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diff --git a/ml/dlib/python_examples/face_clustering.py b/ml/dlib/python_examples/face_clustering.py
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-#!/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
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  This code will also use CUDA if you have CUDA and cuDNN
-#   installed.
-#
-#   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 scikit-image which can be installed
-#   via the command:
-#       pip install 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) != 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 = io.imread(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)
-    
-    
-
-