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fix(autoware_image_projection_based_fusion): fix unusedFunction #8567

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Aug 23, 2024
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fix(autoware_image_projection_based_fusion): fix unusedFunction #8567

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8 changes: 0 additions & 8 deletions ...ge_projection_based_fusion/include/autoware/image_projection_based_fusion/utils/utils.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -74,9 +74,6 @@ std::optional<geometry_msgs::msg::TransformStamped> getTransformStamped(

Eigen::Affine3d transformToEigen(const geometry_msgs::msg::Transform & t);

void convertCluster2FeatureObject(
const std_msgs::msg::Header & header, const PointCloud & cluster,
DetectedObjectWithFeature & feature_obj);
void closest_cluster(
const PointCloud2 & cluster, const double cluster_2d_tolerance, const int min_cluster_size,
const pcl::PointXYZ & center, PointCloud2 & out_cluster);
Expand All @@ -90,11 +87,6 @@ void updateOutputFusedObjects(

geometry_msgs::msg::Point getCentroid(const sensor_msgs::msg::PointCloud2 & pointcloud);

pcl::PointXYZ getClosestPoint(const pcl::PointCloud<pcl::PointXYZ> & cluster);
void addShapeAndKinematic(
const pcl::PointCloud<pcl::PointXYZ> & cluster,
tier4_perception_msgs::msg::DetectedObjectWithFeature & feature_obj);

} // namespace autoware::image_projection_based_fusion

#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__UTILS__UTILS_HPP_
95 changes: 0 additions & 95 deletions perception/autoware_image_projection_based_fusion/src/utils/utils.cpp
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
// Copyright 2022 TIER IV, Inc.

Check notice on line 1 in perception/autoware_image_projection_based_fusion/src/utils/utils.cpp

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CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

✅ No longer an issue: Overall Code Complexity 

The mean cyclomatic complexity in this module is no longer above the threshold
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -72,18 +72,6 @@
return a;
}

void convertCluster2FeatureObject(
const std_msgs::msg::Header & header, const PointCloud & cluster,
DetectedObjectWithFeature & feature_obj)
{
PointCloud2 ros_cluster;
pcl::toROSMsg(cluster, ros_cluster);
ros_cluster.header = header;
feature_obj.feature.cluster = ros_cluster;
feature_obj.object.kinematics.pose_with_covariance.pose.position = getCentroid(ros_cluster);
feature_obj.object.existence_probability = 1.0f;
}

void closest_cluster(
const PointCloud2 & cluster, const double cluster_2d_tolerance, const int min_cluster_size,
const pcl::PointXYZ & center, PointCloud2 & out_cluster)
Expand Down Expand Up @@ -197,74 +185,7 @@
output_fused_objects.push_back(feature_obj);
}
}

Check notice on line 188 in perception/autoware_image_projection_based_fusion/src/utils/utils.cpp

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CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

✅ No longer an issue: Complex Method 

addShapeAndKinematic is no longer above the threshold for cyclomatic complexity. This function has many conditional statements (e.g. if, for, while), leading to lower code health. Avoid adding more conditionals and code to it without refactoring.
void addShapeAndKinematic(
const pcl::PointCloud<pcl::PointXYZ> & cluster,
tier4_perception_msgs::msg::DetectedObjectWithFeature & feature_obj)
{
if (cluster.empty()) {
return;
}
pcl::PointXYZ centroid = pcl::PointXYZ(0.0, 0.0, 0.0);
float max_z = -1e6;
float min_z = 1e6;
for (const auto & point : cluster) {
centroid.x += point.x;
centroid.y += point.y;
centroid.z += point.z;
max_z = max_z < point.z ? point.z : max_z;
min_z = min_z > point.z ? point.z : min_z;
}
centroid.x = centroid.x / static_cast<double>(cluster.size());
centroid.y = centroid.y / static_cast<double>(cluster.size());
centroid.z = centroid.z / static_cast<double>(cluster.size());

std::vector<cv::Point> cluster2d;
std::vector<cv::Point> cluster2d_convex;

for (size_t i = 0; i < cluster.size(); ++i) {
cluster2d.push_back(
cv::Point((cluster.at(i).x - centroid.x) * 1000.0, (cluster.at(i).y - centroid.y) * 1000.));
}
cv::convexHull(cluster2d, cluster2d_convex);
if (cluster2d_convex.empty()) {
return;
}
pcl::PointXYZ polygon_centroid = pcl::PointXYZ(0.0, 0.0, 0.0);
for (size_t i = 0; i < cluster2d_convex.size(); ++i) {
polygon_centroid.x += static_cast<double>(cluster2d_convex.at(i).x) / 1000.0;
polygon_centroid.y += static_cast<double>(cluster2d_convex.at(i).y) / 1000.0;
}
polygon_centroid.x = polygon_centroid.x / static_cast<double>(cluster2d_convex.size());
polygon_centroid.y = polygon_centroid.y / static_cast<double>(cluster2d_convex.size());

autoware_perception_msgs::msg::Shape shape;
for (size_t i = 0; i < cluster2d_convex.size(); ++i) {
geometry_msgs::msg::Point32 point;
point.x = cluster2d_convex.at(i).x / 1000.0;
point.y = cluster2d_convex.at(i).y / 1000.0;
point.z = 0.0;
shape.footprint.points.push_back(point);
}
shape.type = autoware_perception_msgs::msg::Shape::POLYGON;
constexpr float eps = 0.01;
shape.dimensions.x = 0;
shape.dimensions.y = 0;
shape.dimensions.z = std::max((max_z - min_z), eps);
feature_obj.object.shape = shape;
feature_obj.object.kinematics.pose_with_covariance.pose.position.x =
centroid.x + polygon_centroid.x;
feature_obj.object.kinematics.pose_with_covariance.pose.position.y =
centroid.y + polygon_centroid.y;
feature_obj.object.kinematics.pose_with_covariance.pose.position.z =
min_z + shape.dimensions.z * 0.5;
feature_obj.object.existence_probability = 1.0;
feature_obj.object.kinematics.pose_with_covariance.pose.orientation.x = 0;
feature_obj.object.kinematics.pose_with_covariance.pose.orientation.y = 0;
feature_obj.object.kinematics.pose_with_covariance.pose.orientation.z = 0;
feature_obj.object.kinematics.pose_with_covariance.pose.orientation.w = 1;
}

geometry_msgs::msg::Point getCentroid(const sensor_msgs::msg::PointCloud2 & pointcloud)
{
geometry_msgs::msg::Point centroid;
Expand All @@ -285,20 +206,4 @@
return centroid;
}

pcl::PointXYZ getClosestPoint(const pcl::PointCloud<pcl::PointXYZ> & cluster)
{
pcl::PointXYZ closest_point;
double min_dist = 1e6;
pcl::PointXYZ orig_point = pcl::PointXYZ(0.0, 0.0, 0.0);
for (std::size_t i = 0; i < cluster.points.size(); ++i) {
pcl::PointXYZ point = cluster.points.at(i);
double dist_closest_point = autoware::universe_utils::calcDistance2d(point, orig_point);
if (min_dist > dist_closest_point) {
min_dist = dist_closest_point;
closest_point = pcl::PointXYZ(point.x, point.y, point.z);
}
}
return closest_point;
}

} // namespace autoware::image_projection_based_fusion
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