关于在opencv aruco中使用detect_markers.cpp的问题？

Question

我正在使用opencv网站上的detect_markers.cpp来使用相机检测标记的姿势。在没有错误的编译之后，我得到了这样的结果，那么我如何输入参数呢？http://docs.opencv.org/3.1.0/d5/dae/tutorial_aruco_detection.html

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#include <opencv2/highgui.hpp>
#include <opencv2/aruco.hpp>
#include <iostream>
#include <opencv/cv.h>
#include <opencv/cvaux.h>
#include <opencv/highgui.h>
using namespace std;
using namespace cv;

namespace {
const char* about = "Basic marker detection";
const char* keys  =
        "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
        "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
        "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"
        "DICT_7X7_100=13, DICT_7X7_250=14, DICT_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16}"
        "{v        |       | Input from video file, if ommited, input comes from camera }"
        "{ci       | 0     | Camera id if input doesnt come from video (-v) }"
        "{c        |       | Camera intrinsic parameters. Needed for camera pose }"
        "{l        | 0.1   | Marker side lenght (in meters). Needed for correct scale in camera pose }"
        "{dp       |       | File of marker detector parameters }"
        "{r        |       | show rejected candidates too }";
}

/**
 */
static bool readCameraParameters(string filename, Mat &camMatrix, Mat &distCoeffs) {
    FileStorage fs(filename, FileStorage::READ);
    if(!fs.isOpened())
        return false;
    fs["camera_matrix"] >> camMatrix;
    fs["distortion_coefficients"] >> distCoeffs;
    return true;
}



/**
 */
static bool readDetectorParameters(string filename, Ptr<aruco::DetectorParameters> &params) {
    FileStorage fs(filename, FileStorage::READ);
    if(!fs.isOpened())
        return false;
    fs["adaptiveThreshWinSizeMin"] >> params->adaptiveThreshWinSizeMin;
    fs["adaptiveThreshWinSizeMax"] >> params->adaptiveThreshWinSizeMax;
    fs["adaptiveThreshWinSizeStep"] >> params->adaptiveThreshWinSizeStep;
    fs["adaptiveThreshConstant"] >> params->adaptiveThreshConstant;
    fs["minMarkerPerimeterRate"] >> params->minMarkerPerimeterRate;
    fs["maxMarkerPerimeterRate"] >> params->maxMarkerPerimeterRate;
    fs["polygonalApproxAccuracyRate"] >> params->polygonalApproxAccuracyRate;
    fs["minCornerDistanceRate"] >> params->minCornerDistanceRate;
    fs["minDistanceToBorder"] >> params->minDistanceToBorder;
    fs["minMarkerDistanceRate"] >> params->minMarkerDistanceRate;
    fs["doCornerRefinement"] >> params->doCornerRefinement;
    fs["cornerRefinementWinSize"] >> params->cornerRefinementWinSize;
    fs["cornerRefinementMaxIterations"] >> params->cornerRefinementMaxIterations;
    fs["cornerRefinementMinAccuracy"] >> params->cornerRefinementMinAccuracy;
    fs["markerBorderBits"] >> params->markerBorderBits;
    fs["perspectiveRemovePixelPerCell"] >> params->perspectiveRemovePixelPerCell;
    fs["perspectiveRemoveIgnoredMarginPerCell"] >> params->perspectiveRemoveIgnoredMarginPerCell;
    fs["maxErroneousBitsInBorderRate"] >> params->maxErroneousBitsInBorderRate;
    fs["minOtsuStdDev"] >> params->minOtsuStdDev;
    fs["errorCorrectionRate"] >> params->errorCorrectionRate;
    return true;
}



/**
 */
int main(int argc, char *argv[]) {
    CommandLineParser parser(argc, argv, keys);
    parser.about(about);

    if(argc < 2) {
        parser.printMessage();
        return 0;
    }

    int dictionaryId = parser.get<int>("d");
    bool showRejected = parser.has("r");
    bool estimatePose = parser.has("c");
    float markerLength = parser.get<float>("l");

    Ptr<aruco::DetectorParameters> detectorParams = aruco::DetectorParameters::create();
    if(parser.has("dp")) {
        bool readOk = readDetectorParameters(parser.get<string>("dp"), detectorParams);
        if(!readOk) {
            cerr << "Invalid detector parameters file" << endl;
            return 0;
        }
    }
    detectorParams->doCornerRefinement = true; // do corner refinement in markers

    int camId = parser.get<int>("ci");

    String video;
    if(parser.has("v")) {
        video = parser.get<String>("v");
    }

    if(!parser.check()) {
        parser.printErrors();
        return 0;
    }

    Ptr<aruco::Dictionary> dictionary =
        aruco::getPredefinedDictionary(aruco::PREDEFINED_DICTIONARY_NAME(dictionaryId));

    Mat camMatrix, distCoeffs;
    if(estimatePose) {
        bool readOk = readCameraParameters(parser.get<string>("c"), camMatrix, distCoeffs);
        if(!readOk) {
            cerr << "Invalid camera file" << endl;
            return 0;
        }
    }

    VideoCapture inputVideo;
    int waitTime;
    if(!video.empty()) {
        inputVideo.open(video);
        waitTime = 0;
    } else {
        inputVideo.open(camId);
        waitTime = 10;
    }

    double totalTime = 0;
    int totalIterations = 0;

    while(inputVideo.grab()) {
        Mat image, imageCopy;
        inputVideo.retrieve(image);

        double tick = (double)getTickCount();

        vector< int > ids;
        vector< vector< Point2f > > corners, rejected;
        vector< Vec3d > rvecs, tvecs;

        // detect markers and estimate pose
        aruco::detectMarkers(image, dictionary, corners, ids, detectorParams, rejected);
        if(estimatePose && ids.size() > 0)
            aruco::estimatePoseSingleMarkers(corners, markerLength, camMatrix, distCoeffs, rvecs,
                                             tvecs);

        double currentTime = ((double)getTickCount() - tick) / getTickFrequency();
        totalTime += currentTime;
        totalIterations++;
        if(totalIterations % 30 == 0) {
            cout << "Detection Time = " << currentTime * 1000 << " ms "
                 << "(Mean = " << 1000 * totalTime / double(totalIterations) << " ms)" << endl;
        }

        // draw results
        image.copyTo(imageCopy);
        if(ids.size() > 0) {
            aruco::drawDetectedMarkers(imageCopy, corners, ids);

            if(estimatePose) {
                for(unsigned int i = 0; i < ids.size(); i++)
                    aruco::drawAxis(imageCopy, camMatrix, distCoeffs, rvecs[i], tvecs[i],
                                    markerLength * 0.5f);
            }
        }

        if(showRejected && rejected.size() > 0)
            aruco::drawDetectedMarkers(imageCopy, rejected, noArray(), Scalar(100, 0, 255));

        imshow("out", imageCopy);
        char key = (char)waitKey(waitTime);
        if(key == 27) break;
    }

    return 0;
}

这是我得到的消息:基本标记检测用法: detecttest params

-c
    Camera intrinsic parameters. Needed for camera pose
--ci (value:0)
    Camera id if input doesnt come from video (-v)
-d
    dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,DICT_7X7_100=13, DICT_7X7_250=14, DICT_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16
--dp
    File of marker detector parameters
-l (value:0.1)
    Marker side lenght (in meters). Needed for correct scale in camera pose
-r
    show rejected candidates too
-v
    Input from video file, if ommited, input comes from camera

Answer 1

我也在学习这个库。我用以下命令创建了一个标记：

./create_marker --bb=1 -d=0 -ms=400 -id=0 marker.png

并把它打印出来。然后我运行：

./detect_markers -d=0

而且它运行得很好！

这可能有些夸大其词，但这是我在OS X上用brew编译的方法：

g++ -I/usr/local/Cellar/opencv/3.3.0_3/include/opencv -I/usr/local/Cellar/opencv/3.3.0_3/include -L/usr/local/Cellar/opencv/3.3.0_3/lib -lopencv_stitching -lopencv_superres -lopencv_videostab -lopencv_photo -lopencv_aruco -lopencv_bgsegm -lopencv_bioinspired -lopencv_ccalib -lopencv_dpm -lopencv_face -lopencv_fuzzy -lopencv_img_hash -lopencv_line_descriptor -lopencv_optflow -lopencv_reg -lopencv_rgbd -lopencv_saliency -lopencv_stereo -lopencv_structured_light -lopencv_phase_unwrapping -lopencv_surface_matching -lopencv_tracking -lopencv_datasets -lopencv_text -lopencv_dnn -lopencv_plot -lopencv_ml -lopencv_xfeatures2d -lopencv_shape -lopencv_video -lopencv_ximgproc -lopencv_calib3d -lopencv_features2d -lopencv_highgui -lopencv_videoio -lopencv_flann -lopencv_xobjdetect -lopencv_imgcodecs -lopencv_objdetect -lopencv_xphoto -lopencv_imgproc -lopencv_core -o detect_markers detect_markers.cpp