old_contents: import sensor, time, image
  
  NORMALIZED=False
  MATCHING_THRESH=70
  KEYPOINTS_THRESH=32
  
  # Reset sensor
  sensor.reset()
  
  # Sensor settings
  sensor.set_contrast(1)
  sensor.set_gainceiling(16)
  sensor.set_framesize(sensor.HQVGA)
  sensor.set_pixformat(sensor.GRAYSCALE)
  
  # Skip a few frames to allow the sensor settle down
  # Note: This takes more time when exec from the IDE.
  for i in range(0, 30):
      img = sensor.snapshot()
      img.draw_string(0, 0, "Please wait...")
  
  # Load Haar Cascade
  # By default this will use all stages, lower satges is faster but less accurate.
  face_cascade = image.HaarCascade("frontalface", stages=25)
  print(face_cascade)
  
  # First set of keypoints
  kpts1 = None
  
  # Find a face!
  while (kpts1 == None):
      img = sensor.snapshot()
      img.draw_string(0, 0, "Looking for a face...")
      # Find faces
      objects = img.find_features(face_cascade, threshold=0.5, scale=1.5)
      if objects:
          # Draw a rectangle around the first face
          img.draw_rectangle(objects[0])
          # Extract keypoints using the detect face size as the ROI
          kpts1 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=objects[0])
          if kpts1:
              img.draw_keypoints(kpts1)
              time.sleep(1000)
  
  # FPS clock
  clock = time.clock()
  
  while (True):
      clock.tick()
      img = sensor.snapshot()
  
      # Extract keypoints using the whole image.
      kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED)
  
      # Match the first set of keypoints with the second one
      if (kpts2):
          c=img.match_keypoints(kpts1, kpts2, MATCHING_THRESH)
  
      # If a match was found, draw the matching keypoints
      if (c):
          img.draw_cross(c[0], c[1], size=5)
          img.draw_string(0, 0, "Tracking face...")
      print (clock.fps())
  
new_contents: import sensor, time, image
  
  NORMALIZED=False
  MATCHING_THRESH=70
  KEYPOINTS_THRESH=32
  
  # Reset sensor
  sensor.reset()
  
  # Sensor settings
  sensor.set_contrast(1)
  sensor.set_gainceiling(16)
  sensor.set_framesize(sensor.QQVGA)
  sensor.set_pixformat(sensor.GRAYSCALE)
  
  # Skip a few frames to allow the sensor settle down
  # Note: This takes more time when exec from the IDE.
  for i in range(0, 30):
      img = sensor.snapshot()
      img.draw_string(0, 0, "Please wait...")
  
  # Load Haar Cascade
  # By default this will use all stages, lower satges is faster but less accurate.
  face_cascade = image.HaarCascade("frontalface", stages=25)
  print(face_cascade)
  
  # First set of keypoints
  kpts1 = None
  
  # Find a face!
  while (kpts1 == None):
      img = sensor.snapshot()
      img.draw_string(0, 0, "Looking for a face...")
      # Find faces
      objects = img.find_features(face_cascade, threshold=0.5, scale=1.5)
      if objects:
          # Draw a rectangle around the first face
          img.draw_rectangle(objects[0])
          # Extract keypoints using the detect face size as the ROI
          kpts1 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=objects[0])
  
  # Draw keypoints
  img.draw_keypoints(kpts1)
  time.sleep(1000)
  
  # FPS clock
  clock = time.clock()
  
  while (True):
      clock.tick()
      img = sensor.snapshot()
  
      # Find faces
      objects = img.find_features(face_cascade, threshold=0.5, scale=1.5)
      if objects:
          # Extract keypoints using the detect face size as the ROI
          kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=objects[0])
  
          # Match the first set of keypoints with the second one
          if (kpts2):
              c=img.match_keypoints(kpts1, kpts2, MATCHING_THRESH)
  
          # If more than 10% of the keypoints match draw the matching set
          if (c[2]>10):
              img.draw_rectangle(objects[0])
              img.draw_cross(c[0], c[1], size=5)
              img.draw_string(0, 0, "Match %d%%"%(c[2]))
      print (clock.fps())
  
subject: Update face_tracking script.