LineCorrespondenceProjectiveTransformation2DRefiner.java
/*
* Copyright (C) 2017 Alberto Irurueta Carro (alberto@irurueta.com)
*
* 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 com.irurueta.geometry.refiners;
import com.irurueta.algebra.AlgebraException;
import com.irurueta.algebra.Matrix;
import com.irurueta.geometry.Line2D;
import com.irurueta.geometry.ProjectiveTransformation2D;
import com.irurueta.geometry.estimators.LockedException;
import com.irurueta.geometry.estimators.NotReadyException;
import com.irurueta.numerical.EvaluationException;
import com.irurueta.numerical.GradientEstimator;
import com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter;
import com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFunctionEvaluator;
import com.irurueta.numerical.robust.InliersData;
import java.util.BitSet;
import java.util.List;
/**
* A 2D projective transformation refiner using line correspondences.
* This class takes into account an initial estimation, inlier line matches
* and their residuals to find a solution that minimizes error of inliers in
* LMSE terms.
* Typically, a refiner is used by a robust estimator, however it can also be
* useful in some other situations.
*/
@SuppressWarnings("DuplicatedCode")
public class LineCorrespondenceProjectiveTransformation2DRefiner extends
ProjectiveTransformation2DRefiner<Line2D, Line2D> {
/**
* Line to be reused when computing residuals.
*/
private final Line2D residualTestLine = new Line2D();
/**
* Constructor.
*/
public LineCorrespondenceProjectiveTransformation2DRefiner() {
}
/**
* Constructor.
*
* @param initialEstimation initial estimation to be set.
* @param keepCovariance true if covariance of estimation must be kept after
* refinement, false otherwise.
* @param inliers set indicating which of the provided matches are inliers.
* @param residuals residuals for matched samples.
* @param numInliers number of inliers on initial estimation.
* @param samples1 1st set of paired samples.
* @param samples2 2nd set of paired samples.
* @param refinementStandardDeviation standard deviation used for
* Levenberg-Marquardt fitting.
*/
public LineCorrespondenceProjectiveTransformation2DRefiner(
final ProjectiveTransformation2D initialEstimation,
final boolean keepCovariance, final BitSet inliers, final double[] residuals,
final int numInliers, final List<Line2D> samples1, final List<Line2D> samples2,
final double refinementStandardDeviation) {
super(initialEstimation, keepCovariance, inliers, residuals, numInliers, samples1, samples2,
refinementStandardDeviation);
}
/**
* Constructor.
*
* @param initialEstimation initial estimation to be set.
* @param keepCovariance true if covariance of estimation must be kept after
* refinement, false otherwise.
* @param inliersData inlier data, typically obtained from a robust
* estimator.
* @param samples1 1st set of paired samples.
* @param samples2 2nd set of paired samples.
* @param refinementStandardDeviation standard deviation used for
* Levenberg-Marquardt fitting.
*/
public LineCorrespondenceProjectiveTransformation2DRefiner(
final ProjectiveTransformation2D initialEstimation,
final boolean keepCovariance, final InliersData inliersData,
final List<Line2D> samples1, final List<Line2D> samples2,
final double refinementStandardDeviation) {
super(initialEstimation, keepCovariance, inliersData, samples1, samples2, refinementStandardDeviation);
}
/**
* Refines provided initial estimation.
* This method always sets a value into provided result instance regardless
* of the fact that error has actually improved in LMSE terms or not.
*
* @param result instance where refined estimation will be stored.
* @return true if result improves (error decreases) in LMSE terms respect
* to initial estimation, false if no improvement has been achieved.
* @throws NotReadyException if not enough input data has been provided.
* @throws LockedException if estimator is locked because refinement is
* already in progress.
* @throws RefinerException if refinement fails for some reason (e.g. unable
* to converge to a result).
*/
@Override
public boolean refine(final ProjectiveTransformation2D result) throws NotReadyException, LockedException,
RefinerException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
locked = true;
if (listener != null) {
listener.onRefineStart(this, initialEstimation);
}
initialEstimation.normalize();
final var initialTotalResidual = totalResidual(initialEstimation);
try {
final var initParams = new double[
ProjectiveTransformation2D.HOM_COORDS * ProjectiveTransformation2D.HOM_COORDS];
// copy values
System.arraycopy(initialEstimation.getT().getBuffer(), 0, initParams, 0, initParams.length);
// output values to be fitted/optimized will contain residuals
final var y = new double[numInliers];
// input values will contain 2 sets of 2D lines to compute residuals
final var nDims = 2 * Line2D.LINE_NUMBER_PARAMS;
final var x = new Matrix(numInliers, nDims);
final var nSamples = inliers.length();
var pos = 0;
for (var i = 0; i < nSamples; i++) {
if (inliers.get(i)) {
// sample is inlier
final var inputLine = samples1.get(i);
final var outputLine = samples2.get(i);
inputLine.normalize();
outputLine.normalize();
x.setElementAt(pos, 0, inputLine.getA());
x.setElementAt(pos, 1, inputLine.getB());
x.setElementAt(pos, 2, inputLine.getC());
x.setElementAt(pos, 3, outputLine.getA());
x.setElementAt(pos, 4, outputLine.getB());
x.setElementAt(pos, 5, outputLine.getC());
y[pos] = residuals[i];
pos++;
}
}
final var evaluator = new LevenbergMarquardtMultiDimensionFunctionEvaluator() {
private final Line2D inputLine = new Line2D();
private final Line2D outputLine = new Line2D();
private final ProjectiveTransformation2D transformation = new ProjectiveTransformation2D();
private final GradientEstimator gradientEstimator = new GradientEstimator(params -> {
// copy values
System.arraycopy(params, 0, transformation.getT().getBuffer(), 0, params.length);
return residual(transformation, inputLine, outputLine);
});
@Override
public int getNumberOfDimensions() {
return nDims;
}
@Override
public double[] createInitialParametersArray() {
return initParams;
}
@Override
public double evaluate(final int i, final double[] point, final double[] params,
final double[] derivatives) throws EvaluationException {
inputLine.setParameters(point[0], point[1], point[2]);
outputLine.setParameters(point[3], point[4], point[5]);
// copy values
System.arraycopy(params, 0, transformation.getT().getBuffer(), 0, params.length);
final var y = residual(transformation, inputLine, outputLine);
gradientEstimator.gradient(params, derivatives);
return y;
}
};
final var fitter = new LevenbergMarquardtMultiDimensionFitter(evaluator, x, y,
getRefinementStandardDeviation());
fitter.fit();
// obtain estimated params
final var params = fitter.getA();
// update transformation
// copy values
System.arraycopy(params, 0, result.getT().getBuffer(), 0, params.length);
if (keepCovariance) {
// keep covariance
covariance = fitter.getCovar();
}
final var finalTotalResidual = totalResidual(result);
final var errorDecreased = finalTotalResidual < initialTotalResidual;
if (listener != null) {
listener.onRefineEnd(this, initialEstimation, result, errorDecreased);
}
return errorDecreased;
} catch (final Exception e) {
throw new RefinerException(e);
} finally {
locked = false;
}
}
/**
* Computes the residual between the affine transformation and a pair of
* matched lines.
*
* @param transformation a transformation.
* @param inputLine input 2D line.
* @param outputLine output 2D line.
* @return residual.
*/
private double residual(final ProjectiveTransformation2D transformation, final Line2D inputLine,
final Line2D outputLine) {
try {
inputLine.normalize();
outputLine.normalize();
transformation.transform(inputLine, residualTestLine);
return 1.0 - Math.abs(outputLine.dotProduct(residualTestLine));
} catch (final AlgebraException e) {
return 1.0;
}
}
/**
* Computes total residual among all provided inlier samples.
*
* @param transformation a transformation.
* @return total residual.
*/
private double totalResidual(final ProjectiveTransformation2D transformation) {
var result = 0.0;
final var nSamples = inliers.length();
for (var i = 0; i < nSamples; i++) {
if (inliers.get(i)) {
// sample is inlier
final var inputLine = samples1.get(i);
final var outputLine = samples2.get(i);
inputLine.normalize();
outputLine.normalize();
result += residual(transformation, inputLine, outputLine);
}
}
return result;
}
}