opencv 采用多边形进行区域筛选

说明

• 最近在做关于区域筛选问题的相关内容，考虑了一些聚类分析算法，但是对于ROI区域还是不能完全做到独立和分割，因此采用这种方法在一定范围内可以时边缘区域模糊掉，只处理中心区域或者中间区域的图片信息，当然对于目标离散的图片，不适合这种方法进行目标处理，还需要进一步挖掘相关算法进行优化。

代码如下

#include "stdafx.h"

#include"cv.h"

#include"highgui.h"

#include

using namespace std;

using namespace cv;

int _tmain(int argc, _TCHAR* argv[])

{

Mat img = imread("C:\\Users\\Administrator\\Desktop\\B0.bmp");

Point root_points[1][6];  

root_points[0][0] = Point(5,5);  

root_points[0][1] = Point(img.cols,5);  

root_points[0][2] = Point(img.cols,225);  

root_points[0][3] = Point(465,225);

root_points[0][4] = Point(215,220);

root_points[0][5] = Point(5,220);

const Point* ppt[1] = {root_points[0]};  

int npt[] = {6};  

polylines(img, ppt, npt, 1, 1, Scalar(255),1,8,0);  

fillPoly(img, ppt, npt, 1, Scalar(255,255,255));  

Point root_points_1[1][8];  

root_points_1[0][0] = Point(0,860);  

root_points_1[0][1] = Point(img.cols,860);  

root_points_1[0][2] = Point(img.cols,860);  

root_points_1[0][3] = Point(img.cols,860);

root_points_1[0][4] = Point(img.cols,860);

root_points_1[0][5] = Point(img.cols,860);

root_points_1[0][6] = Point(img.cols,img.rows);

root_points_1[0][7] = Point(0,img.rows);

const Point* ppt_1[1] = {root_points_1[0]};      

int npt_1[] = {8};  

polylines(img, ppt_1, npt_1, 1, 1, Scalar(255),1,8,0);  

imshow("Test", img);  

waitKey(1000);  

fillPoly(img, ppt_1, npt_1, 1, Scalar(255,255,255));  

imshow("img",img);

waitKey(0);

return 0;

}

结果展示