sgf6.h
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00001 /*
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00037 
00038 #ifndef PCL_FEATURES_SGF6_H_
00039 #define PCL_FEATURES_SGF6_H_
00040 
00041 #include <pcl17/features/feature.h>
00042 
00043 namespace pcl17
00044 {
00045   const int SGF6_SIZE = 2;
00046 
00047   template <typename PointInT, typename PointOutT>
00048   class SGF6Estimation : 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       SGF6Estimation ()
00063       {
00064         feature_name_ = "SGF6Estimation";
00065         k_ = 1;
00066         axis1_ << 0, 0, 1;
00067         axis2_ << 1, 0, 0;
00068       };
00069 
00070       void
00071       setAxis1(Eigen::Vector3f axis)
00072       {
00073         axis1_[0] = axis[0];
00074         axis1_[1] = axis[1];
00075         axis1_[2] = axis[2];
00076       }
00077 
00078       void
00079       setAxis2(Eigen::Vector3f axis)
00080       {
00081         axis2_[0] = axis[0];
00082         axis2_[1] = axis[1];
00083         axis2_[2] = axis[2];
00084       }
00085 
00086 
00088       void
00089       computeFeature (PointCloudOut &output)
00090       {
00091         // Copy the points specified by the index vector into a new cloud
00092         typename PointCloud<PointInT>::Ptr cloud (new PointCloud<PointInT> ());
00093         cloud->width = indices_->size ();
00094         cloud->height = 1;
00095         cloud->points.resize (cloud->width * cloud->height);
00096         for (size_t idx = 0; idx < indices_->size (); ++idx)
00097         {
00098           cloud->points[idx] = input_->points[(*indices_)[idx]];
00099         }
00100 
00101 
00102         // Compute eigenvectors and eigenvalues
00103         EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
00104         Eigen::Vector4f centroid3;
00105         compute3DCentroid (*cloud, centroid3);
00106         computeCovarianceMatrix (*cloud, centroid3, covariance_matrix);
00107         EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
00108         EIGEN_ALIGN16 Eigen::Matrix3f eigen_vectors;
00109         pcl17::eigen33 (covariance_matrix, eigen_vectors, eigen_values);
00110 
00111 
00112         // The eigenvector corresponding to the smallest eigenvalue
00113         Eigen::Vector3f e (eigen_vectors (0, 0), eigen_vectors (1, 0), eigen_vectors (2, 0));
00114 
00115 
00116         // Compute the feature vector
00117         output.points[0].histogram[0] = acos (e.dot(axis1_) / axis1_.norm ());
00118         output.points[0].histogram[1] = acos (e.dot(axis2_) / axis1_.norm ());
00119       }
00121 
00122 
00123     private:
00124 
00125       Eigen::Vector3f axis1_;
00126       Eigen::Vector3f axis2_;
00127 
00131       void
00132       computeFeatureEigen (pcl17::PointCloud<Eigen::MatrixXf> &) {}
00133   };
00134 }
00135 
00136 #endif  //#ifndef PCL_FEATURES_SGF6_H_
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furniture_features
Author(s): Stefan
autogenerated on Thu May 23 2013 16:09:52