##

    #!/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 use dlib's implementation of the paper:
    #   One Millisecond Face Alignment with an Ensemble of Regression Trees by
    #   Vahid Kazemi and Josephine Sullivan, CVPR 2014
    #
    #   In particular, we will train a face landmarking model based on a small
    #   dataset and then evaluate it.  If you want to visualize the output of the
    #   trained model on some images then you can run the
    #   face_landmark_detection.py example program with predictor.dat as the input
    #   model.
    #
    #   It should also be noted that this kind of model, while often used for face
    #   landmarking, is quite general and can be used for a variety of shape
    #   prediction tasks.  But here we demonstrate it only on a simple face
    #   landmarking task.
    #
    # 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 os
    import sys
    import glob

    import dlib

    # In this example we are going to train a face detector based on the small
    # faces dataset in the examples/faces directory.  This means you need to supply
    # the path to this faces folder as a command line argument so we will know
    # where it is.
    if len(sys.argv) != 2:
        print(
            "Give the path to the examples/faces directory as the argument to this "
            "program. For example, if you are in the python_examples folder then "
            "execute this program by running:\n"
            "    ./train_shape_predictor.py ../examples/faces")
        exit()
    faces_folder = sys.argv[1]

    options = dlib.shape_predictor_training_options()
    # Now make the object responsible for training the model.
    # This algorithm has a bunch of parameters you can mess with.  The
    # documentation for the shape_predictor_trainer explains all of them.
    # You should also read Kazemi's paper which explains all the parameters
    # in great detail.  However, here I'm just setting three of them
    # differently than their default values.  I'm doing this because we
    # have a very small dataset.  In particular, setting the oversampling
    # to a high amount (300) effectively boosts the training set size, so
    # that helps this example.
    options.oversampling_amount = 300
    # I'm also reducing the capacity of the model by explicitly increasing
    # the regularization (making nu smaller) and by using trees with
    # smaller depths.
    options.nu = 0.05
    options.tree_depth = 2
    options.be_verbose = True

    # dlib.train_shape_predictor() does the actual training.  It will save the
    # final predictor to predictor.dat.  The input is an XML file that lists the
    # images in the training dataset and also contains the positions of the face
    # parts.
    training_xml_path = os.path.join(faces_folder, "training_with_face_landmarks.xml")
    dlib.train_shape_predictor(training_xml_path, "predictor.dat", options)

    # Now that we have a model we can test it.  dlib.test_shape_predictor()
    # measures the average distance between a face landmark output by the
    # shape_predictor and where it should be according to the truth data.
    print("\nTraining accuracy: {}".format(
        dlib.test_shape_predictor(training_xml_path, "predictor.dat")))
    # The real test is to see how well it does on data it wasn't trained on.  We
    # trained it on a very small dataset so the accuracy is not extremely high, but
    # it's still doing quite good.  Moreover, if you train it on one of the large
    # face landmarking datasets you will obtain state-of-the-art results, as shown
    # in the Kazemi paper.
    testing_xml_path = os.path.join(faces_folder, "testing_with_face_landmarks.xml")
    print("Testing accuracy: {}".format(
        dlib.test_shape_predictor(testing_xml_path, "predictor.dat")))

    # Now let's use it as you would in a normal application.  First we will load it
    # from disk. We also need to load a face detector to provide the initial
    # estimate of the facial location.
    predictor = dlib.shape_predictor("predictor.dat")
    detector = dlib.get_frontal_face_detector()

    # Now let's run the detector and shape_predictor over the images in the faces
    # folder and display the results.
    print("Showing detections and predictions on the images in the faces folder...")
    win = dlib.image_window()
    for f in glob.glob(os.path.join(faces_folder, "*.jpg")):
        print("Processing file: {}".format(f))
        img = dlib.load_rgb_image(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()