Apply clang-format to sample_consensus_prerejective.h

Author: koide3

registration/include/pcl/registration/sample_consensus_prerejective.h

@@ -249,10 +249,12 @@ class SampleConsensusPrerejective : public Registration<PointSource, PointTarget
   }
 
   /** \brief Set the number of threads to use.
-   * \param nr_threads the number of hardware threads to use (0 sets the value back to automatic)
+   * \param nr_threads the number of hardware threads to use (0 sets the value back to
+   * automatic)
    */
   void
-  setNumberOfThreads(unsigned int nr_threads = 0) {
+  setNumberOfThreads(unsigned int nr_threads = 0)
+  {
 #ifdef _OPENMP
     num_threads_ = nr_threads ? nr_threads : omp_get_num_procs();
 #else
@@ -305,28 +307,31 @@ class SampleConsensusPrerejective : public Registration<PointSource, PointTarget
                         const Eigen::Matrix4f& guess) override;
 
   /** \brief Obtain the fitness of a transformation
-    * The following metrics are calculated, based on
-    * \b final_transformation_ and \b corr_dist_threshold_:
-    *   - Inliers: the number of transformed points which are closer than threshold to NN
-    *   - Error score: the MSE of the inliers  
-    * \param inliers indices of source point cloud inliers
-    * \param fitness_score output fitness_score output fitness score as the MSE of the inliers 
-    */
-  PCL_DEPRECATED(1, 15, "Please use `getFitness(final_transformation_, inliers)` instead")
-  void 
-  getFitness (std::vector<int>& inliers, float& fitness_score) const;
+   * The following metrics are calculated, based on
+   * \b final_transformation_ and \b corr_dist_threshold_:
+   *   - Inliers: the number of transformed points which are closer than threshold to NN
+   *   - Error score: the MSE of the inliers
+   * \param inliers indices of source point cloud inliers
+   * \param fitness_score output fitness_score output fitness score as the MSE of the
+   * inliers
+   */
+  PCL_DEPRECATED(1,
+                 15,
+                 "Please use `getFitness(final_transformation_, inliers)` instead")
+  void
+  getFitness(std::vector<int>& inliers, float& fitness_score) const;
 
   /** \brief Obtain the fitness of a transformation
-    * The following metrics are calculated, based on
-    * \b transformation and \b corr_dist_threshold_:
-    *   - Inliers: the number of transformed points which are closer than threshold to NN
-    *   - Error score: the MSE of the inliers  
-    * \param transformation transformation to be evaluated
-    * \param inliers indices of source point cloud inliers
-    * \return fitness_score fitness_score output fitness score as the MSE of the inliers 
-    */
+   * The following metrics are calculated, based on
+   * \b transformation and \b corr_dist_threshold_:
+   *   - Inliers: the number of transformed points which are closer than threshold to NN
+   *   - Error score: the MSE of the inliers
+   * \param transformation transformation to be evaluated
+   * \param inliers indices of source point cloud inliers
+   * \return fitness_score fitness_score output fitness score as the MSE of the inliers
+   */
   float
-  getFitness (const Eigen::Matrix4f& transformation, std::vector<int>& inliers) const;
+  getFitness(const Eigen::Matrix4f& transformation, std::vector<int>& inliers) const;
 
   /** \brief The source point cloud's feature descriptors. */
   FeatureCloudConstPtr input_features_;