Face_Recognition_v3a.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Face Recognition\n",
    "\n",
    "It is a face recognition system. Many of the ideas presented here are from [FaceNet](https://arxiv.org/pdf/1503.03832.pdf). In lecture, we also talked about [DeepFace](https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf). \n",
    "\n",
    "Face recognition problems commonly fall into two categories: \n",
    "\n",
    "- **Face Verification** - \"is this the claimed person?\". For example, at some airports, you can pass through customs by letting a system scan your passport and then verifying that you (the person carrying the passport) are the correct person. A mobile phone that unlocks using your face is also using face verification. This is a 1:1 matching problem. \n",
    "- **Face Recognition** - \"who is this person?\". For example, the video lecture showed a [face recognition video](https://www.youtube.com/watch?v=wr4rx0Spihs) of Baidu employees entering the office without needing to otherwise identify themselves. This is a 1:K matching problem. \n",
    "\n",
    "FaceNet learns a neural network that encodes a face image into a vector of 128 numbers. By comparing two such vectors, we can then determine if two pictures are of the same person.\n",
    "\n",
    "- Implement the triplet loss function\n",
    "- Use a pretrained model to map face images into 128-dimensional encodings\n",
    "- Use these encodings to perform face verification and face recognition\n",
    "\n",
    "#### Channels-first notation\n",
    "\n",
    "* In this exercise, we will be using a pre-trained model which represents ConvNet activations using a **\"channels first\"** convention, as opposed to the \"channels last\" convention used in lecture and previous programming assignments. \n",
    "* In other words, a batch of images will be of shape $(m, n_C, n_H, n_W)$ instead of $(m, n_H, n_W, n_C)$. \n",
    "* Both of these conventions have a reasonable amount of traction among open-source implementations; there isn't a uniform standard yet within the deep learning community. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "Keras requires TensorFlow 2.2 or higher. Install TensorFlow via `pip install tensorflow`",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/keras/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRandomRotation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow.keras.layers.experimental.preprocessing'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-7bae747a1a03>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mConv2D\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZeroPadding2D\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mActivation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalization\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBatchNormalization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpooling\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMaxPooling2D\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAveragePooling2D\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/keras/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     raise ImportError(\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0;34m'Keras requires TensorFlow 2.2 or higher. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         'Install TensorFlow via `pip install tensorflow`')\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: Keras requires TensorFlow 2.2 or higher. Install TensorFlow via `pip install tensorflow`"
     ]
    }
   ],
   "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": "markdown",
   "metadata": {},
   "source": [
    "##  Naive Face Verification\n",
    "\n",
    "In Face Verification, we're given two images and you have to determine if they are of the same person. The simplest way to do this is to compare the two images pixel-by-pixel. If the distance between the raw images are less than a chosen threshold, it may be the same person! \n",
    "<caption><center> <u> <font color='purple'> **Figure 1** </u></center></caption>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Of course, this algorithm performs really poorly, since the pixel values change dramatically due to variations in lighting, orientation of the person's face, even minor changes in head position, and so on. \n",
    "* We'll see that rather than using the raw image, we can learn an encoding, $f(img)$.  \n",
    "* By using an encoding for each image, an element-wise comparison produces a more accurate judgement as to whether two pictures are of the same person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Encoding face images into a 128-dimensional vector \n",
    "\n",
    "### 1.1 - Using a ConvNet  to compute encodings\n",
    "\n",
    "The FaceNet model takes a lot of data and a long time to train. So following common practice in applied deep learning, let's  load weights that someone else has already trained. The network architecture follows the Inception model from [Szegedy *et al.*](https://arxiv.org/abs/1409.4842). We have provided an inception network implementation. You can look in the file `inception_blocks_v2.py` to see how it is implemented (do so by going to \"File->Open...\" at the top of the Jupyter notebook.  This opens the file directory that contains the '.py' file). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The key things we need to know are:\n",
    "\n",
    "- This network uses 96x96 dimensional RGB images as its input. Specifically, inputs a face image (or batch of $m$ face images) as a tensor of shape $(m, n_C, n_H, n_W) = (m, 3, 96, 96)$ \n",
    "- It outputs a matrix of shape $(m, 128)$ that encodes each input face image into a 128-dimensional vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FRmodel = faceRecoModel(input_shape=(3, 96, 96))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Params: 3743280\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Params:\", FRmodel.count_params())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### 1.2 - The Triplet Loss\n",
    "\n",
    "For an image $x$, we denote its encoding $f(x)$, where $f$ is the function computed by the neural network.\n",
    "\n",
    "<!--\n",
    "We will also add a normalization step at the end of our model so that $\\mid \\mid f(x) \\mid \\mid_2 = 1$ (means the vector of encoding should be of norm 1).\n",
    "!-->\n",
    "\n",
    "Training will use triplets of images $(A, P, N)$:  \n",
    "\n",
    "- A is an \"Anchor\" image--a picture of a person. \n",
    "- P is a \"Positive\" image--a picture of the same person as the Anchor image.\n",
    "- N is a \"Negative\" image--a picture of a different person than the Anchor image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: triplet_loss\n",
    "\n",
    "def triplet_loss(y_true, y_pred, alpha = 0.2):\n",
    "    \"\"\"\n",
    "    Implementation of the triplet loss as defined by formula (3)\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",
    "    \n",
    "    anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]\n",
    "    \n",
    "    # Step 1: Compute the (encoding) distance between the anchor and the positive\n",
    "    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)\n",
    "    # Step 2: Compute the (encoding) distance between the anchor and the negative\n",
    "    neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)\n",
    "    # Step 3: subtract the two previous distances and add alpha.\n",
    "    basic_loss = pos_dist - neg_dist + alpha\n",
    "    # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.\n",
    "    loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss = 528.143\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as test:\n",
    "    tf.set_random_seed(1)\n",
    "    y_true = (None, None, None)\n",
    "    y_pred = (tf.random_normal([3, 128], mean=6, stddev=0.1, seed = 1),\n",
    "              tf.random_normal([3, 128], mean=1, stddev=1, seed = 1),\n",
    "              tf.random_normal([3, 128], mean=3, stddev=4, seed = 1))\n",
    "    loss = triplet_loss(y_true, y_pred)\n",
    "    \n",
    "    print(\"loss = \" + str(loss.eval()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **loss**\n",
    "        </td>\n",
    "        <td>\n",
    "           528.143\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Loading the pre-trained model\n",
    "\n",
    "FaceNet is trained by minimizing the triplet loss. But since training requires a lot of data and a lot of computation, we won't train it from scratch here. Instead, we load a previously trained model. Load a model using the following cell; this might take a couple of minutes to run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])\n",
    "load_weights_from_FaceNet(FRmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Face Verification\n",
    "\n",
    "Let's build a database containing one encoding vector for each person who is allowed to enter the office. To generate the encoding we use `img_to_encoding(image_path, model)`, which runs the forward propagation of the model on the specified image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "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": 22,
   "metadata": {
    "collapsed": true
   },
   "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 an employee who works in the office.\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",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    \n",
    "    # Step 1: Compute the encoding for the image. Use img_to_encoding() see example above. (≈ 1 line)\n",
    "    encoding = img_to_encoding(image_path, model)\n",
    "    \n",
    "    # Step 2: Compute distance with identity's image (≈ 1 line)\n",
    "    dist = np.linalg.norm(encoding - database[identity])\n",
    "    \n",
    "    # Step 3: Open the door if dist < 0.7, else don't open (≈ 3 lines)\n",
    "    if dist < 0.7:\n",
    "        print(\"It's \" + str(identity) + \", welcome in!\")\n",
    "        door_open = True\n",
    "    else:\n",
    "        print(\"It's not \" + str(identity) + \", please go away\")\n",
    "        door_open = False\n",
    "        \n",
    "    ### END CODE HERE ###\n",
    "        \n",
    "    return dist, door_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It's younes, welcome in!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.65939289, True)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verify(\"images/camera_0.jpg\", \"younes\", database, FRmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **It's younes, welcome in!**\n",
    "        </td>\n",
    "        <td>\n",
    "           (0.65939283, True)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It's not kian, please go away\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.86224014, False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verify(\"images/camera_2.jpg\", \"kian\", database, FRmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **It's not kian, please go away**\n",
    "        </td>\n",
    "        <td>\n",
    "           (0.86224014, False)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 - Face Recognition\n",
    "\n",
    "Your face verification system is mostly working well. But since Kian got his ID card stolen, when he came back to the office the next day and couldn't get in! \n",
    "\n",
    "To solve this, you'd like to change your face verification system to a face recognition system. This way, no one has to carry an ID card anymore. An authorized person can just walk up to the building, and the door will unlock for them! \n",
    "\n",
    "You'll implement a face recognition system that takes as input an image, and figures out if it is one of the authorized persons (and if so, who). Unlike the previous face verification system, we will no longer get a person's name as one of the inputs. \n",
    "\n",
    "**Exercise**: Implement `who_is_it()`. You will have to go through the following steps:\n",
    "1. Compute the target encoding of the image from image_path\n",
    "2. Find the encoding from the database that has smallest distance with the target encoding. \n",
    "    - Initialize the `min_dist` variable to a large enough number (100). It will help you keep track of what is the closest encoding to the input's encoding.\n",
    "    - Loop over the database dictionary's names and encodings. To loop use `for (name, db_enc) in database.items()`.\n",
    "        - Compute the L2 distance between the target \"encoding\" and the current \"encoding\" from the database.\n",
    "        - If this distance is less than the min_dist, then set `min_dist` to `dist`, and `identity` to `name`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def who_is_it(image_path, database, model):\n",
    "    \"\"\"\n",
    "    Implements face recognition for the office 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",
    "    min_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",
    "    \n",
    "    \n",
    "    ## Step 1: Compute the target \"encoding\" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)\n",
    "    encoding = img_to_encoding(image_path, model)\n",
    "    \n",
    "    ## Step 2: Find the closest encoding ##\n",
    "    \n",
    "    # Initialize \"min_dist\" to a large value, say 100 (≈1 line)\n",
    "    min_dist = 100\n",
    "    \n",
    "    # Loop over the database dictionary's names and encodings.\n",
    "    for (name, db_enc) in database.items():\n",
    "        \n",
    "        # Compute L2 distance between the target \"encoding\" and the current db_enc from the database. (≈ 1 line)\n",
    "        dist = np.linalg.norm(encoding - db_enc)\n",
    "\n",
    "        # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)\n",
    "        if dist<min_dist:\n",
    "            min_dist = dist\n",
    "            identity = name    \n",
    "    if min_dist > 0.7:\n",
    "        print(\"Not in the database.\")\n",
    "    else:\n",
    "        print (\"it's \" + str(identity) + \", the distance is \" + str(min_dist))\n",
    "        \n",
    "    return min_dist, identity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Younes is at the front-door and the camera takes a picture of him (\"images/camera_0.jpg\"). Let's see if your who_it_is() algorithm identifies Younes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it's younes, the distance is 0.659393\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.65939289, 'younes')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "who_is_it(\"images/camera_0.jpg\", database, FRmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **it's younes, the distance is 0.659393**\n",
    "        </td>\n",
    "        <td>\n",
    "           (0.65939283, 'younes')\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change \"`camera_0.jpg`\" (picture of younes) to \"`camera_1.jpg`\" (picture of bertrand) and see the result."
   ]
  }
 ],
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