TransformationsImpl.cpp

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/*
 
Copyright (c) 2010--2012,
François Pomerleau and Stephane Magnenat, ASL, ETHZ, Switzerland
You can contact the authors at <f dot pomerleau at gmail dot com> and
<stephane at magnenat dot net>
 
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      derived from this software without specific prior written permission.
 
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*/
 
#include "TransformationsImpl.h"
#include "Functions.h"
 
#include <iostream>
 
using namespace PointMatcherSupport;
 
TransformationError::TransformationError(const std::string& reason):
        runtime_error(reason)
{}
 
template<typename T>
typename PointMatcher<T>::DataPoints TransformationsImpl<T>::RigidTransformation::compute(
        const DataPoints& input,
        const TransformationParameters& parameters) const
{
        assert(input.features.rows() == parameters.rows());
        assert(parameters.rows() == parameters.cols());
 
        const unsigned int nbRows = parameters.rows()-1;
        const unsigned int nbCols = parameters.cols()-1;
 
        const TransformationParameters R(parameters.topLeftCorner(nbRows, nbCols));
 
        if(this->checkParameters(parameters) == false)  
                throw TransformationError("RigidTransformation: Error, rotation matrix is not orthogonal.");    
        
        //DataPoints transformedCloud(input.featureLabels, input.descriptorLabels, input.timeLabels, input.features.cols());
        DataPoints transformedCloud = input;
        
        // Apply the transformation to features
        transformedCloud.features = parameters * input.features;
        
        // Apply the transformation to descriptors
        int row(0);
        const int descCols(input.descriptors.cols());
        for (size_t i = 0; i < input.descriptorLabels.size(); ++i)
        {
                const int span(input.descriptorLabels[i].span);
                const std::string& name(input.descriptorLabels[i].text);
                const BOOST_AUTO(inputDesc, input.descriptors.block(row, 0, span, descCols));
                BOOST_AUTO(outputDesc, transformedCloud.descriptors.block(row, 0, span, descCols));
                if (name == "normals" || name == "observationDirections")
                        outputDesc = R * inputDesc;
                
                row += span;
        }
 
        return transformedCloud;
}
 
template<typename T>
bool TransformationsImpl<T>::RigidTransformation::checkParameters(const TransformationParameters& parameters) const
{
        //FIXME: FP - should we put that as function argument?
        const T epsilon = 0.001;
        const unsigned int nbRows = parameters.rows()-1;
        const unsigned int nbCols = parameters.cols()-1;
 
        const TransformationParameters R(parameters.topLeftCorner(nbRows, nbCols));
        
        if(anyabs(1 - R.determinant()) > epsilon)
                return false;
        else
                return true;
 
}
 
template<typename T>
typename PointMatcher<T>::TransformationParameters TransformationsImpl<T>::RigidTransformation::correctParameters(const TransformationParameters& parameters) const
{
        TransformationParameters ortho = parameters;
        if(ortho.cols() == 4)
        {
                //const Eigen::Matrix<T, 3, 1> col0 = parameters.block(0, 0, 3, 1).normalized();
                const Eigen::Matrix<T, 3, 1> col1 = parameters.block(0, 1, 3, 1).normalized();
                const Eigen::Matrix<T, 3, 1> col2 = parameters.block(0, 2, 3, 1).normalized();
 
                const Eigen::Matrix<T, 3, 1> newCol0 = col1.cross(col2);
                const Eigen::Matrix<T, 3, 1> newCol1 = col2.cross(newCol0);
                const Eigen::Matrix<T, 3, 1> newCol2 = col2;
 
                ortho.block(0, 0, 3, 1) = newCol0;
                ortho.block(0, 1, 3, 1) = newCol1;
                ortho.block(0, 2, 3, 1) = newCol2;
        }
        else if(ortho.cols() == 3)
        {
                const T epsilon = 0.001;
 
                // R = [ a b]
                //     [-b a]
                if(anyabs(parameters(0,0) - parameters(1,1)) > epsilon || anyabs(parameters(1,0) + parameters(0,1)) > epsilon)
                {
                        throw TransformationError("RigidTransformation: Error, only proper rigid transformations are supported.");
                }
                
                // mean of a and b
                T a = (parameters(0,0) + parameters(1,1))/2;    
                T b = (-parameters(1,0) + parameters(0,1))/2;
                T sum = sqrt(pow(a,2) + pow(b,2));
 
                a = a/sum;
                b = b/sum;
 
                ortho(0,0) =  a; ortho(0,1) = b;
                ortho(1,0) = -b; ortho(1,1) = a;
        }
 
 
        return ortho;
}
 
template struct TransformationsImpl<float>::RigidTransformation;
template struct TransformationsImpl<double>::RigidTransformation;
 
template<typename T>
typename PointMatcher<T>::DataPoints TransformationsImpl<T>::SimilarityTransformation::compute(
        const DataPoints& input,
        const TransformationParameters& parameters) const
{
        assert(input.features.rows() == parameters.rows());
        assert(parameters.rows() == parameters.cols());
 
        const unsigned int nbRows = parameters.rows()-1;
        const unsigned int nbCols = parameters.cols()-1;
 
        const TransformationParameters R(parameters.topLeftCorner(nbRows, nbCols));
 
        if(this->checkParameters(parameters) == false)
                throw TransformationError("SimilarityTransformation: Error, invalid similarity transform.");
        
        //DataPoints transformedCloud(input.featureLabels, input.descriptorLabels, input.timeLabels, input.features.cols());
        DataPoints transformedCloud = input;
        
        // Apply the transformation to features
        transformedCloud.features = parameters * input.features;
        
        // Apply the transformation to descriptors
        int row(0);
        const int descCols(input.descriptors.cols());
        for (size_t i = 0; i < input.descriptorLabels.size(); ++i)
        {
                const int span(input.descriptorLabels[i].span);
                const std::string& name(input.descriptorLabels[i].text);
                const BOOST_AUTO(inputDesc, input.descriptors.block(row, 0, span, descCols));
                BOOST_AUTO(outputDesc, transformedCloud.descriptors.block(row, 0, span, descCols));
                if (name == "normals" || name == "observationDirections")
                        outputDesc = R * inputDesc;
                
                row += span;
        }
 
        return transformedCloud;
}
 
template<typename T>
bool TransformationsImpl<T>::SimilarityTransformation::checkParameters(const TransformationParameters& parameters) const
{
        //FIXME: FP - should we put that as function argument?
        return true;
}
 
template<typename T>
typename PointMatcher<T>::TransformationParameters TransformationsImpl<T>::SimilarityTransformation::correctParameters(const TransformationParameters& parameters) const
{
        return parameters;
}
 
template struct TransformationsImpl<float>::SimilarityTransformation;
template struct TransformationsImpl<double>::SimilarityTransformation;
 
template<typename T>
typename PointMatcher<T>::DataPoints TransformationsImpl<T>::PureTranslation::compute(const DataPoints& input,
                const TransformationParameters& parameters) const {
        assert(input.features.rows() == parameters.rows());
        assert(parameters.rows() == parameters.cols());
 
        if(this->checkParameters(parameters) == false)
                throw PointMatcherSupport::TransformationError("PureTranslation: Error, left part  not identity.");
 
        //DataPoints transformedCloud(input.featureLabels, input.descriptorLabels, input.features.cols());
        DataPoints transformedCloud = input;
 
        // Apply the transformation to features
        transformedCloud.features = parameters * input.features;
 
        return transformedCloud;
}
 
template<typename T>
typename PointMatcher<T>::TransformationParameters TransformationsImpl<T>::PureTranslation::correctParameters(
                const TransformationParameters& parameters) const {
        const int rows = parameters.rows();
        const int cols = parameters.cols();
 
        // make a copy of the parameters to perform corrections on
        TransformationParameters correctedParameters(parameters);
 
        // set the top left block to the identity matrix
        correctedParameters.block(0,0,rows-1,cols-1).setIdentity();
 
        // fix the bottom row
        correctedParameters.block(rows-1,0,1,cols-1).setZero();
        correctedParameters(rows-1,cols-1) = 1;
 
        return correctedParameters;
}
 
template<typename T>
bool TransformationsImpl<T>::PureTranslation::checkParameters(
                const TransformationParameters& parameters) const {
        const int rows = parameters.rows();
        const int cols = parameters.cols();
 
        // make a copy of parameters to perform the check
        TransformationParameters parameters_(parameters);
 
        // set the translation components of the transformation matrix to 0
        parameters_.block(0,cols-1,rows-1,1).setZero();
 
        // If we have the identity matrix, than this is indeed a pure translation
        if (parameters_.isApprox(TransformationParameters::Identity(rows,cols)))
                return true;
        else
                return false;
}
 
template struct TransformationsImpl<float>::PureTranslation;
template struct TransformationsImpl<double>::PureTranslation;