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gasd.hpp
/*
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#ifndef PCL_FEATURES_IMPL_GASD_H_
#define PCL_FEATURES_IMPL_GASD_H_
#include <pcl/features/gasd.h>
#include <pcl/common/common.h> // for getMinMax3D
#include <pcl/common/transforms.h>
#include <vector>
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::GASDEstimation<PointInT, PointOutT>::compute (PointCloudOut &output)
{
if (!Feature<PointInT, PointOutT>::initCompute ())
{
output.width = output.height = 0;
output.clear ();
return;
}
// Resize the output dataset
output.resize (1);
// Copy header and is_dense flag from input
output.header = surface_->header;
output.is_dense = surface_->is_dense;
// Perform the actual feature computation
computeFeature (output);
Feature<PointInT, PointOutT>::deinitCompute ();
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::GASDEstimation<PointInT, PointOutT>::computeAlignmentTransform ()
{
Eigen::Vector4f centroid;
Eigen::Matrix3f covariance_matrix;
// compute centroid of the object's partial view, then compute covariance matrix from points and centroid of the object's partial view
if (pcl::compute3DCentroid (*surface_, *indices_, centroid) == 0 ||
pcl::computeCovarianceMatrix (*surface_, *indices_, centroid, covariance_matrix) == 0) {
PCL_ERROR("[pcl::GASDEstimation::computeAlignmentTransform] Surface cloud or indices are empty!\n");
return;
}
Eigen::Matrix3f eigenvectors;
Eigen::Vector3f eigenvalues;
// compute eigenvalues and eigenvectors of the covariance matrix
pcl::eigen33 (covariance_matrix, eigenvectors, eigenvalues);
// z axis of the reference frame is the eigenvector associated with the minimal eigenvalue
Eigen::Vector3f z_axis = eigenvectors.col (0);
// if angle between z axis and viewing direction is in the [-90 deg, 90 deg] range, then z axis is negated
if (z_axis.dot (view_direction_) > 0)
{
z_axis = -z_axis;
}
// x axis of the reference frame is the eigenvector associated with the maximal eigenvalue
const Eigen::Vector3f x_axis = eigenvectors.col (2);
// y axis is the cross product of z axis and x axis
const Eigen::Vector3f y_axis = z_axis.cross (x_axis);
const Eigen::Vector3f centroid_xyz = centroid.head<3> ();
// compute alignment transform from axes and centroid
transform_ << x_axis.transpose (), -x_axis.dot (centroid_xyz),
y_axis.transpose (), -y_axis.dot (centroid_xyz),
z_axis.transpose (), -z_axis.dot (centroid_xyz),
0.0f, 0.0f, 0.0f, 1.0f;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::GASDEstimation<PointInT, PointOutT>::addSampleToHistograms (const Eigen::Vector4f &p,
const float max_coord,
const std::size_t half_grid_size,
const HistogramInterpolationMethod interp,
const float hbin,
const float hist_incr,
std::vector<Eigen::VectorXf> &hists)
{
const std::size_t grid_size = half_grid_size * 2;
// compute normalized coordinates with respect to axis-aligned bounding cube centered on the origin
const Eigen::Vector3f scaled ( (p[0] / max_coord) * half_grid_size, (p[1] / max_coord) * half_grid_size, (p[2] / max_coord) * half_grid_size);
// compute histograms array coords
Eigen::Vector4f coords (scaled[0] + half_grid_size, scaled[1] + half_grid_size, scaled[2] + half_grid_size, hbin);
// if using histogram interpolation, subtract 0.5 so samples with the central value of the bin have full weight in it
if (interp != INTERP_NONE)
{
coords -= Eigen::Vector4f (0.5f, 0.5f, 0.5f, 0.5f);
}
// compute histograms bins indices
const Eigen::Vector4f bins (std::floor (coords[0]), std::floor (coords[1]), std::floor (coords[2]), std::floor (coords[3]));
// compute indices of the bin where the sample falls into
const std::size_t grid_idx = ( (bins[0] + 1) * (grid_size + 2) + bins[1] + 1) * (grid_size + 2) + bins[2] + 1;
const std::size_t h_idx = bins[3] + 1;
if (interp == INTERP_NONE)
{
// no interpolation
hists[grid_idx][h_idx] += hist_incr;
}
else
{
// if using histogram interpolation, compute trilinear interpolation
coords -= Eigen::Vector4f (bins[0], bins[1], bins[2], 0.0f);
const float v_x1 = hist_incr * coords[0];
const float v_x0 = hist_incr - v_x1;
const float v_xy11 = v_x1 * coords[1];
const float v_xy10 = v_x1 - v_xy11;
const float v_xy01 = v_x0 * coords[1];
const float v_xy00 = v_x0 - v_xy01;
const float v_xyz111 = v_xy11 * coords[2];
const float v_xyz110 = v_xy11 - v_xyz111;
const float v_xyz101 = v_xy10 * coords[2];
const float v_xyz100 = v_xy10 - v_xyz101;
const float v_xyz011 = v_xy01 * coords[2];
const float v_xyz010 = v_xy01 - v_xyz011;
const float v_xyz001 = v_xy00 * coords[2];
const float v_xyz000 = v_xy00 - v_xyz001;
if (interp == INTERP_TRILINEAR)
{
// trilinear interpolation
hists[grid_idx][h_idx] += v_xyz000;
hists[grid_idx + 1][h_idx] += v_xyz001;
hists[grid_idx + (grid_size + 2)][h_idx] += v_xyz010;
hists[grid_idx + (grid_size + 3)][h_idx] += v_xyz011;
hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx] += v_xyz100;
hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx] += v_xyz101;
hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx] += v_xyz110;
hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx] += v_xyz111;
}
else
{
// quadrilinear interpolation
coords[3] -= bins[3];
const float v_xyzh1111 = v_xyz111 * coords[3];
const float v_xyzh1110 = v_xyz111 - v_xyzh1111;
const float v_xyzh1101 = v_xyz110 * coords[3];
const float v_xyzh1100 = v_xyz110 - v_xyzh1101;
const float v_xyzh1011 = v_xyz101 * coords[3];
const float v_xyzh1010 = v_xyz101 - v_xyzh1011;
const float v_xyzh1001 = v_xyz100 * coords[3];
const float v_xyzh1000 = v_xyz100 - v_xyzh1001;
const float v_xyzh0111 = v_xyz011 * coords[3];
const float v_xyzh0110 = v_xyz011 - v_xyzh0111;
const float v_xyzh0101 = v_xyz010 * coords[3];
const float v_xyzh0100 = v_xyz010 - v_xyzh0101;
const float v_xyzh0011 = v_xyz001 * coords[3];
const float v_xyzh0010 = v_xyz001 - v_xyzh0011;
const float v_xyzh0001 = v_xyz000 * coords[3];
const float v_xyzh0000 = v_xyz000 - v_xyzh0001;
hists[grid_idx][h_idx] += v_xyzh0000;
hists[grid_idx][h_idx + 1] += v_xyzh0001;
hists[grid_idx + 1][h_idx] += v_xyzh0010;
hists[grid_idx + 1][h_idx + 1] += v_xyzh0011;
hists[grid_idx + (grid_size + 2)][h_idx] += v_xyzh0100;
hists[grid_idx + (grid_size + 2)][h_idx + 1] += v_xyzh0101;
hists[grid_idx + (grid_size + 3)][h_idx] += v_xyzh0110;
hists[grid_idx + (grid_size + 3)][h_idx + 1] += v_xyzh0111;
hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx] += v_xyzh1000;
hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx + 1] += v_xyzh1001;
hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx] += v_xyzh1010;
hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx + 1] += v_xyzh1011;
hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx] += v_xyzh1100;
hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx + 1] += v_xyzh1101;
hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx] += v_xyzh1110;
hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx + 1] += v_xyzh1111;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::GASDEstimation<PointInT, PointOutT>::copyShapeHistogramsToOutput (const std::size_t grid_size,
const std::size_t hists_size,
const std::vector<Eigen::VectorXf> &hists,
PointCloudOut &output,
std::size_t &pos)
{
for (std::size_t i = 0; i < grid_size; ++i)
{
for (std::size_t j = 0; j < grid_size; ++j)
{
for (std::size_t k = 0; k < grid_size; ++k)
{
const std::size_t idx = ( (i + 1) * (grid_size + 2) + (j + 1)) * (grid_size + 2) + (k + 1);
std::copy (hists[idx].data () + 1, hists[idx].data () + 1 + hists_size, output[0].histogram + pos);
pos += hists_size;
}
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::GASDEstimation<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
// compute alignment transform using reference frame
computeAlignmentTransform ();
// align point cloud
pcl::transformPointCloud (*surface_, *indices_, shape_samples_, transform_);
const std::size_t shape_grid_size = shape_half_grid_size_ * 2;
// each histogram dimension has 2 additional bins, 1 in each boundary, for performing interpolation
std::vector<Eigen::VectorXf> shape_hists ((shape_grid_size + 2) * (shape_grid_size + 2) * (shape_grid_size + 2),
Eigen::VectorXf::Zero (shape_hists_size_ + 2));
Eigen::Vector4f centroid_p = Eigen::Vector4f::Zero ();
// compute normalization factor for distances between samples and centroid
Eigen::Vector4f far_pt;
pcl::getMaxDistance (shape_samples_, centroid_p, far_pt);
far_pt[3] = 0;
const float distance_normalization_factor = (centroid_p - far_pt).norm ();
// compute normalization factor with respect to axis-aligned bounding cube centered on the origin
Eigen::Vector4f min_pt, max_pt;
pcl::getMinMax3D (shape_samples_, min_pt, max_pt);
max_coord_ = std::max (min_pt.head<3> ().cwiseAbs ().maxCoeff (), max_pt.head<3> ().cwiseAbs ().maxCoeff ());
// normalize sample contribution with respect to the total number of points in the cloud
hist_incr_ = 100.0f / static_cast<float> (shape_samples_.size () - 1);
// for each sample
for (const auto& sample: shape_samples_)
{
// compute shape histogram array coord based on distance between sample and centroid
const Eigen::Vector4f p (sample.x, sample.y, sample.z, 0.0f);
const float d = p.norm ();
const float shape_grid_step = distance_normalization_factor / shape_half_grid_size_;
float integral;
const float dist_hist_val = std::modf(d / shape_grid_step, &integral);
const float dbin = dist_hist_val * shape_hists_size_;
// add sample to shape histograms, optionally performing interpolation
addSampleToHistograms (p, max_coord_, shape_half_grid_size_, shape_interp_, dbin, hist_incr_, shape_hists);
}
pos_ = 0;
// copy shape histograms to output
copyShapeHistogramsToOutput (shape_grid_size, shape_hists_size_, shape_hists, output, pos_);
// set remaining values of the descriptor to zero (if any)
std::fill (output[0].histogram + pos_, output[0].histogram + output[0].descriptorSize (), 0.0f);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::GASDColorEstimation<PointInT, PointOutT>::copyColorHistogramsToOutput (const std::size_t grid_size,
const std::size_t hists_size,
std::vector<Eigen::VectorXf> &hists,
PointCloudOut &output,
std::size_t &pos)
{
for (std::size_t i = 0; i < grid_size; ++i)
{
for (std::size_t j = 0; j < grid_size; ++j)
{
for (std::size_t k = 0; k < grid_size; ++k)
{
const std::size_t idx = ( (i + 1) * (grid_size + 2) + (j + 1)) * (grid_size + 2) + (k + 1);
hists[idx][1] += hists[idx][hists_size + 1];
hists[idx][hists_size] += hists[idx][0];
std::copy (hists[idx].data () + 1, hists[idx].data () + 1 + hists_size, output[0].histogram + pos);
pos += hists_size;
}
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::GASDColorEstimation<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
// call shape feature computation
GASDEstimation<PointInT, PointOutT>::computeFeature (output);
const std::size_t color_grid_size = color_half_grid_size_ * 2;
// each histogram dimension has 2 additional bins, 1 in each boundary, for performing interpolation
std::vector<Eigen::VectorXf> color_hists ((color_grid_size + 2) * (color_grid_size + 2) * (color_grid_size + 2),
Eigen::VectorXf::Zero (color_hists_size_ + 2));
// for each sample
for (const auto& sample: shape_samples_)
{
// compute shape histogram array coord based on distance between sample and centroid
const Eigen::Vector4f p (sample.x, sample.y, sample.z, 0.0f);
// compute hue value
float hue = 0.f;
const unsigned char max = std::max (sample.r, std::max (sample.g, sample.b));
const unsigned char min = std::min (sample.r, std::min (sample.g, sample.b));
const float diff_inv = 1.f / static_cast <float> (max - min);
if (std::isfinite (diff_inv))
{
if (max == sample.r)
{
hue = 60.f * (static_cast <float> (sample.g - sample.b) * diff_inv);
}
else if (max == sample.g)
{
hue = 60.f * (2.f + static_cast <float> (sample.b - sample.r) * diff_inv);
}
else
{
hue = 60.f * (4.f + static_cast <float> (sample.r - sample.g) * diff_inv); // max == b
}
if (hue < 0.f)
{
hue += 360.f;
}
}
// compute color histogram array coord based on hue value
const float hbin = (hue / 360) * color_hists_size_;
// add sample to color histograms, optionally performing interpolation
GASDEstimation<PointInT, PointOutT>::addSampleToHistograms (p, max_coord_, color_half_grid_size_, color_interp_, hbin, hist_incr_, color_hists);
}
// copy color histograms to output
copyColorHistogramsToOutput (color_grid_size, color_hists_size_, color_hists, output, pos_);
// set remaining values of the descriptor to zero (if any)
std::fill (output[0].histogram + pos_, output[0].histogram + output[0].descriptorSize (), 0.0f);
}
#define PCL_INSTANTIATE_GASDEstimation(InT, OutT) template class PCL_EXPORTS pcl::GASDEstimation<InT, OutT>;
#define PCL_INSTANTIATE_GASDColorEstimation(InT, OutT) template class PCL_EXPORTS pcl::GASDColorEstimation<InT, OutT>;
#endif // PCL_FEATURES_IMPL_GASD_H_
pcl::Feature
Feature represents the base feature class.
Definition feature.h:107
pcl::GASDColorEstimation
GASDColorEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given...
Definition gasd.h:257
pcl::GASDEstimation
GASDEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given poin...
Definition gasd.h:75
pcl::GASDEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate GASD descriptor.
Definition gasd.hpp:253
pcl::GASDEstimation::compute
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition gasd.hpp:50
pcl::GASDEstimation::addSampleToHistograms
void addSampleToHistograms(const Eigen::Vector4f &p, const float max_coord, const std::size_t half_grid_size, const HistogramInterpolationMethod interp, const float hbin, const float hist_incr, std::vector< Eigen::VectorXf > &hists)
add a sample to its respective histogram, optionally performing interpolation.
Definition gasd.hpp:118
pcl::PointCloud< PointOutT >
pcl::PointCloud::is_dense
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition point_cloud.h:403
pcl::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition point_cloud.h:462
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition point_cloud.h:398
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition point_cloud.h:392
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition point_cloud.h:400
pcl::PointCloud::clear
void clear()
Removes all points in a cloud and sets the width and height to 0.
Definition point_cloud.h:885
common.h
Define standard C methods and C++ classes that are common to all methods.
pcl::getMaxDistance
void getMaxDistance(const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
Get the point at maximum distance from a given point and a given pointcloud.
Definition common.hpp:197
pcl::getMinMax3D
void getMinMax3D(const pcl::PointCloud< PointT > &cloud, PointT &min_pt, PointT &max_pt)
Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
Definition common.hpp:295
pcl::computeCovarianceMatrix
unsigned int computeCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix)
Compute the 3x3 covariance matrix of a given set of points.
Definition centroid.hpp:192
pcl::transformPointCloud
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform, bool copy_all_fields)
Apply a rigid transform defined by a 4x4 matrix.
Definition transforms.hpp:221
pcl::eigen33
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition eigen.hpp:295
pcl::compute3DCentroid
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition centroid.hpp:57
pcl::HistogramInterpolationMethod
HistogramInterpolationMethod
Different histogram interpolation methods.
Definition gasd.h:47
pcl::INTERP_NONE
@ INTERP_NONE
no interpolation
Definition gasd.h:48
pcl::INTERP_TRILINEAR
@ INTERP_TRILINEAR
trilinear interpolation
Definition gasd.h:49