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我正在尝试开发一种线跟踪算法，其中无人机将通过颜色过滤检测感兴趣的区域。我正在过滤所有颜色以仅看到白色。一旦检测到该颜色，我希望无人机从该区域的一端直线进入另一端，基本上是跟随线。也许在openCV上使用画线功能更容易，但我不确定。无论如何，我最大的问题是告诉无人机跟随颜色或检测到的静止物体。我正在使用来自 GitHub 的这个存储库：https ://github.com/tekkies/cvdrone/blob/master/src/main.cpp 。

 #include "ardrone/ardrone.h"



int main(int argc, char *argv[])

{

    // AR.Drone class

    ARDrone ardrone;



    // Initialize

    if (!ardrone.open()) {

        std::cout << "Failed to initialize." << std::endl;

        return -1;

    }



    // Thresholds

    int minH = 0, maxH = 255;

    int minS = 0, maxS = 255;

    int minV = 0, maxV = 255;



    // XML save data

    std::string filename("thresholds.xml");

    cv::FileStorage fs(filename, cv::FileStorage::READ);



    // If there is a save file then read it

    if (fs.isOpened()) {

        maxH = fs["H_MAX"];

        minH = fs["H_MIN"];

        maxS = fs["S_MAX"];

        minS = fs["S_MIN"];

        maxV = fs["V_MAX"];

        minV = fs["V_MIN"];

        fs.release();

    }



    // Create a window

    cv::namedWindow("binalized");

    cv::createTrackbar("H max", "binalized", &maxH, 255);

    cv::createTrackbar("H min", "binalized", &minH, 255);

    cv::createTrackbar("S max", "binalized", &maxS, 255);

    cv::createTrackbar("S min", "binalized", &minS, 255);

    cv::createTrackbar("V max", "binalized", &maxV, 255);

    cv::createTrackbar("V min", "binalized", &minV, 255);

    cv::resizeWindow("binalized", 0, 0);



    // Kalman filter

    cv::KalmanFilter kalman(4, 2, 0);



    // Sampling time [s]

    const double dt = 1.0;



    // Transition matrix (x, y, vx, vy)

    cv::Mat1f A(4, 4);

    A << 1.0, 0.0,  dt, 0.0,

         0.0, 1.0, 0.0,  dt,

         0.0, 0.0, 1.0, 0.0,

         0.0, 0.0, 0.0, 1.0;

    kalman.transitionMatrix = A;



    // Measurement matrix (x, y)

    cv::Mat1f H(2, 4);

    H << 1, 0, 0, 0,

         0, 1, 0, 0;

    kalman.measurementMatrix = H;



    // Process noise covairance (x, y, vx, vy)

    cv::Mat1f Q(4, 4);

    Q << 1e-5,  0.0,  0.0,  0.0,

          0.0, 1e-5,  0.0,  0.0,

          0.0,  0.0, 1e-5,  0.0,

          0.0,  0.0,  0.0, 1e-5;

    kalman.processNoiseCov = Q;



    // Measurement noise covariance (x, y)

    cv::Mat1f R(2, 2);

    R << 1e-1,  0.0,

          0.0, 1e-1;

    kalman.measurementNoiseCov = R;



    char textBuffer[80];

    cv::Scalar green = CV_RGB(0,255,0);

    float speed = 0.0;

    bool learnMode = false;



    // Main loop

    while (1) {

        // Key input

        int key = cv::waitKey(33);

        if (key == 0x1b) break;



        // Get an image

        cv::Mat image = ardrone.getImage();



        // HSV image

        cv::Mat hsv;

        cv::cvtColor(image, hsv, cv::COLOR_BGR2HSV_FULL);



        // Binalize

        cv::Mat binalized;

        cv::Scalar lower(minH, minS, minV);

        cv::Scalar upper(maxH, maxS, maxV);

        cv::inRange(hsv, lower, upper, binalized);



        // Show result

        cv::imshow("binalized", binalized);



        // De-noising

        cv::Mat kernel = getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));

        cv::morphologyEx(binalized, binalized, cv::MORPH_CLOSE, kernel);

        //cv::imshow("morphologyEx", binalized);



        // Detect contours

        std::vector<std::vector<cv::Point>> contours;

        cv::findContours(binalized.clone(), contours, cv::RETR_CCOMP, cv::CHAIN_APPROX_SIMPLE);



        // Find largest contour

        int contour_index = -1;

        double max_area = 0.0;

        for (size_t i = 0; i < contours.size(); i++) {

            double area = fabs(cv::contourArea(contours[i]));

            if (area > max_area) {

                contour_index = i;

                max_area = area;

            }

        }



        // Object detected

        if (contour_index >= 0) {

            // Moments

            cv::Moments moments = cv::moments(contours[contour_index], true);

            double marker_y = (int)(moments.m01 / moments.m00);

            double marker_x = (int)(moments.m10 / moments.m00);



            // Measurements

            cv::Mat measurement = (cv::Mat1f(2, 1) << marker_x, marker_y);



            // Correction

            cv::Mat estimated = kalman.correct(measurement);



            // Show result

            cv::Rect rect = cv::boundingRect(contours[contour_index]);

            cv::rectangle(image, rect, cv::Scalar(0, 255, 0));

        }



        // Prediction

        cv::Mat1f prediction = kalman.predict();

        int radius = 1e+3 * kalman.errorCovPre.at<float>(0, 0);



        // Calculate object heading fraction

        float heading = -((image.cols/2)-prediction(0, 0))/(image.cols/2);

        sprintf(textBuffer, "heading = %+3.2f", heading);

        putText(image, textBuffer, cvPoint(30,30), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, green, 1, CV_AA);



        // Show predicted position

        cv::circle(image, cv::Point(prediction(0, 0), prediction(0, 1)), radius, green, 2);



        //Speed

        if ((key >= '0') && (key <= '9')) 

        {

            speed = (key-'0')*0.1;

            //printf("speed = %3.2f\n", speed);

        }

        sprintf(textBuffer, "speed = %3.2f", speed);

        putText(image, textBuffer, cvPoint(30,60), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, green, 1, CV_AA);

        // Drone control

        double vx = 0.0, vy = 0.0, vz = 0.0, vr = 0.0;



        // Auto-follow

        vx = speed;

        vr = -heading;

             if (key == 0x260000) vx =  1.0;

    if (key == 0x280000) vx = -1.0;

    if (key == 0x250000) vr =  1.0;

    if (key == 0x270000) vr = -1.0;

    if (key == 'q')      vz =  1.0;

    if (key == 'a')      vz = -1.0;
        ardrone.move3D(vx, vy, vz, vr);



    // See you

    ardrone.close();



    return 0;

}
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