Adding docstrings and commenting the code

skimage/feature/fast.py

@@ -6,17 +6,45 @@ from fast_cy import _corner_fast
 
 def corner_fast(image, n=12, threshold=0.15):
 
+    """Extract FAST corners for a given image.
+
+    Parameters
+    ----------
+    image : 2D ndarray
+        Input image.
+    n : integer
+        Number of consecutive pixels out of 16 pixels on the circle that
+        should be brighter or darker with respect to test pixel above the
+        `threshold` so as to classify the test pixel as a FAST corner. Also
+        stands for the n in `FAST-n` corner detector.
+    threshold : float
+        Threshold used in deciding whether the pixels on the circle are
+        brighter, darker or similar to the test pixel. Decrease the threshold
+        when more corners are desired and vice-versa.
+
+    Returns
+    -------
+    corners : (N, 2) ndarray
+        Location i.e. (row, col) of extracted FAST corners.
+
+    References
+    ----------
+    .. [1] Edward Rosten and Tom Drummond
+           "Machine Learning for high-speed corner detection",
+           http://www.edwardrosten.com/work/rosten_2006_machine.pdf
+
+    """
     image = np.squeeze(image)
     if image.ndim != 2:
         raise ValueError("Only 2-D gray-scale images supported.")
 
     image = np.ascontiguousarray(image, dtype=np.double)
-    corner = _corner_fast(image, n, threshold)
+    corner_response = _corner_fast(image, n, threshold)
 
-    corner_zero_mask = corner != 0
-    c, d = np.nonzero(corner)
-
-    maximas = (maximum_filter(corner, (3, 3)) == corner) & corner_zero_mask
+    # Non-maximal Suppression
+    corner_zero_mask = corner_response != 0
+    maximas = (maximum_filter(corner_response, (3, 3)) == corner_response) & corner_zero_mask
     x, y = np.where(maximas == True)
 
-    return np.squeeze(np.dstack((x, y)))
+    corners = np.squeeze(np.dstack((x, y)))
+    return corners