OpenCV
4.10.0-dev
Open Source Computer Vision
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Classes | |
struct | cv::Accumulator< T > |
struct | cv::Accumulator< char > |
struct | cv::Accumulator< short > |
struct | cv::Accumulator< unsigned char > |
struct | cv::Accumulator< unsigned short > |
class | cv::AffineFeature |
Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in [312] . More... | |
class | cv::AgastFeatureDetector |
Wrapping class for feature detection using the AGAST method. : More... | |
class | cv::AKAZE |
Class implementing the AKAZE keypoint detector and descriptor extractor, described in [10]. More... | |
class | cv::BRISK |
Class implementing the BRISK keypoint detector and descriptor extractor, described in [159] . More... | |
class | cv::FastFeatureDetector |
Wrapping class for feature detection using the FAST method. : More... | |
class | cv::Feature2D |
Abstract base class for 2D image feature detectors and descriptor extractors. More... | |
class | cv::GFTTDetector |
Wrapping class for feature detection using the goodFeaturesToTrack function. : More... | |
class | cv::KAZE |
Class implementing the KAZE keypoint detector and descriptor extractor, described in [9] . More... | |
class | cv::KeyPointsFilter |
A class filters a vector of keypoints. More... | |
struct | cv::L1< T > |
struct | cv::L2< T > |
class | cv::MSER |
Maximally stable extremal region extractor. More... | |
class | cv::ORB |
Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. More... | |
class | cv::SIFT |
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [174] . More... | |
class | cv::SimpleBlobDetector |
Class for extracting blobs from an image. : More... | |
struct | cv::SL2< T > |
Typedefs | |
typedef AffineFeature | cv::AffineDescriptorExtractor |
typedef AffineFeature | cv::AffineFeatureDetector |
typedef Feature2D | cv::DescriptorExtractor |
typedef Feature2D | cv::FeatureDetector |
typedef SIFT | cv::SiftDescriptorExtractor |
typedef SIFT | cv::SiftFeatureDetector |
Functions | |
void | cv::AGAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression, AgastFeatureDetector::DetectorType type) |
Detects corners using the AGAST algorithm. | |
void | cv::AGAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true) |
void | cv::computeRecallPrecisionCurve (const std::vector< std::vector< DMatch > > &matches1to2, const std::vector< std::vector< uchar > > &correctMatches1to2Mask, std::vector< Point2f > &recallPrecisionCurve) |
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 >()) |
void | cv::FAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression, FastFeatureDetector::DetectorType type) |
Detects corners using the FAST algorithm. | |
void | cv::FAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true) |
int | cv::getNearestPoint (const std::vector< Point2f > &recallPrecisionCurve, float l_precision) |
float | cv::getRecall (const std::vector< Point2f > &recallPrecisionCurve, float l_precision) |
#include <opencv2/features2d.hpp>
#include <opencv2/features2d.hpp>
typedef Feature2D cv::DescriptorExtractor |
#include <opencv2/features2d.hpp>
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.
typedef Feature2D cv::FeatureDetector |
#include <opencv2/features2d.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.
typedef SIFT cv::SiftDescriptorExtractor |
#include <opencv2/features2d.hpp>
typedef SIFT cv::SiftFeatureDetector |
#include <opencv2/features2d.hpp>
void cv::AGAST | ( | InputArray | image, |
std::vector< KeyPoint > & | keypoints, | ||
int | threshold, | ||
bool | nonmaxSuppression, | ||
AgastFeatureDetector::DetectorType | type | ||
) |
#include <opencv2/features2d.hpp>
Detects corners using the AGAST algorithm.
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 corners (keypoints). |
type | one of the four neighborhoods as defined in the paper: AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d, AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16 |
For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. The 32-bit binary tree tables were generated automatically from original code using perl script. The perl script and examples of tree generation are placed in features2d/doc folder. Detects corners using the AGAST algorithm by [180] .
void cv::AGAST | ( | InputArray | image, |
std::vector< KeyPoint > & | keypoints, | ||
int | threshold, | ||
bool | nonmaxSuppression = true |
||
) |
#include <opencv2/features2d.hpp>
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
void cv::computeRecallPrecisionCurve | ( | const std::vector< std::vector< DMatch > > & | matches1to2, |
const std::vector< std::vector< uchar > > & | correctMatches1to2Mask, | ||
std::vector< Point2f > & | recallPrecisionCurve | ||
) |
#include <opencv2/features2d.hpp>
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/features2d.hpp>
void cv::FAST | ( | InputArray | image, |
std::vector< KeyPoint > & | keypoints, | ||
int | threshold, | ||
bool | nonmaxSuppression, | ||
FastFeatureDetector::DetectorType | type | ||
) |
#include <opencv2/features2d.hpp>
Detects corners using the FAST algorithm.
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 corners (keypoints). |
type | one of the three neighborhoods as defined in the paper: FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, FastFeatureDetector::TYPE_5_8 |
Detects corners using the FAST algorithm by [228] .
void cv::FAST | ( | InputArray | image, |
std::vector< KeyPoint > & | keypoints, | ||
int | threshold, | ||
bool | nonmaxSuppression = true |
||
) |
#include <opencv2/features2d.hpp>
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
int cv::getNearestPoint | ( | const std::vector< Point2f > & | recallPrecisionCurve, |
float | l_precision | ||
) |
#include <opencv2/features2d.hpp>
float cv::getRecall | ( | const std::vector< Point2f > & | recallPrecisionCurve, |
float | l_precision | ||
) |
#include <opencv2/features2d.hpp>