How to turn 3D image matrix to 2d matrix without a for loop? Python and numpy

Question

Currently working on a lab from a Data course at school. What I want to do is convert matrix_a with shape (26,64,64) to a new matrix_b (64,1664). The (64,64) inside matrix_a is the bits that make up a series of images, and the (64,1664) matrix_b should result in a strip of the images. I tried using np.reshape, which does reshape the matrix correctly, but the images are lost due to the ordering used. I could use a for loop to iteratively insert each 64x64 image into matrix_b, yet they're asking that you do not use a for loop. They mentioned something about using splicing? I'm writing this in python with numpy. Apologies if this post makes no sense it's my first one. Thanks

Answer 1

With numpy, you can try this (assume data_3d is your 3d array):

data_2d = data_3d.swapaxes(1,2).reshape(3,-1)

Answer 2

import numpy as np

arr = np.random.randint(10, size= [26,64,64])
arr.shape

>>> (26, 64, 64)

transpose() reorders axes from [0, 1, 2] to [1, 0, 2] .

arr = arr.transpose([1,0,2])   # edit suggested by @wwii 
arr.shape

>>> (64, 64, 26)

arr = arr.reshape([64, -1])
arr.shape

>>> (64, 1664)

Answer 3

>>> a = np.arange(2*2*3).reshape(3,2,2)
>>> a
array([[[ 0,  1],
        [ 2,  3]],

       [[ 4,  5],
        [ 6,  7]],

       [[ 8,  9],
        [10, 11]]])

Concatenate each image.

Horizontal strip - which is what you asked for in the question.

>>> np.concatenate(list(a),-1)
array([[ 0,  1,  4,  5,  8,  9],
       [ 2,  3,  6,  7, 10, 11]])

Vertical strip

>>> np.concatenate(list(a),0)
array([[ 0,  1],
       [ 2,  3],
       [ 4,  5],
       [ 6,  7],
       [ 8,  9],
       [10, 11]])
>>>

np.hstack(tuple(a)) and np.vstack(tuple(a)) produce identical results to concatenate .

np.vsplit(a,a.shape[0]) is equivalent to list(a) or tuple(a) .