/*
Copyright (c) 2010-2016, 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
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER 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.
*/
#include "rtabmap/core/Graph.h"

#include <rtabmap/utilite/ULogger.h>
#include <rtabmap/utilite/UStl.h>
#include <rtabmap/utilite/UMath.h>
#include <rtabmap/utilite/UConversion.h>
#include <rtabmap/utilite/UTimer.h>
#include <set>

#include <rtabmap/core/OptimizerGTSAM.h>

#ifdef RTABMAP_GTSAM
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/inference/Key.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/DoglegOptimizer.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/NonlinearOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/nonlinear/Values.h>

#ifdef RTABMAP_VERTIGO
#include "vertigo/gtsam/betweenFactorMaxMix.h"
#include "vertigo/gtsam/betweenFactorSwitchable.h"
#include "vertigo/gtsam/switchVariableLinear.h"
#include "vertigo/gtsam/switchVariableSigmoid.h"
#endif
#endif // end RTABMAP_GTSAM

namespace rtabmap {

bool OptimizerGTSAM::available()
{
#ifdef RTABMAP_GTSAM
	return true;
#else
	return false;
#endif
}

void OptimizerGTSAM::parseParameters(const ParametersMap & parameters)
{
	Optimizer::parseParameters(parameters);
	Parameters::parse(parameters, Parameters::kGTSAMOptimizer(), optimizer_);
}

std::map<int, Transform> OptimizerGTSAM::optimize(
		int rootId,
		const std::map<int, Transform> & poses,
		const std::multimap<int, Link> & edgeConstraints,
		std::list<std::map<int, Transform> > * intermediateGraphes,
		double * finalError,
		int * iterationsDone)
{
	std::map<int, Transform> optimizedPoses;
#ifdef RTABMAP_GTSAM

#ifndef RTABMAP_VERTIGO
	if(this->isRobust())
	{
		UWARN("Vertigo robust optimization is not available! Robust optimization is now disabled.");
		setRobust(false);
	}
#endif

	UDEBUG("Optimizing graph...");
	if(edgeConstraints.size()>=1 && poses.size()>=2 && iterations() > 0)
	{
		gtsam::NonlinearFactorGraph graph;

		// detect if there is a global pose prior set, if so remove rootId
		if(!priorsIgnored())
		{
			for(std::multimap<int, Link>::const_iterator iter=edgeConstraints.begin(); iter!=edgeConstraints.end(); ++iter)
			{
				if(iter->second.from() == iter->second.to())
				{
					rootId = 0;
					break;
				}
			}
		}

		//prior first pose
		if(rootId != 0)
		{
			UASSERT(uContains(poses, rootId));
			const Transform & initialPose = poses.at(rootId);
			if(isSlam2d())
			{
				gtsam::noiseModel::Diagonal::shared_ptr priorNoise = gtsam::noiseModel::Diagonal::Variances(gtsam::Vector3(0.01, 0.01, 0.01));
				graph.add(gtsam::PriorFactor<gtsam::Pose2>(rootId, gtsam::Pose2(initialPose.x(), initialPose.y(), initialPose.theta()), priorNoise));
			}
			else
			{
				gtsam::noiseModel::Diagonal::shared_ptr priorNoise = gtsam::noiseModel::Diagonal::Variances((gtsam::Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4).finished());
				graph.add(gtsam::PriorFactor<gtsam::Pose3>(rootId, gtsam::Pose3(initialPose.toEigen4d()), priorNoise));
			}
		}

		UDEBUG("fill poses to gtsam...");
		gtsam::Values initialEstimate;
		for(std::map<int, Transform>::const_iterator iter = poses.begin(); iter!=poses.end(); ++iter)
		{
			UASSERT(!iter->second.isNull());
			if(isSlam2d())
			{
				initialEstimate.insert(iter->first, gtsam::Pose2(iter->second.x(), iter->second.y(), iter->second.theta()));
			}
			else
			{
				initialEstimate.insert(iter->first, gtsam::Pose3(iter->second.toEigen4d()));
			}
		}

		UDEBUG("fill edges to gtsam...");
		int switchCounter = poses.rbegin()->first+1;
		for(std::multimap<int, Link>::const_iterator iter=edgeConstraints.begin(); iter!=edgeConstraints.end(); ++iter)
		{
			int id1 = iter->second.from();
			int id2 = iter->second.to();
			UASSERT(!iter->second.transform().isNull());
			if(id1 == id2)
			{
				if(!priorsIgnored())
				{
					if(isSlam2d())
					{
						Eigen::Matrix<double, 3, 3> information = Eigen::Matrix<double, 3, 3>::Identity();
						if(!isCovarianceIgnored())
						{
							information(0,0) = iter->second.infMatrix().at<double>(0,0); // x-x
							information(0,1) = iter->second.infMatrix().at<double>(0,1); // x-y
							information(0,2) = iter->second.infMatrix().at<double>(0,5); // x-theta
							information(1,0) = iter->second.infMatrix().at<double>(1,0); // y-x
							information(1,1) = iter->second.infMatrix().at<double>(1,1); // y-y
							information(1,2) = iter->second.infMatrix().at<double>(1,5); // y-theta
							information(2,0) = iter->second.infMatrix().at<double>(5,0); // theta-x
							information(2,1) = iter->second.infMatrix().at<double>(5,1); // theta-y
							information(2,2) = iter->second.infMatrix().at<double>(5,5); // theta-theta
						}

						gtsam::noiseModel::Gaussian::shared_ptr model = gtsam::noiseModel::Gaussian::Information(information);
						graph.add(gtsam::PriorFactor<gtsam::Pose2>(id1, gtsam::Pose2(iter->second.transform().x(), iter->second.transform().y(), iter->second.transform().theta()), model));
					}
					else
					{
						Eigen::Matrix<double, 6, 6> information = Eigen::Matrix<double, 6, 6>::Identity();
						if(!isCovarianceIgnored())
						{
							memcpy(information.data(), iter->second.infMatrix().data, iter->second.infMatrix().total()*sizeof(double));
						}

						Eigen::Matrix<double, 6, 6> mgtsam = Eigen::Matrix<double, 6, 6>::Identity();
						mgtsam.block(0,0,3,3) = information.block(3,3,3,3); // cov rotation
						mgtsam.block(3,3,3,3) = information.block(0,0,3,3); // cov translation
						mgtsam.block(0,3,3,3) = information.block(0,3,3,3); // off diagonal
						mgtsam.block(3,0,3,3) = information.block(3,0,3,3); // off diagonal
						gtsam::SharedNoiseModel model = gtsam::noiseModel::Gaussian::Information(mgtsam);

						graph.add(gtsam::PriorFactor<gtsam::Pose3>(id1, gtsam::Pose3(iter->second.transform().toEigen4d()), model));
					}
				}
			}
			else
			{
#ifdef RTABMAP_VERTIGO
				if(this->isRobust() &&
				   iter->second.type() != Link::kNeighbor &&
				   iter->second.type() != Link::kNeighborMerged &&
				   iter->second.type() != Link::kPosePrior)
				{
					// create new switch variable
					// Sunderhauf IROS 2012:
					// "Since it is reasonable to initially accept all loop closure constraints,
					//  a proper and convenient initial value for all switch variables would be
					//  sij = 1 when using the linear switch function"
					double prior = 1.0;
					initialEstimate.insert(gtsam::Symbol('s',switchCounter), vertigo::SwitchVariableLinear(prior));

					// create switch prior factor
					// "If the front-end is not able to assign sound individual values
					//  for Ξij , it is save to set all Ξij = 1, since this value is close
					//  to the individual optimal choice of Ξij for a large range of
					//  outliers."
					gtsam::noiseModel::Diagonal::shared_ptr switchPriorModel = gtsam::noiseModel::Diagonal::Sigmas(gtsam::Vector1(1.0));
					graph.add(gtsam::PriorFactor<vertigo::SwitchVariableLinear> (gtsam::Symbol('s',switchCounter), vertigo::SwitchVariableLinear(prior), switchPriorModel));
				}
#endif

				if(isSlam2d())
				{
					Eigen::Matrix<double, 3, 3> information = Eigen::Matrix<double, 3, 3>::Identity();
					if(!isCovarianceIgnored())
					{
						information(0,0) = iter->second.infMatrix().at<double>(0,0); // x-x
						information(0,1) = iter->second.infMatrix().at<double>(0,1); // x-y
						information(0,2) = iter->second.infMatrix().at<double>(0,5); // x-theta
						information(1,0) = iter->second.infMatrix().at<double>(1,0); // y-x
						information(1,1) = iter->second.infMatrix().at<double>(1,1); // y-y
						information(1,2) = iter->second.infMatrix().at<double>(1,5); // y-theta
						information(2,0) = iter->second.infMatrix().at<double>(5,0); // theta-x
						information(2,1) = iter->second.infMatrix().at<double>(5,1); // theta-y
						information(2,2) = iter->second.infMatrix().at<double>(5,5); // theta-theta
					}
					gtsam::noiseModel::Gaussian::shared_ptr model = gtsam::noiseModel::Gaussian::Information(information);

#ifdef RTABMAP_VERTIGO
					if(this->isRobust() &&
					   iter->second.type()!=Link::kNeighbor &&
					   iter->second.type() != Link::kNeighborMerged)
					{
						// create switchable edge factor
						graph.add(vertigo::BetweenFactorSwitchableLinear<gtsam::Pose2>(id1, id2, gtsam::Symbol('s', switchCounter++), gtsam::Pose2(iter->second.transform().x(), iter->second.transform().y(), iter->second.transform().theta()), model));
					}
					else
#endif
					{
						graph.add(gtsam::BetweenFactor<gtsam::Pose2>(id1, id2, gtsam::Pose2(iter->second.transform().x(), iter->second.transform().y(), iter->second.transform().theta()), model));
					}
				}
				else
				{
					Eigen::Matrix<double, 6, 6> information = Eigen::Matrix<double, 6, 6>::Identity();
					if(!isCovarianceIgnored())
					{
						memcpy(information.data(), iter->second.infMatrix().data, iter->second.infMatrix().total()*sizeof(double));
					}

					Eigen::Matrix<double, 6, 6> mgtsam = Eigen::Matrix<double, 6, 6>::Identity();
					mgtsam.block(0,0,3,3) = information.block(3,3,3,3); // cov rotation
					mgtsam.block(3,3,3,3) = information.block(0,0,3,3); // cov translation
					mgtsam.block(0,3,3,3) = information.block(0,3,3,3); // off diagonal
					mgtsam.block(3,0,3,3) = information.block(3,0,3,3); // off diagonal
					gtsam::SharedNoiseModel model = gtsam::noiseModel::Gaussian::Information(mgtsam);

#ifdef RTABMAP_VERTIGO
					if(this->isRobust() &&
					   iter->second.type() != Link::kNeighbor &&
					   iter->second.type() != Link::kNeighborMerged &&
					   iter->second.type() != Link::kPosePrior)
					{
						// create switchable edge factor
						graph.add(vertigo::BetweenFactorSwitchableLinear<gtsam::Pose3>(id1, id2, gtsam::Symbol('s', switchCounter++), gtsam::Pose3(iter->second.transform().toEigen4d()), model));
					}
					else
#endif
					{
						graph.add(gtsam::BetweenFactor<gtsam::Pose3>(id1, id2, gtsam::Pose3(iter->second.transform().toEigen4d()), model));
					}
				}
			}
		}

		UDEBUG("create optimizer");
		gtsam::NonlinearOptimizer * optimizer;

		if(optimizer_ == 2)
		{
			gtsam::DoglegParams parameters;
			parameters.relativeErrorTol = epsilon();
			parameters.maxIterations = iterations();
			optimizer = new gtsam::DoglegOptimizer(graph, initialEstimate, parameters);
		}
		else if(optimizer_ == 1)
		{
			gtsam::GaussNewtonParams parameters;
			parameters.relativeErrorTol = epsilon();
			parameters.maxIterations = iterations();
			optimizer = new gtsam::GaussNewtonOptimizer(graph, initialEstimate, parameters);
		}
		else
		{
			gtsam::LevenbergMarquardtParams parameters;
			parameters.relativeErrorTol = epsilon();
			parameters.maxIterations = iterations();
			optimizer = new gtsam::LevenbergMarquardtOptimizer(graph, initialEstimate, parameters);
		}

		UINFO("GTSAM optimizing begin (max iterations=%d, robust=%d)", iterations(), isRobust()?1:0);
		UTimer timer;
		int it = 0;
		double lastError = optimizer->error();
		for(int i=0; i<iterations(); ++i)
		{
			if(intermediateGraphes && i > 0)
			{
				std::map<int, Transform> tmpPoses;
				for(gtsam::Values::const_iterator iter=optimizer->values().begin(); iter!=optimizer->values().end(); ++iter)
				{
					if(iter->value.dim() > 1)
					{
						if(isSlam2d())
						{
							gtsam::Pose2 p = iter->value.cast<gtsam::Pose2>();
							tmpPoses.insert(std::make_pair((int)iter->key, Transform(p.x(), p.y(), p.theta())));
						}
						else
						{
							gtsam::Pose3 p = iter->value.cast<gtsam::Pose3>();
							tmpPoses.insert(std::make_pair((int)iter->key, Transform::fromEigen4d(p.matrix())));
						}
					}
				}
				intermediateGraphes->push_back(tmpPoses);
			}
			try
			{
				optimizer->iterate();
				++it;
			}
			catch(gtsam::IndeterminantLinearSystemException & e)
			{
				UERROR("GTSAM exception caught: %s", e.what());
				delete optimizer;
				return optimizedPoses;
			}

			// early stop condition
			double error = optimizer->error();
			UDEBUG("iteration %d error =%f", i+1, error);
			double errorDelta = lastError - error;
			if(i>0 && errorDelta < this->epsilon())
			{
				if(errorDelta < 0)
				{
					UDEBUG("Negative improvement?! Ignore and continue optimizing... (%f < %f)", errorDelta, this->epsilon());
				}
				else
				{
					UINFO("Stop optimizing, not enough improvement (%f < %f)", errorDelta, this->epsilon());
					break;
				}
			}
			else if(i==0 && error < this->epsilon())
			{
				UINFO("Stop optimizing, error is already under epsilon (%f < %f)", error, this->epsilon());
				break;
			}
			lastError = error;
		}
		if(finalError)
		{
			*finalError = lastError;
		}
		if(iterationsDone)
		{
			*iterationsDone = it;
		}
		UINFO("GTSAM optimizing end (%d iterations done, error=%f (initial=%f final=%f), time=%f s)",
				optimizer->iterations(), optimizer->error(), graph.error(initialEstimate), graph.error(optimizer->values()), timer.ticks());

		for(gtsam::Values::const_iterator iter=optimizer->values().begin(); iter!=optimizer->values().end(); ++iter)
		{
			if(iter->value.dim() > 1)
			{
				if(isSlam2d())
				{
					gtsam::Pose2 p = iter->value.cast<gtsam::Pose2>();
					optimizedPoses.insert(std::make_pair((int)iter->key, Transform(p.x(), p.y(), p.theta())));
				}
				else
				{
					gtsam::Pose3 p = iter->value.cast<gtsam::Pose3>();
					optimizedPoses.insert(std::make_pair((int)iter->key, Transform::fromEigen4d(p.matrix())));
				}
			}
		}
		delete optimizer;
	}
	else if(poses.size() == 1 || iterations() <= 0)
	{
		optimizedPoses = poses;
	}
	else
	{
		UWARN("This method should be called at least with 1 pose!");
	}
	UDEBUG("Optimizing graph...end!");
#else
	UERROR("Not built with GTSAM support!");
#endif
	return optimizedPoses;
}

} /* namespace rtabmap */
