目标是检测图像中的5个白色圆圈。必须检测圆圈的测试图像是此处显示的640x480
请在此处下载原始图片1280x1024
我使用不同的方法来评估各种圆/椭圆检测方法。但不知何故,我无法修复我的简单Hough变换代码。它没有检测到任何圆圈。我不清楚问题是在预处理步骤,还是HoughCircle的参数。我在论坛中经历了所有类似的问题,但仍无法解决问题。这是我的代码。请帮助我...
标头文件
#ifndef IMGPROCESSOR_H
#define IMGPROCESSOR_H
// OpenCV Library
#include <opencv2\opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
class ImgProcessor{
public:
Mat OpImg ;
ImgProcessor();
~ImgProcessor();
//aquire filter methods to image
int Do_Hough(Mat IpImg);
};
#endif /* ImgProcessor_H */
源文件
#include "ImgProcessor.h"
#include <opencv2\opencv.hpp>
#include "opencv2\imgproc\imgproc.hpp"
#include "opencv2\imgproc\imgproc_c.h"
#include <vector>
using namespace cv;
ImgProcessor::ImgProcessor(){
return;
}
ImgProcessor::~ImgProcessor(){
return;
}
//Apply filtering for the input image
int ImgProcessor::Do_Hough(Mat IpImg)
{
//Parameter Initialization________________________________________________________
double sigma_x, sigma_y, thresh=250, max_thresh = 255;
int ksize_w = 5 ;
int ksize_h = 5;
sigma_x = 0.3*((ksize_w-1)*0.5 - 1) + 0.8 ;
sigma_y = 0.3*((ksize_h-1)*0.5 - 1) + 0.8 ;
vector<Vec3f> circles;
//Read the image as a matrix
Mat TempImg;
//resize(IpImg, IpImg ,Size(), 0.5,0.5, INTER_AREA);
//Preprocessing__________________________________________________________
//Perform initial smoothing
GaussianBlur( IpImg, TempImg, Size(ksize_w, ksize_h),2,2);
//perform thresholding
threshold(TempImg,TempImg, thresh,thresh, 0);
//Remove noise by gaussian smoothing
GaussianBlur( TempImg, TempImg, Size(ksize_w, ksize_h),2,2);
/*imshow("Noisefree Image", TempImg);
waitKey(10000);*/
//Obtain edges
Canny(TempImg, TempImg, 255,240 , 3);
imshow("See Edges", TempImg);
waitKey(10000);
//Increase the line thickness
//dilate(TempImg,TempImg,0,Point(-1,-1),3);
//Hough Circle Method______________________________________________________________
// Apply the Hough Transform to find the circles
HoughCircles( TempImg, circles, 3, 1, TempImg.rows/32, 255, 240, 5, 0 );
// Draw the circles detected
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 center
circle( IpImg, center, 3, Scalar(0,255,0), -1, 8, 0 );
// circle outline
circle( IpImg, center, radius, Scalar(0,0,255), 3, 8, 0 );
}
// Show your results
namedWindow( "Hough Circle Transform", WINDOW_AUTOSIZE );
imshow( "Hough Circle Transform", IpImg );
// waitKey(0);
return 0;
}
int main(int argc, char** argv)
{
ImgProcessor Iclass;
//char* imageName = argv[1];
string imageName = "D:/Projects/test_2707/test_2707/1.bmp";
Mat IpImg = imread( imageName );
cvtColor(IpImg, IpImg,6,CV_8UC1);
Iclass.Do_Hough(IpImg);
/*Iclass.Do_Contours(IpImg);*/
return 0;
}
答案 0 :(得分:0)
代码似乎很好,除了:
HoughCircles( TempImg, circles, 3, 1, TempImg.rows/32, 255, 240, 5, 0 );
参数列表中的数字3是否对应于CV_HOUGH_GRADIENT?使用定义而不是数字总是更好。
可能你应该首先使用带有更大圆圈的图像进行测试。一旦确定其余代码正确,就可以调整HoughCircles的参数。