void callback(const sensor_msgs::PointCloud2ConstPtr& cloud)
{
ros::Time whole_start = ros::Time::now();
ros::Time declare_types_start = ros::Time::now();
// filter
pcl::VoxelGrid<sensor_msgs::PointCloud2> voxel_grid;
pcl::PassThrough<sensor_msgs::PointCloud2> pass;
pcl::ExtractIndices<pcl::PointXYZ> extract_indices;
pcl::ExtractIndices<pcl::Normal> extract_normals;
// Normal estimation
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimation;
pcl::SACSegmentationFromNormals<pcl::PointXYZ, pcl::Normal> segmentation_from_normals;
// Create the segmentation object
pcl::SACSegmentation<pcl::PointXYZ> seg;
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree2 (new pcl::search::KdTree<pcl::PointXYZ> ());
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree3 (new pcl::search::KdTree<pcl::PointXYZ> ());
// The plane and sphere coefficients
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients ());
pcl::ModelCoefficients::Ptr coefficients_cylinder (new pcl::ModelCoefficients ());
pcl::ModelCoefficients::Ptr coefficients_sphere (new pcl::ModelCoefficients ());
// The plane and sphere inliers
pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());
pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices ());
pcl::PointIndices::Ptr inliers_cylinder (new pcl::PointIndices ());
pcl::PointIndices::Ptr inliers_sphere (new pcl::PointIndices ());
// The point clouds
sensor_msgs::PointCloud2::Ptr voxelgrid_filtered (new sensor_msgs::PointCloud2);
sensor_msgs::PointCloud2::Ptr plane_output_cloud (new sensor_msgs::PointCloud2);
sensor_msgs::PointCloud2::Ptr rest_output_cloud (new sensor_msgs::PointCloud2);
sensor_msgs::PointCloud2::Ptr rest_cloud_filtered (new sensor_msgs::PointCloud2);
sensor_msgs::PointCloud2::Ptr cylinder_output_cloud (new sensor_msgs::PointCloud2);
sensor_msgs::PointCloud2::Ptr sphere_output_cloud (new sensor_msgs::PointCloud2);
// The PointCloud
pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr remove_transformed_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cylinder_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cylinder_output (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr sphere_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr sphere_output (new pcl::PointCloud<pcl::PointXYZ>);
// The cloud normals
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal> ()); // for plane
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal> ()); // for cylinder
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals3 (new pcl::PointCloud<pcl::Normal> ()); // for sphere
ros::Time declare_types_end = ros::Time::now();
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
* Voxel grid Filtering
*/
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// // Create VoxelGrid filtering
// voxel_grid.setInputCloud (cloud);
// voxel_grid.setLeafSize (0.01, 0.01, 0.01);
// voxel_grid.filter (*voxelgrid_filtered);
//
// // Convert the sensor_msgs/PointCloud2 data to pcl/PointCloud
// pcl::fromROSMsg (*voxelgrid_filtered, *transformed_cloud);
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
* Passthrough Filtering
*/
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// pass through filter
// pass.setInputCloud (cloud);
// pass.setFilterFieldName ("z");
// pass.setFilterLimits (0, 1.5);
// pass.filter (*cloud_filtered);
//
// // Convert the sensor_msgs/PointCloud2 data to pcl/PointCloud
// pcl::fromROSMsg (*cloud_filtered, *transformed_cloud);
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
* Estimate point normals
*/
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// ros::Time estimate_start = ros::Time::now();
//
// // Convert the sensor_msgs/PointCloud2 data to pcl/PointCloud
// pcl::fromROSMsg (*cloud, *transformed_cloud);
//
// // Estimate point normals
// normal_estimation.setSearchMethod (tree);
// normal_estimation.setInputCloud (transformed_cloud);
// normal_estimation.setKSearch (50);
// normal_estimation.compute (*cloud_normals);
//
// ros::Time estimate_end = ros::Time::now();
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
* Create and processing the plane extraction
*/
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// ros::Time plane_start = ros::Time::now();
//
// // Create the segmentation object for the planar model and set all the parameters
// segmentation_from_normals.setOptimizeCoefficients (true);
// segmentation_from_normals.setModelType (pcl::SACMODEL_NORMAL_PLANE);
// segmentation_from_normals.setNormalDistanceWeight (0.1);
// segmentation_from_normals.setMethodType (pcl::SAC_RANSAC);
// segmentation_from_normals.setMaxIterations (100);
// segmentation_from_normals.setDistanceThreshold (0.03);
// segmentation_from_normals.setInputCloud (transformed_cloud);
// segmentation_from_normals.setInputNormals (cloud_normals);
//
// // Obtain the plane inliers and coefficients
// segmentation_from_normals.segment (*inliers_plane, *coefficients_plane);
// //std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;
//
// // Extract the planar inliers from the input cloud
// extract_indices.setInputCloud (transformed_cloud);
// extract_indices.setIndices (inliers_plane);
// extract_indices.setNegative (false);
// extract_indices.filter (*cloud_plane);
//
// pcl::toROSMsg (*cloud_plane, *plane_output_cloud);
// plane_pub.publish(plane_output_cloud);
//
// ros::Time plane_end = ros::Time::now();
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
ros::Time plane_start = ros::Time::now();
pcl::fromROSMsg (*cloud, *transformed_cloud);
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (1000);
seg.setDistanceThreshold (0.01);
seg.setInputCloud (transformed_cloud);
seg.segment (*inliers_plane, *coefficients_plane);
extract_indices.setInputCloud(transformed_cloud);
extract_indices.setIndices(inliers_plane);
extract_indices.setNegative(false);
extract_indices.filter(*cloud_plane);
pcl::toROSMsg (*cloud_plane, *plane_output_cloud);
plane_pub.publish(plane_output_cloud);
ros::Time plane_end = ros::Time::now();
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
* Extract rest plane and passthrough filtering
*/
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
ros::Time rest_pass_start = ros::Time::now();
// Create the filtering object
// Remove the planar inliers, extract the rest
extract_indices.setNegative (true);
extract_indices.filter (*remove_transformed_cloud);
transformed_cloud.swap (remove_transformed_cloud);
// publish result of Removal the planar inliers, extract the rest
pcl::toROSMsg (*transformed_cloud, *rest_output_cloud);
rest_pub.publish(rest_output_cloud);
// Convert the sensor_msgs/PointCloud2 data to pcl/PointCloud
// pcl::fromROSMsg (*rest_output_cloud, *cylinder_cloud);
// pass through filter
pass.setInputCloud (rest_output_cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0, 2.5);
pass.filter (*rest_cloud_filtered);
ros::Time rest_pass_end = ros::Time::now();
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
* for cylinder features
*/
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
// Convert the sensor_msgs/PointCloud2 data to pcl/PointCloud
pcl::fromROSMsg (*rest_cloud_filtered, *cylinder_cloud);
// Estimate point normals
normal_estimation.setSearchMethod (tree2);
normal_estimation.setInputCloud (cylinder_cloud);
normal_estimation.setKSearch (50);
normal_estimation.compute (*cloud_normals2);
// Create the segmentation object for sphere segmentation and set all the paopennirameters
segmentation_from_normals.setOptimizeCoefficients (true);
segmentation_from_normals.setModelType (pcl::SACMODEL_CYLINDER);
segmentation_from_normals.setMethodType (pcl::SAC_RANSAC);
segmentation_from_normals.setNormalDistanceWeight (0.1);
segmentation_from_normals.setMaxIterations (10000);
segmentation_from_normals.setDistanceThreshold (0.05);
segmentation_from_normals.setRadiusLimits (0, 0.5);
segmentation_from_normals.setInputCloud (cylinder_cloud);
segmentation_from_normals.setInputNormals (cloud_normals2);
// Obtain the sphere inliers and coefficients
segmentation_from_normals.segment (*inliers_cylinder, *coefficients_cylinder);
//std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;
// Publish the sphere cloud
extract_indices.setInputCloud (cylinder_cloud);
extract_indices.setIndices (inliers_cylinder);
extract_indices.setNegative (false);
extract_indices.filter (*cylinder_output);
if (cylinder_output->points.empty ())
std::cerr << "Can't find the cylindrical component." << std::endl;
pcl::toROSMsg (*cylinder_output, *cylinder_output_cloud);
cylinder_pub.publish(cylinder_output_cloud);
*/
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*
* for sphere features
*/
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
ros::Time sphere_start = ros::Time::now();
// Convert the sensor_msgs/PointCloud2 data to pcl/PointCloud
pcl::fromROSMsg (*rest_cloud_filtered, *sphere_cloud);
// Estimate point normals
normal_estimation.setSearchMethod (tree3);
normal_estimation.setInputCloud (sphere_cloud);
normal_estimation.setKSearch (50);
normal_estimation.compute (*cloud_normals3);
// Create the segmentation object for sphere segmentation and set all the paopennirameters
segmentation_from_normals.setOptimizeCoefficients (true);
//segmentation_from_normals.setModelType (pcl::SACMODEL_SPHERE);
segmentation_from_normals.setModelType (pcl::SACMODEL_SPHERE);
segmentation_from_normals.setMethodType (pcl::SAC_RANSAC);
segmentation_from_normals.setNormalDistanceWeight (0.1);
segmentation_from_normals.setMaxIterations (10000);
segmentation_from_normals.setDistanceThreshold (0.05);
segmentation_from_normals.setRadiusLimits (0, 0.2);
segmentation_from_normals.setInputCloud (sphere_cloud);
segmentation_from_normals.setInputNormals (cloud_normals3);
// Obtain the sphere inliers and coefficients
segmentation_from_normals.segment (*inliers_sphere, *coefficients_sphere);
//std::cerr << "Sphere coefficients: " << *coefficients_sphere << std::endl;
// Publish the sphere cloud
extract_indices.setInputCloud (sphere_cloud);
extract_indices.setIndices (inliers_sphere);
extract_indices.setNegative (false);
extract_indices.filter (*sphere_output);
if (sphere_output->points.empty ())
std::cerr << "Can't find the sphere component." << std::endl;
pcl::toROSMsg (*sphere_output, *sphere_output_cloud);
sphere_pub.publish(sphere_output_cloud);
ros::Time sphere_end = ros::Time::now();
std::cout << "cloud size : " << cloud->width * cloud->height << std::endl;
std::cout << "plane size : " << transformed_cloud->width * transformed_cloud->height << std::endl;
//std::cout << "plane size : " << cloud_normals->width * cloud_normals->height << std::endl;
//std::cout << "cylinder size : " << cloud_normals2->width * cloud_normals2->height << std::endl;
std::cout << "sphere size : " << cloud_normals3->width * cloud_normals3->height << std::endl;
ros::Time whole_now = ros::Time::now();
printf("\n");
std::cout << "whole time : " << whole_now - whole_start << " sec" << std::endl;
std::cout << "declare types time : " << declare_types_end - declare_types_start << " sec" << std::endl;
//std::cout << "estimate time : " << estimate_end - estimate_start << " sec" << std::endl;
std::cout << "plane time : " << plane_end - plane_start << " sec" << std::endl;
std::cout << "rest and pass time : " << rest_pass_end - rest_pass_start << " sec" << std::endl;
std::cout << "sphere time : " << sphere_end - sphere_start << " sec" << std::endl;
printf("\n");
}
void cloud_callback(const sensor_msgs::PointCloud2ConstPtr& input) {
ROS_DEBUG("Got new pointcloud.");
// Convert the sensor_msgs/PointCloud2 data to pcl/PointCloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);
std_msgs::Header header = input->header;
pcl::fromROSMsg(*input, *cloud);
//all the objects needed
pcl::PCDReader reader;
pcl::PassThrough<PointT> pass;
pcl::NormalEstimation<PointT, pcl::Normal> ne;
pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;
pcl::PCDWriter writer;
pcl::ExtractIndices<PointT> extract;
pcl::ExtractIndices<pcl::Normal> extract_normals;
pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());
//data sets
pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_sphere (new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_sphere (new pcl::PointIndices);
ROS_DEBUG("PointCloud before filtering has: %i data points.", (int)cloud->points.size());
//filter our NaNs
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0, 2.0);
pass.filter (*cloud_filtered);
ROS_DEBUG("PointCloud after filtering has: %i data points." , (int)cloud_filtered->points.size());
*cloud_filtered = *cloud;//remove the pass through filter basically
//estimate normal points
ne.setSearchMethod (tree);
ne.setInputCloud (cloud_filtered);
//ne.setKSearch(10);
ne.setRadiusSearch(0.025);
ne.compute (*cloud_normals);
// Create the segmentation object for the planar model and set all the parameters
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_NORMAL_PLANE);
seg.setNormalDistanceWeight (0.05);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (5.0);
seg.setInputCloud (cloud_filtered);
seg.setInputNormals (cloud_normals);
// Obtain the plane inliers and coefficients
seg.segment (*inliers_plane, *coefficients_plane);
std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;
// Extract the planar inliers from the input cloud
extract.setInputCloud (cloud_filtered);
extract.setIndices (inliers_plane);
extract.setNegative (false);
// Remove the planar inliers, extract the rest
extract.setNegative (true);
extract.filter (*cloud_filtered2);
extract_normals.setNegative (true);
extract_normals.setInputCloud (cloud_normals);
extract_normals.setIndices (inliers_plane);
extract_normals.filter (*cloud_normals2);
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_SPHERE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setNormalDistanceWeight (0.1);
seg.setMaxIterations (100);
seg.setDistanceThreshold (5.0);
seg.setRadiusLimits (0, 1.0);
seg.setInputCloud (cloud_filtered2);
seg.setInputNormals (cloud_normals2);
extract.setInputCloud (cloud_filtered2);
extract.setIndices (inliers_sphere);
extract.setNegative (false);
pcl::PointCloud<PointT>::Ptr sphere_cloud (new pcl::PointCloud<PointT> ());
extract.filter(*sphere_cloud);
pcl::PointCloud<pcl::PointXYZ> sphere_cloud_in = *sphere_cloud;
sensor_msgs::PointCloud2 sphere_cloud_out;
pcl::toROSMsg(sphere_cloud_in, sphere_cloud_out);
sphere_cloud_out.header = header;
buoy_cloud_pub.publish(sphere_cloud_out);
}
// --------------
// -----Main-----
// --------------
int
main (int argc, char** argv)
{
// --------------------------------------
// -----Parse Command Line Arguments-----
// --------------------------------------
if (pcl::console::find_argument (argc, argv, "-h") >= 0)
{
printUsage (argv[0]);
return 0;
}
bool simple(false), rgb(false), custom_c(false), normals(false),
shapes(false), viewports(false), interaction_customization(false);
if (pcl::console::find_argument (argc, argv, "-s") >= 0)
{
simple = true;
std::cout << "Simple visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-c") >= 0)
{
custom_c = true;
std::cout << "Custom colour visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-r") >= 0)
{
rgb = true;
std::cout << "RGB colour visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-n") >= 0)
{
normals = true;
std::cout << "Normals visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-a") >= 0)
{
shapes = true;
std::cout << "Shapes visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-v") >= 0)
{
viewports = true;
std::cout << "Viewports example\n";
}
else if (pcl::console::find_argument (argc, argv, "-i") >= 0)
{
interaction_customization = true;
std::cout << "Interaction Customization example\n";
}
else
{
printUsage (argv[0]);
return 0;
}
// ------------------------------------
// -----Create example point cloud-----
// ------------------------------------
pcl::PointCloud<pcl::PointXYZ>::Ptr basic_cloud_ptr (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr point_cloud_ptr (new pcl::PointCloud<pcl::PointXYZRGB>);
std::cout << "Genarating example point clouds.\n\n";
// We're going to make an ellipse extruded along the z-axis. The colour for
// the XYZRGB cloud will gradually go from red to green to blue.
uint8_t r(255), g(15), b(15);
for (float z(-1.0); z <= 1.0; z += 0.05)
{
for (float angle(0.0); angle <= 360.0; angle += 5.0)
{
pcl::PointXYZ basic_point;
basic_point.x = 0.5 * cosf (pcl::deg2rad(angle));
basic_point.y = sinf (pcl::deg2rad(angle));
basic_point.z = z;
basic_cloud_ptr->points.push_back(basic_point);
pcl::PointXYZRGB point;
point.x = basic_point.x;
point.y = basic_point.y;
point.z = basic_point.z;
uint32_t rgb = (static_cast<uint32_t>(r) << 16 |
static_cast<uint32_t>(g) << 8 | static_cast<uint32_t>(b));
point.rgb = *reinterpret_cast<float*>(&rgb);
point_cloud_ptr->points.push_back (point);
}
if (z < 0.0)
{
r -= 12;
g += 12;
}
else
{
g -= 12;
b += 12;
}
}
basic_cloud_ptr->width = (int) basic_cloud_ptr->points.size ();
basic_cloud_ptr->height = 1;
point_cloud_ptr->width = (int) point_cloud_ptr->points.size ();
point_cloud_ptr->height = 1;
// ----------------------------------------------------------------
// -----Calculate surface normals with a search radius of 0.05-----
// ----------------------------------------------------------------
pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> ne;
ne.setInputCloud (point_cloud_ptr);
pcl::search::KdTree<pcl::PointXYZRGB>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZRGB> ());
ne.setSearchMethod (tree);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals1 (new pcl::PointCloud<pcl::Normal>);
ne.setRadiusSearch (0.05);
ne.compute (*cloud_normals1);
// ---------------------------------------------------------------
// -----Calculate surface normals with a search radius of 0.1-----
// ---------------------------------------------------------------
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
ne.setRadiusSearch (0.1);
ne.compute (*cloud_normals2);
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer;
if (simple)
{
viewer = simpleVis(basic_cloud_ptr);
}
else if (rgb)
{
viewer = rgbVis(point_cloud_ptr);
}
else if (custom_c)
{
viewer = customColourVis(basic_cloud_ptr);
}
else if (normals)
{
viewer = normalsVis(point_cloud_ptr, cloud_normals2);
}
else if (shapes)
{
viewer = shapesVis(point_cloud_ptr);
}
else if (viewports)
{
viewer = viewportsVis(point_cloud_ptr, cloud_normals1, cloud_normals2);
}
else if (interaction_customization)
{
viewer = interactionCustomizationVis();
}
//--------------------
// -----Main loop-----
//--------------------
while (!viewer->wasStopped ())
{
viewer->spinOnce (100);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
}
}
int
main (int argc, char** argv)
{
// All the objects needed
pcl::PCDReader reader;
pcl::PassThrough<PointT> pass;
pcl::NormalEstimation<PointT, pcl::Normal> ne;
pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;
pcl::PCDWriter writer;
pcl::ExtractIndices<PointT> extract;
pcl::ExtractIndices<pcl::Normal> extract_normals;
pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());
// Datasets
pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices);
// Read in the cloud data
reader.read ("table_scene_mug_stereo_textured.pcd", *cloud);
std::cerr << "PointCloud has: " << cloud->points.size () << " data points." << std::endl;
// Build a passthrough filter to remove spurious NaNs
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0, 1.5);
pass.filter (*cloud_filtered);
std::cerr << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl;
// Estimate point normals
ne.setSearchMethod (tree);
ne.setInputCloud (cloud_filtered);
ne.setKSearch (50);
ne.compute (*cloud_normals);
// Create the segmentation object for the planar model and set all the parameters
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_NORMAL_PLANE);
seg.setNormalDistanceWeight (0.1);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (0.03);
seg.setInputCloud (cloud_filtered);
seg.setInputNormals (cloud_normals);
// Obtain the plane inliers and coefficients
seg.segment (*inliers_plane, *coefficients_plane);
std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;
// Extract the planar inliers from the input cloud
extract.setInputCloud (cloud_filtered);
extract.setIndices (inliers_plane);
extract.setNegative (false);
// Write the planar inliers to disk
pcl::PointCloud<PointT>::Ptr cloud_plane (new pcl::PointCloud<PointT> ());
extract.filter (*cloud_plane);
std::cerr << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;
writer.write ("table_scene_mug_stereo_textured_plane.pcd", *cloud_plane, false);
// Remove the planar inliers, extract the rest
extract.setNegative (true);
extract.filter (*cloud_filtered2);
extract_normals.setNegative (true);
extract_normals.setInputCloud (cloud_normals);
extract_normals.setIndices (inliers_plane);
extract_normals.filter (*cloud_normals2);
// Create the segmentation object for cylinder segmentation and set all the parameters
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_CYLINDER);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setNormalDistanceWeight (0.1);
seg.setMaxIterations (10000);
seg.setDistanceThreshold (0.05);
seg.setRadiusLimits (0, 0.1);
seg.setInputCloud (cloud_filtered2);
seg.setInputNormals (cloud_normals2);
// Obtain the cylinder inliers and coefficients
seg.segment (*inliers_cylinder, *coefficients_cylinder);
std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;
// Write the cylinder inliers to disk
extract.setInputCloud (cloud_filtered2);
extract.setIndices (inliers_cylinder);
extract.setNegative (false);
pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ());
extract.filter (*cloud_cylinder);
if (cloud_cylinder->points.empty ())
std::cerr << "Can't find the cylindrical component." << std::endl;
else
{
std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->points.size () << " data points." << std::endl;
writer.write ("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);
}
return (0);
}
// --------------
// -----Main-----
// --------------
int
main (int argc, char** argv)
{
// --------------------------------------
// -----Parse Command Line Arguments-----
// --------------------------------------
if (pcl::console::find_argument (argc, argv, "-h") >= 0)
{
printUsage (argv[0]);
return 0;
}
bool simple(false), rgb(false), custom_c(false), normals(false),
shapes(false), viewports(false), interaction_customization(false);
if (pcl::console::find_argument (argc, argv, "-s") >= 0)
{
simple = true;
std::cout << "Simple visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-c") >= 0)
{
custom_c = true;
std::cout << "Custom colour visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-r") >= 0)
{
rgb = true;
std::cout << "RGB colour visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-n") >= 0)
{
normals = true;
std::cout << "Normals visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-a") >= 0)
{
shapes = true;
std::cout << "Shapes visualisation example\n";
}
else if (pcl::console::find_argument (argc, argv, "-v") >= 0)
{
viewports = true;
std::cout << "Viewports example\n";
}
else if (pcl::console::find_argument (argc, argv, "-i") >= 0)
{
interaction_customization = true;
std::cout << "Interaction Customization example\n";
}
else
{
printUsage (argv[0]);
return 0;
}
// ------------------------------------
// -----Create example point cloud-----
// ------------------------------------
pcl::PointCloud<pcl::PointXYZ>::Ptr basic_cloud_ptr (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr point_cloud_ptr (new pcl::PointCloud<pcl::PointXYZRGB>);
std::cout << "Genarating example point clouds.\n\n";
// We're going to make an ellipse extruded along the z-axis. The colour for
// the XYZRGB cloud will gradually go from red to green to blue.
uint8_t r(255), g(15), b(15);
for (float z(-1.0); z <= 1.0; z += 0.05)
{
for (float angle(0.0); angle <= 360.0; angle += 5.0)
{
pcl::PointXYZ basic_point;
basic_point.x = 0.5 * cosf (pcl::deg2rad(angle));
basic_point.y = sinf (pcl::deg2rad(angle));
basic_point.z = z;
basic_cloud_ptr->points.push_back(basic_point);
pcl::PointXYZRGB point;
point.x = basic_point.x;
point.y = basic_point.y;
point.z = basic_point.z;
uint32_t rgb = (static_cast<uint32_t>(r) << 16 |
static_cast<uint32_t>(g) << 8 | static_cast<uint32_t>(b));
point.rgb = *reinterpret_cast<float*>(&rgb);
point_cloud_ptr->points.push_back (point);
}
if (z < 0.0)
{
r -= 12;
g += 12;
}
else
{
g -= 12;
b += 12;
}
}
basic_cloud_ptr->width = (int) basic_cloud_ptr->points.size ();
basic_cloud_ptr->height = 1;
point_cloud_ptr->width = (int) point_cloud_ptr->points.size ();
point_cloud_ptr->height = 1;
// ----------------------------------------------------------------
// -----Calculate surface normals with a search radius of 0.05-----
// ----------------------------------------------------------------
pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> ne;
ne.setInputCloud (point_cloud_ptr);
pcl::search::KdTree<pcl::PointXYZRGB>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZRGB> ());
ne.setSearchMethod (tree);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals1 (new pcl::PointCloud<pcl::Normal>);
ne.setRadiusSearch (0.05);
ne.compute (*cloud_normals1);
// ---------------------------------------------------------------
// -----Calculate surface normals with a search radius of 0.1-----
// ---------------------------------------------------------------
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
ne.setRadiusSearch (0.1);
ne.compute (*cloud_normals2);
// ----------------------------------------------------------------
// -----Load PCD file from Kinect ---------------------------------
// - TF -----------------------------------------------------------
pcl::PointCloud<pcl::PointXYZRGB>::Ptr point_cloud_ptr_kinect (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>& point_cloud_kinect = *point_cloud_ptr_kinect;
pcl::io::loadPCDFile ("/home/taylor/src/data_pcd/top/kinect_top_rgb.pcd", point_cloud_kinect);
// --end import --
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer;
if (simple)
{
viewer = simpleVis(basic_cloud_ptr);
}
else if (rgb)
{
viewer = rgbVis(point_cloud_ptr_kinect);
}
else if (custom_c)
{
viewer = customColourVis(point_cloud_ptr_kinect);
}
else if (normals)
{
viewer = normalsVis(point_cloud_ptr, cloud_normals2);
}
else if (shapes)
{
viewer = shapesVis(point_cloud_ptr);
}
else if (viewports)
{
viewer = viewportsVis(point_cloud_ptr, cloud_normals1, cloud_normals2);
}
else if (interaction_customization)
{
viewer = interactionCustomizationVis();
}
//--------------------
// -----Main loop-----
//--------------------
//TF custom
cout << "begin custom" << endl;
viewer->spinOnce(1000);
viewer->setCameraPosition(0.00, 0.00, -1.25, 0.00, 0.00, 0.625, -0.00, -0.99999, 0.000);
viewer->setCameraFieldOfView(0.523599);
viewer->setCameraClipDistances(0.0, 4.0);
viewer->setSize(1000,1000);
viewer->updateCamera();
viewer->spinOnce(1000);
cout << " " << endl;
cout << "drawing sphere..." << endl;
pcl::PointXYZ p1;
p1.x = -0.031;
p1.y = 0.021;
p1.z = 0.602;
viewer->addSphere(p1, 0.01, 1.0, 0.0, 1.0, "PickedPoint", 0);
cout << "end custom" << endl;
//end TF Custom
while (!viewer->wasStopped ())
{
viewer->spinOnce (10000);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
}
}
// --------------
// -----Main-----
// --------------
int main(int argc, char** argv)
{
// --------------------------------------
// -----Parse Command Line Arguments-----
// --------------------------------------
if (pcl17::console::find_argument(argc, argv, "-h") >= 0)
{
printUsage(argv[0]);
return 0;
}
bool simple(false), rgb(false), custom_c(false), normals(false), shapes(false), viewports(false),
interaction_customization(false);
if (pcl17::console::find_argument(argc, argv, "-s") >= 0)
{
simple = true;
std::cout << "Simple visualisation example\n";
}
else if (pcl17::console::find_argument(argc, argv, "-c") >= 0)
{
custom_c = true;
std::cout << "Custom colour visualisation example\n";
}
else if (pcl17::console::find_argument(argc, argv, "-r") >= 0)
{
rgb = true;
std::cout << "RGB colour visualisation example\n";
}
else if (pcl17::console::find_argument(argc, argv, "-n") >= 0)
{
normals = true;
std::cout << "Normals visualisation example\n";
}
else if (pcl17::console::find_argument(argc, argv, "-a") >= 0)
{
shapes = true;
std::cout << "Shapes visualisation example\n";
}
else if (pcl17::console::find_argument(argc, argv, "-v") >= 0)
{
viewports = true;
std::cout << "Viewports example\n";
}
else if (pcl17::console::find_argument(argc, argv, "-i") >= 0)
{
interaction_customization = true;
std::cout << "Interaction Customization example\n";
}
else
{
printUsage(argv[0]);
return 0;
}
// ------------------------------------
// -----Create example point cloud-----
// ------------------------------------
pcl17::PointCloud<pcl17::PointXYZRGB>::Ptr basic_cloud_ptr(new pcl17::PointCloud<pcl17::PointXYZRGB>);
pcl17::PointCloud<pcl17::PointXYZRGB>::Ptr point_cloud_ptr(new pcl17::PointCloud<pcl17::PointXYZRGB>);
pcl17::PointCloud<pcl17::PointXYZRGB>::Ptr cloud_f(new pcl17::PointCloud<pcl17::PointXYZRGB>);
pcl17::VoxelGrid<pcl17::PointXYZRGB > sor;
//pcl17::PLYReader reader;
//reader.read("box.ply",*point_cloud_ptr,0);
//pcl17::PointXYZRGB point;
if (pcl17::io::loadPCDFile<pcl17::PointXYZRGB>(argv[1], *point_cloud_ptr) == -1) //* load the file
{
PCL17_ERROR("Couldn't read file test_pcd.pcd \n");
return (-1);
}
basic_cloud_ptr = point_cloud_ptr;
/*
std::cout << "Loaded " << point_cloud_ptr->width * point_cloud_ptr->height << " data points" << std::endl;
cout << "gobbel" << endl;
sor.setInputCloud(point_cloud_ptr);
std::cerr << "PointCloud before filtering: " << point_cloud_ptr->width * point_cloud_ptr->height << " data points ("
<< pcl17::getFieldsList(*point_cloud_ptr) << ").";
std::cout << std::endl;
sor.setLeafSize(0.01, 0.01, 0.01);
sor.filter(*cloud_f);
pcl17::io::savePCDFileASCII("BoxFiltered.pcd", *cloud_f); */
/* for (size_t i = 0; i < point_cloud_ptr->points.size (); ++i)
std::cout << " " << point_cloud_ptr->points[i].x
<< " " << point_cloud_ptr->points[i].y
<< " " << point_cloud_ptr->points[i].z << std::endl;
cout << point_cloud_ptr->width << endl;
cout << point_cloud_ptr->height << endl;
for(int i=0;i<point_cloud_ptr->width * point_cloud_ptr->height;i++)
{
point=point_cloud_ptr->points.at(i);
point.r = 255;
point.g = 0;
point.b = 0;
basic_cloud_ptr->points.at(i)=point;
}
cout << basic_cloud_ptr->width << endl;
cout << basic_cloud_ptr->height << endl;
pcl17::PCDWriter writer;
writer.write<pcl17::PointXYZRGB> (argv[1], *basic_cloud_ptr, false);
point_cloud_ptr=basic_cloud_ptr; */
// ----------------------------------------------------------------
// -----Calculate surface normals with a search radius of 0.05-----
// ----------------------------------------------------------------
pcl17::NormalEstimation<pcl17::PointXYZRGB, pcl17::Normal> ne;
ne.setInputCloud(point_cloud_ptr);
pcl17::search::KdTree<pcl17::PointXYZRGB>::Ptr tree(new pcl17::search::KdTree<pcl17::PointXYZRGB>());
ne.setSearchMethod(tree);
pcl17::PointCloud<pcl17::Normal>::Ptr cloud_normals1(new pcl17::PointCloud<pcl17::Normal>);
ne.setRadiusSearch(0.05);
ne.compute(*cloud_normals1);
// ---------------------------------------------------------------
// -----Calculate surface normals with a search radius of 0.1-----
// ---------------------------------------------------------------
pcl17::PointCloud<pcl17::Normal>::Ptr cloud_normals2(new pcl17::PointCloud<pcl17::Normal>);
ne.setRadiusSearch(0.1);
ne.compute(*cloud_normals2);
boost::shared_ptr<pcl17::visualization::PCLVisualizer> viewer;
if (simple)
{
viewer = simpleVis(basic_cloud_ptr);
}
else if (rgb)
{
viewer = rgbVis(point_cloud_ptr);
}
else if (custom_c)
{
viewer = customColourVis(basic_cloud_ptr);
}
else if (normals)
{
viewer = normalsVis(point_cloud_ptr, cloud_normals2);
}
else if (shapes)
{
viewer = shapesVis(point_cloud_ptr);
}
else if (viewports)
{
viewer = viewportsVis(point_cloud_ptr, cloud_normals1, cloud_normals2);
}
else if (interaction_customization)
{
viewer = interactionCustomizationVis();
}
//--------------------
// -----Main loop-----
//--------------------
while (!viewer->wasStopped())
{
viewer->spinOnce(100);
boost::this_thread::sleep(boost::posix_time::microseconds(100000));
}
}
int
main (int argc, char** argv)
{
// All the objects needed
pcl::PCDReader reader;
pcl::PassThrough<PointT> pass;
pcl::NormalEstimation<PointT, pcl::Normal> ne;
pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;
pcl::PCDWriter writer;
pcl::ExtractIndices<PointT> extract;
pcl::ExtractIndices<pcl::Normal> extract_normals;
pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());
// Datasets
pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices);
// Read in the cloud data
std::vector<int> filenames = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");
if (filenames.size() == 0) {
PCL_ERROR ("No pcd files provided");
return 0;
}
if (pcl::io::loadPCDFile<PointType> (argv[filenames[0]], *cloud) == -1) {
PCL_ERROR ("Couldn't read file %s.pcd \n", argv[filenames[0]]);
}
std::cerr << "PointCloud has: " << cloud->points.size () << " data points." << std::endl;
// Estimate point normals
ne.setSearchMethod (tree);
ne.setInputCloud (cloud);
ne.setKSearch (10);
ne.compute (*cloud_normals);
// Create the segmentation object for cylinder segmentation and set all the parameters
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_CYLINDER);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setNormalDistanceWeight (0.1);
seg.setMaxIterations (10000);
seg.setDistanceThreshold (0.05);
seg.setRadiusLimits (0, 0.1);
seg.setInputCloud (cloud);
seg.setInputNormals (cloud_normals);
// Obtain the cylinder inliers and coefficients
seg.segment (*inliers_cylinder, *coefficients_cylinder);
std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;
// Write the cylinder inliers to disk
extract.setInputCloud (cloud);
extract.setIndices (inliers_cylinder);
extract.setNegative (false);
pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ());
extract.filter (*cloud_cylinder);
if (cloud_cylinder->points.empty ())
std::cerr << "Can't find the cylindrical component." << std::endl;
else
{
std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->points.size () << " data points." << std::endl;
writer.write ("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);
}
return (0);
}
int main (int argc, char** argv)
{
// Zmienne ktore uzywamy do segmentacji, filtracji, odczytu i zapisu pliku.
pcl::PCDReader reader;
pcl::PassThrough<PointT> pass;
pcl::NormalEstimation<PointT, pcl::Normal> ne;
pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;
pcl::PCDWriter writer;
pcl::ExtractIndices<PointT> extract;
pcl::ExtractIndices<pcl::Normal> extract_normals;
pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());
pcl::visualization::CloudViewer viewer ("Cylinder Model Segmentation");
// Zmienne ktore przechowuja kolejno chmury naszych punktów
pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices);
// Wczytanie chmury punktów
reader.read ("test_pcd.pcd", *cloud);
std::cerr << "PointCloud has: " << cloud->points.size () << " data points." << std::endl;
// Przefiltorwanie chmury punktów w celu usuniecia "fa³szywych" punktów (NaN)
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0, 2.8);
pass.filter (*cloud_filtered);
std::cerr << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl;
// Obliczenie normalnych dla punktów w chmurze
ne.setSearchMethod (tree);
ne.setInputCloud (cloud_filtered);
ne.setKSearch (50);
ne.compute (*cloud_normals);
// Stworzenie obiektu do segmentacji planarnej, ustawienie odpowiednich parametrów
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_NORMAL_PLANE);
seg.setNormalDistanceWeight (0.1);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (0.03);
seg.setInputCloud (cloud_filtered);
seg.setInputNormals (cloud_normals);
// Segmentacja..
seg.segment (*inliers_plane, *coefficients_plane);
std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;
// Wy³uskanie obiektu ktory segmentowalismy
extract.setInputCloud (cloud_filtered);
extract.setIndices (inliers_plane);
extract.setNegative (true);
extract.filter (*cloud_filtered2);
extract_normals.setNegative (true);
extract_normals.setInputCloud (cloud_normals);
extract_normals.setIndices (inliers_plane);
extract_normals.filter (*cloud_normals2);
// Utworzenie obiektu do segmentacji cylindrycznej, ustawienie odpowiednich parametrów
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_CYLINDER);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setNormalDistanceWeight (0.1);
seg.setMaxIterations (10000);
// Maksymalna odleglosc pomiedzy punktam a prost¹ ktora wyznaczana jest przy segmentacji moze wynosic 33 cm
seg.setDistanceThreshold (0.33);
// Maksymalny promien cylindra moze miec 40 cm
seg.setRadiusLimits (0, 0.45);
seg.setInputCloud (cloud_filtered2);
seg.setInputNormals (cloud_normals2);
// Segmentacja...
seg.segment (*inliers_cylinder, *coefficients_cylinder);
std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;
// Zapisz wynik do pliku na dysku
extract.setInputCloud (cloud_filtered2);
extract.setIndices (inliers_cylinder);
extract.setNegative (false);
pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ());
extract.filter (*cloud_cylinder);
if (cloud_cylinder->points.empty ())
std::cerr << "Can't find the cylindrical component." << std::endl;
else
{
std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->points.size () << " data points." << std::endl;
writer.write ("test_pcd_cylinder.pcd", *cloud_cylinder, false);
viewer.showCloud (cloud_cylinder);
while (!viewer.wasStopped ())
{
}
}
return (0);
}
