缩小时的值插值

Question

I have a 2D array that I would like to down sample to compare it to another.

Lets say my array x is 512x512 , I'd like an array y 128x128 where the elements of y are build using an interpolation of the values overs 4x4 blocks of x (this interpolation could just be taking the average, but other methodes, like geometric average, could be interesting)

So far I looked at scipy.ndimage.interpolation.zoom but I don't get the results I want

>> x = np.arange(16).reshape(4,4)
>> print(x)
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]]
>> y = scipy.ndimage.interpolation.zoom(x, 0.5)
>> print(y)
[[ 0  3]
 [12 15]]

I expected y to be

[[ 2.5  4.5]
 [10.5 12.5]]

Note that simply setting dtype=np.float32 doesn't solve that ...

Answer 1

sklearn.feature_extraction.image.extract_patches cleverly uses np.lib.stride_tricks.as_strided to produce a windowed array that can be operated on.

The sliding_window function, found here Efficient Overlapping Windows with Numpy , produces a windowed array with or without overlap also and let's you get a glimpse of what is happening under the hood.

>>> a = np.arange(16).reshape(4,4)

step_height,step_width determines the overlap for the windows - in your case the steps are the same as the window size, no overlap.

>>> window_height, window_width, step_height, step_width = 2, 2, 2, 2
>>> y = sliding_window(a, (window_height, window_width), (step_height,step_width))
>>> y
array([[[ 0,  1],
        [ 4,  5]],

       [[ 2,  3],
        [ 6,  7]],

       [[ 8,  9],
        [12, 13]],

       [[10, 11],
        [14, 15]]])

Operate on the windows:

>>> y = y.mean(axis = (1,2))
>>> y
array([  2.5,   4.5,  10.5,  12.5])

You need to determine the final shape depending on the number of windows.

>>> final_shape = (2,2)
>>> y = y.reshape(final_shape)
>>> y
array([[  2.5,   4.5],
       [ 10.5,  12.5]])

Searching SO for numpy , window, array should produce numerous other answers and possible solutions.

Answer 2

What you seem to be looking for is the mean over blocks of 4, which is not obtainable with zoom , since zoom uses interpolation (see its docstring)

To obtain what you show, try the following

import numpy as np
x = np.arange(16).reshape(4, 4)

xx = x.reshape(len(x) // 2, 2, x.shape[1] // 2, 2).transpose(0, 2, 1, 3).reshape(len(x) // 2, x.shape[1] // 2, -1).mean(-1)

print xx

This yields

[[  2.5   4.5]
 [ 10.5  12.5]]

Alternatively, this can be done using sklearn.feature_extraction.image.extract_patches

from sklearn.feature_extraction.image import extract_patches

patches = extract_patches(x, patch_shape=(2, 2), extraction_step=(2, 2))

xx = patches.mean(-1).mean(-1)

print xx

However, if your goal is to subsample an image in a graceful way, then taking the mean over blocks of the image is not the right way to do it: It is likely to cause aliasing effects. What you should do in this case is smooth the image ever so slightly using scipy.ndimage.gaussian_filter (eg sigma=0.35 * subsample_factor ) and then subsample simply by indexing [::2, ::2]