Efficient Way To Assign Values Into Random Blocks Of An Array Of Images?

I have a 3d numpy array. It's an array of 2d square images, all of the same size. my task is to block out a random square patch of the images (set all the pixel values to 0). I can

Solution 1:

We can leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows to get sliding windows. More info on use of as_strided based view_as_windows.

from skimage.util.shape import view_as_windows

# Get sliding windows as views
w = view_as_windows(image_array,(1,size,size))[...,0,:,:]

# Index with advanced-indexing into windows array and# assign into it, thus reassigning into input array directly being views
w[np.arange(len(x_array)),x_array,y_array] = 0

Sample run

Setup inputs -

In [60]: image_array # input array
Out[60]: 
array([[[54, 57, 74, 77, 77],
        [19, 93, 31, 46, 97],
        [80, 98, 98, 22, 68],
        [75, 49, 97, 56, 98]],

       [[91, 47, 35, 87, 82],
        [19, 30, 90, 79, 89],
        [57, 74, 92, 98, 59],
        [39, 29, 29, 24, 49]],

       [[42, 75, 19, 67, 42],
        [41, 84, 33, 45, 85],
        [65, 38, 44, 10, 10],
        [46, 63, 15, 48, 27]]])

In [68]: size
Out[68]: 2

# x and y starting indices for0s assignments 
In [65]: x_array
Out[65]: array([1, 0, 1])

In [66]: y_array
Out[66]: array([2, 2, 0])

Use proposed solution -

In [62]: w = view_as_windows(a,(1,size,size))[...,0,:,:]

In [63]: w[np.arange(len(x_array)),x_array,y_array] = 0

In [64]: image_array # verify
Out[64]: 
array([[[54, 57, 74, 77, 77], # start at (1,2)
        [19, 93,  0,  0, 97],
        [80, 98,  0,  0, 68],
        [75, 49, 97, 56, 98]],

       [[91, 47,  0,  0, 82], # start at (0,2)
        [19, 30,  0,  0, 89],
        [57, 74, 92, 98, 59],
        [39, 29, 29, 24, 49]],

       [[42, 75, 19, 67, 42], # start at (1,0)
        [ 0,  0, 33, 45, 85],
        [ 0,  0, 44, 10, 10],
        [46, 63, 15, 48, 27]]])