Merge pull request #5522 from SunBlack/fix_C26814

Use constexpr variables if possible

Author: mvieth

apps/in_hand_scanner/src/icp.cpp

@@ -161,7 +161,7 @@ pcl::ihs::ICP::findTransformation(const MeshConstPtr& mesh_model,
 {
   // Check the input
   // TODO: Double check the minimum number of points necessary for icp
-  const std::size_t n_min = 4;
+  constexpr std::size_t n_min = 4;
 
   if (mesh_model->sizeVertices() < n_min || cloud_data->size() < n_min) {
     std::cerr << "ERROR in icp.cpp: Not enough input points!\n";

apps/in_hand_scanner/src/integration.cpp

@@ -124,7 +124,7 @@ pcl::ihs::Integration::reconstructMesh(const CloudXYZRGBNormalConstPtr& cloud_da
   // *   3 - 0  //
   const int offset_1 = -width;
   const int offset_2 = -width - 1;
-  const int offset_3 = -1;
+  constexpr int offset_3 = -1;
   const int offset_4 = -width - 2;
 
   for (int r = 1; r < height; ++r) {
@@ -262,7 +262,7 @@ pcl::ihs::Integration::merge(const CloudXYZRGBNormalConstPtr& cloud_data,
   // *   3 - 0  //
   const int offset_1 = -width;
   const int offset_2 = -width - 1;
-  const int offset_3 = -1;
+  constexpr int offset_3 = -1;
   const int offset_4 = -width - 2;
 
   const float dot_min = std::cos(max_angle_ * 17.45329252e-3); // deg to rad

apps/in_hand_scanner/src/main_window.cpp

@@ -70,7 +70,7 @@ pcl::ihs::MainWindow::MainWindow(QWidget* parent)
   spacer->setSizePolicy(QSizePolicy::Expanding, QSizePolicy::Expanding);
   ui_->toolBar->insertWidget(ui_->actionHelp, spacer);
 
-  const double max = std::numeric_limits<double>::max();
+  constexpr double max = std::numeric_limits<double>::max();
 
   // In hand scanner
   QHBoxLayout* layout = new QHBoxLayout(ui_->placeholder_in_hand_scanner);

apps/in_hand_scanner/src/opengl_viewer.cpp

@@ -484,7 +484,7 @@ pcl::ihs::OpenGLViewer::addMesh(const CloudXYZRGBNormalConstPtr& cloud,
   const int h = cloud->height;
   const int offset_1 = -w;
   const int offset_2 = -w - 1;
-  const int offset_3 = -1;
+  constexpr int offset_3 = -1;
 
   FaceVertexMeshPtr mesh(new FaceVertexMesh());
   mesh->transformation = T;
@@ -1083,7 +1083,7 @@ pcl::ihs::OpenGLViewer::initializeGL()
 void
 pcl::ihs::OpenGLViewer::setupViewport(const int w, const int h)
 {
-  const float aspect_ratio = 4. / 3.;
+  constexpr float aspect_ratio = 4. / 3.;
 
   // Use the biggest possible area of the window to draw to
   //    case 1 (w < w_scaled):        case 2 (w >= w_scaled):

apps/point_cloud_editor/include/pcl/apps/point_cloud_editor/localTypes.h

@@ -167,25 +167,25 @@ struct IncIndex
 /// A helpful const representing the number of elements in our vectors.
 /// This is used for all operations that require the copying of the vector.
 /// Although this is used in a fairly generic way, the display requires 3D.
-const unsigned int XYZ_SIZE = 3;
+constexpr unsigned int XYZ_SIZE = 3;
 
 /// A helpful const representing the number of elements in each row/col in
 /// our matrices. This is used for all operations that require the copying of
 /// the matrix.
-const unsigned int MATRIX_SIZE_DIM = 4;
+constexpr unsigned int MATRIX_SIZE_DIM = 4;
 
 /// A helpful const representing the number of elements in our matrices.
 /// This is used for all operations that require the copying of the matrix.
-const unsigned int MATRIX_SIZE = MATRIX_SIZE_DIM * MATRIX_SIZE_DIM;
+constexpr unsigned int MATRIX_SIZE = MATRIX_SIZE_DIM * MATRIX_SIZE_DIM;
 
 /// The default window width
-const unsigned int WINDOW_WIDTH = 1200;
+constexpr unsigned int WINDOW_WIDTH = 1200;
 /// The default window height
-const unsigned int WINDOW_HEIGHT = 1000;
+constexpr unsigned int WINDOW_HEIGHT = 1000;
 
 /// The default z translation used to push the world origin in front of the
 /// display
-const float DISPLAY_Z_TRANSLATION = -2.0f;
+constexpr float DISPLAY_Z_TRANSLATION = -2.0f;
 
 /// The radius of the trackball given as a percentage of the screen width
-const float TRACKBALL_RADIUS_SCALE = 0.4f;
+constexpr float TRACKBALL_RADIUS_SCALE = 0.4f;

apps/src/feature_matching.cpp

@@ -324,7 +324,7 @@ ICCVTutorial<FeatureType>::findCorrespondences(
 
   // Find the index of the best match for each keypoint, and store it in
   // "correspondences_out"
-  const int k = 1;
+  constexpr int k = 1;
   pcl::Indices k_indices(k);
   std::vector<float> k_squared_distances(k);
   for (int i = 0; i < static_cast<int>(source->size()); ++i) {

apps/src/multiscale_feature_persistence_example.cpp

@@ -47,7 +47,7 @@
 using namespace pcl;
 
 const Eigen::Vector4f subsampling_leaf_size(0.01f, 0.01f, 0.01f, 0.0f);
-const float normal_estimation_search_radius = 0.05f;
+constexpr float normal_estimation_search_radius = 0.05f;
 
 void
 subsampleAndCalculateNormals(PointCloud<PointXYZ>::Ptr& cloud,

apps/src/openni_feature_persistence.cpp

@@ -69,11 +69,11 @@ using namespace std::chrono_literals;
   } while (false)
 // clang-format on
 
-const float default_subsampling_leaf_size = 0.02f;
-const float default_normal_search_radius = 0.041f;
+constexpr float default_subsampling_leaf_size = 0.02f;
+constexpr float default_normal_search_radius = 0.041f;
 const double aux[] = {0.21, 0.32};
 const std::vector<double> default_scales_vector(aux, aux + 2);
-const float default_alpha = 1.2f;
+constexpr float default_alpha = 1.2f;
 
 template <typename PointType>
 class OpenNIFeaturePersistence {

apps/src/openni_mobile_server.cpp

@@ -79,7 +79,7 @@ CopyPointCloudToBuffers(const pcl::PointCloud<pcl::PointXYZRGBA>::ConstPtr& clou
         point.x > bounds_max.x || point.y > bounds_max.y || point.z > bounds_max.z)
       continue;
 
-    const int conversion_factor = 500;
+    constexpr int conversion_factor = 500;
 
     cloud_buffers.points[j * 3 + 0] = static_cast<short>(point.x * conversion_factor);
     cloud_buffers.points[j * 3 + 1] = static_cast<short>(point.y * conversion_factor);

apps/src/openni_organized_edge_detection.cpp

@@ -68,7 +68,7 @@ class OpenNIOrganizedEdgeDetection {
                                      *this);
     viewer->resetCameraViewpoint("cloud");
 
-    const int point_size = 2;
+    constexpr int point_size = 2;
     viewer->addPointCloud<PointT>(cloud, "nan boundary edges");
     viewer->setPointCloudRenderingProperties(
         pcl::visualization::PCL_VISUALIZER_POINT_SIZE,

apps/src/openni_shift_to_depth_conversion.cpp

@@ -164,7 +164,7 @@ class SimpleOpenNIViewer {
     int centerY = static_cast<int>(height_arg / 2);
 
     const float fl_const = 1.0f / focalLength_arg;
-    static const float bad_point = std::numeric_limits<float>::quiet_NaN();
+    constexpr float bad_point = std::numeric_limits<float>::quiet_NaN();
 
     std::size_t i = 0;
     for (int y = -centerY; y < +centerY; ++y)

apps/src/pcd_organized_edge_detection.cpp

@@ -227,7 +227,7 @@ compute(const pcl::PCLPointCloud2::ConstPtr& input,
   pcl::copyPointCloud(*cloud, label_indices[3].indices, *high_curvature_edges);
   pcl::copyPointCloud(*cloud, label_indices[4].indices, *rgb_edges);
 
-  const int point_size = 2;
+  constexpr int point_size = 2;
   viewer.addPointCloud<pcl::PointXYZRGBA>(nan_boundary_edges, "nan boundary edges");
   viewer.setPointCloudRenderingProperties(
       pcl::visualization::PCL_VISUALIZER_POINT_SIZE, point_size, "nan boundary edges");

apps/src/ppf_object_recognition.cpp

@@ -15,7 +15,7 @@ using namespace pcl;
 using namespace std::chrono_literals;
 
 const Eigen::Vector4f subsampling_leaf_size(0.02f, 0.02f, 0.02f, 0.0f);
-const float normal_estimation_search_radius = 0.05f;
+constexpr float normal_estimation_search_radius = 0.05f;
 
 PointCloud<PointNormal>::Ptr
 subsampleAndCalculateNormals(const PointCloud<PointXYZ>::Ptr& cloud)

apps/src/pyramid_surface_matching.cpp

@@ -9,7 +9,7 @@ using namespace pcl;
 #include <iostream>
 
 const Eigen::Vector4f subsampling_leaf_size(0.02f, 0.02f, 0.02f, 0.0f);
-const float normal_estimation_search_radius = 0.05f;
+constexpr float normal_estimation_search_radius = 0.05f;
 
 void
 subsampleAndCalculateNormals(PointCloud<PointXYZ>::Ptr& cloud,

apps/src/statistical_multiscale_interest_region_extraction_example.cpp

@@ -42,8 +42,8 @@
 
 using namespace pcl;
 
-const float subsampling_leaf_size = 0.003f;
-const float base_scale = 0.005f;
+constexpr float subsampling_leaf_size = 0.003f;
+constexpr float base_scale = 0.005f;
 
 int
 main(int, char** argv)

common/include/pcl/common/impl/copy_point.hpp

@@ -118,10 +118,10 @@ struct CopyPointHelper<PointInT, PointOutT,
     using FieldListInT = typename pcl::traits::fieldList<PointInT>::type;
     using FieldListOutT = typename pcl::traits::fieldList<PointOutT>::type;
     using FieldList = typename pcl::intersect<FieldListInT, FieldListOutT>::type;
-    const std::uint32_t offset_in  = boost::mpl::if_<pcl::traits::has_field<PointInT, pcl::fields::rgb>,
+    constexpr std::uint32_t offset_in  = boost::mpl::if_<pcl::traits::has_field<PointInT, pcl::fields::rgb>,
                                                 pcl::traits::offset<PointInT, pcl::fields::rgb>,
                                                 pcl::traits::offset<PointInT, pcl::fields::rgba> >::type::value;
-    const std::uint32_t offset_out = boost::mpl::if_<pcl::traits::has_field<PointOutT, pcl::fields::rgb>,
+    constexpr std::uint32_t offset_out = boost::mpl::if_<pcl::traits::has_field<PointOutT, pcl::fields::rgb>,
                                                 pcl::traits::offset<PointOutT, pcl::fields::rgb>,
                                                 pcl::traits::offset<PointOutT, pcl::fields::rgba> >::type::value;
     pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointInT, PointOutT> (point_in, point_out));

common/include/pcl/common/impl/eigen.hpp

@@ -89,7 +89,7 @@ computeRoots (const Matrix& m, Roots& roots)
     computeRoots2 (c2, c1, roots);
   else
   {
-    const Scalar s_inv3 = Scalar (1.0 / 3.0);
+    constexpr Scalar s_inv3 = Scalar(1.0 / 3.0);
     const Scalar s_sqrt3 = std::sqrt (Scalar (3.0));
     // Construct the parameters used in classifying the roots of the equation
     // and in solving the equation for the roots in closed form.

common/include/pcl/point_representation.h

@@ -250,7 +250,7 @@ namespace pcl
       template<typename Key> inline void operator() ()
       {
         using FieldT = typename pcl::traits::datatype<PointDefault, Key>::type;
-        const int NrDims = pcl::traits::datatype<PointDefault, Key>::size;
+        constexpr int NrDims = pcl::traits::datatype<PointDefault, Key>::size;
         Helper<Key, FieldT, NrDims>::copyPoint (p1_, p2_, f_idx_);
       }
 

common/include/pcl/register_point_struct.h

@@ -100,7 +100,7 @@ namespace pcl
     plus (std::remove_const_t<T> &l, const T &r)
     {
       using type = std::remove_all_extents_t<T>;
-      static const std::uint32_t count = sizeof (T) / sizeof (type);
+      constexpr std::uint32_t count = sizeof(T) / sizeof(type);
       for (std::uint32_t i = 0; i < count; ++i)
         l[i] += r[i];
     }
@@ -117,7 +117,7 @@ namespace pcl
     plusscalar (T1 &p, const T2 &scalar)
     {
       using type = std::remove_all_extents_t<T1>;
-      static const std::uint32_t count = sizeof (T1) / sizeof (type);
+      constexpr std::uint32_t count = sizeof(T1) / sizeof(type);
       for (std::uint32_t i = 0; i < count; ++i)
         p[i] += scalar;
     }
@@ -134,7 +134,7 @@ namespace pcl
     minus (std::remove_const_t<T> &l, const T &r)
     {
       using type = std::remove_all_extents_t<T>;
-      static const std::uint32_t count = sizeof (T) / sizeof (type);
+      constexpr std::uint32_t count = sizeof(T) / sizeof(type);
       for (std::uint32_t i = 0; i < count; ++i)
         l[i] -= r[i];
     }
@@ -151,7 +151,7 @@ namespace pcl
     minusscalar (T1 &p, const T2 &scalar)
     {
       using type = std::remove_all_extents_t<T1>;
-      static const std::uint32_t count = sizeof (T1) / sizeof (type);
+      constexpr std::uint32_t count = sizeof(T1) / sizeof(type);
       for (std::uint32_t i = 0; i < count; ++i)
         p[i] -= scalar;
     }
@@ -168,7 +168,7 @@ namespace pcl
     mulscalar (T1 &p, const T2 &scalar)
     {
       using type = std::remove_all_extents_t<T1>;
-      static const std::uint32_t count = sizeof (T1) / sizeof (type);
+      constexpr std::uint32_t count = sizeof(T1) / sizeof(type);
       for (std::uint32_t i = 0; i < count; ++i)
         p[i] *= scalar;
     }
@@ -185,7 +185,7 @@ namespace pcl
     divscalar (T1 &p, const T2 &scalar)
     {
       using type = std::remove_all_extents_t<T1>;
-      static const std::uint32_t count = sizeof (T1) / sizeof (type);
+      constexpr std::uint32_t count = sizeof (T1) / sizeof (type);
       for (std::uint32_t i = 0; i < count; ++i)
         p[i] /= scalar;
     }
@@ -202,7 +202,7 @@ namespace pcl
     divscalar2 (ArrayT &p, const ScalarT &scalar)
     {
       using type = std::remove_all_extents_t<ArrayT>;
-      static const std::uint32_t count = sizeof (ArrayT) / sizeof (type);
+      constexpr std::uint32_t count = sizeof (ArrayT) / sizeof (type);
       for (std::uint32_t i = 0; i < count; ++i)
         p[i] = scalar / p[i];
     }

examples/features/example_difference_of_normals.cpp

@@ -99,7 +99,7 @@ int main (int argc, char *argv[])
 
     // Create downsampled point cloud for DoN NN search with large scale
     large_cloud_downsampled = PointCloud<PointT>::Ptr(new pcl::PointCloud<PointT>);
-    const float largedownsample = float (scale2/decimation);
+    constexpr float largedownsample = float(scale2 / decimation);
     sor.setLeafSize (largedownsample, largedownsample, largedownsample);
     sor.filter (*large_cloud_downsampled);
     std::cout << "Using leaf size of " << largedownsample << " for large scale, " << large_cloud_downsampled->size() << " points" << std::endl;

examples/keypoints/example_sift_keypoint_estimation.cpp

@@ -57,10 +57,10 @@ main(int, char** argv)
   std::cout << "points: " << cloud->size () <<std::endl;
 
   // Parameters for sift computation
-  const float min_scale = 0.1f;
-  const int n_octaves = 6;
-  const int n_scales_per_octave = 10;
-  const float min_contrast = 0.5f;
+  constexpr float min_scale = 0.1f;
+  constexpr int n_octaves = 6;
+  constexpr int n_scales_per_octave = 10;
+  constexpr float min_contrast = 0.5f;
 
 
   // Estimate the sift interest points using Intensity values from RGB values

examples/keypoints/example_sift_normal_keypoint_estimation.cpp

@@ -63,10 +63,10 @@ main(int, char** argv)
   std::cout << "points: " << cloud_xyz->size () <<std::endl;
 
   // Parameters for sift computation
-  const float min_scale = 0.01f;
-  const int n_octaves = 3;
-  const int n_scales_per_octave = 4;
-  const float min_contrast = 0.001f;
+  constexpr float min_scale = 0.01f;
+  constexpr int n_octaves = 3;
+  constexpr int n_scales_per_octave = 4;
+  constexpr float min_contrast = 0.001f;
 
   // Estimate the normals of the cloud_xyz
   pcl::NormalEstimation<pcl::PointXYZ, pcl::PointNormal> ne;

examples/keypoints/example_sift_z_keypoint_estimation.cpp

@@ -75,10 +75,10 @@ main(int, char** argv)
   std::cout << "points: " << cloud_xyz->size () <<std::endl;
 
   // Parameters for sift computation
-  const float min_scale = 0.005f;
-  const int n_octaves = 6;
-  const int n_scales_per_octave = 4;
-  const float min_contrast = 0.005f;
+  constexpr float min_scale = 0.005f;
+  constexpr int n_octaves = 6;
+  constexpr int n_scales_per_octave = 4;
+  constexpr float min_contrast = 0.005f;
 
   // Estimate the sift interest points using z values from xyz as the Intensity variants
   pcl::SIFTKeypoint<pcl::PointXYZ, pcl::PointWithScale> sift;

examples/segmentation/example_extract_clusters_normals.cpp

@@ -79,9 +79,9 @@ main (int argc, char **argv)
 
   // Extracting Euclidean clusters using cloud and its normals
   std::vector<pcl::PointIndices> cluster_indices;
-  const float tolerance = 0.5f; // 50cm tolerance in (x, y, z) coordinate system
-  const double eps_angle = 5 * (M_PI / 180.0); // 5degree tolerance in normals
-  const unsigned int min_cluster_size = 50;
+  constexpr float tolerance = 0.5f; // 50cm tolerance in (x, y, z) coordinate system
+  constexpr double eps_angle = 5 * (M_PI / 180.0); // 5degree tolerance in normals
+  constexpr unsigned int min_cluster_size = 50;
 
   pcl::extractEuclideanClusters (*cloud_ptr, *cloud_normals, tolerance, tree_ec, cluster_indices, eps_angle, min_cluster_size);
 

features/include/pcl/features/impl/brisk_2d.hpp

@@ -124,7 +124,7 @@ BRISK2DEstimation<PointInT, PointOutT, KeypointT, IntensityT>::generateKernel (
   scale_list_ = new float[scales_];
   size_list_  = new unsigned int[scales_];
 
-  const float sigma_scale = 1.3f;
+  constexpr float sigma_scale = 1.3f;
 
   for (unsigned int scale = 0; scale < scales_; ++scale)
   {

gpu/kinfu/tools/kinfu_app.cpp

@@ -732,7 +732,7 @@ struct KinFuApp
   {
     if(registration_)
     {
-      const int max_color_integration_weight = 2;
+      constexpr int max_color_integration_weight = 2;
       kinfu_.initColorIntegration(max_color_integration_weight);
       integrate_colors_ = true;      
     }    

gpu/kinfu/tools/record_tsdfvolume.cpp

@@ -138,8 +138,8 @@ DeviceVolume::createFromDepth (const pcl::device::PtrStepSz<const unsigned short
 
   using Matrix3frm = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
 
-  const int rows = 480;
-  const int cols = 640;
+  constexpr int rows = 480;
+  constexpr int cols = 640;
 
   // scale depth values
   gpu::DeviceArray2D<float> device_depth_scaled;
@@ -197,7 +197,7 @@ DeviceVolume::getVolume (pcl::TSDFVolume<VoxelT, WeightT>::Ptr &volume)
 bool
 DeviceVolume::getCloud (pcl::PointCloud<pcl::PointXYZ>::Ptr &cloud)
 {
-  const int DEFAULT_VOLUME_CLOUD_BUFFER_SIZE = 10 * 1000 * 1000;
+  constexpr int DEFAULT_VOLUME_CLOUD_BUFFER_SIZE = 10 * 1000 * 1000;
 
   // point buffer on the device
   pcl::gpu::DeviceArray<pcl::PointXYZ> device_cloud_buffer (DEFAULT_VOLUME_CLOUD_BUFFER_SIZE);

gpu/kinfu_large_scale/tools/kinfuLS_app.cpp

@@ -790,7 +790,7 @@ struct KinFuLSApp
   {
     if(registration_)
     {
-      const int max_color_integration_weight = 2;
+      constexpr int max_color_integration_weight = 2;
       kinfu_->initColorIntegration(max_color_integration_weight);
       integrate_colors_ = true;      
     }