old_contents: # Face Tracking Example
  #
  # This example shows off using the keypoints feature of your OpenMV Cam to track
  # a face after it has been detected by a Haar Cascade. The first part of this
  # script finds a face in the image using the frontalface Haar Cascade.
  # After which the script uses the keypoints feature to automatically learn your
  # face and track it. Keypoints can be used to automatically track anything.
  
  import sensor, time, image
  
  # 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, 10):
      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:
          # Expand the ROI by 31 pixels in every direction
          face = (objects[0][0]-31, objects[0][1]-31,objects[0][2]+31*2, objects[0][3]+31*2)
          # Extract keypoints using the detect face size as the ROI
          kpts1 = img.find_keypoints(scale_factor=1.2, max_keypoints=100, roi=face)
          # Draw a rectangle around the first face
          img.draw_rectangle(objects[0])
  
  # Draw keypoints
  print(kpts1)
  img.draw_keypoints(kpts1, size=12)
  time.sleep(1000)
  
  # FPS clock
  clock = time.clock()
  
  while (True):
      clock.tick()
      img = sensor.snapshot()
      # Extract keypoints using the detect face size as the ROI
      kpts2 = img.find_keypoints(scale_factor=1.2, max_keypoints=100)
  
      if (kpts2):
          # Match the first set of keypoints with the second one
          c=image.match_descriptor(kpts1, kpts2)
          match = c[6] # C[6] contains the number of matches.
          if (match>5):
              img.draw_rectangle(c[2:6])
              img.draw_cross(c[0], c[1], size=10)
              print(kpts2, "matched:%d dt:%d"%(match, c[7]))
  
      # Draw FPS
      img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
  
new_contents: # Face Tracking Example
  #
  # This example shows off using the keypoints feature of your OpenMV Cam to track
  # a face after it has been detected by a Haar Cascade. The first part of this
  # script finds a face in the image using the frontalface Haar Cascade.
  # After which the script uses the keypoints feature to automatically learn your
  # face and track it. Keypoints can be used to automatically track anything.
  import sensor, time, image
  
  # Reset sensor
  sensor.reset()
  sensor.set_contrast(3)
  sensor.set_gainceiling(16)
  sensor.set_framesize(sensor.VGA)
  sensor.set_windowing((320, 240))
  sensor.set_pixformat(sensor.GRAYSCALE)
  
  # Skip a few frames to allow the sensor settle down
  sensor.skip_frames(60)
  
  # 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=1.25)
      if objects:
          # Expand the ROI by 31 pixels in every direction
          face = (objects[0][0]-31, objects[0][1]-31,objects[0][2]+31*2, objects[0][3]+31*2)
          # Extract keypoints using the detect face size as the ROI
          kpts1 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, roi=face)
          # Draw a rectangle around the first face
          img.draw_rectangle(objects[0])
  
  # Draw keypoints
  print(kpts1)
  img.draw_keypoints(kpts1, size=24)
  img = sensor.snapshot()
  time.sleep(2000)
  
  # FPS clock
  clock = time.clock()
  
  while (True):
      clock.tick()
      img = sensor.snapshot()
      # Extract keypoints from the whole frame
      kpts2 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, normalized=True)
  
      if (kpts2):
          # Match the first set of keypoints with the second one
          c=image.match_descriptor(kpts1, kpts2, threshold=85)
          match = c[6] # C[6] contains the number of matches.
          if (match>5):
              img.draw_rectangle(c[2:6])
              img.draw_cross(c[0], c[1], size=10)
              print(kpts2, "matched:%d dt:%d"%(match, c[7]))
  
      # Draw FPS
      img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
  
subject: Update face tracking script.