eye_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "married-jacob",
   "metadata": {},
   "source": [
    "# detecting cotton diseases using cnn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bottom-dominant",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "danish-membrane",
   "metadata": {},
   "source": [
    "### import librases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "worse-conviction",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "intense-victorian",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "revolutionary-southwest",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Conv2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "prescription-friendly",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import MaxPool2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "turkish-thought",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Flatten"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fundamental-penguin",
   "metadata": {},
   "source": [
    "### init the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "descending-relevance",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "subsequent-frank",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.add(Conv2D(filters=8,kernel_size=(3,3),strides=(1,1),input_shape=(128,128,3),activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "amazing-blade",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "selective-officer",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.add(Conv2D(filters=16,kernel_size=(3,3),strides=(1,1),activation='relu'))\n",
    "model2.add(Conv2D(filters=16,kernel_size=(3,3),strides=(1,1),activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "powered-swing",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "correct-newark",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.add(Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "atlantic-danger",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.add(Dense(kernel_initializer=\"random_uniform\",activation=\"relu\",units=150))\n",
    "model2.add(Dense(kernel_initializer=\"random_uniform\",activation=\"relu\",units=150))\n",
    "model2.add(Dense(kernel_initializer=\"random_uniform\",activation=\"relu\",units=150))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "breathing-controversy",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.add(Dense(kernel_initializer=\"random_uniform\",activation=\"softmax\",units=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "driven-excitement",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.compile(loss=\"categorical_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "laden-robertson",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_9 (Conv2D)            (None, 126, 126, 8)       224       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 63, 63, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 61, 61, 16)        1168      \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 59, 59, 16)        2320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 29, 29, 16)        0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 13456)             0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 150)               2018550   \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 150)               22650     \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 150)               22650     \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 4)                 604       \n",
      "=================================================================\n",
      "Total params: 2,068,166\n",
      "Trainable params: 2,068,166\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "higher-hamburg",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "interpreted-candle",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen =ImageDataGenerator(rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "accepted-austria",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "instant-optimization",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2860 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "x_train=train_datagen.flow_from_directory(r'C:\\Users\\91918\\dataset(eye)\\dataset_B_Eye_Images\\train',target_size=(128,128),color_mode='rgb',class_mode=\"categorical\",batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "sealed-graduate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 231 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "x_test=test_datagen.flow_from_directory(r'C:\\Users\\91918\\dataset(eye)\\dataset_B_Eye_Images\\test',target_size=(128,128),color_mode='rgb',class_mode=\"categorical\",batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "empirical-barcelona",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "following-syndication",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "for i,image in enumerate(x_train,1):\n",
    "    if(i==12):\n",
    "        break\n",
    "    plt.subplot(4,3,i)\n",
    "    plt.imshow(image[0][10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "retired-retro",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'closedLeftEyes': 0,\n",
       " 'closedRightEyes': 1,\n",
       " 'openLeftEyes': 2,\n",
       " 'openRightEyes': 3}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "foreign-posting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'closedLeftEyes': 0,\n",
       " 'closedRightEyes': 1,\n",
       " 'openLeftEyes': 2,\n",
       " 'openRightEyes': 3}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "fossil-likelihood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "median-highland",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "overhead-escape",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "tropical-columbia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "90/90 [==============================] - 51s 564ms/step - loss: 1.3703 - accuracy: 0.2836 - val_loss: 1.4480 - val_accuracy: 0.2381\n",
      "Epoch 2/100\n",
      "90/90 [==============================] - 47s 527ms/step - loss: 1.2988 - accuracy: 0.3490 - val_loss: 1.2692 - val_accuracy: 0.4069\n",
      "Epoch 3/100\n",
      "90/90 [==============================] - 45s 498ms/step - loss: 1.1196 - accuracy: 0.4510 - val_loss: 1.0962 - val_accuracy: 0.3983\n",
      "Epoch 4/100\n",
      "90/90 [==============================] - 44s 491ms/step - loss: 1.0520 - accuracy: 0.4741 - val_loss: 0.9968 - val_accuracy: 0.4848\n",
      "Epoch 5/100\n",
      "90/90 [==============================] - 44s 491ms/step - loss: 0.9592 - accuracy: 0.5182 - val_loss: 1.0054 - val_accuracy: 0.4416\n",
      "Epoch 6/100\n",
      "90/90 [==============================] - 44s 493ms/step - loss: 0.9089 - accuracy: 0.5346 - val_loss: 0.8952 - val_accuracy: 0.5238\n",
      "Epoch 7/100\n",
      "90/90 [==============================] - 47s 521ms/step - loss: 0.8640 - accuracy: 0.5374 - val_loss: 0.9412 - val_accuracy: 0.4545\n",
      "Epoch 8/100\n",
      "90/90 [==============================] - 44s 492ms/step - loss: 0.8458 - accuracy: 0.5507 - val_loss: 0.9023 - val_accuracy: 0.4719\n",
      "Epoch 9/100\n",
      "90/90 [==============================] - 47s 527ms/step - loss: 0.8200 - accuracy: 0.5832 - val_loss: 0.7823 - val_accuracy: 0.5887\n",
      "Epoch 10/100\n",
      "90/90 [==============================] - 45s 497ms/step - loss: 0.7843 - accuracy: 0.5909 - val_loss: 0.8127 - val_accuracy: 0.5671\n",
      "Epoch 11/100\n",
      "90/90 [==============================] - 44s 486ms/step - loss: 0.7538 - accuracy: 0.6245 - val_loss: 0.7575 - val_accuracy: 0.6234\n",
      "Epoch 12/100\n",
      "90/90 [==============================] - 46s 513ms/step - loss: 0.7026 - accuracy: 0.6559 - val_loss: 0.8339 - val_accuracy: 0.5974\n",
      "Epoch 13/100\n",
      "90/90 [==============================] - 49s 547ms/step - loss: 0.6683 - accuracy: 0.6937 - val_loss: 0.8105 - val_accuracy: 0.6364\n",
      "Epoch 14/100\n",
      "90/90 [==============================] - 48s 537ms/step - loss: 0.6339 - accuracy: 0.7133 - val_loss: 0.7604 - val_accuracy: 0.6710\n",
      "Epoch 15/100\n",
      "90/90 [==============================] - 44s 492ms/step - loss: 0.6110 - accuracy: 0.7325 - val_loss: 0.7474 - val_accuracy: 0.6797\n",
      "Epoch 16/100\n",
      "90/90 [==============================] - 44s 488ms/step - loss: 0.5755 - accuracy: 0.7497 - val_loss: 0.7638 - val_accuracy: 0.6753\n",
      "Epoch 17/100\n",
      "90/90 [==============================] - 43s 483ms/step - loss: 0.5706 - accuracy: 0.7462 - val_loss: 0.6909 - val_accuracy: 0.6926\n",
      "Epoch 18/100\n",
      "90/90 [==============================] - 44s 487ms/step - loss: 0.5612 - accuracy: 0.7493 - val_loss: 0.7247 - val_accuracy: 0.6883\n",
      "Epoch 19/100\n",
      "90/90 [==============================] - 44s 486ms/step - loss: 0.5598 - accuracy: 0.7517 - val_loss: 0.7626 - val_accuracy: 0.6797\n",
      "Epoch 20/100\n",
      "90/90 [==============================] - 46s 513ms/step - loss: 0.5345 - accuracy: 0.7703 - val_loss: 0.7504 - val_accuracy: 0.6623\n",
      "Epoch 21/100\n",
      "90/90 [==============================] - 44s 483ms/step - loss: 0.4975 - accuracy: 0.7808 - val_loss: 0.7682 - val_accuracy: 0.6450\n",
      "Epoch 22/100\n",
      "90/90 [==============================] - 44s 484ms/step - loss: 0.5082 - accuracy: 0.7822 - val_loss: 0.7043 - val_accuracy: 0.7186\n",
      "Epoch 23/100\n",
      "90/90 [==============================] - 45s 498ms/step - loss: 0.4985 - accuracy: 0.7832 - val_loss: 0.7167 - val_accuracy: 0.6883\n",
      "Epoch 24/100\n",
      "90/90 [==============================] - 50s 555ms/step - loss: 0.4771 - accuracy: 0.7916 - val_loss: 0.8775 - val_accuracy: 0.6320\n",
      "Epoch 25/100\n",
      "90/90 [==============================] - 50s 558ms/step - loss: 0.4720 - accuracy: 0.7972 - val_loss: 0.7758 - val_accuracy: 0.7013\n",
      "Epoch 26/100\n",
      "90/90 [==============================] - 51s 563ms/step - loss: 0.4613 - accuracy: 0.7997 - val_loss: 0.7375 - val_accuracy: 0.7013\n",
      "Epoch 27/100\n",
      "90/90 [==============================] - 58s 641ms/step - loss: 0.4491 - accuracy: 0.8059 - val_loss: 0.7767 - val_accuracy: 0.6797\n",
      "Epoch 28/100\n",
      "90/90 [==============================] - 51s 568ms/step - loss: 0.4146 - accuracy: 0.8199 - val_loss: 0.8340 - val_accuracy: 0.6840\n",
      "Epoch 29/100\n",
      "90/90 [==============================] - 58s 639ms/step - loss: 0.4115 - accuracy: 0.8262 - val_loss: 0.8583 - val_accuracy: 0.6710\n",
      "Epoch 30/100\n",
      "90/90 [==============================] - 51s 562ms/step - loss: 0.4086 - accuracy: 0.8262 - val_loss: 0.7634 - val_accuracy: 0.7273\n",
      "Epoch 31/100\n",
      "90/90 [==============================] - 54s 604ms/step - loss: 0.3982 - accuracy: 0.8269 - val_loss: 0.8067 - val_accuracy: 0.7229\n",
      "Epoch 32/100\n",
      "90/90 [==============================] - 57s 639ms/step - loss: 0.3958 - accuracy: 0.8322 - val_loss: 0.8262 - val_accuracy: 0.6883\n",
      "Epoch 33/100\n",
      "90/90 [==============================] - 51s 567ms/step - loss: 0.3894 - accuracy: 0.8360 - val_loss: 0.7595 - val_accuracy: 0.7056\n",
      "Epoch 34/100\n",
      "76/90 [========================>.....] - ETA: 7s - loss: 0.3746 - accuracy: 0.8396"
     ]
    },
    {
     "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-113-97236101bb30>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m90\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m8\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~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    322\u001b[0m               \u001b[1;34m'in a future version'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'after %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mdate\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    323\u001b[0m               instructions)\n\u001b[1;32m--> 324\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\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    325\u001b[0m     return tf_decorator.make_decorator(\n\u001b[0;32m    326\u001b[0m         \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'deprecated'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   1813\u001b[0m     \"\"\"\n\u001b[0;32m   1814\u001b[0m     \u001b[0m_keras_api_gauge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_cell\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'fit_generator'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1815\u001b[1;33m     return self.fit(\n\u001b[0m\u001b[0;32m   1816\u001b[0m         \u001b[0mgenerator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1817\u001b[0m         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    106\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 108\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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    109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m     \u001b[1;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[0;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1098\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\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   1099\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\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~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    778\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    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\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[0mkwds\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    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\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~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    805\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    806\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 807\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\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[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    808\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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    809\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2827\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\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[1;32m-> 2829\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2830\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2831\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1841\u001b[0m       \u001b[0;31m`\u001b[0m\u001b[0margs\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \"\"\"\n\u001b[1;32m-> 1843\u001b[1;33m     return self._call_flat(\n\u001b[0m\u001b[0;32m   1844\u001b[0m         [t for t in nest.flatten((args, kwargs), expand_composites=True)\n\u001b[0;32m   1845\u001b[0m          if isinstance(t, (ops.Tensor,\n",
      "\u001b[1;32m~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1921\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1922\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1923\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1924\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32m~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    543\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\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[1;32m--> 545\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    546\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\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    547\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\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---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model2.fit_generator(x_train,steps_per_epoch=90,epochs=100,validation_data=x_test,validation_steps=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "musical-comparison",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-e990a8faf683>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'eye_detection.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;31mNameError\u001b[0m: name 'model2' is not defined"
     ]
    }
   ],
   "source": [
    "model2.save('eye_detection.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "neither-chess",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "intense-scheduling",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "excellent-accent",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "import cv2\n",
    "model2 = load_model('eye_detection.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "automatic-grant",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "closedRightEyes\n"
     ]
    }
   ],
   "source": [
    "img =cv2.imread(r'C:\\Users\\91918\\dataset(eye)\\dataset_B_Eye_Images\\test\\closedRightEyes\\closed_eye_1336.jpg_face_2_R.jpg',0)\n",
    "img=cv2.resize(img,(128,128))\n",
    "img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "if (np.max(img)>1):\n",
    "    img=img/255\n",
    "        \n",
    "img=np.array([img])\n",
    "prediction=model2.predict_classes(img)\n",
    "label=['closedLeftEyes','closedRightEyes','openLeftEyes','openRightEyes']\n",
    "prediction=prediction[0]\n",
    "print(label[prediction])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "asian-grounds",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "img = cv2.imread(r'C:\\Users\\91918\\dataset(eye)\\dataset_B_Eye_Images\\test\\closedRightEyes\\closed_eye_1336.jpg_face_2_R.jpg',0)\n",
    "cv2.imshow('image',img)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "moving-selling",
   "metadata": {},
   "outputs": [
    {
     "ename": "SystemError",
     "evalue": "<class 'cv2.CascadeClassifier'> returned a result with an error set",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.0.1) C:\\ci\\opencv-suite_1573470242804\\work\\modules\\core\\src\\persistence.cpp:727: error: (-49:Unknown error code -49) Input file is empty in function 'cv::FileStorage::Impl::open'\n",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mSystemError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-57c8e2071445>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# Loading the cascades\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m## Loading the Trained Models\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mface_cascade\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCascadeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'haarcascade_frontalface_default.xml'\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      6\u001b[0m \u001b[0meye_cascade\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCascadeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'haarcascade_eye.xml'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mSystemError\u001b[0m: <class 'cv2.CascadeClassifier'> returned a result with an error set"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "# Loading the cascades\n",
    "## Loading the Trained Models\n",
    "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n",
    "\n",
    "# Defining a function that will do the detections\n",
    "def detect(gray, frame):\n",
    "    #1. Image\n",
    "    #2.Scaling Factor\n",
    "    #3. No of neighbouring pixels to be considered to detect\n",
    "    faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
    "    for (x, y, w, h) in faces:\n",
    "        print (faces)\n",
    "        print (\"Number of faces detected: \" + str(faces.shape[0]))\n",
    "        #1. Frame we want display the text\n",
    "        #2. Text we want to display\n",
    "        #3. Position of the text (x,y)\n",
    "        #4.Font\n",
    "        #5.Font Size\n",
    "        #6.Color\n",
    "        #7.Thikness\n",
    "        cv2.putText(frame, \"Number of faces detected: \" + str(faces.shape[0]), (10, 30),\n",
    "\t\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
    "        roi_color = frame[y:y+h, x:x+w]\n",
    "        #FaceFileName = \"face_\" + str(y) + \".jpg\"\n",
    "        #cv2.imwrite(FaceFileName, roi_color)\n",
    "    return frame\n",
    "\n",
    "# Doing some Face Recognition with the webcam\n",
    "video_capture = cv2.VideoCapture(0)\n",
    "while True:\n",
    "    data,frame = video_capture.read()\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    canvas = detect(gray, frame)\n",
    "    cv2.imshow('Video', canvas)\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "video_capture.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "objective-pregnancy",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'cv2' has no attribute 'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-402a6c458e1e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mface\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCascadeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhaarcascades\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"haarcascade_frontalface_default.xml\"\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      2\u001b[0m \u001b[1;31m#eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'cv2' has no attribute 'data'"
     ]
    }
   ],
   "source": [
    "face=cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\n",
    "#eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "juvenile-garbage",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.0.1'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "hundred-shepherd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting opencv-contrib-python\n",
      "  Downloading opencv_contrib_python-4.5.1.48-cp38-cp38-win_amd64.whl (41.2 MB)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\91918\\.conda\\envs\\tensorflow\\lib\\site-packages (from opencv-contrib-python) (1.19.2)\n",
      "Installing collected packages: opencv-contrib-python\n",
      "Successfully installed opencv-contrib-python-4.5.1.48\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install opencv-contrib-python --upgrade"
   ]
  }
 ],
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