train_signmatch.py

#!/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
#
#   Also note that this example requires scikit-image which can be installed
#   via the command:
#       pip install -U scikit-image
#   Or downloaded from http://scikit-image.org/download.html. 

import os
import sys
import glob

import dlib
# from skimage import io

TRAINING='speedlimits.svm'

# 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 = False # Speed limit signs aren't symmetric
# 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 = 1.0 # Default for C++ version
options.detection_window_size = 80*80

# Tell the code how many CPU cores your computer has for the fastest training.
options.num_threads = 8
options.be_verbose = True

training_xml_path = "signs.xml"
## testing_xml_path = os.path.join(faces_folder, "testing.xml")
# 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(training_xml_path, TRAINING, 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(training_xml_path, TRAINING)))

# 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(TRAINING)

# 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)