鉴于所示的示例图像，我对以下说法表示怀疑：which I've already used open/close morphology operations on (this is as clean as I can get it, trust me on this)
看了你的评论后For precision, I need it to be fit within about 2 pixels accuracy
我很确定，使用形态学运算可能有很好的近似值。在
请看以下代码：import cv2
# Load image (as BGR for later drawing the circle)
image = cv2.imread('images/hvFJF.jpg', cv2.IMREAD_COLOR)
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Get rid of possible JPG artifacts (when do people learn to use PNG?...)
_, gray = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)
# Downsize image (by factor 4) to speed up morphological operations
gray = cv2.resize(gray, dsize=(0, 0), fx=0.25, fy=0.25)
# Morphological Closing: Get rid of the hole
gray = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)))
# Morphological opening: Get rid of the stuff at the top of the circle
gray = cv2.morphologyEx(gray, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (121, 121)))
# Resize image to original size
gray = cv2.resize(gray, dsize=(image.shape[1], image.shape[0]))
# Find contours (only most external)
cnts, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Draw found contour(s) in input image
image = cv2.drawContours(image, cnts, -1, (0, 0, 255), 2)
cv2.imwrite('images/intermediate.png', gray)
cv2.imwrite('images/result.png', image)
中间图像如下所示：
最终结果如下：
既然你的形象很大，我想，缩小规模不会有什么坏处。下面的形态学操作(严重)加速，这可能对您的设置感兴趣。在
根据你的陈述：NOTE: I do not have prior info as to the size of the circle[...]
您可以从您的输入中找到上述内核大小的适当近似值。由于只给出了一个示例图像，我们无法知道该问题的可变性。在
希望有帮助！在