test_gradient_estimation.cpp

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#include <gtest/gtest.h>
#include <pcl/point_cloud.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/intensity_gradient.h>

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

TEST (PCL, IntensityGradientEstimation)
{
  // Create a test cloud
  PointCloud<PointXYZI> cloud_xyzi;
  cloud_xyzi.height = 1;
  cloud_xyzi.is_dense = true;
  for (float x = -5.0f; x <= 5.0f; x += 0.1f)
  {
    for (float y = -5.0f; y <= 5.0f; y += 0.1f)
    {
      PointXYZI p;
      p.x = x;
      p.y = y;
      p.z = 0.1f * powf (x, 2.0f) + 0.5f * y + 1.0f;
      p.intensity = 0.1f * powf (x, 3.0f) + 0.2f * powf (y, 2.0f) + 1.0f * p.z + 20000.0f;

      cloud_xyzi.points.push_back (p);
    }
  }
  cloud_xyzi.width = static_cast<uint32_t> (cloud_xyzi.points.size ());
  PointCloud<PointXYZI>::ConstPtr cloud_ptr = cloud_xyzi.makeShared ();

  // Estimate surface normals
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  NormalEstimation<PointXYZI, Normal> norm_est;
  norm_est.setInputCloud (cloud_ptr);
  search::KdTree<PointXYZI>::Ptr treept1 (new search::KdTree<PointXYZI> (false));
  norm_est.setSearchMethod (treept1);
  norm_est.setRadiusSearch (0.25);
  norm_est.compute (*normals);

  // Estimate intensity gradient
  PointCloud<IntensityGradient> gradient;
  IntensityGradientEstimation<PointXYZI, Normal, IntensityGradient> grad_est;
  grad_est.setInputCloud (cloud_ptr);
  grad_est.setInputNormals (normals);
  search::KdTree<PointXYZI>::Ptr treept2 (new search::KdTree<PointXYZI> (false));
  grad_est.setSearchMethod (treept2);
  grad_est.setRadiusSearch (0.25);
  grad_est.compute (gradient);

  // Compare to gradient estimates to actual values
  for (size_t i = 0; i < cloud_ptr->points.size (); ++i)
  {
    const PointXYZI &p = cloud_ptr->points[i];

    // A pointer to the estimated gradient values
    const float * g_est = gradient.points[i].gradient;

    // Compute the surface normal analytically.
    float nx = -0.2f * p.x;
    float ny = -0.5f;
    float nz = 1.0f;
    float magnitude = sqrtf (nx * nx + ny * ny + nz * nz);
    nx /= magnitude;
    ny /= magnitude;
    nz /= magnitude;

    // Compute the intensity gradient analytically...
    float tmpx = 0.3f * powf (p.x, 2.0f);
    float tmpy = 0.4f * p.y;
    float tmpz = 1.0f;
    // ...and project the 3-D gradient vector onto the surface's tangent plane.
    float gx = (1 - nx * nx) * tmpx + (-nx * ny) * tmpy + (-nx * nz) * tmpz;
    float gy = (-ny * nx) * tmpx + (1 - ny * ny) * tmpy + (-ny * nz) * tmpz;
    float gz = (-nz * nx) * tmpx + (-nz * ny) * tmpy + (1 - nz * nz) * tmpz;

    // Compare the estimates to the derived values.
    const float tolerance = 0.11f;
    EXPECT_NEAR (g_est[0], gx, tolerance);
    EXPECT_NEAR (g_est[1], gy, tolerance);
    EXPECT_NEAR (g_est[2], gz, tolerance);
  }
}

#ifndef PCL_ONLY_CORE_POINT_TYPES

  TEST (PCL, IntensityGradientEstimationEigen)
  {
    // Create a test cloud
    PointCloud<PointXYZI> cloud_xyzi;
    cloud_xyzi.height = 1;
    cloud_xyzi.is_dense = true;
    for (float x = -5.0f; x <= 5.0f; x += 0.1f)
    {
      for (float y = -5.0f; y <= 5.0f; y += 0.1f)
      {
        PointXYZI p;
        p.x = x;
        p.y = y;
        p.z = 0.1f * powf (x, 2.0f) + 0.5f * y + 1.0f;
        p.intensity = 0.1f * powf (x, 3.0f) + 0.2f * powf (y, 2.0f) + 1.0f * p.z + 20000.0f;

        cloud_xyzi.points.push_back (p);
      }
    }
    cloud_xyzi.width = static_cast<uint32_t> (cloud_xyzi.points.size ());
    PointCloud<PointXYZI>::ConstPtr cloud_ptr = cloud_xyzi.makeShared ();

    // Estimate surface normals
    PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
    NormalEstimation<PointXYZI, Normal> norm_est;
    norm_est.setInputCloud (cloud_ptr);
    search::KdTree<PointXYZI>::Ptr treept1 (new search::KdTree<PointXYZI> (false));
    norm_est.setSearchMethod (treept1);
    norm_est.setRadiusSearch (0.25);
    norm_est.compute (*normals);

    // Estimate intensity gradient
    PointCloud<Eigen::MatrixXf> gradient;
    IntensityGradientEstimation<PointXYZI, Normal, Eigen::MatrixXf> grad_est;
    grad_est.setInputCloud (cloud_ptr);
    grad_est.setInputNormals (normals);
    search::KdTree<PointXYZI>::Ptr treept2 (new search::KdTree<PointXYZI> (false));
    grad_est.setSearchMethod (treept2);
    grad_est.setRadiusSearch (0.25);
    grad_est.computeEigen (gradient);

    // Compare to gradient estimates to actual values
    for (size_t i = 0; i < cloud_ptr->points.size (); ++i)
    {
      const PointXYZI &p = cloud_ptr->points[i];

      // Compute the surface normal analytically.
      float nx = -0.2f * p.x;
      float ny = -0.5f;
      float nz = 1.0f;
      float magnitude = sqrtf (nx * nx + ny * ny + nz * nz);
      nx /= magnitude;
      ny /= magnitude;
      nz /= magnitude;

      // Compute the intensity gradient analytically...
      float tmpx = 0.3f * powf (p.x, 2.0f);
      float tmpy = 0.4f * p.y;
      float tmpz = 1.0f;
      // ...and project the 3-D gradient vector onto the surface's tangent plane.
      float gx = (1 - nx * nx) * tmpx + (-nx * ny) * tmpy + (-nx * nz) * tmpz;
      float gy = (-ny * nx) * tmpx + (1 - ny * ny) * tmpy + (-ny * nz) * tmpz;
      float gz = (-nz * nx) * tmpx + (-nz * ny) * tmpy + (1 - nz * nz) * tmpz;

      // Compare the estimates to the derived values.
      const float tolerance = 0.11f;
      ASSERT_NEAR (gradient.points (i, 0), gx, tolerance);
      ASSERT_NEAR (gradient.points (i, 1), gy, tolerance);
      ASSERT_NEAR (gradient.points (i, 2), gz, tolerance);
    }
  }
#endif

/* ---[ */
int
main (int argc, char** argv)
{
  testing::InitGoogleTest (&argc, argv);
  return (RUN_ALL_TESTS ());
}
/* ]--- */