move rank/ into filter/

doc/examples/applications/plot_benchmark_rank.py

@@ -19,13 +19,14 @@ import numpy as np
 import matplotlib.pyplot as plt
 import time
 
+from scipy.ndimage.filters import percentile_filter
+
 from skimage import data
 from skimage.morphology import dilation,disk
 from skimage.filter import median_filter
-from scipy.ndimage.filters import percentile_filter
-import skimage.rank as rank
+import skimage.filter.rank as rank
 
-def log_timing(func):
+def exec_and_timeit(func):
     """ Decorator that returns both function results and execution time
     (result, ms)
     """
@@ -38,23 +39,23 @@ def log_timing(func):
     return wrapper
 
 
-@log_timing
+@exec_and_timeit
 def cr_med(image,selem):
     return rank.median(image=image,selem = selem)
 
-@log_timing
+@exec_and_timeit
 def cr_max(image,selem):
     return rank.maximum(image=image,selem = selem)
 
-@log_timing
+@exec_and_timeit
 def cm_dil(image,selem):
     return dilation(image=image,selem = selem)
 
-@log_timing
+@exec_and_timeit
 def ctmf_med(image,radius):
     return median_filter(image=image,radius=radius)
 
-@log_timing
+@exec_and_timeit
 def ndi_med(image,n):
     return percentile_filter(image,50,size=n*2-1)
 
@@ -84,8 +85,6 @@ def compare_dilate():
     plt.title('increasing element size')
     plt.plot(e_range,rec)
     plt.legend(['crank.maximum','cmorph.dilate'])
-    plt.figure()
-    plt.imshow(np.hstack((rc,rcm)))
 
     r = 9
     elem = disk(r+1)

doc/examples/plot_16bitbilateral.py

@@ -11,7 +11,7 @@ import matplotlib.pyplot as plt
 
 from skimage import data
 from skimage.morphology import disk
-import skimage.rank as rank
+import skimage.filter.rank as rank
 
 a8 = (data.coins()).astype('uint8')
 
@@ -23,11 +23,13 @@ selem = disk(50)
 f3 = rank.equalize(a16,selem = selem)
 
 # display results
-fig, axes = plt.subplots(nrows=3, figsize=(15,5))
+fig, axes = plt.subplots(nrows=3, figsize=(15,15))
 ax0, ax1, ax2 = axes
 
 ax0.imshow(np.hstack((a8,f1)))
+ax0.set_title('percentile mean')
 ax1.imshow(np.hstack((a16,f2)))
+ax1.set_title('bilateral mean')
 ax2.imshow(np.hstack((a16,f3)))
-
+ax2.set_title('local equalization')
 plt.show()

doc/examples/plot_lena_bilateral_denoise.py

@@ -18,7 +18,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage import data, color, img_as_ubyte
-from skimage.rank import bilateral_mean
+from skimage.filter.rank import bilateral_mean
 from skimage.morphology import disk
 
 l = img_as_ubyte(color.rgb2gray(data.lena()))

doc/examples/plot_local_autolevels.py

@@ -13,7 +13,7 @@ import numpy as np
 
 from skimage import data
 
-from skimage.rank import percentile_autolevel,autolevel
+from skimage.filter.rank import percentile_autolevel,autolevel
 from skimage.morphology import disk
 
 

doc/examples/plot_local_equalize.py

@@ -17,12 +17,12 @@ The local version [2]_ of the histogram equalization emphasized every local gray
 from skimage import data
 from skimage.util.dtype import dtype_range
 from skimage import exposure
-from skimage import rank
 from skimage.morphology import disk
 
 import matplotlib.pyplot as plt
 
 import numpy as np
+from skimage.filter import rank
 
 def plot_img_and_hist(img, axes, bins=256):
     """Plot an image along with its histogram and cumulative histogram.

doc/examples/plot_local_threshold.py

@@ -27,7 +27,7 @@ import matplotlib.pyplot as plt
 from skimage import data
 from skimage.filter import threshold_otsu, threshold_adaptive
 
-from skimage.rank import threshold,morph_contr_enh
+from skimage.filter.rank import threshold,morph_contr_enh
 from skimage.morphology import disk
 
 

doc/examples/plot_marked_watershed.py

@@ -14,15 +14,14 @@ See Wikipedia_ for more details on the algorithm.
 
 """
 
-import numpy as np
 from scipy import ndimage
 import matplotlib.pyplot as plt
 from skimage.morphology import watershed,disk
-from skimage import rank
 from skimage import data
-from scipy import ndimage
 
 # original data
+from skimage.filter import rank
+
 image = data.camera()
 
 # denoise image