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doc add perf.comp.

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odebeir
2012-11-04 14:51:43 +01:00
parent e6199c78cf
commit 8839c1d8f7
2 changed files with 213 additions and 24 deletions
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doc/examples/applications/plot_rank_filters.py
+199 -10
View File
@@ -120,7 +120,7 @@ One may be interested in smoothing an image while preserving important borders (
here we use the **bilateral** filter that restrict the local neighborhood to pixel having a grey level similar to the
central one.
.. note:: a different implementations is available for color images in ``skimage.filter.denoise_bilateral``.
.. note:: a different implementation is available for color images in ``skimage.filter.denoise_bilateral``.
"""
@@ -133,12 +133,16 @@ bilat = bilateral_mean(ima.astype(np.uint16),disk(20),s0=10,s1=10)
# display results
fig = plt.figure(figsize=[10,7])
plt.subplot(1,2,1)
plt.subplot(2,2,1)
plt.imshow(ima,cmap=plt.cm.gray)
plt.xlabel('original')
plt.subplot(1,2,2)
plt.subplot(2,2,3)
plt.imshow(bilat,cmap=plt.cm.gray)
plt.xlabel('bilateral mean')
plt.subplot(2,2,2)
plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray)
plt.subplot(2,2,4)
plt.imshow(bilat[200:350,350:450],cmap=plt.cm.gray)
"""
.. image:: PLOT2RST.current_figure
@@ -266,17 +270,21 @@ enh = morph_contr_enh(ima,disk(5))
# display results
fig = plt.figure(figsize=[10,7])
plt.subplot(1,2,1)
plt.subplot(2,2,1)
plt.imshow(ima,cmap=plt.cm.gray)
plt.xlabel('original')
plt.subplot(1,2,2)
plt.subplot(2,2,3)
plt.imshow(enh,cmap=plt.cm.gray)
plt.xlabel('local morphlogical contrast enhancement')
plt.subplot(2,2,2)
plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray)
plt.subplot(2,2,4)
plt.imshow(enh[200:350,350:450],cmap=plt.cm.gray)
"""
.. image:: PLOT2RST.current_figure
The percentile version of the local morphological contrast enhancement, uses percentile p0 and p1 instead of local
The percentile version of the local morphological contrast enhancement, uses percentile *p0* and *p1* instead of local
minimum and local maximum.
"""
@@ -289,12 +297,16 @@ penh = percentile_morph_contr_enh(ima,disk(5),p0=.1,p1=.9)
# display results
fig = plt.figure(figsize=[10,7])
plt.subplot(1,2,1)
plt.subplot(2,2,1)
plt.imshow(ima,cmap=plt.cm.gray)
plt.xlabel('original')
plt.subplot(1,2,2)
plt.subplot(2,2,3)
plt.imshow(penh,cmap=plt.cm.gray)
plt.xlabel('local morphlogical contrast enhancement')
plt.xlabel('local percentile morphlogical\n contrast enhancement')
plt.subplot(2,2,2)
plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray)
plt.subplot(2,2,4)
plt.imshow(penh[200:350,350:450],cmap=plt.cm.gray)
"""
.. image:: PLOT2RST.current_figure
@@ -304,15 +316,192 @@ Image morphology
Local maximum and local minimum are the base operators for grey level morphology.
.. note:: ``skimage.dilate`` and ``skimage.erode`` are equivalent filters (see below for comparison).
Here is an example of classical morphological grey level filters : opening, closing and morphological gradient.
"""
from skimage.filter.rank import maximum,minimum,gradient
ima = data.camera()
closing = maximum(minimum(ima,disk(5)),disk(5))
opening = minimum(maximum(ima,disk(5)),disk(5))
grad = gradient(ima,disk(5))
# display results
fig = plt.figure(figsize=[10,7])
plt.subplot(2,2,1)
plt.imshow(ima,cmap=plt.cm.gray)
plt.xlabel('original')
plt.subplot(2,2,2)
plt.imshow(closing,cmap=plt.cm.gray)
plt.xlabel('grey level closing')
plt.subplot(2,2,3)
plt.imshow(opening,cmap=plt.cm.gray)
plt.xlabel('grey level opening')
plt.subplot(2,2,4)
plt.imshow(grad,cmap=plt.cm.gray)
plt.xlabel('morphological gradient')
"""
.. image:: PLOT2RST.current_figure
Implementation
================
Implementation comparison w.r.t. image size and structuring element size.
The central part of the ``skimage.rank``filters is build on a sliding window that update local grey level histogram.
This approach limits the algorithm complexity to O(n) where n is the number of image pixels. The complexity is also
limited with respect to the structuring element size.
"""
from time import time
from scipy.ndimage.filters import percentile_filter
from skimage.morphology import dilation
from skimage.filter import median_filter
from skimage.filter.rank import median,maximum
def exec_and_timeit(func):
""" Decorator that returns both function results and execution time
(result, ms)
"""
def wrapper(*arg):
t1 = time()
res = func(*arg)
t2 = time()
ms = (t2-t1)*1000.0
return (res,ms)
return wrapper
@exec_and_timeit
def cr_med(image,selem):
return median(image=image,selem = selem)
@exec_and_timeit
def cr_max(image,selem):
return maximum(image=image,selem = selem)
@exec_and_timeit
def cm_dil(image,selem):
return dilation(image=image,selem = selem)
@exec_and_timeit
def ctmf_med(image,radius):
return median_filter(image=image,radius=radius)
@exec_and_timeit
def ndi_med(image,n):
return percentile_filter(image,50,size=n*2-1)
"""
.. image:: PLOT2RST.current_figure
Comparison between
* rank.maximum
* cmorph.dilate
on increasing structuring element size and increasing image size
"""
a = data.camera()
rec = []
e_range = range(1,20,1)
for r in e_range:
elem = disk(r+1)
rc,ms_rc = cr_max(a,elem)
rcm,ms_rcm = cm_dil(a,elem)
rec.append((ms_rc,ms_rcm))
# same structuring element, the results must match
assert (rc==rcm).all()
rec = np.asarray(rec)
plt.figure()
plt.title('increasing element size')
plt.plot(e_range,rec)
plt.legend(['crank.maximum','cmorph.dilate'])
r = 9
elem = disk(r+1)
rec = []
s_range = range(100,1000,100)
for s in s_range:
a = (np.random.random((s,s))*256).astype('uint8')
(rc,ms_rc) = cr_max(a,elem)
(rcm,ms_rcm) = cm_dil(a,elem)
rec.append((ms_rc,ms_rcm))
# same structuring element, the results must match
assert (rc==rcm).all()
rec = np.asarray(rec)
plt.figure()
plt.title('increasing image size')
plt.plot(s_range,rec)
plt.legend(['crank.maximum','cmorph.dilate'])
"""
.. image:: PLOT2RST.current_figure
Comparison between:
* rank.median
* ctmf.median_filter
* ndimage.percentile
on increasing structuring element size and increasing image size
"""
a = data.camera()
rec = []
e_range = range(2,30,4)
for r in e_range:
elem = disk(r+1)
rc,ms_rc = cr_med(a,elem)
rctmf,ms_rctmf = ctmf_med(a,r)
rndi,ms_ndi = ndi_med(a,r)
rec.append((ms_rc,ms_rctmf,ms_ndi))
rec = np.asarray(rec)
plt.figure()
plt.title('increasing element size')
plt.plot(e_range,rec)
plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile'])
plt.ylabel('time (ms)')
plt.xlabel('element radius')
plt.figure()
plt.imshow(np.hstack((rc,rctmf,rndi)))
plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile')
r = 9
elem = disk(r+1)
rec = []
s_range = [100,200,500,1000]
for s in s_range:
a = (np.random.random((s,s))*256).astype('uint8')
(rc,ms_rc) = cr_med(a,elem)
rctmf,ms_rctmf = ctmf_med(a,r)
rndi,ms_ndi = ndi_med(a,r)
rec.append((ms_rc,ms_rctmf,ms_ndi))
rec = np.asarray(rec)
plt.figure()
plt.title('increasing image size')
plt.plot(s_range,rec)
plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile'])
plt.ylabel('time (ms)')
plt.xlabel('image size')
plt.show()
skimage/filter/rank/rank.pyx
+14 -14
View File
@@ -43,7 +43,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -85,7 +85,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -118,7 +118,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -160,7 +160,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -193,7 +193,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -233,7 +233,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -272,7 +272,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -307,7 +307,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -346,7 +346,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -386,7 +386,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -418,7 +418,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -459,7 +459,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -507,7 +507,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.
@@ -557,7 +557,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Parameters
----------
image : ndarray
Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram,
Image array (uint8 array or uint16). If image is uint16, 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.