Numpy - normalize RGB pixel array

Question

I have a numpy array with shape (34799, 32, 32, 3)which means (num examples, width, height, channels).

Now I normalize the image data with the following code:

def normalize(x):
    return (x - 128) / 128

X_train_norm = normalize(X_train)

But the result seems not right, the value of X_train[0][0][0] is [28 25 24], but the output of X_train_norm[0][0][0] is [1.21875 1.1953125 1.1875].

I use the following test code:

test = np.array([[[[28, 25, 24]]]])
print ((test - 128) / 128)

output:

[[[[-0.78125   -0.8046875 -0.8125   ]]]]

Why the normalize function gets the wrong result?

Answer 1

I think the images are loaded as a numpy array filled with uint8 bytes with values between 0 and 255.

If you perform a subtraction on an uint8 such that the result is negative, a wraparound happens. Like 123 - 128 == 251, and then you divide it by 128. For example:

>>> np.array([28,25,24], dtype=np.uint8) - 128
array([156, 153, 152], dtype=uint8)

and then, we get the reported:

>>> (np.array([28,25,24], dtype=np.uint8) - 128)/128
array([1.21875  , 1.1953125, 1.1875   ])

In order to solve it, you can use .astype(..):

def normalize(x):
    return (x.astype(float) - 128) / 128

Note that this has nothing to do with the fact that you use a function, if you had used the expression with the original array, you would have had the same result.