##

    #!/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 dlib's face recognition tool.  This tool maps
    #   an image of a human face to a 128 dimensional vector space where images of
    #   the same person are near to each other and images from different people are
    #   far apart.  Therefore, you can perform face recognition by mapping faces to
    #   the 128D space and then checking if their Euclidean distance is small
    #   enough.
    #
    #   When using a distance threshold of 0.6, the dlib model obtains an accuracy
    #   of 99.38% on the standard LFW face recognition benchmark, which is
    #   comparable to other state-of-the-art methods for face recognition as of
    #   February 2017. This accuracy means that, when presented with a pair of face
    #   images, the tool will correctly identify if the pair belongs to the same
    #   person or is from different people 99.38% of the time.
    #
    #   Finally, for an in-depth discussion of how dlib's tool works you should
    #   refer to the C++ example program dnn_face_recognition_ex.cpp and the
    #   attendant documentation referenced therein.
    #
    #
    #
    #
    # 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 os
    import dlib
    import glob

    if len(sys.argv) != 4:
        print(
            "Call this program like this:\n"
            "   ./face_recognition.py shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces\n"
            "You can download a trained facial shape predictor and recognition model from:\n"
            "    http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n"
            "    http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2")
        exit()

    predictor_path = sys.argv[1]
    face_rec_model_path = sys.argv[2]
    faces_folder_path = sys.argv[3]

    # Load all the models we need: a detector to find the faces, a shape predictor
    # to find face landmarks so we can precisely localize the face, and finally the
    # face recognition model.
    detector = dlib.get_frontal_face_detector()
    sp = dlib.shape_predictor(predictor_path)
    facerec = dlib.face_recognition_model_v1(face_rec_model_path)

    win = dlib.image_window()

    # Now process all the images
    for f in glob.glob(os.path.join(faces_folder_path, "*.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)))

        # Now process each face we found.
        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 = sp(img, d)
            # Draw the face landmarks on the screen so we can see what face is currently being processed.
            win.clear_overlay()
            win.add_overlay(d)
            win.add_overlay(shape)

            # Compute the 128D vector that describes the face in img identified by
            # shape.  In general, if two face descriptor vectors have a Euclidean
            # distance between them less than 0.6 then they are from the same
            # person, otherwise they are from different people. Here we just print
            # the vector to the screen.
            face_descriptor = facerec.compute_face_descriptor(img, shape)
            print(face_descriptor)
            # It should also be noted that you can also call this function like this:
            #  face_descriptor = facerec.compute_face_descriptor(img, shape, 100, 0.25)
            # The version of the call without the 100 gets 99.13% accuracy on LFW
            # while the version with 100 gets 99.38%.  However, the 100 makes the
            # call 100x slower to execute, so choose whatever version you like.  To
            # explain a little, the 3rd argument tells the code how many times to
            # jitter/resample the image.  When you set it to 100 it executes the
            # face descriptor extraction 100 times on slightly modified versions of
            # the face and returns the average result.  You could also pick a more
            # middle value, such as 10, which is only 10x slower but still gets an
            # LFW accuracy of 99.3%.
            # 4th value (0.25) is padding around the face. If padding == 0 then the chip will
            # be closely cropped around the face. Setting larger padding values will result a looser cropping.
            # In particular, a padding of 0.5 would double the width of the cropped area, a value of 1.
            # would triple it, and so forth.

            # There is another overload of compute_face_descriptor that can take
            # as an input an aligned image.
            #
            # Note that it is important to generate the aligned image as
            # dlib.get_face_chip would do it i.e. the size must be 150x150,
            # centered and scaled.
            #
            # Here is a sample usage of that

            print("Computing descriptor on aligned image ..")

            # Let's generate the aligned image using get_face_chip
            face_chip = dlib.get_face_chip(img, shape)

            # Now we simply pass this chip (aligned image) to the api
            face_descriptor_from_prealigned_image = facerec.compute_face_descriptor(face_chip)
            print(face_descriptor_from_prealigned_image)

            dlib.hit_enter_to_continue()