Face Detection - HOG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f3ef90b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frames per second :  29.97002997002997 FPS\n",
      "Frame count :  93268.0\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File Name</th>\n",
       "      <th>Start time</th>\n",
       "      <th>End time</th>\n",
       "      <th>session_name</th>\n",
       "      <th># of students in video</th>\n",
       "      <th>which assignments</th>\n",
       "      <th># of students with wrong answers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>6.25</td>\n",
       "      <td>9.00</td>\n",
       "      <td>Information Overload &amp; Solutions for Informati...</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>9.22</td>\n",
       "      <td>10.09</td>\n",
       "      <td>Chunking</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>10.10</td>\n",
       "      <td>10.29</td>\n",
       "      <td>Omission</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>10.30</td>\n",
       "      <td>10.51</td>\n",
       "      <td>Queuing</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>10.52</td>\n",
       "      <td>11.17</td>\n",
       "      <td>Capitulation</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>11.40</td>\n",
       "      <td>12.19</td>\n",
       "      <td>Filtering &amp; Giving examples about who uses RecSys</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>16.20</td>\n",
       "      <td>19.45</td>\n",
       "      <td>RecSys</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>19.46</td>\n",
       "      <td>22.14</td>\n",
       "      <td>How RecSys works (Netflix Example)</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>22.15</td>\n",
       "      <td>24.20</td>\n",
       "      <td>How it works</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>24.22</td>\n",
       "      <td>28.07</td>\n",
       "      <td>Traditional Recommendation Algs</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>28.33</td>\n",
       "      <td>29.47</td>\n",
       "      <td>Context-Aware Recommendations</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>29.48</td>\n",
       "      <td>31.00</td>\n",
       "      <td>Multi-Criteria Recommendations</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>31.01</td>\n",
       "      <td>32.00</td>\n",
       "      <td>Group Recommendations</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>32.02</td>\n",
       "      <td>32.27</td>\n",
       "      <td>Cross-Domain Recommendations</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>32.29</td>\n",
       "      <td>33.20</td>\n",
       "      <td>Human Factor Based Recommendations</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>33.22</td>\n",
       "      <td>35.07</td>\n",
       "      <td>Multi-Stakeholder Recommendations</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>36.11</td>\n",
       "      <td>36.47</td>\n",
       "      <td>Recommendations Tasks</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>36.50</td>\n",
       "      <td>40.29</td>\n",
       "      <td>Rating Prediction</td>\n",
       "      <td>18</td>\n",
       "      <td>hw6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>40.29</td>\n",
       "      <td>46.13</td>\n",
       "      <td>Top-N Recommendation</td>\n",
       "      <td>18</td>\n",
       "      <td>hw 6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>46.16</td>\n",
       "      <td>47.26</td>\n",
       "      <td>Relevance &amp; Ranking Metrics (Intro)</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>47.26</td>\n",
       "      <td>51.52</td>\n",
       "      <td>Relevance Metrics</td>\n",
       "      <td>18</td>\n",
       "      <td>hw2, hw6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          File Name  Start time  End time   \n",
       "0   ITMD 525_03.MOV        6.25      9.00  \\\n",
       "1   ITMD 525_03.MOV        9.22     10.09   \n",
       "2   ITMD 525_03.MOV       10.10     10.29   \n",
       "3   ITMD 525_03.MOV       10.30     10.51   \n",
       "4   ITMD 525_03.MOV       10.52     11.17   \n",
       "5   ITMD 525_03.MOV       11.40     12.19   \n",
       "6   ITMD 525_03.MOV       16.20     19.45   \n",
       "7   ITMD 525_03.MOV       19.46     22.14   \n",
       "8   ITMD 525_03.MOV       22.15     24.20   \n",
       "9   ITMD 525_03.MOV       24.22     28.07   \n",
       "10  ITMD 525_03.MOV       28.33     29.47   \n",
       "11  ITMD 525_03.MOV       29.48     31.00   \n",
       "12  ITMD 525_03.MOV       31.01     32.00   \n",
       "13  ITMD 525_03.MOV       32.02     32.27   \n",
       "14  ITMD 525_03.MOV       32.29     33.20   \n",
       "15  ITMD 525_03.MOV       33.22     35.07   \n",
       "16  ITMD 525_03.MOV       36.11     36.47   \n",
       "17  ITMD 525_03.MOV       36.50     40.29   \n",
       "18  ITMD 525_03.MOV       40.29     46.13   \n",
       "19  ITMD 525_03.MOV       46.16     47.26   \n",
       "20  ITMD 525_03.MOV       47.26     51.52   \n",
       "\n",
       "                                         session_name  # of students in video   \n",
       "0   Information Overload & Solutions for Informati...                      13  \\\n",
       "1                                            Chunking                      13   \n",
       "2                                            Omission                      13   \n",
       "3                                             Queuing                      13   \n",
       "4                                        Capitulation                      16   \n",
       "5   Filtering & Giving examples about who uses RecSys                      16   \n",
       "6                                              RecSys                      17   \n",
       "7                  How RecSys works (Netflix Example)                      18   \n",
       "8                                        How it works                      18   \n",
       "9                     Traditional Recommendation Algs                      18   \n",
       "10                      Context-Aware Recommendations                      18   \n",
       "11                     Multi-Criteria Recommendations                      18   \n",
       "12                              Group Recommendations                      18   \n",
       "13                       Cross-Domain Recommendations                      18   \n",
       "14                 Human Factor Based Recommendations                      18   \n",
       "15                  Multi-Stakeholder Recommendations                      18   \n",
       "16                              Recommendations Tasks                      18   \n",
       "17                                  Rating Prediction                      18   \n",
       "18                               Top-N Recommendation                      18   \n",
       "19                Relevance & Ranking Metrics (Intro)                      18   \n",
       "20                                  Relevance Metrics                      18   \n",
       "\n",
       "   which assignments  # of students with wrong answers  \n",
       "0                NaN                               NaN  \n",
       "1                NaN                               NaN  \n",
       "2                NaN                               NaN  \n",
       "3                NaN                               NaN  \n",
       "4                NaN                               NaN  \n",
       "5                NaN                               NaN  \n",
       "6                NaN                               NaN  \n",
       "7                NaN                               NaN  \n",
       "8                NaN                               NaN  \n",
       "9                NaN                               NaN  \n",
       "10               NaN                               NaN  \n",
       "11               NaN                               NaN  \n",
       "12               NaN                               NaN  \n",
       "13               NaN                               NaN  \n",
       "14               NaN                               NaN  \n",
       "15               NaN                               NaN  \n",
       "16               NaN                               NaN  \n",
       "17               hw6                               8.0  \n",
       "18              hw 6                               3.0  \n",
       "19               NaN                               NaN  \n",
       "20          hw2, hw6                               3.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cv2\n",
    "from datetime import datetime\n",
    "from PIL import Image\n",
    "from IPython.display import display, HTML\n",
    "from tqdm import tqdm\n",
    "import face_recognition\n",
    "import dlib\n",
    "\n",
    "\n",
    "\n",
    "filename=\"ITMD 525_03.MOV\"   #filename\n",
    "cap = cv2.VideoCapture(filename) #Read Frame\n",
    "fps=cap.get(cv2.CAP_PROP_FPS) #Extract the frame per second (fps) \n",
    "print('Frames per second : ', fps,'FPS')\n",
    "frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)#get overall no# of frames in the video file\n",
    "print('Frame count : ', frame_count)\n",
    "sessions=pd.read_excel('session_info.xlsx', sheet_name='Sheet2')\n",
    "sessions=sessions.rename(columns=lambda x:x.strip())\n",
    "ses_crop=sessions.iloc[:, 0:7]#just getting FileName, StartTime, EndTime and SessionName\n",
    "ses_crop\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a763fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#selecting only sessions for which we have homework values \n",
    "ses_crop=ses_crop[np.isnan(ses_crop['# of students with wrong answers'])==False]\n",
    "ses_crop.reset_index(drop=True, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64dba826",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File Name</th>\n",
       "      <th>Start time</th>\n",
       "      <th>End time</th>\n",
       "      <th>session_name</th>\n",
       "      <th># of students in video</th>\n",
       "      <th>which assignments</th>\n",
       "      <th># of students with wrong answers</th>\n",
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       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>36.50</td>\n",
       "      <td>40.29</td>\n",
       "      <td>Rating Prediction</td>\n",
       "      <td>18</td>\n",
       "      <td>hw6</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>40.29</td>\n",
       "      <td>46.13</td>\n",
       "      <td>Top-N Recommendation</td>\n",
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       "      <th>2</th>\n",
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       "      <td>47.26</td>\n",
       "      <td>51.52</td>\n",
       "      <td>Relevance Metrics</td>\n",
       "      <td>18</td>\n",
       "      <td>hw2, hw6</td>\n",
       "      <td>3.0</td>\n",
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      "text/plain": [
       "         File Name  Start time  End time          session_name   \n",
       "0  ITMD 525_03.MOV       36.50     40.29     Rating Prediction  \\\n",
       "1  ITMD 525_03.MOV       40.29     46.13  Top-N Recommendation   \n",
       "2  ITMD 525_03.MOV       47.26     51.52     Relevance Metrics   \n",
       "\n",
       "   # of students in video which assignments  # of students with wrong answers  \n",
       "0                      18               hw6                               8.0  \n",
       "1                      18              hw 6                               3.0  \n",
       "2                      18          hw2, hw6                               3.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ses_crop.shape\n",
    "ses_crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ac4e74d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "start time in disected form: [(0.5, 36.0), (0.28999999999999915, 40.0), (0.259999999999998, 47.0)]\n",
      "\n",
      "\n",
      "Start time in Time Format: ['00:36:50', '00:40:29', '00:47:26']\n",
      "\n",
      "End time in disected form: [(0.28999999999999915, 40.0), (0.13000000000000256, 46.0), (0.5200000000000031, 51.0)]\n",
      "\n",
      "\n",
      "End time in Time Format: ['00:40:29', '00:46:13', '00:51:52']\n"
     ]
    }
   ],
   "source": [
    "#let's convert the start time and end time columns into proper time format i.e \"%H:%M:%S\"\n",
    "from time import time\n",
    "import math, time\n",
    "\n",
    "starttime=[]\n",
    "for i in range(len(ses_crop)):\n",
    "    starttime.append(math.modf(ses_crop['Start time'][i])) # (0.5678000000000338, 1234.0)\n",
    "print('\\nstart time in disected form:', starttime)\n",
    "#for i in range(len(starttime)):\n",
    "#    start\n",
    "    \n",
    "seconds_s=[round(x[0]*100) for x in starttime]\n",
    "secondsfromminutes_s=[x[1]*60 for x in starttime]\n",
    "\n",
    "total_seconds_s=[]\n",
    "for i in range(len(seconds_s)):\n",
    "    total_seconds_s.append(seconds_s[i]+secondsfromminutes_s[i])\n",
    "Start_time=[]\n",
    "for i in range(len(total_seconds_s)):\n",
    "    Start_time.append(time.strftime(\"%H:%M:%S\", time.gmtime(total_seconds_s[i])))\n",
    "print('\\n\\nStart time in Time Format:',Start_time)\n",
    "\n",
    "#doing the same for End time\n",
    "endtime=[]\n",
    "for i in range(len(ses_crop)):\n",
    "    endtime.append(math.modf(ses_crop['End time'][i])) # (0.5678000000000338, 1234.0)\n",
    "print('\\nEnd time in disected form:',endtime)\n",
    "\n",
    "seconds_e=[round(x[0]*100) for x in endtime]\n",
    "secondsfromminutes_e=[x[1]*60 for x in endtime]\n",
    "\n",
    "total_seconds_e=[]\n",
    "for i in range(len(seconds_e)):\n",
    "    total_seconds_e.append(seconds_e[i]+secondsfromminutes_e[i])\n",
    "End_time=[]\n",
    "for i in range(len(total_seconds_e)):\n",
    "    End_time.append(time.strftime(\"%H:%M:%S\", time.gmtime(total_seconds_e[i])))\n",
    "print('\\n\\nEnd time in Time Format:',End_time)\n",
    "\n",
    "ses_len=[]\n",
    "for i in range(len(total_seconds_s)):\n",
    "    ses_len.append(total_seconds_e[i]-total_seconds_s[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea79ac4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\munaw\\AppData\\Local\\Temp\\ipykernel_6768\\3526766700.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  ses_crop['Start time']=Start_time\n",
      "C:\\Users\\munaw\\AppData\\Local\\Temp\\ipykernel_6768\\3526766700.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  ses_crop['End time']=End_time\n"
     ]
    },
    {
     "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>File Name</th>\n",
       "      <th>Start time</th>\n",
       "      <th>End time</th>\n",
       "      <th>session_name</th>\n",
       "      <th># of students in video</th>\n",
       "      <th>which assignments</th>\n",
       "      <th># of students with wrong answers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>00:36:50</td>\n",
       "      <td>00:40:29</td>\n",
       "      <td>Rating Prediction</td>\n",
       "      <td>18</td>\n",
       "      <td>hw6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>00:40:29</td>\n",
       "      <td>00:46:13</td>\n",
       "      <td>Top-N Recommendation</td>\n",
       "      <td>18</td>\n",
       "      <td>hw 6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ITMD 525_03.MOV</td>\n",
       "      <td>00:47:26</td>\n",
       "      <td>00:51:52</td>\n",
       "      <td>Relevance Metrics</td>\n",
       "      <td>18</td>\n",
       "      <td>hw2, hw6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         File Name Start time  End time          session_name   \n",
       "0  ITMD 525_03.MOV   00:36:50  00:40:29     Rating Prediction  \\\n",
       "1  ITMD 525_03.MOV   00:40:29  00:46:13  Top-N Recommendation   \n",
       "2  ITMD 525_03.MOV   00:47:26  00:51:52     Relevance Metrics   \n",
       "\n",
       "   # of students in video which assignments  # of students with wrong answers  \n",
       "0                      18               hw6                               8.0  \n",
       "1                      18              hw 6                               3.0  \n",
       "2                      18          hw2, hw6                               3.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assigning the time values in correct format to cropped session dataframe\n",
    "\n",
    "ses_crop['Start time']=Start_time\n",
    "ses_crop['End time']=End_time\n",
    "ses_crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8ab39d82",
   "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>session_name</th>\n",
       "      <th># of students in video</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rating Prediction</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Top-N Recommendation</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Relevance Metrics</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           session_name  # of students in video\n",
       "0     Rating Prediction                      18\n",
       "1  Top-N Recommendation                      18\n",
       "2     Relevance Metrics                      18"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#slicing session-wise attendance for comparison at later stage\n",
    "num_students=ses_crop.iloc[:,3:5]\n",
    "num_students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "995e2185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[66233.76623376623, 72797.2027972028, 85294.70529470529]\n",
      "\n",
      " [66383.61638361638, 72947.05294705294, 85444.55544455544]\n"
     ]
    }
   ],
   "source": [
    "#for each session we have to divide the video into 3 slices begin, dom, end\n",
    "#for each of sub-sessions, we have to determine start frame and end frame for slicing the sessions\n",
    "#let's determine startframe and endframe for begin session\n",
    "startframe_begin=[]\n",
    "for i in range(len(total_seconds_s)):\n",
    "    startframe_begin.append(fps*(total_seconds_s[i]))\n",
    "\n",
    "print(startframe_begin)\n",
    "\n",
    "#after 5 seconds, begin subsession ends, therefore end frame of the begin subsession is located at (total_seconds_ofstarttime+5 seconds)*fps\n",
    "endframe_begin=[]\n",
    "for i in range(len(total_seconds_s)):\n",
    "    endframe_begin.append(fps*(total_seconds_s[i]+5))\n",
    "\n",
    "print('\\n',endframe_begin)\n",
    "\n",
    "#endframe_begin = startframe_dom\n",
    "#endframe_dom = startframe_end\n",
    "#startframe_end & endframe_end can be calculated with by using total_seconds_e\n",
    "startframe_end=[]\n",
    "for i in range(len(total_seconds_e)):\n",
    "    startframe_end.append(fps*(total_seconds_e[i]-5))\n",
    "    \n",
    "endframe_end=[]\n",
    "for i in range(len(total_seconds_e)):\n",
    "    endframe_end.append(fps*total_seconds_e[i])\n",
    "\n",
    "my_dict={'session_name':ses_crop['session_name'],'startframe_begin':startframe_begin, 'endframe_begin':endframe_begin,'startframe_end':startframe_end,'endframe_end':endframe_end, 'session_length':ses_len}\n",
    "ses_timeframes=pd.DataFrame(my_dict \n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e4697e6b",
   "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>session_name</th>\n",
       "      <th>startframe_begin</th>\n",
       "      <th>endframe_begin</th>\n",
       "      <th>startframe_end</th>\n",
       "      <th>endframe_end</th>\n",
       "      <th>session_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rating Prediction</td>\n",
       "      <td>66233.766234</td>\n",
       "      <td>66383.616384</td>\n",
       "      <td>72647.352647</td>\n",
       "      <td>72797.202797</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Top-N Recommendation</td>\n",
       "      <td>72797.202797</td>\n",
       "      <td>72947.052947</td>\n",
       "      <td>82957.042957</td>\n",
       "      <td>83106.893107</td>\n",
       "      <td>344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Relevance Metrics</td>\n",
       "      <td>85294.705295</td>\n",
       "      <td>85444.555445</td>\n",
       "      <td>93116.883117</td>\n",
       "      <td>93266.733267</td>\n",
       "      <td>266.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           session_name  startframe_begin  endframe_begin  startframe_end   \n",
       "0     Rating Prediction      66233.766234    66383.616384    72647.352647  \\\n",
       "1  Top-N Recommendation      72797.202797    72947.052947    82957.042957   \n",
       "2     Relevance Metrics      85294.705295    85444.555445    93116.883117   \n",
       "\n",
       "   endframe_end  session_length  \n",
       "0  72797.202797           219.0  \n",
       "1  83106.893107           344.0  \n",
       "2  93266.733267           266.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ses_timeframes.reset_index(drop=True, inplace=True)\n",
    "ses_timeframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4d68c435",
   "metadata": {},
   "outputs": [],
   "source": [
    "height=int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) #height\n",
    "width=int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) #width\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e9c308a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 93268/93268 [02:03<00:00, 756.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Begin Sessions Number of Frames 450         session_name                                              frame\n",
      "0  Rating Prediction  [[[147, 133, 72], [147, 133, 72], [147, 133, 7...\n",
      "1  Rating Prediction  [[[147, 133, 72], [147, 133, 72], [147, 133, 7...\n",
      "2  Rating Prediction  [[[148, 134, 73], [148, 134, 73], [148, 134, 7...\n",
      "3  Rating Prediction  [[[148, 134, 71], [148, 134, 71], [148, 134, 7...\n",
      "4  Rating Prediction  [[[148, 134, 73], [148, 134, 73], [148, 134, 7...\n",
      "\n",
      "\n",
      "DOM Sessions Number of Frames 23946         session_name                                              frame\n",
      "0  Rating Prediction  [[[151, 139, 77], [151, 139, 77], [151, 139, 7...\n",
      "1  Rating Prediction  [[[150, 138, 76], [150, 138, 76], [150, 138, 7...\n",
      "2  Rating Prediction  [[[150, 138, 76], [150, 138, 76], [150, 138, 7...\n",
      "3  Rating Prediction  [[[150, 138, 76], [150, 138, 76], [150, 138, 7...\n",
      "4  Rating Prediction  [[[150, 138, 74], [150, 138, 74], [150, 138, 7...\n",
      "\n",
      "\n",
      "End Sessions Number of Frames 449         session_name                                              frame\n",
      "0  Rating Prediction  [[[154, 133, 76], [154, 133, 76], [154, 133, 7...\n",
      "1  Rating Prediction  [[[154, 133, 76], [154, 133, 76], [154, 133, 7...\n",
      "2  Rating Prediction  [[[156, 131, 76], [156, 131, 76], [156, 131, 7...\n",
      "3  Rating Prediction  [[[155, 132, 76], [155, 132, 76], [155, 132, 7...\n",
      "4  Rating Prediction  [[[156, 131, 76], [156, 131, 76], [155, 131, 7...\n",
      "Execution Time:  145  sec\n"
     ]
    }
   ],
   "source": [
    "#let's now break down the video files into sessions as given in excel file\n",
    "#further each session is going to be further divided into sub-sessions: begin, dom, end\n",
    "\n",
    "\n",
    "tic=time.time()\n",
    "\n",
    "#initializing lists for storing session-wise values for each subsession\n",
    "begin_list = []\n",
    "dom_list = []\n",
    "end_list =[]\n",
    "useless=[]\n",
    "c =0\n",
    "\n",
    "for i in tqdm(range(int(frame_count))):\n",
    "    #following if-elif statements are only meants for determining value of c for iterating through the ses_timeframes table shown above\n",
    "    \n",
    "    if 66233.766234  <= i <= 72797.202797:\n",
    "        c=0\n",
    "    elif 72797.202797  <= i <= 83106.893107:\n",
    "        c=1\n",
    "    elif 85294.705295  <= i <= 93266.733267:\n",
    "        c=2\n",
    "  \n",
    "       \n",
    "    #read the frame from given video file\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    frame=cv2.resize(frame, (width,height))  #resize the frame\n",
    "    \n",
    "    if i>=ses_timeframes['startframe_begin'][c] and i<=ses_timeframes['endframe_begin'][c]:  \n",
    "        begin_list.append([ses_timeframes['session_name'][c],cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)])            #output\n",
    "    elif i>=ses_timeframes['endframe_begin'][c] and i<=ses_timeframes['startframe_end'][c]:\n",
    "        dom_list.append([ses_timeframes['session_name'][c],cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)])\n",
    "    elif i>=ses_timeframes['startframe_end'][c] and i<=ses_timeframes['endframe_end'][c]:  \n",
    "        end_list.append([ses_timeframes['session_name'][c],cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)])\n",
    "   # else:\n",
    "    #    useless.append(frame)\n",
    "        \n",
    "#creating data frames out of lists\n",
    "df_begin = pd.DataFrame(begin_list, columns=[\"session_name\", \"frame\"])\n",
    "df_dom = pd.DataFrame(dom_list, columns=[\"session_name\", \"frame\"])\n",
    "df_end = pd.DataFrame(end_list, columns=[\"session_name\", \"frame\"]) \n",
    "\n",
    "#printing heads for begin, dom and end dfs\n",
    "print('Begin Sessions Number of Frames', len(df_begin),  df_begin.head())\n",
    "print('\\n\\nDOM Sessions Number of Frames', len(df_dom), df_dom.head())\n",
    "print('\\n\\nEnd Sessions Number of Frames', len(df_end), df_end.head())  \n",
    "\n",
    "tac=time.time()\n",
    "print('Execution Time: ',round(tac-tic),' sec')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c5ea2cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>session_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rating Prediction</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top-N Recommendation</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relevance Metrics</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>session_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rating Prediction</th>\n",
       "      <td>6264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top-N Recommendation</th>\n",
       "      <td>10010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relevance Metrics</th>\n",
       "      <td>7672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>session_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rating Prediction</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top-N Recommendation</th>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relevance Metrics</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#just checking the number of frames per subsession \n",
    "begin_sessions=df_begin.groupby(['session_name'], sort=False).count()\n",
    "display(HTML(begin_sessions.to_html()))\n",
    "dom_sessions=df_dom.groupby(['session_name'],sort=False).count()\n",
    "display(HTML(dom_sessions.to_html()))\n",
    "end_sessions=df_end.groupby(['session_name'],sort=False).count()\n",
    "display(HTML(end_sessions.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5a2354c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "### HISTOGRAM OF GRADIENT ALGORITHM ###\n",
    "#creating function for applying HOG algorithm on each frame to detect faces\n",
    "\n",
    "def NumFaces_HOG (image):\n",
    "    \n",
    "    face_locations = face_recognition.face_locations(image,  number_of_times_to_upsample=3, model='hog')\n",
    "\n",
    "    num_faces = len(face_locations)\n",
    "    return num_faces\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "46d4a0f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 450/450 [16:11<00:00,  2.16s/it]\n",
      "100%|█████████████████████████████████████████████████████████████████████████| 23946/23946 [14:40:07<00:00,  2.21s/it]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 449/449 [16:30<00:00,  2.21s/it]\n"
     ]
    }
   ],
   "source": [
    "#let's apply HOG for face detection\n",
    "\n",
    "#detect number of faces for each frame of begin subsessions\n",
    "face_list_begin=[]\n",
    "for i in tqdm(range(len(df_begin))):\n",
    "    frame_1=df_begin['frame'][i]\n",
    "    num_faces=NumFaces_HOG(frame_1)\n",
    "    face_list_begin.append([df_begin['session_name'][i], num_faces])\n",
    "          \n",
    "faces_begin_hog = pd.DataFrame(face_list_begin, columns=[\"session_name\", \"no_of_Faces\"])\n",
    "\n",
    "#detect number of faces for each frame of dom subsessions\n",
    "face_list_dom=[]\n",
    "for i in tqdm(range(len(df_dom))):\n",
    "    frame_1=df_dom['frame'][i]\n",
    "    num_faces=NumFaces_HOG(frame_1)\n",
    "    face_list_dom.append([df_dom['session_name'][i], num_faces])\n",
    "          \n",
    "faces_dom_hog = pd.DataFrame(face_list_dom, columns=[\"session_name\", \"no_of_Faces\"])\n",
    "\n",
    "#detect number of faces for each frame of end subsessions\n",
    "face_list_end=[]\n",
    "for i in tqdm(range(len(df_end))):\n",
    "    frame_1=df_end['frame'][i]\n",
    "    num_faces=NumFaces_HOG(frame_1)\n",
    "    face_list_end.append([df_end['session_name'][i], num_faces])\n",
    "          \n",
    "faces_end_hog = pd.DataFrame(face_list_end, columns=[\"session_name\", \"no_of_Faces\"])\n",
    "\n",
    "#compute MINIMUM of number detected faces in each subsession\n",
    "face_begin_min_hog=faces_begin_hog.groupby(['session_name'],sort=False).min()\n",
    "face_dom_min_hog=faces_dom_hog.groupby(['session_name'],sort=False).min()\n",
    "face_end_min_hog=faces_end_hog.groupby(['session_name'],sort=False).min()\n",
    "#compute MAXIMUM of number detected faces in each subsession\n",
    "face_begin_max_hog=faces_begin_hog.groupby(['session_name'],sort=False).max()\n",
    "face_dom_max_hog=faces_dom_hog.groupby(['session_name'],sort=False).max()\n",
    "face_end_max_hog=faces_end_hog.groupby(['session_name'],sort=False).max()\n",
    "#compute AVERAGE of number detected faces in each subsession\n",
    "face_begin_avg_hog=faces_begin_hog.groupby(['session_name'],sort=False).mean()\n",
    "face_dom_avg_hog=faces_dom_hog.groupby(['session_name'],sort=False).mean()\n",
    "face_end_avg_hog=faces_end_hog.groupby(['session_name'],sort=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8c5e476e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>begin session name</th>\n",
       "      <th>minimum</th>\n",
       "      <th>average</th>\n",
       "      <th>maximum</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rating Prediction</td>\n",
       "      <td>5</td>\n",
       "      <td>6.840000</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Top-N Recommendation</td>\n",
       "      <td>4</td>\n",
       "      <td>6.246667</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Relevance Metrics</td>\n",
       "      <td>3</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "output_type": "display_data"
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    {
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>begin session name</th>\n",
       "      <th>minimum</th>\n",
       "      <th>average</th>\n",
       "      <th>maximum</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rating Prediction</td>\n",
       "      <td>2</td>\n",
       "      <td>6.361909</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Top-N Recommendation</td>\n",
       "      <td>1</td>\n",
       "      <td>6.614985</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Relevance Metrics</td>\n",
       "      <td>1</td>\n",
       "      <td>6.161496</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>begin session name</th>\n",
       "      <th>minimum</th>\n",
       "      <th>average</th>\n",
       "      <th>maximum</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rating Prediction</td>\n",
       "      <td>4</td>\n",
       "      <td>5.620000</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
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   ],
   "source": [
    "\n",
    "\n",
    "#let's analyze variation in [minimum, average, and maximum] number of faces detected in the begin session \n",
    "merged_2_begin_hog=face_begin_min_hog.merge(face_begin_avg_hog, on='session_name', how='outer')\n",
    "merged_all_begin_hog=merged_2_begin_hog.merge(face_begin_max_hog, on='session_name',how='outer')\n",
    "num_students_begin_hog=merged_all_begin_hog.merge(num_students, on='session_name',how='outer')\n",
    "num_students_begin_hog.columns = ['begin session name', 'minimum', 'average', 'maximum', 'actual']\n",
    "display(HTML(num_students_begin_hog.to_html()))\n",
    "\n",
    "#let's analyze variation in [minimum, average, and maximum] number of faces detected in the dominant session \n",
    "merged_2_dom_hog=face_dom_min_hog.merge(face_dom_avg_hog, on='session_name', how='outer')\n",
    "merged_all_dom_hog=merged_2_dom_hog.merge(face_dom_max_hog, on='session_name',how='outer')\n",
    "num_students_dom_hog=merged_all_dom_hog.merge(num_students, on='session_name',how='outer')\n",
    "num_students_dom_hog.columns = ['begin session name', 'minimum', 'average', 'maximum', 'actual']\n",
    "display(HTML(num_students_dom_hog.to_html()))\n",
    "\n",
    "#let's analyze variation in [minimum, average, and maximum] number of faces detected in the dominant session \n",
    "merged_2_end_hog=face_end_min_hog.merge(face_end_avg_hog, on='session_name', how='outer')\n",
    "merged_all_end_hog=merged_2_end_hog.merge(face_end_max_hog, on='session_name',how='outer')\n",
    "num_students_end_hog=merged_all_end_hog.merge(num_students, on='session_name',how='outer')\n",
    "num_students_begin_hog.columns = ['begin session name', 'minimum', 'average', 'maximum', 'actual']\n",
    "num_students_end_hog.columns = ['begin session name', 'minimum', 'average', 'maximum', 'actual']\n",
    "display(HTML(num_students_end_hog.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b37af855",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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