FaceMask-Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "import cv2\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Flatten,Dense\n",
    "import random\n",
    "\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\n",
    "\n",
    "mobilenet = MobileNetV2(weights = \"imagenet\",include_top = False,input_shape=(150,150,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in mobilenet.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "mobilenetv2_1.00_224 (Functi (None, 5, 5, 1280)        2257984   \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 32000)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 64002     \n",
      "=================================================================\n",
      "Total params: 2,321,986\n",
      "Trainable params: 64,002\n",
      "Non-trainable params: 2,257,984\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Not training the mobilenet layers as they have predefined weights from imagenet\n",
    "for layer in mobilenet.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Initialisation of model\n",
    "model = Sequential()\n",
    "\n",
    "# Adding the mobilenet layer\n",
    "model.add(mobilenet)\n",
    "\n",
    "# Flattening of data\n",
    "model.add(Flatten())\n",
    "\n",
    "# Adding output layer \n",
    "model.add(Dense(2,activation=\"sigmoid\"))\n",
    "\n",
    "#Compiling model\n",
    "model.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics =[\"accuracy\"])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "mobilenetv2_1.00_224 (Functi (None, 5, 5, 1280)        2257984   \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 32000)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 64002     \n",
      "=================================================================\n",
      "Total params: 2,321,986\n",
      "Trainable params: 64,002\n",
      "Non-trainable params: 2,257,984\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics =[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "checkpoint = ModelCheckpoint(\"moblenet_facemask.h5\",monitor=\"val_accuracy\",save_best_only=True,verbose=1)\n",
    "earlystop = EarlyStopping(monitor=\"val_acc\",patience=5,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(\"./face_mask.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "face_clsfr=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_dict={1:'without_mask',0:'with_mask'}\n",
    "color_dict={1:(0,0,255),0:(0,255,0)}\n",
    "\n",
    "size = 4\n",
    "webcam = cv2.VideoCapture(0) #Use camera 0\n",
    "\n",
    "# We load the xml file\n",
    "classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "\n",
    "while True:\n",
    "    (rval, im) = webcam.read()\n",
    "    im=cv2.flip(im,1,1) #Flip to act as a mirror\n",
    "\n",
    "    # Resize the image to speed up detection\n",
    "    mini = cv2.resize(im, (im.shape[1] // size, im.shape[0] // size))\n",
    "\n",
    "    # detect MultiScale / faces \n",
    "    faces = classifier.detectMultiScale(mini)\n",
    "\n",
    "    # Draw rectangles around each face\n",
    "    for f in faces:\n",
    "        (x, y, w, h) = [v * size for v in f] #Scale the shapesize backup\n",
    "        #Save just the rectangle faces in SubRecFaces\n",
    "        face_img = im[y:y+h, x:x+w]\n",
    "        resized=cv2.resize(face_img,(150,150))\n",
    "        normalized=resized/255.0\n",
    "        reshaped=np.reshape(normalized,(1,150,150,3))\n",
    "        reshaped = np.vstack([reshaped])\n",
    "        result=model.predict(reshaped)\n",
    "        #print(result)\n",
    "        \n",
    "        label=np.argmax(result,axis=1)[0]\n",
    "      \n",
    "        cv2.rectangle(im,(x,y),(x+w,y+h),color_dict[label],2)\n",
    "        cv2.rectangle(im,(x,y-40),(x+w,y),color_dict[label],-1)\n",
    "        cv2.putText(im, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)\n",
    "        \n",
    "    # Show the image\n",
    "    cv2.imshow('LIVE',   im)\n",
    "    key = cv2.waitKey(10)\n",
    "    # if Esc key is press then break out of the loop \n",
    "    if key == 27: #The Esc key\n",
    "        break\n",
    "# Stop video\n",
    "webcam.release()\n",
    "\n",
    "# Close all started windows\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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