face_detector_data_provider.h

/*
 * face_detector_data_provider.h
 *
 *  Created on: Sep 2, 2012
 *      Author: aitor
 */
 
#pragma once
 
#include <pcl/memory.h>
#include <pcl/ml/dt/decision_tree_data_provider.h>
#include <pcl/recognition/face_detection/face_common.h>
 
#include <boost/algorithm/string.hpp>
#include <boost/filesystem/operations.hpp>
 
#include <fstream>
#include <string>
 
 
namespace bf = boost::filesystem;
 
namespace pcl
{
  namespace face_detection
  {
    template<class FeatureType, class DataSet, class LabelType, class ExampleIndex, class NodeType>
    class FaceDetectorDataProvider: public pcl::DecisionTreeTrainerDataProvider<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>
    {
      private:
        int num_images_;
        std::vector<std::string> image_files_;
        bool USE_NORMALS_;
        int w_size_;
        int patches_per_image_;
        int min_images_per_bin_;
 
        void getFilesInDirectory(bf::path & dir, std::string & rel_path_so_far, std::vector<std::string> & relative_paths, std::string & ext)
        {
          for (const auto& dir_entry : bf::directory_iterator(dir))
          {
            //check if its a directory, then get models in it
            if (bf::is_directory (dir_entry))
            {
              std::string so_far = rel_path_so_far + (dir_entry.path ().filename ()).string () + "/";
              bf::path curr_path = dir_entry.path ();
              getFilesInDirectory (curr_path, so_far, relative_paths, ext);
            } else
            {
              //check that it is a ply file and then add, otherwise ignore..
              std::vector < std::string > strs;
              std::string file = (dir_entry.path ().filename ()).string ();
              boost::split (strs, file, boost::is_any_of ("."));
              std::string extension = strs[strs.size () - 1];
 
              if (extension == ext)
              {
                std::string path = rel_path_so_far + (dir_entry.path ().filename ()).string ();
                relative_paths.push_back (path);
              }
            }
          }
        }
 
        inline bool readMatrixFromFile(const std::string& file, Eigen::Matrix4f & matrix)
        {
 
          std::ifstream in;
          in.open (file.c_str (), std::ifstream::in);
          if (!in.is_open ())
          {
            return false;
          }
 
          char linebuf[1024];
          in.getline (linebuf, 1024);
          std::string line (linebuf);
          std::vector < std::string > strs_2;
          boost::split (strs_2, line, boost::is_any_of (" "));
 
          for (int i = 0; i < 16; i++)
          {
            matrix (i / 4, i % 4) = static_cast<float> (atof (strs_2[i].c_str ()));
          }
 
          return true;
        }
 
        bool check_inside(int col, int row, int min_col, int max_col, int min_row, int max_row)
        {
          return col >= min_col && col <= max_col && row >= min_row && row <= max_row;
        }
 
        template<class PointInT>
        void cropCloud(int min_col, int max_col, int min_row, int max_row, pcl::PointCloud<PointInT> & cloud_in, pcl::PointCloud<PointInT> & cloud_out)
        {
          cloud_out.width = max_col - min_col + 1;
          cloud_out.height = max_row - min_row + 1;
          cloud_out.resize (cloud_out.width * cloud_out.height);
          for (unsigned int u = 0; u < cloud_out.width; u++)
          {
            for (unsigned int v = 0; v < cloud_out.height; v++)
            {
              cloud_out.at (u, v) = cloud_in.at (min_col + u, min_row + v);
            }
          }
 
          cloud_out.is_dense = cloud_in.is_dense;
        }
 
      public:
 
        using Ptr = shared_ptr<FaceDetectorDataProvider<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>>;
        using ConstPtr = shared_ptr<const FaceDetectorDataProvider<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>>;
 
        FaceDetectorDataProvider()
        {
          w_size_ = 80;
          USE_NORMALS_ = false;
          num_images_ = 10;
          patches_per_image_ = 20;
          min_images_per_bin_ = -1;
        }
 
        void setPatchesPerImage(int n)
        {
          patches_per_image_ = n;
        }
 
        void setMinImagesPerBin(int n)
        {
          min_images_per_bin_ = n;
        }
 
        void setUseNormals(bool use)
        {
          USE_NORMALS_ = use;
        }
 
        void setWSize(int size)
        {
          w_size_ = size;
        }
 
        void setNumImages(int n)
        {
          num_images_ = n;
        }
 
        void initialize(std::string & data_dir);
 
        //shuffle file and get the first num_images_ as requested by a tree
        //extract positive and negative samples
        //create training examples and labels
        void getDatasetAndLabels(DataSet & data_set, std::vector<LabelType> & label_data, std::vector<ExampleIndex> & examples) override;
    };
  }
}