old_contents: """
  =======================
  BRIEF binary descriptor
  =======================
  
  This example demonstrates the BRIEF binary description algorithm.
  
  The descriptor consists of relatively few bits and can be computed using
  a set of intensity difference tests. The short binary descriptor results
  in low memory footprint and very efficient matching based on the Hamming
  distance metric.
  
  However, BRIEF does not provide rotation-invariance and scale scale-invariance
  can be achieved by detecting and extracting features at different scales.
  
  The ORB feature detection and binary description algorithm is an extension to
  the BRIEF method and provides rotation and scale-invariance, see
  `skimage.feature.ORB`.
  
  """
  from skimage import data
  from skimage import transform as tf
  from skimage.feature import (match_descriptors, corner_peaks, corner_harris,
                               plot_matches, BRIEF)
  from skimage.color import rgb2gray
  import matplotlib.pyplot as plt
  
  
  img1 = rgb2gray(data.lena())
  tform = tf.AffineTransform(scale=(1.2, 1.2), translation=(0, -100))
  img2 = tf.warp(img1, tform)
  img3 = tf.rotate(img1, 25)
  
  keypoints1 = corner_peaks(corner_harris(img1), min_distance=5)
  keypoints2 = corner_peaks(corner_harris(img2), min_distance=5)
  keypoints3 = corner_peaks(corner_harris(img3), min_distance=5)
  
  extractor = BRIEF()
  
  descriptors1, mask1 = extractor.extract(img1, keypoints1)
  descriptors2, mask2 = extractor.extract(img2, keypoints2)
  descriptors3, mask3 = extractor.extract(img3, keypoints3)
  
  keypoints1 = keypoints1[mask1]
  keypoints2 = keypoints2[mask2]
  keypoints3 = keypoints3[mask3]
  
  matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
  matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)
  
  fig, ax = plt.subplots(nrows=2, ncols=1)
  
  plt.gray()
  
  plot_matches(ax[0], img1, img2, keypoints1, keypoints2, matches12)
  ax[0].axis('off')
  
  plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13)
  ax[1].axis('off')
  
  plt.show()
  
new_contents: """
  =======================
  BRIEF binary descriptor
  =======================
  
  This example demonstrates the BRIEF binary description algorithm.
  
  The descriptor consists of relatively few bits and can be computed using
  a set of intensity difference tests. The short binary descriptor results
  in low memory footprint and very efficient matching based on the Hamming
  distance metric.
  
  However, BRIEF does not provide rotation-invariance and scale-invariance can be
  achieved by detecting and extracting features at different scales.
  
  The ORB feature detection and binary description algorithm is an extension to
  the BRIEF method and provides rotation and scale-invariance, see
  `skimage.feature.ORB`.
  
  """
  from skimage import data
  from skimage import transform as tf
  from skimage.feature import (match_descriptors, corner_peaks, corner_harris,
                               plot_matches, BRIEF)
  from skimage.color import rgb2gray
  import matplotlib.pyplot as plt
  
  
  img1 = rgb2gray(data.lena())
  tform = tf.AffineTransform(scale=(1.2, 1.2), translation=(0, -100))
  img2 = tf.warp(img1, tform)
  img3 = tf.rotate(img1, 25)
  
  keypoints1 = corner_peaks(corner_harris(img1), min_distance=5)
  keypoints2 = corner_peaks(corner_harris(img2), min_distance=5)
  keypoints3 = corner_peaks(corner_harris(img3), min_distance=5)
  
  extractor = BRIEF()
  
  descriptors1, mask1 = extractor.extract(img1, keypoints1)
  descriptors2, mask2 = extractor.extract(img2, keypoints2)
  descriptors3, mask3 = extractor.extract(img3, keypoints3)
  
  keypoints1 = keypoints1[mask1]
  keypoints2 = keypoints2[mask2]
  keypoints3 = keypoints3[mask3]
  
  matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
  matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)
  
  fig, ax = plt.subplots(nrows=2, ncols=1)
  
  plt.gray()
  
  plot_matches(ax[0], img1, img2, keypoints1, keypoints2, matches12)
  ax[0].axis('off')
  
  plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13)
  ax[1].axis('off')
  
  plt.show()
  
subject: Fix description