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DFPointCloud.cc
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315 lines (280 loc) · 12 KB
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#include "DFPointCloud.hh"
#include "diffCheck/log.hh"
#include "diffCheck/IOManager.hh"
namespace diffCheck::geometry
{
void DFPointCloud::Cvt2DFPointCloud(const std::shared_ptr<open3d::geometry::PointCloud> &O3DPointCloud)
{
this->Points.clear();
this->Colors.clear();
this->Normals.clear();
if (O3DPointCloud->points_.size() != 0)
for (auto &point : O3DPointCloud->points_)
this->Points.push_back(point);
if (O3DPointCloud->HasColors())
for (auto &color : O3DPointCloud->colors_)
this->Colors.push_back(color);
if (O3DPointCloud->HasNormals())
for (auto &normal : O3DPointCloud->normals_)
this->Normals.push_back(normal);
}
void DFPointCloud::Cvt2DFPointCloud(const std::shared_ptr<cilantro::PointCloud3f> &cilantroPointCloud)
{
this->Points.clear();
this->Colors.clear();
this->Normals.clear();
auto ptt = cilantroPointCloud->points;
int n_pt = (int)ptt.cols();
auto col = cilantroPointCloud->colors;
auto nor = cilantroPointCloud->normals;
if (n_pt == 0)
throw std::invalid_argument("The point cloud is empty.");
for (int i = 0; i < n_pt; i++)
{
Eigen::Vector3d pt_d = ptt.col(i).cast<double>();
this->Points.push_back(pt_d);
}
if (cilantroPointCloud->hasColors())
{
for (int i = 0; i < n_pt; i++)
{
Eigen::Vector3d cl_d = col.col(i).cast <double>();
this->Colors.push_back(cl_d);
}
}
if (cilantroPointCloud->hasNormals())
{
for (int i = 0; i < n_pt; i++)
{
Eigen::Vector3d no_d = nor.col(i).cast <double>();
this->Normals.push_back(no_d);
}
}
}
std::shared_ptr<open3d::geometry::PointCloud> DFPointCloud::Cvt2O3DPointCloud()
{
std::shared_ptr<open3d::geometry::PointCloud> O3DPointCloud(new open3d::geometry::PointCloud());
if (this->Points.size() != 0)
for (auto &point : this->Points)
O3DPointCloud->points_.push_back(point);
if (this->Colors.size() != 0)
for (auto &color : this->Colors)
O3DPointCloud->colors_.push_back(color);
if (this->Normals.size() != 0)
for (auto &normal : this->Normals)
O3DPointCloud->normals_.push_back(normal);
return O3DPointCloud;
}
std::shared_ptr<cilantro::PointCloud3f> DFPointCloud::Cvt2CilantroPointCloud()
{
std::shared_ptr<cilantro::PointCloud3f> cilantroPointCloud = std::make_shared<cilantro::PointCloud3f>();
cilantro::VectorSet3f points;
for (auto& pt : this->Points)
{
Eigen::Vector3f pt_f = pt.cast <float>();
points.conservativeResize(points.rows(), points.cols() + 1);
points.col(points.cols() - 1) = pt_f;
}
cilantroPointCloud->points = points;
cilantro::VectorSet3f colors;
if (this->HasColors())
{
for (auto& color : this->Colors)
{
Eigen::Vector3f color_f = color.cast <float>();
colors.conservativeResize(colors.rows(), colors.cols() + 1);
colors.col(colors.cols() - 1) = color_f;
}
}
cilantroPointCloud->colors = colors;
cilantro::VectorSet3f normals;
if (this->HasNormals())
{
for (auto& normal : this->Normals)
{
Eigen::Vector3f normal_f = normal.cast <float>();
normals.conservativeResize(normals.rows(), normals.cols() + 1);
normals.col(normals.cols() - 1) = normal_f;
}
}
cilantroPointCloud->normals = normals;
return cilantroPointCloud;
}
std::vector<Eigen::Vector3d> DFPointCloud::GetAxixAlignedBoundingBox()
{
auto O3DPointCloud = this->Cvt2O3DPointCloud();
auto boundingBox = O3DPointCloud->GetAxisAlignedBoundingBox();
std::vector<Eigen::Vector3d> extremePoints;
extremePoints.push_back(boundingBox.GetMinBound());
extremePoints.push_back(boundingBox.GetMaxBound());
return extremePoints;
}
void DFPointCloud::EstimateNormals(
bool useCilantroEvaluator,
std::optional<int> knn,
std::optional<double> searchRadius
)
{
if (!useCilantroEvaluator)
{
this->Normals.clear();
auto O3DPointCloud = this->Cvt2O3DPointCloud();
if (knn.value() != 30 && searchRadius.has_value() == false)
{
open3d::geometry::KDTreeSearchParamKNN knnSearchParam(knn.value());
O3DPointCloud->EstimateNormals(knnSearchParam);
DIFFCHECK_INFO(("Estimating normals with knn = " + std::to_string(knn.value())).c_str());
}
else if (searchRadius.has_value())
{
open3d::geometry::KDTreeSearchParamHybrid hybridSearchParam(searchRadius.value(), knn.value());
O3DPointCloud->EstimateNormals(hybridSearchParam);
DIFFCHECK_INFO(("Estimating normals with hybrid search radius = " + std::to_string(searchRadius.value()) + "and knn = " + std::to_string(knn.value())).c_str());
}
else
{
O3DPointCloud->EstimateNormals();
DIFFCHECK_INFO("Default estimation of normals with knn = 30");
}
for (auto &normal : O3DPointCloud->normals_)
this->Normals.push_back(normal);
}
else
{
std::shared_ptr<cilantro::PointCloud3f> cilantroPointCloud = this->Cvt2CilantroPointCloud();
cilantro::KNNNeighborhoodSpecification<int> neighborhood(knn.value());
cilantroPointCloud->estimateNormals(neighborhood, false);
this->Normals.clear();
for (int i = 0; i < cilantroPointCloud->normals.cols(); i++)
this->Normals.push_back(cilantroPointCloud->normals.col(i).cast<double>());
DIFFCHECK_INFO(("Estimating normals with cilantro evaluator with knn = " + std::to_string(knn.value())).c_str());
}
}
void DFPointCloud::VoxelDownsample(double voxelSize)
{
if (voxelSize <= 0)
throw std::invalid_argument("Voxel size must be greater than 0.");
auto O3DPointCloud = this->Cvt2O3DPointCloud();
auto O3DPointCloudDown = O3DPointCloud->VoxelDownSample(voxelSize);
this->Points.clear();
for (auto &point : O3DPointCloudDown->points_)
this->Points.push_back(point);
this->Colors.clear();
for (auto &color : O3DPointCloudDown->colors_)
this->Colors.push_back(color);
this->Normals.clear();
for (auto &normal : O3DPointCloudDown->normals_)
this->Normals.push_back(normal);
}
void DFPointCloud::ApplyColor(const Eigen::Vector3d &color)
{
this->Colors.clear();
for (auto &point : this->Points)
this->Colors.push_back(color);
}
void DFPointCloud::ApplyColor(int r, int g, int b)
{
Eigen::Vector3d color = Eigen::Vector3d(r / 255.0, g / 255.0, b / 255.0);
this->ApplyColor(color);
}
void DFPointCloud::RemoveStatisticalOutliers(int nbNeighbors, double stdRatio)
{
std::shared_ptr<open3d::geometry::PointCloud> O3DPointCloud = this->Cvt2O3DPointCloud();
std::tuple<std::shared_ptr<open3d::geometry::PointCloud>, std::vector<size_t>> returnedTuple = O3DPointCloud->RemoveStatisticalOutliers(nbNeighbors, stdRatio);
std::shared_ptr<open3d::geometry::PointCloud> O3DPointCloudWithoutOutliers = std::get<0>(returnedTuple);
this->Points.clear();
for (auto &point : O3DPointCloudWithoutOutliers->points_)
this->Points.push_back(point);
this->Colors.clear();
for (auto &color : O3DPointCloudWithoutOutliers->colors_)
this->Colors.push_back(color);
this->Normals.clear();
for (auto &normal : O3DPointCloudWithoutOutliers->normals_)
this->Normals.push_back(normal);
}
void DFPointCloud::UniformDownsample(int everyKPoints)
{
auto O3DPointCloud = this->Cvt2O3DPointCloud();
auto O3DPointCloudDown = O3DPointCloud->UniformDownSample(everyKPoints);
this->Points.clear();
for (auto &point : O3DPointCloudDown->points_)
this->Points.push_back(point);
this->Colors.clear();
for (auto &color : O3DPointCloudDown->colors_)
this->Colors.push_back(color);
this->Normals.clear();
for (auto &normal : O3DPointCloudDown->normals_)
this->Normals.push_back(normal);
}
void DFPointCloud::DownsampleBySize(int targetSize)
{
// get the number of points and confront it with the targetSize and find the corresponding ratio (0 to 1) to downsample
int numPoints = this->Points.size();
if (numPoints <= targetSize)
throw std::invalid_argument("The target size must be smaller than the number of points in the cloud.");
double ratio = (double)targetSize / (double)numPoints;
auto O3DPointCloud = this->Cvt2O3DPointCloud();
auto O3DPointCloudDown = O3DPointCloud->RandomDownSample(ratio);
this->Points.clear();
for (auto &point : O3DPointCloudDown->points_)
this->Points.push_back(point);
this->Colors.clear();
for (auto &color : O3DPointCloudDown->colors_)
this->Colors.push_back(color);
this->Normals.clear();
for (auto &normal : O3DPointCloudDown->normals_)
this->Normals.push_back(normal);
}
std::vector<Eigen::Vector3d> DFPointCloud::GetTightBoundingBox()
{
open3d::geometry::OrientedBoundingBox tightOOBB = this->Cvt2O3DPointCloud()->GetMinimalOrientedBoundingBox();
std::vector<Eigen::Vector3d> bboxPts = tightOOBB.GetBoxPoints();
return bboxPts;
}
void DFPointCloud::ApplyTransformation(const diffCheck::transformation::DFTransformation &transformation)
{
auto O3DPointCloud = this->Cvt2O3DPointCloud();
O3DPointCloud->Transform(transformation.TransformationMatrix);
this->Points.clear();
this->Colors.clear();
this->Normals.clear();
this->Cvt2DFPointCloud(O3DPointCloud);
}
void DFPointCloud::LoadFromPLY(const std::string &path)
{
auto cloud = diffCheck::io::ReadPLYPointCloud(path);
this->Points = cloud->Points;
this->Colors = cloud->Colors;
this->Normals = cloud->Normals;
}
void DFPointCloud::SaveToPLY(const std::string &path)
{
auto cloud_ptr = std::make_shared<DFPointCloud>(this->Points, this->Colors, this->Normals);
diffCheck::io::WritePLYPointCloud(cloud_ptr, path);
}
std::vector<double> DFPointCloud::ComputeDistance(std::shared_ptr<geometry::DFPointCloud> target)
{
std::vector<double> errors;
auto O3DSourcePointCloud = this->Cvt2O3DPointCloud();
auto O3DTargetPointCloud = target->Cvt2O3DPointCloud();
std::vector<double> distances;
distances = O3DSourcePointCloud->ComputePointCloudDistance(*O3DTargetPointCloud);
return distances;
}
void DFPointCloud::AddPoints(const DFPointCloud &pointCloud)
{
this->Points.insert(this->Points.end(), pointCloud.Points.begin(), pointCloud.Points.end());
this->Colors.insert(this->Colors.end(), pointCloud.Colors.begin(), pointCloud.Colors.end());
this->Normals.insert(this->Normals.end(), pointCloud.Normals.begin(), pointCloud.Normals.end());
}
Eigen::Vector3d DFPointCloud::GetCenterPoint()
{
if (this->Points.size() == 0)
throw std::invalid_argument("The point cloud is empty.");
Eigen::Vector3d center = Eigen::Vector3d::Zero();
for (auto &point : this->Points)
center += point;
center /= this->Points.size();
return center;
}
}
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