#!/usr/bin/python
# The contents of this file are in the 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
#
# 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 sys
import os
import dlib
import glob
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"
 " http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
 exit()
predictor_path = sys.argv[1]
faces_folder_path = sys.argv[2]
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
win = dlib.image_window()
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
 print("Processing file: {}".format(f))
 img = dlib.load_rgb_image(f)
 win.clear_overlay()
 win.set_image(img)
 # Ask the detector to find the bounding boxes of each face. The 1 in the
 # second argument indicates that we should upsample the image 1 time. This
 # will make everything bigger and allow us to detect more faces.
 dets = detector(img, 1)
 print("Number of faces detected: {}".format(len(dets)))
 for k, d in enumerate(dets):
 print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
 k, d.left(), d.top(), d.right(), d.bottom()))
 # Get the landmarks/parts for the face in box d.
 shape = predictor(img, d)
 print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
 shape.part(1)))
 # Draw the face landmarks on the screen.
 win.add_overlay(shape)
 win.add_overlay(dets)
 dlib.hit_enter_to_continue()