opencv/modules/objdetect/test/test_arucodetection.cpp

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.

#include "test_precomp.hpp"
#include "opencv2/objdetect/aruco_detector.hpp"
#include "opencv2/calib3d.hpp"

namespace cv {
    namespace aruco {
        bool operator==(const Dictionary& d1, const Dictionary& d2);
        bool operator==(const Dictionary& d1, const Dictionary& d2) {
            return d1.markerSize == d2.markerSize
                && std::equal(d1.bytesList.begin<Vec<uint8_t, 4>>(), d1.bytesList.end<Vec<uint8_t, 4>>(), d2.bytesList.begin<Vec<uint8_t, 4>>())
                && std::equal(d2.bytesList.begin<Vec<uint8_t, 4>>(), d2.bytesList.end<Vec<uint8_t, 4>>(), d1.bytesList.begin<Vec<uint8_t, 4>>())
                && d1.maxCorrectionBits == d2.maxCorrectionBits;
        };
    }
}

namespace opencv_test { namespace {

/**
 * @brief Draw 2D synthetic markers and detect them
 */
class CV_ArucoDetectionSimple : public cvtest::BaseTest {
    public:
    CV_ArucoDetectionSimple();

    protected:
    void run(int);
};


CV_ArucoDetectionSimple::CV_ArucoDetectionSimple() {}


void CV_ArucoDetectionSimple::run(int) {
    aruco::ArucoDetector detector(aruco::getPredefinedDictionary(aruco::DICT_6X6_250));

    // 20 images
    for(int i = 0; i < 20; i++) {

        const int markerSidePixels = 100;
        int imageSize = markerSidePixels * 2 + 3 * (markerSidePixels / 2);

        // draw synthetic image and store marker corners and ids
        vector<vector<Point2f> > groundTruthCorners;
        vector<int> groundTruthIds;
        Mat img = Mat(imageSize, imageSize, CV_8UC1, Scalar::all(255));
        for(int y = 0; y < 2; y++) {
            for(int x = 0; x < 2; x++) {
                Mat marker;
                int id = i * 4 + y * 2 + x;
                aruco::generateImageMarker(detector.getDictionary(), id, markerSidePixels, marker);
                Point2f firstCorner =
                    Point2f(markerSidePixels / 2.f + x * (1.5f * markerSidePixels),
                            markerSidePixels / 2.f + y * (1.5f * markerSidePixels));
                Mat aux = img.colRange((int)firstCorner.x, (int)firstCorner.x + markerSidePixels)
                              .rowRange((int)firstCorner.y, (int)firstCorner.y + markerSidePixels);
                marker.copyTo(aux);
                groundTruthIds.push_back(id);
                groundTruthCorners.push_back(vector<Point2f>());
                groundTruthCorners.back().push_back(firstCorner);
                groundTruthCorners.back().push_back(firstCorner + Point2f(markerSidePixels - 1, 0));
                groundTruthCorners.back().push_back(
                    firstCorner + Point2f(markerSidePixels - 1, markerSidePixels - 1));
                groundTruthCorners.back().push_back(firstCorner + Point2f(0, markerSidePixels - 1));
            }
        }
        if(i % 2 == 1) img.convertTo(img, CV_8UC3);

        // detect markers
        vector<vector<Point2f> > corners;
        vector<int> ids;

        detector.detectMarkers(img, corners, ids);

        // check detection results
        for(unsigned int m = 0; m < groundTruthIds.size(); m++) {
            int idx = -1;
            for(unsigned int k = 0; k < ids.size(); k++) {
                if(groundTruthIds[m] == ids[k]) {
                    idx = (int)k;
                    break;
                }
            }
            if(idx == -1) {
                ts->printf(cvtest::TS::LOG, "Marker not detected");
                ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
                return;
            }

            for(int c = 0; c < 4; c++) {
                double dist = cv::norm(groundTruthCorners[m][c] - corners[idx][c]);  // TODO cvtest
                if(dist > 0.001) {
                    ts->printf(cvtest::TS::LOG, "Incorrect marker corners position");
                    ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
                    return;
                }
            }
        }
    }
}


static double deg2rad(double deg) { return deg * CV_PI / 180.; }

/**
 * @brief Get rvec and tvec from yaw, pitch and distance
 */
static void getSyntheticRT(double yaw, double pitch, double distance, Mat &rvec, Mat &tvec) {

    rvec = Mat(3, 1, CV_64FC1);
    tvec = Mat(3, 1, CV_64FC1);

    // Rvec
    // first put the Z axis aiming to -X (like the camera axis system)
    Mat rotZ(3, 1, CV_64FC1);
    rotZ.ptr<double>(0)[0] = 0;
    rotZ.ptr<double>(0)[1] = 0;
    rotZ.ptr<double>(0)[2] = -0.5 * CV_PI;

    Mat rotX(3, 1, CV_64FC1);
    rotX.ptr<double>(0)[0] = 0.5 * CV_PI;
    rotX.ptr<double>(0)[1] = 0;
    rotX.ptr<double>(0)[2] = 0;

    Mat camRvec, camTvec;
    composeRT(rotZ, Mat(3, 1, CV_64FC1, Scalar::all(0)), rotX, Mat(3, 1, CV_64FC1, Scalar::all(0)),
              camRvec, camTvec);

    // now pitch and yaw angles
    Mat rotPitch(3, 1, CV_64FC1);
    rotPitch.ptr<double>(0)[0] = 0;
    rotPitch.ptr<double>(0)[1] = pitch;
    rotPitch.ptr<double>(0)[2] = 0;

    Mat rotYaw(3, 1, CV_64FC1);
    rotYaw.ptr<double>(0)[0] = yaw;
    rotYaw.ptr<double>(0)[1] = 0;
    rotYaw.ptr<double>(0)[2] = 0;

    composeRT(rotPitch, Mat(3, 1, CV_64FC1, Scalar::all(0)), rotYaw,
              Mat(3, 1, CV_64FC1, Scalar::all(0)), rvec, tvec);

    // compose both rotations
    composeRT(camRvec, Mat(3, 1, CV_64FC1, Scalar::all(0)), rvec,
              Mat(3, 1, CV_64FC1, Scalar::all(0)), rvec, tvec);

    // Tvec, just move in z (camera) direction the specific distance
    tvec.ptr<double>(0)[0] = 0.;
    tvec.ptr<double>(0)[1] = 0.;
    tvec.ptr<double>(0)[2] = distance;
}

/**
 * @brief Create a synthetic image of a marker with perspective
 */
static Mat projectMarker(const aruco::Dictionary &dictionary, int id, Mat cameraMatrix, double yaw,
                         double pitch, double distance, Size imageSize, int markerBorder,
                         vector<Point2f> &corners, int encloseMarker=0) {

    // canonical image
    Mat marker, markerImg;
    const int markerSizePixels = 100;

    aruco::generateImageMarker(dictionary, id, markerSizePixels, marker, markerBorder);
    marker.copyTo(markerImg);

    if(encloseMarker){ //to enclose the marker
        int enclose = int(marker.rows/4);
        markerImg = Mat::zeros(marker.rows+(2*enclose), marker.cols+(enclose*2), CV_8UC1);

        Mat field= markerImg.rowRange(int(enclose), int(markerImg.rows-enclose))
                            .colRange(int(0), int(markerImg.cols));
        field.setTo(255);
        field= markerImg.rowRange(int(0), int(markerImg.rows))
                            .colRange(int(enclose), int(markerImg.cols-enclose));
        field.setTo(255);

        field = markerImg(Rect(enclose,enclose,marker.rows,marker.cols));
        marker.copyTo(field);
    }

    // get rvec and tvec for the perspective
    Mat rvec, tvec;
    getSyntheticRT(yaw, pitch, distance, rvec, tvec);

    const float markerLength = 0.05f;
    vector<Point3f> markerObjPoints;
    markerObjPoints.push_back(Point3f(-markerLength / 2.f, +markerLength / 2.f, 0));
    markerObjPoints.push_back(markerObjPoints[0] + Point3f(markerLength, 0, 0));
    markerObjPoints.push_back(markerObjPoints[0] + Point3f(markerLength, -markerLength, 0));
    markerObjPoints.push_back(markerObjPoints[0] + Point3f(0, -markerLength, 0));

    // project markers and draw them
    Mat distCoeffs(5, 1, CV_64FC1, Scalar::all(0));
    projectPoints(markerObjPoints, rvec, tvec, cameraMatrix, distCoeffs, corners);

    vector<Point2f> originalCorners;
    originalCorners.push_back(Point2f(0+float(encloseMarker*markerSizePixels/4), 0+float(encloseMarker*markerSizePixels/4)));
    originalCorners.push_back(originalCorners[0]+Point2f((float)markerSizePixels, 0));
    originalCorners.push_back(originalCorners[0]+Point2f((float)markerSizePixels, (float)markerSizePixels));
    originalCorners.push_back(originalCorners[0]+Point2f(0, (float)markerSizePixels));

    Mat transformation = getPerspectiveTransform(originalCorners, corners);

    Mat img(imageSize, CV_8UC1, Scalar::all(255));
    Mat aux;
    const char borderValue = 127;
    warpPerspective(markerImg, aux, transformation, imageSize, INTER_NEAREST, BORDER_CONSTANT,
                    Scalar::all(borderValue));

    // copy only not-border pixels
    for(int y = 0; y < aux.rows; y++) {
        for(int x = 0; x < aux.cols; x++) {
            if(aux.at<unsigned char>(y, x) == borderValue) continue;
            img.at<unsigned char>(y, x) = aux.at<unsigned char>(y, x);
        }
    }

    return img;
}

enum class ArucoAlgParams
{
    USE_DEFAULT = 0,
    USE_APRILTAG=1,             /// Detect marker candidates :: using AprilTag
    DETECT_INVERTED_MARKER,     /// Check if there is a white marker
    USE_ARUCO3                  /// Check if aruco3 should be used
};


/**
 * @brief Draws markers in perspective and detect them
 */
class CV_ArucoDetectionPerspective : public cvtest::BaseTest {
    public:
    CV_ArucoDetectionPerspective(ArucoAlgParams arucoAlgParam) : arucoAlgParams(arucoAlgParam) {}

    protected:
    void run(int);
    ArucoAlgParams arucoAlgParams;
};


void CV_ArucoDetectionPerspective::run(int) {

    int iter = 0;
    int szEnclosed = 0;
    Mat cameraMatrix = Mat::eye(3, 3, CV_64FC1);
    Size imgSize(500, 500);
    cameraMatrix.at<double>(0, 0) = cameraMatrix.at<double>(1, 1) = 650;
    cameraMatrix.at<double>(0, 2) = imgSize.width / 2;
    cameraMatrix.at<double>(1, 2) = imgSize.height / 2;
    aruco::DetectorParameters params;
    params.minDistanceToBorder = 1;
    aruco::ArucoDetector detector(aruco::getPredefinedDictionary(aruco::DICT_6X6_250), params);

    // detect from different positions
    for(double distance : {0.1, 0.3, 0.5, 0.7}) {
        for(int pitch = 0; pitch < 360; pitch += (distance == 0.1? 60:180)) {
            for(int yaw = 70; yaw <= 120; yaw += 40){
                int currentId = iter % 250;
                int markerBorder = iter % 2 + 1;
                iter++;
                vector<Point2f> groundTruthCorners;
                aruco::DetectorParameters detectorParameters = params;
                detectorParameters.markerBorderBits = markerBorder;

                /// create synthetic image
                Mat img=
                    projectMarker(detector.getDictionary(), currentId, cameraMatrix, deg2rad(yaw), deg2rad(pitch),
                                      distance, imgSize, markerBorder, groundTruthCorners, szEnclosed);
                // marker :: Inverted
                if(ArucoAlgParams::DETECT_INVERTED_MARKER == arucoAlgParams){
                    img = ~img;
                    detectorParameters.detectInvertedMarker = true;
                }

                if(ArucoAlgParams::USE_APRILTAG == arucoAlgParams){
                    detectorParameters.cornerRefinementMethod = (int)aruco::CORNER_REFINE_APRILTAG;
                }

                if (ArucoAlgParams::USE_ARUCO3 == arucoAlgParams) {
                    detectorParameters.useAruco3Detection = true;
                    detectorParameters.cornerRefinementMethod = (int)aruco::CORNER_REFINE_SUBPIX;
                }
                detector.setDetectorParameters(detectorParameters);

                // detect markers
                vector<vector<Point2f> > corners;
                vector<int> ids;
                detector.detectMarkers(img, corners, ids);

                // check results
                if(ids.size() != 1 || (ids.size() == 1 && ids[0] != currentId)) {
                    if(ids.size() != 1)
                        ts->printf(cvtest::TS::LOG, "Incorrect number of detected markers");
                    else
                        ts->printf(cvtest::TS::LOG, "Incorrect marker id");
                    ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
                    return;
                }
                for(int c = 0; c < 4; c++) {
                    double dist = cv::norm(groundTruthCorners[c] - corners[0][c]);  // TODO cvtest
                    if(dist > 5) {
                            ts->printf(cvtest::TS::LOG, "Incorrect marker corners position");
                            ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
                            return;
                    }
                }
            }
        }
        // change the state :: to detect an enclosed inverted marker
        if(ArucoAlgParams::DETECT_INVERTED_MARKER == arucoAlgParams && distance == 0.1){
            distance -= 0.1;
            szEnclosed++;
        }
    }
}

// Helper struct and functions for CV_ArucoDetectionConfidence

// Inverts a square subregion inside selected cells of a marker to simulate a confidence drop
enum class MarkerRegionToTemper {
    BORDER, // Only invert cells within the marker border bits
    INNER,  // Only invert cells in the inner part of the marker (excluding borders)
    ALL     // Invert any cells
};

// Define the characteristics of cell inversions
struct MarkerTemperingConfig {
    float cellRatioToTemper;                   // [0,1] ratio of the cell to invert
    int numCellsToTemper;                      // Number of cells to invert
    MarkerRegionToTemper markerRegionToTemper; // Which cells to invert (BORDER, INNER, ALL)
};

// Test configs for CV_ArucoDetectionConfidence
struct ArucoConfidenceTestConfig {
    MarkerTemperingConfig markerTemperingConfig; // Configuration of cells to invert (percentage, number and markerRegionToTemper)
    float perspectiveRemoveIgnoredMarginPerCell; // Width of the margin of pixels on each cell not considered for the marker identification
    int markerBorderBits;                        // Number of bits of the marker border
    float distortionRatio;                       // Percentage of offset used for perspective distortion, bigger means more distorted
};

enum class markerRot
{
    NONE = 0,
    ROT_90,
    ROT_180,
    ROT_270
};

struct markerDetectionGT {
    int id;               // Marker identification
    double confidence;    // Pixel-based confidence defined as 1 - (inverted area / total area)
    bool expectDetection; // True if we expect to detect the marker
};

struct MarkerCreationConfig {
    int id;               // Marker identification
    int markerSidePixels; // Marker size (in pixels)
    markerRot rotation;   // Rotation of the marker in degrees (0, 90, 180, 270)
};

void rotateMarker(Mat &marker, const markerRot rotation)
{
    if(rotation == markerRot::NONE)
        return;

    if (rotation == markerRot::ROT_90) {
        cv::transpose(marker, marker);
        cv::flip(marker, marker, 0);
    } else if (rotation == markerRot::ROT_180) {
        cv::flip(marker, marker, -1);
    } else if (rotation == markerRot::ROT_270) {
        cv::transpose(marker, marker);
        cv::flip(marker, marker, 1);
    }
}

void distortMarker(Mat &marker, const float distortionRatio)
{

    if (distortionRatio < FLT_EPSILON)
        return;

    // apply a distortion (a perspective warp) to simulate a non-ideal capture
    vector<Point2f> src = { {0, 0},
                            {static_cast<float>(marker.cols), 0},
                            {static_cast<float>(marker.cols), static_cast<float>(marker.rows)},
                            {0, static_cast<float>(marker.rows)} };
    float offset = marker.cols * distortionRatio; // distortionRatio % offset for distortion
    vector<Point2f> dst = { {offset, offset},
                            {marker.cols - offset, 0},
                            {marker.cols - offset, marker.rows - offset},
                            {0, marker.rows - offset} };
    Mat M = getPerspectiveTransform(src, dst);
    warpPerspective(marker, marker, M, marker.size(), INTER_LINEAR, BORDER_CONSTANT, Scalar(255));
}

/**
 * @brief Inverts a square subregion inside selected cells of a marker image to simulate confidence degradation.
 *
 * The function computes the marker grid parameters and then applies a bitwise inversion
 * on a square markerRegionToTemper inside the chosen cells. The number of cells to be inverted is determined by
 * the parameter 'numCellsToTemper'. The candidate cells can be filtered to only include border cells,
 * inner cells, or all cells according to the parameter 'markerRegionToTemper'.
 *
 * @param marker           The marker image
 * @param markerSidePixels The total size of the marker in pixels (inner and border).
 * @param markerId         The id of the marker
 * @param params           The Aruco detector configuration (provides border bits, margin ratios, etc.).
 * @param dictionary       The Aruco marker dictionary (used to determine marker grid size).
 * @param cellTempConfig   Cell tempering config as defined in MarkerTemperingConfig
 * @return Cell tempering ground truth as defined in markerDetectionGT
 */
markerDetectionGT applyTemperingToMarkerCells(cv::Mat &marker,
                                 const int markerSidePixels,
                                 const int markerId,
                                 const aruco::DetectorParameters &params,
                                 const aruco::Dictionary &dictionary,
                                 const MarkerTemperingConfig &cellTempConfig)
{

    // nothing to invert
    if(cellTempConfig.numCellsToTemper <= 0 || cellTempConfig.cellRatioToTemper <= FLT_EPSILON)
        return {markerId, 1.0, true};

    // compute the overall grid dimensions.
    const int markerSizeWithBorders = dictionary.markerSize + 2 * params.markerBorderBits;
    const int cellSidePixelsSize = markerSidePixels / markerSizeWithBorders;

    // compute the margin within each cell used for identification.
    const int cellMarginPixels = static_cast<int>(params.perspectiveRemoveIgnoredMarginPerCell * cellSidePixelsSize);
    const int innerCellSizePixels = cellSidePixelsSize - 2 * cellMarginPixels;

    // determine the size of the square that will be inverted in each cell.
    // (cellSidePixelsInvert / innerCellSizePixels)^2 should equal cellRatioToTemper.
    const int cellSidePixelsInvert = min(cellSidePixelsSize, static_cast<int>(innerCellSizePixels * std::sqrt(cellTempConfig.cellRatioToTemper)));
    const int inversionOffsetPixels = (cellSidePixelsSize - cellSidePixelsInvert) / 2;

    // nothing to invert
    if(cellSidePixelsInvert <= 0)
        return {markerId, 1.0, true};

    int cellsTempered = 0;
    int borderErrors = 0;
    int innerCellsErrors = 0;
    // iterate over each cell in the grid.
    for (int row = 0; row < markerSizeWithBorders; row++) {
        for (int col = 0; col < markerSizeWithBorders; col++) {

            // decide if this cell falls in the markerRegionToTemper to temper.
            const bool isBorder = (row < params.markerBorderBits ||
                                   col < params.markerBorderBits ||
                                   row >= markerSizeWithBorders - params.markerBorderBits ||
                                   col >= markerSizeWithBorders - params.markerBorderBits);

            const bool inRegion = (cellTempConfig.markerRegionToTemper == MarkerRegionToTemper::ALL ||
                        (isBorder && cellTempConfig.markerRegionToTemper == MarkerRegionToTemper::BORDER) ||
                        (!isBorder && cellTempConfig.markerRegionToTemper == MarkerRegionToTemper::INNER));

            // apply the inversion to simulate tempering.
            if (inRegion && (cellsTempered < cellTempConfig.numCellsToTemper)) {
                const int xStart = col * cellSidePixelsSize + inversionOffsetPixels;
                const int yStart = row * cellSidePixelsSize + inversionOffsetPixels;
                cv::Rect cellRect(xStart, yStart, cellSidePixelsInvert, cellSidePixelsInvert);
                cv::Mat cellROI = marker(cellRect);
                cv::bitwise_not(cellROI, cellROI);
                ++cellsTempered;

                // cell too tempered, no detection expected
                if(cellTempConfig.cellRatioToTemper > params.validBitIdThreshold) {
                    if(isBorder){
                        ++borderErrors;
                    } else {
                        ++innerCellsErrors;
                    }
                }
            }

            if(cellsTempered >= cellTempConfig.numCellsToTemper)
                break;
        }

        if(cellsTempered >= cellTempConfig.numCellsToTemper)
            break;
    }

    // compute the ground-truth confidence
    const double invertedArea = cellsTempered * cellSidePixelsInvert * cellSidePixelsInvert;
    const double totalDetectionArea = markerSizeWithBorders * innerCellSizePixels * markerSizeWithBorders * innerCellSizePixels;
    const double groundTruthConfidence = std::max(0.0, 1.0 - invertedArea / totalDetectionArea);

    // check if marker is expected to be detected
    const int maximumErrorsInBorder = static_cast<int>(dictionary.markerSize * dictionary.markerSize * params.maxErroneousBitsInBorderRate);
    const int maxCorrectionRecalculed = static_cast<int>(dictionary.maxCorrectionBits * params.errorCorrectionRate);
    const bool expectDetection = static_cast<bool>(borderErrors <= maximumErrorsInBorder && innerCellsErrors <= maxCorrectionRecalculed);

    return {markerId, groundTruthConfidence, expectDetection};
}

/**
 * @brief Create an image of a marker with inverted (tempered) regions to simulate detection confidence
 *
 * Applies an optional rotation and an optional perspective warp to simulate a distorted marker.
 * Inverts a square subregion inside selected cells of a marker image to simulate a drop in confidence.
 * Computes the ground-truth confidence as one minus the ratio of inverted area to the total marker area used for identification.
 *
 */
markerDetectionGT generateTemperedMarkerImage(Mat &marker, const MarkerCreationConfig &markerConfig, const MarkerTemperingConfig &markerTemperingConfig,
                        const aruco::DetectorParameters &params, const aruco::Dictionary &dictionary, const float distortionRatio = 0.f)
{
    // generate the synthetic marker image
    aruco::generateImageMarker(dictionary, markerConfig.id, markerConfig.markerSidePixels,
                               marker, params.markerBorderBits);

    // rotate marker if necessary
    rotateMarker(marker, markerConfig.rotation);

    // temper with cells to simulate detection confidence drops
    markerDetectionGT groundTruth = applyTemperingToMarkerCells(marker, markerConfig.markerSidePixels, markerConfig.id, params, dictionary, markerTemperingConfig);

    // apply a distortion (a perspective warp) to simulate a non-ideal capture
    distortMarker(marker, distortionRatio);

    return groundTruth;
}


/**
 * @brief Copies a marker image into a larger image at the given top-left position.
 */
void placeMarker(Mat &img, const Mat &marker, const Point2f &topLeft)
{
    Rect roi(Point(static_cast<int>(topLeft.x), static_cast<int>(topLeft.y)), marker.size());
    marker.copyTo(img(roi));
}


/**
 * @brief Test the marker confidence computations
 *
 * Loops over a set of detector configurations (e.g. expected confidence, distortion, DetectorParameters)
 * For each configuration, it creates a synthetic image containing four markers arranged in a 2x2 grid.
 * Each marker is generated with its own configuration (id, size, rotation).
 * Finally, it runs the detector and checks that each marker is detected and
 * that its computed confidence is close to the ground truth value.
 *
 */
static void runArucoDetectionConfidence(ArucoAlgParams arucoAlgParam) {
    aruco::DetectorParameters params;
    // make sure there are no bits have any detection errors
    params.maxErroneousBitsInBorderRate = 0.0;
    params.errorCorrectionRate = 0.0;
    params.perspectiveRemovePixelPerCell = 8; // ensure that there is enough resolution to properly handle distortions
    aruco::ArucoDetector detector(aruco::getPredefinedDictionary(aruco::DICT_6X6_250), params);

    const bool detectInvertedMarker = (arucoAlgParam == ArucoAlgParams::DETECT_INVERTED_MARKER);

    // define several detector configurations to test different settings
    // {{MarkerTemperingConfig}, perspectiveRemoveIgnoredMarginPerCell, markerBorderBits, distortionRatio}
    vector<ArucoConfidenceTestConfig> detectorConfigs = {
        // No margins, No distortion
        {{0.f,   64, MarkerRegionToTemper::ALL}, 0.0f, 1, 0.f},
        {{0.01f, 64, MarkerRegionToTemper::ALL}, 0.0f, 1, 0.f},
        {{0.05f, 100, MarkerRegionToTemper::ALL}, 0.0f, 2, 0.f},
        {{0.1f,  64, MarkerRegionToTemper::ALL}, 0.0f, 1, 0.f},
        {{0.15f, 30, MarkerRegionToTemper::ALL}, 0.0f, 1, 0.f},
        {{0.20f, 55, MarkerRegionToTemper::ALL}, 0.0f, 2, 0.f},
        // Margins, No distortion
        {{0.f,   26, MarkerRegionToTemper::BORDER}, 0.05f, 1, 0.f},
        {{0.01f, 56, MarkerRegionToTemper::BORDER}, 0.05f, 2, 0.f},
        {{0.05f, 144, MarkerRegionToTemper::ALL}, 0.1f,  3, 0.f},
        {{0.10f, 49, MarkerRegionToTemper::ALL}, 0.15f, 1, 0.f},
        // No margins, distortion
        {{0.f,   36, MarkerRegionToTemper::INNER}, 0.0f, 1, 0.01f},
        {{0.01f, 36, MarkerRegionToTemper::INNER}, 0.0f, 1, 0.02f},
        {{0.05f, 12, MarkerRegionToTemper::INNER}, 0.0f, 2, 0.05f},
        {{0.1f,  64, MarkerRegionToTemper::ALL}, 0.0f, 1, 0.1f},
        {{0.1f,  81, MarkerRegionToTemper::ALL}, 0.0f, 2, 0.2f},
        // Margins, distortion
        {{0.f,   81, MarkerRegionToTemper::ALL}, 0.05f, 2, 0.01f},
        {{0.01f, 64, MarkerRegionToTemper::ALL}, 0.05f, 1, 0.02f},
        {{0.05f, 81, MarkerRegionToTemper::ALL}, 0.1f,  2, 0.05f},
        {{0.1f,  64, MarkerRegionToTemper::ALL}, 0.15f, 1, 0.1f},
        {{0.1f,  64, MarkerRegionToTemper::ALL}, 0.0f,  1, 0.2f},
        // no marker detection, too much tempering
        {{0.9f, 1, MarkerRegionToTemper::ALL}, 0.05f, 2, 0.0f},
        {{0.9f, 1, MarkerRegionToTemper::BORDER}, 0.05f, 2, 0.0f},
        {{0.9f, 1, MarkerRegionToTemper::INNER}, 0.05f, 2, 0.0f},
    };

    // define marker configurations for the 4 markers in each image
    const int markerSidePixels = 480; // To simplify the cell division, markerSidePixels is a multiple of 8. (6x6 dict + 2 border bits)
    vector<MarkerCreationConfig> markerCreationConfig = {
        {0, markerSidePixels, markerRot::ROT_90},     // {id, markerSidePixels, rotation}
        {1, markerSidePixels, markerRot::ROT_270},
        {2, markerSidePixels, markerRot::NONE},
        {3, markerSidePixels, markerRot::ROT_180}
    };

    // loop over each detector configuration
    for (size_t cfgIdx = 0; cfgIdx < detectorConfigs.size(); cfgIdx++) {
        ArucoConfidenceTestConfig detCfg = detectorConfigs[cfgIdx];
        SCOPED_TRACE(cv::format("detectorConfig=%zu", cfgIdx));

        // update detector parameters
        params.perspectiveRemoveIgnoredMarginPerCell = detCfg.perspectiveRemoveIgnoredMarginPerCell;
        params.markerBorderBits = detCfg.markerBorderBits;
        params.detectInvertedMarker = detectInvertedMarker;
        detector.setDetectorParameters(params);

        // create a blank image large enough to hold 4 markers in a 2x2 grid
        const int margin = markerSidePixels / 2;
        const int imageSize = (markerSidePixels * 2) + margin * 3;
        Mat img(imageSize, imageSize, CV_8UC1, Scalar(255));

        vector<markerDetectionGT> groundTruths;
        const aruco::Dictionary &dictionary = detector.getDictionary();

        // place each marker into the image
        for (int row = 0; row < 2; row++) {
            for (int col = 0; col < 2; col++) {
                int index = row * 2 + col;
                MarkerCreationConfig markerCfg = markerCreationConfig[index];
                // adjust marker id to be unique for each detector configuration
                markerCfg.id += static_cast<int>(cfgIdx * markerCreationConfig.size());

                // generate img
                Mat markerImg;
                markerDetectionGT gt = generateTemperedMarkerImage(markerImg, markerCfg, detCfg.markerTemperingConfig, params, dictionary, detCfg.distortionRatio);
                groundTruths.push_back(gt);

                // place marker in the image
                Point2f topLeft(static_cast<float>(margin + col * (markerSidePixels + margin)),
                                static_cast<float>(margin + row * (markerSidePixels + margin)));
                placeMarker(img, markerImg, topLeft);
            }
        }

        // if testing inverted markers globally, invert the whole image
        if (detectInvertedMarker) {
            bitwise_not(img, img);
        }

        // run detection.
        vector<vector<Point2f>> corners, rejected;
        vector<int> ids;
        vector<float> markerConfidence;
        detector.detectMarkersWithConfidence(img, corners, ids, markerConfidence, rejected);

        ASSERT_EQ(ids.size(), corners.size());
        ASSERT_EQ(ids.size(), markerConfidence.size());

        std::map<int, float> confidenceById;
        for (size_t i = 0; i < ids.size(); i++) {
            confidenceById[ids[i]] = markerConfidence[i];
        }

        // verify that every marker is detected and its confidence is within tolerance
        for (const auto& currentGT : groundTruths) {
            const bool detected = confidenceById.find(currentGT.id) != confidenceById.end();
            EXPECT_EQ(currentGT.expectDetection, detected) << "Marker id: " << currentGT.id;

            if (currentGT.expectDetection && detected) {
                EXPECT_NEAR(currentGT.confidence, confidenceById[currentGT.id], 0.05)
                    << "Marker id: " << currentGT.id;
            }
        }
    }
}


// Helper struc and functions for CV_ArucoDetectionUnc
struct ArucoThresholdTestConfig {
    MarkerTemperingConfig markerTemperingConfig; // Configuration of cells to invert (percentage, number and markerRegionToTemper)
    float validBitIdThreshold;                   // range [0,1], define the acceptable threshold when comparing the detected marker to the dictionary during marker identification.
    float perspectiveRemoveIgnoredMarginPerCell; // Width of the margin of pixels on each cell not considered for the marker identification
    int markerBorderBits;                        // Number of bits of the marker border
    float distortionRatio;                       // Percentage of offset used for perspective distortion, bigger means more distorted
};

/**
 * @brief Test the param validBitIdThreshold
 * Loops over a set of detector configurations (validBitIdThreshold, distortion, DetectorParameters such as markerBorderBits)
 * For each configuration, it creates a synthetic image containing four markers arranged in a 2x2 grid.
 * Each marker is generated with its own configuration (id, size, rotation).
 * Make sure that markers are detected or not based on validBitIdThreshold and percentage of tempering.
 * Finally, it runs the detector and checks that each marker is detected or not based on the threshold.
 *
 */
static void runArucoDetectionThreshold(ArucoAlgParams arucoAlgParam) {

    aruco::DetectorParameters params;
    // make sure there are no bits have any detection errors
    params.perspectiveRemovePixelPerCell = 20; // ensure that there is enough resolution to properly handle distortions
    params.maxErroneousBitsInBorderRate = 0.f;
    params.errorCorrectionRate = 0.f;
    aruco::ArucoDetector detector(aruco::getPredefinedDictionary(aruco::DICT_5X5_250), params); // Max correction: 6bits

    const bool detectInvertedMarker = (arucoAlgParam == ArucoAlgParams::DETECT_INVERTED_MARKER);

    // define several detector configurations to test different settings
    // {{MarkerTemperingConfig}, validBitIdThreshold, perspectiveRemoveIgnoredMarginPerCell, markerBorderBits, distortionRatio}

    vector<ArucoThresholdTestConfig> detectorConfigs = {
        // No tempering, expect detection for every threshold
        {{0.f, 0, MarkerRegionToTemper::ALL}, 0.3f, 0.f, 1, 0.f},
        {{0.f, 0, MarkerRegionToTemper::ALL}, 0.5f, 0.f, 1, 0.f},
        {{0.f, 0, MarkerRegionToTemper::ALL}, 0.9f, 0.f, 1, 0.f},
        // Include distortions
        {{0.f, 0, MarkerRegionToTemper::ALL}, 0.3f, 0.f, 1, 0.05f},
        {{0.f, 0, MarkerRegionToTemper::ALL}, 0.5f, 0.f, 1, 0.1f},
        {{0.f, 0, MarkerRegionToTemper::ALL}, 0.9f, 0.f, 1, 0.2f},

        // 20% temper, expect detection with threshold above 0.2
        {{0.2f, 5, MarkerRegionToTemper::BORDER}, 0.30f, 0.f, 1, 0.f}, // Detection

        {{0.2f, 1, MarkerRegionToTemper::BORDER}, 0.18f, 0.f, 1, 0.f}, // No detection
        {{0.2f, 1, MarkerRegionToTemper::BORDER}, 0.18f, 0.f, 1, 0.f}, // No detection
        {{0.2f, 10, MarkerRegionToTemper::INNER}, 0.22f, 0.f, 1, 0.f}, // Detection
        {{0.2f, 1, MarkerRegionToTemper::INNER},  0.18f, 0.f, 1, 0.f}  // No detection

        // distortions
    };

    // define marker configurations for the 4 markers in each image
    const int markerSidePixels = 700; // To simplify the cell division, markerSidePixels is a multiple of 7. (5x5 dict + 2 border bits)
    vector<MarkerCreationConfig> markerCreationConfig = {
        {0, markerSidePixels, markerRot::ROT_90},     // {id, markerSidePixels, rotation}
        {1, markerSidePixels, markerRot::ROT_270},
        {2, markerSidePixels, markerRot::NONE},
        {3, markerSidePixels, markerRot::ROT_180}
    };

    // loop over each detector configuration
    for (size_t cfgIdx = 0; cfgIdx < detectorConfigs.size(); cfgIdx++) {
        ArucoThresholdTestConfig detCfg = detectorConfigs[cfgIdx];

        // update detector parameters
        params.validBitIdThreshold =detCfg.validBitIdThreshold;
        params.perspectiveRemoveIgnoredMarginPerCell = detCfg.perspectiveRemoveIgnoredMarginPerCell;
        params.markerBorderBits = detCfg.markerBorderBits;
        params.detectInvertedMarker = detectInvertedMarker;
        detector.setDetectorParameters(params);

        // create a blank image large enough to hold 4 markers in a 2x2 grid
        const int margin = markerSidePixels / 2;
        const int imageSize = (markerSidePixels * 2) + margin * 3;
        Mat img(imageSize, imageSize, CV_8UC1, Scalar(255));

        vector<markerDetectionGT> groundTruths;
        const aruco::Dictionary &dictionary = detector.getDictionary();

        // place each marker into the image
        for (int row = 0; row < 2; row++) {
            for (int col = 0; col < 2; col++) {
                int index = row * 2 + col;
                MarkerCreationConfig markerCfg = markerCreationConfig[index];
                // adjust marker id to be unique for each detector configuration
                markerCfg.id += static_cast<int>(cfgIdx * markerCreationConfig.size());

                // generate img
                Mat markerImg;
                markerDetectionGT gt = generateTemperedMarkerImage(markerImg, markerCfg, detCfg.markerTemperingConfig, params, dictionary, detCfg.distortionRatio);

                groundTruths.push_back(gt);

                // place marker in the image
                Point2f topLeft(static_cast<float>(margin + col * (markerSidePixels + margin)),
                                static_cast<float>(margin + row * (markerSidePixels + margin)));
                placeMarker(img, markerImg, topLeft);
            }
        }

        // if testing inverted markers globally, invert the whole image
        if (detectInvertedMarker) {
            bitwise_not(img, img);
        }

        // run detection.
        vector<vector<Point2f>> corners, rejected;
        vector<int> ids;
        vector<float> markerConfidence;
        detector.detectMarkersWithConfidence(img, corners, ids, markerConfidence, rejected);

        ASSERT_EQ(ids.size(), corners.size());
        ASSERT_EQ(ids.size(), markerConfidence.size());

        std::map<int, float> confidenceById;
        for (size_t i = 0; i < ids.size(); i++) {
            confidenceById[ids[i]] = markerConfidence[i];
        }

        // verify that every marker is detected and its confidence is within tolerance
        for (const auto& currentGT : groundTruths) {
            const auto it = confidenceById.find(currentGT.id);
            const bool detected = it != confidenceById.end();
            EXPECT_EQ(currentGT.expectDetection, detected)
                << "Marker id: " << currentGT.id << " (detector config " << cfgIdx << ")";

            if (currentGT.expectDetection && detected) {
                EXPECT_NEAR(currentGT.confidence, it->second, 0.05)
                    << "Marker id: " << currentGT.id << " (detector config " << cfgIdx << ")";
            }
        }
    }
}


/**
 * @brief Check max and min size in marker detection parameters
 */
class CV_ArucoDetectionMarkerSize : public cvtest::BaseTest {
    public:
    CV_ArucoDetectionMarkerSize();

    protected:
    void run(int);
};


CV_ArucoDetectionMarkerSize::CV_ArucoDetectionMarkerSize() {}


void CV_ArucoDetectionMarkerSize::run(int) {
    aruco::DetectorParameters params;
    aruco::ArucoDetector detector(aruco::getPredefinedDictionary(aruco::DICT_6X6_250), params);
    int markerSide = 20;
    int imageSize = 200;

    // 10 cases
    for(int i = 0; i < 10; i++) {
        Mat marker;
        int id = 10 + i * 20;

        // create synthetic image
        Mat img = Mat(imageSize, imageSize, CV_8UC1, Scalar::all(255));
        aruco::generateImageMarker(detector.getDictionary(), id, markerSide, marker);
        Mat aux = img.colRange(30, 30 + markerSide).rowRange(50, 50 + markerSide);
        marker.copyTo(aux);

        vector<vector<Point2f> > corners;
        vector<int> ids;

        // set a invalid minMarkerPerimeterRate
        aruco::DetectorParameters detectorParameters = params;
        detectorParameters.minMarkerPerimeterRate = min(4., (4. * markerSide) / float(imageSize) + 0.1);
        detector.setDetectorParameters(detectorParameters);
        detector.detectMarkers(img, corners, ids);
        if(corners.size() != 0) {
            ts->printf(cvtest::TS::LOG, "Error in DetectorParameters::minMarkerPerimeterRate");
            ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
            return;
        }

        // set an valid minMarkerPerimeterRate
        detectorParameters = params;
        detectorParameters.minMarkerPerimeterRate = max(0., (4. * markerSide) / float(imageSize) - 0.1);
        detector.setDetectorParameters(detectorParameters);
        detector.detectMarkers(img, corners, ids);
        if(corners.size() != 1 || (corners.size() == 1 && ids[0] != id)) {
            ts->printf(cvtest::TS::LOG, "Error in DetectorParameters::minMarkerPerimeterRate");
            ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
            return;
        }

        // set a invalid maxMarkerPerimeterRate
        detectorParameters = params;
        detectorParameters.maxMarkerPerimeterRate = min(4., (4. * markerSide) / float(imageSize) - 0.1);
        detector.setDetectorParameters(detectorParameters);
        detector.detectMarkers(img, corners, ids);
        if(corners.size() != 0) {
            ts->printf(cvtest::TS::LOG, "Error in DetectorParameters::maxMarkerPerimeterRate");
            ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
            return;
        }

        // set an valid maxMarkerPerimeterRate
        detectorParameters = params;
        detectorParameters.maxMarkerPerimeterRate = max(0., (4. * markerSide) / float(imageSize) + 0.1);
        detector.setDetectorParameters(detectorParameters);
        detector.detectMarkers(img, corners, ids);
        if(corners.size() != 1 || (corners.size() == 1 && ids[0] != id)) {
            ts->printf(cvtest::TS::LOG, "Error in DetectorParameters::maxMarkerPerimeterRate");
            ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
            return;
        }
    }
}


/**
 * @brief Check error correction in marker bits
 */
class CV_ArucoBitCorrection : public cvtest::BaseTest {
    public:
    CV_ArucoBitCorrection();

    protected:
    void run(int);
};


CV_ArucoBitCorrection::CV_ArucoBitCorrection() {}


void CV_ArucoBitCorrection::run(int) {

    aruco::Dictionary dictionary1 = aruco::getPredefinedDictionary(aruco::DICT_6X6_250);
    aruco::Dictionary dictionary2 = aruco::getPredefinedDictionary(aruco::DICT_6X6_250);
    aruco::DetectorParameters params;
    aruco::ArucoDetector detector1(dictionary1, params);
    int markerSide = 50;
    int imageSize = 150;

    // 10 markers
    for(int l = 0; l < 10; l++) {
        Mat marker;
        int id = 10 + l * 20;

        Mat currentCodeBytes = dictionary1.bytesList.rowRange(id, id + 1);
        aruco::DetectorParameters detectorParameters = detector1.getDetectorParameters();
        // 5 valid cases
        for(int i = 0; i < 5; i++) {
            // how many bit errors (the error is low enough so it can be corrected)
            detectorParameters.errorCorrectionRate = 0.2 + i * 0.1;
            detector1.setDetectorParameters(detectorParameters);
            int errors =
                (int)std::floor(dictionary1.maxCorrectionBits * detector1.getDetectorParameters().errorCorrectionRate - 1.);

            // create erroneous marker in currentCodeBits
            Mat currentCodeBits =
                aruco::Dictionary::getBitsFromByteList(currentCodeBytes, dictionary1.markerSize);
            for(int e = 0; e < errors; e++) {
                currentCodeBits.ptr<unsigned char>()[2 * e] =
                    !currentCodeBits.ptr<unsigned char>()[2 * e];
            }

            // add erroneous marker to dictionary2 in order to create the erroneous marker image
            Mat currentCodeBytesError = aruco::Dictionary::getByteListFromBits(currentCodeBits);
            currentCodeBytesError.copyTo(dictionary2.bytesList.rowRange(id, id + 1));
            Mat img = Mat(imageSize, imageSize, CV_8UC1, Scalar::all(255));
            dictionary2.generateImageMarker(id, markerSide, marker);
            Mat aux = img.colRange(30, 30 + markerSide).rowRange(50, 50 + markerSide);
            marker.copyTo(aux);

            // try to detect using original dictionary
            vector<vector<Point2f> > corners;
            vector<int> ids;
            detector1.detectMarkers(img, corners, ids);
            if(corners.size() != 1 || (corners.size() == 1 && ids[0] != id)) {
                ts->printf(cvtest::TS::LOG, "Error in bit correction");
                ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
                return;
            }
        }

        // 5 invalid cases
        for(int i = 0; i < 5; i++) {
            // how many bit errors (the error is too high to be corrected)
            detectorParameters.errorCorrectionRate = 0.2 + i * 0.1;
            detector1.setDetectorParameters(detectorParameters);
            int errors =
                (int)std::floor(dictionary1.maxCorrectionBits * detector1.getDetectorParameters().errorCorrectionRate + 1.);

            // create erroneous marker in currentCodeBits
            Mat currentCodeBits =
                aruco::Dictionary::getBitsFromByteList(currentCodeBytes, dictionary1.markerSize);
            for(int e = 0; e < errors; e++) {
                currentCodeBits.ptr<unsigned char>()[2 * e] =
                    !currentCodeBits.ptr<unsigned char>()[2 * e];
            }

            // dictionary3 is only composed by the modified marker (in its original form)
            aruco::Dictionary _dictionary3 = aruco::Dictionary(
                    dictionary2.bytesList.rowRange(id, id + 1).clone(),
                    dictionary1.markerSize,
                    dictionary1.maxCorrectionBits);
            aruco::ArucoDetector detector3(_dictionary3, detector1.getDetectorParameters());
            // add erroneous marker to dictionary2 in order to create the erroneous marker image
            Mat currentCodeBytesError = aruco::Dictionary::getByteListFromBits(currentCodeBits);
            currentCodeBytesError.copyTo(dictionary2.bytesList.rowRange(id, id + 1));
            Mat img = Mat(imageSize, imageSize, CV_8UC1, Scalar::all(255));
            dictionary2.generateImageMarker(id, markerSide, marker);
            Mat aux = img.colRange(30, 30 + markerSide).rowRange(50, 50 + markerSide);
            marker.copyTo(aux);

            // try to detect using dictionary3, it should fail
            vector<vector<Point2f> > corners;
            vector<int> ids;
            detector3.detectMarkers(img, corners, ids);
            if(corners.size() != 0) {
                ts->printf(cvtest::TS::LOG, "Error in DetectorParameters::errorCorrectionRate");
                ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
                return;
            }
        }
    }
}

typedef CV_ArucoDetectionPerspective CV_AprilTagDetectionPerspective;
typedef CV_ArucoDetectionPerspective CV_InvertedArucoDetectionPerspective;
typedef CV_ArucoDetectionPerspective CV_Aruco3DetectionPerspective;

TEST(CV_InvertedArucoDetectionPerspective, algorithmic) {
    CV_InvertedArucoDetectionPerspective test(ArucoAlgParams::DETECT_INVERTED_MARKER);
    test.safe_run();
}

TEST(CV_AprilTagDetectionPerspective, algorithmic) {
    CV_AprilTagDetectionPerspective test(ArucoAlgParams::USE_APRILTAG);
    test.safe_run();
}

TEST(CV_Aruco3DetectionPerspective, algorithmic) {
    CV_Aruco3DetectionPerspective test(ArucoAlgParams::USE_ARUCO3);
    test.safe_run();
}

TEST(CV_ArucoDetectionSimple, algorithmic) {
    CV_ArucoDetectionSimple test;
    test.safe_run();
}

TEST(CV_ArucoDetectionPerspective, algorithmic) {
    CV_ArucoDetectionPerspective test(ArucoAlgParams::USE_DEFAULT);
    test.safe_run();
}

TEST(CV_ArucoDetectionMarkerSize, algorithmic) {
    CV_ArucoDetectionMarkerSize test;
    test.safe_run();
}

TEST(CV_ArucoBitCorrection, algorithmic) {
    CV_ArucoBitCorrection test;
    test.safe_run();
}

TEST(CV_ArucoDetectionConfidence, algorithmic) {
    runArucoDetectionConfidence(ArucoAlgParams::USE_DEFAULT);
}

TEST(CV_InvertedArucoDetectionConfidence, algorithmic) {
    runArucoDetectionConfidence(ArucoAlgParams::DETECT_INVERTED_MARKER);
}

TEST(CV_InvertedFlagArucoDetectionConfidence, algorithmic) {
    aruco::DetectorParameters params;
    params.maxErroneousBitsInBorderRate = 0.0;
    params.errorCorrectionRate = 0.0;
    params.perspectiveRemovePixelPerCell = 8;
    params.detectInvertedMarker = false;

    const aruco::Dictionary dictionary = aruco::getPredefinedDictionary(aruco::DICT_6X6_250);

    // create a blank image large enough to hold 4 markers in a 2x2 grid
    const int markerSidePixels = 480;
    const int margin = markerSidePixels / 2;
    const int imageSize = (markerSidePixels * 2) + margin * 3;
    Mat img(imageSize, imageSize, CV_8UC1, Scalar(255));

    // place 4 markers into the image
    for (int row = 0; row < 2; row++) {
        for (int col = 0; col < 2; col++) {
            const int id = row * 2 + col;
            Mat markerImg;
            aruco::generateImageMarker(dictionary, id, markerSidePixels, markerImg, params.markerBorderBits);

            Point2f topLeft(static_cast<float>(margin + col * (markerSidePixels + margin)),
                            static_cast<float>(margin + row * (markerSidePixels + margin)));
            placeMarker(img, markerImg, topLeft);
        }
    }

    // run detection with detectInvertedMarker = false (baseline)
    aruco::ArucoDetector detector(dictionary, params);
    vector<vector<Point2f>> corners, rejected;
    vector<int> ids;
    vector<float> confidenceDefault;
    detector.detectMarkersWithConfidence(img, corners, ids, confidenceDefault, rejected);
    ASSERT_EQ(ids.size(), corners.size());
    ASSERT_EQ(ids.size(), confidenceDefault.size());

    std::map<int, float> confidenceByIdDefault;
    for (size_t i = 0; i < ids.size(); i++) {
        confidenceByIdDefault[ids[i]] = confidenceDefault[i];
    }

    // run detection with detectInvertedMarker = true, without inverting the image
    params.detectInvertedMarker = true;
    aruco::ArucoDetector detectorInvertedFlag(dictionary, params);
    vector<float> confidenceInvertedFlag;
    detectorInvertedFlag.detectMarkersWithConfidence(img, corners, ids, confidenceInvertedFlag, rejected);
    ASSERT_EQ(ids.size(), corners.size());
    ASSERT_EQ(ids.size(), confidenceInvertedFlag.size());

    std::map<int, float> confidenceByIdInvertedFlag;
    for (size_t i = 0; i < ids.size(); i++) {
        confidenceByIdInvertedFlag[ids[i]] = confidenceInvertedFlag[i];
    }

    // detectInvertedMarker should not invert/flip confidence for non-inverted markers.
    for (int id = 0; id < 4; id++) {
        ASSERT_NE(confidenceByIdDefault.find(id), confidenceByIdDefault.end()) << "Marker id: " << id;
        ASSERT_NE(confidenceByIdInvertedFlag.find(id), confidenceByIdInvertedFlag.end()) << "Marker id: " << id;

        const float confDefault = confidenceByIdDefault[id];
        const float confInvertedFlag = confidenceByIdInvertedFlag[id];

        EXPECT_GT(confDefault, 0.8f) << "Marker id: " << id;
        EXPECT_GT(confInvertedFlag, 0.8f) << "Marker id: " << id;
        EXPECT_NEAR(confDefault, confInvertedFlag, 0.2f) << "Marker id: " << id;
    }
}

TEST(CV_ArucoDetectionThreshold, algorithmic) {
    runArucoDetectionThreshold(ArucoAlgParams::USE_DEFAULT);
}

TEST(CV_InvertedArucoDetectionThreshold, algorithmic) {
    runArucoDetectionThreshold(ArucoAlgParams::DETECT_INVERTED_MARKER);
}

TEST(CV_ArucoDetectMarkers, regression_3192)
{
    aruco::ArucoDetector detector(aruco::getPredefinedDictionary(aruco::DICT_4X4_50));
    vector<int> markerIds;
    vector<vector<Point2f> > markerCorners;
    string imgPath = cvtest::findDataFile("aruco/regression_3192.png");
    Mat image = imread(imgPath);
    const size_t N = 2ull;
    const int goldCorners[N][8] = { {345,120, 520,120, 520,295, 345,295}, {101,114, 270,112, 276,287, 101,287} };
    const int goldCornersIds[N] = { 6, 4 };
    map<int, const int*> mapGoldCorners;
    for (size_t i = 0; i < N; i++)
        mapGoldCorners[goldCornersIds[i]] = goldCorners[i];

    detector.detectMarkers(image, markerCorners, markerIds);

    ASSERT_EQ(N, markerIds.size());
    for (size_t i = 0; i < N; i++)
    {
        int arucoId = markerIds[i];
        ASSERT_EQ(4ull, markerCorners[i].size());
        ASSERT_TRUE(mapGoldCorners.find(arucoId) != mapGoldCorners.end());
        for (int j = 0; j < 4; j++)
        {
            EXPECT_NEAR(static_cast<float>(mapGoldCorners[arucoId][j * 2]), markerCorners[i][j].x, 1.f);
            EXPECT_NEAR(static_cast<float>(mapGoldCorners[arucoId][j * 2 + 1]), markerCorners[i][j].y, 1.f);
        }
    }
}

TEST(CV_ArucoDetectMarkers, regression_2492)
{
    aruco::ArucoDetector detector(aruco::getPredefinedDictionary(aruco::DICT_5X5_50));
    aruco::DetectorParameters detectorParameters = detector.getDetectorParameters();
    detectorParameters.minMarkerDistanceRate = 0.026;
    detector.setDetectorParameters(detectorParameters);
    vector<int> markerIds;
    vector<vector<Point2f> > markerCorners;
    string imgPath = cvtest::findDataFile("aruco/regression_2492.png");
    Mat image = imread(imgPath);
    const size_t N = 8ull;
    const int goldCorners[N][8] = { {179,139, 179,95, 223,95, 223,139}, {99,139, 99,95, 143,95, 143,139},
                                    {19,139, 19,95, 63,95, 63,139},     {256,140, 256,93, 303,93, 303,140},
                                    {256,62, 259,21, 300,23, 297,64},   {99,21, 143,17, 147,60, 103,64},
                                    {69,61, 28,61, 14,21, 58,17},       {174,62, 182,13, 230,19, 223,68} };
    const int goldCornersIds[N] = {13, 13, 13, 13, 1, 15, 14, 4};
    map<int, vector<const int*> > mapGoldCorners;
    for (size_t i = 0; i < N; i++)
        mapGoldCorners[goldCornersIds[i]].push_back(goldCorners[i]);

    detector.detectMarkers(image, markerCorners, markerIds);

    ASSERT_EQ(N, markerIds.size());
    for (size_t i = 0; i < N; i++)
    {
        int arucoId = markerIds[i];
        ASSERT_EQ(4ull, markerCorners[i].size());
        ASSERT_TRUE(mapGoldCorners.find(arucoId) != mapGoldCorners.end());
        float totalDist = 8.f;
        for (size_t k = 0ull; k < mapGoldCorners[arucoId].size(); k++)
        {
            float dist = 0.f;
            for (int j = 0; j < 4; j++) // total distance up to 4 points
            {
                dist += abs(mapGoldCorners[arucoId][k][j * 2] - markerCorners[i][j].x);
                dist += abs(mapGoldCorners[arucoId][k][j * 2 + 1] - markerCorners[i][j].y);
            }
            totalDist = min(totalDist, dist);
        }
        EXPECT_LT(totalDist, 8.f);
    }
}


TEST(CV_ArucoDetectMarkers, regression_contour_24220)
{
    aruco::ArucoDetector detector;
    vector<int> markerIds;
    vector<vector<Point2f> > markerCorners;
    string imgPath = cvtest::findDataFile("aruco/failmask9.png");
    Mat image = imread(imgPath);

    const size_t N = 1ull;
    const int goldCorners[8] = {392,175, 99,257, 117,109, 365,44};
    const int goldCornersId = 0;

    detector.detectMarkers(image, markerCorners, markerIds);

    ASSERT_EQ(N, markerIds.size());
    ASSERT_EQ(4ull, markerCorners[0].size());
    ASSERT_EQ(goldCornersId, markerIds[0]);
    for (int j = 0; j < 4; j++)
    {
        EXPECT_NEAR(static_cast<float>(goldCorners[j * 2]), markerCorners[0][j].x, 1.f);
        EXPECT_NEAR(static_cast<float>(goldCorners[j * 2 + 1]), markerCorners[0][j].y, 1.f);
    }
}

TEST(CV_ArucoDetectMarkers, regression_26922)
{
    const auto arucoDict = aruco::getPredefinedDictionary(aruco::DICT_4X4_1000);
    const aruco::GridBoard gridBoard(Size(19, 10), 1, 0.25, arucoDict);

    const Size imageSize(7200, 3825);

    Mat boardImage;
    gridBoard.generateImage(imageSize, boardImage, 75, 1);

    const aruco::ArucoDetector detector(arucoDict);

    vector<vector<Point2f>> corners;
    vector<int> ids;
    detector.detectMarkers(boardImage, corners, ids);

    EXPECT_EQ(ids.size(), 190ull);
    EXPECT_TRUE(find(ids.begin(), ids.end(), 76) != ids.end());
    EXPECT_TRUE(find(ids.begin(), ids.end(), 172) != ids.end());

    float transformMatrixData[9] = {1, -0.2f, 300, 0.4f, 1, -1000, 0, 0, 1};
    const Mat transformMatrix(Size(3, 3), CV_32FC1, transformMatrixData);

    Mat warpedImage;
    warpPerspective(boardImage, warpedImage, transformMatrix, imageSize);

    detector.detectMarkers(warpedImage, corners, ids);

    EXPECT_EQ(ids.size(), 133ull);
    // markers with id 76 and 172 are on border and should not be detected
    EXPECT_FALSE(find(ids.begin(), ids.end(), 76) != ids.end());
    EXPECT_FALSE(find(ids.begin(), ids.end(), 172) != ids.end());
}

TEST(CV_ArucoMultiDict, setGetDictionaries)
{
    vector<aruco::Dictionary> dictionaries = {aruco::getPredefinedDictionary(aruco::DICT_4X4_50), aruco::getPredefinedDictionary(aruco::DICT_5X5_100)};
    aruco::ArucoDetector detector(dictionaries);
    vector<aruco::Dictionary> dicts = detector.getDictionaries();
    ASSERT_EQ(dicts.size(), 2ul);
    EXPECT_EQ(dicts[0].markerSize, 4);
    EXPECT_EQ(dicts[1].markerSize, 5);
    dictionaries.clear();
    dictionaries.push_back(aruco::getPredefinedDictionary(aruco::DICT_6X6_100));
    dictionaries.push_back(aruco::getPredefinedDictionary(aruco::DICT_7X7_250));
    dictionaries.push_back(aruco::getPredefinedDictionary(aruco::DICT_APRILTAG_25h9));
    detector.setDictionaries(dictionaries);
    dicts = detector.getDictionaries();
    ASSERT_EQ(dicts.size(), 3ul);
    EXPECT_EQ(dicts[0].markerSize, 6);
    EXPECT_EQ(dicts[1].markerSize, 7);
    EXPECT_EQ(dicts[2].markerSize, 5);
    auto dict = detector.getDictionary();
    EXPECT_EQ(dict.markerSize, 6);
    detector.setDictionary(aruco::getPredefinedDictionary(aruco::DICT_APRILTAG_16h5));
    dicts = detector.getDictionaries();
    ASSERT_EQ(dicts.size(), 3ul);
    EXPECT_EQ(dicts[0].markerSize, 4);
    EXPECT_EQ(dicts[1].markerSize, 7);
    EXPECT_EQ(dicts[2].markerSize, 5);
}


TEST(CV_ArucoMultiDict, noDict)
{
    aruco::ArucoDetector detector;
    EXPECT_THROW({
        detector.setDictionaries({});
    }, Exception);
}


TEST(CV_ArucoMultiDict, multiMarkerDetection)
{
    const int markerSidePixels = 100;
    const int imageSize = markerSidePixels * 2 + 3 * (markerSidePixels / 2);
    vector<aruco::Dictionary> usedDictionaries;

    // draw synthetic image
    Mat img = Mat(imageSize, imageSize, CV_8UC1, Scalar::all(255));
    for(int y = 0; y < 2; y++) {
        for(int x = 0; x < 2; x++) {
            Mat marker;
            int id = y * 2 + x;
            int dictId = x * 4 + y * 8;
            auto dict = aruco::getPredefinedDictionary(dictId);
            usedDictionaries.push_back(dict);
            aruco::generateImageMarker(dict, id, markerSidePixels, marker);
            Point2f firstCorner(markerSidePixels / 2.f + x * (1.5f * markerSidePixels),
                        markerSidePixels / 2.f + y * (1.5f * markerSidePixels));
            Mat aux = img(Rect((int)firstCorner.x, (int)firstCorner.y, markerSidePixels, markerSidePixels));
            marker.copyTo(aux);
        }
    }
    img.convertTo(img, CV_8UC3);

    aruco::ArucoDetector detector(usedDictionaries);

    vector<vector<Point2f> > markerCorners;
    vector<int> markerIds;
    vector<vector<Point2f> > rejectedImgPts;
    vector<int> dictIds;
    detector.detectMarkersMultiDict(img, markerCorners, markerIds, rejectedImgPts, dictIds);
    ASSERT_EQ(markerIds.size(), 4u);
    ASSERT_EQ(dictIds.size(), 4u);
    for (size_t i = 0; i < dictIds.size(); ++i) {
        EXPECT_EQ(dictIds[i], (int)i);
    }
}


TEST(CV_ArucoMultiDict, multiMarkerDoubleDetection)
{
    const int markerSidePixels = 100;
    const int imageWidth = 2 * markerSidePixels + 3 * (markerSidePixels / 2);
    const int imageHeight = markerSidePixels + 2 * (markerSidePixels / 2);
    vector<aruco::Dictionary> usedDictionaries = {
        aruco::getPredefinedDictionary(aruco::DICT_5X5_50),
        aruco::getPredefinedDictionary(aruco::DICT_5X5_100)
    };

    // draw synthetic image
    Mat img = Mat(imageHeight, imageWidth, CV_8UC1, Scalar::all(255));
    for(int y = 0; y < 2; y++) {
        Mat marker;
        int id = 49 + y;
        auto dict = aruco::getPredefinedDictionary(aruco::DICT_5X5_100);
        aruco::generateImageMarker(dict, id, markerSidePixels, marker);
        Point2f firstCorner(markerSidePixels / 2.f + y * (1.5f * markerSidePixels),
                    markerSidePixels / 2.f);
        Mat aux = img(Rect((int)firstCorner.x, (int)firstCorner.y, markerSidePixels, markerSidePixels));
        marker.copyTo(aux);
    }
    img.convertTo(img, CV_8UC3);

    aruco::ArucoDetector detector(usedDictionaries);

    vector<vector<Point2f> > markerCorners;
    vector<int> markerIds;
    vector<vector<Point2f> > rejectedImgPts;
    vector<int> dictIds;
    detector.detectMarkersMultiDict(img, markerCorners, markerIds, rejectedImgPts, dictIds);
    ASSERT_EQ(markerIds.size(), 3u);
    ASSERT_EQ(dictIds.size(), 3u);
    EXPECT_EQ(dictIds[0], 0); // 5X5_50
    EXPECT_EQ(dictIds[1], 1); // 5X5_100
    EXPECT_EQ(dictIds[2], 1); // 5X5_100
}


TEST(CV_ArucoMultiDict, serialization)
{
    aruco::ArucoDetector detector;
    {
        FileStorage fs_out(".json", FileStorage::WRITE + FileStorage::MEMORY);
        ASSERT_TRUE(fs_out.isOpened());
        detector.write(fs_out);
        std::string serialized_string = fs_out.releaseAndGetString();
        FileStorage test_fs(serialized_string, FileStorage::Mode::READ + FileStorage::MEMORY);
        ASSERT_TRUE(test_fs.isOpened());
        aruco::ArucoDetector test_detector;
        test_detector.read(test_fs.root());
        // compare default constructor result
        EXPECT_EQ(aruco::getPredefinedDictionary(aruco::DICT_4X4_50), test_detector.getDictionary());
    }
    detector.setDictionaries({aruco::getPredefinedDictionary(aruco::DICT_4X4_50), aruco::getPredefinedDictionary(aruco::DICT_5X5_100)});
    {
        FileStorage fs_out(".json", FileStorage::WRITE + FileStorage::MEMORY);
        ASSERT_TRUE(fs_out.isOpened());
        detector.write(fs_out);
        std::string serialized_string = fs_out.releaseAndGetString();
        FileStorage test_fs(serialized_string, FileStorage::Mode::READ + FileStorage::MEMORY);
        ASSERT_TRUE(test_fs.isOpened());
        aruco::ArucoDetector test_detector;
        test_detector.read(test_fs.root());
        // check for one additional dictionary
        auto dicts = test_detector.getDictionaries();
        ASSERT_EQ(2ul, dicts.size());
        EXPECT_EQ(aruco::getPredefinedDictionary(aruco::DICT_4X4_50), dicts[0]);
        EXPECT_EQ(aruco::getPredefinedDictionary(aruco::DICT_5X5_100), dicts[1]);
    }
}


struct ArucoThreading: public testing::TestWithParam<aruco::CornerRefineMethod>
{
    struct NumThreadsSetter {
        NumThreadsSetter(const int num_threads)
        : original_num_threads_(getNumThreads()) {
            setNumThreads(num_threads);
        }

        ~NumThreadsSetter() {
            setNumThreads(original_num_threads_);
        }
     private:
        int original_num_threads_;
    };
};

TEST_P(ArucoThreading, number_of_threads_does_not_change_results)
{
    // We are not testing against different dictionaries
    // As we are interested mostly in small images, smaller
    // markers is better -> 4x4
    aruco::ArucoDetector detector(aruco::getPredefinedDictionary(aruco::DICT_4X4_50));

    // Height of the test image can be chosen quite freely
    // We aim to test against small images as in those the
    // number of threads has most effect
    const int height_img = 20;
    // Just to get nice white boarder
    const int shift = height_img > 10 ? 5 : 1;
    const int height_marker = height_img-2*shift;

    // Create a test image
    Mat img_marker;
    aruco::generateImageMarker(detector.getDictionary(), 23, height_marker, img_marker, 1);

    // Copy to bigger image to get a white border
    Mat img(height_img, height_img, CV_8UC1, Scalar(255));
    img_marker.copyTo(img(Rect(shift, shift, height_marker, height_marker)));

    aruco::DetectorParameters detectorParameters = detector.getDetectorParameters();
    detectorParameters.cornerRefinementMethod = (int)GetParam();
    detectorParameters.validBitIdThreshold = 0.5f;
    detector.setDetectorParameters(detectorParameters);

    vector<vector<Point2f> > original_corners;
    vector<int> original_ids;
    {
        NumThreadsSetter thread_num_setter(1);
        detector.detectMarkers(img, original_corners, original_ids);
    }

    ASSERT_EQ(original_ids.size(), 1ull);
    ASSERT_EQ(original_corners.size(), 1ull);

    int num_threads_to_test[] = { 2, 8, 16, 32, height_img-1, height_img, height_img+1};

    for (size_t i_num_threads = 0; i_num_threads < sizeof(num_threads_to_test)/sizeof(int); ++i_num_threads) {
        NumThreadsSetter thread_num_setter(num_threads_to_test[i_num_threads]);

        vector<vector<Point2f> > corners;
        vector<int> ids;
        detector.detectMarkers(img, corners, ids);

        // If we don't find any markers, the test is broken
        ASSERT_EQ(ids.size(), 1ull);

        // Make sure we got the same result as the first time
        ASSERT_EQ(corners.size(), original_corners.size());
        ASSERT_EQ(ids.size(), original_ids.size());
        ASSERT_EQ(ids.size(), corners.size());
        for (size_t i = 0; i < corners.size(); ++i) {
            EXPECT_EQ(ids[i], original_ids[i]);
            for (size_t j = 0; j < corners[i].size(); ++j) {
                EXPECT_NEAR(corners[i][j].x, original_corners[i][j].x, 0.1f);
                EXPECT_NEAR(corners[i][j].y, original_corners[i][j].y, 0.1f);
            }
        }
    }
}

INSTANTIATE_TEST_CASE_P(
        CV_ArucoDetectMarkers, ArucoThreading,
        ::testing::Values(
            aruco::CORNER_REFINE_NONE,
            aruco::CORNER_REFINE_SUBPIX,
            aruco::CORNER_REFINE_CONTOUR,
            aruco::CORNER_REFINE_APRILTAG
        ));

}} // namespace