integral_image_normal.h

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#ifndef PCL_INTEGRALIMAGE_BASED_NORMAL_ESTIMATOR_H_
#define PCL_INTEGRALIMAGE_BASED_NORMAL_ESTIMATOR_H_

#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include "pcl/features/feature.h"
#include "pcl/features/integral_image2D.h"

namespace pcl
{
  template <typename PointInT, typename PointOutT>
  class IntegralImageNormalEstimation: public Feature<PointInT, PointOutT>
  {
    using Feature<PointInT, PointOutT>::input_;
    using Feature<PointInT, PointOutT>::feature_name_;
    using Feature<PointInT, PointOutT>::tree_;
    using Feature<PointInT, PointOutT>::k_;

    public:

      enum NormalEstimationMethod
      {
        COVARIANCE_MATRIX,
        AVERAGE_3D_GRADIENT,
        AVERAGE_DEPTH_CHANGE
      };

      typedef typename Feature<PointInT, PointOutT>::PointCloudIn  PointCloudIn;
      typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;

      IntegralImageNormalEstimation ()
      : normal_estimation_method_(AVERAGE_3D_GRADIENT)
      , integral_image_X_(false)
      , integral_image_Y_(false)
      , integral_image_depth_ (true)
      , integral_image_XYZ_ (true)
      , diff_x_(NULL)
      , diff_y_(NULL)
      , depth_data_(NULL)
      , use_depth_dependent_smoothing_(false)
      , max_depth_change_factor_(20.0f*0.001f)
      , normal_smoothing_size_(10.0f)
      , init_covariance_matrix_(false)
      , init_average_3d_gradient_(false)
      , init_depth_change_(false)
      {
        feature_name_ = "IntegralImagesNormalEstimation";
        tree_.reset ();
        k_ = 1;
      }


      virtual ~IntegralImageNormalEstimation ();

      void
      setRectSize (const int width, const int height);

      void
      computePointNormal (const int pos_x, const int pos_y, PointOutT &normal);

      void
      setMaxDepthChangeFactor (float max_depth_change_factor)
      {
        max_depth_change_factor_ = max_depth_change_factor;
      }

      void
      setNormalSmoothingSize (float normal_smoothing_size)
      {
        normal_smoothing_size_ = normal_smoothing_size;
      }

      void
      setNormalEstimationMethod (NormalEstimationMethod normal_estimation_method)
      {
        normal_estimation_method_ = normal_estimation_method;
      }

      void
      setDepthDependentSmoothing (bool use_depth_dependent_smoothing)
      {
        use_depth_dependent_smoothing_ = use_depth_dependent_smoothing;
      }

      virtual inline void
      setInputCloud (const typename PointCloudIn::ConstPtr &cloud)
      {
        input_ = cloud;

        init_covariance_matrix_ = init_average_3d_gradient_ = init_depth_change_ = false;

        // Initialize the correct data structure based on the normal estimation method chosen
        initData ();
      }

    protected:

      void
      computeFeature (PointCloudOut &output);

      void
      initData ();

    private:
      NormalEstimationMethod normal_estimation_method_;

      int rect_width_;
      int rect_height_;

      float distance_threshold_;

      IntegralImage2D<float, 3> integral_image_X_;
      IntegralImage2D<float, 3> integral_image_Y_;
      IntegralImage2D<float, 1> integral_image_depth_;
      IntegralImage2D<float, 3> integral_image_XYZ_;

      float *diff_x_;
      float *diff_y_;

      float *depth_data_;

      bool use_depth_dependent_smoothing_;

      float max_depth_change_factor_;

      float normal_smoothing_size_;

      bool init_covariance_matrix_;

      bool init_average_3d_gradient_;

      bool init_depth_change_;

      void
      initCovarianceMatrixMethod ();

      void
      initAverage3DGradientMethod ();

      void
      initAverageDepthChangeMethod ();

    private:
      void
      computeFeature (pcl::PointCloud<Eigen::MatrixXf> &output) {}
  };
}

#endif