How to do data augmentation on an 8 by 8 greyscale image?

I want to to data augmentation on an 8*8-pixel greayscale image through the codes below on Keras (the pixel values are only 0 and 1):

from ctypes import sizeof
from re import X
from turtle import shape
from keras.preprocessing.image import ImageDataGenerator
from skimage import io
import numpy as np
from PIL import Image

datagen = ImageDataGenerator(
        rotation_range=45,     #Random rotation between 0 and 45
        width_shift_range=0.2,   #% shift
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')    #Also try nearest, constant, reflect, wrap

    
# forming a binary 8*8 array
array = np.array([[0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0],[0,0,1,1,1,0,0,0], 
 [0,0,0,1,1,1,0,0],[0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0],[0,0,0,0,0,0,1,0],[0,0,0,0,0,0,0,0]])

# scale values to uint8 maximum 255, and convert it to greyscale image
array = ((array) * 255).astype(np.uint8)
x = Image.fromarray(array)


i = 0
for batch in datagen.flow(x, batch_size=16,  
                          save_to_dir='augmented', 
                          save_prefix='aug', 
                          save_format='png'):
i += 1
if i > 20:
    break  # otherwise the generator would loop indefinitely  

But I get this error in the output (when I have .flow function):

ValueError: ('Input data in `NumpyArrayIterator` should have rank 4. You passed an array with shape', (8, 8))

Could anyone give me some hands please?

ImageDataGenerator accepts input as 4-dimensional tensor, where first dimension is sample number and last dimension are color channels. In your code you should convert this (8,8) tensor to (1,8,8,1) tensor. This can be done by

array = np.expand_dims(array, (0, -1))

Also you should not convert array to image before passing it to generator as you did it here

x = Image.fromarray(array)

you should simply pass array to generator.