com.irurueta.ar.calibration.estimators.WeightedImageOfAbsoluteConicEstimator 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) 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.algebra.AlgebraException;
import com.irurueta.algebra.Matrix;
import com.irurueta.algebra.SingularValueDecomposer;
import com.irurueta.ar.calibration.ImageOfAbsoluteConic;
import com.irurueta.geometry.ProjectiveTransformation2D;
import com.irurueta.geometry.Transformation2D;
import com.irurueta.geometry.estimators.LockedException;
import com.irurueta.geometry.estimators.NotReadyException;
import com.irurueta.numerical.robust.WeightSelection;
import com.irurueta.sorting.SortingException;
import java.util.List;
/**
* This class implements an Image of Absolute Conic (IAC) estimator using a
* weighted algorithm and correspondences.
* Weights can be used so that homographies assumed to have a better quality
* (i.e. more precisely estimated) are considered to be more relevant.
* Aside from enabling constraints whenever possible to obtain more stable and
* accurate results, its is discouraged to use a large number of homographies,
* even if they are correctly weighted, since as the number of homographies
* increase so do the rounding errors.
*/
@SuppressWarnings("DuplicatedCode")
public class WeightedImageOfAbsoluteConicEstimator extends
ImageOfAbsoluteConicEstimator {
/**
* Default number of homographies to be weighted and taken into account.
*/
public static final int DEFAULT_MAX_HOMOGRAPHIES = 50;
/**
* Indicates if weights are sorted by default so that largest weighted
* correspondences are used first.
*/
public static final boolean DEFAULT_SORT_WEIGHTS = true;
/**
* Maximum number of homographies (i.e. correspondences) to be weighted and
* taken into account.
*/
private int mMaxHomographies;
/**
* Indicates if weights are sorted by default so that largest weighted
* correspondences are used first.
*/
private boolean mSortWeights;
/**
* Array containing weights for all homographies.
*/
private double[] mWeights;
/**
* Constructor.
*/
public WeightedImageOfAbsoluteConicEstimator() {
super();
mMaxHomographies = DEFAULT_MAX_HOMOGRAPHIES;
mSortWeights = DEFAULT_SORT_WEIGHTS;
}
/**
* Constructor with listener.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or estimation progress changes.
*/
public WeightedImageOfAbsoluteConicEstimator(
final ImageOfAbsoluteConicEstimatorListener listener) {
super(listener);
mMaxHomographies = DEFAULT_MAX_HOMOGRAPHIES;
mSortWeights = DEFAULT_SORT_WEIGHTS;
}
/**
* 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 weights array containing a weight amount for each homography.
* The larger the value of a weight, the most significant the correspondence
* will be.
* @throws IllegalArgumentException if not enough homographies are provided
* for default constraints during IAC estimation.
*/
public WeightedImageOfAbsoluteConicEstimator(
final List homographies, final double[] weights) {
super();
internalSetHomographiesAndWeights(homographies, weights);
mMaxHomographies = DEFAULT_MAX_HOMOGRAPHIES;
mSortWeights = DEFAULT_SORT_WEIGHTS;
}
/**
* 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 weights array containing a weight amount for each homography.
* The larger the value of a weight, the most significant the correspondence
* will be.
* @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 constraints during IAC estimation.
*/
public WeightedImageOfAbsoluteConicEstimator(
final List homographies, final double[] weights,
final ImageOfAbsoluteConicEstimatorListener listener) {
super(listener);
internalSetHomographiesAndWeights(homographies, weights);
mMaxHomographies = DEFAULT_MAX_HOMOGRAPHIES;
mSortWeights = DEFAULT_SORT_WEIGHTS;
}
/**
* Sets list of homographies to estimate IAC.
* This method override always throws an IllegalArgumentException because
* it is expected to provide both homographies and their weights.
*
* @param homographies list of homographies to estimate IAC.
* @throws IllegalArgumentException always thrown in this implementation.
*/
@Override
public void setHomographies(final List homographies) {
throw new IllegalArgumentException();
}
/**
* Sets list of homographies to estimate IAC along with their corresponding
* weights.
*
* @param homographies list of homographies to estimate IAC.
* @param weights array containing a weight amount for each homography.
* The larger the value of a weight, the most significant the correspondence
* will be.
* @throws LockedException if estimator is locked.
* @throws IllegalArgumentException if not enough homographies are provided
* for default constraints during IAC estimation.
*/
public void setHomographiesAndWeights(final List homographies,
final double[] weights) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
internalSetHomographiesAndWeights(homographies, weights);
}
/**
* Returns array containing a weight amount for each homography.
* The larger the value of a weight, the most significant the correspondence
* will be.
*
* @return array containing weights for each correspondence.
*/
public double[] getWeights() {
return mWeights;
}
/**
* Returns boolean indicating whether weights have been provided and are
* available for retrieval.
*
* @return true if weights are available, false otherwise.
*/
public boolean areWeightsAvailable() {
return mWeights != null;
}
/**
* Returns maximum number of homographies to be weighted and taken into
* account.
*
* @return maximum number of homographies to be weighted.
*/
public int getMaxHomographies() {
return mMaxHomographies;
}
/**
* Sets maximum number of homographies to be weighted and taken into
* account.
* This method must be called after enforcing constraints, because the
* minimum number of required homographies will be checked based on current
* settings.
*
* @param maxHomographies maximum number of homographies to be weighted.
* @throws IllegalArgumentException if provided value is less than the
* minimum allowed number of homographies.
* @throws LockedException if this instance is locked.
*/
public void setMaxHomographies(final int maxHomographies) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (maxHomographies < getMinNumberOfRequiredHomographies()) {
throw new IllegalArgumentException();
}
mMaxHomographies = maxHomographies;
}
/**
* Indicates if weights are sorted by so that largest weighted homographies
* are used first.
*
* @return true if weights are sorted, false otherwise.
*/
public boolean isSortWeightsEnabled() {
return mSortWeights;
}
/**
* Specifies whether weights are sorted by so that largest weighted
* homographies are used first.
*
* @param sortWeights true if weights are sorted, false otherwise.
* @throws LockedException if this instance is locked.
*/
public void setSortWeightsEnabled(final boolean sortWeights)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mSortWeights = sortWeights;
}
/**
* Indicates if this estimator is ready to start the estimation.
* Estimator will be ready once both list of homographies and weights are
* available and enough homobraphies have been provided
*
* @return true if estimator is ready, false otherwise.
*/
@Override
public boolean isReady() {
return super.isReady() && areWeightsAvailable() &&
mHomographies.size() == mWeights.length;
}
/**
* Estimates Image of Absolute Conic (IAC).
*
* @return estimated IAC.
* @throws LockedException if estimator is locked.
* @throws NotReadyException if input has not yet been provided.
* @throws ImageOfAbsoluteConicEstimatorException if an error occurs during
* estimation, usually because repeated homographies are provided, or
* homographies corresponding to degenerate camera movements such as pure
* parallel translations where no additional data is really provided.
* Indeed, if provided homographies belong to the group of affine
* transformations (or other groups contained within such as metric or
* euclidean ones), this exception will raise because camera movements will
* be degenerate. To avoid this exception, homographies must be purely
* projective.
*/
@Override
public ImageOfAbsoluteConic estimate() throws LockedException,
NotReadyException, ImageOfAbsoluteConicEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
try {
mLocked = true;
if (mListener != null) {
mListener.onEstimateStart(this);
}
final ImageOfAbsoluteConic iac;
if (mZeroSkewness && mPrincipalPointAtOrigin) {
if (mFocalDistanceAspectRatioKnown) {
iac = estimateZeroSkewnessPrincipalPointAtOriginAndKnownFocalDistanceAspectRatio();
} else {
iac = estimateZeroSkewnessAndPrincipalPointAtOrigin();
}
} else if (mZeroSkewness) { //&& !mPrincipalPointAtOrigin
if (mFocalDistanceAspectRatioKnown) {
iac = estimateZeroSkewnessAndKnownFocalDistanceAspectRatio();
} else {
iac = estimateZeroSkewness();
}
} else if (mPrincipalPointAtOrigin) { //&& !mZeroSkewness
iac = estimatePrincipalPointAtOrigin();
} else {
iac = estimateNoConstraints();
}
if (mListener != null) {
mListener.onEstimateEnd(this);
}
return iac;
} finally {
mLocked = false;
}
}
/**
* Returns type of IAC estimator.
*
* @return type of IAC estimator.
*/
@Override
public ImageOfAbsoluteConicEstimatorType getType() {
return ImageOfAbsoluteConicEstimatorType.WEIGHTED_IAC_ESTIMATOR;
}
/**
* Sets list of homographies to estimate IAC.
* This method does not check whether estimator is locked.
*
* @param homographies list of homographies to estimate IAC.
* @param weights array containing a weight amount for each homography.
* The larger the value of a weight, the more significant the correspondence
* will be.
* @throws IllegalArgumentException if provided list of homographies does not
* contain enough elements to estimate the IAC using current settings.
*/
private void internalSetHomographiesAndWeights(
final List homographies, double[] weights) {
if (weights == null || homographies == null ||
weights.length != homographies.size()) {
throw new IllegalArgumentException();
}
internalSetHomographies(homographies);
mWeights = weights;
}
/**
* Estimates Image of Absolute Conic (IAC) without constraints.
*
* @return estimated IAC.
* @throws ImageOfAbsoluteConicEstimatorException if an error occurs during
* estimation, usually because repeated homographies are provided, or
* homographies corresponding to degenerate camera movements such as pure
* parallel translations where no additional data is really provided.
*/
private ImageOfAbsoluteConic estimateNoConstraints()
throws ImageOfAbsoluteConicEstimatorException {
try {
final WeightSelection selection = WeightSelection.selectWeights(mWeights,
mSortWeights, mMaxHomographies);
final boolean[] selected = selection.getSelected();
final int nHomographies = selection.getNumSelected();
final Matrix a = new Matrix(2 * nHomographies, 6);
int index = 0;
int counter = 0;
ProjectiveTransformation2D t = null;
final Matrix h = new Matrix(ProjectiveTransformation2D.HOM_COORDS,
ProjectiveTransformation2D.HOM_COORDS);
// elements ij of homography (last column is not required)
double h11;
double h12;
double h21;
double h22;
double h31;
double h32;
double rowNorm;
double weight;
double factor;
for (final Transformation2D homography : mHomographies) {
if (selected[index]) {
weight = mWeights[index];
// convert homography into projective so it can be normalized
homography.asMatrix(h);
if (t == null) {
t = new ProjectiveTransformation2D(h);
} else {
t.setT(h);
}
// normalize
t.normalize();
// obtain elements of projective transformation matrix
// there is no need to retrieve internal matrix h, as we already
// hold a reference
h11 = h.getElementAt(0, 0);
h12 = h.getElementAt(0, 1);
h21 = h.getElementAt(1, 0);
h22 = h.getElementAt(1, 1);
h31 = h.getElementAt(2, 0);
h32 = h.getElementAt(2, 1);
// fill first equation
a.setElementAt(counter, 0, h11 * h12);
a.setElementAt(counter, 1, h11 * h22 + h21 * h12);
a.setElementAt(counter, 2, h21 * h22);
a.setElementAt(counter, 3, h11 * h32 + h31 * h12);
a.setElementAt(counter, 4, h21 * h32 + h31 * h22);
a.setElementAt(counter, 5, h31 * h32);
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0) +
Math.pow(a.getElementAt(counter, 3), 2.0) +
Math.pow(a.getElementAt(counter, 4), 2.0) +
Math.pow(a.getElementAt(counter, 5), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
a.setElementAt(counter, 3, a.getElementAt(counter, 3) *
factor);
a.setElementAt(counter, 4, a.getElementAt(counter, 4) *
factor);
a.setElementAt(counter, 5, a.getElementAt(counter, 5) *
factor);
counter++;
// fill second equation
a.setElementAt(counter, 0, Math.pow(h11, 2.0) - Math.pow(h12, 2.0));
a.setElementAt(counter, 1, 2.0 * (h11 * h21 - h12 * h22));
a.setElementAt(counter, 2, Math.pow(h21, 2.0) - Math.pow(h22, 2.0));
a.setElementAt(counter, 3, 2.0 * (h11 * h31 - h12 * h32));
a.setElementAt(counter, 4, 2.0 * (h21 * h31 - h22 * h32));
a.setElementAt(counter, 5, Math.pow(h31, 2.0) - Math.pow(h32, 2.0));
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0) +
Math.pow(a.getElementAt(counter, 3), 2.0) +
Math.pow(a.getElementAt(counter, 4), 2.0) +
Math.pow(a.getElementAt(counter, 5), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
a.setElementAt(counter, 3, a.getElementAt(counter, 3) *
factor);
a.setElementAt(counter, 4, a.getElementAt(counter, 4) *
factor);
a.setElementAt(counter, 5, a.getElementAt(counter, 5) *
factor);
counter++;
}
index++;
}
final SingularValueDecomposer decomposer = new SingularValueDecomposer(a);
decomposer.decompose();
if (decomposer.getNullity() > 1) {
// homographies constitute a degenerate camera movement.
// A linear combination of possible IAC's exist (i.e. solution is
// not unique up to scale)
throw new ImageOfAbsoluteConicEstimatorException();
}
final Matrix v = decomposer.getV();
// use last column of V as IAC vector
// the last column of V contains IAC matrix (B), which is symmetric
// and positive definite, ordered as follows: B11, B12, B22, B13,
// B23, B33
final double b11 = v.getElementAt(0, 5);
final double b12 = v.getElementAt(1, 5);
final double b22 = v.getElementAt(2, 5);
final double b13 = v.getElementAt(3, 5);
final double b23 = v.getElementAt(4, 5);
final double b33 = v.getElementAt(5, 5);
// A conic is defined as [A B D]
// [B C E]
// [D E F]
return new ImageOfAbsoluteConic(b11, b12, b22, b13, b23, b33);
} catch (final AlgebraException | SortingException e) {
throw new ImageOfAbsoluteConicEstimatorException(e);
}
}
/**
* Estimates Image of Absolute Conic (IAC) assuming that skewness is zero.
*
* @return estimated IAC.
* @throws ImageOfAbsoluteConicEstimatorException if an error occurs during
* estimation, usually because repeated homographies are provided, or
* homographies corresponding to degenerate camera movements such as pure
* parallel translations where no additional data is really provided.
*/
private ImageOfAbsoluteConic estimateZeroSkewness()
throws ImageOfAbsoluteConicEstimatorException {
try {
final int nHomographies = mHomographies.size();
final WeightSelection selection = WeightSelection.selectWeights(mWeights,
mSortWeights, mMaxHomographies);
final boolean[] selected = selection.getSelected();
final Matrix a = new Matrix(2 * nHomographies, 5);
int index = 0;
int counter = 0;
ProjectiveTransformation2D t = null;
final Matrix h = new Matrix(ProjectiveTransformation2D.HOM_COORDS,
ProjectiveTransformation2D.HOM_COORDS);
// elements ij of homography (last column is not required)
double h11;
double h12;
double h21;
double h22;
double h31;
double h32;
double rowNorm;
double weight;
double factor;
for (final Transformation2D homography : mHomographies) {
if (selected[index]) {
weight = mWeights[index];
// convert homography into projective so it can be normalized
homography.asMatrix(h);
if (t == null) {
t = new ProjectiveTransformation2D(h);
} else {
t.setT(h);
}
// normalize
t.normalize();
// obtain elements of projective transformation matrix
// there is no need to retrieve internal matrix h, as we already
// hold a reference
h11 = h.getElementAt(0, 0);
h12 = h.getElementAt(0, 1);
h21 = h.getElementAt(1, 0);
h22 = h.getElementAt(1, 1);
h31 = h.getElementAt(2, 0);
h32 = h.getElementAt(2, 1);
// fill first equation
a.setElementAt(counter, 0, h11 * h12);
a.setElementAt(counter, 1, h21 * h22);
a.setElementAt(counter, 2, h11 * h32 + h31 * h12);
a.setElementAt(counter, 3, h21 * h32 + h31 * h22);
a.setElementAt(counter, 4, h31 * h32);
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0) +
Math.pow(a.getElementAt(counter, 3), 2.0) +
Math.pow(a.getElementAt(counter, 4), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
a.setElementAt(counter, 3, a.getElementAt(counter, 3) *
factor);
a.setElementAt(counter, 4, a.getElementAt(counter, 4) *
factor);
counter++;
// fill second equation
a.setElementAt(counter, 0, Math.pow(h11, 2.0) - Math.pow(h12, 2.0));
a.setElementAt(counter, 1, Math.pow(h21, 2.0) - Math.pow(h22, 2.0));
a.setElementAt(counter, 2, 2.0 * (h11 * h31 - h12 * h32));
a.setElementAt(counter, 3, 2.0 * (h21 * h31 - h22 * h32));
a.setElementAt(counter, 4, Math.pow(h31, 2.0) - Math.pow(h32, 2.0));
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0) +
Math.pow(a.getElementAt(counter, 3), 2.0) +
Math.pow(a.getElementAt(counter, 4), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
a.setElementAt(counter, 3, a.getElementAt(counter, 3) *
factor);
a.setElementAt(counter, 4, a.getElementAt(counter, 4) *
factor);
counter++;
}
index++;
}
final SingularValueDecomposer decomposer = new SingularValueDecomposer(a);
decomposer.decompose();
if (decomposer.getNullity() > 1) {
// homographies constitute a degenerate camera movement.
// A linear combination of possible IAC's exist (i.e. solution is
// not unique up to scale)
throw new ImageOfAbsoluteConicEstimatorException();
}
final Matrix v = decomposer.getV();
// use last column of V as IAC vector
// the last column of V contains IAC matrix (B), which is symmetric
// and positive definite, ordered as follows: B11, B12, B22, B13,
// B23, B33
final double b11 = v.getElementAt(0, 4);
final double b22 = v.getElementAt(1, 4);
final double b13 = v.getElementAt(2, 4);
final double b23 = v.getElementAt(3, 4);
final double b33 = v.getElementAt(4, 4);
// A conic is defined as [A B D]
// [B C E]
// [D E F]
// Since skewness is zero b12 = B = 0.0
return new ImageOfAbsoluteConic(b11, 0.0, b22, b13, b23, b33);
} catch (final AlgebraException | SortingException e) {
throw new ImageOfAbsoluteConicEstimatorException(e);
}
}
/**
* Estimates Image of Absolute Conic (IAC) assuming that principal point is
* located at origin of coordinates.
*
* @return estimated IAC.
* @throws ImageOfAbsoluteConicEstimatorException if an error occurs during
* estimation, usually because repeated homographies are provided, or
* homographies corresponding to degenerate camera movements such as pure
* parallel translations where no additional data is really provided.
*/
private ImageOfAbsoluteConic estimatePrincipalPointAtOrigin()
throws ImageOfAbsoluteConicEstimatorException {
try {
final int nHomographies = mHomographies.size();
final WeightSelection selection = WeightSelection.selectWeights(mWeights,
mSortWeights, mMaxHomographies);
final boolean[] selected = selection.getSelected();
final Matrix a = new Matrix(2 * nHomographies, 4);
int index = 0;
int counter = 0;
ProjectiveTransformation2D t = null;
final Matrix h = new Matrix(ProjectiveTransformation2D.HOM_COORDS,
ProjectiveTransformation2D.HOM_COORDS);
// elements ij of homography (last column is not required)
double h11;
double h12;
double h21;
double h22;
double h31;
double h32;
double rowNorm;
double weight;
double factor;
for (final Transformation2D homography : mHomographies) {
if (selected[index]) {
weight = mWeights[index];
// convert homography into projective so it can be normalized
homography.asMatrix(h);
if (t == null) {
t = new ProjectiveTransformation2D(h);
} else {
t.setT(h);
}
// normalize
t.normalize();
// obtain elements of projective transformation matrix
// there is no need to retrieve internal matrix h, as we already
// hold a reference
h11 = h.getElementAt(0, 0);
h12 = h.getElementAt(0, 1);
h21 = h.getElementAt(1, 0);
h22 = h.getElementAt(1, 1);
h31 = h.getElementAt(2, 0);
h32 = h.getElementAt(2, 1);
// fill first equation
a.setElementAt(counter, 0, h11 * h12);
a.setElementAt(counter, 1, h11 * h22 + h21 * h12);
a.setElementAt(counter, 2, h21 * h22);
a.setElementAt(counter, 3, h31 * h32);
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0) +
Math.pow(a.getElementAt(counter, 3), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
a.setElementAt(counter, 3, a.getElementAt(counter, 3) *
factor);
counter++;
// fill second equation
a.setElementAt(counter, 0, Math.pow(h11, 2.0) - Math.pow(h12, 2.0));
a.setElementAt(counter, 1, 2.0 * (h11 * h21 - h12 * h22));
a.setElementAt(counter, 2, Math.pow(h21, 2.0) - Math.pow(h22, 2.0));
a.setElementAt(counter, 3, Math.pow(h31, 2.0) - Math.pow(h32, 2.0));
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0) +
Math.pow(a.getElementAt(counter, 3), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
a.setElementAt(counter, 3, a.getElementAt(counter, 3) *
factor);
counter++;
}
index++;
}
final SingularValueDecomposer decomposer = new SingularValueDecomposer(a);
decomposer.decompose();
if (decomposer.getNullity() > 1) {
// homographies constitute a degenerate camera movement.
// A linear combination of possible IAC's exist (i.e. solution is
// not unique up to scale)
throw new ImageOfAbsoluteConicEstimatorException();
}
Matrix v = decomposer.getV();
// use last column of V as IAC vector
// the last column of V contains IAC matrix (B), which is symmetric
// and positive definite, ordered as follows: B11, B12, B22, B13,
// B23, B33
final double b11 = v.getElementAt(0, 3);
final double b12 = v.getElementAt(1, 3);
final double b22 = v.getElementAt(2, 3);
final double b33 = v.getElementAt(3, 3);
// A conic is defined as [A B D]
// [B C E]
// [D E F]
// Since principal point is at origin of coordinates
// b13 = D = 0.0, b23 = E = 0.0
return new ImageOfAbsoluteConic(b11, b12, b22, 0.0, 0.0, b33);
} catch (final AlgebraException | SortingException e) {
throw new ImageOfAbsoluteConicEstimatorException(e);
}
}
/**
* Estimates Image of Absolute Conic (IAC) assuming that skewness is zero
* and that principal point is located at origin of coordinates.
*
* @return estimated IAC.
* @throws ImageOfAbsoluteConicEstimatorException if an error occurs during
* estimation, usually because repeated homographies are provided, or
* homographies corresponding to degenerate camera movements such as pure
* parallel translations where no additional data is really provided.
*/
private ImageOfAbsoluteConic estimateZeroSkewnessAndPrincipalPointAtOrigin()
throws ImageOfAbsoluteConicEstimatorException {
try {
final int nHomographies = mHomographies.size();
final WeightSelection selection = WeightSelection.selectWeights(mWeights,
mSortWeights, mMaxHomographies);
final boolean[] selected = selection.getSelected();
final Matrix a = new Matrix(2 * nHomographies, 3);
int index = 0;
int counter = 0;
ProjectiveTransformation2D t = null;
final Matrix h = new Matrix(ProjectiveTransformation2D.HOM_COORDS,
ProjectiveTransformation2D.HOM_COORDS);
// elements ij of homography (last column is not required)
double h11;
double h12;
double h21;
double h22;
double h31;
double h32;
double rowNorm;
double weight;
double factor;
for (final Transformation2D homography : mHomographies) {
if (selected[index]) {
weight = mWeights[index];
// convert homography into projective so it can be normalized
homography.asMatrix(h);
if (t == null) {
t = new ProjectiveTransformation2D(h);
} else {
t.setT(h);
}
// normalize
t.normalize();
// obtain elements of projective transformation matrix
// there is no need to retrieve internal matrix h, as we already
// hold a reference
h11 = h.getElementAt(0, 0);
h12 = h.getElementAt(0, 1);
h21 = h.getElementAt(1, 0);
h22 = h.getElementAt(1, 1);
h31 = h.getElementAt(2, 0);
h32 = h.getElementAt(2, 1);
// fill first equation
a.setElementAt(counter, 0, h11 * h12);
a.setElementAt(counter, 1, h21 * h22);
a.setElementAt(counter, 2, h31 * h32);
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
counter++;
// fill second equation
a.setElementAt(counter, 0, Math.pow(h11, 2.0) - Math.pow(h12, 2.0));
a.setElementAt(counter, 1, Math.pow(h21, 2.0) - Math.pow(h22, 2.0));
a.setElementAt(counter, 2, Math.pow(h31, 2.0) - Math.pow(h32, 2.0));
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
counter++;
}
index++;
}
final SingularValueDecomposer decomposer = new SingularValueDecomposer(a);
decomposer.decompose();
if (decomposer.getNullity() > 1) {
// homographies constitute a degenerate camera movement.
// A linear combination of possible IAC's exist (i.e. solution is
// not unique up to scale)
throw new ImageOfAbsoluteConicEstimatorException();
}
final Matrix v = decomposer.getV();
// use last column of V as IAC vector
// the last column of V contains IAC matrix (B), which is symmetric
// and positive definite, ordered as follows: B11, B12, B22, B13,
// B23, B33
final double b11 = v.getElementAt(0, 2);
final double b22 = v.getElementAt(1, 2);
final double b33 = v.getElementAt(2, 2);
// A conic is defined as [A B D]
// [B C E]
// [D E F]
// Since principal point is at origin of coordinates
// b12 = B = 0, b13 = D = 0.0, b23 = E = 0.0
return new ImageOfAbsoluteConic(b11, 0.0, b22, 0.0, 0.0, b33);
} catch (final AlgebraException | SortingException e) {
throw new ImageOfAbsoluteConicEstimatorException(e);
}
}
/**
* Estimates Image of Absolute Conic (IAC) assuming that skewness is zero
* and that aspect ratio of focal distances is known.
*
* @return estimated IAC.
* @throws ImageOfAbsoluteConicEstimatorException if an error occurs during
* estimation, usually because repeated homographies are provided, or
* homographies corresponding to degenerate camera movements such as pure
* parallel translations where no additional data is really provided.
*/
private ImageOfAbsoluteConic estimateZeroSkewnessAndKnownFocalDistanceAspectRatio()
throws ImageOfAbsoluteConicEstimatorException {
try {
final int nHomographies = mHomographies.size();
final WeightSelection selection = WeightSelection.selectWeights(mWeights,
mSortWeights, mMaxHomographies);
final boolean[] selected = selection.getSelected();
final Matrix a = new Matrix(2 * nHomographies, 4);
final double sqrAspectRatio = Math.pow(mFocalDistanceAspectRatio, 2.0);
int index = 0;
int counter = 0;
ProjectiveTransformation2D t = null;
final Matrix h = new Matrix(ProjectiveTransformation2D.HOM_COORDS,
ProjectiveTransformation2D.HOM_COORDS);
// elements ij of homography (last column is not required)
double h11;
double h12;
double h21;
double h22;
double h31;
double h32;
double rowNorm;
double weight;
double factor;
for (final Transformation2D homography : mHomographies) {
if (selected[index]) {
weight = mWeights[index];
// convert homography into projective so it can be normalized
homography.asMatrix(h);
if (t == null) {
t = new ProjectiveTransformation2D(h);
} else {
t.setT(h);
}
// normalize
t.normalize();
// obtain elements of projective transformation matrix
// there is no need to retrieve internal matrix h, as we already
// hold a reference
h11 = h.getElementAt(0, 0);
h12 = h.getElementAt(0, 1);
h21 = h.getElementAt(1, 0);
h22 = h.getElementAt(1, 1);
h31 = h.getElementAt(2, 0);
h32 = h.getElementAt(2, 1);
// fill first equation
a.setElementAt(counter, 0, h11 * h12 +
h21 * h22 / sqrAspectRatio);
a.setElementAt(counter, 1, h11 * h32 + h31 * h12);
a.setElementAt(counter, 2, h21 * h32 + h31 * h22);
a.setElementAt(counter, 3, h31 * h32);
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0) +
Math.pow(a.getElementAt(counter, 3), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
a.setElementAt(counter, 3, a.getElementAt(counter, 3) *
factor);
counter++;
// fill second equation
a.setElementAt(counter, 0, Math.pow(h11, 2.0) - Math.pow(h12, 2.0) +
(Math.pow(h21, 2.0) - Math.pow(h22, 2.0)) / sqrAspectRatio);
a.setElementAt(counter, 1, 2.0 * (h11 * h31 - h12 * h32));
a.setElementAt(counter, 2, 2.0 * (h21 * h31 - h22 * h32));
a.setElementAt(counter, 3, Math.pow(h31, 2.0) - Math.pow(h32, 2.0));
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0) +
Math.pow(a.getElementAt(counter, 2), 2.0) +
Math.pow(a.getElementAt(counter, 3), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
a.setElementAt(counter, 2, a.getElementAt(counter, 2) *
factor);
a.setElementAt(counter, 3, a.getElementAt(counter, 3) *
factor);
counter++;
}
index++;
}
final SingularValueDecomposer decomposer = new SingularValueDecomposer(a);
decomposer.decompose();
if (decomposer.getNullity() > 1) {
// homographies constitute a degenerate camera movement.
// A linear combination of possible IAC's exist (i.e. solution is
// not unique up to scale)
throw new ImageOfAbsoluteConicEstimatorException();
}
final Matrix v = decomposer.getV();
// use last column of V as IAC vector
// the last column of V contains IAC matrix (B), which is symmetric
// and positive definite, ordered as follows: B11, B12, B22, B13,
// B23, B33
final double b11 = v.getElementAt(0, 3);
final double b13 = v.getElementAt(1, 3);
final double b23 = v.getElementAt(2, 3);
final double b33 = v.getElementAt(3, 3);
final double b22 = b11 / sqrAspectRatio;
// A conic is defined as [A B D]
// [B C E]
// [D E F]
// Since skewness is zero b12 = B = 0.0
return new ImageOfAbsoluteConic(b11, 0.0, b22, b13, b23, b33);
} catch (final AlgebraException | SortingException e) {
throw new ImageOfAbsoluteConicEstimatorException(e);
}
}
/**
* Estimates Image of Absolute Conic (IAC) assuming that skewness is zero,
* principal point is located at origin of coordinates and that aspect ratio
* of focal distances is known.
*
* @return estimated IAC.
* @throws ImageOfAbsoluteConicEstimatorException if an error occurs during
* estimation, usually because repeated homographies are provided, or
* homographies corresponding to degenerate camera movements such as pure
* parallel translations where no additional data is really provided.
*/
private ImageOfAbsoluteConic estimateZeroSkewnessPrincipalPointAtOriginAndKnownFocalDistanceAspectRatio()
throws ImageOfAbsoluteConicEstimatorException {
try {
final int nHomographies = mHomographies.size();
final WeightSelection selection = WeightSelection.selectWeights(mWeights,
mSortWeights, mMaxHomographies);
final boolean[] selected = selection.getSelected();
final Matrix a = new Matrix(2 * nHomographies, 2);
final double sqrAspectRatio = Math.pow(mFocalDistanceAspectRatio, 2.0);
int index = 0;
int counter = 0;
ProjectiveTransformation2D t = null;
final Matrix h = new Matrix(ProjectiveTransformation2D.HOM_COORDS,
ProjectiveTransformation2D.HOM_COORDS);
// elements ij of homography (last column is not required)
double h11;
double h12;
double h21;
double h22;
double h31;
double h32;
double rowNorm;
double weight;
double factor;
for (final Transformation2D homography : mHomographies) {
if (selected[index]) {
weight = mWeights[index];
// convert homography into projective so it can be normalized
homography.asMatrix(h);
if (t == null) {
t = new ProjectiveTransformation2D(h);
} else {
t.setT(h);
}
// normalize
t.normalize();
// obtain elements of projective transformation matrix
// there is no need to retrieve internal matrix h, as we already
// hold a reference
h11 = h.getElementAt(0, 0);
h12 = h.getElementAt(0, 1);
h21 = h.getElementAt(1, 0);
h22 = h.getElementAt(1, 1);
h31 = h.getElementAt(2, 0);
h32 = h.getElementAt(2, 1);
// fill first equation
a.setElementAt(counter, 0, h11 * h12 +
h21 * h22 / sqrAspectRatio);
a.setElementAt(counter, 1, h31 * h32);
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
counter++;
// fill second equation
a.setElementAt(counter, 0, Math.pow(h11, 2.0) - Math.pow(h12, 2.0) +
(Math.pow(h21, 2.0) - Math.pow(h22, 2.0)) / sqrAspectRatio);
a.setElementAt(counter, 1, Math.pow(h31, 2.0) - Math.pow(h32, 2.0));
// normalize row
rowNorm = Math.sqrt(
Math.pow(a.getElementAt(counter, 0), 2.0) +
Math.pow(a.getElementAt(counter, 1), 2.0));
factor = weight / rowNorm;
a.setElementAt(counter, 0, a.getElementAt(counter, 0) *
factor);
a.setElementAt(counter, 1, a.getElementAt(counter, 1) *
factor);
counter++;
}
index++;
}
final SingularValueDecomposer decomposer = new SingularValueDecomposer(a);
decomposer.decompose();
if (decomposer.getNullity() > 1) {
// homographies constitute a degenerate camera movement.
// A linear combination of possible IAC's exist (i.e. solution is
// not unique up to scale)
throw new ImageOfAbsoluteConicEstimatorException();
}
final Matrix v = decomposer.getV();
// use last column of V as IAC vector
// the last column of V contains IAC matrix (B), which is symmetric
// and positive definite, ordered as follows: B11, B12, B22, B13,
// B23, B33
final double b11 = v.getElementAt(0, 1);
final double b33 = v.getElementAt(1, 1);
final double b22 = b11 / sqrAspectRatio;
// A conic is defined as [A B D]
// [B C E]
// [D E F]
// Since principal point is at origin of coordinates
// b12 = B = 0, b13 = D = 0.0, b23 = E = 0.0
return new ImageOfAbsoluteConic(b11, 0.0, b22, 0.0, 0.0, b33);
} catch (final AlgebraException | SortingException e) {
throw new ImageOfAbsoluteConicEstimatorException(e);
}
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy