Added a test for slic_zero and some other changes based on feedback from Github.

skimage/segmentation/_slic.pyx

@@ -16,8 +16,7 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
                  float step,
                  Py_ssize_t max_iter,
                  double[::1] spacing,
-                 bint slic_zero
-                 ):
+                 bint slic_zero):
     """Helper function for SLIC segmentation.
 
     Parameters
@@ -99,8 +98,8 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
     cdef double[::1] max_dist_color = np.ones(n_segments, dtype=np.double)
     cdef double dist_color
 
-    # The reference implementation calls this invxywt
-    cdef double zyx_wt = float(1) / (step ** 2)
+    # The reference implementation (Achanta et al.) calls this invxywt
+    cdef double spatial_weight = float(1) / (step ** 2)
 
     for i in range(max_iter):
         change = 0
@@ -127,7 +126,7 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
                 for y in range(y_min, y_max):
                     dy = (sy * (cy - y)) ** 2
                     for x in range(x_min, x_max):
-                        dist_center = (dz + dy + (sx * (cx - x)) ** 2) * zyx_wt
+                        dist_center = (dz + dy + (sx * (cx - x)) ** 2) * spatial_weight
                         dist_color = 0
                         for c in range(3, n_features):
                             dist_color += (image_zyx[z, y, x, c - 3]

skimage/segmentation/tests/test_slic.py

@@ -148,6 +148,29 @@ def test_enforce_connectivity():
     assert_equal(segments_disconnected, result_disconnected)
 
 
+def test_slic_zero():
+    # Same as test_color_2d but with slic_zero=True
+    rnd = np.random.RandomState(0)
+    img = np.zeros((20, 21, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    img += 0.01 * rnd.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        seg = slic(img, n_segments=4, sigma=0, slic_zero=True)
+
+    # we expect 4 segments
+    assert_equal(len(np.unique(seg)), 4)
+    assert_equal(seg.shape, img.shape[:-1])
+    assert_equal(seg[:10, :10], 0)
+    assert_equal(seg[10:, :10], 2)
+    assert_equal(seg[:10, 10:], 1)
+    assert_equal(seg[10:, 10:], 3)
+
+
 if __name__ == '__main__':
     from numpy import testing