Difference between absdiff and normal subtraction in OpenCV

Question

I am currently planning on training a binary image classification model. The images I want to train on are the difference between two original pictures. In other words, for each data entry, I start out with 2 pictures, take their difference, and the label that difference as a 0 or 1. My question is what is the best way to find this difference. I know about cv2.absdiff and then normal subtraction of images - what is the most effective way to go about this?

About the data: The images I'm training on are screenshots that usually are the same but may have small differences. I found that normal subtraction seems to show the differences less than absdiff.

This is the code I use for absdiff:

diff = cv2.absdiff(img1, img2)
        mask = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
    
        th = 1
        imask =  mask>1
    
        canvas = np.zeros_like(img2, np.uint8)
        canvas[imask] = img2[imask]

And then this for normal subtraction:

def extract_diff(self,imageA, imageB, image_name, path):
       subtract = imageB.astype(np.float32) - imageA.astype(np.float32)
        mask = cv2.inRange(np.abs(subtract),(30,30,30),(255,255,255))
        
        th = 1
        imask =  mask>1
    
        canvas = np.zeros_like(imageA, np.uint8)
        canvas[imask] = imageA[imask]

Thanks!

Answer 1

a difference can be negative or positive.

for some number types, such as uint8, which can't be negative, a negative value wraps around and the value would make no sense anymore.

that's why absdiff exists. it always gives you unsigned differences, which don't wrap around.

Example with numbers: a = 4, b = 6. a-b should be -2, right? That value, as an uint8, will wrap around to become 0xFE, or 254 in decimal. The 254 value has some relation to the true -2 difference, but it also incorporates the range of values of the data type (8 bits: 256 values).