Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
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// If you do not agree to this license, do not download, install,
// copy or use the software.
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//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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//
// Redistribution and use in source and binary forms, with or without modification,
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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//M*/
#include "precomp.hpp"
using namespace std;
namespace cv
{
/*
* FeatureDetector
*/
FeatureDetector::~FeatureDetector()
{}
void FeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
keypoints.clear();
if( image.empty() )
return;
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) );
detectImpl( image, keypoints, mask );
}
void FeatureDetector::detect(const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, const vector<Mat>& masks ) const
{
pointCollection.resize( imageCollection.size() );
for( size_t i = 0; i < imageCollection.size(); i++ )
detect( imageCollection[i], pointCollection[i], masks.empty() ? Mat() : masks[i] );
}
/*void FeatureDetector::read( const FileNode& )
{}
void FeatureDetector::write( FileStorage& ) const
{}*/
bool FeatureDetector::empty() const
{
return false;
}
void FeatureDetector::removeInvalidPoints( const Mat& mask, vector<KeyPoint>& keypoints )
{
KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )
{
if( detectorType.find("Grid") == 0 )
{
return new GridAdaptedFeatureDetector(FeatureDetector::create(
detectorType.substr(strlen("Grid"))));
}
if( detectorType.find("Pyramid") == 0 )
{
return new PyramidAdaptedFeatureDetector(FeatureDetector::create(
detectorType.substr(strlen("Pyramid"))));
}
if( detectorType.find("Dynamic") == 0 )
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{
return new DynamicAdaptedFeatureDetector(AdjusterAdapter::create(
detectorType.substr(strlen("Dynamic"))));
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}
if( detectorType.compare( "HARRIS" ) == 0 )
{
Ptr<FeatureDetector> fd = FeatureDetector::create("GFTT");
fd->set("useHarrisDetector", true);
return fd;
}
return Algorithm::create<FeatureDetector>("Feature2D." + detectorType);
}
GFTTDetector::GFTTDetector( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize,
bool _useHarrisDetector, double _k )
: nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
{
}
void GFTTDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
vector<Point2f> corners;
goodFeaturesToTrack( grayImage, corners, nfeatures, 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, (float)blockSize );
}
}
static Algorithm* createGFTT() { return new GFTTDetector; }
static Algorithm* createHarris()
{
GFTTDetector* d = new GFTTDetector;
d->set("useHarris", true);
return d;
}
static AlgorithmInfo gftt_info("Feature2D.GFTT", createGFTT);
static AlgorithmInfo harris_info("Feature2D.HARRIS", createHarris);
AlgorithmInfo* GFTTDetector::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
gftt_info.addParam(this, "nfeatures", nfeatures);
gftt_info.addParam(this, "qualityLevel", qualityLevel);
gftt_info.addParam(this, "minDistance", minDistance);
gftt_info.addParam(this, "useHarrisDetector", useHarrisDetector);
gftt_info.addParam(this, "k", k);
harris_info.addParam(this, "nfeatures", nfeatures);
harris_info.addParam(this, "qualityLevel", qualityLevel);
harris_info.addParam(this, "minDistance", minDistance);
harris_info.addParam(this, "useHarrisDetector", useHarrisDetector);
harris_info.addParam(this, "k", k);
initialized = true;
}
return &gftt_info;
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}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
* DenseFeatureDetector
*/
DenseFeatureDetector::DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels,
float _featureScaleMul, int _initXyStep,
int _initImgBound, bool _varyXyStepWithScale,
bool _varyImgBoundWithScale ) :
initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels),
featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound),
varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale)
{}
void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
float curScale = initFeatureScale;
int curStep = initXyStep;
int curBound = initImgBound;
for( int curLevel = 0; curLevel < featureScaleLevels; curLevel++ )
{
for( int x = curBound; x < image.cols - curBound; x += curStep )
{
for( int y = curBound; y < image.rows - curBound; y += curStep )
{
keypoints.push_back( KeyPoint(static_cast<float>(x), static_cast<float>(y), curScale) );
}
}
curScale = curScale * featureScaleMul;
if( varyXyStepWithScale ) curStep = static_cast<int>( curStep * featureScaleMul + 0.5f );
if( varyImgBoundWithScale ) curBound = static_cast<int>( curBound * featureScaleMul + 0.5f );
}
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KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
static Algorithm* createDense() { return new DenseFeatureDetector; }
AlgorithmInfo* DenseFeatureDetector::info() const
{
static AlgorithmInfo info_("Feature2D.Dense", createDense);
static volatile bool initialized = false;
if( !initialized )
{
info_.addParam(this, "initFeatureScale", initFeatureScale);
info_.addParam(this, "featureScaleLevels", featureScaleLevels);
info_.addParam(this, "featureScaleMul", featureScaleMul);
info_.addParam(this, "initXyStep", initXyStep);
info_.addParam(this, "initImgBound", initImgBound);
info_.addParam(this, "varyXyStepWithScale", varyXyStepWithScale);
info_.addParam(this, "varyImgBoundWithScale", varyImgBoundWithScale);
initialized = true;
}
return &info_;
}
/*
* GridAdaptedFeatureDetector
*/
GridAdaptedFeatureDetector::GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector,
int _maxTotalKeypoints, int _gridRows, int _gridCols )
: detector(_detector), maxTotalKeypoints(_maxTotalKeypoints), gridRows(_gridRows), gridCols(_gridCols)
{}
bool GridAdaptedFeatureDetector::empty() const
{
return detector.empty() || (FeatureDetector*)detector->empty();
}
struct ResponseComparator
{
bool operator() (const KeyPoint& a, const KeyPoint& b)
{
return std::abs(a.response) > std::abs(b.response);
}
};
void keepStrongest( int N, vector<KeyPoint>& keypoints )
{
if( (int)keypoints.size() > N )
{
vector<KeyPoint>::iterator nth = keypoints.begin() + N;
std::nth_element( keypoints.begin(), nth, keypoints.end(), ResponseComparator() );
keypoints.erase( nth, keypoints.end() );
}
}
void GridAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
keypoints.reserve(maxTotalKeypoints);
int maxPerCell = maxTotalKeypoints / (gridRows * gridCols);
for( int i = 0; i < gridRows; ++i )
{
Range row_range((i*image.rows)/gridRows, ((i+1)*image.rows)/gridRows);
for( int j = 0; j < gridCols; ++j )
{
Range col_range((j*image.cols)/gridCols, ((j+1)*image.cols)/gridCols);
Mat sub_image = image(row_range, col_range);
Mat sub_mask;
if( !mask.empty() )
sub_mask = mask(row_range, col_range);
vector<KeyPoint> sub_keypoints;
detector->detect( sub_image, sub_keypoints, sub_mask );
keepStrongest( maxPerCell, sub_keypoints );
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std::vector<cv::KeyPoint>::iterator it = sub_keypoints.begin(),
end = sub_keypoints.end();
for( ; it != end; ++it )
{
it->pt.x += col_range.start;
it->pt.y += row_range.start;
}
keypoints.insert( keypoints.end(), sub_keypoints.begin(), sub_keypoints.end() );
}
}
}
/*
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* PyramidAdaptedFeatureDetector
*/
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PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector, int _maxLevel )
: detector(_detector), maxLevel(_maxLevel)
{}
bool PyramidAdaptedFeatureDetector::empty() const
{
return detector.empty() || (FeatureDetector*)detector->empty();
}
void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
Mat src = image;
Mat src_mask = mask;
Mat dilated_mask;
if( !mask.empty() )
{
dilate( mask, dilated_mask, Mat() );
Mat mask255( mask.size(), CV_8UC1, Scalar(0) );
mask255.setTo( Scalar(255), dilated_mask != 0 );
dilated_mask = mask255;
}
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for( int l = 0, multiplier = 1; l <= maxLevel; ++l, multiplier *= 2 )
{
// Detect on current level of the pyramid
vector<KeyPoint> new_pts;
detector->detect( src, new_pts, src_mask );
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vector<KeyPoint>::iterator it = new_pts.begin(),
end = new_pts.end();
for( ; it != end; ++it)
{
it->pt.x *= multiplier;
it->pt.y *= multiplier;
it->size *= multiplier;
it->octave = l;
}
keypoints.insert( keypoints.end(), new_pts.begin(), new_pts.end() );
// Downsample
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if( l < maxLevel )
{
Mat dst;
pyrDown( src, dst );
src = dst;
if( !mask.empty() )
resize( dilated_mask, src_mask, src.size(), 0, 0, CV_INTER_AREA );
}
}
if( !mask.empty() )
KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
}