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

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