Feature Detection and Description#

Detailed Description#

Classes#

Name	Description
`struct` `cv::Accumulator`	View details
`struct` `cv::Accumulator< char >`	View details
`struct` `cv::Accumulator< short >`	View details
`struct` `cv::Accumulator< unsigned char >`	View details
`struct` `cv::Accumulator< unsigned short >`	View details
`class` `cv::AffineFeature`	Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in [349] . View details
`class` `cv::ALIKED`	ALIKED feature detector and descriptor extractor. View details
`class` `cv::DISK`	DISK feature detector and descriptor, based on a DNN model. View details
`class` `cv::FastFeatureDetector`	Wrapping class for feature detection using the FAST method. View details
`class` `cv::Feature2D`	Abstract base class for 2D image feature detectors and descriptor extractors. View details
`class` `cv::GFTTDetector`	Wrapping class for feature detection using the goodFeaturesToTrack function. : View details
`class` `cv::KeyPointsFilter`	A class filters a vector of keypoints. View details
`struct` `cv::L1`	View details
`struct` `cv::L2`	View details
`class` `cv::MSER`	Maximally stable extremal region extractor. View details
`class` `cv::ORB`	Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. View details
`class` `cv::SIFT`	Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [194] . View details
`class` `cv::SimpleBlobDetector`	Class for extracting blobs from an image. : View details
`struct` `cv::SL2`	View details

Typedef Documentation#

Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. This section is devoted to computing descriptors represented as vectors in a multidimensional space. All objects that implement the vector descriptor extractors inherit the DescriptorExtractor interface.

FeatureDetector#

typedef Feature2D cv::FeatureDetector

#include <opencv2/features.hpp>

Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. All objects that implement keypoint detectors inherit the FeatureDetector interface.

SiftDescriptorExtractor#

typedef SIFT cv::SiftDescriptorExtractor

#include <opencv2/features.hpp>

SiftFeatureDetector#

typedef SIFT cv::SiftFeatureDetector

#include <opencv2/features.hpp>

Function Documentation#

computeRecallPrecisionCurve()#

void cv::computeRecallPrecisionCurve(
const std::vector< std::vector< DMatch > > & matches1to2,
const std::vector< std::vector< uchar > > & correctMatches1to2Mask,
std::vector< Point2f > & recallPrecisionCurve )

#include <opencv2/features.hpp>

evaluateFeatureDetector()#

void cv::evaluateFeatureDetector(
const Mat & img1,
const Mat & img2,
const Mat & H1to2,
std::vector< KeyPoint > * keypoints1,
std::vector< KeyPoint > * keypoints2,
float & repeatability,
int & correspCount,
const Ptr< FeatureDetector > & fdetector = Ptr< FeatureDetector >() )

#include <opencv2/features.hpp>

FAST()#

void cv::FAST(
InputArray image,
std::vector< KeyPoint > & keypoints,
int threshold,
bool nonmaxSuppression = true,
FastFeatureDetector::DetectorType type = FastFeatureDetector::TYPE_9_16 )

#include <opencv2/features.hpp>

Detects corners using the FAST algorithm.

Detects corners using the FAST algorithm by [253] .

Check the corresponding tutorial for more details.

Parameters

image — grayscale image where keypoints (corners) are detected.
keypoints — keypoints detected on the image.
threshold — threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.
nonmaxSuppression — if true, non-maximum suppression is applied to detected keypoints (corners).
type — one of the three neighborhoods as defined in the paper: FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, FastFeatureDetector::TYPE_5_8

getNearestPoint()#

int cv::getNearestPoint(
const std::vector< Point2f > & recallPrecisionCurve,
float l_precision )

#include <opencv2/features.hpp>

getRecall()#

float cv::getRecall(
const std::vector< Point2f > & recallPrecisionCurve,
float l_precision )

#include <opencv2/features.hpp>

goodFeaturesToTrack()#

void cv::goodFeaturesToTrack(
InputArray image,
OutputArray corners,
int maxCorners,
double qualityLevel,
double minDistance,
InputArray mask,
int blockSize,
int gradientSize,
bool useHarrisDetector = false,
double k = 0.04 )

#include <opencv2/features.hpp>

Python:

cv.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> corners

cv.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance, mask, blockSize, gradientSize[, corners[, useHarrisDetector[, k]]]) -> corners

cv.goodFeaturesToTrackWithQuality(image, maxCorners, qualityLevel, minDistance, mask[, corners[, cornersQuality[, blockSize[, gradientSize[, useHarrisDetector[, k]]]]]]) -> corners, cornersQuality

goodFeaturesToTrack()#

void cv::goodFeaturesToTrack(
InputArray image,
OutputArray corners,
int maxCorners,
double qualityLevel,
double minDistance,
InputArray mask,
OutputArray cornersQuality,
int blockSize = 3,
int gradientSize = 3,
bool useHarrisDetector = false,
double k = 0.04 )

#include <opencv2/features.hpp>

Python:

cv.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> corners

cv.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance, mask, blockSize, gradientSize[, corners[, useHarrisDetector[, k]]]) -> corners

cv.goodFeaturesToTrackWithQuality(image, maxCorners, qualityLevel, minDistance, mask[, corners[, cornersQuality[, blockSize[, gradientSize[, useHarrisDetector[, k]]]]]]) -> corners, cornersQuality

Same as above, but returns also quality measure of the detected corners.

Parameters

image — Input 8-bit or floating-point 32-bit, single-channel image.
corners — Output vector of detected corners.
maxCorners — Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel — Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance — Minimum possible Euclidean distance between the returned corners.
mask — Region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
cornersQuality — Output vector of quality measure of the detected corners.
blockSize — Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
gradientSize — Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
useHarrisDetector — Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k — Free parameter of the Harris detector.

goodFeaturesToTrack()#

void cv::goodFeaturesToTrack(
InputArray image,
OutputArray corners,
int maxCorners,
double qualityLevel,
double minDistance,
InputArray mask = noArray(),
int blockSize = 3,
bool useHarrisDetector = false,
double k = 0.04 )

#include <opencv2/features.hpp>

Python:

cv.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> corners

cv.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance, mask, blockSize, gradientSize[, corners[, useHarrisDetector[, k]]]) -> corners

cv.goodFeaturesToTrackWithQuality(image, maxCorners, qualityLevel, minDistance, mask[, corners[, cornersQuality[, blockSize[, gradientSize[, useHarrisDetector[, k]]]]]]) -> corners, cornersQuality

Determines strong corners on an image.

The function finds the most prominent corners in the image or in the specified image region, as described in [272]

Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
The remaining corners are sorted by the quality measure in the descending order.
Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note

If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

Feature Detection and Description#

Detailed Description#

Classes#

Typedef Documentation#

AffineDescriptorExtractor#

AffineFeatureDetector#

DescriptorExtractor#

FeatureDetector#

SiftDescriptorExtractor#

SiftFeatureDetector#

Function Documentation#

computeRecallPrecisionCurve()#

evaluateFeatureDetector()#

FAST()#

getNearestPoint()#

getRecall()#

goodFeaturesToTrack()#

goodFeaturesToTrack()#

goodFeaturesToTrack()#