How can I efficiently process a numpy array in blocks similar to Matlab's blkproc (blockproc) function

Question

I\'m looking for a good approach for efficiently dividing an image into small regions, processing each region separately, and then re-assembling the results from each proces

Answer 1

Bit late to the game, but this would do overlapping blocks. I haven't done it here, but you could easily adapt for step sizes for shifting the window, I think:

from numpy.lib.stride_tricks import as_strided
def rolling_block(A, block=(3, 3)):
    shape = (A.shape[0] - block[0] + 1, A.shape[1] - block[1] + 1) + block
    strides = (A.strides[0], A.strides[1]) + A.strides
    return as_strided(A, shape=shape, strides=strides)