# -*- coding: utf-8 -*-
# http://pointclouds.org/documentation/tutorials/implicit_shape_model.php#implicit-shape-model

import pcl

#   if (argc == 0 || argc % 2 == 0)
#     return (-1);


#   unsigned int number_of_training_clouds = (argc - 3) / 2;


#   pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;
#   normal_estimator.setRadiusSearch (25.0);


#   std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> training_clouds;
#   std::vector<pcl::PointCloud<pcl::Normal>::Ptr> training_normals;
#   std::vector<unsigned int> training_classes;


#   for (unsigned int i_cloud = 0; i_cloud < number_of_training_clouds - 1; i_cloud++)
#   {
#     pcl::PointCloud<pcl::PointXYZ>::Ptr tr_cloud(new pcl::PointCloud<pcl::PointXYZ> ());
#     if ( pcl::io::loadPCDFile <pcl::PointXYZ> (argv[i_cloud * 2 + 1], *tr_cloud) == -1 )
#       return (-1);
# 
#     pcl::PointCloud<pcl::Normal>::Ptr tr_normals = (new pcl::PointCloud<pcl::Normal>)->makeShared ();
#     normal_estimator.setInputCloud (tr_cloud);
#     normal_estimator.compute (*tr_normals);
# 
#     unsigned int tr_class = static_cast<unsigned int> (strtol (argv[i_cloud * 2 + 2], 0, 10));
# 
#     training_clouds.push_back (tr_cloud);
#     training_normals.push_back (tr_normals);
#     training_classes.push_back (tr_class);
#   }

for i_cloud = 0 in (i_cloud, number_of_training_clouds - 1):
	pass
#     pcl::PointCloud<pcl::PointXYZ>::Ptr tr_cloud(new pcl::PointCloud<pcl::PointXYZ> ());
#     if ( pcl::io::loadPCDFile <pcl::PointXYZ> (argv[i_cloud * 2 + 1], *tr_cloud) == -1 )
#       return (-1);
# 
#     pcl::PointCloud<pcl::Normal>::Ptr tr_normals = (new pcl::PointCloud<pcl::Normal>)->makeShared ();
#     normal_estimator.setInputCloud (tr_cloud);
#     normal_estimator.compute (*tr_normals);
# 
#     unsigned int tr_class = static_cast<unsigned int> (strtol (argv[i_cloud * 2 + 2], 0, 10));
# 
#     training_clouds.push_back (tr_cloud);
#     training_normals.push_back (tr_normals);
#     training_classes.push_back (tr_class);


#   pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::Histogram<153> >::Ptr fpfh
#     (new pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::Histogram<153> >);
#   fpfh->setRadiusSearch (30.0);
#   pcl::Feature< pcl::PointXYZ, pcl::Histogram<153> >::Ptr feature_estimator(fpfh);


#   pcl::ism::ImplicitShapeModelEstimation<153, pcl::PointXYZ, pcl::Normal> ism;
#   ism.setFeatureEstimator(feature_estimator);
#   ism.setTrainingClouds (training_clouds);
#   ism.setTrainingNormals (training_normals);
#   ism.setTrainingClasses (training_classes);
#   ism.setSamplingSize (2.0f);


#   pcl::ism::ImplicitShapeModelEstimation<153, pcl::PointXYZ, pcl::Normal>::ISMModelPtr model = boost::shared_ptr<pcl::features::ISMModel>
#     (new pcl::features::ISMModel);
#   ism.trainISM (model);


#   std::string file ("trained_ism_model.txt");
#   model->saveModelToFile (file);
#   model->loadModelFromfile (file);


#   unsigned int testing_class = static_cast<unsigned int> (strtol (argv[argc - 1], 0, 10));
#   pcl::PointCloud<pcl::PointXYZ>::Ptr testing_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
#   if ( pcl::io::loadPCDFile <pcl::PointXYZ> (argv[argc - 2], *testing_cloud) == -1 )
#     return (-1);

#   pcl::PointCloud<pcl::Normal>::Ptr testing_normals = (new pcl::PointCloud<pcl::Normal>)->makeShared ();
#   normal_estimator.setInputCloud (testing_cloud);
#   normal_estimator.compute (*testing_normals);

#   boost::shared_ptr<pcl::features::ISMVoteList<pcl::PointXYZ> > vote_list = ism.findObjects (
#     model,
#     testing_cloud,
#     testing_normals,
#     testing_class);

#   double radius = model->sigmas_[testing_class] * 10.0;
#   double sigma = model->sigmas_[testing_class];
#   std::vector<pcl::ISMPeak, Eigen::aligned_allocator<pcl::ISMPeak> > strongest_peaks;
#   vote_list->findStrongestPeaks (strongest_peaks, testing_class, radius, sigma);

#   pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = (new pcl::PointCloud<pcl::PointXYZRGB>)->makeShared ();
#   colored_cloud->height = 0;
#   colored_cloud->width = 1;


#   pcl::PointXYZRGB point;
#   point.r = 255;
#   point.g = 255;
#   point.b = 255;
# 
#   for (size_t i_point = 0; i_point < testing_cloud->points.size (); i_point++)
#   {
#     point.x = testing_cloud->points[i_point].x;
#     point.y = testing_cloud->points[i_point].y;
#     point.z = testing_cloud->points[i_point].z;
#     colored_cloud->points.push_back (point);
#   }
#   colored_cloud->height += testing_cloud->points.size ();
# 


#   point.r = 255;
#   point.g = 0;
#   point.b = 0;
#   for (size_t i_vote = 0; i_vote < strongest_peaks.size (); i_vote++)
#   {
#     point.x = strongest_peaks[i_vote].x;
#     point.y = strongest_peaks[i_vote].y;
#     point.z = strongest_peaks[i_vote].z;
#     colored_cloud->points.push_back (point);
#   }
#   colored_cloud->height += strongest_peaks.size ();
# 

# pcl::visualization::CloudViewer viewer ("Result viewer");
# viewer.showCloud (colored_cloud);
# while (!viewer.wasStopped ())
