##

    #!/usr/bin/python
    # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
    #
    #   This example program shows how you can use dlib to make an object detector
    #   for things like faces, pedestrians, and any other semi-rigid object.  In
    #   particular, we go though the steps to train the kind of sliding window
    #   object detector first published by Dalal and Triggs in 2005 in the paper
    #   Histograms of Oriented Gradients for Human Detection.
    #
    #
    # COMPILING THE DLIB PYTHON INTERFACE
    #   Dlib comes with a compiled python interface for python 2.7 on MS Windows.  If
    #   you are using another python version or operating system then you need to
    #   compile the dlib python interface before you can use this file.  To do this,
    #   run compile_dlib_python_module.bat.  This 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

    import dlib, sys, glob
    from skimage import io

    # In this example we are going to train a face detector based on the small
    # faces dataset in the examples/faces directory.  This means you need to supply
    # the path to this faces folder as a command line argument so we will know
    # where it is.
    if (len(sys.argv) != 2):
        print "Give the path to the examples/faces directory as the argument to this"
        print "program.  For example, if you are in the python_examples folder then "
        print "execute this program by running:"
        print "  ./train_object_detector.py ../examples/faces"
        exit()
    faces_folder = sys.argv[1]

    # Now let's do the training.  The train_simple_object_detector() function has a
    # bunch of options, all of which come with reasonable default values.  The next
    # few lines goes over some of these options.
    options = dlib.simple_object_detector_training_options()
    # Since faces are left/right symmetric we can tell the trainer to train a
    # symmetric detector.  This helps it get the most value out of the training
    # data.
    options.add_left_right_image_flips = True
    # The trainer is a kind of support vector machine and therefore has the usual
    # SVM C parameter.  In general, a bigger C encourages it to fit the training
    # data better but might lead to overfitting.  You must find the best C value
    # empirically by checking how well the trained detector works on a test set of
    # images you haven't trained on.  Don't just leave the value set at 5.  Try a
    # few different C values and see what works best for your data.
    options.C = 5
    # Tell the code how many CPU cores your computer has for the fastest training.
    options.num_threads = 4
    options.be_verbose = True

    # This function does the actual training.  It will save the final detector to
    # detector.svm.  The input is an XML file that lists the images in the training
    # dataset and also contains the positions of the face boxes.  To create your
    # own XML files you can use the imglab tool which can be found in the
    # tools/imglab folder.  It is a simple graphical tool for labeling objects in
    # images with boxes.  To see how to use it read the tools/imglab/README.txt
    # file.  But for this example, we just use the training.xml file included with
    # dlib.
    dlib.train_simple_object_detector(faces_folder+"/training.xml","detector.svm", options)

    # Now that we have a face detector we can test it.  The first statement tests
    # it on the training data.  It will print the precision, recall, and then
    # average precision.
    print "\ntraining accuracy:", dlib.test_simple_object_detector(faces_folder+"/training.xml", "detector.svm")
    # However, to get an idea if it really worked without overfitting we need to
    # run it on images it wasn't trained on.  The next line does this.  Happily, we
    # see that the object detector works perfectly on the testing images.
    print "testing accuracy: ", dlib.test_simple_object_detector(faces_folder+"/testing.xml", "detector.svm")

    # Now let's use the detector as you would in a normal application.  First we
    # will load it from disk.
    detector = dlib.simple_object_detector("detector.svm")

    # We can look at the HOG filter we learned.  It should look like a face.  Neat!
    win_det = dlib.image_window()
    win_det.set_image(detector)

    # Now let's run the detector over the images in the faces folder and display the
    # results.
    print "\nShowing detections on the images in the faces folder..."
    win = dlib.image_window()
    for f in glob.glob(faces_folder+"/*.jpg"):
        print "processing file:", f
        img = io.imread(f)
        dets = detector(img)
        print "number of faces detected:", len(dets)
        for d in dets:
            print "  detection position left,top,right,bottom:", d.left(), d.top(), d.right(), d.bottom()

        win.clear_overlay()
        win.set_image(img)
        win.add_overlay(dets)
        raw_input("Hit enter to continue")