Replace '.points[' with just '[' (#4219)

* Reuse code, add exception in cuda

CHANGES.md

@@ -3062,7 +3062,7 @@ pcl::OrganizedDataIndex -> pcl::search::OrganizedNeighbor
 * added new templated methods for `nearestKSearch` and `radiusSearch` for situations when PointT is different than the one the KdTree object was created with (e.g., KdTree<PointT1> vs nearestKSearch (PointT2 &p...)
 * added two new methods for `getApproximateIndices` where given a reference cloud of point type T1 we're trying to find the corresponding indices in a different cloud of point type T2
 * refactorized a lot of code in search and octree to make it look more consistent with the rest of the API
-* fixed a bug in octree_search which was semantically doing something bad: for each `radiusSearch`/`nearestKSearch`/`approxNearestSearch` call with a PointCloudConstPtr, the octree was getting recreated. Changed the API to be consistent with the rest of PCL (including pcl_search and pcl_kdtree) where we pass in a PointCloud instead of a PointCloudConstPtr which simply calls searchMethod (cloud.points[i], ...)
+* fixed a bug in octree_search which was semantically doing something bad: for each `radiusSearch`/`nearestKSearch`/`approxNearestSearch` call with a PointCloudConstPtr, the octree was getting recreated. Changed the API to be consistent with the rest of PCL (including pcl_search and pcl_kdtree) where we pass in a PointCloud instead of a PointCloudConstPtr which simply calls searchMethod (cloud[i], ...)
 * minor code optimizations
 * renamed organized_neighbor.h header in pcl_search (unreleased, therefore API changes OK!) to organized.h
 * disabled the auto-search tuning as it wasn't really working. must clean this up further
@@ -3117,7 +3117,7 @@ pcl::OrganizedDataIndex -> pcl::search::OrganizedNeighbor
 * added unit test for `getIntersectedVoxelCentersRecursive`
 * added method `getIntersectedVoxelIndices` for getting indices of intersected voxels and updated unit test
 * refactorized a lot of code in search and octree to make it look more consistent with the rest of the API
-* fixed a bug in octree_search which was semantically doing something bad: for each `radiusSearch`/`nearestKSearch`/`approxNearestSearch` call with a PointCloudConstPtr, the octree was getting recreated. Changed the API to be consistent with the rest of PCL (including pcl_search and pcl_kdtree) where we pass in a PointCloud instead of a PointCloudConstPtr which simply calls searchMethod (cloud.points[i], ...)
+* fixed a bug in octree_search which was semantically doing something bad: for each `radiusSearch`/`nearestKSearch`/`approxNearestSearch` call with a PointCloudConstPtr, the octree was getting recreated. Changed the API to be consistent with the rest of PCL (including pcl_search and pcl_kdtree) where we pass in a PointCloud instead of a PointCloudConstPtr which simply calls searchMethod (cloud[i], ...)
 * minor code optimizations
 * renamed organized_neighbor.h header in pcl_search (unreleased, therefore API changes OK!) to organized.h
 * disabled the auto-search tuning as it wasn't really working. must clean this up further

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

@@ -136,7 +136,7 @@ class CVFHEstimation : public GlobalEstimator<PointInT, FeatureT> {
       vfh_signature.points.resize(1);
       vfh_signature.width = vfh_signature.height = 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

@@ -165,7 +165,7 @@ class OURCVFHEstimator : public GlobalEstimator<PointInT, FeatureT> {
       vfh_signature.points.resize(1);
       vfh_signature.width = vfh_signature.height = 1;
       for (int d = 0; d < 308; ++d)
-        vfh_signature.points[0].histogram[d] = point.histogram[d];
+        vfh_signature[0].histogram[d] = point.histogram[d];
 
       signatures.push_back(vfh_signature);
     }

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

@@ -97,8 +97,8 @@ pcl::rec_3d_framework::GlobalNNPipeline<Distance, PointInT, FeatureT>::classify(
 
   if (!signatures.empty()) {
     for (std::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;
 

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

@@ -197,8 +197,8 @@ pcl::rec_3d_framework::GlobalNNCRHRecognizer<Distance, PointInT, FeatureT>::reco
       pcl::ScopeTime t_matching("Matching and roll...");
       for (std::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;
 

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

@@ -253,8 +253,8 @@ pcl::rec_3d_framework::GlobalNNCVFHRecognizer<Distance, PointInT, FeatureT>::rec
         for (std::size_t c = 0; c < categories_to_be_searched_.size(); c++) {
           std::cout << "Using category:" << categories_to_be_searched_[c] << std::endl;
           for (std::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);
 
             flann_model histogram;
@@ -293,8 +293,8 @@ pcl::rec_3d_framework::GlobalNNCVFHRecognizer<Distance, PointInT, FeatureT>::rec
       else {
         for (std::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);
 
           flann_model histogram;

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/utils/vtk_model_sampling.h

@@ -114,9 +114,9 @@ uniform_sampling(const vtkSmartPointer<vtkPolyData>& polydata,
   for (i = 0; i < n_samples; i++) {
     Eigen::Vector4f p(0.f, 0.f, 0.f, 0.f);
     randPSurface(polydata, &cumulativeAreas, totalArea, p);
-    cloud_out.points[i].x = static_cast<float>(p[0]);
-    cloud_out.points[i].y = static_cast<float>(p[1]);
-    cloud_out.points[i].z = static_cast<float>(p[2]);
+    cloud_out[i].x = static_cast<float>(p[0]);
+    cloud_out[i].y = static_cast<float>(p[1]);
+    cloud_out[i].z = static_cast<float>(p[2]);
   }
 }
 
@@ -167,9 +167,9 @@ getVerticesAsPointCloud(const vtkSmartPointer<vtkPolyData>& polydata,
   for (vtkIdType i = 0; i < points->GetNumberOfPoints(); i++) {
     double p[3];
     points->GetPoint(i, p);
-    cloud_out.points[i].x = static_cast<float>(p[0]);
-    cloud_out.points[i].y = static_cast<float>(p[1]);
-    cloud_out.points[i].z = static_cast<float>(p[2]);
+    cloud_out[i].x = static_cast<float>(p[0]);
+    cloud_out[i].y = static_cast<float>(p[1]);
+    cloud_out[i].z = static_cast<float>(p[2]);
   }
 }
 

apps/modeler/src/normal_estimation_worker.cpp

@@ -131,8 +131,8 @@ pcl::modeler::NormalEstimationWorker::processImpl(CloudMeshItem* cloud_mesh_item
 
   for (std::size_t i = 0, i_end = indices->size(); i < i_end; ++i) {
     std::size_t dest = (*indices)[i];
-    cloud->points[dest].normal_x = normals.points[i].normal_x;
-    cloud->points[dest].normal_y = normals.points[i].normal_y;
-    cloud->points[dest].normal_z = normals.points[i].normal_z;
+    cloud->points[dest].normal_x = normals[i].normal_x;
+    cloud->points[dest].normal_y = normals[i].normal_y;
+    cloud->points[dest].normal_z = normals[i].normal_z;
   }
 }

apps/point_cloud_editor/src/cloud.cpp

@@ -227,7 +227,7 @@ Cloud::drawWithRGB () const
 {
   glEnableClientState(GL_COLOR_ARRAY);
   glColorPointer(3, GL_UNSIGNED_BYTE, sizeof(Point3D),
-                 &(cloud_.points[0].b));
+                 &(cloud_[0].b));
   draw();
 }
 
@@ -267,7 +267,7 @@ Cloud::draw (bool disable_highlight) const
         glTranslatef(-center_xyz_[0], -center_xyz_[1], -center_xyz_[2]);
 
         glEnableClientState(GL_VERTEX_ARRAY);
-        glVertexPointer(3, GL_FLOAT, sizeof(Point3D), &(cloud_.points[0].x));
+        glVertexPointer(3, GL_FLOAT, sizeof(Point3D), &(cloud_[0].x));
 
         if (disable_highlight || (!selection_ptr) || selection_ptr->empty())
         {
@@ -328,7 +328,7 @@ Cloud::remove(const Selection& selection)
 {
   unsigned int pos = cloud_.size();
   for (auto rit = selection.rbegin(); rit != selection.rend(); ++rit)
-    std::swap(cloud_.points[--pos], cloud_.points[*rit]);
+    std::swap(cloud_[--pos], cloud_[*rit]);
   resize(cloud_.size()-selection.size());
 }
 
@@ -439,7 +439,7 @@ Cloud::restore (const CopyBuffer& copy_buffer, const Selection& selection)
   append(copied_cloud);
   unsigned int pos = cloud_.size();
   for (auto rit = selection.rbegin(); rit != selection.rend(); ++rit)
-    std::swap(cloud_.points[--pos], cloud_.points[*rit]);
+    std::swap(cloud_[--pos], cloud_[*rit]);
 }
 
 std::string
@@ -459,15 +459,15 @@ Cloud::updateCloudMembers ()
 
   std::fill_n(min_xyz_, XYZ_SIZE, 0.0f);
   std::fill_n(max_xyz_, XYZ_SIZE, 0.0f);
-  float *pt = &(cloud_.points[0].data[X]);
+  float *pt = &(cloud_[0].data[X]);
   std::copy(pt, pt+XYZ_SIZE, max_xyz_);
   std::copy(max_xyz_, max_xyz_+XYZ_SIZE, min_xyz_);
   for (std::size_t i = 1; i < cloud_.size(); ++i)
   {
     for (unsigned int j = 0; j < XYZ_SIZE; ++j)
     {
-      min_xyz_[j] = std::min(min_xyz_[j], cloud_.points[i].data[j]);
-      max_xyz_[j] = std::max(max_xyz_[j], cloud_.points[i].data[j]);
+      min_xyz_[j] = std::min(min_xyz_[j], cloud_[i].data[j]);
+      max_xyz_[j] = std::max(max_xyz_[j], cloud_[i].data[j]);
     }
   }
   float range = 0.0f;
@@ -493,7 +493,7 @@ Cloud::enableTexture () const
   glEnable(GL_TEXTURE_1D);
   glEnableClientState(GL_TEXTURE_COORD_ARRAY);
   glTexCoordPointer(1, GL_FLOAT, sizeof(Point3D),
-                    &(cloud_.points[0].data[color_ramp_axis_]));
+                    &(cloud_[0].data[color_ramp_axis_]));
   glMatrixMode(GL_TEXTURE);
   glPushMatrix();
   glLoadIdentity();

apps/src/ni_agast.cpp

@@ -235,9 +235,9 @@ class AGASTDemo {
       if (!std::isfinite(pt.x) || !std::isfinite(pt.y) || !std::isfinite(pt.z))
         continue;
 
-      keypoints3d.points[j].x = pt.x;
-      keypoints3d.points[j].y = pt.y;
-      keypoints3d.points[j].z = pt.z;
+      keypoints3d[j].x = pt.x;
+      keypoints3d[j].y = pt.y;
+      keypoints3d[j].z = pt.z;
       ++j;
     }
 

apps/src/ni_brisk.cpp

@@ -185,9 +185,9 @@ class BRISKDemo {
       PointT pt =
           bilinearInterpolation(cloud, keypoints->points[i].x, keypoints->points[i].y);
 
-      keypoints3d.points[j].x = pt.x;
-      keypoints3d.points[j].y = pt.y;
-      keypoints3d.points[j].z = pt.z;
+      keypoints3d[j].x = pt.x;
+      keypoints3d[j].y = pt.y;
+      keypoints3d[j].z = pt.z;
       ++j;
     }
 

apps/src/organized_segmentation_demo.cpp

@@ -81,15 +81,15 @@ displayCurvature(pcl::PointCloud<PointT>& cloud,
 {
   pcl::PointCloud<pcl::PointXYZRGBA> curvature_cloud = cloud;
   for (std::size_t i = 0; i < cloud.points.size(); i++) {
-    if (normals.points[i].curvature < 0.04) {
-      curvature_cloud.points[i].r = 0;
-      curvature_cloud.points[i].g = 255;
-      curvature_cloud.points[i].b = 0;
+    if (normals[i].curvature < 0.04) {
+      curvature_cloud[i].r = 0;
+      curvature_cloud[i].g = 255;
+      curvature_cloud[i].b = 0;
     }
     else {
-      curvature_cloud.points[i].r = 255;
-      curvature_cloud.points[i].g = 0;
-      curvature_cloud.points[i].b = 0;
+      curvature_cloud[i].r = 255;
+      curvature_cloud[i].g = 0;
+      curvature_cloud[i].b = 0;
     }
   }
 
@@ -105,14 +105,14 @@ displayDistanceMap(pcl::PointCloud<PointT>& cloud,
   pcl::PointCloud<pcl::PointXYZRGBA> distance_map_cloud = cloud;
   for (std::size_t i = 0; i < cloud.points.size(); i++) {
     if (distance_map[i] < 5.0) {
-      distance_map_cloud.points[i].r = 255;
-      distance_map_cloud.points[i].g = 0;
-      distance_map_cloud.points[i].b = 0;
+      distance_map_cloud[i].r = 255;
+      distance_map_cloud[i].g = 0;
+      distance_map_cloud[i].b = 0;
     }
     else {
-      distance_map_cloud.points[i].r = 0;
-      distance_map_cloud.points[i].g = 255;
-      distance_map_cloud.points[i].b = 0;
+      distance_map_cloud[i].r = 0;
+      distance_map_cloud[i].g = 255;
+      distance_map_cloud[i].b = 0;
     }
   }
 

common/include/pcl/common/distances.h

@@ -112,8 +112,8 @@ namespace pcl
       for (std::size_t j = i; j < cloud.points.size (); ++j)
       {
         // Compute the distance 
-        double dist = (cloud.points[i].getVector4fMap () - 
-                       cloud.points[j].getVector4fMap ()).squaredNorm ();
+        double dist = (cloud[i].getVector4fMap () - 
+                       cloud[j].getVector4fMap ()).squaredNorm ();
         if (dist <= max_dist)
           continue;
 
@@ -126,8 +126,8 @@ namespace pcl
     if (i_min == token || i_max == token)
       return (max_dist = std::numeric_limits<double>::min ());
 
-    pmin = cloud.points[i_min];
-    pmax = cloud.points[i_max];
+    pmin = cloud[i_min];
+    pmax = cloud[i_max];
     return (std::sqrt (max_dist));
   }
 
@@ -152,8 +152,8 @@ namespace pcl
       for (std::size_t j = i; j < indices.size (); ++j)
       {
         // Compute the distance 
-        double dist = (cloud.points[indices[i]].getVector4fMap () - 
-                       cloud.points[indices[j]].getVector4fMap ()).squaredNorm ();
+        double dist = (cloud[indices[i]].getVector4fMap () - 
+                       cloud[indices[j]].getVector4fMap ()).squaredNorm ();
         if (dist <= max_dist)
           continue;
 
@@ -166,8 +166,8 @@ namespace pcl
     if (i_min == token || i_max == token)
       return (max_dist = std::numeric_limits<double>::min ());
 
-    pmin = cloud.points[indices[i_min]];
-    pmax = cloud.points[indices[i_max]];
+    pmin = cloud[indices[i_min]];
+    pmax = cloud[indices[i_max]];
     return (std::sqrt (max_dist));
   }
 

common/include/pcl/common/impl/centroid.hpp

@@ -759,7 +759,7 @@ demeanPointCloud (const pcl::PointCloud<PointT> &cloud_in,
     cloud_out (1, i) = cloud_in[i].y - centroid[1];
     cloud_out (2, i) = cloud_in[i].z - centroid[2];
     // One column at a time
-    //cloud_out.block<4, 1> (0, i) = cloud_in.points[i].getVector4fMap () - centroid;
+    //cloud_out.block<4, 1> (0, i) = cloud_in[i].getVector4fMap () - centroid;
   }
 
   // Make sure we zero the 4th dimension out (1 row, N columns)
@@ -783,7 +783,7 @@ demeanPointCloud (const pcl::PointCloud<PointT> &cloud_in,
     cloud_out (1, i) = cloud_in[indices[i]].y - centroid[1];
     cloud_out (2, i) = cloud_in[indices[i]].z - centroid[2];
     // One column at a time
-    //cloud_out.block<4, 1> (0, i) = cloud_in.points[indices[i]].getVector4fMap () - centroid;
+    //cloud_out.block<4, 1> (0, i) = cloud_in[indices[i]].getVector4fMap () - centroid;
   }
 
   // Make sure we zero the 4th dimension out (1 row, N columns)