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BoofCV is an open source Java library for real-time computer vision and robotics applications.
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/*
 * 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.alg.geo.f;

import boofcv.alg.geo.LowLevelMultiViewOps;
import boofcv.alg.geo.PerspectiveOps;
import boofcv.struct.geo.AssociatedPair;
import org.ejml.data.DMatrixRMaj;

import java.util.List;

/**
 * 
 * Given a set of 8 or more points this class computes the essential or fundamental matrix. The result is
 * often used as an initial guess for more accurate non-linear approaches.
 * 
 *
 * 
 * The computed fundamental matrix follow the following convention (with no noise) for the associated pair:
 * x2^T*F*x1 = 0

 * x1 = keyLoc and x2 = currLoc.
 * 
 *
 * 
 * References:
 * 

 *  Y. Ma, S. Soatto, J. Kosecka, and S. S. Sastry, "An Invitation to 3-D Vision" Springer-Verlad, 2004 
 *  R. Hartley, and A. Zisserman, "Multiple View Geometry in Computer Vision", 2nd Ed, Cambridge 2003 
 * 
 *
 * @author Peter Abeles
 */
public class FundamentalLinear8 extends FundamentalLinear {

	/**
	 * Specifies which type of matrix is to be computed
	 *
	 * @param computeFundamental true it computes a fundamental matrix and false for essential
	 */
	public FundamentalLinear8( boolean computeFundamental ) {
		super(computeFundamental);
	}

	/**
	 * 
	 * Computes a fundamental or essential matrix from a set of associated point correspondences.
	 * 
	 *
	 * @param points List of corresponding image coordinates. In pixel for fundamental matrix or
	 * normalized coordinates for essential matrix.
	 * @return true If successful or false if it failed
	 */
	public boolean process( List points, DMatrixRMaj solution ) {
		if (points.size() < 8)
			throw new IllegalArgumentException("Must be at least 8 points. Was only " + points.size());

		// use normalized coordinates for pixel and calibrated
		// TODO re-evaluate decision to normalize for calibrated case
		LowLevelMultiViewOps.computeNormalization(points, N1, N2);
		createA(points, A);

		if (process(A, solution))
			return false;

		// undo normalization on F
		PerspectiveOps.multTranA(N2.matrix(null), solution, N1.matrix(null), solution);

		if (computeFundamental)
			return projectOntoFundamentalSpace(solution);
		else
			return projectOntoEssential(solution);
	}

	/**
	 * Computes the SVD of A and extracts the essential/fundamental matrix from its null space
	 */
	protected boolean process( DMatrixRMaj A, DMatrixRMaj F ) {
		if (!solverNull.process(A, 1, F))
			return true;

		F.numRows = 3;
		F.numCols = 3;

		return false;
	}
}