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/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2012, Willow Garage, Inc.
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* $Id$
*
*/
#include <pcl/test/gtest.h>
#include <pcl/point_cloud.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/intensity_gradient.h>
using namespace pcl;
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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.push_back (p);
}
}
cloud_xyzi.width = cloud_xyzi.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 (std::size_t i = 0; i < cloud_ptr->size (); ++i)
{
const PointXYZI &p = (*cloud_ptr)[i];
// A pointer to the estimated gradient values
const float * g_est = gradient[i].gradient;
// Compute the surface normal analytically.
float nx = -0.2f * p.x;
float ny = -0.5f;
float nz = 1.0f;
float magnitude = std::sqrt (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);
}
}
/* ---[ */
int
main (int argc, char** argv)
{
testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
/* ]--- */
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