mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-24 11:10:21 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

minor changes

Browse Source
This commit is contained in:
Maria Dimashova 2010-11-03 10:00:24 +00:00
parent 7bda826671
commit 4a43041309
3 changed files with 88 additions and 79 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

161
modules/features2d/include/opencv2/features2d/features2d.hpp
View File

@ -1252,8 +1252,8 @@ public:
*/
void detect( const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, const vector<Mat>& masks=vector<Mat>() ) const;
virtual void read(const FileNode&) {}
virtual void write(FileStorage&) const {}
virtual void read( const FileNode& ) {}
virtual void write( FileStorage& ) const {}
protected:
/*
@ -1268,11 +1268,11 @@ protected:
class CV_EXPORTS FastFeatureDetector : public FeatureDetector
{
public:
FastFeatureDetector( int _threshold = 1, bool _nonmaxSuppression = true );
FastFeatureDetector( int _threshold=1, bool _nonmaxSuppression=true );
virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
virtual void read (const FileNode& fn);
virtual void write (FileStorage& fs) const;
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
int threshold;
@ -1287,8 +1287,8 @@ public:
int _blockSize=3, bool _useHarrisDetector=false, double _k=0.04 );
virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
virtual void read (const FileNode& fn);
virtual void write (FileStorage& fs) const;
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
int maxCorners;
@ -1302,13 +1302,13 @@ protected:
class CV_EXPORTS MserFeatureDetector : public FeatureDetector
{
public:
MserFeatureDetector( CvMSERParams params = cvMSERParams () );
MserFeatureDetector( CvMSERParams params=cvMSERParams () );
MserFeatureDetector( int delta, int minArea, int maxArea, double maxVariation, double minDiversity,
int maxEvolution, double areaThreshold, double minMargin, int edgeBlurSize );
virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
virtual void read (const FileNode& fn);
virtual void write (FileStorage& fs) const;
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
MSER mser;
@ -1321,8 +1321,8 @@ public:
int lineThresholdBinarized=8, int suppressNonmaxSize=5 );
virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
virtual void read (const FileNode& fn);
virtual void write (FileStorage& fs) const;
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
StarDetector star;
@ -1339,8 +1339,8 @@ public:
int angleMode=SIFT::CommonParams::FIRST_ANGLE );
virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
virtual void read (const FileNode& fn);
virtual void write (FileStorage& fs) const;
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
SIFT sift;
@ -1351,8 +1351,8 @@ class CV_EXPORTS SurfFeatureDetector : public FeatureDetector
public:
SurfFeatureDetector( double hessianThreshold = 400., int octaves = 3, int octaveLayers = 4 );
virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
virtual void read (const FileNode& fn);
virtual void write (FileStorage& fs) const;
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
SURF surf;
@ -1388,10 +1388,20 @@ protected:
class CV_EXPORTS GridAdaptedFeatureDetector : public FeatureDetector
{
public:
GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector, int _maxTotalKeypoints,
int _gridRows=4, int _gridCols=4 );
/*
* detector Detector that will be adapted.
* maxTotalKeypoints Maximum count of keypoints detected on the image. Only the strongest keypoints
* will be keeped.
* gridRows Grid rows count.
* gridCols Grid column count.
*/
GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxTotalKeypoints,
int gridRows=4, int gridCols=4 );
virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
// todo read/write
virtual void read( const FileNode& fn ) {}
virtual void write( FileStorage& fs ) const {}
protected:
Ptr<FeatureDetector> detector;
@ -1407,9 +1417,12 @@ protected:
class PyramidAdaptedFeatureDetector : public FeatureDetector
{
public:
PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector, int _levels=2 );
PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int levels=2 );
virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
// todo read/write
virtual void read( const FileNode& fn ) {}
virtual void write( FileStorage& fs ) const {}
protected:
Ptr<FeatureDetector> detector;
@ -1663,7 +1676,7 @@ struct CV_EXPORTS DMatch
float distance;
//less is better
// less is better
bool operator<( const DMatch &m) const
{
return distance < m.distance;
@ -1788,13 +1801,61 @@ protected:
};
/*
* Next two functions are used to implement BruteForceMatcher class specialization
* Brute-force descriptor matcher.
*
* For each descriptor in the first set, this matcher finds the closest
* descriptor in the second set by trying each one.
*
* For efficiency, BruteForceMatcher is templated on the distance metric.
* For float descriptors, a common choice would be cv::L2<float>.
*/
template<class Distance>
class BruteForceMatcher;
class CV_EXPORTS BruteForceMatcher : public DescriptorMatcher
{
public:
BruteForceMatcher( Distance d = Distance() ) : distance(d) {}
virtual ~BruteForceMatcher() {}
virtual void train() {}
virtual bool supportMask() { return true; }
protected:
virtual Ptr<DescriptorMatcher> cloneWithoutData() const { return new BruteForceMatcher(distance); }
virtual void knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult );
virtual void radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
Distance distance;
private:
/*
* Next two methods are used to implement specialization
*/
static void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult );
static void bfRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
};
template<class Distance>
inline void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
void BruteForceMatcher<Distance>::knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult )
{
bfKnnMatchImpl( *this, queryDescs, matches, knn, masks, compactResult );
}
template<class Distance>
void BruteForceMatcher<Distance>::radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult )
{
bfRadiusMatchImpl( *this, queryDescs, matches, maxDistance, masks, compactResult );
}
template<class Distance>
inline void BruteForceMatcher<Distance>::bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult )
{
@ -1869,7 +1930,7 @@ inline void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
}
template<class Distance>
inline void bfRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
inline void BruteForceMatcher<Distance>::bfRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult )
{
@ -1920,58 +1981,6 @@ inline void bfRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
}
}
/*
* Brute-force descriptor matcher.
*
* For each descriptor in the first set, this matcher finds the closest
* descriptor in the second set by trying each one.
*
* For efficiency, BruteForceMatcher is templated on the distance metric.
* For float descriptors, a common choice would be cv::L2<float>.
*/
template<class Distance>
class CV_EXPORTS BruteForceMatcher : public DescriptorMatcher
{
public:
template<class bfDistance>
friend void bfKnnMatchImpl( BruteForceMatcher<bfDistance>& matcher,
const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult );
template<class bfDistance>
friend void bfRadiusMatchImpl( BruteForceMatcher<bfDistance>& matcher,
const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
BruteForceMatcher( Distance d = Distance() ) : distance(d) {}
virtual ~BruteForceMatcher() {}
virtual void train() {}
virtual bool supportMask() { return true; }
protected:
virtual Ptr<DescriptorMatcher> cloneWithoutData() const { return new BruteForceMatcher(distance); }
virtual void knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult );
virtual void radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
Distance distance;
};
template<class Distance>
void BruteForceMatcher<Distance>::knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult )
{
bfKnnMatchImpl<Distance>( *this, queryDescs, matches, knn, masks, compactResult );
}
template<class Distance>
void BruteForceMatcher<Distance>::radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult )
{
bfRadiusMatchImpl<Distance>( *this, queryDescs, matches, maxDistance, masks, compactResult );
}
/*
* BruteForceMatcher L2 specialization
*/

2
modules/features2d/src/detectors.cpp
View File

@ -425,7 +425,7 @@ void GridAdaptedFeatureDetector::detect( const Mat& image, vector<KeyPoint>& key
}
/*
* GridAdaptedFeatureDetector
* PyramidAdaptedFeatureDetector
*/
PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector, int _levels )
: detector(_detector), levels(_levels)

4
modules/features2d/src/matchers.cpp
View File

@ -231,7 +231,7 @@ void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescs, vector<
const vector<Mat>& masks, bool compactResult )
{
#ifndef HAVE_EIGEN2
bfKnnMatchImpl<L2<float> >( *this, queryDescs, matches, knn, masks, compactResult );
bfKnnMatchImpl( *this, queryDescs, matches, knn, masks, compactResult );
#else
CV_Assert( queryDescs.type() == CV_32FC1 || queryDescs.empty() );
CV_Assert( masks.empty() || masks.size() == trainDescCollection.size() );
@ -319,7 +319,7 @@ void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescs, vect
const vector<Mat>& masks, bool compactResult )
{
#ifndef HAVE_EIGEN2
bfRadiusMatchImpl<L2<float> >( *this, queryDescs, matches, maxDistance, masks, compactResult );
bfRadiusMatchImpl( *this, queryDescs, matches, maxDistance, masks, compactResult );
#else
CV_Assert( queryDescs.type() == CV_32FC1 || queryDescs.empty() );
CV_Assert( masks.empty() || masks.size() == trainDescCollection.size() );