void
compute (const sensor_msgs::PointCloud2::ConstPtr &input, sensor_msgs::PointCloud2 &output,
int k, double radius)
{
// Convert data to PointCloud<T>
PointCloud<PointXYZ>::Ptr xyz (new PointCloud<PointXYZ>);
fromROSMsg (*input, *xyz);
// Estimate
TicToc tt;
tt.tic ();
print_highlight (stderr, "Computing ");
NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setInputCloud (xyz);
// ne.setSearchMethod (pcl::search::KdTree<pcl::PointXYZ>::Ptr (new pcl::search::KdTree<pcl::PointXYZ>));
ne.setKSearch (k);
ne.setRadiusSearch (radius);
PointCloud<Normal> normals;
ne.compute (normals);
print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", normals.width * normals.height); print_info (" points]\n");
// Convert data back
sensor_msgs::PointCloud2 output_normals;
toROSMsg (normals, output_normals);
concatenateFields (*input, output_normals, output);
}
TEST (PCL, CVFHEstimationMilk)
{
// Estimate normals first
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
n.setInputCloud (cloud_milk);
n.setSearchMethod (tree);
n.setRadiusSearch (leaf_size_ * 4); //2cm to estimate normals
n.compute (*normals);
CVFHEstimation<PointXYZ, Normal, VFHSignature308> cvfh;
cvfh.setInputCloud (cloud_milk);
cvfh.setInputNormals (normals);
cvfh.setSearchMethod (tree_milk);
cvfh.setClusterTolerance (leaf_size_ * 3);
cvfh.setEPSAngleThreshold (0.13f);
cvfh.setCurvatureThreshold (0.025f);
cvfh.setNormalizeBins (false);
cvfh.setRadiusNormals (leaf_size_ * 4);
// Object
PointCloud<VFHSignature308>::Ptr vfhs (new PointCloud<VFHSignature308> ());
// estimate
cvfh.compute (*vfhs);
EXPECT_EQ (static_cast<int>(vfhs->points.size ()), 2);
}
void
estimateNormals (const typename PointCloud<PointT>::ConstPtr &input, PointCloud<Normal> &normals)
{
if (input->isOrganized ())
{
IntegralImageNormalEstimation<PointT, Normal> ne;
// Set the parameters for normal estimation
ne.setNormalEstimationMethod (ne.COVARIANCE_MATRIX);
ne.setMaxDepthChangeFactor (0.02f);
ne.setNormalSmoothingSize (20.0f);
// Estimate normals in the cloud
ne.setInputCloud (input);
ne.compute (normals);
// Save the distance map for the plane comparator
float *map=ne.getDistanceMap ();// This will be deallocated with the IntegralImageNormalEstimation object...
distance_map_.assign(map, map+input->size() ); //...so we must copy the data out
plane_comparator_->setDistanceMap(distance_map_.data());
}
else
{
NormalEstimation<PointT, Normal> ne;
ne.setInputCloud (input);
ne.setRadiusSearch (0.02f);
ne.setSearchMethod (search_);
ne.compute (normals);
}
}
void SurfaceTriangulation::perform(int ksearch)
{
PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
search::KdTree<PointXYZ>::Ptr tree;
search::KdTree<PointNormal>::Ptr tree2;
cloud->reserve(myPoints.size());
for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
cloud->push_back(PointXYZ(it->x, it->y, it->z));
}
// Create search tree
tree.reset (new search::KdTree<PointXYZ> (false));
tree->setInputCloud (cloud);
// Normal estimation
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
n.setInputCloud (cloud);
//n.setIndices (indices[B);
n.setSearchMethod (tree);
n.setKSearch (ksearch);
n.compute (*normals);
// Concatenate XYZ and normal information
pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);
// Create search tree
tree2.reset (new search::KdTree<PointNormal>);
tree2->setInputCloud (cloud_with_normals);
// Init objects
GreedyProjectionTriangulation<PointNormal> gp3;
// Set parameters
gp3.setInputCloud (cloud_with_normals);
gp3.setSearchMethod (tree2);
gp3.setSearchRadius (searchRadius);
gp3.setMu (mu);
gp3.setMaximumNearestNeighbors (100);
gp3.setMaximumSurfaceAngle(M_PI/4); // 45 degrees
gp3.setMinimumAngle(M_PI/18); // 10 degrees
gp3.setMaximumAngle(2*M_PI/3); // 120 degrees
gp3.setNormalConsistency(false);
gp3.setConsistentVertexOrdering(true);
// Reconstruct
PolygonMesh mesh;
gp3.reconstruct (mesh);
MeshConversion::convert(mesh, myMesh);
// Additional vertex information
//std::vector<int> parts = gp3.getPartIDs();
//std::vector<int> states = gp3.getPointStates();
}
void FeatSegment::segment(const PointCloud<PointXYZRGB >::Ptr cloud, PointCloud<PointXYZRGB >::Ptr outcloud)
{
// PARAM - Lots of them
int min_pts_per_cluster = 10;
int max_pts_per_cluster = INT_MAX; //3000000;
assert(max_pts_per_cluster > 3000000); // Avoid overflow
int number_neighbours = 50; // PARAM
float radius = 0.025; // 0.025 PARAM
float angle = 0.52; // PARAM
// Required KdTrees
pcl::search::KdTree<PointXYZRGB >::Ptr NormalsTree(new pcl::search::KdTree<PointXYZRGB >);
pcl::search::KdTree<PointXYZRGB >::Ptr ClustersTree(new pcl::search::KdTree<PointXYZRGB >);
PointCloud<Normal >::Ptr CloudNormals(new PointCloud<Normal >);
NormalEstimation<PointXYZRGB, Normal > NormalsFinder;
std::vector<PointIndices > clusters;
ClustersTree->setInputCloud(cloud);
// Set normal estimation parameters
NormalsFinder.setKSearch(number_neighbours);
NormalsFinder.setSearchMethod(NormalsTree);
NormalsFinder.setInputCloud(cloud);
NormalsFinder.compute(*CloudNormals);
extractEuclideanClusters(cloud, CloudNormals, ClustersTree, radius, clusters, angle, min_pts_per_cluster, max_pts_per_cluster);
fprintf(stderr, "Number of clusters found matching the given constraints: %d.", (int)clusters.size ());
std::ofstream FileStr2;
FileStr2.open("live_segments.txt", ios::app); // NOTE: Don't add the ios:trunc flag here!
// Copy to clusters to segments
for (size_t i = 0; i < clusters.size (); ++i)
{
if(FileStr2.is_open())
{
for(int j = 0; j < clusters[i].indices.size(); ++j)
{
if(j == clusters[i].indices.size() - 1)
{
FileStr2 << clusters[i].indices.at(j) << endl;
continue; // Also break;
}
FileStr2 << clusters[i].indices.at(j) << " ";
}
FeatPointCloud * Segment = new FeatPointCloud(m_SceneCloud, clusters[i].indices, m_ConfigFName);
m_Segments.push_back(Segment);
}
else
cout << "Failed to open file.\n";
}
FileStr2.close();
}
void PoissonReconstruction::perform(int ksearch)
{
PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
search::KdTree<PointXYZ>::Ptr tree;
search::KdTree<PointNormal>::Ptr tree2;
cloud->reserve(myPoints.size());
for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
cloud->push_back(PointXYZ(it->x, it->y, it->z));
}
// Create search tree
tree.reset (new search::KdTree<PointXYZ> (false));
tree->setInputCloud (cloud);
// Normal estimation
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
n.setInputCloud (cloud);
//n.setIndices (indices[B);
n.setSearchMethod (tree);
n.setKSearch (ksearch);
n.compute (*normals);
// Concatenate XYZ and normal information
pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);
// Create search tree
tree2.reset (new search::KdTree<PointNormal>);
tree2->setInputCloud (cloud_with_normals);
// Init objects
Poisson<PointNormal> poisson;
// Set parameters
poisson.setInputCloud (cloud_with_normals);
poisson.setSearchMethod (tree2);
if (depth >= 1)
poisson.setDepth(depth);
if (solverDivide >= 1)
poisson.setSolverDivide(solverDivide);
if (samplesPerNode >= 1.0f)
poisson.setSamplesPerNode(samplesPerNode);
// Reconstruct
PolygonMesh mesh;
poisson.reconstruct (mesh);
MeshConversion::convert(mesh, myMesh);
}
int
main (int argc, char **argv)
{
bool use_device = false;
bool use_file = false;
if (argc >= 2)
use_device = true;
if (argc >= 3)
use_file = true;
NormalEstimation v;
v.run (use_device, use_file);
return 0;
}
void Reen::MarchingCubesRBF::perform(int ksearch)
{
PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
search::KdTree<PointXYZ>::Ptr tree;
search::KdTree<PointNormal>::Ptr tree2;
cloud->reserve(myPoints.size());
for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
cloud->push_back(PointXYZ(it->x, it->y, it->z));
}
// Create search tree
tree.reset (new search::KdTree<PointXYZ> (false));
tree->setInputCloud (cloud);
// Normal estimation
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
n.setInputCloud (cloud);
//n.setIndices (indices[B);
n.setSearchMethod (tree);
n.setKSearch (ksearch);
n.compute (*normals);
// Concatenate XYZ and normal information
pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);
// Create search tree
tree2.reset (new search::KdTree<PointNormal>);
tree2->setInputCloud (cloud_with_normals);
// Init objects
pcl::MarchingCubesRBF<PointNormal> rbf;
// Set parameters
rbf.setIsoLevel (0);
rbf.setGridResolution (60, 60, 60);
rbf.setPercentageExtendGrid (0.1f);
rbf.setOffSurfaceDisplacement (0.02f);
rbf.setInputCloud (cloud_with_normals);
rbf.setSearchMethod (tree2);
// Reconstruct
PolygonMesh mesh;
rbf.reconstruct (mesh);
MeshConversion::convert(mesh, myMesh);
}
void GridReconstruction::perform(int ksearch)
{
PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
search::KdTree<PointXYZ>::Ptr tree;
search::KdTree<PointNormal>::Ptr tree2;
cloud->reserve(myPoints.size());
for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
cloud->push_back(PointXYZ(it->x, it->y, it->z));
}
// Create search tree
tree.reset (new search::KdTree<PointXYZ> (false));
tree->setInputCloud (cloud);
// Normal estimation
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
n.setInputCloud (cloud);
//n.setIndices (indices[B);
n.setSearchMethod (tree);
n.setKSearch (ksearch);
n.compute (*normals);
// Concatenate XYZ and normal information
pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);
// Create search tree
tree2.reset (new search::KdTree<PointNormal>);
tree2->setInputCloud (cloud_with_normals);
// Init objects
GridProjection<PointNormal> grid;
// Set parameters
grid.setResolution(0.005);
grid.setPaddingSize(3);
grid.setNearestNeighborNum(100);
grid.setMaxBinarySearchLevel(10);
grid.setInputCloud (cloud_with_normals);
grid.setSearchMethod (tree2);
// Reconstruct
PolygonMesh mesh;
grid.reconstruct (mesh);
MeshConversion::convert(mesh, myMesh);
}
void create_normals (typename PointCloud<PointT>::Ptr cloud,
typename PointCloud<NormalT>::Ptr normals,
float normal_radius=0.03)
{
NormalEstimation<PointT, NormalT> nest;
cout << "[PFHTransformationStrategy::create_normals] Input cloud "
<< cloud->points.size() << " points" << endl;
nest.setInputCloud(cloud);
nest.setSearchMethod (typename search::KdTree<PointT>::Ptr
(new search::KdTree<PointT>));
nest.setRadiusSearch(normal_radius);
nest.compute(*normals);
};
void getCylClusters (boost::shared_ptr<PointCloud<T> > sceneCloud, vector<boost::shared_ptr<PointCloud<T> > > &outVector) {
typedef typename pcl::search::KdTree<T>::Ptr my_KdTreePtr;
typedef typename pcl::PointCloud<T>::Ptr my_PointCloudPtr;
NormalEstimation<T, Normal> ne;
SACSegmentationFromNormals<T, Normal> seg;
my_KdTreePtr tree (new pcl::search::KdTree<T> ());
PointCloud<Normal>::Ptr cloud_normals (new PointCloud<pcl::Normal>);
ModelCoefficients::Ptr coefficients_cylinder (new pcl::ModelCoefficients);
PointIndices::Ptr inliers_cylinder (new pcl::PointIndices);
ExtractIndices<T> extract;
// Estimate point normals
ne.setSearchMethod (tree);
ne.setInputCloud (sceneCloud);
ne.setKSearch (50);
ne.compute (*cloud_normals);
seg.setOptimizeCoefficients (true);
seg.setModelType (SACMODEL_CYLINDER);
seg.setMethodType (SAC_RANSAC);
seg.setNormalDistanceWeight (0.1);
seg.setMaxIterations (10000);
seg.setDistanceThreshold (0.05);
seg.setRadiusLimits (0, 0.1);
seg.setInputCloud (sceneCloud);
seg.setInputNormals (cloud_normals);
// Obtain the cylinder inliers and coefficients
seg.segment (*inliers_cylinder, *coefficients_cylinder);
cout << " Number of inliers vs total number of points " << inliers_cylinder->indices.size() << " vs " << sceneCloud->size() << endl;
// Write the cylinder inliers to disk
extract.setInputCloud (sceneCloud);
extract.setIndices (inliers_cylinder);
extract.setNegative (false);
my_PointCloudPtr cloud_cylinder (new PointCloud<T> ());
extract.filter (*cloud_cylinder);
outVector.push_back(cloud_cylinder);
}
TEST (PCL, CVFHEstimation)
{
// Estimate normals first
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
n.setInputCloud (cloud.makeShared ());
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
// estimate
n.compute (*normals);
CVFHEstimation<PointXYZ, Normal, VFHSignature308> cvfh;
cvfh.setInputNormals (normals);
// Object
PointCloud<VFHSignature308>::Ptr vfhs (new PointCloud<VFHSignature308> ());
// set parameters
cvfh.setInputCloud (cloud.makeShared ());
cvfh.setIndices (indicesptr);
cvfh.setSearchMethod (tree);
// estimate
cvfh.compute (*vfhs);
EXPECT_EQ (static_cast<int>(vfhs->points.size ()), 1);
}
TEST (PCL, SHOTGlobalReferenceFrame)
{
// Estimate normals first
double mr = 0.002;
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
n.setInputCloud (cloud.makeShared ());
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setRadiusSearch (20 * mr);
n.compute (*normals);
EXPECT_NEAR (normals->points[103].normal_x, 0.36683175, 1e-4);
EXPECT_NEAR (normals->points[103].normal_y, -0.44696972, 1e-4);
EXPECT_NEAR (normals->points[103].normal_z, -0.81587529, 1e-4);
EXPECT_NEAR (normals->points[200].normal_x, -0.71414840, 1e-4);
EXPECT_NEAR (normals->points[200].normal_y, -0.06002361, 1e-4);
EXPECT_NEAR (normals->points[200].normal_z, -0.69741613, 1e-4);
EXPECT_NEAR (normals->points[140].normal_x, -0.45109111, 1e-4);
EXPECT_NEAR (normals->points[140].normal_y, -0.19499126, 1e-4);
EXPECT_NEAR (normals->points[140].normal_z, -0.87091631, 1e-4);
boost::shared_ptr<vector<int> > test_indices (new vector<int> (0));
for (size_t i = 0; i < cloud.size (); i+=3)
test_indices->push_back (static_cast<int> (i));
testGSHOTGlobalReferenceFrame<GSHOTEstimation<PointXYZ, Normal, SHOT352>, PointXYZ, Normal, SHOT352> (cloud.makeShared (), normals, test_indices);
}
/* ---[ */
int
main (int argc, char** argv)
{
if (argc < 2)
{
std::cerr << "No test file given. Please download `sac_plane_test.pcd` and pass its path to the test." << std::endl;
return (-1);
}
// Load a standard PCD file from disk
sensor_msgs::PointCloud2 cloud_blob;
if (loadPCDFile (argv[1], cloud_blob) < 0)
{
std::cerr << "Failed to read test file. Please download `sac_plane_test.pcd` and pass its path to the test." << std::endl;
return (-1);
}
fromROSMsg (cloud_blob, *cloud_);
indices_.resize (cloud_->points.size ());
for (size_t i = 0; i < indices_.size (); ++i) { indices_[i] = int (i); }
// Estimate surface normals
NormalEstimation<PointXYZ, Normal> n;
search::Search<PointXYZ>::Ptr tree (new search::KdTree<PointXYZ>);
tree->setInputCloud (cloud_);
n.setInputCloud (cloud_);
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices_));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setRadiusSearch (0.02); // Use 2cm radius to estimate the normals
n.compute (*normals_);
testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
TEST (PCL, VFHEstimation)
{
// Estimate normals first
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
n.setInputCloud (cloud.makeShared ());
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
// estimate
n.compute (*normals);
VFHEstimation<PointXYZ, Normal, VFHSignature308> vfh;
vfh.setInputNormals (normals);
// PointCloud<PointNormal> cloud_normals;
// concatenateFields (cloud, normals, cloud_normals);
// savePCDFile ("bun0_n.pcd", cloud_normals);
// Object
PointCloud<VFHSignature308>::Ptr vfhs (new PointCloud<VFHSignature308> ());
// set parameters
vfh.setInputCloud (cloud.makeShared ());
vfh.setIndices (indicesptr);
vfh.setSearchMethod (tree);
// estimate
vfh.compute (*vfhs);
EXPECT_EQ (int (vfhs->points.size ()), 1);
//for (size_t d = 0; d < 308; ++d)
// std::cerr << vfhs.points[0].histogram[d] << std::endl;
}
TEST (PCL, NormalEstimation)
{
tree.reset (new search::KdTree<PointXYZ> (false));
n.setSearchMethod (tree);
n.setKSearch (10);
n.setInputCloud (cloud.makeShared ());
PointCloud<Normal> output;
n.compute (output);
EXPECT_EQ (output.points.size (), cloud.points.size ());
EXPECT_EQ (output.width, cloud.width);
EXPECT_EQ (output.height, cloud.height);
for (size_t i = 0; i < cloud.points.size (); ++i)
{
EXPECT_NEAR (fabs (output.points[i].normal_x), 0, 1e-2);
EXPECT_NEAR (fabs (output.points[i].normal_y), 0, 1e-2);
EXPECT_NEAR (fabs (output.points[i].normal_z), 1.0, 1e-2);
}
}
void
computeFeatureViaNormals (const pcl::PCLPointCloud2::ConstPtr &input, pcl::PCLPointCloud2 &output,
int argc, char** argv, bool set_search_flag = true)
{
int n_k = default_n_k;
int f_k = default_f_k;
double n_radius = default_n_radius;
double f_radius = default_f_radius;
parse_argument (argc, argv, "-n_k", n_k);
parse_argument (argc, argv, "-n_radius", n_radius);
parse_argument (argc, argv, "-f_k", f_k);
parse_argument (argc, argv, "-f_radius", f_radius);
// Convert data to PointCloud<PointIn>
typename PointCloud<PointIn>::Ptr xyz (new PointCloud<PointIn>);
fromPCLPointCloud2 (*input, *xyz);
// Estimate
TicToc tt;
tt.tic ();
print_highlight (stderr, "Computing ");
NormalEstimation<PointIn, NormalT> ne;
ne.setInputCloud (xyz);
ne.setSearchMethod (typename pcl::search::KdTree<PointIn>::Ptr (new pcl::search::KdTree<PointIn>));
ne.setKSearch (n_k);
ne.setRadiusSearch (n_radius);
typename PointCloud<NormalT>::Ptr normals = typename PointCloud<NormalT>::Ptr (new PointCloud<NormalT>);
ne.compute (*normals);
FeatureAlgorithm feature_est;
feature_est.setInputCloud (xyz);
feature_est.setInputNormals (normals);
feature_est.setSearchMethod (typename pcl::search::KdTree<PointIn>::Ptr (new pcl::search::KdTree<PointIn>));
PointCloud<PointOut> output_features;
if (set_search_flag) {
feature_est.setKSearch (f_k);
feature_est.setRadiusSearch (f_radius);
}
feature_est.compute (output_features);
print_info ("[done, ");
print_value ("%g", tt.toc ());
print_info (" ms : ");
print_value ("%d", output.width * output.height);
print_info (" points]\n");
// Convert data back
toPCLPointCloud2 (output_features, output);
}
/** estimate the normals of a point cloud */
static PointCloud<Normal>::Ptr
compute_pcn(PointCloud<PointXYZ>::ConstPtr in, float vx, float vy, float vz)
{
PointCloud<Normal>::Ptr pcn (new PointCloud<Normal>());
NormalEstimation<PointXYZ, Normal> ne;
search::KdTree<PointXYZ>::Ptr kdt (new search::KdTree<PointXYZ>());
ne.setInputCloud(in);
ne.setSearchMethod(kdt);
ne.setKSearch(20);
ne.setViewPoint(vx, vy, vz);
ne.compute(*pcn);
return pcn;
}
// Subsample cloud for faster matching and processing, while filling in normals.
void PointcloudProc::reduceCloud(const PointCloud<PointXYZRGB>& input, PointCloud<PointXYZRGBNormal>& output) const
{
PointCloud<PointXYZRGB> cloud_nan_filtered, cloud_box_filtered, cloud_voxel_reduced;
PointCloud<Normal> normals;
PointCloud<PointXYZRGBNormal> cloud_normals;
std::vector<int> indices;
// Filter out nans.
removeNaNFromPointCloud(input, cloud_nan_filtered, indices);
indices.clear();
// Filter out everything outside a [200x200x200] box.
Eigen::Vector4f min_pt(-100, -100, -100, -100);
Eigen::Vector4f max_pt(100, 100, 100, 100);
getPointsInBox(cloud_nan_filtered, min_pt, max_pt, indices);
ExtractIndices<PointXYZRGB> boxfilter;
boxfilter.setInputCloud(boost::make_shared<const PointCloud<PointXYZRGB> >(cloud_nan_filtered));
boxfilter.setIndices (boost::make_shared<vector<int> > (indices));
boxfilter.filter(cloud_box_filtered);
// Reduce pointcloud
VoxelGrid<PointXYZRGB> voxelfilter;
voxelfilter.setInputCloud (boost::make_shared<const PointCloud<PointXYZRGB> > (cloud_box_filtered));
voxelfilter.setLeafSize (0.05, 0.05, 0.05);
// voxelfilter.setLeafSize (0.1, 0.1, 0.1);
voxelfilter.filter (cloud_voxel_reduced);
// Compute normals
NormalEstimation<PointXYZRGB, Normal> normalest;
normalest.setViewPoint(0, 0, 0);
normalest.setSearchMethod (boost::make_shared<search::KdTree<PointXYZRGB> > ());
//normalest.setKSearch (10);
normalest.setRadiusSearch (0.25);
// normalest.setRadiusSearch (0.4);
normalest.setInputCloud(boost::make_shared<const PointCloud<PointXYZRGB> >(cloud_voxel_reduced));
normalest.compute(normals);
pcl::concatenateFields (cloud_voxel_reduced, normals, cloud_normals);
// Filter based on curvature
PassThrough<PointXYZRGBNormal> normalfilter;
normalfilter.setFilterFieldName("curvature");
// normalfilter.setFilterLimits(0.0, 0.2);
normalfilter.setFilterLimits(0.0, 0.2);
normalfilter.setInputCloud(boost::make_shared<const PointCloud<PointXYZRGBNormal> >(cloud_normals));
normalfilter.filter(output);
}
TEST (PCL, GSHOTRadius)
{
float radius = radius_local_shot / 4.0f;
// Estimate normals first
double mr = 0.002;
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
n.setInputCloud (cloud.makeShared ());
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setRadiusSearch (20 * mr);
n.compute (*normals);
// Objects
PointCloud<SHOT352>::Ptr gshots352 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr shots352 (new PointCloud<SHOT352> ());
// SHOT352 (local)
SHOTEstimation<PointXYZ, Normal, SHOT352> shot352;
shot352.setInputNormals (normals);
shot352.setRadiusSearch (radius);
shot352.setInputCloud (cloud_for_lrf.makeShared ());
boost::shared_ptr<vector<int> > indices_local_shot_ptr (new vector<int> (indices_local_shot));
shot352.setIndices (indices_local_shot_ptr);
shot352.setSearchSurface (cloud.makeShared());
shot352.compute (*shots352);
// SHOT352 (global)
GSHOTEstimation<PointXYZ, Normal, SHOT352> gshot352;
gshot352.setInputNormals (normals);
// set parameters
gshot352.setInputCloud (cloud.makeShared ());
gshot352.setIndices (indicesptr);
gshot352.setSearchMethod (tree);
gshot352.setRadiusSearch (radius);
EXPECT_EQ (gshot352.getRadiusSearch (), shot352.getRadiusSearch ());
// estimate
gshot352.compute (*gshots352);
checkDescNear (*gshots352, *shots352, 1E-7);
}
/* ---[ */
int
main (int argc, char** argv)
{
// Load two standard PCD files from disk
if (argc < 3)
{
std::cerr << "No test files given. Please download `sac_plane_test.pcd` and 'cturtle.pcd' and pass them path to the test." << std::endl;
return (-1);
}
// Load in the point clouds
io::loadPCDFile (argv[1], *cloud_walls);
io::loadPCDFile (argv[2], *cloud_turtle);
// Compute the normals for each cloud, and then clean them up of any NaN values
NormalEstimation<PointXYZ,PointNormal> ne;
ne.setInputCloud (cloud_walls);
ne.setRadiusSearch (0.05);
ne.compute (*cloud_walls_normals);
copyPointCloud (*cloud_walls, *cloud_walls_normals);
std::vector<int> aux_indices;
removeNaNFromPointCloud (*cloud_walls_normals, *cloud_walls_normals, aux_indices);
removeNaNNormalsFromPointCloud (*cloud_walls_normals, *cloud_walls_normals, aux_indices);
ne = NormalEstimation<PointXYZ, PointNormal> ();
ne.setInputCloud (cloud_turtle);
ne.setKSearch (5);
ne.compute (*cloud_turtle_normals);
copyPointCloud (*cloud_turtle, *cloud_turtle_normals);
removeNaNFromPointCloud (*cloud_turtle_normals, *cloud_turtle_normals, aux_indices);
removeNaNNormalsFromPointCloud (*cloud_turtle_normals, *cloud_turtle_normals, aux_indices);
testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
void
compute (const sensor_msgs::PointCloud2::ConstPtr &input, sensor_msgs::PointCloud2 &output,
int k, double radius)
{
// Convert data to PointCloud<T>
PointCloud<PointXYZ>::Ptr xyz (new PointCloud<PointXYZ>);
fromROSMsg (*input, *xyz);
TicToc tt;
tt.tic ();
PointCloud<Normal> normals;
// Try our luck with organized integral image based normal estimation
if (xyz->isOrganized ())
{
IntegralImageNormalEstimation<PointXYZ, Normal> ne;
ne.setInputCloud (xyz);
ne.setNormalEstimationMethod (IntegralImageNormalEstimation<PointXYZ, Normal>::COVARIANCE_MATRIX);
ne.setNormalSmoothingSize (float (radius));
ne.setDepthDependentSmoothing (true);
ne.compute (normals);
}
else
{
NormalEstimation<PointXYZ, Normal> ne;
ne.setInputCloud (xyz);
ne.setSearchMethod (search::KdTree<PointXYZ>::Ptr (new search::KdTree<PointXYZ>));
ne.setKSearch (k);
ne.setRadiusSearch (radius);
ne.compute (normals);
}
print_highlight ("Computed normals in "); print_value ("%g", tt.toc ()); print_info (" ms for "); print_value ("%d", normals.width * normals.height); print_info (" points.\n");
// Convert data back
sensor_msgs::PointCloud2 output_normals;
toROSMsg (normals, output_normals);
concatenateFields (*input, output_normals, output);
}
TEST (PCL, BoundaryEstimation)
{
Eigen::Vector4f u = Eigen::Vector4f::Zero ();
Eigen::Vector4f v = Eigen::Vector4f::Zero ();
// Estimate normals first
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
n.setInputCloud (cloud.makeShared ());
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setKSearch (static_cast<int> (indices.size ()));
// estimate
n.compute (*normals);
BoundaryEstimation<PointXYZ, Normal, Boundary> b;
b.setInputNormals (normals);
EXPECT_EQ (b.getInputNormals (), normals);
// getCoordinateSystemOnPlane
for (size_t i = 0; i < normals->points.size (); ++i)
{
b.getCoordinateSystemOnPlane (normals->points[i], u, v);
Vector4fMap n4uv = normals->points[i].getNormalVector4fMap ();
EXPECT_NEAR (n4uv.dot(u), 0, 1e-4);
EXPECT_NEAR (n4uv.dot(v), 0, 1e-4);
EXPECT_NEAR (u.dot(v), 0, 1e-4);
}
// isBoundaryPoint (indices)
bool pt = false;
pt = b.isBoundaryPoint (cloud, 0, indices, u, v, float (M_PI) / 2.0);
EXPECT_EQ (pt, false);
pt = b.isBoundaryPoint (cloud, static_cast<int> (indices.size ()) / 3, indices, u, v, float (M_PI) / 2.0);
EXPECT_EQ (pt, false);
pt = b.isBoundaryPoint (cloud, static_cast<int> (indices.size ()) / 2, indices, u, v, float (M_PI) / 2.0);
EXPECT_EQ (pt, false);
pt = b.isBoundaryPoint (cloud, static_cast<int> (indices.size ()) - 1, indices, u, v, float (M_PI) / 2.0);
EXPECT_EQ (pt, true);
// isBoundaryPoint (points)
pt = false;
pt = b.isBoundaryPoint (cloud, cloud.points[0], indices, u, v, float (M_PI) / 2.0);
EXPECT_EQ (pt, false);
pt = b.isBoundaryPoint (cloud, cloud.points[indices.size () / 3], indices, u, v, float (M_PI) / 2.0);
EXPECT_EQ (pt, false);
pt = b.isBoundaryPoint (cloud, cloud.points[indices.size () / 2], indices, u, v, float (M_PI) / 2.0);
EXPECT_EQ (pt, false);
pt = b.isBoundaryPoint (cloud, cloud.points[indices.size () - 1], indices, u, v, float (M_PI) / 2.0);
EXPECT_EQ (pt, true);
// Object
PointCloud<Boundary>::Ptr bps (new PointCloud<Boundary> ());
// set parameters
b.setInputCloud (cloud.makeShared ());
b.setIndices (indicesptr);
b.setSearchMethod (tree);
b.setKSearch (static_cast<int> (indices.size ()));
// estimate
b.compute (*bps);
EXPECT_EQ (bps->points.size (), indices.size ());
pt = bps->points[0].boundary_point;
EXPECT_EQ (pt, false);
pt = bps->points[indices.size () / 3].boundary_point;
EXPECT_EQ (pt, false);
pt = bps->points[indices.size () / 2].boundary_point;
EXPECT_EQ (pt, false);
pt = bps->points[indices.size () - 1].boundary_point;
EXPECT_EQ (pt, true);
}
TEST (PCL, PrincipalCurvaturesEstimation)
{
float pcx, pcy, pcz, pc1, pc2;
// Estimate normals first
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
n.setInputCloud (cloud.makeShared ());
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
// estimate
n.compute (*normals);
PrincipalCurvaturesEstimation<PointXYZ, Normal, PrincipalCurvatures> pc;
pc.setInputNormals (normals);
EXPECT_EQ (pc.getInputNormals (), normals);
// computePointPrincipalCurvatures (indices)
pc.computePointPrincipalCurvatures (*normals, 0, indices, pcx, pcy, pcz, pc1, pc2);
EXPECT_NEAR (fabs (pcx), 0.98509, 1e-4);
EXPECT_NEAR (fabs (pcy), 0.10714, 1e-4);
EXPECT_NEAR (fabs (pcz), 0.13462, 1e-4);
EXPECT_NEAR (pc1, 0.23997423052787781, 1e-4);
EXPECT_NEAR (pc2, 0.19400238990783691, 1e-4);
pc.computePointPrincipalCurvatures (*normals, 2, indices, pcx, pcy, pcz, pc1, pc2);
EXPECT_NEAR (pcx, 0.98079, 1e-4);
EXPECT_NEAR (pcy, -0.04019, 1e-4);
EXPECT_NEAR (pcz, 0.19086, 1e-4);
EXPECT_NEAR (pc1, 0.27207490801811218, 1e-4);
EXPECT_NEAR (pc2, 0.19464978575706482, 1e-4);
int indices_size = static_cast<int> (indices.size ());
pc.computePointPrincipalCurvatures (*normals, indices_size - 3, indices, pcx, pcy, pcz, pc1, pc2);
EXPECT_NEAR (pcx, 0.86725, 1e-4);
EXPECT_NEAR (pcy, -0.37599, 1e-4);
EXPECT_NEAR (pcz, 0.32635, 1e-4);
EXPECT_NEAR (pc1, 0.25900053977966309, 1e-4);
EXPECT_NEAR (pc2, 0.17906945943832397, 1e-4);
pc.computePointPrincipalCurvatures (*normals, indices_size - 1, indices, pcx, pcy, pcz, pc1, pc2);
EXPECT_NEAR (pcx, 0.86725, 1e-4);
EXPECT_NEAR (pcy, -0.375851, 1e-3);
EXPECT_NEAR (pcz, 0.32636, 1e-4);
EXPECT_NEAR (pc1, 0.2590005099773407, 1e-4);
EXPECT_NEAR (pc2, 0.17906956374645233, 1e-4);
// Object
PointCloud<PrincipalCurvatures>::Ptr pcs (new PointCloud<PrincipalCurvatures> ());
// set parameters
pc.setInputCloud (cloud.makeShared ());
pc.setIndices (indicesptr);
pc.setSearchMethod (tree);
pc.setKSearch (indices_size);
// estimate
pc.compute (*pcs);
EXPECT_EQ (pcs->points.size (), indices.size ());
// Adjust for small numerical inconsitencies (due to nn_indices not being sorted)
EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[0]), 0.98509, 1e-4);
EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[1]), 0.10713, 1e-4);
EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[2]), 0.13462, 1e-4);
EXPECT_NEAR (fabs (pcs->points[0].pc1), 0.23997458815574646, 1e-4);
EXPECT_NEAR (fabs (pcs->points[0].pc2), 0.19400238990783691, 1e-4);
EXPECT_NEAR (pcs->points[2].principal_curvature[0], 0.98079, 1e-4);
EXPECT_NEAR (pcs->points[2].principal_curvature[1], -0.04019, 1e-4);
EXPECT_NEAR (pcs->points[2].principal_curvature[2], 0.19086, 1e-4);
EXPECT_NEAR (pcs->points[2].pc1, 0.27207502722740173, 1e-4);
EXPECT_NEAR (pcs->points[2].pc2, 0.1946497857570648, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[0], 0.86725, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[1], -0.37599, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[2], 0.32636, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 3].pc1, 0.2590007483959198, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 3].pc2, 0.17906941473484039, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[0], 0.86725, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[1], -0.375851, 1e-3);
EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[2], 0.32636, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 1].pc1, 0.25900065898895264, 1e-4);
EXPECT_NEAR (pcs->points[indices.size () - 1].pc2, 0.17906941473484039, 1e-4);
}
TEST (PCL, SpinImageEstimation)
{
// Estimate normals first
double mr = 0.002;
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
n.setInputCloud (cloud.makeShared ());
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setRadiusSearch (20 * mr);
n.compute (*normals);
EXPECT_NEAR (normals->points[103].normal_x, 0.36683175, 1e-4);
EXPECT_NEAR (normals->points[103].normal_y, -0.44696972, 1e-4);
EXPECT_NEAR (normals->points[103].normal_z, -0.81587529, 1e-4);
EXPECT_NEAR (normals->points[200].normal_x, -0.71414840, 1e-4);
EXPECT_NEAR (normals->points[200].normal_y, -0.06002361, 1e-4);
EXPECT_NEAR (normals->points[200].normal_z, -0.69741613, 1e-4);
EXPECT_NEAR (normals->points[140].normal_x, -0.45109111, 1e-4);
EXPECT_NEAR (normals->points[140].normal_y, -0.19499126, 1e-4);
EXPECT_NEAR (normals->points[140].normal_z, -0.87091631, 1e-4);
typedef Histogram<153> SpinImage;
SpinImageEstimation<PointXYZ, Normal, SpinImage> spin_est(8, 0.5, 16);
// set parameters
//spin_est.setInputWithNormals (cloud.makeShared (), normals);
spin_est.setInputCloud (cloud.makeShared ());
spin_est.setInputNormals (normals);
spin_est.setIndices (indicesptr);
spin_est.setSearchMethod (tree);
spin_est.setRadiusSearch (40*mr);
// Object
PointCloud<SpinImage>::Ptr spin_images (new PointCloud<SpinImage> ());
// radial SI
spin_est.setRadialStructure();
// estimate
spin_est.compute (*spin_images);
EXPECT_EQ (spin_images->points.size (), indices.size ());
EXPECT_NEAR (spin_images->points[100].histogram[0], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[12], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[24], 0.00233226, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[36], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[48], 8.48662e-005, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[60], 0.0266387, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[72], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[84], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[96], 0.0414662, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[108], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[120], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[132], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[144], 0.0128513, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[0], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[12], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[24], 0.00932424, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[36], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[48], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[60], 0.0145733, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[72], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[84], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[96], 0.00034457, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[108], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[120], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[132], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[144], 0.0121195, 1e-4);
// radial SI, angular spin-images
spin_est.setAngularDomain ();
// estimate
spin_est.compute (*spin_images);
EXPECT_EQ (spin_images->points.size (), indices.size ());
EXPECT_NEAR (spin_images->points[100].histogram[0], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[12], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[24], 0.132139, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[36], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[48], 0.908814, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[60], 0.63875, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[72], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[84], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[96], 0.550392, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[108], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[120], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[132], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[144], 0.257136, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[0], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[12], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[24], 0.230605, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[36], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[48], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[60], 0.764872, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[72], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[84], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[96], 1.02824, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[108], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[120], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[132], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[144], 0.293567, 1e-4);
// rectangular SI
spin_est.setRadialStructure (false);
spin_est.setAngularDomain (false);
// estimate
spin_est.compute (*spin_images);
EXPECT_EQ (spin_images->points.size (), indices.size ());
EXPECT_NEAR (spin_images->points[100].histogram[0], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[12], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[24], 0.000889345, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[36], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[48], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[60], 0.0489534, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[72], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[84], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[96], 0.0747141, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[108], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[120], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[132], 0.0173423, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[144], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[0], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[12], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[24], 0.0267132, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[36], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[48], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[60], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[72], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[84], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[96], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[108], 0.0209709, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[120], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[132], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[144], 0.029372, 1e-4);
// rectangular SI, angular spin-images
spin_est.setAngularDomain ();
// estimate
spin_est.compute (*spin_images);
EXPECT_EQ (spin_images->points.size (), indices.size ());
EXPECT_NEAR (spin_images->points[100].histogram[0], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[12], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[24], 0.132139, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[36], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[48], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[60], 0.38800787925720215, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[72], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[84], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[96], 0.468881, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[108], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[120], 0, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[132], 0.67901438474655151, 1e-4);
EXPECT_NEAR (spin_images->points[100].histogram[144], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[0], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[12], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[24], 0.143845, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[36], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[48], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[60], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[72], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[84], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[96], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[108], 0.706084, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[120], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[132], 0, 1e-4);
EXPECT_NEAR (spin_images->points[300].histogram[144], 0.272542, 1e-4);
}
TEST (PCL, SpinImageEstimationEigen)
{
// Estimate normals first
double mr = 0.002;
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
n.setInputCloud (cloud.makeShared ());
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setRadiusSearch (20 * mr);
n.compute (*normals);
EXPECT_NEAR (normals->points[103].normal_x, 0.36683175, 1e-4);
EXPECT_NEAR (normals->points[103].normal_y, -0.44696972, 1e-4);
EXPECT_NEAR (normals->points[103].normal_z, -0.81587529, 1e-4);
EXPECT_NEAR (normals->points[200].normal_x, -0.71414840, 1e-4);
EXPECT_NEAR (normals->points[200].normal_y, -0.06002361, 1e-4);
EXPECT_NEAR (normals->points[200].normal_z, -0.69741613, 1e-4);
EXPECT_NEAR (normals->points[140].normal_x, -0.45109111, 1e-4);
EXPECT_NEAR (normals->points[140].normal_y, -0.19499126, 1e-4);
EXPECT_NEAR (normals->points[140].normal_z, -0.87091631, 1e-4);
SpinImageEstimation<PointXYZ, Normal, Eigen::MatrixXf> spin_est (8, 0.5, 16);
// set parameters
//spin_est.setInputWithNormals (cloud.makeShared (), normals);
spin_est.setInputCloud (cloud.makeShared ());
spin_est.setInputNormals (normals);
spin_est.setIndices (indicesptr);
spin_est.setSearchMethod (tree);
spin_est.setRadiusSearch (40*mr);
// Object
PointCloud<Eigen::MatrixXf>::Ptr spin_images (new PointCloud<Eigen::MatrixXf>);
// radial SI
spin_est.setRadialStructure ();
// estimate
spin_est.computeEigen (*spin_images);
EXPECT_EQ (spin_images->points.rows (), indices.size ());
EXPECT_NEAR (spin_images->points (100, 0), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 12), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 24), 0.00233226, 1e-4);
EXPECT_NEAR (spin_images->points (100, 36), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 48), 8.48662e-005, 1e-4);
EXPECT_NEAR (spin_images->points (100, 60), 0.0266387, 1e-4);
EXPECT_NEAR (spin_images->points (100, 72), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 84), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 96), 0.0414662, 1e-4);
EXPECT_NEAR (spin_images->points (100, 108), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 120), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 132), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 144), 0.0128513, 1e-4);
EXPECT_NEAR (spin_images->points (300, 0), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 12), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 24), 0.00932424, 1e-4);
EXPECT_NEAR (spin_images->points (300, 36), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 48), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 60), 0.0145733, 1e-4);
EXPECT_NEAR (spin_images->points (300, 72), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 84), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 96), 0.00034457, 1e-4);
EXPECT_NEAR (spin_images->points (300, 108), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 120), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 132), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 144), 0.0121195, 1e-4);
// radial SI, angular spin-images
spin_est.setAngularDomain ();
// estimate
spin_est.computeEigen (*spin_images);
EXPECT_EQ (spin_images->points.rows (), indices.size ());
EXPECT_NEAR (spin_images->points (100, 0), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 12), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 24), 0.13213, 1e-4);
EXPECT_NEAR (spin_images->points (100, 36), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 48), 0.908804, 1.1e-4);
EXPECT_NEAR (spin_images->points (100, 60), 0.63875, 1e-4);
EXPECT_NEAR (spin_images->points (100, 72), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 84), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 96), 0.550392, 1e-4);
EXPECT_NEAR (spin_images->points (100, 108), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 120), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 132), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 144), 0.25713, 1e-4);
EXPECT_NEAR (spin_images->points (300, 0), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 12), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 24), 0.230605, 1e-4);
EXPECT_NEAR (spin_images->points (300, 36), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 48), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 60), 0.764872, 1e-4);
EXPECT_NEAR (spin_images->points (300, 72), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 84), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 96), 1.02824, 1e-4);
EXPECT_NEAR (spin_images->points (300, 108), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 120), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 132), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 144), 0.293567, 1e-4);
// rectangular SI
spin_est.setRadialStructure (false);
spin_est.setAngularDomain (false);
// estimate
spin_est.computeEigen (*spin_images);
EXPECT_EQ (spin_images->points.rows (), indices.size ());
EXPECT_NEAR (spin_images->points (100, 0), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 12), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 24), 0.000889345, 1e-4);
EXPECT_NEAR (spin_images->points (100, 36), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 48), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 60), 0.0489534, 1e-4);
EXPECT_NEAR (spin_images->points (100, 72), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 84), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 96), 0.0747141, 1e-4);
EXPECT_NEAR (spin_images->points (100, 108), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 120), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 132), 0.0173423, 1e-4);
EXPECT_NEAR (spin_images->points (100, 144), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 0), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 12), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 24), 0.0267132, 1e-4);
EXPECT_NEAR (spin_images->points (300, 36), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 48), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 60), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 72), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 84), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 96), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 108), 0.0209709, 1e-4);
EXPECT_NEAR (spin_images->points (300, 120), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 132), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 144), 0.029372, 1e-4);
// rectangular SI, angular spin-images
spin_est.setAngularDomain ();
// estimate
spin_est.computeEigen (*spin_images);
EXPECT_EQ (spin_images->points.rows (), indices.size ());
EXPECT_NEAR (spin_images->points (100, 0), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 12), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 24), 0.132126, 1e-4);
EXPECT_NEAR (spin_images->points (100, 36), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 48), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 60), 0.388011, 1e-4);
EXPECT_NEAR (spin_images->points (100, 72), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 84), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 96), 0.468881, 1e-4);
EXPECT_NEAR (spin_images->points (100, 108), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 120), 0, 1e-4);
EXPECT_NEAR (spin_images->points (100, 132), 0.678995, 1e-4);
EXPECT_NEAR (spin_images->points (100, 144), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 0), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 12), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 24), 0.143845, 1e-4);
EXPECT_NEAR (spin_images->points (300, 36), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 48), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 60), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 72), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 84), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 96), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 108), 0.706084, 1e-4);
EXPECT_NEAR (spin_images->points (300, 120), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 132), 0, 1e-4);
EXPECT_NEAR (spin_images->points (300, 144), 0.272542, 1e-4);
}
TEST (PCL, GSHOTShapeEstimation)
{
// Estimate normals first
double mr = 0.002;
NormalEstimation<PointXYZ, Normal> n;
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setInputCloud (cloud.makeShared ());
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setRadiusSearch (20 * mr);
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
n.compute (*normals);
EXPECT_NEAR (normals->points[103].normal_x, 0.36683175, 1e-4);
EXPECT_NEAR (normals->points[103].normal_y, -0.44696972, 1e-4);
EXPECT_NEAR (normals->points[103].normal_z, -0.81587529, 1e-4);
EXPECT_NEAR (normals->points[200].normal_x, -0.71414840, 1e-4);
EXPECT_NEAR (normals->points[200].normal_y, -0.06002361, 1e-4);
EXPECT_NEAR (normals->points[200].normal_z, -0.69741613, 1e-4);
EXPECT_NEAR (normals->points[140].normal_x, -0.45109111, 1e-4);
EXPECT_NEAR (normals->points[140].normal_y, -0.19499126, 1e-4);
EXPECT_NEAR (normals->points[140].normal_z, -0.87091631, 1e-4);
// Objects
PointCloud<SHOT352>::Ptr gshots352 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr shots352 (new PointCloud<SHOT352> ());
// SHOT352 (local)
SHOTEstimation<PointXYZ, Normal, SHOT352> shot352;
shot352.setInputNormals (normals);
shot352.setRadiusSearch (radius_local_shot);
shot352.setInputCloud (cloud_for_lrf.makeShared ());
boost::shared_ptr<vector<int> > indices_local_shot_ptr (new vector<int> (indices_local_shot));
shot352.setIndices (indices_local_shot_ptr);
shot352.setSearchSurface (cloud.makeShared());
shot352.compute (*shots352);
EXPECT_NEAR (shots352->points[0].descriptor[9 ], 0.0f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[10], 0.0f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[11], 0.317935f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[19], 0.0f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[20], 0.0f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[21], 0.0f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[42], 0.0f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[53], 0.0f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[54], 0.0f, 1E-4);
EXPECT_NEAR (shots352->points[0].descriptor[55], 0.089004f, 1E-4);
// SHOT352 (global)
GSHOTEstimation<PointXYZ, Normal, SHOT352> gshot352;
gshot352.setSearchMethod (tree);
gshot352.setInputNormals (normals);
EXPECT_EQ (gshot352.getInputNormals (), normals);
// set parameters
gshot352.setInputCloud (cloud.makeShared ());
gshot352.setIndices (indicesptr);
// estimate
int gshot_size = 1;
gshot352.compute (*gshots352);
EXPECT_EQ (gshots352->points.size (), gshot_size);
checkDescNear (*gshots352, *shots352, 1E-7);
}
TEST (PCL, GSHOTWithRTransNoised)
{
PointCloud<PointNormal>::Ptr cloud_nr (new PointCloud<PointNormal> ());
PointCloud<PointNormal>::Ptr cloud_rot (new PointCloud<PointNormal> ());
PointCloud<PointNormal>::Ptr cloud_trans (new PointCloud<PointNormal> ());
PointCloud<PointNormal>::Ptr cloud_rot_trans (new PointCloud<PointNormal> ());
PointCloud<PointXYZ>::Ptr cloud_noise (new PointCloud<PointXYZ> ());
Eigen::Affine3f rot = Eigen::Affine3f::Identity ();
float rot_x = static_cast <float> (rand ()) / static_cast <float> (RAND_MAX);
float rot_y = static_cast <float> (rand ()) / static_cast <float> (RAND_MAX);
float rot_z = static_cast <float> (rand ()) / static_cast <float> (RAND_MAX);
rot.prerotate (Eigen::AngleAxisf (rot_x * M_PI, Eigen::Vector3f::UnitX ()));
rot.prerotate (Eigen::AngleAxisf (rot_y * M_PI, Eigen::Vector3f::UnitY ()));
rot.prerotate (Eigen::AngleAxisf (rot_z * M_PI, Eigen::Vector3f::UnitZ ()));
//std::cout << "rot = (" << (rot_x * M_PI) << ", " << (rot_y * M_PI) << ", " << (rot_z * M_PI) << ")" << std::endl;
Eigen::Affine3f trans = Eigen::Affine3f::Identity ();
float HI = 5.0f;
float LO = -HI;
float trans_x = LO + static_cast<float> (rand ()) / (static_cast<float> (RAND_MAX / (HI - LO)));
float trans_y = LO + static_cast<float> (rand ()) / (static_cast<float> (RAND_MAX / (HI - LO)));
float trans_z = LO + static_cast<float> (rand ()) / (static_cast<float> (RAND_MAX / (HI - LO)));
//std::cout << "trans = (" << trans_x << ", " << trans_y << ", " << trans_z << ")" << std::endl;
trans.translate (Eigen::Vector3f (trans_x, trans_y, trans_z));
// Estimate normals first
float mr = 0.002;
NormalEstimation<PointXYZ, pcl::Normal> n;
PointCloud<Normal>::Ptr normals1 (new PointCloud<Normal> ());
n.setViewPoint (0.0, 0.0, 1.0);
n.setInputCloud (cloud.makeShared ());
n.setRadiusSearch (20 * mr);
n.compute (*normals1);
pcl::concatenateFields<PointXYZ, Normal, PointNormal> (cloud, *normals1, *cloud_nr);
pcl::transformPointCloudWithNormals<PointNormal, float> (*cloud_nr, *cloud_rot, rot);
pcl::transformPointCloudWithNormals<PointNormal, float> (*cloud_nr, *cloud_trans, trans);
pcl::transformPointCloudWithNormals<PointNormal, float> (*cloud_rot, *cloud_rot_trans, trans);
add_gaussian_noise (cloud.makeShared (), cloud_noise, 0.005);
PointCloud<Normal>::Ptr normals_noise (new PointCloud<Normal> ());
n.setInputCloud (cloud_noise);
n.compute (*normals_noise);
PointCloud<Normal>::Ptr normals2 (new PointCloud<Normal> ());
n.setInputCloud (cloud2.makeShared ());
n.compute (*normals2);
PointCloud<Normal>::Ptr normals3 (new PointCloud<Normal> ());
n.setInputCloud (cloud3.makeShared ());
n.compute (*normals3);
// Objects
// Descriptors for ground truth (using SHOT)
PointCloud<SHOT352>::Ptr desc01 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc02 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc03 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc04 (new PointCloud<SHOT352> ());
// Descriptors for test GSHOT
PointCloud<SHOT352>::Ptr desc1 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc2 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc3 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc4 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc5 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc6 (new PointCloud<SHOT352> ());
PointCloud<SHOT352>::Ptr desc7 (new PointCloud<SHOT352> ());
// SHOT352 (global)
GSHOTEstimation<PointNormal, PointNormal, SHOT352> gshot1;
gshot1.setInputNormals (cloud_nr);
gshot1.setInputCloud (cloud_nr);
gshot1.compute (*desc1);
// Eigen::Vector4f center_desc1 = gshot.getCentralPoint ();
gshot1.setInputNormals (cloud_rot);
gshot1.setInputCloud (cloud_rot);
gshot1.compute (*desc2);
// Eigen::Vector4f center_desc2 = gshot.getCentralPoint ();
gshot1.setInputNormals (cloud_trans);
gshot1.setInputCloud (cloud_trans);
gshot1.compute (*desc3);
// Eigen::Vector4f center_desc3 = gshot.getCentralPoint ();
gshot1.setInputNormals (cloud_rot_trans);
gshot1.setInputCloud (cloud_rot_trans);
gshot1.compute (*desc4);
// Eigen::Vector4f center_desc4 = gshot.getCentralPoint ();
GSHOTEstimation<PointXYZ, Normal, SHOT352> gshot2;
gshot2.setInputNormals (normals1);
gshot2.setInputCloud (cloud_noise);
gshot2.compute (*desc5);
gshot2.setInputNormals (normals2);
gshot2.setInputCloud (cloud2.makeShared ());
gshot2.compute (*desc6);
gshot2.setInputNormals (normals3);
gshot2.setInputCloud (cloud3.makeShared ());
gshot2.compute (*desc7);
// Eigen::Vector3f distance_desc = (center_desc3.head<3> () - center_desc1.head<3> ());
// std::cout << "dist of desc0 and desc3 -> (" << distance_desc[0] << ", " << distance_desc[1] << ", " << distance_desc[2] << ")\n";
// SHOT352 (local)
GSHOTEstimation<PointNormal, PointNormal, SHOT352> shot;
shot.setInputNormals (cloud_nr);
shot.setInputCloud (ground_truth.makeShared());
shot.setSearchSurface (cloud_nr);
shot.setRadiusSearch (radius_local_shot);
shot.compute (*desc01);
shot.setInputNormals (cloud_rot);
shot.setInputCloud (ground_truth.makeShared());
shot.setSearchSurface (cloud_rot);
shot.setRadiusSearch (radius_local_shot);
shot.compute (*desc02);
shot.setInputNormals (cloud_trans);
shot.setInputCloud (ground_truth.makeShared());
shot.setSearchSurface (cloud_trans);
shot.setRadiusSearch (radius_local_shot);
shot.compute (*desc03);
shot.setInputNormals (cloud_rot_trans);
shot.setInputCloud (ground_truth.makeShared());
shot.setSearchSurface (cloud_rot_trans);
shot.setRadiusSearch (radius_local_shot);
shot.compute (*desc04);
// CHECK GSHOT
checkDesc(*desc01, *desc1);
checkDesc(*desc02, *desc2);
checkDesc(*desc03, *desc3);
checkDesc(*desc04, *desc4);
std::vector<float> d0, d1, d2, d3, d4, d5, d6;
for(int i = 0; i < 352; ++i)
{
d0.push_back(desc1->points[0].descriptor[i]);
d1.push_back(desc2->points[0].descriptor[i]);
d2.push_back(desc3->points[0].descriptor[i]);
d3.push_back(desc4->points[0].descriptor[i]);
d4.push_back(desc5->points[0].descriptor[i]);
d5.push_back(desc6->points[0].descriptor[i]);
d6.push_back(desc7->points[0].descriptor[i]);
}
float dist_0 = pcl::selectNorm< std::vector<float> > (d0, d0, 352, pcl::HIK) ;
float dist_1 = pcl::selectNorm< std::vector<float> > (d0, d1, 352, pcl::HIK) ;
float dist_2 = pcl::selectNorm< std::vector<float> > (d0, d2, 352, pcl::HIK) ;
float dist_3 = pcl::selectNorm< std::vector<float> > (d0, d3, 352, pcl::HIK) ;
float dist_4 = pcl::selectNorm< std::vector<float> > (d0, d4, 352, pcl::HIK) ;
float dist_5 = pcl::selectNorm< std::vector<float> > (d0, d5, 352, pcl::HIK) ;
float dist_6 = pcl::selectNorm< std::vector<float> > (d0, d6, 352, pcl::HIK) ;
std::cout << ">> Itself[HIK]: " << dist_0 << std::endl
<< ">> Rotation[HIK]: " << dist_1 << std::endl
<< ">> Translate[HIK]: " << dist_2 << std::endl
<< ">> Rot+Trans[HIK] " << dist_3 << std::endl
<< ">> GaussNoise[HIK]: " << dist_4 << std::endl
<< ">> bun03[HIK]: " << dist_5 << std::endl
<< ">> milk[HIK]: " << dist_6 << std::endl;
float high_barrier = dist_0 * 0.999f;
float noise_barrier = dist_0 * 0.75f;
float cut_barrier = dist_0 * 0.20f;
float low_barrier = dist_0 * 0.02f;
EXPECT_GT (dist_1, high_barrier);
EXPECT_GT (dist_2, high_barrier);
//EXPECT_GT (dist_3, high_barrier);
EXPECT_GT (dist_4, noise_barrier);
EXPECT_GT (dist_5, cut_barrier);
EXPECT_LT (dist_6, low_barrier);
}
/* ---[ */
int
main (int argc, char** argv)
{
if (argc < 2)
{
std::cerr << "No test file given. Please download `bun0.pcd` and pass its path to the test." << std::endl;
return (-1);
}
// Load file
sensor_msgs::PointCloud2 cloud_blob;
loadPCDFile (argv[1], cloud_blob);
fromROSMsg (cloud_blob, *cloud);
// Create search tree
tree.reset (new search::KdTree<PointXYZ> (false));
tree->setInputCloud (cloud);
// Normal estimation
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
n.setInputCloud (cloud);
//n.setIndices (indices[B);
n.setSearchMethod (tree);
n.setKSearch (20);
n.compute (*normals);
// Concatenate XYZ and normal information
pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);
// Create search tree
tree2.reset (new search::KdTree<PointNormal>);
tree2->setInputCloud (cloud_with_normals);
// Process for update cloud
if(argc == 3){
sensor_msgs::PointCloud2 cloud_blob1;
loadPCDFile (argv[2], cloud_blob1);
fromROSMsg (cloud_blob1, *cloud1);
// Create search tree
tree3.reset (new search::KdTree<PointXYZ> (false));
tree3->setInputCloud (cloud1);
// Normal estimation
NormalEstimation<PointXYZ, Normal> n1;
PointCloud<Normal>::Ptr normals1 (new PointCloud<Normal> ());
n1.setInputCloud (cloud1);
n1.setSearchMethod (tree3);
n1.setKSearch (20);
n1.compute (*normals1);
// Concatenate XYZ and normal information
pcl::concatenateFields (*cloud1, *normals1, *cloud_with_normals1);
// Create search tree
tree4.reset (new search::KdTree<PointNormal>);
tree4->setInputCloud (cloud_with_normals1);
}
// Testing
testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
TEST (PCL, NormalEstimation)
{
Eigen::Vector4f plane_parameters;
float curvature;
NormalEstimation<PointXYZ, Normal> n;
// computePointNormal (indices, Vector)
computePointNormal (cloud, indices, plane_parameters, curvature);
EXPECT_NEAR (fabs (plane_parameters[0]), 0.035592, 1e-4);
EXPECT_NEAR (fabs (plane_parameters[1]), 0.369596, 1e-4);
EXPECT_NEAR (fabs (plane_parameters[2]), 0.928511, 1e-4);
EXPECT_NEAR (fabs (plane_parameters[3]), 0.0622552, 1e-4);
EXPECT_NEAR (curvature, 0.0693136, 1e-4);
float nx, ny, nz;
// computePointNormal (indices)
n.computePointNormal (cloud, indices, nx, ny, nz, curvature);
EXPECT_NEAR (fabs (nx), 0.035592, 1e-4);
EXPECT_NEAR (fabs (ny), 0.369596, 1e-4);
EXPECT_NEAR (fabs (nz), 0.928511, 1e-4);
EXPECT_NEAR (curvature, 0.0693136, 1e-4);
// computePointNormal (Vector)
computePointNormal (cloud, plane_parameters, curvature);
EXPECT_NEAR (plane_parameters[0], 0.035592, 1e-4);
EXPECT_NEAR (plane_parameters[1], 0.369596, 1e-4);
EXPECT_NEAR (plane_parameters[2], 0.928511, 1e-4);
EXPECT_NEAR (plane_parameters[3], -0.0622552, 1e-4);
EXPECT_NEAR (curvature, 0.0693136, 1e-4);
// flipNormalTowardsViewpoint (Vector)
flipNormalTowardsViewpoint (cloud.points[0], 0, 0, 0, plane_parameters);
EXPECT_NEAR (plane_parameters[0], -0.035592, 1e-4);
EXPECT_NEAR (plane_parameters[1], -0.369596, 1e-4);
EXPECT_NEAR (plane_parameters[2], -0.928511, 1e-4);
EXPECT_NEAR (plane_parameters[3], 0.0799743, 1e-4);
// flipNormalTowardsViewpoint
flipNormalTowardsViewpoint (cloud.points[0], 0, 0, 0, nx, ny, nz);
EXPECT_NEAR (nx, -0.035592, 1e-4);
EXPECT_NEAR (ny, -0.369596, 1e-4);
EXPECT_NEAR (nz, -0.928511, 1e-4);
// Object
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
// set parameters
PointCloud<PointXYZ>::Ptr cloudptr = cloud.makeShared ();
n.setInputCloud (cloudptr);
EXPECT_EQ (n.getInputCloud (), cloudptr);
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
n.setIndices (indicesptr);
EXPECT_EQ (n.getIndices (), indicesptr);
n.setSearchMethod (tree);
EXPECT_EQ (n.getSearchMethod (), tree);
n.setKSearch (static_cast<int> (indices.size ()));
// estimate
n.compute (*normals);
EXPECT_EQ (normals->points.size (), indices.size ());
for (size_t i = 0; i < normals->points.size (); ++i)
{
EXPECT_NEAR (normals->points[i].normal[0], -0.035592, 1e-4);
EXPECT_NEAR (normals->points[i].normal[1], -0.369596, 1e-4);
EXPECT_NEAR (normals->points[i].normal[2], -0.928511, 1e-4);
EXPECT_NEAR (normals->points[i].curvature, 0.0693136, 1e-4);
}
PointCloud<PointXYZ>::Ptr surfaceptr = cloudptr;
n.setSearchSurface (surfaceptr);
EXPECT_EQ (n.getSearchSurface (), surfaceptr);
// Additional test for searchForNeigbhors
surfaceptr.reset (new PointCloud<PointXYZ>);
*surfaceptr = *cloudptr;
surfaceptr->points.resize (640 * 480);
surfaceptr->width = 640;
surfaceptr->height = 480;
EXPECT_EQ (surfaceptr->points.size (), surfaceptr->width * surfaceptr->height);
n.setSearchSurface (surfaceptr);
tree.reset ();
n.setSearchMethod (tree);
// estimate
n.compute (*normals);
EXPECT_EQ (normals->points.size (), indices.size ());
}