Pythonic way of slicing 2-d numpy arrays into smaller squares

Question

I am trying to handle a RAW image using numpy.

Printing the raw image numpy array returns this:

[[372 387 247 ... 560 359 364]
 [380 392 243 ... 599 342 356]
 [236 238 358 ... 355 600 564]
 ...
 [547 553 344 ...  74  69  69]
 [349 328 560 ...  68  74  73]
 [341 334 537 ...  70  71  73]]

with shape 4384, 5632 .

I want to obtain a list of lists (2D list), such that each entry will correspond to a 8 * 8 square of the 2d numpy array.

This would indicate a list of dimensions 4384/8 , 5632/8 .

I can only think of using while loops and appending each square into the list at the moment, but I was thinking there must be a better way.

Is there a more pythonic way of doing it, maybe using list comprehension and slicing?

Answer 1

Please find below a short explanation of the answer

# 1.  Reshape Image as (height/8, 8, width/8,8)
print(rawimg.reshape(rawimg.shape[0]//8, 8, -1, 8).shape)
# 2. Swap 1 and 2nd index
print(rawimg.reshape(rawimg.shape[0]//8, 8, -1, 8).swapaxes(1,2).shape)
# 3. Reshape again (-1,8,8) with -1 being combined both (548*704) arrays of shape 8x8
print(rawimg.reshape(rawimg.shape[0]//8, 8, -1, 8).swapaxes(1,2).reshape(-1,8,8).shape)


(548, 8, 704, 8)
(548, 704, 8, 8)
(385792, 8, 8)