MaskDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "import scipy\n",
    "import os\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_dir = 'Train'\n",
    "test_dir = 'Test'\n",
    "val_dir = 'Validation'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10000 images belonging to 2 classes.\n",
      "Found 800 images belonging to 2 classes.\n",
      "Found 992 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale=1.0/255, horizontal_flip=True, zoom_range=0.2,shear_range=0.2)\n",
    "train_generator = train_datagen.flow_from_directory(directory=train_dir,target_size=(128,128),class_mode='categorical',batch_size=32)\n",
    "\n",
    "val_datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "val_generator = train_datagen.flow_from_directory(directory=val_dir,target_size=(128,128),class_mode='categorical',batch_size=32)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "test_generator = train_datagen.flow_from_directory(directory=test_dir,target_size=(128,128),class_mode='categorical',batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(32, (3, 3), activation = \"relu\", input_shape = (128, 128, 3)),\n",
    "                                    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "                                    tf.keras.layers.Dropout(0.25),\n",
    "                                    tf.keras.layers.Conv2D(64, (3, 3), activation = \"relu\"),\n",
    "                                    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "                                    tf.keras.layers.Dropout(0.25),\n",
    "                                    tf.keras.layers.Conv2D(128, (3, 3), activation = \"relu\"),\n",
    "                                    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "                                    tf.keras.layers.Dropout(0.25),\n",
    "                                    tf.keras.layers.Flatten(),\n",
    "                                    tf.keras.layers.Dense(2, activation = \"sigmoid\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 126, 126, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 63, 63, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 63, 63, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 61, 61, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 30, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 30, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 28, 28, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 50178     \n",
      "=================================================================\n",
      "Total params: 143,426\n",
      "Trainable params: 143,426\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = RMSprop(lr=0.001), loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "313/313 [==============================] - 406s 1s/step - loss: 0.4072 - accuracy: 0.8627 - val_loss: 0.1032 - val_accuracy: 0.9812\n",
      "Epoch 2/5\n",
      "313/313 [==============================] - 261s 832ms/step - loss: 0.1072 - accuracy: 0.9637 - val_loss: 0.0818 - val_accuracy: 0.9750\n",
      "Epoch 3/5\n",
      "313/313 [==============================] - 256s 818ms/step - loss: 0.0699 - accuracy: 0.9740 - val_loss: 0.0703 - val_accuracy: 0.9850\n",
      "Epoch 4/5\n",
      "313/313 [==============================] - 259s 828ms/step - loss: 0.0681 - accuracy: 0.9760 - val_loss: 0.0242 - val_accuracy: 0.9912\n",
      "Epoch 5/5\n",
      "313/313 [==============================] - 267s 854ms/step - loss: 0.0475 - accuracy: 0.9846 - val_loss: 0.0269 - val_accuracy: 0.9925\n"
     ]
    }
   ],
   "source": [
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs = {}):\n",
    "    if(logs.get('accuracy') > 0.98):\n",
    "      self.model.stop_training = True\n",
    "\n",
    "callbacks = myCallback()\n",
    "history = model.fit(train_generator, epochs=5, validation_data = val_generator, callbacks = [callbacks])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(history.history[\"val_accuracy\"], label=\"validation_accuracy\", linewidth=4)\n",
    "plt.plot(history.history[\"accuracy\"], label=\"training_accuracy\", linewidth=4)\n",
    "plt.legend()\n",
    "plt.title(\"ACC\" ,fontsize=18)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(history.history[\"val_loss\"], label=\"validation_loss\", color=\"red\", linewidth=4)\n",
    "plt.plot(history.history[\"loss\"], label=\"training_loss\", color=\"cyan\", linewidth=4)\n",
    "plt.legend()\n",
    "\n",
    "plt.title(\"LOSS\" ,fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}