python: performance image slicing numpy

Question

I have a function that does the task I need, but is not very efficient. The function takes a color image represented by a numpy array and creates a new array of every complete, 3x3 slice of my image. At the end I reshape it for my desired purpose, effectively reshaping to (columns*rows, 3*3*3 slice) Here is the code:

def get_kernels(im, k_size):
    X, Y = im.shape[1] + 1 - k_size, im.shape[0] + 1 - k_size
    new = np.zeros((Y, X, k_size, k_size, 3))
    for y in range(Y):
        for x in range(X):
            new[y, x] = im[y:y + k_size, x:x + k_size]
    return new.reshape(X * Y, k_size ** 2 * 3)

Divakar · Accepted Answer · 2018-01-15 18:42:10Z

2

You can use scikit-image's view_as_windows to create those sliding windows and then we need to permute axes and reshape -

from skimage.util.shape import view_as_windows

def get_kernels_vectorized(im, k_size):
    X, Y = im.shape[1] + 1 - k_size, im.shape[0] + 1 - k_size
    new = view_as_windows(im,(k_size,k_size,1))[...,0].transpose(0,1,3,4,2)
    return new.reshape(X * Y, k_size ** 2 * 3)

Explanation on usage of view_as_windows

The idea with view_as_windows is that we feed in the input arg window_shape as a tuple of length same as the number of dimensions in the input array whose sliding windows are needed. Also, the axes along which we don't need to slide are fed as 1s. So, the input value for window_shape here is (k_size, k_size, 1) as the last axis is of color channels, along which we don't need to slide.

Sample run for verification -

In [186]: np.random.seed(0)
     ...: im = np.random.randint(0,9,(6,8,3))

In [189]: out1 = get_kernels(im, k_size=3)

In [190]: out2 = get_kernels_vectorized(im, k_size=3)

In [191]: np.array_equal(out1, out2)
Out[191]: True

Timings on 3264x2448 image with kernel size = 3 -

In [177]: np.random.seed(0)
     ...: im = np.random.randint(0,9,(3264,2448,3))

In [178]: %timeit get_kernels(im, k_size=3)
1 loop, best of 3: 5.46 s per loop

In [179]: %timeit get_kernels_vectorized(im, k_size=3)
1 loop, best of 3: 327 ms per loop

16x+ speedup here.

edited Jan 15, 2018 at 18:42

answered Jan 15, 2018 at 18:24

Divakar

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2 Comments

Jonathan Over a year ago

It's over 30x faster for me! Does np.allclose just make sure that the outputs are identical?

Divakar Over a year ago

@Jonathan We need to use np.array_equal for that. Edited.

sascha · Accepted Answer · 2018-01-15 18:23:41Z

0

The use-case is not entirely clear to me.

It might be possible you are asking for something like skimage's view_as_windows. If that's the case, either use it or learn from it (numpy.lib.stride_tricks).

With the correct input, it's a view (no new array will be created) and will be as fast as it gets!

answered Jan 15, 2018 at 18:23

sascha

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