test_ppf_estimation.cpp

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#include <gtest/gtest.h>
#include <pcl/point_cloud.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/ppf.h>
#include <pcl/io/pcd_io.h>

using namespace pcl;
using namespace pcl::io;
using namespace std;

typedef search::KdTree<PointXYZ>::Ptr KdTreePtr;

PointCloud<PointXYZ> cloud;
vector<int> indices;
KdTreePtr tree;

TEST (PCL, PPFEstimation)
{
  // Estimate normals
  NormalEstimation<PointXYZ, Normal> normal_estimation;
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  normal_estimation.setInputCloud (cloud.makeShared ());
  boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
  normal_estimation.setIndices (indicesptr);
  normal_estimation.setSearchMethod (tree);
  normal_estimation.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
  normal_estimation.compute (*normals);

  PPFEstimation <PointXYZ, Normal, PPFSignature> ppf_estimation;
  ppf_estimation.setInputCloud (cloud.makeShared ());
  ppf_estimation.setInputNormals (normals);
  PointCloud<PPFSignature>::Ptr feature_cloud (new PointCloud<PPFSignature> ());
  ppf_estimation.compute (*feature_cloud);

  // Check for size of output
  EXPECT_EQ (feature_cloud->points.size (), indices.size () * cloud.points.size ());

  // Now check for a few values in the feature cloud
  EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].f1));
  EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].f2));
  EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].f3));
  EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].f4));
  EXPECT_TRUE (pcl_isnan (feature_cloud->points[0].alpha_m));

  EXPECT_NEAR (feature_cloud->points[15127].f1, -2.51637, 1e-4);
  EXPECT_NEAR (feature_cloud->points[15127].f2, -0.00365916, 1e-4);
  EXPECT_NEAR (feature_cloud->points[15127].f3, -0.521141, 1e-4);
  EXPECT_NEAR (feature_cloud->points[15127].f4, 0.0106809, 1e-4);
  EXPECT_NEAR (feature_cloud->points[15127].alpha_m, -0.255664, 1e-4);
  EXPECT_NEAR (feature_cloud->points[30254].f1, 0.185142, 1e-4);
  EXPECT_NEAR (feature_cloud->points[30254].f2, 0.0425001, 1e-4);
  EXPECT_NEAR (feature_cloud->points[30254].f3, -0.191276, 1e-4);
  EXPECT_NEAR (feature_cloud->points[30254].f4, 0.0138508, 1e-4);
  EXPECT_NEAR (feature_cloud->points[30254].alpha_m, 2.42955, 1e-4);
  EXPECT_NEAR (feature_cloud->points[45381].f1, -1.96263, 1e-4);
  EXPECT_NEAR (feature_cloud->points[45381].f2, -0.431919, 1e-4);
  EXPECT_NEAR (feature_cloud->points[45381].f3, 0.868716, 1e-4);
  EXPECT_NEAR (feature_cloud->points[45381].f4, 0.140129, 1e-4);
  EXPECT_NEAR (feature_cloud->points[45381].alpha_m, -1.97276, 1e-4);
}

/* ---[ */
int
main (int argc, char** argv)
{
  if (argc < 2)
  {
    std::cerr << "No test file given. Please download `bun0.pcd` and pass its path to the test." << std::endl;
    return (-1);
  }

  if (loadPCDFile<PointXYZ> (argv[1], cloud) < 0)
  {
    std::cerr << "Failed to read test file. Please download `bun0.pcd` and pass its path to the test." << std::endl;
    return (-1);
  }

  indices.resize (cloud.points.size ());
  for (int i = 0; i < static_cast<int> (indices.size ()); ++i)
    indices[i] = i;

  tree.reset (new search::KdTree<PointXYZ> (false));
  tree->setInputCloud (cloud.makeShared ());

  testing::InitGoogleTest (&argc, argv);
  return (RUN_ALL_TESTS ());
}
/* ]--- */