ellipse detection in opencv python

My image is here:

i'm looking for a better solution or algorithm to detect the ellipse part (dish) in this photo and mask it in an other photo in Opencv. could you please give me some advice or solution. and my code is :

 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)))

There is an alternative for it in skimage made by Xie, Yonghong, and Qiang Ji and published as...

“A new efficient ellipse detection method.” Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002.

Their Ellipse detection code is relatively slow and the example takes about 70 seconds; compared to website claimed "28 seconds".

If you have conda or pip: "name" install scikit-image and give it a shot...

Their code can be found here or as copy/pasted below:

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-forced'})

ax1.set_title('Original picture')
ax1.imshow(image_rgb)

ax2.set_title('Edge (white) and result (red)')
ax2.imshow(edges)

plt.show()

APPROACH 1:

As suggested by Miki, I was able to detect the ellipse in the given image using contour properties (in this I used the area property).

CODE:

#--- 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)

This is what I obtained: