Running a pose ID model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running an existing model of pose ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just run in case this have not been installed previuosly\n",
    "!pip install mediapipe opencv-python pandas scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mediapipe as mp\n",
    "import cv2 \n",
    "\n",
    "# From step 2\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "# From step 3\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "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\n",
    "\n",
    "# From step 4\n",
    "from sklearn.metrics import accuracy_score # accuracy metrics\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_holistic = mp.solutions.holistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the model that was saved\n",
    "with open('body_language.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Sad [0.02 0.58 0.4 ]\n",
      "Winning [0.18 0.13 0.69]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Winning [0.06 0.04 0.9 ]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Winning [0.03 0.   0.97]\n",
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      "Sad [0.01 0.5  0.49]\n",
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      "Winning [0.02 0.17 0.81]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Winning [0.  0.2 0.8]\n",
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      "Sad [0.09 0.63 0.28]\n",
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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     ]
    }
   ],
   "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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