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Commit 8462deed authored by Maria Dimashova's avatar Maria Dimashova
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split descriptors.cpp

parent 9e9d4b9e
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modules/features2d/src/descriptors.cpp
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......@@ -41,237 +41,11 @@
#include "precomp.hpp"
#ifdef HAVE_EIGEN2
#include <Eigen/Array>
#endif
//#define _KDTREE
using namespace std;
const int draw_shift_bits = 4;
const int draw_multiplier = 1 << draw_shift_bits;
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;
}
/*
* Drawing functions
*/
static inline void _drawKeypoint( Mat& img, const KeyPoint& p, const Scalar& color, int flags )
{
Point center( cvRound(p.pt.x * draw_multiplier), cvRound(p.pt.y * draw_multiplier) );
if( flags & DrawMatchesFlags::DRAW_RICH_KEYPOINTS )
{
int radius = cvRound(p.size/2 * draw_multiplier); // KeyPoint::size is a diameter
// draw the circles around keypoints with the keypoints size
circle( img, center, radius, color, 1, CV_AA, draw_shift_bits );
// draw orientation of the keypoint, if it is applicable
if( p.angle != -1 )
{
float srcAngleRad = p.angle*(float)CV_PI/180.f;
Point orient(cvRound(cos(srcAngleRad)*radius),
cvRound(sin(srcAngleRad)*radius));
line( img, center, center+orient, color, 1, CV_AA, draw_shift_bits );
}
#if 0
else
{
// draw center with R=1
int radius = 1 * draw_multiplier;
circle( img, center, radius, color, 1, CV_AA, draw_shift_bits );
}
#endif
}
else
{
// draw center with R=3
int radius = 3 * draw_multiplier;
circle( img, center, radius, color, 1, CV_AA, draw_shift_bits );
}
}
void drawKeypoints( const Mat& image, const vector<KeyPoint>& keypoints, Mat& outImg,
const Scalar& _color, int flags )
{
if( !(flags & DrawMatchesFlags::DRAW_OVER_OUTIMG) )
cvtColor( image, outImg, CV_GRAY2BGR );
RNG& rng=theRNG();
bool isRandColor = _color == Scalar::all(-1);
for( vector<KeyPoint>::const_iterator i = keypoints.begin(), ie = keypoints.end(); i != ie; ++i )
{
Scalar color = isRandColor ? Scalar(rng(256), rng(256), rng(256)) : _color;
_drawKeypoint( outImg, *i, color, flags );
}
}
static void _prepareImgAndDrawKeypoints( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2, const vector<KeyPoint>& keypoints2,
Mat& outImg, Mat& outImg1, Mat& outImg2,
const Scalar& singlePointColor, int flags )
{
Size size( img1.cols + img2.cols, MAX(img1.rows, img2.rows) );
if( flags & DrawMatchesFlags::DRAW_OVER_OUTIMG )
{
if( size.width > outImg.cols || size.height > outImg.rows )
CV_Error( CV_StsBadSize, "outImg has size less than need to draw img1 and img2 together" );
outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
}
else
{
outImg.create( size, CV_MAKETYPE(img1.depth(), 3) );
outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
if( img1.type() == CV_8U )
cvtColor( img1, outImg1, CV_GRAY2BGR );
else
img1.copyTo( outImg1 );
if( img2.type() == CV_8U )
cvtColor( img2, outImg2, CV_GRAY2BGR );
else
img2.copyTo( outImg2 );
}
// draw keypoints
if( !(flags & DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS) )
{
Mat outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
drawKeypoints( outImg1, keypoints1, outImg1, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG );
Mat outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
drawKeypoints( outImg2, keypoints2, outImg2, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG );
}
}
static inline void _drawMatch( Mat& outImg, Mat& outImg1, Mat& outImg2 ,
const KeyPoint& kp1, const KeyPoint& kp2, const Scalar& matchColor, int flags )
{
RNG& rng = theRNG();
bool isRandMatchColor = matchColor == Scalar::all(-1);
Scalar color = isRandMatchColor ? Scalar( rng(256), rng(256), rng(256) ) : matchColor;
_drawKeypoint( outImg1, kp1, color, flags );
_drawKeypoint( outImg2, kp2, color, flags );
Point2f pt1 = kp1.pt,
pt2 = kp2.pt,
dpt2 = Point2f( std::min(pt2.x+outImg1.cols, float(outImg.cols-1)), pt2.y );
line( outImg,
Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)),
Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)),
color, 1, CV_AA, draw_shift_bits );
}
void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2,const vector<KeyPoint>& keypoints2,
const vector<int>& matches1to2, Mat& outImg,
const Scalar& matchColor, const Scalar& singlePointColor,
const vector<char>& matchesMask, int flags )
{
if( matches1to2.size() != keypoints1.size() )
CV_Error( CV_StsBadSize, "matches1to2 must have the same size as keypoints1" );
if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
CV_Error( CV_StsBadSize, "matchesMask must have the same size as matches1to2" );
Mat outImg1, outImg2;
_prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
outImg, outImg1, outImg2, singlePointColor, flags );
// draw matches
for( size_t i1 = 0; i1 < keypoints1.size(); i1++ )
{
int i2 = matches1to2[i1];
if( (matchesMask.empty() || matchesMask[i1] ) && i2 >= 0 )
{
const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
_drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags );
}
}
}
void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2, const vector<KeyPoint>& keypoints2,
const vector<DMatch>& matches1to2, Mat& outImg,
const Scalar& matchColor, const Scalar& singlePointColor,
const vector<char>& matchesMask, int flags )
{
if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
CV_Error( CV_StsBadSize, "matchesMask must have the same size as matches1to2" );
Mat outImg1, outImg2;
_prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
outImg, outImg1, outImg2, singlePointColor, flags );
// draw matches
for( size_t m = 0; m < matches1to2.size(); m++ )
{
int i1 = matches1to2[m].indexQuery;
int i2 = matches1to2[m].indexTrain;
if( matchesMask.empty() || matchesMask[m] )
{
const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
_drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags );
}
}
}
void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2, const vector<KeyPoint>& keypoints2,
const vector<vector<DMatch> >& matches1to2, Mat& outImg,
const Scalar& matchColor, const Scalar& singlePointColor,
const vector<vector<char> >& matchesMask, int flags )
{
if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
CV_Error( CV_StsBadSize, "matchesMask must have the same size as matches1to2" );
Mat outImg1, outImg2;
_prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
outImg, outImg1, outImg2, singlePointColor, flags );
// draw matches
for( size_t i = 0; i < matches1to2.size(); i++ )
{
for( size_t j = 0; j < matches1to2[i].size(); j++ )
{
int i1 = matches1to2[i][j].indexQuery;
int i2 = matches1to2[i][j].indexTrain;
if( matchesMask.empty() || matchesMask[i][j] )
{
const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
_drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags );
}
}
}
}
/****************************************************************************************\
* DescriptorExtractor *
\****************************************************************************************/
......@@ -493,7 +267,7 @@ void OpponentColorDescriptorExtractor::write( FileStorage& fs ) const
}
/****************************************************************************************\
* Factory functions for descriptor extractor and matcher creating *
* Factory function for descriptor extractor creating *
\****************************************************************************************/
Ptr<DescriptorExtractor> createDescriptorExtractor( const string& descriptorExtractorType )
......@@ -518,719 +292,4 @@ Ptr<DescriptorExtractor> createDescriptorExtractor( const string& descriptorExtr
return de;
}
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;
}
/****************************************************************************************\
* 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
}
/****************************************************************************************\
* 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();
}
/*
* 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;
}
/****************************************************************************************\
* 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);
}
}
modules/features2d/src/draw.cpp 0 → 100755
View file @ 8462deed
/*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;
const int draw_shift_bits = 4;
const int draw_multiplier = 1 << draw_shift_bits;
namespace cv
{
/*
* Functions to draw keypoints and matches.
*/
static inline void _drawKeypoint( Mat& img, const KeyPoint& p, const Scalar& color, int flags )
{
Point center( cvRound(p.pt.x * draw_multiplier), cvRound(p.pt.y * draw_multiplier) );
if( flags & DrawMatchesFlags::DRAW_RICH_KEYPOINTS )
{
int radius = cvRound(p.size/2 * draw_multiplier); // KeyPoint::size is a diameter
// draw the circles around keypoints with the keypoints size
circle( img, center, radius, color, 1, CV_AA, draw_shift_bits );
// draw orientation of the keypoint, if it is applicable
if( p.angle != -1 )
{
float srcAngleRad = p.angle*(float)CV_PI/180.f;
Point orient(cvRound(cos(srcAngleRad)*radius),
cvRound(sin(srcAngleRad)*radius));
line( img, center, center+orient, color, 1, CV_AA, draw_shift_bits );
}
#if 0
else
{
// draw center with R=1
int radius = 1 * draw_multiplier;
circle( img, center, radius, color, 1, CV_AA, draw_shift_bits );
}
#endif
}
else
{
// draw center with R=3
int radius = 3 * draw_multiplier;
circle( img, center, radius, color, 1, CV_AA, draw_shift_bits );
}
}
void drawKeypoints( const Mat& image, const vector<KeyPoint>& keypoints, Mat& outImg,
const Scalar& _color, int flags )
{
if( !(flags & DrawMatchesFlags::DRAW_OVER_OUTIMG) )
cvtColor( image, outImg, CV_GRAY2BGR );
RNG& rng=theRNG();
bool isRandColor = _color == Scalar::all(-1);
for( vector<KeyPoint>::const_iterator i = keypoints.begin(), ie = keypoints.end(); i != ie; ++i )
{
Scalar color = isRandColor ? Scalar(rng(256), rng(256), rng(256)) : _color;
_drawKeypoint( outImg, *i, color, flags );
}
}
static void _prepareImgAndDrawKeypoints( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2, const vector<KeyPoint>& keypoints2,
Mat& outImg, Mat& outImg1, Mat& outImg2,
const Scalar& singlePointColor, int flags )
{
Size size( img1.cols + img2.cols, MAX(img1.rows, img2.rows) );
if( flags & DrawMatchesFlags::DRAW_OVER_OUTIMG )
{
if( size.width > outImg.cols || size.height > outImg.rows )
CV_Error( CV_StsBadSize, "outImg has size less than need to draw img1 and img2 together" );
outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
}
else
{
outImg.create( size, CV_MAKETYPE(img1.depth(), 3) );
outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
if( img1.type() == CV_8U )
cvtColor( img1, outImg1, CV_GRAY2BGR );
else
img1.copyTo( outImg1 );
if( img2.type() == CV_8U )
cvtColor( img2, outImg2, CV_GRAY2BGR );
else
img2.copyTo( outImg2 );
}
// draw keypoints
if( !(flags & DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS) )
{
Mat outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
drawKeypoints( outImg1, keypoints1, outImg1, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG );
Mat outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
drawKeypoints( outImg2, keypoints2, outImg2, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG );
}
}
static inline void _drawMatch( Mat& outImg, Mat& outImg1, Mat& outImg2 ,
const KeyPoint& kp1, const KeyPoint& kp2, const Scalar& matchColor, int flags )
{
RNG& rng = theRNG();
bool isRandMatchColor = matchColor == Scalar::all(-1);
Scalar color = isRandMatchColor ? Scalar( rng(256), rng(256), rng(256) ) : matchColor;
_drawKeypoint( outImg1, kp1, color, flags );
_drawKeypoint( outImg2, kp2, color, flags );
Point2f pt1 = kp1.pt,
pt2 = kp2.pt,
dpt2 = Point2f( std::min(pt2.x+outImg1.cols, float(outImg.cols-1)), pt2.y );
line( outImg,
Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)),
Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)),
color, 1, CV_AA, draw_shift_bits );
}
void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2,const vector<KeyPoint>& keypoints2,
const vector<int>& matches1to2, Mat& outImg,
const Scalar& matchColor, const Scalar& singlePointColor,
const vector<char>& matchesMask, int flags )
{
if( matches1to2.size() != keypoints1.size() )
CV_Error( CV_StsBadSize, "matches1to2 must have the same size as keypoints1" );
if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
CV_Error( CV_StsBadSize, "matchesMask must have the same size as matches1to2" );
Mat outImg1, outImg2;
_prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
outImg, outImg1, outImg2, singlePointColor, flags );
// draw matches
for( size_t i1 = 0; i1 < keypoints1.size(); i1++ )
{
int i2 = matches1to2[i1];
if( (matchesMask.empty() || matchesMask[i1] ) && i2 >= 0 )
{
const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
_drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags );
}
}
}
void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2, const vector<KeyPoint>& keypoints2,
const vector<DMatch>& matches1to2, Mat& outImg,
const Scalar& matchColor, const Scalar& singlePointColor,
const vector<char>& matchesMask, int flags )
{
if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
CV_Error( CV_StsBadSize, "matchesMask must have the same size as matches1to2" );
Mat outImg1, outImg2;
_prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
outImg, outImg1, outImg2, singlePointColor, flags );
// draw matches
for( size_t m = 0; m < matches1to2.size(); m++ )
{
int i1 = matches1to2[m].indexQuery;
int i2 = matches1to2[m].indexTrain;
if( matchesMask.empty() || matchesMask[m] )
{
const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
_drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags );
}
}
}
void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2, const vector<KeyPoint>& keypoints2,
const vector<vector<DMatch> >& matches1to2, Mat& outImg,
const Scalar& matchColor, const Scalar& singlePointColor,
const vector<vector<char> >& matchesMask, int flags )
{
if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
CV_Error( CV_StsBadSize, "matchesMask must have the same size as matches1to2" );
Mat outImg1, outImg2;
_prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
outImg, outImg1, outImg2, singlePointColor, flags );
// draw matches
for( size_t i = 0; i < matches1to2.size(); i++ )
{
for( size_t j = 0; j < matches1to2[i].size(); j++ )
{
int i1 = matches1to2[i][j].indexQuery;
int i2 = matches1to2[i][j].indexTrain;
if( matchesMask.empty() || matchesMask[i][j] )
{
const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
_drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags );
}
}
}
}
}
modules/features2d/src/matchers.cpp 0 → 100755
View file @ 8462deed
/*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"
#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;
}
}
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