From 5ad8ae0b9c337c1830b5cf8084f578a8d7f6a19e Mon Sep 17 00:00:00 2001 From: Anh Nguyen <anh.nguyen.2@tier4.jp> Date: Fri, 9 Aug 2024 12:59:17 +0700 Subject: [PATCH] Applied clang format 17.0.5 Signed-off-by: Anh Nguyen <anh.nguyen.2@tier4.jp> --- include/multigrid_pclomp/multigrid_ndt_omp.h | 98 +++------------ include/pclomp/gicp_omp.h | 30 +---- include/pclomp/ndt_omp.h | 119 ++++-------------- include/pclomp/voxel_grid_covariance_omp.h | 20 +-- .../pclomp/voxel_grid_covariance_omp_impl.hpp | 4 +- 5 files changed, 56 insertions(+), 215 deletions(-) diff --git a/include/multigrid_pclomp/multigrid_ndt_omp.h b/include/multigrid_pclomp/multigrid_ndt_omp.h index 9f0b6e1..48a89f8 100644 --- a/include/multigrid_pclomp/multigrid_ndt_omp.h +++ b/include/multigrid_pclomp/multigrid_ndt_omp.h @@ -125,9 +125,7 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour MultiGridNormalDistributionsTransform && other) noexcept; /** \brief Empty destructor */ - virtual ~MultiGridNormalDistributionsTransform() - { - } + virtual ~MultiGridNormalDistributionsTransform() {} void setNumThreads(int n) { @@ -136,10 +134,7 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour target_cells_.setThreadNum(params_.num_threads); } - inline int getNumThreads() const - { - return params_.num_threads; - } + inline int getNumThreads() const { return params_.num_threads; } inline void setInputSource(const PointCloudSourceConstPtr & input) { @@ -170,10 +165,7 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour target_cells_.setInputCloudAndFilter(cloud, target_id); } - inline void removeTarget(const std::string target_id) - { - target_cells_.removeCloud(target_id); - } + inline void removeTarget(const std::string target_id) { target_cells_.removeCloud(target_id); } inline void createVoxelKdtree() { @@ -196,50 +188,32 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour /** \brief Get voxel grid resolution. * \return side length of voxels */ - inline float getResolution() const - { - return (params_.resolution); - } + inline float getResolution() const { return (params_.resolution); } /** \brief Get the newton line search maximum step length. * \return maximum step length */ - inline double getStepSize() const - { - return (params_.step_size); - } + inline double getStepSize() const { return (params_.step_size); } /** \brief Set/change the newton line search maximum step length. * \param[in] step_size maximum step length */ - inline void setStepSize(double step_size) - { - params_.step_size = step_size; - } + inline void setStepSize(double step_size) { params_.step_size = step_size; } /** \brief Get the point cloud outlier ratio. * \return outlier ratio */ - inline double getOutlierRatio() const - { - return (outlier_ratio_); - } + inline double getOutlierRatio() const { return (outlier_ratio_); } /** \brief Set/change the point cloud outlier ratio. * \param[in] outlier_ratio outlier ratio */ - inline void setOutlierRatio(double outlier_ratio) - { - outlier_ratio_ = outlier_ratio; - } + inline void setOutlierRatio(double outlier_ratio) { outlier_ratio_ = outlier_ratio; } /** \brief Get the registration alignment probability. * \return transformation probability */ - inline double getTransformationProbability() const - { - return (trans_probability_); - } + inline double getTransformationProbability() const { return (trans_probability_); } inline double getNearestVoxelTransformationLikelihood() const { @@ -249,16 +223,10 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour /** \brief Get the number of iterations required to calculate alignment. * \return final number of iterations */ - inline int getFinalNumIteration() const - { - return (nr_iterations_); - } + inline int getFinalNumIteration() const { return (nr_iterations_); } /** \brief Return the hessian matrix */ - inline Eigen::Matrix<double, 6, 6> getHessian() const - { - return hessian_; - } + inline Eigen::Matrix<double, 6, 6> getHessian() const { return hessian_; } /** \brief Return the transformation array */ inline const std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f>> & @@ -305,10 +273,7 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour regularization_pose_ = regularization_pose; } - inline void unsetRegularizationPose() - { - regularization_pose_ = boost::none; - } + inline void unsetRegularizationPose() { regularization_pose_ = boost::none; } NdtResult getResult() { @@ -331,18 +296,12 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour * transformation epsilon in order for an optimization to be considered as having * converged to the final solution. */ - inline void setTransformationEpsilon(double epsilon) - { - params_.trans_epsilon = epsilon; - } + inline void setTransformationEpsilon(double epsilon) { params_.trans_epsilon = epsilon; } /** \brief Get the transformation epsilon (maximum allowable translation squared * difference between two consecutive transformations) as set by the user. */ - inline double getTransformationEpsilon() - { - return (params_.trans_epsilon); - } + inline double getTransformationEpsilon() { return (params_.trans_epsilon); } inline void setMaximumIterations(int max_iterations) { @@ -350,15 +309,9 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour max_iterations_ = params_.max_iterations; } - inline int getMaxIterations() const - { - return params_.max_iterations; - } + inline int getMaxIterations() const { return params_.max_iterations; } - inline int getMaxIterations() - { - return params_.max_iterations; - } + inline int getMaxIterations() { return params_.max_iterations; } void setParams(const NdtParams & ndt_params) { @@ -368,20 +321,11 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour target_cells_.setThreadNum(params_.num_threads); } - NdtParams getParams() const - { - return params_; - } + NdtParams getParams() const { return params_; } - pcl::PointCloud<PointTarget> getVoxelPCD() const - { - return target_cells_.getVoxelPCD(); - } + pcl::PointCloud<PointTarget> getVoxelPCD() const { return target_cells_.getVoxelPCD(); } - std::vector<std::string> getCurrentMapIDs() const - { - return target_cells_.getCurrentMapIDs(); - } + std::vector<std::string> getCurrentMapIDs() const { return target_cells_.getCurrentMapIDs(); } protected: using BaseRegType::converged_; @@ -413,9 +357,7 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour virtual void computeTransformation(PointCloudSource & output, const Eigen::Matrix4f & guess); /** \brief Initiate covariance voxel structure. */ - void inline init() - { - } + void inline init() {} /** \brief Compute derivatives of probability function w.r.t. the transformation vector. * \note Equation 6.10, 6.12 and 6.13 [Magnusson 2009]. diff --git a/include/pclomp/gicp_omp.h b/include/pclomp/gicp_omp.h index 1e05274..c71d77e 100644 --- a/include/pclomp/gicp_omp.h +++ b/include/pclomp/gicp_omp.h @@ -217,18 +217,12 @@ class GeneralizedIterativeClosestPoint : public pcl::IterativeClosestPoint<Point * converged to the final solution. * \param epsilon the rotation epsilon */ - inline void setRotationEpsilon(double epsilon) - { - rotation_epsilon_ = epsilon; - } + inline void setRotationEpsilon(double epsilon) { rotation_epsilon_ = epsilon; } /** \brief Get the rotation epsilon (maximum allowable difference between two * consecutive rotations) as set by the user. */ - inline double getRotationEpsilon() - { - return (rotation_epsilon_); - } + inline double getRotationEpsilon() { return (rotation_epsilon_); } /** \brief Set the number of neighbors used when selecting a point neighbourhood * to compute covariances. @@ -236,32 +230,20 @@ class GeneralizedIterativeClosestPoint : public pcl::IterativeClosestPoint<Point * covariances computation slower. * \param k the number of neighbors to use when computing covariances */ - void setCorrespondenceRandomness(int k) - { - k_correspondences_ = k; - } + void setCorrespondenceRandomness(int k) { k_correspondences_ = k; } /** \brief Get the number of neighbors used when computing covariances as set by * the user */ - int getCorrespondenceRandomness() - { - return (k_correspondences_); - } + int getCorrespondenceRandomness() { return (k_correspondences_); } /** set maximum number of iterations at the optimization step * \param[in] max maximum number of iterations for the optimizer */ - void setMaximumOptimizerIterations(int max) - { - max_inner_iterations_ = max; - } + void setMaximumOptimizerIterations(int max) { max_inner_iterations_ = max; } ///\return maximum number of iterations at the optimization step - int getMaximumOptimizerIterations() - { - return (max_inner_iterations_); - } + int getMaximumOptimizerIterations() { return (max_inner_iterations_); } protected: /** \brief The number of neighbors used for covariances computation. diff --git a/include/pclomp/ndt_omp.h b/include/pclomp/ndt_omp.h index da0228f..8706215 100644 --- a/include/pclomp/ndt_omp.h +++ b/include/pclomp/ndt_omp.h @@ -114,19 +114,11 @@ class NormalDistributionsTransform : public pcl::Registration<PointSource, Point NormalDistributionsTransform(); /** \brief Empty destructor */ - virtual ~NormalDistributionsTransform() - { - } + virtual ~NormalDistributionsTransform() {} - void setNumThreads(int n) - { - params_.num_threads = n; - } + void setNumThreads(int n) { params_.num_threads = n; } - inline int getNumThreads() const - { - return params_.num_threads; - } + inline int getNumThreads() const { return params_.num_threads; } /** \brief Provide a pointer to the input target (e.g., the point cloud that we want to align the * input source to). \param[in] cloud the input point cloud target @@ -152,60 +144,39 @@ class NormalDistributionsTransform : public pcl::Registration<PointSource, Point /** \brief Get voxel grid resolution. * \return side length of voxels */ - inline float getResolution() const - { - return (params_.resolution); - } + inline float getResolution() const { return (params_.resolution); } /** \brief Get the newton line search maximum step length. * \return maximum step length */ - inline double getStepSize() const - { - return (params_.step_size); - } + inline double getStepSize() const { return (params_.step_size); } /** \brief Set/change the newton line search maximum step length. * \param[in] step_size maximum step length */ - inline void setStepSize(double step_size) - { - params_.step_size = step_size; - } + inline void setStepSize(double step_size) { params_.step_size = step_size; } /** \brief Get the point cloud outlier ratio. * \return outlier ratio */ - inline double getOutlierRatio() const - { - return (outlier_ratio_); - } + inline double getOutlierRatio() const { return (outlier_ratio_); } /** \brief Set/change the point cloud outlier ratio. * \param[in] outlier_ratio outlier ratio */ - inline void setOutlierRatio(double outlier_ratio) - { - outlier_ratio_ = outlier_ratio; - } + inline void setOutlierRatio(double outlier_ratio) { outlier_ratio_ = outlier_ratio; } inline void setNeighborhoodSearchMethod(NeighborSearchMethod method) { params_.search_method = method; } - inline NeighborSearchMethod getNeighborhoodSearchMethod() const - { - return params_.search_method; - } + inline NeighborSearchMethod getNeighborhoodSearchMethod() const { return params_.search_method; } /** \brief Get the registration alignment probability. * \return transformation probability */ - inline double getTransformationProbability() const - { - return (trans_probability_); - } + inline double getTransformationProbability() const { return (trans_probability_); } inline double getNearestVoxelTransformationLikelihood() const { @@ -215,16 +186,10 @@ class NormalDistributionsTransform : public pcl::Registration<PointSource, Point /** \brief Get the number of iterations required to calculate alignment. * \return final number of iterations */ - inline int getFinalNumIteration() const - { - return (nr_iterations_); - } + inline int getFinalNumIteration() const { return (nr_iterations_); } /** \brief Return the hessian matrix */ - inline Eigen::Matrix<double, 6, 6> getHessian() const - { - return hessian_; - } + inline Eigen::Matrix<double, 6, 6> getHessian() const { return hessian_; } /** \brief Return the transformation array */ inline const std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f>> & @@ -257,30 +222,15 @@ class NormalDistributionsTransform : public pcl::Registration<PointSource, Point } // add at 20220721 konishi - inline const std::vector<double> getScores() const - { - return scores_; - } + inline const std::vector<double> getScores() const { return scores_; } - inline const TargetGrid getTargetCells() const - { - return target_cells_; - } + inline const TargetGrid getTargetCells() const { return target_cells_; } - inline const std::map<size_t, double> getScoreMap() const - { - return voxel_score_map_; - } + inline const std::map<size_t, double> getScoreMap() const { return voxel_score_map_; } - inline const std::map<size_t, size_t> getNoPointMap() const - { - return nomap_points_num_; - } + inline const std::map<size_t, size_t> getNoPointMap() const { return nomap_points_num_; } - std::set<Eigen::Vector3i, EigenCmp> & getEmptyVoxels() - { - return empty_voxels_; - } + std::set<Eigen::Vector3i, EigenCmp> & getEmptyVoxels() { return empty_voxels_; } // For debug void cleanScores() @@ -306,10 +256,7 @@ class NormalDistributionsTransform : public pcl::Registration<PointSource, Point regularization_pose_ = regularization_pose; } - inline void unsetRegularizationPose() - { - regularization_pose_ = boost::none; - } + inline void unsetRegularizationPose() { regularization_pose_ = boost::none; } NdtResult getResult() { @@ -329,37 +276,19 @@ class NormalDistributionsTransform : public pcl::Registration<PointSource, Point * transformation epsilon in order for an optimization to be considered as having * converged to the final solution. */ - inline void setTransformationEpsilon(double epsilon) - { - params_.trans_epsilon = epsilon; - } + inline void setTransformationEpsilon(double epsilon) { params_.trans_epsilon = epsilon; } /** \brief Get the transformation epsilon (maximum allowable translation squared * difference between two consecutive transformations) as set by the user. */ - inline double getTransformationEpsilon() - { - return (params_.trans_epsilon); - } + inline double getTransformationEpsilon() { return (params_.trans_epsilon); } - inline void setMaximumIterations(int max_iterations) - { - params_.max_iterations = max_iterations; - } - inline int getMaxIterations() const - { - return params_.max_iterations; - } + inline void setMaximumIterations(int max_iterations) { params_.max_iterations = max_iterations; } + inline int getMaxIterations() const { return params_.max_iterations; } - void setParams(const NdtParams & ndt_params) - { - params_ = ndt_params; - } + void setParams(const NdtParams & ndt_params) { params_ = ndt_params; } - NdtParams getParams() const - { - return params_; - } + NdtParams getParams() const { return params_; } protected: using pcl::Registration<PointSource, PointTarget>::reg_name_; diff --git a/include/pclomp/voxel_grid_covariance_omp.h b/include/pclomp/voxel_grid_covariance_omp.h index b088fe6..373a52a 100644 --- a/include/pclomp/voxel_grid_covariance_omp.h +++ b/include/pclomp/voxel_grid_covariance_omp.h @@ -226,10 +226,7 @@ class VoxelGridCovariance : public pcl::VoxelGrid<PointT> /** \brief Get the minimum number of points required for a cell to be used. * \return the minimum number of points for required for a voxel to be used */ - inline int getMinPointPerVoxel() - { - return min_points_per_voxel_; - } + inline int getMinPointPerVoxel() { return min_points_per_voxel_; } /** \brief Set the minimum allowable ratio between eigenvalues to prevent singular covariance * matrices. \param[in] min_covar_eigvalue_mult the minimum allowable ratio between eigenvalues @@ -242,10 +239,7 @@ class VoxelGridCovariance : public pcl::VoxelGrid<PointT> /** \brief Get the minimum allowable ratio between eigenvalues to prevent singular covariance * matrices. \return the minimum allowable ratio between eigenvalues */ - inline double getCovEigValueInflationRatio() - { - return min_covar_eigvalue_mult_; - } + inline double getCovEigValueInflationRatio() { return min_covar_eigvalue_mult_; } /** \brief Filter cloud and initializes voxel structure. * \param[out] output cloud containing centroids of voxels containing a sufficient number of @@ -390,19 +384,13 @@ class VoxelGridCovariance : public pcl::VoxelGrid<PointT> /** \brief Get the leaf structure map * \return a map containing all leaves */ - inline const Map & getLeaves() - { - return leaves_; - } + inline const Map & getLeaves() { return leaves_; } /** \brief Get a pointcloud containing the voxel centroids * \note Only voxels containing a sufficient number of points are used. * \return a map containing all leaves */ - inline PointCloudPtr getCentroids() - { - return voxel_centroids_; - } + inline PointCloudPtr getCentroids() { return voxel_centroids_; } /** \brief Get a cloud to visualize each voxels normal distribution. * \param[out] cell_cloud a cloud created by sampling the normal distributions of each voxel diff --git a/include/pclomp/voxel_grid_covariance_omp_impl.hpp b/include/pclomp/voxel_grid_covariance_omp_impl.hpp index 0a78ad5..5d0a843 100644 --- a/include/pclomp/voxel_grid_covariance_omp_impl.hpp +++ b/include/pclomp/voxel_grid_covariance_omp_impl.hpp @@ -197,7 +197,7 @@ void pclomp::VoxelGridCovariance<PointT>::applyFilter(PointCloud & output) sizeof(unsigned int)); centroid[centroid_size - 3] = static_cast<float>((rgb >> 16u) & 0x0000ffu); centroid[centroid_size - 2] = static_cast<float>((rgb >> 8u) & 0x0000ffu); - centroid[centroid_size - 1] = static_cast<float>((rgb)&0x0000ffu); + centroid[centroid_size - 1] = static_cast<float>((rgb) & 0x0000ffu); } pcl::for_each_type<FieldList>( pcl::NdCopyPointEigenFunctor<PointT>(input_->points[cp], centroid)); @@ -253,7 +253,7 @@ void pclomp::VoxelGridCovariance<PointT>::applyFilter(PointCloud & output) &rgb, reinterpret_cast<const char *>(&input_->points[cp]) + rgba_index, sizeof(int)); centroid[centroid_size - 3] = static_cast<float>((rgb >> 16u) & 0x0000ffu); centroid[centroid_size - 2] = static_cast<float>((rgb >> 8u) & 0x0000ffu); - centroid[centroid_size - 1] = static_cast<float>((rgb)&0x0000ffu); + centroid[centroid_size - 1] = static_cast<float>((rgb) & 0x0000ffu); } pcl::for_each_type<FieldList>( pcl::NdCopyPointEigenFunctor<PointT>(input_->points[cp], centroid));