webcam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from utils import LRN2D\n",
    "import utils\n",
    "import glob\n",
    "import imutils\n",
    "from imutils.video import VideoStream\n",
    "\n",
    "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.engine.topology import Layer\n",
    "from keras import backend as K\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "np.set_printoptions(threshold=sys.maxsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_capture(name):\n",
    "    n=0\n",
    "    print('Press C to Capture...')\n",
    "    detector=cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')\n",
    "    vid=VideoStream(src=0).start()\n",
    "    while True:\n",
    "        frame=vid.read()\n",
    "        temp=frame.copy()\n",
    "        frame=imutils.resize(frame, width=400)\n",
    "        rects=detector.detectMultiScale(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))\n",
    "        for (x, y, w, h) in rects:\n",
    "            cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)    \n",
    "        cv2.imshow(\"Facial Recognition\", frame)\n",
    "        key=cv2.waitKey(1) & 0xFF\n",
    "        if key==ord(\"c\"):\n",
    "            cv2.imwrite(\"./images/\"+name+\".jpg\",frame[y:y+h,x:x+w])\n",
    "            n+=1\n",
    "        elif key==ord(\"q\") or n==1:\n",
    "            break        \n",
    "    print(\"{} Image has been stored in the dataset!\".format(n))\n",
    "    cv2.destroyAllWindows()\n",
    "    vid.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many people do you want to enroll : 0\n"
     ]
    }
   ],
   "source": [
    "num = int(input('How many people do you want to enroll : '))\n",
    "for i in range(num):\n",
    "    name = input('Enter the name of the person to Enroll : ')\n",
    "    image_capture(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0107 11:45:55.017829 139979959375680 deprecation_wrapper.py:119] From /home/entropy/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0107 11:45:55.030514 139979959375680 deprecation_wrapper.py:119] From /home/entropy/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0107 11:45:55.038996 139979959375680 deprecation_wrapper.py:119] From /home/entropy/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0107 11:45:55.081820 139979959375680 deprecation_wrapper.py:119] From /home/entropy/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W0107 11:45:55.082856 139979959375680 deprecation_wrapper.py:119] From /home/entropy/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "W0107 11:45:55.143621 139979959375680 deprecation_wrapper.py:119] From /home/entropy/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "W0107 11:45:55.225985 139979959375680 deprecation_wrapper.py:119] From /home/entropy/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W0107 11:45:56.509289 139979959375680 deprecation_wrapper.py:119] From /home/entropy/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3980: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "myInput = Input(shape=(96, 96, 3))\n",
    "\n",
    "x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)\n",
    "x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)\n",
    "x = BatchNormalization(axis=3, epsilon=0.00001, name='bn1')(x)\n",
    "x = Activation('relu')(x)\n",
    "x = ZeroPadding2D(padding=(1, 1))(x)\n",
    "x = MaxPooling2D(pool_size=3, strides=2)(x)\n",
    "x = Lambda(LRN2D, name='lrn_1')(x)\n",
    "x = Conv2D(64, (1, 1), name='conv2')(x)\n",
    "x = BatchNormalization(axis=3, epsilon=0.00001, name='bn2')(x)\n",
    "x = Activation('relu')(x)\n",
    "x = ZeroPadding2D(padding=(1, 1))(x)\n",
    "x = Conv2D(192, (3, 3), name='conv3')(x)\n",
    "x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)\n",
    "x = Activation('relu')(x)\n",
    "x = Lambda(LRN2D, name='lrn_2')(x)\n",
    "x = ZeroPadding2D(padding=(1, 1))(x)\n",
    "x = MaxPooling2D(pool_size=3, strides=2)(x)\n",
    "\n",
    "# Inception3a\n",
    "inception_3a_3x3 = Conv2D(96, (1, 1), name='inception_3a_3x3_conv1')(x)\n",
    "inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn1')(inception_3a_3x3)\n",
    "inception_3a_3x3 = Activation('relu')(inception_3a_3x3)\n",
    "inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)\n",
    "inception_3a_3x3 = Conv2D(128, (3, 3), name='inception_3a_3x3_conv2')(inception_3a_3x3)\n",
    "inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn2')(inception_3a_3x3)\n",
    "inception_3a_3x3 = Activation('relu')(inception_3a_3x3)\n",
    "\n",
    "inception_3a_5x5 = Conv2D(16, (1, 1), name='inception_3a_5x5_conv1')(x)\n",
    "inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn1')(inception_3a_5x5)\n",
    "inception_3a_5x5 = Activation('relu')(inception_3a_5x5)\n",
    "inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)\n",
    "inception_3a_5x5 = Conv2D(32, (5, 5), name='inception_3a_5x5_conv2')(inception_3a_5x5)\n",
    "inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn2')(inception_3a_5x5)\n",
    "inception_3a_5x5 = Activation('relu')(inception_3a_5x5)\n",
    "\n",
    "inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)\n",
    "inception_3a_pool = Conv2D(32, (1, 1), name='inception_3a_pool_conv')(inception_3a_pool)\n",
    "inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_pool_bn')(inception_3a_pool)\n",
    "inception_3a_pool = Activation('relu')(inception_3a_pool)\n",
    "inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)\n",
    "\n",
    "inception_3a_1x1 = Conv2D(64, (1, 1), name='inception_3a_1x1_conv')(x)\n",
    "inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_1x1_bn')(inception_3a_1x1)\n",
    "inception_3a_1x1 = Activation('relu')(inception_3a_1x1)\n",
    "\n",
    "inception_3a = concatenate([inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3)\n",
    "\n",
    "# Inception3b\n",
    "inception_3b_3x3 = Conv2D(96, (1, 1), name='inception_3b_3x3_conv1')(inception_3a)\n",
    "inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn1')(inception_3b_3x3)\n",
    "inception_3b_3x3 = Activation('relu')(inception_3b_3x3)\n",
    "inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)\n",
    "inception_3b_3x3 = Conv2D(128, (3, 3), name='inception_3b_3x3_conv2')(inception_3b_3x3)\n",
    "inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn2')(inception_3b_3x3)\n",
    "inception_3b_3x3 = Activation('relu')(inception_3b_3x3)\n",
    "\n",
    "inception_3b_5x5 = Conv2D(32, (1, 1), name='inception_3b_5x5_conv1')(inception_3a)\n",
    "inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn1')(inception_3b_5x5)\n",
    "inception_3b_5x5 = Activation('relu')(inception_3b_5x5)\n",
    "inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)\n",
    "inception_3b_5x5 = Conv2D(64, (5, 5), name='inception_3b_5x5_conv2')(inception_3b_5x5)\n",
    "inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn2')(inception_3b_5x5)\n",
    "inception_3b_5x5 = Activation('relu')(inception_3b_5x5)\n",
    "\n",
    "inception_3b_pool = Lambda(lambda x: x**2, name='power2_3b')(inception_3a)\n",
    "inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)\n",
    "inception_3b_pool = Lambda(lambda x: x*9, name='mult9_3b')(inception_3b_pool)\n",
    "inception_3b_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_3b')(inception_3b_pool)\n",
    "inception_3b_pool = Conv2D(64, (1, 1), name='inception_3b_pool_conv')(inception_3b_pool)\n",
    "inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_pool_bn')(inception_3b_pool)\n",
    "inception_3b_pool = Activation('relu')(inception_3b_pool)\n",
    "inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)\n",
    "\n",
    "inception_3b_1x1 = Conv2D(64, (1, 1), name='inception_3b_1x1_conv')(inception_3a)\n",
    "inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_1x1_bn')(inception_3b_1x1)\n",
    "inception_3b_1x1 = Activation('relu')(inception_3b_1x1)\n",
    "\n",
    "inception_3b = concatenate([inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3)\n",
    "\n",
    "# Inception3c\n",
    "inception_3c_3x3 = utils.conv2d_bn(inception_3b,\n",
    "                                   layer='inception_3c_3x3',\n",
    "                                   cv1_out=128,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   cv2_out=256,\n",
    "                                   cv2_filter=(3, 3),\n",
    "                                   cv2_strides=(2, 2),\n",
    "                                   padding=(1, 1))\n",
    "\n",
    "inception_3c_5x5 = utils.conv2d_bn(inception_3b,\n",
    "                                   layer='inception_3c_5x5',\n",
    "                                   cv1_out=32,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   cv2_out=64,\n",
    "                                   cv2_filter=(5, 5),\n",
    "                                   cv2_strides=(2, 2),\n",
    "                                   padding=(2, 2))\n",
    "\n",
    "inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)\n",
    "inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)\n",
    "\n",
    "inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)\n",
    "\n",
    "#inception 4a\n",
    "inception_4a_3x3 = utils.conv2d_bn(inception_3c,\n",
    "                                   layer='inception_4a_3x3',\n",
    "                                   cv1_out=96,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   cv2_out=192,\n",
    "                                   cv2_filter=(3, 3),\n",
    "                                   cv2_strides=(1, 1),\n",
    "                                   padding=(1, 1))\n",
    "inception_4a_5x5 = utils.conv2d_bn(inception_3c,\n",
    "                                   layer='inception_4a_5x5',\n",
    "                                   cv1_out=32,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   cv2_out=64,\n",
    "                                   cv2_filter=(5, 5),\n",
    "                                   cv2_strides=(1, 1),\n",
    "                                   padding=(2, 2))\n",
    "\n",
    "inception_4a_pool = Lambda(lambda x: x**2, name='power2_4a')(inception_3c)\n",
    "inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)\n",
    "inception_4a_pool = Lambda(lambda x: x*9, name='mult9_4a')(inception_4a_pool)\n",
    "inception_4a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_4a')(inception_4a_pool)\n",
    "inception_4a_pool = utils.conv2d_bn(inception_4a_pool,\n",
    "                                   layer='inception_4a_pool',\n",
    "                                   cv1_out=128,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   padding=(2, 2))\n",
    "inception_4a_1x1 = utils.conv2d_bn(inception_3c,\n",
    "                                   layer='inception_4a_1x1',\n",
    "                                   cv1_out=256,\n",
    "                                   cv1_filter=(1, 1))\n",
    "inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3)\n",
    "\n",
    "#inception4e\n",
    "inception_4e_3x3 = utils.conv2d_bn(inception_4a,\n",
    "                                   layer='inception_4e_3x3',\n",
    "                                   cv1_out=160,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   cv2_out=256,\n",
    "                                   cv2_filter=(3, 3),\n",
    "                                   cv2_strides=(2, 2),\n",
    "                                   padding=(1, 1))\n",
    "inception_4e_5x5 = utils.conv2d_bn(inception_4a,\n",
    "                                   layer='inception_4e_5x5',\n",
    "                                   cv1_out=64,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   cv2_out=128,\n",
    "                                   cv2_filter=(5, 5),\n",
    "                                   cv2_strides=(2, 2),\n",
    "                                   padding=(2, 2))\n",
    "inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)\n",
    "inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)\n",
    "\n",
    "inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)\n",
    "\n",
    "#inception5a\n",
    "inception_5a_3x3 = utils.conv2d_bn(inception_4e,\n",
    "                                   layer='inception_5a_3x3',\n",
    "                                   cv1_out=96,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   cv2_out=384,\n",
    "                                   cv2_filter=(3, 3),\n",
    "                                   cv2_strides=(1, 1),\n",
    "                                   padding=(1, 1))\n",
    "\n",
    "inception_5a_pool = Lambda(lambda x: x**2, name='power2_5a')(inception_4e)\n",
    "inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)\n",
    "inception_5a_pool = Lambda(lambda x: x*9, name='mult9_5a')(inception_5a_pool)\n",
    "inception_5a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_5a')(inception_5a_pool)\n",
    "inception_5a_pool = utils.conv2d_bn(inception_5a_pool,\n",
    "                                   layer='inception_5a_pool',\n",
    "                                   cv1_out=96,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   padding=(1, 1))\n",
    "inception_5a_1x1 = utils.conv2d_bn(inception_4e,\n",
    "                                   layer='inception_5a_1x1',\n",
    "                                   cv1_out=256,\n",
    "                                   cv1_filter=(1, 1))\n",
    "\n",
    "inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)\n",
    "\n",
    "#inception_5b\n",
    "inception_5b_3x3 = utils.conv2d_bn(inception_5a,\n",
    "                                   layer='inception_5b_3x3',\n",
    "                                   cv1_out=96,\n",
    "                                   cv1_filter=(1, 1),\n",
    "                                   cv2_out=384,\n",
    "                                   cv2_filter=(3, 3),\n",
    "                                   cv2_strides=(1, 1),\n",
    "                                   padding=(1, 1))\n",
    "inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)\n",
    "inception_5b_pool = utils.conv2d_bn(inception_5b_pool,\n",
    "                                   layer='inception_5b_pool',\n",
    "                                   cv1_out=96,\n",
    "                                   cv1_filter=(1, 1))\n",
    "inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)\n",
    "\n",
    "inception_5b_1x1 = utils.conv2d_bn(inception_5a,\n",
    "                                   layer='inception_5b_1x1',\n",
    "                                   cv1_out=256,\n",
    "                                   cv1_filter=(1, 1))\n",
    "inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)\n",
    "\n",
    "av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)\n",
    "reshape_layer = Flatten()(av_pool)\n",
    "dense_layer = Dense(128, name='dense_layer')(reshape_layer)\n",
    "norm_layer = Lambda(lambda  x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)\n",
    "\n",
    "\n",
    "# Final Model\n",
    "model = Model(inputs=[myInput], outputs=norm_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = utils.weights\n",
    "weights_dict = utils.load_weights()\n",
    "\n",
    "for name in weights:\n",
    "  if model.get_layer(name) != None:\n",
    "    model.get_layer(name).set_weights(weights_dict[name])\n",
    "  elif model.get_layer(name) != None:\n",
    "    model.get_layer(name).set_weights(weights_dict[name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_to_embedding(image, model):\n",
    "    image = cv2.resize(image, (96, 96)) \n",
    "    img = image[...,::-1]\n",
    "    img = np.around(np.transpose(img, (0,1,2))/255.0, decimals=12)\n",
    "    x_train = np.array([img])\n",
    "    embedding = model.predict_on_batch(x_train)\n",
    "    return embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recognize_face(face_image, input_embeddings, model):\n",
    "    embedding = image_to_embedding(face_image, model)\n",
    "    minimum_distance = 200\n",
    "    name = None\n",
    "    for (input_name, input_embedding) in input_embeddings.items():   \n",
    "        euclidean_distance = np.linalg.norm(embedding-input_embedding)\n",
    "        print('Euclidean distance from %s is %s' %(input_name, euclidean_distance))\n",
    "        if euclidean_distance < minimum_distance:\n",
    "            minimum_distance = euclidean_distance\n",
    "            name = input_name\n",
    "    if minimum_distance < 0.68:\n",
    "        return str(name)\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_input_image_embeddings():\n",
    "    input_embeddings = {}\n",
    "\n",
    "    for file in glob.glob(\"images/*\"):\n",
    "        person_name = os.path.splitext(os.path.basename(file))[0]\n",
    "        image_file = cv2.imread(file, 1)\n",
    "        input_embeddings[person_name] = image_to_embedding(image_file, model)\n",
    "\n",
    "    return input_embeddings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recognize_faces_in_cam(input_embeddings):\n",
    "    cv2.namedWindow(\"Deep Face Recognition\")\n",
    "    vc = cv2.VideoCapture(0)\n",
    "    font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "    while vc.isOpened():\n",
    "        _, frame = vc.read()\n",
    "        img = frame\n",
    "        height, width, channels = frame.shape\n",
    "        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "        faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
    "        identities = []\n",
    "        for (x, y, w, h) in faces:\n",
    "            x1 = x\n",
    "            y1 = y\n",
    "            x2 = x+w\n",
    "            y2 = y+h    \n",
    "            face_image = frame[max(0, y1):min(height, y2), max(0, x1):min(width, x2)]    \n",
    "            identity = recognize_face(face_image, input_embeddings, model)\n",
    "            if identity is not None:\n",
    "                img = cv2.rectangle(frame,(x1, y1),(x2, y2),(255,255,255),2)\n",
    "                cv2.putText(img, str(identity), (x1+5,y1-5), font, 1, (255,255,255), 2)\n",
    "            else:\n",
    "                identity = 'Unknown'\n",
    "                img = cv2.rectangle(frame,(x1, y1),(x2, y2),(255,255,255),2)\n",
    "                cv2.putText(img, str(identity), (x1+5,y1-5), font, 1, (255,255,255), 2)\n",
    "        key = cv2.waitKey(100)\n",
    "        cv2.imshow(\"Face Recognizer\", img)\n",
    "        if key == 27: # exit on ESC\n",
    "            break\n",
    "    vc.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean distance from Atharva is 0.57481265\n",
      "Euclidean distance from Unnikrishnan is 0.677498\n",
      "Euclidean distance from Manasa is 1.1321733\n",
      "Euclidean distance from Atharva is 0.58296216\n",
      "Euclidean distance from Unnikrishnan is 0.688513\n",
      "Euclidean distance from Manasa is 1.1069851\n",
      "Euclidean distance from Atharva is 0.56800324\n",
      "Euclidean distance from Unnikrishnan is 0.7792927\n",
      "Euclidean distance from Manasa is 1.1152834\n",
      "Euclidean distance from Atharva is 0.6655138\n",
      "Euclidean distance from Unnikrishnan is 0.88053006\n",
      "Euclidean distance from Manasa is 1.2413948\n",
      "Euclidean distance from Atharva is 0.5909839\n",
      "Euclidean distance from Unnikrishnan is 0.6025092\n",
      "Euclidean distance from Manasa is 1.025818\n",
      "Euclidean distance from Atharva is 0.7926595\n",
      "Euclidean distance from Unnikrishnan is 0.8757469\n",
      "Euclidean distance from Manasa is 1.066733\n",
      "Euclidean distance from Atharva is 0.61657006\n",
      "Euclidean distance from Unnikrishnan is 0.80336726\n",
      "Euclidean distance from Manasa is 1.2237556\n",
      "Euclidean distance from Atharva is 0.5513126\n",
      "Euclidean distance from Unnikrishnan is 0.60845685\n",
      "Euclidean distance from Manasa is 0.965378\n",
      "Euclidean distance from Atharva is 0.57012105\n",
      "Euclidean distance from Unnikrishnan is 0.66151613\n",
      "Euclidean distance from Manasa is 1.0103139\n",
      "Euclidean distance from Atharva is 0.49884686\n",
      "Euclidean distance from Unnikrishnan is 0.71114296\n",
      "Euclidean distance from Manasa is 1.101646\n",
      "Euclidean distance from Atharva is 0.66948247\n",
      "Euclidean distance from Unnikrishnan is 0.72336805\n",
      "Euclidean distance from Manasa is 1.0180844\n",
      "Euclidean distance from Atharva is 0.7773086\n",
      "Euclidean distance from Unnikrishnan is 0.91376483\n",
      "Euclidean distance from Manasa is 1.0996327\n",
      "Euclidean distance from Atharva is 0.7993162\n",
      "Euclidean distance from Unnikrishnan is 0.7809482\n",
      "Euclidean distance from Manasa is 0.9966657\n",
      "Euclidean distance from Atharva is 0.65537727\n",
      "Euclidean distance from Unnikrishnan is 0.7693195\n",
      "Euclidean distance from Manasa is 1.0828964\n",
      "Euclidean distance from Atharva is 0.72861326\n",
      "Euclidean distance from Unnikrishnan is 0.85347223\n",
      "Euclidean distance from Manasa is 1.1594243\n",
      "Euclidean distance from Atharva is 0.72973055\n",
      "Euclidean distance from Unnikrishnan is 0.8277061\n",
      "Euclidean distance from Manasa is 1.041183\n",
      "Euclidean distance from Atharva is 0.7635921\n",
      "Euclidean distance from Unnikrishnan is 0.81781405\n",
      "Euclidean distance from Manasa is 0.9965071\n",
      "Euclidean distance from Atharva is 0.89215994\n",
      "Euclidean distance from Unnikrishnan is 0.84831136\n",
      "Euclidean distance from Manasa is 0.9645905\n",
      "Euclidean distance from Atharva is 0.7530037\n",
      "Euclidean distance from Unnikrishnan is 0.74075884\n",
      "Euclidean distance from Manasa is 0.95960706\n",
      "Euclidean distance from Atharva is 0.71569675\n",
      "Euclidean distance from Unnikrishnan is 0.8011171\n",
      "Euclidean distance from Manasa is 1.0867752\n",
      "Euclidean distance from Atharva is 0.67459834\n",
      "Euclidean distance from Unnikrishnan is 0.7470008\n",
      "Euclidean distance from Manasa is 1.0124724\n",
      "Euclidean distance from Atharva is 0.71144557\n",
      "Euclidean distance from Unnikrishnan is 0.6773421\n",
      "Euclidean distance from Manasa is 0.951769\n",
      "Euclidean distance from Atharva is 0.5937143\n",
      "Euclidean distance from Unnikrishnan is 0.5398728\n",
      "Euclidean distance from Manasa is 0.8842343\n",
      "Euclidean distance from Atharva is 0.8068383\n",
      "Euclidean distance from Unnikrishnan is 0.9510994\n",
      "Euclidean distance from Manasa is 1.1823261\n",
      "Euclidean distance from Atharva is 0.6528453\n",
      "Euclidean distance from Unnikrishnan is 0.7145139\n",
      "Euclidean distance from Manasa is 0.9426182\n",
      "Euclidean distance from Atharva is 0.6597593\n",
      "Euclidean distance from Unnikrishnan is 0.4379425\n",
      "Euclidean distance from Manasa is 0.8129009\n",
      "Euclidean distance from Atharva is 0.6780768\n",
      "Euclidean distance from Unnikrishnan is 0.7951204\n",
      "Euclidean distance from Manasa is 1.0479677\n",
      "Euclidean distance from Atharva is 0.65439737\n",
      "Euclidean distance from Unnikrishnan is 0.8615949\n",
      "Euclidean distance from Manasa is 1.1893861\n",
      "Euclidean distance from Atharva is 0.67139804\n",
      "Euclidean distance from Unnikrishnan is 0.47557688\n",
      "Euclidean distance from Manasa is 0.9309903\n",
      "Euclidean distance from Atharva is 0.6874942\n",
      "Euclidean distance from Unnikrishnan is 0.81098306\n",
      "Euclidean distance from Manasa is 1.1689802\n",
      "Euclidean distance from Atharva is 0.70686615\n",
      "Euclidean distance from Unnikrishnan is 0.8910198\n",
      "Euclidean distance from Manasa is 1.1969417\n",
      "Euclidean distance from Atharva is 0.70562285\n",
      "Euclidean distance from Unnikrishnan is 0.8108751\n",
      "Euclidean distance from Manasa is 1.0495055\n",
      "Euclidean distance from Atharva is 0.74665517\n",
      "Euclidean distance from Unnikrishnan is 0.7178348\n",
      "Euclidean distance from Manasa is 0.9141773\n",
      "Euclidean distance from Atharva is 0.74853903\n",
      "Euclidean distance from Unnikrishnan is 0.8851827\n",
      "Euclidean distance from Manasa is 1.3181269\n"
     ]
    }
   ],
   "source": [
    "input_embeddings = create_input_image_embeddings()\n",
    "recognize_faces_in_cam(input_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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