moved example to doc

doc/examples/plot_benchmark_rank.py

@@ -0,0 +1,174 @@
+"""
+==============================
+Compare execution time for
+    - skimage.rank.median,
+    - skimage.filter import median_filter
+    - scipy.ndimage.filters import percentile_filter,
+
+    and
+
+    - skimage.cmorph.dilate
+    - skimage.rank.maximum
+
+==============================
+
+to complete
+
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+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
+
+def log_timing(func):
+    """ Decorator that returns both function results and execution time
+    (result, ms)
+    """
+    def wrapper(*arg):
+        t1 = time.time()
+        res = func(*arg)
+        t2 = time.time()
+        ms = (t2-t1)*1000.0
+        return (res,ms)
+    return wrapper
+
+
+@log_timing
+def cr_med(image,selem):
+    return rank.median(image=image,selem = selem)
+
+@log_timing
+def cr_max(image,selem):
+    return rank.maximum(image=image,selem = selem)
+
+@log_timing
+def cm_dil(image,selem):
+    return dilation(image=image,selem = selem)
+
+@log_timing
+def ctmf_med(image,radius):
+    return median_filter(image=image,radius=radius)
+
+@log_timing
+def ndi_med(image,n):
+    return percentile_filter(image,50,size=n*2-1)
+
+def compare_dilate():
+    """ Comparison between
+    - crank.maximum rankfilter implementation
+    - cmorph.dilate cython implementation
+
+    on increasing structuring element size and increasing image size
+    """
+    a = data.camera()
+
+    rec = []
+    e_range = range(1,20,1)
+    for r in e_range:
+        elem = disk(r+1)
+        #        elem = (np.random.random((r,r))>.5).astype('uint8')
+        rc,ms_rc = cr_max(a,elem)
+        rcm,ms_rcm = cm_dil(a,elem)
+        rec.append((ms_rc,ms_rcm))
+        # same structuring element, the results must match
+        assert  (rc==rcm).all()
+
+    rec = np.asarray(rec)
+
+    plt.figure()
+    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)
+
+    rec = []
+    s_range = range(100,1000,100)
+    for s in s_range:
+        a = (np.random.random((s,s))*256).astype('uint8')
+        (rc,ms_rc) = cr_max(a,elem)
+        (rcm,ms_rcm) = cm_dil(a,elem)
+        rec.append((ms_rc,ms_rcm))
+        # same structuring element, the results must match
+        assert  (rc==rcm).all()
+
+    rec = np.asarray(rec)
+
+    plt.figure()
+    plt.title('increasing image size')
+    plt.plot(s_range,rec)
+    plt.legend(['crank.maximum','cmorph.dilate'])
+    plt.figure()
+    plt.imshow(np.hstack((rc,rcm)))
+
+
+def compare_median():
+    """ Comparison between
+    - crank.median rankfilter implementation
+    - ctmf.median_filter filter
+
+    on increasing structuring element size and increasing image size
+    """
+    a = data.camera()
+
+    rec = []
+    e_range = range(2,30,4)
+    for r in e_range:
+        elem = disk(r+1)
+        rc,ms_rc = cr_med(a,elem)
+        rctmf,ms_rctmf = ctmf_med(a,r)
+        rndi,ms_ndi = ndi_med(a,r)
+        rec.append((ms_rc,ms_rctmf,ms_ndi))
+        # check if results are identical
+        # obviously they cannot be identical since structuring element are different (octagon<>disk)
+        # assert  (rc==rctmf).all()
+
+    rec = np.asarray(rec)
+
+    plt.figure()
+    plt.title('increasing element size')
+    plt.plot(e_range,rec)
+    plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile'])
+    plt.ylabel('time (ms)')
+    plt.xlabel('element radius')
+    plt.figure()
+    plt.imshow(np.hstack((rc,rctmf,rndi)))
+    plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile')
+
+    r = 9
+    elem = disk(r+1)
+
+    rec = []
+    s_range = [100,200,500,1000,2000]
+    for s in s_range:
+        a = (np.random.random((s,s))*256).astype('uint8')
+        (rc,ms_rc) = cr_med(a,elem)
+        rctmf,ms_rctmf = ctmf_med(a,r)
+        rndi,ms_ndi = ndi_med(a,r)
+        rec.append((ms_rc,ms_rctmf,ms_ndi))
+        # check if results are identical
+        # obviously they cannot be identical since structuring element are different (octagon<>disk)
+        # assert  (rc==rctmf).all()
+
+    rec = np.asarray(rec)
+
+    plt.figure()
+    plt.title('increasing image size')
+    plt.plot(s_range,rec)
+    plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile'])
+    plt.ylabel('time (ms)')
+    plt.xlabel('image size')
+
+
+
+compare_dilate()
+compare_median()
+plt.show()