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Move cv::KeyPoint and cv::DMatch to core

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This commit is contained in:
Andrey Kamaev
2013-03-26 14:58:09 +04:00
parent 8eff34ee0b
commit 49f6dad13f
37 changed files with 308 additions and 250 deletions
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89
modules/features2d/include/opencv2/features2d.hpp
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@@ -54,64 +54,10 @@ namespace cv
CV_EXPORTS bool initModule_features2d();
/*!
The Keypoint Class
The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint detectors, such as
Harris corner detector, cv::FAST, cv::StarDetector, cv::SURF, cv::SIFT, cv::LDetector etc.
The keypoint is characterized by the 2D position, scale
(proportional to the diameter of the neighborhood that needs to be taken into account),
orientation and some other parameters. The keypoint neighborhood is then analyzed by another algorithm that builds a descriptor
(usually represented as a feature vector). The keypoints representing the same object in different images can then be matched using
cv::KDTree or another method.
*/
class CV_EXPORTS_W_SIMPLE KeyPoint
{
public:
//! the default constructor
CV_WRAP KeyPoint() : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {}
//! the full constructor
KeyPoint(Point2f _pt, float _size, float _angle=-1,
float _response=0, int _octave=0, int _class_id=-1)
: pt(_pt), size(_size), angle(_angle),
response(_response), octave(_octave), class_id(_class_id) {}
//! another form of the full constructor
CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1,
float _response=0, int _octave=0, int _class_id=-1)
: pt(x, y), size(_size), angle(_angle),
response(_response), octave(_octave), class_id(_class_id) {}
size_t hash() const;
//! converts vector of keypoints to vector of points
static void convert(const std::vector<KeyPoint>& keypoints,
CV_OUT std::vector<Point2f>& points2f,
const std::vector<int>& keypointIndexes=std::vector<int>());
//! converts vector of points to the vector of keypoints, where each keypoint is assigned the same size and the same orientation
static void convert(const std::vector<Point2f>& points2f,
CV_OUT std::vector<KeyPoint>& keypoints,
float size=1, float response=1, int octave=0, int class_id=-1);
//! computes overlap for pair of keypoints;
//! overlap is a ratio between area of keypoint regions intersection and
//! area of keypoint regions union (now keypoint region is circle)
static float overlap(const KeyPoint& kp1, const KeyPoint& kp2);
CV_PROP_RW Point2f pt; //!< coordinates of the keypoints
CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood
CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable);
//!< it's in [0,360) degrees and measured relative to
//!< image coordinate system, ie in clockwise.
CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling
CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted
CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to)
};
//! writes vector of keypoints to the file storage
CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector<KeyPoint>& keypoints);
//! reads vector of keypoints from the specified file storage node
CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector<KeyPoint>& keypoints);
// //! writes vector of keypoints to the file storage
// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector<KeyPoint>& keypoints);
// //! reads vector of keypoints from the specified file storage node
// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector<KeyPoint>& keypoints);
/*
* A class filters a vector of keypoints.
@@ -1028,33 +974,6 @@ template<int cellsize> struct CV_EXPORTS HammingMultilevel
}
};
/****************************************************************************************\
* DMatch *
\****************************************************************************************/
/*
* Struct for matching: query descriptor index, train descriptor index, train image index and distance between descriptors.
*/
struct CV_EXPORTS_W_SIMPLE DMatch
{
CV_WRAP DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {}
CV_WRAP DMatch( int _queryIdx, int _trainIdx, float _distance ) :
queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {}
CV_WRAP DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {}
CV_PROP_RW int queryIdx; // query descriptor index
CV_PROP_RW int trainIdx; // train descriptor index
CV_PROP_RW int imgIdx; // train image index
CV_PROP_RW float distance;
// less is better
bool operator<( const DMatch &m ) const
{
return distance < m.distance;
}
};
/****************************************************************************************\
* DescriptorMatcher *
\****************************************************************************************/