ENH: Execute median filter without mask array by default.

scikits/image/filter/_ctmf.pyx

@@ -761,21 +761,15 @@ def median_filter(
     mask : (M,N) array, dtype uint8
         A value of 1 indicates a significant pixel, 0
         that a pixel is masked.
-    output : (M,N) array, dtype uint8, optional
+    output : (M,N) array, dtype uint8
         Array of same size as the input in which to store
         the filtered image.
     radius : int
         Radius of the inscribed circle to the octagon.
-    percent : int
-        Sort the unmasked pixels within the octagon into and array
-        (conceptually) and take the value indexed by the size of that
-        array times `percent` divided by 100.  50 gives the median.
-
-    Returns
-    -------
-    output : (M,N) ndarray, dtype uint8
-        A reference to `output`, if specified, otherwise
-        the new output array.
+    percent : int, optional
+        The unmasked pixels within the octagon are sorted, and the
+        value at the `percent`-th index chosen.  For example, the
+        default value of 50 chooses the median pixel.
 
     """
     if percent < 0:

scikits/image/filter/ctmf.py

@@ -15,19 +15,38 @@ import numpy as np
 import _ctmf
 from rank_order import rank_order
 
-def median_filter(data, mask, radius, percent=50):
-    '''Masked median filter with octagonal shape
-    
-    data - array of data to be median filtered.
-    mask - mask of significant pixels in data
-    radius - the radius of a circle inscribed into the filtering octagon
-    percent - conceptually, order the significant pixels in the octagon,
-              count them and choose the pixel indexed by the percent
-              times the count divided by 100. More simply, 50 = median
-    returns a filtered array.  In areas where the median filter does
-      not overlap the mask, the filtered result is undefined, but in
-      practice, it will be the lowest value in the valid area.
+def median_filter(data, mask=None, radius=1, percent=50):
+    '''Masked median filter with octagon shape.
+
+    Parameters
+    ----------
+    data : (M,N) ndarray, dtype uint8
+        Input image.
+    mask : (M,N) ndarray, dtype uint8, optional
+        A value of 1 indicates a significant pixel, 0
+        that a pixel is masked.  By default, all pixels
+        are considered.
+    radius : {int, 1}, optional
+        The radius of a circle inscribed into the filtering
+        octagon. Default radius is 1.
+    percent : {int, 50}, optional
+        The unmasked pixels within the octagon are sorted, and the
+        value at the `percent`-th index chosen.  For example, the
+        default value of 50 chooses the median pixel.
+
+    Returns
+    -------
+    out : (M,N) ndarray, dtype uint8
+        Filtered array.  In areas where the median filter does
+        not overlap the mask, the filtered result is underfined, but
+        in practice, it will be the lowest value in the valid area.
+
     '''
+
+    if mask is None:
+        mask = np.ones(data.shape, dtype=np.bool)
+    mask = np.ascontiguousarray(mask, dtype=np.bool)
+
     if np.all(~ mask):
         return data.copy()
     #
@@ -47,12 +66,11 @@ def median_filter(data, mask, radius, percent=50):
         was_ranked = False
     input = np.zeros(data.shape, np.uint8 )
     input[mask] = ranked_data
-    
-    mmask = np.ascontiguousarray(mask, np.uint8)
-    
+
+    mask.dtype = np.uint8
     output = np.zeros(data.shape, np.uint8)
-    
-    _ctmf.median_filter(input, mmask, output, radius, percent)
+
+    _ctmf.median_filter(input, mask, output, radius, percent)
     if was_ranked:
         #
         # The translation gives the original value at each ranking.
@@ -66,4 +84,3 @@ def median_filter(data, mask, radius, percent=50):
     else:
         result = output
     return result
-

scikits/image/filter/tests/test_ctmf.py

@@ -94,5 +94,12 @@ def test_04_01_half_masked():
     # in zero coverage areas, the result should be the lowest valud in the valid area
     assert (np.all(result[15:, :] == np.min(img[mask])))
 
+def test_default_values():
+    img = (np.random.random((20, 20)) * 255).astype(np.uint8)
+    mask = np.ones((20, 20), dtype=np.uint8)
+    result1 = median_filter(img, mask, radius=1, percent=50)
+    result2 = median_filter(img)
+    assert_array_equal(result1, result2)
+
 if __name__ == "__main__":
     run_module_suite()