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#ifndef __OPENCV_CUDASTEREO_HPP__
#define __OPENCV_CUDASTEREO_HPP__

#ifndef __cplusplus
#  error cudastereo.hpp header must be compiled as C++
#endif

#include "opencv2/core/cuda.hpp"
#include "opencv2/calib3d.hpp"

/**
  @addtogroup cuda
  @{
    @defgroup cudastereo Stereo Correspondence
  @}
 */

namespace cv { namespace cuda {

//! @addtogroup cudastereo
//! @{

/////////////////////////////////////////
// StereoBM

/** @brief Class computing stereo correspondence (disparity map) using the block matching algorithm. :

@sa StereoBM
 */
class CV_EXPORTS StereoBM : public cv::StereoBM
{
public:
    using cv::StereoBM::compute;

    virtual void compute(InputArray left, InputArray right, OutputArray disparity, Stream& stream) = 0;
};

/** @brief Creates StereoBM object.

@param 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.
@param 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.
 */
CV_EXPORTS Ptr<cuda::StereoBM> createStereoBM(int numDisparities = 64, int blockSize = 19);

/////////////////////////////////////////
// StereoBeliefPropagation

/** @brief Class computing stereo correspondence using the belief propagation algorithm. :

The class implements algorithm described in @cite 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:

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

    and for data cost storage:

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

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

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

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

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

For more details, see @cite Felzenszwalb2006 .

By default, 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:

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

@sa StereoMatcher
 */
class CV_EXPORTS StereoBeliefPropagation : public cv::StereoMatcher
{
public:
    using cv::StereoMatcher::compute;

    /** @overload */
    virtual void compute(InputArray left, InputArray right, OutputArray disparity, Stream& stream) = 0;

    /** @brief Enables the stereo correspondence operator that finds the disparity for the specified data cost.

    @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.
     */
    virtual void compute(InputArray data, OutputArray disparity, Stream& stream = Stream::Null()) = 0;

    //! number of BP iterations on each level
    virtual int getNumIters() const = 0;
    virtual void setNumIters(int iters) = 0;

    //! number of levels
    virtual int getNumLevels() const = 0;
    virtual void setNumLevels(int levels) = 0;

    //! truncation of data cost
    virtual double getMaxDataTerm() const = 0;
    virtual void setMaxDataTerm(double max_data_term) = 0;

    //! data weight
    virtual double getDataWeight() const = 0;
    virtual void setDataWeight(double data_weight) = 0;

    //! truncation of discontinuity cost
    virtual double getMaxDiscTerm() const = 0;
    virtual void setMaxDiscTerm(double max_disc_term) = 0;

    //! discontinuity single jump
    virtual double getDiscSingleJump() const = 0;
    virtual void setDiscSingleJump(double disc_single_jump) = 0;

    //! type for messages (CV_16SC1 or CV_32FC1)
    virtual int getMsgType() const = 0;
    virtual void setMsgType(int msg_type) = 0;

    /** @brief Uses a heuristic method to compute the recommended parameters ( ndisp, iters and levels ) for the
    specified image size ( width and height ).
     */
    static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels);
};

/** @brief Creates StereoBeliefPropagation object.

@param ndisp Number of disparities.
@param iters Number of BP iterations on each level.
@param levels Number of levels.
@param msg_type Type for messages. CV_16SC1 and CV_32FC1 types are supported.
 */
CV_EXPORTS Ptr<cuda::StereoBeliefPropagation>
    createStereoBeliefPropagation(int ndisp = 64, int iters = 5, int levels = 5, int msg_type = CV_32F);

/////////////////////////////////////////
// StereoConstantSpaceBP

/** @brief Class computing stereo correspondence using the constant space belief propagation algorithm. :

The class implements algorithm described in @cite 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 .

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

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

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

For more details, see @cite 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:

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

 */
class CV_EXPORTS StereoConstantSpaceBP : public cuda::StereoBeliefPropagation
{
public:
    //! number of active disparity on the first level
    virtual int getNrPlane() const = 0;
    virtual void setNrPlane(int nr_plane) = 0;

    virtual bool getUseLocalInitDataCost() const = 0;
    virtual void setUseLocalInitDataCost(bool use_local_init_data_cost) = 0;

    /** @brief Uses a heuristic method to compute parameters (ndisp, iters, levelsand nrplane) for the specified
    image size (widthand height).
     */
    static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane);
};

/** @brief Creates StereoConstantSpaceBP object.

@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 msg_type Type for messages. CV_16SC1 and CV_32FC1 types are supported.
 */
CV_EXPORTS Ptr<cuda::StereoConstantSpaceBP>
    createStereoConstantSpaceBP(int ndisp = 128, int iters = 8, int levels = 4, int nr_plane = 4, int msg_type = CV_32F);

/////////////////////////////////////////
// DisparityBilateralFilter

/** @brief Class refining a disparity map using joint bilateral filtering. :

The class implements @cite Yang2010 algorithm.
 */
class CV_EXPORTS DisparityBilateralFilter : public cv::Algorithm
{
public:
    /** @brief Refines a disparity map using joint bilateral filtering.

    @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.
     */
    virtual void apply(InputArray disparity, InputArray image, OutputArray dst, Stream& stream = Stream::Null()) = 0;

    virtual int getNumDisparities() const = 0;
    virtual void setNumDisparities(int numDisparities) = 0;

    virtual int getRadius() const = 0;
    virtual void setRadius(int radius) = 0;

    virtual int getNumIters() const = 0;
    virtual void setNumIters(int iters) = 0;

    //! truncation of data continuity
    virtual double getEdgeThreshold() const = 0;
    virtual void setEdgeThreshold(double edge_threshold) = 0;

    //! truncation of disparity continuity
    virtual double getMaxDiscThreshold() const = 0;
    virtual void setMaxDiscThreshold(double max_disc_threshold) = 0;

    //! filter range sigma
    virtual double getSigmaRange() const = 0;
    virtual void setSigmaRange(double sigma_range) = 0;
};

/** @brief Creates DisparityBilateralFilter object.

@param ndisp Number of disparities.
@param radius Filter radius.
@param iters Number of iterations.
 */
CV_EXPORTS Ptr<cuda::DisparityBilateralFilter>
    createDisparityBilateralFilter(int ndisp = 64, int radius = 3, int iters = 1);

/////////////////////////////////////////
// Utility

/** @brief Reprojects a disparity image to 3D space.

@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 \f$4 \times 4\f$ perspective transformation matrix that can be obtained via stereoRectify .
@param dst_cn The number of channels for output image. Can be 3 or 4.
@param stream Stream for the asynchronous version.

@sa reprojectImageTo3D
 */
CV_EXPORTS void reprojectImageTo3D(InputArray disp, OutputArray xyzw, InputArray Q, int dst_cn = 4, Stream& stream = Stream::Null());

/** @brief Colors a disparity image.

@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.
 */
CV_EXPORTS void drawColorDisp(InputArray src_disp, OutputArray dst_disp, int ndisp, Stream& stream = Stream::Null());

//! @}

}} // namespace cv { namespace cuda {

#endif /* __OPENCV_CUDASTEREO_HPP__ */