SecuritySystemOpenCV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython.display import clear_output # Extra\n",
    "#multiple cascades: https://github.com/Itseez/opencv/tree/master/data/haarcascades\n",
    "newpath = 'C:/Users/ViksIsBck/Anaconda3/pkgs/opencv-3.4.4-py36hb76ac4c_1203/Library/etc/haarcascades/'\n",
    "#https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml\n",
    "face_cascade = cv2.CascadeClassifier(newpath + 'haarcascade_frontalface_default.xml')\n",
    "#https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xml\n",
    "eye_cascade = cv2.CascadeClassifier(newpath + 'haarcascade_eye.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cut_faces(image, faces_coord):\n",
    "    faces = []\n",
    "      \n",
    "    for (x, y, w, h) in faces_coord:\n",
    "        w_rm = int(0.2 * w / 2)\n",
    "        faces.append(image[y: y + h, x + w_rm: x + w - w_rm])\n",
    "         \n",
    "    return faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_intensity(images):\n",
    "    images_norm = []\n",
    "    for image in images:\n",
    "        is_color = len(image.shape) == 3 \n",
    "        if is_color:\n",
    "            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "        images_norm.append(cv2.equalizeHist(image))\n",
    "    return images_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resize(images, size=(50, 50)):\n",
    "    images_norm = []\n",
    "    for image in images:\n",
    "        if image.shape < size:\n",
    "            image_norm = cv2.resize(image, size, \n",
    "                                    interpolation = cv2.INTER_AREA)\n",
    "        else:\n",
    "            image_norm = cv2.resize(image, size, \n",
    "                                    interpolation = cv2.INTER_CUBIC)\n",
    "        images_norm.append(image_norm)\n",
    "\n",
    "    return images_norm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_faces(frame, faces_coord):\n",
    "    faces = cut_faces(frame, faces_coord)\n",
    "    faces = normalize_intensity(faces)\n",
    "    faces = resize(faces)\n",
    "    return faces\n",
    "\n",
    "def draw_rectangle(image, coords):\n",
    "    for (x, y, w, h) in coords:\n",
    "        w_rm = int(0.2 * w / 2) \n",
    "        cv2.rectangle(image, (x + w_rm, y), (x + w - w_rm, y + h), \n",
    "                              (150, 150, 0), 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person: Vivek\n"
     ]
    }
   ],
   "source": [
    "folder = \"Desktop/People/\" + input('Person: ').lower() # input name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[WinError 183] Cannot create a file when that file already exists: 'Desktop/People/vivek'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-94672d881d62>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mtimer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileExistsError\u001b[0m: [WinError 183] Cannot create a file when that file already exists: 'Desktop/People/vivek'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.mkdir(folder)\n",
    "counter = 1\n",
    "timer = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter=0\n",
    "cap = cv2.VideoCapture(0)\n",
    "while counter < 21 : # take 20 pictures\n",
    "        ret, frame = cap.read()\n",
    "        faces_coord = face_cascade.detectMultiScale(frame, 1.3, 5)\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        if len(faces_coord) and timer % 700 == 50: \n",
    "            faces = normalize_faces(frame, faces_coord) \n",
    "            cv2.imwrite(folder + '/' + str(counter) + '.jpg', faces[0])\n",
    "            clear_output(wait = True) #saved face in notebook\n",
    "            counter += 1\n",
    "            \n",
    "        draw_rectangle(frame, faces_coord) #rectangle around face\n",
    "        \n",
    "        cv2.imshow('Mirror Mirror On The Wall',frame)\n",
    "        k = cv2.waitKey(30) & 0xff\n",
    "        if k == 27:\n",
    "            break\n",
    "        timer += 50\n",
    "\n",
    "cap.release()        \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_dataset():\n",
    "    images = []\n",
    "    labels = []\n",
    "    labels_dic = {}\n",
    "    people = [person for person in os.listdir('C:/Users/ViksIsBck/Desktop/People/')]\n",
    "    for i, person in enumerate(people):\n",
    "        labels_dic[i] = person\n",
    "        for image in os.listdir('C:/Users/ViksIsBck/Desktop/People/' + person):\n",
    "            images.append(cv2.imread(\"C:/Users/ViksIsBck/Desktop/People/\" + person + '/' + image, \n",
    "                                     0))\n",
    "            labels.append(i)\n",
    "    return (images, np.array(labels), labels_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models Trained Succesfully\n"
     ]
    }
   ],
   "source": [
    "images, labels, labels_dic = collect_dataset()\n",
    "\n",
    "rec_lbph = cv2.face.LBPHFaceRecognizer_create()\n",
    "rec_lbph.train(images, labels)\n",
    "\n",
    "print (\"Models Trained Succesfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: Somar\n",
      "Confidence: 135\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "while True: \n",
    "        ret, frame = cap.read()\n",
    "        faces_coord = face_cascade.detectMultiScale(frame, 1.3, 5)\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        if len(faces_coord):\n",
    "            faces = normalize_faces(frame, faces_coord) \n",
    "            for i, face in enumerate(faces): # for each detected face\n",
    "                collector = cv2.face.StandardCollector_create()\n",
    "                rec_lbph.predict_collect(face, collector)\n",
    "                conf = collector.getMinDist()\n",
    "                pred = collector.getMinLabel()\n",
    "                threshold = 140\n",
    "                print (\"Prediction: \" + labels_dic[pred].capitalize() + \"\\nConfidence: \" + str(round(conf)))\n",
    "                cv2.putText(frame, labels_dic[pred].capitalize(),\n",
    "                        (faces_coord[i][0], faces_coord[i][1] - 10),\n",
    "                        cv2.FONT_HERSHEY_PLAIN, 3, (66, 53, 243), 2)\n",
    "            \n",
    "            clear_output(wait = True)\n",
    "            draw_rectangle(frame, faces_coord)\n",
    "        \n",
    "        cv2.putText(frame, \"ESC to exit\", (5, frame.shape[0] - 5),\n",
    "                    cv2.FONT_HERSHEY_PLAIN, 1.3, (66, 53, 243), 2, cv2.LINE_AA)\n",
    "        cv2.imshow(\"PyData Tutorial\", frame) # live feed in external\n",
    "        if cv2.waitKey(40) & 0xFF == 27:\n",
    "            cv2.destroyAllWindows()\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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