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Merge pull request #627 from ahojnnes/travis-examples

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Let Travis also run long example scripts. Fix rank filter dtype issue.
This commit is contained in:
Stefan van der Walt
2013-08-01 14:10:00 -07:00
parent cf468711fa 37dfd294f2
commit 38152219ef
2 changed files with 11 additions and 8 deletions
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.travis.yml
+2
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@@ -37,4 +37,6 @@ script:
# Change back to repository root directory and run all doc examples
- cd ..
- for f in doc/examples/*.py; do $PYTHON "$f"; if [ $? -ne 0 ]; then exit 1; fi done
- for f in doc/examples/applications/*.py; do $PYTHON "$f"; if [ $? -ne 0 ]; then exit 1; fi done
# Run pep8 and flake tests
- flake8 --exit-zero --exclude=test_*,six.py skimage doc/examples viewer_examples
doc/examples/applications/plot_rank_filters.py
+9 -8
View File
@@ -38,9 +38,10 @@ from `skimage.data` for all comparisons.
import numpy as np
import matplotlib.pyplot as plt
from skimage import img_as_ubyte
from skimage import data
noisy_image = data.camera()
noisy_image = img_as_ubyte(data.camera())
hist = np.histogram(noisy_image, bins=np.arange(0, 256))
plt.figure(figsize=(8, 3))
@@ -72,7 +73,7 @@ from skimage.filter.rank import median
from skimage.morphology import disk
noise = np.random.random(noisy_image.shape)
noisy_image = data.camera()
noisy_image = img_as_ubyte(data.camera())
noisy_image[noise > 0.99] = 255
noisy_image[noise < 0.01] = 0
@@ -150,7 +151,7 @@ the central one.
from skimage.filter.rank import bilateral_mean
noisy_image = data.camera()
noisy_image = img_as_ubyte(data.camera())
selem = disk(10)
bilat = bilateral_mean(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
@@ -200,7 +201,7 @@ equalization emphasizes every local gray-level variations.
from skimage import exposure
from skimage.filter import rank
noisy_image = data.camera()
noisy_image = img_as_ubyte(data.camera())
# equalize globally and locally
glob = exposure.equalize(noisy_image) * 255
@@ -252,7 +253,7 @@ picture.
from skimage.filter.rank import autolevel
noisy_image = data.camera()
noisy_image = img_as_ubyte(data.camera())
selem = disk(10)
auto = autolevel(noisy_image.astype(np.uint16), disk(20))
@@ -323,7 +324,7 @@ otherwise by the minimum local.
from skimage.filter.rank import enhance_contrast
noisy_image = data.camera()
noisy_image = img_as_ubyte(data.camera())
enh = enhance_contrast(noisy_image, disk(5))
@@ -357,7 +358,7 @@ percentile *p0* and *p1* instead of the local minimum and maximum.
from skimage.filter.rank import enhance_contrast_percentile
noisy_image = data.camera()
noisy_image = img_as_ubyte(data.camera())
penh = enhance_contrast_percentile(noisy_image, disk(5), p0=.1, p1=.9)
@@ -495,7 +496,7 @@ closing and morphological gradient.
from skimage.filter.rank import maximum, minimum, gradient
noisy_image = data.camera()
noisy_image = img_as_ubyte(data.camera())
closing = maximum(minimum(noisy_image, disk(5)), disk(5))
opening = minimum(maximum(noisy_image, disk(5)), disk(5))