Clarify `compute_unary` label `0`. Fixes #15

README.md

@@ -77,6 +77,8 @@ There's two common ways of getting unary potentials:
 2. From a probability distribution computed by, e.g. the softmax output of a
    deep network. For this, see `from pydensecrf.utils import softmax_to_unary`.
 
+For usage of both of these, please refer to their docstrings or have a look at [the example](examples/utils_example.py).
+
 Pairwise potentials
 -------------------
 

pydensecrf/utils.py

@@ -3,16 +3,18 @@ import numpy as np
 
 def compute_unary(labels, M, GT_PROB=0.5):
     """
-    Simple classifier that is 50% certain that the annotation is correct
+    Simple classifier that is 50% certain that the annotation is correct.
     (same as in the inference example).
 
 
     Parameters
     ----------
     labels: nummpy.array
-        The label-map.
+        The label-map. The label value `0` is not a label, but the special
+        value indicating that the location has no label/information and thus
+        every label is equally likely.
     M: int
-        The number of labels there are.
+        The number of labels there are, not including the special `0` value.
     GT_PROB: float
         The certainty of the ground-truth (must be within (0,1)).
     """