121 lines
4.4 KiB
C++
121 lines
4.4 KiB
C++
#include <pcl/point_types.h>
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#include <pcl/io/pcd_io.h>
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#include <pcl/console/time.h>
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#include <pcl/filters/voxel_grid.h>
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#include <pcl/features/normal_3d.h>
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#include <pcl/segmentation/conditional_euclidean_clustering.h>
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typedef pcl::PointXYZI PointTypeIO;
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typedef pcl::PointXYZINormal PointTypeFull;
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bool
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enforceIntensitySimilarity (const PointTypeFull& point_a, const PointTypeFull& point_b, float squared_distance)
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{
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if (fabs (point_a.intensity - point_b.intensity) < 5.0f)
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return (true);
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else
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return (false);
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}
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bool
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enforceCurvatureOrIntensitySimilarity (const PointTypeFull& point_a, const PointTypeFull& point_b, float squared_distance)
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{
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Eigen::Map<const Eigen::Vector3f> point_a_normal(point_a.normal);
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Eigen::Map<const Eigen::Vector3f> point_b_normal(point_b.normal);
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if (fabs (point_a.intensity - point_b.intensity) < 5.0f)
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return (true);
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if (fabs (point_a_normal.dot (point_b_normal)) < 0.05)
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return (true);
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return (false);
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}
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bool
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customRegionGrowing (const PointTypeFull& point_a, const PointTypeFull& point_b, float squared_distance)
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{
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Eigen::Map<const Eigen::Vector3f> point_a_normal(point_a.normal);
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Eigen::Map<const Eigen::Vector3f> point_b_normal(point_b.normal);
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if (squared_distance < 10000)
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{
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if (fabs (point_a.intensity - point_b.intensity) < 8.0f)
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return (true);
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if (fabs (point_a_normal.dot (point_b_normal)) < 0.06)
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return (true);
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}
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else
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{
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if (fabs (point_a.intensity - point_b.intensity) < 3.0f)
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return (true);
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}
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return (false);
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}
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int
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main (int argc, char** argv)
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{
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// Data containers used
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pcl::PointCloud<PointTypeIO>::Ptr cloud_in (new pcl::PointCloud<PointTypeIO>), cloud_out (new pcl::PointCloud<PointTypeIO>);
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pcl::PointCloud<PointTypeFull>::Ptr cloud_with_normals (new pcl::PointCloud<PointTypeFull>);
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pcl::IndicesClustersPtr clusters (new pcl::IndicesClusters), small_clusters (new pcl::IndicesClusters), large_clusters (new pcl::IndicesClusters);
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pcl::search::KdTree<PointTypeIO>::Ptr search_tree (new pcl::search::KdTree<PointTypeIO>);
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pcl::console::TicToc tt;
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// Load the input point cloud
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std::cerr << "Loading...\n", tt.tic ();
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pcl::io::loadPCDFile ("Statues_4.pcd", *cloud_in);
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std::cerr << ">> Done: " << tt.toc () << " ms, " << cloud_in->points.size () << " points\n";
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// Downsample the cloud using a Voxel Grid class
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std::cerr << "Downsampling...\n", tt.tic ();
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pcl::VoxelGrid<PointTypeIO> vg;
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vg.setInputCloud (cloud_in);
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vg.setLeafSize (80.0, 80.0, 80.0);
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vg.setDownsampleAllData (true);
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vg.filter (*cloud_out);
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std::cerr << ">> Done: " << tt.toc () << " ms, " << cloud_out->points.size () << " points\n";
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// Set up a Normal Estimation class and merge data in cloud_with_normals
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std::cerr << "Computing normals...\n", tt.tic ();
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pcl::copyPointCloud (*cloud_out, *cloud_with_normals);
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pcl::NormalEstimation<PointTypeIO, PointTypeFull> ne;
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ne.setInputCloud (cloud_out);
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ne.setSearchMethod (search_tree);
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ne.setRadiusSearch (300.0);
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ne.compute (*cloud_with_normals);
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std::cerr << ">> Done: " << tt.toc () << " ms\n";
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// Set up a Conditional Euclidean Clustering class
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std::cerr << "Segmenting to clusters...\n", tt.tic ();
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pcl::ConditionalEuclideanClustering<PointTypeFull> cec (true);
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cec.setInputCloud (cloud_with_normals);
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cec.setConditionFunction (&customRegionGrowing);
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cec.setClusterTolerance (500.0);
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cec.setMinClusterSize (cloud_with_normals->points.size () / 1000);
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cec.setMaxClusterSize (cloud_with_normals->points.size () / 5);
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cec.segment (*clusters);
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cec.getRemovedClusters (small_clusters, large_clusters);
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std::cerr << ">> Done: " << tt.toc () << " ms\n";
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// Using the intensity channel for lazy visualization of the output
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for (int i = 0; i < small_clusters->size (); ++i)
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for (int j = 0; j < (*small_clusters)[i].indices.size (); ++j)
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cloud_out->points[(*small_clusters)[i].indices[j]].intensity = -2.0;
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for (int i = 0; i < large_clusters->size (); ++i)
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for (int j = 0; j < (*large_clusters)[i].indices.size (); ++j)
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cloud_out->points[(*large_clusters)[i].indices[j]].intensity = +10.0;
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for (int i = 0; i < clusters->size (); ++i)
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{
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int label = rand () % 8;
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for (int j = 0; j < (*clusters)[i].indices.size (); ++j)
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cloud_out->points[(*clusters)[i].indices[j]].intensity = label;
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}
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// Save the output point cloud
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std::cerr << "Saving...\n", tt.tic ();
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pcl::io::savePCDFile ("output.pcd", *cloud_out);
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std::cerr << ">> Done: " << tt.toc () << " ms\n";
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return (0);
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}
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