MSACEuclideanTransformation2DRobustEstimator.java
/*
* Copyright (C) 2017 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.geometry.CoordinatesType;
import com.irurueta.geometry.EuclideanTransformation2D;
import com.irurueta.geometry.Point2D;
import com.irurueta.numerical.robust.MSACRobustEstimator;
import com.irurueta.numerical.robust.MSACRobustEstimatorListener;
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 Euclidean 2D transformation for provided collections of
* matched 2D points using MSAC algorithm.
*/
@SuppressWarnings("DuplicatedCode")
public class MSACEuclideanTransformation2DRobustEstimator extends EuclideanTransformation2DRobustEstimator {
/**
* Constant defining default threshold to determine whether points are
* inliers or not.
* By default, 1.0 is considered a good value for cases where measures are
* done on pixels, since typically the minimum resolution is 1 pixel.
*/
public static final double DEFAULT_THRESHOLD = 1.0;
/**
* Minimum value that can be set as threshold.
* Threshold must be strictly greater than 0.0.
*/
public static final double MIN_THRESHOLD = 0.0;
/**
* Threshold to determine whether points are inliers or not when testing
* possible estimation solutions.
* The threshold refers to the amount of error (i.e. distance) a possible
* solution has on a matched pair of points.
*/
private double threshold;
/**
* Constructor.
*/
public MSACEuclideanTransformation2DRobustEstimator() {
super();
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor with lists of points to be used to estimate an Euclidean 2D
* transformation.
* Points 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 inputPoints list of input points ot be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public MSACEuclideanTransformation2DRobustEstimator(
final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
super(inputPoints, outputPoints);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public MSACEuclideanTransformation2DRobustEstimator(
final EuclideanTransformation2DRobustEstimatorListener listener) {
super(listener);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor with listener and lists of points to be used to estimate an
* Euclidean 2D transformation.
* Points 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 inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public MSACEuclideanTransformation2DRobustEstimator(
final EuclideanTransformation2DRobustEstimatorListener listener,
final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
super(listener, inputPoints, outputPoints);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param weakMinimumSizeAllowed true allows 3 points, false requires 4.
*/
public MSACEuclideanTransformation2DRobustEstimator(final boolean weakMinimumSizeAllowed) {
super(weakMinimumSizeAllowed);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor with lists of points to be used to estimate an Euclidean 2D
* transformation.
* Points 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 inputPoints list of input points ot be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param weakMinimumSizeAllowed true allows 3 points, false requires 4.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public MSACEuclideanTransformation2DRobustEstimator(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed) {
super(inputPoints, outputPoints, weakMinimumSizeAllowed);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param weakMinimumSizeAllowed true allows 3 points, false requires 4.
*/
public MSACEuclideanTransformation2DRobustEstimator(
final EuclideanTransformation2DRobustEstimatorListener listener, final boolean weakMinimumSizeAllowed) {
super(listener, weakMinimumSizeAllowed);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor with listener and lists of points to be used to estimate an
* Euclidean 2D transformation.
* Points 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 inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param weakMinimumSizeAllowed true allows 3 points, false requires 4.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public MSACEuclideanTransformation2DRobustEstimator(
final EuclideanTransformation2DRobustEstimatorListener listener,
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed) {
super(listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
threshold = DEFAULT_THRESHOLD;
}
/**
* Returns threshold to determine whether points are inliers or not when
* testing possible estimation solutions.
* The threshold refers to the amount of error (i.e. Euclidean distance) a
* possible solution has on a matched pair of points.
*
* @return threshold to determine whether points are inliers or not when
* testing possible estimation solutions.
*/
public double getThreshold() {
return threshold;
}
/**
* Sets threshold to determine whether points are inliers or not when
* testing possible estimation solutions.
* The threshold refers to the amount of error (i.e. Euclidean distance) a
* possible solution has on a matched pair of points.
*
* @param threshold threshold to determine whether points are inliers or
* not.
* @throws IllegalArgumentException if provided value is equal or less than
* zero.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
*/
public void setThreshold(final double threshold) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (threshold <= MIN_THRESHOLD) {
throw new IllegalArgumentException();
}
this.threshold = threshold;
}
/**
* Estimates an Euclidean 2D transformation using a robust estimator and
* the best set of matched 2D point correspondences found using the robust
* estimator.
*
* @return an Euclidean 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 EuclideanTransformation2D estimate() throws LockedException, NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final var innerEstimator = new MSACRobustEstimator<>(
new MSACRobustEstimatorListener<EuclideanTransformation2D>() {
// point to be reused when computing residuals
private final Point2D testPoint = Point2D.create(CoordinatesType.HOMOGENEOUS_COORDINATES);
private final EuclideanTransformation2DEstimator nonRobustEstimator =
new EuclideanTransformation2DEstimator(isWeakMinimumSizeAllowed());
private final List<Point2D> subsetInputPoints = new ArrayList<>();
private final List<Point2D> subsetOutputPoints = new ArrayList<>();
@Override
public double getThreshold() {
return threshold;
}
@Override
public int getTotalSamples() {
return inputPoints.size();
}
@Override
public int getSubsetSize() {
return nonRobustEstimator.getMinimumPoints();
}
@Override
public void estimatePreliminarSolutions(
final int[] samplesIndices, final List<EuclideanTransformation2D> solutions) {
subsetInputPoints.clear();
subsetOutputPoints.clear();
for (final var samplesIndex : samplesIndices) {
subsetInputPoints.add(inputPoints.get(samplesIndex));
subsetOutputPoints.add(outputPoints.get(samplesIndex));
}
try {
nonRobustEstimator.setPoints(subsetInputPoints, subsetOutputPoints);
solutions.add(nonRobustEstimator.estimate());
} catch (final Exception e) {
// if points are coincident, no solution is added
}
}
@Override
public double computeResidual(final EuclideanTransformation2D currentEstimation, final int i) {
final var inputPoint = inputPoints.get(i);
final var outputPoint = outputPoints.get(i);
// transform input point and store result in mTestPoint
currentEstimation.transform(inputPoint, testPoint);
return outputPoint.distanceTo(testPoint);
}
@Override
public boolean isReady() {
return MSACEuclideanTransformation2DRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<EuclideanTransformation2D> estimator) {
if (listener != null) {
listener.onEstimateStart(MSACEuclideanTransformation2DRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<EuclideanTransformation2D> estimator) {
if (listener != null) {
listener.onEstimateEnd(MSACEuclideanTransformation2DRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator<EuclideanTransformation2D> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(
MSACEuclideanTransformation2DRobustEstimator.this, iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator<EuclideanTransformation2D> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(
MSACEuclideanTransformation2DRobustEstimator.this, progress);
}
}
});
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.MSAC;
}
/**
* 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() {
return threshold;
}
}