bool TOMPCFilter<T>::segmentation(CloudPtr &cloud_in, VecCloud &clouds_out, double tolerance, double minSize, double maxSize)
{
// std::cout << "Object segmentation" << std::endl;
// object clustering
// Creating the KdTree object for the search method of the extraction
typename pcl::search::KdTree<T>::Ptr tree (new pcl::search::KdTree<T>);
tree->setInputCloud (cloud_in);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<T> ec;
ec.setClusterTolerance (tolerance); // 2cm
ec.setMinClusterSize (minSize);
ec.setMaxClusterSize (maxSize);
ec.setSearchMethod (tree);
ec.setInputCloud( cloud_in);
ec.extract (cluster_indices);
int j = 0;
clouds_out.clear();
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
{
CloudPtr cloud_cluster (new pcl::PointCloud<T>);
for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); pit++)
cloud_cluster->points.push_back (cloud_in->points[*pit]); //*
clouds_out.push_back(cloud_cluster);
}
return 1;
}
void performTracking(const PointCloudConstPtr& cloud)
{
typename pcl::search::KdTree<PointType>::Ptr search(
new pcl::search::KdTree<PointType>());
search->setInputCloud(cloud);
performTracking(cloud, search);
}
void ICCVTutorial<FeatureType>::segmentation (typename pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr source, typename pcl::PointCloud<pcl::PointXYZRGB>::Ptr segmented) const
{
cout << "segmentation..." << std::flush;
// fit plane and keep points above that plane
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
// Create the segmentation object
pcl::SACSegmentation<pcl::PointXYZRGB> seg;
// Optional
seg.setOptimizeCoefficients (true);
// Mandatory
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setDistanceThreshold (0.02);
seg.setInputCloud (source);
seg.segment (*inliers, *coefficients);
pcl::ExtractIndices<pcl::PointXYZRGB> extract;
extract.setInputCloud (source);
extract.setIndices (inliers);
extract.setNegative (true);
extract.filter (*segmented);
std::vector<int> indices;
pcl::removeNaNFromPointCloud(*segmented, *segmented, indices);
cout << "OK" << endl;
cout << "clustering..." << std::flush;
// euclidean clustering
typename pcl::search::KdTree<pcl::PointXYZRGB>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZRGB>);
tree->setInputCloud (segmented);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZRGB> clustering;
clustering.setClusterTolerance (0.02); // 2cm
clustering.setMinClusterSize (1000);
clustering.setMaxClusterSize (250000);
clustering.setSearchMethod (tree);
clustering.setInputCloud(segmented);
clustering.extract (cluster_indices);
if (!cluster_indices.empty ())//use largest cluster
{
cout << cluster_indices.size() << " clusters found";
if (cluster_indices.size() > 1)
cout <<" Using largest one...";
cout << endl;
typename pcl::IndicesPtr indices (new std::vector<int>);
*indices = cluster_indices[0].indices;
extract.setInputCloud (segmented);
extract.setIndices (indices);
extract.setNegative (false);
extract.filter (*segmented);
}
}
template<typename PointT> void
pcl::GeneralizedIterativeClosestPoint<PointSource, PointTarget>::computeCovariances(typename pcl::PointCloud<PointT>::ConstPtr cloud,
const typename pcl::search::KdTree<PointT>::Ptr kdtree,
std::vector<Eigen::Matrix3d>& cloud_covariances)
{
if (k_correspondences_ > int (cloud->size ()))
{
PCL_ERROR ("[pcl::GeneralizedIterativeClosestPoint::computeCovariances] Number or points in cloud (%lu) is less than k_correspondences_ (%lu)!\n", cloud->size (), k_correspondences_);
return;
}
Eigen::Vector3d mean;
std::vector<int> nn_indecies; nn_indecies.reserve (k_correspondences_);
std::vector<float> nn_dist_sq; nn_dist_sq.reserve (k_correspondences_);
// We should never get there but who knows
if(cloud_covariances.size () < cloud->size ())
cloud_covariances.resize (cloud->size ());
typename pcl::PointCloud<PointT>::const_iterator points_iterator = cloud->begin ();
std::vector<Eigen::Matrix3d>::iterator matrices_iterator = cloud_covariances.begin ();
for(;
points_iterator != cloud->end ();
++points_iterator, ++matrices_iterator)
{
const PointT &query_point = *points_iterator;
Eigen::Matrix3d &cov = *matrices_iterator;
// Zero out the cov and mean
cov.setZero ();
mean.setZero ();
// Search for the K nearest neighbours
kdtree->nearestKSearch(query_point, k_correspondences_, nn_indecies, nn_dist_sq);
// Find the covariance matrix
for(int j = 0; j < k_correspondences_; j++) {
const PointT &pt = (*cloud)[nn_indecies[j]];
mean[0] += pt.x;
mean[1] += pt.y;
mean[2] += pt.z;
cov(0,0) += pt.x*pt.x;
cov(1,0) += pt.y*pt.x;
cov(1,1) += pt.y*pt.y;
cov(2,0) += pt.z*pt.x;
cov(2,1) += pt.z*pt.y;
cov(2,2) += pt.z*pt.z;
}
mean /= static_cast<double> (k_correspondences_);
// Get the actual covariance
for (int k = 0; k < 3; k++)
for (int l = 0; l <= k; l++)
{
cov(k,l) /= static_cast<double> (k_correspondences_);
cov(k,l) -= mean[k]*mean[l];
cov(l,k) = cov(k,l);
}
// Compute the SVD (covariance matrix is symmetric so U = V')
Eigen::JacobiSVD<Eigen::Matrix3d> svd(cov, Eigen::ComputeFullU);
cov.setZero ();
Eigen::Matrix3d U = svd.matrixU ();
// Reconstitute the covariance matrix with modified singular values using the column // vectors in V.
for(int k = 0; k < 3; k++) {
Eigen::Vector3d col = U.col(k);
double v = 1.; // biggest 2 singular values replaced by 1
if(k == 2) // smallest singular value replaced by gicp_epsilon
v = gicp_epsilon_;
cov+= v * col * col.transpose();
}
}
}
void
pcl::apps::DominantPlaneSegmentation<PointType>::compute_full (std::vector<CloudPtr> & clusters)
{
// Has the input dataset been set already?
if (!input_)
{
PCL_WARN ("[pcl::apps::DominantPlaneSegmentation] No input dataset given!\n");
return;
}
CloudConstPtr cloud_;
CloudPtr cloud_filtered_ (new Cloud ());
CloudPtr cloud_downsampled_ (new Cloud ());
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals_ (new pcl::PointCloud<pcl::Normal> ());
pcl::PointIndices::Ptr table_inliers_ (new pcl::PointIndices ());
pcl::ModelCoefficients::Ptr table_coefficients_ (new pcl::ModelCoefficients ());
CloudPtr table_projected_ (new Cloud ());
CloudPtr table_hull_ (new Cloud ());
CloudPtr cloud_objects_ (new Cloud ());
CloudPtr cluster_object_ (new Cloud ());
typename pcl::search::KdTree<PointType>::Ptr normals_tree_ (new pcl::search::KdTree<PointType>);
typename pcl::search::KdTree<PointType>::Ptr clusters_tree_ (new pcl::search::KdTree<PointType>);
clusters_tree_->setEpsilon (1);
// Normal estimation parameters
n3d_.setKSearch (10);
n3d_.setSearchMethod (normals_tree_);
// Table model fitting parameters
seg_.setDistanceThreshold (sac_distance_threshold_);
seg_.setMaxIterations (2000);
seg_.setNormalDistanceWeight (0.1);
seg_.setOptimizeCoefficients (true);
seg_.setModelType (pcl::SACMODEL_NORMAL_PLANE);
seg_.setMethodType (pcl::SAC_MSAC);
seg_.setProbability (0.98);
proj_.setModelType (pcl::SACMODEL_NORMAL_PLANE);
bb_cluster_proj_.setModelType (pcl::SACMODEL_NORMAL_PLANE);
prism_.setHeightLimits (object_min_height_, object_max_height_);
// Clustering parameters
cluster_.setClusterTolerance (object_cluster_tolerance_);
cluster_.setMinClusterSize (object_cluster_min_size_);
cluster_.setSearchMethod (clusters_tree_);
// ---[ PassThroughFilter
pass_.setFilterLimits (min_z_bounds_, max_z_bounds_);
pass_.setFilterFieldName ("z");
pass_.setInputCloud (input_);
pass_.filter (*cloud_filtered_);
if (int (cloud_filtered_->points.size ()) < k_)
{
PCL_WARN ("[DominantPlaneSegmentation] Filtering returned %zu points! Aborting.",
cloud_filtered_->points.size ());
return;
}
// Downsample the point cloud
grid_.setLeafSize (downsample_leaf_, downsample_leaf_, downsample_leaf_);
grid_.setDownsampleAllData (false);
grid_.setInputCloud (cloud_filtered_);
grid_.filter (*cloud_downsampled_);
PCL_INFO ("[DominantPlaneSegmentation] Number of points left after filtering&downsampling (%f -> %f): %zu out of %zu\n",
min_z_bounds_, max_z_bounds_, cloud_downsampled_->points.size (), input_->points.size ());
// ---[ Estimate the point normals
n3d_.setInputCloud (cloud_downsampled_);
n3d_.compute (*cloud_normals_);
PCL_INFO ("[DominantPlaneSegmentation] %zu normals estimated. \n", cloud_normals_->points.size ());
// ---[ Perform segmentation
seg_.setInputCloud (cloud_downsampled_);
seg_.setInputNormals (cloud_normals_);
seg_.segment (*table_inliers_, *table_coefficients_);
if (table_inliers_->indices.size () == 0)
{
PCL_WARN ("[DominantPlaneSegmentation] No Plane Inliers points! Aborting.");
return;
}
// ---[ Extract the plane
proj_.setInputCloud (cloud_downsampled_);
proj_.setIndices (table_inliers_);
proj_.setModelCoefficients (table_coefficients_);
proj_.filter (*table_projected_);
// ---[ Estimate the convex hull
std::vector<pcl::Vertices> polygons;
CloudPtr table_hull (new Cloud ());
hull_.setInputCloud (table_projected_);
hull_.reconstruct (*table_hull, polygons);
// Compute the plane coefficients
Eigen::Vector4f model_coefficients;
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
model_coefficients[0] = table_coefficients_->values[0];
model_coefficients[1] = table_coefficients_->values[1];
model_coefficients[2] = table_coefficients_->values[2];
model_coefficients[3] = table_coefficients_->values[3];
// Need to flip the plane normal towards the viewpoint
Eigen::Vector4f vp (0, 0, 0, 0);
// See if we need to flip any plane normals
vp -= table_hull->points[0].getVector4fMap ();
vp[3] = 0;
// Dot product between the (viewpoint - point) and the plane normal
float cos_theta = vp.dot (model_coefficients);
// Flip the plane normal
if (cos_theta < 0)
{
model_coefficients *= -1;
model_coefficients[3] = 0;
// Hessian form (D = nc . p_plane (centroid here) + p)
model_coefficients[3] = -1 * (model_coefficients.dot (table_hull->points[0].getVector4fMap ()));
}
//Set table_coeffs
table_coeffs_ = model_coefficients;
// ---[ Get the objects on top of the table
pcl::PointIndices cloud_object_indices;
prism_.setInputCloud (cloud_filtered_);
prism_.setInputPlanarHull (table_hull);
prism_.segment (cloud_object_indices);
pcl::ExtractIndices<PointType> extract_object_indices;
extract_object_indices.setInputCloud (cloud_downsampled_);
extract_object_indices.setIndices (boost::make_shared<const pcl::PointIndices> (cloud_object_indices));
extract_object_indices.filter (*cloud_objects_);
if (cloud_objects_->points.size () == 0)
return;
// ---[ Split the objects into Euclidean clusters
std::vector<pcl::PointIndices> clusters2;
cluster_.setInputCloud (cloud_filtered_);
cluster_.setIndices (boost::make_shared<const pcl::PointIndices> (cloud_object_indices));
cluster_.extract (clusters2);
PCL_INFO ("[DominantPlaneSegmentation::compute_full()] Number of clusters found matching the given constraints: %zu.\n",
clusters2.size ());
clusters.resize (clusters2.size ());
for (size_t i = 0; i < clusters2.size (); ++i)
{
clusters[i] = CloudPtr (new Cloud ());
pcl::copyPointCloud (*cloud_filtered_, clusters2[i].indices, *clusters[i]);
}
}
template<typename PointType> void
pcl::apps::DominantPlaneSegmentation<PointType>::compute_fast (std::vector<CloudPtr> & clusters)
{
// Has the input dataset been set already?
if (!input_)
{
PCL_WARN ("[pcl::apps::DominantPlaneSegmentation] No input dataset given!\n");
return;
}
// Is the input dataset organized?
if (input_->is_dense)
{
PCL_WARN ("[pcl::apps::DominantPlaneSegmentation] compute_fast can only be used with organized point clouds!\n");
return;
}
CloudConstPtr cloud_;
CloudPtr cloud_filtered_ (new Cloud ());
CloudPtr cloud_downsampled_ (new Cloud ());
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals_ (new pcl::PointCloud<pcl::Normal> ());
pcl::PointIndices::Ptr table_inliers_ (new pcl::PointIndices ());
pcl::ModelCoefficients::Ptr table_coefficients_ (new pcl::ModelCoefficients ());
CloudPtr table_projected_ (new Cloud ());
CloudPtr table_hull_ (new Cloud ());
CloudPtr cloud_objects_ (new Cloud ());
CloudPtr cluster_object_ (new Cloud ());
typename pcl::search::KdTree<PointType>::Ptr normals_tree_ (new pcl::search::KdTree<PointType>);
typename pcl::search::KdTree<PointType>::Ptr clusters_tree_ (new pcl::search::KdTree<PointType>);
clusters_tree_->setEpsilon (1);
// Normal estimation parameters
n3d_.setKSearch (k_);
n3d_.setSearchMethod (normals_tree_);
// Table model fitting parameters
seg_.setDistanceThreshold (sac_distance_threshold_);
seg_.setMaxIterations (2000);
seg_.setNormalDistanceWeight (0.1);
seg_.setOptimizeCoefficients (true);
seg_.setModelType (pcl::SACMODEL_NORMAL_PLANE);
seg_.setMethodType (pcl::SAC_RANSAC);
seg_.setProbability (0.99);
proj_.setModelType (pcl::SACMODEL_NORMAL_PLANE);
bb_cluster_proj_.setModelType (pcl::SACMODEL_NORMAL_PLANE);
prism_.setHeightLimits (object_min_height_, object_max_height_);
// Clustering parameters
cluster_.setClusterTolerance (object_cluster_tolerance_);
cluster_.setMinClusterSize (object_cluster_min_size_);
cluster_.setSearchMethod (clusters_tree_);
// ---[ PassThroughFilter
pass_.setFilterLimits (min_z_bounds_, max_z_bounds_);
pass_.setFilterFieldName ("z");
pass_.setInputCloud (input_);
pass_.filter (*cloud_filtered_);
if (int (cloud_filtered_->points.size ()) < k_)
{
PCL_WARN ("[DominantPlaneSegmentation] Filtering returned %zu points! Aborting.",
cloud_filtered_->points.size ());
return;
}
// Downsample the point cloud
grid_.setLeafSize (downsample_leaf_, downsample_leaf_, downsample_leaf_);
grid_.setDownsampleAllData (false);
grid_.setInputCloud (cloud_filtered_);
grid_.filter (*cloud_downsampled_);
// ---[ Estimate the point normals
n3d_.setInputCloud (cloud_downsampled_);
n3d_.compute (*cloud_normals_);
// ---[ Perform segmentation
seg_.setInputCloud (cloud_downsampled_);
seg_.setInputNormals (cloud_normals_);
seg_.segment (*table_inliers_, *table_coefficients_);
if (table_inliers_->indices.size () == 0)
{
PCL_WARN ("[DominantPlaneSegmentation] No Plane Inliers points! Aborting.");
return;
}
// ---[ Extract the plane
proj_.setInputCloud (cloud_downsampled_);
proj_.setIndices (table_inliers_);
proj_.setModelCoefficients (table_coefficients_);
proj_.filter (*table_projected_);
// ---[ Estimate the convex hull
std::vector<pcl::Vertices> polygons;
CloudPtr table_hull (new Cloud ());
hull_.setInputCloud (table_projected_);
hull_.reconstruct (*table_hull, polygons);
// Compute the plane coefficients
Eigen::Vector4f model_coefficients;
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
model_coefficients[0] = table_coefficients_->values[0];
model_coefficients[1] = table_coefficients_->values[1];
model_coefficients[2] = table_coefficients_->values[2];
model_coefficients[3] = table_coefficients_->values[3];
// Need to flip the plane normal towards the viewpoint
Eigen::Vector4f vp (0, 0, 0, 0);
// See if we need to flip any plane normals
vp -= table_hull->points[0].getVector4fMap ();
vp[3] = 0;
// Dot product between the (viewpoint - point) and the plane normal
float cos_theta = vp.dot (model_coefficients);
// Flip the plane normal
if (cos_theta < 0)
{
model_coefficients *= -1;
model_coefficients[3] = 0;
// Hessian form (D = nc . p_plane (centroid here) + p)
model_coefficients[3] = -1 * (model_coefficients.dot (table_hull->points[0].getVector4fMap ()));
}
//Set table_coeffs
table_coeffs_ = model_coefficients;
// ---[ Get the objects on top of the table
pcl::PointIndices cloud_object_indices;
prism_.setInputCloud (input_);
prism_.setInputPlanarHull (table_hull);
prism_.segment (cloud_object_indices);
pcl::ExtractIndices<PointType> extract_object_indices;
extract_object_indices.setInputCloud (input_);
extract_object_indices.setIndices (boost::make_shared<const pcl::PointIndices> (cloud_object_indices));
extract_object_indices.filter (*cloud_objects_);
//create new binary pointcloud with intensity values (0 and 1), 0 for non-object pixels and 1 otherwise
pcl::PointCloud<pcl::PointXYZI>::Ptr binary_cloud (new pcl::PointCloud<pcl::PointXYZI>);
{
binary_cloud->width = input_->width;
binary_cloud->height = input_->height;
binary_cloud->points.resize (input_->points.size ());
binary_cloud->is_dense = input_->is_dense;
size_t idx;
for (size_t i = 0; i < cloud_object_indices.indices.size (); ++i)
{
idx = cloud_object_indices.indices[i];
binary_cloud->points[idx].getVector4fMap () = input_->points[idx].getVector4fMap ();
binary_cloud->points[idx].intensity = 1.0;
}
}
//connected components on the binary image
std::map<float, float> connected_labels;
float c_intensity = 0.1f;
float intensity_incr = 0.1f;
{
int wsize = wsize_;
for (int i = 0; i < int (binary_cloud->width); i++)
{
for (int j = 0; j < int (binary_cloud->height); j++)
{
if (binary_cloud->at (i, j).intensity != 0)
{
//check neighboring pixels, first left and then top
//be aware of margins
if ((i - 1) < 0 && (j - 1) < 0)
{
//top-left pixel
(*binary_cloud) (i, j).intensity = c_intensity;
}
else
{
if ((j - 1) < 0)
{
//top-row, check on the left of pixel to assign a new label or not
int left = check ((*binary_cloud) (i - 1, j), (*binary_cloud) (i, j), c_intensity, object_cluster_tolerance_);
if (left)
{
//Nothing found on the left, check bigger window
bool found = false;
for (int kk = 2; kk < wsize && !found; kk++)
{
if ((i - kk) < 0)
continue;
int left = check ((*binary_cloud) (i - kk, j), (*binary_cloud) (i, j), c_intensity, object_cluster_tolerance_);
if (left == 0)
{
found = true;
}
}
if (!found)
{
c_intensity += intensity_incr;
(*binary_cloud) (i, j).intensity = c_intensity;
}
}
}
else
{
if ((i - 1) == 0)
{
//check only top
int top = check ((*binary_cloud) (i, j - 1), (*binary_cloud) (i, j), c_intensity, object_cluster_tolerance_);
if (top)
{
bool found = false;
for (int kk = 2; kk < wsize && !found; kk++)
{
if ((j - kk) < 0)
continue;
int top = check ((*binary_cloud) (i, j - kk), (*binary_cloud) (i, j), c_intensity, object_cluster_tolerance_);
if (top == 0)
{
found = true;
}
}
if (!found)
{
c_intensity += intensity_incr;
(*binary_cloud) (i, j).intensity = c_intensity;
}
}
}
else
{
//check left and top
int left = check ((*binary_cloud) (i - 1, j), (*binary_cloud) (i, j), c_intensity, object_cluster_tolerance_);
int top = check ((*binary_cloud) (i, j - 1), (*binary_cloud) (i, j), c_intensity, object_cluster_tolerance_);
if (left == 0 && top == 0)
{
//both top and left had labels, check if they are different
//if they are, take the smallest one and mark labels to be connected..
if ((*binary_cloud) (i - 1, j).intensity != (*binary_cloud) (i, j - 1).intensity)
{
float smaller_intensity = (*binary_cloud) (i - 1, j).intensity;
float bigger_intensity = (*binary_cloud) (i, j - 1).intensity;
if ((*binary_cloud) (i - 1, j).intensity > (*binary_cloud) (i, j - 1).intensity)
{
smaller_intensity = (*binary_cloud) (i, j - 1).intensity;
bigger_intensity = (*binary_cloud) (i - 1, j).intensity;
}
connected_labels[bigger_intensity] = smaller_intensity;
(*binary_cloud) (i, j).intensity = smaller_intensity;
}
}
if (left == 1 && top == 1)
{
//if none had labels, increment c_intensity
//search first on bigger window
bool found = false;
for (int dist = 2; dist < wsize && !found; dist++)
{
if (((i - dist) < 0) || ((j - dist) < 0))
continue;
int left = check ((*binary_cloud) (i - dist, j), (*binary_cloud) (i, j), c_intensity, object_cluster_tolerance_);
int top = check ((*binary_cloud) (i, j - dist), (*binary_cloud) (i, j), c_intensity, object_cluster_tolerance_);
if (left == 0 && top == 0)
{
if ((*binary_cloud) (i - dist, j).intensity != (*binary_cloud) (i, j - dist).intensity)
{
float smaller_intensity = (*binary_cloud) (i - dist, j).intensity;
float bigger_intensity = (*binary_cloud) (i, j - dist).intensity;
if ((*binary_cloud) (i - dist, j).intensity > (*binary_cloud) (i, j - dist).intensity)
{
smaller_intensity = (*binary_cloud) (i, j - dist).intensity;
bigger_intensity = (*binary_cloud) (i - dist, j).intensity;
}
connected_labels[bigger_intensity] = smaller_intensity;
(*binary_cloud) (i, j).intensity = smaller_intensity;
found = true;
}
}
else if (left == 0 || top == 0)
{
//one had label
found = true;
}
}
if (!found)
{
//none had label in the bigger window
c_intensity += intensity_incr;
(*binary_cloud) (i, j).intensity = c_intensity;
}
}
}
}
}
}
}
}
}
std::map<float, pcl::PointIndices> clusters_map;
{
std::map<float, float>::iterator it;
for (int i = 0; i < int (binary_cloud->width); i++)
{
for (int j = 0; j < int (binary_cloud->height); j++)
{
if (binary_cloud->at (i, j).intensity != 0)
{
//check if this is a root label...
it = connected_labels.find (binary_cloud->at (i, j).intensity);
while (it != connected_labels.end ())
{
//the label is on the list, change pixel intensity until it has a root label
(*binary_cloud) (i, j).intensity = (*it).second;
it = connected_labels.find (binary_cloud->at (i, j).intensity);
}
std::map<float, pcl::PointIndices>::iterator it_indices;
it_indices = clusters_map.find (binary_cloud->at (i, j).intensity);
if (it_indices == clusters_map.end ())
{
pcl::PointIndices indices;
clusters_map[binary_cloud->at (i, j).intensity] = indices;
}
clusters_map[binary_cloud->at (i, j).intensity].indices.push_back (static_cast<int> (j * binary_cloud->width + i));
}
}
}
}
clusters.resize (clusters_map.size ());
std::map<float, pcl::PointIndices>::iterator it_indices;
int k = 0;
for (it_indices = clusters_map.begin (); it_indices != clusters_map.end (); it_indices++)
{
if (int ((*it_indices).second.indices.size ()) >= object_cluster_min_size_)
{
clusters[k] = CloudPtr (new Cloud ());
pcl::copyPointCloud (*input_, (*it_indices).second.indices, *clusters[k]);
k++;
indices_clusters_.push_back((*it_indices).second);
}
}
clusters.resize (k);
}
template <typename PointT> void
pcl::people::GroundBasedPeopleDetectionApp<PointT>::compute (std::vector<pcl::people::PersonCluster<PointT> >& clusters)
{
// Check if all mandatory variables have been set:
if (sqrt_ground_coeffs_ != sqrt_ground_coeffs_)
{
PCL_ERROR ("[pcl::people::GroundBasedPeopleDetectionApp::compute] Floor parameters have not been set or they are not valid!\n");
return;
}
if (cloud_ == NULL)
{
PCL_ERROR ("[pcl::people::GroundBasedPeopleDetectionApp::compute] Input cloud has not been set!\n");
return;
}
if (intrinsics_matrix_(0) == 0)
{
PCL_ERROR ("[pcl::people::GroundBasedPeopleDetectionApp::compute] Camera intrinsic parameters have not been set!\n");
return;
}
if (!person_classifier_set_flag_)
{
PCL_ERROR ("[pcl::people::GroundBasedPeopleDetectionApp::compute] Person classifier has not been set!\n");
return;
}
if (!dimension_limits_set_) // if dimension limits have not been set by the user
{
// Adapt thresholds for clusters points number to the voxel size:
max_points_ = int(float(max_points_) * std::pow(0.06/voxel_size_, 2));
if (voxel_size_ > 0.06)
min_points_ = int(float(min_points_) * std::pow(0.06/voxel_size_, 2));
}
// Fill rgb image:
rgb_image_->points.clear(); // clear RGB pointcloud
extractRGBFromPointCloud(cloud_, rgb_image_); // fill RGB pointcloud
// Voxel grid filtering:
PointCloudPtr cloud_filtered(new PointCloud);
pcl::VoxelGrid<PointT> voxel_grid_filter_object;
voxel_grid_filter_object.setInputCloud(cloud_);
voxel_grid_filter_object.setLeafSize (voxel_size_, voxel_size_, voxel_size_);
voxel_grid_filter_object.filter (*cloud_filtered);
// Ground removal and update:
pcl::IndicesPtr inliers(new std::vector<int>);
boost::shared_ptr<pcl::SampleConsensusModelPlane<PointT> > ground_model(new pcl::SampleConsensusModelPlane<PointT>(cloud_filtered));
ground_model->selectWithinDistance(ground_coeffs_, voxel_size_, *inliers);
no_ground_cloud_ = PointCloudPtr (new PointCloud);
pcl::ExtractIndices<PointT> extract;
extract.setInputCloud(cloud_filtered);
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(*no_ground_cloud_);
if((inliers->size() >= 300*0.06/voxel_size_))
ground_model->optimizeModelCoefficients(*inliers, ground_coeffs_, ground_coeffs_);
else
std::cout << "No groundplane update!" << std::endl;
// Euclidean Clustering:
std::vector<pcl::PointIndices> cluster_indices;
typename pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT>);
tree->setInputCloud(no_ground_cloud_);
pcl::EuclideanClusterExtraction<PointT> ec;
ec.setClusterTolerance(2 * 0.06);
ec.setMinClusterSize(min_points_);
ec.setMaxClusterSize(max_points_);
ec.setSearchMethod(tree);
ec.setInputCloud(no_ground_cloud_);
ec.extract(cluster_indices);
// Head based sub-clustering //
pcl::people::HeadBasedSubclustering<PointT> subclustering;
subclustering.setInputCloud(no_ground_cloud_);
subclustering.setGround(ground_coeffs_);
subclustering.setInitialClusters(cluster_indices);
subclustering.setHeightLimits(min_height_, max_height_);
subclustering.setMinimumDistanceBetweenHeads(heads_minimum_distance_);
subclustering.setSensorPortraitOrientation(vertical_);
subclustering.subcluster(clusters);
// Person confidence evaluation with HOG+SVM:
if (vertical_) // Rotate the image if the camera is vertical
{
swapDimensions(rgb_image_);
}
for(typename std::vector<pcl::people::PersonCluster<PointT> >::iterator it = clusters.begin(); it != clusters.end(); ++it)
{
//Evaluate confidence for the current PersonCluster:
Eigen::Vector3f centroid = intrinsics_matrix_ * (it->getTCenter());
centroid /= centroid(2);
Eigen::Vector3f top = intrinsics_matrix_ * (it->getTTop());
top /= top(2);
Eigen::Vector3f bottom = intrinsics_matrix_ * (it->getTBottom());
bottom /= bottom(2);
it->setPersonConfidence(person_classifier_.evaluate(rgb_image_, bottom, top, centroid, intrinsics_matrix_, vertical_));
}
}
template <typename PointT> bool
pcl::people::GroundBasedPeopleDetectionApp<PointT>::compute (std::vector<pcl::people::PersonCluster<PointT> >& clusters)
{
// Check if all mandatory variables have been set:
if (!ground_coeffs_set_)
{
PCL_ERROR ("[pcl::people::GroundBasedPeopleDetectionApp::compute] Floor parameters have not been set or they are not valid!\n");
return (false);
}
if (cloud_ == NULL)
{
PCL_ERROR ("[pcl::people::GroundBasedPeopleDetectionApp::compute] Input cloud has not been set!\n");
return (false);
}
if (!intrinsics_matrix_set_)
{
PCL_ERROR ("[pcl::people::GroundBasedPeopleDetectionApp::compute] Camera intrinsic parameters have not been set!\n");
return (false);
}
if (!person_classifier_set_flag_)
{
PCL_ERROR ("[pcl::people::GroundBasedPeopleDetectionApp::compute] Person classifier has not been set!\n");
return (false);
}
// Fill rgb image:
rgb_image_->points.clear(); // clear RGB pointcloud
extractRGBFromPointCloud(cloud_, rgb_image_); // fill RGB pointcloud
// Downsample of sampling_factor in every dimension:
if (sampling_factor_ != 1)
{
PointCloudPtr cloud_downsampled(new PointCloud);
cloud_downsampled->width = (cloud_->width)/sampling_factor_;
cloud_downsampled->height = (cloud_->height)/sampling_factor_;
cloud_downsampled->points.resize(cloud_downsampled->height*cloud_downsampled->width);
cloud_downsampled->is_dense = cloud_->is_dense;
for (uint32_t j = 0; j < cloud_downsampled->width; j++)
{
for (uint32_t i = 0; i < cloud_downsampled->height; i++)
{
(*cloud_downsampled)(j,i) = (*cloud_)(sampling_factor_*j,sampling_factor_*i);
}
}
(*cloud_) = (*cloud_downsampled);
}
applyTransformationPointCloud();
filter();
// Ground removal and update:
pcl::IndicesPtr inliers(new std::vector<int>);
boost::shared_ptr<pcl::SampleConsensusModelPlane<PointT> > ground_model(new pcl::SampleConsensusModelPlane<PointT>(cloud_filtered_));
ground_model->selectWithinDistance(ground_coeffs_transformed_, 2 * voxel_size_, *inliers);
no_ground_cloud_ = PointCloudPtr (new PointCloud);
pcl::ExtractIndices<PointT> extract;
extract.setInputCloud(cloud_filtered_);
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(*no_ground_cloud_);
if (inliers->size () >= (300 * 0.06 / voxel_size_ / std::pow (static_cast<double> (sampling_factor_), 2)))
ground_model->optimizeModelCoefficients (*inliers, ground_coeffs_transformed_, ground_coeffs_transformed_);
else
PCL_INFO ("No groundplane update!\n");
// Euclidean Clustering:
std::vector<pcl::PointIndices> cluster_indices;
typename pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT>);
tree->setInputCloud(no_ground_cloud_);
pcl::EuclideanClusterExtraction<PointT> ec;
ec.setClusterTolerance(2 * voxel_size_);
ec.setMinClusterSize(min_points_);
ec.setMaxClusterSize(max_points_);
ec.setSearchMethod(tree);
ec.setInputCloud(no_ground_cloud_);
ec.extract(cluster_indices);
// Head based sub-clustering //
pcl::people::HeadBasedSubclustering<PointT> subclustering;
subclustering.setInputCloud(no_ground_cloud_);
subclustering.setGround(ground_coeffs_transformed_);
subclustering.setInitialClusters(cluster_indices);
subclustering.setHeightLimits(min_height_, max_height_);
subclustering.setMinimumDistanceBetweenHeads(heads_minimum_distance_);
subclustering.setSensorPortraitOrientation(vertical_);
subclustering.subcluster(clusters);
// Person confidence evaluation with HOG+SVM:
if (vertical_) // Rotate the image if the camera is vertical
{
swapDimensions(rgb_image_);
}
for(typename std::vector<pcl::people::PersonCluster<PointT> >::iterator it = clusters.begin(); it != clusters.end(); ++it)
{
//Evaluate confidence for the current PersonCluster:
Eigen::Vector3f centroid = intrinsics_matrix_transformed_ * (it->getTCenter());
centroid /= centroid(2);
Eigen::Vector3f top = intrinsics_matrix_transformed_ * (it->getTTop());
top /= top(2);
Eigen::Vector3f bottom = intrinsics_matrix_transformed_ * (it->getTBottom());
bottom /= bottom(2);
it->setPersonConfidence(person_classifier_.evaluate(rgb_image_, bottom, top, centroid, vertical_));
}
return (true);
}
template <typename PointT> bool
open_ptrack::detection::GroundBasedPeopleDetectionApp<PointT>::compute (std::vector<pcl::people::PersonCluster<PointT> >& clusters)
{
frame_counter_++;
// Define if debug info should be written or not for this frame:
bool debug_flag = false;
if ((frame_counter_ % 60) == 0)
{
debug_flag = true;
}
// Check if all mandatory variables have been set:
if (debug_flag)
{
if (sqrt_ground_coeffs_ != sqrt_ground_coeffs_)
{
PCL_ERROR ("[open_ptrack::detection::GroundBasedPeopleDetectionApp::compute] Floor parameters have not been set or they are not valid!\n");
return (false);
}
if (cloud_ == NULL)
{
PCL_ERROR ("[open_ptrack::detection::GroundBasedPeopleDetectionApp::compute] Input cloud has not been set!\n");
return (false);
}
if (intrinsics_matrix_(0) == 0)
{
PCL_ERROR ("[open_ptrack::detection::GroundBasedPeopleDetectionApp::compute] Camera intrinsic parameters have not been set!\n");
return (false);
}
if (!person_classifier_set_flag_)
{
PCL_ERROR ("[open_ptrack::detection::GroundBasedPeopleDetectionApp::compute] Person classifier has not been set!\n");
return (false);
}
}
if (!dimension_limits_set_) // if dimension limits have not been set by the user
{
// Adapt thresholds for clusters points number to the voxel size:
max_points_ = int(float(max_points_) * std::pow(0.06/voxel_size_, 2));
if (voxel_size_ > 0.06)
min_points_ = int(float(min_points_) * std::pow(0.06/voxel_size_, 2));
}
// Fill rgb image:
rgb_image_->points.clear(); // clear RGB pointcloud
extractRGBFromPointCloud(cloud_, rgb_image_); // fill RGB pointcloud
// Downsample of sampling_factor in every dimension:
if (sampling_factor_ != 1)
{
PointCloudPtr cloud_downsampled(new PointCloud);
cloud_downsampled->width = (cloud_->width)/sampling_factor_;
cloud_downsampled->height = (cloud_->height)/sampling_factor_;
cloud_downsampled->points.resize(cloud_downsampled->height*cloud_downsampled->width);
cloud_downsampled->is_dense = cloud_->is_dense;
cloud_downsampled->header = cloud_->header;
for (int j = 0; j < cloud_downsampled->width; j++)
{
for (int i = 0; i < cloud_downsampled->height; i++)
{
(*cloud_downsampled)(j,i) = (*cloud_)(sampling_factor_*j,sampling_factor_*i);
}
}
(*cloud_) = (*cloud_downsampled);
}
if (use_rgb_)
{
// Compute mean luminance:
int n_points = cloud_->points.size();
double sumR, sumG, sumB = 0.0;
for (int j = 0; j < cloud_->width; j++)
{
for (int i = 0; i < cloud_->height; i++)
{
sumR += (*cloud_)(j,i).r;
sumG += (*cloud_)(j,i).g;
sumB += (*cloud_)(j,i).b;
}
}
mean_luminance_ = 0.3 * sumR/n_points + 0.59 * sumG/n_points + 0.11 * sumB/n_points;
// mean_luminance_ = 0.2126 * sumR/n_points + 0.7152 * sumG/n_points + 0.0722 * sumB/n_points;
}
// Voxel grid filtering:
PointCloudPtr cloud_filtered(new PointCloud);
pcl::VoxelGrid<PointT> voxel_grid_filter_object;
voxel_grid_filter_object.setInputCloud(cloud_);
voxel_grid_filter_object.setLeafSize (voxel_size_, voxel_size_, voxel_size_);
voxel_grid_filter_object.setFilterFieldName("z");
voxel_grid_filter_object.setFilterLimits(0.0, max_distance_);
voxel_grid_filter_object.filter (*cloud_filtered);
// Ground removal and update:
pcl::IndicesPtr inliers(new std::vector<int>);
boost::shared_ptr<pcl::SampleConsensusModelPlane<PointT> > ground_model(new pcl::SampleConsensusModelPlane<PointT>(cloud_filtered));
ground_model->selectWithinDistance(ground_coeffs_, voxel_size_, *inliers);
no_ground_cloud_ = PointCloudPtr (new PointCloud);
pcl::ExtractIndices<PointT> extract;
extract.setInputCloud(cloud_filtered);
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(*no_ground_cloud_);
if (inliers->size () >= (300 * 0.06 / voxel_size_ / std::pow (static_cast<double> (sampling_factor_), 2)))
ground_model->optimizeModelCoefficients (*inliers, ground_coeffs_, ground_coeffs_);
else
{
if (debug_flag)
{
PCL_INFO ("No groundplane update!\n");
}
}
// Background Subtraction (optional):
if (background_subtraction_)
{
PointCloudPtr foreground_cloud(new PointCloud);
for (unsigned int i = 0; i < no_ground_cloud_->points.size(); i++)
{
if (not (background_octree_->isVoxelOccupiedAtPoint(no_ground_cloud_->points[i].x, no_ground_cloud_->points[i].y, no_ground_cloud_->points[i].z)))
{
foreground_cloud->points.push_back(no_ground_cloud_->points[i]);
}
}
no_ground_cloud_ = foreground_cloud;
}
if (no_ground_cloud_->points.size() > 0)
{
// Euclidean Clustering:
std::vector<pcl::PointIndices> cluster_indices;
typename pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT>);
tree->setInputCloud(no_ground_cloud_);
pcl::EuclideanClusterExtraction<PointT> ec;
ec.setClusterTolerance(2 * 0.06);
ec.setMinClusterSize(min_points_);
ec.setMaxClusterSize(max_points_);
ec.setSearchMethod(tree);
ec.setInputCloud(no_ground_cloud_);
ec.extract(cluster_indices);
// Sensor tilt compensation to improve people detection:
PointCloudPtr no_ground_cloud_rotated(new PointCloud);
Eigen::VectorXf ground_coeffs_new;
if(sensor_tilt_compensation_)
{
// We want to rotate the point cloud so that the ground plane is parallel to the xOz plane of the sensor:
Eigen::Vector3f input_plane, output_plane;
input_plane << ground_coeffs_(0), ground_coeffs_(1), ground_coeffs_(2);
output_plane << 0.0, -1.0, 0.0;
Eigen::Vector3f axis = input_plane.cross(output_plane);
float angle = acos( input_plane.dot(output_plane)/ ( input_plane.norm()/output_plane.norm() ) );
transform_ = Eigen::AngleAxisf(angle, axis);
// Setting also anti_transform for later
anti_transform_ = transform_.inverse();
no_ground_cloud_rotated = rotateCloud(no_ground_cloud_, transform_);
ground_coeffs_new.resize(4);
ground_coeffs_new = rotateGround(ground_coeffs_, transform_);
}
else
{
transform_ = transform_.Identity();
anti_transform_ = transform_.inverse();
no_ground_cloud_rotated = no_ground_cloud_;
ground_coeffs_new = ground_coeffs_;
}
// To avoid PCL warning:
if (cluster_indices.size() == 0)
cluster_indices.push_back(pcl::PointIndices());
// Head based sub-clustering //
pcl::people::HeadBasedSubclustering<PointT> subclustering;
subclustering.setInputCloud(no_ground_cloud_rotated);
subclustering.setGround(ground_coeffs_new);
subclustering.setInitialClusters(cluster_indices);
subclustering.setHeightLimits(min_height_, max_height_);
subclustering.setMinimumDistanceBetweenHeads(heads_minimum_distance_);
subclustering.setSensorPortraitOrientation(vertical_);
subclustering.subcluster(clusters);
// for (unsigned int i = 0; i < rgb_image_->points.size(); i++)
// {
// if ((rgb_image_->points[i].r < 0) | (rgb_image_->points[i].r > 255) | isnan(rgb_image_->points[i].r))
// {
// std::cout << "ERROR in RGB data!" << std::endl;
// }
// }
if (use_rgb_) // if RGB information can be used
{
// Person confidence evaluation with HOG+SVM:
if (vertical_) // Rotate the image if the camera is vertical
{
swapDimensions(rgb_image_);
}
for(typename std::vector<pcl::people::PersonCluster<PointT> >::iterator it = clusters.begin(); it != clusters.end(); ++it)
{
//Evaluate confidence for the current PersonCluster:
Eigen::Vector3f centroid = intrinsics_matrix_ * (anti_transform_ * it->getTCenter());
centroid /= centroid(2);
Eigen::Vector3f top = intrinsics_matrix_ * (anti_transform_ * it->getTTop());
top /= top(2);
Eigen::Vector3f bottom = intrinsics_matrix_ * (anti_transform_ * it->getTBottom());
bottom /= bottom(2);
it->setPersonConfidence(person_classifier_.evaluate(rgb_image_, bottom, top, centroid, vertical_));
}
}
else
{
for(typename std::vector<pcl::people::PersonCluster<PointT> >::iterator it = clusters.begin(); it != clusters.end(); ++it)
{
it->setPersonConfidence(-100.0);
}
}
}
return (true);
}