CEVI_AI_GYM_TRAINER_FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "09174337",
   "metadata": {},
   "source": [
    "#  Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9baea13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import csv\n",
    "import tensorflow as tf\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ffc8475",
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_pose = mp.solutions.pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "601cde51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e094f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "with mp_pose.Pose(min_detection_confidence = 0.8, min_tracking_confidence = 0.8) as pose:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        #converting BGR to RGB\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False\n",
    "        \n",
    "        results = pose.process(image)\n",
    "        \n",
    "        #recolor back\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                 mp_drawing.DrawingSpec(color = (255,255,255), thickness = 2, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color = (255,255,0), thickness = 2, circle_radius = 2))\n",
    "        \n",
    "        cv2.imshow('Exercise', image)\n",
    "        \n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be7497d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_coords = len(results.pose_landmarks.landmark)\n",
    "num_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "172faabd",
   "metadata": {},
   "outputs": [],
   "source": [
    "landmarks = ['class']\n",
    "for val in range(1, num_coords+1):\n",
    "    landmarks+=['x{}'.format(val), 'y{}'.format(val), 'z{}'.format(val)]\n",
    "for val in range(34, 34+8):\n",
    "    landmarks+=['A{}'.format(val-33)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7908702",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_angle(a,b,c):\n",
    "    a=np.array(a)\n",
    "    b=np.array(b)\n",
    "    c=np.array(c)\n",
    "    \n",
    "    radians = np.arctan2(c[1]-b[1],c[0]-b[0]) - np.arctan2(a[1]-b[1],a[0]-b[0])\n",
    "    angle=np.abs(radians*180.0/np.pi)\n",
    "    \n",
    "    if angle > 180.0:\n",
    "        angle = 360-angle\n",
    "    \n",
    "    return angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "624589ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Opening the file\n",
    "with open('CEVII.csv', mode = 'w', newline = '') as f:\n",
    "    csv_writer = csv.writer(f, delimiter = ',', quotechar = '\"', quoting = csv.QUOTE_MINIMAL)\n",
    "    csv_writer.writerow(landmarks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b71fb2e6",
   "metadata": {},
   "source": [
    "# Creating Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b0c7aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_name = \"PushUps\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec5af67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "with mp_pose.Pose(min_detection_confidence = 0.8, min_tracking_confidence = 0.8) as posee:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        #converting BGR to RGB\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False\n",
    "        \n",
    "        results = posee.process(image)\n",
    "        \n",
    "        #recolor back\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        #draw landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                 mp_drawing.DrawingSpec(color = (255,255,255), thickness = 2, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color = (255,255,0), thickness = 2, circle_radius = 2))\n",
    "        \n",
    "        \n",
    "        \n",
    "        try:\n",
    "            angles = cal_angles()\n",
    "            pose = results.pose_landmarks.landmark\n",
    "            row = list(np.array([[landmark.x, landmark.y, landmark.z] for landmark in pose]).flatten())\n",
    "            \n",
    "            row += angles\n",
    "            row.insert(0, class_name)\n",
    "            \n",
    "            with open('CEVII.csv', mode = 'a', newline = '') as f:\n",
    "                csv_writer = csv.writer(f, delimiter = ',', quotechar = '\"', quoting = csv.QUOTE_MINIMAL)\n",
    "                csv_writer.writerow(row)\n",
    "                \n",
    "        except:\n",
    "            pass\n",
    "            \n",
    "            \n",
    "        cv2.imshow('Exercise', image)\n",
    "        \n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef418252",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_angles(landmarks):\n",
    "    landmarks = results.pose_landmarks.landmark\n",
    "    \n",
    "    ls = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
    "    rs = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]\n",
    "\n",
    "    le = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
    "    re = [landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].y]\n",
    "\n",
    "    lw = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]\n",
    "    rw = [landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y]\n",
    "\n",
    "    lh = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]\n",
    "    rh = [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y]\n",
    "\n",
    "    lk = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
    "    rk = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]\n",
    "\n",
    "    la = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]\n",
    "    ra = [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y]\n",
    "\n",
    "                \n",
    "                \n",
    "                #Left hand angle/13th\n",
    "    angles = [calculate_angle(ls,le,lw), calculate_angle(rs,re,rw), calculate_angle(le,ls,lh), calculate_angle(re,rs,rh),\n",
    "              calculate_angle(rh,rk,ra),calculate_angle(lh,lk,la), calculate_angle(ls,lh,lk), calculate_angle(rs,rh,rk)]\n",
    "\n",
    "#                 #right hand angle/14th\n",
    "#                 rhanda = calculate_angle(rs,re,rw)\n",
    "                \n",
    "#                 # left shoulder angle/11th\n",
    "#                 lshouldera = calculate_angle(le,ls,lh)\n",
    "                \n",
    "#                 # right shoulder angle/12th\n",
    "#                 rshouldera = calculate_angle(re,rs,rh)\n",
    "                \n",
    "#                 # right leg angle/26th\n",
    "#                 rlega = calculate_angle(rh,rk,ra)\n",
    "                \n",
    "#                 # left leg angle/25th\n",
    "#                 llega = calculate_angle(lh,lk,la)\n",
    "                \n",
    "#                 # left hip angle/23rd\n",
    "#                 lhipa = calculate_angle(ls,lh,lk)\n",
    "                \n",
    "#                 # right hip angle/24th\n",
    "#                 rhipa = calculate_angle(rs,rh,rk)\n",
    "                \n",
    "    return angles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d80aeb4",
   "metadata": {},
   "source": [
    "# Extracting data from CSV to Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffd5a50a",
   "metadata": {},
   "outputs": [
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       "      <td>0.361792</td>\n",
       "      <td>0.352775</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.810904</td>\n",
       "      <td>-0.009321</td>\n",
       "      <td>127.751084</td>\n",
       "      <td>119.758679</td>\n",
       "      <td>16.506078</td>\n",
       "      <td>25.484878</td>\n",
       "      <td>99.730310</td>\n",
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       "      <td>173.375926</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.353289</td>\n",
       "      <td>-0.001079</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "<p>5 rows × 108 columns</p>\n",
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       "  classed        x1        y1        z1        x2        y2        z2  \\\n",
       "0  lunges  0.375140  0.366302 -0.002710  0.369248  0.355232  0.011720   \n",
       "1  lunges  0.371450  0.365952 -0.005700  0.365491  0.354565  0.008465   \n",
       "2  lunges  0.369216  0.364465 -0.006156  0.362911  0.353157  0.007764   \n",
       "3  lunges  0.377259  0.353289 -0.001079  0.371428  0.341200  0.013093   \n",
       "4  lunges  0.390199  0.340179  0.001722  0.383578  0.328456  0.016191   \n",
       "\n",
       "         x3        y3        z3  ...       y33       z33          A1  \\\n",
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       "3  0.370303  0.340980  0.013144  ...  0.811840 -0.029050  121.954798   \n",
       "4  0.382382  0.328547  0.016233  ...  0.811278 -0.032875  120.152939   \n",
       "\n",
       "           A2         A3         A4         A5          A6          A7  \\\n",
       "0  117.301780  17.365454  27.609158  98.050940   99.247953  172.756898   \n",
       "1  119.433106  16.781966  25.823022  98.945887   99.277704  173.089117   \n",
       "2  119.758679  16.506078  25.484878  99.730310   99.632449  173.375926   \n",
       "3  114.489100  19.892014  27.578208  97.928908  100.832694  169.485794   \n",
       "4  114.453955  20.759404  27.676385  95.221128  102.752793  168.634658   \n",
       "\n",
       "           A8  \n",
       "0  106.558356  \n",
       "1  106.287879  \n",
       "2  107.011162  \n",
       "3  107.715799  \n",
       "4  105.150726  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"CEVI2.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "788fe2d9",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lunges</td>\n",
       "      <td>0.375140</td>\n",
       "      <td>0.366302</td>\n",
       "      <td>-0.002710</td>\n",
       "      <td>0.369248</td>\n",
       "      <td>0.355232</td>\n",
       "      <td>0.011720</td>\n",
       "      <td>0.368186</td>\n",
       "      <td>0.354934</td>\n",
       "      <td>0.011766</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120542</td>\n",
       "      <td>0.441167</td>\n",
       "      <td>0.809704</td>\n",
       "      <td>-0.012663</td>\n",
       "      <td>0.222185</td>\n",
       "      <td>0.802200</td>\n",
       "      <td>0.098078</td>\n",
       "      <td>0.499090</td>\n",
       "      <td>0.810125</td>\n",
       "      <td>-0.022348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lunges</td>\n",
       "      <td>0.371450</td>\n",
       "      <td>0.365952</td>\n",
       "      <td>-0.005700</td>\n",
       "      <td>0.365491</td>\n",
       "      <td>0.354565</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>0.364397</td>\n",
       "      <td>0.354220</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122606</td>\n",
       "      <td>0.440457</td>\n",
       "      <td>0.809688</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.220159</td>\n",
       "      <td>0.802948</td>\n",
       "      <td>0.102906</td>\n",
       "      <td>0.499027</td>\n",
       "      <td>0.810140</td>\n",
       "      <td>-0.006010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lunges</td>\n",
       "      <td>0.369216</td>\n",
       "      <td>0.364465</td>\n",
       "      <td>-0.006156</td>\n",
       "      <td>0.362911</td>\n",
       "      <td>0.353157</td>\n",
       "      <td>0.007764</td>\n",
       "      <td>0.361792</td>\n",
       "      <td>0.352775</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.439839</td>\n",
       "      <td>0.810888</td>\n",
       "      <td>-0.001415</td>\n",
       "      <td>0.218197</td>\n",
       "      <td>0.804284</td>\n",
       "      <td>0.120269</td>\n",
       "      <td>0.498923</td>\n",
       "      <td>0.810904</td>\n",
       "      <td>-0.009321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lunges</td>\n",
       "      <td>0.377259</td>\n",
       "      <td>0.353289</td>\n",
       "      <td>-0.001079</td>\n",
       "      <td>0.371428</td>\n",
       "      <td>0.341200</td>\n",
       "      <td>0.013093</td>\n",
       "      <td>0.370303</td>\n",
       "      <td>0.340980</td>\n",
       "      <td>0.013144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153299</td>\n",
       "      <td>0.456854</td>\n",
       "      <td>0.810747</td>\n",
       "      <td>-0.025250</td>\n",
       "      <td>0.228339</td>\n",
       "      <td>0.805464</td>\n",
       "      <td>0.140062</td>\n",
       "      <td>0.520068</td>\n",
       "      <td>0.811840</td>\n",
       "      <td>-0.029050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lunges</td>\n",
       "      <td>0.390199</td>\n",
       "      <td>0.340179</td>\n",
       "      <td>0.001722</td>\n",
       "      <td>0.383578</td>\n",
       "      <td>0.328456</td>\n",
       "      <td>0.016191</td>\n",
       "      <td>0.382382</td>\n",
       "      <td>0.328547</td>\n",
       "      <td>0.016233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167441</td>\n",
       "      <td>0.474087</td>\n",
       "      <td>0.807228</td>\n",
       "      <td>-0.028489</td>\n",
       "      <td>0.242031</td>\n",
       "      <td>0.805242</td>\n",
       "      <td>0.154361</td>\n",
       "      <td>0.535254</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>-0.032875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14014</th>\n",
       "      <td>PushUps</td>\n",
       "      <td>0.695071</td>\n",
       "      <td>0.621896</td>\n",
       "      <td>-0.023561</td>\n",
       "      <td>0.703143</td>\n",
       "      <td>0.602503</td>\n",
       "      <td>-0.005921</td>\n",
       "      <td>0.702596</td>\n",
       "      <td>0.600876</td>\n",
       "      <td>-0.005882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232862</td>\n",
       "      <td>0.089371</td>\n",
       "      <td>0.779592</td>\n",
       "      <td>-0.087921</td>\n",
       "      <td>0.144410</td>\n",
       "      <td>0.844310</td>\n",
       "      <td>0.157486</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>0.869368</td>\n",
       "      <td>-0.201255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14015</th>\n",
       "      <td>PushUps</td>\n",
       "      <td>0.695201</td>\n",
       "      <td>0.599583</td>\n",
       "      <td>-0.019629</td>\n",
       "      <td>0.702313</td>\n",
       "      <td>0.579115</td>\n",
       "      <td>-0.002258</td>\n",
       "      <td>0.701567</td>\n",
       "      <td>0.577403</td>\n",
       "      <td>-0.002229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228260</td>\n",
       "      <td>0.089661</td>\n",
       "      <td>0.780016</td>\n",
       "      <td>-0.084739</td>\n",
       "      <td>0.144484</td>\n",
       "      <td>0.844485</td>\n",
       "      <td>0.152518</td>\n",
       "      <td>0.129264</td>\n",
       "      <td>0.867920</td>\n",
       "      <td>-0.197678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14016</th>\n",
       "      <td>PushUps</td>\n",
       "      <td>0.692619</td>\n",
       "      <td>0.591667</td>\n",
       "      <td>-0.041868</td>\n",
       "      <td>0.699654</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>-0.025013</td>\n",
       "      <td>0.698902</td>\n",
       "      <td>0.569628</td>\n",
       "      <td>-0.024989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259157</td>\n",
       "      <td>0.089869</td>\n",
       "      <td>0.781406</td>\n",
       "      <td>-0.056804</td>\n",
       "      <td>0.144068</td>\n",
       "      <td>0.844686</td>\n",
       "      <td>0.186262</td>\n",
       "      <td>0.129641</td>\n",
       "      <td>0.867888</td>\n",
       "      <td>-0.166963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14017</th>\n",
       "      <td>PushUps</td>\n",
       "      <td>0.690892</td>\n",
       "      <td>0.587303</td>\n",
       "      <td>-0.034541</td>\n",
       "      <td>0.697518</td>\n",
       "      <td>0.566698</td>\n",
       "      <td>-0.017549</td>\n",
       "      <td>0.696794</td>\n",
       "      <td>0.565230</td>\n",
       "      <td>-0.017520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265426</td>\n",
       "      <td>0.089195</td>\n",
       "      <td>0.782080</td>\n",
       "      <td>-0.054907</td>\n",
       "      <td>0.143954</td>\n",
       "      <td>0.844819</td>\n",
       "      <td>0.194634</td>\n",
       "      <td>0.130336</td>\n",
       "      <td>0.867899</td>\n",
       "      <td>-0.164014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14018</th>\n",
       "      <td>PushUps</td>\n",
       "      <td>0.689410</td>\n",
       "      <td>0.588071</td>\n",
       "      <td>-0.034263</td>\n",
       "      <td>0.695893</td>\n",
       "      <td>0.568201</td>\n",
       "      <td>-0.017450</td>\n",
       "      <td>0.695169</td>\n",
       "      <td>0.566499</td>\n",
       "      <td>-0.017420</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254764</td>\n",
       "      <td>0.089356</td>\n",
       "      <td>0.783040</td>\n",
       "      <td>-0.066287</td>\n",
       "      <td>0.143900</td>\n",
       "      <td>0.845012</td>\n",
       "      <td>0.182734</td>\n",
       "      <td>0.131171</td>\n",
       "      <td>0.867800</td>\n",
       "      <td>-0.175795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14019 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       classed        x1        y1        z1        x2        y2        z2  \\\n",
       "0       lunges  0.375140  0.366302 -0.002710  0.369248  0.355232  0.011720   \n",
       "1       lunges  0.371450  0.365952 -0.005700  0.365491  0.354565  0.008465   \n",
       "2       lunges  0.369216  0.364465 -0.006156  0.362911  0.353157  0.007764   \n",
       "3       lunges  0.377259  0.353289 -0.001079  0.371428  0.341200  0.013093   \n",
       "4       lunges  0.390199  0.340179  0.001722  0.383578  0.328456  0.016191   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "14014  PushUps  0.695071  0.621896 -0.023561  0.703143  0.602503 -0.005921   \n",
       "14015  PushUps  0.695201  0.599583 -0.019629  0.702313  0.579115 -0.002258   \n",
       "14016  PushUps  0.692619  0.591667 -0.041868  0.699654  0.571182 -0.025013   \n",
       "14017  PushUps  0.690892  0.587303 -0.034541  0.697518  0.566698 -0.017549   \n",
       "14018  PushUps  0.689410  0.588071 -0.034263  0.695893  0.568201 -0.017450   \n",
       "\n",
       "             x3        y3        z3  ...       z30       x31       y31  \\\n",
       "0      0.368186  0.354934  0.011766  ...  0.120542  0.441167  0.809704   \n",
       "1      0.364397  0.354220  0.008511  ...  0.122606  0.440457  0.809688   \n",
       "2      0.361792  0.352775  0.007812  ...  0.138424  0.439839  0.810888   \n",
       "3      0.370303  0.340980  0.013144  ...  0.153299  0.456854  0.810747   \n",
       "4      0.382382  0.328547  0.016233  ...  0.167441  0.474087  0.807228   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "14014  0.702596  0.600876 -0.005882  ...  0.232862  0.089371  0.779592   \n",
       "14015  0.701567  0.577403 -0.002229  ...  0.228260  0.089661  0.780016   \n",
       "14016  0.698902  0.569628 -0.024989  ...  0.259157  0.089869  0.781406   \n",
       "14017  0.696794  0.565230 -0.017520  ...  0.265426  0.089195  0.782080   \n",
       "14018  0.695169  0.566499 -0.017420  ...  0.254764  0.089356  0.783040   \n",
       "\n",
       "            z31       x32       y32       z32       x33       y33       z33  \n",
       "0     -0.012663  0.222185  0.802200  0.098078  0.499090  0.810125 -0.022348  \n",
       "1      0.000650  0.220159  0.802948  0.102906  0.499027  0.810140 -0.006010  \n",
       "2     -0.001415  0.218197  0.804284  0.120269  0.498923  0.810904 -0.009321  \n",
       "3     -0.025250  0.228339  0.805464  0.140062  0.520068  0.811840 -0.029050  \n",
       "4     -0.028489  0.242031  0.805242  0.154361  0.535254  0.811278 -0.032875  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "14014 -0.087921  0.144410  0.844310  0.157486  0.128036  0.869368 -0.201255  \n",
       "14015 -0.084739  0.144484  0.844485  0.152518  0.129264  0.867920 -0.197678  \n",
       "14016 -0.056804  0.144068  0.844686  0.186262  0.129641  0.867888 -0.166963  \n",
       "14017 -0.054907  0.143954  0.844819  0.194634  0.130336  0.867899 -0.164014  \n",
       "14018 -0.066287  0.143900  0.845012  0.182734  0.131171  0.867800 -0.175795  \n",
       "\n",
       "[14019 rows x 100 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the angles\n",
    "df.drop(df.iloc[:,100:], inplace = True, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0076074e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>y1</th>\n",
       "      <th>z1</th>\n",
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       "      <th>y2</th>\n",
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       "      <th>z30</th>\n",
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       "      <th>y33</th>\n",
       "      <th>z33</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.375140</td>\n",
       "      <td>0.366302</td>\n",
       "      <td>-0.002710</td>\n",
       "      <td>0.369248</td>\n",
       "      <td>0.355232</td>\n",
       "      <td>0.011720</td>\n",
       "      <td>0.368186</td>\n",
       "      <td>0.354934</td>\n",
       "      <td>0.011766</td>\n",
       "      <td>0.366900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120542</td>\n",
       "      <td>0.441167</td>\n",
       "      <td>0.809704</td>\n",
       "      <td>-0.012663</td>\n",
       "      <td>0.222185</td>\n",
       "      <td>0.802200</td>\n",
       "      <td>0.098078</td>\n",
       "      <td>0.499090</td>\n",
       "      <td>0.810125</td>\n",
       "      <td>-0.022348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.371450</td>\n",
       "      <td>0.365952</td>\n",
       "      <td>-0.005700</td>\n",
       "      <td>0.365491</td>\n",
       "      <td>0.354565</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>0.364397</td>\n",
       "      <td>0.354220</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>0.363131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122606</td>\n",
       "      <td>0.440457</td>\n",
       "      <td>0.809688</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.220159</td>\n",
       "      <td>0.802948</td>\n",
       "      <td>0.102906</td>\n",
       "      <td>0.499027</td>\n",
       "      <td>0.810140</td>\n",
       "      <td>-0.006010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.369216</td>\n",
       "      <td>0.364465</td>\n",
       "      <td>-0.006156</td>\n",
       "      <td>0.362911</td>\n",
       "      <td>0.353157</td>\n",
       "      <td>0.007764</td>\n",
       "      <td>0.361792</td>\n",
       "      <td>0.352775</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.360516</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.439839</td>\n",
       "      <td>0.810888</td>\n",
       "      <td>-0.001415</td>\n",
       "      <td>0.218197</td>\n",
       "      <td>0.804284</td>\n",
       "      <td>0.120269</td>\n",
       "      <td>0.498923</td>\n",
       "      <td>0.810904</td>\n",
       "      <td>-0.009321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.377259</td>\n",
       "      <td>0.353289</td>\n",
       "      <td>-0.001079</td>\n",
       "      <td>0.371428</td>\n",
       "      <td>0.341200</td>\n",
       "      <td>0.013093</td>\n",
       "      <td>0.370303</td>\n",
       "      <td>0.340980</td>\n",
       "      <td>0.013144</td>\n",
       "      <td>0.368988</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153299</td>\n",
       "      <td>0.456854</td>\n",
       "      <td>0.810747</td>\n",
       "      <td>-0.025250</td>\n",
       "      <td>0.228339</td>\n",
       "      <td>0.805464</td>\n",
       "      <td>0.140062</td>\n",
       "      <td>0.520068</td>\n",
       "      <td>0.811840</td>\n",
       "      <td>-0.029050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.390199</td>\n",
       "      <td>0.340179</td>\n",
       "      <td>0.001722</td>\n",
       "      <td>0.383578</td>\n",
       "      <td>0.328456</td>\n",
       "      <td>0.016191</td>\n",
       "      <td>0.382382</td>\n",
       "      <td>0.328547</td>\n",
       "      <td>0.016233</td>\n",
       "      <td>0.380924</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167441</td>\n",
       "      <td>0.474087</td>\n",
       "      <td>0.807228</td>\n",
       "      <td>-0.028489</td>\n",
       "      <td>0.242031</td>\n",
       "      <td>0.805242</td>\n",
       "      <td>0.154361</td>\n",
       "      <td>0.535254</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>-0.032875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1        y1        z1        x2        y2        z2        x3  \\\n",
       "0  0.375140  0.366302 -0.002710  0.369248  0.355232  0.011720  0.368186   \n",
       "1  0.371450  0.365952 -0.005700  0.365491  0.354565  0.008465  0.364397   \n",
       "2  0.369216  0.364465 -0.006156  0.362911  0.353157  0.007764  0.361792   \n",
       "3  0.377259  0.353289 -0.001079  0.371428  0.341200  0.013093  0.370303   \n",
       "4  0.390199  0.340179  0.001722  0.383578  0.328456  0.016191  0.382382   \n",
       "\n",
       "         y3        z3        x4  ...       z30       x31       y31       z31  \\\n",
       "0  0.354934  0.011766  0.366900  ...  0.120542  0.441167  0.809704 -0.012663   \n",
       "1  0.354220  0.008511  0.363131  ...  0.122606  0.440457  0.809688  0.000650   \n",
       "2  0.352775  0.007812  0.360516  ...  0.138424  0.439839  0.810888 -0.001415   \n",
       "3  0.340980  0.013144  0.368988  ...  0.153299  0.456854  0.810747 -0.025250   \n",
       "4  0.328547  0.016233  0.380924  ...  0.167441  0.474087  0.807228 -0.028489   \n",
       "\n",
       "        x32       y32       z32       x33       y33       z33  \n",
       "0  0.222185  0.802200  0.098078  0.499090  0.810125 -0.022348  \n",
       "1  0.220159  0.802948  0.102906  0.499027  0.810140 -0.006010  \n",
       "2  0.218197  0.804284  0.120269  0.498923  0.810904 -0.009321  \n",
       "3  0.228339  0.805464  0.140062  0.520068  0.811840 -0.029050  \n",
       "4  0.242031  0.805242  0.154361  0.535254  0.811278 -0.032875  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the class column\n",
    "X = df.drop('classed',axis = 1)\n",
    "y = df['classed']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "950b16a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if any null values present in the datasets\n",
    "check_for_nan = df.isnull().values.any()\n",
    "check_for_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "33049b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    lunges\n",
       "1    lunges\n",
       "2    lunges\n",
       "3    lunges\n",
       "4    lunges\n",
       "Name: classed, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a5c47fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   classes_PushUps  classes_Squats  classes_kneecrunches  classes_leglifts  \\\n",
      "0                0               0                     0                 0   \n",
      "1                0               0                     0                 0   \n",
      "2                0               0                     0                 0   \n",
      "3                0               0                     0                 0   \n",
      "4                0               0                     0                 0   \n",
      "\n",
      "   classes_lunges  classes_planks  classes_sideplanks  \n",
      "0               1               0                   0  \n",
      "1               1               0                   0  \n",
      "2               1               0                   0  \n",
      "3               1               0                   0  \n",
      "4               1               0                   0  \n"
     ]
    }
   ],
   "source": [
    "#Getting dummy values for classes \n",
    "y = pd.get_dummies(df.classed, prefix='classes')\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cac8030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 1, 0, 0],\n",
       "       [0, 0, 0, ..., 1, 0, 0],\n",
       "       [0, 0, 0, ..., 1, 0, 0],\n",
       "       ...,\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting input labels into binary values\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "y = LabelBinarizer().fit_transform(df.classed)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2b7eb74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data samples into Training and Testing samples\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b319ef9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9813, 99)\n",
      "(9813, 7)\n",
      "(4206, 99)\n",
      "(4206, 7)\n"
     ]
    }
   ],
   "source": [
    "# Shape of X training samples\n",
    "print(X_train.shape)\n",
    "\n",
    "# Shape of Y training samples\n",
    "print(y_train.shape)\n",
    "\n",
    "# Shape of X testing samples\n",
    "print(X_test.shape)\n",
    "\n",
    "# Shape of Y training samples\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec029f1a",
   "metadata": {},
   "source": [
    "# Training a Deep neural network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "30b41f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7a5cea4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten (Flatten)           (None, 99)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 25)                2500      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 15)                390       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 7)                 112       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,002\n",
      "Trainable params: 3,002\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential(\n",
    "    [\n",
    "        tf.keras.layers.Flatten(input_shape=(99,)),\n",
    "        Dense(units = 25, activation = 'relu'),\n",
    "        Dense(units = 15, activation = 'relu'),\n",
    "        Dense(units = 7, activation = 'softmax'),\n",
    "    ]\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b0d85ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss = 'categorical_crossentropy',\n",
    "    optimizer = tf.keras.optimizers.Adam(0.001),\n",
    "    metrics = ['accuracy']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8bdd592f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "307/307 [==============================] - 2s 3ms/step - loss: 0.6924 - accuracy: 0.8297\n",
      "Epoch 2/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0511 - accuracy: 0.9918\n",
      "Epoch 3/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0222 - accuracy: 0.9962\n",
      "Epoch 4/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0126 - accuracy: 0.9983\n",
      "Epoch 5/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0083 - accuracy: 0.9988\n",
      "Epoch 6/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0065 - accuracy: 0.9991\n",
      "Epoch 7/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0048 - accuracy: 0.9995\n",
      "Epoch 8/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0041 - accuracy: 0.9993\n",
      "Epoch 9/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0036 - accuracy: 0.9994\n",
      "Epoch 10/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0034 - accuracy: 0.9993\n",
      "Epoch 11/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0026 - accuracy: 0.9996\n",
      "Epoch 12/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0023 - accuracy: 0.9996\n",
      "Epoch 13/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0025 - accuracy: 0.9994\n",
      "Epoch 14/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0032 - accuracy: 0.9992\n",
      "Epoch 15/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0025 - accuracy: 0.9993\n",
      "Epoch 16/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0015 - accuracy: 0.9998\n",
      "Epoch 17/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0018 - accuracy: 0.9997\n",
      "Epoch 18/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0017 - accuracy: 0.9996\n",
      "Epoch 19/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0018 - accuracy: 0.9996\n",
      "Epoch 20/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0015 - accuracy: 0.9998\n",
      "Epoch 21/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0017 - accuracy: 0.9995\n",
      "Epoch 22/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 9.6300e-04 - accuracy: 0.9997\n",
      "Epoch 23/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0016 - accuracy: 0.9995\n",
      "Epoch 24/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 8.5436e-04 - accuracy: 0.9998\n",
      "Epoch 25/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0020 - accuracy: 0.9995\n",
      "Epoch 26/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0015 - accuracy: 0.9994\n",
      "Epoch 27/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0014 - accuracy: 0.9997\n",
      "Epoch 28/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 6.3731e-04 - accuracy: 0.9999\n",
      "Epoch 29/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0017 - accuracy: 0.9994\n",
      "Epoch 30/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 7.1753e-04 - accuracy: 0.9999\n",
      "Epoch 31/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0015 - accuracy: 0.9995\n",
      "Epoch 32/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 6.9893e-04 - accuracy: 0.9999\n",
      "Epoch 33/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 6.8650e-04 - accuracy: 0.9998\n",
      "Epoch 34/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0029 - accuracy: 0.9991\n",
      "Epoch 35/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0012 - accuracy: 0.9998\n",
      "Epoch 36/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 7.8373e-04 - accuracy: 0.9998\n",
      "Epoch 37/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 7.2512e-04 - accuracy: 0.9998\n",
      "Epoch 38/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 4.6027e-04 - accuracy: 0.9998\n",
      "Epoch 39/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 8.5425e-04 - accuracy: 0.9996\n",
      "Epoch 40/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 4.9492e-04 - accuracy: 0.9998\n",
      "Epoch 41/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 5.5413e-04 - accuracy: 0.9998\n",
      "Epoch 42/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 3.7845e-04 - accuracy: 0.9999\n",
      "Epoch 43/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 4.2228e-04 - accuracy: 0.9999\n",
      "Epoch 44/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 7.8583e-04 - accuracy: 0.9998\n",
      "Epoch 45/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 2.9237e-04 - accuracy: 0.9999\n",
      "Epoch 46/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 2.3647e-04 - accuracy: 0.9999\n",
      "Epoch 47/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 9.4150e-04 - accuracy: 0.9995\n",
      "Epoch 48/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0021 - accuracy: 0.9994\n",
      "Epoch 49/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 3.7093e-04 - accuracy: 0.9999\n",
      "Epoch 50/50\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0011 - accuracy: 0.9997\n"
     ]
    }
   ],
   "source": [
    "#Fitting a model\n",
    "\n",
    "fit=model.fit(\n",
    "    X_train, y_train, \n",
    "    epochs = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51730b06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "# predicting\n",
    "\n",
    "array=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57c4455",
   "metadata": {},
   "source": [
    "# Predicting in Real-Time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f09072b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "sideplanks\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 4ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 22ms/step\n",
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    },
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      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "f\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 58ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 74ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 70ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 59ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 41ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "Squats\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "sideplanks\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "lunges\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "Squats\n"
     ]
    }
   ],
   "source": [
    "out = ''\n",
    "cap = cv2.VideoCapture(0)\n",
    "# Initiate holistic model\n",
    "with mp_pose.Pose(min_detection_confidence=0.8, min_tracking_confidence=0.8) as posee:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor Feed\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        # Make Detections\n",
    "        results = posee.process(image)\n",
    "        # print(results.face_landmarks)\n",
    "        \n",
    "        # face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\n",
    "        \n",
    "        # Recolor image back to BGR for rendering\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "\n",
    "#         mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "#                                  mp_drawing.DrawingSpec(color = (245,117,66), thickness = 1, circle_radius = 1),\n",
    "#                                  mp_drawing.DrawingSpec(color = (245,66,230), thickness = 1, circle_radius = 1))\n",
    "         \n",
    "        \n",
    "        #Export coordinates\n",
    "        try:\n",
    "            #Extract Pose Landmarks\n",
    "            pose = results.pose_landmarks.landmark\n",
    "            pose_row = list(np.array([[landmark.x, landmark.y, landmark.z] for landmark in pose]).flatten())\n",
    "            \n",
    "#             #Extract Face Landmarks\n",
    "#             face = results.face_landmarks.landmark\n",
    "#             face_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in pose]).flatten())\n",
    "        \n",
    "            #Concate rows\n",
    "            row = pose_row \n",
    "            \n",
    "            angle=cal_angles(pose)\n",
    "            \n",
    "    \n",
    "            X = pd.DataFrame([row])\n",
    "            body_language_class = model.predict(X)\n",
    "\n",
    "            body_language_class = body_language_class.tolist()\n",
    "            \n",
    "            if body_language_class[0].index(max(body_language_class[0]))==0: \n",
    "                out = 'PushUps'\n",
    "                if (angle[0] >= 160 and angle[0] <= 180) and  (angle[1] >= 160 and angle[1] <= 180) \\\n",
    "                    and (angle[2] >= 60 and angle[2] <= 80) \\\n",
    "                    and (angle[3] >= 60 and angle[3] <= 80) \\\n",
    "                    and  (angle[4] >= 160 and angle[4] <= 180) and  (angle[5] >= 160 and angle[5] <= 180) \\\n",
    "                    and  (angle[6] >= 160 and angle[6] <= 180) and  (angle[7] >= 160 and angle[7] <= 180):\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "                \n",
    "                \n",
    "                #pose2\n",
    "                elif (angle[0] >= 80 and angle[0] <= 100) and  (angle[1] >= 80 and angle[1] <= 100) \\\n",
    "                    or (angle[2] >= 10 and angle[2] <= 50) \\\n",
    "                    or (angle[3] >= 10 and angle[3] <= 50) \\\n",
    "                    or  (angle[4] >= 160 and angle[4] <= 180) and  (angle[5] >= 160 and angle[5] <= 180) \\\n",
    "                    and  (angle[6] >= 160 and angle[6] <= 180) and  (angle[7] >= 160 and angle[7] <= 180):\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "                else:\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,255), thickness=2, circle_radius = 2))\n",
    "                    \n",
    "               \n",
    "                \n",
    "            elif body_language_class[0].index(max(body_language_class[0]))==1:\n",
    "                out = 'Squats'\n",
    "                mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "                \n",
    "            elif body_language_class[0].index(max(body_language_class[0]))==2: \n",
    "                out = 'KneeCrunches'\n",
    "                mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "                \n",
    "            elif body_language_class[0].index(max(body_language_class[0]))==3: \n",
    "                out = 'leglift'\n",
    "                if ((angle[6] >= 80 and angle[6] <= 90) or  (angle[7] >= 80 and angle[7] <= 90))\\\n",
    "                or ((angle[5] >= 170 and angle[5] <= 180) or  (angle[4] >= 170 and angle[4] <= 180) \\\n",
    "                    or  (angle[0] >= 170 and angle[0] <= 180)or  (angle[1] >= 170 and angle[1] <= 180)):\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "                \n",
    "                \n",
    "                else:\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,255), thickness=2, circle_radius = 2))\n",
    "                    \n",
    "             \n",
    "                \n",
    "                \n",
    "            elif body_language_class[0].index(max(body_language_class[0]))==4:\n",
    "                out = 'lunges'\n",
    "                if (((angle[5] >= 70 and angle[5] <= 90) or  (angle[4] >= 90 and angle[4] <= 120)) or ((angle[7] >= 170 and angle[7] <= 200) and  (angle[6] >= 80 and angle[6] <= 100)) or ((angle[5] >= 90 and angle[5] <= 120) and  (angle[4] >= 70 and angle[4] <= 90)) and ((angle[7] >= 80 and angle[7] <= 100) and  (angle[6] >= 170 and angle[6] <= 200))):\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "#                 elif :\n",
    "#                     mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "#                                      mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "#                                      mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "                else:\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,255), thickness=2, circle_radius = 2))\n",
    "                \n",
    "                \n",
    "            elif body_language_class[0].index(max(body_language_class[0]))==5: \n",
    "                out = 'planks'\n",
    "                if (angle[0] >= 80 and angle[0] <= 100) and  (angle[1] >= 80 and angle[1] <= 100) \\\n",
    "                    and  (angle[5] >= 150 and angle[5] <= 200) and  (angle[4] >= 150 and angle[4] <= 200) \\\n",
    "                    and  (angle[6] >= 150 and angle[6] <= 200) and  (angle[7] >= 150 and angle[7] <= 200):\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "                else:\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,255), thickness=2, circle_radius = 2))\n",
    "                    \n",
    "                \n",
    "                \n",
    "                \n",
    "            elif body_language_class[0].index(max(body_language_class[0]))==6: \n",
    "                out = 'sideplanks'\n",
    "                if (((angle[0] >= 80 and angle[0] <= 100) or  (angle[1] >= 80 and angle[1] <= 100)\\\n",
    "                or (angle[0] >= 170 and angle[5] <= 180)or (angle[1] >= 170 and angle[1] <= 180)) and  (angle[5] >= 160 and angle[5] <= 180) \\\n",
    "                    and  (angle[4] >= 150 and angle[4] <= 180) and  (angle[6] >= 150 and angle[6] <= 200) and  (angle[7] >= 150 and angle[7] <= 200)):\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius = 2))\n",
    "                \n",
    "                \n",
    "                else:\n",
    "                    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius = 2),\n",
    "                                     mp_drawing.DrawingSpec(color=(0,0,255), thickness=2, circle_radius = 2))\n",
    "                    \n",
    "                \n",
    "                \n",
    "                \n",
    "            else: out='None'\n",
    "                \n",
    "            coords = tuple(np.multiply(\n",
    "            np.array((results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].x,\n",
    "                    results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].y))\n",
    "                          ,[640,480]).astype(int))\n",
    "            \n",
    "            cv2.rectangle(image,\n",
    "                         (coords[0], coords[1]+5),\n",
    "                         (coords[0]+len(out)*20, coords[1]-30),\n",
    "                         (245,117,16),-1)\n",
    "            \n",
    "            cv2.putText(image, out, coords,\n",
    "                       cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "            \n",
    "            print(out)\n",
    "                \n",
    "        except:\n",
    "            print('f')\n",
    "            pass\n",
    "            \n",
    "        cv2.imshow('Raw Webcam Feed', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c356fda4",
   "metadata": {},
   "source": [
    "### Confusion matrix and classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "274797fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[484   0   0   0   0   0   0]\n",
      " [  0 462   0   0   1   0   0]\n",
      " [  0   0 425   0   0   0   0]\n",
      " [  0   0   0 641   0   0   0]\n",
      " [  0   4   0   0 619   0   0]\n",
      " [  0   0   0   0   0 726   0]\n",
      " [  0   0   0   0   0   0 844]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       484\n",
      "           1       0.99      1.00      0.99       463\n",
      "           2       1.00      1.00      1.00       425\n",
      "           3       1.00      1.00      1.00       641\n",
      "           4       1.00      0.99      1.00       623\n",
      "           5       1.00      1.00      1.00       726\n",
      "           6       1.00      1.00      1.00       844\n",
      "\n",
      "    accuracy                           1.00      4206\n",
      "   macro avg       1.00      1.00      1.00      4206\n",
      "weighted avg       1.00      1.00      1.00      4206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "y_pred=np.argmax(array, axis=1)\n",
    "y_test=np.argmax(y_test, axis=1)\n",
    "# print(y_pred)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42792db",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ec4090",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "47f3e2bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Predicted', ylabel='Actual'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "data=confusion_matrix(y_test,y_pred)\n",
    "df_cm = pd.DataFrame(data, columns=np.unique(y_test), index = np.unique(y_test))\n",
    "df_cm.index.name = 'Actual'\n",
    "df_cm.columns.name = 'Predicted'\n",
    "plt.figure(figsize = (8,7))\n",
    "sn.set(font_scale=1.4)#or label size\n",
    "sn.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac0c12e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}