I recently had a task to create a vignette of a picture. This is a technique in Computer Vision or digital signal processing whereby as you move closer and closer towards the centre of the image, the pixel intensity increases:  

When first approaching this problem, I could not understand how, from a dense matrix of colour information, you could determine how far the pixel that you were processing was from the centre of the image. I confirmed, that no position information is available within the pixel itself (it just contains raw colour information). Finally, it dawned on me that you could use the coordinates of the image matrix to create a vector to represent the centre point, ie. the offset x and y from an origin.

But where is the origin?

I first thought I'd have to use the centre of the image as the origin, meaning all my pixels co-ordinates would need to be relative to that, which would have been a pain as I'd have to work out how, say each pixel in the matrix[x][y] would probably need to be represented differently when relative to not matrix[0][0] but the centre of the image!

Then I realised that it could keep the origin as [0][0] in the matrix ie image[0,0] for both the centre point and each respective pixel and then it could thus be represented by a vector displacement from that same origin. This was a breakthrough for me. Not only that, you could then generate a new vector for each pixel this way - all using [0,0] in the image matrix to represent a distance of that pixel from the same origin. 

So, now you have two 2D vectors from the same origin, one that points at the centre of the image ie [max_cols/2, max_rows/2] and you have a vector that is [x,y] for each pixel you are currently processing. You can now subtract the vector representing the centre point from the pixel vector you are currently at, ie this would result in the vector between the two, which if you can calculate the magnitude thereof, will be the distance between the pixel you are on and the centre of the screen - ie its the hypotenuse between the two sides (of the two vectors). 

The length of the resulting vector can be easily by passing in the vector to np.linalg.norm() - ie get the norm of the vector ie the (length or magnitude) and this the distance. I guess you could