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分享opencv用霍夫变换得出圆，怎么提取圆上的特征点
我目前是用霍夫变换得到了圆如图
然后我想把这个圆上的点提取出来，再进行验证（利用提取的点重新画出圆，跟最先的作比较）。这一步，我现在不知道从何下手，网上也没有找到合适的答案。
请求帮助！！
ps：能对圆弧进行霍夫变换以及上面说的一系列操作嘛？
然后我想把这个圆上的点提取出来，再进行验证（利用提取的点重新画出圆，跟最先的作比较）。这一步，我现在不知道从何下手，网上也没有找到合适的答案。
请求帮助！！
ps：能对圆弧进行霍夫变换以及上面说的一系列操作嘛？
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赵4老师 2017-04-13
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harry-white 2017-04-13
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打从心底膜拜赵老师，这个代码我先试试看
赵4老师 2017-04-13
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approxPolyDP
仅供参考：
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;
#define _ShowImage 1
////////////////////////////////////////////////////////
/**
* @name getAllImgsFilePath
* @brief 获取指定路径下的所有文件全路径(还是这个方便啊.)
* @param[in] szDir:指定路径(比如C:\\_Sunyard\\Project\\浙江农信\\data\\照片\\Merge表格\\*.*";)
* @param[in/out] testImgFiles,所有文件全路径存在这里
* @return 0 成功 / -1 失败
* @author [lintao 2015-10-09]
**/
////////////////////////////////////////////////////////
int getAllImgsFilePath(char *szDir,vector<string>& testImgFiles)
{
WIN32_FIND_DATA ffd;
HANDLE hFind = FindFirstFile((szDir), &ffd);
//testImgFiles.push_back(ffd.cFileName);//do 是先做..会push进去的
// List all the files in the directory with some info about them.
do
{
if (ffd.dwFileAttributes & FILE_ATTRIBUTE_DIRECTORY)
{
//_tprintf(TEXT(" %s <DIR>\n"), ffd.cFileName);
//cout<<ffd.cFileName<<endl;
}
else
{
//_tprintf(TEXT(" %s \n"), ffd.cFileName);
//cout<<ffd.cFileName<<endl;
string searchImgDirWithType =szDir; //搜索目录带有搜索文件类型的
int npos=searchImgDirWithType.find_last_of("\\"); //
string searchImgDir =searchImgDirWithType.substr(0,npos+1); //纯目录 //substr 返回pos开始的n个字符组成的字符串
string imgWholePath = searchImgDir + ffd.cFileName;
testImgFiles.push_back(imgWholePath);
}
} while (FindNextFile(hFind, &ffd) != 0);
FindClose(hFind);
return 0;
}
//////////////////////////////////////////////////////////////////////////
//判断轮廓是不是一个圆，通过轮廓的面积pi*r*r的差异，判断是不是圆。
//contour：输入轮廓
//minRadius: 半径长度至少大于minRadius
//////////////////////////////////////////////////////////////////////////
BOOL isCircleContour(vector<Point>& contour,double minRadius)
{
BOOL isCircle = FALSE;
//double perimeter = arcLength(contour,TRUE);
double perimeter = arcLength(contour,FALSE);
double perimeter2 = arcLength(contour,TRUE);
Rect rect =boundingRect(contour);
double r = (rect.width+rect.height)/4.0; //半径约等于轮廓外接矩的边长/2
double area=contourArea(contour);
double pi=3.1415926;
//cout<<"x/y/width/height\t"<<rect.x<<" "<<rect.y<<" "<<rect.width<<" "<<rect.height<<endl;
//cout<<"perimeter:FALSE\t"<<perimeter<<endl;
//cout<<"perimeter2:TRUE\t"<<perimeter2<<endl;
//cout<<"area:\t"<<area<<endl;
//cout<<"r: "<<r<<" 计算出周长: "<<2*pi*r<<" 计算出面积: "<<pi*r*r<<endl;
double perimeterDiffRatio = fabs(2*pi*r - perimeter)*2.0/(2*pi*r + perimeter) *100;
double areaDiffRatio = fabs(pi*r*r - area)*2.0/(pi*r*r + area) *100;
double sideDiffRatio = abs(rect.width-rect.height)*2.0/(rect.width+rect.height)*100;
//CString CstrInfo3;
//CstrInfo3.Format(_T(" perimeterDiffRatio: %f\n areaDiffRatio: %f \n sideDiffRatio:%f"),perimeterDiffRatio,areaDiffRatio,sideDiffRatio);
//AfxMessageBox(CstrInfo3);
if (r>minRadius)
{
cout<<"周长差异百分比: "<<perimeterDiffRatio<<"%"<<endl;;
cout<<"面积差异百分比: "<<areaDiffRatio<<"%"<<endl;
cout<<"边长差异百分比: "<<sideDiffRatio<<"%"<<endl;;
cout<<endl<<endl;
}
//原始的
//const double areaDiffThresh = 18.0; //目前统计的，最大的面积差2.58%
//const double perimeterDiffThresh = 15; //目前统计的，最大的周长差5.65%
//const double sideDiffThresh = 15; //目前统计的，最大的边长差7.3%
//const double avgDiffThresh = 15.0; //三个差异比值的加权平均要求小于5.0;
const double areaDiffThresh = 5; //目前统计的，最大的面积差2.58%
const double perimeterDiffThresh =7.25; //目前统计的，最大的周长差5.65%
const double sideDiffThresh = 10; //目前统计的，最大的边长差7.3%
const double avgDiffThresh = 5.0; //三个差异比值的加权平均要求小于5.0;
double avdDiffRatio = (areaDiffRatio+perimeterDiffRatio+sideDiffRatio)/3.0;
//计算出的轮廓周长是半径的2*pi倍数
if( (areaDiffRatio <areaDiffThresh)
&& (perimeterDiffRatio <perimeterDiffThresh)
&& (sideDiffRatio <sideDiffThresh)
&&(avdDiffRatio<avgDiffThresh)
&& (r>minRadius) )
{
isCircle = TRUE;
}
return isCircle;
}
int countShootingRingsNum(Mat& orgImg)
{
if (orgImg.empty())
return -1;
//转换到灰度图
Mat grayImage;
if (orgImg.channels()==3)
cvtColor(orgImg,grayImage,CV_BGR2GRAY);
if (orgImg.channels()==1)
grayImage = orgImg.clone();
imshow("grayImage",grayImage);
//模块：二值化
int isBlackCharacter= FALSE;
Mat binImg;
double otsuThresh = 0.0;
if(isBlackCharacter)
otsuThresh = threshold(grayImage, binImg, 0, 255, CV_THRESH_BINARY_INV|CV_THRESH_OTSU);
else
otsuThresh = threshold(grayImage, binImg, 0, 255, CV_THRESH_BINARY|CV_THRESH_OTSU);
//CString CstrInfo3;
//CstrInfo3.Format(_T(" otsuThresh: %f"),otsuThresh);
//AfxMessageBox(CstrInfo3);
imshow("binImg",binImg);
//腐蚀膨胀
int erodeSize = 3;
int dilateSize = 3;
Mat elementForErode = getStructuringElement(MORPH_RECT, Size(erodeSize, erodeSize), Point(-1, -1) );
Mat elementForDilate = getStructuringElement(MORPH_RECT, Size(dilateSize, dilateSize), Point(-1, -1) );
dilate(binImg ,binImg , elementForDilate);
//erode(binImg ,binImg , elementForErode );
imshow("膨胀腐蚀后的Binary Image", binImg);
//waitKey();
//模块：提取轮廓(保留轮廓面积大于_tooLessPixels)
vector< vector<Point> > contours; // 轮廓
vector< vector<Point> > filterContours; // 筛选后的轮廓
vector< Vec4i > hierarchy; // 轮廓的结构信息
contours.clear();
hierarchy.clear();
filterContours.clear();
Mat binaryImageCopy1 = binImg.clone();
//findContours(binaryImageCopy1, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
findContours(binaryImageCopy1, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
// 去除伪轮廓: 轮廓上像素点数太少的
const int _tooLessPixels = 10*10;
for (size_t i = 0; i < contours.size(); i++)
{
if (fabs(contourArea(contours[i])) > _tooLessPixels)
{
//先都push进去
filterContours.push_back(contours[i]);
}
}
Mat rgbImg = binImg.clone();
cvtColor(rgbImg,rgbImg,CV_GRAY2BGR);
//binaryImage.setTo(0);
drawContours(rgbImg, filterContours, -1, Scalar(0,255,0), 5); //8, hierarchy);
//cvNamedWindow("filterContours",0);
imshow("filterContours",rgbImg);
//模块：外接矩的宽高比比例失调的过滤掉
const double w2hThresh2 = 2.0; //外接矩的宽高比比例大于3过滤掉
const double h2wThresh2 = 2.0; //外接矩的宽高比比例大于3过滤掉
for (int i=0; i<filterContours.size(); i++)
{
Rect tmpRect = boundingRect(filterContours[i]);
if( //(tmpRect.width>maxRectWidth)
// (tmpRect.height>maxRectHeight)
( (double)tmpRect.width/(double)tmpRect.height > w2hThresh2)
|| ( (double)tmpRect.height/(double)tmpRect.width > h2wThresh2)
)
{
filterContours.erase(filterContours.begin()+i);
i--;//删除掉一个后，索引回退一个
}
}
//不是圆的过滤掉
double minRadius = 20/2; //目前最小的圆直径30
for (int i=0; i<filterContours.size(); i++)
{
if(!isCircleContour(filterContours[i], minRadius))
{
//selectContours.push_back(filterContours[i]);
filterContours.erase(filterContours.begin()+i);
i--;//删除掉一个后，索引回退一个
}
}
Mat rgbImg2 = binImg.clone();
cvtColor(rgbImg2,rgbImg2,CV_GRAY2BGR);
//binaryImage.setTo(0);
drawContours(rgbImg2, filterContours, -1, Scalar(0,255,0), 5); //8, hierarchy);
//cvNamedWindow("filterContours2",0);
imshow("filterContours2",rgbImg2);
Mat grayImg;
cvtColor(orgImg,grayImg,CV_BGR2GRAY);
Mat diffImg;
absdiff(grayImg,GreenImg,diffImg);
normalize(diffImg, diffImg, 0, 255, NORM_MINMAX);
imshow("绿色通道和灰度图差异图图",diffImg);
return 0;
}
int testCountShootingRingsNum()
{
//char *szDir = "C:\\Projects\\ComputerVisionProject\\网上看到的项目\\数打靶的环数\\data\\webData\\*.*";
char *szDir = "C:\\Projects\\ComputerVisionProject\\网上看到的项目\\数打靶的环数\\data\\*.*";
vector<string> testImgFiles;
getAllImgsFilePath(szDir,testImgFiles);
//system("time");
for (int i=0; i<testImgFiles.size(); i++)
{
//输出文件名
int npos=testImgFiles[i].find_last_of("\\");
string fileName =testImgFiles[i].substr(npos+1,testImgFiles[i].length()-(npos+1)); //获取目录
cout<<fileName<<endl;
Mat orgImg =imread(testImgFiles[i]);
if (orgImg.empty())
continue;
imshow("orgImg",orgImg);
countShootingRingsNum(orgImg);
waitKey();
}
system("time");
return 0;
}
void main()
{
testCountShootingRingsNum();
}
harry-white 2017-04-13
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赵老师，我有个问题想请教，opencv中有最小二乘拟合吗？我想拟合出圆弧
赵4老师 2017-04-12
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建议楼主先编译链接调试OpenCV自带的相关例子代码。
比如：C:\OpenCV31\opencv\sources\samples\cpp\openni_capture.cpp
