opencv学习笔记之删除面积小于阈值的二值图像

Mat remove_block(double minarea, Mat& src)
{
CvSeq* contour = NULL;
double tmparea = 0.0;
CvMemStorage* storage = cvCreateMemStorage(0);
//const char* tmpaddsum1 = tmp_string.c_str();  


//IplImage* img_src = cvLoadImage(tmpaddsum1, CV_LOAD_IMAGE_ANYCOLOR);  
IplImage* img_src = &IplImage(src);
IplImage* img_Clone = cvCloneImage(img_src);
//访问二值图像每个点的值  
uchar *pp;
IplImage* img_dst = cvCreateImage(cvGetSize(img_src), IPL_DEPTH_8U, 1);


//-----搜索2值图中的轮廓，并从轮廓树中删除面积小于minarea的区域-----//  
//CvScalar color = cvScalar(255, 0, 0);//CV_RGB(128,0,0)  
CvContourScanner scanner = NULL;
scanner = cvStartFindContours(img_src, storage, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE, cvPoint(0, 0));
//开始遍历轮廓树  
CvRect rect;


while (contour = cvFindNextContour(scanner)){
tmparea = fabs(cvContourArea(contour));
rect = cvBoundingRect(contour, 0);


if (tmparea < minarea){
//当连通区域的中心点为白色时，而且面积较小则用黑色进行填充  
pp = (uchar*)(img_Clone->imageData + img_Clone->widthStep*(rect.y + rect.height / 2) + rect.x + rect.width / 2);
if (pp[0] == 255){
for (int y = rect.y; y<rect.y + rect.height; y++)
{
for (int x = rect.x; x<rect.x + rect.width; x++)
{
pp = (uchar*)(img_Clone->imageData + img_Clone->widthStep*y + x);


if (pp[0] == 255)
{
pp[0] = 0;
}
}
}
}


}
}
Mat dst_img = cvarrToMat(img_Clone);
if (dst_img.channels() == 3)
cvtColor(dst_img, dst_img, CV_RGB2GRAY);
return dst_img;
}