mirror of
https://github.com/davisking/dlib.git
synced 2024-11-01 10:14:53 +08:00
65 lines
2.2 KiB
Python
Executable File
65 lines
2.2 KiB
Python
Executable File
#!/usr/bin/python
|
|
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
|
|
#
|
|
#
|
|
# This is an example illustrating the use of a binary SVM classifier tool from
|
|
# the dlib C++ Library. In this example, we will create a simple test dataset
|
|
# and show how to learn a classifier from it.
|
|
#
|
|
#
|
|
# 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
|
|
#
|
|
|
|
import dlib
|
|
try:
|
|
import cPickle as pickle
|
|
except ImportError:
|
|
import pickle
|
|
|
|
x = dlib.vectors()
|
|
y = dlib.array()
|
|
|
|
# Make a training dataset. Here we have just two training examples. Normally
|
|
# you would use a much larger training dataset, but for the purpose of example
|
|
# this is plenty. For binary classification, the y labels should all be either +1 or -1.
|
|
x.append(dlib.vector([1, 2, 3, -1, -2, -3]))
|
|
y.append(+1)
|
|
|
|
x.append(dlib.vector([-1, -2, -3, 1, 2, 3]))
|
|
y.append(-1)
|
|
|
|
|
|
# Now make a training object. This object is responsible for turning a
|
|
# training dataset into a prediction model. This one here is a SVM trainer
|
|
# that uses a linear kernel. If you wanted to use a RBF kernel or histogram
|
|
# intersection kernel you could change it to one of these lines:
|
|
# svm = dlib.svm_c_trainer_histogram_intersection()
|
|
# svm = dlib.svm_c_trainer_radial_basis()
|
|
svm = dlib.svm_c_trainer_linear()
|
|
svm.be_verbose()
|
|
svm.set_c(10)
|
|
|
|
# Now train the model. The return value is the trained model capable of making predictions.
|
|
classifier = svm.train(x, y)
|
|
|
|
# Now run the model on our data and look at the results.
|
|
print("prediction for first sample: {}".format(classifier(x[0])))
|
|
print("prediction for second sample: {}".format(classifier(x[1])))
|
|
|
|
|
|
# classifier models can also be pickled in the same was as any other python object.
|
|
with open('saved_model.pickle', 'wb') as handle:
|
|
pickle.dump(classifier, handle, 2)
|
|
|