主要是利用霍夫圆检测、面积筛选等完成多个圆形检测，具体代码及结果如下。
第一部分是头文件（common.h）：
#pragma once
#include<opencv2/opencv.hpp>
#include<opencv2/highgui.hpp>
#include<iostream>
using namespace std;
using namespace cv;
extern Mat src;
void imageBasicInformation(Mat& src);//图像基本信息
const Mat houghCirclePre(Mat& srcPre);//霍夫圆检测预处理
void houghCircle(Mat& srcPreHough);//霍夫圆检测
const Mat RectCirclePre(Mat& srcPre);//面积筛选拟合圆的预处理
void AreaCircles(Mat& AreaInput);//面积筛选拟合圆检测第二部分是主函数：
#include"common.h"
Mat src;
int main()
{
src = imread("1.jpg",1);
if (src.empty())
{
cout << "图像不存在！" << endl;
}
else
{
namedWindow("原图", 1);
imshow("原图", src);
imageBasicInformation(src);
Mat srcPreHough = houghCirclePre(src);
houghCircle(srcPreHough);
Mat RectCir = RectCirclePre(src);
AreaCircles(RectCir);
waitKey(0);
destroyAllWindows();
}
return 0；
}第三部分为霍夫圆检测函数（hough.cpp）
主要包括输出图像的基本信息函数：void imageBasicInformation(Mat& src)
霍夫圆检测预处理函数：const Mat houghCirclePre(Mat& srcPre)
霍夫圆检测函数：void houghCircle(Mat& srcPreHough)
#include"common.h"
Mat graySrc, srcPre;//灰度图，霍夫检测预处理，
Mat threshold_grayaSrc;//二值化图
Mat erode_threshold_graySrc, dilate_threshold_graySrc;//二值化后腐蚀，二值化后膨胀
void imageBasicInformation(Mat& src)
{
int cols = src.cols;
int rows = src.rows;
int channels = src.channels();
cout << "图像宽为：" << cols << endl;
cout << "图像高为：" << rows << endl;
cout << "图像通道数：" << channels << endl;
}
const Mat houghCirclePre(Mat& srcPre)
{
double houghCirclePreTime = static_cast<double>(getTickCount());
cvtColor(srcPre, graySrc, COLOR_BGR2GRAY);
GaussianBlur(graySrc, graySrc, Size(3, 3), 2, 2);//滤波
threshold(graySrc, threshold_grayaSrc, 150, 255, 1);//二值化
Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
dilate(threshold_grayaSrc, dilate_threshold_graySrc, element);//膨胀
erode(dilate_threshold_graySrc, erode_threshold_graySrc, element);//腐蚀
houghCirclePreTime = ((double)getTickCount() - houghCirclePreTime) / getTickFrequency();
cout << "霍夫圆预处理时间为：" << houghCirclePreTime << "秒" << endl;
return erode_threshold_graySrc;
}
void houghCircle(Mat& srcPreHough)
{
cout << "进入霍夫圆检测" << endl;
vector<Vec3f> circles;
HoughCircles(srcPreHough, circles, HOUGH_GRADIENT, 1, 60, 1, 35, 0, 0);
cout << "圆的个数" << circles.size() << endl;
for (size_t i = 0;i < circles.size();i++)
{
Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
int radius = cvRound(circles[i][2]);
circle(src, center, 3, Scalar(0, 255, 0), -1, 8, 0);//画圆心
circle(src, center, radius, Scalar(0, 0, 255), 3, 8, 0);//画圆
}
namedWindow("霍夫检测结果", 0);
imshow("霍夫检测结果", src);
imwrite("霍夫圆检测结果.jpg", src);//保存检测结果
}第四部分为利用面积筛选拟合圆检测（AreaCircle.cpp）
主要包括预处理函数：const Mat RectCirclePre(Mat& srcPre)
面积筛选拟合圆检测函数：void AreaCircles(Mat& AreaInput)
#include"common.h"
Mat graySrcArea, thresholdGraySrc;//灰度图像，二值化图像
Mat dilateThresholdGraySrc, erodeThresholdGraySrc;//二值化后膨胀图像，膨胀之后的腐蚀图像
const Mat RectCirclePre(Mat& srcPre)
{
cvtColor(srcPre, graySrcArea, COLOR_BGR2GRAY);
GaussianBlur(graySrcArea, graySrcArea, Size(3, 3), 2, 2);
threshold(graySrcArea, thresholdGraySrc, 100, 255, 1);//二值化，阈值要根据自己的图像自己调整
Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
dilate(thresholdGraySrc, dilateThresholdGraySrc, element);//膨胀
erode(dilateThresholdGraySrc, erodeThresholdGraySrc, element);//腐蚀
return erodeThresholdGraySrc;
}
void AreaCircles(Mat& AreaInput)
{
vector<vector<Point>> RectContours;
vector<Vec4i> Hierarchy;
findContours(AreaInput, RectContours, Hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));
Mat drawing = Mat::zeros(src.size(), CV_8UC3);
for (int i = 0;i < RectContours.size();i++)
{
double area = contourArea(RectContours[i]);
cout << area << endl;//输出所有计算出来的面积，方便下一步设置阈值
if (area > 15000 && area < 100000)//根据上一步计算的阈值设置范围
{
drawContours(drawing, RectContours, i, Scalar(0, 255, 0), 2,8, Hierarchy, 0, Point());
RotatedRect Rect = fitEllipse(RectContours[i]);
circle(src, Rect.center, 2, Scalar(0, 255, 0), 2, 8, 0);//在原图画出圆心
ellipse(src, Rect, Scalar(0, 0, 255), 2);//在原图画出轮廓
}
}
namedWindow("面积筛选拟合圆", 0);
imshow("面积筛选拟合圆", src);
imwrite("面积筛选拟合圆.jpg", src);//保存检测结果
}结果如下（自己画的两个圆）：
原图：
以下为霍夫圆检测结果：
以下为面积筛选拟合圆结果：
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