##

    #!/usr/bin/python
    # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
    #
    #   This example program shows how to find frontal human faces in an image.  In
    #   particular, it shows how you can take a list of images from the command
    #   line and display each on the screen with red boxes overlaid on each human
    #   face.
    #
    #   The examples/faces folder contains some jpg images of people.  You can run
    #   this program on them and see the detections by executing the
    #   following command:
    #       ./face_detector.py ../examples/faces/*.jpg
    #
    #   This face detector is made using the now classic Histogram of Oriented
    #   Gradients (HOG) feature combined with a linear classifier, an image
    #   pyramid, and sliding window detection scheme.  This type of object detector
    #   is fairly general and capable of detecting many types of semi-rigid objects
    #   in addition to human faces.  Therefore, if you are interested in making
    #   your own object detectors then read the train_object_detector.py example
    #   program.
    #
    #
    # 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
    #
    #   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 Numpy which can be installed
    #   via the command:
    #       pip install numpy

    import sys

    import dlib

    detector = dlib.get_frontal_face_detector()
    win = dlib.image_window()

    for f in sys.argv[1:]:
        print("Processing file: {}".format(f))
        img = dlib.load_rgb_image(f)
        # The 1 in the second argument indicates that we should upsample the image
        # 1 time.  This will make everything bigger and allow us to detect more
        # faces.
        dets = detector(img, 1)
        print("Number of faces detected: {}".format(len(dets)))
        for i, d in enumerate(dets):
            print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
                i, d.left(), d.top(), d.right(), d.bottom()))

        win.clear_overlay()
        win.set_image(img)
        win.add_overlay(dets)
        dlib.hit_enter_to_continue()

    # Finally, if you really want to you can ask the detector to tell you the score
    # for each detection.  The score is bigger for more confident detections.
    # The third argument to run is an optional adjustment to the detection threshold,
    # where a negative value will return more detections and a positive value fewer.
    # Also, the idx tells you which of the face sub-detectors matched.  This can be
    # used to broadly identify faces in different orientations.
    if (len(sys.argv[1:]) > 0):
        img = dlib.load_rgb_image(sys.argv[1])
        dets, scores, idx = detector.run(img, 1, -1)
        for i, d in enumerate(dets):
            print("Detection {}, score: {}, face_type:{}".format(
                d, scores[i], idx[i]))