LMedSSphereRobustEstimator.java
/*
* Copyright (C) 2015 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.estimators;
import com.irurueta.geometry.CoplanarPointsException;
import com.irurueta.geometry.Point3D;
import com.irurueta.geometry.Sphere;
import com.irurueta.numerical.robust.LMedSRobustEstimator;
import com.irurueta.numerical.robust.LMedSRobustEstimatorListener;
import com.irurueta.numerical.robust.RobustEstimator;
import com.irurueta.numerical.robust.RobustEstimatorException;
import com.irurueta.numerical.robust.RobustEstimatorMethod;
import java.util.List;
/**
* Finds the best sphere for provided collection of 3D points using LMedS
* algorithm.
*/
public class LMedSSphereRobustEstimator extends SphereRobustEstimator {
/**
* Default value to be used for stop threshold. Stop threshold can be used
* to keep the algorithm iterating in case that best estimated threshold
* using median of residuals is not small enough. Once a solution is found
* that generates a threshold below this value, the algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*/
public static final double DEFAULT_STOP_THRESHOLD = 1e-3;
/**
* Minimum allowed stop threshold value.
*/
public static final double MIN_STOP_THRESHOLD = 0.0;
/**
* Threshold to be used to keep the algorithm iterating in case that best
* estimated threshold using median of residuals is not small enough. Once
* a solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*/
private double mStopThreshold;
/**
* Constructor.
*/
public LMedSSphereRobustEstimator() {
super();
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor with points.
*
* @param points 3D points to estimate a sphere.
* @throws IllegalArgumentException if provided list of points don't have
* a size greater or equal than MINIMUM_SIZE.
*/
public LMedSSphereRobustEstimator(final List<Point3D> points) {
super(points);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public LMedSSphereRobustEstimator(final SphereRobustEstimatorListener listener) {
super(listener);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param points 3D points to estimate a sphere.
* @throws IllegalArgumentException if provided list of points don't have
* a size greater or equal than MINIMUM_SIZE.
*/
public LMedSSphereRobustEstimator(final SphereRobustEstimatorListener listener, final List<Point3D> points) {
super(listener, points);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Returns threshold to be used to keep the algorithm iterating in case that
* best estimated threshold using median of residuals is not small enough.
* Once a solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*
* @return stop threshold to stop the algorithm prematurely when a certain
* accuracy has been reached.
*/
public double getStopThreshold() {
return mStopThreshold;
}
/**
* Sets threshold to be used to keep the algorithm iterating in case that
* best estimated threshold using median of residuals is not small enough.
* Once a solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*
* @param stopThreshold stop threshold to stop the algorithm prematurely
* when a certain accuracy has been reached.
* @throws IllegalArgumentException if provided value is zero or negative.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
*/
public void setStopThreshold(final double stopThreshold) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (stopThreshold <= MIN_STOP_THRESHOLD) {
throw new IllegalArgumentException();
}
mStopThreshold = stopThreshold;
}
/**
* Estimates a sphere using a robust estimator and the best set of 3D points
* that fit into the locus of the estimated sphere found using the robust
* estimator.
*
* @return a sphere.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
* @throws NotReadyException if provided input data is not enough to start
* the estimation.
* @throws RobustEstimatorException if estimation fails for any reason
* (i.e. numerical instability, no solution available, etc).
*/
@SuppressWarnings("DuplicatedCode")
@Override
public Sphere estimate() throws LockedException, NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final var innerEstimator = new LMedSRobustEstimator<>(new LMedSRobustEstimatorListener<Sphere>() {
@Override
public int getTotalSamples() {
return points.size();
}
@Override
public int getSubsetSize() {
return SphereRobustEstimator.MINIMUM_SIZE;
}
@Override
public void estimatePreliminarSolutions(final int[] samplesIndices, final List<Sphere> solutions) {
final var point1 = points.get(samplesIndices[0]);
final var point2 = points.get(samplesIndices[1]);
final var point3 = points.get(samplesIndices[2]);
final var point4 = points.get(samplesIndices[3]);
try {
final var sphere = new Sphere(point1, point2, point3, point4);
solutions.add(sphere);
} catch (final CoplanarPointsException e) {
// if points are coincident, no solution is added
}
}
@Override
public double computeResidual(final Sphere currentEstimation, final int i) {
return residual(currentEstimation, points.get(i));
}
@Override
public boolean isReady() {
return LMedSSphereRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<Sphere> estimator) {
if (listener != null) {
listener.onEstimateStart(LMedSSphereRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<Sphere> estimator) {
if (listener != null) {
listener.onEstimateEnd(LMedSSphereRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(final RobustEstimator<Sphere> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(LMedSSphereRobustEstimator.this, iteration);
}
}
@Override
public void onEstimateProgressChange(final RobustEstimator<Sphere> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(LMedSSphereRobustEstimator.this, progress);
}
}
});
try {
locked = true;
innerEstimator.setConfidence(confidence);
innerEstimator.setMaxIterations(maxIterations);
innerEstimator.setProgressDelta(progressDelta);
innerEstimator.setStopThreshold(mStopThreshold);
return innerEstimator.estimate();
} catch (final com.irurueta.numerical.LockedException e) {
throw new LockedException(e);
} catch (final com.irurueta.numerical.NotReadyException e) {
throw new NotReadyException(e);
} finally {
locked = false;
}
}
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
@Override
public RobustEstimatorMethod getMethod() {
return RobustEstimatorMethod.LMEDS;
}
}