scipy.signal.medfilt2d#

scipy.signal.medfilt2d(input, kernel_size=3)[source]#

Median filter a 2-dimensional array.

Apply a median filter to the input array using a local window-size given by kernel_size (must be odd). The array is zero-padded automatically.

Parameters:

inputarray_like: A 2-dimensional input array.
kernel_sizearray_like, optional: A scalar or a list of length 2, giving the size of the median filter window in each dimension. Elements of kernel_size should be odd. If kernel_size is a scalar, then this scalar is used as the size in each dimension. Default is a kernel of size (3, 3).

Returns:

outndarray: An array the same size as input containing the median filtered result.

See also

scipy.ndimage.median_filter

Notes

This is faster than medfilt when the input dtype is uint8, float32, or float64; for other types, this falls back to medfilt. In some situations, scipy.ndimage.median_filter may be faster than this function.

Examples

>>> import numpy as np
>>> from scipy import signal
>>> x = np.arange(25).reshape(5, 5)
>>> x
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])

# Replaces i,j with the median out of 5*5 window

>>> signal.medfilt2d(x, kernel_size=5)
array([[ 0,  0,  2,  0,  0],
       [ 0,  3,  7,  4,  0],
       [ 2,  8, 12,  9,  4],
       [ 0,  8, 12,  9,  0],
       [ 0,  0, 12,  0,  0]])

# Replaces i,j with the median out of default 3*3 window

>>> signal.medfilt2d(x)
array([[ 0,  1,  2,  3,  0],
       [ 1,  6,  7,  8,  4],
       [ 6, 11, 12, 13,  9],
       [11, 16, 17, 18, 14],
       [ 0, 16, 17, 18,  0]])

# Replaces i,j with the median out of default 5*3 window

>>> signal.medfilt2d(x, kernel_size=[5,3])
array([[ 0,  1,  2,  3,  0],
       [ 0,  6,  7,  8,  3],
       [ 5, 11, 12, 13,  8],
       [ 5, 11, 12, 13,  8],
       [ 0, 11, 12, 13,  0]])

# Replaces i,j with the median out of default 3*5 window

>>> signal.medfilt2d(x, kernel_size=[3,5])
array([[ 0,  0,  2,  1,  0],
       [ 1,  5,  7,  6,  3],
       [ 6, 10, 12, 11,  8],
       [11, 15, 17, 16, 13],
       [ 0, 15, 17, 16,  0]])

# As seen in the examples, # kernel numbers must be odd and not exceed original array dim