Camera Calibration and 3D Reconstruction

ocl::StereoBM_OCL

Class computing stereo correspondence (disparity map) using the block matching algorithm.

class CV_EXPORTS StereoBM_OCL
{
public:
    enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };

    enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };

    //! the default constructor
    StereoBM_OCL();
    //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
    StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);

    //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
    //! Output disparity has CV_8U type.
    void operator() ( const oclMat &left, const oclMat &right, oclMat &disparity);

    //! Some heuristics that tries to estmate
    // if current GPU will be faster then CPU in this algorithm.
    // It queries current active device.
    static bool checkIfGpuCallReasonable();

    int preset;
    int ndisp;
    int winSize;

    // If avergeTexThreshold  == 0 => post procesing is disabled
    // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
    // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
    // i.e. input left image is low textured.
    float avergeTexThreshold;
private:
    /* hidden */
};

The class also performs pre- and post-filtering steps: Sobel pre-filtering (if PREFILTER_XSOBEL flag is set) and low textureness filtering (if averageTexThreshols > 0 ). If avergeTexThreshold = 0 , low textureness filtering is disabled. Otherwise, the disparity is set to 0 in each point (x, y) , where for the left image

\sum HorizontalGradiensInWindow(x, y, winSize) < (winSize \cdot winSize) \cdot avergeTexThreshold

This means that the input left image is low textured.

ocl::StereoBM_OCL::StereoBM_OCL

Enables :ocv:class:`ocl::StereoBM_OCL` constructors.

ocl::StereoBM_OCL::operator ()

Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair.

ocl::StereoBM_OCL::checkIfGpuCallReasonable

Uses a heuristic method to estimate whether the current GPU is faster than the CPU in this algorithm. It queries the currently active device.

ocl::StereoBeliefPropagation

Class computing stereo correspondence using the belief propagation algorithm.

class CV_EXPORTS StereoBeliefPropagation
{
public:
    enum { DEFAULT_NDISP  = 64 };
    enum { DEFAULT_ITERS  = 5  };
    enum { DEFAULT_LEVELS = 5  };
    static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels);
    explicit StereoBeliefPropagation(int ndisp  = DEFAULT_NDISP,
                                     int iters  = DEFAULT_ITERS,
                                     int levels = DEFAULT_LEVELS,
                                     int msg_type = CV_16S);
    StereoBeliefPropagation(int ndisp, int iters, int levels,
                            float max_data_term, float data_weight,
                            float max_disc_term, float disc_single_jump,
                            int msg_type = CV_32F);
    void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
    void operator()(const oclMat &data, oclMat &disparity);
    int ndisp;
    int iters;
    int levels;
    float max_data_term;
    float data_weight;
    float max_disc_term;
    float disc_single_jump;
    int msg_type;
private:
    /* hidden */
};

The class implements algorithm described in [Felzenszwalb2006]_ . It can compute own data cost (using a truncated linear model) or use a user-provided data cost.

width \_ step  \cdot height  \cdot ndisp  \cdot 4  \cdot (1 + 0.25)

width\_step \cdot height \cdot ndisp \cdot (1 + 0.25 + 0.0625 +  \dotsm + \frac{1}{4^{levels}})

ocl::StereoBeliefPropagation::StereoBeliefPropagation

Enables the :ocv:class:`ocl::StereoBeliefPropagation` constructors.

StereoBeliefPropagation uses a truncated linear model for the data cost and discontinuity terms:

DataCost = data \_ weight  \cdot \min ( \lvert Img_Left(x,y)-Img_Right(x-d,y)  \rvert , max \_ data \_ term)

DiscTerm =  \min (disc \_ single \_ jump  \cdot \lvert f_1-f_2  \rvert , max \_ disc \_ term)

For more details, see [Felzenszwalb2006]_.

By default, :ocv:class:`ocl::StereoBeliefPropagation` uses floating-point arithmetics and the CV_32FC1 type for messages. But it can also use fixed-point arithmetics and the CV_16SC1 message type for better performance. To avoid an overflow in this case, the parameters must satisfy the following requirement:

10  \cdot 2^{levels-1}  \cdot max \_ data \_ term < SHRT \_ MAX

ocl::StereoBeliefPropagation::estimateRecommendedParams

Uses a heuristic method to compute the recommended parameters ( ndisp, iters and levels ) for the specified image size ( width and height ).

ocl::StereoBeliefPropagation::operator ()

Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair or data cost.

ocl::StereoConstantSpaceBP

Class computing stereo correspondence using the constant space belief propagation algorithm.

class CV_EXPORTS StereoConstantSpaceBP
{
public:
    enum { DEFAULT_NDISP    = 128 };
    enum { DEFAULT_ITERS    = 8   };
    enum { DEFAULT_LEVELS   = 4   };
    enum { DEFAULT_NR_PLANE = 4   };
    static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels, int &nr_plane);
    explicit StereoConstantSpaceBP(
        int ndisp    = DEFAULT_NDISP,
        int iters    = DEFAULT_ITERS,
        int levels   = DEFAULT_LEVELS,
        int nr_plane = DEFAULT_NR_PLANE,
        int msg_type = CV_32F);
    StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
        float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
        int min_disp_th = 0,
        int msg_type = CV_32F);
    void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
    int ndisp;
    int iters;
    int levels;
    int nr_plane;
    float max_data_term;
    float data_weight;
    float max_disc_term;
    float disc_single_jump;
    int min_disp_th;
    int msg_type;
    bool use_local_init_data_cost;
private:
    /* hidden */
};

The class implements algorithm described in [Yang2010]_. StereoConstantSpaceBP supports both local minimum and global minimum data cost initialization algorithms. For more details, see the paper mentioned above. By default, a local algorithm is used. To enable a global algorithm, set use_local_init_data_cost to false .

ocl::StereoConstantSpaceBP::StereoConstantSpaceBP

Enables the :ocv:class:`ocl::StereoConstantSpaceBP` constructors.

StereoConstantSpaceBP uses a truncated linear model for the data cost and discontinuity terms:

DataCost = data \_ weight  \cdot \min ( \lvert I_2-I_1  \rvert , max \_ data \_ term)

DiscTerm =  \min (disc \_ single \_ jump  \cdot \lvert f_1-f_2  \rvert , max \_ disc \_ term)

For more details, see [Yang2010]_.

By default, StereoConstantSpaceBP uses floating-point arithmetics and the CV_32FC1 type for messages. But it can also use fixed-point arithmetics and the CV_16SC1 message type for better performance. To avoid an overflow in this case, the parameters must satisfy the following requirement:

10  \cdot 2^{levels-1}  \cdot max \_ data \_ term < SHRT \_ MAX

ocl::StereoConstantSpaceBP::estimateRecommendedParams

Uses a heuristic method to compute parameters (ndisp, iters, levelsand nrplane) for the specified image size (widthand height).

ocl::StereoConstantSpaceBP::operator ()

Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair.