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BoofCV is an open source Java library for real-time computer vision and robotics applications.
/*
* Copyright (c) 2022, Peter Abeles. All Rights Reserved.
*
* This file is part of BoofCV (http://boofcv.org).
*
* 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 boofcv.abst.geo.selfcalib;
import boofcv.alg.geo.GeometricResult;
import boofcv.alg.geo.MetricCameras;
import boofcv.alg.geo.MultiViewOps;
import boofcv.alg.geo.PerspectiveOps;
import boofcv.alg.geo.selfcalib.RefineDualQuadraticAlgebraicError;
import boofcv.alg.geo.selfcalib.RefineDualQuadraticAlgebraicError.CameraState;
import boofcv.alg.geo.selfcalib.ResolveSignAmbiguityPositiveDepth;
import boofcv.alg.geo.selfcalib.SelfCalibrationLinearDualQuadratic;
import boofcv.alg.geo.structure.DecomposeAbsoluteDualQuadratic;
import boofcv.misc.BoofMiscOps;
import boofcv.struct.calib.CameraPinhole;
import boofcv.struct.calib.ElevateViewInfo;
import boofcv.struct.geo.AssociatedTuple;
import georegression.geometry.ConvertRotation3D_F64;
import georegression.struct.se.Se3_F64;
import lombok.Getter;
import org.ddogleg.struct.DogArray;
import org.ddogleg.struct.DogArray_I32;
import org.ddogleg.struct.FastAccess;
import org.ddogleg.struct.VerbosePrint;
import org.ejml.data.DMatrix3;
import org.ejml.data.DMatrixRMaj;
import org.ejml.dense.row.CommonOps_DDRM;
import org.jetbrains.annotations.Nullable;
import java.io.PrintStream;
import java.util.List;
import java.util.Objects;
import java.util.Set;
import static boofcv.misc.BoofMiscOps.checkEq;
/**
* Wrapper around {@link SelfCalibrationLinearDualQuadratic} for {@link ProjectiveToMetricCameras}.
*
* @author Peter Abeles
*/
public class ProjectiveToMetricCameraDualQuadratic implements ProjectiveToMetricCameras, VerbosePrint {
/** Self calibration algorithm used internally */
final @Getter SelfCalibrationLinearDualQuadratic selfCalib;
/** Refines intrinsic parameter estimate using algebraic error */
final @Getter RefineDualQuadraticAlgebraicError refiner = new RefineDualQuadraticAlgebraicError();
/** Accept a solution if the number of invalid features is less than or equal to this fraction */
public double invalidFractionAccept = 0.15;
// used to get camera matrices from the trifocal tensor
final DecomposeAbsoluteDualQuadratic decomposeDualQuad = new DecomposeAbsoluteDualQuadratic();
// storage for camera matrices
final DMatrixRMaj P1 = CommonOps_DDRM.identity(3, 4);
// rectifying homography
final DMatrixRMaj H = new DMatrixRMaj(4, 4);
// intrinsic calibration matrix
final DMatrixRMaj K = new DMatrixRMaj(3, 3);
final ResolveSignAmbiguityPositiveDepth resolveSign = new ResolveSignAmbiguityPositiveDepth();
@Nullable PrintStream verbose;
// Workspace for refining the camera estimates
DogArray workCameras = new DogArray<>(CameraPinhole::new, CameraPinhole::reset);
// Number of times each camera observed an image
DogArray_I32 cameraCounts = new DogArray_I32();
public ProjectiveToMetricCameraDualQuadratic( SelfCalibrationLinearDualQuadratic selfCalib ) {
this.selfCalib = selfCalib;
}
@Override
public boolean process( List views, List cameraMatrices,
List observations, MetricCameras metricViews ) {
checkEq(cameraMatrices.size() + 1, views.size(), "View[0] is implicitly identity and not included");
metricViews.reset();
// Perform self calibration and estimate the metric views
if (!solveThenDecompose(cameraMatrices))
return false;
FastAccess solutions = selfCalib.getIntrinsics();
if (solutions.size != views.size()) {
if (verbose != null) verbose.println("FAILED solution.size miss match");
return false;
}
// Figure out how many cameras there are
int numCameras = 0;
for (int i = 0; i < views.size(); i++) {
numCameras = Math.max(numCameras, views.get(i).cameraID);
}
numCameras += 1;
// Average cameras if observed across multiple views
averageCommonCameras(views, solutions, numCameras);
// Refine the linear estimate and apply non-linear constraints, like same camera
refineCamerasAlgebraic(views, cameraMatrices, numCameras);
// Copy results into output and compute extrinsics
if (!reformatResults(views, cameraMatrices, metricViews))
return false;
// Need to resolve the sign ambiguity
resolveSign.process(observations, metricViews);
// Sanity check results by seeing if too many points are behind the camera, which is physically impossible
if (checkBehindCamera(cameraMatrices.size(), observations.size()))
return true;
// Failed, but print what it found to help debug
if (verbose != null) printFoundMetric(views, metricViews);
return false;
}
/**
* Performs self calibration and then finds rectifying homography
*
* @return true if successful
*/
boolean solveThenDecompose( List views ) {
// Determine metric parameters
selfCalib.reset();
selfCalib.addCameraMatrix(P1);
for (int i = 0; i < views.size(); i++) {
selfCalib.addCameraMatrix(views.get(i));
}
GeometricResult results = selfCalib.solve();
if (results != GeometricResult.SUCCESS) {
if (verbose != null) verbose.println("FAILED geometric");
return false;
}
// Convert results into results format
if (!decomposeDualQuad.decompose(selfCalib.getQ())) {
if (verbose != null) verbose.println("FAILED decompose Dual Quad");
return false;
}
if (!decomposeDualQuad.computeRectifyingHomography(H)) {
if (verbose != null) verbose.println("FAILED rectify homography");
return false;
}
return true;
}
/**
* This refines intrinsic parameters by minimizing algebraic error. If anything goes wrong it doesn't update
* the intrinsics. Also updates the rectifying homography.
*/
void refineCamerasAlgebraic( List views, List cameraMatrices, int numCameras ) {
// Refiner can't handle non-zero skew yet
if (!selfCalib.zeroSkew) {
if (verbose != null) verbose.println("Skipping refine since skew is not zero");
return;
}
// Just skip everything if it has been turned off
if (refiner.converge.maxIterations <= 0)
return;
// Sanity check the P0 is implicit
BoofMiscOps.checkEq(views.size(), cameraMatrices.size() + 1);
// Make sure refiner applies the same constraints that the linear estimator applies
refiner.knownAspect = selfCalib.isKnownAspect();
refiner.knownPrinciplePoint = true;
// Configure the refiner. If multiple views use the same camera this constraint is applied
refiner.initialize(numCameras, views.size());
DMatrix3 planeAtInfinity = selfCalib.getPlaneAtInfinity();
refiner.setPlaneAtInfinity(planeAtInfinity.a1, planeAtInfinity.a2, planeAtInfinity.a3);
for (int cameraIdx = 0; cameraIdx < numCameras; cameraIdx++) {
CameraPinhole merged = workCameras.get(cameraIdx);
refiner.setCamera(cameraIdx, merged.fx, merged.cx, merged.cy, merged.fy/merged.fx);
}
for (int viewIdx = 0; viewIdx < views.size(); viewIdx++) {
if (viewIdx == 0)
refiner.setProjective(0, P1);
else
refiner.setProjective(viewIdx, cameraMatrices.get(viewIdx - 1));
refiner.setViewToCamera(viewIdx, views.get(viewIdx).cameraID);
}
// Refine and change nothing if it fails
if (!refiner.refine()) {
if (verbose != null) verbose.println("Refine failed! Ignoring results");
return;
}
if (verbose != null) {
for (int i = 0; i < numCameras; i++) {
CameraState refined = refiner.getCameras().get(i);
verbose.printf("refined[%d] fx=%.1f fy=%.1f\n", i, refined.fx, refined.fx*refined.aspectRatio);
}
}
// Save the refined intrinsic parameters
for (int i = 0; i < views.size(); i++) {
ElevateViewInfo info = views.get(i);
CameraState refined = refiner.getCameras().get(info.cameraID);
CameraPinhole estimated = workCameras.get(info.cameraID);
estimated.fx = refined.fx;
estimated.fy = refined.aspectRatio*refined.fx;
// refiner doesn't support non-zero skew yet
}
// Update rectifying homography using the new parameters
// NOTE: This formulation of H requires P1=[I|0] which is true in this case
PerspectiveOps.pinholeToMatrix(workCameras.get(views.get(0).cameraID), K);
MultiViewOps.canonicalRectifyingHomographyFromKPinf(K, refiner.planeAtInfinity, H);
}
/**
* If multiple views use the same camera the found intrinsics will be averaged across those views.
*/
void averageCommonCameras( List views,
FastAccess solutions,
int numCameras ) {
cameraCounts.reset().resize(numCameras, 0);
workCameras.reset().resize(numCameras);
for (int i = 0; i < views.size(); i++) {
ElevateViewInfo info = views.get(i);
CameraPinhole merged = workCameras.get(info.cameraID);
SelfCalibrationLinearDualQuadratic.Intrinsic estimated = solutions.get(i);
merged.fx += estimated.fx;
merged.fy += estimated.fy;
merged.skew += estimated.skew;
cameraCounts.data[info.cameraID]++;
if (verbose != null) {
verbose.printf("view[%d] fx=%.1f fy=%.1f skew=%.2f\n", i, estimated.fx, estimated.fy, estimated.skew);
}
}
for (int i = 0; i < numCameras; i++) {
CameraPinhole merged = workCameras.get(i);
int divisor = cameraCounts.get(i);
if (divisor == 1)
continue;
merged.fx /= divisor;
merged.fy /= divisor;
merged.skew /= divisor;
// Principle point must be zero. This is here to emphasize that
merged.cx = 0.0;
merged.cy = 0.0;
}
if (verbose != null) {
for (int i = 0; i < numCameras; i++) {
CameraPinhole cam = workCameras.get(i);
verbose.printf("camera[%d] fx=%.1f fy=%.1f skew=%.2f, count=%d\n",
i, cam.fx, cam.fy, cam.skew, cameraCounts.get(i));
}
}
}
/**
* Given the results from self calibration, reformat them so fit the expected format specified by the interface
*
* @param cameraMatrices (Input)
* @param metricViews (Output)
* @return true if successful
*/
boolean reformatResults( List views,
List cameraMatrices,
MetricCameras metricViews ) {
// Copy found intrinsics over
for (int i = 0; i < views.size(); i++) {
metricViews.intrinsics.grow().setTo(workCameras.get(views.get(i).cameraID));
}
// skip the first view since it's the origin and already known
double largestT = 0.0;
for (int i = 0; i < cameraMatrices.size(); i++) {
DMatrixRMaj P = cameraMatrices.get(i);
PerspectiveOps.pinholeToMatrix(metricViews.intrinsics.get(i + 1), K);
if (!MultiViewOps.projectiveToMetricKnownK(P, H, K, metricViews.motion_1_to_k.grow())) {
if (verbose != null) verbose.println("FAILED projectiveToMetricKnownK");
return false;
}
largestT = Math.max(largestT, metricViews.motion_1_to_k.getTail().T.norm());
}
// Ensure the found motion has a scale around 1.0
for (int i = 0; i < metricViews.motion_1_to_k.size; i++) {
metricViews.motion_1_to_k.get(i).T.divide(largestT);
}
return true;
}
/**
* When resolving for the sign ambiguity it has to check to see if triangulated points are behind the camera.
* This looks at the number of points behind the camera and decides if it can trust this solution or not
*
* @return true If it passes the test
*/
boolean checkBehindCamera( int numViews, int numObservations ) {
// bestInvalid is the sum across all views. Use the average fraction across all views as the test
double fractionInvalid = (resolveSign.bestInvalid/(double)numViews)/numObservations;
if (fractionInvalid <= invalidFractionAccept) {
return true;
}
if (verbose != null) {
verbose.printf("FAILED: Features behind camera. fraction=%.3f threshold=%.3f\n",
fractionInvalid, invalidFractionAccept);
}
return false;
}
private void printFoundMetric( List views, MetricCameras metricViews ) {
PrintStream verbose = Objects.requireNonNull(this.verbose);
for (int i = 0; i < views.size(); i++) {
CameraPinhole cam = metricViews.intrinsics.get(i);
verbose.printf("metric[%d] fx=%.1f fy=%.1f", i, cam.fx, cam.fy);
if (i == 0) {
verbose.println();
continue;
}
Se3_F64 m = metricViews.motion_1_to_k.get(i - 1);
double theta = ConvertRotation3D_F64.matrixToRodrigues(m.R, null).theta;
verbose.printf(" T=(%.2f %.2f %.2f) R=%.4f\n", m.T.x, m.T.y, m.T.z, theta);
}
}
@Override public int getMinimumViews() {return selfCalib.getMinimumProjectives();}
@Override public void setVerbose( @Nullable PrintStream out, @Nullable Set configuration ) {
this.verbose = BoofMiscOps.addPrefix(this, out);
BoofMiscOps.verboseChildren(verbose, configuration, resolveSign, refiner);
}
}