mirror of
https://github.com/PhotonVision/photonvision
synced 2026-06-19 00:41:41 +00:00
> there is a problem if the found tags are a valid one plus an invalid tag, so eg it sees a tag but finds noise on the wall, and so lists tags 2 and 15. Lines 90-99 will ignore tag 15, and put 4 corners into the list. It will get past the test on line 100 (there are 4 corners). BUT then at line 106, there are 2 entries in visTags, so it will go to the "else" and try to use solvePNP_SQPNP().
139 lines
5.9 KiB
Java
139 lines
5.9 KiB
Java
/*
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* MIT License
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*
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* Copyright (c) PhotonVision
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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package org.photonvision.estimation;
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import edu.wpi.first.apriltag.AprilTag;
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import edu.wpi.first.apriltag.AprilTagFieldLayout;
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import edu.wpi.first.math.Matrix;
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import edu.wpi.first.math.geometry.Pose3d;
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import edu.wpi.first.math.geometry.Transform3d;
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import edu.wpi.first.math.geometry.Translation3d;
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import edu.wpi.first.math.numbers.*;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Objects;
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import java.util.stream.Collectors;
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import org.opencv.core.Point;
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import org.photonvision.targeting.PhotonTrackedTarget;
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import org.photonvision.targeting.TargetCorner;
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public class VisionEstimation {
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/** Get the visible {@link AprilTag}s which are in the tag layout using the visible tag IDs. */
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public static List<AprilTag> getVisibleLayoutTags(
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List<PhotonTrackedTarget> visTags, AprilTagFieldLayout tagLayout) {
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return visTags.stream()
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.map(
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t -> {
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int id = t.getFiducialId();
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var maybePose = tagLayout.getTagPose(id);
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return maybePose.map(pose3d -> new AprilTag(id, pose3d)).orElse(null);
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})
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.filter(Objects::nonNull)
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.collect(Collectors.toList());
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}
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/**
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* Performs solvePNP using 3d-2d point correspondences of visible AprilTags to estimate the
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* field-to-camera transformation. If only one tag is visible, the result may have an alternate
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* solution.
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*
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* <p><b>Note:</b> The returned transformation is from the field origin to the camera pose!
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*
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* <p>With only one tag: {@link OpenCVHelp#solvePNP_SQUARE}
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*
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* <p>With multiple tags: {@link OpenCVHelp#solvePNP_SQPNP}
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*
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* @param cameraMatrix The camera intrinsics matrix in standard opencv form
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* @param distCoeffs The camera distortion matrix in standard opencv form
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* @param visTags The visible tags reported by PV. Non-tag targets are automatically excluded.
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* @param tagLayout The known tag layout on the field
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* @return The transformation that maps the field origin to the camera pose. Ensure the {@link
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* PNPResults} are present before utilizing them.
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*/
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public static PNPResults estimateCamPosePNP(
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Matrix<N3, N3> cameraMatrix,
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Matrix<N5, N1> distCoeffs,
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List<PhotonTrackedTarget> visTags,
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AprilTagFieldLayout tagLayout) {
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if (tagLayout == null
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|| visTags == null
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|| tagLayout.getTags().size() == 0
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|| visTags.size() == 0) {
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return new PNPResults();
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}
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var corners = new ArrayList<TargetCorner>();
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var knownTags = new ArrayList<AprilTag>();
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// ensure these are AprilTags in our layout
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for (var tgt : visTags) {
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int id = tgt.getFiducialId();
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tagLayout
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.getTagPose(id)
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.ifPresent(
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pose -> {
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knownTags.add(new AprilTag(id, pose));
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corners.addAll(tgt.getDetectedCorners());
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});
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}
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if (knownTags.size() == 0 || corners.size() == 0 || corners.size() % 4 != 0) {
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return new PNPResults();
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}
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Point[] points = OpenCVHelp.cornersToPoints(corners);
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// single-tag pnp
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if (knownTags.size() == 1) {
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var camToTag =
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OpenCVHelp.solvePNP_SQUARE(
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cameraMatrix, distCoeffs, TargetModel.kTag16h5.vertices, points);
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if (!camToTag.isPresent) return new PNPResults();
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var bestPose = knownTags.get(0).pose.transformBy(camToTag.best.inverse());
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var altPose = new Pose3d();
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if (camToTag.ambiguity != 0)
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altPose = knownTags.get(0).pose.transformBy(camToTag.alt.inverse());
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var o = new Pose3d();
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return new PNPResults(
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new Transform3d(o, bestPose),
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new Transform3d(o, altPose),
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camToTag.ambiguity,
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camToTag.bestReprojErr,
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camToTag.altReprojErr);
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}
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// multi-tag pnp
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else {
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var objectTrls = new ArrayList<Translation3d>();
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for (var tag : knownTags) objectTrls.addAll(TargetModel.kTag16h5.getFieldVertices(tag.pose));
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var camToOrigin = OpenCVHelp.solvePNP_SQPNP(cameraMatrix, distCoeffs, objectTrls, points);
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if (!camToOrigin.isPresent) return new PNPResults();
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return new PNPResults(
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camToOrigin.best.inverse(),
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camToOrigin.alt.inverse(),
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camToOrigin.ambiguity,
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camToOrigin.bestReprojErr,
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camToOrigin.altReprojErr);
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}
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}
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}
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