eye blink.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, Activation, Dropout, Conv2D, MaxPooling2D\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3780 images belonging to 2 classes.\n",
      "Found 1068 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "IMG_SIZE = 24\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,shear_range=0.2,brightness_range=[0.5, 1.5],zoom_range=[0.9, 1.25] )\n",
    "\n",
    "val_datagen = ImageDataGenerator(rescale=1./255,shear_range=0.2,brightness_range= [0.5, 1.5],zoom_range=[0.9, 1.25])\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(directory=\"dataset/train\",target_size=(IMG_SIZE, IMG_SIZE),\n",
    "        color_mode=\"grayscale\",batch_size=16,class_mode=\"binary\",shuffle=True,seed=42)\n",
    "\n",
    "val_generator = val_datagen.flow_from_directory(directory=\"dataset/val\",target_size=(IMG_SIZE, IMG_SIZE),\n",
    "        color_mode=\"grayscale\",batch_size=16,class_mode=\"binary\",shuffle=True,seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\pc\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\pc\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From C:\\Users\\pc\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Epoch 1/100\n",
      "236/236 [==============================] - 64s 271ms/step - loss: 0.5333 - accuracy: 0.7404 - val_loss: 0.3335 - val_accuracy: 0.8409\n",
      "Epoch 2/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.3100 - accuracy: 0.8775 - val_loss: 0.3556 - val_accuracy: 0.9030\n",
      "Epoch 3/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.2394 - accuracy: 0.9036 - val_loss: 0.4402 - val_accuracy: 0.9116\n",
      "Epoch 4/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.2128 - accuracy: 0.9222 - val_loss: 0.1642 - val_accuracy: 0.9335\n",
      "Epoch 5/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1906 - accuracy: 0.9272 - val_loss: 0.1517 - val_accuracy: 0.9354\n",
      "Epoch 6/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.1803 - accuracy: 0.9346 - val_loss: 0.0401 - val_accuracy: 0.9430\n",
      "Epoch 7/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1587 - accuracy: 0.9439 - val_loss: 0.1578 - val_accuracy: 0.9458\n",
      "Epoch 8/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.1653 - accuracy: 0.9416 - val_loss: 0.0812 - val_accuracy: 0.9411\n",
      "Epoch 9/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1533 - accuracy: 0.9410 - val_loss: 0.2405 - val_accuracy: 0.9534\n",
      "Epoch 10/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.1461 - accuracy: 0.9453 - val_loss: 0.1569 - val_accuracy: 0.9458\n",
      "Epoch 11/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1321 - accuracy: 0.9538 - val_loss: 0.4212 - val_accuracy: 0.9382\n",
      "Epoch 12/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1211 - accuracy: 0.9527 - val_loss: 0.1464 - val_accuracy: 0.9468\n",
      "Epoch 13/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1228 - accuracy: 0.9519 - val_loss: 0.0109 - val_accuracy: 0.9601\n",
      "Epoch 14/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1187 - accuracy: 0.9527 - val_loss: 0.0105 - val_accuracy: 0.9601\n",
      "Epoch 15/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.1174 - accuracy: 0.9546 - val_loss: 0.0258 - val_accuracy: 0.9382\n",
      "Epoch 16/100\n",
      "236/236 [==============================] - 12s 49ms/step - loss: 0.1188 - accuracy: 0.9535 - val_loss: 0.1622 - val_accuracy: 0.9449\n",
      "Epoch 17/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1117 - accuracy: 0.9586 - val_loss: 0.2139 - val_accuracy: 0.9553\n",
      "Epoch 18/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.1170 - accuracy: 0.9559 - val_loss: 0.2628 - val_accuracy: 0.9477\n",
      "Epoch 19/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1074 - accuracy: 0.9607 - val_loss: 0.4667 - val_accuracy: 0.9477\n",
      "Epoch 20/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1095 - accuracy: 0.9596 - val_loss: 0.2301 - val_accuracy: 0.9430\n",
      "Epoch 21/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.1029 - accuracy: 0.9609 - val_loss: 0.0342 - val_accuracy: 0.9534\n",
      "Epoch 22/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.1049 - accuracy: 0.9604 - val_loss: 0.2579 - val_accuracy: 0.9544\n",
      "Epoch 23/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0974 - accuracy: 0.9625 - val_loss: 0.1415 - val_accuracy: 0.9468\n",
      "Epoch 24/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0988 - accuracy: 0.9621 - val_loss: 0.0188 - val_accuracy: 0.9572\n",
      "Epoch 25/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0983 - accuracy: 0.9603 - val_loss: 0.1106 - val_accuracy: 0.9515\n",
      "Epoch 26/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0999 - accuracy: 0.9639 - val_loss: 0.1990 - val_accuracy: 0.9553\n",
      "Epoch 27/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0864 - accuracy: 0.9679 - val_loss: 0.0970 - val_accuracy: 0.9534\n",
      "Epoch 28/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0959 - accuracy: 0.9612 - val_loss: 0.0774 - val_accuracy: 0.9563\n",
      "Epoch 29/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0829 - accuracy: 0.9694 - val_loss: 0.3922 - val_accuracy: 0.9525\n",
      "Epoch 30/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0894 - accuracy: 0.9660 - val_loss: 0.0092 - val_accuracy: 0.9601\n",
      "Epoch 31/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0897 - accuracy: 0.9617 - val_loss: 0.1004 - val_accuracy: 0.9563\n",
      "Epoch 32/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0975 - accuracy: 0.9657 - val_loss: 0.0381 - val_accuracy: 0.9553\n",
      "Epoch 33/100\n",
      "236/236 [==============================] - 10s 40ms/step - loss: 0.0844 - accuracy: 0.9693 - val_loss: 0.0543 - val_accuracy: 0.9582\n",
      "Epoch 34/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0903 - accuracy: 0.9643 - val_loss: 0.1177 - val_accuracy: 0.9553\n",
      "Epoch 35/100\n",
      "236/236 [==============================] - 10s 40ms/step - loss: 0.0841 - accuracy: 0.9684 - val_loss: 0.0443 - val_accuracy: 0.9496\n",
      "Epoch 36/100\n",
      "236/236 [==============================] - 10s 40ms/step - loss: 0.0763 - accuracy: 0.9710 - val_loss: 0.3021 - val_accuracy: 0.9534\n",
      "Epoch 37/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0798 - accuracy: 0.9705 - val_loss: 0.0062 - val_accuracy: 0.9477\n",
      "Epoch 38/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0774 - accuracy: 0.9676 - val_loss: 0.0215 - val_accuracy: 0.9715\n",
      "Epoch 39/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0843 - accuracy: 0.9660 - val_loss: 0.1232 - val_accuracy: 0.9610\n",
      "Epoch 40/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0878 - accuracy: 0.9697 - val_loss: 0.0387 - val_accuracy: 0.9591\n",
      "Epoch 41/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0723 - accuracy: 0.9727 - val_loss: 0.0675 - val_accuracy: 0.9544\n",
      "Epoch 42/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0745 - accuracy: 0.9712 - val_loss: 0.1714 - val_accuracy: 0.9572\n",
      "Epoch 43/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0618 - accuracy: 0.9769 - val_loss: 0.0035 - val_accuracy: 0.9620\n",
      "Epoch 44/100\n",
      "236/236 [==============================] - 10s 40ms/step - loss: 0.0726 - accuracy: 0.9710 - val_loss: 0.0517 - val_accuracy: 0.9553\n",
      "Epoch 45/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0741 - accuracy: 0.9716 - val_loss: 0.0208 - val_accuracy: 0.9591\n",
      "Epoch 46/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0636 - accuracy: 0.9745 - val_loss: 0.0822 - val_accuracy: 0.9696\n",
      "Epoch 47/100\n",
      "236/236 [==============================] - 11s 47ms/step - loss: 0.0736 - accuracy: 0.9732 - val_loss: 0.0495 - val_accuracy: 0.9610\n",
      "Epoch 48/100\n",
      "236/236 [==============================] - 12s 50ms/step - loss: 0.0655 - accuracy: 0.9729 - val_loss: 0.1614 - val_accuracy: 0.9620\n",
      "Epoch 49/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0718 - accuracy: 0.9750 - val_loss: 0.0263 - val_accuracy: 0.9658\n",
      "Epoch 50/100\n",
      "236/236 [==============================] - 9s 40ms/step - loss: 0.0667 - accuracy: 0.9740 - val_loss: 0.0850 - val_accuracy: 0.9544\n",
      "Epoch 51/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0686 - accuracy: 0.9740 - val_loss: 0.0281 - val_accuracy: 0.9658\n",
      "Epoch 52/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "236/236 [==============================] - 9s 37ms/step - loss: 0.0611 - accuracy: 0.9758 - val_loss: 0.1601 - val_accuracy: 0.9572\n",
      "Epoch 53/100\n",
      "236/236 [==============================] - 10s 40ms/step - loss: 0.0624 - accuracy: 0.9759 - val_loss: 0.0146 - val_accuracy: 0.9686\n",
      "Epoch 54/100\n",
      "236/236 [==============================] - 16s 67ms/step - loss: 0.0682 - accuracy: 0.9741 - val_loss: 0.0395 - val_accuracy: 0.9544\n",
      "Epoch 55/100\n",
      "236/236 [==============================] - 13s 55ms/step - loss: 0.0579 - accuracy: 0.9753 - val_loss: 0.1133 - val_accuracy: 0.9610\n",
      "Epoch 56/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0594 - accuracy: 0.9801 - val_loss: 0.0777 - val_accuracy: 0.9525\n",
      "Epoch 57/100\n",
      "236/236 [==============================] - 13s 57ms/step - loss: 0.0629 - accuracy: 0.9779 - val_loss: 0.2254 - val_accuracy: 0.9563\n",
      "Epoch 58/100\n",
      "236/236 [==============================] - 16s 66ms/step - loss: 0.0556 - accuracy: 0.9811 - val_loss: 0.3074 - val_accuracy: 0.9591 loss: 0.0556 \n",
      "Epoch 59/100\n",
      "236/236 [==============================] - 14s 60ms/step - loss: 0.0596 - accuracy: 0.9801 - val_loss: 0.1335 - val_accuracy: 0.9496\n",
      "Epoch 60/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0583 - accuracy: 0.9766 - val_loss: 0.0423 - val_accuracy: 0.9572\n",
      "Epoch 61/100\n",
      "236/236 [==============================] - 9s 39ms/step - loss: 0.0523 - accuracy: 0.9790 - val_loss: 0.0923 - val_accuracy: 0.9610\n",
      "Epoch 62/100\n",
      "236/236 [==============================] - 11s 48ms/step - loss: 0.0513 - accuracy: 0.9830 - val_loss: 0.0179 - val_accuracy: 0.9572\n",
      "Epoch 63/100\n",
      "236/236 [==============================] - 12s 50ms/step - loss: 0.0530 - accuracy: 0.9778 - val_loss: 0.0937 - val_accuracy: 0.9582\n",
      "Epoch 64/100\n",
      "236/236 [==============================] - 12s 49ms/step - loss: 0.0515 - accuracy: 0.9800 - val_loss: 0.0453 - val_accuracy: 0.9620\n",
      "Epoch 65/100\n",
      "236/236 [==============================] - 11s 47ms/step - loss: 0.0521 - accuracy: 0.9812 - val_loss: 0.0561 - val_accuracy: 0.9591\n",
      "Epoch 66/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0603 - accuracy: 0.9776 - val_loss: 0.0065 - val_accuracy: 0.9601\n",
      "Epoch 67/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0515 - accuracy: 0.9793 - val_loss: 0.0020 - val_accuracy: 0.9525\n",
      "Epoch 68/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0582 - accuracy: 0.9793 - val_loss: 0.0874 - val_accuracy: 0.9612\n",
      "Epoch 69/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0545 - accuracy: 0.9806 - val_loss: 5.8680e-04 - val_accuracy: 0.9601\n",
      "Epoch 70/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0493 - accuracy: 0.9815 - val_loss: 0.1056 - val_accuracy: 0.9572\n",
      "Epoch 71/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0544 - accuracy: 0.9797 - val_loss: 0.1084 - val_accuracy: 0.9629\n",
      "Epoch 72/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0480 - accuracy: 0.9828 - val_loss: 0.0021 - val_accuracy: 0.9667\n",
      "Epoch 73/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0475 - accuracy: 0.9825 - val_loss: 0.0664 - val_accuracy: 0.9563\n",
      "Epoch 74/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0527 - accuracy: 0.9800 - val_loss: 0.0334 - val_accuracy: 0.9572\n",
      "Epoch 75/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0430 - accuracy: 0.9851 - val_loss: 0.0409 - val_accuracy: 0.9610\n",
      "Epoch 76/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0464 - accuracy: 0.9819 - val_loss: 0.4179 - val_accuracy: 0.9553\n",
      "Epoch 77/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0409 - accuracy: 0.9862 - val_loss: 0.0155 - val_accuracy: 0.9610\n",
      "Epoch 78/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0463 - accuracy: 0.9832 - val_loss: 4.5380e-04 - val_accuracy: 0.9572\n",
      "Epoch 79/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0432 - accuracy: 0.9851 - val_loss: 0.4854 - val_accuracy: 0.9525\n",
      "Epoch 80/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0504 - accuracy: 0.9814 - val_loss: 0.3177 - val_accuracy: 0.9544\n",
      "Epoch 81/100\n",
      "236/236 [==============================] - 11s 48ms/step - loss: 0.0411 - accuracy: 0.9849 - val_loss: 0.0080 - val_accuracy: 0.9648\n",
      "Epoch 82/100\n",
      "236/236 [==============================] - 11s 47ms/step - loss: 0.0439 - accuracy: 0.9836 - val_loss: 0.0132 - val_accuracy: 0.9677\n",
      "Epoch 83/100\n",
      "236/236 [==============================] - 11s 47ms/step - loss: 0.0480 - accuracy: 0.9808 - val_loss: 0.2362 - val_accuracy: 0.9572\n",
      "Epoch 84/100\n",
      "236/236 [==============================] - 11s 49ms/step - loss: 0.0465 - accuracy: 0.9843 - val_loss: 0.1089 - val_accuracy: 0.9439\n",
      "Epoch 85/100\n",
      "236/236 [==============================] - 12s 51ms/step - loss: 0.0386 - accuracy: 0.9849 - val_loss: 0.3853 - val_accuracy: 0.9639\n",
      "Epoch 86/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0462 - accuracy: 0.9787 - val_loss: 0.0297 - val_accuracy: 0.9620\n",
      "Epoch 87/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0459 - accuracy: 0.9838 - val_loss: 0.0021 - val_accuracy: 0.9677\n",
      "Epoch 88/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0408 - accuracy: 0.9819 - val_loss: 3.2226e-04 - val_accuracy: 0.9591\n",
      "Epoch 89/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0353 - accuracy: 0.9878 - val_loss: 0.0047 - val_accuracy: 0.9534\n",
      "Epoch 90/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0465 - accuracy: 0.9833 - val_loss: 0.0516 - val_accuracy: 0.9610\n",
      "Epoch 91/100\n",
      "236/236 [==============================] - 11s 47ms/step - loss: 0.0355 - accuracy: 0.9878 - val_loss: 0.0022 - val_accuracy: 0.9601\n",
      "Epoch 92/100\n",
      "236/236 [==============================] - 11s 46ms/step - loss: 0.0460 - accuracy: 0.9830 - val_loss: 0.0479 - val_accuracy: 0.9572\n",
      "Epoch 93/100\n",
      "236/236 [==============================] - 11s 47ms/step - loss: 0.0352 - accuracy: 0.9888 - val_loss: 0.2425 - val_accuracy: 0.9534\n",
      "Epoch 94/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0401 - accuracy: 0.9830 - val_loss: 0.0207 - val_accuracy: 0.9648\n",
      "Epoch 95/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0337 - accuracy: 0.9873 - val_loss: 0.4913 - val_accuracy: 0.9629\n",
      "Epoch 96/100\n",
      "236/236 [==============================] - 11s 48ms/step - loss: 0.0410 - accuracy: 0.9837 - val_loss: 0.0151 - val_accuracy: 0.9629\n",
      "Epoch 97/100\n",
      "236/236 [==============================] - 11s 47ms/step - loss: 0.0365 - accuracy: 0.9878 - val_loss: 0.0770 - val_accuracy: 0.9696\n",
      "Epoch 98/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0329 - accuracy: 0.9886 - val_loss: 0.3312 - val_accuracy: 0.9582\n",
      "Epoch 99/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0437 - accuracy: 0.9833 - val_loss: 0.5818 - val_accuracy: 0.9658\n",
      "Epoch 100/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0423 - accuracy: 0.9869 - val_loss: 3.6882e-04 - val_accuracy: 0.9563\n"
     ]
    }
   ],
   "source": [
    "STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
    "STEP_SIZE_VALID=val_generator.n//val_generator.batch_size\n",
    "\n",
    "#Intialize Neural Network'\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(IMG_SIZE,IMG_SIZE,1)))\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(units=128, activation='relu'))\n",
    "\n",
    "\n",
    "\n",
    "model.add(Dense(units=1, activation = 'sigmoid'))\n",
    "\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "model.fit_generator(generator=train_generator,\n",
    "                    steps_per_epoch=STEP_SIZE_TRAIN,\n",
    "                    validation_data=val_generator,\n",
    "                    validation_steps=STEP_SIZE_VALID,\n",
    "                    epochs=100)\n",
    "model.save_weights(\"model11.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"model15.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "236/236 [==============================] - 11s 46ms/step - loss: 0.0313 - accuracy: 0.9886 - val_loss: 0.2802 - val_accuracy: 0.9612\n",
      "Epoch 2/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0420 - accuracy: 0.9843 - val_loss: 0.0138 - val_accuracy: 0.9639\n",
      "Epoch 3/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0420 - accuracy: 0.9846 - val_loss: 0.0026 - val_accuracy: 0.9563\n",
      "Epoch 4/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0358 - accuracy: 0.9867 - val_loss: 0.1877 - val_accuracy: 0.9553\n",
      "Epoch 5/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0285 - accuracy: 0.9904 - val_loss: 0.3636 - val_accuracy: 0.9610\n",
      "Epoch 6/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0352 - accuracy: 0.9851 - val_loss: 0.2007 - val_accuracy: 0.9525\n",
      "Epoch 7/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0363 - accuracy: 0.9835 - val_loss: 0.8482 - val_accuracy: 0.9610\n",
      "Epoch 8/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0320 - accuracy: 0.9872 - val_loss: 0.0017 - val_accuracy: 0.9572\n",
      "Epoch 9/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0346 - accuracy: 0.9875 - val_loss: 7.7710e-04 - val_accuracy: 0.9648\n",
      "Epoch 10/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0344 - accuracy: 0.9886 - val_loss: 0.0916 - val_accuracy: 0.9715\n",
      "Epoch 11/100\n",
      "236/236 [==============================] - 11s 44ms/step - loss: 0.0300 - accuracy: 0.9894 - val_loss: 0.6918 - val_accuracy: 0.9639\n",
      "Epoch 12/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0399 - accuracy: 0.9851 - val_loss: 0.0354 - val_accuracy: 0.9620\n",
      "Epoch 13/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0356 - accuracy: 0.9872 - val_loss: 0.0022 - val_accuracy: 0.9648\n",
      "Epoch 14/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0335 - accuracy: 0.9867 - val_loss: 0.1301 - val_accuracy: 0.9639\n",
      "Epoch 15/100\n",
      "236/236 [==============================] - 11s 46ms/step - loss: 0.0333 - accuracy: 0.9896 - val_loss: 0.3356 - val_accuracy: 0.9534\n",
      "Epoch 16/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0323 - accuracy: 0.9896 - val_loss: 0.6685 - val_accuracy: 0.9639\n",
      "Epoch 17/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0328 - accuracy: 0.9894 - val_loss: 0.0039 - val_accuracy: 0.9629\n",
      "Epoch 18/100\n",
      "236/236 [==============================] - 11s 46ms/step - loss: 0.0302 - accuracy: 0.9896 - val_loss: 0.0297 - val_accuracy: 0.9601\n",
      "Epoch 19/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0332 - accuracy: 0.9854 - val_loss: 0.0091 - val_accuracy: 0.9572\n",
      "Epoch 20/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0338 - accuracy: 0.9883 - val_loss: 0.2284 - val_accuracy: 0.9449\n",
      "Epoch 21/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0404 - accuracy: 0.9843 - val_loss: 0.2803 - val_accuracy: 0.9658\n",
      "Epoch 22/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0274 - accuracy: 0.9896 - val_loss: 0.3347 - val_accuracy: 0.9629\n",
      "Epoch 23/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0301 - accuracy: 0.9886 - val_loss: 0.3445 - val_accuracy: 0.9563\n",
      "Epoch 24/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0411 - accuracy: 0.9859 - val_loss: 0.0766 - val_accuracy: 0.9667\n",
      "Epoch 25/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0280 - accuracy: 0.9894 - val_loss: 0.2090 - val_accuracy: 0.9563\n",
      "Epoch 26/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0267 - accuracy: 0.9907 - val_loss: 1.0028e-04 - val_accuracy: 0.9648\n",
      "Epoch 27/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0257 - accuracy: 0.9920 - val_loss: 0.0747 - val_accuracy: 0.9468\n",
      "Epoch 28/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0239 - accuracy: 0.9912 - val_loss: 0.0933 - val_accuracy: 0.9610\n",
      "Epoch 29/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0295 - accuracy: 0.9880 - val_loss: 0.1728 - val_accuracy: 0.9648\n",
      "Epoch 30/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0233 - accuracy: 0.9918 - val_loss: 0.0836 - val_accuracy: 0.9487\n",
      "Epoch 31/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0419 - accuracy: 0.9865 - val_loss: 0.2591 - val_accuracy: 0.9525\n",
      "Epoch 32/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0276 - accuracy: 0.9905 - val_loss: 4.2920e-04 - val_accuracy: 0.9563\n",
      "Epoch 33/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0312 - accuracy: 0.9880 - val_loss: 1.3779e-04 - val_accuracy: 0.9487\n",
      "Epoch 34/100\n",
      "236/236 [==============================] - 11s 47ms/step - loss: 0.0249 - accuracy: 0.9913 - val_loss: 0.0817 - val_accuracy: 0.9572\n",
      "Epoch 35/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0245 - accuracy: 0.9893 - val_loss: 0.1048 - val_accuracy: 0.9648\n",
      "Epoch 36/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0316 - accuracy: 0.9883 - val_loss: 0.0188 - val_accuracy: 0.9696\n",
      "Epoch 37/100\n",
      "236/236 [==============================] - 12s 53ms/step - loss: 0.0273 - accuracy: 0.9888 - val_loss: 0.3506 - val_accuracy: 0.9582\n",
      "Epoch 38/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0290 - accuracy: 0.9888 - val_loss: 2.8627e-04 - val_accuracy: 0.9629\n",
      "Epoch 39/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0320 - accuracy: 0.9891 - val_loss: 0.5471 - val_accuracy: 0.9496\n",
      "Epoch 40/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0348 - accuracy: 0.9862 - val_loss: 0.0278 - val_accuracy: 0.9534\n",
      "Epoch 41/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0273 - accuracy: 0.9904 - val_loss: 0.4161 - val_accuracy: 0.9658\n",
      "Epoch 42/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0279 - accuracy: 0.9896 - val_loss: 0.0038 - val_accuracy: 0.9601\n",
      "Epoch 43/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0249 - accuracy: 0.9912 - val_loss: 6.2345e-05 - val_accuracy: 0.9639\n",
      "Epoch 44/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0232 - accuracy: 0.9915 - val_loss: 0.2412 - val_accuracy: 0.9411\n",
      "Epoch 45/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0348 - accuracy: 0.9872 - val_loss: 0.0013 - val_accuracy: 0.9572\n",
      "Epoch 46/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0237 - accuracy: 0.9915 - val_loss: 0.0082 - val_accuracy: 0.9572\n",
      "Epoch 47/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0223 - accuracy: 0.9915 - val_loss: 0.4112 - val_accuracy: 0.9582\n",
      "Epoch 48/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0187 - accuracy: 0.9947 - val_loss: 0.0189 - val_accuracy: 0.9601\n",
      "Epoch 49/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0181 - accuracy: 0.9928 - val_loss: 7.8223e-05 - val_accuracy: 0.9677\n",
      "Epoch 50/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0265 - accuracy: 0.9915 - val_loss: 0.5583 - val_accuracy: 0.9591\n",
      "Epoch 51/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0208 - accuracy: 0.9913 - val_loss: 0.1450 - val_accuracy: 0.9563\n",
      "Epoch 52/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0186 - accuracy: 0.9931 - val_loss: 7.7739e-05 - val_accuracy: 0.9582\n",
      "Epoch 53/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0363 - accuracy: 0.9875 - val_loss: 2.2753e-05 - val_accuracy: 0.9563\n",
      "Epoch 54/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0197 - accuracy: 0.9931 - val_loss: 0.0218 - val_accuracy: 0.9601\n",
      "Epoch 55/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0272 - accuracy: 0.9913 - val_loss: 0.1242 - val_accuracy: 0.9582\n",
      "Epoch 56/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0308 - accuracy: 0.9883 - val_loss: 0.0282 - val_accuracy: 0.9515\n",
      "Epoch 57/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0249 - accuracy: 0.9926 - val_loss: 4.4164e-05 - val_accuracy: 0.9525\n",
      "Epoch 58/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0251 - accuracy: 0.9904 - val_loss: 0.0141 - val_accuracy: 0.9572\n",
      "Epoch 59/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0260 - accuracy: 0.9904 - val_loss: 0.3509 - val_accuracy: 0.9572\n",
      "Epoch 60/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0244 - accuracy: 0.9928 - val_loss: 0.5140 - val_accuracy: 0.9572\n",
      "Epoch 61/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0278 - accuracy: 0.9904 - val_loss: 0.2045 - val_accuracy: 0.9601\n",
      "Epoch 62/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0220 - accuracy: 0.9921 - val_loss: 1.1237 - val_accuracy: 0.9582\n",
      "Epoch 63/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0258 - accuracy: 0.9907 - val_loss: 0.2281 - val_accuracy: 0.9667\n",
      "Epoch 64/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0189 - accuracy: 0.9947 - val_loss: 0.0566 - val_accuracy: 0.9696\n",
      "Epoch 65/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0160 - accuracy: 0.9944 - val_loss: 0.1074 - val_accuracy: 0.9591\n",
      "Epoch 66/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0191 - accuracy: 0.9931 - val_loss: 0.0038 - val_accuracy: 0.9591\n",
      "Epoch 67/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0218 - accuracy: 0.9928 - val_loss: 4.6462e-04 - val_accuracy: 0.9582\n",
      "Epoch 68/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0249 - accuracy: 0.9902 - val_loss: 0.0356 - val_accuracy: 0.9631\n",
      "Epoch 69/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0247 - accuracy: 0.9894 - val_loss: 0.0090 - val_accuracy: 0.9629\n",
      "Epoch 70/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0235 - accuracy: 0.9917 - val_loss: 1.4519 - val_accuracy: 0.9582\n",
      "Epoch 71/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0235 - accuracy: 0.9928 - val_loss: 0.0014 - val_accuracy: 0.9610\n",
      "Epoch 72/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0230 - accuracy: 0.9910 - val_loss: 0.0885 - val_accuracy: 0.9591\n",
      "Epoch 73/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0360 - accuracy: 0.9867 - val_loss: 0.1292 - val_accuracy: 0.9534\n",
      "Epoch 74/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0188 - accuracy: 0.9936 - val_loss: 0.0834 - val_accuracy: 0.9677\n",
      "Epoch 75/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0202 - accuracy: 0.9926 - val_loss: 3.9162e-05 - val_accuracy: 0.9639\n",
      "Epoch 76/100\n",
      "236/236 [==============================] - 10s 41ms/step - loss: 0.0258 - accuracy: 0.9920 - val_loss: 0.0745 - val_accuracy: 0.9610\n",
      "Epoch 77/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0171 - accuracy: 0.9947 - val_loss: 0.0053 - val_accuracy: 0.9563\n",
      "Epoch 78/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0247 - accuracy: 0.9918 - val_loss: 0.0221 - val_accuracy: 0.9639\n",
      "Epoch 79/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0200 - accuracy: 0.9936 - val_loss: 0.1445 - val_accuracy: 0.9563\n",
      "Epoch 80/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0291 - accuracy: 0.9893 - val_loss: 0.0012 - val_accuracy: 0.9620\n",
      "Epoch 81/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0207 - accuracy: 0.9939 - val_loss: 0.3633 - val_accuracy: 0.9553\n",
      "Epoch 82/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0223 - accuracy: 0.9926 - val_loss: 1.1235e-04 - val_accuracy: 0.9582\n",
      "Epoch 83/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0213 - accuracy: 0.9936 - val_loss: 0.0011 - val_accuracy: 0.9601\n",
      "Epoch 84/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0268 - accuracy: 0.9909 - val_loss: 0.2157 - val_accuracy: 0.9591\n",
      "Epoch 85/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0200 - accuracy: 0.9939 - val_loss: 4.8347e-04 - val_accuracy: 0.9591\n",
      "Epoch 86/100\n",
      "236/236 [==============================] - 10s 42ms/step - loss: 0.0261 - accuracy: 0.9907 - val_loss: 0.1364 - val_accuracy: 0.9591\n",
      "Epoch 87/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0214 - accuracy: 0.9915 - val_loss: 4.0884e-04 - val_accuracy: 0.9610\n",
      "Epoch 88/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0134 - accuracy: 0.9952 - val_loss: 0.6695 - val_accuracy: 0.9572\n",
      "Epoch 89/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0278 - accuracy: 0.9893 - val_loss: 0.2540 - val_accuracy: 0.9591\n",
      "Epoch 90/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0200 - accuracy: 0.9936 - val_loss: 0.1381 - val_accuracy: 0.9572\n",
      "Epoch 91/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0244 - accuracy: 0.9912 - val_loss: 2.7238 - val_accuracy: 0.9648\n",
      "Epoch 92/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0224 - accuracy: 0.9931 - val_loss: 0.1106 - val_accuracy: 0.9658\n",
      "Epoch 93/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0235 - accuracy: 0.9928 - val_loss: 3.9766e-04 - val_accuracy: 0.9544\n",
      "Epoch 94/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0146 - accuracy: 0.9936 - val_loss: 0.0401 - val_accuracy: 0.9525\n",
      "Epoch 95/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0185 - accuracy: 0.9928 - val_loss: 0.1292 - val_accuracy: 0.9591\n",
      "Epoch 96/100\n",
      "236/236 [==============================] - 10s 44ms/step - loss: 0.0227 - accuracy: 0.9910 - val_loss: 0.2905 - val_accuracy: 0.9477\n",
      "Epoch 97/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0201 - accuracy: 0.9944 - val_loss: 0.1232 - val_accuracy: 0.9610\n",
      "Epoch 98/100\n",
      "236/236 [==============================] - 11s 45ms/step - loss: 0.0228 - accuracy: 0.9910 - val_loss: 4.1550e-04 - val_accuracy: 0.9591\n",
      "Epoch 99/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0212 - accuracy: 0.9926 - val_loss: 0.7284 - val_accuracy: 0.9572\n",
      "Epoch 100/100\n",
      "236/236 [==============================] - 10s 43ms/step - loss: 0.0171 - accuracy: 0.9942 - val_loss: 0.0183 - val_accuracy: 0.9544\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import RMSprop\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "                   \n",
    "checkpoint = ModelCheckpoint(\"model7.h5\",\n",
    "                             monitor=\"val_loss\",\n",
    "                             mode=\"min\",\n",
    "                             save_best_only = True,\n",
    "                             verbose=1)\n",
    "\n",
    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
    "                          min_delta = 0, \n",
    "                          patience =3,\n",
    "                          verbose = 1,\n",
    "                          restore_best_weights = True)\n",
    "\n",
    "# put our call backs into a callback list\n",
    "callbacks = [earlystop, checkpoint]\n",
    "\n",
    "# use a very small learning rate \n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics = ['accuracy'])\n",
    "\n",
    "STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
    "STEP_SIZE_VALID=val_generator.n//val_generator.batch_size\n",
    "\n",
    "\n",
    "model.fit_generator(generator=train_generator,\n",
    "                    steps_per_epoch=STEP_SIZE_TRAIN,\n",
    "                    validation_data=val_generator,\n",
    "                    validation_steps=STEP_SIZE_VALID,\n",
    "                    epochs=100)\n",
    "\n",
    "model.save(\"model7.h5\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot create group in read-only mode.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-f68cfb884437>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mload_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'model11.h5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\engine\\saving.py\u001b[0m in \u001b[0;36mload_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    490\u001b[0m                 \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtmp_filepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 492\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mload_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    494\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mload_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\engine\\saving.py\u001b[0m in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[0;32m    582\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mH5Dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_supported_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    583\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mH5Dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mh5dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 584\u001b[1;33m             \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_deserialize_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh5dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    585\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'write'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    586\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mload_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh5file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\engine\\saving.py\u001b[0m in \u001b[0;36m_deserialize_model\u001b[1;34m(h5dict, custom_objects, compile)\u001b[0m\n\u001b[0;32m    268\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 270\u001b[1;33m     \u001b[0mmodel_config\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'model_config'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    271\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmodel_config\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    272\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'No model found in config.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\utils\\io_utils.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m    316\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_only\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 318\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot create group in read-only mode.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    319\u001b[0m                 \u001b[0mval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mH5Dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    320\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot create group in read-only mode."
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "model=load_model('model11.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 22, 22, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 11, 11, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 9, 9, 16)          4624      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 16)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 4, 4, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 37,969\n",
      "Trainable params: 37,969\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "from keras.preprocessing.image import img_to_array\n",
    "model1=load_model('model15.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "face_classifier=cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')\n",
    "left_eye_classifier = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_lefteye_2splits.xml')\n",
    "right_eye_classifier =cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_righteye_2splits.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE=24\n",
    "flag1,flag2,flag3,flag4,flag5,flag6=0,0,0,0,0,0\n",
    "\n",
    "def predict(img, model):\n",
    "\n",
    "    image = cv2.resize(img, (IMG_SIZE,IMG_SIZE))\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    image = image.astype(\"float\") / 255.0\n",
    "    image = img_to_array(image)\n",
    "    image = np.expand_dims(image, axis=0)    \n",
    "    prediction = model.predict(image)[0]\n",
    "    print(prediction)\n",
    "    if prediction < 0.3:\n",
    "        prediction = 'closed'\n",
    "    elif prediction > 0.6:\n",
    "        prediction = 'open'\n",
    "    return prediction\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.78683025]\n",
      "open\n",
      "[0.8272072]\n",
      "open\n",
      "[0.57327247]\n",
      "[0.57327247]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:34: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:36: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.814317]\n",
      "[0.9389632]\n",
      "open\n",
      "[0.9871132]\n",
      "[0.8603858]\n",
      "open\n",
      "[0.80988437]\n",
      "[0.90704286]\n",
      "open\n",
      "[0.6878865]\n",
      "open\n",
      "[0.8594904]\n",
      "open\n",
      "[0.89860624]\n",
      "open\n",
      "[0.924516]\n",
      "open\n",
      "[0.47598752]\n",
      "[0.8244151]\n",
      "open\n",
      "[0.90681165]\n",
      "open\n",
      "[0.90681165]\n",
      "open\n",
      "[0.8102834]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:28: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "open\n",
      "[0.28174832]\n",
      "[0.7488052]\n",
      "open\n",
      "[0.21603344]\n",
      "[0.8288323]\n",
      "open\n",
      "[0.8321018]\n",
      "open\n",
      "[0.91009986]\n",
      "open\n",
      "[0.90480393]\n",
      "open\n",
      "[0.96700376]\n",
      "open\n",
      "[0.60425323]\n",
      "open\n",
      "[0.89516544]\n",
      "open\n",
      "[0.8061137]\n",
      "open\n",
      "[0.73018503]\n",
      "open\n",
      "[0.73018503]\n",
      "open\n",
      "[0.91067135]\n",
      "open\n",
      "[0.7522817]\n",
      "open\n",
      "[0.7522817]\n",
      "open\n",
      "[0.09362941]\n",
      "[0.8129993]\n",
      "open\n",
      "[0.8257993]\n",
      "open\n",
      "[0.8975916]\n",
      "open\n",
      "[0.8975916]\n",
      "open\n",
      "[0.87065494]\n",
      "open\n",
      "[0.87572205]\n",
      "open\n",
      "[0.25638634]\n",
      "[0.8096973]\n",
      "open\n",
      "[0.8520804]\n",
      "open\n",
      "[0.83183646]\n",
      "open\n",
      "[0.8629922]\n",
      "open\n",
      "[0.90612644]\n",
      "open\n",
      "[0.9356633]\n",
      "open\n",
      "[0.86538494]\n",
      "open\n",
      "[0.7173588]\n",
      "open\n",
      "[0.7173588]\n",
      "open\n",
      "[0.7884402]\n",
      "open\n",
      "[0.8544349]\n",
      "open\n",
      "[0.8544349]\n",
      "open\n",
      "[0.8790747]\n",
      "open\n",
      "[0.720179]\n",
      "open\n",
      "[0.7206288]\n",
      "open\n",
      "[0.9136776]\n",
      "open\n",
      "[0.9136776]\n",
      "open\n",
      "[0.94176435]\n",
      "open\n",
      "[0.9135518]\n",
      "open\n",
      "[0.9135518]\n",
      "open\n",
      "[0.86609507]\n",
      "[0.9696144]\n",
      "open\n",
      "[0.93722606]\n",
      "open\n",
      "[0.9238152]\n",
      "open\n",
      "[0.9810785]\n",
      "[0.86905897]\n",
      "open\n",
      "[0.42953917]\n",
      "[0.42953917]\n",
      "[0.22373456]\n",
      "closed\n",
      "[0.47592175]\n",
      "[0.47592175]\n",
      "[0.96406555]\n",
      "open\n",
      "[0.91080135]\n",
      "[0.9486734]\n",
      "open\n",
      "[0.86960506]\n",
      "open\n",
      "[0.9857728]\n",
      "open\n",
      "[0.88491195]\n",
      "open\n",
      "[0.88491195]\n",
      "open\n",
      "[0.97039527]\n",
      "open\n",
      "[0.80851793]\n",
      "open\n",
      "[0.80851793]\n",
      "open\n",
      "[0.93413305]\n",
      "open\n",
      "[0.98412377]\n",
      "[0.98957694]\n",
      "open\n",
      "[0.98412377]\n",
      "[0.98957694]\n",
      "open\n",
      "[0.9926717]\n",
      "open\n",
      "[0.9888416]\n",
      "[0.9540051]\n",
      "open\n",
      "[0.9753464]\n",
      "[0.99016225]\n",
      "open\n",
      "[0.9753464]\n",
      "[0.99016225]\n",
      "open\n",
      "[0.98278934]\n",
      "[0.99771607]\n",
      "open\n",
      "[0.9977157]\n",
      "[0.98722494]\n",
      "open\n",
      "[0.9816383]\n",
      "[0.992792]\n",
      "open\n",
      "[0.9816383]\n",
      "[0.992792]\n",
      "open\n",
      "[0.9730585]\n",
      "[0.98736143]\n",
      "open\n",
      "[0.994865]\n",
      "[0.99274737]\n",
      "open\n",
      "[0.38592616]\n",
      "[0.972627]\n",
      "open\n",
      "[0.97603387]\n",
      "[0.9814789]\n",
      "open\n",
      "[0.97603387]\n",
      "[0.9814789]\n",
      "open\n",
      "[0.98455435]\n",
      "[0.9779353]\n",
      "open\n",
      "[0.99195236]\n",
      "[0.9786941]\n",
      "open\n",
      "[0.94671386]\n",
      "[0.9901419]\n",
      "open\n",
      "[0.91639477]\n",
      "[0.63789755]\n",
      "[0.63789755]\n",
      "[0.10689482]\n",
      "[0.6198176]\n",
      "[0.6198176]\n",
      "[0.73845226]\n",
      "[0.6293066]\n",
      "[0.824895]\n",
      "[0.824895]\n",
      "[0.39877367]\n",
      "[0.3751974]\n",
      "[0.50164604]\n",
      "[0.80841696]\n",
      "[0.85509956]\n",
      "[0.75471914]\n",
      "[0.39893466]\n",
      "[0.61239445]\n",
      "[0.71694726]\n",
      "[0.81399095]\n",
      "[0.17737363]\n",
      "[0.78841454]\n",
      "[0.8857593]\n",
      "[0.56155366]\n",
      "[0.56155366]\n",
      "[0.5746858]\n",
      "[0.8705535]\n",
      "[0.8705535]\n",
      "[0.4351624]\n",
      "[0.79864824]\n",
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      "[0.2551679]\n",
      "closed\n",
      "[0.0035971]\n",
      "[0.00083348]\n",
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      "closed\n",
      "[0.00024426]\n",
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      "[8.2454375e-05]\n",
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      "closed\n",
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      "open\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99935406]\n",
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      "closed\n",
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      "open\n",
      "[0.98984957]\n",
      "[0.30494133]\n",
      "[0.30494133]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-ef5860dd53fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m         \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Live face dectector'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     58\u001b[0m         \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "while True:\n",
    "    ret, frame=cap.read()\n",
    "    if ret:        \n",
    "        frame = cv2.resize(frame, (0, 0), fx=0.6, fy=0.6)\n",
    "        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "        faces=face_classifier.detectMultiScale(gray,1.3,5)\n",
    "        for (x,y,w,h) in faces:\n",
    "            left_face = frame[y:y+h, x+int(w/2):x+w]\n",
    "            left_face_gray = gray[y:y+h, x+int(w/2):x+w]\n",
    "            \n",
    "            right_face = frame[y:y+h, x:x+int(w/2)]\n",
    "            right_face_gray = gray[y:y+h, x:x+int(w/2)]\n",
    "\n",
    "                # Detect the left eye\n",
    "            left_eye = left_eye_classifier.detectMultiScale(left_face_gray,scaleFactor=1.1,minNeighbors=5,minSize=(30, 30),flags = cv2.CASCADE_SCALE_IMAGE)\n",
    "\n",
    "                # Detect the right eye\n",
    "            right_eye =right_eye_classifier.detectMultiScale(right_face_gray,scaleFactor=1.1,minNeighbors=5,minSize=(30, 30),flags = cv2.CASCADE_SCALE_IMAGE)\n",
    "\n",
    "                \n",
    "            for (ex,ey,ew,eh) in right_eye:\n",
    "                pred = predict(right_face[ey:ey+eh,ex:ex+ew],model1)\n",
    "     \n",
    "                if pred=='closed':\n",
    "                    flag1=1\n",
    "\n",
    "                elif pred=='open':\n",
    "                    flag2=1\n",
    "                    \n",
    "            for (ex,ey,ew,eh) in left_eye:\n",
    "                pred = predict(left_face[ey:ey+eh,ex:ex+ew],model1)\n",
    "                print(pred)\n",
    "                if pred=='closed':\n",
    "                    flag3=1\n",
    "                elif pred=='open':\n",
    "                    flag4=1\n",
    "                    \n",
    "            if flag1==1 and flag3==1:\n",
    "                cv2.putText(frame,'Closed', (10, 50), cv2.FONT_HERSHEY_SIMPLEX,0.75, (255, 255, 0), 2)\n",
    "                flag1=0\n",
    "                flag3=0\n",
    "                flag5=1\n",
    "            elif flag2==1 and flag4==1:\n",
    "                cv2.putText(frame,'Open', (10, 200), cv2.FONT_HERSHEY_SIMPLEX,0.75, (255, 255, 0), 2)\n",
    "                flag2=0\n",
    "                flag4=0\n",
    "                flag6=1\n",
    "                \n",
    "            if flag5==1 and flag6==1:\n",
    "                cv2.putText(frame,'********Blink*******', (50, 150), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 255, 255), 2)\n",
    "                flag5=0\n",
    "                flag6=0\n",
    "                \n",
    "            \n",
    "        cv2.imshow('Live face dectector',frame)\n",
    "    if cv2.waitKey(1)==13:\n",
    "        break\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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