Added max_size as a parameter

skimage/segmentation/_slic.pyx

@@ -11,7 +11,8 @@ from skimage.util import regular_grid
 
 def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] nearest_segments,
                                 Py_ssize_t n_segments,
-                                int min_size):
+                                int min_size,
+                                int max_size):
     """ Helper function to remove small disconnected regions from the labels
 
     Parameters
@@ -22,7 +23,9 @@ def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] nearest_segments,
         number of specified segments
     min_size: int
         minimum size of the segment
-
+    max_size: int
+        maximum size of the segment. This is done for performance reasons,
+        to pre-allocate a sufficiently large array for the breadth first search
     Returns
     -------
     connected_nearest_segments : 3D array of int, shape (Z, Y, X)
@@ -40,9 +43,6 @@ def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] nearest_segments,
     cdef Py_ssize_t[:] ddy = np.array((0,0,1,-1,0,0))
     cdef Py_ssize_t[:] ddz = np.array((0,0,0,0,1,-1))
 
-    cdef double size = height*width*depth / n_segments
-    cdef double max_size = 3*size
-
     #new object with connected segments
     cdef Py_ssize_t[:, :, ::1] new_nearest_segments \
         = np.zeros((depth, height, width), dtype=np.intp)

skimage/segmentation/slic_superpixels.py

@@ -12,7 +12,7 @@ from skimage.color import rgb2lab
 
 def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
          spacing=None, multichannel=True, convert2lab=True, ratio=None,
-         enforce_connectivity=True, min_size_factor=0.5):
+         enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3):
     """Segments image using k-means clustering in Color-(x,y,z) space.
 
     Parameters
@@ -53,7 +53,9 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
     min_size_factor: float
         proportion of the minimum segment size to be removed with respect
         to the supposed segment size (depth*width*height/n_segments)
-
+    max_size_factor: float
+        proportion of the maximum connected segment size. A value of 3 works
+        in most of the cases.
     Returns
     -------
     labels : 2D or 3D array
@@ -170,7 +172,11 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
     labels = _slic_cython(image, segments, max_iter, spacing, enforce_connectivity)
 
     if (enforce_connectivity):
-        labels = _enforce_label_connectivity_cython(labels, n_segments, min_size_factor*depth*height*width/n_segments)
+        segment_size = depth*height*width/n_segments
+        labels = _enforce_label_connectivity_cython(labels,
+                                                    n_segments,
+                                                    min_size_factor*segment_size,
+                                                    max_size_factor*segment_size)
 
     if is_2d:
         labels = labels[0]