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主要是利用霍夫圓檢測、面積篩選等完成多個圓形檢測,具體代碼及結果如下。
第一部分是頭文件(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);//圖像基本信息
const Mat houghCirclePre(Mat& srcPre);//霍夫圓檢測預處理
void houghCircle(Mat& srcPreHough);//霍夫圓檢測
const Mat RectCirclePre(Mat& srcPre);//面積篩選擬合圓的預處理
void AreaCircles(Mat& AreaInput);//面積篩選擬合圓檢測
第二部分是主函數:
#include"common.h"
Mat src;
int main()
{
src = imread("1.jpg",1);
if (src.empty())
{
cout << "圖像不存在!" << endl;
}
else
{
namedWindow("原圖", 1);
imshow("原圖", src);
imageBasicInformation(src);
Mat srcPreHough = houghCirclePre(src);
houghCircle(srcPreHough);
Mat RectCir = RectCirclePre(src);
AreaCircles(RectCir);
waitKey(0);
destroyAllWindows();
}
return 0;
}
第三部分為霍夫圓檢測函數(hough.cpp)
主要包括輸出圖像的基本信息函數:void imageBasicInformation(Mat& src)
霍夫圓檢測預處理函數:const Mat houghCirclePre(Mat& srcPre)
霍夫圓檢測函數:void houghCircle(Mat& srcPreHough)
#include"common.h"
Mat graySrc, srcPre;//灰度圖,霍夫檢測預處理,
Mat threshold_grayaSrc;//二值化圖
Mat erode_threshold_graySrc, dilate_threshold_graySrc;//二值化后腐蝕,二值化后膨脹
void imageBasicInformation(Mat& src)
{
int cols = src.cols;
int rows = src.rows;
int channels = src.channels();
cout << "圖像寬為:" << cols << endl;
cout << "圖像高為:" << rows << endl;
cout << "圖像通道數:" << 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);//濾波
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);//膨脹
erode(dilate_threshold_graySrc, erode_threshold_graySrc, element);//腐蝕
houghCirclePreTime = ((double)getTickCount() - houghCirclePreTime) / getTickFrequency();
cout << "霍夫圓預處理時間為:" << houghCirclePreTime << "秒" << endl;
return erode_threshold_graySrc;
}
void houghCircle(Mat& srcPreHough)
{
cout << "進入霍夫圓檢測" << endl;
vector<Vec3f> circles;
HoughCircles(srcPreHough, circles, HOUGH_GRADIENT, 1, 60, 1, 35, 0, 0);
cout << "圓的個數" << 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);//畫圓心
circle(src, center, radius, Scalar(0, 0, 255), 3, 8, 0);//畫圓
}
namedWindow("霍夫檢測結果", 0);
imshow("霍夫檢測結果", src);
imwrite("霍夫圓檢測結果.jpg", src);//保存檢測結果
}
第四部分為利用面積篩選擬合圓檢測(AreaCircle.cpp)
主要包括預處理函數:const Mat RectCirclePre(Mat& srcPre)
面積篩選擬合圓檢測函數:void AreaCircles(Mat& AreaInput)
#include"common.h"
Mat graySrc, srcPre;//灰度圖,霍夫檢測預處理,
Mat threshold_grayaSrc;//二值化圖
Mat erode_threshold_graySrc, dilate_threshold_graySrc;//二值化后腐蝕,二值化后膨脹
void imageBasicInformation(Mat& src)
{
int cols = src.cols;
int rows = src.rows;
int channels = src.channels();
cout << "圖像寬為:" << cols << endl;
cout << "圖像高為:" << rows << endl;
cout << "圖像通道數:" << 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);//濾波
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);//膨脹
erode(dilate_threshold_graySrc, erode_threshold_graySrc, element);//腐蝕
houghCirclePreTime = ((double)getTickCount() - houghCirclePreTime) / getTickFrequency();
cout << "霍夫圓預處理時間為:" << houghCirclePreTime << "秒" << endl;
return erode_threshold_graySrc;
}
void houghCircle(Mat& srcPreHough)
{
cout << "進入霍夫圓檢測" << endl;
vector<Vec3f> circles;
HoughCircles(srcPreHough, circles, HOUGH_GRADIENT, 1, 60, 1, 35, 0, 0);
cout << "圓的個數" << 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);//畫圓心
circle(src, center, radius, Scalar(0, 0, 255), 3, 8, 0);//畫圓
}
namedWindow("霍夫檢測結果", 0);
imshow("霍夫檢測結果", src);
imwrite("霍夫圓檢測結果.jpg", src);//保存檢測結果
}
結果如下(自己畫的兩個圓):
原圖:
以下為霍夫圓檢測結果:
以下為面積篩選擬合圓結果:
原文鏈接:https://blog.csdn.net/weixin_45718019/article/details/126196955
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