Why is the output of element-wise addition/subtraction different depending on whether my numpy array is of type int64 or uint8?

Question

I\'m doing image comparisons and calculating diff\'s and have noticed that element-wise subtraction only seems to work when I read the data in as a numpy array with dtype=\'int6

Answer 1

uint8 means "8 bit unsigned integer" and only has valid values in 0-255. This is because 256 distinct values is the maximum amount that can be represented using 8 bits of data. If you add two uint8 images together, you'll very likely overflow 255 somewhere. For example:

>>> np.uint8(130) + np.uint8(131)
5

Similarly, if you subtract two images, you'll very likely get negative numbers - which get wrapped around to the high end of the range again:

>>> np.uint8(130) - np.uint8(131)
255

If you need to add or subtract images like this, you'll want to work with a dtype that won't underflow/overflow as easily (e.g. int64 or float), then normalize and convert back to uint8 as the last step.