PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator.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.algebra.AlgebraException;
import com.irurueta.geometry.CoincidentLinesException;
import com.irurueta.geometry.Line2D;
import com.irurueta.geometry.ProjectiveTransformation2D;
import com.irurueta.numerical.robust.PROMedSRobustEstimator;
import com.irurueta.numerical.robust.PROMedSRobustEstimatorListener;
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 projective 2D transformation for provided collections of
* matched 2D lines using PROMedS algorithm.
*/
@SuppressWarnings("DuplicatedCode")
public class PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator
extends LineCorrespondenceProjectiveTransformation2DRobustEstimator {
/**
* 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-6;
/**
* 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 stopThreshold;
/**
* Quality scores corresponding to each pair of matched lines.
* The larger the score value the better the quality of the matching
*/
private double[] qualityScores;
/**
* Constructor.
*/
public PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator() {
super();
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor with lists of lines to be used to estimate a projective 2D
* transformation.
* Lines in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param inputLines list of input lines to be used to estimate a projective
* 2D transformation.
* @param outputLines list of output lines to be used to estimate a
* projective 2D transformation.
* @throws IllegalArgumentException if provided lists of lines don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator(
final List<Line2D> inputLines, final List<Line2D> outputLines) {
super(inputLines, outputLines);
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 PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator(
final ProjectiveTransformation2DRobustEstimatorListener listener) {
super(listener);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor with listener and lists of lines to be used to estimate a
* projective 2D transformation.
* Lines in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputLines list of input lines to be used to estimate a projective
* 2D transformation.
* @param outputLines list of output lines to be used to estimate a
* projective 2D transformation.
* @throws IllegalArgumentException if provided lists of lines don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator(
final ProjectiveTransformation2DRobustEstimatorListener listener,
final List<Line2D> inputLines, final List<Line2D> outputLines) {
super(listener, inputLines, outputLines);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 3 samples).
*/
public PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator(final double[] qualityScores) {
super();
stopThreshold = DEFAULT_STOP_THRESHOLD;
internalSetQualityScores(qualityScores);
}
/**
* Constructor with lists of lines to be used to estimate a projective 2D
* transformation.
* Lines in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param inputLines list of input lines to be used to estimate a projective
* 2D transformation.
* @param outputLines list of output lines to be used to estimate a
* projective 2D transformation.
* @param qualityScores quality scores corresponding to each pair of matched
* lines.
* @throws IllegalArgumentException if provided lists of lines and array
* of quality scores don't have the same size or their size is smaller than
* MINIMUM_SIZE.
*/
public PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator(
final List<Line2D> inputLines, final List<Line2D> outputLines, final double[] qualityScores) {
super(inputLines, outputLines);
if (qualityScores.length != inputLines.size()) {
throw new IllegalArgumentException();
}
stopThreshold = DEFAULT_STOP_THRESHOLD;
internalSetQualityScores(qualityScores);
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param qualityScores quality scores corresponding to each pair of matched
* lines.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 3 samples).
*/
public PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator(
final ProjectiveTransformation2DRobustEstimatorListener listener, final double[] qualityScores) {
super(listener);
stopThreshold = DEFAULT_STOP_THRESHOLD;
internalSetQualityScores(qualityScores);
}
/**
* Constructor with listener and lists of lines to be used to estimate a
* projective 2D transformation.
* Lines in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputLines list of input lines to be used to estimate a projective
* 2D transformation.
* @param outputLines list of output lines to be used to estimate a
* projective 2D transformation.
* @param qualityScores quality scores corresponding to each pair of matched
* lines.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator(
final ProjectiveTransformation2DRobustEstimatorListener listener,
final List<Line2D> inputLines, final List<Line2D> outputLines, final double[] qualityScores) {
super(listener, inputLines, outputLines);
if (qualityScores.length != inputLines.size()) {
throw new IllegalArgumentException();
}
stopThreshold = DEFAULT_STOP_THRESHOLD;
internalSetQualityScores(qualityScores);
}
/**
* 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 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();
}
this.stopThreshold = stopThreshold;
}
/**
* Returns quality scores corresponding to each pair of matched lines.
* The larger the score value the better the quality of the matching.
*
* @return quality scores corresponding to each pair of matched lines.
*/
@Override
public double[] getQualityScores() {
return qualityScores;
}
/**
* Sets quality scores corresponding to each pair of matched lines.
* The larger the score value the better the quality of the matching.
*
* @param qualityScores quality scores corresponding to each pair of matched
* lines.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 3 samples).
*/
@Override
public void setQualityScores(final double[] qualityScores) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
internalSetQualityScores(qualityScores);
}
/**
* Indicates if estimator is ready to start the affine 2D transformation
* estimation.
* This is true when input data (i.e. lists of matched lines and quality
* scores) are provided and a minimum of MINIMUM_SIZE lines are available.
*
* @return true if estimator is ready, false otherwise.
*/
@Override
public boolean isReady() {
return super.isReady() && qualityScores != null && qualityScores.length == inputLines.size();
}
/**
* Estimates a projective 2D transformation using a robust estimator and
* the best set of matched 2D lines correspondences found using the robust
* estimator.
*
* @return a projective 2D transformation.
* @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 ProjectiveTransformation2D estimate() throws LockedException, NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final var innerEstimator = new PROMedSRobustEstimator<>(
new PROMedSRobustEstimatorListener<ProjectiveTransformation2D>() {
// line to be reused when computing residuals
private final Line2D testLine = new Line2D();
@Override
public double getThreshold() {
return stopThreshold;
}
@Override
public int getTotalSamples() {
return inputLines.size();
}
@Override
public int getSubsetSize() {
return ProjectiveTransformation2DRobustEstimator.MINIMUM_SIZE;
}
@Override
public void estimatePreliminarSolutions(
final int[] samplesIndices, final List<ProjectiveTransformation2D> solutions) {
final var inputLine1 = inputLines.get(samplesIndices[0]);
final var inputLine2 = inputLines.get(samplesIndices[1]);
final var inputLine3 = inputLines.get(samplesIndices[2]);
final var inputLine4 = inputLines.get(samplesIndices[3]);
final var outputLine1 = outputLines.get(samplesIndices[0]);
final var outputLine2 = outputLines.get(samplesIndices[1]);
final var outputLine3 = outputLines.get(samplesIndices[2]);
final var outputLine4 = outputLines.get(samplesIndices[3]);
try {
final var transformation = new ProjectiveTransformation2D(inputLine1, inputLine2,
inputLine3, inputLine4, outputLine1, outputLine2, outputLine3, outputLine4);
solutions.add(transformation);
} catch (final CoincidentLinesException e) {
// if lines are coincident, no solution is added
}
}
@Override
public double computeResidual(final ProjectiveTransformation2D currentEstimation, final int i) {
final var inputLine = inputLines.get(i);
final var outputLine = outputLines.get(i);
// transform input line and store result in mTestLine
try {
currentEstimation.transform(inputLine, testLine);
return getResidual(outputLine, testLine);
} catch (final AlgebraException e) {
// this happens when internal matrix of affine transformation
// cannot be reverse (i.e. transformation is not well-defined,
// numerical instabilities, etc.)
return Double.MAX_VALUE;
}
}
@Override
public boolean isReady() {
return PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<ProjectiveTransformation2D> estimator) {
if (listener != null) {
listener.onEstimateStart(
PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<ProjectiveTransformation2D> estimator) {
if (listener != null) {
listener.onEstimateEnd(
PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator<ProjectiveTransformation2D> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(
PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator.this,
iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator<ProjectiveTransformation2D> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(
PROMedSLineCorrespondenceProjectiveTransformation2DRobustEstimator.this,
progress);
}
}
@Override
public double[] getQualityScores() {
return qualityScores;
}
});
try {
locked = true;
inliersData = null;
innerEstimator.setConfidence(confidence);
innerEstimator.setMaxIterations(maxIterations);
innerEstimator.setProgressDelta(progressDelta);
final var transformation = innerEstimator.estimate();
inliersData = innerEstimator.getInliersData();
return attemptRefine(transformation);
} 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.PROMEDS;
}
/**
* 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 var inliersData = (PROMedSRobustEstimator.PROMedSInliersData) getInliersData();
// avoid setting a threshold too strict
final var threshold = inliersData.getEstimatedThreshold();
return Math.max(threshold, stopThreshold);
}
/**
* Sets quality scores corresponding to each pair of matched lines.
* This method is used internally and does not check whether instance is
* locked or not.
*
* @param qualityScores quality scores to be set.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE.
*/
private void internalSetQualityScores(final double[] qualityScores) {
if (qualityScores.length < MINIMUM_SIZE) {
throw new IllegalArgumentException();
}
this.qualityScores = qualityScores;
}
}