Reputation: 948
I've seen this: Slice 2d array into smaller 2d arrays
However, I have images that cannot be evenly split into some N by N blocks. Ideally, I'd like for the following behavior to occur:
>>> import numpy as np
>>> a = np.arange(70*85).reshape([70,85])
>>> arrlist = split_2d(a, [16, 16])
>>> dd = dict()
>>> for arr in arrlist:
... dd[arr.shape] = dd.get(arr.shape, 0) + 1
>>> print(dd)
{(16, 16): 16, (10, 16): 4, (16, 5): 4, (10, 5): 1}
Which reflects splitting a (70x85) array into the following:
(16x16) (16x16) (16x16) (16x16) (10x16)
(16x16) (16x16) (16x16) (16x16) (10x16)
(16x16) (16x16) (16x16) (16x16) (10x16)
(16x16) (16x16) (16x16) (16x16) (10x16)
(16x16) (16x16) (16x16) (16x16) (10x16)
(16x5) (16x5) (16x5) (16x5) (10x5)
The return value can either be a list of 2D arrays, or a 2D array of 2D arrays.
Upvotes: 1
Views: 2571
Reputation: 294586
This builds the generator you need.
from itertools import product
import numpy as np
n, m = 70, 85
a = np.arange(n * m).reshape(n, m)
def igen(a, n, m):
i_ = np.arange(a.shape[0]) // n
j_ = np.arange(a.shape[1]) // m
for i, j in product(np.unique(i_), np.unique(j_)):
yield (i, j), a[i_ == i][:, j_ == j]
dict_of_arrays = dict(igen(a, 16, 16))
another alternative
pad with np.nan
and reshape
+ transpose
def cover_multiple(current_length, multiple):
return ((current_length - 1) // multiple + 1) * multiple
def slicer(a, chunk_i, chunk_j, two_d=True):
n = cover_multiple(a.shape[0], chunk_i)
m = cover_multiple(a.shape[1], chunk_j)
c = np.empty((n, m))
c.fill(np.nan)
c[:a.shape[0], :a.shape[1]] = a
c = c.reshape(n // chunk_i, chunk_i, m // chunk_j, chunk_j)
c = c.transpose(0, 2, 1, 3)
if not two_d:
c = c.reshape(-1, chunk_i, chunk_j)
return c
demo
a = np.arange(64).reshape(8, 8)
a
[[ 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]]
print(slicer(a, 3, 5))
[[[[ 0. 1. 2. 3. 4.]
[ 8. 9. 10. 11. 12.]
[ 16. 17. 18. 19. 20.]]
[[ 5. 6. 7. nan nan]
[ 13. 14. 15. nan nan]
[ 21. 22. 23. nan nan]]]
[[[ 24. 25. 26. 27. 28.]
[ 32. 33. 34. 35. 36.]
[ 40. 41. 42. 43. 44.]]
[[ 29. 30. 31. nan nan]
[ 37. 38. 39. nan nan]
[ 45. 46. 47. nan nan]]]
[[[ 48. 49. 50. 51. 52.]
[ 56. 57. 58. 59. 60.]
[ nan nan nan nan nan]]
[[ 53. 54. 55. nan nan]
[ 61. 62. 63. nan nan]
[ nan nan nan nan nan]]]]
Upvotes: 2