camera_calibration_and_3d_reconstruction.rst

Camera Calibration and 3D Reconstruction
========================================

.. highlight:: cpp



gpu::StereoBM_GPU
-----------------
.. ocv:class:: gpu::StereoBM_GPU

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

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

        enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };

        StereoBM_GPU();
        StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP,
                     int winSize = DEFAULT_WINSZ);

        void operator() (const GpuMat& left, const GpuMat& right,
                         GpuMat& disparity, Stream& stream = Stream::Null());

        static bool checkIfGpuCallReasonable();

        int preset;
        int ndisp;
        int winSize;

        float avergeTexThreshold;

        ...
    };


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

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

This means that the input left image is low textured.



gpu::StereoBM_GPU::StereoBM_GPU
-----------------------------------
Enables :ocv:class:`gpu::StereoBM_GPU` constructors.

.. ocv:function:: gpu::StereoBM_GPU::StereoBM_GPU()

.. ocv:function:: gpu::StereoBM_GPU::StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ)

    :param preset: Parameter presetting:

        * **BASIC_PRESET** Basic mode without pre-processing.

        * **PREFILTER_XSOBEL** Sobel pre-filtering mode.

    :param ndisparities: Number of disparities. It must be a multiple of 8 and less or equal to 256.

    :param winSize: Block size.



gpu::StereoBM_GPU::operator ()
----------------------------------
Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair.

.. ocv:function:: void gpu::StereoBM_GPU::operator ()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null())

    :param left: Left image. Only  ``CV_8UC1``  type is supported.

    :param right: Right image with the same size and the same type as the left one.

    :param disparity: Output disparity map. It is a  ``CV_8UC1``  image with the same size as the input images.

    :param stream: Stream for the asynchronous version.



gpu::StereoBM_GPU::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.

.. ocv:function:: bool gpu::StereoBM_GPU::checkIfGpuCallReasonable()



gpu::StereoBeliefPropagation
----------------------------
.. ocv:class:: gpu::StereoBeliefPropagation

Class computing stereo correspondence using the belief propagation algorithm. ::

    class 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_32F);
        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 GpuMat& left, const GpuMat& right,
                        GpuMat& disparity, Stream& stream = Stream::Null());
        void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null());

        int ndisp;

        int iters;
        int levels;

        float max_data_term;
        float data_weight;
        float max_disc_term;
        float disc_single_jump;

        int msg_type;

        ...
    };

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.

.. note::

    ``StereoBeliefPropagation`` requires a lot of memory for message storage:

    .. math::

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

    and for data cost storage:

    .. math::

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

    ``width_step`` is the number of bytes in a line including padding.



gpu::StereoBeliefPropagation::StereoBeliefPropagation
---------------------------------------------------------
Enables the :ocv:class:`gpu::StereoBeliefPropagation` constructors.

.. ocv:function:: gpu::StereoBeliefPropagation::StereoBeliefPropagation(int ndisp = DEFAULT_NDISP, int iters = DEFAULT_ITERS, int levels = DEFAULT_LEVELS, int msg_type = CV_32F)

.. ocv:function:: gpu::StereoBeliefPropagation::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)

    :param ndisp: Number of disparities.

    :param iters: Number of BP iterations on each level.

    :param levels: Number of levels.

    :param max_data_term: Threshold for data cost truncation.

    :param data_weight: Data weight.

    :param max_disc_term: Threshold for discontinuity truncation.

    :param disc_single_jump: Discontinuity single jump.

    :param msg_type: Type for messages.  ``CV_16SC1``  and  ``CV_32FC1`` types are supported.

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

.. math::

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

.. math::

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

For more details, see [Felzenszwalb2006]_.

By default, :ocv:class:`gpu::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:

.. math::

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



gpu::StereoBeliefPropagation::estimateRecommendedParams
-----------------------------------------------------------
Uses a heuristic method to compute the recommended parameters ( ``ndisp``, ``iters`` and ``levels`` ) for the specified image size ( ``width`` and ``height`` ).

.. ocv:function:: void gpu::StereoBeliefPropagation::estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels)



gpu::StereoBeliefPropagation::operator ()
---------------------------------------------
Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair or data cost.

.. ocv:function:: void gpu::StereoBeliefPropagation::operator ()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null())

.. ocv:function:: void gpu::StereoBeliefPropagation::operator ()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null())

    :param left: Left image. ``CV_8UC1`` , ``CV_8UC3``  and  ``CV_8UC4``  types are supported.

    :param right: Right image with the same size and the same type as the left one.

    :param data: User-specified data cost, a matrix of ``msg_type`` type and ``Size(<image columns>*ndisp, <image rows>)`` size.

    :param disparity: Output disparity map. If  ``disparity``  is empty, the output type is  ``CV_16SC1`` . Otherwise, the type is retained.

    :param stream: Stream for the asynchronous version.



gpu::StereoConstantSpaceBP
--------------------------
.. ocv:class:: gpu::StereoConstantSpaceBP

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

    class 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 GpuMat& left, const GpuMat& right,
                        GpuMat& disparity, Stream& stream = Stream::Null());

        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;

        ...
    };


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`` .



gpu::StereoConstantSpaceBP::StereoConstantSpaceBP
-----------------------------------------------------
Enables the :ocv:class:`gpu::StereoConstantSpaceBP` constructors.

.. ocv:function:: gpu::StereoConstantSpaceBP::StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP, int iters = DEFAULT_ITERS, int levels = DEFAULT_LEVELS, int nr_plane = DEFAULT_NR_PLANE, int msg_type = CV_32F)

.. ocv:function:: gpu::StereoConstantSpaceBP::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)

    :param ndisp: Number of disparities.

    :param iters: Number of BP iterations on each level.

    :param levels: Number of levels.

    :param nr_plane: Number of disparity levels on the first level.

    :param max_data_term: Truncation of data cost.

    :param data_weight: Data weight.

    :param max_disc_term: Truncation of discontinuity.

    :param disc_single_jump: Discontinuity single jump.

    :param min_disp_th: Minimal disparity threshold.

    :param msg_type: Type for messages.  ``CV_16SC1``  and  ``CV_32FC1`` types are supported.

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

.. math::

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

.. math::

    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:

.. math::

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



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

.. ocv:function:: void gpu::StereoConstantSpaceBP::estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane)



gpu::StereoConstantSpaceBP::operator ()
-------------------------------------------
Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair.

.. ocv:function:: void gpu::StereoConstantSpaceBP::operator ()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null())

    :param left: Left image. ``CV_8UC1`` , ``CV_8UC3``  and  ``CV_8UC4``  types are supported.

    :param right: Right image with the same size and the same type as the left one.

    :param disparity: Output disparity map. If  ``disparity``  is empty, the output type is  ``CV_16SC1`` . Otherwise, the output type is  ``disparity.type()`` .

    :param stream: Stream for the asynchronous version.



gpu::DisparityBilateralFilter
-----------------------------
.. ocv:class:: gpu::DisparityBilateralFilter

Class refining a disparity map using joint bilateral filtering. ::

    class CV_EXPORTS DisparityBilateralFilter
    {
    public:
        enum { DEFAULT_NDISP  = 64 };
        enum { DEFAULT_RADIUS = 3 };
        enum { DEFAULT_ITERS  = 1 };

        explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP,
            int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS);

        DisparityBilateralFilter(int ndisp, int radius, int iters,
            float edge_threshold, float max_disc_threshold,
            float sigma_range);

        void operator()(const GpuMat& disparity, const GpuMat& image,
                        GpuMat& dst, Stream& stream = Stream::Null());

        ...
    };


The class implements [Yang2010]_ algorithm.



gpu::DisparityBilateralFilter::DisparityBilateralFilter
-----------------------------------------------------------
Enables the :ocv:class:`gpu::DisparityBilateralFilter` constructors.

.. ocv:function:: gpu::DisparityBilateralFilter::DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS)

.. ocv:function:: gpu::DisparityBilateralFilter::DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range)

    :param ndisp: Number of disparities.

    :param radius: Filter radius.

    :param iters: Number of iterations.

    :param edge_threshold: Threshold for edges.

    :param max_disc_threshold: Constant to reject outliers.

    :param sigma_range: Filter range.



gpu::DisparityBilateralFilter::operator ()
----------------------------------------------
Refines a disparity map using joint bilateral filtering.

.. ocv:function:: void gpu::DisparityBilateralFilter::operator ()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null())

    :param disparity: Input disparity map.  ``CV_8UC1``  and  ``CV_16SC1``  types are supported.

    :param image: Input image. ``CV_8UC1``  and  ``CV_8UC3``  types are supported.

    :param dst: Destination disparity map. It has the same size and type as  ``disparity`` .

    :param stream: Stream for the asynchronous version.



gpu::drawColorDisp
----------------------
Colors a disparity image.

.. ocv:function:: void gpu::drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, Stream& stream = Stream::Null())

    :param src_disp: Source disparity image.  ``CV_8UC1``  and  ``CV_16SC1``  types are supported.

    :param dst_disp: Output disparity image. It has the same size as  ``src_disp`` . The  type is ``CV_8UC4``  in  ``BGRA``  format (alpha = 255).

    :param ndisp: Number of disparities.

    :param stream: Stream for the asynchronous version.

This function draws a colored disparity map by converting disparity values from ``[0..ndisp)`` interval first to ``HSV`` color space (where different disparity values correspond to different hues) and then converting the pixels to ``RGB`` for visualization.



gpu::reprojectImageTo3D
---------------------------
Reprojects a disparity image to 3D space.

.. ocv:function:: void gpu::reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, int dst_cn = 4, Stream& stream = Stream::Null())

    :param disp: Input disparity image.  ``CV_8U``  and  ``CV_16S``  types are supported.

    :param xyzw: Output 3- or 4-channel floating-point image of the same size as  ``disp`` . Each element of  ``xyzw(x,y)``  contains 3D coordinates ``(x,y,z)`` or ``(x,y,z,1)``  of the point  ``(x,y)`` , computed from the disparity map.

    :param Q: :math:`4 \times 4`  perspective transformation matrix that can be obtained via  :ocv:func:`stereoRectify` .

    :param dst_cn: The number of channels for output image. Can be 3 or 4.

    :param stream: Stream for the asynchronous version.

.. seealso:: :ocv:func:`reprojectImageTo3D`



gpu::solvePnPRansac
-------------------
Finds the object pose from 3D-2D point correspondences.

.. ocv:function:: void gpu::solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat, const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false, int num_iters=100, float max_dist=8.0, int min_inlier_count=100, vector<int>* inliers=NULL)

    :param object: Single-row matrix of object points.

    :param image: Single-row matrix of image points.

    :param camera_mat: 3x3 matrix of intrinsic camera parameters.

    :param dist_coef: Distortion coefficients. See :ocv:func:`undistortPoints` for details.

    :param rvec: Output 3D rotation vector.

    :param tvec: Output 3D translation vector.

    :param use_extrinsic_guess: Flag to indicate that the function must use ``rvec`` and ``tvec`` as an initial transformation guess. It is not supported for now.

    :param num_iters: Maximum number of RANSAC iterations.

    :param max_dist: Euclidean distance threshold to detect whether point is inlier or not.

    :param min_inlier_count: Flag to indicate that the function must stop if greater or equal number of inliers is achieved. It is not supported for now.

    :param inliers: Output vector of inlier indices.

.. seealso:: :ocv:func:`solvePnPRansac`



.. [Felzenszwalb2006] Pedro F. Felzenszwalb algorithm [Pedro F. Felzenszwalb and Daniel P. Huttenlocher. *Efficient belief propagation for early vision*. International Journal of Computer Vision, 70(1), October 2006

.. [Yang2010] Q. Yang, L. Wang, and N. Ahuja. *A constant-space belief propagation algorithm for stereo matching*. In CVPR, 2010.