Add support for list sigma input in SLIC

Previously, having a different `sigma` for different dimensions
required an array input. This allows the user to use a simple list,
which gets converted to an array internally.

Importantly, it removes a very unhelpful error:

```python
>>> im = np.random.rand(10, 20)
>>> from skimage import segmentation as seg
Exception AttributeError: "'UmfpackContext' object has no attribute '_symbolic'" in <bound method UmfpackContext.__del__ of <scipy.sparse.linalg.dsolve.umfpack.umfpack.UmfpackContext object at 0x1045ff5d0>> ignored

>>> s = seg.slic(im, 2, sigma=[2, 1])
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-689b36a2f0ef> in <module>()
----> 1 s = seg.slic(im, 2, sigma=[2, 1])

/Users/nuneziglesiasj/venv/skimdev2/lib/python2.7/site-packages/scikit_image-0.9dev-py2.7-macosx-10.5-x86_64.egg/skimage/segmentation/slic_superpixels.pyc in slic(image, n_segments, compactness, max_iter, sigma, multichannel, convert2lab, ratio)
    106     if not isinstance(sigma, coll.Iterable):
    107         sigma = np.array([sigma, sigma, sigma])
--> 108     if (sigma > 0).any():
    109         sigma = list(sigma) + [0]
    110         image = ndimage.gaussian_filter(image, sigma)

AttributeError: 'bool' object has no attribute 'any'
```

skimage/segmentation/slic_superpixels.py

@@ -27,7 +27,7 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
         infinity, superpixel shapes become square/cubic.
     max_iter : int, optional
         Maximum number of iterations of k-means.
-    sigma : float or (3,) array of floats, optional
+    sigma : float or (3,) array-like of floats, optional
         Width of Gaussian smoothing kernel for pre-processing for each
         dimension of the image. The same sigma is applied to each dimension in
         case of a scalar value. Zero means no smoothing.
@@ -104,7 +104,9 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
         image = image[..., np.newaxis]
 
     if not isinstance(sigma, coll.Iterable):
-        sigma = np.array([sigma, sigma, sigma])
+        sigma = np.array([sigma, sigma, sigma], float)
+    elif type(sigma) in [list, tuple]:
+        sigma = np.array(sigma, float)
     if (sigma > 0).any():
         sigma = list(sigma) + [0]
         image = ndimage.gaussian_filter(image, sigma)

skimage/segmentation/tests/test_slic.py

@@ -90,6 +90,17 @@ def test_gray_3d():
         assert_array_equal(seg[s], c)
 
 
+def test_list_sigma():
+    rnd = np.random.RandomState(0)
+    img = np.array([[1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 1, 1, 1]], np.float)
+    img += 0.1 * rnd.normal(size=img.shape)
+    result_sigma = np.array([[0, 0, 0, 1, 1, 1],
+                             [0, 0, 0, 1, 1, 1]], np.int)
+    seg_sigma = slic(img, n_segments=2, sigma=[1, 50, 1], multichannel=False)
+    assert_equal(seg_sigma, result_sigma)
+
+
 if __name__ == '__main__':
     from numpy import testing
     testing.run_module_suite()