Mediapipe_hand.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79ff1c1b",
   "metadata": {},
   "source": [
    "# Import กับ Keypoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b587a156",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow==2.5.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (2.5.1)\n",
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      "Requirement already satisfied: opencv-python in c:\\users\\asus\\anaconda3\\lib\\site-packages (4.7.0.72)\n",
      "Requirement already satisfied: mediapipe in c:\\users\\asus\\anaconda3\\lib\\site-packages (0.8.11)\n",
      "Requirement already satisfied: sklearn in c:\\users\\asus\\anaconda3\\lib\\site-packages (0.0.post1)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\asus\\anaconda3\\lib\\site-packages (3.5.1)\n",
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      "Requirement already satisfied: google-pasta~=0.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (0.2.0)\n",
      "Requirement already satisfied: opt-einsum~=3.3.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (3.3.0)\n",
      "Requirement already satisfied: numpy~=1.19.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (1.19.5)\n",
      "Requirement already satisfied: flatbuffers~=1.12.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (1.12)\n",
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      "Requirement already satisfied: tensorflow-estimator<2.6.0,>=2.5.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (2.5.0)\n",
      "Requirement already satisfied: tensorboard~=2.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (2.11.2)\n",
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      "Requirement already satisfied: gast==0.4.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (0.4.0)\n",
      "Requirement already satisfied: six~=1.15.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (1.15.0)\n",
      "Requirement already satisfied: wrapt~=1.12.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorflow==2.5.1) (1.12.1)\n",
      "Requirement already satisfied: attrs>=19.1.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from mediapipe) (21.4.0)\n",
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      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (9.0.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.3.2)\n",
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      "Requirement already satisfied: packaging>=20.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (21.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorboard~=2.5->tensorflow==2.5.1) (0.6.1)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorboard~=2.5->tensorflow==2.5.1) (61.2.0)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorboard~=2.5->tensorflow==2.5.1) (1.8.1)\n",
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      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorboard~=2.5->tensorflow==2.5.1) (1.33.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from tensorboard~=2.5->tensorflow==2.5.1) (0.4.6)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.5->tensorflow==2.5.1) (4.7.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.5->tensorflow==2.5.1) (0.2.8)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.5->tensorflow==2.5.1) (4.2.2)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow==2.5.1) (1.3.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard~=2.5->tensorflow==2.5.1) (0.4.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow==2.5.1) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow==2.5.1) (2021.10.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow==2.5.1) (1.26.9)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow==2.5.1) (2.0.4)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow==2.5.1) (3.2.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow==2.5.1 tensorflow-gpu==2.5.1 opencv-python mediapipe sklearn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38707af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import time\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "765baaef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Keypoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5990ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.holistic\n",
    "mp_drawing = mp.solutions.drawing_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a4ee4e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#เปลี่ยน BGR เป็น RGB\n",
    "def mediapipe_detection(image, model):\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    image.flags.writeable = False\n",
    "    results = model.process(image)\n",
    "    image.flags.writeable = True\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "    return image, results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "704b48ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#กำหนดlandmark\n",
    "def draw_landmarks(image, results):\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS)\n",
    "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)\n",
    "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)\n",
    "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a0075e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "#customตัวlandmark\n",
    "def draw_styled_landmarks(image, results):\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",
    "    \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=(80,244,121), thickness=2, circle_radius=2)\n",
    "                             )\n",
    "    \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",
    "    \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": 15,
   "id": "d6057400",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n"
     ]
    }
   ],
   "source": [
    "#ทดสอบOpenCV\n",
    "cap = cv2.VideoCapture(0)\n",
    "#set mediapipe model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        \n",
    "        draw_styled_landmarks(image, results)\n",
    "        \n",
    "\n",
    "        cv2.imshow('OpenCV video test', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9172c2e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "468"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results.face_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "772cab7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_landmarks(frame, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41a0b696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27efc086250>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0cfab701",
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_drawing.draw_landmarks??"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1db5956",
   "metadata": {},
   "source": [
    "# Extract Keypoint Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c8fc539e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "landmark {\n",
       "  x: 0.60208905\n",
       "  y: 0.56985575\n",
       "  z: -1.7378805\n",
       "  visibility: 0.9993266\n",
       "}\n",
       "landmark {\n",
       "  x: 0.644667\n",
       "  y: 0.4659274\n",
       "  z: -1.6353569\n",
       "  visibility: 0.9988932\n",
       "}\n",
       "landmark {\n",
       "  x: 0.6727349\n",
       "  y: 0.46601662\n",
       "  z: -1.6352812\n",
       "  visibility: 0.9988775\n",
       "}\n",
       "landmark {\n",
       "  x: 0.6996428\n",
       "  y: 0.46861637\n",
       "  z: -1.6354315\n",
       "  visibility: 0.99864537\n",
       "}\n",
       "landmark {\n",
       "  x: 0.5528044\n",
       "  y: 0.46518335\n",
       "  z: -1.6610817\n",
       "  visibility: 0.99903125\n",
       "}\n",
       "landmark {\n",
       "  x: 0.5151648\n",
       "  y: 0.46634057\n",
       "  z: -1.6599388\n",
       "  visibility: 0.9991407\n",
       "}\n",
       "landmark {\n",
       "  x: 0.48314458\n",
       "  y: 0.4695173\n",
       "  z: -1.6604264\n",
       "  visibility: 0.9991317\n",
       "}\n",
       "landmark {\n",
       "  x: 0.73638046\n",
       "  y: 0.49281663\n",
       "  z: -0.920451\n",
       "  visibility: 0.9985589\n",
       "}\n",
       "landmark {\n",
       "  x: 0.4313733\n",
       "  y: 0.5000828\n",
       "  z: -1.0077394\n",
       "  visibility: 0.9994088\n",
       "}\n",
       "landmark {\n",
       "  x: 0.6404625\n",
       "  y: 0.68264884\n",
       "  z: -1.468264\n",
       "  visibility: 0.9994934\n",
       "}\n",
       "landmark {\n",
       "  x: 0.54025567\n",
       "  y: 0.683419\n",
       "  z: -1.4928143\n",
       "  visibility: 0.9996513\n",
       "}\n",
       "landmark {\n",
       "  x: 0.9011203\n",
       "  y: 0.93007827\n",
       "  z: -0.44530386\n",
       "  visibility: 0.9909907\n",
       "}\n",
       "landmark {\n",
       "  x: 0.26389033\n",
       "  y: 0.9986616\n",
       "  z: -0.62238705\n",
       "  visibility: 0.99580616\n",
       "}\n",
       "landmark {\n",
       "  x: 1.0517104\n",
       "  y: 1.4744678\n",
       "  z: -0.3560341\n",
       "  visibility: 0.09743804\n",
       "}\n",
       "landmark {\n",
       "  x: 0.08184131\n",
       "  y: 1.5729128\n",
       "  z: -0.6318781\n",
       "  visibility: 0.35262933\n",
       "}\n",
       "landmark {\n",
       "  x: 1.0308015\n",
       "  y: 1.853619\n",
       "  z: -1.003392\n",
       "  visibility: 0.049419634\n",
       "}\n",
       "landmark {\n",
       "  x: 0.18938823\n",
       "  y: 1.9457551\n",
       "  z: -1.4499058\n",
       "  visibility: 0.16189264\n",
       "}\n",
       "landmark {\n",
       "  x: 1.067062\n",
       "  y: 1.996876\n",
       "  z: -1.1967604\n",
       "  visibility: 0.06626139\n",
       "}\n",
       "landmark {\n",
       "  x: 0.18980357\n",
       "  y: 2.1008558\n",
       "  z: -1.6651428\n",
       "  visibility: 0.1697606\n",
       "}\n",
       "landmark {\n",
       "  x: 1.0062864\n",
       "  y: 1.973652\n",
       "  z: -1.2866803\n",
       "  visibility: 0.0952854\n",
       "}\n",
       "landmark {\n",
       "  x: 0.24958737\n",
       "  y: 2.0393355\n",
       "  z: -1.7840271\n",
       "  visibility: 0.23329742\n",
       "}\n",
       "landmark {\n",
       "  x: 0.97840273\n",
       "  y: 1.9258766\n",
       "  z: -1.0766903\n",
       "  visibility: 0.09781066\n",
       "}\n",
       "landmark {\n",
       "  x: 0.2607295\n",
       "  y: 1.9828212\n",
       "  z: -1.5213851\n",
       "  visibility: 0.22085771\n",
       "}\n",
       "landmark {\n",
       "  x: 0.8208273\n",
       "  y: 2.0965652\n",
       "  z: -0.09735578\n",
       "  visibility: 0.0010826793\n",
       "}\n",
       "landmark {\n",
       "  x: 0.36763895\n",
       "  y: 2.114217\n",
       "  z: 0.10485697\n",
       "  visibility: 0.0009405925\n",
       "}\n",
       "landmark {\n",
       "  x: 0.824244\n",
       "  y: 3.0427516\n",
       "  z: 0.043253772\n",
       "  visibility: 0.001137633\n",
       "}\n",
       "landmark {\n",
       "  x: 0.3989665\n",
       "  y: 3.058764\n",
       "  z: 0.39745617\n",
       "  visibility: 0.00046561495\n",
       "}\n",
       "landmark {\n",
       "  x: 0.8299514\n",
       "  y: 3.896242\n",
       "  z: 1.079433\n",
       "  visibility: 0.00033733196\n",
       "}\n",
       "landmark {\n",
       "  x: 0.41583157\n",
       "  y: 3.8989592\n",
       "  z: 1.4004796\n",
       "  visibility: 2.790626e-05\n",
       "}\n",
       "landmark {\n",
       "  x: 0.8383816\n",
       "  y: 4.0269814\n",
       "  z: 1.1164486\n",
       "  visibility: 0.00020270469\n",
       "}\n",
       "landmark {\n",
       "  x: 0.40728408\n",
       "  y: 4.0307884\n",
       "  z: 1.4683205\n",
       "  visibility: 9.0803005e-05\n",
       "}\n",
       "landmark {\n",
       "  x: 0.7677575\n",
       "  y: 4.167258\n",
       "  z: 0.07788824\n",
       "  visibility: 0.00017276315\n",
       "}\n",
       "landmark {\n",
       "  x: 0.4995594\n",
       "  y: 4.16342\n",
       "  z: 0.3431701\n",
       "  visibility: 0.00016607517\n",
       "}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.pose_landmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99bd9a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_keypoints(results):\n",
    "    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(132)\n",
    "    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(1404)\n",
    "    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
    "    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n",
    "    return np.concatenate([pose, face, lh, rh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e3e86129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "468"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results.face_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97a78a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1662,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_keypoints(results).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e1f718",
   "metadata": {},
   "source": [
    "# Setup Folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "61b76934",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH= os.path.join('MP_Data')\n",
    "\n",
    "#actions_train = np.array(['สวัสดี', 'ตก', 'รอ', 'กลับ', 'ขอบคุณ', 'ตัด', 'ลง', 'ขึ้น', 'เฝ้า','คุย', 'ช่วย', 'เชื่อ', 'ฟัง', 'มอง', 'พา', 'ชวน หลีกภัย', 'ทักษิณ ชินวัตร', 'ขนม', 'พิธา ลิ้มเจริญรัตน์', 'ศิริกัญญา ตันสกุล','แบก', 'อนุทิน ชาญวีรกูล', 'รังสิมันต์ โรม', 'พีระพันธุ์ สาลีรัฐวิภาค', 'คุณหญิงสุดารัตน์ เกยุราพันธุ์', 'พลเอกอนุพงษ์ เผ่าจินดา', 'สุวัจน์ ลิปตพัลลภ', 'กรณ์ จาติกวณิช', 'วราวุธ ศิลปะอาชา', 'พล.ต.อ.เสรีพิศุทธ์ เตมียเวสวราวุธ ศิลปะอาชา', 'ศักดิ์สยาม ชิดชอบ', 'ชาดา ไทยเศรษฐ์', 'สุชัชวีร์ สุวรรณสวัสดิ์', 'จุรินทร์ ลักษณวิศิษฏ์', 'ไตรรงค์ สุวรรณคีรี', 'พลเอกประยุทธ์ จันทร์โอชา', 'นฤมล ภิญโญสินวัฒน์', 'ธรรมนัส พรหมเผ่า', 'ชัยวุฒิ ธนาคมานุสรณ์', 'ไพบูลย์ นิติตะวัน', 'พลเอกประวิตร วงษ์สุวรรณ', 'พริษฐ์ วัชรสินธุ', 'ยิ่งลักษณ์ ชินวัตร', 'ณัฐวุฒิ ใสยเกื้อ', 'นพ.ชลน่าน ศรีแก้ว', 'เศรษฐา ทวีสิน', 'แพทองธาร ชินวัตร', 'อะไร', 'สมัคร', 'กระโดด', 'ยก', 'ชน', 'ผ่าน', 'แนะนำ', 'จำได้', 'ปลูก', 'รวม', 'ทำหาย', 'เจอ', 'หาย', 'พนักงานขาย', 'นักเขียนโปรแกรม', 'พ่อครัว', 'เจ้าหน้าที่ตำรวจ', 'เนื้อลูกแกะ', 'เนื้อหมู', 'คุณสบายดีไหม', 'ดูเหมือน', 'วาง', 'อยู่', 'ก', 'ข', 'ค', 'ฆ', 'ต', 'ถ', 'ง', 'จ', 'ฉ', 'ช', 'ซ', 'ฏ', 'ฐ', 'ฑ', 'ฒ', 'ณ', 'ด', 'ท', 'ธ', 'น', 'บ', 'ป', 'ผ', 'ฝ', 'พ', 'ฟ', 'ภ', 'ม', 'ย', 'ร', 'ล', 'ว', 'ศ', 'ษ', 'ส', 'ห', 'ฬ', 'อ', 'ฮ')\n",
    "actions = np.array(['อ', 'ฮ'])\n",
    "#actions = np.array(['สวัสดี', 'ตก', 'รอ', 'กลับ', 'ขอบคุณ', 'ตัด', 'ลง', 'ขึ้น', 'เฝ้า','คุย', 'ช่วย', 'เชื่อ', 'ฟัง', 'มอง', 'พา', 'ชวน หลีกภัย', 'ทักษิณ ชินวัตร', 'ขนม', 'พิธา ลิ้มเจริญรัตน์', 'ศิริกัญญา ตันสกุล','แบก', 'อนุทิน ชาญวีรกูล', 'รังสิมันต์ โรม', 'พีระพันธุ์ สาลีรัฐวิภาค', 'คุณหญิงสุดารัตน์ เกยุราพันธุ์', 'พลเอกอนุพงษ์ เผ่าจินดา', 'สุวัจน์ ลิปตพัลลภ', 'กรณ์ จาติกวณิช', 'วราวุธ ศิลปะอาชา', 'พล.ต.อ.เสรีพิศุทธ์ เตมียเวสวราวุธ ศิลปะอาชา', 'ศักดิ์สยาม ชิดชอบ', 'ชาดา ไทยเศรษฐ์', 'สุชัชวีร์ สุวรรณสวัสดิ์', 'จุรินทร์ ลักษณวิศิษฏ์', 'ไตรรงค์ สุวรรณคีรี', 'พลเอกประยุทธ์ จันทร์โอชา', 'นฤมล ภิญโญสินวัฒน์', 'ธรรมนัส พรหมเผ่า', 'ชัยวุฒิ ธนาคมานุสรณ์', 'ไพบูลย์ นิติตะวัน', 'พลเอกประวิตร วงษ์สุวรรณ', 'พริษฐ์ วัชรสินธุ', 'ยิ่งลักษณ์ ชินวัตร', 'ณัฐวุฒิ ใสยเกื้อ', 'นพ.ชลน่าน ศรีแก้ว', 'เศรษฐา ทวีสิน', 'แพทองธาร ชินวัตร', 'อะไร', 'สมัคร', 'กระโดด', 'ยก', 'ชน', 'ผ่าน', 'แนะนำ', 'จำได้', 'ปลูก', 'รวม', 'ทำหาย', 'เจอ', 'หาย', 'พนักงานขาย', 'นักเขียนโปรแกรม', 'พ่อครัว', 'เจ้าหน้าที่ตำรวจ', 'เนื้อลูกแกะ', 'เนื้อหมู', 'คุณสบายดีไหม', 'ดูเหมือน', 'วาง', 'อยู่', 'เนื้อวัว', 'ผู้จัดการ', 'วิศวกร', 'ชา', 'ศูนย์', 'ใกล้', 'น่าเกลียด', 'น่าเกลียด', 'นาฬิกา', 'ปฏิทิน', 'ปลากระเบน', 'แมงดา', 'วุ้น', 'เต้าส่วน', 'เต้าฮวยน้ำขิง', 'ซาหริ่ม', 'ลอดช่องกะทิ', 'เมี่ยงคำ', 'เรือข้ามฟาก', 'พรรคพลังท้องถิ่นไท', 'พรรศชาติพัฒนากล้า', 'พรรศชาติพัฒนากล้า', 'พรรคเศรษฐกิจใหม่', 'พรรคไทยธรรม', 'พรรคพลังธรรมใหม่', 'พรรคประชานิยม', 'พรรคทวงคืนผืนป่าประเทศไทย', 'พรรคภราดรภาพ', 'พรรครวมใจไทย', 'พรรคประชาชนปฏิรูป'])\n",
    "\n",
    "# 30 videos of data\n",
    "no_sequences = 30\n",
    "\n",
    "# videos are 30 frames length\n",
    "sequence_length = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "af62f3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for action in actions:\n",
    "    for sequence in range(no_sequences):\n",
    "        try:\n",
    "            os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8577787f",
   "metadata": {},
   "source": [
    "# เก็บ Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "78fdc6e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n"
     ]
    }
   ],
   "source": [
    "#ทดสอบOpenCV\n",
    "cap = cv2.VideoCapture(0)\n",
    "#set mediapipe model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    for action in actions:\n",
    "        \n",
    "        for sequence in range(no_sequences):\n",
    "            \n",
    "            for frame_num in range(sequence_length):\n",
    "                \n",
    "                ret, frame = cap.read()\n",
    "\n",
    "                image, results = mediapipe_detection(frame, holistic)\n",
    "                print(results)\n",
    "\n",
    "                draw_styled_landmarks(image, results)\n",
    "\n",
    "\n",
    "                if frame_num == 0:\n",
    "                    cv2.putText(image, 'STARTING COLLECTION', (120, 200),\n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 4, cv2.LINE_AA)\n",
    "                    cv2.putText(image, 'Collecting frame for {} video number {}'.format(action, sequence), (15, 12),\n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 1, cv2.LINE_AA)\n",
    "                    cv2.waitKey(1000)\n",
    "                else:\n",
    "                    cv2.putText(image, 'Collecting frame for {} video number {}'.format(action, sequence), (15, 12),\n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 1, cv2.LINE_AA)\n",
    "\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",
    "                cv2.imshow('OpenCV video test', image)\n",
    "\n",
    "                if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "                    break\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af97081",
   "metadata": {},
   "source": [
    "# Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4f6ed7c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "9cb1c03a",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {label:num for num, label in enumerate(actions)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63520241",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "525199d2",
   "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": 185,
   "id": "982c7912",
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.array(sequences).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "4ec9f1bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.array(labels).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "f1119e36",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "3de10437",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "44cd07d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "e74a6363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 1],\n",
       "       [0, 0, 0, ..., 0, 0, 1],\n",
       "       [0, 0, 0, ..., 0, 0, 1]])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "ef5991fa",
   "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": 195,
   "id": "f4e21a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0e4db65",
   "metadata": {},
   "source": [
    "# LSTM Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "d5ad7349",
   "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": 197,
   "id": "f63bbe4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir=log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "a6f66136",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30, 1662)))\n",
    "model.add(LSTM(128, return_sequences=True, activation='relu'))\n",
    "model.add(LSTM(64, return_sequences=False, activation='relu'))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(actions.shape[0], activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "45ac7f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#actions.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "a392c10d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"Adam\", loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4431322",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train, epochs=1000, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "96ff3a1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_9 (LSTM)                (None, 30, 64)            442112    \n",
      "_________________________________________________________________\n",
      "lstm_10 (LSTM)               (None, 30, 128)           98816     \n",
      "_________________________________________________________________\n",
      "lstm_11 (LSTM)               (None, 64)                49408     \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_11 (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": "markdown",
   "id": "3d5ee66a",
   "metadata": {},
   "source": [
    "# Prediction and Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a7b27c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0678dd7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a53eb8ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.37384814, 0.30111825, 0.32503358], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "dc301d6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'thanks'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(y_test[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1d7af94f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.save('action.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3729f033",
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "03c5664b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('action.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2f0eb9",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "91226c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "6ecbb077",
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "9a5db476",
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_test, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ab39e33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[53,  2],\n",
       "        [ 0, 30]],\n",
       "\n",
       "       [[58,  0],\n",
       "        [ 2, 25]],\n",
       "\n",
       "       [[56,  1],\n",
       "        [ 1, 27]]], dtype=int64)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "8de36d3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9647058823529412"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b31a01de",
   "metadata": {},
   "source": [
    "# ใช้จริง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c277cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [(245, 117, 16), (117, 245, 16), (16, 117, 245)]\n",
    "def prob_viz(res, actions, input_frame, colors):\n",
    "    output_frame = input-frame.copy()\n",
    "    for num, prob in enumerate(res):\n",
    "        cv2.rectangle(output_frame, (0, 60+num*40), (int(prob*100), 90+num*40), colors[num], -1)\n",
    "        cv2.putText(output_frame, actions[num], (0, 85+num*40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "    \n",
    "    return output_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83da9215",
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence = []\n",
    "sentence = []\n",
    "predictions = []\n",
    "threshold = 0.9\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "#set mediapipe model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        \n",
    "        draw_styled_landmarks(image, results)\n",
    "        \n",
    "        keypoints = extract_keypoints(results)\n",
    "        sequence.append(keypoints)\n",
    "        sequence = sequence[-30:]\n",
    "        \n",
    "        if len(sequence) == 30:\n",
    "            res = model.predict(np.expand_dims(sequence, axis=0))[0]\n",
    "            print(actions[np.argmax(res)])\n",
    "            predictions.append(np.argmax(res))\n",
    "            \n",
    "            if np.unique(predictions[-10:])[0]==np.argmax(res):\n",
    "                if res[np.argmax(res)] > threshold:\n",
    "\n",
    "                    if len(sequence) > 0:\n",
    "                        if actions[np.argmax(res)] != sentence[-1]:\n",
    "                            sentence.append(actions[np.argmax(res)])\n",
    "                    else:\n",
    "                        sentence.append(actions[np.argmax(res)])\n",
    "        \n",
    "            if len(sentence) > 5:\n",
    "                sentence = sentence[-5:]\n",
    "\n",
    "            image = prob_viz(res, actions, image, colors)\n",
    "\n",
    "        cv2.rectangle(image, (0,0), (640,40), (245, 117, 16), -1)\n",
    "        \n",
    "        fontpath = \"./angsana.ttc\" # <== 这里是宋体路径 \n",
    "        font = ImageFont.truetype(fontpath, 30)\n",
    "        img_pil = Image.fromarray(image)\n",
    "        draw = ImageDraw.Draw(img_pil)\n",
    "        draw.text((50, 80),  ''.join(sentence), font = font, fill = (b, g, r, a))\n",
    "        image = np.array(img_pil)\n",
    "        #cv2.putText(image, ''.join(sentence), (3, 30),\n",
    "                    #cv2.FONT_HERSHEY_SIMPLEX, 0, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        \n",
    "\n",
    "        cv2.imshow('OpenCV video test', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "d8c10a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 30, 1662)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.expand_dims(X_test[0], axis=0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e42f2843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00146406, 0.9774131 , 0.02112286]], dtype=float32)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.expand_dims(X_test[0], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bb99550",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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