C++17 filesystem for doc/tutorials (#5934)

* C++17 filesystem for doc/tutorials

* Review changes

Author: cybaol

doc/tutorials/content/sources/iccv2011/src/build_all_object_models.cpp

@@ -6,20 +6,27 @@
 #include <pcl/console/parse.h>
 #include <pcl/io/pcd_io.h>
 #include <pcl/visualization/pcl_visualizer.h>
+
+#if (__cplusplus >= 201703L)
+#include <filesystem>
+namespace pcl_fs = std::filesystem;
+#else
 #include <boost/filesystem.hpp>
+namespace pcl_fs = boost::filesystem;
+#endif
+
 #include <boost/algorithm/string.hpp> // for split, is_any_of
-namespace bf = boost::filesystem;
 
 inline void
-getModelsInDirectory (bf::path & dir, std::string & rel_path_so_far, std::vector<std::string> & relative_paths)
+getModelsInDirectory (pcl_fs::path & dir, std::string & rel_path_so_far, std::vector<std::string> & relative_paths)
 {
-  for (const auto& dir_entry : bf::directory_iterator(dir))
+  for (const auto& dir_entry : pcl_fs::directory_iterator(dir))
   {
     //check if its a directory, then get models in it
-    if (bf::is_directory (dir_entry))
+    if (pcl_fs::is_directory (dir_entry))
     {
       std::string so_far = rel_path_so_far + dir_entry.path ().filename ().string () + "/";
-      bf::path curr_path = dir_entry.path ();
+      pcl_fs::path curr_path = dir_entry.path ();
       getModelsInDirectory (curr_path, so_far, relative_paths);
     }
     else
@@ -189,7 +196,7 @@ main (int argc, char ** argv)
 
   std::string directory (argv[1]);
   //Find all raw* files in input_directory
-  bf::path dir_path = directory;
+  pcl_fs::path dir_path = directory;
   std::vector < std::string > files;
   std::string start = "";
   getModelsInDirectory (dir_path, start, files);

doc/tutorials/content/sources/iros2011/src/build_all_object_models.cpp

@@ -6,20 +6,27 @@
 #include <pcl/console/parse.h>
 #include <pcl/io/pcd_io.h>
 #include <pcl/visualization/pcl_visualizer.h>
+
+#if (__cplusplus >= 201703L)
+#include <filesystem>
+namespace pcl_fs = std::filesystem;
+#else
 #include <boost/filesystem.hpp>
+namespace pcl_fs = boost::filesystem;
+#endif
+
 #include <boost/algorithm/string.hpp> // for split, is_any_of
-namespace bf = boost::filesystem;
 
 inline void
-getModelsInDirectory (bf::path & dir, std::string & rel_path_so_far, std::vector<std::string> & relative_paths)
+getModelsInDirectory (pcl_fs::path & dir, std::string & rel_path_so_far, std::vector<std::string> & relative_paths)
 {
-  for (const auto& dir_entry : bf::directory_iterator(dir))
+  for (const auto& dir_entry : pcl_fs::directory_iterator(dir))
   {
     //check if its a directory, then get models in it
-    if (bf::is_directory (dir_entry))
+    if (pcl_fs::is_directory (dir_entry))
     {
       std::string so_far = rel_path_so_far + dir_entry.path ().filename ().string () + "/";
-      bf::path curr_path = dir_entry.path ();
+      pcl_fs::path curr_path = dir_entry.path ();
       getModelsInDirectory (curr_path, so_far, relative_paths);
     }
     else
@@ -189,7 +196,7 @@ main (int argc, char ** argv)
 
   std::string directory (argv[1]);
   //Find all raw* files in input_directory
-  bf::path dir_path = directory;
+  pcl_fs::path dir_path = directory;
   std::vector < std::string > files;
   std::string start = "";
   getModelsInDirectory (dir_path, start, files);

doc/tutorials/content/sources/vfh_recognition/build_tree.cpp

@@ -2,7 +2,15 @@
 #include <pcl/point_cloud.h>
 #include <pcl/console/print.h>
 #include <pcl/io/pcd_io.h>
+
+#if (__cplusplus >= 201703L)
+#include <filesystem>
+namespace pcl_fs = std::filesystem;
+#else
 #include <boost/filesystem.hpp>
+namespace pcl_fs = boost::filesystem;
+#endif
+
 #include <flann/flann.h>
 #include <flann/io/hdf5.h>
 #include <fstream>
@@ -15,7 +23,7 @@ typedef std::pair<std::string, std::vector<float> > vfh_model;
   * \param vfh the resultant VFH model
   */
 bool
-loadHist (const boost::filesystem::path &path, vfh_model &vfh)
+loadHist (const pcl_fs::path &path, vfh_model &vfh)
 {
   int vfh_idx;
   // Load the file as a PCD
@@ -63,22 +71,22 @@ loadHist (const boost::filesystem::path &path, vfh_model &vfh)
   * \param models the resultant vector of histogram models
   */
 void
-loadFeatureModels (const boost::filesystem::path &base_dir, const std::string &extension, 
+loadFeatureModels (const pcl_fs::path &base_dir, const std::string &extension, 
                    std::vector<vfh_model> &models)
 {
-  if (!boost::filesystem::exists (base_dir) && !boost::filesystem::is_directory (base_dir))
+  if (!pcl_fs::exists (base_dir) && !pcl_fs::is_directory (base_dir))
     return;
 
-  for (boost::filesystem::directory_iterator it (base_dir); it != boost::filesystem::directory_iterator (); ++it)
+  for (pcl_fs::directory_iterator it (base_dir); it != pcl_fs::directory_iterator (); ++it)
   {
-    if (boost::filesystem::is_directory (it->status ()))
+    if (pcl_fs::is_directory (it->status ()))
     {
       std::stringstream ss;
       ss << it->path ();
       pcl::console::print_highlight ("Loading %s (%lu models loaded so far).\n", ss.str ().c_str (), (unsigned long)models.size ());
       loadFeatureModels (it->path (), extension, models);
     }
-    if (boost::filesystem::is_regular_file (it->status ()) && it->path ().extension ().string () == extension)
+    if (pcl_fs::is_regular_file (it->status ()) && it->path ().extension ().string () == extension)
     {
       vfh_model m;
       if (loadHist (base_dir / it->path ().filename (), m))

doc/tutorials/content/sources/vfh_recognition/nearest_neighbors.cpp

@@ -10,7 +10,15 @@
 #include <limits>
 #include <flann/flann.h>
 #include <flann/io/hdf5.h>
+
+#if (__cplusplus >= 201703L)
+#include <filesystem>
+namespace pcl_fs = std::filesystem;
+#else
 #include <boost/filesystem.hpp>
+namespace pcl_fs = boost::filesystem;
+#endif
+
 #include <boost/algorithm/string/replace.hpp> // for replace_last
 typedef std::pair<std::string, std::vector<float> > vfh_model;
 
@@ -19,7 +27,7 @@ typedef std::pair<std::string, std::vector<float> > vfh_model;
   * \param vfh the resultant VFH model
   */
 bool
-loadHist (const boost::filesystem::path &path, vfh_model &vfh)
+loadHist (const pcl_fs::path &path, vfh_model &vfh)
 {
   int vfh_idx;
   // Load the file as a PCD
@@ -152,7 +160,7 @@ main (int argc, char** argv)
   flann::Matrix<float> k_distances;
   flann::Matrix<float> data;
   // Check if the data has already been saved to disk
-  if (!boost::filesystem::exists ("training_data.h5") || !boost::filesystem::exists ("training_data.list"))
+  if (!pcl_fs::exists ("training_data.h5") || !pcl_fs::exists ("training_data.list"))
   {
     pcl::console::print_error ("Could not find training data models files %s and %s!\n", 
         training_data_h5_file_name.c_str (), training_data_list_file_name.c_str ());
@@ -167,7 +175,7 @@ main (int argc, char** argv)
   }
 
   // Check if the tree index has already been saved to disk
-  if (!boost::filesystem::exists (kdtree_idx_file_name))
+  if (!pcl_fs::exists (kdtree_idx_file_name))
   {
     pcl::console::print_error ("Could not find kd-tree index in file %s!", kdtree_idx_file_name.c_str ());
     return (-1);

doc/tutorials/content/vfh_recognition.rst

@@ -110,21 +110,21 @@ Once all VFH features have been loaded, we convert them to FLANN format, using:
 
 .. literalinclude:: sources/vfh_recognition/build_tree.cpp
    :language: cpp
-   :lines: 113-118
+   :lines: 121-126
 
 
 Since we're lazy, and we want to use this data (and not reload it again by crawling the directory structure in the testing phase), we dump the data to disk:
 
 .. literalinclude:: sources/vfh_recognition/build_tree.cpp
    :language: cpp
-   :lines: 120-126
+   :lines: 128-134
 
 
 Finally, we create the KdTree, and save its structure to disk:
 
 .. literalinclude:: sources/vfh_recognition/build_tree.cpp
    :language: cpp
-   :lines: 129-133
+   :lines: 137-141
 
 
 Here we will use a ``LinearIndex``, which does a brute-force search using a
@@ -164,7 +164,7 @@ In lines:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 132-143
+   :lines: 141-152
 
 
 we load the first given user histogram (and ignore the rest). Then we proceed
@@ -177,15 +177,15 @@ In lines:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 162-163
+   :lines: 171-172
 
 
 we load the training data from disk, together with the list of file names that
 we previously stored in ``build_tree.cpp``. Then, we read the kd-tree and rebuild the index:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 176-177
+   :lines: 185-186
 
 
 Here we need to make sure that we use the **exact** distance metric
@@ -194,53 +194,53 @@ the tree. The most important part of the code comes here:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 178
+   :lines: 187
 
 Inside ``nearestKSearch``, we first convert the query point to FLANN format:
 
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 75-76
+   :lines: 84-85
 
 Followed by obtaining the resultant nearest neighbor indices and distances for the query in:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 77-80
+   :lines: 86-89
 
 
 Lines:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 177-191
+   :lines: 186-200
 
 create a ``PCLVisualizer`` object, and sets up a set of different viewports (e.g., splits the screen into different chunks), which will be enabled in:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 211
+   :lines: 220
 
 Using the file names representing the models that we previously obtained in
 ``loadFileList``, we proceed at loading the model file names using:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 219-226
+   :lines: 228-235
 
 For visualization purposes, we demean the point cloud by computing its centroid and then subtracting it:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 238-243
+   :lines: 247-252
 
 
 Finally we check if the distance obtained by ``nearestKSearch`` is larger than the user given threshold, and if it is, we display a red line over the cloud that is being rendered in the viewport:
 
 .. literalinclude:: sources/vfh_recognition/nearest_neighbors.cpp
    :language: cpp
-   :lines: 252-258
+   :lines: 261-267
 
 
 Compiling and running the code