save_and_fetch_from_redis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting redis\n",
      "  Obtaining dependency information for redis from https://files.pythonhosted.org/packages/0b/34/a01250ac1fc9bf9161e07956d2d580413106ce02d5591470130a25c599e3/redis-5.0.1-py3-none-any.whl.metadata\n",
      "  Downloading redis-5.0.1-py3-none-any.whl.metadata (8.9 kB)\n",
      "Collecting async-timeout>=4.0.2 (from redis)\n",
      "  Obtaining dependency information for async-timeout>=4.0.2 from https://files.pythonhosted.org/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl.metadata\n",
      "  Downloading async_timeout-4.0.3-py3-none-any.whl.metadata (4.2 kB)\n",
      "Downloading redis-5.0.1-py3-none-any.whl (250 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.3/250.3 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hUsing cached async_timeout-4.0.3-py3-none-any.whl (5.7 kB)\n",
      "Installing collected packages: async-timeout, redis\n",
      "Successfully installed async-timeout-4.0.3 redis-5.0.1\n"
     ]
    }
   ],
   "source": [
    "!pip install redis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import redis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configrue Redis Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# redis-12830.c302.asia-northeast1-1.gce.cloud.redislabs.com:12830\n",
    "# 0k137l1gcAW3TQMjUeQ06oc7vQTMKsAq\n",
    "\n",
    "host = 'redis-12830.c302.asia-northeast1-1.gce.cloud.redislabs.com'\n",
    "port_no = 12830\n",
    "password = '0k137l1gcAW3TQMjUeQ06oc7vQTMKsAq'\n",
    "\n",
    "r = redis.StrictRedis(host=host, port=port_no, password=password)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.ping() # if true then connection successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# r.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Data to Redis from Numpy Zipped File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['arr_0', 'arr_1']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load numpy zip and convert back to df\n",
    "npz_file = np.load('df_students_teachers.npz', allow_pickle=True) # pickle helps in deserializing python objects and complex structures\n",
    "print(type(npz_file.files))\n",
    "npz_file.files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Role</th>\n",
       "      <th>Facial_Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Angelina Jolie</td>\n",
       "      <td>Student</td>\n",
       "      <td>[0.19883595, 1.4748771, -0.09800474, -0.880633...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barack Obama</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>[-0.22199515, 1.5761706, -0.5030543, 0.1360307...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chris Evans</td>\n",
       "      <td>Student</td>\n",
       "      <td>[0.35688442, -0.41513723, 0.039245885, -0.1545...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Morgan Freeman</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>[0.7628859, 0.89053726, 0.46874213, 0.2592453,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>Student</td>\n",
       "      <td>[-0.1354247, -0.28220648, -0.42639893, -0.2821...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name     Role  \\\n",
       "0      Angelina Jolie  Student   \n",
       "1        Barack Obama  Teacher   \n",
       "2         Chris Evans  Student   \n",
       "3      Morgan Freeman  Teacher   \n",
       "4  Scarlett Johansson  Student   \n",
       "\n",
       "                                     Facial_Features  \n",
       "0  [0.19883595, 1.4748771, -0.09800474, -0.880633...  \n",
       "1  [-0.22199515, 1.5761706, -0.5030543, 0.1360307...  \n",
       "2  [0.35688442, -0.41513723, 0.039245885, -0.1545...  \n",
       "3  [0.7628859, 0.89053726, 0.46874213, 0.2592453,...  \n",
       "4  [-0.1354247, -0.28220648, -0.42639893, -0.2821...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_df = pd.DataFrame(npz_file['arr_0'], columns=npz_file['arr_1'])\n",
    "loaded_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Role</th>\n",
       "      <th>Facial_Features</th>\n",
       "      <th>name_role</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Angelina Jolie</td>\n",
       "      <td>Student</td>\n",
       "      <td>[0.19883595, 1.4748771, -0.09800474, -0.880633...</td>\n",
       "      <td>Angelina Jolie@Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barack Obama</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>[-0.22199515, 1.5761706, -0.5030543, 0.1360307...</td>\n",
       "      <td>Barack Obama@Teacher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chris Evans</td>\n",
       "      <td>Student</td>\n",
       "      <td>[0.35688442, -0.41513723, 0.039245885, -0.1545...</td>\n",
       "      <td>Chris Evans@Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Morgan Freeman</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>[0.7628859, 0.89053726, 0.46874213, 0.2592453,...</td>\n",
       "      <td>Morgan Freeman@Teacher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>Student</td>\n",
       "      <td>[-0.1354247, -0.28220648, -0.42639893, -0.2821...</td>\n",
       "      <td>Scarlett Johansson@Student</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name     Role  \\\n",
       "0      Angelina Jolie  Student   \n",
       "1        Barack Obama  Teacher   \n",
       "2         Chris Evans  Student   \n",
       "3      Morgan Freeman  Teacher   \n",
       "4  Scarlett Johansson  Student   \n",
       "\n",
       "                                     Facial_Features  \\\n",
       "0  [0.19883595, 1.4748771, -0.09800474, -0.880633...   \n",
       "1  [-0.22199515, 1.5761706, -0.5030543, 0.1360307...   \n",
       "2  [0.35688442, -0.41513723, 0.039245885, -0.1545...   \n",
       "3  [0.7628859, 0.89053726, 0.46874213, 0.2592453,...   \n",
       "4  [-0.1354247, -0.28220648, -0.42639893, -0.2821...   \n",
       "\n",
       "                    name_role  \n",
       "0      Angelina Jolie@Student  \n",
       "1        Barack Obama@Teacher  \n",
       "2         Chris Evans@Student  \n",
       "3      Morgan Freeman@Teacher  \n",
       "4  Scarlett Johansson@Student  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_df['name_role'] = loaded_df['Name'] + '@' + loaded_df['Role']\n",
    "loaded_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name_role': 'Angelina Jolie@Student',\n",
       "  'Facial_Features': array([ 1.98835954e-01,  1.47487712e+00, -9.80047435e-02, -8.80633175e-01,\n",
       "         -4.95720476e-01,  6.36558652e-01,  7.20502973e-01,  4.32292402e-01,\n",
       "         -1.01362300e+00, -2.19202489e-01, -1.25162446e+00,  6.23574853e-03,\n",
       "         -1.08989584e+00, -9.10528958e-01,  9.79953408e-01, -8.08836937e-01,\n",
       "         -3.21867943e-01, -3.24543655e-01, -1.63320929e-01, -2.94713378e-01,\n",
       "          6.83515608e-01,  3.98809254e-01, -3.36343616e-01, -3.36887270e-01,\n",
       "         -1.28304154e-01,  1.73715562e-01,  5.91930330e-01, -6.08671486e-01,\n",
       "          1.05130899e+00,  7.50654221e-01,  5.73521852e-02, -4.44655001e-01,\n",
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       "          1.19324289e-01,  1.17329180e-01, -1.18487239e+00, -9.72978115e-01,\n",
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       "         -1.62272525e+00, -2.35561848e-01, -1.67543316e+00, -1.45299828e+00,\n",
       "          2.05186903e-02, -3.22832912e-01, -1.85851187e-01,  4.26681280e-01,\n",
       "          6.68314815e-01, -3.09224695e-01, -2.98501283e-01,  1.48307896e+00,\n",
       "         -3.09884012e-01,  1.40212858e+00,  3.90581369e-01,  3.87120366e-01,\n",
       "         -8.88285160e-01,  1.54763460e-03,  7.82855868e-01,  1.82716697e-01,\n",
       "         -1.29056346e+00, -6.87075019e-01,  1.23005822e-01,  4.73679155e-01,\n",
       "          6.38789773e-01, -1.09768617e+00, -1.63425237e-01,  4.48143594e-02,\n",
       "          8.11626554e-01, -1.38433778e+00, -5.94928622e-01,  5.32481074e-01,\n",
       "          7.91828871e-01,  7.84104645e-01, -1.47279334e+00, -8.37020874e-01,\n",
       "         -4.67202365e-01, -1.02844369e+00, -4.51889224e-02, -7.36951828e-01,\n",
       "         -4.37854826e-01,  7.32439697e-01, -3.30705673e-01,  7.13808537e-01,\n",
       "         -7.61765838e-01, -1.38199580e+00, -2.65003502e-01, -1.45969296e+00,\n",
       "         -1.07923484e+00,  1.82994652e+00, -6.89225256e-01, -8.03603530e-01,\n",
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       "          6.02537215e-01, -4.03400600e-01, -7.82189548e-01,  6.11089051e-01,\n",
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       "          1.54427558e-01,  1.65325880e-01,  9.54439044e-02, -4.93535101e-02,\n",
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       "         -2.73735851e-01,  6.09840393e-01, -1.15152967e+00,  4.93083060e-01,\n",
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       "         -2.26343319e-01,  3.26473594e-01, -3.29597443e-01,  5.65747499e-01,\n",
       "         -9.12805557e-01,  3.23796302e-01, -4.52500805e-02,  8.41766521e-02,\n",
       "          4.77010876e-01,  3.10110062e-01,  1.24546790e+00, -1.25428349e-01,\n",
       "         -1.08234167e+00, -4.57606427e-02, -3.59496504e-01,  1.26488364e+00,\n",
       "         -6.21468186e-01,  9.56548080e-02,  2.59749055e-01, -1.15631916e-01,\n",
       "         -4.84646231e-01, -5.53778589e-01, -2.37625048e-01, -9.26460624e-01,\n",
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       "         -7.88211644e-01, -5.08244812e-01, -4.19753611e-01,  5.69062233e-01,\n",
       "         -8.84025216e-01,  7.47892186e-02,  7.21587390e-02, -5.33572495e-01,\n",
       "          2.99318701e-01,  1.04130137e+00,  1.37569928e+00, -1.25925982e+00,\n",
       "          2.50041217e-01, -1.29889989e+00,  1.10026801e+00,  8.19089234e-01,\n",
       "          9.13699269e-02,  3.06497574e-01,  4.34920974e-02, -5.15500665e-01,\n",
       "          6.31846309e-01,  4.67064381e-01, -6.00268066e-01,  6.80252686e-02,\n",
       "         -1.47503074e-02, -6.35128856e-01,  4.85116810e-01,  2.36412376e-01,\n",
       "         -3.05120260e-01, -1.32911265e+00, -7.05708861e-01,  5.09669244e-01,\n",
       "          1.71414793e-01, -5.25387526e-01,  1.44749790e-01, -2.14650825e-01,\n",
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       " {'name_role': 'Scarlett Johansson@Student',\n",
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       "         -5.17099798e-01, -1.74253061e-01,  8.67130399e-01, -8.35920990e-01,\n",
       "          4.65172231e-01,  1.61670434e+00, -9.37394917e-01,  8.94328713e-01,\n",
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       "         -2.80192435e-01,  4.90093321e-01,  1.11279178e+00,  1.54970431e+00,\n",
       "         -6.42601132e-01,  1.83063596e-01, -1.64017904e+00, -1.40337855e-01,\n",
       "          1.10690606e+00,  1.54343337e-01,  7.40464851e-02, -4.30954657e-02,\n",
       "         -9.40772831e-01,  7.46774971e-01,  1.49955702e+00,  5.49925447e-01,\n",
       "         -2.02854320e-01, -6.13241076e-01,  3.81972492e-01,  2.87493646e-01,\n",
       "          2.30515456e+00,  4.27566707e-01,  1.28668928e+00,  2.05768037e+00,\n",
       "          2.29785055e-01, -2.03881457e-01, -6.86487079e-01, -2.32294723e-01,\n",
       "          7.40579247e-01,  6.97546840e-01, -6.25030041e-01, -1.63921431e-01,\n",
       "          1.07728922e+00,  9.73302647e-02, -4.77782190e-01,  1.18000031e+00,\n",
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       "          4.97620642e-01,  2.68671215e-01,  7.08865643e-01,  2.04348773e-01,\n",
       "          2.01475286e+00,  7.82317400e-01, -8.25341403e-01, -1.22919604e-01,\n",
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       "         -1.01419055e+00, -5.62027633e-01, -4.64485705e-01,  6.40104234e-01,\n",
       "          5.74065745e-01,  3.28799576e-01,  1.65384555e+00, -4.61386770e-01,\n",
       "         -3.58583748e-01,  1.42924309e-01,  1.25585711e+00, -1.24361610e+00,\n",
       "         -3.89334738e-01, -4.25015688e-01, -1.92979544e-01,  1.88306898e-01,\n",
       "          9.10179019e-01,  7.54405379e-01,  9.39567208e-01,  6.12869203e-01,\n",
       "         -1.25555649e-01, -9.62056041e-01, -4.25121561e-02,  1.32531023e+00,\n",
       "          2.40482427e-02, -9.85606074e-01,  7.72406161e-01,  2.43718680e-02,\n",
       "         -1.10046220e+00,  8.83433819e-01,  5.85869849e-01, -3.96579027e-01],\n",
       "        dtype=float32)}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records = loaded_df[['name_role', 'Facial_Features']].to_dict(orient='records') # records converts dict to list view\n",
    "records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angelina Jolie@Student\n",
      "Barack Obama@Teacher\n",
      "Chris Evans@Student\n",
      "Morgan Freeman@Teacher\n",
      "Scarlett Johansson@Student\n"
     ]
    }
   ],
   "source": [
    "# saving records to redis as hashes\n",
    "# in order to store facial features it has to be converted to bytes from array\n",
    "\n",
    "for record in records:\n",
    "    name_role = record['name_role']\n",
    "    print(name_role)\n",
    "    vector = record['Facial_Features']\n",
    "\n",
    "    # convert array to bytes\n",
    "    vector_bytes = vector.tobytes()\n",
    "    # print(vector_bytes)\n",
    "\n",
    "    # saving in redis cloud as hash\n",
    "    r.hset(name='academy:register', key=name_role, value=vector_bytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Registration Form Prototype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from insightface.app import FaceAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/boss/miniconda3/envs/tensorflow/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:69: UserWarning: Specified provider 'CUDAExecutionProvider' is not in available provider names.Available providers: 'AzureExecutionProvider, CPUExecutionProvider'\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}\n",
      "find model: /Users/boss/Desktop/Notes/4_attendance_app/insightface_model/models/buffalo_l/models/buffalo_l/1k3d68.onnx landmark_3d_68 ['None', 3, 192, 192] 0.0 1.0\n",
      "Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}\n",
      "find model: /Users/boss/Desktop/Notes/4_attendance_app/insightface_model/models/buffalo_l/models/buffalo_l/2d106det.onnx landmark_2d_106 ['None', 3, 192, 192] 0.0 1.0\n",
      "Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}\n",
      "find model: /Users/boss/Desktop/Notes/4_attendance_app/insightface_model/models/buffalo_l/models/buffalo_l/det_10g.onnx detection [1, 3, '?', '?'] 127.5 128.0\n",
      "Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}\n",
      "find model: /Users/boss/Desktop/Notes/4_attendance_app/insightface_model/models/buffalo_l/models/buffalo_l/genderage.onnx genderage ['None', 3, 96, 96] 0.0 1.0\n",
      "Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}\n",
      "find model: /Users/boss/Desktop/Notes/4_attendance_app/insightface_model/models/buffalo_l/models/buffalo_l/w600k_r50.onnx recognition ['None', 3, 112, 112] 127.5 127.5\n",
      "set det-size: (640, 640)\n"
     ]
    }
   ],
   "source": [
    "# buffalo_l model\n",
    "\n",
    "model_l = FaceAnalysis(name='buffalo_l',\n",
    "                        root='/Users/boss/Desktop/Notes/4_attendance_app/insightface_model/models/buffalo_l', \n",
    "                        providers=['CUDAExecutionProvider'])\n",
    "\n",
    "model_l.prepare(ctx_id=0, det_size=(640, 640), det_thresh=0.5) # input size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect Info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name and Role"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name Entered:  Rahul\n",
      "Role Chosen:  Teacher\n",
      "Key Generated:  Rahul@Teacher\n"
     ]
    }
   ],
   "source": [
    "person_name = input('Enter you resgistrant name: ')\n",
    "\n",
    "while True:\n",
    "    role = int(input('Enter Choice: \\n 1. Student \\n 2.Teacher'))\n",
    "\n",
    "    if role not in (1, 2):\n",
    "        print('Try Again...')\n",
    "\n",
    "    else:\n",
    "        if role == 1:\n",
    "            role = 'Student'\n",
    "        else:\n",
    "            role = 'Teacher'\n",
    "        break\n",
    "\n",
    "key = person_name + '@' + role\n",
    "print('Name Entered: ', person_name)\n",
    "print('Role Chosen: ', role)\n",
    "print('Key Generated: ', key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/boss/miniconda3/envs/tensorflow/lib/python3.10/site-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "embedding_list = []\n",
    "samples = 0\n",
    "\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "    if ret == False:\n",
    "        print('Camera Initialization Failed')\n",
    "        break\n",
    "\n",
    "    # insightface model inference   \n",
    "    results = model_l.get(frame, max_num=1) # information for only one face\n",
    "    for res in results:\n",
    "        samples += 1\n",
    "       \n",
    "        x1, y1, x2, y2 = res['bbox'].astype(int)\n",
    "        embedding = res['embedding']\n",
    "        embedding_list.append(embedding)\n",
    "\n",
    "        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0))\n",
    "        if samples % 10 == 0:\n",
    "         print(samples) # just for reference\n",
    "\n",
    "    if samples >= 100:\n",
    "        break\n",
    "    cv2.imshow('frame', frame)\n",
    "\n",
    "    if cv2.waitKey(1) == ord('q'):\n",
    "        break\n",
    "cv2.destroyAllWindows()\n",
    "cap.release()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facial_features = np.asarray(embedding_list).mean(axis=0)\n",
    "facial_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert numpy array to bytes\n",
    "facial_features_bytes = facial_features.tobytes()\n",
    "\n",
    "# save to cloud\n",
    "r.hset(name='academy:register', key=key, value=facial_features_bytes) # academy:register is the name for hash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'academy:register']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next Phase is predictions in predictions notebook and main functions in face_recognition.py.**"
   ]
  }
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