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::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);
}