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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 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")