/**
 * Copyright (C) 2010  Hauke Strasdat
 *                     Imperial College London
 * Copyright (c) 2005-2023, Individual contributors, see AUTHORS file
 *   See: https://www.mrpt.org/Authors - All rights reserved.
 *
 * bundle_adjuster.h is part of RobotVision.
 *
 * RobotVision is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation, either version 3 of the
 * License, or any later version.
 *
 * RobotVision is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * and the GNU Lesser General Public License along with this program.
 * If not, see <http://www.gnu.org/licenses/>.
 */

#include "vision-lgpl-precomp.h"  // Precompiled headers
//
#include <mrpt/math/robust_kernels.h>
#include <mrpt/poses/Lie/SE.h>
#include <mrpt/vision/bundle_adjustment.h>
#include <mrpt/vision/pinhole.h>

#include "ba_internals.h"

using namespace std;
using namespace mrpt;
using namespace mrpt::vision;
using namespace mrpt::img;
using namespace mrpt::math;
using namespace mrpt::poses;

/* -------------------------------------------------------------------------------------
							ba_initial_estimate

 Fills the frames & landmark points maps with an initial gross estimate
 from the sequence \a observations, so they can be fed to bundle adjustment
 methods.
   ------------------------------------------------------------------------------------
 */
void mrpt::vision::ba_initial_estimate(
	const TSequenceFeatureObservations& observations,
	[[maybe_unused]] const TCamera& camera_params, TFramePosesMap& frame_poses,
	TLandmarkLocationsMap& landmark_points)
{
	MRPT_START
	// VERY CRUDE APPROACH: All camera poses at the origin, all points at
	// (0,0,1)
	// TODO: Improve with the data in "observations"...

	// Clear previous contents:
	frame_poses.clear();
	landmark_points.clear();

	// Go thru the obs list and insert poses:
	for (TSequenceFeatureObservations::const_iterator it = observations.begin();
		 it != observations.end(); ++it)
	{
		const TFeatureID feat_ID = it->id_feature;
		const TCameraPoseID frame_ID = it->id_frame;

		frame_poses[frame_ID] = CPose3D(0, 0, 0, 0, 0, 0);
		landmark_points[feat_ID] = TPoint3D(0, 0, 1);
	}
	MRPT_END
}

void mrpt::vision::ba_initial_estimate(
	const TSequenceFeatureObservations& observations,
	[[maybe_unused]] const TCamera& camera_params, TFramePosesVec& frame_poses,
	TLandmarkLocationsVec& landmark_points)
{
	MRPT_START
	// VERY CRUDE APPROACH: All camera poses at the origin, all points at
	// (0,0,1)
	// TODO: Improve with the data in "observations"...

	// Clear previous contents:
	frame_poses.clear();
	landmark_points.clear();

	if (observations.empty()) return;

	// Go thru the obs list and insert poses:

	TFeatureID max_pt_id = 0;
	TCameraPoseID max_fr_id = 0;

	for (TSequenceFeatureObservations::const_iterator it = observations.begin();
		 it != observations.end(); ++it)
	{
		const TFeatureID feat_ID = it->id_feature;
		const TCameraPoseID frame_ID = it->id_frame;
		keep_max(max_fr_id, frame_ID);
		keep_max(max_pt_id, feat_ID);
	}

	// Insert N copies of the same values:
	frame_poses.assign(max_fr_id + 1, CPose3D(0, 0, 0, 0, 0, 0));
	landmark_points.assign(max_pt_id + 1, TPoint3D(0, 0, 1));

	MRPT_END
}

// This function is what to do for each feature in the reprojection loops below.
// -> residual: the raw residual, even if using robust kernel
// -> sum+= scaled squared norm of the residual (which != squared norm if using
// robust kernel)
template <bool POSES_INVERSE>
inline void reprojectionResidualsElement(
	const TCamera& camera_params, const TFeatureObservation& OBS,
	std::array<double, 2>& out_residual,
	const TFramePosesVec::value_type& frame,
	const TLandmarkLocationsVec::value_type& point, double& sum,
	const bool use_robust_kernel, const double kernel_param,
	double* out_kernel_1st_deriv)
{
	const TPixelCoordf z_pred =
		mrpt::vision::pinhole::projectPoint_no_distortion<POSES_INVERSE>(
			camera_params, frame, point);
	const TPixelCoordf& z_meas = OBS.px;

	out_residual[0] = z_meas.x - z_pred.x;
	out_residual[1] = z_meas.y - z_pred.y;

	const double sum_2 = square(out_residual[0]) + square(out_residual[1]);

	if (use_robust_kernel)
	{
		RobustKernel<rkPseudoHuber> kernel;
		kernel.param_sq = square(kernel_param);
		double kernel_1st_deriv, kernel_2nd_deriv;

		sum += kernel.eval(sum_2, kernel_1st_deriv, kernel_2nd_deriv);
		if (out_kernel_1st_deriv) *out_kernel_1st_deriv = kernel_1st_deriv;
	}
	else
	{
		sum += sum_2;
	}
}

/** Compute reprojection error vector (used from within Bundle Adjustment
 * methods, but can be used in general)
 *  See mrpt::vision::bundle_adj_full for a description of most parameters.
 *
 *  \return Overall squared reprojection error.
 */
double mrpt::vision::reprojectionResiduals(
	const TSequenceFeatureObservations& observations,
	const TCamera& camera_params, const TFramePosesMap& frame_poses,
	const TLandmarkLocationsMap& landmark_points,
	std::vector<std::array<double, 2>>& out_residuals,
	const bool frame_poses_are_inverse, const bool use_robust_kernel,
	const double kernel_param, std::vector<double>* out_kernel_1st_deriv)
{
	MRPT_START

	double sum = 0;

	const size_t N = observations.size();
	out_residuals.resize(N);
	if (out_kernel_1st_deriv) out_kernel_1st_deriv->resize(N);

	for (size_t i = 0; i < N; i++)
	{
		const TFeatureObservation& OBS = observations[i];

		const TFeatureID i_p = OBS.id_feature;
		const TCameraPoseID i_f = OBS.id_frame;

		TFramePosesMap::const_iterator itF = frame_poses.find(i_f);
		TLandmarkLocationsMap::const_iterator itP = landmark_points.find(i_p);
		ASSERTMSG_(itF != frame_poses.end(), "Frame ID is not in list!");
		ASSERTMSG_(itP != landmark_points.end(), "Landmark ID is not in list!");

		const TFramePosesMap::mapped_type& frame = itF->second;
		const TLandmarkLocationsMap::mapped_type& point = itP->second;

		double* ptr_1st_deriv =
			out_kernel_1st_deriv ? &((*out_kernel_1st_deriv)[i]) : nullptr;

		if (frame_poses_are_inverse)
			reprojectionResidualsElement<true>(
				camera_params, OBS, out_residuals[i], frame, point, sum,
				use_robust_kernel, kernel_param, ptr_1st_deriv);
		else
			reprojectionResidualsElement<false>(
				camera_params, OBS, out_residuals[i], frame, point, sum,
				use_robust_kernel, kernel_param, ptr_1st_deriv);
	}

	return sum;

	MRPT_END
}

double mrpt::vision::reprojectionResiduals(
	const TSequenceFeatureObservations& observations,
	const TCamera& camera_params, const TFramePosesVec& frame_poses,
	const TLandmarkLocationsVec& landmark_points,
	std::vector<std::array<double, 2>>& out_residuals,
	const bool frame_poses_are_inverse, const bool use_robust_kernel,
	const double kernel_param, std::vector<double>* out_kernel_1st_deriv)
{
	MRPT_START

	double sum = 0;

	const size_t N = observations.size();
	out_residuals.resize(N);
	if (out_kernel_1st_deriv) out_kernel_1st_deriv->resize(N);

	for (size_t i = 0; i < N; i++)
	{
		const TFeatureObservation& OBS = observations[i];

		const TFeatureID i_p = OBS.id_feature;
		const TCameraPoseID i_f = OBS.id_frame;

		ASSERT_LT_(i_p, landmark_points.size());
		ASSERT_LT_(i_f, frame_poses.size());
		const TFramePosesVec::value_type& frame = frame_poses[i_f];
		const TLandmarkLocationsVec::value_type& point = landmark_points[i_p];

		double* ptr_1st_deriv =
			out_kernel_1st_deriv ? &((*out_kernel_1st_deriv)[i]) : nullptr;

		if (frame_poses_are_inverse)
			reprojectionResidualsElement<true>(
				camera_params, OBS, out_residuals[i], frame, point, sum,
				use_robust_kernel, kernel_param, ptr_1st_deriv);
		else
			reprojectionResidualsElement<false>(
				camera_params, OBS, out_residuals[i], frame, point, sum,
				use_robust_kernel, kernel_param, ptr_1st_deriv);
	}

	return sum;
	MRPT_END
}

// Compute gradients & Hessian blocks
void mrpt::vision::ba_build_gradient_Hessians(
	const TSequenceFeatureObservations& observations,
	const vector<std::array<double, 2>>& residual_vec,
	const std::vector<JacData<6, 3, 2>>& jac_data_vec,
	std::vector<CMatrixFixed<double, 6, 6>>& U,
	std::vector<CVectorFixedDouble<6>>& eps_frame,
	std::vector<CMatrixFixed<double, 3, 3>>& V,
	std::vector<CVectorFixedDouble<3>>& eps_point, size_t num_fix_frames,
	size_t num_fix_points, const vector<double>* kernel_1st_deriv)
{
	MRPT_START

	const size_t N = observations.size();
	const bool use_robust_kernel = (kernel_1st_deriv != nullptr);

	for (size_t i = 0; i < N; i++)
	{
		const TFeatureObservation& OBS = observations[i];

		const TFeatureID i_p = OBS.id_feature;
		const TCameraPoseID i_f = OBS.id_frame;

		const Eigen::Matrix<double, 2, 1> RESID(&residual_vec[i][0]);
		const JacData<6, 3, 2>& JACOB = jac_data_vec[i];
		ASSERTDEB_(JACOB.frame_id == i_f && JACOB.point_id == i_p);

		if (i_f >= num_fix_frames)
		{
			const size_t frame_id = i_f - num_fix_frames;
			ASSERTDEB_(JACOB.J_frame_valid);
			ASSERT_LT_(frame_id, U.size());
			CMatrixDouble66 JtJ(UNINITIALIZED_MATRIX);
			JtJ.matProductOf_AtA(JACOB.J_frame);

			CVectorFixedDouble<6> eps_delta;
			// eps_delta = J^t * RESID
			eps_delta = JACOB.J_frame.transpose() * RESID;
			if (!use_robust_kernel) { eps_frame[frame_id] += eps_delta; }
			else
			{
				const double rho_1st_der = (*kernel_1st_deriv)[i];
				auto scaled_eps = eps_delta;
				scaled_eps *= rho_1st_der;
				eps_frame[frame_id] += scaled_eps;
			}
			U[frame_id] += JtJ;
		}
		if (i_p >= num_fix_points)
		{
			const size_t point_id = i_p - num_fix_points;
			ASSERTDEB_(JACOB.J_point_valid);
			ASSERT_LT_(point_id, V.size());
			CMatrixDouble33 JtJ(UNINITIALIZED_MATRIX);
			JtJ.matProductOf_AtA(JACOB.J_point);

			CVectorFixedDouble<3> eps_delta;
			// eps_delta = J^t * RESID
			eps_delta = JACOB.J_point.transpose() * RESID;
			if (!use_robust_kernel) { eps_point[point_id] += eps_delta; }
			else
			{
				const double rho_1st_der = (*kernel_1st_deriv)[i];
				auto scaled_eps = eps_delta;
				scaled_eps *= rho_1st_der;
				eps_point[point_id] += scaled_eps;
			}

			V[point_id] += JtJ;
		}
	}

	MRPT_END
}

void mrpt::vision::add_se3_deltas_to_frames(
	const TFramePosesVec& frame_poses, const CVectorDouble& delta,
	size_t delta_first_idx, size_t delta_num_vals,
	TFramePosesVec& new_frame_poses, const size_t num_fix_frames)
{
	MRPT_START

	new_frame_poses.resize(frame_poses.size());

	for (size_t i = 0; i < num_fix_frames; ++i)
		new_frame_poses[i] = frame_poses[i];

	size_t delta_used_vals = 0;
	const double* delta_val = &delta[delta_first_idx];

	for (size_t i = num_fix_frames; i < frame_poses.size(); i++)
	{
		const CPose3D& old_pose = frame_poses[i];
		CPose3D& new_pose = new_frame_poses[i];

		// Use the Lie Algebra methods for the increment:
		const CPose3D incrPose =
			mrpt::poses::Lie::SE<3>::exp(CVectorFixedDouble<6>(delta_val));

		// new_pose =  old_pose  [+] delta
		//         = exp(delta) (+) old_pose
		new_pose.composeFrom(incrPose, old_pose);

		// Move to the next entry in delta:
		delta_val += 6;
		delta_used_vals += 6;
	}

	ASSERT_(delta_used_vals == delta_num_vals);
	MRPT_END
}
void mrpt::vision::add_3d_deltas_to_points(
	const TLandmarkLocationsVec& landmark_points, const CVectorDouble& delta,
	size_t delta_first_idx, size_t delta_num_vals,
	TLandmarkLocationsVec& new_landmark_points, const size_t num_fix_points)
{
	MRPT_START

	new_landmark_points.resize(landmark_points.size());

	for (size_t i = 0; i < num_fix_points; ++i)
		new_landmark_points[i] = landmark_points[i];

	size_t delta_used_vals = 0;
	const double* delta_val = &delta[delta_first_idx];

	for (size_t i = num_fix_points; i < landmark_points.size(); i++)
	{
		const TPoint3D& old_point = landmark_points[i];
		TPoint3D& new_point = new_landmark_points[i];

		for (size_t j = 0; j < 3; j++)
			new_point[j] = old_point[j] + delta_val[j];

		// Move to the next entry in delta:
		delta_val += 3;
		delta_used_vals += 3;
	}

	ASSERT_(delta_used_vals == delta_num_vals);
	MRPT_END
}
