====== The distance of 2 images ======
If we [[.:image|represent the images as vectors of numbers]], we need to compute the distance of two vectors. (Which does not sound that weird as "computing a distance of two images", right?)

Using functions from ''numpy'', we can compute the (Euclidean) distance in the following two ways (see ''d1'' and ''d2''):
<code python>
from PIL import Image
import numpy as np

im1 = np.array(Image.open('train_data/img_1112.png')).astype(int).flatten()
im2 = np.array(Image.open('train_data/img_1113.png')).astype(int).flatten()

diff = im1 - im2
d1 = np.sqrt(np.sum(np.square(diff)))
d2 = np.linalg.norm(diff)
</code> 
Variables ''d1'' and ''d2'' should contain the same result.

<WRAP center round important 100%>
Our images are grayscale with shades of gray ranging from 0 to 255 (represented by ''numpy.uint8'' datatype). If we used them with their native datatype ''uint8'', underflow errors may happen when computing the difference ''im1 - im2'' (''uint'' cannot represent negative numbers), and overflow errors may happen when computing the squares of differences. That's why it is better to transform all numbers to ''int'' which can represent both large and negative numbers (it is important to use ''.astype(int)'' after reading in the image.)
</WRAP>