Base Tester.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import face_recognition\n",
    "import cv2\n",
    "import numpy as np\n",
    "import PrebuiltBill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you want to use a prebuilt model, type in 'Y'.\n",
      "The model is trained on Obama, Elon Musk, Scarlet Johansen, and The Creator!\n",
      "Y\n",
      "Importing data from pre-built model...\n"
     ]
    }
   ],
   "source": [
    "db, bill = 0, 0\n",
    "\n",
    "print(\"If you want to use a prebuilt model, type in 'Y'.\")\n",
    "print(\"The model is trained on Obama, Elon Musk, Scarlet Johansen, and The Creator!\")\n",
    "a = input()\n",
    "if a == 'Y':\n",
    "    import PrebuiltDB\n",
    "    db = PrebuiltDB.DB\n",
    "    print('Importing data from pre-built model...')\n",
    "else:\n",
    "    import DataStore\n",
    "    db = DataStore.DB\n",
    "\n",
    "n = len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from csv import writer\n",
    "from csv import DictWriter\n",
    "\n",
    "field_names = ['ID', 'Name', 'Age', 'Gender', 'Number', 'Balance', 'FileName']\n",
    "file_name = 'PeoplesList.csv'\n",
    "\n",
    "with open(file_name, 'a+', newline='') as write_obj:\n",
    "    csv_writer = writer(write_obj)\n",
    "    csv_writer.writerow(field_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "checklist = [False]*n\n",
    "BigCheck = True\n",
    "def ExtractInfo(mathes):\n",
    "    if not False in checklist: BigCheck = False\n",
    "        \n",
    "    if not True in matches: \n",
    "        pass #print('Unknown')\n",
    "    else:\n",
    "        for i in range(n):\n",
    "            if (checklist[i] == False) and (matches[i] == True):\n",
    "                checklist[i] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "known_face_encodings = []\n",
    "known_face_names =[]\n",
    "\n",
    "for j in db:\n",
    "    i = str(db[j]['ID'])\n",
    "    \n",
    "    known_face_names.append(db[j]['Name'])\n",
    "    \n",
    "    ImCode = 'image' + i + ' = face_recognition.load_image_file(\"' + db[j]['FileName'] + '\")'\n",
    "    exec(ImCode)\n",
    "    \n",
    "    EnCode = 'face_encoding_' + i + '= face_recognition.face_encodings(image' + i + ')[0]'\n",
    "    exec(EnCode)\n",
    "    \n",
    "    ApCode = 'known_face_encodings.append(face_encoding_' + i + ')'\n",
    "    exec(ApCode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_capture = cv2.VideoCapture(0)\n",
    "\n",
    "# Initialize these variables to pre allot space\n",
    "face_encodings, face_locations, face_names, process_this_frame = [], [], [], True\n",
    "\n",
    "# Run loop to capture and give output\n",
    "while True:\n",
    "    \n",
    "    ret, frame = video_capture.read()\n",
    "    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) #Resizing captured frame\n",
    "    rgb_small_frame = small_frame[:, :, ::-1] #BGR -> RGB\n",
    "\n",
    "    if process_this_frame: # Processing only half the frames for speed\n",
    "        face_locations = face_recognition.face_locations(rgb_small_frame)\n",
    "        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)\n",
    "        face_names = []\n",
    "        for face_encoding in face_encodings:\n",
    "            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)\n",
    "            name = \"Unknown\"\n",
    "            if BigCheck: ExtractInfo(matches)\n",
    "            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)\n",
    "            best_match_index = np.argmin(face_distances)\n",
    "            if matches[best_match_index]:\n",
    "                name = known_face_names[best_match_index]\n",
    "            face_names.append(name)\n",
    "\n",
    "    process_this_frame = not process_this_frame\n",
    "\n",
    "\n",
    "    for (top, right, bottom, left), name in zip(face_locations, face_names): #Rescale\n",
    "        top *= 4\n",
    "        right *= 4\n",
    "        bottom *= 4\n",
    "        left *= 4\n",
    "        \n",
    "        #Style the Box\n",
    "        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)\n",
    "        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)\n",
    "        font = cv2.FONT_HERSHEY_DUPLEX\n",
    "        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)\n",
    "\n",
    "    cv2.imshow('Video', frame) #Display output box in video\n",
    "\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'): # Quit if Q/q is pressed\n",
    "        break\n",
    "\n",
    "video_capture.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you want to use a automatic billing mechanism, type in 'Y'.\n",
      "N\n",
      "Preparing bill for Jaspreet Singh :\n",
      "Enter Item Name : Cheetos \n",
      "Enter price of Item: 5\n",
      "Enter quantity of Item purchased: 3\n",
      "Enter \"Y\" to enter another item.N\n",
      "Bill Completed\n",
      "\n",
      "\n",
      "Preparing bill for Obama :\n",
      "Enter Item Name : White House\n",
      "Enter price of Item: 10000\n",
      "Enter quantity of Item purchased: 2\n",
      "Enter \"Y\" to enter another item.N\n",
      "Bill Completed\n",
      "\n",
      "\n",
      "Preparing bill for Ben Affleck :\n",
      "Enter Item Name : BatCave\n",
      "Enter price of Item: 30000\n",
      "Enter quantity of Item purchased: 1\n",
      "Enter \"Y\" to enter another item.Y\n",
      "Bill Completed\n",
      "\n",
      "\n",
      "Enter Item Name : Bat Mobile\n",
      "Enter price of Item: 30000\n",
      "Enter quantity of Item purchased: 4\n",
      "Enter \"Y\" to enter another item.N\n",
      "Bill Completed\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"If you want to use a automatic billing mechanism, type in 'Y'.\")\n",
    "b = input()\n",
    "if b == 'Y':\n",
    "    bill = PrebuiltBill.BILL\n",
    "    print('Importing bill...')\n",
    "    print()\n",
    "    print()\n",
    "else:\n",
    "    import BillMaker\n",
    "    bill = BillMaker.Billing(checklist, known_face_names, db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating bill for Jaspreet Singh\n",
      "Here\n",
      "Item Name => Item Price x Item Quantity = Total\n",
      "Cheetos  => 5 x 3 = 15\n",
      "The total bill amounts up to 15\n",
      "\n",
      "\n",
      "Calculating bill for Obama\n",
      "Here\n",
      "else\n",
      "Here\n",
      "Item Name => Item Price x Item Quantity = Total\n",
      "White House => 10000 x 2 = 20000\n",
      "The total bill amounts up to 20000\n",
      "\n",
      "\n",
      "Calculating bill for Ben Affleck\n",
      "Here\n",
      "else\n",
      "Here\n",
      "else\n",
      "Here\n",
      "Item Name => Item Price x Item Quantity = Total\n",
      "BatCave => 30000 x 1 = 30000\n",
      "Bat Mobile => 30000 x 4 = 120000\n",
      "The total bill amounts up to 150000\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def HandleFace(name):\n",
    "    for j in db:\n",
    "        if name == db[j]['Name']:\n",
    "        \n",
    "            with open(file_name, 'a+', newline='') as write_obj:\n",
    "                dict_writer = DictWriter(write_obj, fieldnames=field_names)\n",
    "                dict_writer.writerow(db[j])\n",
    "                \n",
    "                ID = db[j]['ID']\n",
    "                print('Calculating bill for ' + db[j]['Name'])\n",
    "                if b == 'Y':\n",
    "                    PrebuiltBill.DoCalc(ID, bill)\n",
    "                else:\n",
    "                    BillMaker.DoCalc(ID, bill)\n",
    "                print()\n",
    "                print()\n",
    "                \n",
    "def FinalBilling(checklist):\n",
    "    for i in range(n):\n",
    "        if checklist[i] == True:\n",
    "            HandleFace(known_face_names[i])\n",
    "            \n",
    "    \n",
    "    \n",
    "# Printing the Bill\n",
    "FinalBilling(checklist)"
   ]
  }
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