OpenCV 删除轮廓的方法（一）

浅浅的花香味﹌ 2022-07-13 02:18 226阅读 0赞

一种比较方便的删除轮廓的处理方式，是我刚刚学习到的一个方法，在这之前，如果我想删除一个不需要的轮廓，用的方法是将该轮廓填充为背景色，之前的博客提到过，在countours容器中，如果把轮廓填充为背景色，那么只是视觉上看不到该轮廓，但是实际上还存在在容器中。所以之前总是要填充之后从新copyto一下，然后重新找一遍轮廓，达到删除轮廓的效果。这种方式实在是low。  
\[见之前的博客http://blog.csdn.net/chaipp0607/article/details/52858661  
代码如下：

swap(contours_all[j], contours_all[contours_all.size() - 1]);  
    contours_all.pop_back();

swap用于数据交换，将找到的轮廓放在容器的最后面，和j交换的轮廓是就是原来最后面那个，因为contours\_all.size()是轮廓的总个数，轮廓个数标号从0开始，所以contours\_all.size()-1就是最后面那个轮廓，将两者交互之后，用pop\_back()函数删除最后面那个数据，完成删除指定轮廓的功能。

还是举个栗子：

确定炉口位置，废渣，如图  
![这里写图片描述][827ac95fefe6ca5495eb92b3ec19894e.png]  
代码如下

#include<windows.h>
    #include<ctime>
    #include <iostream>   
    #include <stdio.h>
    #include <string>
    #include <stdlib.h>
    #include <opencv2/opencv.hpp>  
    #include <opencv2/legacy/compat.hpp> 
    #include <opencv2/core/core.hpp>
    #include<opencv2/imgproc/imgproc.hpp>
    #include "opencv2/highgui/highgui.hpp"
    #include "opencv2/features2d/features2d.hpp"
    #include "opencv2/nonfree/nonfree.hpp"  
    #include "opencv2/features2d/features2d.hpp"  
    
    using namespace std;
    using namespace cv; 
    
    int  otsu(cv::Mat srcImage);
    int main( )
    {	
    	double scale=0.2; 
        Mat g_SrcImage=imread("2.jpg");
    	Size dsize = Size(g_SrcImage.cols*scale,g_SrcImage.rows*scale);
    	Mat g_DsizeImage = Mat(dsize,CV_32S);
    	resize(g_SrcImage, g_DsizeImage,dsize);
    	
       //二值化确定炉口
    	Mat g_PositionImage;
    	Mat g_ThresholdImage;
    	Mat g_HistImage;
    	Mat g_ContrastAndBrightImage;
    	Mat g_FindOvenImage_all;
    	Mat g_FindOvenImage_outside;
    	cvtColor(g_DsizeImage,g_HistImage,CV_BGR2GRAY);
     
    	g_ContrastAndBrightImage= Mat::zeros( g_HistImage.size(), g_HistImage.type() ); 
    	for(int y = 0; y < g_HistImage.rows; y++ )  
    	{  
    		for(int x = 0; x <g_HistImage.cols; x++ )  
    		{  
    			g_ContrastAndBrightImage.at<uchar>(y,x)= saturate_cast<uchar>( 2*(g_HistImage.at<uchar>(y,x) ));  	
    		}  
    	} 
    
    	int  ostuThreshold = otsu(g_ContrastAndBrightImage);
    	printf("阈值%d\n",ostuThreshold);
    	threshold(g_ContrastAndBrightImage,g_ThresholdImage, ostuThreshold, 255,  THRESH_BINARY_INV);
    	g_ThresholdImage.copyTo(g_FindOvenImage_all);
    	g_ThresholdImage.copyTo(g_FindOvenImage_outside);
    	g_DsizeImage.copyTo(g_PositionImage);
    
    	vector<vector<Point> > contours_outside;
    	vector<Vec4i> hierarchy_outside;
    	findContours(g_FindOvenImage_outside,contours_outside,hierarchy_outside,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE);  //输入必须是二值	
    
    	vector<vector<Point> > contours_all;
    	vector<Vec4i> hierarchy_all;
    	findContours(g_FindOvenImage_all,contours_all,hierarchy_all,CV_RETR_TREE,CV_CHAIN_APPROX_NONE);  //输入必须是二值	
    	 //if (contours_all.size() == contours_outside.size()) return -1;
    	//
    	 for (int i = 0; i < contours_outside.size(); i++)  
    	 {  
    		 int conloursize = contours_outside[i].size();  
    		 for (int j = 0; j < contours_all.size(); j++)  
    		 {  
    			 int tem_size = contours_all[j].size();  
    			 if (conloursize == tem_size)  
    			 {  
    				 swap(contours_all[j], contours_all[contours_all.size() - 1]);  
    				 contours_all.pop_back();  
    				 break;  
    			 }  
    		 }  
    	 }  
    	 //contours_all中只剩下内轮廓  
    	 //查找最大轮廓  
    	 double maxarea = 0;  
    	 int maxAreaIdx = 0;  
    	 for (int index = contours_all.size() - 1; index >= 0; index--)  
    	 {  
    		 double tmparea = fabs(contourArea(contours_all[index]));  
    		 if (tmparea>maxarea)  
    		 {  
    			 maxarea = tmparea;  
    			 maxAreaIdx = index;//记录最大轮廓的索引号  
    		 }  
    	 }  
    	    drawContours(g_PositionImage, contours_all,maxAreaIdx, Scalar(150), 1);
       imshow("缩小图",g_DsizeImage);
       imshow("灰度化",g_HistImage);
       imshow("对比度亮度增强",g_ContrastAndBrightImage);
       imshow("二值化",g_ThresholdImage);
       imshow("找外轮廓",g_FindOvenImage_outside);
       imshow("找全部轮廓",g_FindOvenImage_all);
       imshow("找炉口",g_PositionImage);
       imwrite("C:\\Users\\Administrator\\Desktop\\save\\7.jpg",g_PositionImage);
          imwrite("C:\\Users\\Administrator\\Desktop\\save\\8.jpg",g_ThresholdImage);
    	  imwrite("C:\\Users\\Administrator\\Desktop\\save\\9.jpg",g_FindOvenImage_outside);
    	  imwrite("C:\\Users\\Administrator\\Desktop\\save\\10.jpg",g_FindOvenImage_all);
    	waitKey();
    	getchar();
    	return 0;
    }
    int otsu(cv::Mat srcImage)
    {
    	int nCols = srcImage.cols; //列
    	int nRows = srcImage.rows; //行
    	int threshold = 0;
    	// 初始化统计参数
    	int nSumPix[256];
    	float nProDis[256];
    	for (int i = 0; i < 256; i++)
    	{
    		nSumPix[i] = 0;
    		nProDis[i] = 0;
    	}
    	//统计灰度级中每个像素在整幅图像中的个数 
    	for (int i = 0; i <nRows ; i++)
    	{
    		for (int j = 0; j <nCols ; j++)
    		{
    			nSumPix[(int)srcImage.at<uchar>(i, j)]++;
    		}
    	}
    	 //计算每个灰度级占图像中的概率分布
    	for (int i = 0; i < 256; i++)
    	{
    		nProDis[i] = (float)nSumPix[i] / (nCols * nRows);
    	}
    
    	// 遍历灰度级[0,255],计算出最大类间方差下的阈值  
    	float w0, w1, u0_temp, u1_temp, u0, u1, delta_temp;
    	double delta_max = 0.0;
    	for (int i = 0; i < 256; i++)
    	{
    		// 初始化相关参数
    		w0 = w1 = u0_temp = u1_temp = u0 = u1 = delta_temp = 0;
    		for (int j = 0; j < 256; j++)
    		{
    			//背景部分 
    			if (j <= i)
    			{
    				// 当前i为分割阈值，第一类总的概率  
    				w0 += nProDis[j];
    				u0_temp += j * nProDis[j];
    			}
    			//前景部分   
    			else
    			{
    				// 当前i为分割阈值，第一类总的概率
    				w1 += nProDis[j];
    				u1_temp += j * nProDis[j];
    			}
    		}
    		// 分别计算各类的平均灰度 
    		u0 = u0_temp / w0;
    		u1 = u1_temp / w1;
    		delta_temp = (float)(w0 *w1* pow((u0 - u1), 2));
    		// 依次找到最大类间方差下的阈值    
    		if (delta_temp > delta_max)
    		{
    			delta_max = delta_temp;
    			threshold = i;
    		}
    	}
    	return threshold;
    }

效果图：蓝色框出炉口的位置  
![2df9aedfe0a6baeb27c9082205c72301.png][]

![这里写图片描述][b5b376e70b04f89b53a52baf49aa391e.png]

[827ac95fefe6ca5495eb92b3ec19894e.png]: /images/20220713/b8827dcf3e20449abc31c5e243a81fad.png
[2df9aedfe0a6baeb27c9082205c72301.png]: /images/20220713/75a719e3f19a4a20bee2c5fe95f9654b.png
[b5b376e70b04f89b53a52baf49aa391e.png]: /images/20220713/0ae22d2cc49444bba1e87536a9b0b20c.png