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add bilateral filters pop and meanµ

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Olivier Debeir
2012-10-29 18:07:47 +01:00
parent 304cac7ecd
commit 8222a58ec8
1 changed files with 153 additions and 2 deletions
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skimage/rank/bilateral_rank.py
+153 -2
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@@ -8,11 +8,162 @@ __docformat__ = 'restructuredtext en'
import warnings
from skimage import img_as_ubyte
import numpy as np
from generic import find_bitdepth
import _crank16_bilateral
__all__ = ['bilateral_mean']
def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10):
"""Return greyscale local bilateral_mean of an image.
def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10):
pass
bilateral mean is computed on the given structuring element. Only levels between [g-s0,g+s1] ,are used.
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
an exception will be raised if image has a value > 4095
selem : ndarray
The neighborhood expressed as a 2-D array of 1's and 0's.
out : ndarray
The array to store the result of the morphology. If None is
passed, a new array will be allocated.
mask : ndarray (uint8)
Mask array that defines (>0) area of the image included in the local neighborhood.
If None, the complete image is used (default).
shift_x, shift_y : bool
shift structuring element about center point. This only affects
eccentric structuring elements (i.e. selem with even numbered sides).
Shift is bounded to the structuring element sizes.
s0, s1 : int
define the [s0,s1] interval to be considered for computing the value.
Returns
-------
local bilateral mean : uint16 array (uint8 image are casted to uint16)
The result of the local bilateral mean.
Examples
--------
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> bilateral_mean(ima8, square(3), s0=10,s1=10)
array([[ 0, 0, 0, 0, 0],
[ 0, 255, 255, 255, 0],
[ 0, 255, 255, 255, 0],
[ 0, 255, 255, 255, 0],
[ 0, 0, 0, 0, 0]], dtype=uint16)
>>> ima16 = 4095*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> bilateral_mean(ima16, square(3), s0=10,s1=10)
array([[ 0, 0, 0, 0, 0],
[ 0, 4095, 4095, 4095, 0],
[ 0, 4095, 4095, 4095, 0],
[ 0, 4095, 4095, 4095, 0],
[ 0, 0, 0, 0, 0]], dtype=uint16)
"""
selem = img_as_ubyte(selem)
if mask is not None:
mask = img_as_ubyte(mask)
if image.dtype == np.uint8:
image = image.astype(np.uint16)
elif image.dtype == np.uint16:
pass
else:
raise TypeError("only uint8 and uint16 image supported!")
bitdepth = find_bitdepth(image)
if bitdepth>11:
raise ValueError("only uint16 <4096 image (12bit) supported!")
return _crank16_bilateral.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1)
def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10):
"""Return greyscale local bilateral_pop of an image.
bilateral pop is computed on the given structuring element. Only levels between [g-s0,g+s1] ,are used.
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
an exception will be raised if image has a value > 4095
selem : ndarray
The neighborhood expressed as a 2-D array of 1's and 0's.
out : ndarray
The array to store the result of the morphology. If None is
passed, a new array will be allocated.
mask : ndarray (uint8)
Mask array that defines (>0) area of the image included in the local neighborhood.
If None, the complete image is used (default).
shift_x, shift_y : bool
shift structuring element about center point. This only affects
eccentric structuring elements (i.e. selem with even numbered sides).
Shift is bounded to the structuring element sizes.
s0, s1 : int
define the [s0,s1] interval to be considered for computing the value.
Returns
-------
local bilateral pop : uint16 array (uint8 image are casted to uint16)
The result of the local bilateral pop.
Examples
--------
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> bilateral_pop(ima8, square(3), s0=10,s1=10)
array([[3, 4, 3, 4, 3],
[4, 4, 6, 4, 4],
[3, 6, 9, 6, 3],
[4, 4, 6, 4, 4],
[3, 4, 3, 4, 3]], dtype=uint16)
>>> ima16 = 4095*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> bilateral_pop(ima16, square(3), s0=10,s1=10)
array([[3, 4, 3, 4, 3],
[4, 4, 6, 4, 4],
[3, 6, 9, 6, 3],
[4, 4, 6, 4, 4],
[3, 4, 3, 4, 3]], dtype=uint16)
"""
selem = img_as_ubyte(selem)
if mask is not None:
mask = img_as_ubyte(mask)
if image.dtype == np.uint8:
image = image.astype(np.uint16)
elif image.dtype == np.uint16:
pass
else:
raise TypeError("only uint8 and uint16 image supported!")
bitdepth = find_bitdepth(image)
if bitdepth>11:
raise ValueError("only uint16 <4096 image (12bit) supported!")
return _crank16_bilateral.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1)