FaceNet_Recognition.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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     "name": "stderr",
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     "text": [
      "/Users/fengjinzhao/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate\n",
    "from keras.models import Model\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.pooling import MaxPooling2D, AveragePooling2D\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.layers.core import Lambda, Flatten, Dense\n",
    "from keras.initializers import glorot_uniform\n",
    "from keras.engine.topology import Layer\n",
    "from keras import backend as K\n",
    "K.set_image_data_format('channels_first')\n",
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from fr_utils import *\n",
    "from inception_blocks_v2 import *\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "np.set_printoptions(threshold=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/fengjinzhao/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:1205: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /Users/fengjinzhao/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:2901: calling l2_normalize (from tensorflow.python.ops.nn_impl) with dim is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "dim is deprecated, use axis instead\n"
     ]
    }
   ],
   "source": [
    "FRmodel = faceRecoModel(input_shape=(3, 96, 96))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3743280\n"
     ]
    }
   ],
   "source": [
    "print FRmodel.count_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/distance_kiank.png\" style=\"width:680px;height:250px;\">\n",
    "$$\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2 + \\alpha < \\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2$$\n",
    "$$\\mathcal{J} = \\sum^{m}_{i=1} \\large[ \\small \\underbrace{\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2}_\\text{(1)} - \\underbrace{\\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2}_\\text{(2)} + \\alpha \\large ] \\small_+ \\tag{3}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def triplet_loss(y_true,y_pred,alpha=0.2):\n",
    "    '''\n",
    "    Arguments:\n",
    "    y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.\n",
    "    y_pred -- python list containing three objects:\n",
    "            anchor -- the encodings for the anchor images, of shape (None, 128)\n",
    "            positive -- the encodings for the positive images, of shape (None, 128)\n",
    "            negative -- the encodings for the negative images, of shape (None, 128)\n",
    "    \n",
    "    Returns:\n",
    "    loss -- real number, value of the loss\n",
    "    '''\n",
    "    anchor, positive, negative = y_pred[0],y_pred[1],y_pred[2]\n",
    "    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,positive)),axis=-1)\n",
    "    neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,negative)),axis=-1)\n",
    "    basic_loss = tf.add(tf.subtract(pos_dist,neg_dist),alpha)\n",
    "    loss = tf.reduce_sum(tf.maximum(basic_loss,0.0))\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/fengjinzhao/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:1290: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n"
     ]
    }
   ],
   "source": [
    "load_weights_from_FaceNet(FRmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "FRmodel.compile(optimizer='adam',loss=triplet_loss,metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = {}\n",
    "database[\"danielle\"] = img_to_encoding(\"images/danielle.png\", FRmodel)\n",
    "database[\"younes\"] = img_to_encoding(\"images/younes.jpg\", FRmodel)\n",
    "database[\"tian\"] = img_to_encoding(\"images/tian.jpg\", FRmodel)\n",
    "database[\"andrew\"] = img_to_encoding(\"images/andrew.jpg\", FRmodel)\n",
    "database[\"kian\"] = img_to_encoding(\"images/kian.jpg\", FRmodel)\n",
    "database[\"dan\"] = img_to_encoding(\"images/dan.jpg\", FRmodel)\n",
    "database[\"sebastiano\"] = img_to_encoding(\"images/sebastiano.jpg\", FRmodel)\n",
    "database[\"bertrand\"] = img_to_encoding(\"images/bertrand.jpg\", FRmodel)\n",
    "database[\"kevin\"] = img_to_encoding(\"images/kevin.jpg\", FRmodel)\n",
    "database[\"felix\"] = img_to_encoding(\"images/felix.jpg\", FRmodel)\n",
    "database[\"benoit\"] = img_to_encoding(\"images/benoit.jpg\", FRmodel)\n",
    "database[\"arnaud\"] = img_to_encoding(\"images/arnaud.jpg\", FRmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def verify(image_path, identity, database, model):\n",
    "    \"\"\"\n",
    "    Function that verifies if the person on the \"image_path\" image is \"identity\".\n",
    "    \n",
    "    Arguments:\n",
    "    image_path -- path to an image\n",
    "    identity -- string, name of the person you'd like to verify the identity. Has to be a resident of the Happy house.\n",
    "    database -- python dictionary mapping names of allowed people's names (strings) to their encodings (vectors).\n",
    "    model -- your Inception model instance in Keras\n",
    "    \n",
    "    Returns:\n",
    "    dist -- distance between the image_path and the image of \"identity\" in the database.\n",
    "    door_open -- True, if the door should open. False otherwise.\n",
    "    \"\"\"\n",
    "    encoding = img_to_encoding(image_path,model)\n",
    "    dist = np.linalg.norm(encoding-database[identity])\n",
    "    if dist<0.7:\n",
    "        print(\"It's \" + str(identity) + \", welcome home!\")\n",
    "        door_open = True\n",
    "    else:\n",
    "        print(\"It's not \" + str(identity) + \", please go away\")\n",
    "        door_open = False\n",
    "    return dist, door_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It's younes, welcome home!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.65938437, True)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verify(\"images/camera_0.jpg\", \"younes\", database, FRmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/camera_0.jpg\" style=\"width:100px;height:100px;\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It's not kian, please go away\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8622572, False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verify(\"images/camera_2.jpg\", \"kian\", database, FRmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/camera_2.jpg\" style=\"width:100px;height:100px;\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def who(image_path,database,model):\n",
    "    \"\"\"\n",
    "    Implements face recognition for the happy house by finding who is the person on the image_path image.\n",
    "    \n",
    "    Arguments:\n",
    "    image_path -- path to an image\n",
    "    database -- database containing image encodings along with the name of the person on the image\n",
    "    model -- your Inception model instance in Keras\n",
    "    \n",
    "    Returns:\n",
    "    dist -- the minimum distance between image_path encoding and the encodings from the database\n",
    "    identity -- string, the name prediction for the person on image_path\n",
    "    \"\"\"\n",
    "    encoding = img_to_encoding(image_path,model)\n",
    "    \n",
    "    dist = 100\n",
    "    \n",
    "    for (name,database_encoding) in database.items():\n",
    "        dist_temp = np.linalg.norm(encoding-database_encoding)\n",
    "        if dist_temp < dist:\n",
    "            dist = dist_temp\n",
    "            identity = name\n",
    "    if dist > 0.7:\n",
    "        print(\"Not in the database.\")\n",
    "    else:\n",
    "        print (\"it's \" + str(identity) + \", the distance is \" + str(dist))\n",
    "        \n",
    "    return dist, identity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it's younes, the distance is 0.65938437\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.65938437, 'younes')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "who(\"images/camera_0.jpg\", database, FRmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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