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Refactoring ``skimage.filters.gabor_filter` to `skimage.filters.gabor``

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Refactoring ```skimage.filters.gaussian_filter``` to ```skimage.filters.gaussian```
This commit is contained in:
sumitbinnani
2015-10-04 00:15:53 +05:30
parent 953fbfc539
commit 811b6895cf
9 changed files with 43 additions and 43 deletions
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skimage/color/tests/test_adapt_rgb.py
+3 -3
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@@ -12,7 +12,7 @@ COLOR_IMAGE = data.astronaut()[::5, ::5]
GRAY_IMAGE = data.camera()[::5, ::5]
SIGMA = 3
smooth = partial(filters.gaussian_filter, sigma=SIGMA)
smooth = partial(filters.gaussian, sigma=SIGMA)
assert_allclose = partial(np.testing.assert_allclose, atol=1e-8)
@@ -23,7 +23,7 @@ def edges_each(image):
@adapt_rgb(each_channel)
def smooth_each(image, sigma):
return filters.gaussian_filter(image, sigma)
return filters.gaussian(image, sigma)
@adapt_rgb(hsv_value)
@@ -33,7 +33,7 @@ def edges_hsv(image):
@adapt_rgb(hsv_value)
def smooth_hsv(image, sigma):
return filters.gaussian_filter(image, sigma)
return filters.gaussian(image, sigma)
@adapt_rgb(hsv_value)
skimage/data/_binary_blobs.py
+2 -2
View File
@@ -1,5 +1,5 @@
import numpy as np
from ..filters import gaussian_filter
from ..filters import gaussian
def binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2,
@@ -48,6 +48,6 @@ def binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2,
n_pts = max(int(1. / blob_size_fraction) ** n_dim, 1)
points = (length * rs.rand(n_dim, n_pts)).astype(np.int)
mask[[indices for indices in points]] = 1
mask = gaussian_filter(mask, sigma=0.25 * length * blob_size_fraction)
mask = gaussian(mask, sigma=0.25 * length * blob_size_fraction)
threshold = np.percentile(mask, 100 * (1 - volume_fraction))
return np.logical_not(mask < threshold)
skimage/filter/__init__.py
+4 -4
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@@ -12,7 +12,7 @@ del warn
del skimage_deprecation
from ..filters.lpi_filter import inverse, wiener, LPIFilter2D
from ..filters._gaussian import gaussian_filter
from ..filters._gaussian import gaussian
from ..filters.edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
scharr, hscharr, vscharr, scharr_h, scharr_v,
prewitt, hprewitt, vprewitt, prewitt_h, prewitt_v,
@@ -20,7 +20,7 @@ from ..filters.edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
roberts_negative_diagonal, roberts_pos_diag,
roberts_neg_diag)
from ..filters._rank_order import rank_order
from ..filters._gabor import gabor_kernel, gabor_filter
from ..filters._gabor import gabor_kernel, gabor
from ..filters.thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
threshold_isodata)
from ..filters import rank
@@ -46,7 +46,7 @@ def canny(*args, **kwargs):
__all__ = ['inverse',
'wiener',
'LPIFilter2D',
'gaussian_filter',
'gaussian',
'median',
'canny',
'sobel',
@@ -74,7 +74,7 @@ __all__ = ['inverse',
'denoise_tv_bregman',
'rank_order',
'gabor_kernel',
'gabor_filter',
'gabor',
'threshold_adaptive',
'threshold_otsu',
'threshold_yen',
skimage/filters/__init__.py
+4 -4
View File
@@ -1,5 +1,5 @@
from .lpi_filter import inverse, wiener, LPIFilter2D
from ._gaussian import gaussian_filter
from ._gaussian import gaussian
from .edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
scharr, hscharr, vscharr, scharr_h, scharr_v,
prewitt, hprewitt, vprewitt, prewitt_h, prewitt_v,
@@ -7,7 +7,7 @@ from .edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
roberts_negative_diagonal, roberts_pos_diag,
roberts_neg_diag)
from ._rank_order import rank_order
from ._gabor import gabor_kernel, gabor_filter
from ._gabor import gabor_kernel, gabor
from .thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
threshold_isodata, threshold_li)
from . import rank
@@ -33,7 +33,7 @@ def canny(*args, **kwargs):
__all__ = ['inverse',
'wiener',
'LPIFilter2D',
'gaussian_filter',
'gaussian',
'median',
'canny',
'sobel',
@@ -61,7 +61,7 @@ __all__ = ['inverse',
'denoise_tv_bregman',
'rank_order',
'gabor_kernel',
'gabor_filter',
'gabor',
'threshold_adaptive',
'threshold_otsu',
'threshold_yen',
skimage/filters/_gabor.py
+5 -5
View File
@@ -3,7 +3,7 @@ from scipy import ndimage as ndi
from .._shared.utils import assert_nD
__all__ = ['gabor_kernel', 'gabor_filter']
__all__ = ['gabor_kernel', 'gabor']
def _sigma_prefactor(bandwidth):
@@ -94,7 +94,7 @@ def gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None,
return g
def gabor_filter(image, frequency, theta=0, bandwidth=1, sigma_x=None,
def gabor(image, frequency, theta=0, bandwidth=1, sigma_x=None,
sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0):
"""Return real and imaginary responses to Gabor filter.
@@ -148,19 +148,19 @@ def gabor_filter(image, frequency, theta=0, bandwidth=1, sigma_x=None,
Examples
--------
>>> from skimage.filter import gabor_filter
>>> from skimage.filter import gabor
>>> from skimage import data, io
>>> from matplotlib import pyplot as plt # doctest: +SKIP
>>> image = data.coins()
>>> # detecting edges in a coin image
>>> filt_real, filt_imag = gabor_filter(image, frequency=0.6)
>>> filt_real, filt_imag = gabor(image, frequency=0.6)
>>> plt.figure() # doctest: +SKIP
>>> io.imshow(filt_real) # doctest: +SKIP
>>> io.show() # doctest: +SKIP
>>> # less sensitivity to finer details with the lower frequency kernel
>>> filt_real, filt_imag = gabor_filter(image, frequency=0.1)
>>> filt_real, filt_imag = gabor(image, frequency=0.1)
>>> plt.figure() # doctest: +SKIP
>>> io.imshow(filt_real) # doctest: +SKIP
>>> io.show() # doctest: +SKIP
skimage/filters/_gaussian.py
+6 -6
View File
@@ -6,10 +6,10 @@ import warnings
from ..util import img_as_float
from ..color import guess_spatial_dimensions
__all__ = ['gaussian_filter']
__all__ = ['gaussian']
def gaussian_filter(image, sigma, output=None, mode='nearest', cval=0,
def gaussian(image, sigma, output=None, mode='nearest', cval=0,
multichannel=None):
"""Multi-dimensional Gaussian filter
@@ -66,23 +66,23 @@ def gaussian_filter(image, sigma, output=None, mode='nearest', cval=0,
array([[ 0., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 0.]])
>>> gaussian_filter(a, sigma=0.4) # mild smoothing
>>> gaussian(a, sigma=0.4) # mild smoothing
array([[ 0.00163116, 0.03712502, 0.00163116],
[ 0.03712502, 0.84496158, 0.03712502],
[ 0.00163116, 0.03712502, 0.00163116]])
>>> gaussian_filter(a, sigma=1) # more smooting
>>> gaussian(a, sigma=1) # more smooting
array([[ 0.05855018, 0.09653293, 0.05855018],
[ 0.09653293, 0.15915589, 0.09653293],
[ 0.05855018, 0.09653293, 0.05855018]])
>>> # Several modes are possible for handling boundaries
>>> gaussian_filter(a, sigma=1, mode='reflect')
>>> gaussian(a, sigma=1, mode='reflect')
array([[ 0.08767308, 0.12075024, 0.08767308],
[ 0.12075024, 0.16630671, 0.12075024],
[ 0.08767308, 0.12075024, 0.08767308]])
>>> # For RGB images, each is filtered separately
>>> from skimage.data import astronaut
>>> image = astronaut()
>>> filtered_img = gaussian_filter(image, sigma=1, multichannel=True)
>>> filtered_img = gaussian(image, sigma=1, multichannel=True)
"""
skimage/filters/tests/test_gabor.py
+3 -3
View File
@@ -2,7 +2,7 @@ import numpy as np
from numpy.testing import (assert_equal, assert_almost_equal,
assert_array_almost_equal)
from skimage.filters._gabor import gabor_kernel, gabor_filter, _sigma_prefactor
from skimage.filters._gabor import gabor_kernel, gabor, _sigma_prefactor
def test_gabor_kernel_size():
@@ -59,13 +59,13 @@ def test_gabor_kernel_theta():
np.abs(kernel180))
def test_gabor_filter():
def test_gabor():
Y, X = np.mgrid[:40, :40]
frequencies = (0.1, 0.3)
wave_images = [np.sin(2 * np.pi * X * f) for f in frequencies]
def match_score(image, frequency):
gabor_responses = gabor_filter(image, frequency)
gabor_responses = gabor(image, frequency)
return np.mean(np.hypot(*gabor_responses))
# Gabor scores: diagonals are frequency-matched, off-diagonals are not.
skimage/filters/tests/test_gaussian.py
+9 -9
View File
@@ -1,35 +1,35 @@
import numpy as np
from numpy.testing import assert_raises
from skimage.filters._gaussian import gaussian_filter
from skimage.filters._gaussian import gaussian
from skimage._shared._warnings import expected_warnings
def test_negative_sigma():
a = np.zeros((3, 3))
a[1, 1] = 1.
assert_raises(ValueError, gaussian_filter, a, sigma=-1.0)
assert_raises(ValueError, gaussian_filter, a, sigma=[-1.0, 1.0])
assert_raises(ValueError, gaussian_filter, a,
assert_raises(ValueError, gaussian, a, sigma=-1.0)
assert_raises(ValueError, gaussian, a, sigma=[-1.0, 1.0])
assert_raises(ValueError, gaussian, a,
sigma=np.asarray([-1.0, 1.0]))
def test_null_sigma():
a = np.zeros((3, 3))
a[1, 1] = 1.
assert np.all(gaussian_filter(a, 0) == a)
assert np.all(gaussian(a, 0) == a)
def test_energy_decrease():
a = np.zeros((3, 3))
a[1, 1] = 1.
gaussian_a = gaussian_filter(a, sigma=1, mode='reflect')
gaussian_a = gaussian(a, sigma=1, mode='reflect')
assert gaussian_a.std() < a.std()
def test_multichannel():
a = np.zeros((5, 5, 3))
a[1, 1] = np.arange(1, 4)
gaussian_rgb_a = gaussian_filter(a, sigma=1, mode='reflect',
gaussian_rgb_a = gaussian(a, sigma=1, mode='reflect',
multichannel=True)
# Check that the mean value is conserved in each channel
# (color channels are not mixed together)
@@ -37,13 +37,13 @@ def test_multichannel():
[gaussian_rgb_a[..., i].mean() for i in range(3)])
# Test multichannel = None
with expected_warnings(['multichannel']):
gaussian_rgb_a = gaussian_filter(a, sigma=1, mode='reflect')
gaussian_rgb_a = gaussian(a, sigma=1, mode='reflect')
# Check that the mean value is conserved in each channel
# (color channels are not mixed together)
assert np.allclose([a[..., i].mean() for i in range(3)],
[gaussian_rgb_a[..., i].mean() for i in range(3)])
# Iterable sigma
gaussian_rgb_a = gaussian_filter(a, sigma=[1, 2], mode='reflect',
gaussian_rgb_a = gaussian(a, sigma=[1, 2], mode='reflect',
multichannel=True)
assert np.allclose([a[..., i].mean() for i in range(3)],
[gaussian_rgb_a[..., i].mean() for i in range(3)])
skimage/util/tests/test_apply_parallel.py
+7 -7
View File
@@ -3,7 +3,7 @@ from __future__ import absolute_import
import numpy as np
from numpy.testing import assert_array_almost_equal
from skimage.filters import threshold_adaptive, gaussian_filter
from skimage.filters import threshold_adaptive, gaussian
from skimage.util.apply_parallel import apply_parallel
@@ -20,9 +20,9 @@ def test_apply_parallel():
assert_array_almost_equal(result1, expected1)
def wrapped_gauss(arr):
return gaussian_filter(arr, 1, mode='reflect')
return gaussian(arr, 1, mode='reflect')
expected2 = gaussian_filter(a, 1, mode='reflect')
expected2 = gaussian(a, 1, mode='reflect')
result2 = apply_parallel(wrapped_gauss, a, chunks=(6, 6), depth=5)
assert_array_almost_equal(result2, expected2)
@@ -42,9 +42,9 @@ def test_no_chunks():
def test_apply_parallel_wrap():
def wrapped(arr):
return gaussian_filter(arr, 1, mode='wrap')
return gaussian(arr, 1, mode='wrap')
a = np.arange(144).reshape(12, 12).astype(float)
expected = gaussian_filter(a, 1, mode='wrap')
expected = gaussian(a, 1, mode='wrap')
result = apply_parallel(wrapped, a, chunks=(6, 6), depth=5, mode='wrap')
assert_array_almost_equal(result, expected)
@@ -52,9 +52,9 @@ def test_apply_parallel_wrap():
def test_apply_parallel_nearest():
def wrapped(arr):
return gaussian_filter(arr, 1, mode='nearest')
return gaussian(arr, 1, mode='nearest')
a = np.arange(144).reshape(12, 12).astype(float)
expected = gaussian_filter(a, 1, mode='nearest')
expected = gaussian(a, 1, mode='nearest')
result = apply_parallel(wrapped, a, chunks=(6, 6), depth={0: 5, 1: 5},
mode='nearest')