使用Opencv去除小斑点

我在visualstudio2010上使用opencv2.2。我已经写了一个代码来预处理OCR图像。我使用了很多轨迹条来改变参数。预处理功能之一是通过绘制轮廓并根据大小过滤掉小斑点来去除小斑点。然而,当我运行程序时,cvDrawContours函数会给我一个错误。基本上,我得到弹出窗口和一个错误说R6010-abort已经被调用。命令行说在第641行的matrix.cpp中有一个未知的数组类型。我在这里包括我的代码。该问题由BlobFunc函数中的cvDrawContours函数调用。

#include <C:\OpenCV2.2\modules\core\include\opencv2\core\core.hpp>  
#include <C:\OpenCV2.2\modules\highgui\include\opencv2\highgui\highgui.hpp>  
#include <iostream>  
#include <string.h>
#include <C:\OpenCV2.2\include\opencv\cv.h>  
#include <stdlib.h>
#include <C:\OpenCV2.2\modules\highgui\include\opencv2\highgui\highgui_c.h>
#include <C:\Users\Administrator\Documents\blobs\blob.h>
#include <C:\Users\Administrator\Documents\blobs\BlobResult.h>

using namespace cv;
using namespace std;

int MAX_KERNEL_LENGTH = 30;
int counter=0;
int Blurtype=0;
int Kern=5;
int *Kernp=&Kern;
int betaval=50;
int *betavalp=&betaval;
int Threshtype=0;
int Threshval=30;
int size=10;

Mat src1, src2, dst1, dst2, dst3;
CvScalar black=CV_RGB( 0, 0, 0 ); // black color
CvScalar white=CV_RGB( 255, 255, 255 );   // white color
double area;

void BlobFunc(int,void*);
int main( int argc, char** argv )
{
    //Read Input
    src1 = imread(argv[1]);
    if( !src1.data ) { printf("Error loading src1 \n"); return -1; }
    //Create Windows
    namedWindow("Original Image",1);
    namedWindow("Binarized Image",1);
    namedWindow("Gray Image",1);
    namedWindow("Sharpened Image",1);
    namedWindow("Blurred Image",1);
    imshow("Original Image",src1);
    namedWindow("Blobs",1);
    //Create Trackbars
    cvtColor(src1,src2,CV_RGB2GRAY);
    imshow("Gray Image",src2);
    threshold( src2, dst1, Threshval, 255,Threshtype);
    imshow("Binarized Image",dst1);
    createTrackbar("Kernel","Blurred Image",&Kern,MAX_KERNEL_LENGTH,BlobFunc); 
    createTrackbar("BlurType","Blurred Image",&Blurtype,3,BlobFunc); 
    createTrackbar("Betaval","Sharpened Image",&betaval,100,BlobFunc);
    createTrackbar("Threshold value","Binarized Image",&Threshval,255,BlobFunc);
    createTrackbar("Type","Binarized Image",&Threshtype,4,BlobFunc);
    createTrackbar("Size","Blobs",&size,100,BlobFunc); //Size of Blob
    waitKey(0);
    return 0;
}

void BlobFunc(int,void*)
{
    CvMemStorage *storage=cvCreateMemStorage(0);
    CvSeq *contours=0;
    cvtColor(src1,src2,CV_RGB2GRAY);
    imshow("Gray Image",src2);
    threshold( src2, dst1, Threshval, 255,Threshtype);
    imshow("Binarized Image",dst1);
    for ( int i = 1; i < Kern; i = i + 2 )
    {
        if (Blurtype==0)
        {
            blur(dst1,dst2, Size( i, i ), Point(-1,-1) );
        }
    else if (Blurtype==1)
        {
            GaussianBlur( dst1, dst2, Size( i, i ), 0, 0 );
        }
    else if (Blurtype==2)
        {
            medianBlur ( dst1, dst2, i );
        }
    else if (Blurtype==3)
        {
            bilateralFilter ( dst1, dst2, i, i*2, i/2 );
        }       
    }
    imshow("Blurred Image",dst2);
    addWeighted( dst1, 1, dst2, -double(betaval)/100, 0.0, dst3);
    imshow("Sharpened Image",dst3);
    IplImage img=dst3;
    cvFindContours(&img,storage,&contours,sizeof(CvContour),CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE);                                                      
    IplImage *img_out=cvCreateImage( cvGetSize(&img), 8, 3 );
    cvZero( &img_out );
    for( ; contours != 0; contours = contours->h_next )
    {
     cvDrawContours( &img_out, contours, black, black, -1, CV_FILLED, 8 );
    }

    Mat imgout=img_out;
    cvReleaseMemStorage( &storage );
    imshow("Blobs",imgout);
}