这里的主要问题是为你的半径找到一个好的范围。你可以看看你的图片，猜猜半径。
从你给我的照片，我猜180 - 220将是一个很好的范围。
你的代码看起来像这样：

cv.HoughCircles(processed, storage, cv.CV_HOUGH_GRADIENT, 1, 16.0, 180, 220)

只要尝试为minRadius和maxRadius找到好的值，这应该可以正常工作。

这是我问题解决办法

import numpy as np
import cv2

im = cv2.imread('sun0016.bmp')
height, width, depth = im.shape
print height, width, depth
thresh = 132
imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(imgray,(5,5),0)
edges = cv2.Canny(blur,thresh,thresh*2)
contours, hierarchy = cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[0]
cv2.drawContours(im,contours,-1,(0,255,0),-1)

#centroid_x = M10/M00 and centroid_y = M01/M00
M = cv2.moments(cnt)
x = int(M['m10']/M['m00'])
y = int(M['m01']/M['m00'])
print x,y
print width/2.0,height/2.0
print width/2-x,height/2-y

cv2.circle(im,(x,y),1,(0,0,255),2)
cv2.putText(im,"center of Sun contour", (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255))
cv2.circle(im,(width/2,height/2),1,(255,0,0),2)
cv2.putText(im,"center of image", (width/2,height/2), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0))
cv2.imshow('contour',im)
cv2.waitKey(0)

我想我会附和一个替代的解决方案，以防将来有人偶然发现这个问题。
下面的函数使用cv2.inRange代替cv2.Canny，使用cv2.minEnclosingCircle代替cv2.moments。它通过测量候选人的最小包围圆的半径来选择cv2.findContours找到的最大轮廓。这种过滤有助于拒绝来自例如水印或灰尘的误报，但根据您的需求，您可能希望以不同的方式执行此步骤或完全省略它。
该函数返回x，y坐标以及检测到的磁盘的半径，这是我正在进行的项目的要求。

import cv2

def find_disk(img, threshold=10):
    """Finds the center and radius of a single solar disk present in the supplied image.

    Uses cv2.inRange, cv2.findContours and cv2.minEnclosingCircle to determine the centre and 
    radius of the solar disk present in the supplied image.

    Args:
        img (numpy.ndarray): greyscale image containing a solar disk against a background that is below `threshold`.
        threshold (int): threshold of min pixel value to consider as part of the solar disk

    Returns:
        tuple: center coordinates in x,y form (int) 
        int: radius
    """
    if img is None:
        raise TypeError("img argument is None - check that the path of the loaded image is correct.")

    if len(img.shape) > 2:
        raise TypeError("Expected single channel (grayscale) image.")

    blurred = cv2.GaussianBlur(img, (5, 5), 0)
    mask = cv2.inRange(blurred, threshold, 255)
    img_mod, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Find and use the biggest contour
    r = 0
    for cnt in contours:
        (c_x, c_y), c_r = cv2.minEnclosingCircle(cnt)
        # cv2.circle(img, (round(c_x), round(c_y)), round(c_r), (255, 255, 255), 2)
        if c_r > r:
            x = c_x
            y = c_y
            r = c_r

    # print("Number of contours found: {}".format(len(contours)))
    # cv2.imwrite("mask.jpg", mask)
    # cv2.imwrite("circled_contours.jpg", img)

    if x is None:
        raise RuntimeError("No disks detected in the image.")

    return (round(x), round(y)), round(r)

if __name__ == "__main__":
    image = cv2.imread("path/to/your/image.jpg")
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    center, radius = find_disk(img=gray, threshold=20)

    print("circle x,y: {},{}".format(center[0], center[1]))
    print("circle radius: {}".format(radius))

    # Output the original image with the detected disk superimposed
    cv2.circle(image, center, radius, (0, 0, 255), 1)
    cv2.rectangle(image, (center[0] - 2, center[1] - 2), (center[0] + 2, center[1] + 2), (0, 0, 255), -1)
    cv2.imwrite("disk_superimposed.jpg", image)

我留下了一些注解的调试语句，如果您发现需要进一步修改，这些语句可能会派上用场。
如果图像包含大量眩光，则可能需要使用更高的阈值。