If you have only these regular shapes, there is a simple procedure as follows : Find Contours in the image ( image should be binary as given in your question) Approximate each contour using approxPolyDP function. First, check number of elements in the approximated contours of all the shapes. It is to recognize the shape. … Read more
Hmm, are you looking for “handsome” or “accurate”? Camera calibration is one of the very few subjects in computer vision where accuracy can be directly quantified in physical terms, and verified by a physical experiment. And the usual lesson is that (a) your numbers are just as good as the effort (and money) you put … Read more
Use houghCircle directly on your image, don’t extract edges first. Then test for each detected circle, how much percentage is really present in the image: int main() { cv::Mat color = cv::imread(“../houghCircles.png”); cv::namedWindow(“input”); cv::imshow(“input”, color); cv::Mat canny; cv::Mat gray; /// Convert it to gray cv::cvtColor( color, gray, CV_BGR2GRAY ); // compute canny (don’t blur with … Read more
OpenCV can help you find edges using Canny Edge Detector You can refer for the implementation in this link OpenCV’s Canny Edge Detection in C++ Furthermore, I would like you to first do a bit of research on your own before actually asking for questions here.