我的图片在这里：
我正在寻找一种更好的解决方案或算法来检测这张照片中椭圆形部分（盘子），并在OpenCV中将其遮罩到另一张照片上。请问您能给我一些建议或解决方案吗？下面是我的代码：
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1.2, 1, param1=128, minRadius=200, maxRadius=600)
# draw detected circles on image
circles = circles.tolist()
for cir in circles:
for x, y, r in cir:
x, y, r = int(x), int(y), int(r)
cv2.circle(img, (x, y), r, (0, 255, 0), 4)
# show the output image
cv2.imshow("output", cv2.resize(img, (500, 500)))
import matplotlib.pyplot as plt
from skimage import data, color, img_as_ubyte
from skimage.feature import canny
from skimage.transform import hough_ellipse
from skimage.draw import ellipse_perimeter
# Load picture, convert to grayscale and detect edges
image_rgb = data.coffee()[0:220, 160:420]
image_gray = color.rgb2gray(image_rgb)
edges = canny(image_gray, sigma=2.0,
low_threshold=0.55, high_threshold=0.8)
# Perform a Hough Transform
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
result = hough_ellipse(edges, accuracy=20, threshold=250,
min_size=100, max_size=120)
result.sort(order='accumulator')
# Estimated parameters for the ellipse
best = list(result[-1])
yc, xc, a, b = [int(round(x)) for x in best[1:5]]
orientation = best[5]
# Draw the ellipse on the original image
cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
image_rgb[cy, cx] = (0, 0, 255)
# Draw the edge (white) and the resulting ellipse (red)
edges = color.gray2rgb(img_as_ubyte(edges))
edges[cy, cx] = (250, 0, 0)
fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True,
sharey=True,
subplot_kw={'adjustable':'box'})
ax1.set_title('Original picture')
ax1.imshow(image_rgb)
ax2.set_title('Edge (white) and result (red)')
ax2.imshow(edges)
plt.show()
方法一：
根据Miki的建议，我使用轮廓属性（这里使用了面积属性）成功地检测出给定图像中的椭圆。
代码：
#--- First obtain the threshold using the greyscale image ---
ret,th = cv2.threshold(gray,127,255, 0)
#--- Find all the contours in the binary image ---
_, contours,hierarchy = cv2.findContours(th,2,1)
cnt = contours
big_contour = []
max = 0
for i in cnt:
area = cv2.contourArea(i) #--- find the contour having biggest area ---
if(area > max):
max = area
big_contour = i
final = cv2.drawContours(img, big_contour, -1, (0,255,0), 3)
cv2.imshow('final', final)
这是我得到的：
方法二：
在这种情况下，您也可以使用您建议的方法。进行椭圆/圆形的霍夫检测。
您需要对图像进行预处理。我执行了自适应阈值并得到了以下结果：
