void PC_to_Mat(PointCloudT::Ptr &cloud, cv::Mat &result){
if (cloud->isOrganized()) {
std::cout << "PointCloud is organized..." << std::endl;
result = cv::Mat(cloud->height, cloud->width, CV_8UC3);
if (!cloud->empty()) {
for (int h=0; h<result.rows; h++) {
for (int w=0; w<result.cols; w++) {
PointT point = cloud->at(w, h);
Eigen::Vector3i rgb = point.getRGBVector3i();
result.at<cv::Vec3b>(h,w)[0] = rgb[2];
result.at<cv::Vec3b>(h,w)[1] = rgb[1];
result.at<cv::Vec3b>(h,w)[2] = rgb[0];
}
}
}
}
}
void filter_PC_from_BB(PointCloudT::Ptr &cloud, cv::Mat &result, int x, int y, int width, int height){
const float bad_point = std::numeric_limits<float>::quiet_NaN();
if (cloud->isOrganized()) {
std::cout << "PointCloud is organized..." << std::endl;
result = cv::Mat(cloud->height, cloud->width, CV_8UC3);
if (!cloud->empty()) {
for (int h=0; h<result.rows; h++) {
for (int w=0; w<result.cols; w++) {
// Check if in bounding window
if ( (h>y && h<(y+height)) && ((w > x) && w < (x+width)) ){
// do nothing
} else {
// remove point
//PointT point = cloud->at(w, h);
//cloud->at(w, h);
cloud->at(w, h).x = bad_point;
cloud->at(w, h).y = bad_point;
cloud->at(w, h).z = bad_point;
cloud->at(w, h).r = bad_point;
cloud->at(w, h).g = bad_point;
cloud->at(w, h).b = bad_point;
}
}
}
}
}
}
int
main (int argc, char ** argv)
{
if (argc < 2)
{
pcl::console::print_info ("Syntax is: %s {-p <pcd-file> OR -r <rgb-file> -d <depth-file>} \n --NT (disables use of single camera transform) \n -o <output-file> \n -O <refined-output-file> \n-l <output-label-file> \n -L <refined-output-label-file> \n-v <voxel resolution> \n-s <seed resolution> \n-c <color weight> \n-z <spatial weight> \n-n <normal_weight>] \n", argv[0]);
return (1);
}
/////////////////////////////// //////////////////////////////
////////////////////////////// //////////////////////////////
////// THIS IS ALL JUST INPUT HANDLING - Scroll down until
////// pcl::SupervoxelClustering<pcl::PointXYZRGB> super
////////////////////////////// //////////////////////////////
std::string rgb_path;
bool rgb_file_specified = pcl::console::find_switch (argc, argv, "-r");
if (rgb_file_specified)
pcl::console::parse (argc, argv, "-r", rgb_path);
std::string depth_path;
bool depth_file_specified = pcl::console::find_switch (argc, argv, "-d");
if (depth_file_specified)
pcl::console::parse (argc, argv, "-d", depth_path);
PointCloudT::Ptr cloud = boost::make_shared < PointCloudT >();
NormalCloudT::Ptr input_normals = boost::make_shared < NormalCloudT > ();
bool pcd_file_specified = pcl::console::find_switch (argc, argv, "-p");
std::string pcd_path;
if (!depth_file_specified || !rgb_file_specified)
{
std::cout << "Using point cloud\n";
if (!pcd_file_specified)
{
std::cout << "No cloud specified!\n";
return (1);
}else
{
pcl::console::parse (argc,argv,"-p",pcd_path);
}
}
bool disable_transform = pcl::console::find_switch (argc, argv, "--NT");
bool ignore_provided_normals = pcl::console::find_switch (argc, argv, "--nonormals");
bool has_normals = false;
std::string out_path = "test_output.png";;
pcl::console::parse (argc, argv, "-o", out_path);
std::string out_label_path = "test_output_labels.png";
pcl::console::parse (argc, argv, "-l", out_label_path);
std::string refined_out_path = "refined_test_output.png";
pcl::console::parse (argc, argv, "-O", refined_out_path);
std::string refined_out_label_path = "refined_test_output_labels.png";;
pcl::console::parse (argc, argv, "-L", refined_out_label_path);
float voxel_resolution = 0.008f;
pcl::console::parse (argc, argv, "-v", voxel_resolution);
float seed_resolution = 0.08f;
pcl::console::parse (argc, argv, "-s", seed_resolution);
float color_importance = 0.2f;
pcl::console::parse (argc, argv, "-c", color_importance);
float spatial_importance = 0.4f;
pcl::console::parse (argc, argv, "-z", spatial_importance);
float normal_importance = 1.0f;
pcl::console::parse (argc, argv, "-n", normal_importance);
if (!pcd_file_specified)
{
//Read the images
vtkSmartPointer<vtkImageReader2Factory> reader_factory = vtkSmartPointer<vtkImageReader2Factory>::New ();
vtkImageReader2* rgb_reader = reader_factory->CreateImageReader2 (rgb_path.c_str ());
//qDebug () << "RGB File="<< QString::fromStdString(rgb_path);
if ( ! rgb_reader->CanReadFile (rgb_path.c_str ()))
{
std::cout << "Cannot read rgb image file!";
return (1);
}
rgb_reader->SetFileName (rgb_path.c_str ());
rgb_reader->Update ();
//qDebug () << "Depth File="<<QString::fromStdString(depth_path);
vtkImageReader2* depth_reader = reader_factory->CreateImageReader2 (depth_path.c_str ());
if ( ! depth_reader->CanReadFile (depth_path.c_str ()))
{
std::cout << "Cannot read depth image file!";
return (1);
}
depth_reader->SetFileName (depth_path.c_str ());
depth_reader->Update ();
vtkSmartPointer<vtkImageFlip> flipXFilter = vtkSmartPointer<vtkImageFlip>::New();
flipXFilter->SetFilteredAxis(0); // flip x axis
flipXFilter->SetInputConnection(rgb_reader->GetOutputPort());
flipXFilter->Update();
vtkSmartPointer<vtkImageFlip> flipXFilter2 = vtkSmartPointer<vtkImageFlip>::New();
flipXFilter2->SetFilteredAxis(0); // flip x axis
flipXFilter2->SetInputConnection(depth_reader->GetOutputPort());
flipXFilter2->Update();
vtkSmartPointer<vtkImageData> rgb_image = flipXFilter->GetOutput ();
int *rgb_dims = rgb_image->GetDimensions ();
vtkSmartPointer<vtkImageData> depth_image = flipXFilter2->GetOutput ();
int *depth_dims = depth_image->GetDimensions ();
if (rgb_dims[0] != depth_dims[0] || rgb_dims[1] != depth_dims[1])
{
std::cout << "Depth and RGB dimensions to not match!";
std::cout << "RGB Image is of size "<<rgb_dims[0] << " by "<<rgb_dims[1];
std::cout << "Depth Image is of size "<<depth_dims[0] << " by "<<depth_dims[1];
return (1);
}
cloud->points.reserve (depth_dims[0] * depth_dims[1]);
cloud->width = depth_dims[0];
cloud->height = depth_dims[1];
cloud->is_dense = false;
// Fill in image data
int centerX = static_cast<int>(cloud->width / 2.0);
int centerY = static_cast<int>(cloud->height / 2.0);
unsigned short* depth_pixel;
unsigned char* color_pixel;
float scale = 1.0f/1000.0f;
float focal_length = 525.0f;
float fl_const = 1.0f / focal_length;
depth_pixel = static_cast<unsigned short*>(depth_image->GetScalarPointer (depth_dims[0]-1,depth_dims[1]-1,0));
color_pixel = static_cast<unsigned char*> (rgb_image->GetScalarPointer (depth_dims[0]-1,depth_dims[1]-1,0));
for (size_t y=0; y<cloud->height; ++y)
{
for (size_t x=0; x<cloud->width; ++x, --depth_pixel, color_pixel-=3)
{
PointT new_point;
// uint8_t* p_i = &(cloud_blob->data[y * cloud_blob->row_step + x * cloud_blob->point_step]);
float depth = static_cast<float>(*depth_pixel) * scale;
if (depth == 0.0f)
{
new_point.x = new_point.y = new_point.z = std::numeric_limits<float>::quiet_NaN ();
}
else
{
new_point.x = (static_cast<float> (x) - centerX) * depth * fl_const;
new_point.y = (static_cast<float> (centerY) - y) * depth * fl_const; // vtk seems to start at the bottom left image corner
new_point.z = depth;
}
uint32_t rgb = static_cast<uint32_t>(color_pixel[0]) << 16 | static_cast<uint32_t>(color_pixel[1]) << 8 | static_cast<uint32_t>(color_pixel[2]);
new_point.rgb = *reinterpret_cast<float*> (&rgb);
cloud->points.push_back (new_point);
}
}
}
else
{
/// check if the provided pcd file contains normals
pcl::PCLPointCloud2 input_pointcloud2;
if (pcl::io::loadPCDFile (pcd_path, input_pointcloud2))
{
PCL_ERROR ("ERROR: Could not read input point cloud %s.\n", pcd_path.c_str ());
return (3);
}
pcl::fromPCLPointCloud2 (input_pointcloud2, *cloud);
if (!ignore_provided_normals)
{
if (hasField (input_pointcloud2,"normal_x"))
{
std::cout << "Using normals contained in file. Set --nonormals option to disable this.\n";
pcl::fromPCLPointCloud2 (input_pointcloud2, *input_normals);
has_normals = true;
}
}
}
std::cout << "Done making cloud!\n";
/////////////////////////////// //////////////////////////////
////////////////////////////// //////////////////////////////
////// This is how to use supervoxels
////////////////////////////// //////////////////////////////
////////////////////////////// //////////////////////////////
// If we're using the single camera transform no negative z allowed since we use log(z)
if (!disable_transform)
{
for (PointCloudT::iterator cloud_itr = cloud->begin (); cloud_itr != cloud->end (); ++cloud_itr)
if (cloud_itr->z < 0)
{
PCL_ERROR ("Points found with negative Z values, this is not compatible with the single camera transform!\n");
PCL_ERROR ("Set the --NT option to disable the single camera transform!\n");
return 1;
}
std::cout <<"You have the single camera transform enabled - this should be used with point clouds captured from a single camera.\n";
std::cout <<"You can disable the transform with the --NT flag\n";
}
pcl::SupervoxelClustering<PointT> super (voxel_resolution, seed_resolution,!disable_transform);
super.setInputCloud (cloud);
if (has_normals)
super.setNormalCloud (input_normals);
super.setColorImportance (color_importance);
super.setSpatialImportance (spatial_importance);
super.setNormalImportance (normal_importance);
std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr > supervoxel_clusters;
std::cout << "Extracting supervoxels!\n";
super.extract (supervoxel_clusters);
std::cout << "Found " << supervoxel_clusters.size () << " Supervoxels!\n";
PointLCloudT::Ptr labeled_voxel_cloud = super.getLabeledVoxelCloud ();
PointCloudT::Ptr voxel_centroid_cloud = super.getVoxelCentroidCloud ();
PointNCloudT::Ptr sv_normal_cloud = super.makeSupervoxelNormalCloud (supervoxel_clusters);
PointLCloudT::Ptr full_labeled_cloud = super.getLabeledCloud ();
std::cout << "Getting supervoxel adjacency\n";
std::multimap<uint32_t, uint32_t> label_adjacency;
super.getSupervoxelAdjacency (label_adjacency);
std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr > refined_supervoxel_clusters;
std::cout << "Refining supervoxels \n";
super.refineSupervoxels (3, refined_supervoxel_clusters);
PointLCloudT::Ptr refined_labeled_voxel_cloud = super.getLabeledVoxelCloud ();
PointNCloudT::Ptr refined_sv_normal_cloud = super.makeSupervoxelNormalCloud (refined_supervoxel_clusters);
PointLCloudT::Ptr refined_full_labeled_cloud = super.getLabeledCloud ();
// THESE ONLY MAKE SENSE FOR ORGANIZED CLOUDS
if (cloud->isOrganized ())
{
pcl::io::savePNGFile (out_label_path, *full_labeled_cloud, "label");
pcl::io::savePNGFile (refined_out_label_path, *refined_full_labeled_cloud, "label");
//Save RGB from labels
pcl::io::PointCloudImageExtractorFromLabelField<PointLT> pcie (pcie.io::PointCloudImageExtractorFromLabelField<PointLT>::COLORS_RGB_GLASBEY);
//We need to set this to account for NAN points in the organized cloud
pcie.setPaintNaNsWithBlack (true);
pcl::PCLImage image;
pcie.extract (*full_labeled_cloud, image);
pcl::io::savePNGFile (out_path, image);
pcie.extract (*refined_full_labeled_cloud, image);
pcl::io::savePNGFile (refined_out_path, image);
}
std::cout << "Constructing Boost Graph Library Adjacency List...\n";
typedef boost::adjacency_list<boost::setS, boost::setS, boost::undirectedS, uint32_t, float> VoxelAdjacencyList;
typedef VoxelAdjacencyList::vertex_descriptor VoxelID;
typedef VoxelAdjacencyList::edge_descriptor EdgeID;
VoxelAdjacencyList supervoxel_adjacency_list;
super.getSupervoxelAdjacencyList (supervoxel_adjacency_list);
std::cout << "Loading visualization...\n";
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
viewer->setBackgroundColor (0, 0, 0);
viewer->registerKeyboardCallback(keyboard_callback, 0);
bool refined_normal_shown = show_refined;
bool refined_sv_normal_shown = show_refined;
bool sv_added = false;
bool normals_added = false;
bool graph_added = false;
std::vector<std::string> poly_names;
std::cout << "Loading viewer...\n";
while (!viewer->wasStopped ())
{
if (show_supervoxels)
{
if (!viewer->updatePointCloud ((show_refined)?refined_labeled_voxel_cloud:labeled_voxel_cloud, "colored voxels"))
{
viewer->addPointCloud ((show_refined)?refined_labeled_voxel_cloud:labeled_voxel_cloud, "colored voxels");
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE,3.0, "colored voxels");
}
}
else
{
viewer->removePointCloud ("colored voxels");
}
if (show_voxel_centroids)
{
if (!viewer->updatePointCloud (voxel_centroid_cloud, "voxel centroids"))
{
viewer->addPointCloud (voxel_centroid_cloud, "voxel centroids");
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE,2.0, "voxel centroids");
}
}
else
{
viewer->removePointCloud ("voxel centroids");
}
if (show_supervoxel_normals)
{
if (refined_sv_normal_shown != show_refined || !sv_added)
{
viewer->removePointCloud ("supervoxel_normals");
viewer->addPointCloudNormals<PointNormal> ((show_refined)?refined_sv_normal_cloud:sv_normal_cloud,1,0.05f, "supervoxel_normals");
sv_added = true;
}
refined_sv_normal_shown = show_refined;
}
else if (!show_supervoxel_normals)
{
viewer->removePointCloud ("supervoxel_normals");
}
if (show_normals)
{
std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr>::iterator sv_itr,sv_itr_end;
sv_itr = ((show_refined)?refined_supervoxel_clusters.begin ():supervoxel_clusters.begin ());
sv_itr_end = ((show_refined)?refined_supervoxel_clusters.end ():supervoxel_clusters.end ());
for (; sv_itr != sv_itr_end; ++sv_itr)
{
std::stringstream ss;
ss << sv_itr->first <<"_normal";
if (refined_normal_shown != show_refined || !normals_added)
{
viewer->removePointCloud (ss.str ());
viewer->addPointCloudNormals<PointT,Normal> ((sv_itr->second)->voxels_,(sv_itr->second)->normals_,10,0.02f,ss.str ());
// std::cout << (sv_itr->second)->normals_->points[0]<<"\n";
}
}
normals_added = true;
refined_normal_shown = show_refined;
}
else if (!show_normals)
{
std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr>::iterator sv_itr,sv_itr_end;
sv_itr = ((show_refined)?refined_supervoxel_clusters.begin ():supervoxel_clusters.begin ());
sv_itr_end = ((show_refined)?refined_supervoxel_clusters.end ():supervoxel_clusters.end ());
for (; sv_itr != sv_itr_end; ++sv_itr)
{
std::stringstream ss;
ss << sv_itr->first << "_normal";
viewer->removePointCloud (ss.str ());
}
}
if (show_graph && !graph_added)
{
poly_names.clear ();
std::multimap<uint32_t,uint32_t>::iterator label_itr = label_adjacency.begin ();
for ( ; label_itr != label_adjacency.end (); )
{
//First get the label
uint32_t supervoxel_label = label_itr->first;
//Now get the supervoxel corresponding to the label
pcl::Supervoxel<PointT>::Ptr supervoxel = supervoxel_clusters.at (supervoxel_label);
//Now we need to iterate through the adjacent supervoxels and make a point cloud of them
PointCloudT adjacent_supervoxel_centers;
std::multimap<uint32_t,uint32_t>::iterator adjacent_itr = label_adjacency.equal_range (supervoxel_label).first;
for ( ; adjacent_itr!=label_adjacency.equal_range (supervoxel_label).second; ++adjacent_itr)
{
pcl::Supervoxel<PointT>::Ptr neighbor_supervoxel = supervoxel_clusters.at (adjacent_itr->second);
adjacent_supervoxel_centers.push_back (neighbor_supervoxel->centroid_);
}
//Now we make a name for this polygon
std::stringstream ss;
ss << "supervoxel_" << supervoxel_label;
poly_names.push_back (ss.str ());
addSupervoxelConnectionsToViewer (supervoxel->centroid_, adjacent_supervoxel_centers, ss.str (), viewer);
//Move iterator forward to next label
label_itr = label_adjacency.upper_bound (supervoxel_label);
}
graph_added = true;
}
else if (!show_graph && graph_added)
{
for (std::vector<std::string>::iterator name_itr = poly_names.begin (); name_itr != poly_names.end (); ++name_itr)
{
viewer->removeShape (*name_itr);
}
graph_added = false;
}
if (show_help)
{
viewer->removeShape ("help_text");
printText (viewer);
}
else
{
removeText (viewer);
if (!viewer->updateText("Press h to show help", 5, 10, 12, 1.0, 1.0, 1.0,"help_text") )
viewer->addText("Press h to show help", 5, 10, 12, 1.0, 1.0, 1.0,"help_text");
}
viewer->spinOnce (100);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
}
return (0);
}
int main (int argc, char** argv)
{
ros::init(argc, argv, "cv_proj");
//std::string input_file = "/home/igor/pcds/assembly_objs/ardrone_02_indoor.pcd";
//std::string input_file = "/home/igor/pcds/assembly_objs/ardrone_03_outdoor.pcd";
/*
std::string input_file = "/home/igor/pcds/cluttered/3_objs_ardrone_indoor.pcd";
std::string output_file = "/home/igor/pcds/cv_proj_out/out_cluttered_indoor_01.pcd";
std::string template_file = "/home/igor/pcds/templates/indoor_template.pcd";
std::string out_rgb = "/home/igor/pcds/cv_proj_out/out_result_05.jpg";
*/
std::string input_file, out_pcd, template_file, out_rgb, out_transf_pcd;
ros::param::param<std::string>("/cv_proj/input_file", input_file, "/home/igor/pcds/cluttered/3_objs_ardrone_indoor.pcd");
//ros::param::param<std::string>("/cv_proj/out_pcd", out_pcd, "/home/igor/pcds/cv_proj_out/out_cluttered_indoor_01.pcd");
ros::param::param<std::string>("/cv_proj/template_file", template_file, "/home/igor/pcds/templates/indoor_template.pcd");
//ros::param::param<std::string>("/cv_proj/out_rgb", out_rgb, "/home/igor/pcds/cv_proj_out/out_result_05.jpg");
boost::filesystem::path filepath(input_file);
boost::filesystem::path filename = filepath.filename();
filename = filename.stem(); // Get rid of the extension
boost::filesystem::path dir = filepath.parent_path();
std::string opencv_out_ext = "_filtered.png";
std::string pcl_out_ext = "_filtered.pcd";
std::string output_folder = "/output_cv_proj/";
std::string output_stem;
output_stem = dir.string() + output_folder + filename.string();
out_rgb = output_stem + opencv_out_ext;
out_pcd = output_stem + pcl_out_ext;
out_transf_pcd = output_stem + "_templ" + pcl_out_ext;
std::cout << out_rgb << std::endl;
std::cout << out_pcd << std::endl;
// Read in the cloud data
pcl::PCDReader reader;
PointCloudT::Ptr cloud (new PointCloudT), cloud_f (new PointCloudT);
reader.read (input_file, *cloud);
std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*
if (cloud->isOrganized()) {
std::cout << "-- PointCloud cloud is organized" << std::endl;
std::cout << "-- PointCloud cloud width: " << cloud->width << std::endl;
std::cout << "-- PointCloud cloud height: " << cloud->height << std::endl;
}
/*
// Create the filtering object: downsample the dataset using a leaf size of 1cm
pcl::VoxelGrid<PointT> vg;
PointCloudT::Ptr cloud_filtered (new PointCloudT);
vg.setInputCloud (cloud);
//vg.setLeafSize (0.01f, 0.01f, 0.01f);
vg.setLeafSize (0.001f, 0.001f, 0.001f);
//
vg.filter (*cloud_filtered);
std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl; //*
*/
/**/
PointCloudT::Ptr cloud_filtered (new PointCloudT);
*cloud_filtered = *cloud;
// Create the segmentation object for the planar model and set all the parameters
pcl::SACSegmentation<PointT> seg;
pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
PointCloudT::Ptr cloud_plane (new PointCloudT ());
pcl::PCDWriter writer;
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (0.02);
int i=0, nr_points = (int) cloud_filtered->points.size ();
while (cloud_filtered->points.size () > 0.3 * nr_points)
{
// Segment the largest planar component from the remaining cloud
seg.setInputCloud (cloud_filtered);
seg.segment (*inliers, *coefficients);
if (inliers->indices.size () == 0)
{
std::cout << "Could not estimate a planar model for the given dataset." << std::endl;
break;
}
// Extract the planar inliers from the input cloud
pcl::ExtractIndices<PointT> extract;
extract.setInputCloud (cloud_filtered);
extract.setIndices (inliers);
extract.setNegative (false);
//extract.setUserFilterValue(999);
extract.setKeepOrganized(true);
// Get the points associated with the planar surface
extract.filter (*cloud_plane);
//extract.filterDirectly(cloud_plane);
std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;
// To keep point cloud organized
//extract.setKeepOrganized(true);
// Remove the planar inliers, extract the rest
extract.setNegative (true);
extract.filter (*cloud_f);
//extract.filterDirectly(cloud_f);
*cloud_filtered = *cloud_f;
}
cv::Mat result;
PC_to_Mat(cloud_filtered,result);
imwrite( out_rgb, result );
int rgb_x, rgb_y, rgb_width, rgb_height;
rgb_x = 240;
rgb_y = 150;
rgb_width = 200;
rgb_height = 200;
filter_PC_from_BB ( cloud_filtered, result, rgb_x, rgb_y, rgb_width, rgb_height);
// For templates
/*
// Box filter
pcl::PassThrough<PointT> pass_x;
pass_x.setInputCloud (cloud_filtered);
pass_x.setFilterFieldName ("x");
pass_x.setFilterLimits (-0.2, 0.4);
//pass.setFilterLimitsNegative (true);
pass_x.filter (*cloud_filtered);
pcl::PassThrough<PointT> pass_y;
pass_y.setInputCloud (cloud_filtered);
pass_y.setFilterFieldName ("y");
pass_y.setFilterLimits (-0.2, 0.4);
//pass.setFilterLimitsNegative (true);
pass_y.filter (*cloud_filtered);
*/
// Save file
pcl::io::savePCDFileASCII (out_pcd, *cloud_filtered);
std::cerr << "Saved " << (*cloud_filtered).points.size () << " data points to PCD file." << std::endl;
// Read in template file
PointCloudT::Ptr cloud_template (new PointCloudT);
reader.read (template_file, *cloud_template);
pcl::VoxelGrid<PointT> vg_in;
PointCloudT::Ptr cloud_filtered_vg (new PointCloudT);
vg_in.setInputCloud (cloud_filtered);
vg_in.setLeafSize (0.001f, 0.001f, 0.001f); //vg.setLeafSize (0.01f, 0.01f, 0.01f);
vg_in.filter (*cloud_filtered_vg);
pcl::VoxelGrid<PointT> vg_temp;
PointCloudT::Ptr cloud_template_vg (new PointCloudT);
vg_temp.setInputCloud (cloud_template);
vg_temp.setLeafSize (0.001f, 0.001f, 0.001f); //vg.setLeafSize (0.01f, 0.01f, 0.01f);
vg_temp.filter (*cloud_template_vg);
// ICP
PointCloudT::Ptr cloud_in (new PointCloudT), cloud_out (new PointCloudT);
*cloud_in = *cloud_template_vg;
*cloud_out = *cloud_filtered_vg;
pcl::IterativeClosestPoint<PointT, PointT> icp;
icp.setInputCloud(cloud_in);
icp.setInputTarget(cloud_out);
PointCloudT Final;
icp.align(Final);
std::cout << "has converged:" << icp.hasConverged() << " score: " <<
icp.getFitnessScore() << std::endl;
Eigen::Matrix4f transform = icp.getFinalTransformation ();
std::cout << transform << std::endl;
// Transform template to original frame
PointCloudT::Ptr cloud_out2 (new PointCloudT);
pcl::transformPointCloud(*cloud_in,*cloud_out2,transform);
pcl::io::savePCDFileASCII (out_transf_pcd, *cloud_out2);
std::cerr << "Saved " << (*cloud_out2).points.size () << " data points to PCD file." << std::endl;
return (0);
}