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BUG: Use local lena data file in tv_denoise test.

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Stefan van der Walt
2011-09-26 21:50:48 -07:00
parent 146bef59b3
commit f8a0e9e6e7
1 changed files with 11 additions and 8 deletions
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scikits/image/filter/tests/test_tv_denoise.py
+11 -8
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@@ -1,6 +1,9 @@
import numpy as np
from numpy.testing import run_module_suite
import scikits.image.filter as F
from scikits.image import filter
from scikits.image import data
from scikits.image import color
class TestTvDenoise():
@@ -9,13 +12,12 @@ class TestTvDenoise():
Apply the TV denoising algorithm on the lena image provided
by scipy
"""
import scipy
# lena image
lena = scipy.lena().astype(np.float)
lena = color.rgb2gray(data.lena())
# add noise to lena
lena += 0.5 * lena.std()*np.random.randn(*lena.shape)
# denoise
denoised_lena = F.tv_denoise(lena, weight=60.0)
denoised_lena = filter.tv_denoise(lena, weight=60.0)
# which dtype?
assert denoised_lena.dtype in [np.float, np.float32, np.float64]
from scipy import ndimage
@@ -23,7 +25,7 @@ class TestTvDenoise():
grad_denoised = ndimage.morphological_gradient(denoised_lena, size=((3,3)))
# test if the total variation has decreased
assert np.sqrt((grad_denoised**2).sum()) < np.sqrt((grad**2).sum()) / 2
denoised_lena_int = F.tv_denoise(lena.astype(np.int32), \
denoised_lena_int = filter.tv_denoise(lena.astype(np.int32), \
weight=60.0, keep_type=True)
assert denoised_lena_int.dtype is np.dtype('int32')
@@ -40,16 +42,17 @@ class TestTvDenoise():
mask += 20*np.random.randn(*mask.shape)
mask[mask < 0] = 0
mask[mask > 255] = 255
res = F.tv_denoise(mask.astype(np.uint8), weight=100, keep_type=True)
res = filter.tv_denoise(mask.astype(np.uint8),
weight=100, keep_type=True)
assert res.std() < mask.std()
assert res.dtype is np.dtype('uint8')
res = F.tv_denoise(mask.astype(np.uint8), weight=100)
res = filter.tv_denoise(mask.astype(np.uint8), weight=100)
assert res.std() < mask.std()
assert res.dtype is not np.dtype('uint8')
# test wrong number of dimensions
a = np.random.random((8, 8, 8, 8))
try:
res = F.tv_denoise(a)
res = filter.tv_denoise(a)
except ValueError:
pass