train_dlib_face_detector.py

# USAGE
# python train_dlib_face_detector.py --training dataset/gray/train/dlib_landmarks_train.xml --validation dataset/gray/val/dlib_landmarks_val.xml


#!/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 a HOG based 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/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 multiprocessing
import argparse
import dlib
import os

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--training", required=True,
    help="path to input training XML file")
ap.add_argument("-v", "--validation", required=True,
    help="path to input validation XML file")
args = vars(ap.parse_args())

# 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
options.detection_window_size = 100 * 100 
options.epsilon = 1e-8
options.add_left_right_image_flips = False

# 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 = 7

options.upsample_limit = 0

# Tell the code how many CPU cores your computer has for the fastest training.
options.num_threads = multiprocessing.cpu_count()
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(args["training"], "models/dlib_face_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("")  # Print blank line to create gap from previous output
print("Training accuracy: {}".format(
    dlib.test_simple_object_detector(args["training"], "models/dlib_face_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("Validation accuracy: {}".format(
    dlib.test_simple_object_detector(args["validation"], "models/dlib_face_detector.svm")))