Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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//
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#include "precomp.hpp"
#ifdef HAVE_EIGEN2
#include <Eigen/Array>
#endif
using namespace std;
namespace cv
{
Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
float maxDeltaX, float maxDeltaY )
{
if( keypoints1.empty() || keypoints2.empty() )
return Mat();
Mat mask( keypoints1.size(), keypoints2.size(), CV_8UC1 );
for( size_t i = 0; i < keypoints1.size(); i++ )
{
for( size_t j = 0; j < keypoints2.size(); j++ )
{
Point2f diff = keypoints2[j].pt - keypoints1[i].pt;
mask.at<uchar>(i, j) = std::abs(diff.x) < maxDeltaX && std::abs(diff.y) < maxDeltaY;
}
}
return mask;
}
/****************************************************************************************\
* DescriptorMatcher *
\****************************************************************************************/
void DescriptorMatcher::add( const Mat& descriptors )
{
if( m_train.empty() )
{
m_train = descriptors;
}
else
{
// merge train and descriptors
Mat m( m_train.rows + descriptors.rows, m_train.cols, CV_32F );
Mat m1 = m.rowRange( 0, m_train.rows );
m_train.copyTo( m1 );
Mat m2 = m.rowRange( m_train.rows + 1, m.rows );
descriptors.copyTo( m2 );
m_train = m;
}
}
void DescriptorMatcher::match( const Mat& query, vector<int>& matches ) const
{
matchImpl( query, m_train, matches, Mat() );
}
void DescriptorMatcher::match( const Mat& query, const Mat& mask,
vector<int>& matches ) const
{
matchImpl( query, m_train, matches, mask );
}
void DescriptorMatcher::match( const Mat& query, vector<DMatch>& matches ) const
{
matchImpl( query, m_train, matches, Mat() );
}
void DescriptorMatcher::match( const Mat& query, const Mat& mask,
vector<DMatch>& matches ) const
{
matchImpl( query, m_train, matches, mask );
}
void DescriptorMatcher::match( const Mat& query, const Mat& train, vector<DMatch>& matches, const Mat& mask ) const
{
matchImpl( query, train, matches, mask );
}
void DescriptorMatcher::match( const Mat& query, vector<vector<DMatch> >& matches, float threshold ) const
{
matchImpl( query, m_train, matches, threshold, Mat() );
}
void DescriptorMatcher::match( const Mat& query, const Mat& mask,
vector<vector<DMatch> >& matches, float threshold ) const
{
matchImpl( query, m_train, matches, threshold, mask );
}
void DescriptorMatcher::clear()
{
m_train.release();
}
/*
* BruteForceMatcher L2 specialization
*/
template<>
void BruteForceMatcher<L2<float> >::matchImpl( const Mat& query, const Mat& train, vector<DMatch>& matches, const Mat& mask ) const
{
assert( mask.empty() || (mask.rows == query.rows && mask.cols == train.rows) );
assert( query.cols == train.cols || query.empty() || train.empty() );
matches.clear();
matches.reserve( query.rows );
#if (!defined HAVE_EIGEN2)
Mat norms;
cv::reduce( train.mul( train ), norms, 1, 0);
norms = norms.t();
Mat desc_2t = train.t();
for( int i=0;i<query.rows;i++ )
{
Mat distances = (-2)*query.row(i)*desc_2t;
distances += norms;
DMatch match;
match.indexTrain = -1;
double minVal;
Point minLoc;
if( mask.empty() )
{
minMaxLoc ( distances, &minVal, 0, &minLoc );
}
else
{
minMaxLoc ( distances, &minVal, 0, &minLoc, 0, mask.row( i ) );
}
match.indexTrain = minLoc.x;
if( match.indexTrain != -1 )
{
match.indexQuery = i;
double queryNorm = norm( query.row(i) );
match.distance = (float)sqrt( minVal + queryNorm*queryNorm );
matches.push_back( match );
}
}
#else
Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> desc1t;
Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> desc2;
cv2eigen( query.t(), desc1t);
cv2eigen( train, desc2 );
Eigen::Matrix<float, Eigen::Dynamic, 1> norms = desc2.rowwise().squaredNorm() / 2;
if( mask.empty() )
{
for( int i=0;i<query.rows;i++ )
{
Eigen::Matrix<float, Eigen::Dynamic, 1> distances = desc2*desc1t.col(i);
distances -= norms;
DMatch match;
match.indexQuery = i;
match.distance = sqrt( (-2)*distances.maxCoeff( &match.indexTrain ) + desc1t.col(i).squaredNorm() );
matches.push_back( match );
}
}
else
{
for( int i=0;i<query.rows;i++ )
{
Eigen::Matrix<float, Eigen::Dynamic, 1> distances = desc2*desc1t.col(i);
distances -= norms;
float maxCoeff = -std::numeric_limits<float>::max();
DMatch match;
match.indexTrain = -1;
for( int j=0;j<train.rows;j++ )
{
if( possibleMatch( mask, i, j ) && distances( j, 0 ) > maxCoeff )
{
maxCoeff = distances( j, 0 );
match.indexTrain = j;
}
}
if( match.indexTrain != -1 )
{
match.indexQuery = i;
match.distance = sqrt( (-2)*maxCoeff + desc1t.col(i).squaredNorm() );
matches.push_back( match );
}
}
}
#endif
}
/****************************************************************************************\
* Factory function for descriptor matcher creating *
\****************************************************************************************/
Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherType )
{
DescriptorMatcher* dm = 0;
if( !descriptorMatcherType.compare( "BruteForce" ) )
{
dm = new BruteForceMatcher<L2<float> >();
}
else if( !descriptorMatcherType.compare( "BruteForce-L1" ) )
{
dm = new BruteForceMatcher<L1<float> >();
}
return dm;
}
/****************************************************************************************\
* GenericDescriptorMatch *
\****************************************************************************************/
/*
* KeyPointCollection
*/
void KeyPointCollection::add( const Mat& _image, const vector<KeyPoint>& _points )
{
// update m_start_indices
if( startIndices.empty() )
startIndices.push_back(0);
else
startIndices.push_back((int)(*startIndices.rbegin() + points.rbegin()->size()));
// add image and keypoints
images.push_back(_image);
points.push_back(_points);
}
KeyPoint KeyPointCollection::getKeyPoint( int index ) const
{
size_t i = 0;
for(; i < startIndices.size() && startIndices[i] <= index; i++);
i--;
assert(i < startIndices.size() && (size_t)index - startIndices[i] < points[i].size());
return points[i][index - startIndices[i]];
}
size_t KeyPointCollection::calcKeypointCount() const
{
if( startIndices.empty() )
return 0;
return *startIndices.rbegin() + points.rbegin()->size();
}
void KeyPointCollection::clear()
{
images.clear();
points.clear();
startIndices.clear();
}
/*
* GenericDescriptorMatch
*/
void GenericDescriptorMatch::match( const Mat&, vector<KeyPoint>&, vector<DMatch>& )
{
}
void GenericDescriptorMatch::match( const Mat&, vector<KeyPoint>&, vector<vector<DMatch> >&, float )
{
}
void GenericDescriptorMatch::add( KeyPointCollection& collection )
{
for( size_t i = 0; i < collection.images.size(); i++ )
add( collection.images[i], collection.points[i] );
}
void GenericDescriptorMatch::classify( const Mat& image, vector<cv::KeyPoint>& points )
{
vector<int> keypointIndices;
match( image, points, keypointIndices );
// remap keypoint indices to descriptors
for( size_t i = 0; i < keypointIndices.size(); i++ )
points[i].class_id = collection.getKeyPoint(keypointIndices[i]).class_id;
};
void GenericDescriptorMatch::clear()
{
collection.clear();
}
/****************************************************************************************\
* OneWayDescriptorMatch *
\****************************************************************************************/
OneWayDescriptorMatch::OneWayDescriptorMatch()
{}
OneWayDescriptorMatch::OneWayDescriptorMatch( const Params& _params)
{
initialize(_params);
}
OneWayDescriptorMatch::~OneWayDescriptorMatch()
{}
void OneWayDescriptorMatch::initialize( const Params& _params, OneWayDescriptorBase *_base)
{
base.release();
if (_base != 0)
{
base = _base;
}
params = _params;
}
void OneWayDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
{
if( base.empty() )
base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename,
params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale);
size_t trainFeatureCount = keypoints.size();
base->Allocate( (int)trainFeatureCount );
IplImage _image = image;
for( size_t i = 0; i < keypoints.size(); i++ )
base->InitializeDescriptor( (int)i, &_image, keypoints[i], "" );
collection.add( Mat(), keypoints );
#if defined(_KDTREE)
base->ConvertDescriptorsArrayToTree();
#endif
}
void OneWayDescriptorMatch::add( KeyPointCollection& keypoints )
{
if( base.empty() )
base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename,
params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale);
size_t trainFeatureCount = keypoints.calcKeypointCount();
base->Allocate( (int)trainFeatureCount );
int count = 0;
for( size_t i = 0; i < keypoints.points.size(); i++ )
{
for( size_t j = 0; j < keypoints.points[i].size(); j++ )
{
IplImage img = keypoints.images[i];
base->InitializeDescriptor( count++, &img, keypoints.points[i][j], "" );
}
collection.add( Mat(), keypoints.points[i] );
}
#if defined(_KDTREE)
base->ConvertDescriptorsArrayToTree();
#endif
}
void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<int>& indices)
{
vector<DMatch> matchings( points.size() );
indices.resize(points.size());
match( image, points, matchings );
for( size_t i = 0; i < points.size(); i++ )
indices[i] = matchings[i].indexTrain;
}
void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matches )
{
matches.resize( points.size() );
IplImage _image = image;
for( size_t i = 0; i < points.size(); i++ )
{
int poseIdx = -1;
DMatch match;
match.indexQuery = (int)i;
match.indexTrain = -1;
base->FindDescriptor( &_image, points[i].pt, match.indexTrain, poseIdx, match.distance );
matches[i] = match;
}
}
void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<vector<DMatch> >& matches, float /*threshold*/ )
{
matches.clear();
matches.resize( points.size() );
vector<DMatch> dmatches;
match( image, points, dmatches );
for( size_t i=0;i<matches.size();i++ )
{
matches[i].push_back( dmatches[i] );
}
/*
printf("Start matching %d points\n", points.size());
//std::cout << "Start matching " << points.size() << "points\n";
assert(collection.images.size() == 1);
int n = collection.points[0].size();
printf("n = %d\n", n);
for( size_t i = 0; i < points.size(); i++ )
{
//printf("Matching %d\n", i);
//int poseIdx = -1;
DMatch match;
match.indexQuery = i;
match.indexTrain = -1;
CvPoint pt = points[i].pt;
CvRect roi = cvRect(cvRound(pt.x - 24/4),
cvRound(pt.y - 24/4),
24/2, 24/2);
cvSetImageROI(&_image, roi);
std::vector<int> desc_idxs;
std::vector<int> pose_idxs;
std::vector<float> distances;
std::vector<float> _scales;
base->FindDescriptor(&_image, n, desc_idxs, pose_idxs, distances, _scales);
cvResetImageROI(&_image);
for( int j=0;j<n;j++ )
{
match.indexTrain = desc_idxs[j];
match.distance = distances[j];
matches[i].push_back( match );
}
//sort( matches[i].begin(), matches[i].end(), compareIndexTrain );
//for( int j=0;j<n;j++ )
//{
//printf( "%d %f; ",matches[i][j].indexTrain, matches[i][j].distance);
//}
//printf("\n\n\n");
//base->FindDescriptor( &_image, 100, points[i].pt, match.indexTrain, poseIdx, match.distance );
//matches[i].push_back( match );
}
*/
}
void OneWayDescriptorMatch::read( const FileNode &fn )
{
base = new OneWayDescriptorObject( params.patchSize, params.poseCount, string (), string (), string (),
params.minScale, params.maxScale, params.stepScale );
base->Read (fn);
}
void OneWayDescriptorMatch::write( FileStorage& fs ) const
{
base->Write (fs);
}
void OneWayDescriptorMatch::classify( const Mat& image, vector<KeyPoint>& points )
{
IplImage _image = image;
for( size_t i = 0; i < points.size(); i++ )
{
int descIdx = -1;
int poseIdx = -1;
float distance;
base->FindDescriptor(&_image, points[i].pt, descIdx, poseIdx, distance);
points[i].class_id = collection.getKeyPoint(descIdx).class_id;
}
}
void OneWayDescriptorMatch::clear ()
{
GenericDescriptorMatch::clear();
base->clear ();
}
/****************************************************************************************\
* FernDescriptorMatch *
\****************************************************************************************/
FernDescriptorMatch::Params::Params( int _nclasses, int _patchSize, int _signatureSize,
int _nstructs, int _structSize, int _nviews, int _compressionMethod,
const PatchGenerator& _patchGenerator ) :
nclasses(_nclasses), patchSize(_patchSize), signatureSize(_signatureSize),
nstructs(_nstructs), structSize(_structSize), nviews(_nviews),
compressionMethod(_compressionMethod), patchGenerator(_patchGenerator)
{}
FernDescriptorMatch::Params::Params( const string& _filename )
{
filename = _filename;
}
FernDescriptorMatch::FernDescriptorMatch()
{}
FernDescriptorMatch::FernDescriptorMatch( const Params& _params )
{
params = _params;
}
FernDescriptorMatch::~FernDescriptorMatch()
{}
void FernDescriptorMatch::initialize( const Params& _params )
{
classifier.release();
params = _params;
if( !params.filename.empty() )
{
classifier = new FernClassifier;
FileStorage fs(params.filename, FileStorage::READ);
if( fs.isOpened() )
classifier->read( fs.getFirstTopLevelNode() );
}
}
void FernDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
{
if( params.filename.empty() )
collection.add( image, keypoints );
}
void FernDescriptorMatch::trainFernClassifier()
{
if( classifier.empty() )
{
assert( params.filename.empty() );
vector<vector<Point2f> > points;
for( size_t imgIdx = 0; imgIdx < collection.images.size(); imgIdx++ )
KeyPoint::convert( collection.points[imgIdx], points[imgIdx] );
classifier = new FernClassifier( points, collection.images, vector<vector<int> >(), 0, // each points is a class
params.patchSize, params.signatureSize, params.nstructs, params.structSize,
params.nviews, params.compressionMethod, params.patchGenerator );
}
}
void FernDescriptorMatch::calcBestProbAndMatchIdx( const Mat& image, const Point2f& pt,
float& bestProb, int& bestMatchIdx, vector<float>& signature )
{
(*classifier)( image, pt, signature);
bestProb = -FLT_MAX;
bestMatchIdx = -1;
for( int ci = 0; ci < classifier->getClassCount(); ci++ )
{
if( signature[ci] > bestProb )
{
bestProb = signature[ci];
bestMatchIdx = ci;
}
}
}
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<int>& indices )
{
trainFernClassifier();
indices.resize( keypoints.size() );
vector<float> signature( (size_t)classifier->getClassCount() );
for( size_t pi = 0; pi < keypoints.size(); pi++ )
{
//calcBestProbAndMatchIdx( image, keypoints[pi].pt, bestProb, indices[pi], signature );
//TODO: use octave and image pyramid
indices[pi] = (*classifier)(image, keypoints[pi].pt, signature);
}
}
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<DMatch>& matches )
{
trainFernClassifier();
matches.resize( keypoints.size() );
vector<float> signature( (size_t)classifier->getClassCount() );
for( int pi = 0; pi < (int)keypoints.size(); pi++ )
{
matches[pi].indexQuery = pi;
calcBestProbAndMatchIdx( image, keypoints[pi].pt, matches[pi].distance, matches[pi].indexTrain, signature );
//matching[pi].distance is log of probability so we need to transform it
matches[pi].distance = -matches[pi].distance;
}
}
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<vector<DMatch> >& matches, float threshold )
{
trainFernClassifier();
matches.resize( keypoints.size() );
vector<float> signature( (size_t)classifier->getClassCount() );
for( int pi = 0; pi < (int)keypoints.size(); pi++ )
{
(*classifier)( image, keypoints[pi].pt, signature);
DMatch match;
match.indexQuery = pi;
for( int ci = 0; ci < classifier->getClassCount(); ci++ )
{
if( -signature[ci] < threshold )
{
match.distance = -signature[ci];
match.indexTrain = ci;
matches[pi].push_back( match );
}
}
}
}
void FernDescriptorMatch::classify( const Mat& image, vector<KeyPoint>& keypoints )
{
trainFernClassifier();
vector<float> signature( (size_t)classifier->getClassCount() );
for( size_t pi = 0; pi < keypoints.size(); pi++ )
{
float bestProb = 0;
int bestMatchIdx = -1;
calcBestProbAndMatchIdx( image, keypoints[pi].pt, bestProb, bestMatchIdx, signature );
keypoints[pi].class_id = collection.getKeyPoint(bestMatchIdx).class_id;
}
}
void FernDescriptorMatch::read( const FileNode &fn )
{
params.nclasses = fn["nclasses"];
params.patchSize = fn["patchSize"];
params.signatureSize = fn["signatureSize"];
params.nstructs = fn["nstructs"];
params.structSize = fn["structSize"];
params.nviews = fn["nviews"];
params.compressionMethod = fn["compressionMethod"];
//classifier->read(fn);
}
void FernDescriptorMatch::write( FileStorage& fs ) const
{
fs << "nclasses" << params.nclasses;
fs << "patchSize" << params.patchSize;
fs << "signatureSize" << params.signatureSize;
fs << "nstructs" << params.nstructs;
fs << "structSize" << params.structSize;
fs << "nviews" << params.nviews;
fs << "compressionMethod" << params.compressionMethod;
// classifier->write(fs);
}
void FernDescriptorMatch::clear ()
{
GenericDescriptorMatch::clear();
classifier.release();
}
/****************************************************************************************\
* VectorDescriptorMatch *
\****************************************************************************************/
void VectorDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
{
Mat descriptors;
extractor->compute( image, keypoints, descriptors );
matcher->add( descriptors );
collection.add( Mat(), keypoints );
};
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<int>& keypointIndices )
{
Mat descriptors;
extractor->compute( image, points, descriptors );
matcher->match( descriptors, keypointIndices );
};
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matches )
{
Mat descriptors;
extractor->compute( image, points, descriptors );
matcher->match( descriptors, matches );
}
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points,
vector<vector<DMatch> >& matches, float threshold )
{
Mat descriptors;
extractor->compute( image, points, descriptors );
matcher->match( descriptors, matches, threshold );
}
void VectorDescriptorMatch::clear()
{
GenericDescriptorMatch::clear();
matcher->clear();
}
void VectorDescriptorMatch::read( const FileNode& fn )
{
GenericDescriptorMatch::read(fn);
extractor->read (fn);
}
void VectorDescriptorMatch::write (FileStorage& fs) const
{
GenericDescriptorMatch::write(fs);
extractor->write (fs);
}
/****************************************************************************************\
* Factory function for GenericDescriptorMatch creating *
\****************************************************************************************/
Ptr<GenericDescriptorMatch> createGenericDescriptorMatch( const string& genericDescritptorMatchType,
const string &paramsFilename )
{
GenericDescriptorMatch *descriptorMatch = 0;
if( ! genericDescritptorMatchType.compare("ONEWAY") )
{
descriptorMatch = new OneWayDescriptorMatch();
}
else if( ! genericDescritptorMatchType.compare("FERN") )
{
descriptorMatch = new FernDescriptorMatch();
}
else if( ! genericDescritptorMatchType.compare ("CALONDER") )
{
//descriptorMatch = new CalonderDescriptorMatch ();
}
if( !paramsFilename.empty() && descriptorMatch != 0 )
{
FileStorage fs = FileStorage( paramsFilename, FileStorage::READ );
if( fs.isOpened() )
{
descriptorMatch->read( fs.root() );
fs.release();
}
}
return descriptorMatch;
}
}