypoints2ByPrj )
|
2010-08-05 20:19:26 +08:00
|
|
|
{
|
2010-08-10 00:33:44 +08:00
|
|
|
dmatch->clear();
|
2010-08-05 20:19:26 +08:00
|
|
|
dmatch->add( img2, keypoints2 );
|
2010-08-10 00:33:44 +08:00
|
|
|
// TODO: use more sophisticated strategy to choose threshold
|
2010-08-05 20:19:26 +08:00
|
|
|
dmatch->match( img1, keypoints1, *matches1to2, std::numeric_limits<float>::max() );
|
|
|
|
}
|
|
|
|
float repeatability;
|
|
|
|
int correspCount;
|
|
|
|
SparseMat_<uchar> thresholdedOverlapMask; // thresholded allOverlapErrors
|
|
|
|
calculateRepeatability( img1, img2, H1to2,
|
|
|
|
keypoints1, keypoints2,
|
|
|
|
repeatability, correspCount,
|
|
|
|
&thresholdedOverlapMask );
|
|
|
|
|
|
|
|
correctMatches1to2Mask->resize(matches1to2->size());
|
|
|
|
int ddd = 0;
|
|
|
|
for( size_t i = 0; i < matches1to2->size(); i++ )
|
|
|
|
{
|
|
|
|
(*correctMatches1to2Mask)[i].resize((*matches1to2)[i].size());
|
|
|
|
for( size_t j = 0;j < (*matches1to2)[i].size(); j++ )
|
|
|
|
{
|
|
|
|
int indexQuery = (*matches1to2)[i][j].indexQuery;
|
|
|
|
int indexTrain = (*matches1to2)[i][j].indexTrain;
|
|
|
|
(*correctMatches1to2Mask)[i][j] = thresholdedOverlapMask( indexQuery, indexTrain );
|
|
|
|
ddd += thresholdedOverlapMask( indexQuery, indexTrain ) != 0 ? 1 : 0;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
computeRecallPrecisionCurve( *matches1to2, *correctMatches1to2Mask, recallPrecisionCurve );
|
|
|
|
}
|