Body Language Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Install and Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install mediapipe opencv-python pandas scikit-learn keras tensorflow tflite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mediapipe as mp\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_drawing = mp.solutions.drawing_utils # Drawing helpers\n",
    "mp_holistic = mp.solutions.holistic # Mediapipe Solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Make Some Detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor Feed\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False        \n",
    "        \n",
    "        # Make Detections\n",
    "        results = holistic.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.flags.writeable = True   \n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # 1. Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                                 )\n",
    "        \n",
    "        # 2. Right hand\n",
    "        mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 3. Left Hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 4. Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n",
    "                                 )\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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.face_landmarks.landmark[0].visibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Capture Landmarks & Export to CSV\n",
    "<!--<img src=\"https://i.imgur.com/8bForKY.png\">-->\n",
    "<!--<img src=\"https://i.imgur.com/AzKNp7A.png\">-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_coords = len(results.pose_landmarks.landmark)+len(results.face_landmarks.landmark)\n",
    "num_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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), 'v{}'.format(val)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('coords.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": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_name = \"Pain\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor Feed\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False        \n",
    "        \n",
    "        # Make Detections\n",
    "        results = holistic.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.flags.writeable = True   \n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # 1. Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                                 )\n",
    "        \n",
    "        # 2. Right hand\n",
    "        mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 3. Left Hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 4. Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\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, landmark.visibility] 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 face]).flatten())\n",
    "            \n",
    "            # Concate rows\n",
    "            row = pose_row+face_row\n",
    "            \n",
    "            # Append class name \n",
    "            row.insert(0, class_name)\n",
    "            \n",
    "            # Export to CSV\n",
    "            with open('coords.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",
    "        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",
   "metadata": {},
   "source": [
    "# 3. Train Custom Model Using Scikit Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Read in Collected Data and Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('coords.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>x1</th>\n",
       "      <th>y1</th>\n",
       "      <th>z1</th>\n",
       "      <th>v1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y2</th>\n",
       "      <th>z2</th>\n",
       "      <th>v2</th>\n",
       "      <th>x3</th>\n",
       "      <th>...</th>\n",
       "      <th>z499</th>\n",
       "      <th>v499</th>\n",
       "      <th>x500</th>\n",
       "      <th>y500</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Happy</td>\n",
       "      <td>0.494561</td>\n",
       "      <td>0.629883</td>\n",
       "      <td>-1.055178</td>\n",
       "      <td>0.999964</td>\n",
       "      <td>0.526246</td>\n",
       "      <td>0.568696</td>\n",
       "      <td>-0.995372</td>\n",
       "      <td>0.999930</td>\n",
       "      <td>0.543050</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.564623</td>\n",
       "      <td>0.564677</td>\n",
       "      <td>0.008720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.569697</td>\n",
       "      <td>0.559780</td>\n",
       "      <td>0.009374</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy</td>\n",
       "      <td>0.496609</td>\n",
       "      <td>0.634543</td>\n",
       "      <td>-1.013193</td>\n",
       "      <td>0.999943</td>\n",
       "      <td>0.528843</td>\n",
       "      <td>0.573003</td>\n",
       "      <td>-0.948786</td>\n",
       "      <td>0.999888</td>\n",
       "      <td>0.545678</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.565026</td>\n",
       "      <td>0.562567</td>\n",
       "      <td>0.011578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.569848</td>\n",
       "      <td>0.558903</td>\n",
       "      <td>0.012221</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Happy</td>\n",
       "      <td>0.497161</td>\n",
       "      <td>0.636443</td>\n",
       "      <td>-1.018678</td>\n",
       "      <td>0.999920</td>\n",
       "      <td>0.529438</td>\n",
       "      <td>0.574684</td>\n",
       "      <td>-0.955003</td>\n",
       "      <td>0.999847</td>\n",
       "      <td>0.546307</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.564090</td>\n",
       "      <td>0.562123</td>\n",
       "      <td>0.009436</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.569010</td>\n",
       "      <td>0.558785</td>\n",
       "      <td>0.009918</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Happy</td>\n",
       "      <td>0.497139</td>\n",
       "      <td>0.639510</td>\n",
       "      <td>-1.040067</td>\n",
       "      <td>0.999881</td>\n",
       "      <td>0.529429</td>\n",
       "      <td>0.576933</td>\n",
       "      <td>-0.971950</td>\n",
       "      <td>0.999787</td>\n",
       "      <td>0.546302</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.563264</td>\n",
       "      <td>0.562449</td>\n",
       "      <td>0.009120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.568140</td>\n",
       "      <td>0.559012</td>\n",
       "      <td>0.009602</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Happy</td>\n",
       "      <td>0.496975</td>\n",
       "      <td>0.639019</td>\n",
       "      <td>-1.101477</td>\n",
       "      <td>0.999852</td>\n",
       "      <td>0.529271</td>\n",
       "      <td>0.576253</td>\n",
       "      <td>-1.027935</td>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.546171</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011144</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.562521</td>\n",
       "      <td>0.562701</td>\n",
       "      <td>0.009787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.567392</td>\n",
       "      <td>0.559183</td>\n",
       "      <td>0.010291</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2005 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   class        x1        y1        z1        v1        x2        y2  \\\n",
       "0  Happy  0.494561  0.629883 -1.055178  0.999964  0.526246  0.568696   \n",
       "1  Happy  0.496609  0.634543 -1.013193  0.999943  0.528843  0.573003   \n",
       "2  Happy  0.497161  0.636443 -1.018678  0.999920  0.529438  0.574684   \n",
       "3  Happy  0.497139  0.639510 -1.040067  0.999881  0.529429  0.576933   \n",
       "4  Happy  0.496975  0.639019 -1.101477  0.999852  0.529271  0.576253   \n",
       "\n",
       "         z2        v2        x3  ...      z499  v499      x500      y500  \\\n",
       "0 -0.995372  0.999930  0.543050  ... -0.012576   0.0  0.564623  0.564677   \n",
       "1 -0.948786  0.999888  0.545678  ... -0.010062   0.0  0.565026  0.562567   \n",
       "2 -0.955003  0.999847  0.546307  ... -0.011356   0.0  0.564090  0.562123   \n",
       "3 -0.971950  0.999787  0.546302  ... -0.011725   0.0  0.563264  0.562449   \n",
       "4 -1.027935  0.999740  0.546171  ... -0.011144   0.0  0.562521  0.562701   \n",
       "\n",
       "       z500  v500      x501      y501      z501  v501  \n",
       "0  0.008720   0.0  0.569697  0.559780  0.009374   0.0  \n",
       "1  0.011578   0.0  0.569848  0.558903  0.012221   0.0  \n",
       "2  0.009436   0.0  0.569010  0.558785  0.009918   0.0  \n",
       "3  0.009120   0.0  0.568140  0.559012  0.009602   0.0  \n",
       "4  0.009787   0.0  0.567392  0.559183  0.010291   0.0  \n",
       "\n",
       "[5 rows x 2005 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>x3</th>\n",
       "      <th>...</th>\n",
       "      <th>z499</th>\n",
       "      <th>v499</th>\n",
       "      <th>x500</th>\n",
       "      <th>y500</th>\n",
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       "      <th>v501</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4213</th>\n",
       "      <td>Pain</td>\n",
       "      <td>0.625913</td>\n",
       "      <td>0.073043</td>\n",
       "      <td>-0.585752</td>\n",
       "      <td>0.999295</td>\n",
       "      <td>0.647979</td>\n",
       "      <td>0.030780</td>\n",
       "      <td>-0.546690</td>\n",
       "      <td>0.999082</td>\n",
       "      <td>0.662428</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.672963</td>\n",
       "      <td>0.029102</td>\n",
       "      <td>0.015902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.676614</td>\n",
       "      <td>0.023945</td>\n",
       "      <td>0.016633</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4214</th>\n",
       "      <td>Pain</td>\n",
       "      <td>0.626505</td>\n",
       "      <td>0.070397</td>\n",
       "      <td>-0.584991</td>\n",
       "      <td>0.999360</td>\n",
       "      <td>0.649704</td>\n",
       "      <td>0.028590</td>\n",
       "      <td>-0.547872</td>\n",
       "      <td>0.999164</td>\n",
       "      <td>0.664751</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.682690</td>\n",
       "      <td>0.025097</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.686516</td>\n",
       "      <td>0.020381</td>\n",
       "      <td>0.019334</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4215</th>\n",
       "      <td>Pain</td>\n",
       "      <td>0.644129</td>\n",
       "      <td>0.056613</td>\n",
       "      <td>-0.542262</td>\n",
       "      <td>0.999380</td>\n",
       "      <td>0.663778</td>\n",
       "      <td>0.023275</td>\n",
       "      <td>-0.507565</td>\n",
       "      <td>0.999171</td>\n",
       "      <td>0.677024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.707994</td>\n",
       "      <td>-0.001245</td>\n",
       "      <td>0.017850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.712147</td>\n",
       "      <td>-0.007975</td>\n",
       "      <td>0.018739</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4216</th>\n",
       "      <td>Pain</td>\n",
       "      <td>0.668426</td>\n",
       "      <td>0.046801</td>\n",
       "      <td>-0.556816</td>\n",
       "      <td>0.999397</td>\n",
       "      <td>0.685114</td>\n",
       "      <td>0.012571</td>\n",
       "      <td>-0.521768</td>\n",
       "      <td>0.999165</td>\n",
       "      <td>0.697218</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002208</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.721362</td>\n",
       "      <td>-0.007535</td>\n",
       "      <td>0.022521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.725310</td>\n",
       "      <td>-0.013851</td>\n",
       "      <td>0.023906</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4217</th>\n",
       "      <td>Pain</td>\n",
       "      <td>0.676839</td>\n",
       "      <td>0.040569</td>\n",
       "      <td>-0.553019</td>\n",
       "      <td>0.999391</td>\n",
       "      <td>0.691499</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>-0.519370</td>\n",
       "      <td>0.999116</td>\n",
       "      <td>0.703343</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.730978</td>\n",
       "      <td>-0.015813</td>\n",
       "      <td>0.023860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.735165</td>\n",
       "      <td>-0.021699</td>\n",
       "      <td>0.025202</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2005 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     class        x1        y1        z1        v1        x2        y2  \\\n",
       "4213  Pain  0.625913  0.073043 -0.585752  0.999295  0.647979  0.030780   \n",
       "4214  Pain  0.626505  0.070397 -0.584991  0.999360  0.649704  0.028590   \n",
       "4215  Pain  0.644129  0.056613 -0.542262  0.999380  0.663778  0.023275   \n",
       "4216  Pain  0.668426  0.046801 -0.556816  0.999397  0.685114  0.012571   \n",
       "4217  Pain  0.676839  0.040569 -0.553019  0.999391  0.691499  0.009030   \n",
       "\n",
       "            z2        v2        x3  ...      z499  v499      x500      y500  \\\n",
       "4213 -0.546690  0.999082  0.662428  ... -0.000576   0.0  0.672963  0.029102   \n",
       "4214 -0.547872  0.999164  0.664751  ... -0.000196   0.0  0.682690  0.025097   \n",
       "4215 -0.507565  0.999171  0.677024  ...  0.001027   0.0  0.707994 -0.001245   \n",
       "4216 -0.521768  0.999165  0.697218  ...  0.002208   0.0  0.721362 -0.007535   \n",
       "4217 -0.519370  0.999116  0.703343  ...  0.002835   0.0  0.730978 -0.015813   \n",
       "\n",
       "          z500  v500      x501      y501      z501  v501  \n",
       "4213  0.015902   0.0  0.676614  0.023945  0.016633   0.0  \n",
       "4214  0.018364   0.0  0.686516  0.020381  0.019334   0.0  \n",
       "4215  0.017850   0.0  0.712147 -0.007975  0.018739   0.0  \n",
       "4216  0.022521   0.0  0.725310 -0.013851  0.023906   0.0  \n",
       "4217  0.023860   0.0  0.735165 -0.021699  0.025202   0.0  \n",
       "\n",
       "[5 rows x 2005 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>x1</th>\n",
       "      <th>y1</th>\n",
       "      <th>z1</th>\n",
       "      <th>v1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y2</th>\n",
       "      <th>z2</th>\n",
       "      <th>v2</th>\n",
       "      <th>x3</th>\n",
       "      <th>...</th>\n",
       "      <th>z499</th>\n",
       "      <th>v499</th>\n",
       "      <th>x500</th>\n",
       "      <th>y500</th>\n",
       "      <th>z500</th>\n",
       "      <th>v500</th>\n",
       "      <th>x501</th>\n",
       "      <th>y501</th>\n",
       "      <th>z501</th>\n",
       "      <th>v501</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.488484</td>\n",
       "      <td>0.626194</td>\n",
       "      <td>-0.805227</td>\n",
       "      <td>0.999965</td>\n",
       "      <td>0.521179</td>\n",
       "      <td>0.567260</td>\n",
       "      <td>-0.749725</td>\n",
       "      <td>0.999913</td>\n",
       "      <td>0.539034</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.555681</td>\n",
       "      <td>0.560882</td>\n",
       "      <td>0.023273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.560688</td>\n",
       "      <td>0.556035</td>\n",
       "      <td>0.024310</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.485024</td>\n",
       "      <td>0.628707</td>\n",
       "      <td>-0.909506</td>\n",
       "      <td>0.999952</td>\n",
       "      <td>0.516005</td>\n",
       "      <td>0.567540</td>\n",
       "      <td>-0.847320</td>\n",
       "      <td>0.999888</td>\n",
       "      <td>0.533780</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.548035</td>\n",
       "      <td>0.562400</td>\n",
       "      <td>0.021244</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.553111</td>\n",
       "      <td>0.557692</td>\n",
       "      <td>0.022179</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.481665</td>\n",
       "      <td>0.629282</td>\n",
       "      <td>-0.905353</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>0.512642</td>\n",
       "      <td>0.567530</td>\n",
       "      <td>-0.843626</td>\n",
       "      <td>0.999862</td>\n",
       "      <td>0.530525</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545288</td>\n",
       "      <td>0.561721</td>\n",
       "      <td>0.021342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.550385</td>\n",
       "      <td>0.557264</td>\n",
       "      <td>0.022259</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.473665</td>\n",
       "      <td>0.629666</td>\n",
       "      <td>-0.941813</td>\n",
       "      <td>0.999926</td>\n",
       "      <td>0.503766</td>\n",
       "      <td>0.567250</td>\n",
       "      <td>-0.883020</td>\n",
       "      <td>0.999840</td>\n",
       "      <td>0.522324</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.539540</td>\n",
       "      <td>0.560835</td>\n",
       "      <td>0.020438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.544497</td>\n",
       "      <td>0.557021</td>\n",
       "      <td>0.021202</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.471774</td>\n",
       "      <td>0.631647</td>\n",
       "      <td>-0.962860</td>\n",
       "      <td>0.999904</td>\n",
       "      <td>0.501157</td>\n",
       "      <td>0.567385</td>\n",
       "      <td>-0.899101</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.519776</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.537242</td>\n",
       "      <td>0.560419</td>\n",
       "      <td>0.021292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542195</td>\n",
       "      <td>0.556752</td>\n",
       "      <td>0.022069</td>\n",
       "      <td>0.0</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>891</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.408424</td>\n",
       "      <td>0.760444</td>\n",
       "      <td>-1.765299</td>\n",
       "      <td>0.997130</td>\n",
       "      <td>0.451284</td>\n",
       "      <td>0.671501</td>\n",
       "      <td>-1.732478</td>\n",
       "      <td>0.997550</td>\n",
       "      <td>0.473581</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.497404</td>\n",
       "      <td>0.669046</td>\n",
       "      <td>-0.017883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.503710</td>\n",
       "      <td>0.660127</td>\n",
       "      <td>-0.018144</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.407833</td>\n",
       "      <td>0.757609</td>\n",
       "      <td>-1.673067</td>\n",
       "      <td>0.997244</td>\n",
       "      <td>0.450543</td>\n",
       "      <td>0.668807</td>\n",
       "      <td>-1.630430</td>\n",
       "      <td>0.997649</td>\n",
       "      <td>0.472954</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.495009</td>\n",
       "      <td>0.664736</td>\n",
       "      <td>-0.016783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.501252</td>\n",
       "      <td>0.656103</td>\n",
       "      <td>-0.017102</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.404387</td>\n",
       "      <td>0.755189</td>\n",
       "      <td>-1.673713</td>\n",
       "      <td>0.997319</td>\n",
       "      <td>0.446949</td>\n",
       "      <td>0.666562</td>\n",
       "      <td>-1.628195</td>\n",
       "      <td>0.997719</td>\n",
       "      <td>0.469375</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.491432</td>\n",
       "      <td>0.654330</td>\n",
       "      <td>-0.016761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.497629</td>\n",
       "      <td>0.645556</td>\n",
       "      <td>-0.017094</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.400639</td>\n",
       "      <td>0.743621</td>\n",
       "      <td>-1.533301</td>\n",
       "      <td>0.997383</td>\n",
       "      <td>0.443768</td>\n",
       "      <td>0.653592</td>\n",
       "      <td>-1.481999</td>\n",
       "      <td>0.997776</td>\n",
       "      <td>0.465875</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.488928</td>\n",
       "      <td>0.645551</td>\n",
       "      <td>-0.017285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.494716</td>\n",
       "      <td>0.638876</td>\n",
       "      <td>-0.017876</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.398779</td>\n",
       "      <td>0.731083</td>\n",
       "      <td>-1.542313</td>\n",
       "      <td>0.997486</td>\n",
       "      <td>0.441949</td>\n",
       "      <td>0.641245</td>\n",
       "      <td>-1.487431</td>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.463812</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.033893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.486434</td>\n",
       "      <td>0.637421</td>\n",
       "      <td>-0.015065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.491992</td>\n",
       "      <td>0.631460</td>\n",
       "      <td>-0.015641</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 2005 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    class        x1        y1        z1        v1        x2        y2  \\\n",
       "542   Sad  0.488484  0.626194 -0.805227  0.999965  0.521179  0.567260   \n",
       "543   Sad  0.485024  0.628707 -0.909506  0.999952  0.516005  0.567540   \n",
       "544   Sad  0.481665  0.629282 -0.905353  0.999940  0.512642  0.567530   \n",
       "545   Sad  0.473665  0.629666 -0.941813  0.999926  0.503766  0.567250   \n",
       "546   Sad  0.471774  0.631647 -0.962860  0.999904  0.501157  0.567385   \n",
       "..    ...       ...       ...       ...       ...       ...       ...   \n",
       "891   Sad  0.408424  0.760444 -1.765299  0.997130  0.451284  0.671501   \n",
       "892   Sad  0.407833  0.757609 -1.673067  0.997244  0.450543  0.668807   \n",
       "893   Sad  0.404387  0.755189 -1.673713  0.997319  0.446949  0.666562   \n",
       "894   Sad  0.400639  0.743621 -1.533301  0.997383  0.443768  0.653592   \n",
       "895   Sad  0.398779  0.731083 -1.542313  0.997486  0.441949  0.641245   \n",
       "\n",
       "           z2        v2        x3  ...      z499  v499      x500      y500  \\\n",
       "542 -0.749725  0.999913  0.539034  ... -0.003121   0.0  0.555681  0.560882   \n",
       "543 -0.847320  0.999888  0.533780  ... -0.004222   0.0  0.548035  0.562400   \n",
       "544 -0.843626  0.999862  0.530525  ... -0.004116   0.0  0.545288  0.561721   \n",
       "545 -0.883020  0.999840  0.522324  ... -0.004316   0.0  0.539540  0.560835   \n",
       "546 -0.899101  0.999801  0.519776  ... -0.003737   0.0  0.537242  0.560419   \n",
       "..        ...       ...       ...  ...       ...   ...       ...       ...   \n",
       "891 -1.732478  0.997550  0.473581  ... -0.036483   0.0  0.497404  0.669046   \n",
       "892 -1.630430  0.997649  0.472954  ... -0.035326   0.0  0.495009  0.664736   \n",
       "893 -1.628195  0.997719  0.469375  ... -0.034984   0.0  0.491432  0.654330   \n",
       "894 -1.481999  0.997776  0.465875  ... -0.035665   0.0  0.488928  0.645551   \n",
       "895 -1.487431  0.997855  0.463812  ... -0.033893   0.0  0.486434  0.637421   \n",
       "\n",
       "         z500  v500      x501      y501      z501  v501  \n",
       "542  0.023273   0.0  0.560688  0.556035  0.024310   0.0  \n",
       "543  0.021244   0.0  0.553111  0.557692  0.022179   0.0  \n",
       "544  0.021342   0.0  0.550385  0.557264  0.022259   0.0  \n",
       "545  0.020438   0.0  0.544497  0.557021  0.021202   0.0  \n",
       "546  0.021292   0.0  0.542195  0.556752  0.022069   0.0  \n",
       "..        ...   ...       ...       ...       ...   ...  \n",
       "891 -0.017883   0.0  0.503710  0.660127 -0.018144   0.0  \n",
       "892 -0.016783   0.0  0.501252  0.656103 -0.017102   0.0  \n",
       "893 -0.016761   0.0  0.497629  0.645556 -0.017094   0.0  \n",
       "894 -0.017285   0.0  0.494716  0.638876 -0.017876   0.0  \n",
       "895 -0.015065   0.0  0.491992  0.631460 -0.015641   0.0  \n",
       "\n",
       "[354 rows x 2005 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['class']=='Sad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('class', axis=1) # features\n",
    "y = df['class'] # target value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2148    Surprised\n",
       "2556      Excited\n",
       "1830      Tension\n",
       "961         Angry\n",
       "3542         Pain\n",
       "          ...    \n",
       "551           Sad\n",
       "2555      Excited\n",
       "3623         Pain\n",
       "4120         Pain\n",
       "1517      Tension\n",
       "Name: class, Length: 1266, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Train Machine Learning Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelines = {\n",
    "    'lr':make_pipeline(StandardScaler(), LogisticRegression(max_iter=5000)),\n",
    "    'rc':make_pipeline(StandardScaler(), RidgeClassifier()),\n",
    "    'rf':make_pipeline(StandardScaler(), RandomForestClassifier()),\n",
    "    'gb':make_pipeline(StandardScaler(), GradientBoostingClassifier()),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_models = {}\n",
    "for algo, pipeline in pipelines.items():\n",
    "    model = pipeline.fit(X_train, y_train)\n",
    "    fit_models[algo] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lr': Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('logisticregression', LogisticRegression(max_iter=5000))]),\n",
       " 'rc': Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('ridgeclassifier', RidgeClassifier())]),\n",
       " 'rf': Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('randomforestclassifier', RandomForestClassifier())]),\n",
       " 'gb': Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('gradientboostingclassifier', GradientBoostingClassifier())])}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Surprised', 'Excited', 'Tension', ..., 'Pain', 'Pain', 'Tension'],\n",
       "      dtype='<U9')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models['rc'].predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Evaluate and Serialize Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score # Accuracy metrics \n",
    "import pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 0.995260663507109\n",
      "rc 0.985781990521327\n",
      "rf 0.9881516587677726\n",
      "gb 0.9928909952606635\n"
     ]
    }
   ],
   "source": [
    "for algo, model in fit_models.items():\n",
    "    yhat = model.predict(X_test)\n",
    "    print(algo, accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Surprised', 'Excited', 'Tension', ..., 'Pain', 'Pain', 'Tension'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models['rf'].predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2148    Surprised\n",
       "2556      Excited\n",
       "1830      Tension\n",
       "961         Angry\n",
       "3542         Pain\n",
       "          ...    \n",
       "551           Sad\n",
       "2555      Excited\n",
       "3623         Pain\n",
       "4120         Pain\n",
       "1517      Tension\n",
       "Name: class, Length: 1266, dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('body_language.pkl', 'wb') as f:\n",
    "    pickle.dump(fit_models['rf'], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Make Detections with Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('body_language.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;randomforestclassifier&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;randomforestclassifier&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('randomforestclassifier', RandomForestClassifier())])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happy [0.   0.01 0.   0.05 0.53 0.   0.16 0.25 0.  ]\n",
      "Happy [0.08 0.02 0.   0.1  0.31 0.   0.15 0.29 0.05]\n",
      "Happy [0.09 0.03 0.   0.08 0.33 0.   0.15 0.24 0.08]\n",
      "Happy [0.1  0.03 0.   0.08 0.33 0.   0.17 0.2  0.09]\n",
      "Happy [0.02 0.03 0.   0.07 0.53 0.   0.12 0.23 0.  ]\n",
      "Happy [0.02 0.03 0.   0.06 0.57 0.   0.14 0.18 0.  ]\n",
      "Happy [0.01 0.03 0.   0.06 0.56 0.   0.12 0.22 0.  ]\n",
      "Happy [0.05 0.03 0.   0.06 0.55 0.   0.12 0.19 0.  ]\n",
      "Happy [0.06 0.01 0.   0.08 0.48 0.   0.14 0.19 0.04]\n",
      "Happy [0.07 0.02 0.   0.07 0.46 0.01 0.15 0.17 0.05]\n",
      "Happy [0.02 0.02 0.   0.04 0.59 0.   0.11 0.22 0.  ]\n",
      "Happy [0.   0.07 0.02 0.05 0.59 0.01 0.11 0.11 0.04]\n",
      "Happy [0.   0.05 0.   0.02 0.81 0.   0.05 0.05 0.02]\n",
      "Happy [0.01 0.03 0.   0.03 0.87 0.   0.03 0.03 0.  ]\n",
      "Happy [0.01 0.03 0.   0.03 0.81 0.   0.04 0.07 0.01]\n",
      "Happy [0.01 0.02 0.   0.03 0.82 0.   0.02 0.09 0.01]\n",
      "Happy [0.01 0.01 0.   0.08 0.74 0.   0.06 0.09 0.01]\n",
      "Happy [0.02 0.01 0.   0.07 0.72 0.01 0.11 0.06 0.  ]\n",
      "Happy [0.02 0.01 0.   0.06 0.8  0.   0.06 0.03 0.02]\n",
      "Happy [0.02 0.02 0.   0.07 0.81 0.   0.06 0.02 0.  ]\n",
      "Happy [0.02 0.02 0.   0.07 0.77 0.   0.09 0.03 0.  ]\n",
      "Happy [0.01 0.01 0.   0.09 0.76 0.   0.06 0.06 0.01]\n",
      "Happy [0.02 0.01 0.   0.06 0.7  0.   0.12 0.09 0.  ]\n",
      "Happy [0.01 0.04 0.01 0.09 0.71 0.   0.06 0.06 0.02]\n",
      "Happy [0.02 0.01 0.   0.09 0.72 0.   0.1  0.06 0.  ]\n",
      "Happy [0.02 0.02 0.   0.07 0.77 0.   0.1  0.02 0.  ]\n",
      "Happy [0.09 0.02 0.   0.08 0.51 0.   0.11 0.12 0.07]\n",
      "Happy [0.07 0.02 0.   0.06 0.38 0.02 0.13 0.25 0.07]\n",
      "Surprised [0.   0.08 0.01 0.1  0.28 0.02 0.14 0.3  0.07]\n",
      "Surprised [0.07 0.02 0.02 0.03 0.07 0.   0.1  0.53 0.16]\n",
      "Surprised [0.06 0.02 0.01 0.02 0.1  0.   0.09 0.53 0.17]\n",
      "Tension [0.32 0.   0.   0.1  0.01 0.01 0.13 0.06 0.37]\n",
      "Tension [0.25 0.05 0.05 0.18 0.03 0.   0.05 0.08 0.31]\n",
      "Tension [0.3  0.03 0.05 0.15 0.01 0.01 0.02 0.07 0.36]\n",
      "Angry [0.4  0.05 0.08 0.1  0.02 0.   0.02 0.04 0.29]\n",
      "Angry [0.39 0.05 0.09 0.11 0.02 0.   0.01 0.04 0.29]\n",
      "Angry [0.37 0.05 0.08 0.12 0.01 0.   0.02 0.05 0.3 ]\n",
      "Angry [0.38 0.06 0.1  0.11 0.02 0.   0.02 0.03 0.28]\n",
      "Angry [0.36 0.06 0.09 0.13 0.02 0.   0.01 0.03 0.3 ]\n",
      "Angry [0.39 0.06 0.08 0.12 0.02 0.   0.02 0.02 0.29]\n",
      "Angry [0.4  0.06 0.08 0.12 0.02 0.   0.01 0.03 0.28]\n",
      "Angry [0.41 0.06 0.07 0.13 0.02 0.01 0.01 0.04 0.25]\n",
      "Angry [0.35 0.04 0.07 0.15 0.01 0.01 0.01 0.03 0.33]\n",
      "Angry [0.36 0.04 0.05 0.15 0.02 0.01 0.03 0.04 0.3 ]\n",
      "Angry [0.34 0.04 0.01 0.11 0.   0.01 0.11 0.06 0.32]\n",
      "Tension [0.29 0.02 0.   0.13 0.   0.01 0.13 0.08 0.34]\n",
      "Tension [0.28 0.01 0.01 0.15 0.01 0.01 0.09 0.05 0.39]\n",
      "Angry [0.42 0.03 0.03 0.04 0.01 0.01 0.05 0.05 0.36]\n",
      "Angry [0.42 0.03 0.02 0.05 0.01 0.01 0.07 0.04 0.35]\n",
      "Tension [0.33 0.03 0.01 0.05 0.01 0.01 0.12 0.07 0.37]\n",
      "Tension [0.31 0.03 0.   0.08 0.01 0.01 0.1  0.06 0.4 ]\n",
      "Angry [0.36 0.02 0.   0.08 0.01 0.02 0.09 0.06 0.36]\n",
      "Tension [0.24 0.02 0.01 0.07 0.01 0.02 0.12 0.06 0.45]\n",
      "Tension [0.31 0.03 0.02 0.1  0.01 0.02 0.11 0.04 0.36]\n",
      "Angry [0.38 0.03 0.03 0.09 0.01 0.02 0.09 0.04 0.31]\n",
      "Angry [0.34 0.04 0.03 0.13 0.02 0.02 0.04 0.04 0.34]\n",
      "Tension [0.32 0.02 0.03 0.14 0.02 0.02 0.06 0.04 0.35]\n",
      "Tension [0.33 0.02 0.03 0.14 0.01 0.02 0.05 0.02 0.38]\n",
      "Tension [0.3  0.03 0.03 0.13 0.01 0.02 0.04 0.03 0.41]\n",
      "Tension [0.34 0.04 0.04 0.12 0.01 0.01 0.03 0.05 0.36]\n",
      "Tension [0.33 0.03 0.03 0.11 0.01 0.02 0.05 0.03 0.39]\n",
      "Tension [0.3  0.03 0.03 0.13 0.01 0.02 0.06 0.03 0.39]\n",
      "Tension [0.31 0.03 0.03 0.12 0.01 0.02 0.06 0.03 0.39]\n",
      "Tension [0.33 0.04 0.04 0.11 0.01 0.02 0.05 0.04 0.36]\n",
      "Tension [0.3  0.03 0.03 0.12 0.01 0.02 0.06 0.03 0.4 ]\n",
      "Tension [0.31 0.03 0.04 0.12 0.01 0.02 0.05 0.04 0.38]\n",
      "Tension [0.31 0.03 0.04 0.12 0.01 0.02 0.05 0.05 0.37]\n",
      "Tension [0.3  0.03 0.05 0.1  0.01 0.02 0.04 0.06 0.39]\n",
      "Tension [0.3  0.04 0.04 0.11 0.01 0.02 0.05 0.06 0.37]\n",
      "Tension [0.32 0.03 0.04 0.11 0.01 0.02 0.04 0.04 0.39]\n",
      "Tension [0.31 0.03 0.04 0.12 0.01 0.02 0.05 0.05 0.37]\n",
      "Tension [0.31 0.03 0.03 0.12 0.01 0.02 0.05 0.05 0.38]\n",
      "Tension [0.31 0.02 0.03 0.11 0.01 0.02 0.04 0.04 0.42]\n",
      "Tension [0.31 0.03 0.04 0.12 0.01 0.02 0.05 0.05 0.37]\n",
      "Tension [0.3  0.03 0.03 0.12 0.01 0.02 0.05 0.04 0.4 ]\n",
      "Tension [0.29 0.03 0.03 0.12 0.01 0.02 0.05 0.04 0.41]\n",
      "Tension [0.3  0.03 0.04 0.12 0.01 0.02 0.06 0.04 0.38]\n",
      "Tension [0.32 0.03 0.04 0.11 0.01 0.02 0.05 0.04 0.38]\n",
      "Tension [0.31 0.02 0.03 0.11 0.01 0.02 0.04 0.05 0.41]\n",
      "Tension [0.31 0.04 0.03 0.12 0.01 0.02 0.05 0.05 0.37]\n",
      "Tension [0.29 0.07 0.02 0.09 0.04 0.02 0.11 0.06 0.3 ]\n",
      "Angry [0.38 0.05 0.02 0.06 0.   0.02 0.2  0.1  0.17]\n",
      "Angry [0.39 0.03 0.03 0.04 0.02 0.03 0.22 0.1  0.14]\n",
      "Surprised [0.14 0.02 0.03 0.04 0.15 0.01 0.19 0.21 0.21]\n",
      "Happy [0.1  0.01 0.02 0.02 0.35 0.02 0.09 0.25 0.14]\n",
      "Happy [0.09 0.02 0.02 0.02 0.49 0.   0.11 0.11 0.14]\n",
      "Happy [0.02 0.03 0.   0.03 0.74 0.01 0.1  0.03 0.04]\n",
      "Confused [0.   0.25 0.02 0.08 0.24 0.02 0.24 0.1  0.05]\n",
      "Happy [0.01 0.23 0.02 0.05 0.27 0.02 0.25 0.1  0.05]\n",
      "Happy [0.   0.17 0.03 0.05 0.31 0.04 0.25 0.1  0.05]\n",
      "Happy [0.   0.18 0.03 0.06 0.3  0.03 0.25 0.12 0.03]\n",
      "Happy [0.01 0.19 0.03 0.04 0.3  0.02 0.24 0.12 0.05]\n",
      "Happy [0.01 0.1  0.01 0.03 0.46 0.03 0.2  0.13 0.03]\n",
      "Confused [0.01 0.28 0.01 0.02 0.26 0.02 0.25 0.09 0.06]\n",
      "Happy [0.02 0.25 0.01 0.04 0.29 0.02 0.23 0.11 0.03]\n",
      "Confused [0.01 0.32 0.01 0.06 0.27 0.02 0.22 0.05 0.04]\n",
      "Happy [0.   0.29 0.01 0.06 0.31 0.03 0.21 0.05 0.04]\n",
      "Confused [0.   0.43 0.01 0.04 0.22 0.01 0.19 0.03 0.07]\n",
      "Confused [0.   0.47 0.   0.03 0.18 0.01 0.17 0.04 0.1 ]\n",
      "Confused [0.   0.5  0.   0.04 0.17 0.02 0.14 0.05 0.08]\n",
      "Confused [0.   0.53 0.   0.04 0.14 0.02 0.14 0.05 0.08]\n",
      "Confused [0.   0.5  0.   0.03 0.16 0.02 0.14 0.07 0.08]\n",
      "Confused [0.   0.46 0.   0.02 0.19 0.02 0.15 0.08 0.08]\n",
      "Confused [0.   0.56 0.   0.03 0.14 0.02 0.14 0.06 0.05]\n",
      "Confused [0.   0.55 0.   0.04 0.14 0.03 0.13 0.06 0.05]\n",
      "Confused [0.   0.55 0.   0.03 0.14 0.02 0.14 0.06 0.06]\n",
      "Confused [0.   0.55 0.   0.04 0.14 0.03 0.13 0.06 0.05]\n",
      "Confused [0.   0.52 0.   0.03 0.16 0.02 0.14 0.06 0.07]\n",
      "Confused [0.   0.55 0.   0.03 0.14 0.02 0.14 0.07 0.05]\n",
      "Confused [0.   0.44 0.   0.02 0.19 0.02 0.18 0.08 0.07]\n",
      "Confused [0.   0.32 0.01 0.01 0.29 0.02 0.22 0.1  0.03]\n",
      "Happy [0.   0.   0.   0.01 0.71 0.02 0.02 0.23 0.01]\n",
      "Angry [0.48 0.05 0.06 0.01 0.05 0.01 0.17 0.06 0.11]\n",
      "Angry [0.39 0.03 0.03 0.04 0.03 0.04 0.2  0.05 0.19]\n",
      "Angry [0.39 0.04 0.03 0.04 0.03 0.05 0.16 0.08 0.18]\n",
      "Angry [0.33 0.03 0.   0.05 0.05 0.01 0.17 0.1  0.26]\n",
      "Tension [0.26 0.05 0.   0.03 0.07 0.01 0.16 0.11 0.31]\n",
      "Angry [0.33 0.03 0.01 0.04 0.07 0.03 0.2  0.06 0.23]\n",
      "Angry [0.31 0.02 0.   0.04 0.05 0.03 0.2  0.09 0.26]\n",
      "Angry [0.33 0.03 0.03 0.04 0.03 0.04 0.22 0.06 0.22]\n",
      "Angry [0.3  0.04 0.   0.04 0.09 0.02 0.19 0.09 0.23]\n",
      "Angry [0.27 0.02 0.   0.04 0.07 0.03 0.2  0.11 0.26]\n",
      "Angry [0.29 0.04 0.   0.04 0.09 0.02 0.18 0.11 0.23]\n",
      "Angry [0.31 0.05 0.01 0.04 0.09 0.01 0.15 0.11 0.23]\n",
      "Angry [0.34 0.05 0.   0.04 0.08 0.01 0.16 0.11 0.21]\n",
      "Angry [0.31 0.02 0.01 0.04 0.07 0.03 0.21 0.08 0.23]\n",
      "Angry [0.31 0.04 0.   0.02 0.07 0.03 0.18 0.1  0.25]\n",
      "Angry [0.3  0.04 0.   0.03 0.09 0.02 0.18 0.11 0.23]\n",
      "Angry [0.38 0.04 0.02 0.03 0.01 0.04 0.23 0.08 0.17]\n",
      "Angry [0.37 0.04 0.01 0.02 0.03 0.03 0.24 0.08 0.18]\n",
      "Angry [0.31 0.04 0.05 0.02 0.01 0.04 0.24 0.11 0.18]\n",
      "Angry [0.34 0.06 0.04 0.03 0.02 0.01 0.24 0.08 0.18]\n",
      "Angry [0.34 0.05 0.07 0.03 0.02 0.02 0.2  0.08 0.19]\n",
      "Angry [0.34 0.05 0.07 0.03 0.02 0.02 0.21 0.07 0.19]\n",
      "Angry [0.35 0.05 0.08 0.03 0.02 0.01 0.21 0.07 0.18]\n",
      "Angry [0.33 0.04 0.07 0.03 0.03 0.01 0.2  0.09 0.2 ]\n",
      "Angry [0.34 0.06 0.06 0.04 0.03 0.01 0.18 0.09 0.19]\n",
      "Angry [0.35 0.06 0.07 0.02 0.02 0.02 0.19 0.06 0.21]\n",
      "Angry [0.32 0.07 0.07 0.04 0.02 0.02 0.19 0.06 0.21]\n",
      "Angry [0.32 0.07 0.08 0.04 0.02 0.01 0.2  0.07 0.19]\n",
      "Angry [0.32 0.06 0.04 0.06 0.03 0.01 0.19 0.09 0.2 ]\n",
      "Angry [0.3  0.04 0.06 0.05 0.02 0.01 0.19 0.09 0.24]\n",
      "Angry [0.35 0.07 0.01 0.06 0.01 0.01 0.19 0.11 0.19]\n",
      "Angry [0.31 0.06 0.01 0.1  0.01 0.01 0.17 0.11 0.22]\n",
      "Angry [0.47 0.06 0.02 0.03 0.01 0.01 0.11 0.13 0.16]\n",
      "Angry [0.51 0.02 0.03 0.04 0.01 0.01 0.11 0.11 0.16]\n",
      "Angry [0.54 0.01 0.02 0.05 0.   0.01 0.09 0.11 0.17]\n",
      "Angry [0.54 0.01 0.02 0.06 0.   0.01 0.08 0.11 0.17]\n",
      "Angry [0.39 0.02 0.01 0.1  0.   0.01 0.1  0.15 0.22]\n",
      "Angry [0.42 0.02 0.   0.09 0.   0.01 0.11 0.13 0.22]\n",
      "Angry [0.47 0.01 0.01 0.08 0.   0.01 0.09 0.14 0.19]\n",
      "Tension [0.3  0.02 0.02 0.04 0.   0.   0.11 0.2  0.31]\n",
      "Tension [0.29 0.02 0.02 0.04 0.   0.   0.11 0.17 0.35]\n",
      "Tension [0.26 0.03 0.01 0.05 0.   0.01 0.11 0.18 0.35]\n"
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     "name": "stdout",
     "output_type": "stream",
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      "Tension [0.29 0.02 0.01 0.03 0.01 0.   0.14 0.16 0.34]\n",
      "Surprised [0.06 0.04 0.02 0.01 0.04 0.   0.13 0.45 0.25]\n",
      "Angry [0.29 0.02 0.01 0.02 0.01 0.   0.2  0.23 0.22]\n",
      "Tension [0.24 0.03 0.01 0.02 0.04 0.   0.2  0.19 0.27]\n",
      "Surprised [0.06 0.02 0.03 0.01 0.04 0.   0.18 0.47 0.19]\n",
      "Surprised [0.06 0.03 0.01 0.02 0.12 0.   0.12 0.5  0.14]\n",
      "Surprised [0.09 0.02 0.01 0.02 0.12 0.   0.12 0.49 0.13]\n",
      "Happy [0.04 0.06 0.   0.04 0.37 0.01 0.17 0.26 0.05]\n",
      "Happy [0.03 0.14 0.03 0.03 0.27 0.02 0.26 0.18 0.04]\n",
      "Happy [0.04 0.14 0.03 0.04 0.27 0.02 0.2  0.24 0.02]\n",
      "Happy [0.03 0.16 0.03 0.03 0.31 0.01 0.26 0.14 0.03]\n",
      "Happy [0.03 0.14 0.02 0.03 0.31 0.02 0.22 0.19 0.04]\n",
      "Sad [0.03 0.18 0.02 0.05 0.22 0.01 0.31 0.15 0.03]\n",
      "Sad [0.01 0.22 0.06 0.07 0.12 0.03 0.41 0.05 0.03]\n",
      "Sad [0.01 0.24 0.07 0.07 0.11 0.04 0.43 0.02 0.01]\n",
      "Sad [0.01 0.19 0.03 0.08 0.15 0.02 0.35 0.15 0.02]\n",
      "Sad [0.03 0.13 0.03 0.05 0.19 0.03 0.36 0.16 0.02]\n",
      "Sad [0.03 0.13 0.03 0.03 0.2  0.01 0.38 0.16 0.03]\n",
      "Sad [0.03 0.13 0.04 0.05 0.19 0.03 0.37 0.14 0.02]\n",
      "Sad [0.01 0.2  0.04 0.05 0.16 0.03 0.36 0.13 0.02]\n",
      "Sad [0.01 0.15 0.04 0.04 0.2  0.03 0.34 0.17 0.02]\n",
      "Sad [0.01 0.16 0.03 0.02 0.21 0.01 0.31 0.22 0.03]\n",
      "Surprised [0.02 0.07 0.01 0.01 0.17 0.01 0.2  0.48 0.03]\n",
      "Sad [0.02 0.12 0.02 0.03 0.2  0.01 0.41 0.17 0.02]\n",
      "Sad [0.   0.12 0.01 0.04 0.15 0.   0.39 0.24 0.05]\n",
      "Sad [0.   0.05 0.01 0.02 0.17 0.   0.37 0.32 0.06]\n",
      "Surprised [0.01 0.05 0.   0.04 0.22 0.   0.17 0.48 0.03]\n",
      "Sad [0.   0.07 0.01 0.05 0.18 0.   0.42 0.22 0.05]\n",
      "Sad [0.   0.04 0.01 0.05 0.17 0.   0.37 0.31 0.05]\n",
      "Surprised [0.   0.05 0.   0.02 0.26 0.   0.22 0.4  0.05]\n",
      "Sad [0.   0.02 0.   0.04 0.18 0.01 0.4  0.3  0.05]\n",
      "Sad [0.   0.04 0.01 0.05 0.17 0.01 0.43 0.24 0.05]\n",
      "Sad [0.   0.05 0.   0.04 0.15 0.01 0.39 0.3  0.06]\n",
      "Sad [0.   0.06 0.01 0.04 0.17 0.01 0.43 0.24 0.04]\n",
      "Sad [0.   0.03 0.   0.03 0.16 0.01 0.45 0.26 0.06]\n",
      "Surprised [0.   0.03 0.   0.01 0.14 0.01 0.36 0.39 0.06]\n",
      "Sad [0.   0.06 0.   0.04 0.15 0.   0.5  0.2  0.05]\n",
      "Sad [0.   0.05 0.   0.03 0.17 0.01 0.37 0.31 0.06]\n",
      "Sad [0.   0.05 0.   0.02 0.17 0.01 0.4  0.3  0.05]\n",
      "Sad [0.   0.04 0.   0.03 0.19 0.01 0.43 0.25 0.05]\n",
      "Sad [0.   0.05 0.   0.03 0.17 0.01 0.41 0.28 0.05]\n",
      "Surprised [0.01 0.05 0.   0.   0.15 0.   0.24 0.48 0.07]\n",
      "Surprised [0.01 0.04 0.   0.01 0.13 0.   0.18 0.58 0.05]\n",
      "Surprised [0.01 0.05 0.   0.   0.12 0.   0.24 0.53 0.05]\n",
      "Surprised [0.01 0.06 0.   0.   0.13 0.   0.26 0.48 0.06]\n",
      "Sad [0.   0.04 0.   0.   0.13 0.01 0.37 0.37 0.08]\n",
      "Surprised [0.   0.06 0.   0.   0.13 0.01 0.36 0.37 0.07]\n",
      "Surprised [0.   0.04 0.   0.   0.14 0.01 0.36 0.38 0.07]\n",
      "Surprised [0.01 0.05 0.   0.   0.11 0.   0.21 0.57 0.05]\n",
      "Surprised [0.01 0.06 0.   0.   0.12 0.   0.28 0.47 0.06]\n",
      "Sad [0.   0.06 0.   0.01 0.16 0.01 0.39 0.31 0.06]\n",
      "Sad [0.   0.06 0.   0.05 0.17 0.   0.45 0.19 0.08]\n",
      "Surprised [0.01 0.   0.   0.   0.02 0.   0.05 0.91 0.01]\n",
      "Surprised [0.01 0.06 0.   0.02 0.32 0.   0.17 0.37 0.05]\n",
      "Happy [0.   0.   0.   0.03 0.51 0.   0.15 0.27 0.04]\n",
      "Happy [0.   0.04 0.01 0.06 0.36 0.   0.19 0.3  0.04]\n",
      "Surprised [0.01 0.   0.   0.06 0.34 0.02 0.07 0.45 0.05]\n",
      "Happy [0.01 0.   0.01 0.06 0.46 0.01 0.09 0.31 0.05]\n",
      "Happy [0.01 0.   0.   0.07 0.39 0.02 0.08 0.36 0.07]\n",
      "Surprised [0.01 0.   0.   0.06 0.37 0.02 0.09 0.4  0.05]\n",
      "Happy [0.02 0.   0.01 0.08 0.36 0.01 0.11 0.35 0.06]\n",
      "Happy [0.01 0.   0.01 0.07 0.43 0.01 0.09 0.33 0.05]\n",
      "Happy [0.02 0.   0.03 0.09 0.35 0.   0.17 0.29 0.05]\n",
      "Happy [0.01 0.   0.01 0.07 0.45 0.   0.1  0.3  0.06]\n",
      "Happy [0.01 0.   0.   0.08 0.42 0.01 0.09 0.34 0.05]\n",
      "Surprised [0.01 0.   0.   0.07 0.39 0.01 0.06 0.4  0.06]\n",
      "Happy [0.02 0.01 0.03 0.07 0.33 0.   0.17 0.31 0.06]\n",
      "Happy [0.01 0.03 0.03 0.08 0.32 0.   0.19 0.29 0.05]\n",
      "Sad [0.04 0.06 0.   0.12 0.14 0.02 0.28 0.28 0.06]\n",
      "Surprised [0.03 0.01 0.01 0.12 0.24 0.01 0.2  0.32 0.06]\n",
      "Sad [0.01 0.1  0.01 0.18 0.19 0.   0.38 0.09 0.04]\n",
      "Sad [0.01 0.07 0.02 0.16 0.16 0.   0.35 0.18 0.05]\n",
      "Sad [0.01 0.08 0.02 0.18 0.17 0.01 0.42 0.06 0.05]\n",
      "Sad [0.02 0.1  0.04 0.19 0.09 0.01 0.46 0.04 0.05]\n",
      "Sad [0.02 0.09 0.06 0.21 0.07 0.01 0.44 0.04 0.06]\n",
      "Sad [0.02 0.08 0.06 0.21 0.07 0.02 0.46 0.03 0.05]\n",
      "Sad [0.02 0.1  0.06 0.2  0.07 0.02 0.46 0.02 0.05]\n",
      "Sad [0.02 0.08 0.06 0.22 0.07 0.01 0.46 0.03 0.05]\n",
      "Sad [0.02 0.08 0.06 0.22 0.07 0.02 0.43 0.04 0.06]\n",
      "Sad [0.02 0.08 0.06 0.21 0.08 0.01 0.45 0.04 0.05]\n",
      "Sad [0.02 0.1  0.06 0.21 0.07 0.02 0.44 0.02 0.06]\n",
      "Sad [0.02 0.09 0.06 0.2  0.07 0.02 0.46 0.03 0.05]\n",
      "Sad [0.02 0.09 0.06 0.2  0.07 0.02 0.46 0.03 0.05]\n",
      "Sad [0.02 0.1  0.06 0.19 0.08 0.01 0.47 0.02 0.05]\n",
      "Sad [0.02 0.1  0.06 0.19 0.08 0.01 0.47 0.02 0.05]\n",
      "Sad [0.02 0.1  0.06 0.2  0.08 0.01 0.45 0.03 0.05]\n",
      "Sad [0.02 0.1  0.06 0.2  0.08 0.02 0.44 0.03 0.05]\n",
      "Sad [0.02 0.09 0.06 0.26 0.07 0.03 0.38 0.03 0.06]\n",
      "Sad [0.02 0.1  0.06 0.26 0.05 0.03 0.39 0.03 0.06]\n",
      "Sad [0.02 0.11 0.06 0.26 0.04 0.03 0.41 0.02 0.05]\n",
      "Sad [0.02 0.13 0.06 0.21 0.06 0.02 0.44 0.01 0.05]\n",
      "Sad [0.02 0.13 0.06 0.25 0.05 0.02 0.37 0.05 0.05]\n",
      "Sad [0.02 0.11 0.08 0.21 0.07 0.02 0.38 0.07 0.04]\n",
      "Sad [0.   0.07 0.03 0.08 0.21 0.02 0.4  0.14 0.05]\n",
      "Sad [0.   0.08 0.03 0.09 0.13 0.07 0.36 0.18 0.06]\n",
      "Happy [0.01 0.01 0.01 0.03 0.37 0.08 0.23 0.23 0.03]\n",
      "Happy [0.01 0.02 0.02 0.04 0.44 0.08 0.17 0.19 0.03]\n",
      "Happy [0.01 0.05 0.05 0.05 0.3  0.09 0.25 0.17 0.03]\n",
      "Happy [0.01 0.02 0.04 0.03 0.43 0.07 0.19 0.17 0.04]\n",
      "Happy [0.01 0.01 0.04 0.04 0.46 0.07 0.14 0.18 0.05]\n",
      "Happy [0.   0.01 0.02 0.05 0.52 0.07 0.15 0.14 0.04]\n",
      "Sad [0.01 0.06 0.05 0.05 0.27 0.09 0.28 0.14 0.05]\n",
      "Sad [0.01 0.08 0.05 0.05 0.24 0.1  0.25 0.15 0.07]\n",
      "Sad [0.01 0.08 0.05 0.07 0.21 0.1  0.26 0.16 0.06]\n",
      "Happy [0.   0.09 0.05 0.04 0.25 0.09 0.21 0.21 0.06]\n",
      "Happy [0.   0.07 0.05 0.04 0.26 0.08 0.25 0.22 0.03]\n",
      "Happy [0.01 0.05 0.   0.04 0.52 0.06 0.09 0.21 0.02]\n",
      "Happy [0.   0.03 0.01 0.02 0.43 0.05 0.09 0.35 0.02]\n",
      "Happy [0.02 0.06 0.03 0.02 0.36 0.06 0.21 0.23 0.01]\n",
      "Happy [0.01 0.05 0.03 0.   0.44 0.03 0.17 0.26 0.01]\n",
      "Happy [0.02 0.05 0.03 0.01 0.56 0.02 0.11 0.19 0.01]\n",
      "Happy [0.04 0.09 0.02 0.01 0.31 0.08 0.22 0.19 0.04]\n",
      "Happy [0.06 0.05 0.03 0.   0.41 0.1  0.17 0.13 0.05]\n",
      "Happy [0.08 0.04 0.06 0.   0.37 0.13 0.13 0.16 0.03]\n",
      "Happy [0.06 0.02 0.03 0.   0.41 0.13 0.14 0.15 0.06]\n",
      "Happy [0.03 0.01 0.05 0.   0.3  0.27 0.25 0.06 0.03]\n",
      "Happy [0.03 0.01 0.05 0.   0.33 0.16 0.24 0.14 0.04]\n",
      "Happy [0.04 0.01 0.   0.   0.31 0.27 0.25 0.05 0.07]\n",
      "Pain [0.05 0.01 0.   0.   0.3  0.33 0.17 0.06 0.08]\n",
      "Pain [0.05 0.01 0.   0.   0.28 0.34 0.19 0.06 0.07]\n",
      "Pain [0.07 0.01 0.   0.   0.31 0.32 0.15 0.08 0.06]\n",
      "Pain [0.08 0.01 0.   0.   0.33 0.37 0.11 0.04 0.06]\n",
      "Pain [0.07 0.01 0.   0.   0.2  0.51 0.1  0.03 0.08]\n",
      "Pain [0.08 0.01 0.   0.   0.31 0.41 0.09 0.04 0.06]\n",
      "Pain [0.07 0.   0.   0.   0.19 0.56 0.08 0.04 0.06]\n",
      "Pain [0.09 0.01 0.   0.   0.17 0.54 0.09 0.04 0.06]\n",
      "Pain [0.09 0.01 0.   0.   0.17 0.55 0.09 0.03 0.06]\n",
      "Happy [0.04 0.01 0.01 0.01 0.36 0.03 0.25 0.29 0.  ]\n",
      "Happy [0.05 0.03 0.02 0.06 0.3  0.04 0.2  0.29 0.01]\n",
      "Surprised [0.06 0.   0.   0.09 0.14 0.01 0.2  0.44 0.06]\n",
      "Surprised [0.11 0.01 0.01 0.23 0.05 0.04 0.14 0.33 0.08]\n",
      "Surprised [0.08 0.   0.01 0.21 0.05 0.06 0.15 0.36 0.08]\n",
      "Surprised [0.07 0.   0.   0.2  0.1  0.09 0.18 0.28 0.08]\n",
      "Surprised [0.07 0.01 0.01 0.22 0.04 0.09 0.16 0.35 0.05]\n",
      "Surprised [0.06 0.   0.   0.21 0.06 0.09 0.16 0.35 0.07]\n",
      "Surprised [0.06 0.   0.   0.21 0.09 0.07 0.19 0.31 0.07]\n",
      "Surprised [0.05 0.01 0.01 0.23 0.05 0.07 0.17 0.36 0.05]\n",
      "Surprised [0.07 0.   0.   0.24 0.08 0.1  0.15 0.3  0.06]\n",
      "Surprised [0.07 0.   0.01 0.23 0.05 0.12 0.13 0.34 0.05]\n",
      "Surprised [0.05 0.   0.   0.21 0.06 0.1  0.18 0.33 0.07]\n",
      "Surprised [0.06 0.   0.   0.18 0.08 0.1  0.24 0.28 0.06]\n",
      "Surprised [0.04 0.   0.   0.16 0.2  0.14 0.16 0.26 0.04]\n",
      "Surprised [0.06 0.02 0.   0.18 0.17 0.09 0.18 0.24 0.06]\n",
      "Surprised [0.07 0.   0.   0.11 0.24 0.05 0.19 0.28 0.06]\n",
      "Sad [0.04 0.01 0.01 0.09 0.19 0.09 0.37 0.17 0.03]\n",
      "Sad [0.07 0.01 0.02 0.08 0.24 0.08 0.29 0.18 0.03]\n",
      "Happy [0.04 0.01 0.02 0.02 0.36 0.05 0.3  0.19 0.01]\n",
      "Happy [0.   0.12 0.02 0.03 0.47 0.01 0.23 0.12 0.  ]\n",
      "Happy [0.01 0.06 0.01 0.   0.64 0.   0.17 0.1  0.01]\n",
      "Happy [0.01 0.01 0.   0.01 0.9  0.   0.01 0.04 0.02]\n",
      "Angry [0.47 0.01 0.03 0.02 0.09 0.01 0.14 0.19 0.04]\n",
      "Surprised [0.12 0.01 0.   0.04 0.15 0.   0.22 0.25 0.21]\n",
      "Angry [0.29 0.   0.   0.07 0.04 0.02 0.16 0.26 0.16]\n",
      "Angry [0.27 0.   0.01 0.07 0.04 0.02 0.15 0.23 0.21]\n",
      "Angry [0.36 0.01 0.01 0.09 0.01 0.02 0.12 0.19 0.19]\n",
      "Surprised [0.09 0.   0.   0.11 0.18 0.01 0.11 0.26 0.24]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Tension [0.24 0.01 0.01 0.11 0.02 0.01 0.13 0.15 0.32]\n",
      "Tension [0.08 0.   0.   0.12 0.06 0.   0.11 0.31 0.32]\n",
      "Surprised [0.13 0.01 0.   0.11 0.15 0.   0.09 0.3  0.21]\n",
      "Surprised [0.13 0.01 0.   0.17 0.11 0.   0.05 0.37 0.16]\n",
      "Tension [0.17 0.   0.02 0.14 0.05 0.01 0.1  0.25 0.26]\n",
      "Surprised [0.11 0.01 0.02 0.16 0.08 0.01 0.07 0.33 0.21]\n",
      "Surprised [0.13 0.01 0.01 0.12 0.09 0.01 0.08 0.34 0.21]\n",
      "Surprised [0.19 0.   0.   0.14 0.05 0.01 0.11 0.29 0.21]\n",
      "Surprised [0.09 0.   0.   0.19 0.12 0.   0.06 0.42 0.12]\n",
      "Surprised [0.1  0.   0.   0.19 0.07 0.   0.06 0.43 0.15]\n",
      "Surprised [0.12 0.   0.01 0.14 0.07 0.   0.08 0.37 0.21]\n",
      "Surprised [0.13 0.   0.01 0.14 0.08 0.01 0.07 0.39 0.17]\n",
      "Surprised [0.11 0.01 0.01 0.16 0.08 0.   0.06 0.48 0.09]\n",
      "Surprised [0.04 0.   0.   0.18 0.17 0.   0.06 0.53 0.02]\n",
      "Surprised [0.11 0.   0.   0.17 0.09 0.   0.04 0.51 0.08]\n",
      "Surprised [0.11 0.01 0.01 0.22 0.13 0.   0.04 0.38 0.1 ]\n",
      "Surprised [0.05 0.   0.   0.23 0.21 0.   0.03 0.42 0.06]\n",
      "Surprised [0.23 0.   0.01 0.18 0.06 0.03 0.07 0.24 0.18]\n",
      "Surprised [0.18 0.   0.03 0.25 0.1  0.01 0.03 0.26 0.14]\n",
      "Surprised [0.13 0.01 0.01 0.29 0.09 0.   0.03 0.31 0.13]\n",
      "Excited [0.16 0.   0.02 0.35 0.08 0.01 0.02 0.25 0.11]\n",
      "Excited [0.13 0.   0.03 0.39 0.11 0.   0.01 0.23 0.1 ]\n",
      "Excited [0.11 0.   0.02 0.45 0.07 0.01 0.01 0.17 0.16]\n",
      "Excited [0.26 0.01 0.02 0.47 0.   0.03 0.   0.05 0.16]\n",
      "Excited [0.06 0.01 0.01 0.45 0.   0.03 0.02 0.04 0.38]\n",
      "Excited [0.12 0.   0.02 0.48 0.   0.02 0.02 0.09 0.25]\n",
      "Excited [0.14 0.01 0.02 0.46 0.   0.03 0.02 0.07 0.25]\n",
      "Excited [0.1  0.01 0.02 0.47 0.   0.01 0.02 0.05 0.32]\n",
      "Excited [0.1  0.   0.02 0.44 0.   0.02 0.02 0.04 0.36]\n",
      "Excited [0.07 0.01 0.01 0.46 0.   0.02 0.01 0.07 0.35]\n",
      "Excited [0.05 0.   0.   0.46 0.02 0.01 0.   0.1  0.36]\n",
      "Excited [0.06 0.01 0.   0.46 0.02 0.01 0.   0.08 0.36]\n",
      "Excited [0.05 0.   0.   0.44 0.01 0.01 0.   0.06 0.43]\n",
      "Excited [0.05 0.   0.01 0.45 0.05 0.01 0.   0.13 0.3 ]\n",
      "Excited [0.07 0.   0.   0.48 0.04 0.01 0.   0.12 0.28]\n",
      "Excited [0.06 0.   0.   0.47 0.04 0.   0.01 0.15 0.27]\n",
      "Excited [0.03 0.   0.   0.44 0.1  0.   0.01 0.12 0.3 ]\n",
      "Excited [0.03 0.01 0.   0.41 0.17 0.   0.03 0.1  0.25]\n",
      "Excited [0.01 0.01 0.   0.3  0.3  0.01 0.04 0.16 0.17]\n",
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      "Happy [0.02 0.01 0.   0.04 0.35 0.15 0.17 0.19 0.07]\n",
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      "Surprised [0.02 0.02 0.01 0.04 0.23 0.19 0.18 0.26 0.05]\n",
      "Surprised [0.02 0.02 0.   0.07 0.19 0.15 0.2  0.23 0.12]\n",
      "Surprised [0.03 0.02 0.   0.07 0.13 0.17 0.2  0.22 0.16]\n",
      "Surprised [0.02 0.03 0.   0.08 0.13 0.15 0.19 0.22 0.18]\n",
      "Tension [0.03 0.01 0.   0.09 0.08 0.15 0.22 0.18 0.24]\n",
      "Tension [0.03 0.   0.   0.08 0.04 0.15 0.22 0.16 0.32]\n",
      "Surprised [0.15 0.09 0.05 0.01 0.08 0.19 0.06 0.24 0.13]\n",
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      "Pain [0.06 0.04 0.07 0.02 0.06 0.36 0.07 0.27 0.05]\n",
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      "Pain [0.07 0.04 0.04 0.02 0.05 0.36 0.03 0.28 0.11]\n",
      "Pain [0.05 0.02 0.07 0.   0.06 0.35 0.06 0.31 0.08]\n",
      "Surprised [0.06 0.03 0.1  0.   0.06 0.29 0.07 0.32 0.07]\n",
      "Pain [0.08 0.04 0.1  0.02 0.06 0.31 0.05 0.27 0.07]\n",
      "Pain [0.09 0.07 0.11 0.02 0.06 0.31 0.04 0.23 0.07]\n",
      "Pain [0.1  0.06 0.11 0.02 0.07 0.32 0.04 0.21 0.07]\n",
      "Pain [0.13 0.1  0.11 0.02 0.07 0.34 0.04 0.12 0.07]\n",
      "Pain [0.1  0.08 0.13 0.02 0.07 0.32 0.05 0.16 0.07]\n",
      "Pain [0.14 0.09 0.14 0.02 0.05 0.3  0.05 0.12 0.09]\n",
      "Pain [0.11 0.09 0.11 0.02 0.06 0.33 0.06 0.13 0.09]\n",
      "Pain [0.11 0.07 0.12 0.02 0.06 0.35 0.05 0.13 0.09]\n",
      "Pain [0.12 0.07 0.12 0.02 0.05 0.36 0.06 0.1  0.1 ]\n",
      "Pain [0.06 0.01 0.09 0.   0.05 0.43 0.05 0.23 0.08]\n",
      "Pain [0.09 0.02 0.1  0.   0.05 0.43 0.05 0.2  0.06]\n",
      "Pain [0.09 0.02 0.1  0.01 0.05 0.41 0.06 0.2  0.06]\n",
      "Pain [0.1  0.02 0.11 0.01 0.05 0.4  0.06 0.2  0.05]\n",
      "Pain [0.1  0.02 0.12 0.01 0.04 0.44 0.06 0.15 0.06]\n",
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      "Pain [0.11 0.02 0.13 0.01 0.04 0.4  0.07 0.15 0.07]\n",
      "Pain [0.11 0.02 0.12 0.01 0.04 0.42 0.06 0.15 0.07]\n",
      "Pain [0.1  0.02 0.1  0.   0.06 0.39 0.03 0.22 0.08]\n",
      "Pain [0.09 0.04 0.06 0.02 0.04 0.36 0.04 0.27 0.08]\n",
      "Surprised [0.08 0.05 0.06 0.   0.04 0.26 0.05 0.37 0.09]\n",
      "Surprised [0.08 0.12 0.05 0.02 0.09 0.23 0.04 0.33 0.04]\n",
      "Surprised [0.09 0.05 0.02 0.02 0.27 0.13 0.05 0.31 0.06]\n",
      "Angry [0.2  0.06 0.06 0.02 0.18 0.14 0.08 0.19 0.07]\n",
      "Surprised [0.07 0.04 0.01 0.01 0.3  0.11 0.12 0.31 0.03]\n",
      "Surprised [0.1  0.02 0.02 0.01 0.22 0.08 0.1  0.4  0.05]\n",
      "Happy [0.07 0.05 0.01 0.02 0.36 0.09 0.14 0.23 0.03]\n",
      "Happy [0.01 0.14 0.   0.01 0.35 0.08 0.15 0.2  0.06]\n",
      "Happy [0.01 0.2  0.   0.02 0.27 0.04 0.17 0.26 0.03]\n",
      "Sad [0.   0.08 0.   0.   0.27 0.01 0.33 0.28 0.03]\n",
      "Sad [0.   0.08 0.   0.   0.29 0.01 0.32 0.24 0.06]\n",
      "Sad [0.01 0.08 0.   0.02 0.16 0.01 0.43 0.23 0.06]\n",
      "Happy [0.01 0.06 0.   0.02 0.27 0.02 0.26 0.2  0.16]\n",
      "Happy [0.01 0.05 0.   0.05 0.31 0.03 0.15 0.26 0.14]\n",
      "Surprised [0.03 0.02 0.01 0.07 0.14 0.   0.16 0.37 0.2 ]\n",
      "Happy [0.02 0.06 0.   0.01 0.33 0.   0.17 0.28 0.13]\n",
      "Happy [0.01 0.01 0.   0.02 0.37 0.01 0.28 0.24 0.06]\n",
      "Happy [0.01 0.03 0.02 0.   0.51 0.01 0.13 0.26 0.03]\n",
      "Happy [0.   0.07 0.01 0.02 0.39 0.02 0.23 0.21 0.05]\n",
      "Happy [0.   0.05 0.01 0.   0.46 0.01 0.16 0.28 0.03]\n",
      "Happy [0.   0.04 0.02 0.01 0.45 0.01 0.16 0.29 0.02]\n",
      "Happy [0.   0.05 0.01 0.01 0.52 0.01 0.11 0.26 0.03]\n",
      "Happy [0.   0.09 0.01 0.01 0.3  0.01 0.28 0.22 0.08]\n",
      "Happy [0.   0.05 0.02 0.   0.35 0.01 0.24 0.28 0.05]\n",
      "Surprised [0.   0.05 0.01 0.01 0.32 0.01 0.17 0.38 0.05]\n",
      "Happy [0.   0.04 0.01 0.01 0.4  0.01 0.17 0.33 0.03]\n",
      "Sad [0.   0.07 0.01 0.01 0.3  0.   0.38 0.19 0.04]\n",
      "Sad [0.01 0.1  0.01 0.01 0.28 0.   0.33 0.21 0.05]\n",
      "Sad [0.01 0.11 0.01 0.   0.27 0.   0.33 0.21 0.06]\n",
      "Surprised [0.01 0.08 0.02 0.   0.26 0.   0.29 0.31 0.03]\n",
      "Sad [0.02 0.08 0.02 0.01 0.27 0.   0.31 0.25 0.04]\n",
      "Sad [0.02 0.12 0.01 0.   0.27 0.   0.28 0.24 0.06]\n",
      "Surprised [0.02 0.08 0.01 0.01 0.29 0.01 0.19 0.36 0.03]\n",
      "Sad [0.02 0.11 0.01 0.   0.28 0.   0.3  0.23 0.05]\n",
      "Happy [0.02 0.09 0.01 0.01 0.3  0.   0.29 0.24 0.04]\n",
      "Sad [0.02 0.09 0.02 0.01 0.28 0.   0.29 0.25 0.04]\n",
      "Happy [0.02 0.13 0.01 0.01 0.28 0.   0.28 0.21 0.06]\n",
      "Surprised [0.01 0.08 0.01 0.01 0.31 0.   0.17 0.38 0.03]\n",
      "Surprised [0.07 0.02 0.01 0.02 0.02 0.   0.19 0.44 0.23]\n",
      "Surprised [0.04 0.02 0.01 0.01 0.07 0.   0.12 0.58 0.15]\n",
      "Tension [0.19 0.   0.02 0.02 0.02 0.   0.13 0.26 0.36]\n",
      "Tension [0.22 0.02 0.01 0.   0.02 0.   0.1  0.24 0.39]\n",
      "Tension [0.32 0.02 0.02 0.03 0.03 0.01 0.11 0.12 0.34]\n",
      "Angry [0.36 0.01 0.01 0.13 0.   0.03 0.06 0.1  0.3 ]\n",
      "Angry [0.33 0.02 0.03 0.17 0.   0.03 0.03 0.09 0.3 ]\n",
      "Angry [0.31 0.04 0.02 0.2  0.   0.02 0.03 0.08 0.3 ]\n",
      "Tension [0.29 0.04 0.04 0.15 0.   0.02 0.   0.1  0.36]\n",
      "Tension [0.3  0.04 0.04 0.13 0.   0.02 0.01 0.09 0.37]\n",
      "Tension [0.31 0.04 0.04 0.14 0.   0.02 0.02 0.07 0.36]\n"
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     "name": "stdout",
     "output_type": "stream",
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      "Tension [0.29 0.02 0.03 0.17 0.01 0.02 0.01 0.07 0.38]\n",
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      "Tension [0.3  0.04 0.06 0.15 0.01 0.02 0.01 0.08 0.33]\n",
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      "Tension [0.31 0.03 0.03 0.12 0.01 0.02 0.   0.08 0.4 ]\n",
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      "Tension [0.3  0.02 0.03 0.16 0.01 0.03 0.   0.07 0.38]\n",
      "Tension [0.3  0.02 0.03 0.18 0.01 0.03 0.   0.07 0.36]\n",
      "Tension [0.3  0.02 0.03 0.17 0.01 0.03 0.   0.07 0.37]\n",
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      "Tension [0.32 0.02 0.04 0.17 0.01 0.04 0.03 0.03 0.34]\n",
      "Tension [0.28 0.05 0.01 0.12 0.02 0.05 0.04 0.04 0.39]\n",
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      "Tension [0.23 0.05 0.05 0.15 0.01 0.03 0.04 0.1  0.34]\n",
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      "Tension [0.31 0.03 0.04 0.03 0.02 0.01 0.03 0.19 0.34]\n",
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      "Angry [0.32 0.02 0.06 0.04 0.02 0.02 0.03 0.17 0.32]\n",
      "Tension [0.25 0.04 0.07 0.07 0.02 0.02 0.04 0.16 0.33]\n",
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      "Angry [0.33 0.02 0.1  0.   0.12 0.01 0.06 0.23 0.13]\n",
      "Angry [0.46 0.03 0.08 0.03 0.03 0.   0.03 0.17 0.17]\n",
      "Angry [0.5  0.03 0.05 0.04 0.02 0.   0.   0.16 0.2 ]\n",
      "Angry [0.37 0.06 0.07 0.06 0.01 0.01 0.   0.16 0.26]\n",
      "Angry [0.42 0.04 0.07 0.09 0.01 0.01 0.   0.11 0.25]\n",
      "Angry [0.44 0.07 0.04 0.1  0.01 0.02 0.01 0.12 0.19]\n",
      "Angry [0.44 0.05 0.05 0.1  0.01 0.02 0.01 0.13 0.19]\n",
      "Angry [0.4  0.08 0.07 0.11 0.   0.02 0.02 0.08 0.22]\n",
      "Angry [0.48 0.08 0.04 0.07 0.01 0.02 0.02 0.11 0.17]\n",
      "Angry [0.35 0.08 0.06 0.12 0.01 0.02 0.02 0.09 0.25]\n",
      "Angry [0.34 0.08 0.06 0.11 0.01 0.02 0.02 0.06 0.3 ]\n",
      "Angry [0.31 0.09 0.08 0.14 0.01 0.02 0.01 0.04 0.3 ]\n",
      "Angry [0.26 0.12 0.21 0.08 0.01 0.01 0.03 0.03 0.25]\n",
      "Angry [0.33 0.15 0.13 0.08 0.   0.01 0.02 0.02 0.26]\n",
      "Angry [0.29 0.13 0.14 0.08 0.   0.01 0.02 0.04 0.29]\n",
      "Angry [0.3  0.1  0.18 0.09 0.   0.01 0.01 0.05 0.26]\n",
      "Angry [0.31 0.1  0.18 0.06 0.   0.01 0.01 0.06 0.27]\n",
      "Angry [0.32 0.12 0.18 0.05 0.   0.01 0.02 0.07 0.23]\n",
      "Angry [0.32 0.13 0.17 0.05 0.   0.01 0.02 0.07 0.23]\n",
      "Angry [0.28 0.14 0.17 0.05 0.   0.01 0.01 0.07 0.27]\n",
      "Angry [0.32 0.12 0.15 0.05 0.   0.01 0.01 0.07 0.27]\n",
      "Angry [0.32 0.11 0.12 0.05 0.   0.01 0.01 0.07 0.31]\n",
      "Angry [0.31 0.11 0.14 0.04 0.   0.01 0.01 0.07 0.31]\n",
      "Tension [0.32 0.1  0.11 0.04 0.   0.01 0.02 0.07 0.33]\n",
      "Tension [0.31 0.1  0.12 0.04 0.   0.01 0.02 0.07 0.33]\n",
      "Angry [0.32 0.1  0.15 0.04 0.   0.01 0.03 0.07 0.28]\n",
      "Angry [0.32 0.1  0.16 0.04 0.   0.01 0.02 0.07 0.28]\n",
      "Angry [0.31 0.08 0.16 0.04 0.   0.01 0.02 0.07 0.31]\n",
      "Tension [0.3  0.09 0.14 0.04 0.   0.01 0.03 0.08 0.31]\n",
      "Tension [0.3  0.09 0.15 0.04 0.   0.01 0.03 0.06 0.32]\n",
      "Tension [0.29 0.08 0.16 0.04 0.   0.01 0.02 0.07 0.33]\n",
      "Angry [0.3  0.09 0.08 0.04 0.03 0.01 0.04 0.11 0.3 ]\n",
      "Angry [0.35 0.07 0.1  0.03 0.05 0.01 0.04 0.16 0.19]\n",
      "Angry [0.31 0.05 0.08 0.04 0.07 0.01 0.09 0.15 0.2 ]\n",
      "Angry [0.33 0.04 0.07 0.03 0.08 0.01 0.1  0.17 0.17]\n",
      "Surprised [0.31 0.01 0.05 0.01 0.08 0.01 0.08 0.34 0.11]\n",
      "Angry [0.32 0.02 0.04 0.01 0.08 0.01 0.08 0.2  0.24]\n",
      "Surprised [0.27 0.01 0.06 0.01 0.11 0.01 0.05 0.28 0.2 ]\n",
      "Tension [0.25 0.01 0.04 0.01 0.11 0.01 0.08 0.23 0.26]\n",
      "Tension [0.25 0.02 0.05 0.01 0.09 0.01 0.07 0.2  0.3 ]\n",
      "Angry [0.29 0.   0.04 0.01 0.05 0.01 0.08 0.28 0.24]\n",
      "Tension [0.22 0.03 0.05 0.03 0.05 0.02 0.09 0.25 0.26]\n",
      "Angry [0.27 0.05 0.04 0.04 0.03 0.01 0.07 0.24 0.25]\n",
      "Angry [0.32 0.04 0.03 0.04 0.04 0.02 0.04 0.19 0.28]\n",
      "Angry [0.36 0.04 0.04 0.03 0.02 0.02 0.05 0.16 0.28]\n",
      "Angry [0.35 0.05 0.04 0.04 0.04 0.02 0.06 0.17 0.23]\n",
      "Angry [0.35 0.04 0.06 0.04 0.05 0.02 0.06 0.16 0.22]\n",
      "Angry [0.32 0.03 0.1  0.04 0.07 0.01 0.08 0.16 0.19]\n",
      "Angry [0.35 0.04 0.09 0.02 0.07 0.01 0.08 0.17 0.17]\n",
      "Angry [0.36 0.04 0.09 0.02 0.07 0.01 0.08 0.15 0.18]\n",
      "Angry [0.37 0.03 0.1  0.02 0.06 0.01 0.09 0.15 0.17]\n",
      "Angry [0.32 0.05 0.16 0.03 0.04 0.   0.06 0.1  0.24]\n",
      "Angry [0.35 0.05 0.14 0.02 0.04 0.   0.06 0.1  0.24]\n",
      "Angry [0.36 0.04 0.11 0.03 0.03 0.   0.07 0.1  0.26]\n",
      "Angry [0.35 0.05 0.13 0.03 0.03 0.01 0.05 0.09 0.26]\n",
      "Angry [0.33 0.06 0.13 0.03 0.03 0.01 0.06 0.08 0.27]\n",
      "Angry [0.33 0.07 0.11 0.03 0.02 0.01 0.05 0.11 0.27]\n",
      "Angry [0.36 0.07 0.12 0.03 0.02 0.01 0.04 0.09 0.26]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.36 0.07 0.12 0.03 0.03 0.01 0.05 0.09 0.24]\n",
      "Angry [0.35 0.08 0.14 0.03 0.03 0.01 0.04 0.09 0.23]\n",
      "Angry [0.27 0.05 0.04 0.04 0.06 0.01 0.08 0.21 0.24]\n",
      "Angry [0.3  0.06 0.03 0.04 0.06 0.02 0.07 0.18 0.24]\n",
      "Angry [0.28 0.05 0.03 0.03 0.09 0.01 0.08 0.16 0.27]\n",
      "Angry [0.36 0.02 0.02 0.03 0.04 0.02 0.14 0.14 0.23]\n",
      "Surprised [0.17 0.02 0.   0.05 0.08 0.01 0.07 0.48 0.12]\n",
      "Surprised [0.15 0.01 0.01 0.06 0.1  0.   0.07 0.51 0.09]\n",
      "Surprised [0.   0.02 0.02 0.03 0.29 0.01 0.08 0.54 0.01]\n",
      "Happy [0.04 0.   0.01 0.05 0.44 0.01 0.1  0.31 0.04]\n",
      "Happy [0.1  0.   0.   0.1  0.41 0.01 0.08 0.21 0.09]\n",
      "Happy [0.08 0.01 0.   0.13 0.37 0.02 0.07 0.2  0.12]\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "emotions = []\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor Feed\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False        \n",
    "        \n",
    "        # Make Detections\n",
    "        results = holistic.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.flags.writeable = True   \n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # 1. Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                                 )\n",
    "        \n",
    "        # 2. Right hand\n",
    "        mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 3. Left Hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 4. Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\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, landmark.visibility] 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 face]).flatten())\n",
    "            \n",
    "            # Concate rows\n",
    "            row = pose_row+face_row\n",
    "\n",
    "            # Make Detections\n",
    "            X = pd.DataFrame([row])\n",
    "            body_language_class = model.predict(X)[0]\n",
    "            body_language_prob = model.predict_proba(X)[0]\n",
    "            emotions.append(str(body_language_class))\n",
    "            print(body_language_class, body_language_prob)\n",
    "            \n",
    "            # Grab ear coords\n",
    "            coords = tuple(np.multiply(\n",
    "                            np.array(\n",
    "                                (results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x, \n",
    "                                 results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y))\n",
    "                        , [640,480]).astype(int))\n",
    "            \n",
    "            cv2.rectangle(image, \n",
    "                          (coords[0], coords[1]+5), \n",
    "                          (coords[0]+len(body_language_class)*20, coords[1]-30), \n",
    "                          (245, 117, 16), -1)\n",
    "            cv2.putText(image, body_language_class, coords, \n",
    "                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "            # Get status box\n",
    "            cv2.rectangle(image, (0,0), (250, 60), (245, 117, 16), -1)\n",
    "            \n",
    "            # Display Class\n",
    "            cv2.putText(image, 'CLASS'\n",
    "                        , (95,12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, body_language_class.split(' ')[0]\n",
    "                        , (90,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "            # Display Probability\n",
    "            cv2.putText(image, 'PROB'\n",
    "                        , (15,12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, str(round(body_language_prob[np.argmax(body_language_prob)],2))\n",
    "                        , (10,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "        except:\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",
   "metadata": {},
   "source": [
    "# 5. Emotion Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Pie plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Emotion Distribution')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "# Define a colormap\n",
    "cmap = plt.get_cmap(\"viridis\")\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.bar(labels, sizes, color=cmap(np.linspace(0, 1, len(labels))), width=0.8)\n",
    "plt.title('Emotion Distribution')\n",
    "plt.xlabel('Emotion')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Horizontal bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "# Define a colormap\n",
    "cmap = plt.get_cmap(\"plasma\")\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.barh(labels, sizes, color=cmap(np.linspace(0, 1, len(labels))), height=0.8)\n",
    "plt.title('Emotion Distribution')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Emotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Horizontal bar plot in creasing order of sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "# Sort labels and sizes in decreasing order of sizes\n",
    "labels, sizes = zip(*sorted(zip(labels, sizes), key=lambda x: x[1], reverse=True))\n",
    "\n",
    "# Define a colormap\n",
    "cmap = plt.get_cmap(\"magma\")\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.barh(labels, sizes, color=cmap(np.linspace(0, 1, len(labels))), height=0.8)\n",
    "plt.title('Emotion Distribution')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Emotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 Horizontal bar plot with increasing order of sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "# Sort labels and sizes in increasing order of sizes\n",
    "labels, sizes = zip(*sorted(zip(labels, sizes), key=lambda x: x[1]))\n",
    "\n",
    "# Generate a color gradient\n",
    "start_color = 'blue'\n",
    "end_color = 'red'\n",
    "num_bars = len(labels)\n",
    "colors = plt.cm.get_cmap('coolwarm', num_bars)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.barh(labels, sizes, color=colors(np.arange(num_bars)))\n",
    "plt.title('Emotion Distribution')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Emotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 186)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(np.multiply(np.array((results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x, \n",
    "results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y)), [640,480]).astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Tensorflow-lite Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Train Machine Learning Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Load dataset\n",
    "df = pd.read_csv('coords.csv')\n",
    "\n",
    "X = df.drop('class', axis=1) # features\n",
    "y = df['class'] # target value\n",
    "\n",
    "# Convert string labels to numerical labels\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1234)\n",
    "\n",
    "# Build a simple model\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=[X_train.shape[1]]),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(len(np.unique(y)), activation='softmax') # number of classes is determined by unique values in y\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train, epochs=10)\n",
    "\n",
    "# Convert the model to the TensorFlow Lite format without quantization\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "\n",
    "# Save the model to disk\n",
    "open(\"body_language.tflite\", \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Make Detections with Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Load the TensorFlow Lite model\n",
    "interpreter = tf.lite.Interpreter(model_path='body_language.tflite')\n",
    "interpreter.allocate_tensors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happy [0.01 0.03 0.01 0.05 0.33 0.   0.21 0.3  0.06]\n",
      "Happy [0.   0.06 0.02 0.03 0.39 0.   0.25 0.2  0.05]\n",
      "Sad [0.01 0.06 0.03 0.04 0.26 0.   0.3  0.25 0.05]\n",
      "Sad [0.01 0.05 0.03 0.04 0.29 0.   0.3  0.23 0.05]\n",
      "Happy [0.01 0.05 0.02 0.02 0.37 0.   0.24 0.24 0.05]\n",
      "Happy [0.01 0.06 0.03 0.03 0.32 0.   0.26 0.24 0.05]\n",
      "Happy [0.01 0.05 0.03 0.05 0.31 0.   0.29 0.21 0.05]\n",
      "Happy [0.01 0.06 0.02 0.04 0.39 0.   0.25 0.19 0.04]\n",
      "Happy [0.   0.05 0.02 0.04 0.48 0.   0.19 0.17 0.05]\n",
      "Happy [0.01 0.02 0.01 0.03 0.72 0.01 0.04 0.16 0.  ]\n",
      "Happy [0.   0.01 0.   0.03 0.73 0.   0.14 0.08 0.01]\n",
      "Sad [0.   0.06 0.   0.07 0.25 0.02 0.41 0.17 0.02]\n",
      "Happy [0.   0.   0.02 0.03 0.63 0.01 0.17 0.11 0.03]\n",
      "Happy [0.   0.02 0.   0.07 0.65 0.   0.08 0.13 0.05]\n",
      "Happy [0.   0.   0.   0.08 0.62 0.   0.1  0.13 0.07]\n",
      "Happy [0.01 0.02 0.   0.09 0.48 0.   0.07 0.22 0.11]\n",
      "Happy [0.01 0.01 0.   0.12 0.53 0.   0.11 0.12 0.1 ]\n",
      "Happy [0.01 0.   0.   0.13 0.54 0.   0.1  0.09 0.13]\n",
      "Happy [0.02 0.01 0.   0.15 0.51 0.   0.09 0.1  0.12]\n",
      "Happy [0.01 0.02 0.   0.12 0.51 0.   0.07 0.16 0.11]\n",
      "Happy [0.01 0.02 0.   0.12 0.5  0.01 0.09 0.15 0.1 ]\n",
      "Happy [0.01 0.01 0.   0.14 0.5  0.01 0.07 0.19 0.07]\n",
      "Happy [0.01 0.01 0.   0.11 0.55 0.01 0.11 0.14 0.06]\n",
      "Happy [0.   0.02 0.   0.11 0.5  0.01 0.07 0.18 0.11]\n",
      "Happy [0.   0.02 0.   0.14 0.46 0.01 0.04 0.23 0.1 ]\n",
      "Happy [0.02 0.01 0.   0.09 0.51 0.01 0.07 0.17 0.12]\n",
      "Happy [0.01 0.01 0.   0.14 0.43 0.01 0.04 0.23 0.13]\n",
      "Happy [0.   0.02 0.   0.14 0.46 0.01 0.05 0.19 0.13]\n",
      "Happy [0.01 0.01 0.   0.17 0.43 0.01 0.08 0.15 0.14]\n",
      "Happy [0.02 0.01 0.   0.18 0.31 0.01 0.03 0.22 0.22]\n",
      "Happy [0.01 0.01 0.   0.18 0.31 0.01 0.05 0.23 0.2 ]\n",
      "Happy [0.01 0.01 0.   0.23 0.34 0.   0.06 0.18 0.17]\n",
      "Happy [0.01 0.01 0.   0.25 0.27 0.   0.04 0.19 0.23]\n",
      "Tension [0.03 0.   0.   0.25 0.17 0.   0.05 0.18 0.32]\n",
      "Happy [0.03 0.02 0.   0.25 0.26 0.   0.03 0.18 0.23]\n",
      "Tension [0.   0.01 0.   0.27 0.2  0.   0.04 0.14 0.34]\n",
      "Tension [0.04 0.   0.   0.3  0.16 0.   0.03 0.15 0.32]\n",
      "Tension [0.06 0.   0.   0.25 0.07 0.   0.01 0.08 0.53]\n",
      "Tension [0.1  0.   0.   0.26 0.01 0.   0.03 0.02 0.58]\n",
      "Tension [0.05 0.   0.   0.2  0.01 0.   0.02 0.05 0.67]\n",
      "Tension [0.06 0.   0.   0.24 0.03 0.   0.01 0.04 0.62]\n",
      "Tension [0.05 0.   0.   0.24 0.07 0.   0.03 0.07 0.54]\n",
      "Tension [0.05 0.   0.   0.19 0.   0.   0.02 0.04 0.7 ]\n",
      "Excited [0.01 0.01 0.   0.38 0.23 0.   0.04 0.11 0.22]\n",
      "Excited [0.01 0.01 0.   0.32 0.29 0.   0.07 0.12 0.18]\n",
      "Excited [0.01 0.02 0.   0.3  0.2  0.   0.04 0.16 0.27]\n",
      "Tension [0.05 0.   0.   0.19 0.09 0.   0.02 0.1  0.55]\n",
      "Surprised [0.03 0.01 0.   0.22 0.23 0.   0.04 0.28 0.19]\n",
      "Happy [0.03 0.01 0.   0.2  0.32 0.01 0.06 0.23 0.14]\n",
      "Surprised [0.   0.05 0.01 0.04 0.24 0.01 0.14 0.43 0.08]\n",
      "Happy [0.   0.07 0.02 0.07 0.27 0.01 0.23 0.24 0.09]\n",
      "Surprised [0.03 0.06 0.01 0.08 0.18 0.01 0.19 0.29 0.15]\n",
      "Surprised [0.03 0.01 0.   0.1  0.19 0.01 0.16 0.37 0.13]\n",
      "Surprised [0.07 0.   0.01 0.02 0.05 0.   0.18 0.43 0.24]\n",
      "Tension [0.28 0.01 0.   0.1  0.02 0.02 0.06 0.08 0.43]\n",
      "Tension [0.24 0.01 0.   0.13 0.02 0.02 0.06 0.04 0.48]\n",
      "Tension [0.25 0.01 0.   0.12 0.01 0.01 0.07 0.03 0.5 ]\n",
      "Tension [0.33 0.02 0.01 0.04 0.   0.01 0.04 0.03 0.52]\n",
      "Tension [0.27 0.03 0.03 0.12 0.01 0.01 0.02 0.04 0.47]\n",
      "Tension [0.25 0.02 0.02 0.17 0.01 0.01 0.   0.06 0.46]\n",
      "Tension [0.26 0.03 0.01 0.15 0.01 0.01 0.   0.06 0.47]\n",
      "Tension [0.29 0.02 0.02 0.16 0.01 0.01 0.   0.05 0.44]\n",
      "Tension [0.29 0.02 0.02 0.13 0.01 0.01 0.02 0.06 0.44]\n",
      "Tension [0.21 0.02 0.01 0.06 0.01 0.01 0.04 0.04 0.6 ]\n",
      "Tension [0.2  0.03 0.01 0.03 0.04 0.02 0.08 0.08 0.51]\n",
      "Tension [0.1  0.02 0.01 0.03 0.02 0.02 0.1  0.09 0.61]\n",
      "Tension [0.14 0.02 0.   0.09 0.01 0.01 0.09 0.04 0.6 ]\n",
      "Tension [0.18 0.02 0.   0.04 0.01 0.01 0.04 0.08 0.62]\n",
      "Tension [0.2  0.01 0.   0.05 0.01 0.01 0.03 0.07 0.62]\n",
      "Tension [0.25 0.02 0.02 0.11 0.01 0.01 0.03 0.07 0.48]\n",
      "Tension [0.3  0.04 0.04 0.14 0.01 0.02 0.02 0.05 0.38]\n",
      "Tension [0.27 0.04 0.06 0.14 0.01 0.01 0.02 0.07 0.38]\n",
      "Tension [0.29 0.04 0.06 0.14 0.01 0.   0.02 0.07 0.37]\n",
      "Tension [0.33 0.03 0.07 0.12 0.   0.   0.03 0.05 0.37]\n",
      "Tension [0.34 0.04 0.06 0.12 0.01 0.   0.02 0.05 0.36]\n",
      "Tension [0.35 0.03 0.07 0.11 0.01 0.   0.01 0.05 0.37]\n",
      "Tension [0.31 0.04 0.07 0.13 0.01 0.01 0.01 0.06 0.36]\n",
      "Tension [0.31 0.04 0.07 0.13 0.01 0.01 0.   0.06 0.37]\n",
      "Tension [0.3  0.04 0.06 0.12 0.01 0.01 0.   0.08 0.38]\n",
      "Tension [0.29 0.05 0.07 0.12 0.01 0.01 0.   0.09 0.36]\n",
      "Tension [0.29 0.05 0.05 0.12 0.01 0.01 0.   0.09 0.38]\n",
      "Tension [0.3  0.05 0.06 0.12 0.01 0.01 0.01 0.1  0.34]\n",
      "Tension [0.29 0.05 0.06 0.12 0.01 0.01 0.01 0.09 0.36]\n",
      "Tension [0.27 0.05 0.07 0.12 0.01 0.01 0.01 0.1  0.36]\n",
      "Tension [0.29 0.03 0.07 0.13 0.01 0.02 0.02 0.07 0.36]\n",
      "Tension [0.27 0.03 0.06 0.14 0.01 0.02 0.01 0.07 0.39]\n",
      "Tension [0.28 0.02 0.03 0.09 0.01 0.03 0.02 0.07 0.45]\n",
      "Tension [0.31 0.01 0.   0.05 0.05 0.03 0.09 0.06 0.4 ]\n",
      "Surprised [0.15 0.   0.   0.07 0.07 0.03 0.1  0.31 0.27]\n",
      "Surprised [0.07 0.13 0.   0.06 0.15 0.03 0.1  0.3  0.16]\n",
      "Angry [0.37 0.01 0.   0.12 0.02 0.05 0.08 0.03 0.32]\n",
      "Tension [0.31 0.01 0.   0.05 0.04 0.06 0.1  0.09 0.34]\n",
      "Angry [0.38 0.   0.   0.11 0.02 0.06 0.08 0.04 0.31]\n",
      "Tension [0.32 0.01 0.   0.06 0.01 0.05 0.1  0.09 0.36]\n",
      "Tension [0.26 0.01 0.   0.07 0.03 0.01 0.14 0.12 0.36]\n",
      "Tension [0.06 0.   0.   0.12 0.03 0.   0.14 0.25 0.4 ]\n",
      "Tension [0.25 0.   0.   0.09 0.02 0.01 0.06 0.11 0.46]\n",
      "Tension [0.1  0.   0.02 0.14 0.07 0.   0.07 0.28 0.32]\n",
      "Tension [0.11 0.   0.02 0.11 0.08 0.   0.06 0.17 0.45]\n",
      "Tension [0.14 0.   0.02 0.17 0.08 0.   0.05 0.15 0.39]\n",
      "Tension [0.13 0.   0.   0.23 0.07 0.   0.03 0.08 0.46]\n",
      "Tension [0.12 0.   0.   0.26 0.07 0.   0.04 0.08 0.43]\n",
      "Tension [0.14 0.   0.   0.3  0.04 0.   0.05 0.07 0.4 ]\n",
      "Tension [0.15 0.   0.   0.29 0.04 0.   0.05 0.1  0.37]\n",
      "Tension [0.09 0.   0.   0.26 0.06 0.   0.03 0.13 0.43]\n",
      "Tension [0.08 0.   0.   0.25 0.08 0.   0.04 0.11 0.44]\n",
      "Tension [0.18 0.   0.   0.29 0.04 0.   0.05 0.07 0.37]\n",
      "Tension [0.15 0.   0.   0.29 0.03 0.   0.04 0.09 0.4 ]\n",
      "Tension [0.13 0.   0.   0.25 0.08 0.   0.04 0.14 0.36]\n",
      "Tension [0.28 0.01 0.02 0.18 0.02 0.01 0.09 0.1  0.29]\n",
      "Tension [0.22 0.01 0.04 0.13 0.04 0.01 0.08 0.09 0.38]\n",
      "Tension [0.27 0.01 0.02 0.09 0.06 0.02 0.08 0.12 0.33]\n",
      "Tension [0.23 0.01 0.03 0.03 0.05 0.01 0.11 0.22 0.31]\n",
      "Tension [0.27 0.02 0.05 0.03 0.04 0.   0.13 0.18 0.28]\n",
      "Tension [0.24 0.02 0.03 0.03 0.05 0.   0.08 0.26 0.29]\n",
      "Tension [0.25 0.02 0.04 0.03 0.03 0.   0.11 0.22 0.3 ]\n",
      "Surprised [0.16 0.02 0.01 0.02 0.07 0.   0.11 0.36 0.25]\n",
      "Tension [0.21 0.03 0.02 0.06 0.03 0.   0.17 0.18 0.3 ]\n",
      "Tension [0.21 0.03 0.01 0.02 0.05 0.01 0.2  0.13 0.34]\n",
      "Tension [0.22 0.03 0.03 0.03 0.04 0.01 0.15 0.15 0.34]\n",
      "Tension [0.14 0.02 0.02 0.05 0.07 0.   0.18 0.25 0.27]\n",
      "Tension [0.15 0.02 0.01 0.05 0.06 0.   0.19 0.22 0.3 ]\n",
      "Tension [0.21 0.02 0.02 0.04 0.04 0.01 0.19 0.14 0.33]\n",
      "Surprised [0.1  0.02 0.   0.04 0.13 0.   0.1  0.33 0.28]\n",
      "Happy [0.09 0.02 0.01 0.02 0.32 0.   0.08 0.22 0.24]\n",
      "Happy [0.01 0.16 0.02 0.1  0.39 0.02 0.17 0.09 0.04]\n",
      "Happy [0.01 0.27 0.04 0.06 0.3  0.02 0.17 0.08 0.05]\n",
      "Surprised [0.12 0.   0.   0.06 0.2  0.01 0.13 0.26 0.22]\n",
      "Surprised [0.17 0.01 0.01 0.03 0.08 0.02 0.11 0.33 0.24]\n",
      "Tension [0.26 0.   0.   0.05 0.06 0.03 0.1  0.11 0.39]\n",
      "Tension [0.23 0.   0.   0.06 0.07 0.03 0.13 0.09 0.39]\n",
      "Tension [0.25 0.02 0.02 0.02 0.06 0.02 0.12 0.16 0.33]\n",
      "Surprised [0.17 0.01 0.   0.05 0.05 0.   0.07 0.36 0.29]\n",
      "Angry [0.42 0.   0.01 0.05 0.06 0.02 0.06 0.22 0.16]\n",
      "Angry [0.56 0.   0.03 0.03 0.03 0.02 0.06 0.14 0.13]\n",
      "Angry [0.53 0.   0.02 0.02 0.03 0.03 0.07 0.13 0.17]\n",
      "Angry [0.56 0.   0.02 0.02 0.03 0.03 0.08 0.09 0.17]\n",
      "Angry [0.59 0.   0.02 0.01 0.03 0.02 0.09 0.05 0.19]\n",
      "Angry [0.58 0.01 0.03 0.01 0.03 0.02 0.08 0.05 0.19]\n",
      "Angry [0.61 0.01 0.   0.03 0.03 0.01 0.04 0.06 0.21]\n",
      "Angry [0.63 0.   0.02 0.03 0.03 0.01 0.03 0.04 0.21]\n",
      "Angry [0.64 0.   0.01 0.05 0.01 0.02 0.03 0.04 0.2 ]\n",
      "Angry [0.61 0.01 0.01 0.06 0.   0.02 0.03 0.05 0.21]\n",
      "Angry [0.65 0.01 0.   0.06 0.   0.02 0.03 0.04 0.19]\n",
      "Angry [0.64 0.01 0.02 0.07 0.02 0.02 0.03 0.03 0.16]\n",
      "Angry [0.67 0.01 0.01 0.06 0.01 0.03 0.04 0.02 0.15]\n",
      "Angry [0.68 0.01 0.01 0.05 0.01 0.03 0.04 0.03 0.14]\n",
      "Angry [0.42 0.02 0.01 0.08 0.06 0.03 0.08 0.18 0.12]\n",
      "Surprised [0.23 0.   0.02 0.12 0.1  0.02 0.1  0.3  0.11]\n",
      "Happy [0.01 0.02 0.   0.03 0.42 0.01 0.1  0.4  0.01]\n",
      "Sad [0.   0.04 0.02 0.03 0.29 0.   0.41 0.2  0.01]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Sad [0.   0.04 0.02 0.03 0.28 0.   0.4  0.2  0.03]\n",
      "Sad [0.01 0.07 0.03 0.09 0.21 0.   0.38 0.19 0.02]\n",
      "Sad [0.02 0.05 0.02 0.23 0.14 0.01 0.4  0.08 0.05]\n",
      "Sad [0.02 0.04 0.02 0.08 0.2  0.01 0.37 0.2  0.06]\n",
      "Sad [0.01 0.07 0.02 0.19 0.19 0.   0.36 0.13 0.03]\n",
      "Sad [0.03 0.06 0.02 0.26 0.16 0.01 0.34 0.07 0.05]\n",
      "Sad [0.01 0.08 0.01 0.31 0.1  0.01 0.4  0.02 0.06]\n",
      "Sad [0.02 0.08 0.01 0.31 0.1  0.01 0.39 0.03 0.05]\n",
      "Sad [0.02 0.09 0.01 0.27 0.09 0.01 0.42 0.02 0.07]\n",
      "Sad [0.02 0.1  0.01 0.27 0.08 0.01 0.42 0.03 0.06]\n",
      "Sad [0.02 0.09 0.02 0.27 0.09 0.02 0.41 0.02 0.06]\n",
      "Sad [0.03 0.09 0.01 0.26 0.12 0.01 0.4  0.02 0.06]\n",
      "Sad [0.   0.09 0.01 0.31 0.1  0.02 0.39 0.02 0.06]\n",
      "Sad [0.02 0.11 0.02 0.24 0.08 0.02 0.39 0.04 0.08]\n",
      "Sad [0.01 0.1  0.04 0.24 0.08 0.02 0.38 0.04 0.09]\n",
      "Sad [0.01 0.1  0.02 0.24 0.08 0.02 0.41 0.04 0.08]\n",
      "Sad [0.01 0.07 0.02 0.28 0.09 0.02 0.41 0.04 0.06]\n",
      "Sad [0.   0.11 0.   0.23 0.08 0.03 0.44 0.04 0.07]\n",
      "Surprised [0.01 0.02 0.   0.06 0.26 0.   0.12 0.43 0.1 ]\n",
      "Sad [0.01 0.02 0.   0.14 0.19 0.01 0.28 0.28 0.07]\n",
      "Surprised [0.02 0.01 0.   0.13 0.19 0.   0.13 0.42 0.1 ]\n",
      "Happy [0.01 0.   0.   0.21 0.23 0.02 0.19 0.22 0.12]\n",
      "Surprised [0.18 0.   0.   0.15 0.05 0.01 0.07 0.28 0.26]\n",
      "Tension [0.22 0.   0.01 0.11 0.03 0.02 0.06 0.13 0.42]\n",
      "Tension [0.11 0.   0.   0.13 0.08 0.   0.1  0.2  0.38]\n",
      "Tension [0.07 0.   0.   0.13 0.13 0.   0.06 0.27 0.34]\n",
      "Tension [0.06 0.   0.   0.13 0.12 0.01 0.08 0.25 0.35]\n",
      "Tension [0.1  0.   0.01 0.15 0.08 0.01 0.07 0.2  0.38]\n",
      "Surprised [0.06 0.   0.01 0.14 0.1  0.01 0.06 0.31 0.31]\n",
      "Tension [0.09 0.   0.01 0.13 0.11 0.01 0.06 0.26 0.33]\n",
      "Tension [0.09 0.   0.01 0.22 0.08 0.01 0.04 0.21 0.34]\n",
      "Excited [0.05 0.   0.   0.38 0.2  0.   0.02 0.18 0.17]\n",
      "Tension [0.16 0.01 0.02 0.25 0.02 0.01 0.04 0.11 0.38]\n",
      "Tension [0.15 0.01 0.02 0.28 0.01 0.03 0.07 0.14 0.29]\n",
      "Angry [0.33 0.01 0.02 0.29 0.01 0.03 0.04 0.1  0.17]\n",
      "Tension [0.18 0.01 0.01 0.27 0.01 0.03 0.05 0.1  0.34]\n",
      "Tension [0.15 0.02 0.01 0.3  0.01 0.03 0.07 0.1  0.31]\n",
      "Excited [0.26 0.03 0.01 0.29 0.01 0.02 0.01 0.08 0.29]\n",
      "Tension [0.2  0.02 0.01 0.29 0.01 0.03 0.03 0.09 0.32]\n",
      "Excited [0.23 0.01 0.01 0.36 0.02 0.02 0.02 0.11 0.22]\n",
      "Excited [0.08 0.01 0.01 0.38 0.03 0.01 0.02 0.09 0.37]\n",
      "Excited [0.05 0.01 0.01 0.44 0.01 0.02 0.01 0.08 0.37]\n",
      "Excited [0.11 0.   0.01 0.37 0.02 0.03 0.01 0.1  0.35]\n",
      "Excited [0.09 0.   0.01 0.39 0.02 0.03 0.01 0.08 0.37]\n",
      "Excited [0.04 0.   0.01 0.43 0.02 0.02 0.01 0.07 0.4 ]\n",
      "Excited [0.08 0.   0.01 0.4  0.02 0.02 0.01 0.09 0.37]\n",
      "Excited [0.1  0.01 0.01 0.41 0.01 0.01 0.01 0.11 0.33]\n",
      "Excited [0.1  0.01 0.01 0.46 0.02 0.01 0.01 0.08 0.3 ]\n",
      "Excited [0.12 0.01 0.01 0.41 0.01 0.02 0.01 0.1  0.31]\n",
      "Tension [0.08 0.01 0.01 0.29 0.01 0.01 0.02 0.08 0.49]\n",
      "Tension [0.02 0.   0.   0.31 0.02 0.02 0.01 0.02 0.6 ]\n",
      "Tension [0.01 0.   0.   0.26 0.   0.02 0.01 0.02 0.68]\n",
      "Tension [0.05 0.02 0.   0.14 0.01 0.02 0.04 0.2  0.52]\n",
      "Tension [0.03 0.01 0.01 0.15 0.02 0.02 0.07 0.23 0.46]\n",
      "Tension [0.03 0.04 0.   0.1  0.02 0.03 0.05 0.21 0.52]\n",
      "Tension [0.11 0.06 0.01 0.06 0.1  0.1  0.04 0.22 0.3 ]\n",
      "Tension [0.1  0.06 0.01 0.08 0.11 0.1  0.04 0.22 0.28]\n",
      "Pain [0.03 0.04 0.01 0.09 0.18 0.21 0.07 0.2  0.17]\n",
      "Pain [0.08 0.05 0.02 0.04 0.13 0.3  0.13 0.13 0.12]\n",
      "Pain [0.07 0.07 0.04 0.03 0.11 0.3  0.1  0.1  0.18]\n",
      "Pain [0.06 0.11 0.05 0.03 0.12 0.32 0.09 0.07 0.15]\n",
      "Pain [0.08 0.14 0.05 0.03 0.09 0.25 0.07 0.14 0.15]\n",
      "Tension [0.13 0.13 0.07 0.03 0.06 0.16 0.06 0.15 0.21]\n",
      "Tension [0.12 0.14 0.07 0.03 0.07 0.16 0.07 0.16 0.18]\n",
      "Tension [0.1  0.13 0.07 0.04 0.03 0.15 0.07 0.14 0.27]\n",
      "Tension [0.08 0.15 0.08 0.04 0.02 0.12 0.08 0.1  0.33]\n",
      "Tension [0.17 0.17 0.15 0.04 0.02 0.07 0.1  0.06 0.22]\n",
      "Tension [0.12 0.12 0.11 0.04 0.03 0.1  0.11 0.14 0.23]\n",
      "Tension [0.11 0.13 0.09 0.02 0.03 0.13 0.08 0.13 0.28]\n",
      "Tension [0.14 0.19 0.15 0.03 0.01 0.09 0.09 0.06 0.24]\n",
      "Tension [0.13 0.18 0.16 0.03 0.02 0.09 0.1  0.07 0.22]\n",
      "Confused [0.12 0.21 0.17 0.03 0.02 0.07 0.11 0.07 0.2 ]\n",
      "Confused [0.13 0.21 0.16 0.03 0.03 0.07 0.1  0.06 0.21]\n",
      "Tension [0.13 0.17 0.14 0.04 0.02 0.09 0.11 0.09 0.21]\n",
      "Tension [0.17 0.16 0.15 0.05 0.03 0.08 0.09 0.08 0.19]\n",
      "Tension [0.17 0.17 0.16 0.05 0.01 0.07 0.1  0.07 0.2 ]\n",
      "Confused [0.15 0.18 0.16 0.05 0.03 0.07 0.1  0.08 0.18]\n",
      "Tension [0.16 0.15 0.13 0.05 0.01 0.1  0.11 0.09 0.2 ]\n",
      "Tension [0.16 0.15 0.12 0.05 0.02 0.11 0.1  0.09 0.2 ]\n",
      "Tension [0.17 0.14 0.14 0.05 0.04 0.1  0.08 0.1  0.18]\n",
      "Tension [0.16 0.13 0.15 0.05 0.03 0.1  0.09 0.09 0.2 ]\n",
      "Tension [0.16 0.13 0.11 0.04 0.02 0.11 0.11 0.09 0.23]\n",
      "Tension [0.14 0.14 0.1  0.03 0.04 0.11 0.06 0.13 0.25]\n",
      "Tension [0.14 0.16 0.09 0.03 0.05 0.14 0.04 0.09 0.26]\n",
      "Pain [0.06 0.22 0.08 0.01 0.02 0.29 0.06 0.05 0.21]\n",
      "Confused [0.09 0.23 0.09 0.01 0.04 0.17 0.11 0.03 0.23]\n",
      "Pain [0.04 0.17 0.13 0.01 0.02 0.33 0.07 0.04 0.19]\n",
      "Pain [0.05 0.14 0.15 0.01 0.02 0.32 0.08 0.06 0.17]\n",
      "Pain [0.06 0.14 0.15 0.01 0.02 0.31 0.08 0.06 0.17]\n",
      "Pain [0.06 0.13 0.2  0.01 0.01 0.31 0.07 0.05 0.16]\n",
      "Pain [0.07 0.11 0.15 0.01 0.01 0.36 0.08 0.05 0.16]\n",
      "Pain [0.06 0.11 0.15 0.01 0.01 0.36 0.08 0.06 0.16]\n",
      "Pain [0.05 0.13 0.15 0.01 0.01 0.37 0.09 0.06 0.13]\n",
      "Pain [0.08 0.14 0.16 0.01 0.03 0.27 0.1  0.04 0.17]\n",
      "Pain [0.06 0.14 0.18 0.01 0.01 0.27 0.1  0.05 0.18]\n",
      "Pain [0.06 0.13 0.17 0.01 0.01 0.33 0.07 0.05 0.17]\n",
      "Pain [0.04 0.13 0.14 0.01 0.02 0.34 0.08 0.06 0.18]\n",
      "Pain [0.1  0.14 0.17 0.01 0.03 0.24 0.1  0.03 0.18]\n",
      "Pain [0.06 0.15 0.18 0.01 0.01 0.27 0.09 0.06 0.17]\n",
      "Pain [0.06 0.14 0.15 0.01 0.01 0.34 0.08 0.06 0.15]\n",
      "Pain [0.08 0.15 0.17 0.01 0.02 0.27 0.11 0.03 0.16]\n",
      "Pain [0.05 0.12 0.2  0.01 0.01 0.3  0.09 0.06 0.16]\n",
      "Pain [0.06 0.1  0.13 0.01 0.02 0.41 0.06 0.06 0.15]\n",
      "Pain [0.08 0.13 0.19 0.01 0.03 0.29 0.11 0.03 0.13]\n",
      "Pain [0.08 0.13 0.17 0.01 0.03 0.28 0.11 0.05 0.14]\n",
      "Pain [0.09 0.11 0.16 0.01 0.05 0.28 0.1  0.06 0.14]\n",
      "Tension [0.15 0.1  0.16 0.01 0.1  0.1  0.02 0.17 0.19]\n",
      "Surprised [0.14 0.05 0.06 0.02 0.13 0.16 0.08 0.24 0.12]\n",
      "Surprised [0.13 0.01 0.01 0.03 0.13 0.21 0.06 0.28 0.14]\n",
      "Tension [0.03 0.01 0.   0.07 0.01 0.03 0.1  0.23 0.52]\n",
      "Tension [0.01 0.   0.   0.06 0.03 0.09 0.06 0.02 0.73]\n",
      "Tension [0.03 0.   0.   0.26 0.02 0.02 0.06 0.03 0.58]\n",
      "Tension [0.01 0.01 0.   0.25 0.13 0.11 0.13 0.08 0.28]\n",
      "Excited [0.03 0.   0.   0.29 0.18 0.11 0.03 0.2  0.16]\n",
      "Excited [0.04 0.01 0.   0.3  0.12 0.15 0.06 0.13 0.19]\n",
      "Excited [0.02 0.   0.   0.28 0.11 0.16 0.03 0.28 0.12]\n",
      "Excited [0.05 0.02 0.   0.29 0.05 0.19 0.02 0.25 0.13]\n",
      "Excited [0.05 0.01 0.   0.36 0.05 0.18 0.05 0.18 0.12]\n",
      "Excited [0.06 0.01 0.   0.29 0.05 0.14 0.09 0.29 0.07]\n",
      "Happy [0.03 0.   0.   0.2  0.36 0.02 0.16 0.09 0.14]\n",
      "Happy [0.04 0.01 0.   0.13 0.33 0.02 0.21 0.15 0.11]\n",
      "Happy [0.   0.05 0.   0.05 0.36 0.01 0.19 0.22 0.12]\n",
      "Sad [0.   0.04 0.   0.06 0.27 0.   0.28 0.27 0.08]\n",
      "Sad [0.   0.03 0.   0.04 0.19 0.   0.45 0.23 0.06]\n",
      "Sad [0.   0.02 0.   0.05 0.15 0.   0.44 0.23 0.11]\n",
      "Sad [0.   0.06 0.   0.12 0.11 0.01 0.52 0.13 0.05]\n",
      "Sad [0.01 0.12 0.02 0.21 0.04 0.02 0.47 0.04 0.07]\n",
      "Sad [0.01 0.1  0.02 0.2  0.05 0.02 0.5  0.05 0.05]\n",
      "Sad [0.03 0.07 0.04 0.3  0.06 0.03 0.35 0.06 0.06]\n",
      "Sad [0.03 0.08 0.03 0.31 0.06 0.02 0.35 0.05 0.07]\n",
      "Sad [0.03 0.08 0.03 0.3  0.06 0.02 0.38 0.05 0.05]\n",
      "Sad [0.03 0.08 0.04 0.28 0.06 0.02 0.36 0.05 0.08]\n",
      "Sad [0.03 0.08 0.03 0.26 0.06 0.02 0.41 0.05 0.06]\n",
      "Sad [0.05 0.08 0.05 0.23 0.06 0.02 0.39 0.05 0.07]\n",
      "Sad [0.05 0.08 0.06 0.22 0.06 0.02 0.39 0.05 0.07]\n",
      "Sad [0.05 0.08 0.05 0.22 0.06 0.02 0.4  0.06 0.06]\n",
      "Sad [0.05 0.1  0.05 0.21 0.09 0.01 0.38 0.05 0.06]\n",
      "Sad [0.04 0.1  0.05 0.23 0.08 0.02 0.38 0.05 0.05]\n",
      "Sad [0.03 0.1  0.05 0.24 0.07 0.02 0.38 0.06 0.05]\n",
      "Sad [0.04 0.09 0.05 0.21 0.1  0.01 0.4  0.03 0.07]\n",
      "Sad [0.04 0.1  0.05 0.21 0.09 0.01 0.39 0.04 0.07]\n",
      "Sad [0.05 0.1  0.05 0.21 0.07 0.02 0.38 0.05 0.07]\n",
      "Sad [0.05 0.1  0.05 0.21 0.09 0.01 0.38 0.05 0.06]\n",
      "Sad [0.05 0.09 0.05 0.21 0.09 0.01 0.39 0.05 0.06]\n",
      "Sad [0.05 0.08 0.05 0.21 0.07 0.02 0.41 0.06 0.05]\n",
      "Sad [0.05 0.09 0.05 0.2  0.09 0.01 0.39 0.05 0.07]\n",
      "Sad [0.04 0.1  0.04 0.21 0.09 0.01 0.41 0.05 0.05]\n",
      "Sad [0.03 0.1  0.03 0.23 0.07 0.02 0.42 0.05 0.05]\n",
      "Sad [0.05 0.09 0.04 0.17 0.08 0.02 0.43 0.06 0.06]\n",
      "Sad [0.02 0.08 0.04 0.21 0.08 0.02 0.45 0.05 0.05]\n",
      "Sad [0.02 0.08 0.04 0.2  0.1  0.02 0.44 0.04 0.06]\n",
      "Sad [0.03 0.11 0.06 0.17 0.07 0.02 0.43 0.05 0.06]\n",
      "Sad [0.02 0.11 0.05 0.15 0.07 0.02 0.47 0.05 0.06]\n",
      "Sad [0.01 0.13 0.03 0.17 0.07 0.02 0.43 0.07 0.07]\n",
      "Sad [0.02 0.18 0.07 0.14 0.06 0.01 0.38 0.06 0.08]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Sad [0.   0.16 0.04 0.04 0.24 0.02 0.3  0.15 0.05]\n",
      "Confused [0.   0.32 0.08 0.07 0.1  0.02 0.28 0.02 0.11]\n",
      "Happy [0.   0.06 0.01 0.03 0.42 0.01 0.12 0.33 0.02]\n",
      "Surprised [0.02 0.02 0.   0.04 0.39 0.03 0.07 0.41 0.02]\n",
      "Happy [0.01 0.04 0.03 0.02 0.61 0.03 0.03 0.23 0.  ]\n",
      "Happy [0.04 0.05 0.   0.02 0.41 0.04 0.07 0.27 0.1 ]\n",
      "Happy [0.03 0.06 0.01 0.02 0.39 0.03 0.03 0.39 0.04]\n",
      "Happy [0.01 0.04 0.02 0.02 0.51 0.03 0.02 0.34 0.01]\n",
      "Happy [0.01 0.06 0.02 0.02 0.47 0.03 0.02 0.35 0.02]\n",
      "Happy [0.03 0.05 0.02 0.01 0.43 0.02 0.02 0.41 0.01]\n",
      "Happy [0.01 0.03 0.02 0.01 0.69 0.03 0.02 0.19 0.  ]\n",
      "Happy [0.02 0.06 0.02 0.02 0.56 0.03 0.08 0.21 0.  ]\n",
      "Happy [0.02 0.19 0.02 0.01 0.43 0.03 0.15 0.12 0.03]\n",
      "Happy [0.02 0.31 0.   0.01 0.34 0.03 0.16 0.11 0.02]\n",
      "Happy [0.03 0.23 0.   0.01 0.43 0.03 0.12 0.15 0.  ]\n",
      "Confused [0.   0.59 0.   0.   0.26 0.   0.09 0.05 0.01]\n",
      "Confused [0.   0.66 0.   0.   0.23 0.   0.06 0.02 0.03]\n",
      "Happy [0.03 0.27 0.01 0.   0.38 0.02 0.13 0.15 0.01]\n",
      "Happy [0.04 0.2  0.01 0.   0.45 0.03 0.15 0.11 0.01]\n",
      "Happy [0.03 0.15 0.02 0.   0.5  0.02 0.13 0.13 0.02]\n",
      "Happy [0.04 0.18 0.01 0.   0.46 0.03 0.12 0.15 0.01]\n",
      "Happy [0.04 0.19 0.02 0.   0.46 0.03 0.12 0.13 0.01]\n",
      "Happy [0.02 0.22 0.02 0.01 0.49 0.02 0.09 0.12 0.01]\n",
      "Happy [0.02 0.3  0.01 0.01 0.45 0.02 0.1  0.08 0.01]\n",
      "Happy [0.02 0.3  0.02 0.03 0.44 0.02 0.09 0.07 0.01]\n",
      "Happy [0.03 0.3  0.02 0.02 0.44 0.02 0.07 0.08 0.02]\n",
      "Happy [0.03 0.26 0.02 0.03 0.42 0.01 0.09 0.12 0.02]\n",
      "Happy [0.01 0.38 0.   0.   0.45 0.01 0.05 0.08 0.02]\n",
      "Confused [0.02 0.43 0.   0.   0.38 0.01 0.06 0.08 0.02]\n",
      "Confused [0.03 0.42 0.   0.   0.4  0.01 0.04 0.09 0.01]\n",
      "Happy [0.02 0.35 0.   0.   0.43 0.01 0.06 0.12 0.01]\n",
      "Happy [0.02 0.37 0.   0.   0.38 0.01 0.07 0.14 0.01]\n",
      "Happy [0.02 0.36 0.   0.01 0.4  0.02 0.07 0.1  0.02]\n",
      "Happy [0.02 0.34 0.   0.01 0.41 0.02 0.08 0.1  0.02]\n",
      "Confused [0.03 0.38 0.   0.01 0.36 0.04 0.07 0.08 0.03]\n",
      "Happy [0.05 0.19 0.   0.03 0.44 0.04 0.08 0.12 0.05]\n",
      "Happy [0.04 0.18 0.   0.01 0.4  0.04 0.11 0.16 0.06]\n",
      "Happy [0.05 0.21 0.   0.02 0.38 0.04 0.11 0.15 0.04]\n",
      "Happy [0.02 0.18 0.   0.03 0.42 0.04 0.11 0.17 0.03]\n",
      "Happy [0.02 0.18 0.   0.04 0.42 0.04 0.11 0.17 0.02]\n",
      "Happy [0.04 0.32 0.   0.02 0.43 0.02 0.06 0.07 0.04]\n",
      "Happy [0.01 0.35 0.   0.   0.45 0.04 0.07 0.05 0.03]\n",
      "Confused [0.02 0.44 0.   0.   0.41 0.03 0.03 0.04 0.03]\n",
      "Happy [0.02 0.35 0.   0.   0.48 0.03 0.05 0.04 0.03]\n",
      "Happy [0.02 0.36 0.   0.   0.46 0.01 0.06 0.07 0.02]\n",
      "Happy [0.02 0.35 0.   0.01 0.43 0.02 0.06 0.1  0.01]\n",
      "Confused [0.02 0.55 0.   0.   0.31 0.01 0.04 0.06 0.01]\n",
      "Confused [0.02 0.55 0.   0.   0.3  0.01 0.04 0.06 0.02]\n",
      "Confused [0.02 0.52 0.   0.   0.34 0.01 0.03 0.07 0.01]\n",
      "Confused [0.02 0.54 0.   0.   0.31 0.01 0.04 0.06 0.02]\n",
      "Confused [0.02 0.54 0.   0.   0.3  0.01 0.05 0.07 0.01]\n",
      "Confused [0.01 0.6  0.   0.   0.27 0.01 0.02 0.07 0.02]\n",
      "Confused [0.02 0.55 0.   0.   0.3  0.   0.04 0.07 0.02]\n",
      "Confused [0.01 0.61 0.   0.   0.26 0.   0.03 0.07 0.02]\n",
      "Confused [0.01 0.61 0.   0.   0.26 0.   0.03 0.07 0.02]\n",
      "Confused [0.01 0.6  0.   0.   0.26 0.   0.04 0.07 0.02]\n",
      "Confused [0.01 0.59 0.   0.   0.25 0.   0.05 0.08 0.02]\n",
      "Confused [0.01 0.61 0.   0.   0.27 0.   0.02 0.07 0.02]\n",
      "Confused [0.01 0.61 0.   0.   0.27 0.   0.02 0.07 0.02]\n",
      "Confused [0.02 0.53 0.   0.   0.32 0.01 0.03 0.07 0.02]\n",
      "Confused [0.02 0.54 0.   0.   0.32 0.01 0.03 0.07 0.01]\n",
      "Confused [0.01 0.57 0.   0.   0.3  0.01 0.02 0.07 0.02]\n",
      "Happy [0.02 0.37 0.   0.   0.43 0.   0.07 0.1  0.01]\n",
      "Angry [0.25 0.01 0.08 0.01 0.11 0.05 0.17 0.07 0.25]\n",
      "Tension [0.25 0.01 0.06 0.03 0.11 0.07 0.15 0.05 0.27]\n",
      "Angry [0.31 0.02 0.05 0.02 0.05 0.04 0.19 0.05 0.27]\n",
      "Angry [0.29 0.02 0.06 0.02 0.05 0.04 0.18 0.05 0.29]\n",
      "Angry [0.28 0.02 0.07 0.03 0.06 0.05 0.2  0.1  0.19]\n",
      "Angry [0.28 0.03 0.06 0.02 0.05 0.05 0.18 0.13 0.2 ]\n",
      "Angry [0.29 0.04 0.06 0.02 0.06 0.05 0.17 0.08 0.23]\n",
      "Angry [0.31 0.04 0.09 0.01 0.07 0.06 0.17 0.08 0.17]\n",
      "Angry [0.31 0.04 0.1  0.01 0.07 0.05 0.14 0.12 0.16]\n",
      "Angry [0.32 0.03 0.07 0.02 0.06 0.05 0.16 0.09 0.2 ]\n",
      "Angry [0.32 0.05 0.09 0.01 0.07 0.04 0.13 0.11 0.18]\n",
      "Angry [0.32 0.04 0.1  0.01 0.05 0.04 0.12 0.12 0.2 ]\n",
      "Angry [0.32 0.05 0.1  0.03 0.06 0.04 0.09 0.13 0.18]\n",
      "Angry [0.31 0.04 0.09 0.02 0.06 0.04 0.11 0.12 0.21]\n",
      "Angry [0.27 0.06 0.11 0.02 0.06 0.04 0.16 0.1  0.18]\n",
      "Angry [0.25 0.02 0.11 0.   0.07 0.04 0.15 0.13 0.23]\n",
      "Angry [0.28 0.02 0.05 0.   0.06 0.04 0.15 0.16 0.24]\n",
      "Tension [0.23 0.03 0.05 0.01 0.1  0.02 0.1  0.2  0.26]\n",
      "Tension [0.23 0.03 0.02 0.02 0.11 0.03 0.07 0.24 0.25]\n",
      "Tension [0.24 0.05 0.04 0.07 0.04 0.01 0.1  0.08 0.37]\n",
      "Tension [0.26 0.05 0.03 0.07 0.04 0.01 0.08 0.07 0.39]\n",
      "Tension [0.26 0.04 0.03 0.08 0.01 0.01 0.07 0.05 0.45]\n",
      "Tension [0.22 0.05 0.02 0.07 0.02 0.01 0.08 0.05 0.48]\n",
      "Tension [0.24 0.04 0.02 0.08 0.03 0.01 0.06 0.04 0.48]\n",
      "Tension [0.24 0.04 0.02 0.07 0.02 0.02 0.06 0.04 0.49]\n",
      "Tension [0.23 0.04 0.03 0.07 0.03 0.02 0.09 0.05 0.44]\n",
      "Tension [0.2  0.05 0.03 0.07 0.03 0.02 0.13 0.04 0.43]\n",
      "Tension [0.25 0.06 0.03 0.07 0.03 0.02 0.13 0.04 0.37]\n",
      "Tension [0.19 0.05 0.03 0.05 0.03 0.02 0.12 0.05 0.46]\n",
      "Tension [0.2  0.04 0.02 0.05 0.03 0.02 0.17 0.07 0.4 ]\n",
      "Tension [0.21 0.05 0.02 0.07 0.04 0.02 0.16 0.09 0.34]\n",
      "Tension [0.23 0.05 0.04 0.14 0.02 0.02 0.1  0.07 0.33]\n",
      "Tension [0.23 0.04 0.04 0.12 0.03 0.02 0.12 0.07 0.33]\n",
      "Tension [0.24 0.07 0.03 0.07 0.01 0.03 0.17 0.05 0.33]\n",
      "Tension [0.25 0.04 0.04 0.04 0.04 0.03 0.14 0.05 0.37]\n",
      "Tension [0.25 0.04 0.02 0.08 0.04 0.02 0.17 0.11 0.27]\n",
      "Surprised [0.12 0.02 0.   0.04 0.13 0.   0.15 0.35 0.19]\n",
      "Surprised [0.2  0.   0.02 0.04 0.06 0.   0.22 0.24 0.22]\n",
      "Angry [0.27 0.02 0.01 0.03 0.05 0.   0.18 0.17 0.27]\n",
      "Surprised [0.13 0.01 0.01 0.03 0.11 0.   0.1  0.4  0.21]\n",
      "Surprised [0.13 0.01 0.01 0.04 0.07 0.   0.13 0.39 0.22]\n",
      "Surprised [0.12 0.01 0.01 0.04 0.16 0.   0.13 0.39 0.14]\n",
      "Surprised [0.1  0.02 0.01 0.05 0.05 0.   0.13 0.42 0.22]\n",
      "Surprised [0.03 0.01 0.   0.08 0.25 0.   0.12 0.33 0.18]\n",
      "Surprised [0.   0.02 0.   0.06 0.23 0.   0.17 0.43 0.09]\n",
      "Tension [0.13 0.   0.01 0.07 0.01 0.   0.11 0.28 0.39]\n",
      "Tension [0.05 0.   0.01 0.07 0.18 0.02 0.08 0.29 0.3 ]\n",
      "Surprised [0.05 0.   0.02 0.09 0.09 0.02 0.06 0.36 0.31]\n",
      "Surprised [0.01 0.02 0.01 0.08 0.13 0.01 0.12 0.46 0.16]\n",
      "Surprised [0.   0.02 0.   0.05 0.07 0.01 0.09 0.63 0.13]\n",
      "Surprised [0.04 0.   0.04 0.25 0.06 0.01 0.08 0.38 0.14]\n",
      "Surprised [0.03 0.01 0.01 0.11 0.1  0.02 0.1  0.41 0.21]\n",
      "Surprised [0.07 0.02 0.01 0.05 0.05 0.01 0.18 0.39 0.22]\n",
      "Surprised [0.01 0.03 0.   0.14 0.14 0.02 0.21 0.35 0.1 ]\n",
      "Surprised [0.11 0.02 0.02 0.13 0.24 0.02 0.1  0.27 0.09]\n",
      "Surprised [0.07 0.01 0.01 0.15 0.23 0.04 0.14 0.25 0.1 ]\n",
      "Surprised [0.06 0.01 0.01 0.17 0.14 0.03 0.19 0.35 0.04]\n",
      "Surprised [0.13 0.01 0.02 0.1  0.12 0.03 0.1  0.37 0.12]\n",
      "Surprised [0.13 0.01 0.02 0.15 0.16 0.03 0.12 0.28 0.1 ]\n",
      "Tension [0.22 0.01 0.01 0.02 0.04 0.04 0.14 0.24 0.28]\n",
      "Angry [0.31 0.01 0.   0.04 0.02 0.06 0.13 0.17 0.26]\n",
      "Angry [0.28 0.01 0.   0.04 0.02 0.05 0.16 0.19 0.25]\n",
      "Tension [0.24 0.01 0.01 0.03 0.05 0.04 0.13 0.18 0.31]\n",
      "Angry [0.39 0.   0.   0.04 0.04 0.06 0.14 0.09 0.24]\n",
      "Tension [0.22 0.01 0.02 0.18 0.   0.06 0.08 0.06 0.37]\n",
      "Tension [0.19 0.02 0.   0.08 0.   0.06 0.05 0.07 0.53]\n",
      "Tension [0.27 0.02 0.01 0.1  0.   0.06 0.04 0.1  0.4 ]\n",
      "Tension [0.19 0.   0.02 0.09 0.   0.04 0.05 0.09 0.52]\n",
      "Surprised [0.14 0.01 0.   0.04 0.07 0.05 0.13 0.33 0.23]\n",
      "Tension [0.13 0.01 0.   0.07 0.03 0.05 0.12 0.16 0.43]\n",
      "Tension [0.22 0.01 0.01 0.08 0.02 0.03 0.05 0.12 0.46]\n",
      "Tension [0.22 0.01 0.01 0.07 0.01 0.03 0.05 0.08 0.52]\n",
      "Tension [0.17 0.01 0.   0.07 0.02 0.02 0.04 0.13 0.54]\n",
      "Tension [0.25 0.01 0.   0.09 0.01 0.03 0.04 0.08 0.49]\n",
      "Tension [0.26 0.01 0.   0.08 0.01 0.03 0.04 0.09 0.48]\n",
      "Tension [0.25 0.01 0.   0.09 0.01 0.02 0.04 0.09 0.49]\n",
      "Tension [0.25 0.01 0.   0.1  0.01 0.02 0.04 0.09 0.48]\n",
      "Tension [0.25 0.01 0.02 0.09 0.01 0.02 0.04 0.09 0.47]\n",
      "Tension [0.26 0.01 0.04 0.09 0.01 0.03 0.05 0.09 0.42]\n",
      "Tension [0.29 0.01 0.03 0.09 0.01 0.03 0.04 0.11 0.39]\n",
      "Tension [0.29 0.03 0.04 0.09 0.01 0.03 0.03 0.12 0.36]\n",
      "Tension [0.22 0.04 0.01 0.12 0.04 0.03 0.06 0.09 0.39]\n",
      "Tension [0.19 0.03 0.02 0.08 0.07 0.03 0.12 0.13 0.33]\n",
      "Tension [0.22 0.03 0.02 0.1  0.09 0.07 0.14 0.08 0.25]\n",
      "Tension [0.23 0.03 0.03 0.08 0.06 0.06 0.11 0.1  0.3 ]\n",
      "Tension [0.23 0.07 0.04 0.17 0.03 0.03 0.06 0.09 0.28]\n",
      "Tension [0.3  0.02 0.04 0.2  0.01 0.03 0.03 0.06 0.31]\n",
      "Tension [0.26 0.02 0.05 0.19 0.01 0.03 0.01 0.1  0.33]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tension [0.28 0.02 0.05 0.16 0.01 0.04 0.02 0.09 0.33]\n",
      "Tension [0.28 0.02 0.05 0.19 0.   0.04 0.04 0.07 0.31]\n",
      "Tension [0.28 0.02 0.05 0.19 0.   0.02 0.03 0.1  0.31]\n",
      "Tension [0.29 0.02 0.05 0.17 0.01 0.02 0.02 0.09 0.33]\n",
      "Tension [0.28 0.02 0.04 0.18 0.01 0.02 0.01 0.1  0.34]\n",
      "Angry [0.33 0.01 0.05 0.12 0.02 0.02 0.01 0.14 0.3 ]\n",
      "Angry [0.35 0.01 0.05 0.11 0.02 0.02 0.02 0.14 0.28]\n",
      "Angry [0.32 0.01 0.05 0.15 0.02 0.02 0.02 0.13 0.28]\n",
      "Angry [0.29 0.01 0.07 0.19 0.01 0.03 0.01 0.12 0.27]\n",
      "Tension [0.26 0.02 0.05 0.18 0.01 0.04 0.02 0.13 0.29]\n",
      "Tension [0.25 0.02 0.05 0.18 0.01 0.05 0.02 0.12 0.3 ]\n",
      "Tension [0.26 0.02 0.05 0.18 0.01 0.04 0.02 0.12 0.3 ]\n",
      "Tension [0.24 0.02 0.04 0.17 0.01 0.05 0.02 0.12 0.33]\n",
      "Tension [0.23 0.02 0.04 0.19 0.01 0.05 0.02 0.12 0.32]\n",
      "Tension [0.27 0.02 0.06 0.23 0.01 0.03 0.02 0.08 0.28]\n",
      "Angry [0.3  0.01 0.05 0.18 0.01 0.03 0.02 0.12 0.28]\n",
      "Tension [0.27 0.01 0.05 0.18 0.01 0.04 0.01 0.13 0.3 ]\n",
      "Tension [0.29 0.01 0.05 0.17 0.01 0.03 0.02 0.12 0.3 ]\n",
      "Angry [0.29 0.01 0.07 0.18 0.01 0.03 0.02 0.11 0.28]\n",
      "Angry [0.29 0.02 0.05 0.18 0.01 0.03 0.02 0.12 0.28]\n",
      "Angry [0.3  0.01 0.06 0.18 0.01 0.03 0.02 0.11 0.28]\n",
      "Angry [0.3  0.01 0.06 0.2  0.   0.03 0.03 0.1  0.27]\n",
      "Tension [0.27 0.01 0.08 0.19 0.   0.02 0.03 0.1  0.3 ]\n",
      "Angry [0.3  0.01 0.06 0.17 0.01 0.03 0.03 0.11 0.28]\n",
      "Angry [0.31 0.01 0.06 0.15 0.01 0.03 0.03 0.11 0.29]\n",
      "Angry [0.29 0.03 0.06 0.2  0.   0.03 0.03 0.1  0.26]\n",
      "Angry [0.32 0.01 0.06 0.18 0.   0.03 0.02 0.11 0.27]\n",
      "Angry [0.3  0.01 0.06 0.2  0.   0.03 0.03 0.11 0.26]\n",
      "Angry [0.29 0.03 0.06 0.19 0.   0.03 0.03 0.11 0.26]\n",
      "Tension [0.27 0.03 0.06 0.2  0.   0.03 0.02 0.11 0.28]\n",
      "Angry [0.3  0.01 0.07 0.21 0.   0.03 0.02 0.11 0.25]\n",
      "Angry [0.28 0.01 0.06 0.2  0.   0.03 0.03 0.11 0.28]\n",
      "Angry [0.28 0.03 0.06 0.19 0.   0.03 0.03 0.1  0.28]\n",
      "Angry [0.28 0.01 0.06 0.2  0.   0.03 0.03 0.11 0.28]\n",
      "Angry [0.3  0.01 0.05 0.22 0.   0.03 0.03 0.11 0.25]\n",
      "Angry [0.31 0.01 0.06 0.19 0.   0.03 0.03 0.11 0.26]\n",
      "Angry [0.27 0.03 0.06 0.21 0.   0.03 0.03 0.1  0.27]\n",
      "Angry [0.3  0.01 0.06 0.22 0.   0.03 0.02 0.11 0.25]\n",
      "Angry [0.29 0.01 0.06 0.21 0.   0.03 0.02 0.11 0.27]\n",
      "Tension [0.27 0.03 0.06 0.21 0.   0.03 0.03 0.09 0.28]\n",
      "Angry [0.27 0.03 0.05 0.22 0.   0.03 0.03 0.1  0.27]\n",
      "Angry [0.28 0.03 0.06 0.21 0.   0.03 0.03 0.1  0.26]\n",
      "Angry [0.3  0.03 0.06 0.2  0.   0.03 0.03 0.07 0.28]\n",
      "Angry [0.29 0.01 0.06 0.2  0.   0.03 0.03 0.12 0.26]\n",
      "Angry [0.3  0.01 0.06 0.2  0.   0.03 0.03 0.11 0.26]\n",
      "Angry [0.31 0.01 0.06 0.19 0.   0.03 0.04 0.11 0.25]\n",
      "Angry [0.32 0.03 0.07 0.22 0.   0.04 0.03 0.05 0.24]\n",
      "Angry [0.33 0.02 0.07 0.24 0.   0.04 0.02 0.05 0.23]\n",
      "Angry [0.31 0.02 0.05 0.25 0.   0.04 0.02 0.06 0.25]\n",
      "Angry [0.32 0.02 0.06 0.25 0.   0.04 0.02 0.06 0.23]\n",
      "Angry [0.32 0.02 0.05 0.25 0.   0.04 0.02 0.06 0.24]\n",
      "Angry [0.32 0.02 0.04 0.24 0.   0.04 0.02 0.06 0.26]\n",
      "Angry [0.29 0.01 0.05 0.28 0.   0.05 0.02 0.06 0.24]\n",
      "Excited [0.29 0.02 0.06 0.3  0.   0.04 0.02 0.05 0.22]\n",
      "Angry [0.28 0.01 0.06 0.28 0.   0.05 0.02 0.05 0.25]\n",
      "Angry [0.28 0.01 0.05 0.28 0.   0.05 0.02 0.05 0.26]\n",
      "Angry [0.3  0.01 0.05 0.28 0.   0.05 0.02 0.04 0.25]\n",
      "Angry [0.31 0.03 0.06 0.24 0.   0.04 0.03 0.05 0.24]\n",
      "Angry [0.33 0.01 0.03 0.28 0.   0.05 0.02 0.05 0.23]\n",
      "Tension [0.29 0.02 0.01 0.22 0.   0.07 0.02 0.07 0.3 ]\n",
      "Angry [0.48 0.   0.01 0.07 0.02 0.04 0.04 0.11 0.23]\n",
      "Angry [0.38 0.   0.   0.12 0.   0.06 0.03 0.08 0.33]\n",
      "Angry [0.33 0.   0.   0.08 0.02 0.09 0.07 0.1  0.31]\n",
      "Angry [0.36 0.   0.   0.08 0.03 0.12 0.12 0.08 0.21]\n",
      "Surprised [0.18 0.01 0.   0.14 0.05 0.09 0.13 0.24 0.16]\n",
      "Surprised [0.1  0.01 0.   0.13 0.07 0.09 0.13 0.33 0.14]\n",
      "Surprised [0.16 0.01 0.   0.13 0.05 0.09 0.12 0.3  0.14]\n",
      "Angry [0.23 0.   0.   0.12 0.04 0.08 0.11 0.19 0.23]\n",
      "Angry [0.29 0.   0.   0.08 0.03 0.08 0.13 0.15 0.24]\n",
      "Angry [0.39 0.   0.   0.06 0.01 0.09 0.06 0.14 0.25]\n",
      "Angry [0.4  0.   0.   0.06 0.01 0.1  0.06 0.13 0.24]\n",
      "Angry [0.37 0.   0.   0.08 0.02 0.09 0.07 0.15 0.22]\n",
      "Angry [0.35 0.   0.   0.06 0.02 0.09 0.07 0.11 0.3 ]\n",
      "Angry [0.46 0.   0.   0.04 0.02 0.09 0.04 0.08 0.27]\n",
      "Angry [0.44 0.   0.   0.08 0.01 0.08 0.03 0.06 0.3 ]\n",
      "Angry [0.38 0.   0.   0.06 0.02 0.07 0.05 0.11 0.31]\n",
      "Tension [0.22 0.01 0.01 0.14 0.01 0.09 0.04 0.07 0.41]\n",
      "Tension [0.23 0.01 0.01 0.17 0.   0.09 0.03 0.07 0.39]\n",
      "Tension [0.24 0.01 0.02 0.14 0.   0.09 0.04 0.06 0.4 ]\n",
      "Tension [0.3  0.   0.   0.13 0.03 0.09 0.03 0.07 0.35]\n",
      "Angry [0.37 0.   0.02 0.08 0.02 0.09 0.04 0.14 0.24]\n",
      "Happy [0.02 0.05 0.01 0.08 0.39 0.01 0.12 0.24 0.08]\n",
      "Surprised [0.23 0.   0.   0.08 0.12 0.03 0.2  0.25 0.09]\n",
      "Surprised [0.01 0.05 0.01 0.09 0.16 0.04 0.19 0.33 0.12]\n",
      "Surprised [0.   0.04 0.01 0.03 0.19 0.03 0.24 0.38 0.08]\n",
      "Surprised [0.   0.03 0.01 0.   0.25 0.02 0.26 0.34 0.09]\n",
      "Surprised [0.01 0.02 0.   0.03 0.22 0.03 0.26 0.35 0.08]\n",
      "Surprised [0.01 0.01 0.   0.04 0.21 0.02 0.23 0.41 0.07]\n",
      "Surprised [0.01 0.   0.   0.03 0.21 0.02 0.19 0.41 0.13]\n",
      "Surprised [0.02 0.03 0.   0.03 0.24 0.02 0.22 0.35 0.09]\n",
      "Surprised [0.01 0.04 0.01 0.03 0.19 0.04 0.22 0.35 0.11]\n",
      "Surprised [0.02 0.06 0.01 0.03 0.25 0.04 0.21 0.29 0.09]\n",
      "Surprised [0.04 0.01 0.   0.03 0.18 0.02 0.19 0.39 0.14]\n",
      "Surprised [0.05 0.01 0.   0.03 0.09 0.03 0.2  0.43 0.16]\n",
      "Surprised [0.04 0.02 0.01 0.02 0.12 0.02 0.19 0.48 0.1 ]\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "emotions = []\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor Feed\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False        \n",
    "        \n",
    "        # Make Detections\n",
    "        results = holistic.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.flags.writeable = True   \n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # 1. Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                                 )\n",
    "        \n",
    "        # 2. Right hand\n",
    "        mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 3. Left Hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 4. Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\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, landmark.visibility] 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 face]).flatten())\n",
    "            \n",
    "            # Concate rows\n",
    "            row = pose_row+face_row\n",
    "\n",
    "            # Make Detections\n",
    "            X = pd.DataFrame([row])\n",
    "            body_language_class = model.predict(X)[0]\n",
    "            body_language_prob = model.predict_proba(X)[0]\n",
    "            emotions.append(str(body_language_class))\n",
    "            print(body_language_class, body_language_prob)\n",
    "            \n",
    "            # Grab ear coords\n",
    "            coords = tuple(np.multiply(\n",
    "                            np.array(\n",
    "                                (results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x, \n",
    "                                 results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y))\n",
    "                        , [640,480]).astype(int))\n",
    "            \n",
    "            cv2.rectangle(image, \n",
    "                          (coords[0], coords[1]+5), \n",
    "                          (coords[0]+len(body_language_class)*20, coords[1]-30), \n",
    "                          (245, 117, 16), -1)\n",
    "            cv2.putText(image, body_language_class, coords, \n",
    "                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "            # Get status box\n",
    "            cv2.rectangle(image, (0,0), (250, 60), (245, 117, 16), -1)\n",
    "            \n",
    "            # Display Class\n",
    "            cv2.putText(image, 'CLASS'\n",
    "                        , (95,12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, body_language_class.split(' ')[0]\n",
    "                        , (90,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "            # Display Probability\n",
    "            cv2.putText(image, 'PROB'\n",
    "                        , (15,12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, str(round(body_language_prob[np.argmax(body_language_prob)],2))\n",
    "                        , (10,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "        except:\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",
   "metadata": {},
   "source": [
    "# 8. Emotion Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1 Pie plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Emotion Distribution')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2 Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "# Define a colormap\n",
    "cmap = plt.get_cmap(\"viridis\")\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.bar(labels, sizes, color=cmap(np.linspace(0, 1, len(labels))), width=0.8)\n",
    "plt.title('Emotion Distribution')\n",
    "plt.xlabel('Emotion')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3 Horizontal bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "# Define a colormap\n",
    "cmap = plt.get_cmap(\"plasma\")\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.barh(labels, sizes, color=cmap(np.linspace(0, 1, len(labels))), height=0.8)\n",
    "plt.title('Emotion Distribution')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Emotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.4 Horizontal bar plot in creasing order of sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "# Sort labels and sizes in decreasing order of sizes\n",
    "labels, sizes = zip(*sorted(zip(labels, sizes), key=lambda x: x[1], reverse=True))\n",
    "\n",
    "# Define a colormap\n",
    "cmap = plt.get_cmap(\"magma\")\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.barh(labels, sizes, color=cmap(np.linspace(0, 1, len(labels))), height=0.8)\n",
    "plt.title('Emotion Distribution')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Emotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  8.5 Horizontal bar plot with increasing order of sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = emotions\n",
    "\n",
    "counts = {value: data.count(value) for value in set(data)}\n",
    "labels = list(counts.keys())\n",
    "sizes = list(counts.values())\n",
    "\n",
    "# Sort labels and sizes in increasing order of sizes\n",
    "labels, sizes = zip(*sorted(zip(labels, sizes), key=lambda x: x[1]))\n",
    "\n",
    "# Generate a color gradient\n",
    "start_color = 'blue'\n",
    "end_color = 'red'\n",
    "num_bars = len(labels)\n",
    "colors = plt.cm.get_cmap('coolwarm', num_bars)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.barh(labels, sizes, color=colors(np.arange(num_bars)))\n",
    "plt.title('Emotion Distribution')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Emotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(424, 144)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(np.multiply(np.array((results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x, \n",
    "results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y)), [640,480]).astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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