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BoofCV is an open source Java library for real-time computer vision and robotics applications.

There is a newer version: 1.1.5

/*
 * Copyright (c) 2021, Peter Abeles. All Rights Reserved.
 *
 * This file is part of BoofCV (http://boofcv.org).
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package boofcv.abst.geo.f;

import boofcv.abst.geo.RefineEpipolar;
import boofcv.abst.geo.optimization.ResidualsEpipolarMatrix;
import boofcv.alg.geo.ModelObservationResidual;
import boofcv.alg.geo.f.FundamentalResidualSampson;
import boofcv.alg.geo.f.FundamentalResidualSimple;
import boofcv.alg.geo.f.ParamFundamentalEpipolar;
import boofcv.struct.geo.AssociatedPair;
import org.ddogleg.fitting.modelset.ModelCodec;
import org.ddogleg.optimization.FactoryOptimization;
import org.ddogleg.optimization.UnconstrainedLeastSquares;
import org.ejml.data.DMatrixRMaj;

import java.util.List;

/**
 * Improves upon the initial estimate of the Fundamental matrix by minimizing the error.
 *
 * @author Peter Abeles
 */
public class LeastSquaresFundamental implements RefineEpipolar {
	ModelCodec paramModel;
	ResidualsEpipolarMatrix func;
	double param[];

	UnconstrainedLeastSquares minimizer;

	int maxIterations;
	double convergenceTol;

	public LeastSquaresFundamental( double convergenceTol,
									int maxIterations,
									boolean useSampson ) {
		this(new ParamFundamentalEpipolar(), convergenceTol, maxIterations, useSampson);
	}

	public LeastSquaresFundamental( ModelCodec paramModel,
									double convergenceTol,
									int maxIterations,
									boolean useSampson ) {
		this.paramModel = paramModel;
		this.maxIterations = maxIterations;
		this.convergenceTol = convergenceTol;

		param = new double[paramModel.getParamLength()];

		ModelObservationResidual residual;
		if (useSampson)
			residual = new FundamentalResidualSampson();
		else
			residual = new FundamentalResidualSimple();

		func = new ResidualsEpipolarMatrix(paramModel, residual);

		minimizer = FactoryOptimization.levenbergMarquardt(null, false);
	}

	@Override
	public boolean fitModel( List obs, DMatrixRMaj F, DMatrixRMaj refinedF ) {
		func.setObservations(obs);

		paramModel.encode(F, param);

		minimizer.setFunction(func, null);

		minimizer.initialize(param, 0, convergenceTol*obs.size());

		for (int i = 0; i < maxIterations; i++) {
			if (minimizer.iterate())
				break;
		}

		paramModel.decode(minimizer.getParameters(), refinedF);

		return true;
	}

	@Override
	public double getFitScore() {
		return minimizer.getFunctionValue();
	}
}