Detect circle like shapes opencv

Question

everyone i'm fairly new to OpenCV and computer vision and i'm stuck at this problem , which might seem like a fairly trivial but forgive my noobness :)

I'm trying to detect Rebars from a cross-sectional image.

i'm using this code :

import cv2 import cv2.cv as cv import numpy as np img = cv2.imread('test/t2.jpg',0) img = cv2.equalizeHist(img) cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR) circles = cv2.HoughCircles(img,cv.CV_HOUGH_GRADIENT,1,10,param1=50,param2=30,minRadius=0,maxRadius=25) circles = np.uint16(np.around(circles)) for i in circles[0,:]: # draw the outer circle cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2) cv2.imshow('detected circles',cimg) cv2.waitKey(0) cv2.destroyAllWindows()

This is the result i'm getting currently, which is not good :

I'm looking for pointers on how to proceed with this problem and how to learn more about CV as i'm really interested!

Thanks a ton!

Oh sorry i forgot to mention the main part, i'm not able detect these properly , this is the result i'm getting result — Harpreet Boparai
– Harpreet Boparai, Commented Feb 20, 2016 at 4:39

Community · Accepted Answer · 2017-05-23 11:47:01Z

HoughCircles is not a strong enough way to detect circle in such complex image like your case.

SO has already had some discussion about this. You could refer these post with quality accepted answers

Standard way:

Filled circle detection using CV2 in Python?

What are the possible fast ways to detect circle in an image?

Noise image:

https://dsp.stackexchange.com/questions/5930/find-circle-in-noisy-data

Another method:

Gradient Pair Vectors

Learning Automata

user3654766 · Accepted Answer · 2016-02-20 11:15:17Z

Those results can be slightly improved with setting the parameters better on this line:

circles = cv2.HoughCircles(img,cv.CV_HOUGH_GRADIENT,1,10,param1=50,param2=30,minRadius=0,maxRadius=25)

For example, you can reduce the maxRadius slightly and increase the sensitivity.

In my experience, however, you won't get a good result on an image like this. It is very complex, the circles are irregular and at different angles. If your goal is to practice, then sure, play with the parameters and try different methods to improve it. I don't see much practical use though.

Harshfi6 · Accepted Answer · 2017-03-18 09:30:59Z

You can detect features here, using the module trackpy. You need to vary feature sizes with odd numbers and see which one matches best. You may also need to do some pre-processing like, converting image to grayscale.

import trackpy as tp import numpy as np import pandas as pd import pims import matplotlib.pyplot as plt #%% importing the data frames=pims.ImageSequence('F:/TrapHysteresis/processing/Positions/*.TIF') #%% tracking circles and center positions featuresize=71 f1=tp.locate(frames[0],featuresize) plt.figure() tp.annotate(f1,frames[0])

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