Visualizing edge image and accumulator in Hough Circle Transform

Python
1

Hello,
I am using the Hough Circle Transform to detected circles in images (see script below). I would like to display the corresponding edge images and the accumulator matrix. How can I do that?

import sys
import cv2 as cv
import numpy as np
import pdb
import matplotlib.pyplot as plt
import re


def main(argv):
    
    default_file = 'image.png'
    filename = argv[0] if len(argv) > 0 else default_file
    
    # Loads an image:
    src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_COLOR)

    # Print the dimensions of the images:
    dimensions = src.shape
    print('Image dimensions:', dimensions, 'pixel')

    # Check if image is loaded fine:
    if src is None:
        print ('Error opening image!')
        print ('Usage: hough_circle.py [image_name -- default ' + default_file + '] \n')
        return -1
    
    # Convert image to grayscale:
    gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)

    # Delete original image to free memory:
    del src
    
    # Apply a median blur to reduce noise and avoid false circle detection:
    gray = cv.medianBlur(gray, 7)

    # Save the image with circles (original dimensions):
    cv.imwrite('smoothed_'+argv[0], gray) 

    # Apply the Hough Circle detection method:
    r_min = 30
    r_max = 40
    circles = cv.HoughCircles(gray, cv.HOUGH_GRADIENT, 1, 100,
                               param1=500, param2=30,
                               minRadius=r_min, maxRadius=r_max)

    # Print the number of found circles:
    print('Number of detected circles:', circles.shape[1])

    # Delete smoothed image to free memory:
    del gray
    
    # Reload the image:
    src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_COLOR)
    
    # Define array to store the radius info (for histogram plot):
    arr_radii = np.array([])
    
    # Retrieve the coordinates of all found circles:
    if circles is not None:
        circles = np.uint16(np.around(circles))
        for i in circles[0, :]:
            #print(i)
            center = (i[0], i[1])
            # circle center
            cv.circle(src, center, 1, (0, 100, 100), 3)
            # circle outline
            radius = i[2]
            cv.circle(src, center, radius, (255, 0, 255), 3)
            arr_radii = np.append(arr_radii, i[2]) # Already converted into int type.
    arr_radii=arr_radii.astype(int)

    # Save the image with circles (original dimensions):
    cv.imwrite('DetectedCircles_'+argv[0], src) 
    
    # Plot radius histogram:
    bin = np.arange(r_min-0.5, r_max+0.5, 1.0)
    _,_,arr = plt.hist(arr_radii, bins=bin, facecolor='b', alpha=0.75)

    for pp in arr:
       x = (pp._x0 + pp._x1)/2-0.1
       y = pp._y1 + 1
       if pp._y1 != 0:
          plt.text(x, y, int(pp._y1))
    
    plt.xlim(r_min, r_max)
    plt.xlabel('Radius')
    plt.ylabel('Counts')
    plt.title('Histogram of radius distribution')
    plt.savefig("Rad_histo_"+re.sub('\.tif$', '',argv[0])+".png")
    plt.show()

    return 0
if __name__ == "__main__":
    main(sys.argv[1:])
2

Hello,

In order to see the edge image, I simply call the cv.Canny function with the same parameters I pass to the cv.HoughCircles (the lower threshold is higher_threshold/2):

edges = cv.Canny(my_image, 50, 100)
cv.imwrite('Edges_my_image', edges) #save image into file

For what the accumulator matrix is concerned, someone has any hints on how to visualize it?

3

unfortunately, you cannot access the accumulator matrix, its hidden in the implementation.

at least from c++, you can get the votes for the returned circles:

see docs

circles Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector (x,y,radius) or (x,y,radius,votes) .

in c++, you’d use a vector<Vec4f> to get the votes, but no idea how to do this from python, sorry.