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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.epipolar.estimators;
import com.irurueta.ar.epipolar.FundamentalMatrix;
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;
/**
* Finds the best fundamental matrix for provided collections of matched 2D
* points using LMedS algorithm.
*/
public class LMedSFundamentalMatrixRobustEstimator extends
FundamentalMatrixRobustEstimator {
/**
* 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.
*
* @param fundMatrixEstimatorMethod method for non-robust fundamental matrix
* estimator.
*/
public LMedSFundamentalMatrixRobustEstimator(
final FundamentalMatrixEstimatorMethod fundMatrixEstimatorMethod) {
super(fundMatrixEstimatorMethod);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param fundMatrixEstimatorMethod method for non-robust fundamental matrix
* estimator.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public LMedSFundamentalMatrixRobustEstimator(
final FundamentalMatrixEstimatorMethod fundMatrixEstimatorMethod,
final FundamentalMatrixRobustEstimatorListener listener) {
super(fundMatrixEstimatorMethod, listener);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param fundMatrixEstimatorMethod method for non-robust fundamental matrix
* estimator.
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on right view.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
public LMedSFundamentalMatrixRobustEstimator(
final FundamentalMatrixEstimatorMethod fundMatrixEstimatorMethod,
final List leftPoints, final List rightPoints) {
super(fundMatrixEstimatorMethod, leftPoints, rightPoints);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param fundMatrixEstimatorMethod method for non-robust fundamental matrix
* estimator.
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on right view.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
public LMedSFundamentalMatrixRobustEstimator(
final FundamentalMatrixEstimatorMethod fundMatrixEstimatorMethod,
final List leftPoints, final List rightPoints,
final FundamentalMatrixRobustEstimatorListener listener) {
super(fundMatrixEstimatorMethod, leftPoints, rightPoints, listener);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*/
public LMedSFundamentalMatrixRobustEstimator() {
this(DEFAULT_FUNDAMENTAL_MATRIX_ESTIMATOR_METHOD);
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public LMedSFundamentalMatrixRobustEstimator(
final FundamentalMatrixRobustEstimatorListener listener) {
this(DEFAULT_FUNDAMENTAL_MATRIX_ESTIMATOR_METHOD, listener);
}
/**
* Constructor.
*
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on right view.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
public LMedSFundamentalMatrixRobustEstimator(final List leftPoints,
final List rightPoints) {
this(DEFAULT_FUNDAMENTAL_MATRIX_ESTIMATOR_METHOD, leftPoints,
rightPoints);
}
/**
* Constructor.
*
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on right view.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
public LMedSFundamentalMatrixRobustEstimator(final List leftPoints,
final List rightPoints,
final FundamentalMatrixRobustEstimatorListener listener) {
this(DEFAULT_FUNDAMENTAL_MATRIX_ESTIMATOR_METHOD, leftPoints,
rightPoints, listener);
}
/**
* 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).
*/
@Override
public FundamentalMatrix estimate() throws LockedException,
NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final LMedSRobustEstimator innerEstimator =
new LMedSRobustEstimator<>(
new LMedSRobustEstimatorListener() {
// subset of left points
private final List mSubsetLeftPoints = new ArrayList<>();
// subset of right points
private final List mSubsetRightPoints = new ArrayList<>();
@Override
public int getTotalSamples() {
return mLeftPoints.size();
}
@Override
public int getSubsetSize() {
return getMinRequiredPoints();
}
@Override
public void estimatePreliminarSolutions(final int[] samplesIndices,
final List solutions) {
mSubsetLeftPoints.clear();
mSubsetRightPoints.clear();
for (final int samplesIndex : samplesIndices) {
mSubsetLeftPoints.add(mLeftPoints.get(samplesIndex));
mSubsetRightPoints.add(mRightPoints.get(samplesIndex));
}
nonRobustEstimate(solutions, mSubsetLeftPoints,
mSubsetRightPoints);
}
@Override
public double computeResidual(final FundamentalMatrix currentEstimation,
final int i) {
final Point2D leftPoint = mLeftPoints.get(i);
final Point2D rightPoint = mRightPoints.get(i);
return residual(currentEstimation, leftPoint, rightPoint);
}
@Override
public boolean isReady() {
return LMedSFundamentalMatrixRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(
final RobustEstimator estimator) {
if (mListener != null) {
mListener.onEstimateStart(
LMedSFundamentalMatrixRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(
final RobustEstimator estimator) {
if (mListener != null) {
mListener.onEstimateEnd(
LMedSFundamentalMatrixRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator estimator,
final int iteration) {
if (mListener != null) {
mListener.onEstimateNextIteration(
LMedSFundamentalMatrixRobustEstimator.this,
iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator estimator,
final float progress) {
if (mListener != null) {
mListener.onEstimateProgressChange(
LMedSFundamentalMatrixRobustEstimator.this,
progress);
}
}
});
try {
mLocked = true;
mInliersData = null;
innerEstimator.setConfidence(mConfidence);
innerEstimator.setMaxIterations(mMaxIterations);
innerEstimator.setProgressDelta(mProgressDelta);
innerEstimator.setStopThreshold(mStopThreshold);
FundamentalMatrix result = innerEstimator.estimate();
mInliersData = innerEstimator.getInliersData();
return attemptRefine(result);
} 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;
}
/**
* Gets standard deviation used for Levenberg-Marquardt fitting during
* refinement.
* Returned value gives an indication of how much variance each residual
* has.
* Typically this value is related to the threshold used on each robust
* estimation, since residuals of found inliers are within the range of
* such threshold.
*
* @return standard deviation used for refinement.
*/
@Override
protected double getRefinementStandardDeviation() {
final LMedSRobustEstimator.LMedSInliersData inliersData =
(LMedSRobustEstimator.LMedSInliersData) getInliersData();
return inliersData.getEstimatedThreshold();
}
}
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