Is there a fast way to shuffle numpy image in segments?

Question

I want to write a function that can take small images and return a permutation of them, block-wise.

Basically I want to turn this:

Into this:

There was an excellent answer in Is there a function in Python that shuffle data by data blocks? that helped me write a solution. However for ~50,000 28×28 images this takes a long time to run.

# blocks of 7x7 shuffling
range1 = np.arange(4) 
range2 = np.arange(4)
block_size = int(28 / 4)
print([[x[i*block_size:(i+1)*block_size].shape] for i in range1])
for x in x1:
  np.random.shuffle(range1)
  x[:] = np.block([[x[i*block_size:(i+1)*block_size]] for i in range1])
  for a in x:
    np.random.shuffle(range2)
    a[:] = np.block([a[i*block_size:(i+1)*block_size] for i in range2])

print("x1", time.time() - begin)
begin = time.time()

Asked By: chenjesu

||

Source

Answer 1

I already found a solution that runs much faster. I feel silly because I didn’t really need a double for loop, just two separate shuffle indexes. Leaving this solution here in case anyone wants to shuffle an image block-wise in numpy.

If anyone comes up with another good solution, let me know.


# blocks of 7x7 shuffling
range1 = np.arange(4) 
range2 = np.arange(4)
block_size = int(28 / 4)
for x in x1:
  np.random.shuffle(range1)
  np.random.shuffle(range2)
  x[:] = np.block([[x[i*block_size:(i+1)*block_size]] for i in range1])
  x[:] = np.block([x[:,i*block_size:(i+1)*block_size] for i in range2])

Answered By: chenjesu

Answer 2

Here’s one approach based on this post –

def randomize_tiles_3D(x1, H, W):
    # W,H are width and height of blocks
    m,n,p = x1.shape
    l1,l2 = n//H,p//W
    combs  = np.random.rand(m,l1*l2).argsort(axis=1)
    r,c = np.unravel_index(combs,(l1,l2))
    x1cr = x1.reshape(-1,l1,H,l2,W)
    out = x1cr[np.arange(m)[:,None],r,:,c]
    return out.reshape(-1,l1,l2,H,W).swapaxes(2,3).reshape(-1,n,p)

Sample run –

In [46]: x1
Out[46]: 
array([[[ 0,  1,  2,  3,  4,  5],
        [ 6,  7,  8,  9, 10, 11],
        [12, 13, 14, 15, 16, 17],
        [18, 19, 20, 21, 22, 23],
        [24, 25, 26, 27, 28, 29],
        [30, 31, 32, 33, 34, 35]],

       [[36, 37, 38, 39, 40, 41],
        [42, 43, 44, 45, 46, 47],
        [48, 49, 50, 51, 52, 53],
        [54, 55, 56, 57, 58, 59],
        [60, 61, 62, 63, 64, 65],
        [66, 67, 68, 69, 70, 71]]])

In [47]: np.random.seed(0)

In [48]: randomize_tiles_3D(x1, H=3, W=3)
Out[48]: 
array([[[21, 22, 23,  0,  1,  2],
        [27, 28, 29,  6,  7,  8],
        [33, 34, 35, 12, 13, 14],
        [18, 19, 20,  3,  4,  5],
        [24, 25, 26,  9, 10, 11],
        [30, 31, 32, 15, 16, 17]],

       [[36, 37, 38, 54, 55, 56],
        [42, 43, 44, 60, 61, 62],
        [48, 49, 50, 66, 67, 68],
        [39, 40, 41, 57, 58, 59],
        [45, 46, 47, 63, 64, 65],
        [51, 52, 53, 69, 70, 71]]])

Answered By: Divakar

Answer 3

It will be more efficient to use numpy.lib.stride_tricks.as_strided to break 2D matrices into blocks.

import numpy as np

img_width, block_width = 12, 3
n = img_width // block_width
a = np.arange(img_width * img_width).reshape(img_width, img_width)
print(a)

blocks = np.lib.stride_tricks.as_strided(a, 
         shape=(n, n, block_width, block_width), 
         strides=(a.itemsize * np.array([n * block_width ** 2, block_width, n * block_width, 1])))
print(blocks)

blocks = blocks.reshape((n * n, block_width, block_width)) # flatten for better shuffle
np.random.shuffle(blocks)
print(blocks)

blocks = np.lib.stride_tricks.as_strided(blocks, 
                                shape=(n, block_width, n, block_width), 
                                strides=(a.itemsize * np.array([n * block_width ** 2, block_width, block_width ** 2, 1])))
shuffled = np.reshape(blocks, (img_width, img_width))
print(shuffled)

Output can be found here: blocks_shuffle_example.ipynb

Document: numpy.lib.stride_tricks.as_strided

Answered By: Y4nhu1

Answer 4

Here’s one approach:

Assume that the original image has shape (m, n), and each block has shape (w, h).

import numpy as np

# split image into tiles of w*h blocks with shape = ((m * n) / (w * h), w, h)
tiles = np.array([img_pad[x : x+w, y : y+h] for x in range(0, m, w) for y in range(0, n, h)])
np.random.shuffle(tiles)
# merge back to shape = (m, n)
mb, nb = m // w, n // h
res = np.vstack(np.hstack(tiles[i*nb : (i+1)*nb]) for i in range(mb))

Update:

res = np.vstack(np.hstack(tiles[i*nb : (i+1)*nb]) for i in range(mb))

may cause "FutureWarning: arrays to stack must be passed as a "sequence" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future." while running.

Use

res = np.block([[np.hstack(tiles[i*nb : (i+1)*nb])] for i in range(mb)])

instead and there’re no warnings.

Answered By: Cervol Liu

Is there a fast way to shuffle numpy image in segments?

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