Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 
 

499 lines
18 KiB

/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace std;
namespace cv
{
/*
* FeatureDetector
*/
struct MaskPredicate
{
MaskPredicate( const Mat& _mask ) : mask(_mask)
{}
MaskPredicate& operator=(const MaskPredicate&) { return *this; }
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::read (const FileNode& fn)
{
threshold = fn["threshold"];
nonmaxSuppression = (int)fn["nonmaxSuppression"] ? true : false;
}
void FastFeatureDetector::write (FileStorage& fs) const
{
fs << "threshold" << threshold;
fs << "nonmaxSuppression" << nonmaxSuppression;
}
void FastFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
FAST( grayImage, 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::read (const FileNode& fn)
{
maxCorners = fn["maxCorners"];
qualityLevel = fn["qualityLevel"];
minDistance = fn["minDistance"];
blockSize = fn["blockSize"];
useHarrisDetector = (int)fn["useHarrisDetector"] != 0;
k = fn["k"];
}
void GoodFeaturesToTrackDetector::write (FileStorage& fs) const
{
fs << "maxCorners" << maxCorners;
fs << "qualityLevel" << qualityLevel;
fs << "minDistance" << minDistance;
fs << "blockSize" << blockSize;
fs << "useHarrisDetector" << useHarrisDetector;
fs << "k" << k;
}
void GoodFeaturesToTrackDetector::detectImpl( const Mat& image, const Mat& mask,
vector<KeyPoint>& keypoints ) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
vector<Point2f> corners;
goodFeaturesToTrack( grayImage, 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, (float)blockSize );
}
}
/*
* MserFeatureDetector
*/
MserFeatureDetector::MserFeatureDetector( int delta, int minArea, int maxArea,
double maxVariation, double minDiversity,
int maxEvolution, double areaThreshold,
double minMargin, int edgeBlurSize )
: mser( delta, minArea, maxArea, maxVariation, minDiversity,
maxEvolution, areaThreshold, minMargin, edgeBlurSize )
{}
MserFeatureDetector::MserFeatureDetector( CvMSERParams params )
: mser( params.delta, params.minArea, params.maxArea, params.maxVariation, params.minDiversity,
params.maxEvolution, params.areaThreshold, params.minMargin, params.edgeBlurSize )
{}
void MserFeatureDetector::read (const FileNode& fn)
{
int delta = fn["delta"];
int minArea = fn["minArea"];
int maxArea = fn["maxArea"];
float maxVariation = fn["maxVariation"];
float minDiversity = fn["minDiversity"];
int maxEvolution = fn["maxEvolution"];
double areaThreshold = fn["areaThreshold"];
double minMargin = fn["minMargin"];
int edgeBlurSize = fn["edgeBlurSize"];
mser = MSER( delta, minArea, maxArea, maxVariation, minDiversity,
maxEvolution, areaThreshold, minMargin, edgeBlurSize );
}
void MserFeatureDetector::write (FileStorage& fs) const
{
//fs << "algorithm" << getAlgorithmName ();
fs << "delta" << mser.delta;
fs << "minArea" << mser.minArea;
fs << "maxArea" << mser.maxArea;
fs << "maxVariation" << mser.maxVariation;
fs << "minDiversity" << mser.minDiversity;
fs << "maxEvolution" << mser.maxEvolution;
fs << "areaThreshold" << mser.areaThreshold;
fs << "minMargin" << mser.minMargin;
fs << "edgeBlurSize" << mser.edgeBlurSize;
}
void MserFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const
{
vector<vector<Point> > msers;
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
mser(grayImage, 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 )
{
// TODO check transformation from MSER region to KeyPoint
RotatedRect rect = fitEllipse(Mat(*contour_it));
*keypoint_it = KeyPoint( rect.center, sqrt(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::read (const FileNode& fn)
{
int maxSize = fn["maxSize"];
int responseThreshold = fn["responseThreshold"];
int lineThresholdProjected = fn["lineThresholdProjected"];
int lineThresholdBinarized = fn["lineThresholdBinarized"];
int suppressNonmaxSize = fn["suppressNonmaxSize"];
star = StarDetector( maxSize, responseThreshold, lineThresholdProjected,
lineThresholdBinarized, suppressNonmaxSize);
}
void StarFeatureDetector::write (FileStorage& fs) const
{
//fs << "algorithm" << getAlgorithmName ();
fs << "maxSize" << star.maxSize;
fs << "responseThreshold" << star.responseThreshold;
fs << "lineThresholdProjected" << star.lineThresholdProjected;
fs << "lineThresholdBinarized" << star.lineThresholdBinarized;
fs << "suppressNonmaxSize" << star.suppressNonmaxSize;
}
void StarFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
star(grayImage, keypoints);
removeInvalidPoints(mask, keypoints);
}
/*
* SiftFeatureDetector
*/
SiftFeatureDetector::SiftFeatureDetector(double threshold, double edgeThreshold,
int nOctaves, int nOctaveLayers, int firstOctave, int angleMode) :
sift(threshold, edgeThreshold, nOctaves, nOctaveLayers, firstOctave, angleMode)
{
}
void SiftFeatureDetector::read (const FileNode& fn)
{
double threshold = fn["threshold"];
double edgeThreshold = fn["edgeThreshold"];
int nOctaves = fn["nOctaves"];
int nOctaveLayers = fn["nOctaveLayers"];
int firstOctave = fn["firstOctave"];
int angleMode = fn["angleMode"];
sift = SIFT(threshold, edgeThreshold, nOctaves, nOctaveLayers, firstOctave, angleMode);
}
void SiftFeatureDetector::write (FileStorage& fs) const
{
//fs << "algorithm" << getAlgorithmName ();
SIFT::CommonParams commParams = sift.getCommonParams ();
SIFT::DetectorParams detectorParams = sift.getDetectorParams ();
fs << "threshold" << detectorParams.threshold;
fs << "edgeThreshold" << detectorParams.edgeThreshold;
fs << "nOctaves" << commParams.nOctaves;
fs << "nOctaveLayers" << commParams.nOctaveLayers;
fs << "firstOctave" << commParams.firstOctave;
fs << "angleMode" << commParams.angleMode;
}
void SiftFeatureDetector::detectImpl( const Mat& image, const Mat& mask,
vector<KeyPoint>& keypoints) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
sift(grayImage, mask, keypoints);
}
/*
* SurfFeatureDetector
*/
SurfFeatureDetector::SurfFeatureDetector( double hessianThreshold, int octaves, int octaveLayers)
: surf(hessianThreshold, octaves, octaveLayers)
{}
void SurfFeatureDetector::read (const FileNode& fn)
{
double hessianThreshold = fn["hessianThreshold"];
int octaves = fn["octaves"];
int octaveLayers = fn["octaveLayers"];
surf = SURF( hessianThreshold, octaves, octaveLayers );
}
void SurfFeatureDetector::write (FileStorage& fs) const
{
//fs << "algorithm" << getAlgorithmName ();
fs << "hessianThreshold" << surf.hessianThreshold;
fs << "octaves" << surf.nOctaves;
fs << "octaveLayers" << surf.nOctaveLayers;
}
void SurfFeatureDetector::detectImpl( const Mat& image, const Mat& mask,
vector<KeyPoint>& keypoints) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
surf(grayImage, mask, keypoints);
}
/*
* GridAdaptedFeatureDetector
*/
void DenseFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const
{
keypoints.clear();
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 );
}
removeInvalidPoints( mask, keypoints );
}
/*
* GridAdaptedFeatureDetector
*/
GridAdaptedFeatureDetector::GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector,
int _maxTotalKeypoints, int _gridRows, int _gridCols )
: detector(_detector), maxTotalKeypoints(_maxTotalKeypoints), gridRows(_gridRows), gridCols(_gridCols)
{}
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, const Mat &mask,
vector<KeyPoint> &keypoints ) const
{
keypoints.clear();
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 );
for( std::vector<cv::KeyPoint>::iterator it = sub_keypoints.begin(), end = sub_keypoints.end();
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() );
}
}
}
/*
* GridAdaptedFeatureDetector
*/
PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector, int _levels )
: detector(_detector), levels(_levels)
{}
void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const
{
Mat src = image;
for( int l = 0, multiplier = 1; l <= levels; ++l, multiplier *= 2 )
{
// Detect on current level of the pyramid
vector<KeyPoint> new_pts;
detector->detect(src, new_pts);
for( vector<KeyPoint>::iterator it = new_pts.begin(), end = new_pts.end(); it != end; ++it)
{
it->pt.x *= multiplier;
it->pt.y *= multiplier;
it->size *= multiplier;
it->octave = l;
}
removeInvalidPoints( mask, new_pts );
keypoints.insert( keypoints.end(), new_pts.begin(), new_pts.end() );
// Downsample
if( l < levels )
{
Mat dst;
pyrDown(src, dst);
src = dst;
}
}
}
Ptr<FeatureDetector> createFeatureDetector( const string& detectorType )
{
FeatureDetector* fd = 0;
if( !detectorType.compare( "FAST" ) )
{
fd = new FastFeatureDetector( 10/*threshold*/, true/*nonmax_suppression*/ );
}
else if( !detectorType.compare( "STAR" ) )
{
fd = new StarFeatureDetector( 16/*max_size*/, 5/*response_threshold*/, 10/*line_threshold_projected*/,
8/*line_threshold_binarized*/, 5/*suppress_nonmax_size*/ );
}
else if( !detectorType.compare( "SIFT" ) )
{
fd = new SiftFeatureDetector(SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
SIFT::DetectorParams::GET_DEFAULT_EDGE_THRESHOLD());
}
else if( !detectorType.compare( "SURF" ) )
{
fd = new SurfFeatureDetector( 400./*hessian_threshold*/, 3 /*octaves*/, 4/*octave_layers*/ );
}
else if( !detectorType.compare( "MSER" ) )
{
fd = new MserFeatureDetector( 5/*delta*/, 60/*min_area*/, 14400/*_max_area*/, 0.25f/*max_variation*/,
0.2/*min_diversity*/, 200/*max_evolution*/, 1.01/*area_threshold*/, 0.003/*min_margin*/,
5/*edge_blur_size*/ );
}
else if( !detectorType.compare( "GFTT" ) )
{
fd = new GoodFeaturesToTrackDetector( 1000/*maxCorners*/, 0.01/*qualityLevel*/, 1./*minDistance*/,
3/*int _blockSize*/, false/*useHarrisDetector*/, 0.04/*k*/ );
}
else if( !detectorType.compare( "HARRIS" ) )
{
fd = new GoodFeaturesToTrackDetector( 1000/*maxCorners*/, 0.01/*qualityLevel*/, 1./*minDistance*/,
3/*int _blockSize*/, true/*useHarrisDetector*/, 0.04/*k*/ );
}
return fd;
}
}