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#ifndef __OPENCV_XIMGPROC_HPP__
#define __OPENCV_XIMGPROC_HPP__

#include "ximgproc/edge_filter.hpp"
#include "ximgproc/disparity_filter.hpp"
#include "ximgproc/sparse_match_interpolator.hpp"
#include "ximgproc/structured_edge_detection.hpp"
#include "ximgproc/edgeboxes.hpp"
#include "ximgproc/edge_drawing.hpp"
#include "ximgproc/seeds.hpp"
#include "ximgproc/segmentation.hpp"
#include "ximgproc/fast_hough_transform.hpp"
#include "ximgproc/estimated_covariance.hpp"
#include "ximgproc/weighted_median_filter.hpp"
#include "ximgproc/slic.hpp"
#include "ximgproc/lsc.hpp"
#include "ximgproc/paillou_filter.hpp"
#include "ximgproc/fast_line_detector.hpp"
#include "ximgproc/deriche_filter.hpp"
#include "ximgproc/peilin.hpp"
#include "ximgproc/fourier_descriptors.hpp"
#include "ximgproc/ridgefilter.hpp"
#include "ximgproc/brightedges.hpp"


/** @defgroup ximgproc Extended Image Processing
  @{
    @defgroup ximgproc_edge Structured forests for fast edge detection

This module contains implementations of modern structured edge detection algorithms,
i.e. algorithms which somehow takes into account pixel affinities in natural images.

    @defgroup ximgproc_edgeboxes EdgeBoxes

    @defgroup ximgproc_filters Filters

    @defgroup ximgproc_superpixel Superpixels

    @defgroup ximgproc_segmentation Image segmentation

    @defgroup ximgproc_fast_line_detector Fast line detector

    @defgroup ximgproc_edge_drawing EdgeDrawing

EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION

Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive
approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm
works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level
edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supression that tests
every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the
neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression,
ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for
an additional hysteresis step.

    @defgroup ximgproc_fourier Fourier descriptors
    @}
*/

namespace cv
{
namespace ximgproc
{

enum ThinningTypes{
    THINNING_ZHANGSUEN    = 0, // Thinning technique of Zhang-Suen
    THINNING_GUOHALL      = 1  // Thinning technique of Guo-Hall
};

/**
* @brief Specifies the binarization method to use in cv::ximgproc::niBlackThreshold
*/
enum LocalBinarizationMethods{
	BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
	BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
	BINARIZATION_WOLF = 2,    //!< Wolf's technique. See @cite Wolf2004 .
	BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
};

//! @addtogroup ximgproc
//! @{

/** @brief Performs thresholding on input images using Niblack's technique or some of the
popular variations it inspired.

The function transforms a grayscale image to a binary image according to the formulae:
-   **THRESH_BINARY**
    \f[dst(x,y) =  \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f]
-   **THRESH_BINARY_INV**
    \f[dst(x,y) =  \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f]
where \f$T(x,y)\f$ is a threshold calculated individually for each pixel.

The threshold value \f$T(x, y)\f$ is determined based on the binarization method chosen. For
classic Niblack, it is the mean minus \f$ k \f$ times standard deviation of
\f$\texttt{blockSize} \times\texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$.

The function can't process the image in-place.

@param _src Source 8-bit single-channel image.
@param _dst Destination image of the same size and the same type as src.
@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied,
used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
@param type Thresholding type, see cv::ThresholdTypes.
@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value
for the pixel: 3, 5, 7, and so on.
@param k The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is
normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from
the mean.
@param binarizationMethod Binarization method to use. By default, Niblack's technique is used.
Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods.

@sa  threshold, adaptiveThreshold
 */
CV_EXPORTS_W void niBlackThreshold( InputArray _src, OutputArray _dst,
                                    double maxValue, int type,
                                    int blockSize, double k, int binarizationMethod = BINARIZATION_NIBLACK );

/** @brief Applies a binary blob thinning operation, to achieve a skeletization of the input image.

The function transforms a binary blob image into a skeletized form using the technique of Zhang-Suen.

@param src Source 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values.
@param dst Destination image of the same size and the same type as src. The function can work in-place.
@param thinningType Value that defines which thinning algorithm should be used. See cv::ximgproc::ThinningTypes
 */
CV_EXPORTS_W void thinning( InputArray src, OutputArray dst, int thinningType = THINNING_ZHANGSUEN);

/** @brief Performs anisotropic diffusion on an image.

 The function applies Perona-Malik anisotropic diffusion to an image. This is the solution to the partial differential equation:

 \f[{\frac  {\partial I}{\partial t}}={\mathrm  {div}}\left(c(x,y,t)\nabla I\right)=\nabla c\cdot \nabla I+c(x,y,t)\Delta I\f]

 Suggested functions for c(x,y,t) are:

 \f[c\left(\|\nabla I\|\right)=e^{{-\left(\|\nabla I\|/K\right)^{2}}}\f]

 or

 \f[ c\left(\|\nabla I\|\right)={\frac {1}{1+\left({\frac  {\|\nabla I\|}{K}}\right)^{2}}} \f]

 @param src Source image with 3 channels.
 @param dst Destination image of the same size and the same number of channels as src .
 @param alpha The amount of time to step forward by on each iteration (normally, it's between 0 and 1).
 @param K sensitivity to the edges
 @param niters The number of iterations
*/
CV_EXPORTS_W void anisotropicDiffusion(InputArray src, OutputArray dst, float alpha, float K, int niters );

//! @}

}
}

#endif // __OPENCV_XIMGPROC_HPP__