test_ppf_estimation.cpp
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00001 /*
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00039 
00040 #include <gtest/gtest.h>
00041 #include <pcl/point_cloud.h>
00042 #include <pcl/features/normal_3d.h>
00043 #include <pcl/features/ppf.h>
00044 #include <pcl/io/pcd_io.h>
00045 
00046 using namespace pcl;
00047 using namespace pcl::io;
00048 using namespace std;
00049 
00050 typedef search::KdTree<PointXYZ>::Ptr KdTreePtr;
00051 
00052 PointCloud<PointXYZ> cloud;
00053 vector<int> indices;
00054 KdTreePtr tree;
00055 
00057 TEST (PCL, PPFEstimation)
00058 {
00059   // Estimate normals
00060   NormalEstimation<PointXYZ, Normal> normal_estimation;
00061   PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
00062   normal_estimation.setInputCloud (cloud.makeShared ());
00063   boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
00064   normal_estimation.setIndices (indicesptr);
00065   normal_estimation.setSearchMethod (tree);
00066   normal_estimation.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
00067   normal_estimation.compute (*normals);
00068 
00069   PPFEstimation <PointXYZ, Normal, PPFSignature> ppf_estimation;
00070   ppf_estimation.setInputCloud (cloud.makeShared ());
00071   ppf_estimation.setInputNormals (normals);
00072   PointCloud<PPFSignature>::Ptr feature_cloud (new PointCloud<PPFSignature> ());
00073   ppf_estimation.compute (*feature_cloud);
00074 
00075   // Check for size of output
00076   EXPECT_EQ (feature_cloud->points.size (), indices.size () * cloud.points.size ());
00077 
00078   // Now check for a few values in the feature cloud
00079   EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].f1));
00080   EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].f2));
00081   EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].f3));
00082   EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].f4));
00083   EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].alpha_m));
00084 
00085   EXPECT_NEAR (feature_cloud->points[15127].f1, -2.51637, 1e-4);
00086   EXPECT_NEAR (feature_cloud->points[15127].f2, -0.00365916, 1e-4);
00087   EXPECT_NEAR (feature_cloud->points[15127].f3, -0.521141, 1e-4);
00088   EXPECT_NEAR (feature_cloud->points[15127].f4, 0.0106809, 1e-4);
00089   EXPECT_NEAR (feature_cloud->points[15127].alpha_m, -0.255664, 1e-4);
00090   EXPECT_NEAR (feature_cloud->points[30254].f1, 0.185142, 1e-4);
00091   EXPECT_NEAR (feature_cloud->points[30254].f2, 0.0425001, 1e-4);
00092   EXPECT_NEAR (feature_cloud->points[30254].f3, -0.191276, 1e-4);
00093   EXPECT_NEAR (feature_cloud->points[30254].f4, 0.0138508, 1e-4);
00094   EXPECT_NEAR (feature_cloud->points[30254].alpha_m, 2.42955, 1e-4);
00095   EXPECT_NEAR (feature_cloud->points[45381].f1, -1.96263, 1e-4);
00096   EXPECT_NEAR (feature_cloud->points[45381].f2, -0.431919, 1e-4);
00097   EXPECT_NEAR (feature_cloud->points[45381].f3, 0.868716, 1e-4);
00098   EXPECT_NEAR (feature_cloud->points[45381].f4, 0.140129, 1e-4);
00099   EXPECT_NEAR (feature_cloud->points[45381].alpha_m, -1.97276, 1e-4);
00100 }
00101 
00102 /* ---[ */
00103 int
00104 main (int argc, char** argv)
00105 {
00106   if (argc < 2)
00107   {
00108     std::cerr << "No test file given. Please download `bun0.pcd` and pass its path to the test." << std::endl;
00109     return (-1);
00110   }
00111 
00112   if (loadPCDFile<PointXYZ> (argv[1], cloud) < 0)
00113   {
00114     std::cerr << "Failed to read test file. Please download `bun0.pcd` and pass its path to the test." << std::endl;
00115     return (-1);
00116   }
00117 
00118   indices.resize (cloud.points.size ());
00119   for (int i = 0; i < static_cast<int> (indices.size ()); ++i)
00120     indices[i] = i;
00121 
00122   tree.reset (new search::KdTree<PointXYZ> (false));
00123   tree->setInputCloud (cloud.makeShared ());
00124 
00125   testing::InitGoogleTest (&argc, argv);
00126   return (RUN_ALL_TESTS ());
00127 }
00128 /* ]--- */


pcl
Author(s): Open Perception
autogenerated on Wed Aug 26 2015 15:35:34