"""Performs face alignment and calculates L2 distance between the embeddings of images."""

# MIT License

#

# Copyright (c) 2016 David Sandberg

#

# Permission is hereby granted, free of charge, to any person obtaining a copy

# of this software and associated documentation files (the "Software"), to deal

# in the Software without restriction, including without limitation the rights

# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

# copies of the Software, and to permit persons to whom the Software is

# furnished to do so, subject to the following conditions:

#

# The above copyright notice and this permission notice shall be included in all

# copies or substantial portions of the Software.

#

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

# SOFTWARE.

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

from scipy import misc

import tensorflow as tf

import numpy as np

import sys

import os

import argparse

import facenet

import align.detect_face

def main():

    model = "../models/20170216-091149"

    image_files = ['compare_images/index10.png', 'compare_images/index73.png']

    image_size = 160

    margin = 44

    gpu_memory_fraction = 0.5

    images = load_and_align_data(image_files, image_size, margin, gpu_memory_fraction)

    with tf.Graph().as_default():

        with tf.Session() as sess:

            # Load the model

            facenet.load_model(model)

            # Get input and output tensors

            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")

            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")

            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")

            # Run forward pass to calculate embeddings

            feed_dict = { images_placeholder: images, phase_train_placeholder:False }

            emb = sess.run(embeddings, feed_dict=feed_dict)

            nrof_images = len(image_files)

            print('Images:')

            for i in range(nrof_images):

                print('%1d: %s' % (i, image_files[i]))

            print('')

            # Print distance matrix

            print('Distance matrix')

            print('    ', end='')

            for i in range(nrof_images):

                print('    %1d     ' % i, end='')

            print('')

            for i in range(nrof_images):

                print('%1d  ' % i, end='')

                for j in range(nrof_images):

                    dist = np.sqrt(np.sum(np.square(np.subtract(emb[i,:], emb[j,:]))))

                    print('  %1.4f  ' % dist, end='')

                print('')

def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):

    minsize = 20 # minimum size of face

    threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold

    factor = 0.709 # scale factor

    print('Creating networks and loading parameters')

    with tf.Graph().as_default():

        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)

        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))

        with sess.as_default():

            pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)

    nrof_samples = len(image_paths)

    img_list = [None] * nrof_samples

    for i in range(nrof_samples):

        img = misc.imread(os.path.expanduser(image_paths[i]))

        img_size = np.asarray(img.shape)[0:2]

        bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)

        det = np.squeeze(bounding_boxes[0,0:4])

        bb = np.zeros(4, dtype=np.int32)

        bb[0] = np.maximum(det[0]-margin/2, 0)

        bb[1] = np.maximum(det[1]-margin/2, 0)

        bb[2] = np.minimum(det[2]+margin/2, img_size[1])

        bb[3] = np.minimum(det[3]+margin/2, img_size[0])

        cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]

        aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')

        prewhitened = facenet.prewhiten(aligned)

        img_list[i] = prewhitened

    images = np.stack(img_list)

    return images

# def parse_arguments(argv):

#     parser = argparse.ArgumentParser()

#

#     parser.add_argument('model', type=str, default="./models/20170216-091149",

#         help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')

#     parser.add_argument('image_files', type=str, default="src/compare_images/index10.png src/compare_images/index73.png "

#         , nargs='+', help='Images to compare')

#     parser.add_argument('--image_size', type=int,

#         help='Image size (height, width) in pixels.', default=160)

#     parser.add_argument('--margin', type=int,

#         help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)

#     parser.add_argument('--gpu_memory_fraction', type=float,

#         help='Upper bound on the amount of GPU memory that will be used by the process.', default=0.5)

#     return parser.parse_args(argv)

if __name__ == '__main__':

    main()
"""Validate a face recognizer on the "Labeled Faces in the Wild" dataset (http://vis-www.cs.umass.edu/lfw/).

Embeddings are calculated using the pairs from http://vis-www.cs.umass.edu/lfw/pairs.txt and the ROC curve

is calculated and plotted. Both the model metagraph and the model parameters need to exist

in the same directory, and the metagraph should have the extension '.meta'.

"""

# MIT License

#

# Copyright (c) 2016 David Sandberg

#

# Permission is hereby granted, free of charge, to any person obtaining a copy

# of this software and associated documentation files (the "Software"), to deal

# in the Software without restriction, including without limitation the rights

# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

# copies of the Software, and to permit persons to whom the Software is

# furnished to do so, subject to the following conditions:

#

# The above copyright notice and this permission notice shall be included in all

# copies or substantial portions of the Software.

#

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

# SOFTWARE.

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import tensorflow as tf

import numpy as np

import argparse

import facenet

import lfw

import os

import sys

import math

from sklearn import metrics

from scipy.optimize import brentq

from scipy import interpolate

import numpy

from PIL import Image,ImageDraw

import cv2

from scipy import misc

import argparse

import align.detect_face

def detetet_face_init():

    cap = cv2.VideoCapture(0)

    print(cap.isOpened())

    classifier=cv2.CascadeClassifier("./xml/haarcascade_frontalface_alt.xml")

    count=0

    return cap,classifier,count

def detect_face_clear():

    cap.release()

    cv2.destroyAllWindows()

def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):

    minsize = 20 # minimum size of face

    threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold

    factor = 0.709 # scale factor

    print('Creating networks and loading parameters')

    with tf.Graph().as_default():

        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)

        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))

        with sess.as_default():

            pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)

    nrof_samples = len(image_paths)

    img_list = [None] * nrof_samples

    for i in range(nrof_samples):

        img = misc.imread(os.path.expanduser(image_paths[i]))

        img_size = np.asarray(img.shape)[0:2]

        bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)

        det = np.squeeze(bounding_boxes[0,0:4])

        bb = np.zeros(4, dtype=np.int32)

        bb[0] = np.maximum(det[0]-margin/2, 0)

        bb[1] = np.maximum(det[1]-margin/2, 0)

        bb[2] = np.minimum(det[2]+margin/2, img_size[1])

        bb[3] = np.minimum(det[3]+margin/2, img_size[0])

        cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]

        aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')

        prewhitened = facenet.prewhiten(aligned)

        img_list[i] = prewhitened

    images = np.stack(img_list)

    return images

def compare_facevec(facevec1, facevec2):

    dist = np.sqrt(np.sum(np.square(np.subtract(facevec1, facevec2))))

    #print('  %1.4f  ' % dist, end='')

    return dist

def face_recognition_using_facenet():

    cap,classifier,count = detetet_face_init()

    model = "../models/20170216-091149"

    image_files = ['compare_images/index10.png', 'compare_images/index73.png']

    image_size = 160

    margin = 44

    gpu_memory_fraction = 0.5

    with tf.Graph().as_default():

        with tf.Session() as sess:

            # Load the model

            facenet.load_model(model)

            # Get input and output tensors

            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")

            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")

            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")

            #image_size = images_placeholder.get_shape()[1]  # For some reason this doesn't work for frozen graphs

            image_size = 160

            embedding_size = embeddings.get_shape()[1]

            index = 0

            th = 0.7

            face_recognition_tag = True

            color = (0,255,0)

            exist_face_vec = []

            face_detect_vec = []

            while count > -1:

                ret,img = cap.read()

                faceRects = classifier.detectMultiScale(img, 1.2, 2, cv2.CASCADE_SCALE_IMAGE,(20,20))

                if len(faceRects)>0:

                    for faceRect in faceRects:

                            x, y, w, h = faceRect

                            #cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (0,255,0), 2,0)

                            #print "save faceimg"

                            face_win = img[int(y):int(y) + int(h), int(x):int(x) + int(w)]

                            face_detect = cv2.resize(face_win,(image_size,image_size),interpolation=cv2.INTER_CUBIC)

                            #cv2.imwrite('faceimg/index' + str(index) + '.bmp', face_win)

                            # Run forward pass to calculate embeddings

                            #print('Runnning forward pass on face detect')

                            nrof_samples = 1

                            img_list = [None] * nrof_samples

                            prewhitened = facenet.prewhiten(face_detect)

                            img_list[0] = prewhitened

                            images = np.stack(img_list)

                            if index == 10:

                                feed_dict = {images_placeholder:images, phase_train_placeholder:False }

                                exist_face_vec = sess.run(embeddings, feed_dict=feed_dict)

                            elif index > 10  and index % 10 == 0:

                                feed_dict = {images_placeholder:images, phase_train_placeholder:False }

                                face_detect_vec = sess.run(embeddings, feed_dict=feed_dict)

                                cp = compare_facevec(face_detect_vec, exist_face_vec)

                                print("index ", index, " dist ", cp)

                                if cp < th:

                                    print(True)

                                    face_recognition_tag = True

                                else:

                                    print(False)

                                    face_recognition_tag = False

                            index +=1

                            # if face_recognition_tag == True:

                            #     cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (255,0,0), 2,0)

                            # else:

                            #     cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (0,255,0), 2,0)

                            cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (0,255,0), 2,0)

                cv2.imshow('video',img)

                key=cv2.waitKey(1)

                if key==ord('q'):

                    break

    detect_face_clear(cap)

if __name__ == '__main__':

    face_recognition_using_facenet()

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