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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.calibration.estimators;
import com.irurueta.ar.calibration.ImageOfAbsoluteConic;
import com.irurueta.geometry.Transformation2D;
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 Image of AbsoluteConic (IAC) for provided collection of
* homographies (2D transformations) using LMedS algorithm.
*/
public class LMedSImageOfAbsoluteConicRobustEstimator extends
ImageOfAbsoluteConicRobustEstimator {
/**
* 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 equations h1'*IAC*h2 = 0 and
* h1'*IAC*h1 = h2'*IAC*h2 --< h1'*IAC*h1 - h2'*IAC*h2 = 0, where
* h1 and h2 are the 1st and 2nd columns of an homography (2D
* transformation).
* These equations are derived from the fact that rotation matrices are
* orthonormal.
*/
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 the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*/
private double mStopThreshold;
/**
* Constructor.
*/
public LMedSImageOfAbsoluteConicRobustEstimator() {
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 LMedSImageOfAbsoluteConicRobustEstimator(
final ImageOfAbsoluteConicRobustEstimatorListener listener) {
super(listener);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param homographies list of homographies (2D transformations) used to
* estimate the image of absolute conic (IAC), which can be used to obtain
* pinhole camera intrinsic parameters.
* @throws IllegalArgumentException if not enough homographies are provided
* for default settings. Hence, at least 1 homography must be provided.
*/
public LMedSImageOfAbsoluteConicRobustEstimator(
final List homographies) {
super(homographies);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param homographies list of homographies (2D transformations) used to
* estimate the image of absolute conic (IAC), 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 estimation progress changes.
* @throws IllegalArgumentException if not enough homographies are provided
* for default settings. Hence, at least 1 homography must be provided.
*/
public LMedSImageOfAbsoluteConicRobustEstimator(
final List homographies,
final ImageOfAbsoluteConicRobustEstimatorListener listener) {
super(homographies, 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 Image of Absolute Conic (IAC).
*
* @return estimated IAC.
* @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 ImageOfAbsoluteConic estimate() throws LockedException,
NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final LMedSRobustEstimator innerEstimator =
new LMedSRobustEstimator<>(
new LMedSRobustEstimatorListener() {
// subset of homographies picked on each iteration
private final List mSubsetHomographies =
new ArrayList<>();
@Override
public int getTotalSamples() {
return mHomographies.size();
}
@Override
public int getSubsetSize() {
return mIACEstimator.getMinNumberOfRequiredHomographies();
}
@Override
public void estimatePreliminarSolutions(final int[] samplesIndices,
final List solutions) {
mSubsetHomographies.clear();
for (final int samplesIndex : samplesIndices) {
mSubsetHomographies.add(mHomographies.get(
samplesIndex));
}
try {
mIACEstimator.setLMSESolutionAllowed(false);
mIACEstimator.setHomographies(mSubsetHomographies);
final ImageOfAbsoluteConic iac = mIACEstimator.estimate();
solutions.add(iac);
} catch (final Exception e) {
// if anything fails, no solution is added
}
}
@Override
public double computeResidual(
final ImageOfAbsoluteConic currentEstimation, final int i) {
return residual(currentEstimation, mHomographies.get(i));
}
@Override
public boolean isReady() {
return LMedSImageOfAbsoluteConicRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(
final RobustEstimator estimator) {
if (mListener != null) {
mListener.onEstimateStart(
LMedSImageOfAbsoluteConicRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(
final RobustEstimator estimator) {
if (mListener != null) {
mListener.onEstimateEnd(
LMedSImageOfAbsoluteConicRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator estimator,
final int iteration) {
if (mListener != null) {
mListener.onEstimateNextIteration(
LMedSImageOfAbsoluteConicRobustEstimator.this,
iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator estimator,
final float progress) {
if (mListener != null) {
mListener.onEstimateProgressChange(
LMedSImageOfAbsoluteConicRobustEstimator.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;
}
}
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