I have a numpy array with 1000 RGB images with shape (1000, 90, 90, 3) and I need to work on each image, but sliced in 9 blocks. I've found many solution for slicing a single image, but how can I obtain a (9000, 30, 30, 3) array and then iteratively send to a function 9 contiguous block?

I would do smth like what I do in the code below. In my example I used parts of images from skimage.data to illustrate my method and made the shapes and sizes different so that it will look prettier. But you can do the same for your dta by adjusting those parameters.

from skimage import data
from matplotlib import pyplot as plt
import numpy as npastronaut = data.astronaut()
coffee    = data.coffee()arr = np.stack([coffee[:400, :400, :], astronaut[:400, :400, :]])
plt.imshow(arr[0])
plt.title('arr[0]')
plt.figure()
plt.imshow(arr[1])
plt.title('arr[1]')arr_blocks = arr.reshape(arr.shape[0], 4, 100, 4, 100, 3, ).swapaxes(2, 3)
arr_blocks = arr_blocks.reshape(-1, 100, 100, 3)for i, block in enumerate(arr_blocks):plt.figure(10+i//16, figsize = (10, 10))plt.subplot(4, 4, i%16+1)plt.imshow(block)plt.title(f'block {i}')# batch_size = 9
# some_outputs_list = []
# for i in range(arr_blocks.shape[0]//batch_size + ((arr_blocks.shape[0]%batch_size) > 0)):
#     some_outputs_list.append(some_function(arr_blocks[i*batch_size:(i+1)*batch_size]))

Output: