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/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2009-2011, 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 Willow Garage, Inc. 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 <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/keypoints/sift_keypoint.h>
#include <pcl/features/normal_3d.h>
/* This example shows how to estimate the SIFT points based on the
* Normal gradients i.e. curvature than using the Intensity gradient
* as usually used for SIFT keypoint estimation.
*/
int
main(int, char** argv)
{
std::string filename = argv[1];
std::cout << "Reading " << filename << std::endl;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz (new pcl::PointCloud<pcl::PointXYZ>);
if(pcl::io::loadPCDFile<pcl::PointXYZ> (filename, *cloud_xyz) == -1) // load the file
{
PCL_ERROR("Couldn't read file\n");
return -1;
}
std::cout << "points: " << cloud_xyz->size () <<std::endl;
// Parameters for sift computation
constexpr float min_scale = 0.01f;
constexpr int n_octaves = 3;
constexpr int n_scales_per_octave = 4;
constexpr float min_contrast = 0.001f;
// Estimate the normals of the cloud_xyz
pcl::NormalEstimation<pcl::PointXYZ, pcl::PointNormal> ne;
pcl::PointCloud<pcl::PointNormal>::Ptr cloud_normals (new pcl::PointCloud<pcl::PointNormal>);
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree_n(new pcl::search::KdTree<pcl::PointXYZ>());
ne.setInputCloud(cloud_xyz);
ne.setSearchMethod(tree_n);
ne.setRadiusSearch(0.2);
ne.compute(*cloud_normals);
// Copy the xyz info from cloud_xyz and add it to cloud_normals as the xyz field in PointNormals estimation is zero
for(std::size_t i = 0; i<cloud_normals->size(); ++i)
{
(*cloud_normals)[i].x = (*cloud_xyz)[i].x;
(*cloud_normals)[i].y = (*cloud_xyz)[i].y;
(*cloud_normals)[i].z = (*cloud_xyz)[i].z;
}
// Estimate the sift interest points using normals values from xyz as the Intensity variants
pcl::SIFTKeypoint<pcl::PointNormal, pcl::PointWithScale> sift;
pcl::PointCloud<pcl::PointWithScale> result;
pcl::search::KdTree<pcl::PointNormal>::Ptr tree(new pcl::search::KdTree<pcl::PointNormal> ());
sift.setSearchMethod(tree);
sift.setScales(min_scale, n_octaves, n_scales_per_octave);
sift.setMinimumContrast(min_contrast);
sift.setInputCloud(cloud_normals);
sift.compute(result);
std::cout << "No of SIFT points in the result are " << result.size () << std::endl;
/*
// Copying the pointwithscale to pointxyz so as visualize the cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_temp (new pcl::PointCloud<pcl::PointXYZ>);
copyPointCloud(result, *cloud_temp);
std::cout << "SIFT points in the cloud_temp are " << cloud_temp->size () << std::endl;
// Visualization of keypoints along with the original cloud
pcl::visualization::PCLVisualizer viewer("PCL Viewer");
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> keypoints_color_handler (cloud_temp, 0, 255, 0);
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud_color_handler (cloud_xyz, 255, 0, 0);
viewer.setBackgroundColor( 0.0, 0.0, 0.0 );
viewer.addPointCloud(cloud_xyz, cloud_color_handler, "cloud");
viewer.addPointCloud(cloud_temp, keypoints_color_handler, "keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "keypoints");
while(!viewer.wasStopped ())
{
viewer.spinOnce ();
}
*/
return 0;
}
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