##

    #!/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 for clustering using chinese_whispers.
    #   This is useful when you have a collection of photographs which you know are linked to
    #   a particular person, but the person may be photographed with multiple other people.
    #   In this example, we assume the largest cluster will contain photos of the common person in the
    #   collection of photographs. Then, we save extracted images of the face in the largest cluster in
    #   a 150x150 px format which is suitable for jittering and loading to perform metric learning (as shown
    #   in the dnn_metric_learning_on_images_ex.cpp example.
    #   https://github.com/davisking/dlib/blob/master/examples/dnn_metric_learning_on_images_ex.cpp
    #
    # 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) != 5:
        print(
            "Call this program like this:\n"
            "   ./face_clustering.py shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces output_folder\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]
    output_folder_path = sys.argv[4]

    # 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)

    descriptors = []
    images = []

    # Now find all the faces and compute 128D face descriptors for each face.
    for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
        print("Processing file: {}".format(f))
        img = dlib.load_rgb_image(f)

        # 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):
            # Get the landmarks/parts for the face in box d.
            shape = sp(img, d)

            # Compute the 128D vector that describes the face in img identified by
            # shape.
            face_descriptor = facerec.compute_face_descriptor(img, shape)
            descriptors.append(face_descriptor)
            images.append((img, shape))

    # Now let's cluster the faces.
    labels = dlib.chinese_whispers_clustering(descriptors, 0.5)
    num_classes = len(set(labels))
    print("Number of clusters: {}".format(num_classes))

    # Find biggest class
    biggest_class = None
    biggest_class_length = 0
    for i in range(0, num_classes):
        class_length = len([label for label in labels if label == i])
        if class_length > biggest_class_length:
            biggest_class_length = class_length
            biggest_class = i

    print("Biggest cluster id number: {}".format(biggest_class))
    print("Number of faces in biggest cluster: {}".format(biggest_class_length))

    # Find the indices for the biggest class
    indices = []
    for i, label in enumerate(labels):
        if label == biggest_class:
            indices.append(i)

    print("Indices of images in the biggest cluster: {}".format(str(indices)))

    # Ensure output directory exists
    if not os.path.isdir(output_folder_path):
        os.makedirs(output_folder_path)

    # Save the extracted faces
    print("Saving faces in largest cluster to output folder...")
    for i, index in enumerate(indices):
        img, shape = images[index]
        file_path = os.path.join(output_folder_path, "face_" + str(i))
        # The size and padding arguments are optional with default size=150x150 and padding=0.25
        dlib.save_face_chip(img, shape, file_path, size=150, padding=0.25)