I'm using OpenCV houghcircles to identify all the circles (both hollow and filled). Follow is my code:
import numpy as np
import cv2
img = cv2.imread('images/32x32.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
bilateral = cv2.bilateralFilter(gray,10,50,50)
minDist = 30
param1 = 30
param2 = 50
minRadius = 5
maxRadius = 100
circles = cv2.HoughCircles(bilateral, cv2.HOUGH_GRADIENT, 1, minDist, param1=param1, param2=param2, minRadius=minRadius, maxRadius=maxRadius)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
cv2.circle(img, (i[0], i[1]), i[2], (0, 0, 255), 2)
# Show result for testing:
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
As you can see I'm able identity most of the circles except for few. What am I missing here? I've tried varying the parameters but this is the best i could get.
Also, if I use even more compact circles the script does not identify any circles whatsoever.
An alternative idea is to use find contour method and chek whether the contour is a circle using appox as below.
import cv2
img = cv2.imread('32x32.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
inputImageCopy = img.copy()
# Find the circle blobs on the binary mask:
contours, hierarchy = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Use a list to store the center and radius of the target circles:
detectedCircles = []
# Look for the outer contours:
for i, c in enumerate(contours):
# Approximate the contour to a circle:
(x, y), radius = cv2.minEnclosingCircle(c)
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx)>5: # check if the contour is circle
# Compute the center and radius:
center = (int(x), int(y))
radius = int(radius)
# Draw the circles:
cv2.circle(inputImageCopy, center, radius, (0, 0, 255), 2)
# Store the center and radius:
detectedCircles.append([center, radius])
cv2.imshow("Circles", inputImageCopy)
cv2.waitKey(0)
cv2.destroyAllWindows()