com.irurueta.ar.calibration.estimators.RANSACDualAbsoluteQuadricRobustEstimator Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of irurueta-ar Show documentation
Show all versions of irurueta-ar Show documentation
Augmented Reality and 3D reconstruction library
/*
* Copyright (C) 2016 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.DualAbsoluteQuadric;
import com.irurueta.geometry.PinholeCamera;
import com.irurueta.geometry.estimators.LockedException;
import com.irurueta.geometry.estimators.NotReadyException;
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.ArrayList;
import java.util.List;
/**
* Finds the best dual absolute quadric (DAQ) using RANSAC algorithm.
*/
public class RANSACDualAbsoluteQuadricRobustEstimator extends
DualAbsoluteQuadricRobustEstimator {
/**
* Constant defining default threshold to determine whether cameras are
* inliers or not.
* Threshold is defined by the equations used to estimate the DAQ depending
* on the required settings (zero skewness, principal point at origin, and
* known aspect ratio).
*/
public static final double DEFAULT_THRESHOLD = 1e-3;
/**
* 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 cameras are inliers or not when
* testing possible estimation solutions.
* The threshold refers to the amount of error a possible solution has.
*/
private double mThreshold;
/**
* Constructor.
*/
public RANSACDualAbsoluteQuadricRobustEstimator() {
super();
mThreshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public RANSACDualAbsoluteQuadricRobustEstimator(
final DualAbsoluteQuadricRobustEstimatorListener listener) {
super(listener);
mThreshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param cameras list of cameras used to estimate the dual absolute quadric
* (DAQ), which can be used to obtain pinhole camera intrinsic parameters.
* @throws IllegalArgumentException if not enough cameras are provided for
* default settings. Hence, at least 2 cameras must be provided.
*/
public RANSACDualAbsoluteQuadricRobustEstimator(
final List cameras) {
super(cameras);
mThreshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param cameras list of cameras used to estimate the dual absolute quadric
* (DAQ), 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 cameras are provided for
* default settings. Hence, at least 2 cameras must be provided.
*/
public RANSACDualAbsoluteQuadricRobustEstimator(final List cameras,
final DualAbsoluteQuadricRobustEstimatorListener listener) {
super(cameras, listener);
mThreshold = DEFAULT_THRESHOLD;
}
/**
* Returns threshold to determine whether cameras are inliers or not
* when testing possible estimation solutions.
* The threshold refers to the amount of error a possible solution has.
*
* @return threshold to determine whether cameras are inliers or not.
*/
public double getThreshold() {
return mThreshold;
}
/**
* Sets threshold to determine whether cameras are inliers or not when
* testing possible estimation solutions.
* The threshold refers to the amount of error a possible solution has.
*
* @param threshold threshold to determine whether cameras are inliers or
* not when testing possible estimation solutions.
* @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();
}
mThreshold = threshold;
}
/**
* Estimates the Dual Absolute Quadric using provided cameras.
*
* @return estimated Dual Absolute Quadric (DAQ).
* @throws LockedException if robust estimator is locked.
* @throws NotReadyException if no valid input data has already been
* provided.
* @throws RobustEstimatorException if estimation fails for any reason
* (i.e. numerical instability, no solution available, etc).
*/
@SuppressWarnings("DuplicatedCode")
@Override
public DualAbsoluteQuadric estimate() throws LockedException,
NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final RANSACRobustEstimator innerEstimator =
new RANSACRobustEstimator<>(
new RANSACRobustEstimatorListener() {
// subset of cameras picked on each iteration
private final List mSubsetCameras =
new ArrayList<>();
@Override
public double getThreshold() {
return mThreshold;
}
@Override
public int getTotalSamples() {
return mCameras.size();
}
@Override
public int getSubsetSize() {
return mDAQEstimator.getMinNumberOfRequiredCameras();
}
@Override
public void estimatePreliminarSolutions(final int[] samplesIndices,
final List solutions) {
mSubsetCameras.clear();
for (final int samplesIndex : samplesIndices) {
mSubsetCameras.add(mCameras.get(samplesIndex));
}
try {
mDAQEstimator.setLMSESolutionAllowed(false);
mDAQEstimator.setCameras(mSubsetCameras);
final DualAbsoluteQuadric daq = mDAQEstimator.estimate();
solutions.add(daq);
} catch (final Exception e) {
// if anything fails, no solution is added
}
}
@Override
public double computeResidual(
final DualAbsoluteQuadric currentEstimation, final int i) {
return residual(currentEstimation, mCameras.get(i));
}
@Override
public boolean isReady() {
return RANSACDualAbsoluteQuadricRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(
final RobustEstimator estimator) {
if (mListener != null) {
mListener.onEstimateStart(
RANSACDualAbsoluteQuadricRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(
final RobustEstimator estimator) {
if (mListener != null) {
mListener.onEstimateEnd(
RANSACDualAbsoluteQuadricRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator estimator,
final int iteration) {
if (mListener != null) {
mListener.onEstimateNextIteration(
RANSACDualAbsoluteQuadricRobustEstimator.this,
iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator estimator,
final float progress) {
if (mListener != null) {
mListener.onEstimateProgressChange(
RANSACDualAbsoluteQuadricRobustEstimator.this,
progress);
}
}
});
try {
mLocked = true;
innerEstimator.setConfidence(mConfidence);
innerEstimator.setMaxIterations(mMaxIterations);
innerEstimator.setProgressDelta(mProgressDelta);
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.RANSAC;
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy