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#ifndef __OPENCV_SFM_RECONSTRUCT_HPP__
#define __OPENCV_SFM_RECONSTRUCT_HPP__

#include <vector>
#include <string>

#include <opencv2/core.hpp>

namespace cv
{
namespace sfm
{

//! @addtogroup reconstruction
//! @{

#if defined(CV_DOXYGEN) || defined(CERES_FOUND)

/** @brief Reconstruct 3d points from 2d correspondences while performing autocalibration.
  @param points2d Input vector of vectors of 2d points (the inner vector is per image).
  @param Ps Output vector with the 3x4 projections matrices of each image.
  @param points3d Output array with estimated 3d points.
  @param K Input/Output camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$. Input parameters used as initial guess.
  @param is_projective if true, the cameras are supposed to be projective.

  This method calls below signature and extracts projection matrices from estimated K, R and t.

   @note
    - Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
*/
CV_EXPORTS
void
reconstruct(InputArrayOfArrays points2d, OutputArray Ps, OutputArray points3d, InputOutputArray K,
            bool is_projective = false);

/** @brief Reconstruct 3d points from 2d correspondences while performing autocalibration.
  @param points2d Input vector of vectors of 2d points (the inner vector is per image).
  @param Rs Output vector of 3x3 rotations of the camera.
  @param Ts Output vector of 3x1 translations of the camera.
  @param points3d Output array with estimated 3d points.
  @param K Input/Output camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$. Input parameters used as initial guess.
  @param is_projective if true, the cameras are supposed to be projective.

  Internally calls libmv simple pipeline routine with some default parameters by instatiating SFMLibmvEuclideanReconstruction class.

  @note
    - Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
    - To see a working example for camera motion reconstruction, check the following tutorial: @ref tutorial_sfm_trajectory_estimation.
*/
CV_EXPORTS
void
reconstruct(InputArrayOfArrays points2d, OutputArray Rs, OutputArray Ts, InputOutputArray K,
            OutputArray points3d, bool is_projective = false);

/** @brief Reconstruct 3d points from 2d images while performing autocalibration.
  @param images a vector of string with the images paths.
  @param Ps Output vector with the 3x4 projections matrices of each image.
  @param points3d Output array with estimated 3d points.
  @param K Input/Output camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$. Input parameters used as initial guess.
  @param is_projective if true, the cameras are supposed to be projective.

  This method calls below signature and extracts projection matrices from estimated K, R and t.

   @note
    - The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior.
    - For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images.
*/
CV_EXPORTS
void
reconstruct(const std::vector<String> images, OutputArray Ps, OutputArray points3d,
            InputOutputArray K, bool is_projective = false);

/** @brief Reconstruct 3d points from 2d images while performing autocalibration.
  @param images a vector of string with the images paths.
  @param Rs Output vector of 3x3 rotations of the camera.
  @param Ts Output vector of 3x1 translations of the camera.
  @param points3d Output array with estimated 3d points.
  @param K Input/Output camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$. Input parameters used as initial guess.
  @param is_projective if true, the cameras are supposed to be projective.

  Internally calls libmv simple pipeline routine with some default parameters by instatiating SFMLibmvEuclideanReconstruction class.

   @note
    - The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior.
    - For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images.
    - To see a working example for scene reconstruction, check the following tutorial: @ref tutorial_sfm_scene_reconstruction.
*/
CV_EXPORTS
void
reconstruct(const std::vector<String> images, OutputArray Rs, OutputArray Ts,
            InputOutputArray K, OutputArray points3d, bool is_projective = false);

#endif /* CV_DOXYGEN || CERES_FOUND */

//! @} sfm

} /* namespace cv */
} /* namespace sfm */

#endif

/* End of file. */