feat(euclidean_cluster): diagnostics modified to remove redundant info

Author: lei.gu

perception/autoware_euclidean_cluster/include/autoware/euclidean_cluster/voxel_grid_based_euclidean_cluster.hpp

@@ -58,7 +58,7 @@ class VoxelGridBasedEuclideanCluster : public EuclideanClusterInterface
   int min_points_number_per_voxel_;
 
   void publishDiagnosticsSummary(
-    const std::vector<std::string> & warnings,
+    size_t skipped_cluster_count,
     const sensor_msgs::msg::PointCloud2::ConstSharedPtr & pointcloud_msg);
   autoware_utils::DiagnosticsInterface * diagnostics_interface_ptr_{nullptr};
 };

perception/autoware_euclidean_cluster/lib/voxel_grid_based_euclidean_cluster.cpp

@@ -46,30 +46,24 @@ VoxelGridBasedEuclideanCluster::VoxelGridBasedEuclideanCluster(
 
 // After processing all clusters, publish a summary of diagnostics.
 void VoxelGridBasedEuclideanCluster::publishDiagnosticsSummary(
-  const std::vector<std::string> & warnings,
+  size_t skipped_cluster_count,
   const sensor_msgs::msg::PointCloud2::ConstSharedPtr & pointcloud_msg)
 {
   if (!diagnostics_interface_ptr_) {
     return;
   }
   diagnostics_interface_ptr_->clear();
-
-  std::stringstream summary;
-  if (!warnings.empty()) {
-    summary << warnings.size()
-            << " clusters skipped because cluster size exceeds the maximum allowed "
-            << max_cluster_size_ << " \n";
-    for (const auto & warn : warnings) {
-      summary << " - " << warn << "\n";
-    }
+  std::string summary;
+  if (skipped_cluster_count > 0) {
+    summary = std::to_string(skipped_cluster_count) + " clusters skipped because cluster point size exceeds the maximum allowed "
+              + std::to_string(max_cluster_size_);
     diagnostics_interface_ptr_->add_key_value("is_cluster_data_size_within_range", false);
   } else {
     diagnostics_interface_ptr_->add_key_value("is_cluster_data_size_within_range", true);
   }
   diagnostics_interface_ptr_->update_level_and_message(
-    warnings.empty() ? static_cast<int8_t>(diagnostic_msgs::msg::DiagnosticStatus::OK)
-                     : static_cast<int8_t>(diagnostic_msgs::msg::DiagnosticStatus::WARN),
-    summary.str());
+    skipped_cluster_count > 0 ? static_cast<int8_t>(diagnostic_msgs::msg::DiagnosticStatus::WARN) : static_cast<int8_t>(diagnostic_msgs::msg::DiagnosticStatus::OK),
+    summary);
   diagnostics_interface_ptr_->publish(pointcloud_msg->header.stamp);
 }
 
@@ -168,8 +162,7 @@ bool VoxelGridBasedEuclideanCluster::cluster(
 
   // build output and check cluster size
   {
-    // At the start, create a container to collect warnings.
-    std::vector<std::string> warning_messages;
+    size_t skipped_cluster_count = 0;  // Count the skipped clusters
     for (size_t i = 0; i < temporary_clusters.size(); ++i) {
       auto & i_cluster_data_size = clusters_data_size.at(i);
       int cluster_size = static_cast<int>(i_cluster_data_size / point_step);
@@ -179,8 +172,7 @@ bool VoxelGridBasedEuclideanCluster::cluster(
       }
       if (cluster_size > max_cluster_size_) {
         // Cluster size exceeds the maximum threshold; log a warning.
-        warning_messages.push_back(
-          " Cluster " + std::to_string(i) + " (" + std::to_string(cluster_size) + ").");
+        skipped_cluster_count++;
         continue;
       }
       const auto & cluster = temporary_clusters.at(i);
@@ -205,8 +197,8 @@ bool VoxelGridBasedEuclideanCluster::cluster(
       objects.feature_objects.push_back(feature_object);
     }
     objects.header = pointcloud_msg->header;
-
-    publishDiagnosticsSummary(warning_messages, pointcloud_msg);
+    // Publish the diagnostics summary.
+    publishDiagnosticsSummary(skipped_cluster_count,  pointcloud_msg);
   }
 
   return true;