mirror of https://github.com/davisking/dlib.git
64 lines
2.6 KiB
Python
64 lines
2.6 KiB
Python
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#!/usr/bin/python
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# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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#
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# This example shows how to use the correlation_tracker from the dlib Python
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# library. This object lets you track the position of an object as it moves
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# from frame to frame in a video sequence. To use it, you give the
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# correlation_tracker the bounding box of the object you want to track in the
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# current video frame. Then it will identify the location of the object in
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# subsequent frames.
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#
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# In this particular example, we are going to run on the
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# video sequence that comes with dlib, which can be found in the
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# examples/video_frames folder. This video shows a juice box sitting on a table
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# and someone is waving the camera around. The task is to track the position of
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# the juice box as the camera moves around.
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#
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# COMPILING THE DLIB PYTHON INTERFACE
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# Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
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# you are using another python version or operating system then you need to
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# compile the dlib python interface before you can use this file. To do this,
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# run compile_dlib_python_module.bat. This should work on any operating
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# system so long as you have CMake and boost-python installed.
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# On Ubuntu, this can be done easily by running the command:
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# sudo apt-get install libboost-python-dev cmake
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#
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# Also note that this example requires scikit-image which can be installed
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# via the command:
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# pip install -U scikit-image
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# Or downloaded from http://scikit-image.org/download.html.
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import os
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import glob
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import dlib
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from skimage import io
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# Path to the video frames
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video_folder = os.path.join("..", "examples", "video_frames")
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# Create the correlation tracker - the object needs to be initialized
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# before it can be used
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tracker = dlib.correlation_tracker()
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win = dlib.image_window()
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# We will track the frames as we load them off of disk
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for k, f in enumerate(sorted(glob.glob(os.path.join(video_folder, "*.jpg")))):
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print("Processing Frame {}".format(k))
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img = io.imread(f)
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# We need to initialize the tracker on the first frame
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if k == 0:
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# Start a track on the juice box. If you look at the first frame you
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# will see that the juice box is contained within the bounding
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# box (74, 67, 112, 153).
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tracker.start_track(img, dlib.rectangle(74, 67, 112, 153))
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else:
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# Else we just attempt to track from the previous frame
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tracker.update(img)
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win.clear_overlay()
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win.set_image(img)
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win.add_overlay(tracker.get_position())
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dlib.hit_enter_to_continue()
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