text: # # SIFT (Scale-Invariant Feature Transform)
  
  # ## Import resources and display image
  
  import cv2
  import matplotlib.pyplot as plt
  import numpy as np
  
  # Load the image
  image1 = cv2.imread('./images/face1.jpeg')
  
  # Convert the training image to RGB
  training_image = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)
  
  # Convert the training image to gray scale
  training_gray = cv2.cvtColor(training_image, cv2.COLOR_RGB2GRAY)
  
  # Create test image by adding Scale Invariance and Rotational Invariance
  test_image = cv2.pyrDown(training_image)
  test_image = cv2.pyrDown(test_image)
  num_rows, num_cols = test_image.shape[:2]
  
  rotation_matrix = cv2.getRotationMatrix2D((num_cols/2, num_rows/2), 30, 1)
  test_image = cv2.warpAffine(test_image, rotation_matrix, (num_cols, num_rows))
  
  test_gray = cv2.cvtColor(test_image, cv2.COLOR_RGB2GRAY)
  
  # Display traning image and testing image
  fx, plots = plt.subplots(1, 2, figsize=(10,5))
  
  plots[0].set_title("Training Image")
  plots[0].imshow(training_image)
  
  plots[1].set_title("Testing Image")
  plots[1].imshow(test_image)
  
  
  # ## Detect keypoints and Create Descriptor
  
  #sift = cv2.xfeatures2d.SIFT_create()
  #
  #train_keypoints, train_descriptor = sift.detectAndCompute(training_gray, None)
  #test_keypoints, test_descriptor = sift.detectAndCompute(test_gray, None)
  #
  #keypoints_without_size = np.copy(training_image)
  #keypoints_with_size = np.copy(training_image)
  #
  #cv2.drawKeypoints(training_image, train_keypoints, keypoints_without_size, color = (0, 255, 0))
  #
  #cv2.drawKeypoints(training_image, train_keypoints, keypoints_with_size, flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
  #
  ## Display image with and without keypoints size
  #fx, plots = plt.subplots(1, 2, figsize=(20,10))
  #
  #plots[0].set_title("Train keypoints With Size")
  #plots[0].imshow(keypoints_with_size, cmap='gray')
  #
  #plots[1].set_title("Train keypoints Without Size")
  #plots[1].imshow(keypoints_without_size, cmap='gray')
  #
  ## Print the number of keypoints detected in the training image
  #print("Number of Keypoints Detected In The Training Image: ", len(train_keypoints))
  #
  ## Print the number of keypoints detected in the query image
  #print("Number of Keypoints Detected In The Query Image: ", len(test_keypoints))
  #
  #
  ## ## Matching Keypoints
  #
  #
  ## Create a Brute Force Matcher object.
  #bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck = False)
  #
  ## Perform the matching between the SIFT descriptors of the training image and the test image
  #matches = bf.match(train_descriptor, test_descriptor)
  #
  ## The matches with shorter distance are the ones we want.
  #matches = sorted(matches, key = lambda x : x.distance)
  #
  #result = cv2.drawMatches(training_image, train_keypoints, test_gray, test_keypoints, matches, test_gray, flags = 2)
  #
  ## Display the best matching points
  #plt.rcParams['figure.figsize'] = [14.0, 7.0]
  #plt.title('Best Matching Points')
  #plt.imshow(result)
  #plt.show()
  #
  ## Print total number of matching points between the training and query images
  #print("\nNumber of Matching Keypoints Between The Training and Query Images: ", len(matches))
  #
  