opencv实现边缘梯度直方图+meanshift实现框选物体跟踪，代码出错，不知道错在哪里？

Question

#include<iostream>
#include<opencv2/opencv.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/core/core.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<ctype.h>
using namespace std;
using namespace cv;

Mat image;         //当前帧图像 
Mat imageCopy; //用于拷贝的当前帧图像  
Mat rectImage;   //子图像
Point beginPoint; //矩形框起点  
Point endPoint;  //矩形框终点  
bool leftButtonDownFlag = false; //左键单击后视频暂停播放的标志位  
int frameCount = 0; //帧数统计 
int trackCount = 0;  //等于1时初始化直方图
void onMouse(int event, int x, int y, int flags, void* ustc); //鼠标回调函数  

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

    VideoCapture capture("1.AVI");
    //VideoCapture capture(0);
    int capture_fps = capture.get(CV_CAP_PROP_FPS); //获取视频帧率 
    int capture_count = capture.get(CV_CAP_PROP_FRAME_COUNT);
    int capture_width = capture.get(CV_CAP_PROP_FRAME_WIDTH);
    int capture_height = capture.get(CV_CAP_PROP_FRAME_HEIGHT);
    cout << "视频帧率：" << capture_fps << endl;
    cout << "视频帧数：" << capture_count << endl;
    cout << "视频宽度：" << capture_width << endl;
    cout << "视频高度：" << capture_height << endl;
    int pauseTime = 1000 / capture_fps; //两幅画面中间间隔  

    namedWindow("Video");
    setMouseCallback("Video", onMouse);//函数setMousecallback用于鼠标在图上做标记等作用
    int hbinNum = 16;//每一维上直方图的个数，如果是一维直方图，就是竖条(bin)的个数
    float hranges[] = { 40, 250 };//每一维数值的取值范围数组
    const float* phranges = hranges;//const修饰phranges指针,指针phranges的指向可以修改，但是phranges指向的值不可以改；指针phranges指向的是hranges的首地址
    bool backprojectMode = false;

    namedWindow("Video", 0);
    capture >> image;
    int rows = image.rows;
    int cols = image.cols;
    Mat image1 = image.clone();
    Mat dst, edge, gray;//定义canny边缘检测所需的变量
    Mat abs_grad_x, abs_grad_y;//定义sobel算子求方向梯度所需的变量
    Mat grad_x(rows, cols, CV_8UC1, Scalar::all(0));//定义sobel算子求方向梯度所需的变量
    Mat grad_y(rows, cols, CV_8UC1, Scalar::all(0));//定义sobel算子求方向梯度所需的变量

    Mat theta(rows, cols, CV_32FC1, Scalar::all(0));
    Mat gradient(rows, cols, CV_32FC1, Scalar::all(0));//梯度矩阵
    Mat angle(rows, cols, CV_32FC1, Scalar::all(0));//梯度方向角度
    Mat  mask, hist, histImg = Mat::zeros(image.size(), image.type()), backproj;//初始化histImg存储直方图数据，使其规格与捕获的image相同
    Rect trackWindow;//定义一个捕捉矩形窗口

    //**********************读取视频********************************
    while (true) {
        if (!leftButtonDownFlag) //鼠标左键按下绘制矩形时，视频暂停播放  
        {
            capture >> image;//读取当前帧
            frameCount++;   //帧数增加，视频向前增加 
        }
        if (!image.data || waitKey(pauseTime + 30) == 27)  //当当前帧的数据为空或两帧画面的时间间隔不满足规定的数值时，退出播放
        {
            break;
        }


        //***************camshiftdemo代码的脉络分析********************
        //提取边缘梯度大小与幅值
        if (trackCount > 0) {
            dst.create(image1.size(), image1.type()); 
            dst = Scalar::all(0);
            cvtColor(image1, gray, COLOR_BGR2GRAY);
            GaussianBlur(gray, edge, Size(3, 3), 0, 0);
            Canny(edge, edge, 3, 9, 3);//canny算子获得边缘图
            Sobel(gray, grad_x, CV_16S,1, 0, 3, 1, 1);//求X方向的梯度
            Sobel(gray, grad_y, CV_16S,0, 1, 3, 1, 1);//求Y方向的梯度
            cartToPolar(grad_x, grad_y, gradient, angle);//计算梯度方向与梯度幅值 
            convertScaleAbs(grad_x, grad_x);
            convertScaleAbs(grad_y, grad_y);

            //绘制直方图
            if (trackCount == 1) {
                histImg = Scalar::all(0);
                Mat roi(angle, Rect(beginPoint, endPoint)), maskroi(mask, Rect(beginPoint, endPoint));
                calcHist(&roi, 1, 0, maskroi, hist, 1, &hbinNum, &phranges);//掩膜maskroi是用来确定输入图像的哪些像素被计数;mask也设置ROI， 是要让hue中截取出来放在mask中的 区域大小位置都和hue中一致， 不要改变原来的位置这点很有用。
                normalize(hist, hist, 0, 255, CV_MINMAX);
                trackCount++;
                trackWindow = Rect(beginPoint, endPoint);
            }


            calcBackProject(&angle, 1, 0, hist, backproj, &phranges);//直方图反向投影
            backproj &= mask;
            meanShift(backproj, trackWindow, TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1));//CamshiftDemo

            //***画出跟踪区域的位置
            if (backprojectMode) {
                cvtColor(backproj, image, CV_GRAY2BGR);
            }
            rectangle(image, Point(trackWindow.x, trackWindow.y), Point(trackWindow.x + trackWindow.width, trackWindow.y + trackWindow.height), Scalar(0, 0, 255), 1, CV_AA);//画跟踪矩形框
            trackCount++;

            //  writer << image;
        }
        imshow("Video", image);

    }
    waitKey(0);
    return 0;
}

//鼠标回调函数    
void onMouse(int event, int x, int y, int flags, void *ustc)
{
    if (event == CV_EVENT_LBUTTONDOWN)
    {
        leftButtonDownFlag = true; //标志位  
        beginPoint = Point(x, y);  //设置左键按下点的矩形起点  
        endPoint = beginPoint;
    }
    if (event == CV_EVENT_MOUSEMOVE && leftButtonDownFlag)
    {
        imageCopy = image.clone();
        endPoint = Point(x, y);
        if (beginPoint != endPoint)
        {
            //在复制的图像上绘制矩形  
            rectangle(imageCopy, beginPoint, endPoint, Scalar(0, 0, 255), 2);
        }
        imshow("Video", imageCopy);
    }
    if (event == CV_EVENT_LBUTTONUP)
    {
        leftButtonDownFlag = false;
        Mat subImage = image(Rect(beginPoint, endPoint)); //子图像  
        rectImage = subImage.clone();
        trackCount = 1;
        //imshow("Sub Image", rectImage);
    }
}