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convert image to float before performing tv_denoise operation now

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removed keep_type argument from tv_denoise which becomes obsolete with the previos change
adapted tests
This commit is contained in:
Andreas Wuerl
2012-08-27 19:44:30 +02:00
parent bcf4594170
commit 42ae537a69
2 changed files with 11 additions and 21 deletions
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skimage/filter/_tv_denoise.py
+4 -14
View File
@@ -1,5 +1,5 @@
import numpy as np
from skimage.util import dtype
from skimage import img_as_float
def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200):
@@ -171,7 +171,7 @@ def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200):
i += 1
return out
def tv_denoise(im, weight=50, eps=2.e-4, keep_type=False, n_iter_max=200):
def tv_denoise(im, weight=50, eps=2.e-4, n_iter_max=200):
"""
Perform total-variation denoising on 2-d and 3-d images
@@ -192,11 +192,6 @@ def tv_denoise(im, weight=50, eps=2.e-4, keep_type=False, n_iter_max=200):
(E_(n-1) - E_n) < eps * E_0
keep_type: bool, optional (False)
whether the output has the same dtype as the input array.
keep_type is False by default, and the dtype of the output
is np.float
n_iter_max: int, optional
maximal number of iterations used for the optimization.
@@ -245,7 +240,7 @@ def tv_denoise(im, weight=50, eps=2.e-4, keep_type=False, n_iter_max=200):
"""
im_type = im.dtype
if not im_type.kind == 'f':
im = im.astype(np.float)
im = img_as_float(im)
if im.ndim == 2:
out = _tv_denoise_2d(im, weight, eps, n_iter_max)
@@ -254,9 +249,4 @@ def tv_denoise(im, weight=50, eps=2.e-4, keep_type=False, n_iter_max=200):
else:
raise ValueError('only 2-d and 3-d images may be denoised with this '
'function')
if keep_type:
return out.astype(im_type)
else:
if not im_type.kind == 'f':
out = dtype.convert(out.astype(im_type), np.float)
return out
return out
skimage/filter/tests/test_tv_denoise.py
+7 -7
View File
@@ -2,7 +2,7 @@ import numpy as np
from numpy.testing import run_module_suite
from skimage import filter, data, color
from skimage import img_as_uint
from skimage import img_as_uint, img_as_ubyte
class TestTvDenoise():
@@ -29,8 +29,8 @@ class TestTvDenoise():
# test if the total variation has decreased
assert (np.sqrt((grad_denoised**2).sum())
< np.sqrt((grad**2).sum()) / 2)
denoised_lena_int = filter.tv_denoise(img_as_uint(lena),
weight=60.0, keep_type=True)
denoised_lena_int = img_as_uint(filter.tv_denoise(img_as_ubyte(lena),
weight=60.0))
assert denoised_lena_int.dtype is np.dtype('uint16')
def test_tv_denoise_float_result_range(self):
@@ -55,13 +55,13 @@ class TestTvDenoise():
mask += 20 * np.random.randn(*mask.shape)
mask[mask < 0] = 0
mask[mask > 255] = 255
res = filter.tv_denoise(mask.astype(np.uint8),
weight=100, keep_type=True)
res = img_as_ubyte(filter.tv_denoise(mask.astype(np.uint8),
weight=100))
assert res.std() < mask.std()
assert res.dtype is np.dtype('uint8')
res = filter.tv_denoise(mask.astype(np.uint8), weight=100)
res = img_as_ubyte(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: