Added SLIC-zero to SLIC and changed SLIC implementation slightly

skimage/segmentation/slic_superpixels.py

@@ -25,7 +25,8 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
     compactness : float, optional
         Balances color-space proximity and image-space proximity. Higher
         values give more weight to image-space. As `compactness` tends to
-        infinity, superpixel shapes become square/cubic.
+        infinity, superpixel shapes become square/cubic. In SLICO mode, this
+        is the initial compactness.
     max_iter : int, optional
         Maximum number of iterations of k-means.
     sigma : float or (3,) array-like of floats, optional
@@ -46,6 +47,8 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
         Whether the input should be converted to Lab colorspace prior to
         segmentation. For this purpose, the input is assumed to be RGB. Highly
         recommended.
+    slic_zero: bool, optional
+        True to run SLIC-zero, False to run original SLIC.
     ratio : float, optional
         Synonym for `compactness`. This keyword is deprecated.
     enforce_connectivity: bool, optional (default False)
@@ -171,10 +174,17 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
 
     # we do the scaling of ratio in the same way as in the SLIC paper
     # so the values have the same meaning
-    ratio = float(max((step_z, step_y, step_x))) / compactness
-    image = np.ascontiguousarray(image * ratio)
+    step = float(max((step_z, step_y, step_x)))
+    ratio = float(1) / compactness
+    
+    if slic_zero:
+        image = np.ascontiguousarray(image * ratio)
+    else:
+        image = np.ascontiguousarray(image * ratio)
 
-    labels = _slic_cython(image, segments, max_iter, spacing)
+    # _slic_cython expects the image in zyx format... but isn't image in xyz
+    # format???
+    labels = _slic_cython(image, segments, step, max_iter, spacing, slic_zero)
 
     if enforce_connectivity:
         segment_size = depth * height * width / n_segments
@@ -188,4 +198,4 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
     if is_2d:
         labels = labels[0]
 
-    return labels
+    return labels