class cv::ORB

Overview

Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. More…

#include <features2d.hpp>

class ORB: public cv::Feature2D
{
public:
    // enums

    enum
    {
        kBytes       = 32,
        HARRIS_SCORE =0,
        FAST_SCORE   =1,
    };

    // methods

    static
    Ptr<ORB>
    create(
        int nfeatures = 500,
        float scaleFactor = 1.2f,
        int nlevels = 8,
        int edgeThreshold = 31,
        int firstLevel = 0,
        int WTA_K = 2,
        int scoreType = ORB::HARRIS_SCORE,
        int patchSize = 31,
        int fastThreshold = 20
        );

    virtual
    int
    getEdgeThreshold() const = 0;

    virtual
    int
    getFastThreshold() const = 0;

    virtual
    int
    getFirstLevel() const = 0;

    virtual
    int
    getMaxFeatures() const = 0;

    virtual
    int
    getNLevels() const = 0;

    virtual
    int
    getPatchSize() const = 0;

    virtual
    double
    getScaleFactor() const = 0;

    virtual
    int
    getScoreType() const = 0;

    virtual
    int
    getWTA_K() const = 0;

    virtual
    void
    setEdgeThreshold(int edgeThreshold) = 0;

    virtual
    void
    setFastThreshold(int fastThreshold) = 0;

    virtual
    void
    setFirstLevel(int firstLevel) = 0;

    virtual
    void
    setMaxFeatures(int maxFeatures) = 0;

    virtual
    void
    setNLevels(int nlevels) = 0;

    virtual
    void
    setPatchSize(int patchSize) = 0;

    virtual
    void
    setScaleFactor(double scaleFactor) = 0;

    virtual
    void
    setScoreType(int scoreType) = 0;

    virtual
    void
    setWTA_K(int wta_k) = 0;
};

// direct descendants

class ORB;

Inherited Members

public:
    // methods

    virtual
    void
    clear();

    virtual
    bool
    empty() const;

    virtual
    String
    getDefaultName() const;

    virtual
    void
    read(const FileNode& fn);

    virtual
    void
    save(const String& filename) const;

    virtual
    void
    write(FileStorage& fs) const;

    template <typename _Tp>
    static
    Ptr<_Tp>
    load(
        const String& filename,
        const String& objname = String()
        );

    template <typename _Tp>
    static
    Ptr<_Tp>
    loadFromString(
        const String& strModel,
        const String& objname = String()
        );

    template <typename _Tp>
    static
    Ptr<_Tp>
    read(const FileNode& fn);

    virtual
    void
    compute(
        InputArray image,
        std::vector<KeyPoint>& keypoints,
        OutputArray descriptors
        );

    virtual
    void
    compute(
        InputArrayOfArrays images,
        std::vector<std::vector<KeyPoint>>& keypoints,
        OutputArrayOfArrays descriptors
        );

    virtual
    int
    defaultNorm() const;

    virtual
    int
    descriptorSize() const;

    virtual
    int
    descriptorType() const;

    virtual
    void
    detect(
        InputArray image,
        std::vector<KeyPoint>& keypoints,
        InputArray mask = noArray()
        );

    virtual
    void
    detect(
        InputArrayOfArrays images,
        std::vector<std::vector<KeyPoint>>& keypoints,
        InputArrayOfArrays masks = noArray()
        );

    virtual
    void
    detectAndCompute(
        InputArray image,
        InputArray mask,
        std::vector<KeyPoint>& keypoints,
        OutputArray descriptors,
        bool useProvidedKeypoints = false
        );

    virtual
    bool
    empty() const;

    void
    read(const String& fileName);

    virtual
    void
    read(const FileNode& fn);

    void
    write(const String& fileName) const;

    virtual
    void
    write(FileStorage& fs) const;

protected:
    // methods

    void
    writeFormat(FileStorage& fs) const;

Detailed Documentation

Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor.

described in [72]. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).

Methods

static
Ptr<ORB>
create(
    int nfeatures = 500,
    float scaleFactor = 1.2f,
    int nlevels = 8,
    int edgeThreshold = 31,
    int firstLevel = 0,
    int WTA_K = 2,
    int scoreType = ORB::HARRIS_SCORE,
    int patchSize = 31,
    int fastThreshold = 20
    )

The ORB constructor.

Parameters:

nfeatures	The maximum number of features to retain.
scaleFactor	Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
nlevels	The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels).
edgeThreshold	This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
firstLevel	It should be 0 in the current implementation.
WTA_K	The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
scoreType	The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
patchSize	size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
fastThreshold