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doc entropy

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Olivier Debeir
2012-11-08 12:15:47 +01:00
parent e804f96f02
commit 665e05ff35
8 changed files with 183 additions and 91 deletions
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doc/examples/applications/plot_rank_filters.py
+55
View File
@@ -348,6 +348,61 @@ plt.xlabel('morphological gradient')
"""
.. image:: PLOT2RST.current_figure
Feature extraction
===================
Local histogram can be exploited to compute local entropy, which is related to the local image complexity.
Entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode local
greylevel distribution.
``skimage.rank.entropy`` returns local entropy on a given structuring element.
The following example shows this filter applied on 8- and 16- bit images.
.. note:: to better use the available image bit, the function returns 10x entropy for 8-bit images and 1000x entropy
for 16-bit images.
"""
from skimage import data
from skimage.filter.rank import entropy
from skimage.morphology import disk
import numpy as np
import matplotlib.pyplot as plt
# defining a 8- and a 16-bit test images
a8 = data.camera()
a16 = data.camera().astype(np.uint16)*4
ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy
ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy
# display results
plt.figure(figsize=(10, 10))
plt.subplot(2,2,1)
plt.imshow(a8, cmap=plt.cm.gray)
plt.xlabel('8-bit image')
plt.colorbar()
plt.subplot(2,2,2)
plt.imshow(ent8, cmap=plt.cm.jet)
plt.xlabel('entropy*10')
plt.colorbar()
plt.subplot(2,2,3)
plt.imshow(a16, cmap=plt.cm.gray)
plt.xlabel('16-bit image')
plt.colorbar()
plt.subplot(2,2,4)
plt.imshow(ent16, cmap=plt.cm.jet)
plt.xlabel('entropy*1000')
plt.colorbar()
plt.show()
"""
.. image:: PLOT2RST.current_figure
Implementation
================
doc/examples/plot_entropy.py
+44
View File
@@ -0,0 +1,44 @@
"""
===================
Entropy
===================
"""
from skimage import data
from skimage.filter.rank import entropy
from skimage.morphology import disk
import numpy as np
import matplotlib.pyplot as plt
# defining a 8- and a 16-bit test images
a8 = data.camera()
a16 = data.camera().astype(np.uint16)*4
ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy
ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy
# display results
plt.figure(figsize=(10, 10))
plt.subplot(2,2,1)
plt.imshow(a8, cmap=plt.cm.gray)
plt.xlabel('8-bit image')
plt.colorbar()
plt.subplot(2,2,2)
plt.imshow(ent8, cmap=plt.cm.jet)
plt.xlabel('entropy*10')
plt.colorbar()
plt.subplot(2,2,3)
plt.imshow(a16, cmap=plt.cm.gray)
plt.xlabel('16-bit image')
plt.colorbar()
plt.subplot(2,2,4)
plt.imshow(ent16, cmap=plt.cm.jet)
plt.xlabel('entropy*1000')
plt.colorbar()
plt.show()
skimage/filter/rank/_crank16.pyx
+25 -28
View File
@@ -5,6 +5,7 @@
import numpy as np
cimport numpy as np
from libc.math cimport log2
# import main loop
from skimage.filter.rank._core16 cimport _core16
@@ -222,6 +223,23 @@ cdef inline np.uint16_t kernel_tophat(
return < np.uint16_t > (i - g)
cdef inline np.uint16_t kernel_entropy(
Py_ssize_t * histo, float pop, np.uint16_t g,
Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin,
float p0, float p1, Py_ssize_t s0, Py_ssize_t s1):
cdef Py_ssize_t i
cdef float e,p
e = 0.
for i in range(maxbin):
p = histo[i]/pop
if p>0:
e -= p*log2(p)
return < np.uint16_t > e*1000
# -----------------------------------------------------------------
# python wrappers
# -----------------------------------------------------------------
@@ -232,8 +250,6 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""bottom hat
"""
return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -242,8 +258,6 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""bottom hat
"""
return _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -252,8 +266,6 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""local egalisation of the gray level
"""
return _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -262,8 +274,6 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""local maximum - local minimum gray level
"""
return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -272,8 +282,6 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""local maximum gray level
"""
return _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -282,8 +290,6 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""average gray level (clipped on uint8)
"""
return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -292,8 +298,6 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""(g - average gray level)/2+midbin (clipped on uint8)
"""
return _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -302,8 +306,6 @@ def median(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""local median
"""
return _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -312,8 +314,6 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""local minimum gray level
"""
return _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -322,8 +322,6 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""morphological contrast enhancement
"""
return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -332,8 +330,6 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""local mode
"""
return _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -342,8 +338,6 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""returns the number of actual pixels of the structuring element inside the mask
"""
return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -352,8 +346,6 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""returns maxbin-1 if gray level higher than local mean, 0 else
"""
return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
@@ -362,6 +354,11 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
"""top hat
"""
return _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
def entropy(np.ndarray[np.uint16_t, ndim=2] image,
np.ndarray[np.uint8_t, ndim=2] selem,
np.ndarray[np.uint8_t, ndim=2] mask=None,
np.ndarray[np.uint16_t, ndim=2] out=None,
char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8):
return _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
skimage/filter/rank/_crank8.pyx
+6 -5
View File
@@ -6,6 +6,8 @@
import numpy as np
cimport numpy as np
from libc.math cimport log2
# import main loop
from skimage.filter.rank._core8 cimport _core8
@@ -251,17 +253,16 @@ cdef inline np.uint8_t kernel_entropy(
Py_ssize_t s1):
cdef Py_ssize_t i
cdef Py_ssize_t min_i
cdef float e,p
e = 0
e = 0.
for i in range(256):
p = <float>histo[i]/pop
p = histo[i]/pop
if p>0:
e -= p*np.log2(p)
e -= p*log2(p)
return < np.uint8_t > e
return < np.uint8_t > e*10
# -----------------------------------------------------------------
# python wrappers
skimage/filter/rank/bilateral_rank.pyx
+2 -4
View File
@@ -71,8 +71,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal
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.
If None, 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).
@@ -126,8 +125,7 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals
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.
If None, 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).
skimage/filter/rank/demo/demo_single.py
+4 -1
View File
@@ -25,12 +25,15 @@ if __name__ == '__main__':
plt.imsave('noise.png',noise,cmap=plt.cm.gray)
plt.imsave('cam.png',a8,cmap=plt.cm.gray)
selem = disk(3)
ent = rank.entropy(a16,selem)
plt.figure()
plt.subplot(1,2,1)
plt.imshow(a8)
plt.subplot(1,2,2)
plt.imshow(noise)
plt.imshow(ent)
plt.colorbar()
plt.show()
skimage/filter/rank/percentile_rank.pyx
+8 -16
View File
@@ -55,8 +55,7 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift
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.
If None, 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).
@@ -92,8 +91,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_
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.
If None, 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).
@@ -130,8 +128,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa
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.
If None, 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).
@@ -167,8 +164,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals
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.
If None, 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).
@@ -205,8 +201,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False,
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.
If None, 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).
@@ -243,8 +238,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False,
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.
If None, 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).
@@ -281,8 +275,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal
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.
If None, 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).
@@ -319,8 +312,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift
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.
If None, 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).
skimage/filter/rank/rank.pyx
+39 -37
View File
@@ -19,7 +19,7 @@ from skimage.filter.rank import _crank8, _crank16
from skimage.filter.rank.generic import find_bitdepth
__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum',
'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter']
'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter','entropy']
def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y):
@@ -52,8 +52,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -94,8 +93,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -127,8 +125,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -169,8 +166,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -202,8 +198,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -242,8 +237,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -281,8 +275,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F
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.
If None, 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).
@@ -316,8 +309,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -355,8 +347,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -395,8 +386,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -427,8 +417,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa
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.
If None, 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).
@@ -468,8 +457,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -516,8 +504,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -566,8 +553,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -595,8 +581,7 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False
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.
If None, 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).
@@ -626,7 +611,12 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False
return _apply(_crank8.noise_filter, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""Returns the entropy (in bit) computed locally (precision is limited due to image type used 8- or 16-bit)
"""Returns the entropy [wiki_entropy]_ computed locally. Entropy is computed using base 2 logarithm i.e.
the filter returns the minimum number of bits needed to encode local greylevel distribution.
References
----------
.. [wiki_entropy] http://en.wikipedia.org/wiki/Entropy_(information_theory)
Parameters
----------
@@ -636,8 +626,7 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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.
If None, 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).
@@ -645,12 +634,25 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Offset added to the structuring element center point.
Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns
-------
out : uint8 array or uint16 array (same as input image)
local entropy (in bit)
entropy x10 (uint8 images) and entropy x1000 (uint16 images)
Examples
--------
>>> # Local entropy
>>> from skimage import data
>>> from skimage.filter.rank import entropy
>>> from skimage.morphology import disk
>>> # defining a 8- and a 16-bit test images
>>> a8 = data.camera()
>>> a16 = data.camera().astype(np.uint16)*4
>>> ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy
>>> ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy
"""
return _apply(_crank8.entropy, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)