13. Hough Circles

vector<Vec3f> circles;
HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 0 );

with the arguments:

• src_gray : 입력 영상(Gray Scale)
• circles : A vector that stores sets of 3 values:  for each detected circle.
• CV_HOUGH_GRADIENT : 검출 함수 정의. Currently this is the only one available in OpenCV
• dp = 1 : 누적기 해상도 (영상 크기 /2)
• min_dist = src_gray.rows/8 : 검출된 원간의 최소거리
• param_1 = 200 : 캐니 최대 경계값
• param_2 = 100* : Threshold for center detection.
• min_radius = 0 : Minimum radio to be detected. If unknown, put zero as default.
• max_radius = 0 : Maximum radius to be detected. If unknown, put zero as default

코드

#include <iostream>

#include <opencv2/core/core.hpp>

#include <opencv2/highgui/highgui.hpp>

#include <opencv2/imgproc/imgproc.hpp>

using namespace cv;

using namespace std;

int main()

{

Mat image;

image = imread("image4/circle2.jpg", CV_LOAD_IMAGE_COLOR);

vector<Vec3f> circles;

HoughCircles(

image, // 원본이미지 

circles, 

CV_HOUGH_GRADIENT,

1,   // 누적기 해상도(영상크기/2)

100, // 두 원 간의 최소 거리

100, // 캐니 최대 경계값

50 // 투표 최소 개수

);

cout << "circles.size()= " << circles.size() << endl;

vector<Vec3f>::const_iterator itc = circles.begin();

int k = 0;

while (itc != circles.end()) {

int cx = (*itc)[0];

int cy = (*itc)[1];

int radius = (*itc)[2];

printf("Circles[%d]: (cx , cy)= (%d , %d), r = %d\n", k++, cx, cy, radius);

circle(image, Point(cx, cy), radius, Scalar(0, 0, 255), 2);

++itc;

}

namedWindow("Detected Circles");

imshow("Detected Circles", image);

waitKey(0);

return 0;

}

결과