# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#   This example shows how to use dlib's face recognition tool for image alignment.
#   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 dlib

if len(sys.argv) != 3:
        "Call this program like this:\n"
        "   ./face_alignment.py shape_predictor_5_face_landmarks.dat ../examples/faces/bald_guys.jpg\n"
        "You can download a trained facial shape predictor from:\n"
        "    http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n")

predictor_path = sys.argv[1]
face_file_path = sys.argv[2]

# Load all the models we need: a detector to find the faces, a shape predictor
# to find face landmarks so we can precisely localize the face
detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(predictor_path)

# Load the image using Dlib
img = dlib.load_rgb_image(face_file_path)

# 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)

num_faces = len(dets)
if num_faces == 0:
    print("Sorry, there were no faces found in '{}'".format(face_file_path))

# Find the 5 face landmarks we need to do the alignment.
faces = dlib.full_object_detections()
for detection in dets:
    faces.append(sp(img, detection))

window = dlib.image_window()

# Get the aligned face images
# Optionally: 
# images = dlib.get_face_chips(img, faces, size=160, padding=0.25)
images = dlib.get_face_chips(img, faces, size=320)
for image in images:

# It is also possible to get a single chip
image = dlib.get_face_chip(img, faces[0])