Face recognition by comapring distance (weigth) between image arrays

0.1 set python version and anaconda environment

# refresh session and use the default python path
#.rs.restartR()
#reticulate::use_python("/Users/Mezhoud/miniconda3/envs/Ana_Env/bin/python", required = TRUE)
#reticulate::use_python("/Users/Mezhoud/miniconda3/bin/python", required = TRUE)
reticulate::use_python("/Users/Mezhoud/venv/bin/python3", required = TRUE)
reticulate::py_config()

## python:         /Users/Mezhoud/venv/bin/python3
## libpython:      /Users/Mezhoud/miniconda3/lib/libpython3.7m.dylib
## pythonhome:     /Users/Mezhoud/miniconda3:/Users/Mezhoud/miniconda3
## virtualenv:     /Users/Mezhoud/venv/bin/activate_this.py
## version:        3.7.1 (default, Dec 14 2018, 13:28:58)  [Clang 4.0.1 (tags/RELEASE_401/final)]
## numpy:          /Users/Mezhoud/miniconda3/lib/python3.7/site-packages/numpy
## numpy_version:  1.16.4
## 
## NOTE: Python version was forced by use_python function

setwd("/Volumes/DATA/learn_by_example/face_recognition/Face-Recognition_speak")
library(reticulate)
reticulate::py_module_available("tensorflow")

## [1] TRUE

0.2 Add new face to images folder

import cv2
import numpy as np
import matplotlib.pyplot as plt
from face_functions import speak, add_to_database

## Using TensorFlow backend.

os.environ['KMP_DUPLICATE_LIB_OK']='True'
 
## interactive input does not work with markdown
#name = input("Enter your Name: ")
name = "mezhoud"
speak('saving '+ name +'to database', 2)
#add_to_database(name)

0.3 Compile model using dataset and load weigths into model

from keras import backend as K
import time
from multiprocessing.dummy import Pool
K.set_image_data_format('channels_first')
import cv2
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import glob
import numpy as np
from numpy import genfromtxt
import tensorflow as tf
from fr_utils import *
from inception_network import *
from face_functions import *
from keras.models import load_model
import sys


def triplet_loss_function(y_true,y_pred,alpha = 0.3):
    anchor = y_pred[0]
    positive = y_pred[1]
    negative = y_pred[2]
    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
    neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
    basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
    loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))
    return loss


if __name__=='__main__':

    speak('compiling Model.....', 1)
    model = model(input_shape = (3,96,96))
    model.compile(optimizer = 'adam', loss = triplet_loss_function, metrics = ['accuracy'])
    speak('model compile sucessful', 1)
    speak('loading weights into model, this might take sometime sir!', 1)

    #load_weights_from_FaceNet(model) 
    speak('loading weights sequence complete sir!')

0.4 Take picture for prediction

os.environ['KMP_DUPLICATE_LIB_OK']='True'
#image = webcam('temp.jpg')

0.5 Encode dataset (images database) to arrays

import glob
def img_to_encoding(image_path, model):
    #speak("Start encoding", 0.5)
    image = cv2.imread(image_path, 1)
    image = cv2.resize(image, (96, 96)) 
    img = image[...,::-1]
    img = np.around(np.transpose(img, (2,0,1))/255.0, decimals=12)
    x_train = np.array([img])
    embedding = model.predict_on_batch(x_train)
    #speak("End encoding", 0.5)
    return embedding
    
def prepare_database(model):
    database = {}
    for file in glob.glob("images/*"):
        identity = os.path.splitext(os.path.basename(file))[0]
        database[identity] = img_to_encoding(file, model)
    return(database)
    
database = prepare_database(model)

0.6 Pairwise array-array distance computing

def recognise_face(imagepath, database, model):
    encoding = img_to_encoding(imagepath, model)
    identity = None
    min_dist = 100
    for (name, db_enc) in database.items():
        dist = np.linalg.norm(db_enc - encoding)
        print('distance for %s is %s' %(name, dist))
        if dist < min_dist:
            min_dist = dist
            identity = name
    if min_dist > 0.6:
        #speak('cant recognisethe face', 2)
        return str(0)
    else:
        #speak(identity, 0.5)
        return str("Are you %s ?" %identity)


face = recognise_face("temp.jpg", database, model)

## distance for Mr.musk is 0.14535406
## distance for mamia is 0.13927925
## distance for Karim is 0.12224277
## distance for MEZHOUD is 0.08723267
## distance for badis is 0.120408036

print(face)

## Are you MEZHOUD ?