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Replace deprecated function calls in examples

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Johannes Schönberger
2013-07-08 20:34:40 +02:00
parent e041c27f88
commit b9b5efbdf8
2 changed files with 11 additions and 11 deletions
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doc/examples/applications/plot_rank_filters.py
+9 -9
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@@ -283,16 +283,16 @@ result.
"""
from skimage.filter.rank import percentile_autolevel
from skimage.filter.rank import autolevel_percentile
image = data.camera()
selem = disk(20)
loc_autolevel = autolevel(image, selem=selem)
loc_perc_autolevel0 = percentile_autolevel(image, selem=selem, p0=.00, p1=1.0)
loc_perc_autolevel1 = percentile_autolevel(image, selem=selem, p0=.01, p1=.99)
loc_perc_autolevel2 = percentile_autolevel(image, selem=selem, p0=.05, p1=.95)
loc_perc_autolevel3 = percentile_autolevel(image, selem=selem, p0=.1, p1=.9)
loc_perc_autolevel0 = autolevel_percentile(image, selem=selem, p0=.00, p1=1.0)
loc_perc_autolevel1 = autolevel_percentile(image, selem=selem, p0=.01, p1=.99)
loc_perc_autolevel2 = autolevel_percentile(image, selem=selem, p0=.05, p1=.95)
loc_perc_autolevel3 = autolevel_percentile(image, selem=selem, p0=.1, p1=.9)
fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
ax0, ax1, ax2 = axes
@@ -321,11 +321,11 @@ otherwise by the minimum local.
"""
from skimage.filter.rank import morph_contr_enh
from skimage.filter.rank import enhance_contrast
noisy_image = data.camera()
enh = morph_contr_enh(noisy_image, disk(5))
enh = enhance_contrast(noisy_image, disk(5))
fig = plt.figure(figsize=[10, 7])
plt.subplot(2, 2, 1)
@@ -355,11 +355,11 @@ percentile *p0* and *p1* instead of the local minimum and maximum.
"""
from skimage.filter.rank import percentile_morph_contr_enh
from skimage.filter.rank import enhance_contrast_percentile
noisy_image = data.camera()
penh = percentile_morph_contr_enh(noisy_image, disk(5), p0=.1, p1=.9)
penh = enhance_contrast_percentile(noisy_image, disk(5), p0=.1, p1=.9)
fig = plt.figure(figsize=[10, 7])
plt.subplot(2, 2, 1)
doc/examples/plot_rank_mean.py
+2 -2
View File
@@ -29,8 +29,8 @@ from skimage.filter import rank
image = (data.coins()).astype(np.uint16) * 16
selem = disk(20)
percentile_result = rank.percentile_mean(image, selem=selem, p0=.1, p1=.9)
bilateral_result = rank.bilateral_mean(image, selem=selem, s0=500, s1=500)
percentile_result = rank.mean_percentile(image, selem=selem, p0=.1, p1=.9)
bilateral_result = rank.mean_bilateral(image, selem=selem, s0=500, s1=500)
normal_result = rank.mean(image, selem=selem)