<p>使用不同类型的阈值(adaptivethreshold,它考虑了局部的亮度)已经可以解决您的问题:自适应阈值结果就是您要寻找的。在</p>
<p><a href="https://i.stack.imgur.com/QWbWD.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/QWbWD.png" alt="enter image description here"/></a></p>
<p>[编辑:我冒昧地在Hough circles上添加了一些代码。我承认,为了得到一个好看的结果,我使用了这张图像的参数,尽管我不知道您需要什么类型的精度来解决此类问题]</p>
<pre><code>import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('image.png',0)
thresh = cv2.threshold(img, 210, 255, cv2.ADAPTIVE_THRESH_MEAN_C)[1]
canny = cv2.Canny(thresh,50,150)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(canny,cv2.HOUGH_GRADIENT,1,20, param1=50,param2=23,minRadius=0,maxRadius=0)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
# draw the outer circle
cv2.circle(cimg,(i[0],i[1]),i[2],(255,0,0),3)
# draw the center of the circle
cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
titles = ['Original Image', 'Adaptive Thresholding', "Canny", "Hough Circle"]
images = [img, thresh, canny, cimg]
for i in xrange(4):
plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
plt.show()
</code></pre>
<p>如果这还不够,请告诉我们。在</p>