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121034876d
merge with https://github.com/opencv/opencv_contrib/pull/3394 move Charuco API from contrib to main repo: - add CharucoDetector: ``` CharucoDetector::detectBoard(InputArray image, InputOutputArrayOfArrays markerCorners, InputOutputArray markerIds, OutputArray charucoCorners, OutputArray charucoIds) const // detect charucoCorners and/or markerCorners CharucoDetector::detectDiamonds(InputArray image, InputOutputArrayOfArrays _markerCorners, InputOutputArrayOfArrays _markerIds, OutputArrayOfArrays _diamondCorners, OutputArray _diamondIds) const ``` - add `matchImagePoints()` for `CharucoBoard` - remove contrib aruco dependencies from interactive-calibration tool - move almost all aruco tests to objdetect ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
322 lines
12 KiB
C++
322 lines
12 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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#include "test_aruco_utils.hpp"
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namespace opencv_test { namespace {
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enum class ArucoAlgParams
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{
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USE_DEFAULT = 0,
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USE_ARUCO3 = 1
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};
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/**
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* @brief Check pose estimation of aruco board
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*/
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class CV_ArucoBoardPose : public cvtest::BaseTest {
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public:
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CV_ArucoBoardPose(ArucoAlgParams arucoAlgParams)
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{
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aruco::DetectorParameters params;
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aruco::Dictionary dictionary = aruco::getPredefinedDictionary(aruco::DICT_6X6_250);
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params.minDistanceToBorder = 3;
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if (arucoAlgParams == ArucoAlgParams::USE_ARUCO3) {
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params.useAruco3Detection = true;
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params.cornerRefinementMethod = aruco::CORNER_REFINE_SUBPIX;
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params.minSideLengthCanonicalImg = 16;
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params.errorCorrectionRate = 0.8;
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}
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detector = aruco::ArucoDetector(dictionary, params);
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}
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protected:
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aruco::ArucoDetector detector;
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void run(int);
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};
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void CV_ArucoBoardPose::run(int) {
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int iter = 0;
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Mat cameraMatrix = Mat::eye(3, 3, CV_64FC1);
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Size imgSize(500, 500);
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cameraMatrix.at< double >(0, 0) = cameraMatrix.at< double >(1, 1) = 650;
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cameraMatrix.at< double >(0, 2) = imgSize.width / 2;
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cameraMatrix.at< double >(1, 2) = imgSize.height / 2;
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Mat distCoeffs(5, 1, CV_64FC1, Scalar::all(0));
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const int sizeX = 3, sizeY = 3;
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aruco::DetectorParameters detectorParameters = detector.getDetectorParameters();
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// for different perspectives
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for(double distance = 0.2; distance <= 0.4; distance += 0.15) {
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for(int yaw = -55; yaw <= 50; yaw += 25) {
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for(int pitch = -55; pitch <= 50; pitch += 25) {
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vector<int> tmpIds;
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for(int i = 0; i < sizeX*sizeY; i++)
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tmpIds.push_back((iter + int(i)) % 250);
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aruco::GridBoard gridboard(Size(sizeX, sizeY), 0.02f, 0.005f, detector.getDictionary(), tmpIds);
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int markerBorder = iter % 2 + 1;
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iter++;
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// create synthetic image
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Mat img = projectBoard(gridboard, cameraMatrix, deg2rad(yaw), deg2rad(pitch), distance,
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imgSize, markerBorder);
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vector<vector<Point2f> > corners;
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vector<int> ids;
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detectorParameters.markerBorderBits = markerBorder;
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detector.setDetectorParameters(detectorParameters);
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detector.detectMarkers(img, corners, ids);
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ASSERT_EQ(ids.size(), gridboard.getIds().size());
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// estimate pose
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Mat rvec, tvec;
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{
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Mat objPoints, imgPoints; // get object and image points for the solvePnP function
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gridboard.matchImagePoints(corners, ids, objPoints, imgPoints);
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solvePnP(objPoints, imgPoints, cameraMatrix, distCoeffs, rvec, tvec);
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}
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// check axes
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vector<Point2f> axes = getAxis(cameraMatrix, distCoeffs, rvec, tvec, gridboard.getRightBottomCorner().x);
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vector<Point2f> topLeft = getMarkerById(gridboard.getIds()[0], corners, ids);
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ASSERT_NEAR(topLeft[0].x, axes[0].x, 2.f);
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ASSERT_NEAR(topLeft[0].y, axes[0].y, 2.f);
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vector<Point2f> topRight = getMarkerById(gridboard.getIds()[2], corners, ids);
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ASSERT_NEAR(topRight[1].x, axes[1].x, 2.f);
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ASSERT_NEAR(topRight[1].y, axes[1].y, 2.f);
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vector<Point2f> bottomLeft = getMarkerById(gridboard.getIds()[6], corners, ids);
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ASSERT_NEAR(bottomLeft[3].x, axes[2].x, 2.f);
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ASSERT_NEAR(bottomLeft[3].y, axes[2].y, 2.f);
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// check estimate result
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for(unsigned int i = 0; i < ids.size(); i++) {
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int foundIdx = -1;
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for(unsigned int j = 0; j < gridboard.getIds().size(); j++) {
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if(gridboard.getIds()[j] == ids[i]) {
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foundIdx = int(j);
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break;
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}
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}
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if(foundIdx == -1) {
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ts->printf(cvtest::TS::LOG, "Marker detected with wrong ID in Board test");
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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return;
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}
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vector< Point2f > projectedCorners;
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projectPoints(gridboard.getObjPoints()[foundIdx], rvec, tvec, cameraMatrix, distCoeffs,
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projectedCorners);
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for(int c = 0; c < 4; c++) {
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double repError = cv::norm(projectedCorners[c] - corners[i][c]); // TODO cvtest
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if(repError > 5.) {
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ts->printf(cvtest::TS::LOG, "Corner reprojection error too high");
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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return;
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}
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}
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}
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}
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}
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}
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}
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/**
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* @brief Check refine strategy
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*/
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class CV_ArucoRefine : public cvtest::BaseTest {
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public:
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CV_ArucoRefine(ArucoAlgParams arucoAlgParams)
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{
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aruco::Dictionary dictionary = aruco::getPredefinedDictionary(aruco::DICT_6X6_250);
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aruco::DetectorParameters params;
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params.minDistanceToBorder = 3;
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params.cornerRefinementMethod = aruco::CORNER_REFINE_SUBPIX;
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if (arucoAlgParams == ArucoAlgParams::USE_ARUCO3)
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params.useAruco3Detection = true;
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aruco::RefineParameters refineParams(10.f, 3.f, true);
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detector = aruco::ArucoDetector(dictionary, params, refineParams);
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}
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protected:
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aruco::ArucoDetector detector;
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void run(int);
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};
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void CV_ArucoRefine::run(int) {
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int iter = 0;
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Mat cameraMatrix = Mat::eye(3, 3, CV_64FC1);
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Size imgSize(500, 500);
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cameraMatrix.at< double >(0, 0) = cameraMatrix.at< double >(1, 1) = 650;
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cameraMatrix.at< double >(0, 2) = imgSize.width / 2;
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cameraMatrix.at< double >(1, 2) = imgSize.height / 2;
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Mat distCoeffs(5, 1, CV_64FC1, Scalar::all(0));
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aruco::DetectorParameters detectorParameters = detector.getDetectorParameters();
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// for different perspectives
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for(double distance = 0.2; distance <= 0.4; distance += 0.2) {
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for(int yaw = -60; yaw < 60; yaw += 30) {
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for(int pitch = -60; pitch <= 60; pitch += 30) {
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aruco::GridBoard gridboard(Size(3, 3), 0.02f, 0.005f, detector.getDictionary());
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int markerBorder = iter % 2 + 1;
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iter++;
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// create synthetic image
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Mat img = projectBoard(gridboard, cameraMatrix, deg2rad(yaw), deg2rad(pitch), distance,
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imgSize, markerBorder);
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// detect markers
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vector<vector<Point2f> > corners, rejected;
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vector<int> ids;
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detectorParameters.markerBorderBits = markerBorder;
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detector.setDetectorParameters(detectorParameters);
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detector.detectMarkers(img, corners, ids, rejected);
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// remove a marker from detection
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int markersBeforeDelete = (int)ids.size();
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if(markersBeforeDelete < 2) continue;
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rejected.push_back(corners[0]);
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corners.erase(corners.begin(), corners.begin() + 1);
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ids.erase(ids.begin(), ids.begin() + 1);
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// try to refind the erased marker
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detector.refineDetectedMarkers(img, gridboard, corners, ids, rejected, cameraMatrix,
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distCoeffs, noArray());
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// check result
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if((int)ids.size() < markersBeforeDelete) {
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ts->printf(cvtest::TS::LOG, "Error in refine detected markers");
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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return;
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}
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}
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}
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}
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}
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TEST(CV_ArucoBoardPose, accuracy) {
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CV_ArucoBoardPose test(ArucoAlgParams::USE_DEFAULT);
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test.safe_run();
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}
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typedef CV_ArucoBoardPose CV_Aruco3BoardPose;
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TEST(CV_Aruco3BoardPose, accuracy) {
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CV_Aruco3BoardPose test(ArucoAlgParams::USE_ARUCO3);
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test.safe_run();
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}
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typedef CV_ArucoRefine CV_Aruco3Refine;
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TEST(CV_ArucoRefine, accuracy) {
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CV_ArucoRefine test(ArucoAlgParams::USE_DEFAULT);
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test.safe_run();
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}
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TEST(CV_Aruco3Refine, accuracy) {
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CV_Aruco3Refine test(ArucoAlgParams::USE_ARUCO3);
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test.safe_run();
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}
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TEST(CV_ArucoBoardPose, CheckNegativeZ)
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{
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double matrixData[9] = { -3.9062571886921410e+02, 0., 4.2350000000000000e+02,
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0., 3.9062571886921410e+02, 2.3950000000000000e+02,
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0., 0., 1 };
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cv::Mat cameraMatrix = cv::Mat(3, 3, CV_64F, matrixData);
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vector<cv::Point3f> pts3d1, pts3d2;
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pts3d1.push_back(cv::Point3f(0.326198f, -0.030621f, 0.303620f));
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pts3d1.push_back(cv::Point3f(0.325340f, -0.100594f, 0.301862f));
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pts3d1.push_back(cv::Point3f(0.255859f, -0.099530f, 0.293416f));
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pts3d1.push_back(cv::Point3f(0.256717f, -0.029557f, 0.295174f));
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pts3d2.push_back(cv::Point3f(-0.033144f, -0.034819f, 0.245216f));
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pts3d2.push_back(cv::Point3f(-0.035507f, -0.104705f, 0.241987f));
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pts3d2.push_back(cv::Point3f(-0.105289f, -0.102120f, 0.237120f));
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pts3d2.push_back(cv::Point3f(-0.102926f, -0.032235f, 0.240349f));
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vector<int> tmpIds = {0, 1};
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vector<vector<Point3f> > tmpObjectPoints = {pts3d1, pts3d2};
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aruco::Board board(tmpObjectPoints, aruco::getPredefinedDictionary(0), tmpIds);
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vector<vector<Point2f> > corners;
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vector<Point2f> pts2d;
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pts2d.push_back(cv::Point2f(37.7f, 203.3f));
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pts2d.push_back(cv::Point2f(38.5f, 120.5f));
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pts2d.push_back(cv::Point2f(105.5f, 115.8f));
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pts2d.push_back(cv::Point2f(104.2f, 202.7f));
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corners.push_back(pts2d);
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pts2d.clear();
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pts2d.push_back(cv::Point2f(476.0f, 184.2f));
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pts2d.push_back(cv::Point2f(479.6f, 73.8f));
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pts2d.push_back(cv::Point2f(590.9f, 77.0f));
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pts2d.push_back(cv::Point2f(587.5f, 188.1f));
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corners.push_back(pts2d);
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Vec3d rvec, tvec;
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int nUsed = 0;
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{
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Mat objPoints, imgPoints; // get object and image points for the solvePnP function
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board.matchImagePoints(corners, board.getIds(), objPoints, imgPoints);
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nUsed = (int)objPoints.total()/4;
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solvePnP(objPoints, imgPoints, cameraMatrix, Mat(), rvec, tvec);
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}
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ASSERT_EQ(nUsed, 2);
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cv::Matx33d rotm; cv::Point3d out;
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cv::Rodrigues(rvec, rotm);
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out = cv::Point3d(tvec) + rotm*Point3d(board.getObjPoints()[0][0]);
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ASSERT_GT(out.z, 0);
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corners.clear(); pts2d.clear();
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pts2d.push_back(cv::Point2f(38.4f, 204.5f));
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pts2d.push_back(cv::Point2f(40.0f, 124.7f));
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pts2d.push_back(cv::Point2f(102.0f, 119.1f));
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pts2d.push_back(cv::Point2f(99.9f, 203.6f));
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corners.push_back(pts2d);
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pts2d.clear();
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pts2d.push_back(cv::Point2f(476.0f, 184.3f));
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pts2d.push_back(cv::Point2f(479.2f, 75.1f));
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pts2d.push_back(cv::Point2f(588.7f, 79.2f));
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pts2d.push_back(cv::Point2f(586.3f, 188.5f));
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corners.push_back(pts2d);
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nUsed = 0;
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{
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Mat objPoints, imgPoints; // get object and image points for the solvePnP function
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board.matchImagePoints(corners, board.getIds(), objPoints, imgPoints);
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nUsed = (int)objPoints.total()/4;
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solvePnP(objPoints, imgPoints, cameraMatrix, Mat(), rvec, tvec, true);
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}
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ASSERT_EQ(nUsed, 2);
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cv::Rodrigues(rvec, rotm);
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out = cv::Point3d(tvec) + rotm*Point3d(board.getObjPoints()[0][0]);
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ASSERT_GT(out.z, 0);
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}
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TEST(CV_ArucoGenerateBoard, regression_1226) {
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int bwidth = 1600;
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int bheight = 1200;
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cv::aruco::Dictionary dict = cv::aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_50);
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cv::aruco::CharucoBoard board(Size(7, 5), 1.0, 0.75, dict);
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cv::Size sz(bwidth, bheight);
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cv::Mat mat;
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ASSERT_NO_THROW(
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{
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board.generateImage(sz, mat, 0, 1);
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});
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}
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}} // namespace
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