esf.hpp
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* $Id: pfh.hpp 5027 2012-03-12 03:10:45Z rusu $
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#ifndef PCL_FEATURES_IMPL_ESF_H_
#define PCL_FEATURES_IMPL_ESF_H_
#include <pcl/features/esf.h>
#include <pcl/common/common.h>
#include <pcl/common/distances.h>
#include <pcl/common/transforms.h>
#include <vector>
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ESFEstimation<PointInT, PointOutT>::computeESF (
PointCloudIn &pc, std::vector<float> &hist)
{
const int binsize = 64;
unsigned int sample_size = 20000;
// @TODO: Replace with c++ stdlib uniform_random_generator
srand (static_cast<unsigned int> (time (nullptr)));
int maxindex = static_cast<int> (pc.points.size ());
int index1, index2, index3;
std::vector<float> d2v, d1v, d3v, wt_d3;
std::vector<int> wt_d2;
d1v.reserve (sample_size);
d2v.reserve (sample_size * 3);
d3v.reserve (sample_size);
wt_d2.reserve (sample_size * 3);
wt_d3.reserve (sample_size);
float h_in[binsize] = {0};
float h_out[binsize] = {0};
float h_mix[binsize] = {0};
float h_mix_ratio[binsize] = {0};
float h_a3_in[binsize] = {0};
float h_a3_out[binsize] = {0};
float h_a3_mix[binsize] = {0};
float h_d3_in[binsize] = {0};
float h_d3_out[binsize] = {0};
float h_d3_mix[binsize] = {0};
float ratio=0.0;
float pih = static_cast<float>(M_PI) / 2.0f;
float a,b,c,s;
int th1,th2,th3;
int vxlcnt = 0;
int pcnt1,pcnt2,pcnt3;
for (std::size_t nn_idx = 0; nn_idx < sample_size; ++nn_idx)
{
// get a new random point
index1 = rand()%maxindex;
index2 = rand()%maxindex;
index3 = rand()%maxindex;
if (index1==index2 || index1 == index3 || index2 == index3)
{
nn_idx--;
continue;
}
Eigen::Vector4f p1 = pc.points[index1].getVector4fMap ();
Eigen::Vector4f p2 = pc.points[index2].getVector4fMap ();
Eigen::Vector4f p3 = pc.points[index3].getVector4fMap ();
// A3
Eigen::Vector4f v21 (p2 - p1);
Eigen::Vector4f v31 (p3 - p1);
Eigen::Vector4f v23 (p2 - p3);
a = v21.norm (); b = v31.norm (); c = v23.norm (); s = (a+b+c) * 0.5f;
if (s * (s-a) * (s-b) * (s-c) <= 0.001f)
{
nn_idx--;
continue;
}
v21.normalize ();
v31.normalize ();
v23.normalize ();
//TODO: .dot gives nan's
th1 = static_cast<int> (pcl_round (std::acos (std::abs (v21.dot (v31))) / pih * (binsize-1)));
th2 = static_cast<int> (pcl_round (std::acos (std::abs (v23.dot (v31))) / pih * (binsize-1)));
th3 = static_cast<int> (pcl_round (std::acos (std::abs (v23.dot (v21))) / pih * (binsize-1)));
if (th1 < 0 || th1 >= binsize)
{
nn_idx--;
continue;
}
if (th2 < 0 || th2 >= binsize)
{
nn_idx--;
continue;
}
if (th3 < 0 || th3 >= binsize)
{
nn_idx--;
continue;
}
// D2
d2v.push_back (pcl::euclideanDistance (pc.points[index1], pc.points[index2]));
d2v.push_back (pcl::euclideanDistance (pc.points[index1], pc.points[index3]));
d2v.push_back (pcl::euclideanDistance (pc.points[index2], pc.points[index3]));
int vxlcnt_sum = 0;
int p_cnt = 0;
// IN, OUT, MIXED, Ratio line tracing, index1->index2
{
const int xs = p1[0] < 0.0? static_cast<int>(std::floor(p1[0])+GRIDSIZE_H): static_cast<int>(std::ceil(p1[0])+GRIDSIZE_H-1);
const int ys = p1[1] < 0.0? static_cast<int>(std::floor(p1[1])+GRIDSIZE_H): static_cast<int>(std::ceil(p1[1])+GRIDSIZE_H-1);
const int zs = p1[2] < 0.0? static_cast<int>(std::floor(p1[2])+GRIDSIZE_H): static_cast<int>(std::ceil(p1[2])+GRIDSIZE_H-1);
const int xt = p2[0] < 0.0? static_cast<int>(std::floor(p2[0])+GRIDSIZE_H): static_cast<int>(std::ceil(p2[0])+GRIDSIZE_H-1);
const int yt = p2[1] < 0.0? static_cast<int>(std::floor(p2[1])+GRIDSIZE_H): static_cast<int>(std::ceil(p2[1])+GRIDSIZE_H-1);
const int zt = p2[2] < 0.0? static_cast<int>(std::floor(p2[2])+GRIDSIZE_H): static_cast<int>(std::ceil(p2[2])+GRIDSIZE_H-1);
wt_d2.push_back (this->lci (xs, ys, zs, xt, yt, zt, ratio, vxlcnt, pcnt1));
if (wt_d2.back () == 2)
h_mix_ratio[static_cast<int> (pcl_round (ratio * (binsize-1)))]++;
vxlcnt_sum += vxlcnt;
p_cnt += pcnt1;
}
// IN, OUT, MIXED, Ratio line tracing, index1->index3
{
const int xs = p1[0] < 0.0? static_cast<int>(std::floor(p1[0])+GRIDSIZE_H): static_cast<int>(std::ceil(p1[0])+GRIDSIZE_H-1);
const int ys = p1[1] < 0.0? static_cast<int>(std::floor(p1[1])+GRIDSIZE_H): static_cast<int>(std::ceil(p1[1])+GRIDSIZE_H-1);
const int zs = p1[2] < 0.0? static_cast<int>(std::floor(p1[2])+GRIDSIZE_H): static_cast<int>(std::ceil(p1[2])+GRIDSIZE_H-1);
const int xt = p3[0] < 0.0? static_cast<int>(std::floor(p3[0])+GRIDSIZE_H): static_cast<int>(std::ceil(p3[0])+GRIDSIZE_H-1);
const int yt = p3[1] < 0.0? static_cast<int>(std::floor(p3[1])+GRIDSIZE_H): static_cast<int>(std::ceil(p3[1])+GRIDSIZE_H-1);
const int zt = p3[2] < 0.0? static_cast<int>(std::floor(p3[2])+GRIDSIZE_H): static_cast<int>(std::ceil(p3[2])+GRIDSIZE_H-1);
wt_d2.push_back (this->lci (xs, ys, zs, xt, yt, zt, ratio, vxlcnt, pcnt2));
if (wt_d2.back () == 2)
h_mix_ratio[static_cast<int>(pcl_round (ratio * (binsize-1)))]++;
vxlcnt_sum += vxlcnt;
p_cnt += pcnt2;
}
// IN, OUT, MIXED, Ratio line tracing, index2->index3
{
const int xs = p2[0] < 0.0? static_cast<int>(std::floor(p2[0])+GRIDSIZE_H): static_cast<int>(std::ceil(p2[0])+GRIDSIZE_H-1);
const int ys = p2[1] < 0.0? static_cast<int>(std::floor(p2[1])+GRIDSIZE_H): static_cast<int>(std::ceil(p2[1])+GRIDSIZE_H-1);
const int zs = p2[2] < 0.0? static_cast<int>(std::floor(p2[2])+GRIDSIZE_H): static_cast<int>(std::ceil(p2[2])+GRIDSIZE_H-1);
const int xt = p3[0] < 0.0? static_cast<int>(std::floor(p3[0])+GRIDSIZE_H): static_cast<int>(std::ceil(p3[0])+GRIDSIZE_H-1);
const int yt = p3[1] < 0.0? static_cast<int>(std::floor(p3[1])+GRIDSIZE_H): static_cast<int>(std::ceil(p3[1])+GRIDSIZE_H-1);
const int zt = p3[2] < 0.0? static_cast<int>(std::floor(p3[2])+GRIDSIZE_H): static_cast<int>(std::ceil(p3[2])+GRIDSIZE_H-1);
wt_d2.push_back (this->lci (xs,ys,zs,xt,yt,zt,ratio,vxlcnt,pcnt3));
if (wt_d2.back () == 2)
h_mix_ratio[static_cast<int>(pcl_round(ratio * (binsize-1)))]++;
vxlcnt_sum += vxlcnt;
p_cnt += pcnt3;
}
// D3 ( herons formula )
d3v.push_back (std::sqrt (std::sqrt (s * (s-a) * (s-b) * (s-c))));
if (vxlcnt_sum <= 21)
{
wt_d3.push_back (0);
h_a3_out[th1] += static_cast<float> (pcnt3) / 32.0f;
h_a3_out[th2] += static_cast<float> (pcnt1) / 32.0f;
h_a3_out[th3] += static_cast<float> (pcnt2) / 32.0f;
}
else
if (p_cnt - vxlcnt_sum < 4)
{
h_a3_in[th1] += static_cast<float> (pcnt3) / 32.0f;
h_a3_in[th2] += static_cast<float> (pcnt1) / 32.0f;
h_a3_in[th3] += static_cast<float> (pcnt2) / 32.0f;
wt_d3.push_back (1);
}
else
{
h_a3_mix[th1] += static_cast<float> (pcnt3) / 32.0f;
h_a3_mix[th2] += static_cast<float> (pcnt1) / 32.0f;
h_a3_mix[th3] += static_cast<float> (pcnt2) / 32.0f;
wt_d3.push_back (static_cast<float> (vxlcnt_sum) / static_cast<float> (p_cnt));
}
}
// Normalizing, get max
float maxd2 = 0;
float maxd3 = 0;
for (std::size_t nn_idx = 0; nn_idx < sample_size; ++nn_idx)
{
// get max of Dx
if (d2v[nn_idx] > maxd2)
maxd2 = d2v[nn_idx];
if (d2v[sample_size + nn_idx] > maxd2)
maxd2 = d2v[sample_size + nn_idx];
if (d2v[sample_size*2 +nn_idx] > maxd2)
maxd2 = d2v[sample_size*2 +nn_idx];
if (d3v[nn_idx] > maxd3)
maxd3 = d3v[nn_idx];
}
// Normalize and create histogram
int index;
for (std::size_t nn_idx = 0; nn_idx < sample_size; ++nn_idx)
{
if (wt_d3[nn_idx] >= 0.999) // IN
{
index = static_cast<int>(pcl_round (d3v[nn_idx] / maxd3 * (binsize-1)));
if (index >= 0 && index < binsize)
h_d3_in[index]++;
}
else
{
if (wt_d3[nn_idx] <= 0.001) // OUT
{
index = static_cast<int>(pcl_round (d3v[nn_idx] / maxd3 * (binsize-1)));
if (index >= 0 && index < binsize)
h_d3_out[index]++ ;
}
else
{
index = static_cast<int>(pcl_round (d3v[nn_idx] / maxd3 * (binsize-1)));
if (index >= 0 && index < binsize)
h_d3_mix[index]++;
}
}
}
//normalize and create histogram
for (std::size_t nn_idx = 0; nn_idx < d2v.size(); ++nn_idx )
{
if (wt_d2[nn_idx] == 0)
h_in[static_cast<int>(pcl_round (d2v[nn_idx] / maxd2 * (binsize-1)))]++ ;
if (wt_d2[nn_idx] == 1)
h_out[static_cast<int>(pcl_round (d2v[nn_idx] / maxd2 * (binsize-1)))]++;
if (wt_d2[nn_idx] == 2)
h_mix[static_cast<int>(pcl_round (d2v[nn_idx] / maxd2 * (binsize-1)))]++ ;
}
//float weights[10] = {1, 1, 1, 1, 1, 1, 1, 1 , 1 , 1};
float weights[10] = {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f};
hist.reserve (binsize * 10);
for (const float &i : h_a3_in)
hist.push_back (i * weights[0]);
for (const float &i : h_a3_out)
hist.push_back (i * weights[1]);
for (const float &i : h_a3_mix)
hist.push_back (i * weights[2]);
for (const float &i : h_d3_in)
hist.push_back (i * weights[3]);
for (const float &i : h_d3_out)
hist.push_back (i * weights[4]);
for (const float &i : h_d3_mix)
hist.push_back (i * weights[5]);
for (const float &i : h_in)
hist.push_back (i*0.5f * weights[6]);
for (const float &i : h_out)
hist.push_back (i * weights[7]);
for (const float &i : h_mix)
hist.push_back (i * weights[8]);
for (const float &i : h_mix_ratio)
hist.push_back (i*0.5f * weights[9]);
float sm = 0;
for (const float &i : hist)
sm += i;
for (float &i : hist)
i /= sm;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> int
pcl::ESFEstimation<PointInT, PointOutT>::lci (
const int x1, const int y1, const int z1,
const int x2, const int y2, const int z2,
float &ratio, int &incnt, int &pointcount)
{
int voxelcount = 0;
int voxel_in = 0;
int act_voxel[3];
act_voxel[0] = x1;
act_voxel[1] = y1;
act_voxel[2] = z1;
int x_inc, y_inc, z_inc;
int dx = x2 - x1;
int dy = y2 - y1;
int dz = z2 - z1;
if (dx < 0)
x_inc = -1;
else
x_inc = 1;
int l = std::abs (dx);
if (dy < 0)
y_inc = -1 ;
else
y_inc = 1;
int m = std::abs (dy);
if (dz < 0)
z_inc = -1 ;
else
z_inc = 1;
int n = std::abs (dz);
int dx2 = 2 * l;
int dy2 = 2 * m;
int dz2 = 2 * n;
if ((l >= m) & (l >= n))
{
int err_1 = dy2 - l;
int err_2 = dz2 - l;
for (int i = 1; i<l; i++)
{
voxelcount++;;
voxel_in += static_cast<int>(lut_[act_voxel[0]][act_voxel[1]][act_voxel[2]] == 1);
if (err_1 > 0)
{
act_voxel[1] += y_inc;
err_1 -= dx2;
}
if (err_2 > 0)
{
act_voxel[2] += z_inc;
err_2 -= dx2;
}
err_1 += dy2;
err_2 += dz2;
act_voxel[0] += x_inc;
}
}
else if ((m >= l) & (m >= n))
{
int err_1 = dx2 - m;
int err_2 = dz2 - m;
for (int i=1; i<m; i++)
{
voxelcount++;
voxel_in += static_cast<int>(lut_[act_voxel[0]][act_voxel[1]][act_voxel[2]] == 1);
if (err_1 > 0)
{
act_voxel[0] += x_inc;
err_1 -= dy2;
}
if (err_2 > 0)
{
act_voxel[2] += z_inc;
err_2 -= dy2;
}
err_1 += dx2;
err_2 += dz2;
act_voxel[1] += y_inc;
}
}
else
{
int err_1 = dy2 - n;
int err_2 = dx2 - n;
for (int i=1; i<n; i++)
{
voxelcount++;
voxel_in += static_cast<int>(lut_[act_voxel[0]][act_voxel[1]][act_voxel[2]] == 1);
if (err_1 > 0)
{
act_voxel[1] += y_inc;
err_1 -= dz2;
}
if (err_2 > 0)
{
act_voxel[0] += x_inc;
err_2 -= dz2;
}
err_1 += dy2;
err_2 += dx2;
act_voxel[2] += z_inc;
}
}
voxelcount++;
voxel_in += static_cast<int>(lut_[act_voxel[0]][act_voxel[1]][act_voxel[2]] == 1);
incnt = voxel_in;
pointcount = voxelcount;
if (voxel_in >= voxelcount-1)
return (0);
if (voxel_in <= 7)
return (1);
ratio = static_cast<float>(voxel_in) / static_cast<float>(voxelcount);
return (2);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ESFEstimation<PointInT, PointOutT>::voxelize9 (PointCloudIn &cluster)
{
int xi,yi,zi,xx,yy,zz;
for (std::size_t i = 0; i < cluster.points.size (); ++i)
{
xx = cluster.points[i].x<0.0? static_cast<int>(std::floor(cluster.points[i].x)+GRIDSIZE_H) : static_cast<int>(std::ceil(cluster.points[i].x)+GRIDSIZE_H-1);
yy = cluster.points[i].y<0.0? static_cast<int>(std::floor(cluster.points[i].y)+GRIDSIZE_H) : static_cast<int>(std::ceil(cluster.points[i].y)+GRIDSIZE_H-1);
zz = cluster.points[i].z<0.0? static_cast<int>(std::floor(cluster.points[i].z)+GRIDSIZE_H) : static_cast<int>(std::ceil(cluster.points[i].z)+GRIDSIZE_H-1);
for (int x = -1; x < 2; x++)
for (int y = -1; y < 2; y++)
for (int z = -1; z < 2; z++)
{
xi = xx + x;
yi = yy + y;
zi = zz + z;
if (yi >= GRIDSIZE || xi >= GRIDSIZE || zi>=GRIDSIZE || yi < 0 || xi < 0 || zi < 0)
{
;
}
else
this->lut_[xi][yi][zi] = 1;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ESFEstimation<PointInT, PointOutT>::cleanup9 (PointCloudIn &cluster)
{
int xi,yi,zi,xx,yy,zz;
for (std::size_t i = 0; i < cluster.points.size (); ++i)
{
xx = cluster.points[i].x<0.0? static_cast<int>(std::floor(cluster.points[i].x)+GRIDSIZE_H) : static_cast<int>(std::ceil(cluster.points[i].x)+GRIDSIZE_H-1);
yy = cluster.points[i].y<0.0? static_cast<int>(std::floor(cluster.points[i].y)+GRIDSIZE_H) : static_cast<int>(std::ceil(cluster.points[i].y)+GRIDSIZE_H-1);
zz = cluster.points[i].z<0.0? static_cast<int>(std::floor(cluster.points[i].z)+GRIDSIZE_H) : static_cast<int>(std::ceil(cluster.points[i].z)+GRIDSIZE_H-1);
for (int x = -1; x < 2; x++)
for (int y = -1; y < 2; y++)
for (int z = -1; z < 2; z++)
{
xi = xx + x;
yi = yy + y;
zi = zz + z;
if (yi >= GRIDSIZE || xi >= GRIDSIZE || zi>=GRIDSIZE || yi < 0 || xi < 0 || zi < 0)
{
;
}
else
this->lut_[xi][yi][zi] = 0;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ESFEstimation<PointInT, PointOutT>::scale_points_unit_sphere (
const pcl::PointCloud<PointInT> &pc, float scalefactor, Eigen::Vector4f& centroid)
{
pcl::compute3DCentroid (pc, centroid);
pcl::demeanPointCloud (pc, centroid, local_cloud_);
float max_distance = 0, d;
pcl::PointXYZ cog (0, 0, 0);
for (std::size_t i = 0; i < local_cloud_.points.size (); ++i)
{
d = pcl::euclideanDistance(cog,local_cloud_.points[i]);
if (d > max_distance)
max_distance = d;
}
float scale_factor = 1.0f / max_distance * scalefactor;
Eigen::Affine3f matrix = Eigen::Affine3f::Identity();
matrix.scale (scale_factor);
pcl::transformPointCloud (local_cloud_, local_cloud_, matrix);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointOutT> void
pcl::ESFEstimation<PointInT, PointOutT>::compute (PointCloudOut &output)
{
if (!Feature<PointInT, PointOutT>::initCompute ())
{
output.width = output.height = 0;
output.points.clear ();
return;
}
// Copy the header
output.header = input_->header;
// Resize the output dataset
// Important! We should only allocate precisely how many elements we will need, otherwise
// we risk at pre-allocating too much memory which could lead to bad_alloc
// (see http://dev.pointclouds.org/issues/657)
output.width = output.height = 1;
output.is_dense = input_->is_dense;
output.points.resize (1);
// Perform the actual feature computation
computeFeature (output);
Feature<PointInT, PointOutT>::deinitCompute ();
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ESFEstimation<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
Eigen::Vector4f xyz_centroid;
std::vector<float> hist;
scale_points_unit_sphere (*surface_, static_cast<float>(GRIDSIZE_H), xyz_centroid);
this->voxelize9 (local_cloud_);
this->computeESF (local_cloud_, hist);
this->cleanup9 (local_cloud_);
// We only output _1_ signature
output.points.resize (1);
output.width = 1;
output.height = 1;
for (std::size_t d = 0; d < hist.size (); ++d)
output.points[0].histogram[d] = hist[d];
}
#define PCL_INSTANTIATE_ESFEstimation(T,OutT) template class PCL_EXPORTS pcl::ESFEstimation<T,OutT>;
#endif // PCL_FEATURES_IMPL_ESF_H_
common.h
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:397
pcl::euclideanDistance
float euclideanDistance(const PointType1 &p1, const PointType2 &p2)
Calculate the euclidean distance between the two given points.
Definition: distances.h:200
pcl::demeanPointCloud
void demeanPointCloud(ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Matrix< Scalar, 4, 1 > &centroid, pcl::PointCloud< PointT > &cloud_out, int npts=0)
Subtract a centroid from a point cloud and return the de-meaned representation.
Definition: centroid.hpp:625
pcl::PointCloud< PointInT >
pcl::ESFEstimation::compute
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition: esf.hpp:509
pcl::ESFEstimation::lci
int lci(const int x1, const int y1, const int z1, const int x2, const int y2, const int z2, float &ratio, int &incnt, int &pointcount)
...
Definition: esf.hpp:307
pcl::transformPointCloud
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform, bool copy_all_fields=true)
Apply an affine transform defined by an Eigen Transform.
Definition: transforms.hpp:215
pcl::ESFEstimation::cleanup9
void cleanup9(PointCloudIn &cluster)
...
Definition: esf.hpp:455
pcl::ESFEstimation::scale_points_unit_sphere
void scale_points_unit_sphere(const pcl::PointCloud< PointInT > &pc, float scalefactor, Eigen::Vector4f &centroid)
...
Definition: esf.hpp:484
pcl::PointXYZ
A point structure representing Euclidean xyz coordinates.
Definition: point_types.hpp:291
pcl::ESFEstimation::voxelize9
void voxelize9(PointCloudIn &cluster)
...
Definition: esf.hpp:426
pcl_round
__inline double pcl_round(double number)
Win32 doesn't seem to have rounding functions.
Definition: pcl_macros.h:166
pcl::ESFEstimation::PointCloudOut
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: esf.h:74
pcl::ESFEstimation::computeESF
void computeESF(PointCloudIn &pc, std::vector< float > &hist)
...
Definition: esf.hpp:52
pcl::ESFEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the Ensebmel of Shape Function (ESF) descriptors at a set of points given by <setInputCloud ...
Definition: esf.hpp:537
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:50
distances.h
pcl::Feature
Feature represents the base feature class.
Definition: feature.h:105