Added a python version of the DNN face recognition example program.

python_examples/face_recognition.py

+#!/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
+#   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 and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
+#       sudo apt-get install libboost-python-dev 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) != 4:
+    print(
+        "Call this program like this:\n"
+        "   ./face_recognition.py shape_predictor_68_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_68_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 = io.imread(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.  He 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);
+        # 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%.
+
+
+        dlib.hit_enter_to_continue()
+
+