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. BRIEF does not
  provide rotation-invariance. Scale-invariance can be achieved by detecting and
  extracting features at different scales.
  
  """
  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.astronaut())
  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()
  
  extractor.extract(img1, keypoints1)
  keypoints1 = keypoints1[extractor.mask]
  descriptors1 = extractor.descriptors
  
  extractor.extract(img2, keypoints2)
  keypoints2 = keypoints2[extractor.mask]
  descriptors2 = extractor.descriptors
  
  extractor.extract(img3, keypoints3)
  keypoints3 = keypoints3[extractor.mask]
  descriptors3 = extractor.descriptors
  
  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')
  ax[0].set_title("Original Image VS Transformed Image")
  print('Transformed Image using AffineTransform parameters:')
  print('\tscaleX = 1.2')
  print('\tscaleY = 1.2')
  print('\ttranslateX = 0')
  print('\ttranslateY = -100')
  
  
  plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13)
  ax[1].axis('off')
  ax[1].set_title("Original Image VS Transformed Image")
  print('Transformed Image using AffineTransform parameters:')
  print('\tscaleX = 1.2')
  print('\tscaleY = 1.2')
  print('\trotation = 25 degrees')
  print('\ttranslateX = 0')
  print('\ttranslateY = -100')
  
  
  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. BRIEF does not
  provide rotation-invariance. Scale-invariance can be achieved by detecting and
  extracting features at different scales.
  
  """
  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.astronaut())
  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()
  
  extractor.extract(img1, keypoints1)
  keypoints1 = keypoints1[extractor.mask]
  descriptors1 = extractor.descriptors
  
  extractor.extract(img2, keypoints2)
  keypoints2 = keypoints2[extractor.mask]
  descriptors2 = extractor.descriptors
  
  extractor.extract(img3, keypoints3)
  keypoints3 = keypoints3[extractor.mask]
  descriptors3 = extractor.descriptors
  
  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')
  ax[0].set_title("Original Image VS Transformed Image")
  
  plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13)
  ax[1].axis('off')
  ax[1].set_title("Original Image VS Transformed Image")
  
  
  plt.show()
  
subject: Update plot_brief.py