RANSACConicRobustEstimator.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.geometry.CoincidentPointsException;
import com.irurueta.geometry.Conic;
import com.irurueta.geometry.Point2D;
import com.irurueta.numerical.robust.RANSACRobustEstimator;
import com.irurueta.numerical.robust.RANSACRobustEstimatorListener;
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 conic for provided collection of 2D points using RANSAC
* algorithm.
*/
@SuppressWarnings("DuplicatedCode")
public class RANSACConicRobustEstimator extends ConicRobustEstimator {
/**
* Constant defining default threshold to determine whether points are
* inliers or not.
* Threshold is defined by the equation abs(trans(x) * C * x) < t, where
* trans is the transposition, x is a point, C is a conic and t is a
* threshold.
* This equation determines the points x belonging to the locus of a conic
* C up to a certain threshold.
*/
public static final double DEFAULT_THRESHOLD = 1e-6;
/**
* 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 RANSACConicRobustEstimator() {
super();
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor with points.
*
* @param points 2D points to estimate a conic.
* @throws IllegalArgumentException if provided list of points don't have
* a size greater or equal than MINIMUM_SIZE.
*/
public RANSACConicRobustEstimator(final List<Point2D> points) {
super(points);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public RANSACConicRobustEstimator(final ConicRobustEstimatorListener listener) {
super(listener);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param points 2D points to estimate a conic.
* @throws IllegalArgumentException if provided list of points don't have
* a size greater or equal than MINIMUM_SIZE.
*/
public RANSACConicRobustEstimator(final ConicRobustEstimatorListener listener, final List<Point2D> points) {
super(listener, points);
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 a possible solution has on a
* given point.
*
* @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 a possible solution has on
* a given point.
*
* @param threshold threshold to be set.
* @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 a conic using a robust estimator and the best set of 2D points
* that fit into the locus of the estimated conic found using the robust
* estimator.
*
* @return a conic.
* @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 Conic estimate() throws LockedException, NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final var innerEstimator = new RANSACRobustEstimator<>(new RANSACRobustEstimatorListener<Conic>() {
@Override
public double getThreshold() {
return threshold;
}
@Override
public int getTotalSamples() {
return points.size();
}
@Override
public int getSubsetSize() {
return ConicRobustEstimator.MINIMUM_SIZE;
}
@Override
public void estimatePreliminarSolutions(final int[] samplesIndices, final List<Conic> solutions) {
final var point1 = points.get(samplesIndices[0]);
final var point2 = points.get(samplesIndices[1]);
final var point3 = points.get(samplesIndices[2]);
final var point4 = points.get(samplesIndices[3]);
final var point5 = points.get(samplesIndices[4]);
try {
final var conic = new Conic(point1, point2, point3, point4, point5);
solutions.add(conic);
} catch (final CoincidentPointsException e) {
// if points are coincident, no solution is added
}
}
@Override
public double computeResidual(final Conic currentEstimation, final int i) {
return residual(currentEstimation, points.get(i));
}
@Override
public boolean isReady() {
return RANSACConicRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<Conic> estimator) {
if (listener != null) {
listener.onEstimateStart(RANSACConicRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<Conic> estimator) {
if (listener != null) {
listener.onEstimateEnd(RANSACConicRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(final RobustEstimator<Conic> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(RANSACConicRobustEstimator.this, iteration);
}
}
@Override
public void onEstimateProgressChange(final RobustEstimator<Conic> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(RANSACConicRobustEstimator.this, progress);
}
}
});
try {
locked = true;
innerEstimator.setConfidence(confidence);
innerEstimator.setMaxIterations(maxIterations);
innerEstimator.setProgressDelta(progressDelta);
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 {
locked = false;
}
}
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
@Override
public RobustEstimatorMethod getMethod() {
return RobustEstimatorMethod.RANSAC;
}
}