face_rec_attendance_sys.ipynb

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   "source": [
    "# Face_Recognition_Attendance_System"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "I have used the HOG method for face recognition.\n",
    "Step:1 To find faces in an image, I’ll start by making our image black and white because we don’t need color data to find faces:\n",
    "Step:2 Checking posing and projecting images for which we will use Face landmark estimation alogrithm\n",
    "Step:3 Then Encoding the faces which we will get from webcam and calculate the measurements \n",
    "Step:4 Last and Final finding the image name from oure dataset and adding into the attendance system dataset "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "In code ImageAttendance is folder where some image is already stored.\n",
    "Attendance.csv is the file where we will store the attendance of people of those who matches from our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import face_recognition as fr\n",
    "import os\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bill_Gates.jpg', 'Brack_Obama.jpg', 'mark_zuker.jpg', 'Satya_Nadella.jpg', 'Sundar_Pichai.jpg', 'Tim_Cook.jpg']\n",
      "['Bill_Gates', 'Brack_Obama', 'mark_zuker', 'Satya_Nadella', 'Sundar_Pichai', 'Tim_Cook']\n",
      "Encoding Complete\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "BRACK_OBAMA\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "TIM_COOK\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SUNDAR_PICHAI\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n",
      "SATYA_NADELLA\n"
     ]
    }
   ],
   "source": [
    "#folder of our already dataset\n",
    "path = 'ImageAttendance'\n",
    "images = []\n",
    "classNames = []\n",
    "myList = os.listdir(path)\n",
    "print(myList)\n",
    "\n",
    "for cl in myList:\n",
    "    curImg = cv2.imread(f'{path}/{cl}')\n",
    "    images.append(curImg)\n",
    "    classNames.append(os.path.splitext(cl)[0])\n",
    "    \n",
    "print(classNames)\n",
    "\n",
    "\n",
    "#Fuction for finding the encoding of images\n",
    "def findEncodings(images):\n",
    "    encodeList = []\n",
    "    for img in images:\n",
    "        img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "        encode = fr.face_encodings(img)[0]\n",
    "        encodeList.append(encode)\n",
    "        \n",
    "    return encodeList\n",
    "\n",
    "\n",
    "#Fuction for adding the person name into attendance system dataset\n",
    "def markAtendance(name):\n",
    "    with open('Attendance.csv','r+') as f:\n",
    "        myDataList = f.readlines()\n",
    "        nameList = []\n",
    "        for line in myDataList:\n",
    "            entry = line.split('\\t')\n",
    "            nameList.append(entry[0])\n",
    "        if name not in nameList:\n",
    "            now = datetime.now()\n",
    "            dtString = now.strftime('%H:%M:%S')\n",
    "            f.writelines(f'\\n{name},{dtString}')\n",
    "            \n",
    "encodeListKnown = findEncodings(images)\n",
    "print(\"Encoding Complete\")\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "    success, img = cap.read()\n",
    "    imgsmall = cv2.resize(img,(0,0),None,0.25,0.25)\n",
    "    imgsmall = cv2.cvtColor(imgsmall,cv2.COLOR_BGR2RGB)\n",
    "    \n",
    "    facesCurFrame = fr.face_locations(imgsmall)\n",
    "    encodeCurFrame = fr.face_encodings(imgsmall,facesCurFrame)\n",
    "    \n",
    "    \n",
    "    for encodeFace,facLoc in zip(encodeCurFrame,facesCurFrame):\n",
    "        matches = fr.compare_faces(encodeListKnown,encodeFace)\n",
    "        faceDis = fr.face_distance(encodeListKnown,encodeFace)\n",
    "        #print(faceDis)\n",
    "        matchIndex = np.argmin(faceDis)\n",
    "        \n",
    "        if matches[matchIndex]:\n",
    "            name = classNames[matchIndex].upper()\n",
    "            y1,x2,y2,x1 = facLoc\n",
    "            y1,x2,y2,x1 = y1*4,x2*4,y2*4,x1*4\n",
    "            cv2.rectangle(img,(x1,y1),(x2,y2),(0,255,0),2)\n",
    "            cv2.rectangle(img,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILLED)\n",
    "            cv2.putText(img,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,255),2)\n",
    "            markAtendance(name)\n",
    "            \n",
    "    cv2.imshow('Webcam',img)\n",
    "    cv2.waitKey(1)\n",
    "    "
   ]
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