Python: Faster Local Maximum In 2-d Matrix

Given: R is an mxn float matrix Output: O is an mxn matrix where O[i,j] = R[i,j] if (i,j) is a local max and O[i,j] = 0 otherwise. Local maximum is defined as the maximum element i

Solution 1:

You can use scipy.ndimage.maximum_filter:

In [28]: from scipy.ndimageimport maximum_filter

Here's a sample R:

In [29]: R
Out[29]: 
array([[3, 3, 0, 0, 3],
       [0, 0, 2, 1, 3],
       [0, 1, 1, 1, 2],
       [3, 2, 1, 2, 0],
       [2, 2, 1, 2, 1]])

Get the maximum on 3x3 windows:

In [30]: mx = maximum_filter(R, size=3)

In [31]: mx
Out[31]: 
array([[3, 3, 3, 3, 3],
       [3, 3, 3, 3, 3],
       [3, 3, 2, 3, 3],
       [3, 3, 2, 2, 2],
       [3, 3, 2, 2, 2]])

Compare mx to R; this is a boolean matrix:

In [32]: mx == R
Out[32]: 
array([[ True,  True, False, False,  True],
       [False, False, False, False,  True],
       [False, False, False, False, False],
       [ True, False, False,  True, False],
       [False, False, False,  True, False]], dtype=bool)

Use np.where to create O:

In [33]: O = np.where(mx == R, R, 0)

In [34]: O
Out[34]: 
array([[3, 3, 0, 0, 3],
       [0, 0, 0, 0, 3],
       [0, 0, 0, 0, 0],
       [3, 0, 0, 2, 0],
       [0, 0, 0, 2, 0]])