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"atomic bomb" commit. Reorganized OpenCV directory structure

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Vadim Pisarevsky
2010-05-11 17:44:00 +00:00
commit 127d6649a1
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define_opencv_module(features2d opencv_core opencv_imgproc opencv_calib3d opencv_highgui)

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modules/features2d/include/opencv2/features2d/features2d.hpp Normal file
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/*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;
using namespace cv;
/****************************************************************************************\
* DescriptorExtractor *
\****************************************************************************************/
/*
* DescriptorExtractor
*/
struct RoiPredicate
{
RoiPredicate(float _minX, float _minY, float _maxX, float _maxY)
: minX(_minX), minY(_minY), maxX(_maxX), maxY(_maxY)
{}
bool operator()( const KeyPoint& keyPt) const
{
Point2f pt = keyPt.pt;
return (pt.x < minX) || (pt.x >= maxX) || (pt.y < minY) || (pt.y >= maxY);
}
float minX, minY, maxX, maxY;
};
void DescriptorExtractor::removeBorderKeypoints( vector<KeyPoint>& keypoints,
Size imageSize, int borderPixels )
{
keypoints.erase( remove_if(keypoints.begin(), keypoints.end(),
RoiPredicate((float)borderPixels, (float)borderPixels,
(float)(imageSize.width - borderPixels),
(float)(imageSize.height - borderPixels))),
keypoints.end());
}
/****************************************************************************************\
* SiftDescriptorExtractor *
\****************************************************************************************/
SiftDescriptorExtractor::SiftDescriptorExtractor( double magnification, bool isNormalize,
int nOctaves, int nOctaveLayers, int firstOctave )
: sift( magnification, isNormalize, nOctaves, nOctaveLayers, firstOctave )
{}
void SiftDescriptorExtractor::compute( const Mat& image,
vector<KeyPoint>& keypoints,
Mat& descriptors) const
{
bool useProvidedKeypoints = true;
sift(image, Mat(), keypoints, descriptors, useProvidedKeypoints);
}
/****************************************************************************************\
* SurfDescriptorExtractor *
\****************************************************************************************/
SurfDescriptorExtractor::SurfDescriptorExtractor( int nOctaves,
int nOctaveLayers, bool extended )
: surf( 0.0, nOctaves, nOctaveLayers, extended )
{}
void SurfDescriptorExtractor::compute( const Mat& image,
vector<KeyPoint>& keypoints,
Mat& descriptors) const
{
// Compute descriptors for given keypoints
vector<float> _descriptors;
Mat mask;
bool useProvidedKeypoints = true;
surf(image, mask, keypoints, _descriptors, useProvidedKeypoints);
descriptors.create(keypoints.size(), surf.descriptorSize(), CV_32FC1);
assert( (int)_descriptors.size() == descriptors.rows * descriptors.cols );
std::copy(_descriptors.begin(), _descriptors.end(), descriptors.begin<float>());
}
/****************************************************************************************\
* 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(*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();
}
/*
* GenericDescriptorMatch
*/
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;
};
/****************************************************************************************\
* OneWayDescriptorMatch *
\****************************************************************************************/
OneWayDescriptorMatch::OneWayDescriptorMatch()
{}
OneWayDescriptorMatch::OneWayDescriptorMatch( const Params& _params)
{
initialize(_params);
}
OneWayDescriptorMatch::~OneWayDescriptorMatch()
{}
void OneWayDescriptorMatch::initialize( const Params& _params)
{
base.release();
params = _params;
}
void OneWayDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
{
if( base.empty() )
base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.trainPath.c_str(),
params.pcaConfig.c_str(), params.pcaHrConfig.c_str(),
params.pcaDescConfig.c_str());
size_t trainFeatureCount = keypoints.size();
base->Allocate( trainFeatureCount );
IplImage _image = image;
for( size_t i = 0; i < keypoints.size(); i++ )
base->InitializeDescriptor( 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.trainPath.c_str(),
params.pcaConfig.c_str(), params.pcaHrConfig.c_str(),
params.pcaDescConfig.c_str());
size_t trainFeatureCount = keypoints.calcKeypointCount();
base->Allocate( 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)
{
indices.resize(points.size());
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 );
indices[i] = descIdx;
}
}
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;
}
}
/****************************************************************************************\
* CalonderDescriptorMatch *
\****************************************************************************************/
CalonderDescriptorMatch::Params::Params( const RNG& _rng, const PatchGenerator& _patchGen,
int _numTrees, int _depth, int _views,
size_t _reducedNumDim,
int _numQuantBits,
bool _printStatus,
int _patchSize ) :
rng(_rng), patchGen(_patchGen), numTrees(_numTrees), depth(_depth), views(_views),
patchSize(_patchSize), reducedNumDim(_reducedNumDim), numQuantBits(_numQuantBits), printStatus(_printStatus)
{}
CalonderDescriptorMatch::Params::Params( const string& _filename )
{
filename = _filename;
}
CalonderDescriptorMatch::CalonderDescriptorMatch()
{}
CalonderDescriptorMatch::CalonderDescriptorMatch( const Params& _params )
{
initialize(_params);
}
CalonderDescriptorMatch::~CalonderDescriptorMatch()
{}
void CalonderDescriptorMatch::initialize( const Params& _params )
{
classifier.release();
params = _params;
if( !params.filename.empty() )
{
classifier = new RTreeClassifier;
classifier->read( params.filename.c_str() );
}
}
void CalonderDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
{
if( params.filename.empty() )
collection.add( image, keypoints );
}
Mat CalonderDescriptorMatch::extractPatch( const Mat& image, const Point& pt, int patchSize ) const
{
const int offset = patchSize / 2;
return image( Rect(pt.x - offset, pt.y - offset, patchSize, patchSize) );
}
void CalonderDescriptorMatch::calcBestProbAndMatchIdx( const Mat& image, const Point& pt,
float& bestProb, int& bestMatchIdx, float* signature )
{
IplImage roi = extractPatch( image, pt, params.patchSize );
classifier->getSignature( &roi, signature );
bestProb = 0;
bestMatchIdx = -1;
for( size_t ci = 0; ci < (size_t)classifier->classes(); ci++ )
{
if( signature[ci] > bestProb )
{
bestProb = signature[ci];
bestMatchIdx = ci;
}
}
}
void CalonderDescriptorMatch::trainRTreeClassifier()
{
if( classifier.empty() )
{
assert( params.filename.empty() );
classifier = new RTreeClassifier;
vector<BaseKeypoint> baseKeyPoints;
vector<IplImage> iplImages( collection.images.size() );
for( size_t imageIdx = 0; imageIdx < collection.images.size(); imageIdx++ )
{
iplImages[imageIdx] = collection.images[imageIdx];
for( size_t pointIdx = 0; pointIdx < collection.points[imageIdx].size(); pointIdx++ )
{
BaseKeypoint bkp;
KeyPoint kp = collection.points[imageIdx][pointIdx];
bkp.x = cvRound(kp.pt.x);
bkp.y = cvRound(kp.pt.y);
bkp.image = &iplImages[imageIdx];
baseKeyPoints.push_back(bkp);
}
}
classifier->train( baseKeyPoints, params.rng, params.patchGen, params.numTrees,
params.depth, params.views, params.reducedNumDim, params.numQuantBits,
params.printStatus );
}
}
void CalonderDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<int>& indices )
{
trainRTreeClassifier();
float bestProb = 0;
AutoBuffer<float> signature( classifier->classes() );
indices.resize( keypoints.size() );
for( size_t pi = 0; pi < keypoints.size(); pi++ )
calcBestProbAndMatchIdx( image, keypoints[pi].pt, bestProb, indices[pi], signature );
}
void CalonderDescriptorMatch::classify( const Mat& image, vector<KeyPoint>& keypoints )
{
trainRTreeClassifier();
AutoBuffer<float> signature( classifier->classes() );
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;
}
}
/****************************************************************************************\
* 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<Point2f> points;
vector<Ptr<Mat> > refimgs( collection.images.size() );
vector<int> labels;
for( size_t imageIdx = 0; imageIdx < collection.images.size(); imageIdx++ )
{
refimgs[imageIdx] = &collection.images[imageIdx];
for( size_t pointIdx = 0; pointIdx < collection.points[imageIdx].size(); pointIdx++ )
{
points.push_back(collection.points[imageIdx][pointIdx].pt);
labels.push_back(imageIdx);
}
}
classifier = new FernClassifier( points, refimgs, labels, params.nclasses, 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 = 0;
bestMatchIdx = -1;
for( size_t ci = 0; ci < (size_t)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();
float bestProb = 0;
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 );
}
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;
}
}

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/*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;
using namespace cv;
/*
FeatureDetector
*/
struct MaskPredicate
{
MaskPredicate( const Mat& _mask ) : mask(_mask)
{}
MaskPredicate& operator=(const MaskPredicate&) {}
bool operator() (const KeyPoint& key_pt) const
{
return mask.at<uchar>( (int)(key_pt.pt.y + 0.5f), (int)(key_pt.pt.x + 0.5f) ) != 0;
}
const Mat& mask;
};
void FeatureDetector::removeInvalidPoints( const Mat& mask, vector<KeyPoint>& keypoints )
{
if( mask.empty() )
return;
keypoints.erase(remove_if(keypoints.begin(), keypoints.end(), MaskPredicate(mask)), keypoints.end());
};
/*
FastFeatureDetector
*/
FastFeatureDetector::FastFeatureDetector( int _threshold, bool _nonmaxSuppression )
: threshold(_threshold), nonmaxSuppression(_nonmaxSuppression)
{}
void FastFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints) const
{
FAST( image, keypoints, threshold, nonmaxSuppression );
removeInvalidPoints( mask, keypoints );
}
/*
GoodFeaturesToTrackDetector
*/
GoodFeaturesToTrackDetector::GoodFeaturesToTrackDetector( int _maxCorners, double _qualityLevel, \
double _minDistance, int _blockSize,
bool _useHarrisDetector, double _k )
: maxCorners(_maxCorners), qualityLevel(_qualityLevel), minDistance(_minDistance),
blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
{}
void GoodFeaturesToTrackDetector::detectImpl( const Mat& image, const Mat& mask,
vector<KeyPoint>& keypoints ) const
{
vector<Point2f> corners;
goodFeaturesToTrack( image, corners, maxCorners, qualityLevel, minDistance, mask,
blockSize, useHarrisDetector, k );
keypoints.resize(corners.size());
vector<Point2f>::const_iterator corner_it = corners.begin();
vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
for( ; corner_it != corners.end(); ++corner_it, ++keypoint_it )
{
*keypoint_it = KeyPoint( *corner_it, 1.f );
}
}
/*
MserFeatureDetector
*/
MserFeatureDetector::MserFeatureDetector( int delta, int minArea, int maxArea,
float maxVariation, float minDiversity,
int maxEvolution, double areaThreshold,
double minMargin, int edgeBlurSize )
: mser( delta, minArea, maxArea, maxVariation, minDiversity,
maxEvolution, areaThreshold, minMargin, edgeBlurSize )
{}
void MserFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const
{
vector<vector<Point> > msers;
mser(image, msers, mask);
keypoints.resize( msers.size() );
vector<vector<Point> >::const_iterator contour_it = msers.begin();
vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
for( ; contour_it != msers.end(); ++contour_it, ++keypoint_it )
{
RotatedRect rect = fitEllipse(Mat(*contour_it));
*keypoint_it = KeyPoint( rect.center, min(rect.size.height, rect.size.width), rect.angle);
}
}
/*
StarFeatureDetector
*/
StarFeatureDetector::StarFeatureDetector(int maxSize, int responseThreshold,
int lineThresholdProjected,
int lineThresholdBinarized,
int suppressNonmaxSize)
: star( maxSize, responseThreshold, lineThresholdProjected,
lineThresholdBinarized, suppressNonmaxSize)
{}
void StarFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints) const
{
star(image, keypoints);
removeInvalidPoints(mask, keypoints);
}
/*
SiftFeatureDetector
*/
SiftFeatureDetector::SiftFeatureDetector(double threshold, double edgeThreshold, int angleMode,
int nOctaves, int nOctaveLayers, int firstOctave) :
sift(threshold, edgeThreshold, angleMode, nOctaves, nOctaveLayers, firstOctave)
{
}
void SiftFeatureDetector::detectImpl( const Mat& image, const Mat& mask,
vector<KeyPoint>& keypoints) const
{
sift(image, mask, keypoints);
}
/*
SurfFeatureDetector
*/
SurfFeatureDetector::SurfFeatureDetector( double hessianThreshold, int octaves, int octaveLayers)
: surf(hessianThreshold, octaves, octaveLayers)
{}
void SurfFeatureDetector::detectImpl( const Mat& image, const Mat& mask,
vector<KeyPoint>& keypoints) const
{
surf(image, mask, keypoints);
}

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/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2008, Willow Garage Inc., 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"
namespace cv
{
void write(FileStorage& fs, const string& objname, const vector<KeyPoint>& keypoints)
{
WriteStructContext ws(fs, objname, CV_NODE_SEQ + CV_NODE_FLOW);
int i, npoints = (int)keypoints.size();
for( i = 0; i < npoints; i++ )
{
const KeyPoint& kpt = keypoints[i];
write(fs, kpt.pt.x);
write(fs, kpt.pt.y);
write(fs, kpt.size);
write(fs, kpt.angle);
write(fs, kpt.response);
write(fs, kpt.octave);
}
}
void read(const FileNode& node, vector<KeyPoint>& keypoints)
{
keypoints.resize(0);
FileNodeIterator it = node.begin(), it_end = node.end();
for( ; it != it_end; )
{
KeyPoint kpt;
it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave;
keypoints.push_back(kpt);
}
}
void KeyPoint::convert(const std::vector<KeyPoint>& u, std::vector<Point2f>& v)
{
size_t i, sz = u.size();
v.resize(sz);
for( i = 0; i < sz; i++ )
v[i] = u[i].pt;
}
void KeyPoint::convert( const std::vector<Point2f>& u, std::vector<KeyPoint>& v,
float size, float response, int octave, int class_id )
{
size_t i, sz = u.size();
v.resize(sz);
for( i = 0; i < sz; i++ )
v[i] = KeyPoint(u[i], size, -1, response, octave, class_id);
}
}

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/*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"
/* End of file. */

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/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if _MSC_VER >= 1200
#pragma warning( disable: 4251 4710 4711 4514 4996 ) /* function AAA selected for automatic inline expansion */
#endif
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/internal.hpp"
#endif

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/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2008, Willow Garage Inc., 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"
static void
icvComputeIntegralImages( const CvMat* matI, CvMat* matS, CvMat* matT, CvMat* _FT )
{
int x, y, rows = matI->rows, cols = matI->cols;
const uchar* I = matI->data.ptr;
int *S = matS->data.i, *T = matT->data.i, *FT = _FT->data.i;
int istep = matI->step, step = matS->step/sizeof(S[0]);
assert( CV_MAT_TYPE(matI->type) == CV_8UC1 &&
CV_MAT_TYPE(matS->type) == CV_32SC1 &&
CV_ARE_TYPES_EQ(matS, matT) && CV_ARE_TYPES_EQ(matS, _FT) &&
CV_ARE_SIZES_EQ(matS, matT) && CV_ARE_SIZES_EQ(matS, _FT) &&
matS->step == matT->step && matS->step == _FT->step &&
matI->rows+1 == matS->rows && matI->cols+1 == matS->cols );
for( x = 0; x <= cols; x++ )
S[x] = T[x] = FT[x] = 0;
S += step; T += step; FT += step;
S[0] = T[0] = 0;
FT[0] = I[0];
for( x = 1; x < cols; x++ )
{
S[x] = S[x-1] + I[x-1];
T[x] = I[x-1];
FT[x] = I[x] + I[x-1];
}
S[cols] = S[cols-1] + I[cols-1];
T[cols] = FT[cols] = I[cols-1];
for( y = 2; y <= rows; y++ )
{
I += istep, S += step, T += step, FT += step;
S[0] = S[-step]; S[1] = S[-step+1] + I[0];
T[0] = T[-step + 1];
T[1] = FT[0] = T[-step + 2] + I[-istep] + I[0];
FT[1] = FT[-step + 2] + I[-istep] + I[1] + I[0];
for( x = 2; x < cols; x++ )
{
S[x] = S[x - 1] + S[-step + x] - S[-step + x - 1] + I[x - 1];
T[x] = T[-step + x - 1] + T[-step + x + 1] - T[-step*2 + x] + I[-istep + x - 1] + I[x - 1];
FT[x] = FT[-step + x - 1] + FT[-step + x + 1] - FT[-step*2 + x] + I[x] + I[x-1];
}
S[cols] = S[cols - 1] + S[-step + cols] - S[-step + cols - 1] + I[cols - 1];
T[cols] = FT[cols] = T[-step + cols - 1] + I[-istep + cols - 1] + I[cols - 1];
}
}
typedef struct CvStarFeature
{
int area;
int* p[8];
}
CvStarFeature;
static int
icvStarDetectorComputeResponses( const CvMat* img, CvMat* responses, CvMat* sizes,
const CvStarDetectorParams* params )
{
const int MAX_PATTERN = 17;
static const int sizes0[] = {1, 2, 3, 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128, -1};
static const int pairs[][2] = {{1, 0}, {3, 1}, {4, 2}, {5, 3}, {7, 4}, {8, 5}, {9, 6},
{11, 8}, {13, 10}, {14, 11}, {15, 12}, {16, 14}, {-1, -1}};
float invSizes[MAX_PATTERN][2];
int sizes1[MAX_PATTERN];
#if CV_SSE2
__m128 invSizes4[MAX_PATTERN][2];
__m128 sizes1_4[MAX_PATTERN];
Cv32suf absmask;
absmask.i = 0x7fffffff;
volatile bool useSIMD = cv::checkHardwareSupport(CV_CPU_SSE2);
#endif
CvStarFeature f[MAX_PATTERN];
CvMat *sum = 0, *tilted = 0, *flatTilted = 0;
int y, i=0, rows = img->rows, cols = img->cols, step;
int border, npatterns=0, maxIdx=0;
#ifdef _OPENMP
int nthreads = cvGetNumThreads();
#endif
assert( CV_MAT_TYPE(img->type) == CV_8UC1 &&
CV_MAT_TYPE(responses->type) == CV_32FC1 &&
CV_MAT_TYPE(sizes->type) == CV_16SC1 &&
CV_ARE_SIZES_EQ(responses, sizes) );
while( pairs[i][0] >= 0 && !
( sizes0[pairs[i][0]] >= params->maxSize
|| sizes0[pairs[i+1][0]] + sizes0[pairs[i+1][0]]/2 >= std::min(rows, cols) ) )
{
++i;
}
npatterns = i;
npatterns += (pairs[npatterns-1][0] >= 0);
maxIdx = pairs[npatterns-1][0];
sum = cvCreateMat( rows + 1, cols + 1, CV_32SC1 );
tilted = cvCreateMat( rows + 1, cols + 1, CV_32SC1 );
flatTilted = cvCreateMat( rows + 1, cols + 1, CV_32SC1 );
step = sum->step/CV_ELEM_SIZE(sum->type);
icvComputeIntegralImages( img, sum, tilted, flatTilted );
for( i = 0; i <= maxIdx; i++ )
{
int ur_size = sizes0[i], t_size = sizes0[i] + sizes0[i]/2;
int ur_area = (2*ur_size + 1)*(2*ur_size + 1);
int t_area = t_size*t_size + (t_size + 1)*(t_size + 1);
f[i].p[0] = sum->data.i + (ur_size + 1)*step + ur_size + 1;
f[i].p[1] = sum->data.i - ur_size*step + ur_size + 1;
f[i].p[2] = sum->data.i + (ur_size + 1)*step - ur_size;
f[i].p[3] = sum->data.i - ur_size*step - ur_size;
f[i].p[4] = tilted->data.i + (t_size + 1)*step + 1;
f[i].p[5] = flatTilted->data.i - t_size;
f[i].p[6] = flatTilted->data.i + t_size + 1;
f[i].p[7] = tilted->data.i - t_size*step + 1;
f[i].area = ur_area + t_area;
sizes1[i] = sizes0[i];
}
// negate end points of the size range
// for a faster rejection of very small or very large features in non-maxima suppression.
sizes1[0] = -sizes1[0];
sizes1[1] = -sizes1[1];
sizes1[maxIdx] = -sizes1[maxIdx];
border = sizes0[maxIdx] + sizes0[maxIdx]/2;
for( i = 0; i < npatterns; i++ )
{
int innerArea = f[pairs[i][1]].area;
int outerArea = f[pairs[i][0]].area - innerArea;
invSizes[i][0] = 1.f/outerArea;
invSizes[i][1] = 1.f/innerArea;
}
#if CV_SSE2
if( useSIMD )
{
for( i = 0; i < npatterns; i++ )
{
_mm_store_ps((float*)&invSizes4[i][0], _mm_set1_ps(invSizes[i][0]));
_mm_store_ps((float*)&invSizes4[i][1], _mm_set1_ps(invSizes[i][1]));
}
for( i = 0; i <= maxIdx; i++ )
_mm_store_ps((float*)&sizes1_4[i], _mm_set1_ps((float)sizes1[i]));
}
#endif
for( y = 0; y < border; y++ )
{
float* r_ptr = (float*)(responses->data.ptr + responses->step*y);
float* r_ptr2 = (float*)(responses->data.ptr + responses->step*(rows - 1 - y));
short* s_ptr = (short*)(sizes->data.ptr + sizes->step*y);
short* s_ptr2 = (short*)(sizes->data.ptr + sizes->step*(rows - 1 - y));
memset( r_ptr, 0, cols*sizeof(r_ptr[0]));
memset( r_ptr2, 0, cols*sizeof(r_ptr2[0]));
memset( s_ptr, 0, cols*sizeof(s_ptr[0]));
memset( s_ptr2, 0, cols*sizeof(s_ptr2[0]));
}
#ifdef _OPENMP
#pragma omp parallel for num_threads(nthreads) schedule(static)
#endif
for( y = border; y < rows - border; y++ )
{
int x = border, i;
float* r_ptr = (float*)(responses->data.ptr + responses->step*y);
short* s_ptr = (short*)(sizes->data.ptr + sizes->step*y);
memset( r_ptr, 0, border*sizeof(r_ptr[0]));
memset( s_ptr, 0, border*sizeof(s_ptr[0]));
memset( r_ptr + cols - border, 0, border*sizeof(r_ptr[0]));
memset( s_ptr + cols - border, 0, border*sizeof(s_ptr[0]));
#if CV_SSE2
if( useSIMD )
{
__m128 absmask4 = _mm_set1_ps(absmask.f);
for( ; x <= cols - border - 4; x += 4 )
{
int ofs = y*step + x;
__m128 vals[MAX_PATTERN];
__m128 bestResponse = _mm_setzero_ps();
__m128 bestSize = _mm_setzero_ps();
for( i = 0; i <= maxIdx; i++ )
{
const int** p = (const int**)&f[i].p[0];
__m128i r0 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[0]+ofs)),
_mm_loadu_si128((const __m128i*)(p[1]+ofs)));
__m128i r1 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[3]+ofs)),
_mm_loadu_si128((const __m128i*)(p[2]+ofs)));
__m128i r2 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[4]+ofs)),
_mm_loadu_si128((const __m128i*)(p[5]+ofs)));
__m128i r3 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[7]+ofs)),
_mm_loadu_si128((const __m128i*)(p[6]+ofs)));
r0 = _mm_add_epi32(_mm_add_epi32(r0,r1), _mm_add_epi32(r2,r3));
_mm_store_ps((float*)&vals[i], _mm_cvtepi32_ps(r0));
}
for( i = 0; i < npatterns; i++ )
{
__m128 inner_sum = vals[pairs[i][1]];
__m128 outer_sum = _mm_sub_ps(vals[pairs[i][0]], inner_sum);
__m128 response = _mm_sub_ps(_mm_mul_ps(inner_sum, invSizes4[i][1]),
_mm_mul_ps(outer_sum, invSizes4[i][0]));
__m128 swapmask = _mm_cmpgt_ps(_mm_and_ps(response,absmask4),
_mm_and_ps(bestResponse,absmask4));
bestResponse = _mm_xor_ps(bestResponse,
_mm_and_ps(_mm_xor_ps(response,bestResponse), swapmask));
bestSize = _mm_xor_ps(bestSize,
_mm_and_ps(_mm_xor_ps(sizes1_4[pairs[i][0]], bestSize), swapmask));
}
_mm_storeu_ps(r_ptr + x, bestResponse);
_mm_storel_epi64((__m128i*)(s_ptr + x),
_mm_packs_epi32(_mm_cvtps_epi32(bestSize),_mm_setzero_si128()));
}
}
#endif
for( ; x < cols - border; x++ )
{
int ofs = y*step + x;
int vals[MAX_PATTERN];
float bestResponse = 0;
int bestSize = 0;
for( i = 0; i <= maxIdx; i++ )
{
const int** p = (const int**)&f[i].p[0];
vals[i] = p[0][ofs] - p[1][ofs] - p[2][ofs] + p[3][ofs] +
p[4][ofs] - p[5][ofs] - p[6][ofs] + p[7][ofs];
}
for( i = 0; i < npatterns; i++ )
{
int inner_sum = vals[pairs[i][1]];
int outer_sum = vals[pairs[i][0]] - inner_sum;
float response = inner_sum*invSizes[i][1] - outer_sum*invSizes[i][0];
if( fabs(response) > fabs(bestResponse) )
{
bestResponse = response;
bestSize = sizes1[pairs[i][0]];
}
}
r_ptr[x] = bestResponse;
s_ptr[x] = (short)bestSize;
}
}
cvReleaseMat(&sum);
cvReleaseMat(&tilted);
cvReleaseMat(&flatTilted);
return border;
}
static bool
icvStarDetectorSuppressLines( const CvMat* responses, const CvMat* sizes, CvPoint pt,
const CvStarDetectorParams* params )
{
const float* r_ptr = responses->data.fl;
int rstep = responses->step/sizeof(r_ptr[0]);
const short* s_ptr = sizes->data.s;
int sstep = sizes->step/sizeof(s_ptr[0]);
int sz = s_ptr[pt.y*sstep + pt.x];
int x, y, delta = sz/4, radius = delta*4;
float Lxx = 0, Lyy = 0, Lxy = 0;
int Lxxb = 0, Lyyb = 0, Lxyb = 0;
for( y = pt.y - radius; y <= pt.y + radius; y += delta )
for( x = pt.x - radius; x <= pt.x + radius; x += delta )
{
float Lx = r_ptr[y*rstep + x + 1] - r_ptr[y*rstep + x - 1];
float Ly = r_ptr[(y+1)*rstep + x] - r_ptr[(y-1)*rstep + x];
Lxx += Lx*Lx; Lyy += Ly*Ly; Lxy += Lx*Ly;
}
if( (Lxx + Lyy)*(Lxx + Lyy) >= params->lineThresholdProjected*(Lxx*Lyy - Lxy*Lxy) )
return true;
for( y = pt.y - radius; y <= pt.y + radius; y += delta )
for( x = pt.x - radius; x <= pt.x + radius; x += delta )
{
int Lxb = (s_ptr[y*sstep + x + 1] == sz) - (s_ptr[y*sstep + x - 1] == sz);
int Lyb = (s_ptr[(y+1)*sstep + x] == sz) - (s_ptr[(y-1)*sstep + x] == sz);
Lxxb += Lxb * Lxb; Lyyb += Lyb * Lyb; Lxyb += Lxb * Lyb;
}
if( (Lxxb + Lyyb)*(Lxxb + Lyyb) >= params->lineThresholdBinarized*(Lxxb*Lyyb - Lxyb*Lxyb) )
return true;
return false;
}
static void
icvStarDetectorSuppressNonmax( const CvMat* responses, const CvMat* sizes,
CvSeq* keypoints, int border,
const CvStarDetectorParams* params )
{
int x, y, x1, y1, delta = params->suppressNonmaxSize/2;
int rows = responses->rows, cols = responses->cols;
const float* r_ptr = responses->data.fl;
int rstep = responses->step/sizeof(r_ptr[0]);
const short* s_ptr = sizes->data.s;
int sstep = sizes->step/sizeof(s_ptr[0]);
short featureSize = 0;
for( y = border; y < rows - border; y += delta+1 )
for( x = border; x < cols - border; x += delta+1 )
{
float maxResponse = (float)params->responseThreshold;
float minResponse = (float)-params->responseThreshold;
CvPoint maxPt = {-1,-1}, minPt = {-1,-1};
int tileEndY = MIN(y + delta, rows - border - 1);
int tileEndX = MIN(x + delta, cols - border - 1);
for( y1 = y; y1 <= tileEndY; y1++ )
for( x1 = x; x1 <= tileEndX; x1++ )
{
float val = r_ptr[y1*rstep + x1];
if( maxResponse < val )
{
maxResponse = val;
maxPt = cvPoint(x1, y1);
}
else if( minResponse > val )
{
minResponse = val;
minPt = cvPoint(x1, y1);
}
}
if( maxPt.x >= 0 )
{
for( y1 = maxPt.y - delta; y1 <= maxPt.y + delta; y1++ )
for( x1 = maxPt.x - delta; x1 <= maxPt.x + delta; x1++ )
{
float val = r_ptr[y1*rstep + x1];
if( val >= maxResponse && (y1 != maxPt.y || x1 != maxPt.x))
goto skip_max;
}
if( (featureSize = s_ptr[maxPt.y*sstep + maxPt.x]) >= 4 &&
!icvStarDetectorSuppressLines( responses, sizes, maxPt, params ))
{
CvStarKeypoint kpt = cvStarKeypoint( maxPt, featureSize, maxResponse );
cvSeqPush( keypoints, &kpt );
}
}
skip_max:
if( minPt.x >= 0 )
{
for( y1 = minPt.y - delta; y1 <= minPt.y + delta; y1++ )
for( x1 = minPt.x - delta; x1 <= minPt.x + delta; x1++ )
{
float val = r_ptr[y1*rstep + x1];
if( val <= minResponse && (y1 != minPt.y || x1 != minPt.x))
goto skip_min;
}
if( (featureSize = s_ptr[minPt.y*sstep + minPt.x]) >= 4 &&
!icvStarDetectorSuppressLines( responses, sizes, minPt, params ))
{
CvStarKeypoint kpt = cvStarKeypoint( minPt, featureSize, minResponse );
cvSeqPush( keypoints, &kpt );
}
}
skip_min:
;
}
}
CV_IMPL CvSeq*
cvGetStarKeypoints( const CvArr* _img, CvMemStorage* storage,
CvStarDetectorParams params )
{
CvMat stub, *img = cvGetMat(_img, &stub);
CvSeq* keypoints = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvStarKeypoint), storage );
CvMat* responses = cvCreateMat( img->rows, img->cols, CV_32FC1 );
CvMat* sizes = cvCreateMat( img->rows, img->cols, CV_16SC1 );
int border = icvStarDetectorComputeResponses( img, responses, sizes, &params );
if( border >= 0 )
icvStarDetectorSuppressNonmax( responses, sizes, keypoints, border, &params );
cvReleaseMat( &responses );
cvReleaseMat( &sizes );
return border >= 0 ? keypoints : 0;
}
namespace cv
{
StarDetector::StarDetector()
{
*(CvStarDetectorParams*)this = cvStarDetectorParams();
}
StarDetector::StarDetector(int _maxSize, int _responseThreshold,
int _lineThresholdProjected,
int _lineThresholdBinarized,
int _suppressNonmaxSize)
{
*(CvStarDetectorParams*)this = cvStarDetectorParams(_maxSize, _responseThreshold,
_lineThresholdProjected, _lineThresholdBinarized, _suppressNonmaxSize);
}
void StarDetector::operator()(const Mat& image, vector<KeyPoint>& keypoints) const
{
CvMat _image = image;
MemStorage storage(cvCreateMemStorage(0));
Seq<CvStarKeypoint> kp = cvGetStarKeypoints( &_image, storage, *(const CvStarDetectorParams*)this);
Seq<CvStarKeypoint>::iterator it = kp.begin();
keypoints.resize(kp.size());
size_t i, n = kp.size();
for( i = 0; i < n; i++, ++it )
{
const CvStarKeypoint& kpt = *it;
keypoints[i] = KeyPoint(kpt.pt, (float)kpt.size, -1.f, kpt.response, 0);
}
}
}

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/* Original code has been submitted by Liu Liu. Here is the copyright.
----------------------------------------------------------------------------------
* An OpenCV Implementation of SURF
* Further Information Refer to "SURF: Speed-Up Robust Feature"
* Author: Liu Liu
* liuliu.1987+opencv@gmail.com
*
* There are still serveral lacks for this experimental implementation:
* 1.The interpolation of sub-pixel mentioned in article was not implemented yet;
* 2.A comparision with original libSurf.so shows that the hessian detector is not a 100% match to their implementation;
* 3.Due to above reasons, I recommanded the original one for study and reuse;
*
* However, the speed of this implementation is something comparable to original one.
*
* Copyright© 2008, Liu Liu All rights reserved.
*
* Redistribution and use in source and binary forms, with or
* without modification, are permitted provided that the following
* conditions are met:
* Redistributions of source code must retain the above
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* disclaimer.
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* copyright notice, this list of conditions and the following
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* provided with the distribution.
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* promote products derived from this software without
* specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
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* INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
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/*
The following changes have been made, comparing to the original contribution:
1. A lot of small optimizations, less memory allocations, got rid of global buffers
2. Reversed order of cvGetQuadrangleSubPix and cvResize calls; probably less accurate, but much faster
3. The descriptor computing part (which is most expensive) is threaded using OpenMP
(subpixel-accurate keypoint localization and scale estimation are still TBD)
*/
/*
KeyPoint position and scale interpolation has been implemented as described in
the Brown and Lowe paper cited by the SURF paper.
The sampling step along the x and y axes of the image for the determinant of the
Hessian is now the same for each layer in an octave. While this increases the
computation time, it ensures that a true 3x3x3 neighbourhood exists, with
samples calculated at the same position in the layers above and below. This
results in improved maxima detection and non-maxima suppression, and I think it
is consistent with the description in the SURF paper.
The wavelet size sampling interval has also been made consistent. The wavelet
size at the first layer of the first octave is now 9 instead of 7. Along with
regular position sampling steps, this makes location and scale interpolation
easy. I think this is consistent with the SURF paper and original
implementation.
The scaling of the wavelet parameters has been fixed to ensure that the patterns
are symmetric around the centre. Previously the truncation caused by integer
division in the scaling ratio caused a bias towards the top left of the wavelet,
resulting in inconsistent keypoint positions.
The matrices for the determinant and trace of the Hessian are now reused in each
octave.
The extraction of the patch of pixels surrounding a keypoint used to build a
descriptor has been simplified.
KeyPoint descriptor normalisation has been changed from normalising each 4x4
cell (resulting in a descriptor of magnitude 16) to normalising the entire
descriptor to magnitude 1.
The default number of octaves has been increased from 3 to 4 to match the
original SURF binary default. The increase in computation time is minimal since
the higher octaves are sampled sparsely.
The default number of layers per octave has been reduced from 3 to 2, to prevent
redundant calculation of similar sizes in consecutive octaves. This decreases
computation time. The number of features extracted may be less, however the
additional features were mostly redundant.
The radius of the circle of gradient samples used to assign an orientation has
been increased from 4 to 6 to match the description in the SURF paper. This is
now defined by ORI_RADIUS, and could be made into a parameter.
The size of the sliding window used in orientation assignment has been reduced
from 120 to 60 degrees to match the description in the SURF paper. This is now
defined by ORI_WIN, and could be made into a parameter.
Other options like HAAR_SIZE0, HAAR_SIZE_INC, SAMPLE_STEP0, ORI_SEARCH_INC,
ORI_SIGMA and DESC_SIGMA have been separated from the code and documented.
These could also be made into parameters.
Modifications by Ian Mahon
*/
#include "precomp.hpp"
CvSURFParams cvSURFParams(double threshold, int extended)
{
CvSURFParams params;
params.hessianThreshold = threshold;
params.extended = extended;
params.nOctaves = 4;
params.nOctaveLayers = 2;
return params;
}
struct CvSurfHF
{
int p0, p1, p2, p3;
float w;
};
CV_INLINE float
icvCalcHaarPattern( const int* origin, const CvSurfHF* f, int n )
{
double d = 0;
for( int k = 0; k < n; k++ )
d += (origin[f[k].p0] + origin[f[k].p3] - origin[f[k].p1] - origin[f[k].p2])*f[k].w;
return (float)d;
}
static void
icvResizeHaarPattern( const int src[][5], CvSurfHF* dst, int n, int oldSize, int newSize, int widthStep )
{
float ratio = (float)newSize/oldSize;
for( int k = 0; k < n; k++ )
{
int dx1 = cvRound( ratio*src[k][0] );
int dy1 = cvRound( ratio*src[k][1] );
int dx2 = cvRound( ratio*src[k][2] );
int dy2 = cvRound( ratio*src[k][3] );
dst[k].p0 = dy1*widthStep + dx1;
dst[k].p1 = dy2*widthStep + dx1;
dst[k].p2 = dy1*widthStep + dx2;
dst[k].p3 = dy2*widthStep + dx2;
dst[k].w = src[k][4]/((float)(dx2-dx1)*(dy2-dy1));
}
}
/*
* Maxima location interpolation as described in "Invariant Features from
* Interest Point Groups" by Matthew Brown and David Lowe. This is performed by
* fitting a 3D quadratic to a set of neighbouring samples.
*
* The gradient vector and Hessian matrix at the initial keypoint location are
* approximated using central differences. The linear system Ax = b is then
* solved, where A is the Hessian, b is the negative gradient, and x is the
* offset of the interpolated maxima coordinates from the initial estimate.
* This is equivalent to an iteration of Netwon's optimisation algorithm.
*
* N9 contains the samples in the 3x3x3 neighbourhood of the maxima
* dx is the sampling step in x
* dy is the sampling step in y
* ds is the sampling step in size
* point contains the keypoint coordinates and scale to be modified
*
* Return value is 1 if interpolation was successful, 0 on failure.
*/
CV_INLINE int
icvInterpolateKeypoint( float N9[3][9], int dx, int dy, int ds, CvSURFPoint *point )
{
int solve_ok;
float A[9], x[3], b[3];
CvMat matA = cvMat(3, 3, CV_32F, A);
CvMat _x = cvMat(3, 1, CV_32F, x);
CvMat _b = cvMat(3, 1, CV_32F, b);
b[0] = -(N9[1][5]-N9[1][3])/2; /* Negative 1st deriv with respect to x */
b[1] = -(N9[1][7]-N9[1][1])/2; /* Negative 1st deriv with respect to y */
b[2] = -(N9[2][4]-N9[0][4])/2; /* Negative 1st deriv with respect to s */
A[0] = N9[1][3]-2*N9[1][4]+N9[1][5]; /* 2nd deriv x, x */
A[1] = (N9[1][8]-N9[1][6]-N9[1][2]+N9[1][0])/4; /* 2nd deriv x, y */
A[2] = (N9[2][5]-N9[2][3]-N9[0][5]+N9[0][3])/4; /* 2nd deriv x, s */
A[3] = A[1]; /* 2nd deriv y, x */
A[4] = N9[1][1]-2*N9[1][4]+N9[1][7]; /* 2nd deriv y, y */
A[5] = (N9[2][7]-N9[2][1]-N9[0][7]+N9[0][1])/4; /* 2nd deriv y, s */
A[6] = A[2]; /* 2nd deriv s, x */
A[7] = A[5]; /* 2nd deriv s, y */
A[8] = N9[0][4]-2*N9[1][4]+N9[2][4]; /* 2nd deriv s, s */
solve_ok = cvSolve( &matA, &_b, &_x );
if( solve_ok )
{
point->pt.x += x[0]*dx;
point->pt.y += x[1]*dy;
point->size = cvRound( point->size + x[2]*ds );
}
return solve_ok;
}
/* Wavelet size at first layer of first octave. */
const int HAAR_SIZE0 = 9;
/* Wavelet size increment between layers. This should be an even number,
such that the wavelet sizes in an octave are either all even or all odd.
This ensures that when looking for the neighbours of a sample, the layers
above and below are aligned correctly. */
const int HAAR_SIZE_INC = 6;
static CvSeq* icvFastHessianDetector( const CvMat* sum, const CvMat* mask_sum,
CvMemStorage* storage, const CvSURFParams* params )
{
CvSeq* points = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvSURFPoint), storage );
/* Sampling step along image x and y axes at first octave. This is doubled
for each additional octave. WARNING: Increasing this improves speed,
however keypoint extraction becomes unreliable. */
const int SAMPLE_STEP0 = 1;
/* Wavelet Data */
const int NX=3, NY=3, NXY=4, NM=1;
const int dx_s[NX][5] = { {0, 2, 3, 7, 1}, {3, 2, 6, 7, -2}, {6, 2, 9, 7, 1} };
const int dy_s[NY][5] = { {2, 0, 7, 3, 1}, {2, 3, 7, 6, -2}, {2, 6, 7, 9, 1} };
const int dxy_s[NXY][5] = { {1, 1, 4, 4, 1}, {5, 1, 8, 4, -1}, {1, 5, 4, 8, -1}, {5, 5, 8, 8, 1} };
const int dm[NM][5] = { {0, 0, 9, 9, 1} };
CvSurfHF Dx[NX], Dy[NY], Dxy[NXY], Dm;
CvMat** dets = (CvMat**)cvStackAlloc((params->nOctaveLayers+2)*sizeof(dets[0]));
CvMat** traces = (CvMat**)cvStackAlloc((params->nOctaveLayers+2)*sizeof(traces[0]));
int *sizes = (int*)cvStackAlloc((params->nOctaveLayers+2)*sizeof(sizes[0]));
double dx = 0, dy = 0, dxy = 0;
int octave, layer, sampleStep, size, margin;
int rows, cols;
int i, j, sum_i, sum_j;
const int* s_ptr;
float *det_ptr, *trace_ptr;
/* Allocate enough space for hessian determinant and trace matrices at the
first octave. Clearing these initially or between octaves is not
required, since all values that are accessed are first calculated */
for( layer = 0; layer <= params->nOctaveLayers+1; layer++ )
{
dets[layer] = cvCreateMat( (sum->rows-1)/SAMPLE_STEP0, (sum->cols-1)/SAMPLE_STEP0, CV_32FC1 );
traces[layer] = cvCreateMat( (sum->rows-1)/SAMPLE_STEP0, (sum->cols-1)/SAMPLE_STEP0, CV_32FC1 );
}
for( octave = 0, sampleStep=SAMPLE_STEP0; octave < params->nOctaves; octave++, sampleStep*=2 )
{
/* Hessian determinant and trace sample array size in this octave */
rows = (sum->rows-1)/sampleStep;
cols = (sum->cols-1)/sampleStep;
/* Calculate the determinant and trace of the hessian */
for( layer = 0; layer <= params->nOctaveLayers+1; layer++ )
{
sizes[layer] = size = (HAAR_SIZE0+HAAR_SIZE_INC*layer)<<octave;
icvResizeHaarPattern( dx_s, Dx, NX, 9, size, sum->cols );
icvResizeHaarPattern( dy_s, Dy, NY, 9, size, sum->cols );
icvResizeHaarPattern( dxy_s, Dxy, NXY, 9, size, sum->cols );
/*printf( "octave=%d layer=%d size=%d rows=%d cols=%d\n", octave, layer, size, rows, cols );*/
margin = (size/2)/sampleStep;
for( sum_i=0, i=margin; sum_i<=(sum->rows-1)-size; sum_i+=sampleStep, i++ )
{
s_ptr = sum->data.i + sum_i*sum->cols;
det_ptr = dets[layer]->data.fl + i*dets[layer]->cols + margin;
trace_ptr = traces[layer]->data.fl + i*traces[layer]->cols + margin;
for( sum_j=0, j=margin; sum_j<=(sum->cols-1)-size; sum_j+=sampleStep, j++ )
{
dx = icvCalcHaarPattern( s_ptr, Dx, 3 );
dy = icvCalcHaarPattern( s_ptr, Dy, 3 );
dxy = icvCalcHaarPattern( s_ptr, Dxy, 4 );
s_ptr+=sampleStep;
*det_ptr++ = (float)(dx*dy - 0.81*dxy*dxy);
*trace_ptr++ = (float)(dx + dy);
}
}
}
/* Find maxima in the determinant of the hessian */
for( layer = 1; layer <= params->nOctaveLayers; layer++ )
{
size = sizes[layer];
icvResizeHaarPattern( dm, &Dm, NM, 9, size, mask_sum ? mask_sum->cols : sum->cols );
/* Ignore pixels without a 3x3 neighbourhood in the layer above */
margin = (sizes[layer+1]/2)/sampleStep+1;
for( i = margin; i < rows-margin; i++ )
{
det_ptr = dets[layer]->data.fl + i*dets[layer]->cols;
trace_ptr = traces[layer]->data.fl + i*traces[layer]->cols;
for( j = margin; j < cols-margin; j++ )
{
float val0 = det_ptr[j];
if( val0 > params->hessianThreshold )
{
/* Coordinates for the start of the wavelet in the sum image. There
is some integer division involved, so don't try to simplify this
(cancel out sampleStep) without checking the result is the same */
int sum_i = sampleStep*(i-(size/2)/sampleStep);
int sum_j = sampleStep*(j-(size/2)/sampleStep);
/* The 3x3x3 neighbouring samples around the maxima.
The maxima is included at N9[1][4] */
int c = dets[layer]->cols;
const float *det1 = dets[layer-1]->data.fl + i*c + j;
const float *det2 = dets[layer]->data.fl + i*c + j;
const float *det3 = dets[layer+1]->data.fl + i*c + j;
float N9[3][9] = { { det1[-c-1], det1[-c], det1[-c+1],
det1[-1] , det1[0] , det1[1],
det1[c-1] , det1[c] , det1[c+1] },
{ det2[-c-1], det2[-c], det2[-c+1],
det2[-1] , det2[0] , det2[1],
det2[c-1] , det2[c] , det2[c+1 ] },
{ det3[-c-1], det3[-c], det3[-c+1],
det3[-1 ], det3[0] , det3[1],
det3[c-1] , det3[c] , det3[c+1 ] } };
/* Check the mask - why not just check the mask at the center of the wavelet? */
if( mask_sum )
{
const int* mask_ptr = mask_sum->data.i + mask_sum->cols*sum_i + sum_j;
float mval = icvCalcHaarPattern( mask_ptr, &Dm, 1 );
if( mval < 0.5 )
continue;
}
/* Non-maxima suppression. val0 is at N9[1][4]*/
if( val0 > N9[0][0] && val0 > N9[0][1] && val0 > N9[0][2] &&
val0 > N9[0][3] && val0 > N9[0][4] && val0 > N9[0][5] &&
val0 > N9[0][6] && val0 > N9[0][7] && val0 > N9[0][8] &&
val0 > N9[1][0] && val0 > N9[1][1] && val0 > N9[1][2] &&
val0 > N9[1][3] && val0 > N9[1][5] &&
val0 > N9[1][6] && val0 > N9[1][7] && val0 > N9[1][8] &&
val0 > N9[2][0] && val0 > N9[2][1] && val0 > N9[2][2] &&
val0 > N9[2][3] && val0 > N9[2][4] && val0 > N9[2][5] &&
val0 > N9[2][6] && val0 > N9[2][7] && val0 > N9[2][8] )
{
/* Calculate the wavelet center coordinates for the maxima */
double center_i = sum_i + (double)(size-1)/2;
double center_j = sum_j + (double)(size-1)/2;
CvSURFPoint point = cvSURFPoint( cvPoint2D32f(center_j,center_i),
CV_SIGN(trace_ptr[j]), sizes[layer], 0, val0 );
/* Interpolate maxima location within the 3x3x3 neighbourhood */
int ds = sizes[layer]-sizes[layer-1];
int interp_ok = icvInterpolateKeypoint( N9, sampleStep, sampleStep, ds, &point );
/* Sometimes the interpolation step gives a negative size etc. */
if( interp_ok && point.size >= 1 &&
point.pt.x >= 0 && point.pt.x <= (sum->cols-1) &&
point.pt.y >= 0 && point.pt.y <= (sum->rows-1) )
{
/*printf( "KeyPoint %f %f %d\n", point.pt.x, point.pt.y, point.size );*/
cvSeqPush( points, &point );
}
}
}
}
}
}
}
/* Clean-up */
for( layer = 0; layer <= params->nOctaveLayers+1; layer++ )
{
cvReleaseMat( &dets[layer] );
cvReleaseMat( &traces[layer] );
}
return points;
}
namespace cv
{
struct SURFInvoker
{
enum { ORI_RADIUS = 6, ORI_WIN = 60, PATCH_SZ = 20 };
static const int ORI_SEARCH_INC;
static const float ORI_SIGMA;
static const float DESC_SIGMA;
SURFInvoker( const CvSURFParams* _params,
CvSeq* _keypoints, CvSeq* _descriptors,
const CvMat* _img, const CvMat* _sum,
const CvPoint* _apt, const float* _aptw,
int _nangle0, const float* _DW )
{
params = _params;
keypoints = _keypoints;
descriptors = _descriptors;
img = _img;
sum = _sum;
apt = _apt;
aptw = _aptw;
nangle0 = _nangle0;
DW = _DW;
}
void operator()(const BlockedRange& range) const
{
/* X and Y gradient wavelet data */
const int NX=2, NY=2;
int dx_s[NX][5] = {{0, 0, 2, 4, -1}, {2, 0, 4, 4, 1}};
int dy_s[NY][5] = {{0, 0, 4, 2, 1}, {0, 2, 4, 4, -1}};
const int descriptor_size = params->extended ? 128 : 64;
const int max_ori_samples = (2*ORI_RADIUS+1)*(2*ORI_RADIUS+1);
float X[max_ori_samples], Y[max_ori_samples], angle[max_ori_samples];
uchar PATCH[PATCH_SZ+1][PATCH_SZ+1];
float DX[PATCH_SZ][PATCH_SZ], DY[PATCH_SZ][PATCH_SZ];
CvMat matX = cvMat(1, max_ori_samples, CV_32F, X);
CvMat matY = cvMat(1, max_ori_samples, CV_32F, Y);
CvMat _angle = cvMat(1, max_ori_samples, CV_32F, angle);
CvMat _patch = cvMat(PATCH_SZ+1, PATCH_SZ+1, CV_8U, PATCH);
int k, k1 = range.begin(), k2 = range.end();
int maxSize = 0;
for( k = k1; k < k2; k++ )
maxSize = std::max(maxSize, ((CvSURFPoint*)cvGetSeqElem( keypoints, k ))->size);
maxSize = cvCeil((PATCH_SZ+1)*maxSize*1.2f/9.0f);
Ptr<CvMat> winbuf = cvCreateMat( 1, maxSize*maxSize, CV_8U );
for( k = k1; k < k2; k++ )
{
const int* sum_ptr = sum->data.i;
int sum_cols = sum->cols;
int i, j, kk, x, y, nangle;
float* vec;
CvSurfHF dx_t[NX], dy_t[NY];
CvSURFPoint* kp = (CvSURFPoint*)cvGetSeqElem( keypoints, k );
int size = kp->size;
CvPoint2D32f center = kp->pt;
/* The sampling intervals and wavelet sized for selecting an orientation
and building the keypoint descriptor are defined relative to 's' */
float s = (float)size*1.2f/9.0f;
/* To find the dominant orientation, the gradients in x and y are
sampled in a circle of radius 6s using wavelets of size 4s.
We ensure the gradient wavelet size is even to ensure the
wavelet pattern is balanced and symmetric around its center */
int grad_wav_size = 2*cvRound( 2*s );
if ( sum->rows < grad_wav_size || sum->cols < grad_wav_size )
{
/* when grad_wav_size is too big,
* the sampling of gradient will be meaningless
* mark keypoint for deletion. */
kp->size = -1;
continue;
}
icvResizeHaarPattern( dx_s, dx_t, NX, 4, grad_wav_size, sum->cols );
icvResizeHaarPattern( dy_s, dy_t, NY, 4, grad_wav_size, sum->cols );
for( kk = 0, nangle = 0; kk < nangle0; kk++ )
{
const int* ptr;
float vx, vy;
x = cvRound( center.x + apt[kk].x*s - (float)(grad_wav_size-1)/2 );
y = cvRound( center.y + apt[kk].y*s - (float)(grad_wav_size-1)/2 );
if( (unsigned)y >= (unsigned)(sum->rows - grad_wav_size) ||
(unsigned)x >= (unsigned)(sum->cols - grad_wav_size) )
continue;
ptr = sum_ptr + x + y*sum_cols;
vx = icvCalcHaarPattern( ptr, dx_t, 2 );
vy = icvCalcHaarPattern( ptr, dy_t, 2 );
X[nangle] = vx*aptw[kk]; Y[nangle] = vy*aptw[kk];
nangle++;
}
if ( nangle == 0 )
{
/* No gradient could be sampled because the keypoint is too
* near too one or more of the sides of the image. As we
* therefore cannot find a dominant direction, we skip this
* keypoint and mark it for later deletion from the sequence. */
kp->size = -1;
continue;
}
matX.cols = matY.cols = _angle.cols = nangle;
cvCartToPolar( &matX, &matY, 0, &_angle, 1 );
float bestx = 0, besty = 0, descriptor_mod = 0;
for( i = 0; i < 360; i += ORI_SEARCH_INC )
{
float sumx = 0, sumy = 0, temp_mod;
for( j = 0; j < nangle; j++ )
{
int d = std::abs(cvRound(angle[j]) - i);
if( d < ORI_WIN/2 || d > 360-ORI_WIN/2 )
{
sumx += X[j];
sumy += Y[j];
}
}
temp_mod = sumx*sumx + sumy*sumy;
if( temp_mod > descriptor_mod )
{
descriptor_mod = temp_mod;
bestx = sumx;
besty = sumy;
}
}
float descriptor_dir = cvFastArctan( besty, bestx );
kp->dir = descriptor_dir;
if( !descriptors )
continue;
descriptor_dir *= (float)(CV_PI/180);
/* Extract a window of pixels around the keypoint of size 20s */
int win_size = (int)((PATCH_SZ+1)*s);
CV_Assert( winbuf->cols >= win_size*win_size );
CvMat win = cvMat(win_size, win_size, CV_8U, winbuf->data.ptr);
float sin_dir = sin(descriptor_dir);
float cos_dir = cos(descriptor_dir) ;
/* Subpixel interpolation version (slower). Subpixel not required since
the pixels will all get averaged when we scale down to 20 pixels */
/*
float w[] = { cos_dir, sin_dir, center.x,
-sin_dir, cos_dir , center.y };
CvMat W = cvMat(2, 3, CV_32F, w);
cvGetQuadrangleSubPix( img, &win, &W );
*/
/* Nearest neighbour version (faster) */
float win_offset = -(float)(win_size-1)/2;
float start_x = center.x + win_offset*cos_dir + win_offset*sin_dir;
float start_y = center.y - win_offset*sin_dir + win_offset*cos_dir;
uchar* WIN = win.data.ptr;
for( i = 0; i < win_size; i++, start_x += sin_dir, start_y += cos_dir )
{
float pixel_x = start_x;
float pixel_y = start_y;
for( j = 0; j < win_size; j++, pixel_x += cos_dir, pixel_y -= sin_dir )
{
int x = std::min(std::max(cvRound(pixel_x), 0), img->cols-1);
int y = std::min(std::max(cvRound(pixel_y), 0), img->rows-1);
WIN[i*win_size + j] = img->data.ptr[y*img->step + x];
}
}
/* Scale the window to size PATCH_SZ so each pixel's size is s. This
makes calculating the gradients with wavelets of size 2s easy */
cvResize( &win, &_patch, CV_INTER_AREA );
/* Calculate gradients in x and y with wavelets of size 2s */
for( i = 0; i < PATCH_SZ; i++ )
for( j = 0; j < PATCH_SZ; j++ )
{
float dw = DW[i*PATCH_SZ + j];
float vx = (PATCH[i][j+1] - PATCH[i][j] + PATCH[i+1][j+1] - PATCH[i+1][j])*dw;
float vy = (PATCH[i+1][j] - PATCH[i][j] + PATCH[i+1][j+1] - PATCH[i][j+1])*dw;
DX[i][j] = vx;
DY[i][j] = vy;
}
/* Construct the descriptor */
vec = (float*)cvGetSeqElem( descriptors, k );
for( kk = 0; kk < (int)(descriptors->elem_size/sizeof(vec[0])); kk++ )
vec[kk] = 0;
double square_mag = 0;
if( params->extended )
{
/* 128-bin descriptor */
for( i = 0; i < 4; i++ )
for( j = 0; j < 4; j++ )
{
for( y = i*5; y < i*5+5; y++ )
{
for( x = j*5; x < j*5+5; x++ )
{
float tx = DX[y][x], ty = DY[y][x];
if( ty >= 0 )
{
vec[0] += tx;
vec[1] += (float)fabs(tx);
} else {
vec[2] += tx;
vec[3] += (float)fabs(tx);
}
if ( tx >= 0 )
{
vec[4] += ty;
vec[5] += (float)fabs(ty);
} else {
vec[6] += ty;
vec[7] += (float)fabs(ty);
}
}
}
for( kk = 0; kk < 8; kk++ )
square_mag += vec[kk]*vec[kk];
vec += 8;
}
}
else
{
/* 64-bin descriptor */
for( i = 0; i < 4; i++ )
for( j = 0; j < 4; j++ )
{
for( y = i*5; y < i*5+5; y++ )
{
for( x = j*5; x < j*5+5; x++ )
{
float tx = DX[y][x], ty = DY[y][x];
vec[0] += tx; vec[1] += ty;
vec[2] += (float)fabs(tx); vec[3] += (float)fabs(ty);
}
}
for( kk = 0; kk < 4; kk++ )
square_mag += vec[kk]*vec[kk];
vec+=4;
}
}
/* unit vector is essential for contrast invariance */
vec = (float*)cvGetSeqElem( descriptors, k );
double scale = 1./(sqrt(square_mag) + DBL_EPSILON);
for( kk = 0; kk < descriptor_size; kk++ )
vec[kk] = (float)(vec[kk]*scale);
}
}
const CvSURFParams* params;
const CvMat* img;
const CvMat* sum;
CvSeq* keypoints;
CvSeq* descriptors;
const CvPoint* apt;
const float* aptw;
int nangle0;
const float* DW;
};
const int SURFInvoker::ORI_SEARCH_INC = 5;
const float SURFInvoker::ORI_SIGMA = 2.5f;
const float SURFInvoker::DESC_SIGMA = 3.3f;
}
CV_IMPL void
cvExtractSURF( const CvArr* _img, const CvArr* _mask,
CvSeq** _keypoints, CvSeq** _descriptors,
CvMemStorage* storage, CvSURFParams params,
int useProvidedKeyPts)
{
const int ORI_RADIUS = cv::SURFInvoker::ORI_RADIUS;
const float ORI_SIGMA = cv::SURFInvoker::ORI_SIGMA;
const float DESC_SIGMA = cv::SURFInvoker::DESC_SIGMA;
CvMat *sum = 0, *mask1 = 0, *mask_sum = 0;
if( _keypoints && !useProvidedKeyPts ) // If useProvidedKeyPts!=0 we'll use current contents of "*_keypoints"
*_keypoints = 0;
if( _descriptors )
*_descriptors = 0;
CvSeq *keypoints, *descriptors = 0;
CvMat imghdr, *img = cvGetMat(_img, &imghdr);
CvMat maskhdr, *mask = _mask ? cvGetMat(_mask, &maskhdr) : 0;
const int max_ori_samples = (2*ORI_RADIUS+1)*(2*ORI_RADIUS+1);
int descriptor_size = params.extended ? 128 : 64;
const int descriptor_data_type = CV_32F;
const int PATCH_SZ = 20;
float DW[PATCH_SZ][PATCH_SZ];
CvMat _DW = cvMat(PATCH_SZ, PATCH_SZ, CV_32F, DW);
CvPoint apt[max_ori_samples];
float aptw[max_ori_samples];
int i, j, nangle0 = 0, N;
CV_Assert(img != 0);
CV_Assert(CV_MAT_TYPE(img->type) == CV_8UC1);
CV_Assert(mask == 0 || (CV_ARE_SIZES_EQ(img,mask) && CV_MAT_TYPE(mask->type) == CV_8UC1));
CV_Assert(storage != 0);
CV_Assert(params.hessianThreshold >= 0);
CV_Assert(params.nOctaves > 0);
CV_Assert(params.nOctaveLayers > 0);
sum = cvCreateMat( img->rows+1, img->cols+1, CV_32SC1 );
cvIntegral( img, sum );
// Compute keypoints only if we are not asked for evaluating the descriptors are some given locations:
if (!useProvidedKeyPts)
{
if( mask )
{
mask1 = cvCreateMat( img->height, img->width, CV_8UC1 );
mask_sum = cvCreateMat( img->height+1, img->width+1, CV_32SC1 );
cvMinS( mask, 1, mask1 );
cvIntegral( mask1, mask_sum );
}
keypoints = icvFastHessianDetector( sum, mask_sum, storage, &params );
}
else
{
CV_Assert(useProvidedKeyPts && (_keypoints != 0) && (*_keypoints != 0));
keypoints = *_keypoints;
}
N = keypoints->total;
if( _descriptors )
{
descriptors = cvCreateSeq( 0, sizeof(CvSeq),
descriptor_size*CV_ELEM_SIZE(descriptor_data_type), storage );
cvSeqPushMulti( descriptors, 0, N );
}
/* Coordinates and weights of samples used to calculate orientation */
cv::Mat matG = cv::getGaussianKernel( 2*ORI_RADIUS+1, ORI_SIGMA, CV_32F );
const float* G = (const float*)matG.data;
for( i = -ORI_RADIUS; i <= ORI_RADIUS; i++ )
{
for( j = -ORI_RADIUS; j <= ORI_RADIUS; j++ )
{
if( i*i + j*j <= ORI_RADIUS*ORI_RADIUS )
{
apt[nangle0] = cvPoint(j,i);
aptw[nangle0++] = G[i+ORI_RADIUS]*G[j+ORI_RADIUS];
}
}
}
/* Gaussian used to weight descriptor samples */
double c2 = 1./(DESC_SIGMA*DESC_SIGMA*2);
double gs = 0;
for( i = 0; i < PATCH_SZ; i++ )
{
for( j = 0; j < PATCH_SZ; j++ )
{
double x = j - (float)(PATCH_SZ-1)/2, y = i - (float)(PATCH_SZ-1)/2;
double val = exp(-(x*x+y*y)*c2);
DW[i][j] = (float)val;
gs += val;
}
}
cvScale( &_DW, &_DW, 1./gs );
cv::parallel_for(cv::BlockedRange(0, N),
cv::SURFInvoker(&params, keypoints, descriptors, img, sum,
apt, aptw, nangle0, &DW[0][0]));
//cv::SURFInvoker(&params, keypoints, descriptors, img, sum,
// apt, aptw, nangle0, &DW[0][0])(cv::BlockedRange(0, N));
/* remove keypoints that were marked for deletion */
for ( i = 0; i < N; i++ )
{
CvSURFPoint* kp = (CvSURFPoint*)cvGetSeqElem( keypoints, i );
if ( kp->size == -1 )
{
cvSeqRemove( keypoints, i );
if ( _descriptors )
cvSeqRemove( descriptors, i );
i--;
N--;
}
}
if( _keypoints && !useProvidedKeyPts )
*_keypoints = keypoints;
if( _descriptors )
*_descriptors = descriptors;
cvReleaseMat( &sum );
if (mask1) cvReleaseMat( &mask1 );
if (mask_sum) cvReleaseMat( &mask_sum );
}
namespace cv
{
SURF::SURF()
{
hessianThreshold = 100;
extended = 1;
nOctaves = 4;
nOctaveLayers = 2;
}
SURF::SURF(double _threshold, int _nOctaves, int _nOctaveLayers, bool _extended)
{
hessianThreshold = _threshold;
extended = _extended;
nOctaves = _nOctaves;
nOctaveLayers = _nOctaveLayers;
}
int SURF::descriptorSize() const { return extended ? 128 : 64; }
static int getPointOctave(const CvSURFPoint& kpt, const CvSURFParams& params)
{
int octave = 0, layer = 0, best_octave = 0;
float min_diff = FLT_MAX;
for( octave = 1; octave < params.nOctaves; octave++ )
for( layer = 0; layer < params.nOctaveLayers; layer++ )
{
float diff = std::abs(kpt.size - (float)((HAAR_SIZE0 + HAAR_SIZE_INC*layer) << octave));
if( min_diff > diff )
{
min_diff = diff;
best_octave = octave;
if( min_diff == 0 )
return best_octave;
}
}
return best_octave;
}
void SURF::operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints) const
{
CvMat _img = img, _mask, *pmask = 0;
if( mask.data )
pmask = &(_mask = mask);
MemStorage storage(cvCreateMemStorage(0));
Seq<CvSURFPoint> kp;
cvExtractSURF(&_img, pmask, &kp.seq, 0, storage, *(const CvSURFParams*)this, 0);
Seq<CvSURFPoint>::iterator it = kp.begin();
size_t i, n = kp.size();
keypoints.resize(n);
for( i = 0; i < n; i++, ++it )
{
const CvSURFPoint& kpt = *it;
keypoints[i] = KeyPoint(kpt.pt, (float)kpt.size, kpt.dir,
kpt.hessian, getPointOctave(kpt, *this));
}
}
void SURF::operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints,
vector<float>& descriptors,
bool useProvidedKeypoints) const
{
CvMat _img = img, _mask, *pmask = 0;
if( mask.data )
pmask = &(_mask = mask);
MemStorage storage(cvCreateMemStorage(0));
Seq<CvSURFPoint> kp;
CvSeq* d = 0;
size_t i, n;
if( useProvidedKeypoints )
{
kp = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvSURFPoint), storage);
n = keypoints.size();
for( i = 0; i < n; i++ )
{
const KeyPoint& kpt = keypoints[i];
kp.push_back(cvSURFPoint(kpt.pt, 1, cvRound(kpt.size), kpt.angle, kpt.response));
}
}
cvExtractSURF(&_img, pmask, &kp.seq, &d, storage,
*(const CvSURFParams*)this, useProvidedKeypoints);
if( !useProvidedKeypoints )
{
Seq<CvSURFPoint>::iterator it = kp.begin();
size_t i, n = kp.size();
keypoints.resize(n);
for( i = 0; i < n; i++, ++it )
{
const CvSURFPoint& kpt = *it;
keypoints[i] = KeyPoint(kpt.pt, (float)kpt.size, kpt.dir,
kpt.hessian, getPointOctave(kpt, *this));
}
}
descriptors.resize(d ? d->total*d->elem_size/sizeof(float) : 0);
if(descriptors.size() != 0)
cvCvtSeqToArray(d, &descriptors[0]);
}
}