Class cv::KeyPoint#

Data structure for salient point detectors. View details

#include <opencv2/core/types.hpp>

Collaboration diagram for cv::KeyPoint:

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, FAST, StarDetector, SURF, SIFT 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 KDTree or another method.

Constructor & Destructor Documentation#

KeyPoint()#

cv::KeyPoint::KeyPoint()

Python:

cv.KeyPoint() -> <KeyPoint object>

cv.KeyPoint(x, y, size[, angle[, response[, octave[, class_id]]]]) -> <KeyPoint object>

the default constructor

KeyPoint()#

cv::KeyPoint::KeyPoint(
float x,
float y,
float size,
float angle = -1,
float response = 0,
int octave = 0,
int class_id = -1 )

Python:

cv.KeyPoint() -> <KeyPoint object>

cv.KeyPoint(x, y, size[, angle[, response[, octave[, class_id]]]]) -> <KeyPoint object>

Parameters

x — x-coordinate of the keypoint
y — y-coordinate of the keypoint
size — keypoint diameter
angle — keypoint orientation
response — keypoint detector response on the keypoint (that is, strength of the keypoint)
octave — pyramid octave in which the keypoint has been detected
class_id — object id

KeyPoint()#

cv::KeyPoint::KeyPoint(
Point2f pt,
float size,
float angle = -1,
float response = 0,
int octave = 0,
int class_id = -1 )

Python:

cv.KeyPoint() -> <KeyPoint object>

cv.KeyPoint(x, y, size[, angle[, response[, octave[, class_id]]]]) -> <KeyPoint object>

Parameters

pt — x & y coordinates of the keypoint
size — keypoint diameter
angle — keypoint orientation
response — keypoint detector response on the keypoint (that is, strength of the keypoint)
octave — pyramid octave in which the keypoint has been detected
class_id — object id

Member Function Documentation#

hash()#

size_t cv::KeyPoint::hash()

convert()#

static void cv::KeyPoint::convert(
const std::vector< KeyPoint > & keypoints,
std::vector< Point2f > & points2f,
const std::vector< int > & keypointIndexes = std::vector< int >() )

Python:

cv.KeyPoint.convert(keypoints[, keypointIndexes]) -> points2f

cv.KeyPoint.convert(points2f[, size[, response[, octave[, class_id]]]]) -> keypoints

cv.KeyPoint_convert(keypoints[, keypointIndexes]) -> points2f

cv.KeyPoint_convert(points2f[, size[, response[, octave[, class_id]]]]) -> keypoints

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

keypoints — Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
points2f — Array of (x,y) coordinates of each keypoint
keypointIndexes — Array of indexes of keypoints to be converted to points. (Acts like a mask to convert only specified keypoints)

convert()#

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 )

Python:

cv.KeyPoint.convert(keypoints[, keypointIndexes]) -> points2f

cv.KeyPoint.convert(points2f[, size[, response[, octave[, class_id]]]]) -> keypoints

cv.KeyPoint_convert(keypoints[, keypointIndexes]) -> points2f

cv.KeyPoint_convert(points2f[, size[, response[, octave[, class_id]]]]) -> keypoints

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

Parameters

points2f — Array of (x,y) coordinates of each keypoint
keypoints — Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
size — keypoint diameter
response — keypoint detector response on the keypoint (that is, strength of the keypoint)
octave — pyramid octave in which the keypoint has been detected
class_id — object id

overlap()#

static float cv::KeyPoint::overlap(
const KeyPoint & kp1,
const KeyPoint & kp2 )

Python:

cv.KeyPoint.overlap(kp1, kp2) -> retval

cv.KeyPoint_overlap(kp1, kp2) -> retval

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

kp1 — First keypoint
kp2 — Second 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

Source file#

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

opencv2/core/types.hpp