How to find different centers of various arcs in a almost circular hole using OpenCV?

find circles in image python
hough transform circle detection c
opencv detect circles in video
red circle detection opencv python
real time circle detection - opencv python
hough gradient method
imfindcircles
cv2.houghcircles parameters python

I have a photo of a hole. I am currently assuming that the hole is not a perfect circle, and hence I need to find the change in dimension of the work. Now I was thinking of taking three 30 degrees arcs and find the distance from the centres of those arcs and the centre of the circle (which I will find using Hough circles) and take the mean of those values. Which is what I need for my research. I am attaching a sample photo of one of the holes that I have drilled. Any help will be helpful.

An idea is to threshold then find contours and filter using the largest contour area. From here we use cv2.minEnclosingCircle() to find the center (x,y) point and the radius. Here's the largest contour highlighted in green

Now we simply find the minimum enclosing circle around the contour to determine the center point. Here's the result

the (x,y) coordinate

176.53846740722656 174.33653259277344

Code

import cv2
import numpy as np

image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur,0,255,cv2.THRESH_OTSU + cv2.THRESH_BINARY_INV)[1]
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)

for c in cnts:
    (x,y), radius = cv2.minEnclosingCircle(c)
    cv2.circle(image,(int(x),int(y)),int(radius),(35,255,12),3)
    cv2.circle(image,(int(x),int(y)),1,(35,255,12),2)
    print(x,y)
    break

cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()

Detecting Circles in Images using OpenCV and Hough Circles , Tutorial: In this post I'll show you how to use OpenCV and the cv2. Figure 2: Detecting the top of a soda can using circle detection with OpenCV. in images using OpenCV is substantially harder than detecting other shapes All too often I see developers, students, and researchers wasting their time,� So I have figured out the answer to your first question: determining the center and radius of circles in the image. Initially I am finding all the contours present in the image. Then using a for loop, I found the center and radius using cv2.minEnclosingCircle for every contour in the image. I printed them in the console screen.

If you have a hole you might use a specific color of the background behind your "object". So it should not be a problem to segment the actual shape:

and walk through all the points to find the most distant pairs (so you can find the diameter). With that being said you can find, for example, the centre of the circle you a looking for. Just did a quick test:

Sorry, no code to show. I'm not using OpenCV here, but you say any help is helpful :)

Detect and Measure Circular Objects in an Image, This example shows how to automatically detect circular objects in an image and other and some others that are close together and almost touching each other. A quick way to find the appropriate radius range is to use the interactive tool Note that the outputs centers and radii are empty, which means that no circles� This tutorial explains simple blob detection using OpenCV. What is a Blob ? A Blob is a group of connected pixels in an image that share some common property ( E.g grayscale value ). In the image above, the dark connected regions are blobs, and the goal of blob detection is to identify and mark […]

Find circles using circular Hough transform, centers = imfindcircles( A , radius ) finds the circles in image A whose radii are approximately equal to radius . The output Find all the circles with radius r pixels in the range [15, 30]. The CHT is not a rigorously specified algorithm, rather there are a number of different approaches that can be taken in its implementation. A quick way to find the appropriate radius range is to use the interactive tool imdistline to get an approximate estimate of the radii of various objects. d = imdistline; imdistline creates a draggable tool that can be moved to fit across a chip and the numbers can be read to get an approximate estimate of its radius.

Hough Circle Transform, Use the OpenCV function HoughCircles() to detect circles in an image. The first stage involves edge detection and finding the possible circle centers and For more details, please check the book Learning OpenCV or your favorite gray. rows/16, // change this value to detect circles with different distances to each other. Calculates the area, circular arc, center angle and chord of an arch given the radius and height.

Detecting circular shapes using contours, This image has few different objects. It has a noisy background. It has objects of various shapes, and yet, there are few circular shapes that can be found. I think this OpenCV has provides the below function to find contours. Arc length is the distance from one endpoint of the arc to the other. Finding an arc length requires knowing a bit about the geometry of a circle. Since the arc is a portion of the circumference, if you know what portion of 360 degrees the arc’s central angle is, you can easily find the length of the arc.

[PDF] OpenCV-Python Tutorials Documentation, All the OpenCV array structures are converted to-and-from Numpy arrays. So whatever Similar is the case with other tutorials, documentation etc. To draw a circle, you need its center coordinates and radius. See, OpenCV function is nearly 25x faster than Numpy function. It is also called arc length. Cross-multiply your numbers using the equation: a/C = T/360. A is your arc length, C is your circumference and T is the angle you measured. Multiply C by T. Set the result equal to 360 times a. Divide both sides of the equation by 360 to solve for a.

Comments
  • start by detecting the inner contour and split it into arcs then fit each to circle to find centers .
  • @Ziri can you give me pointers on how to fit the circle part? Can you elaborate more on it? thanks
  • Once your have the Arcs you can apply RANSAC circle fitting : github.com/aerolalit/RANSAC-Algorithm
  • @Ziri I am also having trouble splitting the arcs? The only way I can think of is to take a minEnclosed circle like below and then split it into 30 deg increments and take those, but won't that actually give us the arc with respect to the actual circle and not the actual arc? I am not sure abt this part.
  • Enclosing circle is not what you'are looking for if you need accurate results . I suggest eliminating those defected (non-arc) regions from the inner contour using the distance from the centroid to the edge, then split it into arcs.
  • can u find more than one min enclosing circle using this function ??
  • No this approach is with the assumption that there is only one hole in the image
  • there is only one hole in the image but the hole is not exactly circular and hence I need the arcs as described in the question to get my data
  • I don't think im understanding your question correctly. Could you add an expected output image?
  • do you have any links on how to detect arcs and non-arcs. The only thing I can think of is to take the distance of one point on the contour and compare it to the previous one and see if the error is acceptable. I am sure there is a better way