"""Approximate bilateral rank filter for local (custom kernel) mean.
The local histogram is computed using a sliding window similar to the method
described in [Huang]_.
The pixel neighborhood is defined by:
* the given structuring element
* an interval [g-s0, g+s1] in greylevel around g the processed pixel greylevel
The kernel is flat (i.e. each pixel belonging to the neighborhood contributes
equally).
Result image is 8-/16-bit or double with respect to the input image and the
rank filter operation.
References
----------
.. [Huang] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional
median filtering algorithm", IEEE Transactions on Acoustics, Speech and
Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
"""
__note__ = "Code adpated to 3D images from skimage.filters.rank by Christoph Kirst"
import numpy as np
from . import Rank as rnk
import pyximport;
pyximport.install(setup_args={"include_dirs":np.get_include()}, reload_support=True)
from . import BilateralCode as code
__all__ = ['mean_bilateral', 'pop_bilateral', 'sum_bilateral', 'sum_relative_bilateral', 'mean_scale_bilateral']
###############################################################################
### Bilateral filter
###############################################################################
[docs]
def mean_bilateral(source, selem=None, sink=None, mask=None, s0=10, s1=10, **kwargs):
"""Apply a flat kernel bilateral filter.
This is an edge-preserving and noise reducing denoising filter. It averages
pixels based on their spatial closeness and radiometric similarity.
Spatial closeness is measured by considering only the local pixel
neighborhood given by a structuring element.
Radiometric similarity is defined by the greylevel interval [g-s0, g+s1]
where g is the current pixel greylevel.
Only pixels belonging to the structuring element and having a graylevel
inside this interval are averaged.
Arguments
---------
source : array
Input array.
selem : array
Structuring element, if None use a cube of size 3.
sink : array
Output array, if None, a new array is allocated.
mask : array
Optional mask, if None, the complete source is used.
Pixels in the mask are zero in the output.
s0,s1 : int
The lower and upper widths around the center value
for the bilateral window.
Returns
-------
sink : array
The filtered array.
"""
return rnk._apply_code(code.mean, code.mean_masked, sink_dtype = float,
source=source, selem=selem, sink=sink, mask=mask, parameter_index=[s0,s1], **kwargs);
[docs]
def pop_bilateral(source, selem=None, sink=None, mask=None, s0=10, s1=10, **kwargs):
"""Return the local number (population) of pixels.
The number of pixels is defined as the number of pixels which are included
in the structuring element and the mask. Additionally pixels must have a
graylevel inside the interval [g-s0, g+s1] where g is the gray value of the
center pixel.
Arguments
---------
source : array
Input array.
selem : array
Structuring element, if None use a cube of size 3.
sink : array
Output array, if None, a new array is allocated.
mask : array
Optional mask, if None, the complete source is used.
Pixels in the mask are zero in the output.
s0,s1 : int
The lower and upper widths around the center value
for the bilateral window.
Returns
-------
sink : array
The filtered array.
"""
return rnk._apply_code(code.pop, code.pop_masked, sink_dtype = float,
source=source, selem=selem, sink=sink, mask=mask, parameter_index=[s0,s1], **kwargs);
[docs]
def sum_bilateral(source, selem=None, sink=None, mask=None, s0=10, s1=10, **kwargs):
"""Apply a flat kernel bilateral filter.
This is an edge-preserving and noise reducing denoising filter. It sums
pixels based on their spatial closeness and radiometric similarity.
Spatial closeness is measured by considering only the local pixel
neighborhood given by a structuring element (structural_element).
Radiometric similarity is defined by the greylevel interval [g-s0, g+s1]
where g is the current pixel greylevel.
Only pixels belonging to the structuring element AND having a greylevel
inside this interval are summed.
Note that the sum may overflow depending on the data type of the input
array.
Arguments
---------
source : array
Input array.
selem : array
Structuring element, if None use a cube of size 3.
sink : array
Output array, if None, a new array is allocated.
mask : array
Optional mask, if None, the complete source is used.
Pixels in the mask are zero in the output.
s0,s1 : int
The lower and upper widths around the center value
for the bilateral window.
Returns
-------
sink : array
The filtered array.
"""
return rnk._apply_code(code.sum, code.sum_masked, sink_dtype = float,
source=source, selem=selem, sink=sink, mask=mask, parameter_index=[s0,s1], **kwargs);
[docs]
def sum_relative_bilateral(source, selem=None, sink=None, mask=None, s0=0.5, s1=2.0, **kwargs):
"""Apply a flat kernel bilateral filter.
This is an edge-preserving and noise reducing denoising filter. It sums
pixels based on their spatial closeness and radiometric similarity.
Spatial closeness is measured by considering only the local pixel
neighborhood given by a structuring element (structural_element).
Radiometric similarity is defined by the greylevel interval [g-s0, g+s1]
where g is the current pixel greylevel.
Only pixels belonging to the structuring element AND having a greylevel
inside this interval are summed.
Note that the sum may overflow depending on the data type of the input
array.
Arguments
---------
source : array
Input array.
selem : array
Structuring element, if None use a cube of size 3.
sink : array
Output array, if None, a new array is allocated.
mask : array
Optional mask, if None, the complete source is used.
Pixels in the mask are zero in the output.
s0,s1 : int
The lower and upper widths around the center value
for the bilateral window.
Returns
-------
sink : array
The filtered array.
"""
return rnk._apply_code(code.sum, code.sum_masked, sink_dtype = float,
source=source, selem=selem, sink=sink, mask=mask, parameter_float=[s0,s1], **kwargs);
[docs]
def mean_scale_bilateral(source, selem=None, sink=None, mask=None, s0=10, s1=10, p0=0, p1=0, **kwargs):
"""Mean pixel value using a bilateral window size that scales with local pixel intensity.
The filter linearly scales the biateral window size as [g-s0-p0*g, g+s1+p1*g].
Arguments
---------
source : array
Input array.
selem : array
Structuring element, if None use a cube of size 3.
sink : array
Output array, if None, a new array is allocated.
mask : array
Optional mask, if None, the complete source is used.
Pixels in the mask are zero in the output.
s0,s1 : int
The lower and upper widths around the center value
for the bilateral window.
p0,p1 : float
The lower and upper scale values for the bilateral window.
Returns
-------
sink : array
The filtered array.
"""
return rnk._apply_code(code.mean_scale, code.mean_scale_masked, sink_dtype = float,
source=source, selem=selem, sink=sink, mask=mask,
parameter_index=[s0,s1], parameter_float=[p0,p1], **kwargs);
