I have finally completed the pipeline: fixed incorrect parameters (as were suggested by Dan) developed my own ‘merging line segments’ algorithm. I had bad results when I implemented TAVARES and PADILHA algorithm (as were suggested by Andrew). I have skipped Canny and got better results (as were suggested by Alexander) Please find the code and … Read more
Have you seen a code sample from HoughLinesP function documentation? I think you can use it as starting point for your algorithm. To pick horizontal an vertical lines you just need to filter out other lines by line angle. UPDATE: As I see you need to find not the lines but horizontal an vertical edges … Read more
The function scipy.signal.find_peaks, as its name suggests, is useful for this. But it’s important to understand well its parameters width, threshold, distance and above all prominence to get a good peak extraction. According to my tests and the documentation, the concept of prominence is “the useful concept” to keep the good peaks, and discard the … 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