Circle Detection

Implementation

Importing Libraries

We import the necessary packages we’ll need. We’ll utilize NumPy for numerical processing and cv2 for our OpenCV bindings.

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

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Reading the Image

img = cv.imread('eg.jpg', cv.IMREAD_COLOR)

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Displaying the Image

plt.imshow(img)

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Converting to grayscale

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

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# Blur using 3 * 3 kernel.
gray_blurred = cv.blur(gray, (3, 3))
  
# Apply Hough transform on the blurred image to detect circles.
detected_circles = cv.HoughCircles(gray_blurred, 
                   cv.HOUGH_GRADIENT, 1, 20, param1 = 50,
               param2 = 30, minRadius = 1, maxRadius = 30)

# Drawing circles that we can detect.
if detected_circles is not None:
  
    # Convert the circle parameters h, k and r to integers.
    detected_circles = np.uint16(np.around(detected_circles))
  
    for point in detected_circles[0, :]:
        h, k, r = point[0], point[1], point[2]
  
        # Draw the circumference of the circle.
        cv.circle(img, (a, b), r, (0, 255, 0), 2)
  
        # Drawing a small circle to show the center.
        cv.circle(img, (a, b), 1, (0, 0, 255), 3)
        cv.imshow("Detected Circle", img)
        cv.waitKey(0)

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plt.imshow(img)

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Output

Also read, Sampling and Quantization

Frequently Asked Questions

1. How do you identify a circle?
   For detecting the circles or any geometric shape, we first need to detect or identify the edges of the objects and shapes present in the Image. The edges are the points for which there is a sharp change of color. For instance, the edge of a black ball on a white background is a circle.
    
2. What algorithm is used to detect circles in OpenCV?
   We use cv2. HoughCircles to detect circles. We have to provide some parameters such as image where Image is the image file converted to greyscale. The method is the algorithm used to detect the circles. dp is the inverse ratio of the accumulator resolution to the image resolution. minDist is the Minimum distance between the center coordinates of detected circles.
    
3. Why is Hough transform slow?
   The problem with the Hough Transform algorithm is that it uses ρ (the perpendicular distance of the line from origin) and θ (the angle made by ordinary to this line with the positive direction about the x-axis). This makes it time-consuming.

Key Takeaways

Let us brief the article.

Firstly, we learn about circle detection than its applications in different fields. Moving on, We learned about Hough circles and their various parameters. Finally, we saw the implementation part of the circle detection.
Check out this problem - First Missing Positive 

I hope you all like this article.

Happy Learning Ninajs!