OpenCV删除面积小的区域实现图像二值化分割标记连通区域

小灰灰 2022-06-07 00:27 719阅读 0赞

#  OpenCV删除面积小的区域 实现图像二值化分割 标记连通区域 #

【[尊重原创，转载请注明出处][Link 1]】http://blog.csdn.net/guyuealian/article/details/78142749  
   之前本博客在Matlab实现了《[Matlab形态学图像处理：二值图像分割 标记连通区域和重心位置 删除连通区域][Matlab_ _]》http://blog.csdn.net/guyuealian/article/details/71440949，现在本人使用OpenCV实现这一功能：对图像进行二值化分割，并用“红色矩形”标记连通区域的面积，为了减少噪声的干扰，删除面积小的区域，代码中将连通区域面积（像素个数）不足100的区域认为是噪声点，并将其删除（即置为背景黑色）。本人制作了一个GIF动画图，以便大家观看效果图：

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OpenCV参考代码如下：

#include "stdafx.h"
    #include <iostream>  
    #include<vector>
    #include<algorithm>
    #include <opencv2\opencv.hpp>  
    #include <opencv2\highgui\highgui.hpp>  
    using namespace std;
    using namespace cv;
    
    
    //轮廓按照面积大小升序排序
    bool ascendSort(vector<Point> a, vector<Point> b) {
    	return a.size() < b.size();
    
    }
    
    //轮廓按照面积大小降序排序
    bool descendSort(vector<Point> a, vector<Point> b) {
    	return a.size() > b.size();
    }
    int main() {
    	Mat srcImage = imread("D:\\OpencvTest\\123.jpg");
    	Mat thresholdImage;
    	Mat grayImage;
    	cvtColor(srcImage, grayImage, CV_BGR2GRAY);
    	threshold(grayImage, thresholdImage, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
    	//Mat resultImage;
    	//thresholdImage.copyTo(resultImage);
    	vector< vector< Point> > contours;  //用于保存所有轮廓信息
    	vector< vector< Point> > contours2; //用于保存面积不足100的轮廓
    	vector<Point> tempV;				//暂存的轮廓
    
    	findContours(thresholdImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
    	//cv::Mat labels;
    	//int N = connectedComponents(resultImage, labels, 8, CV_16U);
    	//findContours(labels, contours2, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    
    	//轮廓按照面积大小进行升序排序
    	sort(contours.begin(), contours.end(), ascendSort);//升序排序
    	vector<vector<Point> >::iterator itc = contours.begin();
    	int i = 0;
    	while (itc != contours.end())
    	{
    		//获得轮廓的矩形边界
    		Rect rect = boundingRect(*itc);
    		int x = rect.x;
    		int y = rect.y;
    		int w = rect.width;
    		int h = rect.height;
    		//绘制轮廓的矩形边界
    		cv::rectangle(srcImage, rect, { 0, 0, 255 }, 1);
    		//保存图片
    		char str[10];
    		sprintf(str, "%d.jpg", i++);
    		cv::imshow("srcImage", srcImage);
    		imwrite(str, srcImage);
    		waitKey(1000);
    	
    		if (itc->size() < 100)
    		{
    			//把轮廓面积不足100的区域，放到容器contours2中，
    			tempV.push_back(Point(x, y));
    			tempV.push_back(Point(x, y+h));
    			tempV.push_back(Point(x+w, y+h));
    			tempV.push_back(Point(x+w, y));
    			contours2.push_back(tempV);
    			/*也可以直接用：contours2.push_back(*itc);代替上面的5条语句*/
    			//contours2.push_back(*itc);
    
    			//删除轮廓面积不足100的区域，即用黑色填充轮廓面积不足100的区域：
    			cv::drawContours(srcImage, contours2, -1, Scalar(0,0,0), CV_FILLED);
    		}
    		//保存图片
    		sprintf(str, "%d.jpg", i++);
    		cv::imshow("srcImage", srcImage);
    		imwrite(str, srcImage);
    		cv::waitKey(100);
    		tempV.clear();
    		++itc;
    	}
    	return 0;
    }

**【2】findContours的用法：**

using namespace std;
    using namespace cv;
    using namespace cv::xphoto;
    #include "stdafx.h"
    #include <iostream>  
    #include<vector>
    #include<algorithm>
    #include <opencv2\opencv.hpp>  
    #include <opencv2\highgui\highgui.hpp>  
    using namespace std;
    using namespace cv;
    
    //轮廓按照面积大小升序排序
    bool ascendSort(vector<Point> a, vector<Point> b) {
    	return a.size() < b.size();
    
    }
    
    //轮廓按照面积大小降序排序
    bool descendSort(vector<Point> a, vector<Point> b) {
    	return a.size() > b.size();
    }
    
    //自己实现的将灰度图像转为三通道的BGR图像
    cv::Mat gray2BGR(cv::Mat grayImg) {
    	if (grayImg.channels() == 3)
    		return grayImg;
    	cv::Mat bgrImg = cv::Mat::zeros(grayImg.size(), CV_8UC3);
    	std::vector<cv::Mat> bgr_channels;
    	cv::split(bgrImg, bgr_channels);
    	bgr_channels.at(0) = grayImg;
    	bgr_channels.at(1) = grayImg;
    	bgr_channels.at(2) = grayImg;
    	cv::merge(bgr_channels, bgrImg);
    	return bgrImg;
    }
    
    //自定义的drawImage函数的功能类似于OpenCV的drawContours函数
    cv::Mat drawImage(cv::Mat image, vector< vector< Point> > pointV) {
    	cv::Mat destImage=image.clone();
    	if (destImage.channels()==1)
    	{
    		destImage = gray2BGR(destImage);
    	}
    	for (size_t i=0;i<pointV.size();i++)
    	{
    		for (size_t j = 0; j<pointV.at(i).size(); j++)
    		{
    			cv::Point point = pointV.at(i).at(j);
    			destImage.at<Vec3b>(point) = cv::Vec3b(0, 0, saturate_cast<uchar>(255-i*5));
    		}
    
    	}
    	return destImage;
    }
    int main() {
    	Mat srcImage = imread("D:\\OpencvTest\\mask5.jpg");
    	cv::imshow("srcImage", srcImage);
    	Mat thresholdImage;
    	Mat grayImage;
    	cvtColor(srcImage, grayImage, CV_BGR2GRAY);
    	threshold(grayImage, thresholdImage, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
    	cv::Mat mask = thresholdImage.clone();
    	//(1)CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE
    	vector< vector< Point> > contours1;
    	findContours(mask, contours1, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
    	cv::Mat destImage = drawImage(mask, contours1);
    	imshow("destImage", destImage);
    
    	//(2)CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE
    	vector< vector< Point> > contours2; 
    	findContours(mask, contours2, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    	cv::Mat destImage2 = drawImage(mask, contours2);
    	imshow("destImage2", destImage2);
    
    	//(3)CV_RETR_LIST, CV_CHAIN_APPROX_NONE
    	vector< vector< Point> > contours3; 
    	findContours(mask, contours3, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
    	cv::Mat destImage3 = drawImage(mask, contours3);
    	imshow("destImage3", destImage3);
    
    
    	//(4)CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE
    	vector< vector< Point> > contours4;
    	findContours(mask, contours4, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);
    	cv::Mat destImage4 = drawImage(mask, contours4);
    	imshow("destImage4", destImage4);
    
    
    	//cv::Mat imge2 = gray2BGR(mask);
    	//drawContours(imge2, contours4, -1, cv::Scalar(0, 0, 255), 1);
    	//cv::fillConvexPoly(mask, contours1.at(0), cv::Scalar(255, 255, 255));
    	cv::waitKey(0);
    	return 0;
    }

如果你觉得该帖子帮到你，还望贵人多多支持，鄙人会再接再厉，继续努力的~

![Image 1][]

[Link 1]: http://blog.csdn.net/guyuealian/article/details/78142749
[Matlab_ _]: http://blog.csdn.net/guyuealian/article/details/71440949
[Center]: /images/20220607/4cc2deb5b9ad413187611b2e67f2bc05.png
[Image 1]: