Action Detection Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import and Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow==2.9.1 tensorflow-gpu==2.9.1 opencv-python mediapipe sklearn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import time\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Keypoints using MP Holistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.holistic # Holistic model\n",
    "mp_drawing = mp.solutions.drawing_utils # Drawing utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mediapipe_detection(image, model):\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB\n",
    "    image.flags.writeable = False                  # Image is no longer writeable\n",
    "    results = model.process(image)                 # Make prediction\n",
    "    image.flags.writeable = True                   # Image is now writeable \n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR\n",
    "    return image, results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_landmarks(image, results):\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION) # Draw face connections\n",
    "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) # Draw pose connections\n",
    "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw left hand connections\n",
    "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw right hand connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_styled_landmarks(image, results):\n",
    "    # Draw face connections\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, \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",
    "    # Draw pose connections\n",
    "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_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",
    "    # Draw left hand connections\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",
    "    # Draw right hand connections  \n",
    "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_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",
    "                             ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        \n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks(image, results)\n",
    "\n",
    "        # Show to screen\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results.left_hand_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mediapipe.python.solution_base.SolutionOutputs"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_landmarks(frame, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d362654320>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Extract Keypoint Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'landmark'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20768\\1636665476.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright_hand_landmarks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlandmark\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'landmark'"
     ]
    }
   ],
   "source": [
    "len(results.right_hand_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pose = []\n",
    "for res in results.pose_landmarks.landmark:\n",
    "    test = np.array([res.x, res.y, res.z, res.visibility])\n",
    "    pose.append(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(132)\n",
    "face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(1404)\n",
    "lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
    "rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(1404)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_keypoints(results):\n",
    "    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)\n",
    "    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)\n",
    "    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
    "    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n",
    "    return np.concatenate([pose, face, lh, rh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_test = extract_keypoints(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "result_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1662"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "468*3+33*4+21*3+21*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('0', result_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '0.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_15544\\3826063030.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'0.npy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[0;32m    415\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    416\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 417\u001b[1;33m             \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\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    418\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    419\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '0.npy'"
     ]
    }
   ],
   "source": [
    "np.load('0.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(result_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Setup Folders for Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path for exported data, numpy arrays\n",
    "DATA_PATH = os.path.join('MP_Data') \n",
    "\n",
    "# Actions that we try to detect\n",
    "actions = np.array(['hello', 'thanks', 'healthy'])\n",
    "\n",
    "# Ninety videos worth of data\n",
    "no_sequences = 30\n",
    "\n",
    "# Videos are going to be 30 frames in length\n",
    "sequence_length = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hello\n",
    "## 0\n",
    "## 1\n",
    "## 2\n",
    "## ...\n",
    "## 29\n",
    "# thanks\n",
    "\n",
    "# I love you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for action in actions: \n",
    "    for sequence in range(no_sequences):\n",
    "        try: \n",
    "            os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Collect Keypoint Values for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_22312\\2003275975.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m                 \u001b[1;31m# Make detections\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m                 \u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmediapipe_detection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mholistic\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     18\u001b[0m \u001b[1;31m#                 print(results)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_22312\\2065571785.py\u001b[0m in \u001b[0;36mmediapipe_detection\u001b[1;34m(image, model)\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOLOR_BGR2RGB\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# COLOR CONVERSION BGR 2 RGB\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriteable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m                  \u001b[1;31m# Image is no longer writeable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m                 \u001b[1;31m# Make prediction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriteable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m                   \u001b[1;31m# Image is now writeable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOLOR_RGB2BGR\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# COLOR COVERSION RGB 2 BGR\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\mediapipe\\python\\solutions\\holistic.py\u001b[0m in \u001b[0;36mprocess\u001b[1;34m(self, image)\u001b[0m\n\u001b[0;32m    158\u001b[0m     \"\"\"\n\u001b[0;32m    159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 160\u001b[1;33m     \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'image'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mimage\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    161\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpose_landmarks\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pytype: disable=attribute-error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    162\u001b[0m       \u001b[1;32mfor\u001b[0m \u001b[0mlandmark\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpose_landmarks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlandmark\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pytype: disable=attribute-error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\mediapipe\\python\\solution_base.py\u001b[0m in \u001b[0;36mprocess\u001b[1;34m(self, input_data)\u001b[0m\n\u001b[0;32m    362\u001b[0m                                      data).at(self._simulated_timestamp))\n\u001b[0;32m    363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 364\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait_until_idle\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    365\u001b[0m     \u001b[1;31m# Create a NamedTuple object where the field names are mapping to the graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    366\u001b[0m     \u001b[1;31m# output stream names.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    # NEW LOOP\n",
    "    # Loop through actions\n",
    "    for action in actions:\n",
    "        # Loop through sequences aka videos\n",
    "        for sequence in range(no_sequences):\n",
    "            # Loop through video length aka sequence length\n",
    "            for frame_num in range(sequence_length):\n",
    "\n",
    "                # Read feed\n",
    "                ret, frame = cap.read()\n",
    "\n",
    "                # Make detections\n",
    "                image, results = mediapipe_detection(frame, holistic)\n",
    "#                 print(results)\n",
    "\n",
    "                # Draw landmarks\n",
    "                draw_styled_landmarks(image, results)\n",
    "            \n",
    "                # NEW Apply wait logic\n",
    "                if frame_num == 0: \n",
    "                    cv2.putText(image, 'STARTING COLLECTION', (120,200), \n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255, 0), 4, cv2.LINE_AA)\n",
    "                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), \n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n",
    "                    # Show to screen\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "                    cv2.waitKey(2000)\n",
    "                else: \n",
    "                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), \n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n",
    "                    # Show to screen\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "                \n",
    "                # NEW Export keypoints\n",
    "                keypoints = extract_keypoints(results)\n",
    "                npy_path = os.path.join(DATA_PATH, action, str(sequence), str(frame_num))\n",
    "                np.save(npy_path, keypoints)\n",
    "\n",
    "                # Break gracefully\n",
    "                if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "                    break\n",
    "                    \n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Preprocess Data and Create Labels and Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {label:num for num, label in enumerate(actions)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences, labels = [], []\n",
    "for action in actions:\n",
    "    for sequence in range(no_sequences):\n",
    "        window = []\n",
    "        for frame_num in range(sequence_length):\n",
    "            res = np.load(os.path.join(DATA_PATH, action, str(sequence), \"{}.npy\".format(frame_num)))\n",
    "            window.append(res)\n",
    "        sequences.append(window)\n",
    "        labels.append(label_map[action])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(labels).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Build and Train LSTM Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "from tensorflow.keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir=log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'actions' 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-6-0dd78d67bf2b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'relu'\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[0;32m      6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'relu'\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;32m----> 7\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'softmax'\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[1;31mNameError\u001b[0m: name 'actions' is not defined"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,1662)))\n",
    "model.add(LSTM(128, return_sequences=True, activation='relu'))\n",
    "model.add(LSTM(64, return_sequences=False, activation='relu'))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(actions.shape[0], activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = [.7, 0.2, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actions[np.argmax(res)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actions[np.argmax(res[3])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actions[np.argmax(y_test[3])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Save Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('action.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' 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-bb62a3109bdc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'action.h5'\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 'model' is not defined"
     ]
    }
   ],
   "source": [
    "model.load_weights('action.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Evaluation using Confusion Matrix and Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_test, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Test in Real Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [(245,117,16), (117,245,16), (16,117,245)]\n",
    "def prob_viz(res, actions, input_frame, colors):\n",
    "    output_frame = input_frame.copy()\n",
    "    for num, prob in enumerate(res):\n",
    "        cv2.rectangle(output_frame, (0,60+num*40), (int(prob*100), 90+num*40), colors[num], -1)\n",
    "        cv2.putText(output_frame, actions[num], (0, 85+num*40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "    return output_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(18,18))\n",
    "plt.imshow(prob_viz(res, actions, image, colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence.append('def')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sequence[-30:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. New detection variables\n",
    "sequence = []\n",
    "sentence = []\n",
    "threshold = 0.8\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "cap.set(cv2.CAP_PROP_FRAME_WIDTH, 2000);\n",
    "cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1000);\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        \n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks(image, results)\n",
    "        \n",
    "        # 2. Prediction logic\n",
    "        keypoints = extract_keypoints(results)\n",
    "#         sequence.insert(0,keypoints)\n",
    "#         sequence = sequence[:30]\n",
    "        sequence.append(keypoints)\n",
    "        sequence = sequence[-30:]\n",
    "        \n",
    "        if len(sequence) == 30:\n",
    "            res = model.predict(np.expand_dims(sequence, axis=0))[0]\n",
    "            print(actions[np.argmax(res)])\n",
    "            \n",
    "            \n",
    "        #3. Viz logic\n",
    "            if res[np.argmax(res)] > threshold: \n",
    "                if len(sentence) > 0: \n",
    "                    if actions[np.argmax(res)] != sentence[-1]:\n",
    "                        sentence.append(actions[np.argmax(res)])\n",
    "                else:\n",
    "                    sentence.append(actions[np.argmax(res)])\n",
    "\n",
    "            if len(sentence) > 5: \n",
    "                sentence = sentence[-5:]\n",
    "\n",
    "            # Viz probabilities\n",
    "            image = prob_viz(res, actions, image, colors)\n",
    "            \n",
    "        cv2.rectangle(image, (0,0), (640, 40), (245, 117, 16), -1)\n",
    "        cv2.putText(image, ' '.join(sentence), (3,30), \n",
    "                       cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        # Show to screen\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res[np.argmax(res)] > threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(num_sequences,30,1662)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict(np.expand_dims(X_test[0], axis=0))"
   ]
  }
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