//store icp cloud -> table cloud plane to DB
bool extract(table_detection::db_table_clouds::Request &req, table_detection::db_table_clouds::Response &res)
{
pcl_cloud::Ptr loaded_cloud(new pcl_cloud());
pcl::fromROSMsg(req.cloud, *loaded_cloud);
bool rc = extract_table_msg(loaded_cloud, true, true, true);
return rc;
}
void pcl::face_detection::FaceDetectorDataProvider<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::getDatasetAndLabels(DataSet & data_set,
std::vector<LabelType> & label_data, std::vector<ExampleIndex> & examples)
{
srand (static_cast<unsigned int>(time (NULL)));
std::random_shuffle (image_files_.begin (), image_files_.end ());
std::vector < std::string > files;
files = image_files_;
files.resize (std::min (num_images_, static_cast<int> (files.size ())));
std::vector < TrainingExample > training_examples;
std::vector<float> labels;
int total_neg, total_pos;
total_neg = total_pos = 0;
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::visualization::PCLVisualizer vis("training");
#endif
for (size_t j = 0; j < files.size (); j++)
{
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
vis.removeAllPointClouds();
vis.removeAllShapes();
#endif
if ((j % 50) == 0)
{
std::cout << "Loading image..." << j << std::endl;
}
//1. Load clouds with and without labels
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr loaded_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile (files[j], *loaded_cloud);
pcl::PointCloud<pcl::PointXYZL>::Ptr cloud_labels (new pcl::PointCloud<pcl::PointXYZL>);
pcl::PointCloud<pcl::PointXYZL>::Ptr loaded_cloud_labels (new pcl::PointCloud<pcl::PointXYZL>);
pcl::io::loadPCDFile (files[j], *loaded_cloud_labels);
//crop images to remove as many NaNs as possible and reduce the memory footprint
{
size_t min_col, min_row;
size_t max_col, max_row;
min_col = min_row = std::numeric_limits<size_t>::max ();
max_col = max_row = 0;
for (size_t col = 0; col < loaded_cloud->width; col++)
{
for (size_t row = 0; row < loaded_cloud->height; row++)
{
if (pcl::isFinite (loaded_cloud->at (col, row)))
{
if (row < min_row)
min_row = row;
if (row > max_row)
max_row = row;
if (col < min_col)
min_col = col;
if (col > max_col)
max_col = col;
}
}
}
//std::cout << min_col << " - " << max_col << std::endl;
//std::cout << min_row << " - " << max_row << std::endl;
cropCloud<pcl::PointXYZ> (min_col, max_col, min_row, max_row, *loaded_cloud, *cloud);
cropCloud<pcl::PointXYZL> (min_col, max_col, min_row, max_row, *loaded_cloud_labels, *cloud_labels);
/*pcl::visualization::PCLVisualizer vis ("training");
vis.addPointCloud(loaded_cloud);
vis.spin();*/
}
//Compute integral image over depth
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_depth;
integral_image_depth.reset (new pcl::IntegralImage2D<float, 1> (false));
int element_stride = sizeof(pcl::PointXYZ) / sizeof(float);
int row_stride = element_stride * cloud->width;
const float *data = reinterpret_cast<const float*> (&cloud->points[0]);
integral_image_depth->setInput (data + 2, cloud->width, cloud->height, element_stride, row_stride);
//Compute normals and normal integral images
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);
if (USE_NORMALS_)
{
typedef typename pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> NormalEstimator_;
NormalEstimator_ n3d;
n3d.setNormalEstimationMethod (n3d.COVARIANCE_MATRIX);
n3d.setInputCloud (cloud);
n3d.setRadiusSearch (0.02);
n3d.setKSearch (0);
{
pcl::ScopeTime t ("compute normals...");
n3d.compute (*normals);
}
}
int element_stride_normal = sizeof(pcl::Normal) / sizeof(float);
int row_stride_normal = element_stride_normal * normals->width;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_x;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_y;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_z;
if (USE_NORMALS_)
{
integral_image_normal_x.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_nx = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_x->setInput (data_nx, normals->width, normals->height, element_stride_normal, row_stride_normal);
integral_image_normal_y.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_ny = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_y->setInput (data_ny + 1, normals->width, normals->height, element_stride_normal, row_stride_normal);
integral_image_normal_z.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_nz = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_z->setInput (data_nz + 2, normals->width, normals->height, element_stride_normal, row_stride_normal);
}
//Using cloud labels estimate a 2D window from where to extract positive samples
//Rest can be used to extract negative samples
size_t min_col, min_row;
size_t max_col, max_row;
min_col = min_row = std::numeric_limits<size_t>::max ();
max_col = max_row = 0;
//std::cout << cloud_labels->width << " " << cloud_labels->height << std::endl;
for (size_t col = 0; col < cloud_labels->width; col++)
{
for (size_t row = 0; row < cloud_labels->height; row++)
{
if (cloud_labels->at (col, row).label == 1)
{
if (row < min_row)
min_row = row;
if (row > max_row)
max_row = row;
if (col < min_col)
min_col = col;
if (col > max_col)
max_col = col;
}
}
}
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity(new pcl::PointCloud<pcl::PointXYZI>);
cloud_intensity->width = cloud->width;
cloud_intensity->height = cloud->height;
cloud_intensity->points.resize(cloud->points.size());
cloud_intensity->is_dense = cloud->is_dense;
for (int jjj = 0; jjj < static_cast<int>(cloud->points.size()); jjj++)
{
cloud_intensity->points[jjj].getVector4fMap() = cloud->points[jjj].getVector4fMap();
cloud_intensity->points[jjj].intensity = 0.f;
int row, col;
col = jjj % cloud->width;
row = jjj / cloud->width;
//std::cout << row << " " << col << std::endl;
if (check_inside(col, row, min_col, max_col, min_row, max_row))
{
cloud_intensity->points[jjj].intensity = 1.f;
}
}
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity, "intensity");
vis.addPointCloud(cloud_intensity, handler, "intensity_cloud");
#endif
std::string pose_file (files[j]);
boost::replace_all (pose_file, ".pcd", "_pose.txt");
Eigen::Matrix4f pose_mat;
pose_mat.setIdentity (4, 4);
readMatrixFromFile (pose_file, pose_mat);
Eigen::Vector3f ea = pose_mat.block<3, 3> (0, 0).eulerAngles (0, 1, 2);
Eigen::Vector3f trans_vector = Eigen::Vector3f (pose_mat (0, 3), pose_mat (1, 3), pose_mat (2, 3));
pcl::PointXYZ center_point;
center_point.x = trans_vector[0];
center_point.y = trans_vector[1];
center_point.z = trans_vector[2];
int N_patches = patches_per_image_;
int pos_extracted = 0;
int neg_extracted = 0;
int w_size_2 = static_cast<int> (w_size_ / 2);
//************************************************
//2nd training style, fanelli's journal description
//************************************************
{
typedef std::pair<int, int> pixelpair;
std::vector < pixelpair > negative_p, positive_p;
//get negative and positive indices to sample from
for (int col = 0; col < (static_cast<int> (cloud_labels->width) - w_size_); col++)
{
for (int row = 0; row < (static_cast<int> (cloud_labels->height) - w_size_); row++)
{
if (!pcl::isFinite (cloud->at (col + w_size_2, row + w_size_2)))
continue;
//reject patches with more than percent invalid values
float percent = 0.5f;
if (static_cast<float>(integral_image_depth->getFiniteElementsCount (col, row, w_size_, w_size_)) < (percent * static_cast<float>(w_size_ * w_size_)))
continue;
pixelpair pp = std::make_pair (col, row);
if (cloud_labels->at (col + w_size_2, row + w_size_2).label == 1)
positive_p.push_back (pp);
else
negative_p.push_back (pp);
}
}
//shuffle and resize
std::random_shuffle (positive_p.begin (), positive_p.end ());
std::random_shuffle (negative_p.begin (), negative_p.end ());
positive_p.resize (N_patches);
negative_p.resize (N_patches);
//extract positive patch
for (size_t p = 0; p < positive_p.size (); p++)
{
int col, row;
col = positive_p[p].first;
row = positive_p[p].second;
pcl::PointXYZ patch_center_point;
patch_center_point.x = cloud->at (col + w_size_2, row + w_size_2).x;
patch_center_point.y = cloud->at (col + w_size_2, row + w_size_2).y;
patch_center_point.z = cloud->at (col + w_size_2, row + w_size_2).z;
TrainingExample te;
te.iimages_.push_back (integral_image_depth);
if (USE_NORMALS_)
{
te.iimages_.push_back (integral_image_normal_x);
te.iimages_.push_back (integral_image_normal_y);
te.iimages_.push_back (integral_image_normal_z);
}
te.row_ = row;
te.col_ = col;
te.wsize_ = w_size_;
te.trans_ = center_point.getVector3fMap () - patch_center_point.getVector3fMap ();
te.trans_ *= 1000.f; //transform it to millimeters
te.rot_ = ea;
te.rot_ *= 57.2957795f; //transform it to degrees
labels.push_back (1);
pos_extracted++;
total_pos++;
training_examples.push_back (te);
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity2(new pcl::PointCloud<pcl::PointXYZI>(*cloud_intensity));
for (int jjj = col; jjj < (col + w_size_); jjj++)
{
for (int iii = row; iii < (row + w_size_); iii++)
{
cloud_intensity2->at(jjj, iii).intensity = 2.f;
}
}
vis.removeAllPointClouds();
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity2, "intensity");
vis.addPointCloud(cloud_intensity2, handler, "cloud");
vis.spinOnce();
vis.spin();
sleep(1);
#endif
}
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
std::cout << "Going to extract negative patches..." << std::endl;
sleep(2);
#endif
for (size_t p = 0; p < negative_p.size (); p++)
{
int col, row;
col = negative_p[p].first;
row = negative_p[p].second;
TrainingExample te;
te.iimages_.push_back (integral_image_depth);
if (USE_NORMALS_)
{
te.iimages_.push_back (integral_image_normal_x);
te.iimages_.push_back (integral_image_normal_y);
te.iimages_.push_back (integral_image_normal_z);
}
te.row_ = row;
te.col_ = col;
te.wsize_ = w_size_;
labels.push_back (0);
neg_extracted++;
total_neg++;
training_examples.push_back (te);
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity2(new pcl::PointCloud<pcl::PointXYZI>(*cloud_intensity));
for (int jjj = col; jjj < (col + w_size_); jjj++)
{
for (int iii = row; iii < (row + w_size_); iii++)
{
cloud_intensity2->at(jjj, iii).intensity = 2.f;
}
}
vis.removeAllPointClouds();
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity2, "intensity");
vis.addPointCloud(cloud_intensity2, handler, "cloud");
vis.spinOnce();
vis.spin();
sleep(1);
#endif
}
if (neg_extracted != N_patches)
{
std::cout << "Extracted " << neg_extracted << " negative patches" << std::endl;
std::cout << files[j] << std::endl;
}
if (pos_extracted != N_patches)
{
std::cout << "Extracted " << pos_extracted << " positive patches" << std::endl;
std::cout << files[j] << std::endl;
}
}
}
std::cout << training_examples.size () << " " << labels.size () << " " << total_neg << " " << total_pos << std::endl;
//train random forest and make persistent
std::vector<int> example_indices;
for (size_t i = 0; i < labels.size (); i++)
example_indices.push_back (static_cast<int> (i));
label_data = labels;
data_set = training_examples;
examples = example_indices;
}
