Use PointCloud::operator [] instead of PointCloud::points::operator []

Author: fran6co

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/global/cvfh_estimator.h

@@ -138,7 +138,7 @@ namespace pcl
         {
           pcl::PointCloud<FeatureT> vfh_signature (1);
           for (int d = 0; d < 308; ++d)
-            vfh_signature.points[0].histogram[d] = cvfh_signatures.points[i].histogram[d];
+            vfh_signature[0].histogram[d] = cvfh_signatures[i].histogram[d];
 
           signatures.push_back (vfh_signature);
         }

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/global/ourcvfh_estimator.h

@@ -170,7 +170,7 @@ namespace pcl
           {
             pcl::PointCloud<FeatureT> vfh_signature (1);
             for (int d = 0; d < 308; ++d)
-              vfh_signature.points[0].histogram[d] = cvfh_signatures.points[i].histogram[d];
+              vfh_signature[0].histogram[d] = cvfh_signatures[i].histogram[d];
 
             signatures.push_back (vfh_signature);
           }

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/colorshot_local_estimator.h

@@ -77,11 +77,11 @@ namespace pcl
           signatures->width = static_cast<int> (shots->size ());
           signatures->height = 1;
 
-          int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);
+          int size_feat = sizeof((*signatures)[0].histogram) / sizeof(float);
 
           for (size_t k = 0; k < shots->size (); k++)
             for (int i = 0; i < size_feat; i++)
-              signatures->points[k].histogram[i] = shots->points[k].descriptor[i];
+              (*signatures)[k].histogram[i] = (*shots)[k].descriptor[i];
 
           return true;
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/fpfh_local_estimator.h

@@ -80,7 +80,7 @@ namespace pcl
           int size_feat = 33;
           for (size_t k = 0; k < fpfhs->size (); k++)
             for (int i = 0; i < size_feat; i++)
-              signatures->points[k].histogram[i] = fpfhs->points[k].histogram[i];
+              (*signatures)[k].histogram[i] = (*fpfhs)[k].histogram[i];
 
           return true;
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/fpfh_local_estimator_omp.h

@@ -82,7 +82,7 @@ namespace pcl
           int size_feat = 33;
           for (size_t k = 0; k < fpfhs->size (); k++)
             for (int i = 0; i < size_feat; i++)
-              signatures->points[k].histogram[i] = fpfhs->points[k].histogram[i];
+              (*signatures)[k].histogram[i] = (*fpfhs)[k].histogram[i];
 
           return true;
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/local_estimator.h

@@ -126,7 +126,7 @@ namespace pcl
               if ((fabs (eigenValues[0] - eigenValues[1]) < 1.5e-4) || (eigsum != 0 && fabs (eigenValues[0] / eigsum) > 1.e-2))
               {
                 //region is not planar, add to filtered keypoint
-                keypoints_cloud->points[good] = keypoints_cloud->points[i];
+                (*keypoints_cloud)[good] = (*keypoints_cloud)[i];
                 good++;
               }
             }
@@ -227,8 +227,8 @@ namespace pcl
           typename pcl::PointCloud<pcl::PointNormal>::Ptr input_cloud (new pcl::PointCloud<pcl::PointNormal> (input_->width, input_->height));
           for (size_t i = 0; i < input_->size (); i++)
           {
-            input_cloud->points[i].getVector3fMap () = input_->points[i].getVector3fMap ();
-            input_cloud->points[i].getNormalVector3fMap () = normals_->points[i].getNormalVector3fMap ();
+            (*input_cloud)[i].getVector3fMap () = (*input_)[i].getVector3fMap ();
+            (*input_cloud)[i].getNormalVector3fMap () = (*normals_)[i].getNormalVector3fMap ();
           }
 
           typename pcl::PointCloud<pcl::PointXYZI>::Ptr intensity_keypoints (new pcl::PointCloud<pcl::PointXYZI>);
@@ -501,7 +501,7 @@ namespace pcl
          if ((fabs (eigenValues[0] - eigenValues[1]) < 1.5e-4) || (eigsum != 0 && fabs (eigenValues[0] / eigsum) > 1.e-2))
          {
          //region is not planar, add to filtered keypoint
-         keypoints_cloud.points[good] = keypoints_cloud.points[i];
+         keypoints_cloud[good] = keypoints_cloud[i];
          good++;
          }
          }

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/shot_local_estimator.h

@@ -124,11 +124,11 @@ namespace pcl
           signatures->width = static_cast<int> (shots->size ());
           signatures->height = 1;
 
-          int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);
+          int size_feat = sizeof((*signatures)[0].histogram) / sizeof(float);
 
           for (size_t k = 0; k < shots->size (); k++)
             for (int i = 0; i < size_feat; i++)
-              signatures->points[k].histogram[i] = shots->points[k].descriptor[i];
+              (*signatures)[k].histogram[i] = (*shots)[k].descriptor[i];
 
           return true;
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/shot_local_estimator_omp.h

@@ -118,7 +118,7 @@ namespace pcl
           signatures->width = static_cast<int> (shots->size ());
           signatures->height = 1;
 
-          int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);
+          int size_feat = sizeof((*signatures)[0].histogram) / sizeof(float);
 
           int good = 0;
           for (size_t k = 0; k < shots->size (); k++)
@@ -127,15 +127,15 @@ namespace pcl
             int NaNs = 0;
             for (int i = 0; i < size_feat; i++)
             {
-              if (!pcl_isfinite(shots->points[k].descriptor[i]))
+              if (!pcl_isfinite((*shots)[k].descriptor[i]))
                 NaNs++;
             }
 
             if (NaNs == 0)
             {
               for (int i = 0; i < size_feat; i++)
               {
-                signatures->points[good].histogram[i] = shots->points[k].descriptor[i];
+                (*signatures)[good].histogram[i] = (*shots)[k].descriptor[i];
               }
 
               good++;

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/normal_estimator.h

@@ -40,7 +40,7 @@ namespace pcl
           // Iterate through the source data set
           for (size_t i = 0; i < input->size (); ++i)
           {
-            tree->nearestKSearch (input->points[i], 9, nn_indices, nn_distances);
+            tree->nearestKSearch ((*input)[i], 9, nn_indices, nn_distances);
 
             float avg_dist_neighbours = 0.0;
             for (size_t j = 1; j < nn_indices.size (); j++)
@@ -207,10 +207,10 @@ namespace pcl
               int j = 0;
               for (size_t i = 0; i < out->size (); ++i)
               {
-                if (!pcl_isfinite (out->points[i].x) || !pcl_isfinite (out->points[i].y) || !pcl_isfinite (out->points[i].z))
+                if (!pcl_isfinite ((*out)[i].x) || !pcl_isfinite ((*out)[i].y) || !pcl_isfinite ((*out)[i].z))
                   continue;
 
-                out->points[j] = out->points[i];
+                (*out)[j] = (*out)[i];
                 j++;
               }
 
@@ -242,12 +242,12 @@ namespace pcl
             int j = 0;
             for (size_t i = 0; i < normals->size (); ++i)
             {
-              if (!pcl_isfinite (normals->points[i].normal_x) || !pcl_isfinite (normals->points[i].normal_y)
-                  || !pcl_isfinite (normals->points[i].normal_z))
+              if (!pcl_isfinite ((*normals)[i].normal_x) || !pcl_isfinite ((*normals)[i].normal_y)
+                  || !pcl_isfinite ((*normals)[i].normal_z))
                 continue;
 
-              normals->points[j] = normals->points[i];
-              out->points[j] = out->points[i];
+              (*normals)[j] = (*normals)[i];
+              (*out)[j] = (*out)[i];
               j++;
             }
 
@@ -261,13 +261,13 @@ namespace pcl
             bool NaNs = false;
             for (size_t i = 0; i < normals->size (); ++i)
             {
-              if (pcl_isfinite (normals->points[i].normal_x) && pcl_isfinite (normals->points[i].normal_y)
-                  && pcl_isfinite (normals->points[i].normal_z))
+              if (pcl_isfinite ((*normals)[i].normal_x) && pcl_isfinite ((*normals)[i].normal_y)
+                  && pcl_isfinite ((*normals)[i].normal_z))
                 continue;
 
               NaNs = true;
 
-              out->points[i].x = out->points[i].y = out->points[i].z = std::numeric_limits<float>::quiet_NaN ();
+              (*out)[i].x = (*out)[i].y = (*out)[i].z = std::numeric_limits<float>::quiet_NaN ();
             }
 
             if (NaNs)
@@ -280,9 +280,9 @@ namespace pcl
           /*for (size_t i = 0; i < out->size (); i++)
           {
             int r, g, b;
-            r = static_cast<int> (out->points[i].r);
-            g = static_cast<int> (out->points[i].g);
-            b = static_cast<int> (out->points[i].b);
+            r = static_cast<int> ((*out)[i].r);
+            g = static_cast<int> ((*out)[i].g);
+            b = static_cast<int> ((*out)[i].b);
             std::cout << "in normal estimator:" << r << " " << g << " " << b << std::endl;
           }*/
         }

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/pipeline/impl/global_nn_classifier.hpp

@@ -40,9 +40,9 @@ template<template<class > class Distance, typename PointInT, typename FeatureT>
 
           flann_model descr_model;
           descr_model.first = models->at (i);
-          int size_feat = sizeof(signature->points[0].histogram) / sizeof(float);
+          int size_feat = sizeof((*signature)[0].histogram) / sizeof(float);
           descr_model.second.resize (size_feat);
-          memcpy (&descr_model.second[0], &signature->points[0].histogram[0], size_feat * sizeof(float));
+          memcpy (&descr_model.second[0], &(*signature)[0].histogram[0], size_feat * sizeof(float));
 
           flann_models_.push_back (descr_model);
         }
@@ -101,8 +101,8 @@ template<template<class > class Distance, typename PointInT, typename FeatureT>
     {
       for (size_t idx = 0; idx < signatures.size (); idx++)
       {
-        float* hist = signatures[idx].points[0].histogram;
-        int size_feat = sizeof(signatures[idx].points[0].histogram) / sizeof(float);
+        float* hist = signatures[idx][0].histogram;
+        int size_feat = sizeof(signatures[idx][0].histogram) / sizeof(float);
         std::vector<float> std_hist (hist, hist + size_feat);
         ModelT empty;