facenet.py

from keras import backend as K
import time
from multiprocessing.dummy import Pool
K.set_image_data_format('channels_first')
import cv2
import os
import glob
import numpy as np
from numpy import genfromtxt
import tensorflow as tf
from fr_utils import *
from inception_blocks_v2 import *
import talkey


#import win32com.client as wincl
tts = talkey.engines.FestivalTTS()

PADDING = 50
ready_to_detect_identity = True
#windows10_voice_interface = wincl.Dispatch("SAPI.SpVoice")

FRmodel = faceRecoModel(input_shape=(3, 96, 96))

def triplet_loss(y_true, y_pred, alpha = 0.3):
    """
    Implementation of the triplet loss as defined by formula (3)

    Arguments:
    y_pred -- python list containing three objects:
            anchor -- the encodings for the anchor images, of shape (None, 128)
            positive -- the encodings for the positive images, of shape (None, 128)
            negative -- the encodings for the negative images, of shape (None, 128)

    Returns:
    loss -- real number, value of the loss
    """

    anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]

    # Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
    # Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
    neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
    # Step 3: subtract the two previous distances and add alpha.
    basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
    # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
    loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))

    return loss

FRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])
load_weights_from_FaceNet(FRmodel)

def prepare_database():
    database = {}

    # load all the images of individuals to recognize into the database
    for file in glob.glob("images/*"):
        identity = os.path.splitext(os.path.basename(file))[0]
        database[identity] = img_path_to_encoding(file, FRmodel)

    return database

def webcam_face_recognizer(database):
    """
    Runs a loop that extracts images from the computer's webcam and determines whether or not
    it contains the face of a person in our database.

    If it contains a face, an audio message will be played welcoming the user.
    If not, the program will process the next frame from the webcam
    """
    global ready_to_detect_identity

    cv2.namedWindow("preview")
    vc = cv2.VideoCapture(0)

    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

    while vc.isOpened():
        _, frame = vc.read()
        img = frame

        # We do not want to detect a new identity while the program is in the process of identifying another person
        if ready_to_detect_identity:
            img = process_frame(img, frame, face_cascade)

        key = cv2.waitKey(100)
        cv2.imshow("preview", img)

        if key == 27: # exit on ESC
            break
    cv2.destroyWindow("preview")

def process_frame(img, frame, face_cascade):
    """
    Determine whether the current frame contains the faces of people from our database
    """
    global ready_to_detect_identity
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    # Loop through all the faces detected and determine whether or not they are in the database
    identities = []
    for (x, y, w, h) in faces:
        x1 = x-PADDING
        y1 = y-PADDING
        x2 = x+w+PADDING
        y2 = y+h+PADDING

        img = cv2.rectangle(frame,(x1, y1),(x2, y2),(255,0,0),2)

        identity = find_identity(frame, x1, y1, x2, y2)

        if identity is not None:
            identities.append(identity)

    if identities != []:
        cv2.imwrite('example.png',img)

        ready_to_detect_identity = False
        pool = Pool(processes=1)
        # We run this as a separate process so that the camera feedback does not freeze
        pool.apply_async(welcome_users, [identities])
    return img

def find_identity(frame, x1, y1, x2, y2):
    """
    Determine whether the face contained within the bounding box exists in our database

    x1,y1_____________
    |                 |
    |                 |
    |_________________x2,y2

    """
    height, width, channels = frame.shape
    # The padding is necessary since the OpenCV face detector creates the bounding box around the face and not the head
    part_image = frame[max(0, y1):min(height, y2), max(0, x1):min(width, x2)]

    return who_is_it(part_image, database, FRmodel)

def who_is_it(image, database, model):
    """
    Implements face recognition for the happy house by finding who is the person on the image_path image.

    Arguments:
    image_path -- path to an image
    database -- database containing image encodings along with the name of the person on the image
    model -- your Inception model instance in Keras

    Returns:
    min_dist -- the minimum distance between image_path encoding and the encodings from the database
    identity -- string, the name prediction for the person on image_path
    """
    encoding = img_to_encoding(image, model)

    min_dist = 100
    identity = None

    # Loop over the database dictionary's names and encodings.
    for (name, db_enc) in database.items():

        # Compute L2 distance between the target "encoding" and the current "emb" from the database.
        dist = np.linalg.norm(db_enc - encoding)

        print('distance for %s is %s' %(name, dist))

        # If this distance is less than the min_dist, then set min_dist to dist, and identity to name
        if dist < min_dist:
            min_dist = dist
            identity = name

    if min_dist > 0.90:
        return None
    else:
        return str(identity)

def welcome_users(identities):
    """ Outputs a welcome audio message to the users """
    global ready_to_detect_identity
    welcome_message = 'Welcome '

    if len(identities) == 1:
        welcome_message += '%s, have a nice day.' % identities[0]
    else:
        for identity_id in range(len(identities)-1):
            welcome_message += '%s, ' % identities[identity_id]
        welcome_message += 'and %s, ' % identities[-1]
        welcome_message += 'have a nice day!'
    print("Its working.....")
    tts.say(welcome_message)


    # Allow the program to start detecting identities again
    ready_to_detect_identity = True

if __name__ == "__main__":
    database = prepare_database()
    webcam_face_recognizer(database)

# ### References:
#
# - Florian Schroff, Dmitry Kalenichenko, James Philbin (2015). [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/pdf/1503.03832.pdf)
# - Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf (2014). [DeepFace: Closing the gap to human-level performance in face verification](https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf)
# - The pretrained model we use is inspired by Victor Sy Wang's implementation and was loaded using his code: https://github.com/iwantooxxoox/Keras-OpenFace.
# - Our implementation also took a lot of inspiration from the official FaceNet github repository: https://github.com/davidsandberg/facenet
#