#!/usr/bin/python
# 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.
#
# 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.  This code will also use CUDA if you have CUDA and cuDNN
#   installed.
#
#   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 OpenCV and Numpy which can be installed
#   via the command:
#       pip install opencv-python numpy
#   Or downloaded from http://opencv.org/releases.html

import sys
import os
import dlib
import glob
import cv2
import numpy as np

if len(sys.argv) != 4:
    print(
        "Call this program like this:\n"
        "   ./face_alignment.py shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces/bald_guys.jpg\n"
        "You can download a trained facial shape predictor and recognition model from:\n"
        "    http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n"
        "    http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2")
    exit()

predictor_path = sys.argv[1]
face_rec_model_path = sys.argv[2]
face_file_path = sys.argv[3]

# 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, and finally the
# face recognition model.
detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(predictor_path)
facerec = dlib.face_recognition_model_v1(face_rec_model_path)

# Load the image using OpenCV
bgr_img = cv2.imread(face_file_path)
if bgr_img is None:
    print("Sorry, we could not load '{}' as an image".format(face_file_path))
    exit()

# Convert to RGB since dlib uses RGB images
img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)

# 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))
    exit()

# The full object detection object
faces = dlib.full_object_detections()
for detection in dets:
    faces.append(sp(img, detection))

# 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:
    cv_rgb_image = np.array(image).astype(np.uint8)
    cv_bgr_img = cv2.cvtColor(cv_rgb_image, cv2.COLOR_RGB2BGR)
    cv2.imshow('image',cv_bgr_img)
    cv2.waitKey(0)

# It is also possible to get a single chip
image = dlib.get_face_chip(img, faces[0])
cv_rgb_image = np.array(image).astype(np.uint8)
cv_bgr_img = cv2.cvtColor(cv_rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('image',cv_bgr_img)
cv2.waitKey(0)

cv2.destroyAllWindows()