##

    #!/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 and
    #   estimate their pose.  The pose takes the form of 68 landmarks.  These are
    #   points on the face such as the corners of the mouth, along the eyebrows, on
    #   the eyes, and so forth.
    #
    #   This face detector is made using the classic Histogram of Oriented
    #   Gradients (HOG) feature combined with a linear classifier, an image pyramid,
    #   and sliding window detection scheme.  The pose estimator was created by
    #   using dlib's implementation of the paper:
    #      One Millisecond Face Alignment with an Ensemble of Regression Trees by
    #      Vahid Kazemi and Josephine Sullivan, CVPR 2014
    #   and was trained on the iBUG 300-W face landmark dataset.
    #
    #   Also, note that you can train your own models using dlib's machine learning
    #   tools. See train_shape_predictor.py to see an example.
    #
    #   You can get the shape_predictor_68_face_landmarks.dat file from:
    #   http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
    #
    # COMPILING THE DLIB PYTHON INTERFACE
    #   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
    #   you are using another python version or operating system then you need to
    #   compile the dlib python interface before you can use this file.  To do this,
    #   run compile_dlib_python_module.bat.  This should work on any operating
    #   system so long as you have CMake and boost-python installed.
    #   On Ubuntu, this can be done easily by running the command:
    #       sudo apt-get install libboost-python-dev cmake
    #
    #   Also note that this example requires scikit-image which can be installed
    #   via the command:
    #       pip install -U scikit-image
    #   Or downloaded from http://scikit-image.org/download.html.

    import sys
    import os
    import dlib
    import glob
    from skimage import io

    if len(sys.argv) != 3:
        print(
            "Give the path to the trained shape predictor model as the first "
            "argument and then the directory containing the facial images.\n"
            "For example, if you are in the python_examples folder then "
            "execute this program by running:\n"
            "    ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
            "You can download a trained facial shape predictor from:\n"
            "    http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2")
        exit()

    predictor_path = sys.argv[1]
    faces_folder_path = sys.argv[2]

    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(predictor_path)
    win = dlib.image_window()

    for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
        print("Processing file: {}".format(f))
        img = io.imread(f)

        win.clear_overlay()
        win.set_image(img)

        # Ask the detector to find the bounding boxes of each face. 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 k, d in enumerate(dets):
            print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
                k, d.left(), d.top(), d.right(), d.bottom()))
            # Get the landmarks/parts for the face in box d.
            shape = predictor(img, d)
            print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
                                                      shape.part(1)))
            # Draw the face landmarks on the screen.
            win.add_overlay(shape)

        win.add_overlay(dets)
        dlib.hit_enter_to_continue()