Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "287b8dd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pygame 2.5.2 (SDL 2.28.3, Python 3.10.7)\n",
      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import load_model\n",
    "import numpy as np\n",
    "from pygame import mixer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d1a0dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')\n",
    "model = load_model(r'D:\\models\\model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2194d4e8",
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    }
   ],
   "source": [
    "mixer.init()\n",
    "sound= mixer.Sound(r'C:\\Users\\DELL\\Desktop\\jupyter\\Drivers Drowsiness Detection using Deep Learning\\alarm.wav')\n",
    "cap = cv2.VideoCapture(0)\n",
    "Score = 0\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "    height,width = frame.shape[0:2]\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    faces= face_cascade.detectMultiScale(gray, scaleFactor= 1.2, minNeighbors=3)\n",
    "    eyes= eye_cascade.detectMultiScale(gray, scaleFactor= 1.1, minNeighbors=1)\n",
    "    \n",
    "    cv2.rectangle(frame, (0,height-50),(200,height),(0,0,0),thickness=cv2.FILLED)\n",
    "    \n",
    "    for (x,y,w,h) in faces:\n",
    "        cv2.rectangle(frame,pt1=(x,y),pt2=(x+w,y+h), color= (255,0,0), thickness=3 )\n",
    "        \n",
    "    for (ex,ey,ew,eh) in eyes:\n",
    "        #cv2.rectangle(frame,pt1=(ex,ey),pt2=(ex+ew,ey+eh), color= (255,0,0), thickness=3 )\n",
    "        \n",
    "        # preprocessing steps\n",
    "        eye= frame[ey:ey+eh,ex:ex+w]\n",
    "        eye= cv2.resize(eye,(80,80))\n",
    "        eye= eye/255\n",
    "        eye= eye.reshape(80,80,3)\n",
    "        eye= np.expand_dims(eye,axis=0)\n",
    "        # preprocessing is done now model prediction\n",
    "        prediction = model.predict(eye)\n",
    "        \n",
    "        # if eyes are closed\n",
    "        if prediction[0][0]>0.30:\n",
    "            cv2.putText(frame,'closed',(10,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255),\n",
    "                       thickness=1,lineType=cv2.LINE_AA)\n",
    "            cv2.putText(frame,'Score'+str(Score),(100,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255),\n",
    "                       thickness=1,lineType=cv2.LINE_AA)\n",
    "            Score=Score+1\n",
    "            if(Score>15):\n",
    "                try:\n",
    "                    sound.play()\n",
    "                except:\n",
    "                    pass\n",
    "            \n",
    "        # if eyes are open\n",
    "        elif prediction[0][1]>0.90:\n",
    "            cv2.putText(frame,'open',(10,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255),\n",
    "                       thickness=1,lineType=cv2.LINE_AA)      \n",
    "            cv2.putText(frame,'Score'+str(Score),(100,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255),\n",
    "                       thickness=1,lineType=cv2.LINE_AA)\n",
    "            Score = Score-1\n",
    "            if (Score<0):\n",
    "                Score=0\n",
    "            \n",
    "        \n",
    "    cv2.imshow('frame',frame)\n",
    "    if cv2.waitKey(33) & 0xFF==ord('q'):\n",
    "        break\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe172bec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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