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common.hpp
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#ifndef PCL_COMMON_IMPL_H_
#define PCL_COMMON_IMPL_H_
#include <pcl/point_types.h>
#include <pcl/common/common.h>
#include <limits>
//////////////////////////////////////////////////////////////////////////////////////////////
inline double
pcl::getAngle3D (const Eigen::Vector4f &v1, const Eigen::Vector4f &v2, const bool in_degree)
{
// Compute the actual angle
double rad = v1.normalized ().dot (v2.normalized ());
if (rad < -1.0)
rad = -1.0;
else if (rad > 1.0)
rad = 1.0;
return (in_degree ? std::acos (rad) * 180.0 / M_PI : std::acos (rad));
}
inline double
pcl::getAngle3D (const Eigen::Vector3f &v1, const Eigen::Vector3f &v2, const bool in_degree)
{
// Compute the actual angle
double rad = v1.normalized ().dot (v2.normalized ());
if (rad < -1.0)
rad = -1.0;
else if (rad > 1.0)
rad = 1.0;
return (in_degree ? std::acos (rad) * 180.0 / M_PI : std::acos (rad));
}
#ifdef __SSE__
inline __m128
pcl::acos_SSE (const __m128 &x)
{
/*
This python code generates the coefficients:
import math, numpy, scipy.optimize
def get_error(S):
err_sum=0.0
for x in numpy.arange(0.0, 1.0, 0.0025):
if (S[3]+S[4]*x)<0.0:
err_sum+=10.0
else:
err_sum+=((S[0]+x*(S[1]+x*S[2]))*numpy.sqrt(S[3]+S[4]*x)+S[5]+x*(S[6]+x*S[7])-math.acos(x))**2.0
return err_sum/400.0
print(scipy.optimize.minimize(fun=get_error, x0=[1.57, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0, 0.0], method='Nelder-Mead', options={'maxiter':42000, 'maxfev':42000, 'disp':True, 'xatol':1e-6, 'fatol':1e-6}))
*/
const __m128 mul_term = _mm_add_ps (_mm_set1_ps (1.59121552f), _mm_mul_ps (x, _mm_add_ps (_mm_set1_ps (-0.15461442f), _mm_mul_ps (x, _mm_set1_ps (0.05354897f)))));
const __m128 add_term = _mm_add_ps (_mm_set1_ps (0.06681017f), _mm_mul_ps (x, _mm_add_ps (_mm_set1_ps (-0.09402311f), _mm_mul_ps (x, _mm_set1_ps (0.02708663f)))));
return _mm_add_ps (_mm_mul_ps (mul_term, _mm_sqrt_ps (_mm_add_ps (_mm_set1_ps (0.89286965f), _mm_mul_ps (_mm_set1_ps (-0.89282669f), x)))), add_term);
}
inline __m128
pcl::getAcuteAngle3DSSE (const __m128 &x1, const __m128 &y1, const __m128 &z1, const __m128 &x2, const __m128 &y2, const __m128 &z2)
{
const __m128 dot_product = _mm_add_ps (_mm_add_ps (_mm_mul_ps (x1, x2), _mm_mul_ps (y1, y2)), _mm_mul_ps (z1, z2));
// The andnot-function realizes an abs-operation: the sign bit is removed
// -0.0f (negative zero) means that all bits are 0, only the sign bit is 1
return acos_SSE (_mm_min_ps (_mm_set1_ps (1.0f), _mm_andnot_ps (_mm_set1_ps (-0.0f), dot_product)));
}
#endif // ifdef __SSE__
#ifdef __AVX__
inline __m256
pcl::acos_AVX (const __m256 &x)
{
const __m256 mul_term = _mm256_add_ps (_mm256_set1_ps (1.59121552f), _mm256_mul_ps (x, _mm256_add_ps (_mm256_set1_ps (-0.15461442f), _mm256_mul_ps (x, _mm256_set1_ps (0.05354897f)))));
const __m256 add_term = _mm256_add_ps (_mm256_set1_ps (0.06681017f), _mm256_mul_ps (x, _mm256_add_ps (_mm256_set1_ps (-0.09402311f), _mm256_mul_ps (x, _mm256_set1_ps (0.02708663f)))));
return _mm256_add_ps (_mm256_mul_ps (mul_term, _mm256_sqrt_ps (_mm256_add_ps (_mm256_set1_ps (0.89286965f), _mm256_mul_ps (_mm256_set1_ps (-0.89282669f), x)))), add_term);
}
inline __m256
pcl::getAcuteAngle3DAVX (const __m256 &x1, const __m256 &y1, const __m256 &z1, const __m256 &x2, const __m256 &y2, const __m256 &z2)
{
const __m256 dot_product = _mm256_add_ps (_mm256_add_ps (_mm256_mul_ps (x1, x2), _mm256_mul_ps (y1, y2)), _mm256_mul_ps (z1, z2));
// The andnot-function realizes an abs-operation: the sign bit is removed
// -0.0f (negative zero) means that all bits are 0, only the sign bit is 1
return acos_AVX (_mm256_min_ps (_mm256_set1_ps (1.0f), _mm256_andnot_ps (_mm256_set1_ps (-0.0f), dot_product)));
}
#endif // ifdef __AVX__
//////////////////////////////////////////////////////////////////////////////////////////////
inline void
pcl::getMeanStd (const std::vector<float> &values, double &mean, double &stddev)
{
// throw an exception when the input array is empty
if (values.empty ())
{
PCL_THROW_EXCEPTION (BadArgumentException, "Input array must have at least 1 element.");
}
// when the array has only one element, mean is the number itself and standard dev is 0
if (values.size () == 1)
{
mean = values.at (0);
stddev = 0;
return;
}
double sum = 0, sq_sum = 0;
for (const float &value : values)
{
sum += value;
sq_sum += value * value;
}
mean = sum / static_cast<double>(values.size ());
double variance = (sq_sum - sum * sum / static_cast<double>(values.size ())) / (static_cast<double>(values.size ()) - 1);
stddev = sqrt (variance);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> inline void
pcl::getPointsInBox (const pcl::PointCloud<PointT> &cloud,
Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt,
Indices &indices)
{
indices.resize (cloud.size ());
int l = 0;
// If the data is dense, we don't need to check for NaN
if (cloud.is_dense)
{
for (std::size_t i = 0; i < cloud.size (); ++i)
{
// Check if the point is inside bounds
if (cloud[i].x < min_pt[0] || cloud[i].y < min_pt[1] || cloud[i].z < min_pt[2])
continue;
if (cloud[i].x > max_pt[0] || cloud[i].y > max_pt[1] || cloud[i].z > max_pt[2])
continue;
indices[l++] = static_cast<int>(i);
}
}
// NaN or Inf values could exist => check for them
else
{
for (std::size_t i = 0; i < cloud.size (); ++i)
{
// Check if the point is invalid
if (!std::isfinite (cloud[i].x) ||
!std::isfinite (cloud[i].y) ||
!std::isfinite (cloud[i].z))
continue;
// Check if the point is inside bounds
if (cloud[i].x < min_pt[0] || cloud[i].y < min_pt[1] || cloud[i].z < min_pt[2])
continue;
if (cloud[i].x > max_pt[0] || cloud[i].y > max_pt[1] || cloud[i].z > max_pt[2])
continue;
indices[l++] = static_cast<int>(i);
}
}
indices.resize (l);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointT> inline void
pcl::getMaxDistance (const pcl::PointCloud<PointT> &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
{
float max_dist = std::numeric_limits<float>::lowest();
int max_idx = -1;
float dist;
const Eigen::Vector3f pivot_pt3 = pivot_pt.head<3> ();
// If the data is dense, we don't need to check for NaN
if (cloud.is_dense)
{
for (std::size_t i = 0; i < cloud.size (); ++i)
{
pcl::Vector3fMapConst pt = cloud[i].getVector3fMap ();
dist = (pivot_pt3 - pt).norm ();
if (dist > max_dist)
{
max_idx = static_cast<int>(i);
max_dist = dist;
}
}
}
// NaN or Inf values could exist => check for them
else
{
for (std::size_t i = 0; i < cloud.size (); ++i)
{
// Check if the point is invalid
if (!std::isfinite (cloud[i].x) || !std::isfinite (cloud[i].y) || !std::isfinite (cloud[i].z))
continue;
pcl::Vector3fMapConst pt = cloud[i].getVector3fMap ();
dist = (pivot_pt3 - pt).norm ();
if (dist > max_dist)
{
max_idx = static_cast<int>(i);
max_dist = dist;
}
}
}
if(max_idx != -1)
max_pt = cloud[max_idx].getVector4fMap ();
else
max_pt = Eigen::Vector4f(std::numeric_limits<float>::quiet_NaN(),std::numeric_limits<float>::quiet_NaN(),std::numeric_limits<float>::quiet_NaN(),std::numeric_limits<float>::quiet_NaN());
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointT> inline void
pcl::getMaxDistance (const pcl::PointCloud<PointT> &cloud, const Indices &indices,
const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
{
float max_dist = std::numeric_limits<float>::lowest();
int max_idx = -1;
float dist;
const Eigen::Vector3f pivot_pt3 = pivot_pt.head<3> ();
// If the data is dense, we don't need to check for NaN
if (cloud.is_dense)
{
for (std::size_t i = 0; i < indices.size (); ++i)
{
pcl::Vector3fMapConst pt = cloud[indices[i]].getVector3fMap ();
dist = (pivot_pt3 - pt).norm ();
if (dist > max_dist)
{
max_idx = static_cast<int> (i);
max_dist = dist;
}
}
}
// NaN or Inf values could exist => check for them
else
{
for (std::size_t i = 0; i < indices.size (); ++i)
{
// Check if the point is invalid
if (!std::isfinite (cloud[indices[i]].x) || !std::isfinite (cloud[indices[i]].y)
||
!std::isfinite (cloud[indices[i]].z))
continue;
pcl::Vector3fMapConst pt = cloud[indices[i]].getVector3fMap ();
dist = (pivot_pt3 - pt).norm ();
if (dist > max_dist)
{
max_idx = static_cast<int> (i);
max_dist = dist;
}
}
}
if(max_idx != -1)
max_pt = cloud[indices[max_idx]].getVector4fMap ();
else
max_pt = Eigen::Vector4f(std::numeric_limits<float>::quiet_NaN(),std::numeric_limits<float>::quiet_NaN(),std::numeric_limits<float>::quiet_NaN(),std::numeric_limits<float>::quiet_NaN());
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> inline void
pcl::getMinMax3D (const pcl::PointCloud<PointT> &cloud, PointT &min_pt, PointT &max_pt)
{
Eigen::Vector4f min_p, max_p;
pcl::getMinMax3D (cloud, min_p, max_p);
min_pt.x = min_p[0]; min_pt.y = min_p[1]; min_pt.z = min_p[2];
max_pt.x = max_p[0]; max_pt.y = max_p[1]; max_pt.z = max_p[2];
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> inline void
pcl::getMinMax3D (const pcl::PointCloud<PointT> &cloud, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt)
{
min_pt.setConstant (std::numeric_limits<float>::max());
max_pt.setConstant (std::numeric_limits<float>::lowest());
// If the data is dense, we don't need to check for NaN
if (cloud.is_dense)
{
for (const auto& point: cloud.points)
{
const pcl::Vector4fMapConst pt = point.getVector4fMap ();
min_pt = min_pt.cwiseMin (pt);
max_pt = max_pt.cwiseMax (pt);
}
}
// NaN or Inf values could exist => check for them
else
{
for (const auto& point: cloud.points)
{
// Check if the point is invalid
if (!std::isfinite (point.x) ||
!std::isfinite (point.y) ||
!std::isfinite (point.z))
continue;
const pcl::Vector4fMapConst pt = point.getVector4fMap ();
min_pt = min_pt.cwiseMin (pt);
max_pt = max_pt.cwiseMax (pt);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> inline void
pcl::getMinMax3D (const pcl::PointCloud<PointT> &cloud, const pcl::PointIndices &indices,
Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt)
{
pcl::getMinMax3D (cloud, indices.indices, min_pt, max_pt);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> inline void
pcl::getMinMax3D (const pcl::PointCloud<PointT> &cloud, const Indices &indices,
Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt)
{
min_pt.setConstant (std::numeric_limits<float>::max());
max_pt.setConstant (std::numeric_limits<float>::lowest());
// If the data is dense, we don't need to check for NaN
if (cloud.is_dense)
{
for (const auto &index : indices)
{
const pcl::Vector4fMapConst pt = cloud[index].getVector4fMap ();
min_pt = min_pt.cwiseMin (pt);
max_pt = max_pt.cwiseMax (pt);
}
}
// NaN or Inf values could exist => check for them
else
{
for (const auto &index : indices)
{
// Check if the point is invalid
if (!std::isfinite (cloud[index].x) ||
!std::isfinite (cloud[index].y) ||
!std::isfinite (cloud[index].z))
continue;
const pcl::Vector4fMapConst pt = cloud[index].getVector4fMap ();
min_pt = min_pt.cwiseMin (pt);
max_pt = max_pt.cwiseMax (pt);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> inline double
pcl::getCircumcircleRadius (const PointT &pa, const PointT &pb, const PointT &pc)
{
Eigen::Vector4f p1 (pa.x, pa.y, pa.z, 0);
Eigen::Vector4f p2 (pb.x, pb.y, pb.z, 0);
Eigen::Vector4f p3 (pc.x, pc.y, pc.z, 0);
double p2p1 = (p2 - p1).norm (), p3p2 = (p3 - p2).norm (), p1p3 = (p1 - p3).norm ();
// Calculate the area of the triangle using Heron's formula
// (https://en.wikipedia.org/wiki/Heron's_formula)
double semiperimeter = (p2p1 + p3p2 + p1p3) / 2.0;
double area = sqrt (semiperimeter * (semiperimeter - p2p1) * (semiperimeter - p3p2) * (semiperimeter - p1p3));
// Compute the radius of the circumscribed circle
return ((p2p1 * p3p2 * p1p3) / (4.0 * area));
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> inline void
pcl::getMinMax (const PointT &histogram, int len, float &min_p, float &max_p)
{
min_p = std::numeric_limits<float>::max();
max_p = std::numeric_limits<float>::lowest();
for (int i = 0; i < len; ++i)
{
min_p = (histogram[i] > min_p) ? min_p : histogram[i];
max_p = (histogram[i] < max_p) ? max_p : histogram[i];
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> inline float
pcl::calculatePolygonArea (const pcl::PointCloud<PointT> &polygon)
{
float area = 0.0f;
int num_points = polygon.size ();
Eigen::Vector3f va,vb,res;
res(0) = res(1) = res(2) = 0.0f;
for (int i = 0; i < num_points; ++i)
{
int j = (i + 1) % num_points;
va = polygon[i].getVector3fMap ();
vb = polygon[j].getVector3fMap ();
res += va.cross (vb);
}
area = res.norm ();
return (area*0.5);
}
#endif //#ifndef PCL_COMMON_IMPL_H_
pcl::BadArgumentException
An exception that is thrown when the arguments number or type is wrong/unhandled.
Definition exceptions.h:259
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:174
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:404
pcl::PointCloud::size
std::size_t size() const
Definition point_cloud.h:444
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition point_cloud.h:396
common.h
Define standard C methods and C++ classes that are common to all methods.
point_types.h
Defines all the PCL implemented PointT point type structures.
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::calculatePolygonArea
float calculatePolygonArea(const pcl::PointCloud< PointT > &polygon)
Calculate the area of a polygon given a point cloud that defines the polygon.
Definition common.hpp:414
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::getMeanStd
void getMeanStd(const std::vector< float > &values, double &mean, double &stddev)
Compute both the mean and the standard deviation of an array of values.
Definition common.hpp:124
pcl::getAngle3D
double getAngle3D(const Eigen::Vector4f &v1, const Eigen::Vector4f &v2, const bool in_degree=false)
Compute the smallest angle between two 3D vectors in radians (default) or degree.
Definition common.hpp:47
pcl::getPointsInBox
void getPointsInBox(const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, Indices &indices)
Get a set of points residing in a box given its bounds.
Definition common.hpp:154
pcl::getMinMax
void getMinMax(const PointT &histogram, int len, float &min_p, float &max_p)
Get the minimum and maximum values on a point histogram.
Definition common.hpp:400
pcl::getCircumcircleRadius
double getCircumcircleRadius(const PointT &pa, const PointT &pb, const PointT &pc)
Compute the radius of a circumscribed circle for a triangle formed of three points pa,...
Definition common.hpp:383
pcl::Vector3fMapConst
const Eigen::Map< const Eigen::Vector3f > Vector3fMapConst
Definition point_types.hpp:200
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl::Vector4fMapConst
const Eigen::Map< const Eigen::Vector4f, Eigen::Aligned > Vector4fMapConst
Definition point_types.hpp:202
M_PI
#define M_PI
Definition pcl_macros.h:203
pcl::PointIndices
Definition PointIndices.h:12
pcl::PointIndices::indices
Indices indices
Definition PointIndices.h:20