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#include <gtest/gtest.h>
#include <pcl/point_cloud.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/principal_curvatures.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, PrincipalCurvaturesEstimation)
{
  float pcx, pcy, pcz, pc1, pc2;

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

  PrincipalCurvaturesEstimation<PointXYZ, Normal, PrincipalCurvatures> pc;
  pc.setInputNormals (normals);
  EXPECT_EQ (pc.getInputNormals (), normals);

  // computePointPrincipalCurvatures (indices)
  pc.computePointPrincipalCurvatures (*normals, 0, indices, pcx, pcy, pcz, pc1, pc2);
  EXPECT_NEAR (fabs (pcx), 0.98509, 1e-4);
  EXPECT_NEAR (fabs (pcy), 0.10714, 1e-4);
  EXPECT_NEAR (fabs (pcz), 0.13462, 1e-4);
  EXPECT_NEAR (pc1, 0.23997423052787781, 1e-4);
  EXPECT_NEAR (pc2, 0.19400238990783691, 1e-4);

  pc.computePointPrincipalCurvatures (*normals, 2, indices, pcx, pcy, pcz, pc1, pc2);
  EXPECT_NEAR (pcx, 0.98079, 1e-4);
  EXPECT_NEAR (pcy, -0.04019, 1e-4);
  EXPECT_NEAR (pcz, 0.19086, 1e-4);
  EXPECT_NEAR (pc1, 0.27207490801811218, 1e-4);
  EXPECT_NEAR (pc2, 0.19464978575706482, 1e-4);

  int indices_size = static_cast<int> (indices.size ());
  pc.computePointPrincipalCurvatures (*normals, indices_size - 3, indices, pcx, pcy, pcz, pc1, pc2);
  EXPECT_NEAR (pcx, 0.86725, 1e-4);
  EXPECT_NEAR (pcy, -0.37599, 1e-4);
  EXPECT_NEAR (pcz, 0.32635, 1e-4);
  EXPECT_NEAR (pc1, 0.25900053977966309, 1e-4);
  EXPECT_NEAR (pc2, 0.17906945943832397, 1e-4);

  pc.computePointPrincipalCurvatures (*normals, indices_size - 1, indices, pcx, pcy, pcz, pc1, pc2);
  EXPECT_NEAR (pcx, 0.86725, 1e-4);
  EXPECT_NEAR (pcy, -0.375851, 1e-3);
  EXPECT_NEAR (pcz, 0.32636, 1e-4);
  EXPECT_NEAR (pc1, 0.2590005099773407,  1e-4);
  EXPECT_NEAR (pc2, 0.17906956374645233, 1e-4);

  // Object
  PointCloud<PrincipalCurvatures>::Ptr pcs (new PointCloud<PrincipalCurvatures> ());

  // set parameters
  pc.setInputCloud (cloud.makeShared ());
  pc.setIndices (indicesptr);
  pc.setSearchMethod (tree);
  pc.setKSearch (indices_size);

  // estimate
  pc.compute (*pcs);
  EXPECT_EQ (pcs->points.size (), indices.size ());

  // Adjust for small numerical inconsitencies (due to nn_indices not being sorted)
  EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[0]), 0.98509, 1e-4);
  EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[1]), 0.10713, 1e-4);
  EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[2]), 0.13462, 1e-4);
  EXPECT_NEAR (fabs (pcs->points[0].pc1), 0.23997458815574646, 1e-4);
  EXPECT_NEAR (fabs (pcs->points[0].pc2), 0.19400238990783691, 1e-4);

  EXPECT_NEAR (pcs->points[2].principal_curvature[0], 0.98079, 1e-4);
  EXPECT_NEAR (pcs->points[2].principal_curvature[1], -0.04019, 1e-4);
  EXPECT_NEAR (pcs->points[2].principal_curvature[2], 0.19086, 1e-4);
  EXPECT_NEAR (pcs->points[2].pc1, 0.27207502722740173, 1e-4);
  EXPECT_NEAR (pcs->points[2].pc2, 0.1946497857570648,  1e-4);

  EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[0], 0.86725, 1e-4);
  EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[1], -0.37599, 1e-4);
  EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[2], 0.32636, 1e-4);
  EXPECT_NEAR (pcs->points[indices.size () - 3].pc1, 0.2590007483959198,  1e-4);
  EXPECT_NEAR (pcs->points[indices.size () - 3].pc2, 0.17906941473484039, 1e-4);

  EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[0], 0.86725, 1e-4);
  EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[1], -0.375851, 1e-3);
  EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[2], 0.32636, 1e-4);
  EXPECT_NEAR (pcs->points[indices.size () - 1].pc1, 0.25900065898895264, 1e-4);
  EXPECT_NEAR (pcs->points[indices.size () - 1].pc2, 0.17906941473484039, 1e-4);
}

#ifndef PCL_ONLY_CORE_POINT_TYPES
  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  TEST (PCL, PrincipalCurvaturesEstimationEigen)
  {
    float pcx, pcy, pcz, pc1, pc2;

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

    PrincipalCurvaturesEstimation<PointXYZ, Normal, Eigen::MatrixXf> pc;
    pc.setInputNormals (normals);
    EXPECT_EQ (pc.getInputNormals (), normals);

    // computePointPrincipalCurvatures (indices)
    pc.computePointPrincipalCurvatures (*normals, 0, indices, pcx, pcy, pcz, pc1, pc2);
    EXPECT_NEAR (fabs (pcx), 0.98509, 1e-4);
    EXPECT_NEAR (fabs (pcy), 0.10714, 1e-4);
    EXPECT_NEAR (fabs (pcz), 0.13462, 1e-4);
    EXPECT_NEAR (pc1, 0.23997423052787781, 1e-4);
    EXPECT_NEAR (pc2, 0.19400238990783691, 1e-4);

    pc.computePointPrincipalCurvatures (*normals, 2, indices, pcx, pcy, pcz, pc1, pc2);
    EXPECT_NEAR (pcx, 0.98079, 1e-4);
    EXPECT_NEAR (pcy, -0.04019, 1e-4);
    EXPECT_NEAR (pcz, 0.19086, 1e-4);
    EXPECT_NEAR (pc1, 0.27207490801811218, 1e-4);
    EXPECT_NEAR (pc2, 0.19464978575706482, 1e-4);

    int indices_size = static_cast<int> (indices.size ());
    pc.computePointPrincipalCurvatures (*normals, indices_size - 3, indices, pcx, pcy, pcz, pc1, pc2);
    EXPECT_NEAR (pcx, 0.86725, 1e-4);
    EXPECT_NEAR (pcy, -0.37599, 1e-4);
    EXPECT_NEAR (pcz, 0.32635, 1e-4);
    EXPECT_NEAR (pc1, 0.25900053977966309, 1e-4);
    EXPECT_NEAR (pc2, 0.17906945943832397, 1e-4);

    pc.computePointPrincipalCurvatures (*normals, indices_size - 1, indices, pcx, pcy, pcz, pc1, pc2);
    EXPECT_NEAR (pcx, 0.86725, 1e-4);
    EXPECT_NEAR (pcy, -0.375851, 1e-3);
    EXPECT_NEAR (pcz, 0.32636, 1e-4);
    EXPECT_NEAR (pc1, 0.2590005099773407,  1e-4);
    EXPECT_NEAR (pc2, 0.17906956374645233, 1e-4);

    // Object
    PointCloud<Eigen::MatrixXf>::Ptr pcs (new PointCloud<Eigen::MatrixXf>);

    // set parameters
    pc.setInputCloud (cloud.makeShared ());
    pc.setIndices (indicesptr);
    pc.setSearchMethod (tree);
    pc.setKSearch (indices_size);

    // estimate
    pc.computeEigen (*pcs);
    EXPECT_EQ (pcs->points.rows (), indices.size ());

    // Adjust for small numerical inconsitencies (due to nn_indices not being sorted)
    EXPECT_NEAR (fabs (pcs->points (0, 0)), 0.98509, 1e-4);
    EXPECT_NEAR (fabs (pcs->points (0, 1)), 0.10713, 1e-4);
    EXPECT_NEAR (fabs (pcs->points (0, 2)), 0.13462, 1e-4);
    EXPECT_NEAR (fabs (pcs->points (0, 3)), 0.23997458815574646, 1e-4);
    EXPECT_NEAR (fabs (pcs->points (0, 4)), 0.19400238990783691, 1e-4);

    EXPECT_NEAR (pcs->points (2, 0), 0.98079, 1e-4);
    EXPECT_NEAR (pcs->points (2, 1), -0.04019, 1e-4);
    EXPECT_NEAR (pcs->points (2, 2), 0.19086, 1e-4);
    EXPECT_NEAR (pcs->points (2, 3), 0.27207502722740173, 1e-4);
    EXPECT_NEAR (pcs->points (2, 4), 0.1946497857570648,  1e-4);

    EXPECT_NEAR (pcs->points (indices.size () - 3, 0), 0.86725, 1e-4);
    EXPECT_NEAR (pcs->points (indices.size () - 3, 1), -0.37599, 1e-4);
    EXPECT_NEAR (pcs->points (indices.size () - 3, 2), 0.32636, 1e-4);
    EXPECT_NEAR (pcs->points (indices.size () - 3, 3), 0.2590007483959198,  1e-4);
    EXPECT_NEAR (pcs->points (indices.size () - 3, 4), 0.17906941473484039, 1e-4);

    EXPECT_NEAR (pcs->points (indices.size () - 1, 0), 0.86725, 1e-4);
    EXPECT_NEAR (pcs->points (indices.size () - 1, 1), -0.375851, 1e-3);
    EXPECT_NEAR (pcs->points (indices.size () - 1, 2), 0.32636, 1e-4);
    EXPECT_NEAR (pcs->points (indices.size () - 1, 3), 0.25900065898895264, 1e-4);
    EXPECT_NEAR (pcs->points (indices.size () - 1, 4), 0.17906941473484039, 1e-4);
  }
#endif

/* ---[ */
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 (size_t i = 0; i < indices.size (); ++i)
    indices[i] = static_cast<int> (i);

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

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