def medfilt2d(input, kernel_size=3):
"""Median filter two 2-dimensional arrays.
Description:
Apply a median filter to the input array using a local window-size
given by kernel_size (must be odd).
Inputs:
in -- An 2 dimensional input array.
kernel_size -- A scalar or an length-2 list 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.
Outputs: (out,)
out -- An array the same size as input containing the median filtered
result.
"""
image = asarray(input)
if kernel_size is None:
kernel_size = [3] * 2
kernel_size = asarray(kernel_size)
if len(kernel_size.shape) == 0:
kernel_size = [kernel_size.item()] * 2
kernel_size = asarray(kernel_size)
for size in kernel_size:
if (size % 2) != 1:
raise ValueError, "Each element of kernel_size should be odd."
return sigtools._medfilt2d(image, kernel_size)
def medfilt2d(input, kernel_size=3):
"""Median filter two 2-dimensional arrays.
Description:
Apply a median filter to the input array using a local window-size
given by kernel_size (must be odd).
Inputs:
in -- An 2 dimensional input array.
kernel_size -- A scalar or an length-2 list 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.
Outputs: (out,)
out -- An array the same size as input containing the median filtered
result.
"""
image = asarray(input)
if kernel_size is None:
kernel_size = [3] * 2
kernel_size = asarray(kernel_size)
if len(kernel_size.shape) == 0:
kernel_size = [kernel_size.item()] * 2
kernel_size = asarray(kernel_size)
for size in kernel_size:
if (size % 2) != 1:
raise ValueError, "Each element of kernel_size should be odd."
return sigtools._medfilt2d(image, kernel_size)
def medfilt2d(input, kernel_size=3):
"""
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).
Parameters
----------
input : array_like
A 2-dimensional input array.
kernel_size : array_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
-------
out : ndarray
An array the same size as input containing the median filtered
result.
"""
image = asarray(input)
if kernel_size is None:
kernel_size = [3] * 2
kernel_size = asarray(kernel_size)
if len(kernel_size.shape) == 0:
kernel_size = [kernel_size.item()] * 2
kernel_size = asarray(kernel_size)
for size in kernel_size:
if (size % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
return sigtools._medfilt2d(image, kernel_size)
def medfilt2d(input, kernel_size=3):
"""
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).
Parameters
----------
input : array_like
A 2-dimensional input array.
kernel_size : array_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
-------
out : ndarray
An array the same size as input containing the median filtered
result.
"""
image = asarray(input)
if kernel_size is None:
kernel_size = [3] * 2
kernel_size = asarray(kernel_size)
if len(kernel_size.shape) == 0:
kernel_size = [kernel_size.item()] * 2
kernel_size = asarray(kernel_size)
for size in kernel_size:
if (size % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
return sigtools._medfilt2d(image, kernel_size)