OpenCV  3.3.1-dev
Open Source Computer Vision
Public Member Functions | Static Public Member Functions | Public Attributes | List of all members
cv::KeyPoint Class Reference

Data structure for salient point detectors. More...

#include "types.hpp"

Inheritance diagram for cv::KeyPoint:
cv::xfeatures2d::Elliptic_KeyPoint

Public Member Functions

 KeyPoint ()
 the default constructor More...
 
 KeyPoint (Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)
 
 KeyPoint (float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)
 
size_t hash () const
 

Static Public Member Functions

static void convert (const std::vector< KeyPoint > &keypoints, std::vector< Point2f > &points2f, const std::vector< int > &keypointIndexes=std::vector< int >())
 
static void convert (const std::vector< Point2f > &points2f, std::vector< KeyPoint > &keypoints, float size=1, float response=1, int octave=0, int class_id=-1)
 
static float overlap (const KeyPoint &kp1, const KeyPoint &kp2)
 

Public Attributes

float angle
 
int class_id
 object class (if the keypoints need to be clustered by an object they belong to) More...
 
int octave
 octave (pyramid layer) from which the keypoint has been extracted More...
 
Point2f pt
 coordinates of the keypoints More...
 
float response
 the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling More...
 
float size
 diameter of the meaningful keypoint neighborhood More...
 

Detailed Description

Data structure for salient point detectors.

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.

Constructor & Destructor Documentation

§ KeyPoint() [1/3]

cv::KeyPoint::KeyPoint ( )
Python:
<KeyPoint object>=cv.KeyPoint()
<KeyPoint object>=cv.KeyPoint(x, y, _size[, _angle[, _response[, _octave[, _class_id]]]])

the default constructor

§ KeyPoint() [2/3]

cv::KeyPoint::KeyPoint ( Point2f  _pt,
float  _size,
float  _angle = -1,
float  _response = 0,
int  _octave = 0,
int  _class_id = -1 
)
Python:
<KeyPoint object>=cv.KeyPoint()
<KeyPoint object>=cv.KeyPoint(x, y, _size[, _angle[, _response[, _octave[, _class_id]]]])
Parameters
_ptx & y coordinates of the keypoint
_sizekeypoint diameter
_anglekeypoint orientation
_responsekeypoint detector response on the keypoint (that is, strength of the keypoint)
_octavepyramid octave in which the keypoint has been detected
_class_idobject id

§ KeyPoint() [3/3]

cv::KeyPoint::KeyPoint ( float  x,
float  y,
float  _size,
float  _angle = -1,
float  _response = 0,
int  _octave = 0,
int  _class_id = -1 
)
Python:
<KeyPoint object>=cv.KeyPoint()
<KeyPoint object>=cv.KeyPoint(x, y, _size[, _angle[, _response[, _octave[, _class_id]]]])
Parameters
xx-coordinate of the keypoint
yy-coordinate of the keypoint
_sizekeypoint diameter
_anglekeypoint orientation
_responsekeypoint detector response on the keypoint (that is, strength of the keypoint)
_octavepyramid octave in which the keypoint has been detected
_class_idobject id

Member Function Documentation

§ convert() [1/2]

static void cv::KeyPoint::convert ( const std::vector< KeyPoint > &  keypoints,
std::vector< Point2f > &  points2f,
const std::vector< int > &  keypointIndexes = std::vector< int >() 
)
static
Python:
points2f=cv.KeyPoint_convert(keypoints[, keypointIndexes])
keypoints=cv.KeyPoint_convert(points2f[, size[, response[, octave[, class_id]]]])

This method converts vector of keypoints to vector of points or the reverse, where each keypoint is assigned the same size and the same orientation.

Parameters
keypointsKeypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
points2fArray of (x,y) coordinates of each keypoint
keypointIndexesArray of indexes of keypoints to be converted to points. (Acts like a mask to convert only specified keypoints)

§ convert() [2/2]

static void cv::KeyPoint::convert ( const std::vector< Point2f > &  points2f,
std::vector< KeyPoint > &  keypoints,
float  size = 1,
float  response = 1,
int  octave = 0,
int  class_id = -1 
)
static
Python:
points2f=cv.KeyPoint_convert(keypoints[, keypointIndexes])
keypoints=cv.KeyPoint_convert(points2f[, size[, response[, octave[, class_id]]]])

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters
points2fArray of (x,y) coordinates of each keypoint
keypointsKeypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
sizekeypoint diameter
responsekeypoint detector response on the keypoint (that is, strength of the keypoint)
octavepyramid octave in which the keypoint has been detected
class_idobject id

§ hash()

size_t cv::KeyPoint::hash ( ) const

§ overlap()

static float cv::KeyPoint::overlap ( const KeyPoint kp1,
const KeyPoint kp2 
)
static
Python:
retval=cv.KeyPoint_overlap(kp1, kp2)

This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint regions' intersection and area of keypoint regions' union (considering keypoint region as circle). If they don't overlap, we get zero. If they coincide at same location with same size, we get 1.

Parameters
kp1First keypoint
kp2Second keypoint

Member Data Documentation

§ angle

float cv::KeyPoint::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.

§ class_id

int cv::KeyPoint::class_id

object class (if the keypoints need to be clustered by an object they belong to)

§ octave

int cv::KeyPoint::octave

octave (pyramid layer) from which the keypoint has been extracted

§ pt

Point2f cv::KeyPoint::pt

coordinates of the keypoints

§ response

float cv::KeyPoint::response

the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling

§ size

float cv::KeyPoint::size

diameter of the meaningful keypoint neighborhood


The documentation for this class was generated from the following file: