OPENCV二值化图像内孔洞填充/小区域去除_消除二值图像中面积较小的离散点

来源：http://lib.csdn.net/article/opencv/28353

        原作者：robberjohn  博客已删除了，源码下载链接在

         http://download.csdn.net/download/robberjohn/8474913 

        http://blog.csdn.net/robberjohn/article/details/44081571

对于二值化图像，去除孔洞时采用的方法实际上与去除小区域相同，因此完全可以用同一个函数进行。

这两个功能可以采取区域生长法来实现。须注意，去除小区域时为保存有用信息，可采用8邻域探测，去除孔洞时则4邻域即可，否则容易泄露，出现靠边缘的孔洞未去除的情况。

效果（区域面积阈值为700）： 

原图像：

小面积区域去除：

孔洞填充结果：

源码

#include <cv.h>  
#include <highgui.h>  
#include <opencv2/imgproc/imgproc.hpp>    
#include <opencv2/highgui/highgui.hpp>    
#include <iostream>    
#include <vector>    
  
  
using namespace cv;  
using namespace std;  
  
void RemoveSmallRegion(Mat& Src, Mat& Dst, int AreaLimit=50, int CheckMode=1, int NeihborMode=0);  
  
int main()    
{    
    double t = (double)getTickCount();  
  
    char* imagePath = "E:\\SVM\\局部.jpg";  
    char* OutPath = "E:\\SVM\\局部_去除孔洞.jpg";  
      
    Mat Src = imread(imagePath, CV_LOAD_IMAGE_GRAYSCALE);  
    Mat Dst = Mat::zeros(Src.size(), CV_8UC1);  
      
  
    //二值化处理  
    for(int i = 0; i < Src.rows; ++i)    
    {    
        uchar* iData = Src.ptr<uchar>(i);  
        for(int j = 0; j < Src.cols; ++j)    
        {    
            if(iData[j] == 0 || iData[j]==255) continue;  
            else if (iData[j] < 10)    
            {    
                iData[j] = 0;    
                //cout<<'#';  
            }    
            else if (iData[j] > 10)    
            {    
                iData[j] = 255;   
                //cout<<'!';  
            }    
        }    
    }    
    cout<<"Image Binary processed."<<endl;  
  
    RemoveSmallRegion(Src, Dst, 20, 1, 1);  
    RemoveSmallRegion(Dst, Dst, 20, 0, 0);  
    cout<<"Done!"<<endl;  
    imwrite(OutPath, Dst);  
          
    t = ((double)getTickCount() - t)/getTickFrequency();  
    cout<<"Time cost: "<<t<<" sec."<<endl;  
  
    return 0;    
}    
  
//CheckMode: 0代表去除黑区域，1代表去除白区域; NeihborMode：0代表4邻域，1代表8邻域;  
void RemoveSmallRegion(Mat& Src, Mat& Dst, int AreaLimit, int CheckMode, int NeihborMode)  
{     
    int RemoveCount=0;       //记录除去的个数  
    //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  
    Mat Pointlabel = Mat::zeros( Src.size(), CV_8UC1 );  
      
    if(CheckMode==1)  
    {  
        cout<<"Mode: 去除小区域. ";  
        for(int i = 0; i < Src.rows; ++i)    
        {    
            uchar* iData = Src.ptr<uchar>(i);  
            uchar* iLabel = Pointlabel.ptr<uchar>(i);  
            for(int j = 0; j < Src.cols; ++j)    
            {    
                if (iData[j] < 10)    
                {    
                    iLabel[j] = 3;   
                }    
            }    
        }    
    }  
    else  
    {  
        cout<<"Mode: 去除孔洞. ";  
        for(int i = 0; i < Src.rows; ++i)    
        {    
            uchar* iData = Src.ptr<uchar>(i);  
            uchar* iLabel = Pointlabel.ptr<uchar>(i);  
            for(int j = 0; j < Src.cols; ++j)    
            {    
                if (iData[j] > 10)    
                {    
                    iLabel[j] = 3;   
                }    
            }    
        }    
    }  
  
    vector<Point2i> NeihborPos;  //记录邻域点位置  
    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 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)    
    {    
        uchar* iLabel = Pointlabel.ptr<uchar>(i);  
        for(int j = 0; j < Src.cols; ++j)    
        {    
            if (iLabel[j] == 0)    
            {    
                //********开始该点处的检查**********  
                vector<Point2i> GrowBuffer;                                      //堆栈，用于存储生长点  
                GrowBuffer.push_back( Point2i(j, i) );  
                Pointlabel.at<uchar>(i, j)=1;  
                int CheckResult=0;                                               //用于判断结果（是否超出大小），0为未超出，1为超出  
  
                for ( int z=0; z<GrowBuffer.size(); z++ )  
                {  
  
                    for (int q=0; q<NeihborCount; q++)                                      //检查四个邻域点  
                    {  
                        CurrX=GrowBuffer.at(z).x+NeihborPos.at(q).x;  
                        CurrY=GrowBuffer.at(z).y+NeihborPos.at(q).y;  
                        if (CurrX>=0&&CurrX<Src.cols&&CurrY>=0&&CurrY<Src.rows)  //防止越界  
                        {  
                            if ( Pointlabel.at<uchar>(CurrY, CurrX)==0 )  
                            {  
                                GrowBuffer.push_back( Point2i(CurrX, CurrY) );  //邻域点加入buffer  
                                Pointlabel.at<uchar>(CurrY, CurrX)=1;           //更新邻域点的检查标签，避免重复检查  
                            }  
                        }  
                    }  
  
                }  
                if (GrowBuffer.size()>AreaLimit) CheckResult=2;                 //判断结果（是否超出限定的大小），1为未超出，2为超出  
                else {CheckResult=1;   RemoveCount++;}  
                for (int z=0; z<GrowBuffer.size(); z++)                         //更新Label记录  
                {  
                    CurrX=GrowBuffer.at(z).x;   
                    CurrY=GrowBuffer.at(z).y;  
                    Pointlabel.at<uchar>(CurrY, CurrX) += CheckResult;  
                }  
                //********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)    
    {    
        uchar* iData = Src.ptr<uchar>(i);  
        uchar* iDstData = Dst.ptr<uchar>(i);  
        uchar* iLabel = Pointlabel.ptr<uchar>(i);  
        for(int j = 0; j < Src.cols; ++j)    
        {    
            if (iLabel[j] == 2)    
            {    
                iDstData[j] = CheckMode;   
            }    
            else if(iLabel[j] == 3)  
            {  
                iDstData[j] = iData[j];  
            }  
        }    
    }   
      
    cout<<RemoveCount<<" objects removed."<<endl;  
}  

一、对于二值图，0代表黑色，255代表白色。去除小连通区域与孔洞，小连通区域用8邻域，孔洞用4邻域。

 

  函数名字为：void RemoveSmallRegion(Mat &Src, Mat &Dst,int AreaLimit, int CheckMode, int NeihborMode)

     CheckMode: 0代表去除黑区域，1代表去除白区域; NeihborMode：0代表4邻域，1代表8邻域;  

如果去除小连通区域CheckMode=1,NeihborMode=1去除孔洞CheckMode=0，NeihborMode=0

     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 。

1.先对整个图像扫描，如果是去除小连通区域，则将黑色的背景图作为合格，像素值标记为3，如果是去除孔洞，则将白色的色素点作为合格，像素值标记为3。

2.扫面整个图像，对图像进行处理。

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<uchar>(i, j) < 10)
				{
					PointLabel.at<uchar>(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<uchar>(i, j) > 10)
				{
					PointLabel.at<uchar>(i, j) = 3;//如果原图是白色区域，标记为合格，像素为3
				}
			}
		}
	}
 
 
	vector<Point2i>NeihborPos;//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 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<uchar>(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
				vector<Point2i>GrowBuffer;//记录检查像素点的个数
				GrowBuffer.push_back(Point2i(j, i));
				PointLabel.at<uchar>(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<Src.cols&&CurrY >= 0 && CurrY<Src.rows)  //防止越界  
						{
							if (PointLabel.at<uchar>(CurrY, CurrX) == 0)
							{
								GrowBuffer.push_back(Point2i(CurrX, CurrY));  //邻域点加入buffer  
								PointLabel.at<uchar>(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<uchar>(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<uchar>(i,j)==2)
			{
				Dst.at<uchar>(i, j) = CheckMode;
			}
			else if (PointLabel.at<uchar>(i, j) == 3)
			{
				Dst.at<uchar>(i, j) = Src.at<uchar>(i, j);
				
			}
		}
	}
	cout << RemoveCount << " objects removed." << endl;
}