Reputation: 119
I need to reconstruct 2d array(from image colour channel) of the specific shape from a number of smaller arrays:
import numpy as np
from PIL import Image
def blockshaped(arr, nrows, ncols):
"""
Return an array of shape (n, nrows, ncols) where
n * nrows * ncols = arr.size
If arr is a 2D array, the returned array should look like n subblocks with
each subblock preserving the "physical" layout of arr.
"""
h, w = arr.shape
return (arr.reshape(h//nrows, nrows, -1, ncols)
.swapaxes(1,2)
.reshape(-1, nrows, ncols))
pic = Image.open('testimage.bmp') # open image
(r, g, b) = pic.split()# split to channels
c = np.asarray(b) # channel b as array
ar = np.empty((0,256),int) # empty array for appending
n_a = blockshaped(c,8,8) # dividing array into 4 subarrays
n_a2 = np.concatenate(n_a, axis = 0) #concatenate arrays
for i in n_a2:
ar = np.append(ar, i) # append array elements
ar = ar.reshape((16,16)) # reshaping
This code produces following result:
It differs from the original array:
Which is understandable, since np.concatenate does not concatenate arrays in the order I need. The question is how do I merge arrays to restore them to the original state?
blockshaped function is from here
Upvotes: 1
Views: 163
Reputation: 221564
Reshape to split the first axis into three axes of lengths 2,2,8
, keeping the second axis as it is. Then, permute axes with rollaxis
/swapaxes
/transpose
and a final reshape to have 16 x 16
shape -
n_a2.reshape(2,2,8,8).swapaxes(1,2).reshape(16,16)
Sample run for verification -
In [46]: c = np.random.randint(11,99,(16,16))
In [47]: n_a = blockshaped(c,8,8) # dividing array into 4 subarrays
...: n_a2 = np.concatenate(n_a, axis = 0) #concatenate arrays
...:
In [48]: out = n_a2.reshape(2,2,8,8).swapaxes(1,2).reshape(16,16)
In [49]: np.allclose(out, c)
Out[49]: True
Upvotes: 1