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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()