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added GridAdaptedFeatureDetector, PyramidAdaptedFeatureDetector and funcs to draw keypoints and matches

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This commit is contained in:
Maria Dimashova
2010-08-03 16:28:52 +00:00
parent 9c94a96c40
commit 4e60decad3
4 changed files with 349 additions and 57 deletions
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122
modules/features2d/src/detectors.cpp
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@@ -46,8 +46,8 @@ using namespace std;
namespace cv
{
/*
FeatureDetector
*/
* FeatureDetector
*/
struct MaskPredicate
{
MaskPredicate( const Mat& _mask ) : mask(_mask)
@@ -70,8 +70,8 @@ void FeatureDetector::removeInvalidPoints( const Mat& mask, vector<KeyPoint>& ke
};
/*
FastFeatureDetector
*/
* FastFeatureDetector
*/
FastFeatureDetector::FastFeatureDetector( int _threshold, bool _nonmaxSuppression )
: threshold(_threshold), nonmaxSuppression(_nonmaxSuppression)
{}
@@ -95,8 +95,8 @@ void FastFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<
}
/*
GoodFeaturesToTrackDetector
*/
* GoodFeaturesToTrackDetector
*/
GoodFeaturesToTrackDetector::GoodFeaturesToTrackDetector( int _maxCorners, double _qualityLevel, \
double _minDistance, int _blockSize,
bool _useHarrisDetector, double _k )
@@ -140,8 +140,8 @@ void GoodFeaturesToTrackDetector::detectImpl( const Mat& image, const Mat& mask,
}
/*
MserFeatureDetector
*/
* MserFeatureDetector
*/
MserFeatureDetector::MserFeatureDetector( int delta, int minArea, int maxArea,
double maxVariation, double minDiversity,
int maxEvolution, double areaThreshold,
@@ -204,8 +204,8 @@ void MserFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<
}
/*
StarFeatureDetector
*/
* StarFeatureDetector
*/
StarFeatureDetector::StarFeatureDetector(int maxSize, int responseThreshold,
int lineThresholdProjected,
int lineThresholdBinarized,
@@ -244,8 +244,8 @@ void StarFeatureDetector::detectImpl( const Mat& image, const Mat& mask, vector<
}
/*
SiftFeatureDetector
*/
* SiftFeatureDetector
*/
SiftFeatureDetector::SiftFeatureDetector(double threshold, double edgeThreshold,
int nOctaves, int nOctaveLayers, int firstOctave, int angleMode) :
sift(threshold, edgeThreshold, nOctaves, nOctaveLayers, firstOctave, angleMode)
@@ -286,8 +286,8 @@ void SiftFeatureDetector::detectImpl( const Mat& image, const Mat& mask,
}
/*
SurfFeatureDetector
*/
* SurfFeatureDetector
*/
SurfFeatureDetector::SurfFeatureDetector( double hessianThreshold, int octaves, int octaveLayers)
: surf(hessianThreshold, octaves, octaveLayers)
{}
@@ -360,4 +360,98 @@ Ptr<FeatureDetector> createDetector( const string& detectorType )
return fd;
}
/*
* 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;
}
}
}
}