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he public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#
# This example program shows how to find frontal human faces in an image and
# estimate their pose. The pose takes the form of 68 landmarks. These are
# points on the face such as the corners of the mouth, along the eyebrows, on
# the eyes, and so forth.
#
# The face detector we use is made using the classic Histogram of Oriented
# Gradients (HOG) feature combined with a linear classifier, an image pyramid,
# and sliding window detection scheme. The pose estimator was created by
# using dlib's implementation of the paper:
# One Millisecond Face Alignment with an Ensemble of Regression Trees by
# Vahid Kazemi and Josephine Sullivan, CVPR 2014
# and was trained on the iBUG 300-W face landmark dataset (see
# https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/):
# C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic.
# 300 faces In-the-wild challenge: Database and results.
# Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
# You can get the trained model file from:
# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2.
# Note that the license for the iBUG 300-W dataset excludes commercial use.
# So you should contact Imperial College London to find out if it's OK for
# you to use this model file in a commercial product.
#
#
# Also, note that you can train your own models using dlib's machine learning
# tools. See train_shape_predictor.py to see an example.
#
#
# 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
# or
# python setup.py install --yes USE_AVX_INSTRUCTIONS
# if you have a CPU that supports AVX instructions, since this makes some
# things run faster.
#
# Compiling dlib 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 scikit-image
# Or downloaded from http://scikit-image.org/download.html.
import sys
import os
import dlib
import glob
from skimage import io
if len(sys.argv) != 3:
print(
"Give the path to the trained shape predictor model as the first "
"argument and then the directory containing the facial images.\n"
"For example, if you are in the python_examples folder then "
"execute this program by running:\n"
" ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
"You can download a trained facial shape predictor from:\n"
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