Face_reco.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D1Xo2gr1wztF"
   },
   "source": [
    "**Face Recognition using Transfer Learning from FaceNet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "colab_type": "code",
    "id": "MdPWeDOjXB5u",
    "outputId": "6c6a26d1-dd95-4fd9-b613-69b185058426"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras-facenet in /usr/local/lib/python3.6/dist-packages (0.1a5)\n",
      "Requirement already satisfied: opencv-python>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from keras-facenet) (3.4.7.28)\n",
      "Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (from keras-facenet) (2.2.5)\n",
      "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-python>=3.0.0->keras-facenet) (1.16.5)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras->keras-facenet) (2.8.0)\n",
      "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from keras->keras-facenet) (1.0.8)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from keras->keras-facenet) (1.1.0)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras->keras-facenet) (3.13)\n",
      "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras->keras-facenet) (1.3.1)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras->keras-facenet) (1.12.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install keras-facenet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "Skc-3PSld6Zu",
    "outputId": "c3f7a274-f3a7-4300-c0e0-f053e02235db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "colab_type": "code",
    "id": "AgaFVn_1XKeG",
    "outputId": "46803299-536f-4cad-d75e-ce838d5650b1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "from keras_facenet import FaceNet\n",
    "embedder = FaceNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6NgSsEx_XbU1"
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg \n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RLanAOifv-R0"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t662FTsAbgCE"
   },
   "outputs": [],
   "source": [
    "def load_train_data(n):\n",
    "  lst = []\n",
    "  for i in range(1,n+1):\n",
    "    filename = str(i)+'.jpg'\n",
    "    x = mpimg.imread('/content/drive/My Drive/face reco/train/'+filename)\n",
    "    x = cv2.resize(x,(160,160))\n",
    "    lst.append(x)\n",
    "  df = np.array(lst)\n",
    "  y_train = pd.read_csv('/content/drive/My Drive/face reco/train/y_train.csv',header=None)\n",
    "  y_train = y_train.values\n",
    "  return df,y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "phKwd5IVfkkf"
   },
   "outputs": [],
   "source": [
    "def load_test_data(n):\n",
    "  lst = []\n",
    "  for i in range(1,n+1):\n",
    "    filename = str(i)+'.jpg'\n",
    "    x = mpimg.imread('/content/drive/My Drive/face reco/Test/'+filename)\n",
    "    x = cv2.resize(x,(160,160))\n",
    "    lst.append(x)\n",
    "  df = np.array(lst)\n",
    "  y_test = pd.read_csv('/content/drive/My Drive/face reco/Test/y_test.csv',header=None)\n",
    "  y_test = y_test.values\n",
    "  return df,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "hzR57dbZaruU",
    "outputId": "3546179e-2590-44c2-a96b-a4fc6b9eb2fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_Train data shape= (24, 160, 160, 3)\n",
      "X_Test data shape= (7, 160, 160, 3)\n",
      "y_Train data shape= (24, 1)\n",
      "y_Test data shape= (7, 1)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Loading the data set \n",
    "'''\n",
    "X_train_orig,y_train = load_train_data(24)\n",
    "X_test_orig,y_test = load_test_data(7)\n",
    "print('X_Train data shape=',X_train_orig.shape)\n",
    "print('X_Test data shape=',X_test_orig.shape)\n",
    "print('y_Train data shape=',y_train.shape)\n",
    "print('y_Test data shape=',y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "M27mxGWVX4ll",
    "outputId": "a284f8d2-fdef-4ec5-ad47-77a6425ab4b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train embed shape= (24, 512)\n",
      "Test embed shape= (7, 512)\n"
     ]
    }
   ],
   "source": [
    "X_train = embedder.embeddings(X_train_orig)\n",
    "X_test = embedder.embeddings(X_test_orig)\n",
    "print('Train embed shape=',X_train.shape)\n",
    "print('Test embed shape=',X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4cSLX1CKYrvN"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wW23dLxopdEJ"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DLYB1nDrplYz"
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "O7AtjTnFpo_3",
    "outputId": "8c8c4ec7-2d7b-4b19-9d8f-5b2c8839616a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zRFJcD42pzoi",
    "outputId": "8573ac9d-7a7e-46f6-8131-de6fed38101e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 3, 3, 4, 6])"
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicted faces\n",
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "dC_U-MFpwME9",
    "outputId": "f17ede32-3afb-4bf5-d5ae-6f1a9de5b3fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [2],\n",
       "       [3],\n",
       "       [3],\n",
       "       [4],\n",
       "       [6]])"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#True faces\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Z1upxkI1wUH0",
    "outputId": "b9e3b308-e417-459d-f537-04c0a1250c33"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9fWaX1cplT4s"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "To recognise face of a given input file with path and filename.\n",
    "Returns one of the six classes if face is from the training data faces,\n",
    "else 'Face not found !' message is returned .\n",
    "'''\n",
    "\n",
    "def predict(path, filename):\n",
    "  x = mpimg.imread(path+'/'+filename)\n",
    "  x = cv2.resize(x,(160,160))\n",
    "  x = x.reshape(1,160,160,3)\n",
    "  x = embedder.embeddings(x)\n",
    "  if(max(model.predict_proba(x)[0])<0.25):         #0.25 is the threshold\n",
    "    print(\"Face not found !\")\n",
    "    return\n",
    "  else:\n",
    "    return model.predict(x)"
   ]
  }
 ],
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   "name": "Face_reco.ipynb",
   "provenance": []
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