sign_language_detection_detailed_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ae0bd76b",
   "metadata": {},
   "source": [
    "# Sign Language Detection with LSTM and Mediapipe\n",
    "[Mediapipe](https://mediapipe.dev/).\n",
    "\n",
    "This tutorial was created and mostly copied from [Nicholas Renotte Sign Language Detection using ACTION RECOGNITION with Python | LSTM Deep Learning Model](https://youtu.be/doDUihpj6ro). [Original Project Github Link](https://github.com/nicknochnack/ActionDetectionforSignLanguage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02317557",
   "metadata": {},
   "source": [
    "# Contents\n",
    "\n",
    "1. [Import Dependencies](#1)\n",
    "1. [Keypoints using MP Holistic](#2)\n",
    "    1. [Holistic Detection with FPS Counter](#14)\n",
    "1. [Extract Keypoint Values](#3)\n",
    "    1. [Extract from the Pose Results](#4)\n",
    "        1. [Make a List for Pose of Frame](#5)\n",
    "        1. [Make an Numpy Array for Pose of Frame](#6)\n",
    "        1. [Determine for Input for LSTM Network](#7)\n",
    "    1. [Extract Keypoints Values from Face and Hands Results](#8)\n",
    "    1. [Error Handling for No Results Situations](#9)\n",
    "    1. [The Final Input Arrays and Function](#10)\n",
    "1. [Setup Folders for Collection](#11)\n",
    "1. [Collect Keypoint Values for Training and Testing](#12)\n",
    "1. [Preprocess Data and Create Labels and Features](#13)\n",
    "    1. [Split Train and Test Data](#17)\n",
    "1. [Build and Train LSTM Neural Network](#15)\n",
    "1. [Make Precitions](#16)\n",
    "1. [Save Model](#18)\n",
    "1. [Evaluation using Confusion Matrix and Accuracy](#19)\n",
    "    1. [Evaluating on Training Set](#21)\n",
    "    1. [Evaluating on Test Set](#22)\n",
    "\n",
    "1. [Test in Real Time](#20)\n",
    "    1. [Expected Input for LSTM Model](#23)\n",
    "    1. [Visualization: How to Works](#25)\n",
    "    1. [Real Time Detection with Webcam](#24)\n",
    "1. [Appendix](#99)\n",
    "    1. [Results of Pose Landmarks](#100)\n",
    "    1. [Results of Pose Landmarks](#100)\n",
    "    1. [os.makedirs file already exists error](#102)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07819255",
   "metadata": {},
   "source": [
    "<a id=\"1\"></a>\n",
    "# Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "604acae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "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 keras as k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f208d34b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf: 2.5.0\n",
      "Keras 2.5.0\n",
      "cv2: 4.5.5\n",
      "np: 1.19.5\n"
     ]
    }
   ],
   "source": [
    "print(\"tf:\", tf.__version__)\n",
    "print(\"Keras\", k.__version__)\n",
    "print(\"cv2:\", cv2.__version__)\n",
    "print(\"np:\", np.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd49d9b",
   "metadata": {},
   "source": [
    "<a id=\"2\"></a>\n",
    "# 2. Keypoints using MP Holistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2d7fa5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.holistic # Holistic model\n",
    "mp_drawing = mp.solutions.drawing_utils # Drawing utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f53dcbb7",
   "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": 5,
   "id": "5243dab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_landmarks(image, results):\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS) # 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": 6,
   "id": "c623e2a5",
   "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_CONTOURS, \n",
    "                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1), \n",
    "                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                             ) \n",
    "    # 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": 14,
   "id": "1ff87512",
   "metadata": {},
   "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",
    "    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('Results', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "            \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02891332",
   "metadata": {},
   "source": [
    "<a id=\"14\"></a>\n",
    "## 2.A. Holistic Detection with FPS Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "274a188d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "# used to record the time when we processed last frame\n",
    "prev_frame_time = 0\n",
    "# used to record the time at which we processed current frame\n",
    "new_frame_time = 0\n",
    "\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(model_complexity=1, min_detection_confidence=0.5, \n",
    "                          min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        \n",
    "        # time when we finish processing for this frame\n",
    "        new_frame_time = time.time()\n",
    "        \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",
    "        # fps will be number of frame processed in given time frame\n",
    "        # since their will be most of time error of 0.001 second\n",
    "        # we will be subtracting it to get more accurate result\n",
    "        fps = 1/(new_frame_time-prev_frame_time)\n",
    "        prev_frame_time = new_frame_time\n",
    "\n",
    "        # converting the fps into integer\n",
    "        fps = int(fps)\n",
    "        \n",
    "        # converting the fps to string so that we can display it on frame\n",
    "        # by using putText function\n",
    "        fps = str(fps)\n",
    "        font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "        \n",
    "        # putting the FPS count on the frame\n",
    "        cv2.putText(image, fps, (7, 70), font, 1, (100, 255, 0), 3, cv2.LINE_AA)\n",
    "    \n",
    "        cv2.imshow('Webcam with Detections', image)\n",
    "\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f676484d",
   "metadata": {},
   "source": [
    "<center>\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Info:</b> Getting 15-20 fps right now.\n",
    "</div>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6440e0e7",
   "metadata": {},
   "source": [
    "<a id=\"3\"></a>\n",
    "# 3. Extract Keypoint Values\n",
    "\n",
    "[Results of Pose Landmarks are shown in the appendix.](#100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "19cb5156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mediapipe.python.solution_base.SolutionOutputs"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b6335a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22fd2f8f970>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_landmarks(frame, results)\n",
    "plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f0f8b6",
   "metadata": {},
   "source": [
    "<a id=\"4\"></a>\n",
    "## 3.A. Extract Keypoints Values from the Pose Results\n",
    "[Results of Pose Landmarks are shown in the appendix.](#100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "274b2df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results.pose_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22447e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x: 0.5141614079475403\n",
       "y: 0.6165520548820496\n",
       "z: -1.25775945186615\n",
       "visibility: 0.999890923500061"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example\n",
    "results.pose_landmarks.landmark[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea2ef43d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5141614079475403"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.pose_landmarks.landmark[0].x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f423d4fe",
   "metadata": {},
   "source": [
    "<a id=\"5\"></a>\n",
    "### 3.A.a. Make a List for Pose of Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01a47614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 :  [ 0.51416141  0.61655205 -1.25775945  0.99989092]\n",
      "1 :  [ 0.55111563  0.54507172 -1.17968035  0.99981564]\n",
      "2 :  [ 0.56976366  0.54401785 -1.17947507  0.99971282]\n",
      "3 :  [ 0.58554864  0.54434967 -1.17963982  0.99970639]\n",
      "4 :  [ 0.48639673  0.54793304 -1.16285384  0.99985468]\n",
      "5 :  [ 0.46225613  0.54766738 -1.16211605  0.99984491]\n",
      "6 :  [ 0.44253802  0.54701072 -1.16233289  0.99989635]\n",
      "7 :  [ 0.61597383  0.57685852 -0.67488194  0.99964345]\n",
      "8 :  [ 0.42484012  0.57580578 -0.57358116  0.99991494]\n",
      "9 :  [ 0.55367172  0.68515056 -1.07539344  0.99987185]\n",
      "10 :  [ 0.47169375  0.68425041 -1.0490272   0.999937  ]\n",
      "11 :  [ 0.75430995  0.93799049 -0.38094968  0.99431765]\n",
      "12 :  [ 0.27315301  0.89302522 -0.35905725  0.99938464]\n",
      "13 :  [ 0.89079112  1.4007318  -0.36563292  0.18669201]\n",
      "14 :  [-0.06555679  1.024261   -1.08029437  0.9212842 ]\n",
      "15 :  [ 0.90250504  1.7410413  -0.52428687  0.10095713]\n",
      "16 :  [ 0.13169168  0.63971478 -2.03506994  0.87573814]\n",
      "17 :  [ 0.93885076  1.85246527 -0.60532808  0.1005643 ]\n",
      "18 :  [ 0.21211712  0.48843393 -2.25936913  0.84196973]\n",
      "19 :  [ 0.88130438  1.84434354 -0.63183135  0.14808102]\n",
      "20 :  [ 0.23927827  0.48195806 -2.11360312  0.86006099]\n",
      "21 :  [ 0.85254794  1.80916297 -0.55631638  0.15329427]\n",
      "22 :  [ 0.23165461  0.56120241 -2.01359367  0.84888852]\n",
      "23 :  [ 6.37438178e-01  1.76785994e+00 -1.20335758e-01  7.48293765e-04]\n",
      "24 :  [2.98382699e-01 1.74717104e+00 1.25824362e-01 1.11842249e-03]\n",
      "25 :  [ 6.13167524e-01  2.48493314e+00 -2.74459720e-01  1.07535778e-03]\n",
      "26 :  [3.04855913e-01 2.47705078e+00 4.00484174e-01 7.30420754e-04]\n",
      "27 :  [5.96843660e-01 3.12273359e+00 3.97073299e-01 1.12893220e-04]\n",
      "28 :  [2.94160128e-01 3.10096312e+00 1.09019637e+00 1.65701222e-05]\n",
      "29 :  [6.02725923e-01 3.21748734e+00 4.20719147e-01 5.06659781e-05]\n",
      "30 :  [2.83836752e-01 3.18583441e+00 1.14547956e+00 4.03216982e-05]\n",
      "31 :  [ 5.40249467e-01  3.31736970e+00 -3.65602314e-01  5.76123639e-05]\n",
      "32 :  [3.30280453e-01 3.31952262e+00 3.52497816e-01 9.31877585e-05]\n"
     ]
    }
   ],
   "source": [
    "for i, res in enumerate(results.pose_landmarks.landmark):\n",
    "    test = np.array([res.x, res.y, res.z, res.visibility])\n",
    "    print(i,\": \", test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4940b54e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.51416141,  0.61655205, -1.25775945,  0.99989092]),\n",
       " array([ 0.55111563,  0.54507172, -1.17968035,  0.99981564]),\n",
       " array([ 0.56976366,  0.54401785, -1.17947507,  0.99971282]),\n",
       " array([ 0.58554864,  0.54434967, -1.17963982,  0.99970639]),\n",
       " array([ 0.48639673,  0.54793304, -1.16285384,  0.99985468]),\n",
       " array([ 0.46225613,  0.54766738, -1.16211605,  0.99984491]),\n",
       " array([ 0.44253802,  0.54701072, -1.16233289,  0.99989635]),\n",
       " array([ 0.61597383,  0.57685852, -0.67488194,  0.99964345]),\n",
       " array([ 0.42484012,  0.57580578, -0.57358116,  0.99991494]),\n",
       " array([ 0.55367172,  0.68515056, -1.07539344,  0.99987185]),\n",
       " array([ 0.47169375,  0.68425041, -1.0490272 ,  0.999937  ]),\n",
       " array([ 0.75430995,  0.93799049, -0.38094968,  0.99431765]),\n",
       " array([ 0.27315301,  0.89302522, -0.35905725,  0.99938464]),\n",
       " array([ 0.89079112,  1.4007318 , -0.36563292,  0.18669201]),\n",
       " array([-0.06555679,  1.024261  , -1.08029437,  0.9212842 ]),\n",
       " array([ 0.90250504,  1.7410413 , -0.52428687,  0.10095713]),\n",
       " array([ 0.13169168,  0.63971478, -2.03506994,  0.87573814]),\n",
       " array([ 0.93885076,  1.85246527, -0.60532808,  0.1005643 ]),\n",
       " array([ 0.21211712,  0.48843393, -2.25936913,  0.84196973]),\n",
       " array([ 0.88130438,  1.84434354, -0.63183135,  0.14808102]),\n",
       " array([ 0.23927827,  0.48195806, -2.11360312,  0.86006099]),\n",
       " array([ 0.85254794,  1.80916297, -0.55631638,  0.15329427]),\n",
       " array([ 0.23165461,  0.56120241, -2.01359367,  0.84888852]),\n",
       " array([ 6.37438178e-01,  1.76785994e+00, -1.20335758e-01,  7.48293765e-04]),\n",
       " array([2.98382699e-01, 1.74717104e+00, 1.25824362e-01, 1.11842249e-03]),\n",
       " array([ 6.13167524e-01,  2.48493314e+00, -2.74459720e-01,  1.07535778e-03]),\n",
       " array([3.04855913e-01, 2.47705078e+00, 4.00484174e-01, 7.30420754e-04]),\n",
       " array([5.96843660e-01, 3.12273359e+00, 3.97073299e-01, 1.12893220e-04]),\n",
       " array([2.94160128e-01, 3.10096312e+00, 1.09019637e+00, 1.65701222e-05]),\n",
       " array([6.02725923e-01, 3.21748734e+00, 4.20719147e-01, 5.06659781e-05]),\n",
       " array([2.83836752e-01, 3.18583441e+00, 1.14547956e+00, 4.03216982e-05]),\n",
       " array([ 5.40249467e-01,  3.31736970e+00, -3.65602314e-01,  5.76123639e-05]),\n",
       " array([3.30280453e-01, 3.31952262e+00, 3.52497816e-01, 9.31877585e-05])]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pose = [] # a list for pose results\n",
    "for res in results.pose_landmarks.landmark:\n",
    "    test = np.array([res.x, res.y, res.z, res.visibility])\n",
    "    pose.append(test)\n",
    "pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "afa3051b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2a382f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pose)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bffc5e8",
   "metadata": {},
   "source": [
    "<a id=\"6\"></a>\n",
    "### 3.A.b. Make an Numpy Array for Pose of Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33df3d61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.14161408e-01,  6.16552055e-01, -1.25775945e+00,\n",
       "         9.99890924e-01],\n",
       "       [ 5.51115632e-01,  5.45071721e-01, -1.17968035e+00,\n",
       "         9.99815643e-01],\n",
       "       [ 5.69763660e-01,  5.44017851e-01, -1.17947507e+00,\n",
       "         9.99712825e-01],\n",
       "       [ 5.85548639e-01,  5.44349670e-01, -1.17963982e+00,\n",
       "         9.99706388e-01],\n",
       "       [ 4.86396730e-01,  5.47933042e-01, -1.16285384e+00,\n",
       "         9.99854684e-01],\n",
       "       [ 4.62256134e-01,  5.47667384e-01, -1.16211605e+00,\n",
       "         9.99844909e-01],\n",
       "       [ 4.42538023e-01,  5.47010720e-01, -1.16233289e+00,\n",
       "         9.99896348e-01],\n",
       "       [ 6.15973830e-01,  5.76858521e-01, -6.74881935e-01,\n",
       "         9.99643445e-01],\n",
       "       [ 4.24840122e-01,  5.75805783e-01, -5.73581159e-01,\n",
       "         9.99914944e-01],\n",
       "       [ 5.53671718e-01,  6.85150564e-01, -1.07539344e+00,\n",
       "         9.99871850e-01],\n",
       "       [ 4.71693754e-01,  6.84250414e-01, -1.04902720e+00,\n",
       "         9.99936998e-01],\n",
       "       [ 7.54309952e-01,  9.37990487e-01, -3.80949676e-01,\n",
       "         9.94317651e-01],\n",
       "       [ 2.73153007e-01,  8.93025219e-01, -3.59057248e-01,\n",
       "         9.99384642e-01],\n",
       "       [ 8.90791118e-01,  1.40073180e+00, -3.65632921e-01,\n",
       "         1.86692014e-01],\n",
       "       [-6.55567870e-02,  1.02426100e+00, -1.08029437e+00,\n",
       "         9.21284199e-01],\n",
       "       [ 9.02505040e-01,  1.74104130e+00, -5.24286866e-01,\n",
       "         1.00957133e-01],\n",
       "       [ 1.31691679e-01,  6.39714777e-01, -2.03506994e+00,\n",
       "         8.75738144e-01],\n",
       "       [ 9.38850760e-01,  1.85246527e+00, -6.05328083e-01,\n",
       "         1.00564301e-01],\n",
       "       [ 2.12117121e-01,  4.88433927e-01, -2.25936913e+00,\n",
       "         8.41969728e-01],\n",
       "       [ 8.81304383e-01,  1.84434354e+00, -6.31831348e-01,\n",
       "         1.48081020e-01],\n",
       "       [ 2.39278272e-01,  4.81958061e-01, -2.11360312e+00,\n",
       "         8.60060990e-01],\n",
       "       [ 8.52547944e-01,  1.80916297e+00, -5.56316376e-01,\n",
       "         1.53294265e-01],\n",
       "       [ 2.31654614e-01,  5.61202407e-01, -2.01359367e+00,\n",
       "         8.48888516e-01],\n",
       "       [ 6.37438178e-01,  1.76785994e+00, -1.20335758e-01,\n",
       "         7.48293765e-04],\n",
       "       [ 2.98382699e-01,  1.74717104e+00,  1.25824362e-01,\n",
       "         1.11842249e-03],\n",
       "       [ 6.13167524e-01,  2.48493314e+00, -2.74459720e-01,\n",
       "         1.07535778e-03],\n",
       "       [ 3.04855913e-01,  2.47705078e+00,  4.00484174e-01,\n",
       "         7.30420754e-04],\n",
       "       [ 5.96843660e-01,  3.12273359e+00,  3.97073299e-01,\n",
       "         1.12893220e-04],\n",
       "       [ 2.94160128e-01,  3.10096312e+00,  1.09019637e+00,\n",
       "         1.65701222e-05],\n",
       "       [ 6.02725923e-01,  3.21748734e+00,  4.20719147e-01,\n",
       "         5.06659781e-05],\n",
       "       [ 2.83836752e-01,  3.18583441e+00,  1.14547956e+00,\n",
       "         4.03216982e-05],\n",
       "       [ 5.40249467e-01,  3.31736970e+00, -3.65602314e-01,\n",
       "         5.76123639e-05],\n",
       "       [ 3.30280453e-01,  3.31952262e+00,  3.52497816e-01,\n",
       "         9.31877585e-05]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark])\n",
    "pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3a4c72f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Pose: 33\n",
      "Shape of Pose: (33, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of Pose:\",len(pose))\n",
    "print(\"Shape of Pose:\", pose.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2bb58fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pose)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b421b058",
   "metadata": {},
   "source": [
    "<a id=\"7\"></a>\n",
    "### 3.A.c. Determine for Input for LSTM Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e2e9a006",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is input format for LSTM \n",
    "pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "734e3ec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Pose: (132,)\n",
      "Pose: \n",
      " [ 5.14161408e-01  6.16552055e-01 -1.25775945e+00  9.99890924e-01\n",
      "  5.51115632e-01  5.45071721e-01 -1.17968035e+00  9.99815643e-01\n",
      "  5.69763660e-01  5.44017851e-01 -1.17947507e+00  9.99712825e-01\n",
      "  5.85548639e-01  5.44349670e-01 -1.17963982e+00  9.99706388e-01\n",
      "  4.86396730e-01  5.47933042e-01 -1.16285384e+00  9.99854684e-01\n",
      "  4.62256134e-01  5.47667384e-01 -1.16211605e+00  9.99844909e-01\n",
      "  4.42538023e-01  5.47010720e-01 -1.16233289e+00  9.99896348e-01\n",
      "  6.15973830e-01  5.76858521e-01 -6.74881935e-01  9.99643445e-01\n",
      "  4.24840122e-01  5.75805783e-01 -5.73581159e-01  9.99914944e-01\n",
      "  5.53671718e-01  6.85150564e-01 -1.07539344e+00  9.99871850e-01\n",
      "  4.71693754e-01  6.84250414e-01 -1.04902720e+00  9.99936998e-01\n",
      "  7.54309952e-01  9.37990487e-01 -3.80949676e-01  9.94317651e-01\n",
      "  2.73153007e-01  8.93025219e-01 -3.59057248e-01  9.99384642e-01\n",
      "  8.90791118e-01  1.40073180e+00 -3.65632921e-01  1.86692014e-01\n",
      " -6.55567870e-02  1.02426100e+00 -1.08029437e+00  9.21284199e-01\n",
      "  9.02505040e-01  1.74104130e+00 -5.24286866e-01  1.00957133e-01\n",
      "  1.31691679e-01  6.39714777e-01 -2.03506994e+00  8.75738144e-01\n",
      "  9.38850760e-01  1.85246527e+00 -6.05328083e-01  1.00564301e-01\n",
      "  2.12117121e-01  4.88433927e-01 -2.25936913e+00  8.41969728e-01\n",
      "  8.81304383e-01  1.84434354e+00 -6.31831348e-01  1.48081020e-01\n",
      "  2.39278272e-01  4.81958061e-01 -2.11360312e+00  8.60060990e-01\n",
      "  8.52547944e-01  1.80916297e+00 -5.56316376e-01  1.53294265e-01\n",
      "  2.31654614e-01  5.61202407e-01 -2.01359367e+00  8.48888516e-01\n",
      "  6.37438178e-01  1.76785994e+00 -1.20335758e-01  7.48293765e-04\n",
      "  2.98382699e-01  1.74717104e+00  1.25824362e-01  1.11842249e-03\n",
      "  6.13167524e-01  2.48493314e+00 -2.74459720e-01  1.07535778e-03\n",
      "  3.04855913e-01  2.47705078e+00  4.00484174e-01  7.30420754e-04\n",
      "  5.96843660e-01  3.12273359e+00  3.97073299e-01  1.12893220e-04\n",
      "  2.94160128e-01  3.10096312e+00  1.09019637e+00  1.65701222e-05\n",
      "  6.02725923e-01  3.21748734e+00  4.20719147e-01  5.06659781e-05\n",
      "  2.83836752e-01  3.18583441e+00  1.14547956e+00  4.03216982e-05\n",
      "  5.40249467e-01  3.31736970e+00 -3.65602314e-01  5.76123639e-05\n",
      "  3.30280453e-01  3.31952262e+00  3.52497816e-01  9.31877585e-05]\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of Pose:\", pose.shape)\n",
    "print(\"Pose: \\n\",pose)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23db9a2",
   "metadata": {},
   "source": [
    "<a id=\"8\"></a>\n",
    "## 3.B. Extract Keypoints Values from Face and Hands Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed64139e",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> There are no visibility values for face and hands\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f0436165",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(468, 3)\n"
     ]
    }
   ],
   "source": [
    "face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark])\n",
    "print(face.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0e4f7513",
   "metadata": {},
   "outputs": [],
   "source": [
    "face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "483425e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of face results:  (1404,)\n",
      "[ 0.5259276   0.68227011 -0.01917047 ...  0.59534889  0.53105265\n",
      "  0.00102406]\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of face results: \", face.shape)\n",
    "print(face)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "19722ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark])\n",
    "rh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cf772a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# right hand\n",
    "rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "39246322",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1ffd5a50",
   "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)",
      "Input \u001b[1;32mIn [34]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# left hand\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m lh \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([[res\u001b[38;5;241m.\u001b[39mx, res\u001b[38;5;241m.\u001b[39my, res\u001b[38;5;241m.\u001b[39mz, res\u001b[38;5;241m.\u001b[39mvisibility] \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mleft_hand_landmarks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlandmark\u001b[49m])\u001b[38;5;241m.\u001b[39mflatten()\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'landmark'"
     ]
    }
   ],
   "source": [
    "# left hand\n",
    "lh = np.array([[res.x, res.y, res.z, res.visibility] for res in results.left_hand_landmarks.landmark]).flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1529c1",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> There is no left hand on the image, so there are no results of the left hand.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a887604",
   "metadata": {},
   "source": [
    "<a id=\"9\"></a>\n",
    "## 3.C. Error Handling for No Results Situations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14582dd8",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Info:</b> If there is not some part of the results on the image like the left hand, we need to handle the error when we want to convert results to inputs. In order to fix it, we will create a NumPy array with zeros as it should be shape of the input. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "96a5bf03",
   "metadata": {},
   "outputs": [],
   "source": [
    "pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark])\n",
    "face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark])\n",
    "rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark])\n",
    "# same as right hand\n",
    "# lh = np.array([[res.x, res.y, res.z, res.visibility] for res in results.left_hand_landmarks.landmark])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1b926311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of pose results:  (33, 4)\n",
      "Shape of face results:  (468, 3)\n",
      "Shape of right hand results:  (21, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of pose results: \", pose.shape)\n",
    "print(\"Shape of face results: \", face.shape)\n",
    "print(\"Shape of right hand results: \", rh.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "684d16ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of pose results:  (132,)\n",
      "Shape of face results:  (1404,)\n",
      "Shape of right hand results:  (63,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of pose results: \", pose.flatten().shape)\n",
    "print(\"Shape of face results: \", face.flatten().shape)\n",
    "print(\"Shape of right hand results: \", rh.flatten().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "74729848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for hands results\n",
    "np.zeros(21*3).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b769bf6c",
   "metadata": {},
   "source": [
    "<a id=\"10\"></a>\n",
    "## 3.D. The Final Input Arrays and Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6b40d56f",
   "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(63)\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(63)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2450c3ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of pose results:  (132,)\n",
      "Shape of face results:  (1404,)\n",
      "Shape of right hand results:  (63,)\n",
      "Shape of left hand results:  (63,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of pose results: \", pose.shape)\n",
    "print(\"Shape of face results: \", face.shape)\n",
    "print(\"Shape of right hand results: \", rh.shape)\n",
    "print(\"Shape of left hand results: \", lh.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "042c2139",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The final extract keypoints function\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(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(63)\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(63)\n",
    "    return np.concatenate([pose, face, lh, rh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c5b0e87b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1662,)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_keypoints(results).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f952d5b0",
   "metadata": {},
   "source": [
    "<a id=\"11\"></a>\n",
    "# 4. Setup Folders for Collection\n",
    "\n",
    "We use Turkish Sign Language for 3 words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2703a731",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path for exported data, numpy arrays\n",
    "DATA_PATH = os.path.join('workspace/data') \n",
    "\n",
    "# Actions that we try to detect\n",
    "actions = np.array(['merhaba', 'gunaydin', 'seniseviyorum'])\n",
    "\n",
    "# Thirty videos worth of data. no_sequences : number of sequences\n",
    "no_sequences = 30\n",
    "\n",
    "# Videos are going to be 30 frames in length\n",
    "sequence_length = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aedf18f",
   "metadata": {},
   "source": [
    "- \"Merhaba\" means hello in Turkish. Here is the [link](https://youtu.be/15xh4aNPKAM?list=PLflEAUnN_mr9CJQSe6zxx3iT8by5exh5G&t=203) that shows you how to do that.\n",
    "- \"Gunaydın\" means in Turkish good morning. Here is the [link](https://youtu.be/n6LulS3PFkk?list=PLflEAUnN_mr9CJQSe6zxx3iT8by5exh5G&t=207)\n",
    "- \"Seni Seviyorum\" means I love you. Here is the [link](https://youtu.be/eS2l_yqxLBw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "25142719",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merhaba\n",
    "## 0\n",
    "## 1\n",
    "## 2\n",
    "## ...\n",
    "## 29\n",
    "# gunaydin\n",
    "## 0\n",
    "## 1\n",
    "## 2\n",
    "## ...\n",
    "# seniseviyorum\n",
    "#..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d76fd81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create data class folders\n",
    "for action in actions: \n",
    "    for sequence in range(no_sequences):\n",
    "        try: \n",
    "            # If folder exist, raises error\n",
    "            os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebb8e30",
   "metadata": {},
   "source": [
    "If we want to add new folders for new data, we can add it safely. Because the code above tries to make a new folder but if it exists, it passes.\n",
    "To see [os.makedirs file already exists error](#102)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699b403f",
   "metadata": {},
   "source": [
    "<a id=\"12\"></a>\n",
    "# 5. Collect Keypoint Values for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4b0caebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def progress_bar(image, percentage:int=5, size_x=100, size_y:int=10, height_from_bottom=5):\n",
    "    \"\"\"Progress bar for image\n",
    "    \n",
    "    Args:\n",
    "        image: image that want you put progress bar on it\n",
    "        percentage: an integer value percentage of fullness of bar\n",
    "        size_y: an integer value for height of your bar\n",
    "        size_x: an integer value for width of your bar\n",
    "        height_from_bottom : progress bar height from the bottom of the image\n",
    "    \n",
    "    Returns:\n",
    "        Image: the image that the progress bar added\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    height, width, _ = image.shape\n",
    "    hfb = height - height*height_from_bottom/100\n",
    "\n",
    "    \n",
    "    # Thickness of -1 will fill the entire shape\n",
    "    thickness = -1\n",
    "    \n",
    "    percentage = percentage * size_x / 100\n",
    "    \n",
    "    # FULL PART\n",
    "    # represents the top left corner of rectangle 1\n",
    "    r1_x1 = int(width/2 - size_x/2)\n",
    "    r1_y1 = int(hfb - size_y)\n",
    "    start_point_1 = (r1_x1, r1_y1)\n",
    "    \n",
    "    # represents the bottom right corner of rectangle 1\n",
    "    r1_x2 = int(r1_x1 + percentage)\n",
    "    r1_y2 = int(hfb)\n",
    "    end_point_1 = (r1_x2, r1_y2)\n",
    "    \n",
    "    \n",
    "    # Color in BGR\n",
    "    color1 = (85, 240, 24) \n",
    "    \n",
    "    #rectangle 1 full\n",
    "    image = cv2.rectangle(image, start_point_1, end_point_1, color1, thickness)\n",
    "    \n",
    "    \n",
    "    # EMPTY PART\n",
    "    if not percentage == 100:\n",
    "        r2_x1 = r1_x2\n",
    "        r2_y1 = r1_y1\n",
    "        start_point_2 = (r2_x1, r2_y1)\n",
    "\n",
    "        # represents the bottom right corner of rectangle 1\n",
    "        r2_x2 = int(r2_x1 +(size_x-percentage))\n",
    "        r2_y2 = int(hfb)\n",
    "        end_point_2 = (r2_x2, r2_y2)\n",
    "\n",
    "        color2 = (245, 135, 66)\n",
    "\n",
    "        #rectangle 2 empty\n",
    "        image = cv2.rectangle(image, start_point_2, end_point_2, color2, thickness)\n",
    "        \n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12e3836d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\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",
    "        \n",
    "        ret, frame = cap.read()\n",
    "        cv2.putText(frame, 'New Action({}) will be collected frames.'.format(action), (10,200), \n",
    "                                       cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,255, 0), 2, cv2.LINE_AA)                \n",
    "        cv2.imshow('OpenCV Feed', frame)\n",
    "        cv2.waitKey(30000) \n",
    "        \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",
    "                    for i in range(1,101,1):\n",
    "                        \n",
    "                        # Read feed\n",
    "                        ret, frame = cap.read()\n",
    "                        # Make detections\n",
    "                        image, results = mediapipe_detection(frame, holistic)\n",
    "                        # Draw landmarks\n",
    "                        draw_styled_landmarks(image, results)\n",
    "                \n",
    "                        cv2.putText(image, f'STARTING COLLECTION {int((100-i)/10)+1}', (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",
    "                        \n",
    "                        image2 = image.copy()\n",
    "                        image2 = progress_bar(image2, percentage=i)\n",
    "                        \n",
    "                        \n",
    "                        # Show to screen\n",
    "                        cv2.imshow('OpenCV Feed', image2)\n",
    "                        cv2.waitKey(1)\n",
    "                        \n",
    "                else: \n",
    "                    image = progress_bar(image, percentage=frame_num*100/sequence_length)\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": 16,
   "id": "332bfa8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3b724f",
   "metadata": {},
   "source": [
    "<a id=\"13\"></a>\n",
    "# 6. Preprocess Data and Create Labels and Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82a2d49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1036b537",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {label:num for num, label in enumerate(actions)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2999687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'merhaba': 0, 'gunaydin': 1, 'seniseviyorum': 2}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed5aa1aa",
   "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": 13,
   "id": "7db47ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 30, 1662)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(sequences).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4ec254be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(labels).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2ea526c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a3463b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 30, 1662)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d691c42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74b2693a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9efc085",
   "metadata": {},
   "source": [
    "<a id=\"17\"></a>\n",
    "## 6.A. Split Train and Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28a73e7b",
   "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": 20,
   "id": "4d1e5879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85, 30, 1662)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf02ad38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 30, 1662)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62d1a936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f69963b",
   "metadata": {},
   "source": [
    "<a id=\"15\"></a>\n",
    "# 7. Build and Train LSTM Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cae6c983",
   "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": 24,
   "id": "8dd22f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Log_2022_05_01_time_21_04_24'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of logs name\n",
    "time_of_model = time.strftime('%Y_%m_%d_time_%H_%M_%S',time.localtime())\n",
    "name_of_training_logs = f\"Log_{time_of_model}\"\n",
    "name_of_training_logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "75bab97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_of_model = time.strftime('%Y_%m_%d_time_%H_%M_%S',time.localtime())\n",
    "name_of_training_logs = f\"Log_{time_of_model}\"\n",
    "\n",
    "log_dir = os.path.join(f\"Logs/{name_of_training_logs}\")\n",
    "tb_callback = TensorBoard(log_dir=log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2cecab2f",
   "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"
     ]
    }
   ],
   "source": [
    "def create_model():\n",
    "    \n",
    "    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'))\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18afb439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions.shape[0]  # number of our classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4b951ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "np.argmax : 0\n",
      "Detected Action: merhaba\n"
     ]
    }
   ],
   "source": [
    "# Assume res is our model output. (Probabilities of classes.)\n",
    "res = [.7, 0.2, 0.1]\n",
    "\n",
    "print(\"np.argmax :\",np.argmax(res))  # Returns the indices of the maximum values along an axis.\n",
    "\n",
    "print(\"Detected Action:\", actions[np.argmax(res)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "489c6f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "88315847",
   "metadata": {},
   "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",
      "Total params: 596,675\n",
      "Trainable params: 596,675\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8e945e91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "3/3 [==============================] - 4s 553ms/step - loss: 11.1806 - categorical_accuracy: 0.3176\n",
      "Epoch 2/100\n",
      "3/3 [==============================] - 1s 359ms/step - loss: 1.2771 - categorical_accuracy: 0.3176\n",
      "Epoch 3/100\n",
      "3/3 [==============================] - 1s 336ms/step - loss: 5.5799 - categorical_accuracy: 0.3294\n",
      "Epoch 4/100\n",
      "3/3 [==============================] - 1s 314ms/step - loss: 4.8107 - categorical_accuracy: 0.4000\n",
      "Epoch 5/100\n",
      "3/3 [==============================] - 1s 324ms/step - loss: 11.7758 - categorical_accuracy: 0.3412\n",
      "Epoch 6/100\n",
      "3/3 [==============================] - 1s 319ms/step - loss: 6.1102 - categorical_accuracy: 0.2235\n",
      "Epoch 7/100\n",
      "3/3 [==============================] - 1s 326ms/step - loss: 3.1696 - categorical_accuracy: 0.5294\n",
      "Epoch 8/100\n",
      "3/3 [==============================] - 1s 327ms/step - loss: 15.5262 - categorical_accuracy: 0.3176\n",
      "Epoch 9/100\n",
      "3/3 [==============================] - 1s 343ms/step - loss: 24.8684 - categorical_accuracy: 0.2941\n",
      "Epoch 10/100\n",
      "3/3 [==============================] - 1s 317ms/step - loss: 20.9450 - categorical_accuracy: 0.3059\n",
      "Epoch 11/100\n",
      "3/3 [==============================] - 1s 344ms/step - loss: 25.2601 - categorical_accuracy: 0.3529\n",
      "Epoch 12/100\n",
      "3/3 [==============================] - 1s 333ms/step - loss: 28.1995 - categorical_accuracy: 0.3412\n",
      "Epoch 13/100\n",
      "3/3 [==============================] - 1s 345ms/step - loss: 63.9973 - categorical_accuracy: 0.3765\n",
      "Epoch 14/100\n",
      "3/3 [==============================] - 1s 326ms/step - loss: 48.5521 - categorical_accuracy: 0.1882\n",
      "Epoch 15/100\n",
      "3/3 [==============================] - 1s 365ms/step - loss: 17.4866 - categorical_accuracy: 0.3412\n",
      "Epoch 16/100\n",
      "3/3 [==============================] - 1s 331ms/step - loss: 33.9662 - categorical_accuracy: 0.2706\n",
      "Epoch 17/100\n",
      "3/3 [==============================] - 1s 345ms/step - loss: 36.5248 - categorical_accuracy: 0.3412\n",
      "Epoch 18/100\n",
      "3/3 [==============================] - 1s 328ms/step - loss: 31.2594 - categorical_accuracy: 0.2824\n",
      "Epoch 19/100\n",
      "3/3 [==============================] - 1s 358ms/step - loss: 31.6755 - categorical_accuracy: 0.2941\n",
      "Epoch 20/100\n",
      "3/3 [==============================] - 1s 375ms/step - loss: 35.3240 - categorical_accuracy: 0.3294\n",
      "Epoch 21/100\n",
      "3/3 [==============================] - 1s 319ms/step - loss: 23.3832 - categorical_accuracy: 0.3412\n",
      "Epoch 22/100\n",
      "3/3 [==============================] - 1s 334ms/step - loss: 12.6027 - categorical_accuracy: 0.3294\n",
      "Epoch 23/100\n",
      "3/3 [==============================] - 1s 318ms/step - loss: 10.5633 - categorical_accuracy: 0.3529\n",
      "Epoch 24/100\n",
      "3/3 [==============================] - 1s 310ms/step - loss: 7.7200 - categorical_accuracy: 0.2353\n",
      "Epoch 25/100\n",
      "3/3 [==============================] - 1s 357ms/step - loss: 14.9713 - categorical_accuracy: 0.3059\n",
      "Epoch 26/100\n",
      "3/3 [==============================] - 1s 323ms/step - loss: 13.4157 - categorical_accuracy: 0.3647\n",
      "Epoch 27/100\n",
      "3/3 [==============================] - 1s 320ms/step - loss: 20.3536 - categorical_accuracy: 0.3412\n",
      "Epoch 28/100\n",
      "3/3 [==============================] - 1s 320ms/step - loss: 10.8931 - categorical_accuracy: 0.3647\n",
      "Epoch 29/100\n",
      "3/3 [==============================] - 1s 342ms/step - loss: 4.6818 - categorical_accuracy: 0.3882\n",
      "Epoch 30/100\n",
      "3/3 [==============================] - 1s 339ms/step - loss: 9.1156 - categorical_accuracy: 0.2706\n",
      "Epoch 31/100\n",
      "3/3 [==============================] - 1s 319ms/step - loss: 6.9394 - categorical_accuracy: 0.3294\n",
      "Epoch 32/100\n",
      "3/3 [==============================] - 1s 338ms/step - loss: 3.5966 - categorical_accuracy: 0.4941\n",
      "Epoch 33/100\n",
      "3/3 [==============================] - 1s 318ms/step - loss: 2.9979 - categorical_accuracy: 0.3882\n",
      "Epoch 34/100\n",
      "3/3 [==============================] - 1s 355ms/step - loss: 3.1698 - categorical_accuracy: 0.4000\n",
      "Epoch 35/100\n",
      "3/3 [==============================] - 1s 311ms/step - loss: 2.3504 - categorical_accuracy: 0.4118\n",
      "Epoch 36/100\n",
      "3/3 [==============================] - 1s 339ms/step - loss: 2.3441 - categorical_accuracy: 0.3412\n",
      "Epoch 37/100\n",
      "3/3 [==============================] - 1s 317ms/step - loss: 1.7865 - categorical_accuracy: 0.4000\n",
      "Epoch 38/100\n",
      "3/3 [==============================] - 1s 342ms/step - loss: 1.8261 - categorical_accuracy: 0.2471\n",
      "Epoch 39/100\n",
      "3/3 [==============================] - 1s 319ms/step - loss: 1.4154 - categorical_accuracy: 0.4118\n",
      "Epoch 40/100\n",
      "3/3 [==============================] - 1s 318ms/step - loss: 1.4860 - categorical_accuracy: 0.3882\n",
      "Epoch 41/100\n",
      "3/3 [==============================] - 1s 345ms/step - loss: 1.3139 - categorical_accuracy: 0.4000\n",
      "Epoch 42/100\n",
      "3/3 [==============================] - 1s 316ms/step - loss: 1.1729 - categorical_accuracy: 0.4824\n",
      "Epoch 43/100\n",
      "3/3 [==============================] - 1s 342ms/step - loss: 1.0450 - categorical_accuracy: 0.5412\n",
      "Epoch 44/100\n",
      "3/3 [==============================] - 1s 341ms/step - loss: 0.9113 - categorical_accuracy: 0.6118\n",
      "Epoch 45/100\n",
      "3/3 [==============================] - 1s 339ms/step - loss: 0.8779 - categorical_accuracy: 0.6824\n",
      "Epoch 46/100\n",
      "3/3 [==============================] - 1s 327ms/step - loss: 0.8538 - categorical_accuracy: 0.6235\n",
      "Epoch 47/100\n",
      "3/3 [==============================] - 1s 337ms/step - loss: 0.8280 - categorical_accuracy: 0.6235\n",
      "Epoch 48/100\n",
      "3/3 [==============================] - 1s 338ms/step - loss: 0.7974 - categorical_accuracy: 0.6235\n",
      "Epoch 49/100\n",
      "3/3 [==============================] - 1s 373ms/step - loss: 0.7558 - categorical_accuracy: 0.7059\n",
      "Epoch 50/100\n",
      "3/3 [==============================] - 1s 341ms/step - loss: 0.7235 - categorical_accuracy: 0.6824\n",
      "Epoch 51/100\n",
      "3/3 [==============================] - 1s 332ms/step - loss: 0.7233 - categorical_accuracy: 0.6941\n",
      "Epoch 52/100\n",
      "3/3 [==============================] - 1s 311ms/step - loss: 0.6877 - categorical_accuracy: 0.7647\n",
      "Epoch 53/100\n",
      "3/3 [==============================] - 1s 312ms/step - loss: 0.6739 - categorical_accuracy: 0.7647\n",
      "Epoch 54/100\n",
      "3/3 [==============================] - 1s 362ms/step - loss: 0.6571 - categorical_accuracy: 0.7765\n",
      "Epoch 55/100\n",
      "3/3 [==============================] - 1s 314ms/step - loss: 0.6536 - categorical_accuracy: 0.7412\n",
      "Epoch 56/100\n",
      "3/3 [==============================] - 1s 337ms/step - loss: 0.6356 - categorical_accuracy: 0.7765\n",
      "Epoch 57/100\n",
      "3/3 [==============================] - 1s 337ms/step - loss: 0.6322 - categorical_accuracy: 0.7882\n",
      "Epoch 58/100\n",
      "3/3 [==============================] - 1s 345ms/step - loss: 0.6361 - categorical_accuracy: 0.7765\n",
      "Epoch 59/100\n",
      "3/3 [==============================] - 1s 331ms/step - loss: 0.6239 - categorical_accuracy: 0.7647\n",
      "Epoch 60/100\n",
      "3/3 [==============================] - 1s 365ms/step - loss: 0.6494 - categorical_accuracy: 0.7647\n",
      "Epoch 61/100\n",
      "3/3 [==============================] - 1s 318ms/step - loss: 0.6247 - categorical_accuracy: 0.7647\n",
      "Epoch 62/100\n",
      "3/3 [==============================] - 1s 338ms/step - loss: 0.6559 - categorical_accuracy: 0.7647\n",
      "Epoch 63/100\n",
      "3/3 [==============================] - 1s 342ms/step - loss: 0.6222 - categorical_accuracy: 0.7176\n",
      "Epoch 64/100\n",
      "3/3 [==============================] - 1s 351ms/step - loss: 0.6498 - categorical_accuracy: 0.7647\n",
      "Epoch 65/100\n",
      "3/3 [==============================] - 1s 347ms/step - loss: 0.6286 - categorical_accuracy: 0.7529\n",
      "Epoch 66/100\n",
      "3/3 [==============================] - 1s 334ms/step - loss: 0.6233 - categorical_accuracy: 0.7412\n",
      "Epoch 67/100\n",
      "3/3 [==============================] - 1s 350ms/step - loss: 0.5966 - categorical_accuracy: 0.7647\n",
      "Epoch 68/100\n",
      "3/3 [==============================] - 1s 342ms/step - loss: 0.5938 - categorical_accuracy: 0.8118\n",
      "Epoch 69/100\n",
      "3/3 [==============================] - 1s 353ms/step - loss: 0.5683 - categorical_accuracy: 0.8235\n",
      "Epoch 70/100\n",
      "3/3 [==============================] - 1s 340ms/step - loss: 0.5701 - categorical_accuracy: 0.7882\n",
      "Epoch 71/100\n",
      "3/3 [==============================] - 1s 326ms/step - loss: 0.5561 - categorical_accuracy: 0.8000\n",
      "Epoch 72/100\n",
      "3/3 [==============================] - 1s 344ms/step - loss: 0.5744 - categorical_accuracy: 0.7765\n",
      "Epoch 73/100\n",
      "3/3 [==============================] - 1s 333ms/step - loss: 0.5612 - categorical_accuracy: 0.7529\n",
      "Epoch 74/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 349ms/step - loss: 0.5392 - categorical_accuracy: 0.8235\n",
      "Epoch 75/100\n",
      "3/3 [==============================] - 1s 323ms/step - loss: 0.5414 - categorical_accuracy: 0.7882\n",
      "Epoch 76/100\n",
      "3/3 [==============================] - 1s 339ms/step - loss: 0.5164 - categorical_accuracy: 0.8118\n",
      "Epoch 77/100\n",
      "3/3 [==============================] - 1s 318ms/step - loss: 0.5198 - categorical_accuracy: 0.7765\n",
      "Epoch 78/100\n",
      "3/3 [==============================] - 1s 355ms/step - loss: 0.5162 - categorical_accuracy: 0.7765\n",
      "Epoch 79/100\n",
      "3/3 [==============================] - 1s 341ms/step - loss: 0.5273 - categorical_accuracy: 0.7882\n",
      "Epoch 80/100\n",
      "3/3 [==============================] - 1s 330ms/step - loss: 0.5020 - categorical_accuracy: 0.8118\n",
      "Epoch 81/100\n",
      "3/3 [==============================] - 1s 373ms/step - loss: 0.5296 - categorical_accuracy: 0.8000\n",
      "Epoch 82/100\n",
      "3/3 [==============================] - 1s 322ms/step - loss: 0.5217 - categorical_accuracy: 0.8118\n",
      "Epoch 83/100\n",
      "3/3 [==============================] - 1s 323ms/step - loss: 0.5469 - categorical_accuracy: 0.7529\n",
      "Epoch 84/100\n",
      "3/3 [==============================] - 1s 331ms/step - loss: 0.5335 - categorical_accuracy: 0.7647\n",
      "Epoch 85/100\n",
      "3/3 [==============================] - 1s 325ms/step - loss: 0.5395 - categorical_accuracy: 0.7765\n",
      "Epoch 86/100\n",
      "3/3 [==============================] - 1s 345ms/step - loss: 0.4915 - categorical_accuracy: 0.8353\n",
      "Epoch 87/100\n",
      "3/3 [==============================] - 1s 331ms/step - loss: 0.5354 - categorical_accuracy: 0.7765\n",
      "Epoch 88/100\n",
      "3/3 [==============================] - 1s 336ms/step - loss: 0.5097 - categorical_accuracy: 0.7765\n",
      "Epoch 89/100\n",
      "3/3 [==============================] - 1s 332ms/step - loss: 0.5079 - categorical_accuracy: 0.7882\n",
      "Epoch 90/100\n",
      "3/3 [==============================] - 1s 348ms/step - loss: 0.5428 - categorical_accuracy: 0.7529\n",
      "Epoch 91/100\n",
      "3/3 [==============================] - 1s 338ms/step - loss: 0.5530 - categorical_accuracy: 0.7529\n",
      "Epoch 92/100\n",
      "3/3 [==============================] - 1s 358ms/step - loss: 0.4916 - categorical_accuracy: 0.7765\n",
      "Epoch 93/100\n",
      "3/3 [==============================] - 1s 342ms/step - loss: 0.4944 - categorical_accuracy: 0.7529\n",
      "Epoch 94/100\n",
      "3/3 [==============================] - 1s 332ms/step - loss: 0.5004 - categorical_accuracy: 0.8118\n",
      "Epoch 95/100\n",
      "3/3 [==============================] - 1s 316ms/step - loss: 0.5120 - categorical_accuracy: 0.8000\n",
      "Epoch 96/100\n",
      "3/3 [==============================] - 1s 323ms/step - loss: 0.4769 - categorical_accuracy: 0.8000\n",
      "Epoch 97/100\n",
      "3/3 [==============================] - 1s 360ms/step - loss: 0.4644 - categorical_accuracy: 0.7882\n",
      "Epoch 98/100\n",
      "3/3 [==============================] - 1s 313ms/step - loss: 0.4490 - categorical_accuracy: 0.8353\n",
      "Epoch 99/100\n",
      "3/3 [==============================] - 1s 346ms/step - loss: 0.4506 - categorical_accuracy: 0.8471\n",
      "Epoch 100/100\n",
      "3/3 [==============================] - 1s 339ms/step - loss: 0.4488 - categorical_accuracy: 0.8353\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x205b6a4dc40>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs=100\n",
    "model.fit(X_train, y_train, epochs=epochs, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383299cf",
   "metadata": {},
   "source": [
    "<a id=\"16\"></a>\n",
    "# 8. Make Precitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "84b44989",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ffed1958",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test shape:  (5, 30, 1662)\n",
      "y_head shape:  (5, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_test shape: \", X_test.shape)\n",
    "print(\"y_head shape: \", y_head.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc1d6580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00133037, 0.00167425, 0.99699533],\n",
       "       [0.17314357, 0.07383862, 0.75301784],\n",
       "       [0.456451  , 0.501795  , 0.04175403],\n",
       "       [0.04370437, 0.02803317, 0.92826253],\n",
       "       [0.43388492, 0.46357897, 0.10253619]], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b11ae430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.456451  , 0.501795  , 0.04175403], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_head[2] # the probalities of classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6a307750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_head[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8b5bad5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_head[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b624163b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gunaydin'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(y_head[2])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a52aa609",
   "metadata": {},
   "source": [
    "<a id=\"18\"></a>\n",
    "# 9. Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "8d893ca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sign_lstm_2022_05_01_time_21_04_27_epochs_100.h5\n",
      "workspace/models/sign_lstm_2022_05_01_time_21_04_27_epochs_100.h5\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
      "workspace/models/sign_lstm_2022_05_01_time_21_04_27_epochs_100.h5 SAVED.\n"
     ]
    }
   ],
   "source": [
    "name_of_model = f\"sign_lstm_{time_of_model}_epochs_{epochs}.h5\"\n",
    "print(name_of_model)\n",
    "model_path = \"workspace/models/\" + name_of_model\n",
    "print(model_path)\n",
    "\n",
    "model.save(model_path)\n",
    "print(model_path,\"SAVED.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "4032b43e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "workspace/models/sign_lstm_2022_04_30_time_17_58_55_epochs_200.h5\n"
     ]
    }
   ],
   "source": [
    "name_of_model = \"sign_lstm_2022_04_30_time_17_58_55_epochs_200.h5\"  # our best model\n",
    "\n",
    "model_path = \"workspace/models/\"+name_of_model\n",
    "\n",
    "print(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "afce90e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b022c9cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_30 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_31 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_32 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"
     ]
    }
   ],
   "source": [
    "model = create_model()\n",
    "\n",
    "model.load_weights(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024ee9ed",
   "metadata": {},
   "source": [
    "<a id=\"19\"></a>\n",
    "# 10. Evaluation using Confusion Matrix and Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f232f2e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f52c929",
   "metadata": {},
   "source": [
    "<a id=\"21\"></a>\n",
    "## 10.A. Evaluating on Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "b74c40c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f40e7a5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_train, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "739e3c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[57,  0],\n",
       "        [ 0, 28]],\n",
       "\n",
       "       [[56,  0],\n",
       "        [ 0, 29]],\n",
       "\n",
       "       [[57,  0],\n",
       "        [ 0, 28]]], dtype=int64)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "c6f86b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac054d6a",
   "metadata": {},
   "source": [
    "<a id=\"22\"></a>\n",
    "## 10.B. Evaluating on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "eff01b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "59b9cbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_test, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "08e180b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[3, 0],\n",
       "        [0, 2]],\n",
       "\n",
       "       [[4, 0],\n",
       "        [0, 1]],\n",
       "\n",
       "       [[3, 0],\n",
       "        [0, 2]]], dtype=int64)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3e18265d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef6af61",
   "metadata": {},
   "source": [
    "<a id=\"20\"></a>\n",
    "# 11. Test in Real Time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec755844",
   "metadata": {},
   "source": [
    "<a id=\"23\"></a>\n",
    "##  11.A. Expected Input for LSTM Model\n",
    "\n",
    "The input that LSTM model expects is : **(num_sequences,30,1662)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "df15714a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 30, 1662)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7d374008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85, 30, 1662)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f650ade0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 1662)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5612bea8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_0 shape (1, 30, 1662)\n",
      "test_1 shape (1, 30, 1662)\n"
     ]
    }
   ],
   "source": [
    "# model input\n",
    "test_0 = np.expand_dims(X_test[0], axis=0)\n",
    "test_1 = np.expand_dims(X_test[1], axis=0)\n",
    "\n",
    "print(\"test_0 shape\",test_0.shape)\n",
    "print(\"test_1 shape\",test_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5033cf1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequence shape:  (2, 30, 1662)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2.3032386e-07, 9.9999976e-01, 1.6849479e-10],\n",
       "       [1.0000000e+00, 3.6931600e-11, 2.4099025e-13]], dtype=float32)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence = np.array([])  \n",
    "sequence = np.append(test_0, test_1, axis=0)\n",
    "print(\"sequence shape: \", sequence.shape)\n",
    "\n",
    "results_of_LSTM = model.predict(sequence)\n",
    "results_of_LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "897eccdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0. result: gunaydin\n",
      "1. result: merhaba\n"
     ]
    }
   ],
   "source": [
    "for i in range(results_of_LSTM.shape[0]):\n",
    "    print(f\"{i}. result:\", actions[np.argmax(results_of_LSTM[i])])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4db999",
   "metadata": {},
   "source": [
    "<a id=\"25\"></a>\n",
    "## 11.B. Visualization: How to Works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "36dfc9c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "results_of_LSTM = results_of_LSTM[0]  # we took just one\n",
    "\n",
    "index_of_highest_probability = np.argmax(results_of_LSTM)\n",
    "\n",
    "print(index_of_highest_probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "bace3ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99999976"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability_of_class = results_of_LSTM[index_of_highest_probability]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "79ecaa93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0.4qq\n",
    "\n",
    "results_of_LSTM[index_of_highest_probability] > threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f3c8bb",
   "metadata": {},
   "source": [
    "<a id=\"24\"></a>\n",
    "## 11.C. Real Time Detection with Webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "65bef451",
   "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, (0,90+num*40), (int(prob*100), 125+num*40), colors[num], -1)\n",
    "        cv2.putText(output_frame, actions[num], (0, 115+num*40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "    return output_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "b96ed903",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. New Detection Variables\n",
    "sequence = []  # holds 30 frames to give as a input to LSTM model and predict\n",
    "sentence = []  #\n",
    "threshold = 0.8\n",
    "LSTM_results = np.array([0])\n",
    "results_string = None\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "# used to record the time when we processed last frame\n",
    "prev_frame_time = 0\n",
    "# used to record the time at which we processed current frame\n",
    "new_frame_time = 0\n",
    "\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(model_complexity=1, min_detection_confidence=0.5, \n",
    "                          min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        \n",
    "        # time when we finish processing for this frame\n",
    "        new_frame_time = time.time()\n",
    "        \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",
    "        \n",
    "        # 2. Prediction Logic\n",
    "        keypoints = extract_keypoints(results) # detect keypoints\n",
    "        sequence.append(keypoints)  # append a list\n",
    "        sequence = sequence[-30:]  # hold last keypoints of 30 frame\n",
    "        \n",
    "        if len(sequence) == 30:   # if there are 30 frames, ready to predict\n",
    "            # Prepare to be LSTM model input\n",
    "            LSTM_results = model.predict(np.expand_dims(sequence, axis=0))[0] # [0] cuz there is just 1 input\n",
    "#             print(\"aslkdjals\")\n",
    "            # print result class\n",
    "#             print(actions[np.argmax(LSTM_results)])\n",
    "#             print(\"%\",\" %.1f\" %(100*LSTM_results[np.argmax(LSTM_results)]))\n",
    "            results_string = str(actions[np.argmax(LSTM_results)]) + str(\"%\" + \" %.1f\" %(100*LSTM_results[np.argmax(LSTM_results)]))\n",
    "            \n",
    "            # 3. Viz logic - Filtering\n",
    "            if LSTM_results[np.argmax(LSTM_results)] > threshold: \n",
    "                if len(sentence) > 0:  \n",
    "                    # check the last result is the same as previous\n",
    "                    # if they are same, don't write or show in a row\n",
    "                    # if they are not, append sentence list\n",
    "                    if actions[np.argmax(LSTM_results)] != sentence[-1]:  \n",
    "                        sentence.append(actions[np.argmax(LSTM_results)])\n",
    "                else:\n",
    "                    sentence.append(actions[np.argmax(LSTM_results)])\n",
    "\n",
    "            if len(sentence) > 5:\n",
    "                sentence = sentence[-5:]\n",
    "                \n",
    "            # Viz probabilities\n",
    "            image = prob_viz(LSTM_results, 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, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        \n",
    "        cv2.rectangle(image, (0,45), (640, 80), (0, 0, 255), -1)\n",
    "        if not results_string is None:\n",
    "            cv2.putText(image, results_string, (3,80), \n",
    "                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "\n",
    "        \n",
    "            \n",
    "        # fps will be number of frame processed in given time frame\n",
    "        # since their will be most of time error of 0.001 second\n",
    "        # we will be subtracting it to get more accurate result\n",
    "        fps = 1/(new_frame_time-prev_frame_time)\n",
    "        prev_frame_time = new_frame_time\n",
    "\n",
    "        # converting the fps into integer\n",
    "        fps = int(fps)\n",
    "        \n",
    "        # converting the fps to string so that we can display it on frame\n",
    "        # by using putText function\n",
    "        fps = str(fps)\n",
    "        fps = \"FPS:\" + fps\n",
    "        font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "        \n",
    "        # putting the FPS count on the frame\n",
    "        cv2.putText(image, fps, (500, 70), font, 1, (100, 255, 0), 3, cv2.LINE_AA)\n",
    "    \n",
    "        cv2.imshow('Webcam with Detections', image)\n",
    "\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "37c7229f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "836916ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x206c5613280>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "output_frame = prob_viz(LSTM_results, actions, image, colors)\n",
    "output_frame = cv2.cvtColor(output_frame, cv2.COLOR_BGR2RGB )\n",
    "\n",
    "plt.imshow(output_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f199bc61",
   "metadata": {},
   "source": [
    "<a id=\"99\"></a>\n",
    "#  12. Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3903f6a2",
   "metadata": {},
   "source": [
    "<a id=\"100\"></a>\n",
    "## 12.A. Results of Pose Landmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2d18e4c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "visibility: 0.0007304207538254559\n",
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       "visibility: 0.00011289321992080659\n",
       ", x: 0.2941601276397705\n",
       "y: 3.1009631156921387\n",
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       "visibility: 1.6570122170378454e-05\n",
       ", x: 0.6027259230613708\n",
       "y: 3.217487335205078\n",
       "z: 0.4207191467285156\n",
       "visibility: 5.066597805125639e-05\n",
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       "visibility: 4.032169817946851e-05\n",
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       "visibility: 9.318775846622884e-05\n",
       "]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.pose_landmarks.landmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3333b4d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "results.pose_landmarks"
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  {
   "cell_type": "markdown",
   "id": "f32eafb5",
   "metadata": {},
   "source": [
    "<a id=\"101\"></a>\n",
    "## 12.B. Results of Face and Hands Landmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cfa02016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Face landmarks: 468 \n",
      "\n",
      "Results of Face Landmarks:\n",
      "\n",
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      "z: -0.021827178075909615\n",
      ", x: 0.5463465452194214\n",
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      "y: 0.6253142356872559\n",
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      "y: 0.5174961090087891\n",
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      "y: 0.5159744024276733\n",
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      ", x: 0.5880063772201538\n",
      "y: 0.517086386680603\n",
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      ", x: 0.5965155363082886\n",
      "y: 0.5211944580078125\n",
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      "y: 0.5395906567573547\n",
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      ", x: 0.6342498064041138\n",
      "y: 0.5699620246887207\n",
      "z: 0.0584675595164299\n",
      ", x: 0.596545398235321\n",
      "y: 0.557643711566925\n",
      "z: 0.00647122785449028\n",
      ", x: 0.5863445997238159\n",
      "y: 0.5623942017555237\n",
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      ", x: 0.5732446312904358\n",
      "y: 0.5632894039154053\n",
      "z: -0.002173338085412979\n",
      ", x: 0.5609478950500488\n",
      "y: 0.5609496831893921\n",
      "z: -0.0035300778690725565\n",
      ", x: 0.551753580570221\n",
      "y: 0.5572459697723389\n",
      "z: -0.004506231285631657\n",
      ", x: 0.5454636216163635\n",
      "y: 0.5535362958908081\n",
      "z: -0.005221141967922449\n",
      ", x: 0.6349864602088928\n",
      "y: 0.5680498480796814\n",
      "z: 0.09453485161066055\n",
      ", x: 0.5534749627113342\n",
      "y: 0.6350693106651306\n",
      "z: -0.022741982713341713\n",
      ", x: 0.5379855632781982\n",
      "y: 0.5930017232894897\n",
      "z: -0.03619178757071495\n",
      ", x: 0.5391226410865784\n",
      "y: 0.6367738842964172\n",
      "z: -0.04678158089518547\n",
      ", x: 0.5342112183570862\n",
      "y: 0.6432205438613892\n",
      "z: -0.04180663451552391\n",
      ", x: 0.5394216179847717\n",
      "y: 0.6381337642669678\n",
      "z: -0.04173998907208443\n",
      ", x: 0.5511325001716614\n",
      "y: 0.6387426257133484\n",
      "z: -0.018800294026732445\n",
      ", x: 0.5323160290718079\n",
      "y: 0.645435094833374\n",
      "z: -0.04410790652036667\n",
      ", x: 0.531649112701416\n",
      "y: 0.6469070315361023\n",
      "z: -0.03193139657378197\n",
      ", x: 0.5459661483764648\n",
      "y: 0.5421102643013\n",
      "z: -0.001966917421668768\n",
      ", x: 0.5403817296028137\n",
      "y: 0.5467371344566345\n",
      "z: -0.007448750548064709\n",
      ", x: 0.5375167727470398\n",
      "y: 0.5504732131958008\n",
      "z: -0.014353228732943535\n",
      ", x: 0.5905981063842773\n",
      "y: 0.535531222820282\n",
      "z: 0.0011630295775830746\n",
      ", x: 0.5953488945960999\n",
      "y: 0.5310526490211487\n",
      "z: 0.0010240647243335843\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of Face landmarks:\", len(results.face_landmarks.landmark), \"\\n\")\n",
    "print(\"Results of Face Landmarks:\\n\\n\", results.face_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4863963",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Right Hand landmarks: 21 \n",
      "\n",
      "Results of Right Hand Landmarks:\n",
      "\n",
      " [x: 0.12414760142564774\n",
      "y: 0.6531518697738647\n",
      "z: 4.644348905458173e-07\n",
      ", x: 0.19273778796195984\n",
      "y: 0.6696648597717285\n",
      "z: -0.020147597417235374\n",
      ", x: 0.2625942826271057\n",
      "y: 0.6471019387245178\n",
      "z: -0.02552444487810135\n",
      ", x: 0.31694847345352173\n",
      "y: 0.6255170106887817\n",
      "z: -0.03091582842171192\n",
      ", x: 0.3603905141353607\n",
      "y: 0.624834418296814\n",
      "z: -0.035637494176626205\n",
      ", x: 0.26922911405563354\n",
      "y: 0.504911482334137\n",
      "z: 0.004614424426108599\n",
      ", x: 0.31528547406196594\n",
      "y: 0.44453462958335876\n",
      "z: -0.005729347933083773\n",
      ", x: 0.3412741422653198\n",
      "y: 0.40685179829597473\n",
      "z: -0.01886722445487976\n",
      ", x: 0.3627797067165375\n",
      "y: 0.3757861852645874\n",
      "z: -0.02850322239100933\n",
      ", x: 0.2439296692609787\n",
      "y: 0.4693107008934021\n",
      "z: 0.002997872419655323\n",
      ", x: 0.2923547923564911\n",
      "y: 0.396414577960968\n",
      "z: -0.0043066274374723434\n",
      ", x: 0.3243725299835205\n",
      "y: 0.3516043424606323\n",
      "z: -0.015779340639710426\n",
      ", x: 0.35129594802856445\n",
      "y: 0.31707170605659485\n",
      "z: -0.024339258670806885\n",
      ", x: 0.21223792433738708\n",
      "y: 0.4473881125450134\n",
      "z: -0.0031707657035440207\n",
      ", x: 0.2556781470775604\n",
      "y: 0.37449678778648376\n",
      "z: -0.011938055045902729\n",
      ", x: 0.28630274534225464\n",
      "y: 0.334561288356781\n",
      "z: -0.020870547741651535\n",
      ", x: 0.3136860728263855\n",
      "y: 0.30460548400878906\n",
      "z: -0.027170611545443535\n",
      ", x: 0.17312726378440857\n",
      "y: 0.4356275498867035\n",
      "z: -0.01228791568428278\n",
      ", x: 0.2018146514892578\n",
      "y: 0.3756939172744751\n",
      "z: -0.019929036498069763\n",
      ", x: 0.22460174560546875\n",
      "y: 0.34260284900665283\n",
      "z: -0.022307736799120903\n",
      ", x: 0.24890626966953278\n",
      "y: 0.31588494777679443\n",
      "z: -0.023736068978905678\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of Right Hand landmarks:\", len(results.right_hand_landmarks.landmark), \"\\n\")\n",
    "print(\"Results of Right Hand Landmarks:\\n\\n\", results.right_hand_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "69f9e7ed",
   "metadata": {
    "scrolled": false
   },
   "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)",
      "Input \u001b[1;32mIn [28]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# there is no left hand on the image, so there is no results.\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mleft_hand_landmarks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlandmark\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'landmark'"
     ]
    }
   ],
   "source": [
    "# there is no left hand on the image, so there is no results.\n",
    "results.left_hand_landmarks.landmark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5862c31",
   "metadata": {},
   "source": [
    "<a id=\"102\"></a>\n",
    "## 12.C. os.makedirs file already exists error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6b608465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gunaydin', 'merhaba', 'seniseviyorum']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(\"./workspace/data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f94850b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new data folders\n",
    "\n",
    "# Path for exported data, numpy arrays\n",
    "DATA_PATH = os.path.join('workspace/data') \n",
    "\n",
    "# Actions that we try to detect\n",
    "actions = np.array(['merhaba', 'gunaydin', 'seniseviyorum'])\n",
    "\n",
    "# Thirty videos worth of data. no_sequences : number of sequences\n",
    "no_sequences = 30\n",
    "\n",
    "# Videos are going to be 30 frames in length\n",
    "sequence_length = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f9b693fb",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[WinError 183] Cannot create a file when that file already exists: 'workspace/data\\\\merhaba\\\\0'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "Input \u001b[1;32mIn [41]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m action \u001b[38;5;129;01min\u001b[39;00m actions: \n\u001b[0;32m      3\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m sequence \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(no_sequences): \n\u001b[0;32m      4\u001b[0m         \u001b[38;5;66;03m# If folder exist, raises error\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m         \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mDATA_PATH\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msequence\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\ai_pt\\lib\\os.py:225\u001b[0m, in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    224\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 225\u001b[0m     \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    226\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[0;32m    227\u001b[0m     \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[0;32m    228\u001b[0m     \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[0;32m    229\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\n",
      "\u001b[1;31mFileExistsError\u001b[0m: [WinError 183] Cannot create a file when that file already exists: 'workspace/data\\\\merhaba\\\\0'"
     ]
    }
   ],
   "source": [
    "# create data class folders\n",
    "for action in actions: \n",
    "    for sequence in range(no_sequences): \n",
    "        # If folder exist, raises error\n",
    "        os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))"
   ]
  }
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