spin_image.hpp
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2012, Willow Garage, Inc.
* Copyright (c) 2012-, Open Perception, Inc.
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* $Id$
*
*/
#ifndef PCL_FEATURES_IMPL_SPIN_IMAGE_H_
#define PCL_FEATURES_IMPL_SPIN_IMAGE_H_
#include <limits>
#include <pcl/point_types.h>
#include <pcl/exceptions.h>
#include <pcl/features/spin_image.h>
#include <cmath>
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT>
pcl::SpinImageEstimation<PointInT, PointNT, PointOutT>::SpinImageEstimation (
unsigned int image_width, double support_angle_cos, unsigned int min_pts_neighb) :
input_normals_ (), rotation_axes_cloud_ (),
is_angular_ (false), rotation_axis_ (), use_custom_axis_(false), use_custom_axes_cloud_ (false),
is_radial_ (false), image_width_ (image_width), support_angle_cos_ (support_angle_cos),
min_pts_neighb_ (min_pts_neighb)
{
assert (support_angle_cos_ <= 1.0 && support_angle_cos_ >= 0.0); // may be permit negative cosine?
feature_name_ = "SpinImageEstimation";
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> Eigen::ArrayXXd
pcl::SpinImageEstimation<PointInT, PointNT, PointOutT>::computeSiForPoint (int index) const
{
assert (image_width_ > 0);
assert (support_angle_cos_ <= 1.0 && support_angle_cos_ >= 0.0); // may be permit negative cosine?
const Eigen::Vector3f origin_point ((*input_)[index].getVector3fMap ());
Eigen::Vector3f origin_normal;
origin_normal =
input_normals_ ?
(*input_normals_)[index].getNormalVector3fMap () :
Eigen::Vector3f (); // just a placeholder; should never be used!
const Eigen::Vector3f rotation_axis = use_custom_axis_ ?
rotation_axis_.getNormalVector3fMap () :
use_custom_axes_cloud_ ?
(*rotation_axes_cloud_)[index].getNormalVector3fMap () :
origin_normal;
Eigen::ArrayXXd m_matrix (Eigen::ArrayXXd::Zero (image_width_+1, 2*image_width_+1));
Eigen::ArrayXXd m_averAngles (Eigen::ArrayXXd::Zero (image_width_+1, 2*image_width_+1));
// OK, we are interested in the points of the cylinder of height 2*r and
// base radius r, where r = m_dBinSize * in_iImageWidth
// it can be embedded to the sphere of radius sqrt(2) * m_dBinSize * in_iImageWidth
// suppose that points are uniformly distributed, so we lose ~40%
// according to the volumes ratio
double bin_size = 0.0;
if (is_radial_)
bin_size = search_radius_ / image_width_;
else
bin_size = search_radius_ / image_width_ / sqrt(2.0);
pcl::Indices nn_indices;
std::vector<float> nn_sqr_dists;
const int neighb_cnt = this->searchForNeighbors (index, search_radius_, nn_indices, nn_sqr_dists);
if (neighb_cnt < static_cast<int> (min_pts_neighb_))
{
throw PCLException (
"Too few points for spin image, use setMinPointCountInNeighbourhood() to decrease the threshold or use larger feature radius",
"spin_image.hpp", "computeSiForPoint");
}
// for all neighbor points
for (int i_neigh = 0; i_neigh < neighb_cnt ; i_neigh++)
{
// first, skip the points with distant normals
double cos_between_normals = -2.0; // should be initialized if used
if (support_angle_cos_ > 0.0 || is_angular_) // not bogus
{
cos_between_normals = origin_normal.dot ((*input_normals_)[nn_indices[i_neigh]].getNormalVector3fMap ());
if (std::abs (cos_between_normals) > (1.0 + 10*std::numeric_limits<float>::epsilon ())) // should be okay for numeric stability
{
PCL_ERROR ("[pcl::%s::computeSiForPoint] Normal for the point %d and/or the point %d are not normalized, dot ptoduct is %f.\n",
getClassName ().c_str (), nn_indices[i_neigh], index, cos_between_normals);
throw PCLException ("Some normals are not normalized",
"spin_image.hpp", "computeSiForPoint");
}
cos_between_normals = std::max (-1.0, std::min (1.0, cos_between_normals));
if (std::abs (cos_between_normals) < support_angle_cos_ ) // allow counter-directed normals
{
continue;
}
if (cos_between_normals < 0.0)
{
cos_between_normals = -cos_between_normals; // the normal is not used explicitly from now
}
}
// now compute the coordinate in cylindric coordinate system associated with the origin point
const Eigen::Vector3f direction (
(*surface_)[nn_indices[i_neigh]].getVector3fMap () - origin_point);
const double direction_norm = direction.norm ();
if (std::abs(direction_norm) < 10*std::numeric_limits<double>::epsilon ())
continue; // ignore the point itself; it does not contribute really
assert (direction_norm > 0.0);
// the angle between the normal vector and the direction to the point
double cos_dir_axis = direction.dot(rotation_axis) / direction_norm;
if (std::abs(cos_dir_axis) > (1.0 + 10*std::numeric_limits<float>::epsilon())) // should be okay for numeric stability
{
PCL_ERROR ("[pcl::%s::computeSiForPoint] Rotation axis for the point %d are not normalized, dot ptoduct is %f.\n",
getClassName ().c_str (), index, cos_dir_axis);
throw PCLException ("Some rotation axis is not normalized",
"spin_image.hpp", "computeSiForPoint");
}
cos_dir_axis = std::max (-1.0, std::min (1.0, cos_dir_axis));
// compute coordinates w.r.t. the reference frame
double beta = std::numeric_limits<double>::signaling_NaN ();
double alpha = std::numeric_limits<double>::signaling_NaN ();
if (is_radial_) // radial spin image structure
{
beta = asin (cos_dir_axis); // yes, arc sine! to get the angle against tangent, not normal!
alpha = direction_norm;
}
else // rectangular spin-image structure
{
beta = direction_norm * cos_dir_axis;
alpha = direction_norm * sqrt (1.0 - cos_dir_axis*cos_dir_axis);
if (std::abs (beta) >= bin_size * image_width_ || alpha >= bin_size * image_width_)
{
continue; // outside the cylinder
}
}
assert (alpha >= 0.0);
assert (alpha <= bin_size * image_width_ + 20 * std::numeric_limits<float>::epsilon () );
// bilinear interpolation
double beta_bin_size = is_radial_ ? (M_PI / 2 / image_width_) : bin_size;
int beta_bin = int(std::floor (beta / beta_bin_size)) + int(image_width_);
assert (0 <= beta_bin && beta_bin < m_matrix.cols ());
int alpha_bin = int(std::floor (alpha / bin_size));
assert (0 <= alpha_bin && alpha_bin < m_matrix.rows ());
if (alpha_bin == static_cast<int> (image_width_)) // border points
{
alpha_bin--;
// HACK: to prevent a > 1
alpha = bin_size * (alpha_bin + 1) - std::numeric_limits<double>::epsilon ();
}
if (beta_bin == int(2*image_width_) ) // border points
{
beta_bin--;
// HACK: to prevent b > 1
beta = beta_bin_size * (beta_bin - int(image_width_) + 1) - std::numeric_limits<double>::epsilon ();
}
double a = alpha/bin_size - double(alpha_bin);
double b = beta/beta_bin_size - double(beta_bin-int(image_width_));
assert (0 <= a && a <= 1);
assert (0 <= b && b <= 1);
m_matrix (alpha_bin, beta_bin) += (1-a) * (1-b);
m_matrix (alpha_bin+1, beta_bin) += a * (1-b);
m_matrix (alpha_bin, beta_bin+1) += (1-a) * b;
m_matrix (alpha_bin+1, beta_bin+1) += a * b;
if (is_angular_)
{
m_averAngles (alpha_bin, beta_bin) += (1-a) * (1-b) * std::acos (cos_between_normals);
m_averAngles (alpha_bin+1, beta_bin) += a * (1-b) * std::acos (cos_between_normals);
m_averAngles (alpha_bin, beta_bin+1) += (1-a) * b * std::acos (cos_between_normals);
m_averAngles (alpha_bin+1, beta_bin+1) += a * b * std::acos (cos_between_normals);
}
}
if (is_angular_)
{
// transform sum to average
m_matrix = m_averAngles / (m_matrix + std::numeric_limits<double>::epsilon ()); // +eps to avoid division by zero
}
else if (neighb_cnt > 1) // to avoid division by zero, also no need to divide by 1
{
// normalization
m_matrix /= m_matrix.sum();
}
return m_matrix;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> bool
pcl::SpinImageEstimation<PointInT, PointNT, PointOutT>::initCompute ()
{
if (!Feature<PointInT, PointOutT>::initCompute ())
{
PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
return (false);
}
// Check if input normals are set
if (!input_normals_)
{
PCL_ERROR ("[pcl::%s::initCompute] No input dataset containing normals was given!\n", getClassName ().c_str ());
Feature<PointInT, PointOutT>::deinitCompute ();
return (false);
}
// Check if the size of normals is the same as the size of the surface
if (input_normals_->size () != input_->size ())
{
PCL_ERROR ("[pcl::%s::initCompute] ", getClassName ().c_str ());
PCL_ERROR ("The number of points in the input dataset differs from ");
PCL_ERROR ("the number of points in the dataset containing the normals!\n");
Feature<PointInT, PointOutT>::deinitCompute ();
return (false);
}
// We need a positive definite search radius to continue
if (search_radius_ == 0)
{
PCL_ERROR ("[pcl::%s::initCompute] Need a search radius different than 0!\n", getClassName ().c_str ());
Feature<PointInT, PointOutT>::deinitCompute ();
return (false);
}
if (k_ != 0)
{
PCL_ERROR ("[pcl::%s::initCompute] K-nearest neighbor search for spin images not implemented. Used a search radius instead!\n", getClassName ().c_str ());
Feature<PointInT, PointOutT>::deinitCompute ();
return (false);
}
// If the surface won't be set, make fake surface and fake surface normals
// if we wouldn't do it here, the following method would alarm that no surface normals is given
if (!surface_)
{
surface_ = input_;
fake_surface_ = true;
}
//if (fake_surface_ && !input_normals_)
// input_normals_ = normals_; // normals_ is set, as checked earlier
assert(!(use_custom_axis_ && use_custom_axes_cloud_));
if (!use_custom_axis_ && !use_custom_axes_cloud_ // use input normals as rotation axes
&& !input_normals_)
{
PCL_ERROR ("[pcl::%s::initCompute] No normals for input cloud were given!\n", getClassName ().c_str ());
// Cleanup
Feature<PointInT, PointOutT>::deinitCompute ();
return (false);
}
if ((is_angular_ || support_angle_cos_ > 0.0) // support angle is not bogus NOTE this is for randomly-flipped normals
&& !input_normals_)
{
PCL_ERROR ("[pcl::%s::initCompute] No normals for input cloud were given!\n", getClassName ().c_str ());
// Cleanup
Feature<PointInT, PointOutT>::deinitCompute ();
return (false);
}
if (use_custom_axes_cloud_
&& rotation_axes_cloud_->size () == input_->size ())
{
PCL_ERROR ("[pcl::%s::initCompute] Rotation axis cloud have different size from input!\n", getClassName ().c_str ());
// Cleanup
Feature<PointInT, PointOutT>::deinitCompute ();
return (false);
}
return (true);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::SpinImageEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
for (std::size_t i_input = 0; i_input < indices_->size (); ++i_input)
{
Eigen::ArrayXXd res = computeSiForPoint (indices_->at (i_input));
// Copy into the resultant cloud
for (Eigen::Index iRow = 0; iRow < res.rows () ; iRow++)
{
for (Eigen::Index iCol = 0; iCol < res.cols () ; iCol++)
{
output[i_input].histogram[ iRow*res.cols () + iCol ] = static_cast<float> (res (iRow, iCol));
}
}
}
}
#define PCL_INSTANTIATE_SpinImageEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::SpinImageEstimation<T,NT,OutT>;
#endif // PCL_FEATURES_IMPL_SPIN_IMAGE_H_
pcl::Feature
Feature represents the base feature class.
Definition: feature.h:107
pcl::Feature::feature_name_
std::string feature_name_
The feature name.
Definition: feature.h:223
pcl::Feature::deinitCompute
virtual bool deinitCompute()
This method should get called after ending the actual computation.
Definition: feature.hpp:181
pcl::PCLException
A base class for all pcl exceptions which inherits from std::runtime_error.
Definition: exceptions.h:64
pcl::PointCloud< PointOutT >
pcl::SpinImageEstimation::computeSiForPoint
Eigen::ArrayXXd computeSiForPoint(int index) const
Computes a spin-image for the point of the scan.
Definition: spin_image.hpp:67
pcl::SpinImageEstimation::SpinImageEstimation
SpinImageEstimation(unsigned int image_width=8, double support_angle_cos=0.0, unsigned int min_pts_neighb=0)
Constructs empty spin image estimator.
Definition: spin_image.hpp:52
pcl::SpinImageEstimation::initCompute
bool initCompute() override
initializes computations specific to spin-image.
Definition: spin_image.hpp:237
pcl::SpinImageEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the Spin Image descriptors at a set of points given by setInputWithNormals() using the surfa...
Definition: spin_image.hpp:322
point_types.h
Defines all the PCL implemented PointT point type structures.
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
M_PI
#define M_PI
Definition: pcl_macros.h:201