sac_model_stick.hpp

/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2009, 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: sac_model_line.hpp 2328 2011-08-31 08:11:00Z rusu $
 *
 */
 
#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
 
#include <pcl/sample_consensus/sac_model_stick.h>
#include <pcl/common/centroid.h>
#include <pcl/common/concatenate.h>
#include <pcl/common/eigen.h> // for eigen33
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::isSampleGood (const Indices &samples) const
{
  if (samples.size () != sample_size_)
  {
    PCL_ERROR ("[pcl::SampleConsensusModelStick::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
    return (false);
  }
  if (
      ((*input_)[samples[0]].x != (*input_)[samples[1]].x)
    &&
      ((*input_)[samples[0]].y != (*input_)[samples[1]].y)
    &&
      ((*input_)[samples[0]].z != (*input_)[samples[1]].z))
  {
    return (true);
  }
 
  return (false);
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::computeModelCoefficients (
      const Indices &samples, Eigen::VectorXf &model_coefficients) const
{
  // Need 2 samples
  if (samples.size () != sample_size_)
  {
    PCL_ERROR ("[pcl::SampleConsensusModelStick::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
    return (false);
  }
 
  model_coefficients.resize (model_size_);
  model_coefficients[0] = (*input_)[samples[0]].x;
  model_coefficients[1] = (*input_)[samples[0]].y;
  model_coefficients[2] = (*input_)[samples[0]].z;
 
  model_coefficients[3] = (*input_)[samples[1]].x;
  model_coefficients[4] = (*input_)[samples[1]].y;
  model_coefficients[5] = (*input_)[samples[1]].z;
 
//  model_coefficients[3] = (*input_)[samples[1]].x - model_coefficients[0];
//  model_coefficients[4] = (*input_)[samples[1]].y - model_coefficients[1];
//  model_coefficients[5] = (*input_)[samples[1]].z - model_coefficients[2];
 
//  model_coefficients.template segment<3> (3).normalize ();
  // We don't care about model_coefficients[6] which is the width (radius) of the stick
 
  return (true);
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::getDistancesToModel (
      const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
  {
    PCL_ERROR ("[pcl::SampleConsensusModelStick::getDistancesToModel] Given model is invalid!\n");
    return;
  }
 
  float sqr_threshold = static_cast<float> (radius_max_ * radius_max_);
  distances.resize (indices_->size ());
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
  line_dir.normalize ();
 
  // Iterate through the 3d points and calculate the distances from them to the line
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the line
    // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
    float sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
 
    if (sqr_distance < sqr_threshold)
    {
      // Need to estimate sqrt here to keep MSAC and friends general
      distances[i] = sqrt (sqr_distance);
    }
    else
    {
      // Penalize outliers by doubling the distance
      distances[i] = 2 * sqrt (sqr_distance);
    }
  }
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::selectWithinDistance (
      const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
  {
    PCL_ERROR ("[pcl::SampleConsensusModelStick::selectWithinDistance] Given model is invalid!\n");
    return;
  }
 
  float sqr_threshold = static_cast<float> (threshold * threshold);
 
  inliers.clear ();
  error_sqr_dists_.clear ();
  inliers.reserve (indices_->size ());
  error_sqr_dists_.reserve (indices_->size ());
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt1 (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
  Eigen::Vector4f line_pt2 (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
  Eigen::Vector4f line_dir = line_pt2 - line_pt1;
  //Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
  //Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
  line_dir.normalize ();
  //float norm = line_dir.squaredNorm ();
 
  // Iterate through the 3d points and calculate the distances from them to the line
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the line
    // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
    Eigen::Vector4f dir = (*input_)[(*indices_)[i]].getVector4fMap () - line_pt1;
    //float u = dir.dot (line_dir);
 
    // If the point falls outside of the segment, ignore it
    //if (u < 0.0f || u > 1.0f)
    //  continue;
 
    float sqr_distance = dir.cross3 (line_dir).squaredNorm ();
    if (sqr_distance < sqr_threshold)
    {
      // Returns the indices of the points whose squared distances are smaller than the threshold
      inliers.push_back ((*indices_)[i]);
      error_sqr_dists_.push_back (static_cast<double> (sqr_distance));
    }
  }
}
 
///////////////////////////////////////////////////////////////////////////
template <typename PointT> std::size_t
pcl::SampleConsensusModelStick<PointT>::countWithinDistance (
      const Eigen::VectorXf &model_coefficients, const double threshold) const
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
  {
    PCL_ERROR ("[pcl::SampleConsensusModelStick::countWithinDistance] Given model is invalid!\n");
    return (0);
  }
 
  float sqr_threshold = static_cast<float> (threshold * threshold);
 
  std::size_t nr_i = 0, nr_o = 0;
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt1 (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
  Eigen::Vector4f line_pt2 (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
  Eigen::Vector4f line_dir = line_pt2 - line_pt1;
  line_dir.normalize ();
 
  //Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
  //Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
 
  // Iterate through the 3d points and calculate the distances from them to the line
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the line
    // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
    Eigen::Vector4f dir = (*input_)[(*indices_)[i]].getVector4fMap () - line_pt1;
    //float u = dir.dot (line_dir);
 
    // If the point falls outside of the segment, ignore it
    //if (u < 0.0f || u > 1.0f)
    //  continue;
 
    float sqr_distance = dir.cross3 (line_dir).squaredNorm ();
    // Use a larger threshold (4 times the radius) to get more points in
    if (sqr_distance < sqr_threshold)
    {
      nr_i++;
    }
    else if (sqr_distance < 4.0f * sqr_threshold)
    {
      nr_o++;
    }
  }
 
  return (nr_i <= nr_o ? 0 : nr_i - nr_o);
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::optimizeModelCoefficients (
      const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
  {
    optimized_coefficients = model_coefficients;
    return;
  }
 
  // Need more than the minimum sample size to make a difference
  if (inliers.size () <= sample_size_)
  {
    PCL_ERROR ("[pcl::SampleConsensusModelStick::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
    optimized_coefficients = model_coefficients;
    return;
  }
 
  optimized_coefficients.resize (model_size_);
 
  // Compute the 3x3 covariance matrix
  Eigen::Vector4f centroid;
  Eigen::Matrix3f covariance_matrix;
 
  computeMeanAndCovarianceMatrix (*input_, inliers, covariance_matrix, centroid);
 
  optimized_coefficients[0] = centroid[0];
  optimized_coefficients[1] = centroid[1];
  optimized_coefficients[2] = centroid[2];
 
  // Extract the eigenvalues and eigenvectors
  Eigen::Vector3f eigen_values;
  Eigen::Vector3f eigen_vector;
  pcl::eigen33 (covariance_matrix, eigen_values);
  pcl::computeCorrespondingEigenVector (covariance_matrix, eigen_values [2], eigen_vector);
 
  optimized_coefficients.template segment<3> (3).matrix () = eigen_vector;
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::projectPoints (
      const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
{
  // Needs a valid model coefficients
  if (!isModelValid (model_coefficients))
  {
    PCL_ERROR ("[pcl::SampleConsensusModelStick::projectPoints] Given model is invalid!\n");
    return;
  }
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
 
  projected_points.header = input_->header;
  projected_points.is_dense = input_->is_dense;
 
  // Copy all the data fields from the input cloud to the projected one?
  if (copy_data_fields)
  {
    // Allocate enough space and copy the basics
    projected_points.resize (input_->size ());
    projected_points.width    = input_->width;
    projected_points.height   = input_->height;
 
    using FieldList = typename pcl::traits::fieldList<PointT>::type;
    // Iterate over each point
    for (std::size_t i = 0; i < projected_points.size (); ++i)
    {
      // Iterate over each dimension
      pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
    }
 
    // Iterate through the 3d points and calculate the distances from them to the line
    for (const auto &inlier : inliers)
    {
      Eigen::Vector4f pt ((*input_)[inlier].x, (*input_)[inlier].y, (*input_)[inlier].z, 0.0f);
      // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
      float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
 
      Eigen::Vector4f pp = line_pt + k * line_dir;
      // Calculate the projection of the point on the line (pointProj = A + k * B)
      projected_points[inlier].x = pp[0];
      projected_points[inlier].y = pp[1];
      projected_points[inlier].z = pp[2];
    }
  }
  else
  {
    // Allocate enough space and copy the basics
    projected_points.resize (inliers.size ());
    projected_points.width    = inliers.size ();
    projected_points.height   = 1;
 
    using FieldList = typename pcl::traits::fieldList<PointT>::type;
    // Iterate over each point
    for (std::size_t i = 0; i < inliers.size (); ++i)
    {
      // Iterate over each dimension
      pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
    }
 
    // Iterate through the 3d points and calculate the distances from them to the line
    for (std::size_t i = 0; i < inliers.size (); ++i)
    {
      Eigen::Vector4f pt ((*input_)[inliers[i]].x, (*input_)[inliers[i]].y, (*input_)[inliers[i]].z, 0.0f);
      // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
      float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
 
      Eigen::Vector4f pp = line_pt + k * line_dir;
      // Calculate the projection of the point on the line (pointProj = A + k * B)
      projected_points[i].x = pp[0];
      projected_points[i].y = pp[1];
      projected_points[i].z = pp[2];
    }
  }
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::doSamplesVerifyModel (
      const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
  {
    PCL_ERROR ("[pcl::SampleConsensusModelStick::doSamplesVerifyModel] Given model is invalid!\n");
    return (false);
  }
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
  Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0.0f);
  //Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
  line_dir.normalize ();
 
  float sqr_threshold = static_cast<float> (threshold * threshold);
  // Iterate through the 3d points and calculate the distances from them to the line
  for (const auto &index : indices)
  {
    // Calculate the distance from the point to the line
    // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
    if ((line_pt - (*input_)[index].getVector4fMap ()).cross3 (line_dir).squaredNorm () > sqr_threshold)
    {
      return (false);
    }
  }
 
  return (true);
}
 
#define PCL_INSTANTIATE_SampleConsensusModelStick(T) template class PCL_EXPORTS pcl::SampleConsensusModelStick<T>;
 
#endif    // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_