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adapt tests

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This commit is contained in:
Olivier Debeir
2012-10-05 17:15:33 +02:00
parent c633fb8953
commit 866ea40fbb
5 changed files with 37 additions and 30 deletions
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skimage/rank/README.rst
+6 -6
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@@ -1,13 +1,13 @@
To use this to build your Cython file use the commandline options:
.. sourcecode:: text
$ python setup.py build_ext --inplace
**To do**
* add simple examples
* add doc
* add/check existing doc
.. sourcecode:: text
$ python setup.py build_ext --inplace
skimage/rank/rank.py
+1 -1
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@@ -13,7 +13,7 @@ from generic import find_bitdepth
import _crank16,_crank8
__all__ = ['autolevel','bottomhat','egalise','gradient','maximum','mean'
,'meansubstraction','median','minimum','modal','morph_contr_enh','pop']
,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat']
def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""Return greyscale local autolevel of an image.
skimage/rank/tests/demo_16bitbilateral.py
+11 -4
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@@ -2,16 +2,19 @@ import numpy as np
import matplotlib.pyplot as plt
from skimage import data
from skimage.rank import crank8_percentiles
from skimage.rank import crank16_bilateral
from skimage.morphology import disk
import skimage.rank as rank
if __name__ == '__main__':
a8 = (data.coins()).astype('uint8')
a16 = (data.coins()).astype('uint16')*16
selem = np.ones((20,20),dtype='uint8')
f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9)
f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500)
f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9)
f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500)
selem = disk(50)
f3 = rank.egalise(a16,selem = selem)
plt.figure()
plt.imshow(np.hstack((a8,f1)))
@@ -21,4 +24,8 @@ if __name__ == '__main__':
plt.imshow(np.hstack((a16,f2)))
plt.colorbar()
plt.figure()
plt.imshow(np.hstack((a16,f3)))
plt.colorbar()
plt.show()
skimage/rank/tests/demo_benchmark.py
+2 -2
View File
@@ -3,13 +3,13 @@ import matplotlib.pyplot as plt
from skimage import data
from skimage.morphology import dilation
from skimage.rank import _crank8
import skimage.rank as rank
from tools import log_timing
@log_timing
def cr_max(image,selem):
return _crank8.maximum(image=image,selem = selem)
return rank.maximum(image=image,selem = selem)
@log_timing
def cm_dil(image,selem):
skimage/rank/tests/test_suite.py
+17 -17
View File
@@ -2,8 +2,8 @@ import unittest
import numpy as np
from skimage.rank import crank8,crank8_percentiles
from skimage.rank import crank16,crank16_bilateral,crank16_percentiles
from skimage.rank import _crank8,_crank8_percentiles
from skimage.rank import _crank16,_crank16_bilateral,_crank16_percentiles
from skimage.morphology import cmorph
class TestSequenceFunctions(unittest.TestCase):
@@ -17,23 +17,23 @@ class TestSequenceFunctions(unittest.TestCase):
elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8')
for m,n in np.random.random_integers(1,100,size=(10,2)):
a8 = np.ones((m,n),dtype='uint8')
r = crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0)
r = _crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0)
self.assertTrue(a8.shape == r.shape)
r = crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1)
r = _crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1)
self.assertTrue(a8.shape == r.shape)
for m,n in np.random.random_integers(1,100,size=(10,2)):
a16 = np.ones((m,n),dtype='uint16')
r = crank16.mean(image=a16,selem = elem,shift_x=0,shift_y=0)
r = _crank16.mean(image=a16,selem = elem,shift_x=0,shift_y=0)
self.assertTrue(a16.shape == r.shape)
r = crank16.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1)
r = _crank16.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1)
self.assertTrue(a16.shape == r.shape)
for m,n in np.random.random_integers(1,100,size=(10,2)):
a16 = np.ones((m,n),dtype='uint16')
r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9)
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9)
self.assertTrue(a16.shape == r.shape)
r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9)
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9)
self.assertTrue(a16.shape == r.shape)
def test_compare_with_cmorph(self):
@@ -43,27 +43,27 @@ class TestSequenceFunctions(unittest.TestCase):
for r in range(1,20,1):
elem = np.ones((r,r),dtype='uint8')
# elem = (np.random.random((r,r))>.5).astype('uint8')
rc = crank8.maximum(image=a,selem = elem)
rc = _crank8.maximum(image=a,selem = elem)
cm = cmorph.dilate(image=a,selem = elem)
self.assertTrue((rc==cm).all())
def test_bitdepth(self):
elem = np.ones((3,3),dtype='uint8')
a16 = np.ones((100,100),dtype='uint16')*255
r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8)
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8)
a16 = np.ones((100,100),dtype='uint16')*255*2
r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=9)
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=9)
a16 = np.ones((100,100),dtype='uint16')*255*4
r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=10)
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=10)
a16 = np.ones((100,100),dtype='uint16')*255*8
r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=11)
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=11)
a16 = np.ones((100,100),dtype='uint16')*255*16
r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12)
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12)
def test_population(self):
a = np.zeros((5,5),dtype='uint8')
elem = np.ones((3,3),dtype='uint8')
p = crank8.pop(image=a,selem = elem)
p = _crank8.pop(image=a,selem = elem)
r = np.asarray([[4, 6, 6, 6, 4],
[6, 9, 9, 9, 6],
[6, 9, 9, 9, 6],
@@ -75,7 +75,7 @@ class TestSequenceFunctions(unittest.TestCase):
a = np.zeros((6,6),dtype='uint8')
a[2,2] = 255
elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8')
f = crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1)
f = _crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1)
r = np.asarray([[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 255, 0, 0, 0],
@@ -90,7 +90,7 @@ class TestSequenceFunctions(unittest.TestCase):
# should fail because data bitdepth is too high for the function
a16 = np.ones((100,100),dtype='uint16')*255
elem = np.ones((3,3),dtype='uint8')
f = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4)
f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4)
if __name__ == '__main__':