Opencv检测多个圆形(霍夫圆检测，轮廓面积筛选)
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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 class="lazy" data-src;
void imageBasicInformation(Mat& class="lazy" data-src);//图像基本信息
const Mat houghCirclePre(Mat& class="lazy" data-srcPre);//霍夫圆检测预处理
void houghCircle(Mat& class="lazy" data-srcPreHough);//霍夫圆检测
const Mat RectCirclePre(Mat& class="lazy" data-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 class="lazy" data-src;
int main()
{
class="lazy" data-src = imread("1.jpg",1);
if (class="lazy" data-src.empty())
{
cout << "图像不存在！" << endl;
}
else
{
namedWindow("原图", 1);
imshow("原图", class="lazy" data-src);
imageBasicInformation(class="lazy" data-src);
Mat class="lazy" data-srcPreHough = houghCirclePre(class="lazy" data-src);
houghCircle(class="lazy" data-srcPreHough);
Mat RectCir = RectCirclePre(class="lazy" data-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& class="lazy" data-src)
霍夫圆检测预处理函数：const Mat houghCirclePre(Mat& class="lazy" data-srcPre)
霍夫圆检测函数：void houghCircle(Mat& class="lazy" data-srcPreHough)
#include"common.h"
Mat grayclass="lazy" data-src, class="lazy" data-srcPre;//灰度图，霍夫检测预处理，
Mat threshold_grayaclass="lazy" data-src;//二值化图
Mat erode_threshold_grayclass="lazy" data-src, dilate_threshold_grayclass="lazy" data-src;//二值化后腐蚀，二值化后膨胀
void imageBasicInformation(Mat& class="lazy" data-src)
{
int cols = class="lazy" data-src.cols;
int rows = class="lazy" data-src.rows;
int channels = class="lazy" data-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& class="lazy" data-srcPre)
{
double houghCirclePreTime = static_cast<double>(getTickCount());
cvtColor(class="lazy" data-srcPre, grayclass="lazy" data-src, COLOR_BGR2GRAY);
GaussianBlur(grayclass="lazy" data-src, grayclass="lazy" data-src, Size(3, 3), 2, 2);//滤波
threshold(grayclass="lazy" data-src, threshold_grayaclass="lazy" data-src, 150, 255, 1);//二值化
Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
dilate(threshold_grayaclass="lazy" data-src, dilate_threshold_grayclass="lazy" data-src, element);//膨胀
erode(dilate_threshold_grayclass="lazy" data-src, erode_threshold_grayclass="lazy" data-src, element);//腐蚀
houghCirclePreTime = ((double)getTickCount() - houghCirclePreTime) / getTickFrequency();
cout << "霍夫圆预处理时间为：" << houghCirclePreTime << "秒" << endl;
return erode_threshold_grayclass="lazy" data-src;
}
void houghCircle(Mat& class="lazy" data-srcPreHough)
{
cout << "进入霍夫圆检测" << endl;
vector<Vec3f> circles;
HoughCircles(class="lazy" data-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(class="lazy" data-src, center, 3, Scalar(0, 255, 0), -1, 8, 0);//画圆心
circle(class="lazy" data-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", class="lazy" data-src);
imwrite("霍夫圆检测结果.jpg", class="lazy" data-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& class="lazy" data-srcPre)
面积筛选拟合圆检测函数：void AreaCircles(Mat& AreaInput)
#include"common.h"
Mat grayclass="lazy" data-src, class="lazy" data-srcPre;//灰度图，霍夫检测预处理，
Mat threshold_grayaclass="lazy" data-src;//二值化图
Mat erode_threshold_grayclass="lazy" data-src, dilate_threshold_grayclass="lazy" data-src;//二值化后腐蚀，二值化后膨胀
void imageBasicInformation(Mat& class="lazy" data-src)
{
int cols = class="lazy" data-src.cols;
int rows = class="lazy" data-src.rows;
int channels = class="lazy" data-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& class="lazy" data-srcPre)
{
double houghCirclePreTime = static_cast<double>(getTickCount());
cvtColor(class="lazy" data-srcPre, grayclass="lazy" data-src, COLOR_BGR2GRAY);
GaussianBlur(grayclass="lazy" data-src, grayclass="lazy" data-src, Size(3, 3), 2, 2);//滤波
threshold(grayclass="lazy" data-src, threshold_grayaclass="lazy" data-src, 150, 255, 1);//二值化
Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
dilate(threshold_grayaclass="lazy" data-src, dilate_threshold_grayclass="lazy" data-src, element);//膨胀
erode(dilate_threshold_grayclass="lazy" data-src, erode_threshold_grayclass="lazy" data-src, element);//腐蚀
houghCirclePreTime = ((double)getTickCount() - houghCirclePreTime) / getTickFrequency();
cout << "霍夫圆预处理时间为：" << houghCirclePreTime << "秒" << endl;
return erode_threshold_grayclass="lazy" data-src;
}
void houghCircle(Mat& class="lazy" data-srcPreHough)
{
cout << "进入霍夫圆检测" << endl;
vector<Vec3f> circles;
HoughCircles(class="lazy" data-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(class="lazy" data-src, center, 3, Scalar(0, 255, 0), -1, 8, 0);//画圆心
circle(class="lazy" data-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", class="lazy" data-src);
imwrite("霍夫圆检测结果.jpg", class="lazy" data-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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