com.irurueta.ar.calibration.estimators.LMedSRadialDistortionRobustEstimator Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of irurueta-ar Show documentation
Show all versions of irurueta-ar Show documentation
Augmented Reality and 3D reconstruction library
/*
* Copyright (C) 2015 Alberto Irurueta Carro ([email protected])
*
* 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.RadialDistortion;
import com.irurueta.geometry.CoordinatesType;
import com.irurueta.geometry.Point2D;
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;
import java.util.logging.Level;
import java.util.logging.Logger;
/**
* Finds the best radial distortion for provided collection of 2D points using
* LMedS algorithm
*/
public class LMedSRadialDistortionRobustEstimator extends
RadialDistortionRobustEstimator {
/**
* 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 LMedSRadialDistortionRobustEstimator() {
super();
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 LMedSRadialDistortionRobustEstimator(
final RadialDistortionRobustEstimatorListener listener) {
super(listener);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param distortedPoints list of distorted points. Distorted points are
* obtained after radial distortion is applied to an undistorted point.
* @param undistortedPoints list of undistorted points.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MIN_NUMBER_OF_POINTS.
*/
public LMedSRadialDistortionRobustEstimator(final List distortedPoints,
final List undistortedPoints) {
super(distortedPoints, undistortedPoints);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param distortedPoints list of distorted points. Distorted points are
* obtained after radial distortion is applied to an undistorted point.
* @param undistortedPoints list of undistorted points.
* @param listener listener to be notified of events such as when
* estimation starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MIN_NUMBER_OF_POINTS.
*/
public LMedSRadialDistortionRobustEstimator(final List distortedPoints,
final List undistortedPoints,
final RadialDistortionRobustEstimatorListener listener) {
super(distortedPoints, undistortedPoints, listener);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param distortedPoints list of distorted points. Distorted points are
* obtained after radial distortion is applied to an undistorted point.
* @param undistortedPoints list of undistorted points.
* @param distortionCenter radial distortion center. If null it is assumed
* to be the origin of coordinates, otherwise this is typically equal to
* the camera principal point.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MIN_NUMBER_OF_POINTS.
*/
public LMedSRadialDistortionRobustEstimator(final List distortedPoints,
final List undistortedPoints,
final Point2D distortionCenter) {
super(distortedPoints, undistortedPoints, distortionCenter);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param distortedPoints list of distorted points. Distorted points are
* obtained after radial distortion is applied to an undistorted point.
* @param undistortedPoints list of undistorted points.
* @param distortionCenter radial distortion center. If null it is assumed
* to be the origin of coordinates, otherwise this is typically equal to
* the camera principal point.
* @param listener listener to be notified of events such as when
* estimation starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MIN_NUMBER_OF_POINTS.
*/
public LMedSRadialDistortionRobustEstimator(final List distortedPoints,
final List undistortedPoints,
final Point2D distortionCenter,
final RadialDistortionRobustEstimatorListener listener) {
super(distortedPoints, undistortedPoints, distortionCenter, listener);
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 radial distortion using a robust estimator and
* the best set of matched 2D points found using the robust estimator.
*
* @return a radial distortion.
* @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 RadialDistortion estimate() throws LockedException,
NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final LMedSRobustEstimator innerEstimator =
new LMedSRobustEstimator<>(
new LMedSRobustEstimatorListener() {
// point to be reused when computing residuals
private final Point2D mTestPoint = Point2D.create(
CoordinatesType.INHOMOGENEOUS_COORDINATES);
// non-robust radial distortion estimator
private final LMSERadialDistortionEstimator mRadialDistortionEstimator =
new LMSERadialDistortionEstimator();
// subset of distorted (i.e. measured) points
private final List mSubsetDistorted = new ArrayList<>();
// subset of undistorted (i.e. ideal) points
private final List mSubsetUndistorted = new ArrayList<>();
@Override
public int getTotalSamples() {
return mDistortedPoints.size();
}
@Override
public int getSubsetSize() {
return RadialDistortionRobustEstimator.MIN_NUMBER_OF_POINTS;
}
@Override
public void estimatePreliminarSolutions(
final int[] samplesIndices, final List solutions) {
mSubsetDistorted.clear();
mSubsetDistorted.add(mDistortedPoints.get(samplesIndices[0]));
mSubsetDistorted.add(mDistortedPoints.get(samplesIndices[1]));
mSubsetUndistorted.clear();
mSubsetUndistorted.add(mUndistortedPoints.get(samplesIndices[0]));
mSubsetUndistorted.add(mUndistortedPoints.get(samplesIndices[1]));
try {
mRadialDistortionEstimator.setPoints(mDistortedPoints,
mUndistortedPoints);
mRadialDistortionEstimator.setPoints(mSubsetDistorted,
mSubsetUndistorted);
final RadialDistortion distortion = mRadialDistortionEstimator.
estimate();
solutions.add(distortion);
} catch (final Exception e) {
// if anything fails, no solution is added
}
}
@Override
public double computeResidual(final RadialDistortion currentEstimation, final int i) {
final Point2D distortedPoint = mDistortedPoints.get(i);
final Point2D undistortedPoint = mUndistortedPoints.get(i);
currentEstimation.distort(undistortedPoint, mTestPoint);
return mTestPoint.distanceTo(distortedPoint);
}
@Override
public boolean isReady() {
return LMedSRadialDistortionRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(
final RobustEstimator estimator) {
try {
mRadialDistortionEstimator.setLMSESolutionAllowed(false);
mRadialDistortionEstimator.setIntrinsic(getIntrinsic());
} catch (final Exception e) {
Logger.getLogger(
LMedSRadialDistortionRobustEstimator.class.getName()).
log(Level.WARNING,
"Could not set intrinsic parameters on radial distortion estimator", e);
}
if (mListener != null) {
mListener.onEstimateStart(LMedSRadialDistortionRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(
final RobustEstimator estimator) {
if (mListener != null) {
mListener.onEstimateEnd(LMedSRadialDistortionRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator estimator,
final int iteration) {
if (mListener != null) {
mListener.onEstimateNextIteration(
LMedSRadialDistortionRobustEstimator.this,
iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator estimator,
final float progress) {
if (mListener != null) {
mListener.onEstimateProgressChange(
LMedSRadialDistortionRobustEstimator.this,
progress);
}
}
});
try {
mLocked = true;
innerEstimator.setConfidence(mConfidence);
innerEstimator.setMaxIterations(mMaxIterations);
innerEstimator.setProgressDelta(mProgressDelta);
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 {
mLocked = false;
}
}
/**
* Returns method being used for robust estimation
*
* @return method being used for robust estimation
*/
@Override
public RobustEstimatorMethod getMethod() {
return RobustEstimatorMethod.LMedS;
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy