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diff --git a/ml/dlib/python_examples/correlation_tracker.py b/ml/dlib/python_examples/correlation_tracker.py
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-#!/usr/bin/python
-# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-#
-# This example shows how to use the correlation_tracker from the dlib Python
-# library.  This object lets you track the position of an object as it moves
-# from frame to frame in a video sequence.  To use it, you give the
-# correlation_tracker the bounding box of the object you want to track in the
-# current video frame.  Then it will identify the location of the object in
-# subsequent frames.
-#
-# In this particular example, we are going to run on the
-# video sequence that comes with dlib, which can be found in the
-# examples/video_frames folder.  This video shows a juice box sitting on a table
-# and someone is waving the camera around.  The task is to track the position of
-# the juice box as the camera moves around.
-#
-#
-# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
-#   You can install dlib using the command:
-#       pip install dlib
-#
-#   Alternatively, if you want to compile dlib yourself then go into the dlib
-#   root folder and run:
-#       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
-#
-#   Compiling dlib should work on any operating system so long as you have
-#   CMake installed.  On Ubuntu, this can be done easily by running the
-#   command:
-#       sudo apt-get install cmake
-#
-#   Also note that this example requires scikit-image which can be installed
-#   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html. 
-
-import os
-import glob
-
-import dlib
-from skimage import io
-
-# Path to the video frames
-video_folder = os.path.join("..", "examples", "video_frames")
-
-# Create the correlation tracker - the object needs to be initialized
-# before it can be used
-tracker = dlib.correlation_tracker()
-
-win = dlib.image_window()
-# We will track the frames as we load them off of disk
-for k, f in enumerate(sorted(glob.glob(os.path.join(video_folder, "*.jpg")))):
-    print("Processing Frame {}".format(k))
-    img = io.imread(f)
-
-    # We need to initialize the tracker on the first frame
-    if k == 0:
-        # Start a track on the juice box. If you look at the first frame you
-        # will see that the juice box is contained within the bounding
-        # box (74, 67, 112, 153).
-        tracker.start_track(img, dlib.rectangle(74, 67, 112, 153))
-    else:
-        # Else we just attempt to track from the previous frame
-        tracker.update(img)
-
-    win.clear_overlay()
-    win.set_image(img)
-    win.add_overlay(tracker.get_position())
-    dlib.hit_enter_to_continue()