Circle Detection Without Hough Circles

Question

I have an image of a circle, I want to find a circle, but not using hough circles.

I found a method related here .

But I can not find the coordinates of the transition from white to black, since I do not know the x and y coordinates in the circle. What other methods exist, or how can I make this approach work?

This is my test image:

Answer 1

One possible approach is to first create a threshold image to get rid of some noise around the circle. You can then extract the edge of the circle using the Canny definition of the edge. Finally, findNonZero to get a list of pixel coordinates.

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First I made a quick prototype with Python:

 import cv2 import numpy as np img = cv2.imread('circle.png', 0) mask = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1] edges = cv2.Canny(mask, 20, 100) points = np.array([p[0] for p in cv2.findNonZero(edges)]) 

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And then ported it to C ++, adding additional code to save all intermediate images and build the found pixels.

 #include <opencv2/opencv.hpp> int main() { cv::Mat img(cv::imread("circle.png", 0)); cv::Mat mask; cv::threshold(img, mask, 127, 255, cv::THRESH_BINARY); cv::imwrite("circle_1.png", mask); cv::Mat edges; cv::Canny(mask, edges, 20, 100); cv::imwrite("circle_2.png", edges); std::vector<cv::Point2i> points; cv::findNonZero(edges, points); cv::Mat output(cv::Mat::zeros(edges.size(), CV_8UC3)); for (auto const& p : points) { output.at<cv::Vec3b>(p) = cv::Vec3b(127, 255, 127); } cv::imwrite("circle_3.png", output); } 

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Output threshold :

Canny output:

Redrawn pixels:

Answer 2

Another approach (which is useful not only for circles) is to find the contours of the image and analyze the moment of the image on the circle to find it in the center of mass:

I recommend to study them if you are going to move forward with image processing. These are quite useful approaches that transform images into more useful structures.