sgf4.h
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00001 /*
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00037 
00038 #ifndef PCL_FEATURES_SGF4_H_
00039 #define PCL_FEATURES_SGF4_H_
00040 
00041 #include <pcl17/features/feature.h>
00042 
00043 namespace pcl17
00044 {
00045   const int SGF4_SIZE = 6;
00046 
00047   template <typename PointInT, typename PointOutT>
00048   class SGF4Estimation : public Feature<PointInT, PointOutT>
00049   {
00050 
00051     public:
00052 
00053       using Feature<PointInT, PointOutT>::feature_name_;
00054       using Feature<PointInT, PointOutT>::input_;
00055       using Feature<PointInT, PointOutT>::indices_;
00056       using Feature<PointInT, PointOutT>::k_;
00057 
00058       typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;
00059       typedef typename Feature<PointInT, PointOutT>::PointCloudIn  PointCloudIn;
00060 
00062       SGF4Estimation ()
00063       {
00064         feature_name_ = "SGF4Estimation";
00065         k_ = 1;
00066       };
00067 
00068 
00070       void
00071       computeFeature (PointCloudOut &output)
00072       {
00073         // Copy the points specified by the index vector into a new cloud
00074         typename PointCloud<PointInT>::Ptr cloud (new PointCloud<PointInT> ());
00075         cloud->width = indices_->size ();
00076         cloud->height = 1;
00077         cloud->points.resize (cloud->width * cloud->height);
00078         for (size_t idx = 0; idx < indices_->size (); ++idx)
00079         {
00080           cloud->points[idx] = input_->points[(*indices_)[idx]];
00081         }
00082 
00083 
00084         // Compute eigenvectors and eigenvalues
00085         EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
00086         Eigen::Vector4f centroid3;
00087         compute3DCentroid (*cloud, centroid3);
00088         computeCovarianceMatrix (*cloud, centroid3, covariance_matrix);
00089         EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
00090         EIGEN_ALIGN16 Eigen::Matrix3f eigen_vectors;
00091         pcl17::eigen33 (covariance_matrix, eigen_vectors, eigen_values);
00092 
00093 
00094         // Compute the feature vector
00095         float eigen_sum = eigen_values.sum ();
00096         output.points[0].histogram[0] = eigen_values[1] != 0 ? eigen_values[0] / eigen_values[1] : 0;
00097         output.points[0].histogram[1] = eigen_values[2] != 0 ? eigen_values[1] / eigen_values[2] : 0;
00098         output.points[0].histogram[2] = eigen_values[2] != 0 ? eigen_values[0] / eigen_values[2] : 0;
00099         output.points[0].histogram[3] = eigen_sum != 0 ? eigen_values[0] / eigen_sum : 0;
00100         output.points[0].histogram[4] = eigen_sum != 0 ? eigen_values[1] / eigen_sum : 0;
00101         output.points[0].histogram[5] = eigen_sum != 0 ? eigen_values[2] / eigen_sum : 0;
00102       }
00104 
00105 
00106     private:
00107 
00111       void
00112       computeFeatureEigen (pcl17::PointCloud<Eigen::MatrixXf> &) {}
00113   };
00114 }
00115 
00116 #endif  //#ifndef PCL_FEATURES_SGF4_H_
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furniture_features
Author(s): Stefan
autogenerated on Sun Oct 6 2013 11:58:55