opencv用boundingrect找最大区域时崩溃

Question

代码的功能是调用摄像头，然后找出视频帧中的红色区域，再找到其中最大的区域作为ROI。

用笔记本集成的摄像头的时候可以正常跑。

换成usb外接摄像头，编译连接都没问题，就是run的时候崩溃了

//#include "stdafx.h"

#include "opencv2/opencv.hpp"

#include

#include
using namespace std;
using namespace cv;

//漫水填充
void fillHole(const Mat srcBw, Mat &dstBw)
{
Size m_Size = srcBw.size();
Mat Temp = Mat::zeros(m_Size.height + 2, m_Size.width + 2, srcBw.type());//延展图像
srcBw.copyTo(Temp(Range(1, m_Size.height + 1), Range(1, m_Size.width + 1)));

cv::floodFill(Temp, Point(0, 0), Scalar(255,255,255));
//cv::floodFill(Temp, Point(30, 29), Scalar(255, 0, 0), 0, Scalar(10, 10, 10), Scalar(10, 10, 10));
Mat cutImg;//裁剪延展的图像
Temp(Range(1, m_Size.height + 1), Range(1, m_Size.width + 1)).copyTo(cutImg);

dstBw = srcBw | (~cutImg);

}

int main(int argc, char** argv)
{
//定义扫描图像的循环变量
int i = 0;
int j = 0;

//通过摄像头采集视频

VideoCapture capture(0);



//读取视频.
//VideoCapture capture("E:/weixian.mp4");
//视频总帧数
//long totalFrameNumber = capture.get(CV_CAP_PROP_FRAME_COUNT);
//num是一个计数的flag.
int num = 1;


while (1)
//while (num <= totalFrameNumber)
{
    //IplImage* frame;
    Mat frame1, frame;
    //将捕获图像存入frame变量
    capture >> frame1;




    blur(frame1, frame1, Size(7, 7));


    //缩放倍数
    double fScale = 0.5;
    //目标图像尺寸
    CvSize czSize;
    czSize.width = frame1.cols*fScale;
    czSize.height = frame1.rows*fScale;
    cv::resize(frame1, frame, cv::Size(czSize.width, czSize.height), (0, 0), (0, 0), cv::INTER_LINEAR);
    cv::Mat rgbImage = frame, hsvImage;
    //转换到hsv空间
    cv::cvtColor(rgbImage, hsvImage, cv::COLOR_BGR2HSV);
    //为了获取图像的尺寸
    //这里把图像类型转换
    IplImage* tempImage = &IplImage(hsvImage);
    IplImage* extractionImage = &IplImage(hsvImage);
    IplImage* reversionImage = &IplImage(hsvImage);
    IplImage* grayImage = &IplImage(hsvImage);
    IplImage* binaryImage = &IplImage(hsvImage);
    //显示两个空间下的图像
    imshow("RGB", rgbImage);
    //imshow("HSV", hsvImage);
    for (i = 0; i < tempImage->height; i++)
    {
        for (j = 0; j < tempImage->width; j++)
        {
            //获取像素点为（j, i）点的HSV的值
            CvScalar s_hsv = cvGet2D(tempImage, i, j);
            /*
            opencv 的H范围是0~180，红色的H范围大概是(0~8)∪(160,180)
            S是饱和度，一般是大于一个值,S过低就是灰色（参考值S>80)，
            V是亮度，过低就是黑色，过高就是白色(参考值220>V>50)。
            */
            CvScalar s;
            if (!(((s_hsv.val[0]>0) && (s_hsv.val[0]<8)) || (s_hsv.val[0]>178) && (s_hsv.val[0]<180)))
            {
                s.val[0] = 0;
                s.val[1] = 0;
                s.val[2] = 0;
                cvSet2D(tempImage, i, j, s);
            }

            //else这小段是自己加的，如果是红色，就置为白色.
            else
            {
                s.val[0] = 180;
                s.val[1] = 30;
                s.val[2] = 255;
                cvSet2D(tempImage, i, j, s);
            }
        }
    }
    //提取红色分量
    cvConvert(tempImage, extractionImage);
    //cvNamedWindow("Extraction");
    //cvShowImage("Extraction", extractionImage);
    //颜色空间变换回RGB
    cvCvtColor(extractionImage, reversionImage, cv::COLOR_HSV2BGR);
    //cvNamedWindow("Reversion");
    //cvShowImage("Reversion", reversionImage);
    //这里如果转灰度,会出现内存泄漏.
    //由于前边定义每个图像都用的取址运算符
    //因为grayImge指向的是hsvImage的地址,而后者一直在变
    //所以此处gray也一直在变
    //RGB图像转灰度图
    //cvCvtColor(reversionImage, grayImage, cv::COLOR_BGR2GRAY);
    //cvNamedWindow("Gray");
    //cvShowImage("Gray", grayImage);
    //灰度图转二值图
    cvThreshold(grayImage, binaryImage, 100, 255, CV_THRESH_BINARY);
    //cvThreshold(reversionImage, binaryImage, 150, 255, CV_THRESH_BINARY);
    //cvNamedWindow("Binary");
    //cvShowImage("Binary", binaryImage);
    //5*5正方形，8位uchar型，全1结构元素
    cv::Mat element5(5, 5, CV_8U, cv::Scalar(1));  
    cv::Mat closed,opened,temp,final,cimage;
    //cv::vector<vector<cv::Point>> contours(10000);
    temp = Mat(binaryImage);
    //高级形态学运算函数
    cv::morphologyEx(temp, opened, cv::MORPH_CLOSE, element5);
    cv::morphologyEx(temp, closed, cv::MORPH_CLOSE, element5);
    //cvNamedWindow("Opened");
    //imshow("Opened", opened);
    //cvNamedWindow("Closed");
    //imshow("Closed", closed);
    fillHole(closed, final);
    cvNamedWindow("Final");
    imshow("Final", final);
    Canny(final, cimage, 150, 250);
    //cvNamedWindow("Canny");
    //imshow("Canny", cimage);


    cv::vector<vector<cv::Point>> contours;
    vector<Vec4i> hierarchy;

    cv::findContours(cimage, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE,Point());
    //cv::findContours(cimage, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point());







    // 寻找最大连通域

    double maxArea = 0;
    vector<cv::Point> maxContour;
    for (size_t i = 0; i < contours.size(); i++)
    {
        double area = cv::contourArea(contours[i]);
        if (area > maxArea)
        {
            maxArea = area;
            maxContour = contours[i];
        }

        //cout << maxArea << endl;
    }
    //cout << maxArea << endl;
    //cout << contours.size() << endl;
    //cout << i << endl;

    //cout << maxContour << endl;


    // 将轮廓转为矩形框

    cv::Rect maxRect = cv::boundingRect(maxContour);



    // 显示连通域
    cv::Mat result1, result2;
    final.copyTo(result1);
    final.copyTo(result2);

    for (size_t i = 0; i < contours.size(); i++)
    {
        cv::Rect r = cv::boundingRect(contours[i]);
        cv::rectangle(result1, r, cv::Scalar(255));
    }
    //cv::imshow("all regions", result1);
    //cv::waitKey();
    cv::rectangle(result2, maxRect, cv::Scalar(0, 255, 0), 3);
    cv::imshow("largest region", result2);
    if (maxArea>8000)
    {
        cv::imshow("largest region", result2);
    }



    /*




    */



    waitKey(20);


    //视频帧数加1.
    num = num + 1;
}
return 0;

}

好像是boundingrect那里出错了，但是为什么集成摄像头就可以呢？请教各位

Answer 1

http://jxliucong.blog.163.com/blog/static/182167853201422084431567/