这是后期补充的部分，和前期的代码不太一样

效果图

源代码

//测试

void CCutImageVS2013Dlg::OnBnClickedTestButton1()

{

vector > contours; //轮廓数组

vector centers; //轮廓质心坐标

vector >::iterator itr; //轮廓迭代器

vector::iterator itrc; //质心坐标迭代器

vector > con; //当前轮廓

double area;

double minarea = 1000;

double maxarea = 0;

Moments mom; // 轮廓矩

Mat image, gray, edge, dst;

image = imread("D:\\66.png");

cvtColor(image, gray, COLOR_BGR2GRAY);

Mat rgbImg(gray.size(), CV_8UC3); //创建三通道图

blur(gray, edge, Size(3, 3)); //模糊去噪

threshold(edge, edge, 200, 255, THRESH_BINARY_INV); //二值化处理,黑底白字

//--------去除较小轮廓，并寻找最大轮廓--------------------------

findContours(edge, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); //寻找轮廓

itr = contours.begin(); //使用迭代器去除噪声轮廓

while (itr != contours.end())

{

area = contourArea(*itr); //获得轮廓面积

if (area

{

itr = contours.erase(itr); //itr一旦erase，需要重新赋值

}

else

{

itr++;

}

if (area>maxarea) //寻找最大轮廓

{

maxarea = area;

}

}

dst = Mat::zeros(image.rows, image.cols, CV_8UC3);

/*绘制连通区域轮廓，计算质心坐标*/

Point2d center;

itr = contours.begin();

while (itr != contours.end())

{

area = contourArea(*itr);

con.push_back(*itr); //获取当前轮廓

if (area == maxarea)

{

vector boundRect(1); //定义外接矩形集合

boundRect[0] = boundingRect(Mat(*itr));

cvtColor(gray, rgbImg, COLOR_GRAY2BGR);

Rect select;

select.x = boundRect[0].x;

select.y = boundRect[0].y;

select.width = boundRect[0].width;

select.height = boundRect[0].height;

rectangle(rgbImg, select, Scalar(0, 255, 0), 3, 2); //用矩形画矩形窗

drawContours(dst, con, -1, Scalar(0, 0, 255), 2); //最大面积红色绘制

}

else

drawContours(dst, con, -1, Scalar(255, 0, 0), 2); //其它面积蓝色绘制

con.pop_back();

//计算质心

mom = moments(*itr);

center.x = (int)(mom.m10 / mom.m00);

center.y = (int)(mom.m01 / mom.m00);

centers.push_back(center);

itr++;

}

imshow("rgbImg", rgbImg);

//imshow("gray", gray);

//imshow("edge", edge);

imshow("origin", image);

imshow("connected_region", dst);

waitKey(0);

return;

}

前期做的，方法可能不太一样

一，先看效果图

原图

处理前后图

二，实现源代码

//=======函数实现=====================================================================

void RemoveSmallRegion(Mat &Src, Mat &Dst, int AreaLimit, int CheckMode, int NeihborMode)

{

int RemoveCount = 0;

//新建一幅标签图像初始化为0像素点，为了记录每个像素点检验状态的标签，0代表未检查，1代表正在检查,2代表检查不合格(需要反转颜色)，3代表检查合格或不需检查

//初始化的图像全部为0，未检查

Mat PointLabel = Mat::zeros(Src.size(), CV_8UC1);

if (CheckMode == 1)//去除小连通区域的白色点

{

//cout << "去除小连通域.";

for (int i = 0; i < Src.rows; i++)

{

for (int j = 0; j < Src.cols; j++)

{

if (Src.at(i, j) < 10)

{

PointLabel.at(i, j) = 3;//将背景黑色点标记为合格，像素为3

}

}

}

}

else//去除孔洞，黑色点像素

{

//cout << "去除孔洞";

for (int i = 0; i < Src.rows; i++)

{

for (int j = 0; j < Src.cols; j++)

{

if (Src.at(i, j) > 10)

{

PointLabel.at(i, j) = 3;//如果原图是白色区域，标记为合格，像素为3

}

}

}

}

vectorNeihborPos;//将邻域压进容器

NeihborPos.push_back(Point2i(-1, 0));

NeihborPos.push_back(Point2i(1, 0));

NeihborPos.push_back(Point2i(0, -1));

NeihborPos.push_back(Point2i(0, 1));

if (NeihborMode == 1)

{

//cout << "Neighbor mode: 8邻域." << endl;

NeihborPos.push_back(Point2i(-1, -1));

NeihborPos.push_back(Point2i(-1, 1));

NeihborPos.push_back(Point2i(1, -1));

NeihborPos.push_back(Point2i(1, 1));

}

else int a = 0;//cout << "Neighbor mode: 4邻域." << endl;

int NeihborCount = 4 + 4 * NeihborMode;

int CurrX = 0, CurrY = 0;

//开始检测

for (int i = 0; i < Src.rows; i++)

{

for (int j = 0; j < Src.cols; j++)

{

if (PointLabel.at(i, j) == 0)//标签图像像素点为0，表示还未检查的不合格点

{ //开始检查

vectorGrowBuffer;//记录检查像素点的个数

GrowBuffer.push_back(Point2i(j, i));

PointLabel.at(i, j) = 1;//标记为正在检查

int CheckResult = 0;

for (int z = 0; z < GrowBuffer.size(); z++)

{

for (int q = 0; q < NeihborCount; q++)

{

CurrX = GrowBuffer.at(z).x + NeihborPos.at(q).x;

CurrY = GrowBuffer.at(z).y + NeihborPos.at(q).y;

if (CurrX >= 0 && CurrX= 0 && CurrY

{

if (PointLabel.at(CurrY, CurrX) == 0)

{

GrowBuffer.push_back(Point2i(CurrX, CurrY)); //邻域点加入buffer

PointLabel.at(CurrY, CurrX) = 1; //更新邻域点的检查标签，避免重复检查

}

}

}

}

if (GrowBuffer.size()>AreaLimit) //判断结果(是否超出限定的大小)，1为未超出，2为超出

CheckResult = 2;

else

{

CheckResult = 1;

RemoveCount++;//记录有多少区域被去除

}

for (int z = 0; z < GrowBuffer.size(); z++)

{

CurrX = GrowBuffer.at(z).x;

CurrY = GrowBuffer.at(z).y;

PointLabel.at(CurrY, CurrX) += CheckResult;//标记不合格的像素点，像素值为2

}

//********结束该点处的检查**********

}

}

}

CheckMode = 255 * (1 - CheckMode);

//开始反转面积过小的区域

for (int i = 0; i < Src.rows; ++i)

{

for (int j = 0; j < Src.cols; ++j)

{

if (PointLabel.at(i, j) == 2)

{

Dst.at(i, j) = CheckMode;

}

else if (PointLabel.at(i, j) == 3)

{

Dst.at(i, j) = Src.at(i, j);

}

}

}

//cout << RemoveCount << " objects removed." << endl;

}

//=======函数实现=====================================================================

//=======调用函数=====================================================================

Mat img;

img = imread("D:\\1_1.jpg", 0);//读取图片

threshold(img, img, 128, 255, CV_THRESH_BINARY_INV);

imshow("去除前", img);

Mat img1;

RemoveSmallRegion(img, img, 200, 0, 1);

imshow("去除后", img);

waitKey(0);

//=======调用函数=====================================================================

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