Documenting the example code

doc/examples/plot_orb_matching.py

@@ -1,37 +1,54 @@
 import numpy as np
 from skimage import data
 from skimage import transform as tf
-from skimage.feature import pairwise_hamming_distance, brief, match_binary_descriptors, corner_harris, corner_peaks, keypoints_orb, descriptor_orb
+from skimage.feature import (pairwise_hamming_distance,
+                             match_binary_descriptors, corner_harris,
+                             corner_peaks, keypoints_orb, descriptor_orb)
 from skimage.color import rgb2gray
 from skimage import img_as_float
 import matplotlib.pyplot as plt
 
+# Initializing parameters for transformation
 rotate = 0.5
 translate = (-100, -200)
 scaling = (1.5, 1.5)
-match_threshold = 0.25
-match_cross_check = True
 
+# Creating a transformed image from the original Lena image by scaling and
+# rotating it
 img_color = data.lena()
-tform = tf.AffineTransform(scale = scaling, rotation=rotate, translation=translate)
+tform = tf.AffineTransform(scale = scaling, rotation=rotate,
+                           translation=translate)
 transformed_img_color = tf.warp(img_color, tform)
 img = rgb2gray(img_color)
 transformed_img = rgb2gray(transformed_img_color)
 
+# Extracting oFAST keypoints and computing their rBRIEF descriptors
 keypoints1, orientations1, scales1 = keypoints_orb(img, n_keypoints=500)
 keypoints1.shape
-descriptors1, keypoints1 = descriptor_orb(img, keypoints1, orientations1, scales1)
+descriptors1, keypoints1 = descriptor_orb(img, keypoints1, orientations1,
+                                          scales1)
 keypoints1.shape
 descriptors1.shape
 
-keypoints2, orientations2, scales2 = keypoints_orb(transformed_img, n_keypoints=500)
+keypoints2, orientations2, scales2 = keypoints_orb(transformed_img,
+                                                   n_keypoints=500)
 keypoints2.shape
-descriptors2, keypoints2 = descriptor_orb(transformed_img, keypoints2, orientations2, scales2)
+descriptors2, keypoints2 = descriptor_orb(transformed_img, keypoints2,
+                                          orientations2, scales2)
 keypoints2.shape
 descriptors2.shape
 
+#Initializing parameters for Descriptor matching
+match_threshold = 0.25
+match_cross_check = True
+
 pairwise_hamming_distance(descriptors1, descriptors2)
-matched_keypoints, mask1, mask2 = match_binary_descriptors(keypoints1, descriptors1, keypoints2, descriptors2, cross_check=match_cross_check, threshold=match_threshold)
+matched_keypoints, mask1, mask2 = match_binary_descriptors(keypoints1,
+                                                           descriptors1,
+                                                           keypoints2,
+                                                           descriptors2,
+                                                           cross_check=match_cross_check,
+                                                           threshold=match_threshold)
 
 matched_keypoints.shape
 
@@ -41,7 +58,8 @@ dst = matched_keypoints[:, 1, :]
 src_scale = 10 * (scales1[mask1] + 1) ** 1.5
 dst_scale = 10 * (scales2[mask2] + 1) ** 1.5
 
-img_combined = np.concatenate((img_as_float(img_color), img_as_float(transformed_img_color)), axis=1)
+img_combined = np.concatenate((img_as_float(img_color),
+                               img_as_float(transformed_img_color)), axis=1)
 offset = img.shape
 
 fig, ax = plt.subplots(nrows=1, ncols=1)

skimage/feature/orb.py

@@ -1,6 +1,7 @@
 import numpy as np
 
-from .util import _mask_border_keypoints, _prepare_grayscale_input_2D
+from skimage.feature.util import (_mask_border_keypoints,
+                                  _prepare_grayscale_input_2D)
 
 from skimage.feature import (corner_fast, corner_orientations, corner_peaks,
                              corner_harris)