Opencv求取连通区域重心实例

//otsu算法实现函数
int Otsu(Mat &image)
{
int width = image.cols;
int height = image.rows;
int x = 0, y = 0;
int pixelCount[256];
float pixelPro[256];
int i, j, pixelSum = width * height, threshold = 0;
uchar* data = (uchar*)image.data;
//初始化 
for (i = 0; i < 256; i++)
{
pixelCount[i] = 0;
pixelPro[i] = 0;
}
//统计灰度级中每个像素在整幅图像中的个数 
for (i = y; i < height; i++)
{
for (j = x; j<width; j++)
{
pixelCount[data[i * image.step + j]]++;
}
}
//计算每个像素在整幅图像中的比例 
for (i = 0; i < 256; i++)
{
pixelPro[i] = (float)(pixelCount[i]) / (float)(pixelSum);
}
//经典ostu算法,得到前景和背景的分割 
//遍历灰度级[0,255],计算出方差最大的灰度值,为最佳阈值 
float w0, w1, u0tmp, u1tmp, u0, u1, u, deltaTmp, deltaMax = 0;
for (i = 0; i < 256; i++)
{
w0 = w1 = u0tmp = u1tmp = u0 = u1 = u = deltaTmp = 0;
for (j = 0; j < 256; j++)
{
if (j <= i) //背景部分 
{
//以i为阈值分类，第一类总的概率 
w0 += pixelPro[j];
u0tmp += j * pixelPro[j];
}
else    //前景部分 
{
//以i为阈值分类，第二类总的概率 
w1 += pixelPro[j];
u1tmp += j * pixelPro[j];
}
}
u0 = u0tmp / w0;    //第一类的平均灰度 
u1 = u1tmp / w1;    //第二类的平均灰度 
u = u0tmp + u1tmp;   //整幅图像的平均灰度 
//计算类间方差 
deltaTmp = w0 * (u0 - u)*(u0 - u) + w1 * (u1 - u)*(u1 - u);
//找出最大类间方差以及对应的阈值 
if (deltaTmp   deltaMax)
{
deltaMax = deltaTmp;
threshold = i;
}
}
//返回最佳阈值; 
return threshold;
}
int main()
{
Mat White=imread("white.tif");//读取图像
int threshold_white = otsu(White);//阈值计算，利用otsu
cout << "最佳阈值：" << threshold_white << endl;
Mat thresholded = Mat::zeros(White.size(), White.type());
threshold(White, thresholded, threshold_white, 255, CV_THRESH_BINARY);//二值化
vector<vector<Point  contours;
vector<Vec4i hierarchy;
findContours(thresholded, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);//查找轮廓
int i = 0;
int count = 0;
Point pt[10];//假设有三个连通区域
Moments moment;//矩
vector<Point Center;//创建一个向量保存重心坐标
for (; i  = 0; i = hierarchy[i][0])//读取每一个轮廓求取重心
{
Mat temp(contours.at(i));
Scalar color(0, 0, 255);
moment = moments(temp, false);
if (moment.m00 != 0)//除数不能为0
{
pt[i].x = cvRound(moment.m10 / moment.m00);//计算重心横坐标
pt[i].y = cvRound(moment.m01 / moment.m00);//计算重心纵坐标
}
Point p = Point(pt[i].x, pt[i].y);//重心坐标
circle(White, p, 1, color, 1, 8);//原图画出重心坐标
count++;//重心点数或者是连通区域数
Center.push_back(p);//将重心坐标保存到Center向量中
}
}
cout << "重心点个数：" << Center.size() << endl;
cout << "轮廓数量：" << contours.size() << endl;
imwrite("Center.tif", White);
}

void getAlikeContours(std::vector<cv::Point  Inputlist, cv::Mat InputImage, std::vector<cv::Point  &Outputlist)
{
Mat image;
InputImage.copyTo(image);
vector<vector<Point    contours;
findContours(image, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);//查找最外层轮廓
for (int idx = contours.size() - 1; idx  = 0; idx--)
{
for (int i = contours[idx].size() - 1; i  = 0; i--)
{
if (contours[idx][i].x == 1 || contours[idx][i].y == 1 || contours[idx][i].x == image.cols - 2 || contours[idx][i].y == image.rows - 2)
{
swap(contours[idx][i], contours[idx][contours[idx].size() - 1]);
contours[idx].pop_back();
}
}
//可能会存在空的轮廓，把他们删除
for (int idx = contours.size() - 1; idx  = 0; idx--)
{
if (contours[idx].size() == 0) contours.erase(contours.begin() + idx);
}
while (true)
{
if (contours.size() == 0) break;
if (contours.size() == 1)
{
vector<Point  finalList;
finalList.assign(contours[0].begin(), contours[0].end());
convexHull(Mat(finalList), Outputlist, true);
break;
}
int maxContourIdx = 0;
int maxContourPtNum = 0;
for (int index = contours.size() - 1; index  = 0; index--)
{
if (contours[index].size()   maxContourPtNum)
{
maxContourPtNum = contours[index].size();
maxContourIdx = index;
}
}
//第二大轮廓
int secondContourIdx = 0;
int secondContourPtNum = 0;
for (int index = contours.size() - 1; index  = 0; index--)
{
if (index == maxContourIdx) continue;
if (contours[index].size()   secondContourPtNum)
{
secondContourPtNum = contours[index].size();
secondContourIdx = index;
}
}
vector<Point  maxlist;
vector<Point  maxAndseclist;
vector<Point  maxlistHull;
vector<Point  maxAndseclistHull;
maxlist.insert(maxlist.end(), contours[maxContourIdx].begin(), contours[maxContourIdx].end());
maxAndseclist.insert(maxAndseclist.end(), contours[maxContourIdx].begin(), contours[maxContourIdx].end());
maxAndseclist.insert(maxAndseclist.end(), contours[secondContourIdx].begin(), contours[secondContourIdx].end());
convexHull(Mat(maxlist), maxlistHull, true);
convexHull(Mat(maxAndseclist), maxAndseclistHull, true);
double maxcontourScore = matchShapes(Inputlist, maxlistHull, CV_CONTOURS_MATCH_I1, 0);
double maxandseccontourScore = matchShapes(Inputlist, maxAndseclistHull, CV_CONTOURS_MATCH_I1, 0);
if (maxcontourScore maxandseccontourScore)
{
contours[maxContourIdx].insert(contours[maxContourIdx].end(), contours[secondContourIdx].begin(), contours[secondContourIdx].end());
}
contours.erase(contours.begin() + secondContourIdx);
}
}