272 lines
9.3 KiB
C++
272 lines
9.3 KiB
C++
/*
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* crh_recognition.h
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*
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* Created on: Mar 30, 2012
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* Author: aitor
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*/
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#pragma once
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#include <pcl/common/common.h>
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#include <pcl/features/crh.h>
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#include <pcl/common/fft/kiss_fftr.h>
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#include <pcl/common/transforms.h>
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namespace pcl
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{
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/** \brief CRHAlignment uses two Camera Roll Histograms (CRH) to find the
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* roll rotation that aligns both views. See:
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* - CAD-Model Recognition and 6 DOF Pose Estimation
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* A. Aldoma, N. Blodow, D. Gossow, S. Gedikli, R.B. Rusu, M. Vincze and G. Bradski
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* ICCV 2011, 3D Representation and Recognition (3dRR11) workshop
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* Barcelona, Spain, (2011)
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*
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* \author Aitor Aldoma
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* \ingroup recognition
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*/
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template<typename PointT, int nbins_>
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class PCL_EXPORTS CRHAlignment
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{
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private:
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/** \brief Sorts peaks */
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struct peaks_ordering
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{
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bool
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operator() (std::pair<float, int> const& a, std::pair<float, int> const& b)
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{
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return a.first > b.first;
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}
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};
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using PointTPtr = typename pcl::PointCloud<PointT>::Ptr;
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/** \brief View of the model to be aligned to input_view_ */
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PointTPtr target_view_;
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/** \brief View of the input */
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PointTPtr input_view_;
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/** \brief Centroid of the model_view_ */
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Eigen::Vector3f centroid_target_;
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/** \brief Centroid of the input_view_ */
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Eigen::Vector3f centroid_input_;
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/** \brief transforms from model view to input view */
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std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > transforms_;
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/** \brief Allowed maximum number of peaks */
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int max_peaks_;
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/** \brief Quantile of peaks after sorting to be checked */
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float quantile_;
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/** \brief Threshold for a peak to be accepted.
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* If peak_i >= (max_peak * accept_threhsold_) => peak is accepted
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*/
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float accept_threshold_;
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/** \brief computes the transformation to the z-axis
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* \param[in] centroid
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* \param[out] trasnformation to z-axis
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*/
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void
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computeTransformToZAxes (Eigen::Vector3f & centroid, Eigen::Affine3f & transform)
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{
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Eigen::Vector3f plane_normal;
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plane_normal[0] = -centroid[0];
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plane_normal[1] = -centroid[1];
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plane_normal[2] = -centroid[2];
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Eigen::Vector3f z_vector = Eigen::Vector3f::UnitZ ();
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plane_normal.normalize ();
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Eigen::Vector3f axis = plane_normal.cross (z_vector);
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double rotation = -asin (axis.norm ());
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axis.normalize ();
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transform = Eigen::Affine3f (Eigen::AngleAxisf (static_cast<float>(rotation), axis));
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}
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/** \brief computes the roll transformation
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* \param[in] centroid input
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* \param[in] centroid view
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* \param[in] roll_angle
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* \param[out] roll transformation
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*/
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void
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computeRollTransform (Eigen::Vector3f & centroidInput, Eigen::Vector3f & centroidResult, double roll_angle, Eigen::Affine3f & final_trans)
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{
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Eigen::Affine3f transformInputToZ;
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computeTransformToZAxes (centroidInput, transformInputToZ);
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transformInputToZ = transformInputToZ.inverse ();
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Eigen::Affine3f transformRoll (Eigen::AngleAxisf (-static_cast<float>(roll_angle * M_PI / 180), Eigen::Vector3f::UnitZ ()));
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Eigen::Affine3f transformDBResultToZ;
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computeTransformToZAxes (centroidResult, transformDBResultToZ);
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final_trans = transformInputToZ * transformRoll * transformDBResultToZ;
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}
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public:
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/** \brief Constructor. */
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CRHAlignment() {
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max_peaks_ = 5;
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quantile_ = 0.2f;
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accept_threshold_ = 0.8f;
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}
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/** \brief returns the computed transformations
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* \param[out] transforms transformations
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*/
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void getTransforms(std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > & transforms) {
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transforms = transforms_;
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}
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/** \brief sets model and input views
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* \param[in] input_view
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* \param[in] target_view
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*/
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void
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setInputAndTargetView (PointTPtr & input_view, PointTPtr & target_view)
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{
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target_view_ = target_view;
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input_view_ = input_view;
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}
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/** \brief sets model and input centroids
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* \param[in] c1 model view centroid
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* \param[in] c2 input view centroid
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*/
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void
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setInputAndTargetCentroids (Eigen::Vector3f & c1, Eigen::Vector3f & c2)
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{
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centroid_target_ = c2;
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centroid_input_ = c1;
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}
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/** \brief Computes the transformation aligning model to input
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* \param[in] input_ftt CRH histogram of the input cloud
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* \param[in] target_ftt CRH histogram of the target cloud
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*/
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void
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align (pcl::PointCloud<pcl::Histogram<nbins_> > & input_ftt, pcl::PointCloud<pcl::Histogram<nbins_> > & target_ftt)
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{
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transforms_.clear(); //clear from last round...
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std::vector<float> peaks;
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computeRollAngle (input_ftt, target_ftt, peaks);
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//if the number of peaks is too big, we should try to reduce using siluette matching
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for (const float &peak : peaks)
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{
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Eigen::Affine3f rollToRot;
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computeRollTransform (centroid_input_, centroid_target_, peak, rollToRot);
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Eigen::Matrix4f rollHomMatrix = Eigen::Matrix4f ();
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rollHomMatrix.setIdentity (4, 4);
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rollHomMatrix = rollToRot.matrix ();
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Eigen::Matrix4f translation2;
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translation2.setIdentity (4, 4);
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Eigen::Vector3f centr = rollToRot * centroid_target_;
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translation2 (0, 3) = centroid_input_[0] - centr[0];
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translation2 (1, 3) = centroid_input_[1] - centr[1];
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translation2 (2, 3) = centroid_input_[2] - centr[2];
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Eigen::Matrix4f resultHom (translation2 * rollHomMatrix);
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transforms_.push_back(resultHom.inverse());
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}
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}
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/** \brief Computes the roll angle that aligns input to model.
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* \param[in] input_ftt CRH histogram of the input cloud
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* \param[in] target_ftt CRH histogram of the target cloud
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* \param[out] peaks Vector containing angles where the histograms correlate
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*/
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void
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computeRollAngle (pcl::PointCloud<pcl::Histogram<nbins_> > & input_ftt, pcl::PointCloud<pcl::Histogram<nbins_> > & target_ftt,
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std::vector<float> & peaks)
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{
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pcl::PointCloud<pcl::Histogram<nbins_> > input_ftt_negate (input_ftt);
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for (int i = 2; i < (nbins_); i += 2)
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input_ftt_negate[0].histogram[i] = -input_ftt_negate[0].histogram[i];
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int nr_bins_after_padding = 180;
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int peak_distance = 5;
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int cutoff = nbins_ - 1;
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kiss_fft_cpx * multAB = new kiss_fft_cpx[nr_bins_after_padding];
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for (int i = 0; i < nr_bins_after_padding; i++)
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multAB[i].r = multAB[i].i = 0.f;
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int k = 0;
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multAB[k].r = input_ftt_negate[0].histogram[0] * target_ftt[0].histogram[0];
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k++;
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float a, b, c, d;
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for (int i = 1; i < cutoff; i += 2, k++)
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{
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a = input_ftt_negate[0].histogram[i];
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b = input_ftt_negate[0].histogram[i + 1];
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c = target_ftt[0].histogram[i];
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d = target_ftt[0].histogram[i + 1];
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multAB[k].r = a * c - b * d;
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multAB[k].i = b * c + a * d;
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float tmp = std::sqrt (multAB[k].r * multAB[k].r + multAB[k].i * multAB[k].i);
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multAB[k].r /= tmp;
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multAB[k].i /= tmp;
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}
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multAB[nbins_ - 1].r = input_ftt_negate[0].histogram[nbins_ - 1] * target_ftt[0].histogram[nbins_ - 1];
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kiss_fft_cfg mycfg = kiss_fft_alloc (nr_bins_after_padding, 1, nullptr, nullptr);
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kiss_fft_cpx * invAB = new kiss_fft_cpx[nr_bins_after_padding];
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kiss_fft (mycfg, multAB, invAB);
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std::vector < std::pair<float, int> > scored_peaks (nr_bins_after_padding);
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for (int i = 0; i < nr_bins_after_padding; i++)
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scored_peaks[i] = std::make_pair (invAB[i].r, i);
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std::sort (scored_peaks.begin (), scored_peaks.end (), peaks_ordering ());
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std::vector<int> peaks_indices;
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std::vector<float> peaks_values;
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// we look at the upper quantile_
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float quantile = quantile_;
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int max_inserted= max_peaks_;
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int inserted=0;
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bool stop=false;
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for (int i = 0; (i < static_cast<int> (quantile * static_cast<float> (nr_bins_after_padding))) && !stop; i++)
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{
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if (scored_peaks[i].first >= scored_peaks[0].first * accept_threshold_)
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{
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bool insert = true;
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for (const int &peaks_index : peaks_indices)
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{ //check inserted peaks, first pick always inserted
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if ((std::abs (peaks_index - scored_peaks[i].second) <= peak_distance) ||
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(std::abs (peaks_index - (scored_peaks[i].second - nr_bins_after_padding)) <= peak_distance))
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{
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insert = false;
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break;
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}
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}
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if (insert)
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{
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peaks_indices.push_back (scored_peaks[i].second);
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peaks_values.push_back (scored_peaks[i].first);
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peaks.push_back (static_cast<float> (scored_peaks[i].second * (360 / nr_bins_after_padding)));
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inserted++;
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if(inserted >= max_inserted)
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stop = true;
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}
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}
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}
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}
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};
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}
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