How to remove bell curved shape from image using opencv

Question

I hope you are doing well. I want to remove the bell-curved shape in the image. I have used OpenCV. I have implemented the following code which detects the curve shape, Now How can remove that curve shape and save the new image in the folder.

Input Image 1

I want to remove the area shown in the image below

Area i want to remove

import cv2 
import numpy as np
    # load image as grayscale
cell1 = cv2.imread("/content/savedImage.jpg",0)
    # threshold image
ret,thresh_binary = cv2.threshold(cell1,107,255,cv2.THRESH_BINARY)
    # findcontours
contours, hierarchy = cv2.findContours(image =thresh_binary , mode = cv2.RETR_TREE,method = cv2.CHAIN_APPROX_SIMPLE)

    # create an empty mask
mask = np.zeros(cell1.shape[:2],dtype=np.uint8)

    # loop through the contours
for i,cnt in enumerate(contours):
            # if the contour has no other contours inside of it
    if hierarchy[0][i][2] == -1 :
                    # if the size of the contour is greater than a threshold
       if  cv2.contourArea(cnt) > 10000:
             cv2.drawContours(mask,[cnt], 0, (255), -1)   
fig, ax = plt.subplots(1,2)
ax[0].imshow(cell1,'gray');
ax[1].imshow(mask,'gray');

Ouput Image after the above Code

How can I process to remove that curve shape?

Answer 1

Here is one way to do that in Python/OpenCV.

Get the external contours. Then find the one that has the largest w/h aspect ratio. Draw that contour filled on a black background as a mask. Dilate it a little. Then invert it and use it to blacken out that region.

Input:

import cv2
import numpy as np

# read image
img = cv2.imread('the_image.jpg')
ht, wd = img.shape[:2]

# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# threshold
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

# get external contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

max_aspect=0
for cntr in contours:
    x,y,w,h = cv2.boundingRect(cntr)
    aspect = w/h
    if aspect > max_aspect:
        max_aspect = aspect
        max_contour = cntr

# create mask from max_contour
mask = np.zeros((ht,wd), dtype=np.uint8)
mask = cv2.drawContours(mask, [max_contour], 0, (255), -1)

# dilate mask
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)

# invert mask
mask = 255 - mask

# mask out region in input
result = img.copy()
result = cv2.bitwise_and(result, result, mask=mask)

# save resulting image
cv2.imwrite('the_image_masked.png',result)

# show thresh and result    
cv2.imshow("mask", mask)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Result: