BayesFilter.cpp

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/*
Copyright (c) 2010-2014, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
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 Universite de Sherbrooke 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
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*/

#include "BayesFilter.h"
#include "rtabmap/core/Memory.h"
#include "rtabmap/core/Signature.h"
#include "rtabmap/core/Parameters.h"
#include <iostream>

#include "rtabmap/utilite/UtiLite.h"

namespace rtabmap {

BayesFilter::BayesFilter(const ParametersMap & parameters) :
        _virtualPlacePrior(Parameters::defaultBayesVirtualPlacePriorThr()),
        _fullPredictionUpdate(Parameters::defaultBayesFullPredictionUpdate()),
        _totalPredictionLCValues(0.0f)
{
        this->setPredictionLC(Parameters::defaultBayesPredictionLC());
        this->parseParameters(parameters);
}

BayesFilter::~BayesFilter() {
}

void BayesFilter::parseParameters(const ParametersMap & parameters)
{
        ParametersMap::const_iterator iter;
        if((iter=parameters.find(Parameters::kBayesPredictionLC())) != parameters.end())
        {
                this->setPredictionLC((*iter).second);
        }
        Parameters::parse(parameters, Parameters::kBayesVirtualPlacePriorThr(), _virtualPlacePrior);
        Parameters::parse(parameters, Parameters::kBayesFullPredictionUpdate(), _fullPredictionUpdate);

        UASSERT(_virtualPlacePrior >= 0 && _virtualPlacePrior <= 1.0f);
}

// format = {Virtual place, Loop closure, level1, level2, l3, l4...}
void BayesFilter::setPredictionLC(const std::string & prediction)
{
        std::list<std::string> strValues = uSplit(prediction, ' ');
        if(strValues.size() < 2)
        {
                UERROR("The number of values < 2 (prediction=\"%s\")", prediction.c_str());
        }
        else
        {
                std::vector<double> tmpValues(strValues.size());
                int i=0;
                bool valid = true;
                for(std::list<std::string>::iterator iter = strValues.begin(); iter!=strValues.end(); ++iter)
                {
                        tmpValues[i] = uStr2Float((*iter).c_str());
                        //UINFO("%d=%e", i, tmpValues[i]);
                        if(tmpValues[i] < 0.0 || tmpValues[i]>1.0)
                        {
                                valid = false;
                                break;
                        }
                        ++i;
                }

                if(!valid)
                {
                        UERROR("The prediction is not valid (values must be between >0 && <=1) prediction=\"%s\"", prediction.c_str());
                }
                else
                {
                        _predictionLC = tmpValues;
                }
        }
        _totalPredictionLCValues = 0.0f;
        for(unsigned int j=0; j<_predictionLC.size(); ++j)
        {
                _totalPredictionLCValues += _predictionLC[j];
        }
}

const std::vector<double> & BayesFilter::getPredictionLC() const
{
        // {Vp, Lc, l1, l2, l3, l4...}
        return _predictionLC;
}

std::string BayesFilter::getPredictionLCStr() const
{
        std::string values;
        for(unsigned int i=0; i<_predictionLC.size(); ++i)
        {
                values.append(uNumber2Str(_predictionLC[i]));
                if(i+1 < _predictionLC.size())
                {
                        values.append(" ");
                }
        }
        return values;
}

void BayesFilter::reset()
{
        _posterior.clear();
        _prediction = cv::Mat();
}

const std::map<int, float> & BayesFilter::computePosterior(const Memory * memory, const std::map<int, float> & likelihood)
{
        ULOGGER_DEBUG("");

        if(!memory)
        {
                ULOGGER_ERROR("Memory is Null!");
                return _posterior;
        }

        if(!likelihood.size())
        {
                ULOGGER_ERROR("likelihood is empty!");
                return _posterior;
        }

        if(_predictionLC.size() < 2)
        {
                ULOGGER_ERROR("Prediction is not valid!");
                return _posterior;
        }

        UTimer timer;
        timer.start();

        cv::Mat prior;
        cv::Mat posterior;

        float sum = 0;
        int j=0;
        // Recursive Bayes estimation...
        // STEP 1 - Prediction : Prior*lastPosterior
        _prediction = this->generatePrediction(memory, uKeys(likelihood));

        ULOGGER_DEBUG("STEP1-generate prior=%fs, rows=%d, cols=%d", timer.ticks(), _prediction.rows, _prediction.cols);
        //std::cout << "Prediction=" << _prediction << std::endl;

        // Adjust the last posterior if some images were
        // reactivated or removed from the working memory
        posterior = cv::Mat(likelihood.size(), 1, CV_32FC1);
        this->updatePosterior(memory, uKeys(likelihood));
        j=0;
        for(std::map<int, float>::const_iterator i=_posterior.begin(); i!= _posterior.end(); ++i)
        {
                ((float*)posterior.data)[j++] = (*i).second;
        }
        ULOGGER_DEBUG("STEP1-update posterior=%fs, posterior=%d, _posterior size=%d", posterior.rows, _posterior.size());
        //std::cout << "LastPosterior=" << posterior << std::endl;

        // Multiply prediction matrix with the last posterior
        // (m,m) X (m,1) = (m,1)
        prior = _prediction * posterior;
        ULOGGER_DEBUG("STEP1-matrix mult time=%fs", timer.ticks());
        //std::cout << "ResultingPrior=" << prior << std::endl;

        ULOGGER_DEBUG("STEP1-matrix mult time=%fs", timer.ticks());
        std::vector<float> likelihoodValues = uValues(likelihood);
        //std::cout << "Likelihood=" << cv::Mat(likelihoodValues) << std::endl;

        // STEP 2 - Update : Multiply with observations (likelihood)
        j=0;
        for(std::map<int, float>::const_iterator i=likelihood.begin(); i!= likelihood.end(); ++i)
        {
                std::map<int, float>::iterator p =_posterior.find((*i).first);
                if(p!= _posterior.end())
                {
                        (*p).second = (*i).second * ((float*)prior.data)[j++];
                        sum+=(*p).second;
                }
                else
                {
                        ULOGGER_ERROR("Problem1! can't find id=%d", (*i).first);
                }
        }
        ULOGGER_DEBUG("STEP2-likelihood time=%fs", timer.ticks());
        //std::cout << "Posterior (before normalization)=" << _posterior << std::endl;

        // Normalize
        ULOGGER_DEBUG("sum=%f", sum);
        if(sum != 0)
        {
                for(std::map<int, float>::iterator i=_posterior.begin(); i!= _posterior.end(); ++i)
                {
                        (*i).second /= sum;
                }
        }
        ULOGGER_DEBUG("normalize time=%fs", timer.ticks());
        //std::cout << "Posterior=" << _posterior << std::endl;

        return _posterior;
}

cv::Mat BayesFilter::generatePrediction(const Memory * memory, const std::vector<int> & ids) const
{
        if(!_fullPredictionUpdate && !_prediction.empty())
        {
                return updatePrediction(_prediction, memory, uKeys(_posterior), ids);
        }
        UDEBUG("");

        UASSERT(memory &&
                   _predictionLC.size() >= 2 &&
                   ids.size());

        UTimer timer;
        timer.start();
        UTimer timerGlobal;
        timerGlobal.start();

        std::map<int, int> idToIndexMap;
        for(unsigned int i=0; i<ids.size(); ++i)
        {
                UASSERT_MSG(ids[i] != 0, "Signature id is null ?!?");
                idToIndexMap.insert(idToIndexMap.end(), std::make_pair(ids[i], i));
        }

        //int rows = prediction.rows;
        cv::Mat prediction = cv::Mat::zeros(ids.size(), ids.size(), CV_32FC1);
        int cols = prediction.cols;

        // Each prior is a column vector
        UDEBUG("_predictionLC.size()=%d",_predictionLC.size());
        std::set<int> idsDone;

        for(unsigned int i=0; i<ids.size(); ++i)
        {
                if(idsDone.find(ids[i]) == idsDone.end())
                {
                        if(ids[i] > 0)
                        {
                                // Set high values (gaussians curves) to loop closure neighbors

                                // ADD prob for each neighbors
                                std::map<int, int> neighbors = memory->getNeighborsId(ids[i], _predictionLC.size()-1, 0);
                                std::list<int> idsLoopMargin;
                                //filter neighbors in STM
                                for(std::map<int, int>::iterator iter=neighbors.begin(); iter!=neighbors.end();)
                                {
                                        if(memory->isInSTM(iter->first))
                                        {
                                                neighbors.erase(iter++);
                                        }
                                        else
                                        {
                                                if(iter->second == 0)
                                                {
                                                        idsLoopMargin.push_back(iter->first);
                                                }
                                                ++iter;
                                        }
                                }

                                // should at least have 1 id in idsMarginLoop
                                if(idsLoopMargin.size() == 0)
                                {
                                        UFATAL("No 0 margin neighbor for signature %d !?!?", ids[i]);
                                }

                                // same neighbor tree for loop signatures (margin = 0)
                                for(std::list<int>::iterator iter = idsLoopMargin.begin(); iter!=idsLoopMargin.end(); ++iter)
                                {
                                        float sum = 0.0f; // sum values added
                                        sum += this->addNeighborProb(prediction, i, neighbors, idToIndexMap);
                                        idsDone.insert(*iter);
                                        this->normalize(prediction, i, sum, ids[0]<0);
                                }
                        }
                        else
                        {
                                // Set the virtual place prior
                                if(_virtualPlacePrior > 0)
                                {
                                        if(cols>1) // The first must be the virtual place
                                        {
                                                ((float*)prediction.data)[i] = _virtualPlacePrior;
                                                float val = (1.0-_virtualPlacePrior)/(cols-1);
                                                for(int j=1; j<cols; j++)
                                                {
                                                        ((float*)prediction.data)[i + j*cols] = val;
                                                }
                                        }
                                        else if(cols>0)
                                        {
                                                ((float*)prediction.data)[i] = 1;
                                        }
                                }
                                else
                                {
                                        // Only for some tests...
                                        // when _virtualPlacePrior=0, set all priors to the same value
                                        if(cols>1)
                                        {
                                                float val = 1.0/cols;
                                                for(int j=0; j<cols; j++)
                                                {
                                                        ((float*)prediction.data)[i + j*cols] = val;
                                                }
                                        }
                                        else if(cols>0)
                                        {
                                                ((float*)prediction.data)[i] = 1;
                                        }
                                }
                        }
                }
        }

        ULOGGER_DEBUG("time = %fs", timerGlobal.ticks());

        return prediction;
}

void BayesFilter::normalize(cv::Mat & prediction, unsigned int index, float addedProbabilitiesSum, bool virtualPlaceUsed) const
{
        UASSERT(index < (unsigned int)prediction.rows && index < (unsigned int)prediction.cols);

        int cols = prediction.cols;
        // ADD values of not found neighbors to loop closure
        if(addedProbabilitiesSum < _totalPredictionLCValues-_predictionLC[0])
        {
                float delta = _totalPredictionLCValues-_predictionLC[0]-addedProbabilitiesSum;
                ((float*)prediction.data)[index + index*cols] += delta;
                addedProbabilitiesSum+=delta;
        }

        float allOtherPlacesValue = 0;
        if(_totalPredictionLCValues < 1)
        {
                allOtherPlacesValue = 1.0f - _totalPredictionLCValues;
        }

        // Set all loop events to small values according to the model
        if(allOtherPlacesValue > 0 && cols>1)
        {
                float value = allOtherPlacesValue / float(cols - 1);
                for(int j=virtualPlaceUsed?1:0; j<cols; ++j)
                {
                        if(((float*)prediction.data)[index + j*cols] == 0)
                        {
                                ((float*)prediction.data)[index + j*cols] = value;
                                addedProbabilitiesSum += ((float*)prediction.data)[index + j*cols];
                        }
                }
        }

        //normalize this row
        float maxNorm = 1 - (virtualPlaceUsed?_predictionLC[0]:0); // 1 - virtual place probability
        if(addedProbabilitiesSum<maxNorm-0.0001 || addedProbabilitiesSum>maxNorm+0.0001)
        {
                for(int j=virtualPlaceUsed?1:0; j<cols; ++j)
                {
                        ((float*)prediction.data)[index + j*cols] *= maxNorm / addedProbabilitiesSum;
                }
                addedProbabilitiesSum = maxNorm;
        }

        // ADD virtual place prob
        if(virtualPlaceUsed)
        {
                ((float*)prediction.data)[index] = _predictionLC[0];
                addedProbabilitiesSum += ((float*)prediction.data)[index];
        }

        //debug
        //for(int j=0; j<cols; ++j)
        //{
        //      ULOGGER_DEBUG("test col=%d = %f", i, prediction.data.fl[i + j*cols]);
        //}

        if(addedProbabilitiesSum<0.99 || addedProbabilitiesSum > 1.01)
        {
                UWARN("Prediction is not normalized sum=%f", addedProbabilitiesSum);
        }
}

cv::Mat BayesFilter::updatePrediction(const cv::Mat & oldPrediction,
                const Memory * memory,
                const std::vector<int> & oldIds,
                const std::vector<int> & newIds) const
{
        UTimer timer;
        UDEBUG("");

        UASSERT(memory &&
                oldIds.size() &&
                newIds.size() &&
                oldIds.size() == (unsigned int)oldPrediction.cols &&
                oldIds.size() == (unsigned int)oldPrediction.rows);

        cv::Mat prediction = cv::Mat::zeros(newIds.size(), newIds.size(), CV_32FC1);

        // Create id to index maps
        std::map<int, int> oldIdToIndexMap;
        std::map<int, int> newIdToIndexMap;
        for(unsigned int i=0; i<oldIds.size() || i<newIds.size(); ++i)
        {
                if(i<oldIds.size())
                {
                        UASSERT(oldIds[i]);
                        oldIdToIndexMap.insert(oldIdToIndexMap.end(), std::make_pair(oldIds[i], i));
                        //UDEBUG("oldIdToIndexMap[%d] = %d", oldIds[i], i);
                }
                if(i<newIds.size())
                {
                        UASSERT(newIds[i]);
                        newIdToIndexMap.insert(newIdToIndexMap.end(), std::make_pair(newIds[i], i));
                        //UDEBUG("newIdToIndexMap[%d] = %d", newIds[i], i);
                }
        }
        UDEBUG("time creating id-index maps = %fs", timer.restart());

        //Get removed ids
        std::set<int> removedIds;
        for(unsigned int i=0; i<oldIds.size(); ++i)
        {
                if(!uContains(newIdToIndexMap, oldIds[i]))
                {
                        removedIds.insert(removedIds.end(), oldIds[i]);
                        UDEBUG("removed id=%d at oldIndex=%d", oldIds[i], i);
                }
        }
        UDEBUG("time getting removed ids = %fs", timer.restart());

        int added = 0;
        // get ids to update
        std::set<int> idsToUpdate;
        for(unsigned int i=0; i<oldIds.size() || i<newIds.size(); ++i)
        {
                if(i<oldIds.size())
                {
                        if(removedIds.find(oldIds[i]) != removedIds.end())
                        {
                                unsigned int cols = oldPrediction.cols;
                                for(unsigned int j=0; j<cols; ++j)
                                {
                                        if(((const float *)oldPrediction.data)[i + j*cols] != 0.0f &&
                                           j!=i &&
                                           removedIds.find(oldIds[j]) == removedIds.end())
                                        {
                                                //UDEBUG("to update id=%d from id=%d removed (value=%f)", oldIds[j], oldIds[i], ((const float *)oldPrediction.data)[i + j*cols]);
                                                idsToUpdate.insert(oldIds[j]);
                                        }
                                }
                        }
                }
                if(i<newIds.size() && !uContains(oldIdToIndexMap,newIds[i]))
                {
                        std::map<int, int> neighbors = memory->getNeighborsId(newIds[i], _predictionLC.size()-1, 0);
                        float sum = this->addNeighborProb(prediction, i, neighbors, newIdToIndexMap);
                        this->normalize(prediction, i, sum, newIds[0]<0);
                        ++added;
                        for(std::map<int,int>::iterator iter=neighbors.begin(); iter!=neighbors.end(); ++iter)
                        {
                                if(uContains(oldIdToIndexMap, iter->first) &&
                                   removedIds.find(iter->first) == removedIds.end())
                                {
                                        idsToUpdate.insert(iter->first);
                                }
                        }
                }
        }
        UDEBUG("time getting ids to update = %fs", timer.restart());

        // update modified/added ids
        int modified = 0;
        for(std::set<int>::iterator iter = idsToUpdate.begin(); iter!=idsToUpdate.end(); ++iter)
        {
                std::map<int, int> neighbors = memory->getNeighborsId(*iter, _predictionLC.size()-1, 0);
                int index = newIdToIndexMap.at(*iter);
                float sum = this->addNeighborProb(prediction, index, neighbors, newIdToIndexMap);
                this->normalize(prediction, index, sum, newIds[0]<0);
                ++modified;
        }
        UDEBUG("time updating modified/added ids = %fs", timer.restart());

        //UDEBUG("oldIds.size()=%d, oldPrediction.cols=%d, oldPrediction.rows=%d", oldIds.size(), oldPrediction.cols, oldPrediction.rows);
        //UDEBUG("newIdToIndexMap.size()=%d, prediction.cols=%d, prediction.rows=%d", newIdToIndexMap.size(), prediction.cols, prediction.rows);
        // copy not changed probabilities
        int copied = 0;
        for(unsigned int i=0; i<oldIds.size(); ++i)
        {
                if(oldIds[i]>0 && removedIds.find(oldIds[i]) == removedIds.end() && idsToUpdate.find(oldIds[i]) == idsToUpdate.end())
                {
                        for(int j=0; j<oldPrediction.cols; ++j)
                        {
                                if(removedIds.find(oldIds[j]) == removedIds.end() && ((const float *)oldPrediction.data)[i + j*oldPrediction.cols] != 0.0f)
                                {
                                        //UDEBUG("i=%d, j=%d", i, j);
                                        //UDEBUG("oldIds[i]=%d, oldIds[j]=%d", oldIds[i], oldIds[j]);
                                        //UDEBUG("newIdToIndexMap.at(oldIds[i])=%d", newIdToIndexMap.at(oldIds[i]));
                                        //UDEBUG("newIdToIndexMap.at(oldIds[j])=%d", newIdToIndexMap.at(oldIds[j]));
                                        ((float *)prediction.data)[newIdToIndexMap.at(oldIds[i]) + newIdToIndexMap.at(oldIds[j])*prediction.cols] = ((const float *)oldPrediction.data)[i + j*oldPrediction.cols];
                                }
                        }
                        ++copied;
                }
        }
        UDEBUG("time copying = %fs", timer.restart());

        //update virtual place
        if(newIds[0] < 0)
        {
                if(prediction.cols>1) // The first must be the virtual place
                {
                        ((float*)prediction.data)[0] = _virtualPlacePrior;
                        float val = (1.0-_virtualPlacePrior)/(prediction.cols-1);
                        for(int j=1; j<prediction.cols; j++)
                        {
                                ((float*)prediction.data)[j*prediction.cols] = val;
                        }
                }
                else if(prediction.cols>0)
                {
                        ((float*)prediction.data)[0] = 1;
                }
        }
        UDEBUG("time updating virtual place = %fs", timer.restart());

        UDEBUG("Modified=%d, Added=%d, Copied=%d", modified, added, copied);
        return prediction;
}

void BayesFilter::updatePosterior(const Memory * memory, const std::vector<int> & likelihoodIds)
{
        ULOGGER_DEBUG("");
        std::map<int, float> newPosterior;
        for(std::vector<int>::const_iterator i=likelihoodIds.begin(); i != likelihoodIds.end(); ++i)
        {
                std::map<int, float>::iterator post = _posterior.find(*i);
                if(post == _posterior.end())
                {
                        if(_posterior.size() == 0)
                        {
                                newPosterior.insert(std::pair<int, float>(*i, 1));
                        }
                        else
                        {
                                newPosterior.insert(std::pair<int, float>(*i, 0));
                        }
                }
                else
                {
                        newPosterior.insert(std::pair<int, float>((*post).first, (*post).second));
                }
        }
        _posterior = newPosterior;
}

float BayesFilter::addNeighborProb(cv::Mat & prediction, unsigned int col, const std::map<int, int> & neighbors, const std::map<int, int> & idToIndexMap) const
{
        UASSERT((unsigned int)prediction.cols == idToIndexMap.size() &&
                        (unsigned int)prediction.rows == idToIndexMap.size() &&
                        col < (unsigned int)prediction.cols &&
                        col < (unsigned int)prediction.rows);

        float sum=0;
        for(std::map<int, int>::const_iterator iter=neighbors.begin(); iter!=neighbors.end(); ++iter)
        {
                int index = uValue(idToIndexMap, iter->first, -1);
                if(index >= 0)
                {
                        sum += ((float*)prediction.data)[col + index*prediction.cols] = _predictionLC[iter->second+1];
                }
        }
        return sum;
}


} // namespace rtabmap