LMedSDualAbsoluteQuadricRobustEstimator.java
/*
* Copyright (C) 2016 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.ar.calibration.estimators;
import com.irurueta.ar.calibration.DualAbsoluteQuadric;
import com.irurueta.geometry.PinholeCamera;
import com.irurueta.geometry.estimators.LockedException;
import com.irurueta.geometry.estimators.NotReadyException;
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.ArrayList;
import java.util.List;
/**
* Finds the best Dual Absolute Quadric (DAQ) for provided collection of cameras
* using LMedS algorithm.
*/
public class LMedSDualAbsoluteQuadricRobustEstimator extends DualAbsoluteQuadricRobustEstimator {
/**
* 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.
* Threshold is defined by the equations used to estimate the DAQ depending
* on the required settings (zero skewness, principal point at origin, and
* known aspect ratio).
*/
public static final double DEFAULT_STOP_THRESHOLD = 1e-6;
/**
* Minimum value that can be set as stop threshold.
* Threshold must be strictly greater than 0.0.
*/
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 one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*/
private double stopThreshold;
/**
* Constructor.
*/
public LMedSDualAbsoluteQuadricRobustEstimator() {
super();
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public LMedSDualAbsoluteQuadricRobustEstimator(final DualAbsoluteQuadricRobustEstimatorListener listener) {
super(listener);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param cameras list of cameras used to estimate the Dual Absolute Quadric
* (DAQ), which can be used to obtain pinhole camera intrinsic parameters.
* @throws IllegalArgumentException if not enough cameras are provided
* for default settings. Hence, at least 2 cameras must be provided.
*/
public LMedSDualAbsoluteQuadricRobustEstimator(final List<PinholeCamera> cameras) {
super(cameras);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param cameras list of cameras used to estimate the Dual AbsoluteQuadric
* (DAQ), which can be used to obtain pinhole camera intrinsic parameters.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if not enough cameras are provided
* for default settings. Hence, at least 2 cameras must be provided.
*/
public LMedSDualAbsoluteQuadricRobustEstimator(
final List<PinholeCamera> cameras, final DualAbsoluteQuadricRobustEstimatorListener listener) {
super(cameras, listener);
stopThreshold = 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 stopThreshold;
}
/**
* 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 tat if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value
* lower than the one typically used in RANSAC, and yet the algorithm 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();
}
this.stopThreshold = stopThreshold;
}
/**
* Estimates the Dual Absolute Quadric using provided cameras.
*
* @return estimated Dual Absolute Quadric (DAQ).
* @throws LockedException if robust estimator is locked.
* @throws NotReadyException if no valid input data has already been
* provided.
* @throws RobustEstimatorException if estimation fails for any reason
* (i.e. numerical instability, no solution available, etc).
*/
@SuppressWarnings("DuplicatedCode")
@Override
public DualAbsoluteQuadric estimate() throws LockedException, NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final var innerEstimator = new LMedSRobustEstimator<DualAbsoluteQuadric>(new LMedSRobustEstimatorListener<>() {
// subset of cameras picked on each iteration
private final List<PinholeCamera> subsetCameras = new ArrayList<>();
@Override
public int getTotalSamples() {
return cameras.size();
}
@Override
public int getSubsetSize() {
return daqEstimator.getMinNumberOfRequiredCameras();
}
@Override
public void estimatePreliminarSolutions(
final int[] samplesIndices, final List<DualAbsoluteQuadric> solutions) {
subsetCameras.clear();
for (var samplesIndex : samplesIndices) {
subsetCameras.add(cameras.get(samplesIndex));
}
try {
daqEstimator.setLMSESolutionAllowed(false);
daqEstimator.setCameras(subsetCameras);
var daq = daqEstimator.estimate();
solutions.add(daq);
} catch (final Exception e) {
// if anything fails, no solution is added
}
}
@Override
public double computeResidual(final DualAbsoluteQuadric currentEstimation, final int i) {
return residual(currentEstimation, cameras.get(i));
}
@Override
public boolean isReady() {
return LMedSDualAbsoluteQuadricRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<DualAbsoluteQuadric> estimator) {
if (listener != null) {
listener.onEstimateStart(LMedSDualAbsoluteQuadricRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<DualAbsoluteQuadric> estimator) {
if (listener != null) {
listener.onEstimateEnd(LMedSDualAbsoluteQuadricRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator<DualAbsoluteQuadric> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(LMedSDualAbsoluteQuadricRobustEstimator.this, iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator<DualAbsoluteQuadric> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(LMedSDualAbsoluteQuadricRobustEstimator.this, progress);
}
}
});
try {
locked = true;
innerEstimator.setConfidence(confidence);
innerEstimator.setMaxIterations(maxIterations);
innerEstimator.setProgressDelta(progressDelta);
innerEstimator.setStopThreshold(stopThreshold);
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;
}
}