georegression.fitting.curves.CovarianceToEllipse_F64 Maven / Gradle / Ivy

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GeoRegression is a free Java based geometry library for scientific computing in fields such as robotics and computer vision with a focus on 2D/3D space.

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/*
 * Copyright (C) 2020, Peter Abeles. All Rights Reserved.
 *
 * This file is part of Geometric Regression Library (GeoRegression).
 *
 * 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 georegression.fitting.curves;

import georegression.struct.point.Vector2D_F64;
import org.ejml.data.Complex_F64;
import org.ejml.data.DMatrixRMaj;
import org.ejml.dense.row.factory.DecompositionFactory_DDRM;
import org.ejml.interfaces.decomposition.EigenDecomposition_F64;

/**
 * Computes a containment ellipse given a covariance
 *
 * @author Peter Abeles
 */
public class CovarianceToEllipse_F64 {

	EigenDecomposition_F64 eigen = DecompositionFactory_DDRM.eig(2, true);
	DMatrixRMaj Q = new DMatrixRMaj(2,2);

	// major axis
	Vector2D_F64 x = new Vector2D_F64();
	// minor axis
	Vector2D_F64 y = new Vector2D_F64();

	// major and minor axis length (in that order)
	double lengthX,lengthY;

	// number of standard deviations the ellipse should be
	double numStdev = 1;

	public void setNumStdev(double numStdev) {
		this.numStdev = numStdev;
	}

	/**
	 * Specifies the covariance matrix. Q = [a11 a12; a12 a22]
	 * @param a11 element in covariance matrix.
	 * @param a12 element in covariance matrix.
	 * @param a22  element in covariance matrix.
	 * @return true if it was successful or false if something went wrong
	 */
	public boolean setCovariance( double a11 , double a12, double a22 ) {
		Q.data[0] = a11;
		Q.data[1] = a12;
		Q.data[2] = a12;
		Q.data[3] = a22;

		if( !eigen.decompose(Q) ) {
			System.err.println("Eigenvalue decomposition failed!");
			return false;
		}

		Complex_F64 v0 = eigen.getEigenvalue(0);
		Complex_F64 v1 = eigen.getEigenvalue(1);

		DMatrixRMaj a0,a1;

		if( v0.getMagnitude2() > v1.getMagnitude2() ) {
			a0 = eigen.getEigenVector(0);
			a1 = eigen.getEigenVector(1);
			lengthX = (double) v0.getMagnitude();
			lengthY = (double) v1.getMagnitude();
		} else {
			a0 = eigen.getEigenVector(1);
			a1 = eigen.getEigenVector(0);
			lengthX = (double) v1.getMagnitude();
			lengthY = (double) v0.getMagnitude();
		}

		if( a0 == null || a1 == null ) {
			System.err.println("Complex eigenvalues: "+v0+"  "+v1);
			return false;
		}

		lengthX = Math.sqrt(lengthX);
		lengthY = Math.sqrt(lengthY);

		x.setTo( (double) a0.get(0) , (double) a0.get(1));
		y.setTo( (double) a1.get(0) , (double) a1.get(1));

		return true;
	}

	/**
	 * @return Vector which defines the major axis
	 */
	public Vector2D_F64 getMajorVector() {
		return x;
	}

	/**
	 * @return Vector which defines the minor axis
	 */
	public Vector2D_F64 getMinorVector() {
		return y;
	}

	/**
	 * @return Angle between the major axis and the x-axis
	 */
	public double getAngle() {
		return Math.atan2( x.y, x.x );
	}

	/**
	 * @return Length of the major axis
	 */
	public double getMajorAxis() {
		return numStdev*lengthX;
	}

	/**
	 * @return Length of the minor axis
	 */
	public double getMinorAxis() {
		return numStdev*lengthY;
	}
}