int main (int argc, char** argv) { // All the objects needed pcl::PCDReader reader; pcl::PassThrough<PointT> pass; pcl::NormalEstimation<PointT, pcl::Normal> ne; pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg; pcl::PCDWriter writer; pcl::ExtractIndices<PointT> extract; pcl::ExtractIndices<pcl::Normal> extract_normals; pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ()); // Datasets pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>); pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>); pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>); pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>); pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>); pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients); pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices); // Read in the cloud data std::vector<int> filenames = pcl::console::parse_file_extension_argument (argc, argv, ".pcd"); if (filenames.size() == 0) { PCL_ERROR ("No pcd files provided"); return 0; } if (pcl::io::loadPCDFile<PointType> (argv[filenames[0]], *cloud) == -1) { PCL_ERROR ("Couldn't read file %s.pcd \n", argv[filenames[0]]); } std::cerr << "PointCloud has: " << cloud->points.size () << " data points." << std::endl; // Estimate point normals ne.setSearchMethod (tree); ne.setInputCloud (cloud); ne.setKSearch (10); ne.compute (*cloud_normals); // Create the segmentation object for cylinder segmentation and set all the parameters seg.setOptimizeCoefficients (true); seg.setModelType (pcl::SACMODEL_CYLINDER); seg.setMethodType (pcl::SAC_RANSAC); seg.setNormalDistanceWeight (0.1); seg.setMaxIterations (10000); seg.setDistanceThreshold (0.05); seg.setRadiusLimits (0, 0.1); seg.setInputCloud (cloud); seg.setInputNormals (cloud_normals); // Obtain the cylinder inliers and coefficients seg.segment (*inliers_cylinder, *coefficients_cylinder); std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl; // Write the cylinder inliers to disk extract.setInputCloud (cloud); extract.setIndices (inliers_cylinder); extract.setNegative (false); pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ()); extract.filter (*cloud_cylinder); if (cloud_cylinder->points.empty ()) std::cerr << "Can't find the cylindrical component." << std::endl; else { std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->points.size () << " data points." << std::endl; writer.write ("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false); } return (0); }
int main (int argc, char** argv) { // All the objects needed pcl::PCDReader reader; pcl::PassThrough<PointT> pass; pcl::NormalEstimation<PointT, pcl::Normal> ne; pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg; pcl::PCDWriter writer; pcl::ExtractIndices<PointT> extract; pcl::ExtractIndices<pcl::Normal> extract_normals; pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ()); // Datasets pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>); pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>); pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>); pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>); pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>); pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients); pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices); // Read in the cloud data reader.read ("table_scene_mug_stereo_textured.pcd", *cloud); std::cerr << "PointCloud has: " << cloud->points.size () << " data points." << std::endl; // Build a passthrough filter to remove spurious NaNs pass.setInputCloud (cloud); pass.setFilterFieldName ("z"); pass.setFilterLimits (0, 1.5); pass.filter (*cloud_filtered); std::cerr << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl; // Estimate point normals ne.setSearchMethod (tree); ne.setInputCloud (cloud_filtered); ne.setKSearch (50); ne.compute (*cloud_normals); // Create the segmentation object for the planar model and set all the parameters seg.setOptimizeCoefficients (true); seg.setModelType (pcl::SACMODEL_NORMAL_PLANE); seg.setNormalDistanceWeight (0.1); seg.setMethodType (pcl::SAC_RANSAC); seg.setMaxIterations (100); seg.setDistanceThreshold (0.03); seg.setInputCloud (cloud_filtered); seg.setInputNormals (cloud_normals); // Obtain the plane inliers and coefficients seg.segment (*inliers_plane, *coefficients_plane); std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl; // Extract the planar inliers from the input cloud extract.setInputCloud (cloud_filtered); extract.setIndices (inliers_plane); extract.setNegative (false); // Write the planar inliers to disk pcl::PointCloud<PointT>::Ptr cloud_plane (new pcl::PointCloud<PointT> ()); extract.filter (*cloud_plane); std::cerr << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl; writer.write ("table_scene_mug_stereo_textured_plane.pcd", *cloud_plane, false); // Remove the planar inliers, extract the rest extract.setNegative (true); extract.filter (*cloud_filtered2); extract_normals.setNegative (true); extract_normals.setInputCloud (cloud_normals); extract_normals.setIndices (inliers_plane); extract_normals.filter (*cloud_normals2); // Create the segmentation object for cylinder segmentation and set all the parameters seg.setOptimizeCoefficients (true); seg.setModelType (pcl::SACMODEL_CYLINDER); seg.setMethodType (pcl::SAC_RANSAC); seg.setNormalDistanceWeight (0.1); seg.setMaxIterations (10000); seg.setDistanceThreshold (0.05); seg.setRadiusLimits (0, 0.1); seg.setInputCloud (cloud_filtered2); seg.setInputNormals (cloud_normals2); // Obtain the cylinder inliers and coefficients seg.segment (*inliers_cylinder, *coefficients_cylinder); std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl; // Write the cylinder inliers to disk extract.setInputCloud (cloud_filtered2); extract.setIndices (inliers_cylinder); extract.setNegative (false); pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ()); extract.filter (*cloud_cylinder); if (cloud_cylinder->points.empty ()) std::cerr << "Can't find the cylindrical component." << std::endl; else { std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->points.size () << " data points." << std::endl; writer.write ("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false); } return (0); }
int main (int argc, char** argv) { // Zmienne ktore uzywamy do segmentacji, filtracji, odczytu i zapisu pliku. pcl::PCDReader reader; pcl::PassThrough<PointT> pass; pcl::NormalEstimation<PointT, pcl::Normal> ne; pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg; pcl::PCDWriter writer; pcl::ExtractIndices<PointT> extract; pcl::ExtractIndices<pcl::Normal> extract_normals; pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ()); pcl::visualization::CloudViewer viewer ("Cylinder Model Segmentation"); // Zmienne ktore przechowuja kolejno chmury naszych punktów pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>); pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>); pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>); pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>); pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>); pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients); pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices); // Wczytanie chmury punktów reader.read ("test_pcd.pcd", *cloud); std::cerr << "PointCloud has: " << cloud->points.size () << " data points." << std::endl; // Przefiltorwanie chmury punktów w celu usuniecia "fa³szywych" punktów (NaN) pass.setInputCloud (cloud); pass.setFilterFieldName ("z"); pass.setFilterLimits (0, 2.8); pass.filter (*cloud_filtered); std::cerr << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl; // Obliczenie normalnych dla punktów w chmurze ne.setSearchMethod (tree); ne.setInputCloud (cloud_filtered); ne.setKSearch (50); ne.compute (*cloud_normals); // Stworzenie obiektu do segmentacji planarnej, ustawienie odpowiednich parametrów seg.setOptimizeCoefficients (true); seg.setModelType (pcl::SACMODEL_NORMAL_PLANE); seg.setNormalDistanceWeight (0.1); seg.setMethodType (pcl::SAC_RANSAC); seg.setMaxIterations (100); seg.setDistanceThreshold (0.03); seg.setInputCloud (cloud_filtered); seg.setInputNormals (cloud_normals); // Segmentacja.. seg.segment (*inliers_plane, *coefficients_plane); std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl; // Wy³uskanie obiektu ktory segmentowalismy extract.setInputCloud (cloud_filtered); extract.setIndices (inliers_plane); extract.setNegative (true); extract.filter (*cloud_filtered2); extract_normals.setNegative (true); extract_normals.setInputCloud (cloud_normals); extract_normals.setIndices (inliers_plane); extract_normals.filter (*cloud_normals2); // Utworzenie obiektu do segmentacji cylindrycznej, ustawienie odpowiednich parametrów seg.setOptimizeCoefficients (true); seg.setModelType (pcl::SACMODEL_CYLINDER); seg.setMethodType (pcl::SAC_RANSAC); seg.setNormalDistanceWeight (0.1); seg.setMaxIterations (10000); // Maksymalna odleglosc pomiedzy punktam a prost¹ ktora wyznaczana jest przy segmentacji moze wynosic 33 cm seg.setDistanceThreshold (0.33); // Maksymalny promien cylindra moze miec 40 cm seg.setRadiusLimits (0, 0.45); seg.setInputCloud (cloud_filtered2); seg.setInputNormals (cloud_normals2); // Segmentacja... seg.segment (*inliers_cylinder, *coefficients_cylinder); std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl; // Zapisz wynik do pliku na dysku extract.setInputCloud (cloud_filtered2); extract.setIndices (inliers_cylinder); extract.setNegative (false); pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ()); extract.filter (*cloud_cylinder); if (cloud_cylinder->points.empty ()) std::cerr << "Can't find the cylindrical component." << std::endl; else { std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->points.size () << " data points." << std::endl; writer.write ("test_pcd_cylinder.pcd", *cloud_cylinder, false); viewer.showCloud (cloud_cylinder); while (!viewer.wasStopped ()) { } } return (0); }