Transform classic loops to range-based for loops in module segmentati…

…on (#2859)

Changes are based on the result of run-clang-tidy -header-filter='.*' -checks='-*,modernize-loop-convert' -fix
Use always const reference in for-ranged loop instead of copying primitive data types

segmentation/include/pcl/segmentation/impl/cpc_segmentation.hpp

@@ -136,24 +136,24 @@ pcl::CPCSegmentation<PointT>::applyCuttingPlane (uint32_t depth_levels_left)
   }
   bool cut_found = false;
   // do the following processing for each segment separately
-  for (SegLabel2ClusterMap::iterator itr = seg_to_edge_points_map.begin (); itr != seg_to_edge_points_map.end (); ++itr)
+  for (const auto &seg_to_edge_points : seg_to_edge_points_map)
   {
     // if too small do not process
-    if (itr->second->size () < min_segment_size_for_cutting_)
+    if (seg_to_edge_points.second->size () < min_segment_size_for_cutting_)
     {
       continue;
     }
 
     std::vector<double> weights;
-    weights.resize (itr->second->size ());
-    for (std::size_t cp = 0; cp < itr->second->size (); ++cp)
+    weights.resize (seg_to_edge_points.second->size ());
+    for (std::size_t cp = 0; cp < seg_to_edge_points.second->size (); ++cp)
     {
-      float& cur_weight = itr->second->points[cp].intensity;
+      float& cur_weight = seg_to_edge_points.second->points[cp].intensity;
       cur_weight = cur_weight < concavity_tolerance_threshold_ ? 0 : 1;
       weights[cp] = cur_weight;
     }
 
-    pcl::PointCloud<WeightSACPointType>::Ptr edge_cloud_cluster  = itr->second;
+    pcl::PointCloud<WeightSACPointType>::Ptr edge_cloud_cluster  = seg_to_edge_points.second;
     pcl::SampleConsensusModelPlane<WeightSACPointType>::Ptr model_p (new pcl::SampleConsensusModelPlane<WeightSACPointType> (edge_cloud_cluster));
 
     WeightedRandomSampleConsensus weight_sac (model_p, seed_resolution_, true);
@@ -195,32 +195,29 @@ pcl::CPCSegmentation<PointT>::applyCuttingPlane (uint32_t depth_levels_left)
       euclidean_clusterer.setInputCloud (edge_cloud_cluster);
       euclidean_clusterer.setIndices (boost::make_shared <std::vector <int> > (support_indices));
       euclidean_clusterer.extract (cluster_indices);
-//       sv_adjacency_list_[seg_to_edgeID_map[itr->first][point_index]].used_for_cutting = true;
+//       sv_adjacency_list_[seg_to_edgeID_map[seg_to_edge_points.first][point_index]].used_for_cutting = true;
 
-      for (size_t cc = 0; cc < cluster_indices.size (); ++cc)
+      for (const auto &cluster_index : cluster_indices)
       {
         // get centroids of vertices        
         int cluster_concave_pts = 0;
         float cluster_score = 0;
 //         std::cout << "Cluster has " << cluster_indices[cc].indices.size () << " points" << std::endl;
-        for (size_t cp = 0; cp < cluster_indices[cc].indices.size (); ++cp)
+        for (const int &current_index : cluster_index.indices)
         {
-          int current_index = cluster_indices[cc].indices[cp];
-          double index_score;
+          double index_score = weights[current_index];
           if (use_directed_weights_)
-            index_score = weights[current_index] * 1.414 * (fabsf (plane_normal.dot (edge_cloud_cluster->at (current_index).getNormalVector3fMap ())));
-          else
-            index_score = weights[current_index];
+            index_score *= 1.414 * (fabsf (plane_normal.dot (edge_cloud_cluster->at (current_index).getNormalVector3fMap ())));
           cluster_score += index_score;
           if (weights[current_index] > 0)
             ++cluster_concave_pts;
         }
         // check if the score is below the threshold. If that is the case this segment should not be split
-        cluster_score = cluster_score * 1.0 / cluster_indices[cc].indices.size ();
+        cluster_score = cluster_score * 1.0 / cluster_index.indices.size ();
 //         std::cout << "Cluster score: " << cluster_score << std::endl;
         if (cluster_score >= min_cut_score_)
         {
-          cut_support_indices.insert (cut_support_indices.end (), cluster_indices[cc].indices.begin (), cluster_indices[cc].indices.end ());
+          cut_support_indices.insert (cut_support_indices.end (), cluster_index.indices.begin (), cluster_index.indices.end ());
         }
       }
       if (cut_support_indices.size () == 0)
@@ -242,15 +239,13 @@ pcl::CPCSegmentation<PointT>::applyCuttingPlane (uint32_t depth_levels_left)
     }
 
     int number_connections_cut = 0;
-    for (size_t cs = 0; cs < cut_support_indices.size (); ++cs)
+    for (const int &point_index : cut_support_indices)
     {
-      const int point_index = cut_support_indices[cs];
-
       if (use_clean_cutting_)
       {
         // skip edges where both centroids are on one side of the cutting plane
-        uint32_t source_sv_label = sv_adjacency_list_[boost::source (seg_to_edgeIDs_map[itr->first][point_index], sv_adjacency_list_)];
-        uint32_t target_sv_label = sv_adjacency_list_[boost::target (seg_to_edgeIDs_map[itr->first][point_index], sv_adjacency_list_)];
+        uint32_t source_sv_label = sv_adjacency_list_[boost::source (seg_to_edgeIDs_map[seg_to_edge_points.first][point_index], sv_adjacency_list_)];
+        uint32_t target_sv_label = sv_adjacency_list_[boost::target (seg_to_edgeIDs_map[seg_to_edge_points.first][point_index], sv_adjacency_list_)];
         // get centroids of vertices
         const pcl::PointXYZRGBA source_centroid = sv_label_to_supervoxel_map_[source_sv_label]->centroid_;
         const pcl::PointXYZRGBA target_centroid = sv_label_to_supervoxel_map_[target_sv_label]->centroid_;
@@ -260,11 +255,11 @@ pcl::CPCSegmentation<PointT>::applyCuttingPlane (uint32_t depth_levels_left)
           continue;
         }
       }
-      sv_adjacency_list_[seg_to_edgeIDs_map[itr->first][point_index]].used_for_cutting = true;
-      if (sv_adjacency_list_[seg_to_edgeIDs_map[itr->first][point_index]].is_valid) 
+      sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].used_for_cutting = true;
+      if (sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].is_valid) 
       {
         ++number_connections_cut;
-        sv_adjacency_list_[seg_to_edgeIDs_map[itr->first][point_index]].is_valid = false;
+        sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].is_valid = false;
       }
     }
 //     std::cout << "We cut " << number_connections_cut << " connections" << std::endl;
@@ -333,15 +328,12 @@ pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel (int)
     sac_model_->selectWithinDistance (model_coefficients, threshold_, *current_inliers);
     double current_score = 0;
     Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
-    for (size_t i = 0; i < current_inliers->size (); ++i)
+    for (const int &current_index : *current_inliers)
     {
-      int current_index = current_inliers->at (i);
-      double index_score;
+      double index_score = weights_[current_index];
       if (use_directed_weights_)
         // the sqrt(2) factor was used in the paper and was meant for making the scores better comparable between directed and undirected weights
-        index_score = weights_[current_index] * 1.414 * (fabsf (plane_normal.dot (point_cloud_ptr_->at (current_index).getNormalVector3fMap ())));
-      else
-        index_score = weights_[current_index];
+        index_score *= 1.414 * (fabsf (plane_normal.dot (point_cloud_ptr_->at (current_index).getNormalVector3fMap ())));
 
       current_score += index_score;
     }

segmentation/include/pcl/segmentation/impl/crf_segmentation.hpp

@@ -415,9 +415,9 @@ pcl::CrfSegmentation<PointT>::segmentPoints (pcl::PointCloud<pcl::PointXYZRGBL>
 
 
     std::cout << "labels size: " << labels.size () << std::endl;
-    for (size_t i = 0; i < labels.size (); i++)
+    for (const int &label : labels)
     {
-      std::cout << labels[i] << std::endl;
+      std::cout << label << std::endl;
     }
 
   }

segmentation/include/pcl/segmentation/impl/extract_clusters.hpp

@@ -143,16 +143,16 @@ pcl::extractEuclideanClusters (const PointCloud<PointT> &cloud,
   std::vector<int> nn_indices;
   std::vector<float> nn_distances;
   // Process all points in the indices vector
-  for (int i = 0; i < static_cast<int> (indices.size ()); ++i)
+  for (const int &index : indices)
   {
-    if (processed[indices[i]])
+    if (processed[index])
       continue;
 
     std::vector<int> seed_queue;
     int sq_idx = 0;
-    seed_queue.push_back (indices[i]);
+    seed_queue.push_back (index);
 
-    processed[indices[i]] = true;
+    processed[index] = true;
 
     while (sq_idx < static_cast<int> (seed_queue.size ()))
     {

segmentation/include/pcl/segmentation/impl/extract_labeled_clusters.hpp

@@ -144,8 +144,8 @@ pcl::LabeledEuclideanClusterExtraction<PointT>::extract (std::vector<std::vector
   extractLabeledEuclideanClusters (*input_, tree_, static_cast<float> (cluster_tolerance_), labeled_clusters, min_pts_per_cluster_, max_pts_per_cluster_, max_label_);
 
   // Sort the clusters based on their size (largest one first)
-  for (int i = 0; i < static_cast<int> (labeled_clusters.size ()); i++)
-    std::sort (labeled_clusters[i].rbegin (), labeled_clusters[i].rend (), comparePointClusters);
+  for (auto &labeled_cluster : labeled_clusters)
+    std::sort (labeled_cluster.rbegin (), labeled_cluster.rend (), comparePointClusters);
 
   deinitCompute ();
 }

segmentation/include/pcl/segmentation/impl/grabcut_segmentation.hpp

@@ -161,10 +161,10 @@ pcl::GrabCut<PointT>::setBackgroundPointsIndices (const PointIndicesConstPtr &in
 
   std::fill (trimap_.begin (), trimap_.end (), TrimapBackground);
   std::fill (hard_segmentation_.begin (), hard_segmentation_.end (), SegmentationBackground);
-  for (std::vector<int>::const_iterator idx = indices->indices.begin (); idx != indices->indices.end (); ++idx)
+  for (const int &index : indices->indices)
   {
-    trimap_[*idx] = TrimapUnknown;
-    hard_segmentation_[*idx] = SegmentationForeground;
+    trimap_[index] = TrimapUnknown;
+    hard_segmentation_[index] = SegmentationForeground;
   }
 
   if (!initialized_)
@@ -245,17 +245,17 @@ template <typename PointT> void
 pcl::GrabCut<PointT>::setTrimap (const PointIndicesConstPtr &indices, segmentation::grabcut::TrimapValue t)
 {
   using namespace pcl::segmentation::grabcut;
-  for (auto idx = indices->indices.cbegin (); idx != indices->indices.cend (); ++idx)
-    trimap_[*idx] = t;
+  for (const int &index : indices->indices)
+    trimap_[index] = t;
 
   // Immediately set the hard segmentation as well so that the display will update.
   if (t == TrimapForeground)
-    for (auto idx = indices->indices.cbegin (); idx != indices->indices.cend (); ++idx)
-      hard_segmentation_[*idx] = SegmentationForeground;
+    for (const int &index : indices->indices)
+      hard_segmentation_[index] = SegmentationForeground;
   else
     if (t == TrimapBackground)
-      for (auto idx = indices->indices.cbegin (); idx != indices->indices.cend (); ++idx)
-        hard_segmentation_[*idx] = SegmentationBackground;
+      for (const int &index : indices->indices)
+        hard_segmentation_[index] = SegmentationBackground;
 }
 
 template <typename PointT> void

segmentation/include/pcl/segmentation/impl/lccp_segmentation.hpp

@@ -114,9 +114,9 @@ pcl::LCCPSegmentation<PointT>::relabelCloud (pcl::PointCloud<pcl::PointXYZL> &la
   if (grouping_data_valid_)
   {
     // Relabel all Points in cloud with new labels
-    for (auto voxel_itr = labeled_cloud_arg.begin (); voxel_itr != labeled_cloud_arg.end (); ++voxel_itr)
+    for (auto &voxel : labeled_cloud_arg)
     {
-      voxel_itr->label = sv_label_to_seg_label_map_[voxel_itr->label];
+      voxel.label = sv_label_to_seg_label_map_[voxel.label];
     }
   }
   else
@@ -236,9 +236,9 @@ pcl::LCCPSegmentation<PointT>::mergeSmallSegments ()
     }
 
     // Erase filtered Segments from segment map
-    for (auto filtered_ID_itr = filteredSegLabels.cbegin (); filtered_ID_itr != filteredSegLabels.cend (); ++filtered_ID_itr)
+    for (const unsigned int &filteredSegLabel : filteredSegLabels)
     {
-      seg_label_to_sv_list_map_.erase (*filtered_ID_itr);
+      seg_label_to_sv_list_map_.erase (filteredSegLabel);
     }
 
     // After filtered Segments are deleted, compute completely new adjacency map
@@ -272,11 +272,10 @@ pcl::LCCPSegmentation<PointT>::prepareSegmentation (const std::map<uint32_t, typ
   }
 
   // Add all edges
-  for (std::multimap<uint32_t, uint32_t>::const_iterator sv_neighbors_itr = label_adjaceny_arg.begin (); sv_neighbors_itr != label_adjaceny_arg.end ();
-      ++sv_neighbors_itr)
+  for (const auto &sv_neighbors_itr : label_adjaceny_arg)
   {
-    const uint32_t& sv_label = sv_neighbors_itr->first;
-    const uint32_t& neighbor_label = sv_neighbors_itr->second;
+    const uint32_t& sv_label = sv_neighbors_itr.first;
+    const uint32_t& neighbor_label = sv_neighbors_itr.second;
 
     VertexID u = label_ID_map[sv_label];
     VertexID v = label_ID_map[neighbor_label];

segmentation/include/pcl/segmentation/impl/organized_multi_plane_segmentation.hpp

@@ -134,11 +134,11 @@ pcl::OrganizedMultiPlaneSegmentation<PointT, PointNT, PointLT>::segment (std::ve
   model.values.resize (4);
 
   // Fit Planes to each cluster
-  for (size_t i = 0; i < label_indices.size (); i++)
+  for (const auto &label_index : label_indices)
   {
-    if (static_cast<unsigned> (label_indices[i].indices.size ()) > min_inliers_)
+    if (static_cast<unsigned> (label_index.indices.size ()) > min_inliers_)
     {
-      pcl::computeMeanAndCovarianceMatrix (*input_, label_indices[i].indices, clust_cov, clust_centroid);
+      pcl::computeMeanAndCovarianceMatrix (*input_, label_index.indices, clust_cov, clust_centroid);
       Eigen::Vector4f plane_params;
 
       EIGEN_ALIGN16 Eigen::Vector3f::Scalar eigen_value;
@@ -174,7 +174,7 @@ pcl::OrganizedMultiPlaneSegmentation<PointT, PointNT, PointLT>::segment (std::ve
         model.values[2] = plane_params[2];
         model.values[3] = plane_params[3];
         model_coefficients.push_back (model);
-        inlier_indices.push_back (label_indices[i]);
+        inlier_indices.push_back (label_index);
         centroids.push_back (clust_centroid);
         covariances.push_back (clust_cov);
       }

segmentation/include/pcl/segmentation/impl/seeded_hue_segmentation.hpp

@@ -62,9 +62,8 @@ pcl::seededHueSegmentation (const PointCloud<PointXYZRGB>          &cloud,
   std::vector<float> nn_distances;
 
   // Process all points in the indices vector
-  for (size_t k = 0; k < indices_in.indices.size (); ++k)
+  for (const int &i : indices_in.indices)
   {
-    int i = indices_in.indices[k];
     if (processed[i])
       continue;
 
@@ -111,8 +110,8 @@ pcl::seededHueSegmentation (const PointCloud<PointXYZRGB>          &cloud,
       sq_idx++;
     }
     // Copy the seed queue into the output indices
-    for (size_t l = 0; l < seed_queue.size (); ++l)
-      indices_out.indices.push_back(seed_queue[l]);
+    for (const int &l : seed_queue)
+      indices_out.indices.push_back(l);
   }
   // This is purely esthetical, can be removed for speed purposes
   std::sort (indices_out.indices.begin (), indices_out.indices.end ());
@@ -138,9 +137,8 @@ pcl::seededHueSegmentation (const PointCloud<PointXYZRGB>            &cloud,
   std::vector<float> nn_distances;
 
   // Process all points in the indices vector
-  for (size_t k = 0; k < indices_in.indices.size (); ++k)
+  for (const int &i : indices_in.indices)
   {
-    int i = indices_in.indices[k];
     if (processed[i])
       continue;
 
@@ -186,8 +184,8 @@ pcl::seededHueSegmentation (const PointCloud<PointXYZRGB>            &cloud,
       sq_idx++;
     }
     // Copy the seed queue into the output indices
-    for (size_t l = 0; l < seed_queue.size (); ++l)
-      indices_out.indices.push_back(seed_queue[l]);
+    for (const int &l : seed_queue)
+      indices_out.indices.push_back(l);
   }
   // This is purely esthetical, can be removed for speed purposes
   std::sort (indices_out.indices.begin (), indices_out.indices.end ());

segmentation/include/pcl/segmentation/impl/supervoxel_clustering.hpp

@@ -403,10 +403,10 @@ pcl::SupervoxelClustering<PointT>::selectInitialSupervoxelSeeds (std::vector<int
   // This is 1/20th of the number of voxels which fit in a planar slice through search volume
   // Area of planar slice / area of voxel side. (Note: This is smaller than the value mentioned in the original paper)
   float min_points = 0.05f * (search_radius)*(search_radius) * 3.1415926536f  / (resolution_*resolution_);
-  for (size_t i = 0; i < seed_indices_orig.size (); ++i)
+  for (const int &index_orig : seed_indices_orig)
   {
-    int num = voxel_kdtree_->radiusSearch (seed_indices_orig[i], search_radius , neighbors, sqr_distances);
-    int min_index = seed_indices_orig[i];
+    int num = voxel_kdtree_->radiusSearch (index_orig, search_radius , neighbors, sqr_distances);
+    int min_index = index_orig;
     if ( num > min_points)
     {
       seed_indices.push_back (min_index);
@@ -497,12 +497,12 @@ pcl::SupervoxelClustering<PointT>::getSupervoxelAdjacencyList (VoxelAdjacencyLis
     uint32_t label = sv_itr->getLabel ();
     std::set<uint32_t> neighbor_labels;
     sv_itr->getNeighborLabels (neighbor_labels);
-    for (std::set<uint32_t>::iterator label_itr = neighbor_labels.begin (); label_itr != neighbor_labels.end (); ++label_itr)
+    for (const unsigned int &neighbor_label : neighbor_labels)
     {
       bool edge_added;
       EdgeID edge;
       VoxelID u = (label_ID_map.find (label))->second;
-      VoxelID v = (label_ID_map.find (*label_itr))->second;
+      VoxelID v = (label_ID_map.find (neighbor_label))->second;
       boost::tie (edge, edge_added) = add_edge (u,v,adjacency_list_arg);
       //Calc distance between centers, set as edge weight
       if (edge_added)
@@ -513,7 +513,7 @@ pcl::SupervoxelClustering<PointT>::getSupervoxelAdjacencyList (VoxelAdjacencyLis
 
         for (typename HelperListT::const_iterator neighb_itr = supervoxel_helpers_.cbegin (); neighb_itr != supervoxel_helpers_.cend (); ++neighb_itr)
         {
-          if (neighb_itr->getLabel () == (*label_itr))
+          if (neighb_itr->getLabel () == neighbor_label)
           {
             neighb_centroid_data = neighb_itr->getCentroid ();
             break;
@@ -539,8 +539,8 @@ pcl::SupervoxelClustering<PointT>::getSupervoxelAdjacency (std::multimap<uint32_
     uint32_t label = sv_itr->getLabel ();
     std::set<uint32_t> neighbor_labels;
     sv_itr->getNeighborLabels (neighbor_labels);
-    for (std::set<uint32_t>::iterator label_itr = neighbor_labels.begin (); label_itr != neighbor_labels.end (); ++label_itr)
-      label_adjacency.insert (std::pair<uint32_t,uint32_t> (label, *label_itr) );
+    for (const unsigned int &neighbor_label : neighbor_labels)
+      label_adjacency.insert (std::pair<uint32_t,uint32_t> (label, neighbor_label) );
     //if (neighbor_labels.size () == 0)
     //  std::cout << label<<"(size="<<sv_itr->size () << ") has "<<neighbor_labels.size () << "\n";
   }