/*
 * Software License Agreement (BSD License)
 *
 * Point Cloud Library (PCL) - www.pointclouds.org
 * Copyright (c) 2012-, Open Perception, Inc.
 * Copyright (c) 2004, Sylvain Paris and Francois Sillion

 * All rights reserved.

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 * modification, are permitted provided that the following conditions
 * are met:
 *
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 *    copyright notice, this list of conditions and the following
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 *    with the distribution.
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 *    contributors may be used to endorse or promote products derived
 *    from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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 * $Id$
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 */


#ifndef PCL_FILTERS_FAST_BILATERAL_H_
#define PCL_FILTERS_FAST_BILATERAL_H_

#include <pcl/filters/filter.h>

namespace pcl
{
  /** \brief Implementation of a fast bilateral filter for smoothing depth information in organized point clouds
   *  Based on the following paper:
   *    * Sylvain Paris and FrŽdo Durand
   *      "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach"
   *       European Conference on Computer Vision (ECCV'06)
   *
   *  More details on the webpage: http://people.csail.mit.edu/sparis/bf/
   */
  template<typename PointT>
  class FastBilateralFilter : public Filter<PointT>
  {
    protected:
      using Filter<PointT>::input_;
      typedef typename Filter<PointT>::PointCloud PointCloud;

    public:
    
      typedef boost::shared_ptr< FastBilateralFilter<PointT> > Ptr;
      typedef boost::shared_ptr< const FastBilateralFilter<PointT> > ConstPtr;

      /** \brief Empty constructor. */
      FastBilateralFilter ()
        : sigma_s_ (15.0f)
        , sigma_r_ (0.05f)
        , early_division_ (false)
      { }
      
      /** \brief Empty destructor */
      virtual ~FastBilateralFilter () {}

      /** \brief Set the standard deviation of the Gaussian used by the bilateral filter for
        * the spatial neighborhood/window.
        * \param[in] sigma_s the size of the Gaussian bilateral filter window to use
        */
      inline void
      setSigmaS (float sigma_s)
      { sigma_s_ = sigma_s; }

      /** \brief Get the size of the Gaussian bilateral filter window as set by the user. */
      inline float
      getSigmaS () const
      { return sigma_s_; }


      /** \brief Set the standard deviation of the Gaussian used to control how much an adjacent
        * pixel is downweighted because of the intensity difference (depth in our case).
        * \param[in] sigma_r the standard deviation of the Gaussian for the intensity difference
        */
      inline void
      setSigmaR (float sigma_r)
      { sigma_r_ = sigma_r; }

      /** \brief Get the standard deviation of the Gaussian for the intensity difference */
      inline float
      getSigmaR () const
      { return sigma_r_; }

      /** \brief Filter the input data and store the results into output.
        * \param[out] output the resultant point cloud
        */
      virtual void
      applyFilter (PointCloud &output);

    protected:
      float sigma_s_;
      float sigma_r_;
      bool early_division_;

      class Array3D
      {
        public:
          Array3D (const size_t width, const size_t height, const size_t depth)
          {
            x_dim_ = width;
            y_dim_ = height;
            z_dim_ = depth;
            v_ = std::vector<Eigen::Vector2f> (width*height*depth, Eigen::Vector2f (0.0f, 0.0f));
          }

          inline Eigen::Vector2f&
          operator () (const size_t x, const size_t y, const size_t z)
          { return v_[(x * y_dim_ + y) * z_dim_ + z]; }

          inline const Eigen::Vector2f&
          operator () (const size_t x, const size_t y, const size_t z) const
          { return v_[(x * y_dim_ + y) * z_dim_ + z]; }

          inline void
          resize (const size_t width, const size_t height, const size_t depth)
          {
            x_dim_ = width;
            y_dim_ = height;
            z_dim_ = depth;
            v_.resize (x_dim_ * y_dim_ * z_dim_);
          }

          Eigen::Vector2f
          trilinear_interpolation (const float x,
                                   const float y,
                                   const float z);

          static inline size_t
          clamp (const size_t min_value,
                 const size_t max_value,
                 const size_t x);

          inline size_t
          x_size () const
          { return x_dim_; }

          inline size_t
          y_size () const
          { return y_dim_; }

          inline size_t
          z_size () const
          { return z_dim_; }

          inline std::vector<Eigen::Vector2f >::iterator
          begin ()
          { return v_.begin (); }

          inline std::vector<Eigen::Vector2f >::iterator
          end ()
          { return v_.end (); }

          inline std::vector<Eigen::Vector2f >::const_iterator
          begin () const
          { return v_.begin (); }

          inline std::vector<Eigen::Vector2f >::const_iterator
          end () const
          { return v_.end (); }

        private:
          std::vector<Eigen::Vector2f > v_;
          size_t x_dim_, y_dim_, z_dim_;
      };


  };
}

#ifdef PCL_NO_PRECOMPILE
#include <pcl/filters/impl/fast_bilateral.hpp>
#else
#define PCL_INSTANTIATE_FastBilateralFilter(T) template class PCL_EXPORTS pcl::FastBilateralFilter<T>;
#endif


#endif /* PCL_FILTERS_FAST_BILATERAL_H_ */
