Check if image is all white pixels with OpenCV

Question

I am working on a script with OpenCV (Python) to split up an image into different sections in order to run OCR on each section on it later. I've got the script splitting up the source image into all the boxes I need, but it also comes along with a number of plain white images as well.

I'm curious if there's a way to check if an image is only white pixels or not with OpenCV. I'm very new to this library, so any information on this would be helpful.

Thanks!

Truehearted

Calculate the mean of all the pixels, if it is 255 it's all white. – Dialectical

Answer 1

Method #1: np.mean

Calculate the mean of the image. If it is equal to 255 then the image consists of all white pixels.

if np.mean(image) == 255:
    print('All white')
else:
    print('Not all white')

Method #2: cv2.countNonZero

You can use cv2.countNonZero to count non-zero (white) array elements. The idea is to obtain a binary image then check if the number of white pixels is equal to the area of the image. If it matches then the entire image consists of all white pixels. Here's a minimum example:

---

Input image #1 (invisible since background is white):

All white

Input image #2

Not all white

import cv2
import numpy as np

def all_white_pixels(image):
    '''Returns True if all white pixels or False if not all white'''
    H, W = image.shape[:2]
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

    pixels = cv2.countNonZero(thresh)
    return True if pixels == (H * W) else False

if __name__ == '__main__':
    image = cv2.imread('1.png')
    if all_white_pixels(image):
        print('All white')
    else:
        print('Not all white')
    cv2.imshow('image', image)
    cv2.waitKey()

Pless

You get my vote just for posting an invisible image :-) – Dialectical

Unfortunately, your implementation regarding cv2.threshold is wrong. Set up an all gray image (any value > 0), and you'll always get True, which is due to the use of Otsu here. The correct implementation would be just cv2.threshold(gray, 254, 255, cv2.THRESH_BINARY)[1], which is also way faster (even faster than np.mean or np.all)! Furthermore, Otsu limits cv2.threshold to single-channel images, as so does cv2.countNonZero. So, getting rid of Otsu and switching to np.count_nonzero will allow this approach to also work on multi-channel images. – Indophenol

One more addition: Unfortunately, np.count_nonzero is significantly slower than cv2.countNonZero. So, for multi-channel image support maybe a list comprehension of cv2.countNonZero calls is faster. – Indophenol

@Indophenol interesting I didn't know that. Thanks for pointing that out – Pless