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added bag of words; did some renaming

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Maria Dimashova 2010-09-23 16:17:48 +00:00
parent 9d9453906b
commit 26dbbcc070
5 changed files with 176 additions and 15 deletions
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69
modules/features2d/include/opencv2/features2d/features2d.hpp
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@ -2188,7 +2188,7 @@ CV_EXPORTS void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1
const vector<vector<char> >& matchesMask=vector<vector<char> >(), int flags=DrawMatchesFlags::DEFAULT );
/****************************************************************************************\
* Evaluation functions *
* Functions to evaluate the feature detectors and [generic] descriptor extractors *
\****************************************************************************************/
CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
@ -2201,13 +2201,70 @@ CV_EXPORTS void computeRecallPrecisionCurve( const vector<vector<DMatch> >& matc
vector<Point2f>& recallPrecisionCurve );
CV_EXPORTS float getRecall( const vector<Point2f>& recallPrecisionCurve, float l_precision );
CV_EXPORTS void evaluateDescriptorMatch( 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=Ptr<GenericDescriptorMatch>() );
CV_EXPORTS void 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=Ptr<GenericDescriptorMatch>() );
/****************************************************************************************\
* Bag of visual words *
\****************************************************************************************/
/*
* Abstract base class for training of a 'bag of visual words' vocabulary from a set of descriptors
*/
class BOWTrainer
{
public:
/*
* Train visual words vocabulary, that is cluster training descriptors and
* compute cluster centers.
*
* descriptors Training descriptors computed on images keypoints.
* vocabulary Vocabulary is cluster centers.
*/
virtual void cluster( const Mat& descriptors, Mat& vocabulary ) = 0;
};
/*
* This is BOWTrainer using cv::kmeans to get vocabulary.
*/
class BOWKMeansTrainer : public BOWTrainer
{
public:
BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
int attempts=3, int flags=KMEANS_PP_CENTERS );
virtual void cluster( const Mat& descriptors, Mat& vocabulary );
protected:
int clusterCount;
TermCriteria termcrit;
int attempts;
int flags;
};
/*
* Class to compute image descriptor using bad of visual words.
*/
class BOWImgDescriptorExtractor
{
public:
BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
const Ptr<DescriptorMatcher>& dmatcher );
void set( const Mat& vocabulary );
void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor,
vector<vector<int> >& pointIdxsInClusters );
int descriptorSize() const { return vocabulary.empty() ? 0 : vocabulary.rows; }
int descriptorType() const { return CV_32FC1; }
protected:
Mat vocabulary;
Ptr<DescriptorExtractor> dextractor;
Ptr<DescriptorMatcher> dmatcher;
};
} /* namespace cv */
#endif /* __cplusplus */

104
modules/features2d/src/bagofwords.cpp Executable file
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@ -0,0 +1,104 @@
/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 "precomp.hpp"
using namespace std;
namespace cv
{
BOWKMeansTrainer::BOWKMeansTrainer( int _clusterCount, const TermCriteria& _termcrit,
int _attempts, int _flags ) :
clusterCount(_clusterCount), termcrit(_termcrit), attempts(_attempts), flags(_flags)
{}
void BOWKMeansTrainer::cluster( const Mat& descriptors, Mat& vocabulary )
{
Mat labels;
kmeans( descriptors, clusterCount, labels, termcrit, attempts, flags, &vocabulary );
}
BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& _dextractor,
const Ptr<DescriptorMatcher>& _dmatcher ) :
dextractor(_dextractor), dmatcher(_dmatcher)
{}
void BOWImgDescriptorExtractor::set( const Mat& _vocabulary )
{
dmatcher->clear();
vocabulary = _vocabulary;
dmatcher->add( vocabulary );
}
void BOWImgDescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor,
vector<vector<int> >& pointIdxsInClusters )
{
int clusterCount = descriptorSize(); // = vocabulary.rows
// Compute descriptors for the image.
Mat descriptors;
dextractor->compute( image, keypoints, descriptors );
// Match keypoint descriptors to cluster center (to vocabulary)
vector<DMatch> matches;
dmatcher->match( descriptors, matches );
// Compute image descriptor
pointIdxsInClusters = vector<vector<int> >(clusterCount);
imgDescriptor = Mat( 1, clusterCount, descriptorType(), Scalar::all(0.0) );
float *dptr = (float*)imgDescriptor.data;
for( size_t i = 0; i < matches.size(); i++ )
{
int queryIdx = matches[i].indexQuery;
int trainIdx = matches[i].indexTrain; // cluster index
CV_Assert( queryIdx == (int)i );
dptr[trainIdx] = dptr[trainIdx] + 1.f;
pointIdxsInClusters[trainIdx].push_back( queryIdx );
}
// Normalize image descriptor.
imgDescriptor /= descriptors.rows;
}
}

10
modules/features2d/src/evaluation.cpp
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@ -452,11 +452,11 @@ float cv::getRecall( const vector<Point2f>& recallPrecisionCurve, float l_precis
return recall;
}
void cv::evaluateDescriptorMatch( 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 )
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 )
{
Ptr<GenericDescriptorMatch> dmatch = _dmatch;
dmatch->clear();

2
samples/cpp/descriptor_extractor_matcher.cpp
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@ -73,7 +73,7 @@ void doIteration( const Mat& img1, Mat& img2, bool isWarpPerspective,
cout << "< Evaluate descriptor match..." << endl;
vector<Point2f> curve;
Ptr<GenericDescriptorMatch> gdm = new VectorDescriptorMatch( descriptorExtractor, descriptorMatcher );
evaluateDescriptorMatch( img1, img2, H12, keypoints1, keypoints2, 0, 0, curve, gdm );
evaluateGenericDescriptorMatcher( img1, img2, H12, keypoints1, keypoints2, 0, 0, curve, gdm );
for( float l_p = 0; l_p < 1 - FLT_EPSILON; l_p+=0.1 )
cout << "1-precision = " << l_p << "; recall = " << getRecall( curve, l_p ) << endl;
cout << ">" << endl;

6
tests/cv/src/adetectordescriptor_evaluation.cpp
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@ -1077,9 +1077,9 @@ void DescriptorQualityTest::runDatasetTest (const vector<Mat> &imgs, const vecto
vector<vector<uchar> > correctMatchesMask;
vector<Point2f> recallPrecisionCurve; // not used because we need recallPrecisionCurve for
// all images in dataset
evaluateDescriptorMatch( imgs[0], imgs[ci+1], Hs[ci], keypoints1, keypoints2,
&matches1to2, &correctMatchesMask, recallPrecisionCurve,
descMatch );
evaluateGenericDescriptorMatcher( imgs[0], imgs[ci+1], Hs[ci], keypoints1, keypoints2,
&matches1to2, &correctMatchesMask, recallPrecisionCurve,
descMatch );
allMatches1to2.insert( allMatches1to2.end(), matches1to2.begin(), matches1to2.end() );
allCorrectMatchesMask.insert( allCorrectMatchesMask.end(), correctMatchesMask.begin(), correctMatchesMask.end() );
}