ENH Add generalized Bernsen thresholding

This allows the user to specify their own structuring element.

ChangeLog

@@ -7,7 +7,7 @@ Version 0.9.6+
 	* Add ``verbose`` argument to tests.run()
 	* Add ``circle_se`` to ``morph``
 	* Allow ``loc(max|min)`` to take floating point inputs
-	* Add Bernsen local thresholding
+	* Add Bernsen local thresholding (``bernsen`` and ``gbernsen`` functions)
 
 Version 0.9.6 2012-12-02 by luispedro
 	* Fix distance() of non-boolean images (issue #24 on github)

mahotas/tests/test_thresholding.py

@@ -2,7 +2,7 @@
 # License: MIT
 
 import numpy as np
-from mahotas.thresholding import otsu, rc, bernsen
+from mahotas.thresholding import otsu, rc, bernsen, gbernsen
 from mahotas.histogram import fullhistogram
 
 def slow_otsu(img, ignore_zeros=False):
@@ -102,7 +102,17 @@ def test_soft_threhold():
 def test_bernsen():
     np.random.seed(120)
     for i in range(4):
-        f = 32*np.random.rand(128,128)
+        f = 32*np.random.rand(40,68)
         f = f.astype(np.uint8)
         b = bernsen(f, 8, 15)
         assert f.shape == b.shape
+        b = bernsen(f, 8, 15, 34)
+        assert f.shape == b.shape
+
+def test_gbernsen():
+    np.random.seed(120)
+    for i in range(4):
+        f = 32*np.random.rand(64,96)
+        f = f.astype(np.uint8)
+        b = gbernsen(f, np.ones((3,3), bool), 15, 145)
+        assert f.shape == b.shape

mahotas/thresholding.py

@@ -168,14 +168,47 @@ def bernsen(f, radius, contrast_threshold, gthresh=None):
     Returns
     -------
     thresholded : binary ndarray
+
+    See Also
+    --------
+    gbernsen : function
+        Generalised Bernsen thresholding
     '''
-    from mahotas import morph
-    from mahotas.convolve import rank_filter
+    from mahotas.morph import circle_se
     if gthresh is None:
         gthresh = 128
-    circle = morph.circle_se(radius)
-    fmax = rank_filter(f, circle, circle.sum()-1)
-    fmin = rank_filter(f, circle, 0)
+    return gbernsen(f, circle_se(radius), contrast_threshold, gthresh)
+
+def gbernsen(f, se, contrast_threshold, gthresh):
+    '''
+    thresholded = gbernsen(f, se, contrast_threshold, gthresh)
+
+    Generalised Bernsen local thresholding
+
+    Parameters
+    ----------
+    f : ndarray
+        input image
+    se : boolean ndarray
+        structuring element to use for "locality"
+    contrast_threshold : integer
+        contrast threshold
+    gthresh : numeric, optional
+        global threshold to fall back in low contrast regions
+
+    Returns
+    -------
+    thresholded : binary ndarray
+
+    See Also
+    --------
+    bernsen : function
+        Bernsen thresholding with a circular region
+    '''
+    from mahotas.convolve import rank_filter
+    fmax = rank_filter(f, se, se.sum()-1)
+    fmin = rank_filter(f, se, 0)
     fptp = fmax - fmin
     fmean = fmax/.2 + fmin/.2 # Do not use (fmax + fmin) as that may overflow
     return np.choose(fptp < contrast_threshold, (fmean < gthresh, fmean > f))
+