Short Answer cvCanny is used to detect Edges, as well as increase contrast and remove image noise. HoughLines which uses the Hough Transform is used to determine whether those edges are lines or not. Hough Transform requires edges to be detected well in order to be efficient and provide meaning results. Long Answer The Limitations … Read more
I would try to use a skeleton representation of the image. The problem with your canny, here, is that it basically results in two lines because of the width of the line. Then I would apply the Hough transform on it.
You will most likely need to do the following: Apply the Hough Transform algorithm on the entire page, this should should yield a series of page sections. For each section you get, apply it again. If the current section yielded 2 elements, then you should be dealing with a rectangle similar to the above. Once … 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
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
You don’t want to get the intersections of the parallel lines; only the intersections of the vertical lines with those of the horizontal lines. Also, since you have vertical lines, calculating the slope will likely result in exploding or inf slopes, so you shouldn’t use the y = mx+b equations. You need to do two … Read more
Here’s a very basic, visual explanation of how a Hough Transform works for detecting lines in an image:
I’m Martin’s friend who was working on this earlier this year. This was my first ever coding project, and kinda ended in a bit of a rush, so the code needs some errr…decoding… I’ll give a few tips from what I’ve seen you doing already, and then sort my code on my day off tomorrow. … 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