FACE MASK DETECTION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'with mask': 0, 'without mask': 1}\n",
      "['with mask', 'without mask']\n",
      "[0, 1]\n"
     ]
    }
   ],
   "source": [
    "import cv2,os\n",
    "\n",
    "data_path=(\"C:\\\\Users\\\\soham\\\\3D Objects\\\\ML\\\\FACE MASK\\\\dataset\")\n",
    "categories=os.listdir(data_path)\n",
    "labels=[i for i in range(len(categories))]\n",
    "\n",
    "label_dict=dict(zip(categories,labels)) #empty dictionary\n",
    "\n",
    "print(label_dict)\n",
    "print(categories)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_size=100\n",
    "data=[]\n",
    "target=[]\n",
    "\n",
    "\n",
    "for category in categories:\n",
    "    folder_path=os.path.join(data_path,category)\n",
    "    img_names=os.listdir(folder_path)\n",
    "        \n",
    "    for img_name in img_names:\n",
    "        img_path=os.path.join(folder_path,img_name)\n",
    "        img=cv2.imread(img_path)\n",
    "\n",
    "        try:\n",
    "            gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)           \n",
    "            #Coverting the image into gray scale\n",
    "            resized=cv2.resize(gray,(img_size,img_size))\n",
    "            #resizing the gray scale into 50x50, since we need a fixed common size for all the images in the dataset\n",
    "            data.append(resized)\n",
    "            target.append(label_dict[category])\n",
    "            #appending the image and the label(categorized) into the list (dataset)\n",
    "\n",
    "        except Exception as e:\n",
    "            print('Exception:',e)\n",
    "            #if any exception rasied, the exception will be printed here. And pass to the next image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "data=np.array(data)/255.0\n",
    "data=np.reshape(data,(data.shape[0],img_size,img_size,1))\n",
    "target=np.array(target)\n",
    "\n",
    "from keras.utils import np_utils\n",
    "\n",
    "new_target=np_utils.to_categorical(target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Network Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "data=np.load('C:\\\\Users\\\\soham\\\\3D Objects\\\\ML\\\\FACE MASK\\\\data.npy')\n",
    "target=np.load('C:\\\\Users\\\\soham\\\\3D Objects\\\\ML\\\\FACE MASK\\\\target.npy')\n",
    "\n",
    "#loading the save numpy arrays in the previous code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation,Flatten,Dropout\n",
    "from keras.layers import Conv2D,MaxPooling2D\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "model=Sequential()\n",
    "\n",
    "model.add(Conv2D(200,(3,3),input_shape=data.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "#The first CNN layer followed by Relu and MaxPooling layers\n",
    "\n",
    "model.add(Conv2D(100,(3,3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "#The second convolution layer followed by Relu and MaxPooling layers\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dropout(0.5))\n",
    "#Flatten layer to stack the output convolutions from second convolution layer\n",
    "model.add(Dense(50,activation='relu'))\n",
    "#Dense layer of 64 neurons\n",
    "model.add(Dense(2,activation='softmax'))\n",
    "#The Final layer with two outputs for two categories\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data,test_data,train_target,test_target=train_test_split(data,target,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.7180 - accuracy: 0.5091WARNING:tensorflow:From E:\\anaconda\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "WARNING:tensorflow:From E:\\anaconda\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "INFO:tensorflow:Assets written to: model-001.model\\assets\n",
      "31/31 [==============================] - 60s 2s/step - loss: 0.7180 - accuracy: 0.5091 - val_loss: 0.6844 - val_accuracy: 0.4960\n",
      "Epoch 2/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.6229 - accuracy: 0.6636INFO:tensorflow:Assets written to: model-002.model\\assets\n",
      "31/31 [==============================] - 42s 1s/step - loss: 0.6229 - accuracy: 0.6636 - val_loss: 0.5682 - val_accuracy: 0.6653\n",
      "Epoch 3/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.4271 - accuracy: 0.8273INFO:tensorflow:Assets written to: model-003.model\\assets\n",
      "31/31 [==============================] - 39s 1s/step - loss: 0.4271 - accuracy: 0.8273 - val_loss: 0.4710 - val_accuracy: 0.7661\n",
      "Epoch 4/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.3007 - accuracy: 0.8717INFO:tensorflow:Assets written to: model-004.model\\assets\n",
      "31/31 [==============================] - 39s 1s/step - loss: 0.3007 - accuracy: 0.8717 - val_loss: 0.2460 - val_accuracy: 0.9032\n",
      "Epoch 5/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.2361 - accuracy: 0.9051INFO:tensorflow:Assets written to: model-005.model\\assets\n",
      "31/31 [==============================] - 41s 1s/step - loss: 0.2361 - accuracy: 0.9051 - val_loss: 0.2163 - val_accuracy: 0.9113\n",
      "Epoch 6/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.1858 - accuracy: 0.9212INFO:tensorflow:Assets written to: model-006.model\\assets\n",
      "31/31 [==============================] - 40s 1s/step - loss: 0.1858 - accuracy: 0.9212 - val_loss: 0.2007 - val_accuracy: 0.9153\n",
      "Epoch 7/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.1242 - accuracy: 0.9586INFO:tensorflow:Assets written to: model-007.model\\assets\n",
      "31/31 [==============================] - 40s 1s/step - loss: 0.1242 - accuracy: 0.9586 - val_loss: 0.1922 - val_accuracy: 0.9274\n",
      "Epoch 8/25\n",
      "31/31 [==============================] - 36s 1s/step - loss: 0.1086 - accuracy: 0.9616 - val_loss: 0.2595 - val_accuracy: 0.8871\n",
      "Epoch 9/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.0931 - accuracy: 0.9677INFO:tensorflow:Assets written to: model-009.model\\assets\n",
      "31/31 [==============================] - 38s 1s/step - loss: 0.0931 - accuracy: 0.9677 - val_loss: 0.1753 - val_accuracy: 0.9355\n",
      "Epoch 10/25\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.0549 - accuracy: 0.9848INFO:tensorflow:Assets written to: model-010.model\\assets\n",
      "31/31 [==============================] - 38s 1s/step - loss: 0.0549 - accuracy: 0.9848 - val_loss: 0.1608 - val_accuracy: 0.9476\n",
      "Epoch 11/25\n",
      "31/31 [==============================] - 37s 1s/step - loss: 0.0413 - accuracy: 0.9859 - val_loss: 0.1806 - val_accuracy: 0.9556\n",
      "Epoch 12/25\n",
      "31/31 [==============================] - 38s 1s/step - loss: 0.0361 - accuracy: 0.9899 - val_loss: 0.1985 - val_accuracy: 0.9556\n",
      "Epoch 13/25\n",
      "31/31 [==============================] - 37s 1s/step - loss: 0.0358 - accuracy: 0.9899 - val_loss: 0.1819 - val_accuracy: 0.9476\n",
      "Epoch 14/25\n",
      "31/31 [==============================] - 38s 1s/step - loss: 0.0283 - accuracy: 0.9909 - val_loss: 0.2751 - val_accuracy: 0.9476\n",
      "Epoch 15/25\n",
      "31/31 [==============================] - 37s 1s/step - loss: 0.0329 - accuracy: 0.9909 - val_loss: 0.2041 - val_accuracy: 0.9355\n",
      "Epoch 16/25\n",
      "31/31 [==============================] - 37s 1s/step - loss: 0.0269 - accuracy: 0.9929 - val_loss: 0.2255 - val_accuracy: 0.9556\n",
      "Epoch 17/25\n",
      "31/31 [==============================] - 37s 1s/step - loss: 0.0317 - accuracy: 0.9869 - val_loss: 0.1970 - val_accuracy: 0.9435\n",
      "Epoch 18/25\n",
      "31/31 [==============================] - 36s 1s/step - loss: 0.0441 - accuracy: 0.9808 - val_loss: 0.2056 - val_accuracy: 0.9315\n",
      "Epoch 19/25\n",
      "31/31 [==============================] - 36s 1s/step - loss: 0.0482 - accuracy: 0.9798 - val_loss: 0.2062 - val_accuracy: 0.9516\n",
      "Epoch 20/25\n",
      "31/31 [==============================] - 37s 1s/step - loss: 0.0246 - accuracy: 0.9869 - val_loss: 0.1915 - val_accuracy: 0.9637\n",
      "Epoch 21/25\n",
      "31/31 [==============================] - 37s 1s/step - loss: 0.0549 - accuracy: 0.9818 - val_loss: 0.2290 - val_accuracy: 0.9435\n",
      "Epoch 22/25\n",
      "31/31 [==============================] - 36s 1s/step - loss: 0.0330 - accuracy: 0.9909 - val_loss: 0.1990 - val_accuracy: 0.9435\n",
      "Epoch 23/25\n",
      "31/31 [==============================] - 36s 1s/step - loss: 0.0206 - accuracy: 0.9919 - val_loss: 0.2032 - val_accuracy: 0.9556\n",
      "Epoch 24/25\n",
      "31/31 [==============================] - 38s 1s/step - loss: 0.0212 - accuracy: 0.9909 - val_loss: 0.1929 - val_accuracy: 0.9476\n",
      "Epoch 25/25\n",
      "31/31 [==============================] - 37s 1s/step - loss: 0.0189 - accuracy: 0.9960 - val_loss: 0.2174 - val_accuracy: 0.9476\n"
     ]
    }
   ],
   "source": [
    "checkpoint = ModelCheckpoint('model-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')\n",
    "history=model.fit(train_data,train_target,epochs=25,callbacks=[checkpoint],validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(history.history['loss'],'r',label='training loss')\n",
    "plt.plot(history.history['val_loss'],label='validation loss')\n",
    "plt.xlabel('# epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.savefig('plot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(history.history['accuracy'],'r',label='training accuracy')\n",
    "plt.plot(history.history['val_accuracy'],label='validation accuracy')\n",
    "plt.xlabel('# epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "plt.savefig('plot1.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 1s 214ms/step - loss: 0.1887 - accuracy: 0.9493\n",
      "[0.1886659860610962, 0.9492753744125366]\n"
     ]
    }
   ],
   "source": [
    "print(model.evaluate(test_data,test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('C:\\\\Users\\\\soham\\\\3D Objects\\\\ML\\\\FACE MASK\\\\model-017.model')\n",
    "\n",
    "face_clsfr=cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "\n",
    "source=cv2.VideoCapture(0)\n",
    "\n",
    "labels_dict={0:'MASK',1:'NO MASK'}\n",
    "color_dict={0:(0,255,0),1:(0,0,255)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "while(True):\n",
    "\n",
    "    ret,img=source.read()\n",
    "    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces=face_clsfr.detectMultiScale(gray,1.3,5)  \n",
    "\n",
    "    for (x,y,w,h) in faces:\n",
    "    \n",
    "        face_img=gray[y:y+w,x:x+w]\n",
    "        resized=cv2.resize(face_img,(100,100))\n",
    "        normalized=resized/255.0\n",
    "        reshaped=np.reshape(normalized,(1,100,100,1))\n",
    "        result=model.predict(reshaped)\n",
    "\n",
    "        label=np.argmax(result,axis=1)[0]\n",
    "      \n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),color_dict[label],2)\n",
    "        cv2.rectangle(img,(x,y-40),(x+w,y),color_dict[label],-1)\n",
    "        cv2.putText(img, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)\n",
    "        \n",
    "        \n",
    "    cv2.imshow('LIVE',img)\n",
    "    key=cv2.waitKey(1)\n",
    "    \n",
    "    if(key==27):\n",
    "        break\n",
    "        \n",
    "cv2.destroyAllWindows()\n",
    "source.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}