使用霍夫圆变换检测圆阵列

Question

I am trying to detect all the circles in the array. To achieve this I am using Hough Circle Transform. I am able to detect 100% circles in the array but there are a lot many false positives and when I get rid of the false positives I am not able to detect 100% circles. When I change the dp parameter to 1 in the code given below then all the false positives are gone and when I keep it as 3 then there are many false positives with 100% detection. I would like to get 100% detection with 0 or very few false positives. What would be the best approach to do this.

import cv2
import cv2.cv as cv
import numpy as np

img = cv2.imread('test1.tiff',0)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)

circles = cv2.HoughCircles(img, cv.CV_HOUGH_GRADIENT,3,15,
                        param1=70 ,param2=17,minRadius=1,maxRadius=10)

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.imwrite("output15.jpg", cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

Here there is a sample image:

Answer 1

Since they are circles, and all dark, why not filter them out with a morphological disk and subtract the original image from the filtered one to get good responses? Morphology isn't particularly fast, but faster than Hough. What you'd do is dilate the background (with a disk shaped) until the black is gone, then subtract the original image from that. Then threshold. You can then do size filtering to eliminate any tiny scraps that could get through.

Given this application, I don't think Hough is the strongest choice, unless this is a school project.