class cv::StereoBM

Overview

Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige. More…

#include <calib3d.hpp>

class StereoBM: public cv::StereoMatcher
{
public:
    // enums

    enum
    {
        PREFILTER_NORMALIZED_RESPONSE = 0,
        PREFILTER_XSOBEL              = 1,
    };

    // methods

    virtual
    int
    getPreFilterCap() const = 0;

    virtual
    int
    getPreFilterSize() const = 0;

    virtual
    int
    getPreFilterType() const = 0;

    virtual
    Rect
    getROI1() const = 0;

    virtual
    Rect
    getROI2() const = 0;

    virtual
    int
    getSmallerBlockSize() const = 0;

    virtual
    int
    getTextureThreshold() const = 0;

    virtual
    int
    getUniquenessRatio() const = 0;

    virtual
    void
    setPreFilterCap(int preFilterCap) = 0;

    virtual
    void
    setPreFilterSize(int preFilterSize) = 0;

    virtual
    void
    setPreFilterType(int preFilterType) = 0;

    virtual
    void
    setROI1(Rect roi1) = 0;

    virtual
    void
    setROI2(Rect roi2) = 0;

    virtual
    void
    setSmallerBlockSize(int blockSize) = 0;

    virtual
    void
    setTextureThreshold(int textureThreshold) = 0;

    virtual
    void
    setUniquenessRatio(int uniquenessRatio) = 0;

    static
    Ptr<StereoBM>
    create(
        int numDisparities = 0,
        int blockSize = 21
        );
};

// direct descendants

class StereoBM;

Inherited Members

public:
    // enums

    enum
    {
        DISP_SHIFT = 4,
        DISP_SCALE = (1 <<DISP_SHIFT),
    };

    // 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 left,
        InputArray right,
        OutputArray disparity
        ) = 0;

    virtual
    int
    getBlockSize() const = 0;

    virtual
    int
    getDisp12MaxDiff() const = 0;

    virtual
    int
    getMinDisparity() const = 0;

    virtual
    int
    getNumDisparities() const = 0;

    virtual
    int
    getSpeckleRange() const = 0;

    virtual
    int
    getSpeckleWindowSize() const = 0;

    virtual
    void
    setBlockSize(int blockSize) = 0;

    virtual
    void
    setDisp12MaxDiff(int disp12MaxDiff) = 0;

    virtual
    void
    setMinDisparity(int minDisparity) = 0;

    virtual
    void
    setNumDisparities(int numDisparities) = 0;

    virtual
    void
    setSpeckleRange(int speckleRange) = 0;

    virtual
    void
    setSpeckleWindowSize(int speckleWindowSize) = 0;

protected:
    // methods

    void
    writeFormat(FileStorage& fs) const;

Detailed Documentation

Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige.

Methods

static
Ptr<StereoBM>
create(
    int numDisparities = 0,
    int blockSize = 21
    )

Creates StereoBM object.

The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for a specific stereo pair.

Parameters:

numDisparities	the disparity search range. For each pixel algorithm will find the best disparity from 0 (default minimum disparity) to numDisparities. The search range can then be shifted by changing the minimum disparity.
blockSize	the linear size of the blocks compared by the algorithm. The size should be odd (as the block is centered at the current pixel). Larger block size implies smoother, though less accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher chance for algorithm to find a wrong correspondence.