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There isn't a straightforward way to extract the ellipse. But you may use a mask image to extract the elliptical patch with the rest of the region blacked out.

First, define a mask image which has a white ellipse on a black background. Then use a bitwise and operation to extract the patch.

Python code:

import cv2 import numpy as np import matplotlib.pyplot as plt

image = cv2.imread('baboon.jpg')
# create a mask image of the same shape as input image, filled with 0s (black color)
mask = np.zeros_like(image)
rows, cols,_ = mask.shape
# create a white filled ellipse
mask=cv2.ellipse(mask, center=(rows/2, cols/2), axes=(50,100), angle=0, startAngle=0, endAngle=360, color=(255,255,255), thickness=-1)
# Bitwise AND operation to black out regions outside the mask
result = np.bitwise_and(image,mask)
# Convert from BGR to RGB for displaying correctly in matplotlib
# Note that you needn't do this for displaying using OpenCV's imshow()
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask_rgb = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)
result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
# Plotting the results
plt.subplot(131)
plt.imshow(image_rgb)
plt.subplot(132)
plt.imshow(mask_rgb)
plt.subplot(133)
plt.imshow(result_rgb)
plt.show()

Plot:

image description

There isn't a straightforward way to extract the ellipse. But you may use a mask image to extract the elliptical patch with the rest of the region blacked out.

First, define a mask image which has a white ellipse on a black background. Then use a bitwise and operation to extract the patch.

Python code:

import cv2 import numpy as np import matplotlib.pyplot as pltplt image = cv2.imread('baboon.jpg') # create a mask image of the same shape as input image, filled with 0s (black color) mask = np.zeros_like(image) rows, cols,_ = mask.shape # create a white filled ellipse mask=cv2.ellipse(mask, center=(rows/2, cols/2), axes=(50,100), angle=0, startAngle=0, endAngle=360, color=(255,255,255), thickness=-1) # Bitwise AND operation to black out regions outside the mask result = np.bitwise_and(image,mask) # Convert from BGR to RGB for displaying correctly in matplotlib # Note that you needn't do this for displaying using OpenCV's imshow() image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) mask_rgb = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB) result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) # Plotting the results plt.subplot(131) plt.imshow(image_rgb) plt.subplot(132) plt.imshow(mask_rgb) plt.subplot(133) plt.imshow(result_rgb) plt.show() Plot: