AI_BodyLanguageRecognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Requirements and first setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mediapipe in c:\\users\\julia\\anaconda3\\lib\\site-packages (0.8.6.2)\n",
      "Requirement already satisfied: opencv-python in c:\\users\\julia\\anaconda3\\lib\\site-packages (4.5.3.56)\n",
      "Requirement already satisfied: pandas in c:\\users\\julia\\anaconda3\\lib\\site-packages (1.1.3)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\julia\\anaconda3\\lib\\site-packages (0.23.2)\n",
      "Requirement already satisfied: absl-py in c:\\users\\julia\\anaconda3\\lib\\site-packages (from mediapipe) (0.13.0)\n",
      "Requirement already satisfied: opencv-contrib-python in c:\\users\\julia\\anaconda3\\lib\\site-packages (from mediapipe) (4.5.3.56)\n",
      "Requirement already satisfied: attrs>=19.1.0 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from mediapipe) (20.3.0)\n",
      "Requirement already satisfied: protobuf>=3.11.4 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from mediapipe) (3.17.3)\n",
      "Requirement already satisfied: wheel in c:\\users\\julia\\anaconda3\\lib\\site-packages (from mediapipe) (0.35.1)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\julia\\anaconda3\\lib\\site-packages (from mediapipe) (3.3.2)\n",
      "Requirement already satisfied: six in c:\\users\\julia\\anaconda3\\lib\\site-packages (from mediapipe) (1.15.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\julia\\anaconda3\\lib\\site-packages (from mediapipe) (1.19.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from pandas) (2020.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from scikit-learn) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from scikit-learn) (0.17.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from scikit-learn) (1.5.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (8.0.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (0.10.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\julia\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (2020.6.20)\n"
     ]
    }
   ],
   "source": [
    "!pip install mediapipe opencv-python pandas scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mediapipe as mp\n",
    "import cv2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_holistic = mp.solutions.holistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\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",
    "        \n",
    "        # Recolor back\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS, \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",
    "        # 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=(80,256,121), thickness=2,circle_radius=2)\n",
    "                                 )\n",
    "        # Left hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                  mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                  mp_drawing.DrawingSpec(color=(80,256,121), thickness=2,circle_radius=2)\n",
    "                                 )\n",
    "        # Pose landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                  mp_drawing.DrawingSpec(color=(245,117,10), thickness=2, circle_radius=4),\n",
    "                                  mp_drawing.DrawingSpec(color=(245,256,121), 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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mediapipe.python.solution_base.SolutionOutputs"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Capture landmarks and create CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_coords = len(results.face_landmarks.landmark) + len(results.pose_landmarks.landmark)\n",
    "num_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the landmarks format\n",
    "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the csv file\n",
    "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Class name you're trainig\n",
    "class_name = \"Happy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\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",
    "        \n",
    "        # Recolor back\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS, \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",
    "        # 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=(80,256,121), thickness=2,circle_radius=2)\n",
    "                                 )\n",
    "        # Left hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                  mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                  mp_drawing.DrawingSpec(color=(80,256,121), thickness=2,circle_radius=2)\n",
    "                                 )\n",
    "        # Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                  mp_drawing.DrawingSpec(color=(245,117,10), thickness=2, circle_radius=4),\n",
    "                                  mp_drawing.DrawingSpec(color=(245,256,121), thickness=2,circle_radius=2)\n",
    "                                 )\n",
    "        # Export coordinates\n",
    "        try:\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",
    "            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",
    "            row = pose_row + face_row\n",
    "            row.insert(0, class_name)\n",
    "            \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",
    "        \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": 76,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"coords.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "   class        x1        y1        z1        v1        x2        y2  \\\n",
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "      <td>0.004186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.558827</td>\n",
       "      <td>0.626944</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Winning</td>\n",
       "      <td>0.513182</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>-0.795878</td>\n",
       "      <td>0.999550</td>\n",
       "      <td>0.534919</td>\n",
       "      <td>0.629341</td>\n",
       "      <td>-0.755327</td>\n",
       "      <td>0.999106</td>\n",
       "      <td>0.549350</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.559135</td>\n",
       "      <td>0.629791</td>\n",
       "      <td>0.004248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.563381</td>\n",
       "      <td>0.626241</td>\n",
       "      <td>0.004034</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Winning</td>\n",
       "      <td>0.521317</td>\n",
       "      <td>0.673007</td>\n",
       "      <td>-0.796527</td>\n",
       "      <td>0.999551</td>\n",
       "      <td>0.543582</td>\n",
       "      <td>0.633126</td>\n",
       "      <td>-0.754130</td>\n",
       "      <td>0.999145</td>\n",
       "      <td>0.558801</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.563667</td>\n",
       "      <td>0.629704</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.567943</td>\n",
       "      <td>0.626198</td>\n",
       "      <td>0.004010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Winning</td>\n",
       "      <td>0.527825</td>\n",
       "      <td>0.672980</td>\n",
       "      <td>-0.749958</td>\n",
       "      <td>0.999557</td>\n",
       "      <td>0.547923</td>\n",
       "      <td>0.632879</td>\n",
       "      <td>-0.703969</td>\n",
       "      <td>0.999179</td>\n",
       "      <td>0.562581</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.566912</td>\n",
       "      <td>0.627673</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571098</td>\n",
       "      <td>0.624237</td>\n",
       "      <td>0.004161</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>534</th>\n",
       "      <td>Winning</td>\n",
       "      <td>0.379153</td>\n",
       "      <td>0.671279</td>\n",
       "      <td>-0.824848</td>\n",
       "      <td>0.997462</td>\n",
       "      <td>0.407934</td>\n",
       "      <td>0.611139</td>\n",
       "      <td>-0.818989</td>\n",
       "      <td>0.996456</td>\n",
       "      <td>0.423179</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008366</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.446095</td>\n",
       "      <td>0.595919</td>\n",
       "      <td>-0.013760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.450853</td>\n",
       "      <td>0.588689</td>\n",
       "      <td>-0.015072</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>Winning</td>\n",
       "      <td>0.325317</td>\n",
       "      <td>0.365976</td>\n",
       "      <td>-0.541835</td>\n",
       "      <td>0.999976</td>\n",
       "      <td>0.354183</td>\n",
       "      <td>0.289572</td>\n",
       "      <td>-0.479076</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.375206</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.392981</td>\n",
       "      <td>0.289194</td>\n",
       "      <td>-0.010134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397360</td>\n",
       "      <td>0.282464</td>\n",
       "      <td>-0.010931</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Winning</td>\n",
       "      <td>0.347556</td>\n",
       "      <td>0.375456</td>\n",
       "      <td>-0.571756</td>\n",
       "      <td>0.999974</td>\n",
       "      <td>0.373050</td>\n",
       "      <td>0.303261</td>\n",
       "      <td>-0.513698</td>\n",
       "      <td>0.999963</td>\n",
       "      <td>0.392564</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.412560</td>\n",
       "      <td>0.315234</td>\n",
       "      <td>-0.010160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416993</td>\n",
       "      <td>0.309700</td>\n",
       "      <td>-0.011048</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Winning</td>\n",
       "      <td>0.348621</td>\n",
       "      <td>0.376574</td>\n",
       "      <td>-0.568086</td>\n",
       "      <td>0.999973</td>\n",
       "      <td>0.373164</td>\n",
       "      <td>0.308331</td>\n",
       "      <td>-0.514107</td>\n",
       "      <td>0.999957</td>\n",
       "      <td>0.392834</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.393335</td>\n",
       "      <td>0.317553</td>\n",
       "      <td>-0.021522</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397803</td>\n",
       "      <td>0.311829</td>\n",
       "      <td>-0.023017</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Winning</td>\n",
       "      <td>0.298126</td>\n",
       "      <td>0.387612</td>\n",
       "      <td>-0.776476</td>\n",
       "      <td>0.999973</td>\n",
       "      <td>0.323417</td>\n",
       "      <td>0.318861</td>\n",
       "      <td>-0.782958</td>\n",
       "      <td>0.999959</td>\n",
       "      <td>0.343214</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.335353</td>\n",
       "      <td>0.319365</td>\n",
       "      <td>-0.028043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.340092</td>\n",
       "      <td>0.312391</td>\n",
       "      <td>-0.029970</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>255 rows × 2005 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class        x1        y1        z1        v1        x2        y2  \\\n",
       "284  Winning  0.441137  0.666129 -0.791712  0.999592  0.466263  0.618982   \n",
       "285  Winning  0.492343  0.666716 -0.794794  0.999560  0.515076  0.623053   \n",
       "286  Winning  0.513182  0.669709 -0.795878  0.999550  0.534919  0.629341   \n",
       "287  Winning  0.521317  0.673007 -0.796527  0.999551  0.543582  0.633126   \n",
       "288  Winning  0.527825  0.672980 -0.749958  0.999557  0.547923  0.632879   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "534  Winning  0.379153  0.671279 -0.824848  0.997462  0.407934  0.611139   \n",
       "535  Winning  0.325317  0.365976 -0.541835  0.999976  0.354183  0.289572   \n",
       "536  Winning  0.347556  0.375456 -0.571756  0.999974  0.373050  0.303261   \n",
       "537  Winning  0.348621  0.376574 -0.568086  0.999973  0.373164  0.308331   \n",
       "538  Winning  0.298126  0.387612 -0.776476  0.999973  0.323417  0.318861   \n",
       "\n",
       "           z2        v2        x3  ...      z499  v499      x500      y500  \\\n",
       "284 -0.760779  0.999092  0.480207  ... -0.002153   0.0  0.482003  0.604836   \n",
       "285 -0.759815  0.999092  0.529768  ... -0.003057   0.0  0.554600  0.630891   \n",
       "286 -0.755327  0.999106  0.549350  ... -0.002900   0.0  0.559135  0.629791   \n",
       "287 -0.754130  0.999145  0.558801  ... -0.002850   0.0  0.563667  0.629704   \n",
       "288 -0.703969  0.999179  0.562581  ... -0.002847   0.0  0.566912  0.627673   \n",
       "..        ...       ...       ...  ...       ...   ...       ...       ...   \n",
       "534 -0.818989  0.996456  0.423179  ... -0.008366   0.0  0.446095  0.595919   \n",
       "535 -0.479076  0.999968  0.375206  ... -0.016548   0.0  0.392981  0.289194   \n",
       "536 -0.513698  0.999963  0.392564  ... -0.015392   0.0  0.412560  0.315234   \n",
       "537 -0.514107  0.999957  0.392834  ... -0.018798   0.0  0.393335  0.317553   \n",
       "538 -0.782958  0.999959  0.343214  ... -0.020061   0.0  0.335353  0.319365   \n",
       "\n",
       "         z500  v500      x501      y501      z501  v501  \n",
       "284 -0.004818   0.0  0.487225  0.599934 -0.005975   0.0  \n",
       "285  0.004186   0.0  0.558827  0.626944  0.004005   0.0  \n",
       "286  0.004248   0.0  0.563381  0.626241  0.004034   0.0  \n",
       "287  0.004229   0.0  0.567943  0.626198  0.004010   0.0  \n",
       "288  0.004385   0.0  0.571098  0.624237  0.004161   0.0  \n",
       "..        ...   ...       ...       ...       ...   ...  \n",
       "534 -0.013760   0.0  0.450853  0.588689 -0.015072   0.0  \n",
       "535 -0.010134   0.0  0.397360  0.282464 -0.010931   0.0  \n",
       "536 -0.010160   0.0  0.416993  0.309700 -0.011048   0.0  \n",
       "537 -0.021522   0.0  0.397803  0.311829 -0.023017   0.0  \n",
       "538 -0.028043   0.0  0.340092  0.312391 -0.029970   0.0  \n",
       "\n",
       "[255 rows x 2005 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['class']=='Winning']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('class', axis= 1) # features\n",
    "y = df['class'] # target value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93       Happy\n",
       "347    Winning\n",
       "351    Winning\n",
       "24       Happy\n",
       "514    Winning\n",
       "        ...   \n",
       "279        Sad\n",
       "372    Winning\n",
       "204        Sad\n",
       "53       Happy\n",
       "294    Winning\n",
       "Name: class, Length: 377, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline # allows you to build a ML pipeline\n",
    "from sklearn.preprocessing import StandardScaler # Normalizes your data\n",
    "\n",
    "# Diferent algorithms for diversifing your model training (?)\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier \n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelines = {\n",
    "    'lr': make_pipeline(StandardScaler(), LogisticRegression()),\n",
    "    'rc': make_pipeline(StandardScaler(), RidgeClassifier()),\n",
    "    'rf': make_pipeline(StandardScaler(), RandomForestClassifier()),\n",
    "    'gb': make_pipeline(StandardScaler(), GradientBoostingClassifier()),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\julia\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lr': Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('logisticregression', LogisticRegression())]),\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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sad', 'Happy', 'Happy', 'Happy', 'Winning', 'Happy', 'Winning',\n",
       "       'Happy', 'Happy', 'Happy', 'Happy', 'Winning', 'Sad', 'Happy',\n",
       "       'Happy', 'Sad', 'Winning', 'Sad', 'Winning', 'Winning', 'Winning',\n",
       "       'Sad', 'Winning', 'Happy', 'Winning', 'Happy', 'Winning', 'Sad',\n",
       "       'Happy', 'Sad', 'Sad', 'Happy', 'Happy', 'Happy', 'Winning',\n",
       "       'Winning', 'Sad', 'Happy', 'Happy', 'Winning', 'Winning',\n",
       "       'Winning', 'Happy', 'Sad', 'Winning', 'Happy', 'Winning',\n",
       "       'Winning', 'Happy', 'Winning', 'Happy', 'Winning', 'Sad',\n",
       "       'Winning', 'Winning', 'Winning', 'Winning', 'Winning', 'Sad',\n",
       "       'Happy', 'Winning', 'Sad', 'Happy', 'Winning', 'Happy', 'Happy',\n",
       "       'Winning', 'Sad', 'Winning', 'Happy', 'Winning', 'Winning',\n",
       "       'Winning', 'Winning', 'Winning', 'Happy', 'Winning', 'Winning',\n",
       "       'Winning', 'Happy', 'Winning', 'Winning', 'Happy', 'Happy', 'Sad',\n",
       "       'Sad', 'Winning', 'Happy', 'Happy', 'Winning', 'Happy', 'Winning',\n",
       "       'Happy', 'Winning', 'Winning', 'Happy', 'Winning', 'Winning',\n",
       "       'Winning', 'Winning', 'Happy', 'Winning', 'Happy', 'Winning',\n",
       "       'Happy', 'Winning', 'Winning', 'Winning', 'Winning', 'Sad',\n",
       "       'Happy', 'Winning', 'Sad', 'Winning', 'Sad', 'Winning', 'Sad',\n",
       "       'Happy', 'Winning', 'Sad', 'Winning', 'Happy', 'Winning', 'Happy',\n",
       "       'Winning', 'Sad', 'Winning', 'Winning', 'Winning', 'Happy',\n",
       "       'Winning', 'Winning', 'Winning', 'Winning', 'Happy', 'Winning',\n",
       "       'Winning', 'Sad', 'Happy', 'Winning', 'Sad', 'Sad', 'Sad', 'Happy',\n",
       "       'Winning', 'Sad', 'Winning', 'Happy', 'Winning', 'Happy', 'Happy',\n",
       "       'Sad', 'Happy', 'Happy', 'Happy', 'Sad', 'Happy', 'Winning',\n",
       "       'Happy', 'Winning', 'Winning', 'Happy'], dtype='<U7')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models['rc'].predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Evaluate and serialize model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score # accuracy metrics\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fit_models' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-ec7816b598de>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0malgo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfit_models\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0myhat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malgo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myhat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'fit_models' is not defined"
     ]
    }
   ],
   "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sad', 'Happy', 'Happy', 'Happy', 'Winning', 'Happy', 'Winning',\n",
       "       'Happy', 'Happy', 'Happy', 'Happy', 'Winning', 'Sad', 'Happy',\n",
       "       'Happy', 'Sad', 'Winning', 'Sad', 'Winning', 'Winning', 'Winning',\n",
       "       'Sad', 'Winning', 'Happy', 'Winning', 'Happy', 'Winning', 'Sad',\n",
       "       'Happy', 'Sad', 'Sad', 'Happy', 'Happy', 'Happy', 'Winning',\n",
       "       'Winning', 'Sad', 'Happy', 'Happy', 'Winning', 'Winning',\n",
       "       'Winning', 'Happy', 'Sad', 'Winning', 'Happy', 'Winning',\n",
       "       'Winning', 'Happy', 'Winning', 'Happy', 'Winning', 'Sad',\n",
       "       'Winning', 'Winning', 'Winning', 'Winning', 'Winning', 'Sad',\n",
       "       'Happy', 'Winning', 'Sad', 'Happy', 'Winning', 'Happy', 'Happy',\n",
       "       'Winning', 'Sad', 'Winning', 'Happy', 'Winning', 'Winning',\n",
       "       'Winning', 'Winning', 'Winning', 'Happy', 'Winning', 'Winning',\n",
       "       'Winning', 'Happy', 'Winning', 'Winning', 'Happy', 'Happy', 'Sad',\n",
       "       'Sad', 'Winning', 'Happy', 'Happy', 'Winning', 'Happy', 'Winning',\n",
       "       'Happy', 'Winning', 'Winning', 'Happy', 'Winning', 'Winning',\n",
       "       'Winning', 'Winning', 'Happy', 'Winning', 'Happy', 'Winning',\n",
       "       'Happy', 'Winning', 'Winning', 'Winning', 'Winning', 'Sad',\n",
       "       'Happy', 'Winning', 'Sad', 'Winning', 'Sad', 'Winning', 'Sad',\n",
       "       'Happy', 'Winning', 'Sad', 'Winning', 'Happy', 'Winning', 'Happy',\n",
       "       'Winning', 'Sad', 'Winning', 'Winning', 'Winning', 'Happy',\n",
       "       'Winning', 'Winning', 'Winning', 'Winning', 'Happy', 'Winning',\n",
       "       'Winning', 'Sad', 'Happy', 'Winning', 'Sad', 'Sad', 'Sad', 'Happy',\n",
       "       'Winning', 'Sad', 'Winning', 'Happy', 'Winning', 'Happy', 'Happy',\n",
       "       'Sad', 'Happy', 'Happy', 'Happy', 'Sad', 'Happy', 'Winning',\n",
       "       'Happy', 'Winning', 'Winning', 'Happy'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models['rf'].predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181        Sad\n",
       "138      Happy\n",
       "11       Happy\n",
       "21       Happy\n",
       "523    Winning\n",
       "        ...   \n",
       "484    Winning\n",
       "122      Happy\n",
       "477    Winning\n",
       "469    Winning\n",
       "134      Happy\n",
       "Name: class, Length: 162, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('body_language.pkl','wb') as f:\n",
    "    pickle.dump(fit_models['rf'], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pickle' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-de7faa60e679>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'body_language.pkl'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m        \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pickle' is not defined"
     ]
    }
   ],
   "source": [
    " with open('body_language.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('randomforestclassifier', RandomForestClassifier())])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Winning [0.11 0.15 0.74]\n",
      "Winning [0.1  0.13 0.77]\n",
      "Winning [0.12 0.11 0.77]\n",
      "Winning [0.11 0.11 0.78]\n",
      "Winning [0.1  0.02 0.88]\n",
      "Winning [0.11 0.02 0.87]\n",
      "Winning [0.06 0.06 0.88]\n",
      "Winning [0.01 0.1  0.89]\n",
      "Winning [0.01 0.08 0.91]\n",
      "Winning [0.01 0.09 0.9 ]\n",
      "Winning [0.01 0.09 0.9 ]\n",
      "Winning [0.01 0.07 0.92]\n",
      "Winning [0.01 0.07 0.92]\n",
      "Winning [0.01 0.07 0.92]\n",
      "Winning [0.01 0.03 0.96]\n",
      "Winning [0.01 0.03 0.96]\n",
      "Winning [0.01 0.02 0.97]\n",
      "Winning [0.01 0.02 0.97]\n",
      "Winning [0.   0.03 0.97]\n",
      "Winning [0.   0.03 0.97]\n",
      "Winning [0.   0.02 0.98]\n",
      "Winning [0.   0.02 0.98]\n",
      "Winning [0.   0.02 0.98]\n",
      "Winning [0.   0.02 0.98]\n",
      "Winning [0.   0.01 0.99]\n",
      "Winning [0.   0.01 0.99]\n",
      "Winning [0.   0.03 0.97]\n",
      "Winning [0.   0.02 0.98]\n",
      "Winning [0.   0.03 0.97]\n",
      "Winning [0.   0.02 0.98]\n",
      "Winning [0.   0.01 0.99]\n",
      "Winning [0. 0. 1.]\n",
      "Winning [0.   0.01 0.99]\n",
      "Winning [0.02 0.01 0.97]\n",
      "Winning [0.01 0.02 0.97]\n",
      "Winning [0.02 0.02 0.96]\n",
      "Winning [0.02 0.05 0.93]\n",
      "Winning [0.01 0.04 0.95]\n",
      "Winning [0.01 0.03 0.96]\n",
      "Winning [0.01 0.13 0.86]\n",
      "Winning [0.04 0.1  0.86]\n",
      "Winning [0.04 0.1  0.86]\n",
      "Winning [0.07 0.1  0.83]\n",
      "Winning [0.07 0.1  0.83]\n",
      "Winning [0.07 0.1  0.83]\n",
      "Winning [0.07 0.1  0.83]\n",
      "Winning [0.07 0.1  0.83]\n",
      "Winning [0.07 0.1  0.83]\n",
      "Winning [0.07 0.1  0.83]\n",
      "Winning [0.07 0.1  0.83]\n",
      "Winning [0.06 0.1  0.84]\n",
      "Winning [0.04 0.07 0.89]\n",
      "Winning [0.02 0.06 0.92]\n",
      "Winning [0.02 0.07 0.91]\n",
      "Winning [0.02 0.02 0.96]\n",
      "Winning [0.01 0.03 0.96]\n",
      "Winning [0.03 0.08 0.89]\n",
      "Winning [0.02 0.03 0.95]\n",
      "Winning [0.02 0.   0.98]\n",
      "Winning [0.01 0.   0.99]\n",
      "Winning [0.04 0.1  0.86]\n",
      "Winning [0.07 0.09 0.84]\n",
      "Winning [0.07 0.09 0.84]\n",
      "Winning [0.1  0.09 0.81]\n",
      "Winning [0.11 0.1  0.79]\n",
      "Winning [0.1  0.09 0.81]\n",
      "Winning [0.1  0.09 0.81]\n",
      "Winning [0.1  0.09 0.81]\n",
      "Winning [0.1  0.09 0.81]\n",
      "Winning [0.1 0.1 0.8]\n",
      "Winning [0.1  0.11 0.79]\n",
      "Winning [0.15 0.09 0.76]\n",
      "Winning [0.09 0.08 0.83]\n",
      "Winning [0.06 0.1  0.84]\n",
      "Winning [0.08 0.12 0.8 ]\n",
      "Winning [0.08 0.12 0.8 ]\n",
      "Winning [0.07 0.12 0.81]\n",
      "Winning [0.06 0.11 0.83]\n",
      "Winning [0.07 0.12 0.81]\n",
      "Winning [0.06 0.11 0.83]\n",
      "Winning [0.07 0.14 0.79]\n",
      "Winning [0.06 0.1  0.84]\n",
      "Winning [0.06 0.12 0.82]\n",
      "Winning [0.06 0.12 0.82]\n",
      "Winning [0.06 0.11 0.83]\n",
      "Winning [0.06 0.09 0.85]\n",
      "Winning [0.05 0.03 0.92]\n",
      "Winning [0.05 0.04 0.91]\n",
      "Winning [0.05 0.05 0.9 ]\n",
      "Winning [0.03 0.04 0.93]\n",
      "Winning [0.03 0.03 0.94]\n",
      "Winning [0.04 0.02 0.94]\n",
      "Winning [0.04 0.03 0.93]\n",
      "Winning [0.  0.2 0.8]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.  0.2 0.8]\n",
      "Winning [0.  0.2 0.8]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.25 0.75]\n",
      "Winning [0.   0.27 0.73]\n",
      "Winning [0.   0.25 0.75]\n",
      "Winning [0.   0.25 0.75]\n",
      "Winning [0.   0.23 0.77]\n",
      "Winning [0.   0.22 0.78]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.22 0.78]\n",
      "Winning [0.   0.22 0.78]\n",
      "Winning [0.   0.22 0.78]\n",
      "Winning [0.   0.22 0.78]\n",
      "Winning [0.   0.24 0.76]\n",
      "Winning [0.   0.24 0.76]\n",
      "Winning [0.   0.24 0.76]\n",
      "Winning [0.   0.23 0.77]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.21 0.79]\n",
      "Winning [0.   0.19 0.81]\n",
      "Winning [0.   0.18 0.82]\n",
      "Winning [0.   0.15 0.85]\n",
      "Winning [0.  0.1 0.9]\n",
      "Winning [0.   0.13 0.87]\n",
      "Winning [0.   0.18 0.82]\n",
      "Winning [0.   0.19 0.81]\n",
      "Winning [0.   0.14 0.86]\n",
      "Winning [0.   0.14 0.86]\n",
      "Winning [0.   0.13 0.87]\n",
      "Winning [0.   0.11 0.89]\n",
      "Winning [0.   0.12 0.88]\n",
      "Winning [0.01 0.03 0.96]\n",
      "Winning [0.01 0.01 0.98]\n",
      "Winning [0.01 0.   0.99]\n",
      "Winning [0.01 0.02 0.97]\n",
      "Winning [0.02 0.02 0.96]\n",
      "Winning [0.01 0.02 0.97]\n",
      "Winning [0.01 0.03 0.96]\n",
      "Winning [0.01 0.02 0.97]\n",
      "Winning [0.01 0.02 0.97]\n",
      "Winning [0.01 0.02 0.97]\n",
      "Winning [0.02 0.03 0.95]\n",
      "Winning [0.03 0.04 0.93]\n",
      "Winning [0.03 0.05 0.92]\n",
      "Winning [0.03 0.05 0.92]\n",
      "Winning [0.02 0.08 0.9 ]\n",
      "Winning [0.02 0.07 0.91]\n",
      "Winning [0.01 0.07 0.92]\n",
      "Winning [0.01 0.1  0.89]\n",
      "Winning [0.01 0.11 0.88]\n",
      "Winning [0.   0.14 0.86]\n",
      "Winning [0.   0.13 0.87]\n",
      "Winning [0.   0.09 0.91]\n",
      "Winning [0.  0.1 0.9]\n",
      "Winning [0.  0.1 0.9]\n",
      "Winning [0.01 0.12 0.87]\n",
      "Winning [0.02 0.2  0.78]\n",
      "Winning [0.03 0.21 0.76]\n",
      "Winning [0.07 0.23 0.7 ]\n",
      "Winning [0.14 0.21 0.65]\n",
      "Winning [0.24 0.09 0.67]\n",
      "Winning [0.29 0.06 0.65]\n",
      "Winning [0.29 0.06 0.65]\n",
      "Winning [0.23 0.05 0.72]\n",
      "Winning [0.21 0.04 0.75]\n",
      "Winning [0.21 0.03 0.76]\n",
      "Winning [0.16 0.03 0.81]\n",
      "Winning [0.25 0.06 0.69]\n",
      "Winning [0.16 0.07 0.77]\n",
      "Winning [0.19 0.09 0.72]\n",
      "Winning [0.2 0.1 0.7]\n",
      "Winning [0.19 0.1  0.71]\n",
      "Winning [0.23 0.07 0.7 ]\n",
      "Winning [0.2  0.06 0.74]\n",
      "Winning [0.18 0.03 0.79]\n",
      "Winning [0.19 0.04 0.77]\n",
      "Winning [0.18 0.05 0.77]\n",
      "Winning [0.16 0.04 0.8 ]\n",
      "Winning [0.15 0.05 0.8 ]\n",
      "Winning [0.08 0.05 0.87]\n",
      "Winning [0.1  0.04 0.86]\n",
      "Winning [0.1  0.05 0.85]\n",
      "Winning [0.1  0.06 0.84]\n",
      "Winning [0.11 0.1  0.79]\n",
      "Winning [0.12 0.12 0.76]\n",
      "Winning [0.11 0.11 0.78]\n",
      "Winning [0.12 0.11 0.77]\n",
      "Winning [0.12 0.17 0.71]\n",
      "Winning [0.12 0.09 0.79]\n",
      "Winning [0.09 0.14 0.77]\n",
      "Winning [0.13 0.2  0.67]\n",
      "Winning [0.13 0.19 0.68]\n",
      "Winning [0.14 0.18 0.68]\n",
      "Winning [0.12 0.16 0.72]\n",
      "Winning [0.15 0.18 0.67]\n",
      "Winning [0.15 0.17 0.68]\n",
      "Winning [0.04 0.35 0.61]\n",
      "Winning [0.01 0.26 0.73]\n",
      "Winning [0.01 0.21 0.78]\n",
      "Winning [0.01 0.17 0.82]\n",
      "Winning [0.06 0.15 0.79]\n",
      "Winning [0.08 0.09 0.83]\n",
      "Winning [0.08 0.08 0.84]\n",
      "Winning [0.08 0.11 0.81]\n",
      "Winning [0.07 0.14 0.79]\n",
      "Winning [0.07 0.11 0.82]\n",
      "Winning [0.1 0.1 0.8]\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\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",
    "        \n",
    "        # Recolor back\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS, \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",
    "        # 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=(80,256,121), thickness=2,circle_radius=2)\n",
    "                                 )\n",
    "        # Left hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                  mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                  mp_drawing.DrawingSpec(color=(80,256,121), thickness=2,circle_radius=2)\n",
    "                                 )\n",
    "        # Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                  mp_drawing.DrawingSpec(color=(245,117,10), thickness=2, circle_radius=4),\n",
    "                                  mp_drawing.DrawingSpec(color=(245,256,121), 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",
    "            # Concatenate rows\n",
    "            row = pose_row + face_row\n",
    "            \n",
    "#             # Append class name\n",
    "#             row.insert(0, class_name)\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",
    "            print(body_language_class, body_language_prob)\n",
    "            \n",
    "            # Grab ear coordinates\n",
    "            display_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",
    "                         (display_coords[0], display_coords[1]+5),\n",
    "                         (display_coords[0]+ len(body_language_class)*20, display_coords[1]-30),\n",
    "                         (245, 117, 16), -1)\n",
    "            cv2.putText(image, body_language_class, display_coords, \n",
    "                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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