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# -*- 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 ())
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