learning opencv中介绍的一种可以连通区域的函数

void find_connected_components(IplImage* mask, int poly1_hull0, float perimScale, int number,

CvRect* bbs, CvPoint* centers)
{
static CvMemStorage*    mem_storage = NULL;
static CvSeq*            contours = NULL;
//CLEAN UP RAW MASK
//开运算作用：平滑轮廓，去掉细节,断开缺口
cvMorphologyEx(mask, mask, NULL, NULL, CV_MOP_OPEN, 1);//对输入mask进行开操作，CVCLOSE_ITR为开操作的次数，输出为mask图像
//闭运算作用：平滑轮廓，连接缺口
cvMorphologyEx(mask, mask, NULL, NULL, CV_MOP_CLOSE, 1);//对输入mask进行闭操作，CVCLOSE_ITR为闭操作的次数，输出为mask图像
//FIND CONTOURS AROUND ONLY BIGGER REGIONS
if (mem_storage == NULL) mem_storage = cvCreateMemStorage(0);
else cvClearMemStorage(mem_storage);
//CV_RETR_EXTERNAL=0是在types_c.h中定义的，CV_CHAIN_APPROX_SIMPLE=2也是在该文件中定义的
CvContourScanner scanner = cvStartFindContours(mask, mem_storage, sizeof(CvContour), CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
CvSeq* c;
int numCont = 0;
//该while内部只针对比较大的轮廓曲线进行替换处理
while ((c = cvFindNextContour(scanner)) != NULL)
{
double len = cvContourPerimeter(c);
double q = (mask->height + mask->width) / perimScale;   //calculate perimeter len threshold
if (len < q) //Get rid of blob if it's perimeter is too small
{
cvSubstituteContour(scanner, NULL);    //用NULL代替原来的那个轮廓
}
else //Smooth it's edges if it's large enough
{
CvSeq* c_new;
if (poly1_hull0) //Polygonal approximation of the segmentation
c_new = cvApproxPoly(c, sizeof(CvContour), mem_storage, CV_POLY_APPROX_DP, CVCONTOUR_APPROX_LEVEL, 0);
else //Convex Hull of the segmentation
c_new = cvConvexHull2(c, mem_storage, CV_CLOCKWISE, 1);
cvSubstituteContour(scanner, c_new); //最开始的轮廓用凸包或者多项式拟合曲线替换
numCont++;
}
}
contours = cvEndFindContours(&scanner);    //结束轮廓查找操作
const CvScalar CVX_WHITE = CV_RGB(0xff, 0xff, 0xff);
const CvScalar CVX_BLACK = CV_RGB(0x00, 0x00, 0x00);
// PAINT THE FOUND REGIONS BACK INTO THE IMAGE
cvZero(mask);
IplImage *maskTemp;
//CALC CENTER OF MASS AND OR BOUNDING RECTANGLES
if ( number != NULL)
{
int N = number, numFilled = 0, i = 0;
CvMoments moments;
double M00, M01, M10;
maskTemp = cvCloneImage(mask);
for (i = 0, c = contours; c != NULL; c = c->h_next, i++)        //h_next为轮廓序列中的下一个轮廓
{
if (i < N) //Only process up to *num of them
{
//CV_CVX_WHITE在本程序中是白色的意思
cvDrawContours(maskTemp, c, CV_CVX_WHITE, CV_CVX_WHITE, -1, CV_FILLED, 8);
//Find the center of each contour
if (centers !=NULL)
{
cvMoments(maskTemp, &moments, 1);    //计算mask图像的最高达3阶的矩
M00 = cvGetSpatialMoment(&moments, 0, 0); //提取x的0次和y的0次矩
M10 = cvGetSpatialMoment(&moments, 1, 0); //提取x的1次和y的0次矩
M01 = cvGetSpatialMoment(&moments, 0, 1); //提取x的0次和y的1次矩
centers[i].x = (int)(M10 / M00);    //利用矩的结果求出轮廓的中心点坐标
centers[i].y = (int)(M01 / M00);
}
//Bounding rectangles around blobs
if (bbs !=NULL)
{
bbs[i] = cvBoundingRect(c); //算出轮廓c的外接矩形
}
cvZero(maskTemp);
numFilled++;
}
//Draw filled contours into mask
cvDrawContours(mask, c, CV_CVX_WHITE, CV_CVX_WHITE, -1, CV_FILLED, 8); //draw to central mask
} //end looping over contours
number = numFilled;
cvReleaseImage(&maskTemp);
}
//ELSE JUST DRAW PROCESSED CONTOURS INTO THE MASK
else
{
for (c = contours; c != NULL; c = c->h_next)
{
cvDrawContours(mask, c, CV_CVX_WHITE, CV_CVX_BLACK, -1, CV_FILLED, 8);
}
}
}