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#include <gtest/gtest.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/io/pcd_io.h>
#include <pcl/search/search.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/feature.h>
#include <pcl/features/fpfh.h>
#include <pcl/features/impl/fpfh.hpp>
#include <pcl/recognition/implicit_shape_model.h>
#include <pcl/recognition/impl/implicit_shape_model.hpp>

pcl::PointCloud<pcl::PointXYZ>::Ptr training_cloud;
pcl::PointCloud<pcl::PointXYZ>::Ptr testing_cloud;
pcl::PointCloud<pcl::Normal>::Ptr training_normals;
pcl::PointCloud<pcl::Normal>::Ptr testing_normals;

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (ISM, TrainRecognize)
{
  std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> clouds;
  std::vector<pcl::PointCloud<pcl::Normal>::Ptr > normals;
  std::vector<unsigned int> classes;

  clouds.push_back (training_cloud);
  normals.push_back (training_normals);
  classes.push_back (0);

  pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::Histogram<153> >::Ptr fpfh
    (new pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::Histogram<153> >);
  fpfh->setRadiusSearch (30.0);
  pcl::Feature< pcl::PointXYZ, pcl::Histogram<153> >::Ptr feature_estimator(fpfh);

  pcl::ism::ImplicitShapeModelEstimation<153, pcl::PointXYZ, pcl::Normal>::ISMModelPtr model = boost::shared_ptr<pcl::features::ISMModel> (new pcl::features::ISMModel);

  pcl::ism::ImplicitShapeModelEstimation<153, pcl::PointXYZ, pcl::Normal> ism;
  ism.setFeatureEstimator(feature_estimator);
  ism.setTrainingClouds (clouds);
  ism.setTrainingClasses (classes);
  ism.setTrainingNormals (normals);
  ism.setSamplingSize (2.0f);
  ism.trainISM (model);

  int _class = 0;
  double radius = model->sigmas_[_class] * 10.0;
  double sigma = model->sigmas_[_class];

  boost::shared_ptr<pcl::features::ISMVoteList<pcl::PointXYZ> > vote_list = ism.findObjects (model, testing_cloud, testing_normals, _class);
  EXPECT_NE (vote_list->getNumberOfVotes (), 0);
  std::vector<pcl::ISMPeak, Eigen::aligned_allocator<pcl::ISMPeak> > strongest_peaks;
  vote_list->findStrongestPeaks (strongest_peaks, _class, radius, sigma);

  EXPECT_NE (strongest_peaks.size (), 0);
}

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (ISM, TrainWithWrongParameters)
{
  pcl::ism::ImplicitShapeModelEstimation<153, pcl::PointXYZ, pcl::Normal> ism;

  float prev_sampling_size = ism.getSamplingSize ();
  EXPECT_NE (prev_sampling_size, 0.0);
  ism.setSamplingSize (0.0f);
  float curr_sampling_size = ism.getSamplingSize ();
  EXPECT_EQ (curr_sampling_size, prev_sampling_size);

  unsigned int prev_number_of_clusters = ism.getNumberOfClusters ();
  EXPECT_NE (prev_number_of_clusters, 0);
  ism.setNumberOfClusters (0);
  unsigned int curr_number_of_clusters = ism.getNumberOfClusters ();
  EXPECT_EQ (curr_number_of_clusters, prev_number_of_clusters);
}

/* ---[ */
int
main (int argc, char** argv)
{
  if (argc < 2)
  {
    std::cerr << "This test requires two point clouds (one for training and one for testing)." << std::endl;
    std::cerr << "You can use these two clouds 'ism_train.pcd' and 'ism_test.pcd'." << std::endl;
    return (-1);
  }

  training_cloud = (new pcl::PointCloud<pcl::PointXYZ>)->makeShared();
  if (pcl::io::loadPCDFile (argv[1], *training_cloud) < 0)
  {
    std::cerr << "Failed to read test file. Please download `ism_train.pcd` and pass its path to the test." << std::endl;
    return (-1);
  }
  testing_cloud = (new pcl::PointCloud<pcl::PointXYZ>)->makeShared();
  if (pcl::io::loadPCDFile (argv[2], *testing_cloud) < 0)
  {
    std::cerr << "Failed to read test file. Please download `ism_test.pcd` and pass its path to the test." << std::endl;
    return (-1);
  }

  training_normals = (new pcl::PointCloud<pcl::Normal>)->makeShared();
  testing_normals = (new pcl::PointCloud<pcl::Normal>)->makeShared();
  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;
  normal_estimator.setRadiusSearch (25.0);
  normal_estimator.setInputCloud(training_cloud);
  normal_estimator.compute(*training_normals);
  normal_estimator.setInputCloud(testing_cloud);
  normal_estimator.compute(*testing_normals);

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