Numpy max pooling convolution

Question

Edit:

What I actually wanted to do is finding local maxima, which is explained good below, and the same solution is also explained here:

http://scikit-image.org/docs/dev/auto_examples/plot_peak_local_max.html

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It seems you can do linear convolution in Numpy.

Is it possible to do a non-linear max pooling convolution? Use a NxM patch and stride over the input image, zeroing the current pixel if it's not the maximum in the vicinity?

So a non-linear max convolution works like this, here is my image

  3 4 5 2 3
  3 5 1 2 7
  2 2 5 1 7

And given a 2x2 max pooling gives this output

  0 0 5 0 0
  0 5 0 0 7
  0 0 5 0 7

You have a 2x2 patch that strides over the image, and zeroes everything, only keeping the max value.

Answer 1

You could use Scipy's maximum_filer -

from scipy.ndimage.filters import maximum_filter

arr*(arr == maximum_filter(arr,footprint=np.ones((3,3))))

Sample run -

In [19]: arr
Out[19]: 
array([[3, 4, 5, 2, 3],
       [3, 5, 1, 2, 7],
       [2, 2, 5, 6, 7]])

In [20]: arr*(arr == maximum_filter(arr,footprint=np.ones((3,3))))
Out[20]: 
array([[0, 0, 5, 0, 0],
       [0, 5, 0, 0, 7],
       [0, 0, 0, 0, 7]])