Add missing optional description for kwargs

skimage/feature/brief.py

@@ -24,22 +24,22 @@ class BRIEF(DescriptorExtractor):
 
     Parameters
     ----------
-    descriptor_size : int
+    descriptor_size : int, optional
         Size of BRIEF descriptor for each keypoint. Sizes 128, 256 and 512
         recommended by the authors. Default is 256.
-    patch_size : int
+    patch_size : int, optional
         Length of the two dimensional square patch sampling region around
         the keypoints. Default is 49.
-    mode : {'normal', 'uniform'}
+    mode : {'normal', 'uniform'}, optional
         Probability distribution for sampling location of decision pixel-pairs
         around keypoints.
-    sample_seed : int
+    sample_seed : int, optional
         Seed for the random sampling of the decision pixel-pairs. From a square
         window with length patch_size, pixel pairs are sampled using the `mode`
         parameter to build the descriptors using intensity comparison. The
         value of `sample_seed` must be the same for the images to be matched
         while building the descriptors.
-    sigma : float
+    sigma : float, optional
         Standard deviation of the Gaussian low pass filter applied to the image
         to alleviate noise sensitivity, which is strongly recommended to obtain
         discriminative and good descriptors.

skimage/feature/censure.py

@@ -115,15 +115,15 @@ class CenSurE(FeatureDetector):
 
     """CenSurE keypoint detector.
 
-    min_scale : int
+    min_scale : int, optional
         Minimum scale to extract keypoints from.
-    max_scale : int
+    max_scale : int, optional
         Maximum scale to extract keypoints from. The keypoints will be
         extracted from all the scales except the first and the last i.e.
         from the scales in the range [min_scale + 1, max_scale - 1]. The filter
         sizes for different scales is such that the two adjacent scales
         comprise of an octave.
-    mode : {'DoB', 'Octagon', 'STAR'}
+    mode : {'DoB', 'Octagon', 'STAR'}, optional
         Type of bi-level filter used to get the scales of the input image.
         Possible values are 'DoB', 'Octagon' and 'STAR'. The three modes
         represent the shape of the bi-level filters i.e. box(square), octagon
@@ -132,10 +132,10 @@ class CenSurE(FeatureDetector):
         weights being uniformly negative in both the inner octagon while
         uniformly positive in the difference region. Use STAR and Octagon for
         better features and DoB for better performance.
-    non_max_threshold : float
+    non_max_threshold : float, optional
         Threshold value used to suppress maximas and minimas with a weak
         magnitude response obtained after Non-Maximal Suppression.
-    line_threshold : float
+    line_threshold : float, optional
         Threshold for rejecting interest points which have ratio of principal
         curvatures greater than this value.
 

skimage/feature/orb.py

@@ -25,32 +25,32 @@ class ORB(FeatureDetector, DescriptorExtractor):
 
     Parameters
     ----------
-    n_keypoints : int
+    n_keypoints : int, optional
         Number of keypoints to be returned. The function will return the best
         ``n_keypoints`` according to the Harris corner response if more than
         ``n_keypoints`` are detected. If not, then all the detected keypoints
         are returned.
-    fast_n : int
+    fast_n : int, optional
         The ``n`` parameter in ``feature.corner_fast``. Minimum number of
         consecutive pixels out of 16 pixels on the circle that should all be
         either brighter or darker w.r.t test-pixel. A point c on the circle is
         darker w.r.t test pixel p if ``Ic < Ip - threshold`` and brighter if
         ``Ic > Ip + threshold``. Also stands for the n in ``FAST-n`` corner
         detector.
-    fast_threshold : float
+    fast_threshold : float, optional
         The ``threshold`` parameter in ``feature.corner_fast``. Threshold used
         to decide whether the pixels on the circle are brighter, darker or
         similar w.r.t. the test pixel. Decrease the threshold when more
         corners are desired and vice-versa.
-    harris_k : float
+    harris_k : float, optional
         The ``k`` parameter in ``feature.corner_harris``. Sensitivity factor to
         separate corners from edges, typically in range ``[0, 0.2]``. Small
         values of k result in detection of sharp corners.
-    downscale : float
+    downscale : float, optional
         Downscale factor for the image pyramid. Default value 1.2 is chosen so
         that there are more dense scales which enable robust scale invariance
         for a subsequent feature description.
-    n_scales : int
+    n_scales : int, optional
         Maximum number of scales from the bottom of the image pyramid to
         extract the features from.
 

skimage/feature/util.py

@@ -53,12 +53,12 @@ def plot_matches(ax, image1, image2, keypoints1, keypoints2, matches,
         Indices of corresponding matches in first and second set of
         descriptors, where ``matches[:, 0]`` denote the indices in the first
         and ``matches[:, 1]`` the indices in the second set of descriptors.
-    keypoints_color : matplotlib color
+    keypoints_color : matplotlib color, optional
         Color for keypoint locations.
-    matches_color : matplotlib color
+    matches_color : matplotlib color, optional
         Color for lines which connect keypoint matches. By default the
         color is chosen randomly.
-    only_matches : bool
+    only_matches : bool, optional
         Whether to only plot matches and not plot the keypoint locations.
 
     """