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diff --git a/ml/dlib/python_examples/face_detector.py b/ml/dlib/python_examples/face_detector.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 program shows how to find frontal human faces in an image.  In
+#   particular, it shows how you can take a list of images from the command
+#   line and display each on the screen with red boxes overlaid on each human
+#   face.
+#
+#   The examples/faces folder contains some jpg images of people.  You can run
+#   this program on them and see the detections by executing the
+#   following command:
+#       ./face_detector.py ../examples/faces/*.jpg
+#
+#   This face detector is made using the now classic Histogram of Oriented
+#   Gradients (HOG) feature combined with a linear classifier, an image
+#   pyramid, and sliding window detection scheme.  This type of object detector
+#   is fairly general and capable of detecting many types of semi-rigid objects
+#   in addition to human faces.  Therefore, if you are interested in making
+#   your own object detectors then read the train_object_detector.py example
+#   program.  
+#
+#
+# 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.  
+#
+#   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 dlib
+from skimage import io
+
+
+detector = dlib.get_frontal_face_detector()
+win = dlib.image_window()
+
+for f in sys.argv[1:]:
+    print("Processing file: {}".format(f))
+    img = io.imread(f)
+    # 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 i, d in enumerate(dets):
+        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
+            i, d.left(), d.top(), d.right(), d.bottom()))
+
+    win.clear_overlay()
+    win.set_image(img)
+    win.add_overlay(dets)
+    dlib.hit_enter_to_continue()
+
+
+# Finally, if you really want to you can ask the detector to tell you the score
+# for each detection.  The score is bigger for more confident detections.
+# The third argument to run is an optional adjustment to the detection threshold,
+# where a negative value will return more detections and a positive value fewer.
+# Also, the idx tells you which of the face sub-detectors matched.  This can be
+# used to broadly identify faces in different orientations.
+if (len(sys.argv[1:]) > 0):
+    img = io.imread(sys.argv[1])
+    dets, scores, idx = detector.run(img, 1, -1)
+    for i, d in enumerate(dets):
+        print("Detection {}, score: {}, face_type:{}".format(
+            d, scores[i], idx[i]))
+