RealTimeSignLanguageDetection.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# LSTM Sign Language Detection by Avishake Adhikary\n",
    "\n",
    "![Amity University](https://upload.wikimedia.org/wikipedia/en/thumb/d/df/Amity_University_Kolkata.svg/1200px-Amity_University_Kolkata.svg.png)\n",
    "\n",
    "## Masters of Computer Applications\n",
    "## Amity University Kolkata\n",
    "#### Under the guidance of Prof. Soma Debnath\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QxYU2phiPrqQ"
   },
   "source": [
    "**LSTM MODEL**\n",
    "1. Install and import dependencies\n",
    "2. Keypoints using MP Holistic\n",
    "3. Extract Keypoints\n",
    "4. Setup Folders for Collection\n",
    "5. Collect Keypoints Values for Training and Testing\n",
    "6. Preprocess Data and Create Labels and Features\n",
    "7. Build and Train LSTM Neural Network\n",
    "8. Make Predictions\n",
    "9. Save Weights\n",
    "10. Evaluation using Confusion Matrix and Accuracy\n",
    "11. Test in Real Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B2vsTy0iSs4n"
   },
   "source": [
    "# Install and Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3704,
     "status": "ok",
     "timestamp": 1656251963057,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
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    "outputId": "308f88f6-fdff-4881-bfeb-28e45b711b60",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install tensorflow opencv-python mediapipe scikit-learn matplotlib tensorflowjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1656251964679,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
    "id": "zyk_Cj1lTBx4"
   },
   "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": {
    "id": "S5mNukEBUexq"
   },
   "source": [
    "# Keypoints using MP Holistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 595,
     "status": "ok",
     "timestamp": 1656252228703,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
    "id": "acdAFfVgUl8Q"
   },
   "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": {
    "executionInfo": {
     "elapsed": 616,
     "status": "ok",
     "timestamp": 1656252305033,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
    "id": "xjqvRhIgU3ED"
   },
   "outputs": [],
   "source": [
    "def mediapipe_detection(image, model):\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR-CONVERSION BGR-to-RGB\n",
    "    image.flags.writeable = False                  # Convert image to not-writeable\n",
    "    results = model.process(image)                 # Make prediction\n",
    "    image.flags.writeable = True                   # Convert image to writeable \n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR-COVERSION RGB-to-BGR\n",
    "    return image, results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 378,
     "status": "ok",
     "timestamp": 1656252384608,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
    "id": "dMNNayFJVPsc"
   },
   "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": {
    "executionInfo": {
     "elapsed": 605,
     "status": "ok",
     "timestamp": 1656252433029,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
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   },
   "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": {
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     "status": "ok",
     "timestamp": 1656252693125,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
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    },
    "id": "6vqu_ZooVqvF"
   },
   "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"
     ]
    }
   ],
   "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()"
   ]
  },
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   "metadata": {
    "colab": {
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    },
    "executionInfo": {
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     "status": "error",
     "timestamp": 1656252542997,
     "user": {
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    },
    "id": "YimPM9LgVwgV",
    "outputId": "0a48c792-cf94-4f5c-a40e-1025265bf220"
   },
   "outputs": [],
   "source": [
    "draw_landmarks(frame, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "executionInfo": {
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     "status": "error",
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    },
    "id": "IiG71qE5VxSr",
    "outputId": "db59cc43-275e-44ae-c5d8-cb6598e5892f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19b82035300>"
      ]
     },
     "execution_count": 8,
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Ty29e7QgLXa"
   },
   "source": [
    "# Extract Keypoint Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "x8y1FlSUhCwQ"
   },
   "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": "markdown",
   "metadata": {
    "id": "XCUspdlZhc7f"
   },
   "source": [
    "# Setup Folders for Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 378,
     "status": "ok",
     "timestamp": 1656256035347,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
    "id": "DyPx8mB0hgUs"
   },
   "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(['cat', 'food', 'help'])\n",
    "\n",
    "# Thirty videos worth of data\n",
    "no_sequences = 30\n",
    "\n",
    "# Videos are going to be 30 frames in length\n",
    "sequence_length = 30\n",
    "\n",
    "# Folder start\n",
    "start_folder = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "error",
     "timestamp": 1656256036925,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
    "id": "4PcPkJnshi-8",
    "outputId": "d36e0106-54d6-4b8d-dbed-e8a1c7a1c647"
   },
   "outputs": [],
   "source": [
    "signs = ['cat','food','help']\n",
    "\n",
    "parent_folder = 'MP_Data'\n",
    "\n",
    "if not os.path.exists(parent_folder):\n",
    "    os.mkdir(parent_folder)\n",
    "    for sign in signs:\n",
    "        sign_folder = os.path.join(parent_folder, sign)\n",
    "        os.mkdir(sign_folder)\n",
    "        for i in range(30):\n",
    "            subfolder = os.path.join(sign_folder, str(i))\n",
    "            os.mkdir(subfolder)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OH9h-pJ2hsgz"
   },
   "source": [
    "# Collect Keypoint Values for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 373
    },
    "executionInfo": {
     "elapsed": 642,
     "status": "error",
     "timestamp": 1656255658196,
     "user": {
      "displayName": "Avishake Adhikary",
      "userId": "07253883199703046489"
     },
     "user_tz": -330
    },
    "id": "4m08aX8DhxTk",
    "outputId": "6a36cafb-2354-4bc9-8115-17355dab1479"
   },
   "outputs": [],
   "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",
    "\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(500)\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": 13,
   "metadata": {
    "id": "sommeZnbh8ki"
   },
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t_wqzJTIiB2p"
   },
   "source": [
    "# Preprocess Data and Create Labels and Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "I7gsasOUiFwV"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "0pHKTsQ0iHq9"
   },
   "outputs": [],
   "source": [
    "label_map = {label:num for num, label in enumerate(actions)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "aSLsH2qgpqoH"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cat': 0, 'food': 1, 'help': 2}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "gE7Cgzhlptlo"
   },
   "outputs": [],
   "source": [
    "sequences, labels = [], []\n",
    "for action in actions:\n",
    "    for sequence in np.array(os.listdir(os.path.join(DATA_PATH, action))).astype(int):\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": 18,
   "metadata": {
    "id": "DBN3Pl15p2wK"
   },
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "wUwO3RHWp7aC"
   },
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "dUzql9nSp9bp"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TboyXNdaqBXa"
   },
   "source": [
    "# Build and Train LSTM Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "ftwthsRCqD83"
   },
   "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": 22,
   "metadata": {
    "id": "o5by_FSSqF5z"
   },
   "outputs": [],
   "source": [
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir=log_dir)\n",
    "#While training access tensorboard\n",
    "#tensorboard --logdir=."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "Pe-dANRGqHkD"
   },
   "outputs": [],
   "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": 24,
   "metadata": {
    "id": "mwaSGrThqJ36"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "0qE4K3bvqMLI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "3/3 [==============================] - 5s 88ms/step - loss: 1.2623 - categorical_accuracy: 0.2706\n",
      "Epoch 2/2000\n",
      "3/3 [==============================] - 0s 109ms/step - loss: 3.2268 - categorical_accuracy: 0.3294\n",
      "Epoch 3/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 2.5173 - categorical_accuracy: 0.3529\n",
      "Epoch 4/2000\n",
      "3/3 [==============================] - 0s 100ms/step - loss: 3.1427 - categorical_accuracy: 0.2941\n",
      "Epoch 5/2000\n",
      "3/3 [==============================] - 0s 89ms/step - loss: 3.2227 - categorical_accuracy: 0.2000\n",
      "Epoch 6/2000\n",
      "3/3 [==============================] - 0s 89ms/step - loss: 1.4476 - categorical_accuracy: 0.3765\n",
      "Epoch 7/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 1.0728 - categorical_accuracy: 0.1294\n",
      "Epoch 8/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.9672 - categorical_accuracy: 0.3529\n",
      "Epoch 9/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.8702 - categorical_accuracy: 0.5412\n",
      "Epoch 10/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.8030 - categorical_accuracy: 0.6235\n",
      "Epoch 11/2000\n",
      "3/3 [==============================] - 0s 105ms/step - loss: 0.6635 - categorical_accuracy: 0.8118\n",
      "Epoch 12/2000\n",
      "3/3 [==============================] - 0s 128ms/step - loss: 0.4741 - categorical_accuracy: 0.9059\n",
      "Epoch 13/2000\n",
      "3/3 [==============================] - 0s 125ms/step - loss: 1.9591 - categorical_accuracy: 0.3765\n",
      "Epoch 14/2000\n",
      "3/3 [==============================] - 0s 121ms/step - loss: 1.1219 - categorical_accuracy: 0.3412\n",
      "Epoch 15/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.9713 - categorical_accuracy: 0.3412\n",
      "Epoch 16/2000\n",
      "3/3 [==============================] - 0s 96ms/step - loss: 1.0183 - categorical_accuracy: 0.3412\n",
      "Epoch 17/2000\n",
      "3/3 [==============================] - 0s 128ms/step - loss: 1.0122 - categorical_accuracy: 0.6706\n",
      "Epoch 18/2000\n",
      "3/3 [==============================] - 0s 117ms/step - loss: 0.8712 - categorical_accuracy: 0.6235\n",
      "Epoch 19/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 0.7869 - categorical_accuracy: 0.4118\n",
      "Epoch 20/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.6469 - categorical_accuracy: 0.6706\n",
      "Epoch 21/2000\n",
      "3/3 [==============================] - 0s 152ms/step - loss: 0.5675 - categorical_accuracy: 0.6588\n",
      "Epoch 22/2000\n",
      "3/3 [==============================] - 0s 97ms/step - loss: 1.0486 - categorical_accuracy: 0.5294\n",
      "Epoch 23/2000\n",
      "3/3 [==============================] - 0s 99ms/step - loss: 1.7212 - categorical_accuracy: 0.6118\n",
      "Epoch 24/2000\n",
      "3/3 [==============================] - 0s 103ms/step - loss: 0.6505 - categorical_accuracy: 0.6706\n",
      "Epoch 25/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.8627 - categorical_accuracy: 0.5647\n",
      "Epoch 26/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 1.5865 - categorical_accuracy: 0.3765\n",
      "Epoch 27/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 1.1342 - categorical_accuracy: 0.4118\n",
      "Epoch 28/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.7474 - categorical_accuracy: 0.6118\n",
      "Epoch 29/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.6709 - categorical_accuracy: 0.6235\n",
      "Epoch 30/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.5981 - categorical_accuracy: 0.6706\n",
      "Epoch 31/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.5630 - categorical_accuracy: 0.6588\n",
      "Epoch 32/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 0.5065 - categorical_accuracy: 0.6588\n",
      "Epoch 33/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.4807 - categorical_accuracy: 0.6706\n",
      "Epoch 34/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 0.4747 - categorical_accuracy: 0.6000\n",
      "Epoch 35/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.4532 - categorical_accuracy: 0.6824\n",
      "Epoch 36/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 1.9742 - categorical_accuracy: 0.5529\n",
      "Epoch 37/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 1.2375 - categorical_accuracy: 0.3529\n",
      "Epoch 38/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 1.1723 - categorical_accuracy: 0.3294\n",
      "Epoch 39/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 1.1346 - categorical_accuracy: 0.2824\n",
      "Epoch 40/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 1.1197 - categorical_accuracy: 0.6235\n",
      "Epoch 41/2000\n",
      "3/3 [==============================] - 0s 83ms/step - loss: 1.1113 - categorical_accuracy: 0.4588\n",
      "Epoch 42/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 1.1039 - categorical_accuracy: 0.3412\n",
      "Epoch 43/2000\n",
      "3/3 [==============================] - 0s 98ms/step - loss: 1.0997 - categorical_accuracy: 0.1882\n",
      "Epoch 44/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 1.0937 - categorical_accuracy: 0.0824\n",
      "Epoch 45/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 1.0855 - categorical_accuracy: 0.3294\n",
      "Epoch 46/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 1.0743 - categorical_accuracy: 0.3294\n",
      "Epoch 47/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 1.0577 - categorical_accuracy: 0.3294\n",
      "Epoch 48/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 1.0134 - categorical_accuracy: 0.6706\n",
      "Epoch 49/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.8670 - categorical_accuracy: 0.6118\n",
      "Epoch 50/2000\n",
      "3/3 [==============================] - 0s 89ms/step - loss: 1.0406 - categorical_accuracy: 0.3294\n",
      "Epoch 51/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 1.0374 - categorical_accuracy: 0.3294\n",
      "Epoch 52/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 1.0197 - categorical_accuracy: 0.3294\n",
      "Epoch 53/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.9936 - categorical_accuracy: 0.3294\n",
      "Epoch 54/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.9571 - categorical_accuracy: 0.3294\n",
      "Epoch 55/2000\n",
      "3/3 [==============================] - 0s 83ms/step - loss: 0.9076 - categorical_accuracy: 0.3529\n",
      "Epoch 56/2000\n",
      "3/3 [==============================] - 0s 111ms/step - loss: 0.8202 - categorical_accuracy: 0.7176\n",
      "Epoch 57/2000\n",
      "3/3 [==============================] - 0s 90ms/step - loss: 0.6641 - categorical_accuracy: 0.8353\n",
      "Epoch 58/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.4986 - categorical_accuracy: 0.8588\n",
      "Epoch 59/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.7825 - categorical_accuracy: 0.7647\n",
      "Epoch 60/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.8756 - categorical_accuracy: 0.6000\n",
      "Epoch 61/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 1.3185 - categorical_accuracy: 0.3882\n",
      "Epoch 62/2000\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.6421 - categorical_accuracy: 0.6706\n",
      "Epoch 63/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 0.6140 - categorical_accuracy: 0.9882\n",
      "Epoch 64/2000\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.5341 - categorical_accuracy: 0.9765\n",
      "Epoch 65/2000\n",
      "3/3 [==============================] - 0s 90ms/step - loss: 0.3792 - categorical_accuracy: 0.9294\n",
      "Epoch 66/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.2904 - categorical_accuracy: 0.9059\n",
      "Epoch 67/2000\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.8019 - categorical_accuracy: 0.6353\n",
      "Epoch 68/2000\n",
      "3/3 [==============================] - 0s 90ms/step - loss: 0.4875 - categorical_accuracy: 0.6941\n",
      "Epoch 69/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.6132 - categorical_accuracy: 0.6706\n",
      "Epoch 70/2000\n",
      "3/3 [==============================] - 0s 96ms/step - loss: 0.4499 - categorical_accuracy: 0.8118\n",
      "Epoch 71/2000\n",
      "3/3 [==============================] - 0s 96ms/step - loss: 0.4445 - categorical_accuracy: 0.6706\n",
      "Epoch 72/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.4414 - categorical_accuracy: 0.6706\n",
      "Epoch 73/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 0.4127 - categorical_accuracy: 0.9059\n",
      "Epoch 74/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.4177 - categorical_accuracy: 0.6941\n",
      "Epoch 75/2000\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.3969 - categorical_accuracy: 0.8824\n",
      "Epoch 76/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.3628 - categorical_accuracy: 0.7765\n",
      "Epoch 77/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.3536 - categorical_accuracy: 0.9882\n",
      "Epoch 78/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.3207 - categorical_accuracy: 0.9882\n",
      "Epoch 79/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.2994 - categorical_accuracy: 0.9059\n",
      "Epoch 80/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.2206 - categorical_accuracy: 0.9882\n",
      "Epoch 81/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.1706 - categorical_accuracy: 0.9882\n",
      "Epoch 82/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.2490 - categorical_accuracy: 0.8353\n",
      "Epoch 83/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 0.7454 - categorical_accuracy: 0.6941\n",
      "Epoch 84/2000\n",
      "3/3 [==============================] - 0s 89ms/step - loss: 0.3892 - categorical_accuracy: 0.7647\n",
      "Epoch 85/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.4018 - categorical_accuracy: 0.7294\n",
      "Epoch 86/2000\n",
      "3/3 [==============================] - 0s 90ms/step - loss: 0.2942 - categorical_accuracy: 0.8353\n",
      "Epoch 87/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 0.2075 - categorical_accuracy: 0.9882\n",
      "Epoch 88/2000\n",
      "3/3 [==============================] - 0s 83ms/step - loss: 0.2229 - categorical_accuracy: 0.9765\n",
      "Epoch 89/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.1869 - categorical_accuracy: 0.9882\n",
      "Epoch 90/2000\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.1650 - categorical_accuracy: 0.9882\n",
      "Epoch 91/2000\n",
      "3/3 [==============================] - 0s 83ms/step - loss: 0.1267 - categorical_accuracy: 0.9882\n",
      "Epoch 92/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.1232 - categorical_accuracy: 0.9882\n",
      "Epoch 93/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.0859 - categorical_accuracy: 0.9882\n",
      "Epoch 94/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.1027 - categorical_accuracy: 0.9882\n",
      "Epoch 95/2000\n",
      "3/3 [==============================] - 0s 90ms/step - loss: 0.0911 - categorical_accuracy: 0.9882\n",
      "Epoch 96/2000\n",
      "3/3 [==============================] - 0s 102ms/step - loss: 0.0854 - categorical_accuracy: 0.9882\n",
      "Epoch 97/2000\n",
      "3/3 [==============================] - 0s 96ms/step - loss: 0.0576 - categorical_accuracy: 0.9882\n",
      "Epoch 98/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.1110 - categorical_accuracy: 0.9529\n",
      "Epoch 99/2000\n",
      "3/3 [==============================] - 0s 98ms/step - loss: 0.2405 - categorical_accuracy: 0.9176\n",
      "Epoch 100/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.4874 - categorical_accuracy: 0.8471\n",
      "Epoch 101/2000\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.4789 - categorical_accuracy: 0.8235\n",
      "Epoch 102/2000\n",
      "3/3 [==============================] - 0s 97ms/step - loss: 0.1998 - categorical_accuracy: 0.9412\n",
      "Epoch 103/2000\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.1790 - categorical_accuracy: 0.9294\n",
      "Epoch 104/2000\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.1380 - categorical_accuracy: 0.9882\n",
      "Epoch 105/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.1190 - categorical_accuracy: 0.9882\n",
      "Epoch 106/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.1023 - categorical_accuracy: 0.9882\n",
      "Epoch 107/2000\n",
      "3/3 [==============================] - 0s 100ms/step - loss: 0.0948 - categorical_accuracy: 0.9882\n",
      "Epoch 108/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.0771 - categorical_accuracy: 0.9882\n",
      "Epoch 109/2000\n",
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    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 498/2000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 83ms/step - loss: 2.5525e-07 - categorical_accuracy: 1.0000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 86ms/step - loss: 5.3294e-08 - categorical_accuracy: 1.0000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 113ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 94ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1870/2000\n",
      "3/3 [==============================] - 0s 104ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1871/2000\n",
      "3/3 [==============================] - 0s 97ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1872/2000\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1873/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 95ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1875/2000\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1876/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1877/2000\n",
      "3/3 [==============================] - 0s 123ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1878/2000\n",
      "3/3 [==============================] - 0s 135ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1879/2000\n",
      "3/3 [==============================] - 0s 99ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1880/2000\n",
      "3/3 [==============================] - 0s 99ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 89ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 88ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1883/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1884/2000\n",
      "3/3 [==============================] - 0s 99ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1885/2000\n",
      "3/3 [==============================] - 0s 141ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1886/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1887/2000\n",
      "3/3 [==============================] - 0s 105ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1888/2000\n",
      "3/3 [==============================] - 0s 106ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 100ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 107ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1892/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1893/2000\n",
      "3/3 [==============================] - 0s 77ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "Epoch 1898/2000\n",
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      "Epoch 1899/2000\n",
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      "Epoch 1900/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1901/2000\n",
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      "Epoch 1903/2000\n",
      "3/3 [==============================] - 0s 134ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 128ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 115ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
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      "3/3 [==============================] - 0s 116ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1932/2000\n",
      "3/3 [==============================] - 0s 115ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1933/2000\n",
      "3/3 [==============================] - 0s 119ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1934/2000\n",
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      "Epoch 1935/2000\n",
      "3/3 [==============================] - 0s 109ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1936/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1937/2000\n",
      "3/3 [==============================] - 0s 104ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1938/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1939/2000\n",
      "3/3 [==============================] - 0s 97ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1940/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1941/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1942/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1943/2000\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1944/2000\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1945/2000\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1946/2000\n",
      "3/3 [==============================] - 0s 100ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1947/2000\n",
      "3/3 [==============================] - 0s 105ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1948/2000\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1949/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1950/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1951/2000\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1952/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 86ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1953/2000\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1954/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1955/2000\n",
      "3/3 [==============================] - 0s 89ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1956/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1957/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1958/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1959/2000\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1960/2000\n",
      "3/3 [==============================] - 0s 79ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1961/2000\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1962/2000\n",
      "3/3 [==============================] - 0s 79ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1963/2000\n",
      "3/3 [==============================] - 0s 79ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1964/2000\n",
      "3/3 [==============================] - 0s 80ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1965/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1966/2000\n",
      "3/3 [==============================] - 0s 79ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1967/2000\n",
      "3/3 [==============================] - 0s 103ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1968/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1969/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1970/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1971/2000\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1972/2000\n",
      "3/3 [==============================] - 0s 86ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1973/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1974/2000\n",
      "3/3 [==============================] - 0s 83ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1975/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1976/2000\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1977/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1978/2000\n",
      "3/3 [==============================] - 0s 107ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1979/2000\n",
      "3/3 [==============================] - 0s 133ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1980/2000\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1981/2000\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1982/2000\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1983/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1984/2000\n",
      "3/3 [==============================] - 0s 101ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1985/2000\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1986/2000\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1987/2000\n",
      "3/3 [==============================] - 0s 84ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1988/2000\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1989/2000\n",
      "3/3 [==============================] - 0s 99ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1990/2000\n",
      "3/3 [==============================] - 0s 103ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1991/2000\n",
      "3/3 [==============================] - 0s 103ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1992/2000\n",
      "3/3 [==============================] - 0s 98ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1993/2000\n",
      "3/3 [==============================] - 0s 127ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1994/2000\n",
      "3/3 [==============================] - 0s 104ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1995/2000\n",
      "3/3 [==============================] - 0s 101ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1996/2000\n",
      "3/3 [==============================] - 0s 94ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1997/2000\n",
      "3/3 [==============================] - 0s 128ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1998/2000\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 1999/2000\n",
      "3/3 [==============================] - 0s 109ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n",
      "Epoch 2000/2000\n",
      "3/3 [==============================] - 0s 77ms/step - loss: 0.0000e+00 - categorical_accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x19b80adfe80>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "iDl33ZbFqN2t"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 30, 64)            442112    \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 30, 128)           98816     \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 64)                49408     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 99        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 596,675\n",
      "Trainable params: 596,675\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=664x591>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import visualkeras\n",
    "from PIL import ImageFont\n",
    "font = ImageFont.truetype(\"arial.ttf\", 12)\n",
    "visualkeras.layered_view(model, legend=True, font=font,spacing=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UhYu2JVPqbp-"
   },
   "source": [
    "# Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "7tE8zfpYqeDe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 553ms/step\n"
     ]
    }
   ],
   "source": [
    "res = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "aNRZlz79qiAA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'food'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res[4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "AtZeBCPgqjz4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'food'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(y_test[4])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DPsjE16HqnKo"
   },
   "source": [
    "# Save Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "Ld-yL7oFqpgg"
   },
   "outputs": [],
   "source": [
    "model.save('./model.h5')\n",
    "model.save_weights('./model_weights.h5');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dTL-aUUhqrKK"
   },
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "model = tf.keras.models.load_model('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "3qphZ5ktqsMe"
   },
   "outputs": [],
   "source": [
    "model.load_weights('model_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8daBYvRkqvGx"
   },
   "source": [
    "# Evaluation using Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "lPcHFXpsqyaX"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "8iVft9Y3qz61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 515ms/step\n"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "hNBgddNGq1le"
   },
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_test, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "RgLBnVFnq3iJ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[3, 0],\n",
       "        [0, 2]],\n",
       "\n",
       "       [[3, 0],\n",
       "        [0, 2]],\n",
       "\n",
       "       [[4, 0],\n",
       "        [0, 1]]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "tphmefeRq4Qk"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XoPKuYiFq9eh"
   },
   "source": [
    "# Realtime Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "W6BrEyu7rADM"
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "29FjbYT-rBc-"
   },
   "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": 42,
   "metadata": {
    "id": "sZF5_CJWrU0a"
   },
   "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",
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      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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      "help\n"
     ]
    }
   ],
   "source": [
    "# 1. New detection variables\n",
    "sequence = []\n",
    "sentence = []\n",
    "predictions = []\n",
    "threshold = 0.5\n",
    "\n",
    "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",
    "        # 2. Prediction logic\n",
    "        keypoints = extract_keypoints(results)\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",
    "            predictions.append(np.argmax(res))\n",
    "            \n",
    "            \n",
    "        #3. Viz logic\n",
    "            if np.unique(predictions[-10:])[0]==np.argmax(res): \n",
    "                if res[np.argmax(res)] > threshold: \n",
    "                    \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": [
    "!pip install numpy --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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\n",
    "import tensorflow as tf\n",
    "\n",
    "signs = ['hello', 'thanks', 'iloveyou']\n",
    "actions = np.array(['hello', 'thanks', 'iloveyou'])\n",
    "\n",
    "\n",
    "mp_holistic = mp.solutions.holistic # Holistic model\n",
    "mp_drawing = mp.solutions.drawing_utils # Drawing utilities\n",
    "\n",
    "def mediapipe_detection(image, model):\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR-CONVERSION BGR-to-RGB\n",
    "    image.flags.writeable = False                  # Convert image to not-writeable\n",
    "    results = model.process(image)                 # Make prediction\n",
    "    image.flags.writeable = True                   # Convert image to writeable \n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR-COVERSION RGB-to-BGR\n",
    "    return image, results\n",
    "\n",
    "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\n",
    "\n",
    "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",
    "                             ) \n",
    "    \n",
    "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])\n",
    "    \n",
    "# 1. New detection variables\n",
    "sequence = []\n",
    "sentence = []\n",
    "predictions = []\n",
    "threshold = 0.5\n",
    "\n",
    "model = tf.keras.models.load_model('model.h5')\n",
    "model.load_weights('model_weights.h5')\n",
    "model.summary()\n",
    "\n",
    "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",
    "        # 2. Prediction logic\n",
    "        keypoints = extract_keypoints(results)\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",
    "            predictions.append(np.argmax(res))\n",
    "            \n",
    "            \n",
    "        #3. Viz logic\n",
    "            if np.unique(predictions[-10:])[0]==np.argmax(res): \n",
    "                if res[np.argmax(res)] > threshold: \n",
    "                    \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), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        #print(sentence)\n",
    "        #word = sentence[-1:]\n",
    "        #convert_to_audio(word[0]) if (len(word) != 0) else print(\"word not detected yet\")\n",
    "        #convert_to_audio(word[0])\n",
    "        \n",
    "        # Show to screen\n",
    "        cv2.imshow('Realtime LSTM Sign Language Detection', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  }
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