Merge pull request #3426 from kunaltyagi/std_size

Replace `size_t` with `std::size_t`

2d/include/pcl/2d/impl/edge.hpp

@@ -68,7 +68,7 @@ pcl::Edge<PointInT, PointOutT>::detectEdgeSobel(pcl::PointCloud<PointOutT>& outp
   output.height = height;
   output.width = width;
 
-  for (size_t i = 0; i < output.size(); ++i) {
+  for (std::size_t i = 0; i < output.size(); ++i) {
     output[i].magnitude_x = (*magnitude_x)[i].intensity;
     output[i].magnitude_y = (*magnitude_y)[i].intensity;
     output[i].magnitude =
@@ -110,7 +110,7 @@ pcl::Edge<PointInT, PointOutT>::sobelMagnitudeDirection(
   output.height = height;
   output.width = width;
 
-  for (size_t i = 0; i < output.size(); ++i) {
+  for (std::size_t i = 0; i < output.size(); ++i) {
     output[i].magnitude_x = (*magnitude_x)[i].intensity;
     output[i].magnitude_y = (*magnitude_y)[i].intensity;
     output[i].magnitude =
@@ -149,7 +149,7 @@ pcl::Edge<PointInT, PointOutT>::detectEdgePrewitt(pcl::PointCloud<PointOutT>& ou
   output.height = height;
   output.width = width;
 
-  for (size_t i = 0; i < output.size(); ++i) {
+  for (std::size_t i = 0; i < output.size(); ++i) {
     output[i].magnitude_x = (*magnitude_x)[i].intensity;
     output[i].magnitude_y = (*magnitude_y)[i].intensity;
     output[i].magnitude =
@@ -188,7 +188,7 @@ pcl::Edge<PointInT, PointOutT>::detectEdgeRoberts(pcl::PointCloud<PointOutT>& ou
   output.height = height;
   output.width = width;
 
-  for (size_t i = 0; i < output.size(); ++i) {
+  for (std::size_t i = 0; i < output.size(); ++i) {
     output[i].magnitude_x = (*magnitude_x)[i].intensity;
     output[i].magnitude_y = (*magnitude_y)[i].intensity;
     output[i].magnitude =
@@ -369,7 +369,7 @@ pcl::Edge<PointInT, PointOutT>::detectEdgeCanny(pcl::PointCloud<PointOutT>& outp
   }
 
   // Final thresholding
-  for (size_t i = 0; i < input_->size(); ++i) {
+  for (std::size_t i = 0; i < input_->size(); ++i) {
     if ((*maxima)[i].intensity == std::numeric_limits<float>::max())
       output[i].magnitude = 255;
     else

2d/include/pcl/2d/impl/kernel.hpp

@@ -112,7 +112,7 @@ pcl::kernel<PointT>::gaussianKernel(pcl::PointCloud<PointT>& kernel)
   }
 
   // Normalizing the kernel
-  for (size_t i = 0; i < kernel.size(); ++i)
+  for (std::size_t i = 0; i < kernel.size(); ++i)
     kernel[i].intensity /= sum;
 }
 
@@ -140,7 +140,7 @@ pcl::kernel<PointT>::loGKernel(pcl::PointCloud<PointT>& kernel)
   }
 
   // Normalizing the kernel
-  for (size_t i = 0; i < kernel.size(); ++i)
+  for (std::size_t i = 0; i < kernel.size(); ++i)
     kernel[i].intensity /= sum;
 }
 

2d/include/pcl/2d/impl/keypoint.hpp

@@ -84,12 +84,12 @@ pcl::keypoint::harrisCorner(ImageType& output,
   conv_2d.convolve(M11, kernel_i, I_y2);
 
   /*harris function*/
-  const size_t height = input.size();
-  const size_t width = input[0].size();
+  const std::size_t height = input.size();
+  const std::size_t width = input[0].size();
   output.resize(height);
-  for (size_t i = 0; i < height; i++) {
+  for (std::size_t i = 0; i < height; i++) {
     output[i].resize(width);
-    for (size_t j = 0; j < width; j++) {
+    for (std::size_t j = 0; j < width; j++) {
       output[i][j] = M00[i][j] * M11[i][j] - (M10[i][j] * M10[i][j]) -
                      alpha * ((M00[i][j] + M11[i][j]) * (M00[i][j] + M11[i][j]));
       if (thresh != 0) {
@@ -102,8 +102,8 @@ pcl::keypoint::harrisCorner(ImageType& output,
   }
 
   /*local maxima*/
-  for (size_t i = 1; i < height - 1; i++) {
-    for (size_t j = 1; j < width - 1; j++) {
+  for (std::size_t i = 1; i < height - 1; i++) {
+    for (std::size_t j = 1; j < width - 1; j++) {
       if (output[i][j] > output[i - 1][j - 1] && output[i][j] > output[i - 1][j] &&
           output[i][j] > output[i - 1][j + 1] && output[i][j] > output[i][j - 1] &&
           output[i][j] > output[i][j + 1] && output[i][j] > output[i + 1][j - 1] &&
@@ -141,14 +141,14 @@ pcl::keypoint::hessianBlob(ImageType& output,
   edge_detection.ComputeDerivativeYCentral(I_xy, I_x);
   edge_detection.ComputeDerivativeYCentral(I_yy, I_y);
   /*Determinant of Hessian*/
-  const size_t height = input.size();
-  const size_t width = input[0].size();
+  const std::size_t height = input.size();
+  const std::size_t width = input[0].size();
   float min = std::numeric_limits<float>::max();
   float max = std::numeric_limits<float>::min();
   cornerness.resize(height);
-  for (size_t i = 0; i < height; i++) {
+  for (std::size_t i = 0; i < height; i++) {
     cornerness[i].resize(width);
-    for (size_t j = 0; j < width; j++) {
+    for (std::size_t j = 0; j < width; j++) {
       cornerness[i][j] =
           sigma * sigma * (I_xx[i][j] + I_yy[i][j] - I_xy[i][j] * I_xy[i][j]);
       if (SCALED) {
@@ -163,9 +163,9 @@ pcl::keypoint::hessianBlob(ImageType& output,
     output.resize(height);
     output[0].resize(width);
     output[height - 1].resize(width);
-    for (size_t i = 1; i < height - 1; i++) {
+    for (std::size_t i = 1; i < height - 1; i++) {
       output[i].resize(width);
-      for (size_t j = 1; j < width - 1; j++) {
+      for (std::size_t j = 1; j < width - 1; j++) {
         if (SCALED)
           output[i][j] = ((cornerness[i][j] - min) / (max - min));
         else
@@ -183,8 +183,8 @@ pcl::keypoint::hessianBlob(ImageType& output,
                            const float scaling_factor,
                            const int num_scales)
 {
-  const size_t height = input.size();
-  const size_t width = input[0].size();
+  const std::size_t height = input.size();
+  const std::size_t width = input[0].size();
   const int local_search_radius = 1;
   float scale = start_scale;
   std::vector<ImageType> cornerness;
@@ -195,8 +195,8 @@ pcl::keypoint::hessianBlob(ImageType& output,
   }
   bool non_max_flag = false;
   float scale_max, local_max;
-  for (size_t i = 0; i < height; i++) {
-    for (size_t j = 0; j < width; j++) {
+  for (std::size_t i = 0; i < height; i++) {
+    for (std::size_t j = 0; j < width; j++) {
       scale_max = std::numeric_limits<float>::min();
       /*default output in case of no blob at the current point is 0*/
       output[i][j] = 0;
@@ -245,12 +245,12 @@ pcl::keypoint::imageElementMultiply(ImageType& output,
                                     ImageType& input1,
                                     ImageType& input2)
 {
-  const size_t height = input1.size();
-  const size_t width = input1[0].size();
+  const std::size_t height = input1.size();
+  const std::size_t width = input1[0].size();
   output.resize(height);
-  for (size_t i = 0; i < height; i++) {
+  for (std::size_t i = 0; i < height; i++) {
     output[i].resize(width);
-    for (size_t j = 0; j < width; j++) {
+    for (std::size_t j = 0; j < width; j++) {
       output[i][j] = input1[i][j] * input2[i][j];
     }
   }

2d/include/pcl/2d/impl/morphology.hpp

@@ -282,7 +282,7 @@ pcl::Morphology<PointT>::subtractionBinary(pcl::PointCloud<PointT>& output,
   output.height = height;
   output.resize(height * width);
 
-  for (size_t i = 0; i < output.size(); ++i) {
+  for (std::size_t i = 0; i < output.size(); ++i) {
     if (input1[i].intensity == 1 && input2[i].intensity == 0)
       output[i].intensity = 1;
     else
@@ -303,7 +303,7 @@ pcl::Morphology<PointT>::unionBinary(pcl::PointCloud<PointT>& output,
   output.height = height;
   output.resize(height * width);
 
-  for (size_t i = 0; i < output.size(); ++i) {
+  for (std::size_t i = 0; i < output.size(); ++i) {
     if (input1[i].intensity == 1 || input2[i].intensity == 1)
       output[i].intensity = 1;
     else
@@ -324,7 +324,7 @@ pcl::Morphology<PointT>::intersectionBinary(pcl::PointCloud<PointT>& output,
   output.height = height;
   output.resize(height * width);
 
-  for (size_t i = 0; i < output.size(); ++i) {
+  for (std::size_t i = 0; i < output.size(); ++i) {
     if (input1[i].intensity == 1 && input2[i].intensity == 1)
       output[i].intensity = 1;
     else
@@ -363,7 +363,7 @@ pcl::Morphology<PointT>::structuringElementRectangle(pcl::PointCloud<PointT>& ke
   kernel.height = height;
   kernel.width = width;
   kernel.resize(height * width);
-  for (size_t i = 0; i < kernel.size(); ++i)
+  for (std::size_t i = 0; i < kernel.size(); ++i)
     kernel[i].intensity = 1;
 }
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/global/crh_estimator.h

@@ -67,7 +67,7 @@ namespace pcl
         crh.setInputCloud(processed);
         crh.setInputNormals(normals_);
 
-        for (size_t idx = 0; idx < signatures.size (); idx++)
+        for (std::size_t idx = 0; idx < signatures.size (); idx++)
         {
           Eigen::Vector4f centroid4f(centroids[idx][0],centroids[idx][1],centroids[idx][2],0);
           crh.setCentroid(centroid4f);

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/global/cvfh_estimator.h

@@ -133,7 +133,7 @@ namespace pcl
 
         cvfh.compute (cvfh_signatures);
 
-        for (size_t i = 0; i < cvfh_signatures.points.size (); i++)
+        for (std::size_t i = 0; i < cvfh_signatures.points.size (); i++)
         {
           pcl::PointCloud<FeatureT> vfh_signature;
           vfh_signature.points.resize (1);

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/colorshot_local_estimator.h

@@ -78,7 +78,7 @@ namespace pcl
 
           int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);
 
-          for (size_t k = 0; k < shots->points.size (); k++)
+          for (std::size_t k = 0; k < shots->points.size (); k++)
             for (int i = 0; i < size_feat; i++)
               signatures->points[k].histogram[i] = shots->points[k].descriptor[i];
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/fpfh_local_estimator.h

@@ -77,7 +77,7 @@ namespace pcl
           signatures->height = 1;
 
           int size_feat = 33;
-          for (size_t k = 0; k < fpfhs->points.size (); k++)
+          for (std::size_t k = 0; k < fpfhs->points.size (); k++)
             for (int i = 0; i < size_feat; i++)
               signatures->points[k].histogram[i] = fpfhs->points[k].histogram[i];
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/fpfh_local_estimator_omp.h

@@ -79,7 +79,7 @@ namespace pcl
           signatures->height = 1;
 
           int size_feat = 33;
-          for (size_t k = 0; k < fpfhs->points.size (); k++)
+          for (std::size_t k = 0; k < fpfhs->points.size (); k++)
             for (int i = 0; i < size_feat; i++)
               signatures->points[k].histogram[i] = fpfhs->points[k].histogram[i];
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/local_estimator.h

@@ -106,7 +106,7 @@ namespace pcl
           filtered_keypoints.points.resize (keypoints_cloud->points.size ());
           int good = 0;
 
-          for (size_t i = 0; i < keypoints_cloud->points.size (); i++)
+          for (std::size_t i = 0; i < keypoints_cloud->points.size (); i++)
           {
 
             if (tree->radiusSearch (keypoints_cloud->points[i], radius_, (*neighborhood_indices_)[good], (*neighborhood_dist_)[good]))
@@ -232,7 +232,7 @@ namespace pcl
           input_cloud->width = input_->width;
           input_cloud->height = input_->height;
           input_cloud->points.resize (input_->width * input_->height);
-          for (size_t i = 0; i < input_->points.size (); i++)
+          for (std::size_t i = 0; i < input_->points.size (); i++)
           {
             input_cloud->points[i].getVector3fMap () = input_->points[i].getVector3fMap ();
             input_cloud->points[i].getNormalVector3fMap () = normals_->points[i].getNormalVector3fMap ();
@@ -398,7 +398,7 @@ namespace pcl
         computeKeypoints (PointInTPtr & cloud, PointInTPtr & keypoints, pcl::PointCloud<pcl::Normal>::Ptr & normals)
         {
           keypoints.reset (new pcl::PointCloud<PointInT>);
-          for (size_t i = 0; i < keypoint_extractor_.size (); i++)
+          for (std::size_t i = 0; i < keypoint_extractor_.size (); i++)
           {
             keypoint_extractor_[i]->setInputCloud (cloud);
             if (keypoint_extractor_[i]->needNormals ())

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/shot_local_estimator.h

@@ -125,7 +125,7 @@ namespace pcl
 
           int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);
 
-          for (size_t k = 0; k < shots->points.size (); k++)
+          for (std::size_t k = 0; k < shots->points.size (); k++)
             for (int i = 0; i < size_feat; i++)
               signatures->points[k].histogram[i] = shots->points[k].descriptor[i];
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/normal_estimator.h

@@ -37,12 +37,12 @@ namespace pcl
           float sum_distances = 0.0;
           std::vector<float> avg_distances (input->points.size ());
           // Iterate through the source data set
-          for (size_t i = 0; i < input->points.size (); ++i)
+          for (std::size_t i = 0; i < input->points.size (); ++i)
           {
             tree->nearestKSearch (input->points[i], 9, nn_indices, nn_distances);
 
             float avg_dist_neighbours = 0.0;
-            for (size_t j = 1; j < nn_indices.size (); j++)
+            for (std::size_t j = 1; j < nn_indices.size (); j++)
               avg_dist_neighbours += std::sqrt (nn_distances[j]);
 
             avg_dist_neighbours /= static_cast<float> (nn_indices.size ());
@@ -203,7 +203,7 @@ namespace pcl
             {
               pcl::ScopeTime t ("check nans...");
               int j = 0;
-              for (size_t i = 0; i < out->points.size (); ++i)
+              for (std::size_t i = 0; i < out->points.size (); ++i)
               {
                 if (!std::isfinite (out->points[i].x) || !std::isfinite (out->points[i].y) || !std::isfinite (out->points[i].z))
                   continue;
@@ -239,7 +239,7 @@ namespace pcl
           {
             pcl::ScopeTime t ("check nans...");
             int j = 0;
-            for (size_t i = 0; i < normals->points.size (); ++i)
+            for (std::size_t i = 0; i < normals->points.size (); ++i)
             {
               if (!std::isfinite (normals->points[i].normal_x) || !std::isfinite (normals->points[i].normal_y)
                   || !std::isfinite (normals->points[i].normal_z))
@@ -263,7 +263,7 @@ namespace pcl
             //is is organized, we set the xyz points to NaN
             pcl::ScopeTime t ("check nans organized...");
             bool NaNs = false;
-            for (size_t i = 0; i < normals->points.size (); ++i)
+            for (std::size_t i = 0; i < normals->points.size (); ++i)
             {
               if (std::isfinite (normals->points[i].normal_x) && std::isfinite (normals->points[i].normal_y)
                   && std::isfinite (normals->points[i].normal_z))
@@ -281,7 +281,7 @@ namespace pcl
             }
           }
 
-          /*for (size_t i = 0; i < out->points.size (); i++)
+          /*for (std::size_t i = 0; i < out->points.size (); i++)
           {
             int r, g, b;
             r = static_cast<int> (out->points[i].r);

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/pc_source/mesh_source.h

@@ -211,7 +211,7 @@ namespace pcl
             model.poses_.reset (new std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > ());
             model.self_occlusions_.reset (new std::vector<float> ());
 
-            for (size_t i = 0; i < views_xyz_orig.size (); i++)
+            for (std::size_t i = 0; i < views_xyz_orig.size (); i++)
             {
               model.views_->push_back (views_xyz_orig[i]);
               model.poses_->push_back (poses[i]);
@@ -222,7 +222,7 @@ namespace pcl
             direc << dir << "/" << model.class_ << "/" << model.id_;
             this->createClassAndModelDirectories (dir, model.class_, model.id_);
 
-            for (size_t i = 0; i < model.views_->size (); i++)
+            for (std::size_t i = 0; i < model.views_->size (); i++)
             {
               //save generated model for future use
               std::stringstream path_view;

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/pc_source/registered_views_source.h

@@ -92,7 +92,7 @@ namespace pcl
 
         void
         assembleModelFromViewsAndPoses(ModelT & model, std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > & poses) {
-          for(size_t i=0; i < model.views_->size(); i++) {
+          for(std::size_t i=0; i < model.views_->size(); i++) {
             Eigen::Matrix4f inv = poses[i];
             typename pcl::PointCloud<PointInT>::Ptr global_cloud(new pcl::PointCloud<PointInT>);
             pcl::transformPointCloud(*(model.views_->at(i)),*global_cloud, inv);
@@ -143,7 +143,7 @@ namespace pcl
 
             std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > poses_to_assemble_;
 
-            for (size_t i = 0; i < view_filenames.size (); i++)
+            for (std::size_t i = 0; i < view_filenames.size (); i++)
             {
               std::stringstream view_file;
               view_file << pathmodel.str () << "/" << view_filenames[i];
@@ -214,7 +214,7 @@ namespace pcl
             getViewsFilenames (model_dir, view_filenames);
             std::cout << view_filenames.size () << std::endl;
 
-            for (size_t i = 0; i < view_filenames.size (); i++)
+            for (std::size_t i = 0; i < view_filenames.size (); i++)
             {
               std::stringstream view_file;
               view_file << model_path << "/" << view_filenames[i];
@@ -312,7 +312,7 @@ namespace pcl
 
           models_.reset (new std::vector<ModelT>);
 
-          for (size_t i = 0; i < files.size (); i++)
+          for (std::size_t i = 0; i < files.size (); i++)
           {
             ModelT m;
 

apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/pc_source/source.h

@@ -201,7 +201,7 @@ namespace pcl
       void
       voxelizeAllModels (float resolution)
       {
-        for (size_t i = 0; i < models_->size (); i++)
+        for (std::size_t i = 0; i < models_->size (); i++)
         {
           models_->at (i).getAssembled (resolution);
         }