How to detect a flash / Glare in an image of document using skimage / opencv in python?

Solution for How to detect a flash / Glare in an image of document using skimage / opencv in python?
is Given Below:

Please suggest a new approach or at least a method to make any of these robust enough to detect at good rate

I have some images (mostly taken from computer screen) where some kind of flash from camera or so called is present there. I want to discard these type of images or at least notify the user to retake it. How could I do that?

I do not have enough to train a Deep Learning Classification model such as Fast Glare Detection

Here is the Data of more than 70 images

I have tried these few things in order:

  1. Bright area detection using OpenCV cv2.minMaxLoc function but it ALWAYS returns the area no matter what and mostly it fails for my type of images.

  2. I found this code for removal but it is in Matlab

  3. This code uses Clahe adjustement but the problem is that it removes rather than detection

  4. This one looks promising but is not robust enough for my image type

The final below code I found is somewhat I need but can someone help me for making it robust. for example using these thresholds / changing them / or using Binarization , Closing (Increasing White area with dilation and then removing black Noise with Erosion) such that these are generalised for all.

def get_image_stats(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (25, 25), 0)
    no_text = gray * ((gray/blurred)>0.99)                     # select background only
    no_text[no_text<10] = no_text[no_text>20].mean()           # convert black pixels to mean value
    no_bright = no_text.copy()
    no_bright[no_bright>220] = no_bright[no_bright<220].mean() # disregard bright pixels

    std = no_bright.std()
    bright = (no_text>220).sum()

    if no_text.mean()<200 and bright>8000:
        return True

These are some of few examples:

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