test_non_linear.cpp

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 * $Id: test_segmentation.cpp 3564 2011-12-16 06:11:13Z rusu $
 *
 */

#include <gtest/gtest.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/io/pcd_io.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/PointIndices.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/segmentation/sac_segmentation.h>

using namespace pcl;
using namespace pcl::io;

PointCloud<PointXYZ>::Ptr cloud_;

TEST (SACSegmentation, Segmentation)
{
  ModelCoefficients::Ptr sphere_coefficients (new ModelCoefficients);
  PointIndices::Ptr inliers (new PointIndices);

  SACSegmentation<PointXYZ> seg;
  seg.setOptimizeCoefficients (true);
  seg.setModelType (SACMODEL_SPHERE);
  seg.setMethodType (SAC_RANSAC);
  seg.setMaxIterations (10000);
  seg.setDistanceThreshold (0.01);
  seg.setRadiusLimits (0.03, 0.07); // true radius is 0.05
  seg.setInputCloud (cloud_);
  seg.segment (*inliers, *sphere_coefficients);

  EXPECT_NEAR (sphere_coefficients->values[0], 0.998776,  1e-2);
  EXPECT_NEAR (sphere_coefficients->values[1], 0.752023,  1e-2);
  EXPECT_NEAR (sphere_coefficients->values[2], 1.24558,   1e-2);
  EXPECT_NEAR (sphere_coefficients->values[3], 0.0536238, 1e-2);

  EXPECT_NEAR (static_cast<int> (inliers->indices.size ()), 3516, 10);
}

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

  // Load a standard PCD file from disk
  PointCloud<PointXYZ> cloud;
  if (loadPCDFile (argv[1], cloud) < 0)
  {
    std::cerr << "Failed to read test file. Please download `noisy_slice_displaced.pcd` and pass its path to the test." << std::endl;
    return (-1);
  }

  cloud_   = cloud.makeShared ();

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