如何使用 python 操作霍夫圆以检测给定图像中的所有圆？

Question

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

import copy

image = cv2.imread('C:/Users/Deepak Padhi/Desktop/download.png')

#cv2.imshow('Filtered_original',image)

image = cv2.GaussianBlur(image,(5,5),0)

orig_image = np.copy(image)
gray= image[:,:,1]
output=gray.copy()
##cv2.imshow("gray", gray)
##cv2.waitKey(0)

circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 3, 4.0)

circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(output,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(output,(i[0],i[1]),2,(0,0,255),3)
cv2.imshow('detected circles',output)

This is the code I used to try and detect all circles in the given image: Image of circles

Which parameter and how should I manipulate to detect the smaller as well as the larger circles? I had tried minRadius and maxRadius to possible values as per both circles, but it didn't work, so I let it reset to default.

It feels like the canny edge detector is actually detecting the smaller circles with the default arguments(100,100), as attached canny detection

I took the Green plane of the given image: Original Image

Answer 1

I would recommend you to first read the docs .

If you just change the param1 and param2 which are thresholds for the Canny edge detector, the cv2.HoughCircles works fine.

Here the code:

import cv2
import numpy as np

image = cv2.imread('circles.png')

image = cv2.GaussianBlur(image, (5, 5), 0)

orig_image = np.copy(image)
gray = image[:, :, 1]
output = gray.copy()

circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=20, 
                           minRadius=0, maxRadius=0)

circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(output, (i[0], i[1]), i[2], (0, 255, 0), 2)
    # draw the center of the circle
    cv2.circle(output, (i[0], i[1]), 2, (0, 0, 255), 3)
cv2.imwrite('detected circles.png', output)