Merge pull request #1184 from stefanv/filter_rename

Rename and deprecate filter module to prevent shadowing of built-in keyword

TODO.txt

@@ -3,8 +3,9 @@ Remember to list any API changes below in `doc/source/api_changes.txt`.
 Version 0.13
 ------------
 * Remove deprecated `None` defaults for `skimage.exposure.rescale_intensity`
-* Remove deprecated `skimage.filter.canny` import in filter/__init__.py file (canny is now in `skimage.feature.canny`).
+* Remove deprecated `skimage.filters.canny` import in filters/__init__.py file (canny is now in `skimage.feature.canny`).
     * Don't forget to complete api_changes.txt. (`GitHub discuss <https://github.com/scikit-image/scikit-image/pull/1113>`__ )
+* Remove deprecated ``skimage.filter`` module.
 
 Version 0.12
 ------------
@@ -20,4 +21,4 @@ Version 0.12
   `skimage.transform.ProjectiveTransform._matrix`,
   `skimage.transform.PolynomialTransform._params`,
   `skimage.transform.PiecewiseAffineTransform.affines_*` attributes
-* Remove deprecated functions `skimage.filter.denoise_*`
+* Remove deprecated functions `skimage.filters.denoise_*`

bento.info

@@ -33,7 +33,7 @@ UseBackends: Waf
 Library:
     Packages:
         skimage, skimage.color, skimage.data, skimage.draw, skimage.exposure,
-        skimage.feature, skimage.filter, skimage.graph, skimage.io,
+        skimage.feature, skimage.filters, skimage.graph, skimage.io,
         skimage.io._plugins, skimage.measure, skimage.morphology,
         skimage.scripts, skimage.restoration, skimage.segmentation,
         skimage.transform, skimage.util
@@ -61,9 +61,9 @@ Library:
     Extension: skimage.transform._hough_transform
         Sources:
             skimage/transform/_hough_transform.pyx
-    Extension: skimage.filter._ctmf
+    Extension: skimage.filters._ctmf
         Sources:
-            skimage/filter/_ctmf.pyx
+            skimage/filters/_ctmf.pyx
     Extension: skimage.measure._ccomp
         Sources:
             skimage/measure/_ccomp.pyx
@@ -127,18 +127,18 @@ Library:
     Extension: skimage._shared.geometry
         Sources:
             skimage/_shared/geometry.pyx
-    Extension: skimage.filter.rank.generic_cy
+    Extension: skimage.filters.rank.generic_cy
         Sources:
-            skimage/filter/rank/generic_cy.pyx
-    Extension: skimage.filter.rank.percentile_cy
+            skimage/filters/rank/generic_cy.pyx
+    Extension: skimage.filters.rank.percentile_cy
         Sources:
             skimage/filter/rank/percentile_cy.pyx
-    Extension: skimage.filter.rank.core_cy
+    Extension: skimage.filters.rank.core_cy
         Sources:
             skimage/filter/rank/core_cy.pyx
-    Extension: skimage.filter.rank.bilateral_cy
+    Extension: skimage.filters.rank.bilateral_cy
         Sources:
-            skimage/filter/rank/bilateral_cy.pyx
+            skimage/filters/rank/bilateral_cy.pyx
     Extension: skimage.restoration._unwrap_1d
         Sources:
             skimage/restoration/_unwrap_1d.pyx

doc/examples/applications/plot_coins_segmentation.py

@@ -109,7 +109,7 @@ find an elevation map using the Sobel gradient of the image.
 
 """
 
-from skimage.filter import sobel
+from skimage.filters import sobel
 
 elevation_map = sobel(coins)
 

doc/examples/applications/plot_rank_filters.py

@@ -62,7 +62,7 @@ randomly set to 0. The **median** filter is applied to remove the noise.
 
 """
 
-from skimage.filter.rank import median
+from skimage.filters.rank import median
 from skimage.morphology import disk
 
 noise = np.random.random(noisy_image.shape)
@@ -107,7 +107,7 @@ image.
 
 """
 
-from skimage.filter.rank import mean
+from skimage.filters.rank import mean
 
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7])
 
@@ -133,11 +133,11 @@ the central one.
 .. note::
 
     A different implementation is available for color images in
-    `skimage.filter.denoise_bilateral`.
+    `skimage.filters.denoise_bilateral`.
 
 """
 
-from skimage.filter.rank import mean_bilateral
+from skimage.filters.rank import mean_bilateral
 
 noisy_image = img_as_ubyte(data.camera())
 
@@ -183,7 +183,7 @@ equalization emphasizes every local gray-level variations.
 """
 
 from skimage import exposure
-from skimage.filter import rank
+from skimage.filters import rank
 
 noisy_image = img_as_ubyte(data.camera())
 
@@ -230,7 +230,7 @@ picture.
 
 """
 
-from skimage.filter.rank import autolevel
+from skimage.filters.rank import autolevel
 
 noisy_image = img_as_ubyte(data.camera())
 
@@ -260,7 +260,7 @@ result.
 
 """
 
-from skimage.filter.rank import autolevel_percentile
+from skimage.filters.rank import autolevel_percentile
 
 image = data.camera()
 
@@ -298,7 +298,7 @@ otherwise by the minimum local.
 
 """
 
-from skimage.filter.rank import enhance_contrast
+from skimage.filters.rank import enhance_contrast
 
 noisy_image = img_as_ubyte(data.camera())
 
@@ -330,7 +330,7 @@ percentile *p0* and *p1* instead of the local minimum and maximum.
 
 """
 
-from skimage.filter.rank import enhance_contrast_percentile
+from skimage.filters.rank import enhance_contrast_percentile
 
 noisy_image = img_as_ubyte(data.camera())
 
@@ -366,19 +366,19 @@ threshold is determined by maximizing the variance between two classes of
 pixels of the local neighborhood defined by a structuring element.
 
 The example compares the local threshold with the global threshold
-`skimage.filter.threshold_otsu`.
+`skimage.filters.threshold_otsu`.
 
 .. note::
 
     Local is much slower than global thresholding. A function for global Otsu
-    thresholding can be found in : `skimage.filter.threshold_otsu`.
+    thresholding can be found in : `skimage.filters.threshold_otsu`.
 
 .. [4] http://en.wikipedia.org/wiki/Otsu's_method
 
 """
 
-from skimage.filter.rank import otsu
-from skimage.filter import threshold_otsu
+from skimage.filters.rank import otsu
+from skimage.filters import threshold_otsu
 
 p8 = data.page()
 
@@ -459,7 +459,7 @@ closing and morphological gradient.
 
 """
 
-from skimage.filter.rank import maximum, minimum, gradient
+from skimage.filters.rank import maximum, minimum, gradient
 
 noisy_image = img_as_ubyte(data.camera())
 
@@ -511,7 +511,7 @@ images.
 """
 
 from skimage import data
-from skimage.filter.rank import entropy
+from skimage.filters.rank import entropy
 from skimage.morphology import disk
 import numpy as np
 import matplotlib.pyplot as plt
@@ -549,7 +549,7 @@ from time import time
 
 from scipy.ndimage.filters import percentile_filter
 from skimage.morphology import dilation
-from skimage.filter.rank import median, maximum
+from skimage.filters.rank import median, maximum
 
 
 def exec_and_timeit(func):

doc/examples/plot_edge_filter.py

@@ -11,7 +11,7 @@ gradient of the image intensity function.
 import matplotlib.pyplot as plt
 
 from skimage.data import camera
-from skimage.filter import roberts, sobel
+from skimage.filters import roberts, sobel
 
 
 image = camera()

doc/examples/plot_entropy.py

@@ -10,7 +10,7 @@ coded in an image.
 import matplotlib.pyplot as plt
 
 from skimage import data
-from skimage.filter.rank import entropy
+from skimage.filters.rank import entropy
 from skimage.morphology import disk
 from skimage.util import img_as_ubyte
 

doc/examples/plot_gabor.py

@@ -21,7 +21,7 @@ from scipy import ndimage as nd
 
 from skimage import data
 from skimage.util import img_as_float
-from skimage.filter import gabor_kernel
+from skimage.filters import gabor_kernel
 
 
 def compute_feats(image, kernels):

doc/examples/plot_join_segmentations.py

@@ -14,7 +14,7 @@ import numpy as np
 from scipy import ndimage as nd
 import matplotlib.pyplot as plt
 
-from skimage.filter import sobel
+from skimage.filters import sobel
 from skimage.segmentation import slic, join_segmentations
 from skimage.morphology import watershed
 from skimage.color import label2rgb

doc/examples/plot_label.py

@@ -17,7 +17,7 @@ import matplotlib.pyplot as plt
 import matplotlib.patches as mpatches
 
 from skimage import data
-from skimage.filter import threshold_otsu
+from skimage.filters import threshold_otsu
 from skimage.segmentation import clear_border
 from skimage.morphology import label, closing, square
 from skimage.measure import regionprops

doc/examples/plot_local_equalize.py

@@ -28,7 +28,7 @@ from skimage.util.dtype import dtype_range
 from skimage.util import img_as_ubyte
 from skimage import exposure
 from skimage.morphology import disk
-from skimage.filter import rank
+from skimage.filters import rank
 
 
 matplotlib.rcParams['font.size'] = 9

doc/examples/plot_local_otsu.py

@@ -20,7 +20,7 @@ import matplotlib.pyplot as plt
 
 from skimage import data
 from skimage.morphology import disk
-from skimage.filter import threshold_otsu, rank
+from skimage.filters import threshold_otsu, rank
 from skimage.util import img_as_ubyte
 
 

doc/examples/plot_marked_watershed.py

@@ -19,7 +19,7 @@ import matplotlib.pyplot as plt
 
 from skimage.morphology import watershed, disk
 from skimage import data
-from skimage.filter import rank
+from skimage.filters import rank
 from skimage.util import img_as_ubyte
 
 

doc/examples/plot_otsu.py

@@ -18,7 +18,7 @@ import matplotlib
 import matplotlib.pyplot as plt
 
 from skimage.data import camera
-from skimage.filter import threshold_otsu
+from skimage.filters import threshold_otsu
 
 
 matplotlib.rcParams['font.size'] = 9

doc/examples/plot_rank_mean.py

@@ -23,7 +23,7 @@ import matplotlib.pyplot as plt
 
 from skimage import data
 from skimage.morphology import disk
-from skimage.filter import rank
+from skimage.filters import rank
 
 
 image = (data.coins()).astype(np.uint16) * 16

doc/examples/plot_threshold_adaptive.py

@@ -18,7 +18,7 @@ local neighborhood minus an offset value.
 import matplotlib.pyplot as plt
 
 from skimage import data
-from skimage.filter import threshold_otsu, threshold_adaptive
+from skimage.filters import threshold_otsu, threshold_adaptive
 
 
 image = data.page()

doc/examples/plot_tinting_grayscale_images.py

@@ -116,7 +116,7 @@ thresholding. In practice, you might want to define a region for tinting based
 on segmentation results or blob detection methods.
 """
 
-from skimage.filter import rank
+from skimage.filters import rank
 
 # Square regions defined as slices over the first two dimensions.
 top_left = (slice(100),) * 2

doc/examples/plot_windowed_histogram.py

@@ -13,7 +13,7 @@ extracted and a histogram of its greyscale values is computed.
 
 Next, for each pixel in the test image, a histogram of the greyscale values in
 a region of the image surrounding the pixel is computed.
-`skimage.filter.rank.windowed_histogram` is used for this task, as it employs
+`skimage.filters.rank.windowed_histogram` is used for this task, as it employs
 an efficient sliding window based algorithm that is able to compute these
 histograms quickly [2]_. The local histogram for the region surrounding each
 pixel in the image is compared to that of the single coin, with a similarity
@@ -42,7 +42,7 @@ import matplotlib.pyplot as plt
 from skimage import data, transform
 from skimage.util import img_as_ubyte
 from skimage.morphology import disk
-from skimage.filter import rank
+from skimage.filters import rank
 
 
 matplotlib.rcParams['font.size'] = 9

doc/logo/scikit_image_logo.py

@@ -20,7 +20,7 @@ import skimage.io as sio
 from skimage import img_as_float
 from skimage.color import gray2rgb, rgb2gray
 from skimage.exposure import rescale_intensity
-from skimage.filter import sobel
+from skimage.filters import sobel
 
 import scipy_logo
 

doc/source/api_changes.txt

@@ -1,5 +1,6 @@
 Version 0.11
 ------------
+- The ``skimage.filter`` subpackage has been renamed to ``skimage.filters``.
 - Some edge detectors returned values greater than 1--their results are now
   appropriately scaled with a factor of ``sqrt(2)``.
 

doc/source/user_guide/tutorial_segmentation.txt

@@ -117,7 +117,7 @@ The choice of the elevation map is critical for good segmentation.
 Here, the amplitude of the gradient provides a good elevation map. We
 use the Sobel operator for computing the amplitude of the gradient::
 
-    >>> from skimage.filter import sobel
+    >>> from skimage.filters import sobel
     >>> elevation_map = sobel(coins)
 
 From the 3-D surface plot shown below, we see that high barriers effectively

doc/source/user_guide/viewer.txt

@@ -51,7 +51,7 @@ call the total-variation denoising function, ``denoise_tv_bregman``:
 
 .. code-block:: python
 
-   from skimage.filter import denoise_tv_bregman
+   from skimage.filters import denoise_tv_bregman
    from skimage.viewer.plugins.base import Plugin
 
    denoise_plugin = Plugin(image_filter=denoise_tv_bregman)

doc/tools/build_modref_templates.py

@@ -51,6 +51,7 @@ if __name__ == '__main__':
     docwriter = ApiDocWriter(package)
     docwriter.package_skip_patterns += [r'\.fixes$',
                                         r'\.externals$',
+                                        r'filter$',
                                         ]
     docwriter.write_api_docs(outdir)
     docwriter.write_index(outdir, 'api', relative_to='source/api')

skimage/__init__.py

@@ -19,7 +19,7 @@ exposure
     Image intensity adjustment, e.g., histogram equalization, etc.
 feature
     Feature detection and extraction, e.g., texture analysis corners, etc.
-filter
+filters
     Sharpening, edge finding, rank filters, thresholding, etc.
 graph
     Graph-theoretic operations, e.g., shortest paths.

skimage/feature/_canny.py

@@ -109,7 +109,7 @@ def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
 
     Examples
     --------
-    >>> from skimage import filter
+    >>> from skimage import filters
     >>> # Generate noisy image of a square
     >>> im = np.zeros((256, 256))
     >>> im[64:-64, 64:-64] = 1

skimage/feature/peak.py

@@ -1,6 +1,6 @@
 import numpy as np
 import scipy.ndimage as ndi
-from ..filter import rank_order
+from ..filters import rank_order
 
 
 def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,

skimage/filter/__init__.py

@@ -1,56 +1,14 @@
-from .lpi_filter import inverse, wiener, LPIFilter2D
-from ._gaussian import gaussian_filter
-from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
-                    hprewitt, vprewitt, roberts, roberts_positive_diagonal,
-                    roberts_negative_diagonal)
-from ._rank_order import rank_order
-from ._gabor import gabor_kernel, gabor_filter
-from .thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
-                           threshold_isodata)
-from . import rank
+from skimage._shared.utils import skimage_deprecation
+from warnings import warn
 
-from skimage._shared.utils import deprecated
-from skimage import restoration
-denoise_bilateral = deprecated('skimage.restoration.denoise_bilateral')\
-                        (restoration.denoise_bilateral)
-denoise_tv_bregman = deprecated('skimage.restoration.denoise_tv_bregman')\
-                        (restoration.denoise_tv_bregman)
-denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\
-                        (restoration.denoise_tv_chambolle)
+global _import_warned
 
-# Backward compatibility v<0.11
-@deprecated('skimage.feature.canny')
-def canny(*args, **kwargs):
-    # Hack to avoid circular import
-    from skimage.feature._canny import canny as canny_
-    return canny_(*args, **kwargs)
+warn(skimage_deprecation('The `skimage.filter` module has been renamed '
+                         'to `skimage.filters`.  This placeholder module '
+                         'will be removed in v0.13.'))
+_import_warned = True
 
+del warn
+del skimage_deprecation
 
-__all__ = ['inverse',
-           'wiener',
-           'LPIFilter2D',
-           'gaussian_filter',
-           'canny',
-           'sobel',
-           'hsobel',
-           'vsobel',
-           'scharr',
-           'hscharr',
-           'vscharr',
-           'prewitt',
-           'hprewitt',
-           'vprewitt',
-           'roberts',
-           'roberts_positive_diagonal',
-           'roberts_negative_diagonal',
-           'denoise_tv_chambolle',
-           'denoise_bilateral',
-           'denoise_tv_bregman',
-           'rank_order',
-           'gabor_kernel',
-           'gabor_filter',
-           'threshold_adaptive',
-           'threshold_otsu',
-           'threshold_yen',
-           'threshold_isodata',
-           'rank']
+from ..filters import *

skimage/filters/__init__.py

@@ -0,0 +1,56 @@
+from .lpi_filter import inverse, wiener, LPIFilter2D
+from ._gaussian import gaussian_filter
+from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
+                    hprewitt, vprewitt, roberts, roberts_positive_diagonal,
+                    roberts_negative_diagonal)
+from ._rank_order import rank_order
+from ._gabor import gabor_kernel, gabor_filter
+from .thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
+                           threshold_isodata)
+from . import rank
+
+from skimage._shared.utils import deprecated
+from skimage import restoration
+denoise_bilateral = deprecated('skimage.restoration.denoise_bilateral')\
+                        (restoration.denoise_bilateral)
+denoise_tv_bregman = deprecated('skimage.restoration.denoise_tv_bregman')\
+                        (restoration.denoise_tv_bregman)
+denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\
+                        (restoration.denoise_tv_chambolle)
+
+# Backward compatibility v<0.11
+@deprecated
+def canny(*args, **kwargs):
+    # Hack to avoid circular import
+    from skimage.feature._canny import canny as canny_
+    return canny_(*args, **kwargs)
+
+
+__all__ = ['inverse',
+           'wiener',
+           'LPIFilter2D',
+           'gaussian_filter',
+           'canny',
+           'sobel',
+           'hsobel',
+           'vsobel',
+           'scharr',
+           'hscharr',
+           'vscharr',
+           'prewitt',
+           'hprewitt',
+           'vprewitt',
+           'roberts',
+           'roberts_positive_diagonal',
+           'roberts_negative_diagonal',
+           'denoise_tv_chambolle',
+           'denoise_bilateral',
+           'denoise_tv_bregman',
+           'rank_order',
+           'gabor_kernel',
+           'gabor_filter',
+           'threshold_adaptive',
+           'threshold_otsu',
+           'threshold_yen',
+           'threshold_isodata',
+           'rank']

skimage/filters/lpi_filter.py

@@ -66,9 +66,9 @@ class LPIFilter2D(object):
 
         Examples
         --------
-
         Gaussian filter: Use a 1-D gaussian in each direction without
         normalization coefficients.
+
         >>> def filt_func(r, c, sigma = 1):
         ...     return np.exp(-np.hypot(r, c)/sigma)
         >>> filter = LPIFilter2D(filt_func)

skimage/filters/rank/bilateral.py

@@ -89,13 +89,13 @@ def mean_bilateral(image, selem, out=None, mask=None, shift_x=False,
 
     See also
     --------
-    skimage.filter.denoise_bilateral for a Gaussian bilateral filter.
+    skimage.filters.denoise_bilateral for a Gaussian bilateral filter.
 
     Examples
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import mean_bilateral
+    >>> from skimage.filters.rank import mean_bilateral
     >>> img = data.camera().astype(np.uint16)
     >>> bilat_img = mean_bilateral(img, disk(20), s0=10,s1=10)
 
@@ -142,7 +142,7 @@ def pop_bilateral(image, selem, out=None, mask=None, shift_x=False,
     Examples
     --------
     >>> from skimage.morphology import square
-    >>> import skimage.filter.rank as rank
+    >>> import skimage.filters.rank as rank
     >>> img = 255 * np.array([[0, 0, 0, 0, 0],
     ...                       [0, 1, 1, 1, 0],
     ...                       [0, 1, 1, 1, 0],
@@ -206,13 +206,13 @@ def sum_bilateral(image, selem, out=None, mask=None, shift_x=False,
 
     See also
     --------
-    skimage.filter.denoise_bilateral for a Gaussian bilateral filter.
+    skimage.filters.denoise_bilateral for a Gaussian bilateral filter.
 
     Examples
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import sum_bilateral
+    >>> from skimage.filters.rank import sum_bilateral
     >>> img = data.camera().astype(np.uint16)
     >>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10)
 

skimage/filters/rank/generic.py

@@ -126,7 +126,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import autolevel
+    >>> from skimage.filters.rank import autolevel
     >>> img = data.camera()
     >>> auto = autolevel(img, disk(5))
 
@@ -168,7 +168,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import bottomhat
+    >>> from skimage.filters.rank import bottomhat
     >>> img = data.camera()
     >>> out = bottomhat(img, disk(5))
 
@@ -207,7 +207,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import equalize
+    >>> from skimage.filters.rank import equalize
     >>> img = data.camera()
     >>> equ = equalize(img, disk(5))
 
@@ -246,7 +246,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import gradient
+    >>> from skimage.filters.rank import gradient
     >>> img = data.camera()
     >>> out = gradient(img, disk(5))
 
@@ -287,14 +287,14 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
 
     Notes
     -----
-    The lower algorithm complexity makes the `skimage.filter.rank.maximum`
+    The lower algorithm complexity makes `skimage.filters.rank.maximum`
     more efficient for larger images and structuring elements.
 
     Examples
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import maximum
+    >>> from skimage.filters.rank import maximum
     >>> img = data.camera()
     >>> out = maximum(img, disk(5))
 
@@ -333,7 +333,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import mean
+    >>> from skimage.filters.rank import mean
     >>> img = data.camera()
     >>> avg = mean(img, disk(5))
 
@@ -372,7 +372,7 @@ def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import subtract_mean
+    >>> from skimage.filters.rank import subtract_mean
     >>> img = data.camera()
     >>> out = subtract_mean(img, disk(5))
 
@@ -411,7 +411,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import median
+    >>> from skimage.filters.rank import median
     >>> img = data.camera()
     >>> med = median(img, disk(5))
 
@@ -452,14 +452,14 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
 
     Notes
     -----
-    The lower algorithm complexity makes the `skimage.filter.rank.minimum` more
+    The lower algorithm complexity makes `skimage.filters.rank.minimum` more
     efficient for larger images and structuring elements.
 
     Examples
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import minimum
+    >>> from skimage.filters.rank import minimum
     >>> img = data.camera()
     >>> out = minimum(img, disk(5))
 
@@ -500,7 +500,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import modal
+    >>> from skimage.filters.rank import modal
     >>> img = data.camera()
     >>> out = modal(img, disk(5))
 
@@ -544,7 +544,7 @@ def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import enhance_contrast
+    >>> from skimage.filters.rank import enhance_contrast
     >>> img = data.camera()
     >>> out = enhance_contrast(img, disk(5))
 
@@ -585,7 +585,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     Examples
     --------
     >>> from skimage.morphology import square
-    >>> import skimage.filter.rank as rank
+    >>> import skimage.filters.rank as rank
     >>> img = 255 * np.array([[0, 0, 0, 0, 0],
     ...                       [0, 1, 1, 1, 0],
     ...                       [0, 1, 1, 1, 0],
@@ -635,7 +635,7 @@ def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     Examples
     --------
     >>> from skimage.morphology import square
-    >>> import skimage.filter.rank as rank
+    >>> import skimage.filters.rank as rank
     >>> img = np.array([[0, 0, 0, 0, 0],
     ...                 [0, 1, 1, 1, 0],
     ...                 [0, 1, 1, 1, 0],
@@ -685,7 +685,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     Examples
     --------
     >>> from skimage.morphology import square
-    >>> from skimage.filter.rank import threshold
+    >>> from skimage.filters.rank import threshold
     >>> img = 255 * np.array([[0, 0, 0, 0, 0],
     ...                       [0, 1, 1, 1, 0],
     ...                       [0, 1, 1, 1, 0],
@@ -736,7 +736,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import tophat
+    >>> from skimage.filters.rank import tophat
     >>> img = data.camera()
     >>> out = tophat(img, disk(5))
 
@@ -781,7 +781,7 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False,
     --------
     >>> from skimage import data
     >>> from skimage.morphology import disk
-    >>> from skimage.filter.rank import noise_filter
+    >>> from skimage.filters.rank import noise_filter
     >>> img = data.camera()
     >>> out = noise_filter(img, disk(5))
 
@@ -833,7 +833,7 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     Examples
     --------
     >>> from skimage import data
-    >>> from skimage.filter.rank import entropy
+    >>> from skimage.filters.rank import entropy
     >>> from skimage.morphology import disk
     >>> img = data.camera()
     >>> ent = entropy(img, disk(5))
@@ -877,7 +877,7 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     Examples
     --------
     >>> from skimage import data
-    >>> from skimage.filter.rank import otsu
+    >>> from skimage.filters.rank import otsu
     >>> from skimage.morphology import disk
     >>> img = data.camera()
     >>> local_otsu = otsu(img, disk(5))
@@ -926,7 +926,7 @@ def windowed_histogram(image, selem, out=None, mask=None,
     Examples
     --------
     >>> from skimage import data
-    >>> from skimage.filter.rank import windowed_histogram
+    >>> from skimage.filters.rank import windowed_histogram
     >>> from skimage.morphology import disk
     >>> img = data.camera()
     >>> hist_img = windowed_histogram(img, disk(5))

skimage/filters/rank/tests/test_rank.py

@@ -4,7 +4,7 @@ from numpy.testing import run_module_suite, assert_array_equal, assert_raises
 from skimage import img_as_ubyte, img_as_uint, img_as_float
 from skimage import data, util
 from skimage.morphology import cmorph, disk
-from skimage.filter import rank
+from skimage.filters import rank
 
 np.random.seed(0)
 

skimage/filters/setup.py

@@ -9,7 +9,7 @@ base_path = os.path.abspath(os.path.dirname(__file__))
 def configuration(parent_package='', top_path=None):
     from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs
 
-    config = Configuration('filter', parent_package, top_path)
+    config = Configuration('filters', parent_package, top_path)
     config.add_data_dir('tests')
     config.add_data_dir('rank/tests')
 

skimage/filters/tests/test_edges.py

@@ -2,13 +2,13 @@ import numpy as np
 from numpy.testing import (assert_array_almost_equal as assert_close,
                            assert_, assert_allclose)
 
-import skimage.filter as F
-from skimage.filter.edges import _mask_filter_result
+from skimage import filters
+from skimage.filters.edges import _mask_filter_result
 
 
 def test_roberts_zeros():
     """Roberts' filter on an array of all zeros."""
-    result = F.roberts(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.roberts(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert (np.all(result == 0))
 
 
@@ -18,7 +18,7 @@ def test_roberts_diagonal1():
     expected = ~(np.tri(10, 10, -1).astype(bool) |
                  np.tri(10, 10, -2).astype(bool).transpose())
     expected = _mask_filter_result(expected, None)
-    result = F.roberts(image).astype(bool)
+    result = filters.roberts(image).astype(bool)
     assert_close(result, expected)
 
 
@@ -28,21 +28,21 @@ def test_roberts_diagonal2():
     expected = ~np.rot90(np.tri(10, 10, -1).astype(bool) |
                          np.tri(10, 10, -2).astype(bool).transpose())
     expected = _mask_filter_result(expected, None)
-    result = F.roberts(image).astype(bool)
+    result = filters.roberts(image).astype(bool)
     assert_close(result, expected)
 
 
 def test_sobel_zeros():
     """Sobel on an array of all zeros."""
-    result = F.sobel(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.sobel(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert (np.all(result == 0))
 
 
 def test_sobel_mask():
     """Sobel on a masked array should be zero."""
     np.random.seed(0)
-    result = F.sobel(np.random.uniform(size=(10, 10)),
-                     np.zeros((10, 10), bool))
+    result = filters.sobel(np.random.uniform(size=(10, 10)),
+                           np.zeros((10, 10), bool))
     assert (np.all(result == 0))
 
 
@@ -50,7 +50,7 @@ def test_sobel_horizontal():
     """Sobel on a horizontal edge should be a horizontal line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.sobel(image) * np.sqrt(2)
+    result = filters.sobel(image) * np.sqrt(2)
     # Fudge the eroded points
     i[np.abs(j) == 5] = 10000
     assert_allclose(result[i == 0], 1)
@@ -61,7 +61,7 @@ def test_sobel_vertical():
     """Sobel on a vertical edge should be a vertical line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float)
-    result = F.sobel(image) * np.sqrt(2)
+    result = filters.sobel(image) * np.sqrt(2)
     j[np.abs(i) == 5] = 10000
     assert (np.all(result[j == 0] == 1))
     assert (np.all(result[np.abs(j) > 1] == 0))
@@ -69,15 +69,15 @@ def test_sobel_vertical():
 
 def test_hsobel_zeros():
     """Horizontal sobel on an array of all zeros."""
-    result = F.hsobel(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.hsobel(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert (np.all(result == 0))
 
 
 def test_hsobel_mask():
     """Horizontal Sobel on a masked array should be zero."""
     np.random.seed(0)
-    result = F.hsobel(np.random.uniform(size=(10, 10)),
-                      np.zeros((10, 10), bool))
+    result = filters.hsobel(np.random.uniform(size=(10, 10)),
+                            np.zeros((10, 10), bool))
     assert (np.all(result == 0))
 
 
@@ -85,7 +85,7 @@ def test_hsobel_horizontal():
     """Horizontal Sobel on an edge should be a horizontal line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.hsobel(image)
+    result = filters.hsobel(image)
     # Fudge the eroded points
     i[np.abs(j) == 5] = 10000
     assert (np.all(result[i == 0] == 1))
@@ -96,20 +96,20 @@ def test_hsobel_vertical():
     """Horizontal Sobel on a vertical edge should be zero."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float) * np.sqrt(2)
-    result = F.hsobel(image)
+    result = filters.hsobel(image)
     assert_allclose(result, 0, atol=1e-10)
 
 
 def test_vsobel_zeros():
     """Vertical sobel on an array of all zeros."""
-    result = F.vsobel(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.vsobel(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert_allclose(result, 0)
 
 
 def test_vsobel_mask():
     """Vertical Sobel on a masked array should be zero."""
     np.random.seed(0)
-    result = F.vsobel(np.random.uniform(size=(10, 10)),
+    result = filters.vsobel(np.random.uniform(size=(10, 10)),
                       np.zeros((10, 10), bool))
     assert_allclose(result, 0)
 
@@ -118,7 +118,7 @@ def test_vsobel_vertical():
     """Vertical Sobel on an edge should be a vertical line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float)
-    result = F.vsobel(image)
+    result = filters.vsobel(image)
     # Fudge the eroded points
     j[np.abs(i) == 5] = 10000
     assert (np.all(result[j == 0] == 1))
@@ -129,21 +129,21 @@ def test_vsobel_horizontal():
     """vertical Sobel on a horizontal edge should be zero."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.vsobel(image)
+    result = filters.vsobel(image)
     assert_allclose(result, 0)
 
 
 def test_scharr_zeros():
     """Scharr on an array of all zeros."""
-    result = F.scharr(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.scharr(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert (np.all(result < 1e-16))
 
 
 def test_scharr_mask():
     """Scharr on a masked array should be zero."""
     np.random.seed(0)
-    result = F.scharr(np.random.uniform(size=(10, 10)),
-                      np.zeros((10, 10), bool))
+    result = filters.scharr(np.random.uniform(size=(10, 10)),
+                            np.zeros((10, 10), bool))
     assert_allclose(result, 0)
 
 
@@ -151,7 +151,7 @@ def test_scharr_horizontal():
     """Scharr on an edge should be a horizontal line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.scharr(image) * np.sqrt(2)
+    result = filters.scharr(image) * np.sqrt(2)
     # Fudge the eroded points
     i[np.abs(j) == 5] = 10000
     assert_allclose(result[i == 0], 1)
@@ -162,7 +162,7 @@ def test_scharr_vertical():
     """Scharr on a vertical edge should be a vertical line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float)
-    result = F.scharr(image) * np.sqrt(2)
+    result = filters.scharr(image) * np.sqrt(2)
     j[np.abs(i) == 5] = 10000
     assert_allclose(result[j == 0], 1)
     assert (np.all(result[np.abs(j) > 1] == 0))
@@ -170,15 +170,15 @@ def test_scharr_vertical():
 
 def test_hscharr_zeros():
     """Horizontal Scharr on an array of all zeros."""
-    result = F.hscharr(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.hscharr(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert_allclose(result, 0)
 
 
 def test_hscharr_mask():
     """Horizontal Scharr on a masked array should be zero."""
     np.random.seed(0)
-    result = F.hscharr(np.random.uniform(size=(10, 10)),
-                       np.zeros((10, 10), bool))
+    result = filters.hscharr(np.random.uniform(size=(10, 10)),
+                             np.zeros((10, 10), bool))
     assert_allclose(result, 0)
 
 
@@ -186,7 +186,7 @@ def test_hscharr_horizontal():
     """Horizontal Scharr on an edge should be a horizontal line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.hscharr(image)
+    result = filters.hscharr(image)
     # Fudge the eroded points
     i[np.abs(j) == 5] = 10000
     assert (np.all(result[i == 0] == 1))
@@ -197,21 +197,21 @@ def test_hscharr_vertical():
     """Horizontal Scharr on a vertical edge should be zero."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float)
-    result = F.hscharr(image)
+    result = filters.hscharr(image)
     assert_allclose(result, 0)
 
 
 def test_vscharr_zeros():
     """Vertical Scharr on an array of all zeros."""
-    result = F.vscharr(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.vscharr(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert_allclose(result, 0)
 
 
 def test_vscharr_mask():
     """Vertical Scharr on a masked array should be zero."""
     np.random.seed(0)
-    result = F.vscharr(np.random.uniform(size=(10, 10)),
-                       np.zeros((10, 10), bool))
+    result = filters.vscharr(np.random.uniform(size=(10, 10)),
+                             np.zeros((10, 10), bool))
     assert_allclose(result, 0)
 
 
@@ -219,7 +219,7 @@ def test_vscharr_vertical():
     """Vertical Scharr on an edge should be a vertical line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float)
-    result = F.vscharr(image)
+    result = filters.vscharr(image)
     # Fudge the eroded points
     j[np.abs(i) == 5] = 10000
     assert (np.all(result[j == 0] == 1))
@@ -230,21 +230,21 @@ def test_vscharr_horizontal():
     """vertical Scharr on a horizontal edge should be zero."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.vscharr(image)
+    result = filters.vscharr(image)
     assert_allclose(result, 0)
 
 
 def test_prewitt_zeros():
     """Prewitt on an array of all zeros."""
-    result = F.prewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.prewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert_allclose(result, 0)
 
 
 def test_prewitt_mask():
     """Prewitt on a masked array should be zero."""
     np.random.seed(0)
-    result = F.prewitt(np.random.uniform(size=(10, 10)),
-                       np.zeros((10, 10), bool))
+    result = filters.prewitt(np.random.uniform(size=(10, 10)),
+                             np.zeros((10, 10), bool))
     assert_allclose(np.abs(result), 0)
 
 
@@ -252,7 +252,7 @@ def test_prewitt_horizontal():
     """Prewitt on an edge should be a horizontal line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.prewitt(image) * np.sqrt(2)
+    result = filters.prewitt(image) * np.sqrt(2)
     # Fudge the eroded points
     i[np.abs(j) == 5] = 10000
     assert (np.all(result[i == 0] == 1))
@@ -263,7 +263,7 @@ def test_prewitt_vertical():
     """Prewitt on a vertical edge should be a vertical line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float)
-    result = F.prewitt(image) * np.sqrt(2)
+    result = filters.prewitt(image) * np.sqrt(2)
     j[np.abs(i) == 5] = 10000
     assert_allclose(result[j == 0], 1)
     assert_allclose(result[np.abs(j) > 1], 0, atol=1e-10)
@@ -271,14 +271,14 @@ def test_prewitt_vertical():
 
 def test_hprewitt_zeros():
     """Horizontal prewitt on an array of all zeros."""
-    result = F.hprewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.hprewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert_allclose(result, 0)
 
 
 def test_hprewitt_mask():
     """Horizontal prewitt on a masked array should be zero."""
     np.random.seed(0)
-    result = F.hprewitt(np.random.uniform(size=(10, 10)),
+    result = filters.hprewitt(np.random.uniform(size=(10, 10)),
                         np.zeros((10, 10), bool))
     assert_allclose(result, 0)
 
@@ -287,7 +287,7 @@ def test_hprewitt_horizontal():
     """Horizontal prewitt on an edge should be a horizontal line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.hprewitt(image)
+    result = filters.hprewitt(image)
     # Fudge the eroded points
     i[np.abs(j) == 5] = 10000
     assert (np.all(result[i == 0] == 1))
@@ -298,21 +298,21 @@ def test_hprewitt_vertical():
     """Horizontal prewitt on a vertical edge should be zero."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float)
-    result = F.hprewitt(image)
+    result = filters.hprewitt(image)
     assert_allclose(result, 0, atol=1e-10)
 
 
 def test_vprewitt_zeros():
     """Vertical prewitt on an array of all zeros."""
-    result = F.vprewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
+    result = filters.vprewitt(np.zeros((10, 10)), np.ones((10, 10), bool))
     assert_allclose(result, 0)
 
 
 def test_vprewitt_mask():
     """Vertical prewitt on a masked array should be zero."""
     np.random.seed(0)
-    result = F.vprewitt(np.random.uniform(size=(10, 10)),
-                        np.zeros((10, 10), bool))
+    result = filters.vprewitt(np.random.uniform(size=(10, 10)),
+                              np.zeros((10, 10), bool))
     assert_allclose(result, 0)
 
 
@@ -320,7 +320,7 @@ def test_vprewitt_vertical():
     """Vertical prewitt on an edge should be a vertical line."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (j >= 0).astype(float)
-    result = F.vprewitt(image)
+    result = filters.vprewitt(image)
     # Fudge the eroded points
     j[np.abs(i) == 5] = 10000
     assert (np.all(result[j == 0] == 1))
@@ -331,7 +331,7 @@ def test_vprewitt_horizontal():
     """Vertical prewitt on a horizontal edge should be zero."""
     i, j = np.mgrid[-5:6, -5:6]
     image = (i >= 0).astype(float)
-    result = F.vprewitt(image)
+    result = filters.vprewitt(image)
     assert_allclose(result, 0)
 
 
@@ -347,7 +347,7 @@ def test_horizontal_mask_line():
     expected[1:-1, 1:-1] = 0.2           # constant gradient for most of image,
     expected[4:7, 1:-1] = 0              # but line and neighbors masked
 
-    for grad_func in (F.hprewitt, F.hsobel, F.hscharr):
+    for grad_func in (filters.hprewitt, filters.hsobel, filters.hscharr):
         result = grad_func(vgrad, mask)
         yield assert_close, result, expected
 
@@ -364,7 +364,7 @@ def test_vertical_mask_line():
     expected[1:-1, 1:-1] = 0.2           # constant gradient for most of image,
     expected[1:-1, 4:7] = 0              # but line and neighbors masked
 
-    for grad_func in (F.vprewitt, F.vsobel, F.vscharr):
+    for grad_func in (filters.vprewitt, filters.vsobel, filters.vscharr):
         result = grad_func(hgrad, mask)
         yield assert_close, result, expected
 
@@ -372,7 +372,8 @@ def test_vertical_mask_line():
 def test_range():
     """Output of edge detection should be in [0, 1]"""
     image = np.random.random((100, 100))
-    for detector in (F.sobel, F.scharr, F.prewitt, F.roberts):
+    for detector in (filters.sobel, filters.scharr,
+                     filters.prewitt, filters.roberts):
         out = detector(image)
         assert_(out.min() >= 0,
                 "Minimum of `{0}` is smaller than zero".format(

skimage/filters/tests/test_filter_import.py

@@ -0,0 +1,10 @@
+from warnings import catch_warnings, simplefilter
+
+
+def test_filter_import():
+    with catch_warnings():
+        simplefilter('ignore')
+        from skimage import filter as F
+
+    assert('sobel' in dir(F))
+    assert F._import_warned

skimage/filters/tests/test_gabor.py

@@ -2,7 +2,7 @@ import numpy as np
 from numpy.testing import (assert_equal, assert_almost_equal,
                            assert_array_almost_equal)
 
-from skimage.filter._gabor import gabor_kernel, gabor_filter, _sigma_prefactor
+from skimage.filters._gabor import gabor_kernel, gabor_filter, _sigma_prefactor
 
 
 def test_gabor_kernel_size():

skimage/filters/tests/test_gaussian.py

@@ -1,5 +1,5 @@
 import numpy as np
-from skimage.filter._gaussian import gaussian_filter
+from skimage.filters._gaussian import gaussian_filter
 
 
 def test_null_sigma():

skimage/filters/tests/test_lpi_filter.py

@@ -1,17 +1,13 @@
-import os.path
-
 import numpy as np
-from numpy.testing import *
+from numpy.testing import (assert_raises, assert_, assert_equal,
+                           run_module_suite)
 
-from skimage import data_dir
-from skimage.io import *
-from skimage.filter import *
+from skimage import data
+from skimage.filters import LPIFilter2D, inverse, wiener
 
 
 class TestLPIFilter2D(object):
-
-    img = imread(os.path.join(data_dir, 'camera.png'),
-                 flatten=True)[:50, :50]
+    img = data.camera()[:50, :50]
 
     def filt_func(self, r, c):
         return np.exp(-np.hypot(r, c) / 1)
@@ -36,14 +32,14 @@ class TestLPIFilter2D(object):
         assert_equal(g.shape, self.img.shape)
 
         g1 = inverse(F[::-1, ::-1], predefined_filter=self.f)
-        assert ((g - g1[::-1, ::-1]).sum() < 55)
+        assert_((g - g1[::-1, ::-1]).sum() < 55)
 
         # test cache
         g1 = inverse(F[::-1, ::-1], predefined_filter=self.f)
-        assert ((g - g1[::-1, ::-1]).sum() < 55)
+        assert_((g - g1[::-1, ::-1]).sum() < 55)
 
         g1 = inverse(F[::-1, ::-1], self.filt_func)
-        assert ((g - g1[::-1, ::-1]).sum() < 55)
+        assert_((g - g1[::-1, ::-1]).sum() < 55)
 
     def test_wiener(self):
         F = self.f(self.img)
@@ -51,10 +47,10 @@ class TestLPIFilter2D(object):
         assert_equal(g.shape, self.img.shape)
 
         g1 = wiener(F[::-1, ::-1], predefined_filter=self.f)
-        assert ((g - g1[::-1, ::-1]).sum() < 1)
+        assert_((g - g1[::-1, ::-1]).sum() < 1)
 
         g1 = wiener(F[::-1, ::-1], self.filt_func)
-        assert ((g - g1[::-1, ::-1]).sum() < 1)
+        assert_((g - g1[::-1, ::-1]).sum() < 1)
 
     def test_non_callable(self):
         assert_raises(ValueError, LPIFilter2D, None)

skimage/filters/tests/test_thresholding.py

@@ -3,10 +3,10 @@ from numpy.testing import assert_equal, assert_almost_equal
 
 import skimage
 from skimage import data
-from skimage.filter.thresholding import (threshold_adaptive,
-                                         threshold_otsu,
-                                         threshold_yen,
-                                         threshold_isodata)
+from skimage.filters.thresholding import (threshold_adaptive,
+                                          threshold_otsu,
+                                          threshold_yen,
+                                          threshold_isodata)
 
 
 class TestSimpleImage():

skimage/morphology/greyreconstruct.py

@@ -11,7 +11,7 @@ Original author: Lee Kamentsky
 """
 import numpy as np
 
-from skimage.filter._rank_order import rank_order
+from skimage.filters._rank_order import rank_order
 
 
 def reconstruction(seed, mask, method='dilation', selem=None, offset=None):

skimage/morphology/watershed.py

@@ -27,7 +27,7 @@ Original author: Lee Kamentsky
 from _heapq import heappush, heappop
 import numpy as np
 import scipy.ndimage
-from ..filter import rank_order
+from ..filters import rank_order
 from .._shared.utils import deprecated
 
 from . import _watershed

skimage/segmentation/random_walker_segmentation.py

@@ -39,7 +39,7 @@ except ImportError:
     amg_loaded = False
 from scipy.sparse.linalg import cg
 from ..util import img_as_float
-from ..filter import rank_order
+from ..filters import rank_order
 
 #-----------Laplacian--------------------
 

skimage/setup.py

@@ -13,7 +13,7 @@ def configuration(parent_package='', top_path=None):
     config.add_subpackage('exposure')
     config.add_subpackage('feature')
     config.add_subpackage('restoration')
-    config.add_subpackage('filter')
+    config.add_subpackage('filters')
     config.add_subpackage('graph')
     config.add_subpackage('io')
     config.add_subpackage('measure')

skimage/viewer/tests/test_plugins.py

@@ -2,7 +2,7 @@
 import numpy as np
 import skimage
 import skimage.data as data
-from skimage.filter.rank import median
+from skimage.filters.rank import median
 from skimage.morphology import disk
 from skimage.viewer import ImageViewer, viewer_available
 from numpy.testing import assert_equal, assert_allclose, assert_almost_equal

skimage/viewer/tests/test_viewer.py

@@ -4,7 +4,7 @@ from skimage.viewer.qt import QtGui, QtCore
 from skimage.viewer import ImageViewer, CollectionViewer, viewer_available
 from skimage.transform import pyramid_gaussian
 from skimage.viewer.plugins import OverlayPlugin
-from skimage.filter import sobel
+from skimage.filters import sobel
 from numpy.testing import assert_equal
 from numpy.testing.decorators import skipif
 from skimage._shared.version_requirements import is_installed

viewer_examples/plugins/canny_simple.py

@@ -1,5 +1,5 @@
 from skimage import data
-from skimage.filter import canny
+from skimage.filters import canny
 
 from skimage.viewer import ImageViewer
 from skimage.viewer.widgets import Slider

viewer_examples/plugins/collection_plugin.py

@@ -8,7 +8,7 @@ Demo of a CollectionViewer for viewing collections of images with the
 
 """
 from skimage import data
-from skimage.filter import rank
+from skimage.filters import rank
 from skimage.morphology import disk
 
 from skimage.viewer import CollectionViewer

viewer_examples/plugins/median_filter.py

@@ -1,5 +1,5 @@
 from skimage import data
-from skimage.filter.rank import median
+from skimage.filters.rank import median
 from skimage.morphology import disk
 
 from skimage.viewer import ImageViewer

viewer_examples/plugins/watershed_demo.py

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 
 from skimage import data
-from skimage import filter
+from skimage import filters
 from skimage import morphology
 from skimage.viewer import ImageViewer
 from skimage.viewer.widgets import history