Remove Yellow rectangle from image

Question

I am using this code to remove this yellow stamp from an image :

import cv2
import numpy as np

# read image
img = cv2.imread('input.jpg')

# threshold on yellow
lower = (0, 200, 200)
upper = (100, 255, 255)
thresh = cv2.inRange(img, lower, upper)

# apply dilate morphology
kernel = np.ones((9, 9), np.uint8)
mask = cv2.morphologyEx(thresh, cv2.MORPH_DILATE, kernel)

# get largest contour
contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(big_contour)

# draw filled white contour on input
result = img.copy()
cv2.drawContours(result, [big_contour], 0, (255, 255, 255), -1)


cv2.imwrite('yellow_removed.png', result)

# show the images
cv2.imshow("RESULT", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

I get the following error:

big_contour = max(contours, key=cv2.contourArea) ValueError: max() arg is an empty sequence

Obviously, it is not detecting any contours, and the contours array is empty, but I could not figure out why that is or how to fix it. Help is appreciated!

where's the image? – Christoph Rackwitz May 21 '22 at 15:44 — Christoph Rackwitz, May 21 '22 at 15:44

score 1 · Accepted Answer · answered May 20 '22 at 09:42

1

Check your lower thresholds. It worked for me for both images when I changed the lower threshold to lower = (0, 120, 120).

answered May 20 '22 at 09:42

shounak shastri

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score 1 · Answer 2 · answered May 20 '22 at 09:49

1

The thresholds is the reason due to the second image being darker. Lowering these thresholds captures more of the yellow area, but will still leave some holes when drawing the contour.

lower = (0, 130, 130)

You can fix this by drawing the bounding rectangle instead.

cv2.rectangle(result,(x,y),(x+w,y+h),(255,255,255),-1)

answered May 20 '22 at 09:49

DNy

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Upvoting your answer since I already picked the above as a solution. Thank you! – Mar1941 May 20 '22 at 09:58

score 1 · Answer 3 · answered May 22 '22 at 17:52

Using HSV color space is great for figuring out a particular shade/tone of color. When you have dominant colors to isolate, you can opt for the LAB color space. I have explained as to why this is better in this answer.

Code:

img = cv2.imread('bill.jpg')
# create another copy for the result
img2 = img.copy()

# convert to LAB space and store b-channel
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
b_channel = lab[:,:,-1]

Notice how bright the yellow region is above.

# Perform Otsu threshold
th = cv2.threshold(b_channel, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

# Find the contour with largest area
contours, hierarchy = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
c = max(contours, key = cv2.contourArea)

# draw the contour on plain black image of same shape as original
mask = np.zeros((img.shape[0], img.shape[1]), np.uint8)
mask = cv2.drawContours(mask,[c],0,255, -1)

# dilation to avoid border effects
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
dilate = cv2.dilate(mask, kernel, iterations=1)

img2[dilate == 255] = (255, 255, 255)

Another example:

Input:

Result:

Remove Yellow rectangle from image

3 Answers3