1来源
为了实现一个简单的demo 就是拍取了物体的照片之后确定一下其具体的分布。
本文采用的方式是canny算子+闭运算+最大轮廓+最小包围矩阵的方式求解的。下面是代码。
轮廓提取参考
2代码
#include <Eigen/Dense>
#include <iostream>
#include <string>
#include <boost/format.hpp>
#include <vector>
//opencv
#include <opencv2/core/eigen.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
//#include <opencv2/rgbd.hpp>
#include <opencv2/highgui/highgui.hpp>
#include<opencv2/calib3d/calib3d.hpp>
using namespace std;
using namespace cv;
using namespace Eigen;
float getDistance(Point2f pointO, Point2f pointA)
{
float distance;
distance = powf((pointO.x - pointA.x), 2) + powf((pointO.y - pointA.y), 2);
distance = sqrtf(distance);
return distance;
}
int main()
{
Mat image,image_edge,kernel,image_edge_close,image_copy;
vector<vector<Point>> contours;
image = imread("D:\\vs2019_wkplace\\2DPART\\image1.png");
//深拷贝
image.copyTo(image_copy);
//imshow("test", image);
cv::Canny(image, image_edge, 50, 400);
//imshow("test1", image_edge);
//闭运算 先膨胀后腐蚀,保证是对的
kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
cv::morphologyEx(image_edge, image_edge_close, cv::M