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#ifndef PCL_RECOGNITION_COLOR_GRADIENT_MODALITY
#define PCL_RECOGNITION_COLOR_GRADIENT_MODALITY

#include <pcl/recognition/quantizable_modality.h>

#include <pcl/pcl_base.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/recognition/point_types.h>
#include <pcl/filters/convolution.h>

#include <list>

namespace pcl
{

  /** \brief Modality based on max-RGB gradients.
    * \author Stefan Holzer
    */
  template <typename PointInT>
  class ColorGradientModality
    : public QuantizableModality, public PCLBase<PointInT>
  {
    protected:
      using PCLBase<PointInT>::input_;

      /** \brief Candidate for a feature (used in feature extraction methods). */
      struct Candidate
      {
        /** \brief The gradient. */
        GradientXY gradient;
    
        /** \brief The x-position. */
        int x;
        /** \brief The y-position. */
        int y;	
    
        /** \brief Operator for comparing to candidates (by magnitude of the gradient).
          * \param[in] rhs the candidate to compare with.
          */
        bool operator< (const Candidate & rhs) const
        {
          return (gradient.magnitude > rhs.gradient.magnitude);
        }
      };

    public:
      typedef typename pcl::PointCloud<PointInT> PointCloudIn;

      /** \brief Different methods for feature selection/extraction. */
      enum FeatureSelectionMethod
      {
        MASK_BORDER_HIGH_GRADIENTS,
        MASK_BORDER_EQUALLY, // this gives templates most equally to the OpenCV implementation
        DISTANCE_MAGNITUDE_SCORE
      };

      /** \brief Constructor. */
      ColorGradientModality ();
      /** \brief Destructor. */
      virtual ~ColorGradientModality ();
  
      /** \brief Sets the threshold for the gradient magnitude which is used when quantizing the data.
        *        Gradients with a smaller magnitude are ignored. 
        * \param[in] threshold the new gradient magnitude threshold.
        */
      inline void
      setGradientMagnitudeThreshold (const float threshold)
      {
        gradient_magnitude_threshold_ = threshold;
      }

      /** \brief Sets the threshold for the gradient magnitude which is used for feature extraction.
        *        Gradients with a smaller magnitude are ignored. 
        * \param[in] threshold the new gradient magnitude threshold.
        */
      inline void
      setGradientMagnitudeThresholdForFeatureExtraction (const float threshold)
      {
        gradient_magnitude_threshold_feature_extraction_ = threshold;
      }

      /** \brief Sets the feature selection method.
        * \param[in] method the feature selection method.
        */
      inline void
      setFeatureSelectionMethod (const FeatureSelectionMethod method)
      {
        feature_selection_method_ = method;
      }
  
      /** \brief Sets the spreading size for spreading the quantized data. */
      inline void
      setSpreadingSize (const size_t spreading_size)
      {
        spreading_size_ = spreading_size;
      }

      /** \brief Sets whether variable feature numbers for feature extraction is enabled.
        * \param[in] enabled enables/disables variable feature numbers for feature extraction.
        */
      inline void
      setVariableFeatureNr (const bool enabled)
      {
        variable_feature_nr_ = enabled;
      }

      /** \brief Returns a reference to the internally computed quantized map. */
      inline QuantizedMap &
      getQuantizedMap () 
      { 
        return (filtered_quantized_color_gradients_);
      }
  
      /** \brief Returns a reference to the internally computed spreaded quantized map. */
      inline QuantizedMap &
      getSpreadedQuantizedMap () 
      { 
        return (spreaded_filtered_quantized_color_gradients_);
      }

      /** \brief Returns a point cloud containing the max-RGB gradients. */
      inline pcl::PointCloud<pcl::GradientXY> &
      getMaxColorGradients ()
      {
        return (color_gradients_);
      }
  
      /** \brief Extracts features from this modality within the specified mask.
        * \param[in] mask defines the areas where features are searched in. 
        * \param[in] nr_features defines the number of features to be extracted 
        *            (might be less if not sufficient information is present in the modality).
        * \param[in] modalityIndex the index which is stored in the extracted features.
        * \param[out] features the destination for the extracted features.
        */
      void
      extractFeatures (const MaskMap & mask, size_t nr_features, size_t modalityIndex,
                       std::vector<QuantizedMultiModFeature> & features) const;
  
      /** \brief Extracts all possible features from the modality within the specified mask.
        * \param[in] mask defines the areas where features are searched in. 
        * \param[in] nr_features IGNORED (TODO: remove this parameter).
        * \param[in] modalityIndex the index which is stored in the extracted features.
        * \param[out] features the destination for the extracted features.
        */
      void
      extractAllFeatures (const MaskMap & mask, size_t nr_features, size_t modalityIndex,
                          std::vector<QuantizedMultiModFeature> & features) const;
  
      /** \brief Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
        * \param cloud the const boost shared pointer to a PointCloud message
        */
      virtual void 
      setInputCloud (const typename PointCloudIn::ConstPtr & cloud) 
      { 
        input_ = cloud;
      }

      /** \brief Processes the input data (smoothing, computing gradients, quantizing, filtering, spreading). */
      virtual void
      processInputData ();

      /** \brief Processes the input data assuming that everything up to filtering is already done/available 
        *        (so only spreading is performed). */
      virtual void
      processInputDataFromFiltered ();

    protected:

      /** \brief Computes the Gaussian kernel used for smoothing. 
        * \param[in] kernel_size the size of the Gaussian kernel. 
        * \param[in] sigma the sigma.
        * \param[out] kernel_values the destination for the values of the kernel. */
      void
      computeGaussianKernel (const size_t kernel_size, const float sigma, std::vector <float> & kernel_values);

      /** \brief Computes the max-RGB gradients for the specified cloud.
        * \param[in] cloud the cloud for which the gradients are computed.
        */
      void
      computeMaxColorGradients (const typename pcl::PointCloud<pcl::RGB>::ConstPtr & cloud);

      /** \brief Computes the max-RGB gradients for the specified cloud using sobel.
        * \param[in] cloud the cloud for which the gradients are computed.
        */
      void
      computeMaxColorGradientsSobel (const typename pcl::PointCloud<pcl::RGB>::ConstPtr & cloud);
  
      /** \brief Quantizes the color gradients. */
      void
      quantizeColorGradients ();
  
      /** \brief Filters the quantized gradients. */
      void
      filterQuantizedColorGradients ();

      /** \brief Erodes a mask.
        * \param[in] mask_in the mask which will be eroded.
        * \param[out] mask_out the destination for the eroded mask.
        */
      static void
      erode (const pcl::MaskMap & mask_in, pcl::MaskMap & mask_out);
  
    private:

      /** \brief Determines whether variable numbers of features are extracted or not. */
      bool variable_feature_nr_;

      /** \brief Stores a smoothed verion of the input cloud. */
	    pcl::PointCloud<pcl::RGB>::Ptr smoothed_input_;

      /** \brief Defines which feature selection method is used. */
      FeatureSelectionMethod feature_selection_method_;

      /** \brief The threshold applied on the gradient magnitudes (for quantization). */
      float gradient_magnitude_threshold_;
      /** \brief The threshold applied on the gradient magnitudes for feature extraction. */
      float gradient_magnitude_threshold_feature_extraction_;

      /** \brief The point cloud which holds the max-RGB gradients. */
      pcl::PointCloud<pcl::GradientXY> color_gradients_;

      /** \brief The spreading size. */
      size_t spreading_size_;
  
      /** \brief The map which holds the quantized max-RGB gradients. */
      pcl::QuantizedMap quantized_color_gradients_;
      /** \brief The map which holds the filtered quantized data. */
      pcl::QuantizedMap filtered_quantized_color_gradients_;
      /** \brief The map which holds the spreaded quantized data. */
      pcl::QuantizedMap spreaded_filtered_quantized_color_gradients_;
  
  };

}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
pcl::ColorGradientModality<PointInT>::
ColorGradientModality ()
  : variable_feature_nr_ (false)
  , smoothed_input_ (new pcl::PointCloud<pcl::RGB> ())
  , feature_selection_method_ (DISTANCE_MAGNITUDE_SCORE)
  , gradient_magnitude_threshold_ (10.0f)
  , gradient_magnitude_threshold_feature_extraction_ (55.0f)
  , color_gradients_ ()
  , spreading_size_ (8)
  , quantized_color_gradients_ ()
  , filtered_quantized_color_gradients_ ()
  , spreaded_filtered_quantized_color_gradients_ ()
{
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
pcl::ColorGradientModality<PointInT>::
~ColorGradientModality ()
{
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT> void
pcl::ColorGradientModality<PointInT>::
computeGaussianKernel (const size_t kernel_size, const float sigma, std::vector <float> & kernel_values)
{
  // code taken from OpenCV
  const int n = int (kernel_size);
  const int SMALL_GAUSSIAN_SIZE = 7;
  static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] =
  {
      {1.f},
      {0.25f, 0.5f, 0.25f},
      {0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f},
      {0.03125f, 0.109375f, 0.21875f, 0.28125f, 0.21875f, 0.109375f, 0.03125f}
  };

  const float* fixed_kernel = n % 2 == 1 && n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 ?
      small_gaussian_tab[n>>1] : 0;

  //CV_Assert( ktype == CV_32F || ktype == CV_64F );
  /*Mat kernel(n, 1, ktype);*/
  kernel_values.resize (n);
  float* cf = &(kernel_values[0]);
  //double* cd = (double*)kernel.data;

  double sigmaX = sigma > 0 ? sigma : ((n-1)*0.5 - 1)*0.3 + 0.8;
  double scale2X = -0.5/(sigmaX*sigmaX);
  double sum = 0;

  int i;
  for( i = 0; i < n; i++ )
  {
    double x = i - (n-1)*0.5;
    double t = fixed_kernel ? double (fixed_kernel[i]) : std::exp (scale2X*x*x);

    cf[i] = float (t);
    sum += cf[i];
  }

  sum = 1./sum;
  for (i = 0; i < n; i++ )
  {
    cf[i] = float (cf[i]*sum);
  }
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
processInputData ()
{
  // compute gaussian kernel values
  const size_t kernel_size = 7;
  std::vector<float> kernel_values;
  computeGaussianKernel (kernel_size, 0.0f, kernel_values);

  // smooth input
	pcl::filters::Convolution<pcl::RGB, pcl::RGB> convolution;
	Eigen::ArrayXf gaussian_kernel(kernel_size);
	//gaussian_kernel << 1.f/16, 1.f/8, 3.f/16, 2.f/8, 3.f/16, 1.f/8, 1.f/16;
	//gaussian_kernel << 16.f/1600.f,  32.f/1600.f,  64.f/1600.f, 128.f/1600.f, 256.f/1600.f, 128.f/1600.f,  64.f/1600.f,  32.f/1600.f,  16.f/1600.f;
  gaussian_kernel << kernel_values[0], kernel_values[1], kernel_values[2], kernel_values[3], kernel_values[4], kernel_values[5], kernel_values[6];

  pcl::PointCloud<pcl::RGB>::Ptr rgb_input_ (new pcl::PointCloud<pcl::RGB>());
  
  const uint32_t width = input_->width;
  const uint32_t height = input_->height;

  rgb_input_->resize (width*height);
  rgb_input_->width = width;
  rgb_input_->height = height;
  rgb_input_->is_dense = input_->is_dense;
  for (size_t row_index = 0; row_index < height; ++row_index)
  {
    for (size_t col_index = 0; col_index < width; ++col_index)
    {
      (*rgb_input_) (col_index, row_index).r = (*input_) (col_index, row_index).r;
      (*rgb_input_) (col_index, row_index).g = (*input_) (col_index, row_index).g;
      (*rgb_input_) (col_index, row_index).b = (*input_) (col_index, row_index).b;
    }
  }

	convolution.setInputCloud (rgb_input_);
	convolution.setKernel (gaussian_kernel);

  convolution.convolve (*smoothed_input_);

  // extract color gradients
  computeMaxColorGradientsSobel (smoothed_input_);

  // quantize gradients
  quantizeColorGradients ();

  // filter quantized gradients to get only dominants one + thresholding
  filterQuantizedColorGradients ();

  // spread filtered quantized gradients
  //spreadFilteredQunatizedColorGradients ();
  pcl::QuantizedMap::spreadQuantizedMap (filtered_quantized_color_gradients_,
                                         spreaded_filtered_quantized_color_gradients_, 
                                         spreading_size_);
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
processInputDataFromFiltered ()
{
  // spread filtered quantized gradients
  //spreadFilteredQunatizedColorGradients ();
  pcl::QuantizedMap::spreadQuantizedMap (filtered_quantized_color_gradients_,
                                         spreaded_filtered_quantized_color_gradients_, 
                                         spreading_size_);
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void pcl::ColorGradientModality<PointInT>::
extractFeatures (const MaskMap & mask, const size_t nr_features, const size_t modality_index,
                 std::vector<QuantizedMultiModFeature> & features) const
{
  const size_t width = mask.getWidth ();
  const size_t height = mask.getHeight ();
  
  std::list<Candidate> list1;
  std::list<Candidate> list2;


  if (feature_selection_method_ == DISTANCE_MAGNITUDE_SCORE)
  {
    for (size_t row_index = 0; row_index < height; ++row_index)
    {
      for (size_t col_index = 0; col_index < width; ++col_index)
      {
        if (mask (col_index, row_index) != 0)
        {
          const GradientXY & gradient = color_gradients_ (col_index, row_index);
          if (gradient.magnitude > gradient_magnitude_threshold_feature_extraction_
            && filtered_quantized_color_gradients_ (col_index, row_index) != 0)
          {
            Candidate candidate;
            candidate.gradient = gradient;
            candidate.x = static_cast<int> (col_index);
            candidate.y = static_cast<int> (row_index);

            list1.push_back (candidate);
          }
        }
      }
    }

    list1.sort();

    if (variable_feature_nr_)
    {
      list2.push_back (*(list1.begin ()));
      //while (list2.size () != nr_features)
      bool feature_selection_finished = false;
      while (!feature_selection_finished)
      {
        float best_score = 0.0f;
        typename std::list<Candidate>::iterator best_iter = list1.end ();
        for (typename std::list<Candidate>::iterator iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
        {
          // find smallest distance
          float smallest_distance = std::numeric_limits<float>::max ();
          for (typename std::list<Candidate>::iterator iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
          {
            const float dx = static_cast<float> (iter1->x) - static_cast<float> (iter2->x);
            const float dy = static_cast<float> (iter1->y) - static_cast<float> (iter2->y);

            const float distance = dx*dx + dy*dy;

            if (distance < smallest_distance)
            {
              smallest_distance = distance;
            }
          }

          const float score = smallest_distance * iter1->gradient.magnitude;

          if (score > best_score)
          {
            best_score = score;
            best_iter = iter1;
          }
        }


        float min_min_sqr_distance = std::numeric_limits<float>::max ();
        float max_min_sqr_distance = 0;
        for (typename std::list<Candidate>::iterator iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
        {
          float min_sqr_distance = std::numeric_limits<float>::max ();
          for (typename std::list<Candidate>::iterator iter3 = list2.begin (); iter3 != list2.end (); ++iter3)
          {
            if (iter2 == iter3)
              continue;

            const float dx = static_cast<float> (iter2->x) - static_cast<float> (iter3->x);
            const float dy = static_cast<float> (iter2->y) - static_cast<float> (iter3->y);

            const float sqr_distance = dx*dx + dy*dy;

            if (sqr_distance < min_sqr_distance)
            {
              min_sqr_distance = sqr_distance;
            }

            //std::cerr << min_sqr_distance;
          }
          //std::cerr << std::endl;

          // check current feature
          {
            const float dx = static_cast<float> (iter2->x) - static_cast<float> (best_iter->x);
            const float dy = static_cast<float> (iter2->y) - static_cast<float> (best_iter->y);

            const float sqr_distance = dx*dx + dy*dy;

            if (sqr_distance < min_sqr_distance)
            {
              min_sqr_distance = sqr_distance;
            }
          }

          if (min_sqr_distance < min_min_sqr_distance)
            min_min_sqr_distance = min_sqr_distance;
          if (min_sqr_distance > max_min_sqr_distance)
            max_min_sqr_distance = min_sqr_distance;

          //std::cerr << min_sqr_distance << ", " << min_min_sqr_distance << ", " << max_min_sqr_distance << std::endl;
        }

        if (best_iter != list1.end ())
        {
          //std::cerr << "feature_index: " << list2.size () << std::endl;
          //std::cerr << "min_min_sqr_distance: " << min_min_sqr_distance << std::endl;
          //std::cerr << "max_min_sqr_distance: " << max_min_sqr_distance << std::endl;

          if (min_min_sqr_distance < 50)
          {
            feature_selection_finished = true;
            break;
          }

          list2.push_back (*best_iter);
        }
      } 
    }
    else
    {
      if (list1.size () <= nr_features)
      {
        for (typename std::list<Candidate>::iterator iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
        {
          QuantizedMultiModFeature feature;
          
          feature.x = iter1->x;
          feature.y = iter1->y;
          feature.modality_index = modality_index;
          feature.quantized_value = filtered_quantized_color_gradients_ (iter1->x, iter1->y);

          features.push_back (feature);
        }
        return;
      }

      list2.push_back (*(list1.begin ()));
      while (list2.size () != nr_features)
      {
        float best_score = 0.0f;
        typename std::list<Candidate>::iterator best_iter = list1.end ();
        for (typename std::list<Candidate>::iterator iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
        {
          // find smallest distance
          float smallest_distance = std::numeric_limits<float>::max ();
          for (typename std::list<Candidate>::iterator iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
          {
            const float dx = static_cast<float> (iter1->x) - static_cast<float> (iter2->x);
            const float dy = static_cast<float> (iter1->y) - static_cast<float> (iter2->y);

            const float distance = dx*dx + dy*dy;

            if (distance < smallest_distance)
            {
              smallest_distance = distance;
            }
          }

          const float score = smallest_distance * iter1->gradient.magnitude;

          if (score > best_score)
          {
            best_score = score;
            best_iter = iter1;
          }
        }

        if (best_iter != list1.end ())
        {
          list2.push_back (*best_iter);
        }
        else
        {
          break;
        }
      }  
    }
  }
  else if (feature_selection_method_ == MASK_BORDER_HIGH_GRADIENTS || feature_selection_method_ == MASK_BORDER_EQUALLY)
  {
    MaskMap eroded_mask;
    erode (mask, eroded_mask);

    MaskMap diff_mask;
    MaskMap::getDifferenceMask (mask, eroded_mask, diff_mask);

    for (size_t row_index = 0; row_index < height; ++row_index)
    {
      for (size_t col_index = 0; col_index < width; ++col_index)
      {
        if (diff_mask (col_index, row_index) != 0)
        {
          const GradientXY & gradient = color_gradients_ (col_index, row_index);
          if ((feature_selection_method_ == MASK_BORDER_EQUALLY || gradient.magnitude > gradient_magnitude_threshold_feature_extraction_)
            && filtered_quantized_color_gradients_ (col_index, row_index) != 0)
          {
            Candidate candidate;
            candidate.gradient = gradient;
            candidate.x = static_cast<int> (col_index);
            candidate.y = static_cast<int> (row_index);

            list1.push_back (candidate);
          }
        }
      }
    }

    list1.sort();

    if (list1.size () <= nr_features)
    {
      for (typename std::list<Candidate>::iterator iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
      {
        QuantizedMultiModFeature feature;
          
        feature.x = iter1->x;
        feature.y = iter1->y;
        feature.modality_index = modality_index;
        feature.quantized_value = filtered_quantized_color_gradients_ (iter1->x, iter1->y);

        features.push_back (feature);
      }
      return;
    }

    size_t distance = list1.size () / nr_features + 1; // ??? 
    while (list2.size () != nr_features)
    {
      const size_t sqr_distance = distance*distance;
      for (typename std::list<Candidate>::iterator iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
      {
        bool candidate_accepted = true;

        for (typename std::list<Candidate>::iterator iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
        {
          const int dx = iter1->x - iter2->x;
          const int dy = iter1->y - iter2->y;
          const unsigned int tmp_distance = dx*dx + dy*dy;

          //if (tmp_distance < distance) 
          if (tmp_distance < sqr_distance)
          {
            candidate_accepted = false;
            break;
          }
        }

        if (candidate_accepted)
          list2.push_back (*iter1);

        if (list2.size () == nr_features)
          break;
      }
      --distance;
    }
  }

  for (typename std::list<Candidate>::iterator iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
  {
    QuantizedMultiModFeature feature;
    
    feature.x = iter2->x;
    feature.y = iter2->y;
    feature.modality_index = modality_index;
    feature.quantized_value = filtered_quantized_color_gradients_ (iter2->x, iter2->y);

    features.push_back (feature);
  }
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT> void 
pcl::ColorGradientModality<PointInT>::
extractAllFeatures (const MaskMap & mask, const size_t, const size_t modality_index,
                 std::vector<QuantizedMultiModFeature> & features) const
{
  const size_t width = mask.getWidth ();
  const size_t height = mask.getHeight ();
  
  std::list<Candidate> list1;
  std::list<Candidate> list2;


  for (size_t row_index = 0; row_index < height; ++row_index)
  {
    for (size_t col_index = 0; col_index < width; ++col_index)
    {
      if (mask (col_index, row_index) != 0)
      {
        const GradientXY & gradient = color_gradients_ (col_index, row_index);
        if (gradient.magnitude > gradient_magnitude_threshold_feature_extraction_
          && filtered_quantized_color_gradients_ (col_index, row_index) != 0)
        {
          Candidate candidate;
          candidate.gradient = gradient;
          candidate.x = static_cast<int> (col_index);
          candidate.y = static_cast<int> (row_index);

          list1.push_back (candidate);
        }
      }
    }
  }

  list1.sort();

  for (typename std::list<Candidate>::iterator iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
  {
    QuantizedMultiModFeature feature;
          
    feature.x = iter1->x;
    feature.y = iter1->y;
    feature.modality_index = modality_index;
    feature.quantized_value = filtered_quantized_color_gradients_ (iter1->x, iter1->y);

    features.push_back (feature);
  }
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
computeMaxColorGradients (const typename pcl::PointCloud<pcl::RGB>::ConstPtr & cloud)
{
  const int width = cloud->width;
  const int height = cloud->height;

  color_gradients_.points.resize (width*height);
  color_gradients_.width = width;
  color_gradients_.height = height;

  const float pi = tan (1.0f) * 2;
  for (int row_index = 0; row_index < height-2; ++row_index)
  {
    for (int col_index = 0; col_index < width-2; ++col_index)
    {
      const int index0 = row_index*width+col_index;
      const int index_c = row_index*width+col_index+2;
      const int index_r = (row_index+2)*width+col_index;

      //const int index_d = (row_index+1)*width+col_index+1;

      const unsigned char r0 = cloud->points[index0].r;
      const unsigned char g0 = cloud->points[index0].g;
      const unsigned char b0 = cloud->points[index0].b;

      const unsigned char r_c = cloud->points[index_c].r;
      const unsigned char g_c = cloud->points[index_c].g;
      const unsigned char b_c = cloud->points[index_c].b;

      const unsigned char r_r = cloud->points[index_r].r;
      const unsigned char g_r = cloud->points[index_r].g;
      const unsigned char b_r = cloud->points[index_r].b;

      const float r_dx = static_cast<float> (r_c) - static_cast<float> (r0);
      const float g_dx = static_cast<float> (g_c) - static_cast<float> (g0);
      const float b_dx = static_cast<float> (b_c) - static_cast<float> (b0);

      const float r_dy = static_cast<float> (r_r) - static_cast<float> (r0);
      const float g_dy = static_cast<float> (g_r) - static_cast<float> (g0);
      const float b_dy = static_cast<float> (b_r) - static_cast<float> (b0);

      const float sqr_mag_r = r_dx*r_dx + r_dy*r_dy;
      const float sqr_mag_g = g_dx*g_dx + g_dy*g_dy;
      const float sqr_mag_b = b_dx*b_dx + b_dy*b_dy;

      if (sqr_mag_r > sqr_mag_g && sqr_mag_r > sqr_mag_b)
      {
        GradientXY gradient;
        gradient.magnitude = sqrt (sqr_mag_r);
        gradient.angle = atan2 (r_dy, r_dx) * 180.0f / pi;
        gradient.x = static_cast<float> (col_index);
        gradient.y = static_cast<float> (row_index);

        color_gradients_ (col_index+1, row_index+1) = gradient;
      }
      else if (sqr_mag_g > sqr_mag_b)
      {
        GradientXY gradient;
        gradient.magnitude = sqrt (sqr_mag_g);
        gradient.angle = atan2 (g_dy, g_dx) * 180.0f / pi;
        gradient.x = static_cast<float> (col_index);
        gradient.y = static_cast<float> (row_index);

        color_gradients_ (col_index+1, row_index+1) = gradient;
      }
      else
      {
        GradientXY gradient;
        gradient.magnitude = sqrt (sqr_mag_b);
        gradient.angle = atan2 (b_dy, b_dx) * 180.0f / pi;
        gradient.x = static_cast<float> (col_index);
        gradient.y = static_cast<float> (row_index);

        color_gradients_ (col_index+1, row_index+1) = gradient;
      }

      assert (color_gradients_ (col_index+1, row_index+1).angle >= -180 &&
              color_gradients_ (col_index+1, row_index+1).angle <=  180);
    }
  }

  return;
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
computeMaxColorGradientsSobel (const typename pcl::PointCloud<pcl::RGB>::ConstPtr & cloud)
{
  const int width = cloud->width;
  const int height = cloud->height;

  color_gradients_.points.resize (width*height);
  color_gradients_.width = width;
  color_gradients_.height = height;

  const float pi = tanf (1.0f) * 2.0f;
  for (int row_index = 1; row_index < height-1; ++row_index)
  {
    for (int col_index = 1; col_index < width-1; ++col_index)
    {
      const int r7 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index-1)].r);
      const int g7 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index-1)].g);
      const int b7 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index-1)].b);
      const int r8 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index)].r);
      const int g8 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index)].g);
      const int b8 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index)].b);
      const int r9 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index+1)].r);
      const int g9 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index+1)].g);
      const int b9 = static_cast<int> (cloud->points[(row_index-1)*width + (col_index+1)].b);
      const int r4 = static_cast<int> (cloud->points[(row_index)*width + (col_index-1)].r);
      const int g4 = static_cast<int> (cloud->points[(row_index)*width + (col_index-1)].g);
      const int b4 = static_cast<int> (cloud->points[(row_index)*width + (col_index-1)].b);
      const int r6 = static_cast<int> (cloud->points[(row_index)*width + (col_index+1)].r);
      const int g6 = static_cast<int> (cloud->points[(row_index)*width + (col_index+1)].g);
      const int b6 = static_cast<int> (cloud->points[(row_index)*width + (col_index+1)].b);
      const int r1 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index-1)].r);
      const int g1 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index-1)].g);
      const int b1 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index-1)].b);
      const int r2 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index)].r);
      const int g2 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index)].g);
      const int b2 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index)].b);
      const int r3 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index+1)].r);
      const int g3 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index+1)].g);
      const int b3 = static_cast<int> (cloud->points[(row_index+1)*width + (col_index+1)].b);

      //const int r_tmp1 = - r7 + r3;
      //const int r_tmp2 = - r1 + r9;
      //const int g_tmp1 = - g7 + g3;
      //const int g_tmp2 = - g1 + g9;
      //const int b_tmp1 = - b7 + b3;
      //const int b_tmp2 = - b1 + b9;
      ////const int gx = - r7 - (r4<<2) - r1 + r3 + (r6<<2) + r9;
      ////const int gy = - r7 - (r8<<2) - r9 + r1 + (r2<<2) + r3;
      //const int r_dx = r_tmp1 + r_tmp2 - (r4<<2) + (r6<<2);
      //const int r_dy = r_tmp1 - r_tmp2 - (r8<<2) + (r2<<2);
      //const int g_dx = g_tmp1 + g_tmp2 - (g4<<2) + (g6<<2);
      //const int g_dy = g_tmp1 - g_tmp2 - (g8<<2) + (g2<<2);
      //const int b_dx = b_tmp1 + b_tmp2 - (b4<<2) + (b6<<2);
      //const int b_dy = b_tmp1 - b_tmp2 - (b8<<2) + (b2<<2);

      //const int r_tmp1 = - r7 + r3;
      //const int r_tmp2 = - r1 + r9;
      //const int g_tmp1 = - g7 + g3;
      //const int g_tmp2 = - g1 + g9;
      //const int b_tmp1 = - b7 + b3;
      //const int b_tmp2 = - b1 + b9;
      //const int gx = - r7 - (r4<<2) - r1 + r3 + (r6<<2) + r9;
      //const int gy = - r7 - (r8<<2) - r9 + r1 + (r2<<2) + r3;
      const int r_dx = r9 + 2*r6 + r3 - (r7 + 2*r4 + r1);
      const int r_dy = r1 + 2*r2 + r3 - (r7 + 2*r8 + r9);
      const int g_dx = g9 + 2*g6 + g3 - (g7 + 2*g4 + g1);
      const int g_dy = g1 + 2*g2 + g3 - (g7 + 2*g8 + g9);
      const int b_dx = b9 + 2*b6 + b3 - (b7 + 2*b4 + b1);
      const int b_dy = b1 + 2*b2 + b3 - (b7 + 2*b8 + b9);

      const int sqr_mag_r = r_dx*r_dx + r_dy*r_dy;
      const int sqr_mag_g = g_dx*g_dx + g_dy*g_dy;
      const int sqr_mag_b = b_dx*b_dx + b_dy*b_dy;

      if (sqr_mag_r > sqr_mag_g && sqr_mag_r > sqr_mag_b)
      {
        GradientXY gradient;
        gradient.magnitude = sqrtf (static_cast<float> (sqr_mag_r));
        gradient.angle = atan2f (static_cast<float> (r_dy), static_cast<float> (r_dx)) * 180.0f / pi;
        if (gradient.angle < -180.0f) gradient.angle += 360.0f;
        if (gradient.angle >= 180.0f) gradient.angle -= 360.0f;
        gradient.x = static_cast<float> (col_index);
        gradient.y = static_cast<float> (row_index);

        color_gradients_ (col_index, row_index) = gradient;
      }
      else if (sqr_mag_g > sqr_mag_b)
      {
        GradientXY gradient;
        gradient.magnitude = sqrtf (static_cast<float> (sqr_mag_g));
        gradient.angle = atan2f (static_cast<float> (g_dy), static_cast<float> (g_dx)) * 180.0f / pi;
        if (gradient.angle < -180.0f) gradient.angle += 360.0f;
        if (gradient.angle >= 180.0f) gradient.angle -= 360.0f;
        gradient.x = static_cast<float> (col_index);
        gradient.y = static_cast<float> (row_index);

        color_gradients_ (col_index, row_index) = gradient;
      }
      else
      {
        GradientXY gradient;
        gradient.magnitude = sqrtf (static_cast<float> (sqr_mag_b));
        gradient.angle = atan2f (static_cast<float> (b_dy), static_cast<float> (b_dx)) * 180.0f / pi;
        if (gradient.angle < -180.0f) gradient.angle += 360.0f;
        if (gradient.angle >= 180.0f) gradient.angle -= 360.0f;
        gradient.x = static_cast<float> (col_index);
        gradient.y = static_cast<float> (row_index);

        color_gradients_ (col_index, row_index) = gradient;
      }

      assert (color_gradients_ (col_index, row_index).angle >= -180 &&
              color_gradients_ (col_index, row_index).angle <=  180);
    }
  }

  return;
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
quantizeColorGradients ()
{
  //std::cerr << "quantize this, bastard!!!" << std::endl;

  //unsigned char quantization_map[16] = {0,1,2,3,4,5,6,7,0,1,2,3,4,5,6,7};
  //unsigned char quantization_map[16] = {1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8};

  //for (float angle = 0.0f; angle < 360.0f; angle += 1.0f)
  //{
  //  const int quantized_value = quantization_map[static_cast<int> (angle * angleScale)];
  //  std::cerr << angle << ": " << quantized_value << std::endl;
  //}


  const size_t width = input_->width;
  const size_t height = input_->height;

  quantized_color_gradients_.resize (width, height);

  const float angleScale = 16.0f/360.0f;

  //float min_angle = std::numeric_limits<float>::max ();
  //float max_angle = -std::numeric_limits<float>::max ();
  for (size_t row_index = 0; row_index < height; ++row_index)
  {
    for (size_t col_index = 0; col_index < width; ++col_index)
    {
      if (color_gradients_ (col_index, row_index).magnitude < gradient_magnitude_threshold_) 
      {
        quantized_color_gradients_ (col_index, row_index) = 0;
        continue;
      }

      const float angle = 11.25f + color_gradients_ (col_index, row_index).angle + 180.0f;
      const int quantized_value = (static_cast<int> (angle * angleScale)) & 7;
      quantized_color_gradients_ (col_index, row_index) = static_cast<unsigned char> (quantized_value + 1); 

      //const float angle = color_gradients_ (col_index, row_index).angle + 180.0f;

      //min_angle = std::min (min_angle, angle);
      //max_angle = std::max (max_angle, angle);

      //if (angle < 0.0f || angle >= 360.0f)
      //{
      //  std::cerr << "angle shitty: " << angle << std::endl;
      //}

      //const int quantized_value = quantization_map[static_cast<int> (angle * angleScale)];
      //quantized_color_gradients_ (col_index, row_index) = static_cast<unsigned char> (quantized_value); 

      //assert (0 <= quantized_value && quantized_value < 16);
      //quantized_color_gradients_ (col_index, row_index) = quantization_map[quantized_value];
      //quantized_color_gradients_ (col_index, row_index) = static_cast<unsigned char> ((quantized_value & 7) + 1); // = (quantized_value % 8) + 1
    }
  }

  //std::cerr << ">>>>> " << min_angle << ", " << max_angle << std::endl;
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
filterQuantizedColorGradients ()
{
  const size_t width = input_->width;
  const size_t height = input_->height;

  filtered_quantized_color_gradients_.resize (width, height);

  // filter data
  for (size_t row_index = 1; row_index < height-1; ++row_index)
  {
    for (size_t col_index = 1; col_index < width-1; ++col_index)
    {
      unsigned char histogram[9] = {0,0,0,0,0,0,0,0,0};

      {
        const unsigned char * data_ptr = quantized_color_gradients_.getData () + (row_index-1)*width+col_index-1;
        assert (data_ptr[0] < 9 && data_ptr[1] < 9 && data_ptr[2] < 9);
        ++histogram[data_ptr[0]];
        ++histogram[data_ptr[1]];
        ++histogram[data_ptr[2]];
      }
      {
        const unsigned char * data_ptr = quantized_color_gradients_.getData () + row_index*width+col_index-1;
        assert (data_ptr[0] < 9 && data_ptr[1] < 9 && data_ptr[2] < 9);
        ++histogram[data_ptr[0]];
        ++histogram[data_ptr[1]];
        ++histogram[data_ptr[2]];
      }
      {
        const unsigned char * data_ptr = quantized_color_gradients_.getData () + (row_index+1)*width+col_index-1;
        assert (data_ptr[0] < 9 && data_ptr[1] < 9 && data_ptr[2] < 9);
        ++histogram[data_ptr[0]];
        ++histogram[data_ptr[1]];
        ++histogram[data_ptr[2]];
      }

      unsigned char max_hist_value = 0;
      int max_hist_index = -1;

      // for (int i = 0; i < 8; ++i)
      // {
      //   if (max_hist_value < histogram[i+1])
      //   {
      //     max_hist_index = i;
      //     max_hist_value = histogram[i+1]
      //   }
      // }
      // Unrolled for performance optimization:
      if (max_hist_value < histogram[1]) {max_hist_index = 0; max_hist_value = histogram[1];}
      if (max_hist_value < histogram[2]) {max_hist_index = 1; max_hist_value = histogram[2];}
      if (max_hist_value < histogram[3]) {max_hist_index = 2; max_hist_value = histogram[3];}
      if (max_hist_value < histogram[4]) {max_hist_index = 3; max_hist_value = histogram[4];}
      if (max_hist_value < histogram[5]) {max_hist_index = 4; max_hist_value = histogram[5];}
      if (max_hist_value < histogram[6]) {max_hist_index = 5; max_hist_value = histogram[6];}
      if (max_hist_value < histogram[7]) {max_hist_index = 6; max_hist_value = histogram[7];}
      if (max_hist_value < histogram[8]) {max_hist_index = 7; max_hist_value = histogram[8];}

      if (max_hist_index != -1 && max_hist_value >= 5)
        filtered_quantized_color_gradients_ (col_index, row_index) = static_cast<unsigned char> (0x1 << max_hist_index);
      else
        filtered_quantized_color_gradients_ (col_index, row_index) = 0;

    }
  }
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
erode (const pcl::MaskMap & mask_in, 
       pcl::MaskMap & mask_out)
{
  const size_t width = mask_in.getWidth ();
  const size_t height = mask_in.getHeight ();

  mask_out.resize (width, height);

  for (size_t row_index = 1; row_index < height-1; ++row_index)
  {
    for (size_t col_index = 1; col_index < width-1; ++col_index)
    {
      if (mask_in (col_index, row_index-1) == 0 ||
          mask_in (col_index-1, row_index) == 0 ||
          mask_in (col_index+1, row_index) == 0 ||
          mask_in (col_index, row_index+1) == 0)
      {
        mask_out (col_index, row_index) = 0;
      }
      else
      {
        mask_out (col_index, row_index) = 255;
      }
    }
  }
}

#endif 
