Action Detection 10.10_2.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 필요한 라이브러리 설치 및 import \n",
    "1. Import and Install Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "GPU를 사용하는 tensorflow와 mediapipe 설치 <br>\n",
    "\n",
    " mediapipe란 ? 주로 멀티 모델 오디오, 비디오 또는 모든 시계열 데이터를 구축하는 데 사용되는 프레임 워크입니다. <br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# 필요한 라이브러리 설치 \n",
    "\n",
    "#using tensorflow with GPU, mediapipe detacting movements\n",
    "!pip install tensorflow==2.4.1 tensorflow-gpu==2.4.1 opencv-python mediapipe sklearn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import time\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MP Holistic을 사용하여 이미지의 좌표 출력하기 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#mp_holistic = mp.solutions.holistic <br>\n",
    "#mp.solutions.holistic 이란 ? -> 실시간 모션인식 (얼굴,몸의 분절을 실시간으로 추적한다.)\n",
    "\n",
    "#참고 사이트 <br>\n",
    "#https://google.github.io/mediapipe/solutions/holistic.html\n",
    "\n",
    "\n",
    "\n",
    "#mp_drawing = 점을 연결하여 선을 보여준다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.holistic \n",
    "#mp.solutions.holistic 이란 ? -> 실시간 모션인식 (얼굴,몸의 분절을 실시간으로 추적한다.)\n",
    "\n",
    "#Live perception of simultaneous human pose, face landmarks, \n",
    "#and hand tracking in real-time on mobile devices can enable various modern life applications\n",
    "#https://google.github.io/mediapipe/solutions/holistic.html\n",
    "\n",
    "mp_drawing = mp.solutions.drawing_utils # Drawing utilities\n",
    "# 점을 연결하여 선을 보여준다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 이미지의 좌표를 추적하는 함수 만들기 \n",
    "#이미지 -> RGB형식으로 만들어 모델에 대입한 결과를 results에 담고, 원상태로 만든 이미지와 results를 출력한다. \n",
    "\n",
    "def mediapipe_detection(image, model):   # 대상을 추적한다. \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 모델에 대입 \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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'results' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7720/649385880.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#<class 'mediapipe.python.solution_base.SolutionOutputs'>\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mface_landmarks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlandmark\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#x: 0.5144715905189514\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'results' is not defined"
     ]
    }
   ],
   "source": [
    "print(results)\n",
    "#<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
    "\n",
    "results.face_landmarks.landmark\n",
    "#x: 0.5144715905189514\n",
    "#y: 0.24100039899349213\n",
    "#z: -0.015107447281479836\n",
    "\n",
    "#results -> <face, pose, left_hand, right_hand> 총 네 개의 결과값이 있다,\n",
    "#하나의 값 (results.face_landmarks.landmark) 당 x,y,z 총 3개의 값이 3차원으로 존재한다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 위에서 얻은 results의 값을 numpy형태로 출력하기 \n",
    "Extract Keypoint Values\n",
    "\n",
    "results.pose_landmarks.landmark -> [res.x, res.y, res.z, res.visibility]으로 총 4개의 값으로 구성 <br>\n",
    "그외는 <br>\n",
    "results.face_landmarks.landmark -> [res.x, res.y, res.z]으로 구성 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pose, face, lh, rh의 순서로 저장한다. \n",
    "# np.concatenate 를 하여 네개의 값을 하나의 넘파이 배열로 만든다.  \n",
    "#만약 frame에 값이 출력되지 않았다면, (사진에서 왼손이 보이지 않아 left_hand의 result가 none으로 나올 경우) 0으로 채운다. \n",
    "\n",
    "def extract_keypoints(results):\n",
    "    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)\n",
    "    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)\n",
    "    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
    "    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n",
    "    return np.concatenate([pose, face, lh, rh])\n",
    "\n",
    "#extract_keypoints(results).shape \n",
    "# (1662,) -> 33*4 + 468*3  + 21*3 + 21*3  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 훈련데이터 동영상 넘파이 배열로 저장하기 \n",
    "Convert Keypoint Values from videos\n",
    "\n",
    "< 데이터 출처>  <br>\n",
    ": Ai hub https://aihub.or.kr/keti_data_board/language_intelligence\n",
    "\n",
    "<데이터 구성> <br>\n",
    "데이터 크기 : 3000개의 수화 동영상 <br>\n",
    "녹화 참여 인원수 : 총 10명 <br>\n",
    "단어 종류 : 419개 \n",
    "\n",
    "<데이터프레임 설명> <br>\n",
    "dt : AI hub에서 가져온 동영상 파일 설명 엑셀파일 원본 <br>\n",
    "dt1 : 문장과 단어가 모두 포함되어 있는 dt원본파일에서 단어 동영상만 추출 <br>\n",
    "list_a : dt1의 한국어 컬럼을 단어의 개수 별로 정리한 데이터 프레임 <단어/횟수>로 구성.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 필요한 데이터프레임 생성 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\snrnq\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages (from pandas) (1.19.5)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\snrnq\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\snrnq\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "#tensorflow에서 엑셀파일 여는 라이브러리 다운 \n",
    "\n",
    "#!pip install pandas\n",
    "#!pip install openpyxl\n",
    "#!pip install fsspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting fsspec\n",
      "  Downloading fsspec-2021.10.0-py3-none-any.whl (125 kB)\n",
      "Installing collected packages: fsspec\n",
      "Successfully installed fsspec-2021.10.0\n"
     ]
    }
   ],
   "source": [
    "#!pip install pandas\n",
    "#!pip install openpyxl\n",
    "#!pip install fsspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>번호</th>\n",
       "      <th>언어 제공자 ID</th>\n",
       "      <th>취득연도</th>\n",
       "      <th>방향</th>\n",
       "      <th>타입(단어/문장)</th>\n",
       "      <th>파일명</th>\n",
       "      <th>한국어</th>\n",
       "      <th>번호.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000001.MOV</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000002.MOV</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000003.MOV</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000004.MOV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000005.MOV</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    번호  언어 제공자 ID    취득연도  방향 타입(단어/문장)                     파일명  한국어  번호.1\n",
       "0  1.0        1.0  2017.0  정면        단어  KETI_SL_0000000001.MOV  0.0   1.0\n",
       "1  2.0        1.0  2017.0  정면        단어  KETI_SL_0000000002.MOV  1.0   2.0\n",
       "2  3.0        1.0  2017.0  정면        단어  KETI_SL_0000000003.MOV  2.0   3.0\n",
       "3  4.0        1.0  2017.0  정면        단어  KETI_SL_0000000004.MOV  3.0   4.0\n",
       "4  5.0        1.0  2017.0  정면        단어  KETI_SL_0000000005.MOV  4.0   5.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dt 데이터프레임 생성 \n",
    "import pandas as pd\n",
    "dt=pd.read_excel(\"C://Users//User//sign//10.09.xlsx\")\n",
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>번호</th>\n",
       "      <th>언어 제공자 ID</th>\n",
       "      <th>취득연도</th>\n",
       "      <th>방향</th>\n",
       "      <th>타입(단어/문장)</th>\n",
       "      <th>파일명</th>\n",
       "      <th>한국어</th>\n",
       "      <th>번호.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000001.MOV</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000002.MOV</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000003.MOV</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000004.MOV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>정면</td>\n",
       "      <td>단어</td>\n",
       "      <td>KETI_SL_0000000005.MOV</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    번호  언어 제공자 ID    취득연도  방향 타입(단어/문장)                     파일명  한국어  번호.1\n",
       "0  1.0        1.0  2017.0  정면        단어  KETI_SL_0000000001.MOV  0.0   1.0\n",
       "1  2.0        1.0  2017.0  정면        단어  KETI_SL_0000000002.MOV  1.0   2.0\n",
       "2  3.0        1.0  2017.0  정면        단어  KETI_SL_0000000003.MOV  2.0   3.0\n",
       "3  4.0        1.0  2017.0  정면        단어  KETI_SL_0000000004.MOV  3.0   4.0\n",
       "4  5.0        1.0  2017.0  정면        단어  KETI_SL_0000000005.MOV  4.0   5.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dt 데이터프레임 생성 \n",
    "import pandas as pd\n",
    "dt=pd.read_excel(\"E://Users//User//sign//10.09.xlsx\")\n",
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\solag\\AppData\\Local\\Temp/ipykernel_11512/3882441389.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dt['한국어'][i]=int(dt['한국어'][i])\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(dt['한국어'])):\n",
    "    try:\n",
    "        dt['한국어'][i]=int(dt['한국어'][i])\n",
    "        dt['한국어'][i]=srt(dt['한국어'][i])\n",
    "    except:\n",
    "        dt['한국어'][i]==dt['한국어'][i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\snrnq\\AppData\\Local\\Temp/ipykernel_7720/4080634729.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dt['한국어'][i] = int(dt['한국어'][i])\n",
      "C:\\Users\\snrnq\\AppData\\Local\\Temp/ipykernel_7720/4080634729.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dt['한국어'][i] = str(dt['한국어'][i])\n"
     ]
    }
   ],
   "source": [
    "# dt 한국어컬럼 중 float 형 int형으로 바꾸기 \n",
    "\n",
    "for i in range(len(dt['한국어'])):    #4704\n",
    "    try:\n",
    "        dt['한국어'][i] = int(dt['한국어'][i])\n",
    "        dt['한국어'][i] = str(dt['한국어'][i])\n",
    "    except:\n",
    "        dt['한국어'][i] == dt['한국어'][i]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       KETI_SL_0000000001.avi\n",
       "1       KETI_SL_0000000002.avi\n",
       "2       KETI_SL_0000000003.avi\n",
       "3       KETI_SL_0000000004.avi\n",
       "4       KETI_SL_0000000005.avi\n",
       "                 ...          \n",
       "4699    KETI_SL_0000010371.avi\n",
       "4700    KETI_SL_0000010372.avi\n",
       "4701    KETI_SL_0000010373.avi\n",
       "4702    KETI_SL_0000010374.avi\n",
       "4703    KETI_SL_0000010375.avi\n",
       "Name: 파일명, Length: 4704, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt['파일명'] = dt['파일명'].apply(lambda x:x[:-3]) + dt['파일명'].apply(lambda x:x[-3:]).replace('MOV','avi')\n",
    "dt['파일명']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       KETI_SL_0000000001.avi\n",
       "1       KETI_SL_0000000002.avi\n",
       "2       KETI_SL_0000000003.avi\n",
       "3       KETI_SL_0000000004.avi\n",
       "4       KETI_SL_0000000005.avi\n",
       "                 ...          \n",
       "4699    KETI_SL_0000010371.avi\n",
       "4700    KETI_SL_0000010372.avi\n",
       "4701    KETI_SL_0000010373.avi\n",
       "4702    KETI_SL_0000010374.avi\n",
       "4703    KETI_SL_0000010375.avi\n",
       "Name: 파일명, Length: 4704, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#파일명 마지막 세글자.avi로 바꾸기 \n",
    "dt['파일명'] = dt['파일명'].apply(lambda x:x[:-3]) + dt['파일명'].apply(lambda x:x[-3:]).replace('MOV','avi')\n",
    "dt['파일명']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "dt1=dt[dt['타입(단어/문장)']=='단어']\n",
    "dt1 = dt1[:3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## dt1 데이터 프레임 생성 \n",
    "#전체 데이터 단어만 선택한 후 3000개 데이터 선택 \n",
    "\n",
    "import pandas as pd\n",
    "dt1=dt[dt['타입(단어/문장)']=='단어']\n",
    "dt1 = dt1[:3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'str' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11512/2867496936.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mlist_a\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'한국어'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'횟수'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m#list_a = list_a.sort_values('한국어')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_a\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'한국어'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36msort_values\u001b[1;34m(self, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[0m\n\u001b[0;32m   3453\u001b[0m         \u001b[1;31m# GH 35922. Make sorting stable by leveraging nargsort\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3454\u001b[0m         \u001b[0mvalues_to_sort\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_key_mapped\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3455\u001b[1;33m         \u001b[0msorted_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnargsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues_to_sort\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_position\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m         result = self._constructor(\n",
      "\u001b[1;32mD:\\anaconda\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\core\\sorting.py\u001b[0m in \u001b[0;36mnargsort\u001b[1;34m(items, kind, ascending, na_position, key, mask)\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[0mnon_nans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnon_nans\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    402\u001b[0m         \u001b[0mnon_nan_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnon_nan_idx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 403\u001b[1;33m     \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnon_nan_idx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnon_nans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    404\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mascending\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dt1=dt[dt['타입(단어/문장)']=='단어']  #단어만 골라내기\n",
    "dt1 = dt1[:3000]   #0~2999\n",
    "aa = dt1[['한국어']].copy()    #0~2999단어만 추출\n",
    "list_a =aa['한국어'].value_counts()  #단어   횟수\n",
    "list_a =list_a.reset_index()\n",
    "list_a.columns = ['한국어','횟수']\n",
    "#list_a = list_a.sort_values('한국어')\n",
    "print(list_a['한국어'].sort_values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>한국어</th>\n",
       "      <th>횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      한국어  횟수\n",
       "0       0   8\n",
       "22      1   8\n",
       "49     10   8\n",
       "328   100   7\n",
       "325  1000   7"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 단어별로 동영상이 몇개 있는지 출력하는 list_a 데이터 프레임 생성 \n",
    "\n",
    "#한국어컬럼만 추출\n",
    "aa = dt1[['한국어']].copy()\n",
    "\n",
    "#한국어 컬럼에 대한 개수 추출\n",
    "list_a =aa['한국어'].value_counts()\n",
    "\n",
    "#인덱스를 없애서 총 2개의 컬럼을 가진 데이터 프레임 생성 [한국어.횟수]\n",
    "list_a =list_a.reset_index()\n",
    "\n",
    "# 데이터프레임 컬렴명 변겅 \n",
    "list_a.columns = ['한국어','횟수']\n",
    "list_a = list_a.sort_values('한국어')\n",
    "list_a.head()\n",
    "\n",
    "#len(list_a)\n",
    "#419"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## numpy형태로 변환한 파일 담을 폴더 생성 \n",
    "\n",
    "<폴더 구조> <br>\n",
    "train 폴더 <br>\n",
    "단어의 뜻을 이름으로 하는 폴더 419개  <br>\n",
    "각각의 단어 폴더 안에 단어 당 촬영한 동영상 개수 만큼 폴더 생성   <br>\n",
    "각각의 동영상 개수 폴더 안에 프레임 횟수만큼 폴더를 만들고 하나의 폴더안에 프레임 하나의 넘파이 배열이 담긴 파이썬 파일 첨부 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 단어별 -동영상개수만큼의 폴더 생성하기 \n",
    "import os\n",
    "# train 폴더 생성\n",
    "os.mkdir( 'c://Users//User//sign//train')\n",
    "\n",
    "# 단어별 폴더 생성 \n",
    "for k in range(419): \n",
    "    a = 'C://Users//User//sign//train//' + str(list_a['한국어'][k])  # 동영상 이름 \n",
    "    os.mkdir(a)\n",
    "    # 단어 폴더 안에 동영상의 횟수만큼 폴더 생성 \n",
    "    for i in range(list_a['횟수'][k]):\n",
    "        b= 'C://Users//User//sign//train//' + str(list_a['한국어'][k]) + '//' + str(i)\n",
    "        os.mkdir(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 단어별 -동영상개수만큼의 폴더 생성하기 \n",
    "import os\n",
    "# train 폴더 생성\n",
    "os.mkdir( 'e://Users//User//sign//train')\n",
    "\n",
    "# 단어별 폴더 생성 \n",
    "for k in range(419): \n",
    "    a = 'e://Users//User//sign//train//' + str(list_a['한국어'][k])  # 동영상 이름 \n",
    "    os.mkdir(a)\n",
    "    # 단어 폴더 안에 동영상의 횟수만큼 폴더 생성 \n",
    "    for i in range(list_a['횟수'][k]):\n",
    "        b= 'e://Users//User//sign//train//' + str(list_a['한국어'][k]) + '//' + str(i)\n",
    "        os.mkdir(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## video3000에 있는 동영상 읽어와서 numpy배열로 변환한 후 위에서 만든 train 폴더에 저장 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "낫단어103프레임 추출 완료======================================\n",
      "낯선남자단어72프레임 추출 완료======================================\n",
      "낯선사람단어80프레임 추출 완료======================================\n",
      "낯선여자단어84프레임 추출 완료======================================\n",
      "내년단어69프레임 추출 완료======================================\n",
      "내일단어136프레임 추출 완료======================================\n",
      "냄새나다단어136프레임 추출 완료======================================\n",
      "노원구단어74프레임 추출 완료======================================\n",
      "논단어86프레임 추출 완료======================================\n",
      "농약단어99프레임 추출 완료======================================\n",
      "누나단어136프레임 추출 완료======================================\n",
      "누수단어92프레임 추출 완료======================================\n",
      "누전단어81프레임 추출 완료======================================\n",
      "누출단어88프레임 추출 완료======================================\n",
      "눈단어78프레임 추출 완료======================================\n",
      "다리단어72프레임 추출 완료======================================\n",
      "다음단어136프레임 추출 완료======================================\n",
      "달(월)단어151프레임 추출 완료======================================\n",
      "대문앞단어107프레임 추출 완료======================================\n",
      "도둑단어65프레임 추출 완료======================================\n",
      "절도단어65프레임 추출 완료======================================\n",
      "도로단어132프레임 추출 완료======================================\n",
      "도봉구단어81프레임 추출 완료======================================\n",
      "독극물단어123프레임 추출 완료======================================\n",
      "독버섯단어95프레임 추출 완료======================================\n",
      "독사단어94프레임 추출 완료======================================\n",
      "동대문구단어80프레임 추출 완료======================================\n",
      "동생단어161프레임 추출 완료======================================\n",
      "동작구단어80프레임 추출 완료======================================\n",
      "동전단어150프레임 추출 완료======================================\n",
      "두드러기생기다단어109프레임 추출 완료======================================\n",
      "뒤단어73프레임 추출 완료======================================\n",
      "뒤통수단어136프레임 추출 완료======================================\n",
      "등단어96프레임 추출 완료======================================\n",
      "딸단어155프레임 추출 완료======================================\n",
      "떨어지다단어76프레임 추출 완료======================================\n",
      "쓰러지다단어76프레임 추출 완료======================================\n",
      "뜨거운물단어81프레임 추출 완료======================================\n",
      "마당단어80프레임 추출 완료======================================\n",
      "마포구단어79프레임 추출 완료======================================\n",
      "말려주세요단어81프레임 추출 완료======================================\n",
      "말벌단어106프레임 추출 완료======================================\n",
      "맹견단어93프레임 추출 완료======================================\n",
      "머리단어136프레임 추출 완료======================================\n",
      "멧돼지단어90프레임 추출 완료======================================\n",
      "목단어146프레임 추출 완료======================================\n",
      "목요일단어69프레임 추출 완료======================================\n",
      "무너지다단어59프레임 추출 완료======================================\n",
      "붕괴단어59프레임 추출 완료======================================\n",
      "무릎단어121프레임 추출 완료======================================\n",
      "문틈단어88프레임 추출 완료======================================\n",
      "물단어100프레임 추출 완료======================================\n",
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      "방망이단어87프레임 추출 완료======================================\n",
      "밭단어59프레임 추출 완료======================================\n",
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      "배고프다단어151프레임 추출 완료======================================\n",
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      "병원단어68프레임 추출 완료======================================\n",
      "보건소단어86프레임 추출 완료======================================\n",
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      "볼단어130프레임 추출 완료======================================\n",
      "부엌단어84프레임 추출 완료======================================\n",
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      "불나다단어77프레임 추출 완료======================================\n",
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      "비닐하우스단어101프레임 추출 완료======================================\n",
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      "산단어67프레임 추출 완료======================================\n",
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      "손단어56프레임 추출 완료======================================\n",
      "손가락단어87프레임 추출 완료======================================\n",
      "손목단어66프레임 추출 완료======================================\n",
      "송파구단어84프레임 추출 완료======================================\n",
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      "수요일단어121프레임 추출 완료======================================\n",
      "술취한 사람단어99프레임 추출 완료======================================\n",
      "숨을안쉬다단어89프레임 추출 완료======================================\n",
      "시청단어81프레임 추출 완료======================================\n",
      "신고하세요(경찰)단어121프레임 추출 완료======================================\n",
      "심장마비단어66프레임 추출 완료======================================\n",
      "아기단어62프레임 추출 완료======================================\n",
      "아이들단어62프레임 추출 완료======================================\n",
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      "아내단어74프레임 추출 완료======================================\n",
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      "인대단어89프레임 추출 완료======================================\n",
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      "양천구단어91프레임 추출 완료======================================\n",
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      "언니단어133프레임 추출 완료======================================\n",
      "얼굴단어77프레임 추출 완료======================================\n",
      "엄마단어159프레임 추출 완료======================================\n",
      "엘리베이터단어80프레임 추출 완료======================================\n",
      "여자친구단어89프레임 추출 완료======================================\n",
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      "연기단어91프레임 추출 완료======================================\n",
      "연락해주세요단어83프레임 추출 완료======================================\n",
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      "열나다단어73프레임 추출 완료======================================\n",
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      "오른쪽단어69프레임 추출 완료======================================\n",
      "오른쪽-귀단어121프레임 추출 완료======================================\n",
      "오른쪽-눈단어132프레임 추출 완료======================================\n",
      "오빠단어120프레임 추출 완료======================================\n",
      "옥상단어84프레임 추출 완료======================================\n",
      "올해단어71프레임 추출 완료======================================\n",
      "왼쪽단어73프레임 추출 완료======================================\n",
      "왼쪽-귀단어121프레임 추출 완료======================================\n",
      "왼쪽-눈단어121프레임 추출 완료======================================\n",
      "욕실단어80프레임 추출 완료======================================\n",
      "용산구단어85프레임 추출 완료======================================\n",
      "우리집단어68프레임 추출 완료======================================\n",
      "운동장단어81프레임 추출 완료======================================\n",
      "월요일단어121프레임 추출 완료======================================\n",
      "위단어107프레임 추출 완료======================================\n",
      "위에단어71프레임 추출 완료======================================\n",
      "위협단어78프레임 추출 완료======================================\n",
      "윗집단어75프레임 추출 완료======================================\n",
      "윗집사람단어90프레임 추출 완료======================================\n",
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      "유치원단어63프레임 추출 완료======================================\n",
      "유치원 버스단어93프레임 추출 완료======================================\n",
      "은평구단어80프레임 추출 완료======================================\n",
      "음식물단어88프레임 추출 완료======================================\n",
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      "응급처리단어89프레임 추출 완료======================================\n",
      "의사단어79프레임 추출 완료======================================\n",
      "이마단어115프레임 추출 완료======================================\n",
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      "절단단어75프레임 추출 완료======================================\n",
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      "종로구단어82프레임 추출 완료======================================\n",
      "주단어134프레임 추출 완료======================================\n",
      "중구단어73프레임 추출 완료======================================\n",
      "중랑구단어83프레임 추출 완료======================================\n",
      "지난단어104프레임 추출 완료======================================\n",
      "지혈대단어113프레임 추출 완료======================================\n",
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      "집단폭행단어98프레임 추출 완료======================================\n",
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      "창백하다단어95프레임 추출 완료======================================\n",
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      "학교단어71프레임 추출 완료======================================\n",
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      "함몰되다단어93프레임 추출 완료======================================\n",
      "해(연)단어83프레임 추출 완료======================================\n",
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      "현관앞단어111프레임 추출 완료======================================\n",
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      "형단어106프레임 추출 완료======================================\n",
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      "홍수단어118프레임 추출 완료======================================\n",
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      "화약단어79프레임 추출 완료======================================\n",
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      "장단지단어110프레임 추출 완료======================================\n",
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      "43단어128프레임 추출 완료======================================\n",
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      "48단어78프레임 추출 완료======================================\n",
      "49단어113프레임 추출 완료======================================\n",
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      "결박단어126프레임 추출 완료======================================\n",
      "경운기단어120프레임 추출 완료======================================\n",
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      "구로구단어63프레임 추출 완료======================================\n",
      "구청단어73프레임 추출 완료======================================\n",
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      "귀단어78프레임 추출 완료======================================\n",
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      "깔리다단어89프레임 추출 완료======================================\n",
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      "서대문구단어94프레임 추출 완료======================================\n",
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      "손목단어95프레임 추출 완료======================================\n",
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      "신고하세요(경찰)단어114프레임 추출 완료======================================\n",
      "심장마비단어72프레임 추출 완료======================================\n",
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      "아이들단어64프레임 추출 완료======================================\n",
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      "장단지단어96프레임 추출 완료======================================\n",
      "절단단어77프레임 추출 완료======================================\n",
      "제초제단어116프레임 추출 완료======================================\n",
      "조난단어87프레임 추출 완료======================================\n",
      "종로구단어79프레임 추출 완료======================================\n",
      "주단어136프레임 추출 완료======================================\n",
      "중구단어86프레임 추출 완료======================================\n",
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      "집단어64프레임 추출 완료======================================\n",
      "집단폭행단어111프레임 추출 완료======================================\n",
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      "58단어100프레임 추출 완료======================================\n",
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      "65단어196프레임 추출 완료======================================\n",
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      "62단어102프레임 추출 완료======================================\n",
      "63단어89프레임 추출 완료======================================\n",
      "64단어93프레임 추출 완료======================================\n",
      "65단어205프레임 추출 완료======================================\n",
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      "67단어105프레임 추출 완료======================================\n",
      "68단어96프레임 추출 완료======================================\n",
      "69단어103프레임 추출 완료======================================\n",
      "70단어87프레임 추출 완료======================================\n",
      "71단어86프레임 추출 완료======================================\n",
      "72단어100프레임 추출 완료======================================\n",
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      "76단어93프레임 추출 완료======================================\n",
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      "79단어103프레임 추출 완료======================================\n",
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      "93단어94프레임 추출 완료======================================\n",
      "94단어103프레임 추출 완료======================================\n",
      "95단어106프레임 추출 완료======================================\n",
      "96단어109프레임 추출 완료======================================\n",
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      "112단어108프레임 추출 완료======================================\n",
      "119단어113프레임 추출 완료======================================\n",
      "1000단어210프레임 추출 완료======================================\n",
      "10000단어110프레임 추출 완료======================================\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyError",
     "evalue": "3000",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 3000",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7720/3001882141.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[1;31m#dt1 -> 단어별로 정리한 동영상 파일 3000개\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m         \u001b[0mvideos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"C://Users//User//sign//video3000//\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdt1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'파일명'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    940\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    941\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 942\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    943\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    944\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1049\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1050\u001b[0m         \u001b[1;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1051\u001b[1;33m         \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1052\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\snowdeer_env1\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 3000"
     ]
    }
   ],
   "source": [
    "#폴더의 개수 출력하는 함수 생성 \n",
    "def dir_empty(dir_path):\n",
    "    return len(os.listdir(dir_path))\n",
    "\n",
    "\n",
    "\n",
    "# holistic 모델 사용 \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    # 3000개의 동영상 분석 \n",
    "    for k in range(1000, 3001):\n",
    "        # dt1['파일명']의 동영상 3000개 분석 \n",
    "        #dt1 -> 단어별로 정리한 동영상 파일 3000개 \n",
    "        \n",
    "        videos = \"C://Users//User//sign//video3000//\" + dt1['파일명'][k] \n",
    "\n",
    "        \n",
    "        #openCV를 열고 동영상을 읽어들인다. -> 동영상의 처음부터 끝까지 frame형태로 기록된다. \n",
    "        # -> 출력물 ret. frame \n",
    "        \n",
    "        # ret이란 frame이 존재하는지 존재하지 않는지 True, False로 출력한다. \n",
    "        # 동영상이 끝나 더이상의 frame이 존재하지 않으면 False출력 \n",
    "        \n",
    "        cam = cv2.VideoCapture(videos)\n",
    "        \n",
    "        #출력하는 동영상의 해당 단어 kor에 담기 \n",
    "        kor = dt1['한국어'][k]\n",
    "        \n",
    "        # videocount -> 해당 단어의 몇번째 동영상인지 출력 \n",
    "        videocount =0\n",
    "\n",
    "      # \n",
    "        while (True):\n",
    "            #만약 해당 단어 폴더의 vidoecount의 이름을 가진 폴더의 길이가 0이라면(비어있다면)\n",
    "            #  해당단어 - video count - frame 개수 별로 만든 폴더에 넘파이 파일 저장 \n",
    "            if dir_empty('C://Users//User//sign//train//' + kor + '//'+str(videocount)) == 0:\n",
    "                \n",
    "                #프레임 개수를 알려주는 currentframe \n",
    "                currentframe = 0\n",
    "\n",
    "                while (True):\n",
    "                    # 여기서의 True 가 밑의 ret == True로 연결\n",
    "                    #ret 이 True 라면 계속 반복, 즉 프레임이 남아있다면 계속해서 읽어들인다. \n",
    "\n",
    "\n",
    "\n",
    "                    # 위에서 읽은 cam을 frame별로 하나씩 읽어들이겠다.\n",
    "                    ret,frame = cam.read()\n",
    "\n",
    "\n",
    "                    # 만약 프레임이 남아있다면 \n",
    "                    if ret == True:\n",
    "                        \n",
    "                        image, results = mediapipe_detection(frame, holistic)  # frame에 있는 점들을 추적하여 result에 넣는다. \n",
    "                        keypoints = extract_keypoints(results) #result를 하나의 넘파이 배열로 만든다\n",
    "                        \n",
    "                        # 프레임 번호를 폴더명으로 하는 폴더 생성 \n",
    "                        os.mkdir('C://Users//User//sign//train//' + kor + '//'+str(videocount)+'//'+ str(currentframe))\n",
    "                        \n",
    "                        #위에서 만든 폴더에 넘파이 배열 파일을 넣겠다. \n",
    "                        npy_path = 'C://Users//User//sign//train//' + kor + '//'+str(videocount)+'//'+ str(currentframe)+'//' +str(currentframe)\n",
    "                        np.save(npy_path, keypoints)\n",
    "                        \n",
    "                        #다음프레임으로 이동 \n",
    "                        currentframe +=1\n",
    "                        \n",
    "\n",
    "\n",
    "                    #만약 프레임이 끝났다면 즉, 하나의 동영상이 완료되었다면 종료 (두번째 while 문 종료)\n",
    "                    else:\n",
    "                        break\n",
    "                \n",
    "                # 하나의 동영상 추출 완료 ( if문 종료 )\n",
    "                # dir_empty('C://Users//User//sign//train//' + kor + '//'+str(videocount)) == 0: 종료 \n",
    "                # 완료를 알려주는 문장 출력 \n",
    "                print(kor + '단어' + str(currentframe) + '프레임 추출 완료======================================')\n",
    "                \n",
    "                break\n",
    "                \n",
    "    #첫번째 while 문 \n",
    "    #만약 ir_empty('C://Users//User//sign//train//' + kor + '//'+str(videocount)) == 0 이 아니라면, \n",
    "    # 즉 단어 - 동영상 횟수를 나타내는 폴더가 비어있지 않다면 다음폴더로 이동 \n",
    "    #ex ) 사자- 1번째 동영상의 폴더의 길이가 0이아니라면 사자-2 번째 폴더로 이동하고 위의 문장을 다시 실행 \n",
    "    \n",
    "            else:\n",
    "                videocount +=1\n",
    "                continue\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 3차원 형태의 넘파이 배열을 모델에 넣기 위해서는 모든 프레임의 개수가 같아야 한다.\n",
    "\n",
    "# 프레임의 개수가 가장 많은 영상을 찾아 그 영상에 맞춰 모든 영상의 마지막 프레임을 해당 프레임의 개수만큼 복사한다. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>한국어</th>\n",
       "      <th>횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>화상</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>화약</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>화요일</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>화장실</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>화재</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>419 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      한국어  횟수\n",
       "0       0   8\n",
       "22      1   8\n",
       "49     10   8\n",
       "328   100   7\n",
       "325  1000   7\n",
       "..    ...  ..\n",
       "189    화상   7\n",
       "188    화약   7\n",
       "187   화요일   7\n",
       "186   화장실   7\n",
       "331    화재   7\n",
       "\n",
       "[419 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_a\n",
    "\n",
    "#len(list_a)   #419"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C://Users//User//sign//train//0',\n",
       " 'C://Users//User//sign//train//51',\n",
       " 'C://Users//User//sign//train//58',\n",
       " 'C://Users//User//sign//train//57',\n",
       " 'C://Users//User//sign//train//56',\n",
       " 'C://Users//User//sign//train//55',\n",
       " 'C://Users//User//sign//train//54',\n",
       " 'C://Users//User//sign//train//53',\n",
       " 'C://Users//User//sign//train//52',\n",
       " 'C://Users//User//sign//train//50',\n",
       " 'C://Users//User//sign//train//61',\n",
       " 'C://Users//User//sign//train//49',\n",
       " 'C://Users//User//sign//train//48',\n",
       " 'C://Users//User//sign//train//47',\n",
       " 'C://Users//User//sign//train//46',\n",
       " 'C://Users//User//sign//train//44',\n",
       " 'C://Users//User//sign//train//43',\n",
       " 'C://Users//User//sign//train//42',\n",
       " 'C://Users//User//sign//train//60',\n",
       " 'C://Users//User//sign//train//62',\n",
       " 'C://Users//User//sign//train//40',\n",
       " 'C://Users//User//sign//train//72',\n",
       " 'C://Users//User//sign//train//1',\n",
       " 'C://Users//User//sign//train//78',\n",
       " 'C://Users//User//sign//train//77',\n",
       " 'C://Users//User//sign//train//76',\n",
       " 'C://Users//User//sign//train//75',\n",
       " 'C://Users//User//sign//train//74',\n",
       " 'C://Users//User//sign//train//73',\n",
       " 'C://Users//User//sign//train//71',\n",
       " 'C://Users//User//sign//train//63',\n",
       " 'C://Users//User//sign//train//70',\n",
       " 'C://Users//User//sign//train//69',\n",
       " 'C://Users//User//sign//train//68',\n",
       " 'C://Users//User//sign//train//67',\n",
       " 'C://Users//User//sign//train//66',\n",
       " 'C://Users//User//sign//train//65',\n",
       " 'C://Users//User//sign//train//64',\n",
       " 'C://Users//User//sign//train//41',\n",
       " 'C://Users//User//sign//train//59',\n",
       " 'C://Users//User//sign//train//39',\n",
       " 'C://Users//User//sign//train//20',\n",
       " 'C://Users//User//sign//train//18',\n",
       " 'C://Users//User//sign//train//17',\n",
       " 'C://Users//User//sign//train//16',\n",
       " 'C://Users//User//sign//train//15',\n",
       " 'C://Users//User//sign//train//14',\n",
       " 'C://Users//User//sign//train//12',\n",
       " 'C://Users//User//sign//train//11',\n",
       " 'C://Users//User//sign//train//10',\n",
       " 'C://Users//User//sign//train//9',\n",
       " 'C://Users//User//sign//train//8',\n",
       " 'C://Users//User//sign//train//7',\n",
       " 'C://Users//User//sign//train//6',\n",
       " 'C://Users//User//sign//train//5',\n",
       " 'C://Users//User//sign//train//4',\n",
       " 'C://Users//User//sign//train//3',\n",
       " 'C://Users//User//sign//train//38',\n",
       " 'C://Users//User//sign//train//2',\n",
       " 'C://Users//User//sign//train//19',\n",
       " 'C://Users//User//sign//train//13',\n",
       " 'C://Users//User//sign//train//21',\n",
       " 'C://Users//User//sign//train//29',\n",
       " 'C://Users//User//sign//train//37',\n",
       " 'C://Users//User//sign//train//35',\n",
       " 'C://Users//User//sign//train//34',\n",
       " 'C://Users//User//sign//train//33',\n",
       " 'C://Users//User//sign//train//32',\n",
       " 'C://Users//User//sign//train//31',\n",
       " 'C://Users//User//sign//train//30',\n",
       " 'C://Users//User//sign//train//36',\n",
       " 'C://Users//User//sign//train//28',\n",
       " 'C://Users//User//sign//train//26',\n",
       " 'C://Users//User//sign//train//25',\n",
       " 'C://Users//User//sign//train//24',\n",
       " 'C://Users//User//sign//train//23',\n",
       " 'C://Users//User//sign//train//22',\n",
       " 'C://Users//User//sign//train//27',\n",
       " 'C://Users//User//sign//train//연기',\n",
       " 'C://Users//User//sign//train//옆집 아저씨',\n",
       " 'C://Users//User//sign//train//옆집',\n",
       " 'C://Users//User//sign//train//영등포구',\n",
       " 'C://Users//User//sign//train//엽총',\n",
       " 'C://Users//User//sign//train//열어주세요',\n",
       " 'C://Users//User//sign//train//열나다',\n",
       " 'C://Users//User//sign//train//열',\n",
       " 'C://Users//User//sign//train//연락해주세요',\n",
       " 'C://Users//User//sign//train//엘리베이터',\n",
       " 'C://Users//User//sign//train//여학생',\n",
       " 'C://Users//User//sign//train//여자친구',\n",
       " 'C://Users//User//sign//train//엄마',\n",
       " 'C://Users//User//sign//train//얼굴',\n",
       " 'C://Users//User//sign//train//언니',\n",
       " 'C://Users//User//sign//train//어지러움',\n",
       " 'C://Users//User//sign//train//어제',\n",
       " 'C://Users//User//sign//train//옆집사람',\n",
       " 'C://Users//User//sign//train//옆집 할아버지',\n",
       " 'C://Users//User//sign//train//위협',\n",
       " 'C://Users//User//sign//train//옆쪽',\n",
       " 'C://Users//User//sign//train//욕실',\n",
       " 'C://Users//User//sign//train//윗집',\n",
       " 'C://Users//User//sign//train//양천구',\n",
       " 'C://Users//User//sign//train//위에',\n",
       " 'C://Users//User//sign//train//위',\n",
       " 'C://Users//User//sign//train//월요일',\n",
       " 'C://Users//User//sign//train//운동장',\n",
       " 'C://Users//User//sign//train//우리집',\n",
       " 'C://Users//User//sign//train//용산구',\n",
       " 'C://Users//User//sign//train//왼쪽-눈',\n",
       " 'C://Users//User//sign//train//오늘',\n",
       " 'C://Users//User//sign//train//왼쪽-귀',\n",
       " 'C://Users//User//sign//train//왼쪽',\n",
       " 'C://Users//User//sign//train//올해',\n",
       " 'C://Users//User//sign//train//옥상',\n",
       " 'C://Users//User//sign//train//오빠',\n",
       " 'C://Users//User//sign//train//오른쪽-눈',\n",
       " 'C://Users//User//sign//train//오른쪽-귀',\n",
       " 'C://Users//User//sign//train//오른쪽',\n",
       " 'C://Users//User//sign//train//어깨',\n",
       " 'C://Users//User//sign//train//서랍',\n",
       " 'C://Users//User//sign//train//약사',\n",
       " 'C://Users//User//sign//train//수요일',\n",
       " 'C://Users//User//sign//train//송파구',\n",
       " 'C://Users//User//sign//train//손목',\n",
       " 'C://Users//User//sign//train//손',\n",
       " 'C://Users//User//sign//train//소화전',\n",
       " 'C://Users//User//sign//train//소화기',\n",
       " 'C://Users//User//sign//train//소방차',\n",
       " 'C://Users//User//sign//train//소방관',\n",
       " 'C://Users//User//sign//train//성폭행',\n",
       " 'C://Users//User//sign//train//성북구',\n",
       " 'C://Users//User//sign//train//성동구',\n",
       " 'C://Users//User//sign//train//선생님',\n",
       " 'C://Users//User//sign//train//선반',\n",
       " 'C://Users//User//sign//train//서초구',\n",
       " 'C://Users//User//sign//train//서울시',\n",
       " 'C://Users//User//sign//train//유리',\n",
       " 'C://Users//User//sign//train//서대문구',\n",
       " 'C://Users//User//sign//train//삼키다',\n",
       " 'C://Users//User//sign//train//수영장',\n",
       " 'C://Users//User//sign//train//술취한 사람',\n",
       " 'C://Users//User//sign//train//약국',\n",
       " 'C://Users//User//sign//train//숨을안쉬다',\n",
       " 'C://Users//User//sign//train//앞집',\n",
       " 'C://Users//User//sign//train//앞',\n",
       " 'C://Users//User//sign//train//알려주세요',\n",
       " 'C://Users//User//sign//train//안방',\n",
       " 'C://Users//User//sign//train//인대',\n",
       " 'C://Users//User//sign//train//아파트',\n",
       " 'C://Users//User//sign//train//아줌마',\n",
       " 'C://Users//User//sign//train//아저씨',\n",
       " 'C://Users//User//sign//train//아빠',\n",
       " 'C://Users//User//sign//train//아들',\n",
       " 'C://Users//User//sign//train//아내',\n",
       " 'C://Users//User//sign//train//어린이',\n",
       " 'C://Users//User//sign//train//아이들',\n",
       " 'C://Users//User//sign//train//아기',\n",
       " 'C://Users//User//sign//train//심장마비',\n",
       " 'C://Users//User//sign//train//신고하세요(경찰)',\n",
       " 'C://Users//User//sign//train//시청',\n",
       " 'C://Users//User//sign//train//윗집사람',\n",
       " 'C://Users//User//sign//train//창백하다',\n",
       " 'C://Users//User//sign//train//유치원',\n",
       " 'C://Users//User//sign//train//트럭',\n",
       " 'C://Users//User//sign//train//폭행',\n",
       " 'C://Users//User//sign//train//폭우',\n",
       " 'C://Users//User//sign//train//폭탄',\n",
       " 'C://Users//User//sign//train//폭발',\n",
       " 'C://Users//User//sign//train//팔꿈치',\n",
       " 'C://Users//User//sign//train//팔',\n",
       " 'C://Users//User//sign//train//파편',\n",
       " 'C://Users//User//sign//train//파도',\n",
       " 'C://Users//User//sign//train//트랙터',\n",
       " 'C://Users//User//sign//train//출산',\n",
       " 'C://Users//User//sign//train//통학버스',\n",
       " 'C://Users//User//sign//train//토하다',\n",
       " 'C://Users//User//sign//train//토요일',\n",
       " 'C://Users//User//sign//train//택시',\n",
       " 'C://Users//User//sign//train//코',\n",
       " 'C://Users//User//sign//train//칼',\n",
       " 'C://Users//User//sign//train//침수',\n",
       " 'C://Users//User//sign//train//친구',\n",
       " 'C://Users//User//sign//train//학교',\n",
       " 'C://Users//User//sign//train//학생',\n",
       " 'C://Users//User//sign//train//할머니',\n",
       " 'C://Users//User//sign//train//할아버지',\n",
       " 'C://Users//User//sign//train//화장실',\n",
       " 'C://Users//User//sign//train//화요일',\n",
       " 'C://Users//User//sign//train//화약',\n",
       " 'C://Users//User//sign//train//화상',\n",
       " 'C://Users//User//sign//train//홍수',\n",
       " 'C://Users//User//sign//train//호흡기',\n",
       " 'C://Users//User//sign//train//호흡곤란',\n",
       " 'C://Users//User//sign//train//형',\n",
       " 'C://Users//User//sign//train//협박',\n",
       " 'C://Users//User//sign//train//현관앞',\n",
       " 'C://Users//User//sign//train//현관',\n",
       " 'C://Users//User//sign//train//허벅지',\n",
       " 'C://Users//User//sign//train//허리',\n",
       " 'C://Users//User//sign//train//해열제',\n",
       " 'C://Users//User//sign//train//해독제',\n",
       " 'C://Users//User//sign//train//해(연)',\n",
       " 'C://Users//User//sign//train//함몰되다',\n",
       " 'C://Users//User//sign//train//출혈',\n",
       " 'C://Users//User//sign//train//축사',\n",
       " 'C://Users//User//sign//train//유치원 버스',\n",
       " 'C://Users//User//sign//train//이웃집',\n",
       " 'C://Users//User//sign//train//작은방',\n",
       " 'C://Users//User//sign//train//작년',\n",
       " 'C://Users//User//sign//train//자상',\n",
       " 'C://Users//User//sign//train//자살',\n",
       " 'C://Users//User//sign//train//자동차',\n",
       " 'C://Users//User//sign//train//임신한아내',\n",
       " 'C://Users//User//sign//train//임산부',\n",
       " 'C://Users//User//sign//train//일요일',\n",
       " 'C://Users//User//sign//train//이상한사람',\n",
       " 'C://Users//User//sign//train//추락',\n",
       " 'C://Users//User//sign//train//이번',\n",
       " 'C://Users//User//sign//train//이물질',\n",
       " 'C://Users//User//sign//train//이마',\n",
       " 'C://Users//User//sign//train//의사',\n",
       " 'C://Users//User//sign//train//응급처리',\n",
       " 'C://Users//User//sign//train//응급대원',\n",
       " 'C://Users//User//sign//train//음식물',\n",
       " 'C://Users//User//sign//train//은평구',\n",
       " 'C://Users//User//sign//train//장난감',\n",
       " 'C://Users//User//sign//train//장단지',\n",
       " 'C://Users//User//sign//train//절단',\n",
       " 'C://Users//User//sign//train//제초제',\n",
       " 'C://Users//User//sign//train//총',\n",
       " 'C://Users//User//sign//train//체온계',\n",
       " 'C://Users//User//sign//train//살충제',\n",
       " 'C://Users//User//sign//train//창문',\n",
       " 'C://Users//User//sign//train//차안',\n",
       " 'C://Users//User//sign//train//차밖',\n",
       " 'C://Users//User//sign//train//집단폭행',\n",
       " 'C://Users//User//sign//train//집',\n",
       " 'C://Users//User//sign//train//질식',\n",
       " 'C://Users//User//sign//train//진통제',\n",
       " 'C://Users//User//sign//train//지혈대',\n",
       " 'C://Users//User//sign//train//지난',\n",
       " 'C://Users//User//sign//train//중랑구',\n",
       " 'C://Users//User//sign//train//중구',\n",
       " 'C://Users//User//sign//train//주',\n",
       " 'C://Users//User//sign//train//종로구',\n",
       " 'C://Users//User//sign//train//조난',\n",
       " 'C://Users//User//sign//train//살해',\n",
       " 'C://Users//User//sign//train//무너지다',\n",
       " 'C://Users//User//sign//train//납치',\n",
       " 'C://Users//User//sign//train//광진구',\n",
       " 'C://Users//User//sign//train//놀이터',\n",
       " 'C://Users//User//sign//train//공원',\n",
       " 'C://Users//User//sign//train//공사장',\n",
       " 'C://Users//User//sign//train//곰',\n",
       " 'C://Users//User//sign//train//탈골',\n",
       " 'C://Users//User//sign//train//골절',\n",
       " 'C://Users//User//sign//train//고장',\n",
       " 'C://Users//User//sign//train//고열',\n",
       " 'C://Users//User//sign//train//고압전선',\n",
       " 'C://Users//User//sign//train//고속도로',\n",
       " 'C://Users//User//sign//train//계단',\n",
       " 'C://Users//User//sign//train//계곡',\n",
       " 'C://Users//User//sign//train//경찰차',\n",
       " 'C://Users//User//sign//train//경찰',\n",
       " 'C://Users//User//sign//train//경운기',\n",
       " 'C://Users//User//sign//train//결박',\n",
       " 'C://Users//User//sign//train//걸렸다',\n",
       " 'C://Users//User//sign//train//관악구',\n",
       " 'C://Users//User//sign//train//교통사고',\n",
       " 'C://Users//User//sign//train//강풍',\n",
       " 'C://Users//User//sign//train//구급대',\n",
       " 'C://Users//User//sign//train//남학생',\n",
       " 'C://Users//User//sign//train//남편',\n",
       " 'C://Users//User//sign//train//남자친구',\n",
       " 'C://Users//User//sign//train//끓는물',\n",
       " 'C://Users//User//sign//train//깔리다',\n",
       " 'C://Users//User//sign//train//기절하다',\n",
       " 'C://Users//User//sign//train//기절',\n",
       " 'C://Users//User//sign//train//급류',\n",
       " 'C://Users//User//sign//train//금천구',\n",
       " 'C://Users//User//sign//train//금요일',\n",
       " 'C://Users//User//sign//train//금가다',\n",
       " 'C://Users//User//sign//train//귀',\n",
       " 'C://Users//User//sign//train//구해주세요',\n",
       " 'C://Users//User//sign//train//구청',\n",
       " 'C://Users//User//sign//train//구로구',\n",
       " 'C://Users//User//sign//train//구급차',\n",
       " 'C://Users//User//sign//train//구급대원',\n",
       " 'C://Users//User//sign//train//개',\n",
       " 'C://Users//User//sign//train//강서구',\n",
       " 'C://Users//User//sign//train//낫',\n",
       " 'C://Users//User//sign//train//96',\n",
       " 'C://Users//User//sign//train//94',\n",
       " 'C://Users//User//sign//train//93',\n",
       " 'C://Users//User//sign//train//92',\n",
       " 'C://Users//User//sign//train//91',\n",
       " 'C://Users//User//sign//train//90',\n",
       " 'C://Users//User//sign//train//89',\n",
       " 'C://Users//User//sign//train//88',\n",
       " 'C://Users//User//sign//train//87',\n",
       " 'C://Users//User//sign//train//86',\n",
       " 'C://Users//User//sign//train//85',\n",
       " 'C://Users//User//sign//train//84',\n",
       " 'C://Users//User//sign//train//83',\n",
       " 'C://Users//User//sign//train//82',\n",
       " 'C://Users//User//sign//train//81',\n",
       " 'C://Users//User//sign//train//80',\n",
       " 'C://Users//User//sign//train//79',\n",
       " 'C://Users//User//sign//train//45',\n",
       " 'C://Users//User//sign//train//95',\n",
       " 'C://Users//User//sign//train//97',\n",
       " 'C://Users//User//sign//train//강북구',\n",
       " 'C://Users//User//sign//train//98',\n",
       " 'C://Users//User//sign//train//강동구',\n",
       " 'C://Users//User//sign//train//강남구',\n",
       " 'C://Users//User//sign//train//강',\n",
       " 'C://Users//User//sign//train//감전',\n",
       " 'C://Users//User//sign//train//감금',\n",
       " 'C://Users//User//sign//train//갇히다',\n",
       " 'C://Users//User//sign//train//각목',\n",
       " 'C://Users//User//sign//train//가시',\n",
       " 'C://Users//User//sign//train//가슴',\n",
       " 'C://Users//User//sign//train//가스',\n",
       " 'C://Users//User//sign//train//가렵다',\n",
       " 'C://Users//User//sign//train//10000',\n",
       " 'C://Users//User//sign//train//1000',\n",
       " 'C://Users//User//sign//train//119',\n",
       " 'C://Users//User//sign//train//112',\n",
       " 'C://Users//User//sign//train//100',\n",
       " 'C://Users//User//sign//train//99',\n",
       " 'C://Users//User//sign//train//사이',\n",
       " 'C://Users//User//sign//train//화재',\n",
       " 'C://Users//User//sign//train//낯선남자',\n",
       " 'C://Users//User//sign//train//말려주세요',\n",
       " 'C://Users//User//sign//train//밭',\n",
       " 'C://Users//User//sign//train//방망이',\n",
       " 'C://Users//User//sign//train//발작',\n",
       " 'C://Users//User//sign//train//발목',\n",
       " 'C://Users//User//sign//train//발가락',\n",
       " 'C://Users//User//sign//train//발',\n",
       " 'C://Users//User//sign//train//반점생기다',\n",
       " 'C://Users//User//sign//train//바다',\n",
       " 'C://Users//User//sign//train//밑에',\n",
       " 'C://Users//User//sign//train//물',\n",
       " 'C://Users//User//sign//train//문틈',\n",
       " 'C://Users//User//sign//train//무릎',\n",
       " 'C://Users//User//sign//train//목요일',\n",
       " 'C://Users//User//sign//train//목',\n",
       " 'C://Users//User//sign//train//멧돼지',\n",
       " 'C://Users//User//sign//train//머리',\n",
       " 'C://Users//User//sign//train//맹견',\n",
       " 'C://Users//User//sign//train//배',\n",
       " 'C://Users//User//sign//train//배고프다',\n",
       " 'C://Users//User//sign//train//뱀',\n",
       " 'C://Users//User//sign//train//부엌',\n",
       " 'C://Users//User//sign//train//뼈',\n",
       " 'C://Users//User//sign//train//빌라',\n",
       " 'C://Users//User//sign//train//비상약',\n",
       " 'C://Users//User//sign//train//비닐하우스',\n",
       " 'C://Users//User//sign//train//붕대',\n",
       " 'C://Users//User//sign//train//불나다',\n",
       " 'C://Users//User//sign//train//불',\n",
       " 'C://Users//User//sign//train//볼',\n",
       " 'C://Users//User//sign//train//벌',\n",
       " 'C://Users//User//sign//train//복통',\n",
       " 'C://Users//User//sign//train//보내주세요(구급차)',\n",
       " 'C://Users//User//sign//train//보내주세요(경찰)',\n",
       " 'C://Users//User//sign//train//보건소',\n",
       " 'C://Users//User//sign//train//병원',\n",
       " 'C://Users//User//sign//train//벼락',\n",
       " 'C://Users//User//sign//train//범람',\n",
       " 'C://Users//User//sign//train//낯선사람',\n",
       " 'C://Users//User//sign//train//말벌',\n",
       " 'C://Users//User//sign//train//마포구',\n",
       " 'C://Users//User//sign//train//독극물',\n",
       " 'C://Users//User//sign//train//도둑',\n",
       " 'C://Users//User//sign//train//대문앞',\n",
       " 'C://Users//User//sign//train//달(월)',\n",
       " 'C://Users//User//sign//train//다음',\n",
       " 'C://Users//User//sign//train//다리',\n",
       " 'C://Users//User//sign//train//눈',\n",
       " 'C://Users//User//sign//train//누출',\n",
       " 'C://Users//User//sign//train//누전',\n",
       " 'C://Users//User//sign//train//누수',\n",
       " 'C://Users//User//sign//train//누나',\n",
       " 'C://Users//User//sign//train//농약',\n",
       " 'C://Users//User//sign//train//논',\n",
       " 'C://Users//User//sign//train//노원구',\n",
       " 'C://Users//User//sign//train//냄새나다',\n",
       " 'C://Users//User//sign//train//내일',\n",
       " 'C://Users//User//sign//train//내년',\n",
       " 'C://Users//User//sign//train//낯선여자',\n",
       " 'C://Users//User//sign//train//도로',\n",
       " 'C://Users//User//sign//train//도봉구',\n",
       " 'C://Users//User//sign//train//동작구',\n",
       " 'C://Users//User//sign//train//등',\n",
       " 'C://Users//User//sign//train//뒤통수',\n",
       " 'C://Users//User//sign//train//뒤',\n",
       " 'C://Users//User//sign//train//두드러기생기다',\n",
       " 'C://Users//User//sign//train//독버섯',\n",
       " 'C://Users//User//sign//train//떨어지다',\n",
       " 'C://Users//User//sign//train//동전',\n",
       " 'C://Users//User//sign//train//딸',\n",
       " 'C://Users//User//sign//train//동생',\n",
       " 'C://Users//User//sign//train//동대문구',\n",
       " 'C://Users//User//sign//train//독사',\n",
       " 'C://Users//User//sign//train//뜨거운물',\n",
       " 'C://Users//User//sign//train//마당',\n",
       " 'C://Users//User//sign//train//피나다',\n",
       " 'C://Users//User//sign//train//쓰러지다',\n",
       " 'C://Users//User//sign//train//공장',\n",
       " 'C://Users//User//sign//train//부러지다',\n",
       " 'C://Users//User//sign//train//붕괴',\n",
       " 'C://Users//User//sign//train//아래',\n",
       " 'C://Users//User//sign//train//손가락',\n",
       " 'C://Users//User//sign//train//절도',\n",
       " 'C://Users//User//sign//train//복부',\n",
       " 'C://Users//User//sign//train//거실',\n",
       " 'C://Users//User//sign//train//산']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path=[]\n",
    "\n",
    "for i in range(len(list_a)):\n",
    "\n",
    "    videos2 = \"C://Users//User//sign//train//\" + str(list_a['한국어'][i])\n",
    "    train_path.append(videos2)\n",
    "    \n",
    "# len(train_path)   #419\n",
    "\n",
    "train_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path=[]\n",
    "\n",
    "for i in range(len(list_a)):\n",
    "\n",
    "    videos2 = \"C://Users//User//sign//train//\" + str(list_a['한국어'][i])\n",
    "    train_path.append(videos2)\n",
    "    \n",
    "# len(train_path)   #419\n",
    "\n",
    "train_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "C://Users//User//sign//train//동전//2 150\n",
      "C://Users//User//sign//train//동전//3 144\n",
      "C://Users//User//sign//train//동전//4 136\n",
      "C://Users//User//sign//train//동전//5 196\n",
      "C://Users//User//sign//train//동전//6 177\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C://Users//User//sign//train//딸//0 131\n",
      "C://Users//User//sign//train//딸//1 55\n",
      "C://Users//User//sign//train//딸//2 155\n",
      "C://Users//User//sign//train//딸//3 122\n",
      "C://Users//User//sign//train//딸//4 121\n",
      "C://Users//User//sign//train//딸//5 184\n",
      "C://Users//User//sign//train//딸//6 60\n",
      "C://Users//User//sign//train//동생//0 131\n",
      "C://Users//User//sign//train//동생//1 59\n",
      "C://Users//User//sign//train//동생//2 161\n",
      "C://Users//User//sign//train//동생//3 122\n",
      "C://Users//User//sign//train//동생//4 107\n",
      "C://Users//User//sign//train//동생//5 161\n",
      "C://Users//User//sign//train//동생//6 156\n",
      "C://Users//User//sign//train//동대문구//0 77\n",
      "C://Users//User//sign//train//동대문구//1 99\n",
      "C://Users//User//sign//train//동대문구//2 80\n",
      "C://Users//User//sign//train//동대문구//3 84\n",
      "C://Users//User//sign//train//동대문구//4 110\n",
      "C://Users//User//sign//train//동대문구//5 98\n",
      "C://Users//User//sign//train//동대문구//6 92\n",
      "C://Users//User//sign//train//독사//0 80\n",
      "C://Users//User//sign//train//독사//1 81\n",
      "C://Users//User//sign//train//독사//2 94\n",
      "C://Users//User//sign//train//독사//3 98\n",
      "C://Users//User//sign//train//독사//4 112\n",
      "C://Users//User//sign//train//독사//5 166\n",
      "C://Users//User//sign//train//독사//6 93\n",
      "C://Users//User//sign//train//뜨거운물//0 71\n",
      "C://Users//User//sign//train//뜨거운물//1 70\n",
      "C://Users//User//sign//train//뜨거운물//2 81\n",
      "C://Users//User//sign//train//뜨거운물//3 81\n",
      "C://Users//User//sign//train//뜨거운물//4 88\n",
      "C://Users//User//sign//train//뜨거운물//5 100\n",
      "C://Users//User//sign//train//뜨거운물//6 76\n",
      "C://Users//User//sign//train//마당//0 72\n",
      "C://Users//User//sign//train//마당//1 77\n",
      "C://Users//User//sign//train//마당//2 80\n",
      "C://Users//User//sign//train//마당//3 111\n",
      "C://Users//User//sign//train//마당//4 97\n",
      "C://Users//User//sign//train//마당//5 112\n",
      "C://Users//User//sign//train//마당//6 80\n",
      "C://Users//User//sign//train//피나다//0 78\n",
      "C://Users//User//sign//train//피나다//1 84\n",
      "C://Users//User//sign//train//피나다//2 90\n",
      "C://Users//User//sign//train//피나다//3 98\n",
      "C://Users//User//sign//train//피나다//4 123\n",
      "C://Users//User//sign//train//피나다//5 155\n",
      "C://Users//User//sign//train//쓰러지다//0 61\n",
      "C://Users//User//sign//train//쓰러지다//1 73\n",
      "C://Users//User//sign//train//쓰러지다//2 76\n",
      "C://Users//User//sign//train//쓰러지다//3 77\n",
      "C://Users//User//sign//train//쓰러지다//4 137\n",
      "C://Users//User//sign//train//쓰러지다//5 66\n",
      "C://Users//User//sign//train//공장//0 58\n",
      "C://Users//User//sign//train//공장//1 79\n",
      "C://Users//User//sign//train//공장//2 79\n",
      "C://Users//User//sign//train//공장//3 91\n",
      "C://Users//User//sign//train//공장//4 181\n",
      "C://Users//User//sign//train//공장//5 166\n",
      "C://Users//User//sign//train//부러지다//0 57\n",
      "C://Users//User//sign//train//부러지다//1 167\n",
      "C://Users//User//sign//train//부러지다//2 66\n",
      "C://Users//User//sign//train//부러지다//3 87\n",
      "C://Users//User//sign//train//부러지다//4 111\n",
      "C://Users//User//sign//train//부러지다//5 152\n",
      "C://Users//User//sign//train//붕괴//0 56\n",
      "C://Users//User//sign//train//붕괴//1 58\n",
      "C://Users//User//sign//train//붕괴//2 59\n",
      "C://Users//User//sign//train//붕괴//3 87\n",
      "C://Users//User//sign//train//붕괴//4 82\n",
      "C://Users//User//sign//train//붕괴//5 166\n",
      "C://Users//User//sign//train//아래//0 58\n",
      "C://Users//User//sign//train//아래//1 63\n",
      "C://Users//User//sign//train//아래//2 85\n",
      "C://Users//User//sign//train//아래//3 85\n",
      "C://Users//User//sign//train//아래//4 116\n",
      "C://Users//User//sign//train//아래//5 181\n",
      "C://Users//User//sign//train//손가락//0 85\n",
      "C://Users//User//sign//train//손가락//1 79\n",
      "C://Users//User//sign//train//손가락//2 87\n",
      "C://Users//User//sign//train//손가락//3 101\n",
      "C://Users//User//sign//train//손가락//4 104\n",
      "C://Users//User//sign//train//손가락//5 104\n",
      "C://Users//User//sign//train//절도//0 57\n",
      "C://Users//User//sign//train//절도//1 72\n",
      "C://Users//User//sign//train//절도//2 65\n",
      "C://Users//User//sign//train//절도//3 88\n",
      "C://Users//User//sign//train//절도//4 88\n",
      "C://Users//User//sign//train//절도//5 62\n",
      "C://Users//User//sign//train//복부//0 139\n",
      "C://Users//User//sign//train//복부//1 55\n",
      "C://Users//User//sign//train//복부//2 131\n",
      "C://Users//User//sign//train//복부//3 161\n",
      "C://Users//User//sign//train//복부//4 172\n",
      "C://Users//User//sign//train//복부//5 48\n",
      "C://Users//User//sign//train//거실//0 73\n",
      "C://Users//User//sign//train//거실//1 85\n",
      "C://Users//User//sign//train//거실//2 76\n",
      "C://Users//User//sign//train//거실//3 95\n",
      "C://Users//User//sign//train//거실//4 112\n",
      "C://Users//User//sign//train//거실//5 103\n",
      "C://Users//User//sign//train//산//0 62\n",
      "C://Users//User//sign//train//산//1 79\n",
      "C://Users//User//sign//train//산//2 67\n",
      "C://Users//User//sign//train//산//3 74\n",
      "C://Users//User//sign//train//산//4 87\n",
      "C://Users//User//sign//train//산//5 81\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "train_path_count=[]\n",
    "\n",
    "for k in range(len(train_path)):\n",
    "\n",
    "    file_list = os.listdir(train_path[k])\n",
    "    \n",
    "    for p in range(len(file_list)):\n",
    "        train_path_count.append(train_path[k]+ '//' + file_list[p] )\n",
    "    \n",
    "    \n",
    "path = train_path_count\n",
    "\n",
    "len_file_list=[]\n",
    "path_last=[]\n",
    "\n",
    "for j in range(len(path)):\n",
    "    file_list = os.listdir(path[j])\n",
    "    print(path[j], len(file_list))\n",
    "    len_file_list.append(len(file_list))\n",
    "    path_last.append(path[j])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (Temp/ipykernel_11512/126096718.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\solag\\AppData\\Local\\Temp/ipykernel_11512/126096718.py\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    list_a[['한국어','횟수']][list_a['한국어']==0&&(list_a['한국어']==9)]\u001b[0m\n\u001b[1;37m                                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "list_a[['한국어','횟수']][list_a['한국어']==0 &&(list_a['한국어']==9)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['E://Users//User//sign//test//0',\n",
       " 'E://Users//User//sign//test//51',\n",
       " 'E://Users//User//sign//test//58',\n",
       " 'E://Users//User//sign//test//57',\n",
       " 'E://Users//User//sign//test//56',\n",
       " 'E://Users//User//sign//test//55',\n",
       " 'E://Users//User//sign//test//54',\n",
       " 'E://Users//User//sign//test//53',\n",
       " 'E://Users//User//sign//test//52',\n",
       " 'E://Users//User//sign//test//50',\n",
       " 'E://Users//User//sign//test//61',\n",
       " 'E://Users//User//sign//test//49',\n",
       " 'E://Users//User//sign//test//48',\n",
       " 'E://Users//User//sign//test//47',\n",
       " 'E://Users//User//sign//test//46',\n",
       " 'E://Users//User//sign//test//44',\n",
       " 'E://Users//User//sign//test//43',\n",
       " 'E://Users//User//sign//test//42',\n",
       " 'E://Users//User//sign//test//60',\n",
       " 'E://Users//User//sign//test//62',\n",
       " 'E://Users//User//sign//test//40',\n",
       " 'E://Users//User//sign//test//72',\n",
       " 'E://Users//User//sign//test//1',\n",
       " 'E://Users//User//sign//test//78',\n",
       " 'E://Users//User//sign//test//77',\n",
       " 'E://Users//User//sign//test//76',\n",
       " 'E://Users//User//sign//test//75',\n",
       " 'E://Users//User//sign//test//74',\n",
       " 'E://Users//User//sign//test//73',\n",
       " 'E://Users//User//sign//test//71',\n",
       " 'E://Users//User//sign//test//63',\n",
       " 'E://Users//User//sign//test//70',\n",
       " 'E://Users//User//sign//test//69',\n",
       " 'E://Users//User//sign//test//68',\n",
       " 'E://Users//User//sign//test//67',\n",
       " 'E://Users//User//sign//test//66',\n",
       " 'E://Users//User//sign//test//65',\n",
       " 'E://Users//User//sign//test//64',\n",
       " 'E://Users//User//sign//test//41',\n",
       " 'E://Users//User//sign//test//59',\n",
       " 'E://Users//User//sign//test//39',\n",
       " 'E://Users//User//sign//test//20',\n",
       " 'E://Users//User//sign//test//18',\n",
       " 'E://Users//User//sign//test//17',\n",
       " 'E://Users//User//sign//test//16',\n",
       " 'E://Users//User//sign//test//15',\n",
       " 'E://Users//User//sign//test//14',\n",
       " 'E://Users//User//sign//test//12',\n",
       " 'E://Users//User//sign//test//11',\n",
       " 'E://Users//User//sign//test//10',\n",
       " 'E://Users//User//sign//test//9',\n",
       " 'E://Users//User//sign//test//8',\n",
       " 'E://Users//User//sign//test//7',\n",
       " 'E://Users//User//sign//test//6',\n",
       " 'E://Users//User//sign//test//5',\n",
       " 'E://Users//User//sign//test//4',\n",
       " 'E://Users//User//sign//test//3',\n",
       " 'E://Users//User//sign//test//38',\n",
       " 'E://Users//User//sign//test//2',\n",
       " 'E://Users//User//sign//test//19',\n",
       " 'E://Users//User//sign//test//13',\n",
       " 'E://Users//User//sign//test//21',\n",
       " 'E://Users//User//sign//test//29',\n",
       " 'E://Users//User//sign//test//37',\n",
       " 'E://Users//User//sign//test//35',\n",
       " 'E://Users//User//sign//test//34',\n",
       " 'E://Users//User//sign//test//33',\n",
       " 'E://Users//User//sign//test//32',\n",
       " 'E://Users//User//sign//test//31',\n",
       " 'E://Users//User//sign//test//30',\n",
       " 'E://Users//User//sign//test//36',\n",
       " 'E://Users//User//sign//test//28',\n",
       " 'E://Users//User//sign//test//26',\n",
       " 'E://Users//User//sign//test//25',\n",
       " 'E://Users//User//sign//test//24',\n",
       " 'E://Users//User//sign//test//23',\n",
       " 'E://Users//User//sign//test//22',\n",
       " 'E://Users//User//sign//test//27',\n",
       " 'E://Users//User//sign//test//연기',\n",
       " 'E://Users//User//sign//test//옆집 아저씨',\n",
       " 'E://Users//User//sign//test//옆집',\n",
       " 'E://Users//User//sign//test//영등포구',\n",
       " 'E://Users//User//sign//test//엽총',\n",
       " 'E://Users//User//sign//test//열어주세요',\n",
       " 'E://Users//User//sign//test//열나다',\n",
       " 'E://Users//User//sign//test//열',\n",
       " 'E://Users//User//sign//test//연락해주세요',\n",
       " 'E://Users//User//sign//test//엘리베이터',\n",
       " 'E://Users//User//sign//test//여학생',\n",
       " 'E://Users//User//sign//test//여자친구',\n",
       " 'E://Users//User//sign//test//엄마',\n",
       " 'E://Users//User//sign//test//얼굴',\n",
       " 'E://Users//User//sign//test//언니',\n",
       " 'E://Users//User//sign//test//어지러움',\n",
       " 'E://Users//User//sign//test//어제',\n",
       " 'E://Users//User//sign//test//옆집사람',\n",
       " 'E://Users//User//sign//test//옆집 할아버지',\n",
       " 'E://Users//User//sign//test//위협',\n",
       " 'E://Users//User//sign//test//옆쪽',\n",
       " 'E://Users//User//sign//test//욕실',\n",
       " 'E://Users//User//sign//test//윗집',\n",
       " 'E://Users//User//sign//test//양천구',\n",
       " 'E://Users//User//sign//test//위에',\n",
       " 'E://Users//User//sign//test//위',\n",
       " 'E://Users//User//sign//test//월요일',\n",
       " 'E://Users//User//sign//test//운동장',\n",
       " 'E://Users//User//sign//test//우리집',\n",
       " 'E://Users//User//sign//test//용산구',\n",
       " 'E://Users//User//sign//test//왼쪽-눈',\n",
       " 'E://Users//User//sign//test//오늘',\n",
       " 'E://Users//User//sign//test//왼쪽-귀',\n",
       " 'E://Users//User//sign//test//왼쪽',\n",
       " 'E://Users//User//sign//test//올해',\n",
       " 'E://Users//User//sign//test//옥상',\n",
       " 'E://Users//User//sign//test//오빠',\n",
       " 'E://Users//User//sign//test//오른쪽-눈',\n",
       " 'E://Users//User//sign//test//오른쪽-귀',\n",
       " 'E://Users//User//sign//test//오른쪽',\n",
       " 'E://Users//User//sign//test//어깨',\n",
       " 'E://Users//User//sign//test//서랍',\n",
       " 'E://Users//User//sign//test//약사',\n",
       " 'E://Users//User//sign//test//수요일',\n",
       " 'E://Users//User//sign//test//송파구',\n",
       " 'E://Users//User//sign//test//손목',\n",
       " 'E://Users//User//sign//test//손',\n",
       " 'E://Users//User//sign//test//소화전',\n",
       " 'E://Users//User//sign//test//소화기',\n",
       " 'E://Users//User//sign//test//소방차',\n",
       " 'E://Users//User//sign//test//소방관',\n",
       " 'E://Users//User//sign//test//성폭행',\n",
       " 'E://Users//User//sign//test//성북구',\n",
       " 'E://Users//User//sign//test//성동구',\n",
       " 'E://Users//User//sign//test//선생님',\n",
       " 'E://Users//User//sign//test//선반',\n",
       " 'E://Users//User//sign//test//서초구',\n",
       " 'E://Users//User//sign//test//서울시',\n",
       " 'E://Users//User//sign//test//유리',\n",
       " 'E://Users//User//sign//test//서대문구',\n",
       " 'E://Users//User//sign//test//삼키다',\n",
       " 'E://Users//User//sign//test//수영장',\n",
       " 'E://Users//User//sign//test//술취한 사람',\n",
       " 'E://Users//User//sign//test//약국',\n",
       " 'E://Users//User//sign//test//숨을안쉬다',\n",
       " 'E://Users//User//sign//test//앞집',\n",
       " 'E://Users//User//sign//test//앞',\n",
       " 'E://Users//User//sign//test//알려주세요',\n",
       " 'E://Users//User//sign//test//안방',\n",
       " 'E://Users//User//sign//test//인대',\n",
       " 'E://Users//User//sign//test//아파트',\n",
       " 'E://Users//User//sign//test//아줌마',\n",
       " 'E://Users//User//sign//test//아저씨',\n",
       " 'E://Users//User//sign//test//아빠',\n",
       " 'E://Users//User//sign//test//아들',\n",
       " 'E://Users//User//sign//test//아내',\n",
       " 'E://Users//User//sign//test//어린이',\n",
       " 'E://Users//User//sign//test//아이들',\n",
       " 'E://Users//User//sign//test//아기',\n",
       " 'E://Users//User//sign//test//심장마비',\n",
       " 'E://Users//User//sign//test//신고하세요(경찰)',\n",
       " 'E://Users//User//sign//test//시청',\n",
       " 'E://Users//User//sign//test//윗집사람',\n",
       " 'E://Users//User//sign//test//창백하다',\n",
       " 'E://Users//User//sign//test//유치원',\n",
       " 'E://Users//User//sign//test//트럭',\n",
       " 'E://Users//User//sign//test//폭행',\n",
       " 'E://Users//User//sign//test//폭우',\n",
       " 'E://Users//User//sign//test//폭탄',\n",
       " 'E://Users//User//sign//test//폭발',\n",
       " 'E://Users//User//sign//test//팔꿈치',\n",
       " 'E://Users//User//sign//test//팔',\n",
       " 'E://Users//User//sign//test//파편',\n",
       " 'E://Users//User//sign//test//파도',\n",
       " 'E://Users//User//sign//test//트랙터',\n",
       " 'E://Users//User//sign//test//출산',\n",
       " 'E://Users//User//sign//test//통학버스',\n",
       " 'E://Users//User//sign//test//토하다',\n",
       " 'E://Users//User//sign//test//토요일',\n",
       " 'E://Users//User//sign//test//택시',\n",
       " 'E://Users//User//sign//test//코',\n",
       " 'E://Users//User//sign//test//칼',\n",
       " 'E://Users//User//sign//test//침수',\n",
       " 'E://Users//User//sign//test//친구',\n",
       " 'E://Users//User//sign//test//학교',\n",
       " 'E://Users//User//sign//test//학생',\n",
       " 'E://Users//User//sign//test//할머니',\n",
       " 'E://Users//User//sign//test//할아버지',\n",
       " 'E://Users//User//sign//test//화장실',\n",
       " 'E://Users//User//sign//test//화요일',\n",
       " 'E://Users//User//sign//test//화약',\n",
       " 'E://Users//User//sign//test//화상',\n",
       " 'E://Users//User//sign//test//홍수',\n",
       " 'E://Users//User//sign//test//호흡기',\n",
       " 'E://Users//User//sign//test//호흡곤란',\n",
       " 'E://Users//User//sign//test//형',\n",
       " 'E://Users//User//sign//test//협박',\n",
       " 'E://Users//User//sign//test//현관앞',\n",
       " 'E://Users//User//sign//test//현관',\n",
       " 'E://Users//User//sign//test//허벅지',\n",
       " 'E://Users//User//sign//test//허리',\n",
       " 'E://Users//User//sign//test//해열제',\n",
       " 'E://Users//User//sign//test//해독제',\n",
       " 'E://Users//User//sign//test//해(연)',\n",
       " 'E://Users//User//sign//test//함몰되다',\n",
       " 'E://Users//User//sign//test//출혈',\n",
       " 'E://Users//User//sign//test//축사',\n",
       " 'E://Users//User//sign//test//유치원 버스',\n",
       " 'E://Users//User//sign//test//이웃집',\n",
       " 'E://Users//User//sign//test//작은방',\n",
       " 'E://Users//User//sign//test//작년',\n",
       " 'E://Users//User//sign//test//자상',\n",
       " 'E://Users//User//sign//test//자살',\n",
       " 'E://Users//User//sign//test//자동차',\n",
       " 'E://Users//User//sign//test//임신한아내',\n",
       " 'E://Users//User//sign//test//임산부',\n",
       " 'E://Users//User//sign//test//일요일',\n",
       " 'E://Users//User//sign//test//이상한사람',\n",
       " 'E://Users//User//sign//test//추락',\n",
       " 'E://Users//User//sign//test//이번',\n",
       " 'E://Users//User//sign//test//이물질',\n",
       " 'E://Users//User//sign//test//이마',\n",
       " 'E://Users//User//sign//test//의사',\n",
       " 'E://Users//User//sign//test//응급처리',\n",
       " 'E://Users//User//sign//test//응급대원',\n",
       " 'E://Users//User//sign//test//음식물',\n",
       " 'E://Users//User//sign//test//은평구',\n",
       " 'E://Users//User//sign//test//장난감',\n",
       " 'E://Users//User//sign//test//장단지',\n",
       " 'E://Users//User//sign//test//절단',\n",
       " 'E://Users//User//sign//test//제초제',\n",
       " 'E://Users//User//sign//test//총',\n",
       " 'E://Users//User//sign//test//체온계',\n",
       " 'E://Users//User//sign//test//살충제',\n",
       " 'E://Users//User//sign//test//창문',\n",
       " 'E://Users//User//sign//test//차안',\n",
       " 'E://Users//User//sign//test//차밖',\n",
       " 'E://Users//User//sign//test//집단폭행',\n",
       " 'E://Users//User//sign//test//집',\n",
       " 'E://Users//User//sign//test//질식',\n",
       " 'E://Users//User//sign//test//진통제',\n",
       " 'E://Users//User//sign//test//지혈대',\n",
       " 'E://Users//User//sign//test//지난',\n",
       " 'E://Users//User//sign//test//중랑구',\n",
       " 'E://Users//User//sign//test//중구',\n",
       " 'E://Users//User//sign//test//주',\n",
       " 'E://Users//User//sign//test//종로구',\n",
       " 'E://Users//User//sign//test//조난',\n",
       " 'E://Users//User//sign//test//살해',\n",
       " 'E://Users//User//sign//test//무너지다',\n",
       " 'E://Users//User//sign//test//납치',\n",
       " 'E://Users//User//sign//test//광진구',\n",
       " 'E://Users//User//sign//test//놀이터',\n",
       " 'E://Users//User//sign//test//공원',\n",
       " 'E://Users//User//sign//test//공사장',\n",
       " 'E://Users//User//sign//test//곰',\n",
       " 'E://Users//User//sign//test//탈골',\n",
       " 'E://Users//User//sign//test//골절',\n",
       " 'E://Users//User//sign//test//고장',\n",
       " 'E://Users//User//sign//test//고열',\n",
       " 'E://Users//User//sign//test//고압전선',\n",
       " 'E://Users//User//sign//test//고속도로',\n",
       " 'E://Users//User//sign//test//계단',\n",
       " 'E://Users//User//sign//test//계곡',\n",
       " 'E://Users//User//sign//test//경찰차',\n",
       " 'E://Users//User//sign//test//경찰',\n",
       " 'E://Users//User//sign//test//경운기',\n",
       " 'E://Users//User//sign//test//결박',\n",
       " 'E://Users//User//sign//test//걸렸다',\n",
       " 'E://Users//User//sign//test//관악구',\n",
       " 'E://Users//User//sign//test//교통사고',\n",
       " 'E://Users//User//sign//test//강풍',\n",
       " 'E://Users//User//sign//test//구급대',\n",
       " 'E://Users//User//sign//test//남학생',\n",
       " 'E://Users//User//sign//test//남편',\n",
       " 'E://Users//User//sign//test//남자친구',\n",
       " 'E://Users//User//sign//test//끓는물',\n",
       " 'E://Users//User//sign//test//깔리다',\n",
       " 'E://Users//User//sign//test//기절하다',\n",
       " 'E://Users//User//sign//test//기절',\n",
       " 'E://Users//User//sign//test//급류',\n",
       " 'E://Users//User//sign//test//금천구',\n",
       " 'E://Users//User//sign//test//금요일',\n",
       " 'E://Users//User//sign//test//금가다',\n",
       " 'E://Users//User//sign//test//귀',\n",
       " 'E://Users//User//sign//test//구해주세요',\n",
       " 'E://Users//User//sign//test//구청',\n",
       " 'E://Users//User//sign//test//구로구',\n",
       " 'E://Users//User//sign//test//구급차',\n",
       " 'E://Users//User//sign//test//구급대원',\n",
       " 'E://Users//User//sign//test//개',\n",
       " 'E://Users//User//sign//test//강서구',\n",
       " 'E://Users//User//sign//test//낫',\n",
       " 'E://Users//User//sign//test//96',\n",
       " 'E://Users//User//sign//test//94',\n",
       " 'E://Users//User//sign//test//93',\n",
       " 'E://Users//User//sign//test//92',\n",
       " 'E://Users//User//sign//test//91',\n",
       " 'E://Users//User//sign//test//90',\n",
       " 'E://Users//User//sign//test//89',\n",
       " 'E://Users//User//sign//test//88',\n",
       " 'E://Users//User//sign//test//87',\n",
       " 'E://Users//User//sign//test//86',\n",
       " 'E://Users//User//sign//test//85',\n",
       " 'E://Users//User//sign//test//84',\n",
       " 'E://Users//User//sign//test//83',\n",
       " 'E://Users//User//sign//test//82',\n",
       " 'E://Users//User//sign//test//81',\n",
       " 'E://Users//User//sign//test//80',\n",
       " 'E://Users//User//sign//test//79',\n",
       " 'E://Users//User//sign//test//45',\n",
       " 'E://Users//User//sign//test//95',\n",
       " 'E://Users//User//sign//test//97',\n",
       " 'E://Users//User//sign//test//강북구',\n",
       " 'E://Users//User//sign//test//98',\n",
       " 'E://Users//User//sign//test//강동구',\n",
       " 'E://Users//User//sign//test//강남구',\n",
       " 'E://Users//User//sign//test//강',\n",
       " 'E://Users//User//sign//test//감전',\n",
       " 'E://Users//User//sign//test//감금',\n",
       " 'E://Users//User//sign//test//갇히다',\n",
       " 'E://Users//User//sign//test//각목',\n",
       " 'E://Users//User//sign//test//가시',\n",
       " 'E://Users//User//sign//test//가슴',\n",
       " 'E://Users//User//sign//test//가스',\n",
       " 'E://Users//User//sign//test//가렵다',\n",
       " 'E://Users//User//sign//test//10000',\n",
       " 'E://Users//User//sign//test//1000',\n",
       " 'E://Users//User//sign//test//119',\n",
       " 'E://Users//User//sign//test//112',\n",
       " 'E://Users//User//sign//test//100',\n",
       " 'E://Users//User//sign//test//99',\n",
       " 'E://Users//User//sign//test//사이',\n",
       " 'E://Users//User//sign//test//화재',\n",
       " 'E://Users//User//sign//test//낯선남자',\n",
       " 'E://Users//User//sign//test//말려주세요',\n",
       " 'E://Users//User//sign//test//밭',\n",
       " 'E://Users//User//sign//test//방망이',\n",
       " 'E://Users//User//sign//test//발작',\n",
       " 'E://Users//User//sign//test//발목',\n",
       " 'E://Users//User//sign//test//발가락',\n",
       " 'E://Users//User//sign//test//발',\n",
       " 'E://Users//User//sign//test//반점생기다',\n",
       " 'E://Users//User//sign//test//바다',\n",
       " 'E://Users//User//sign//test//밑에',\n",
       " 'E://Users//User//sign//test//물',\n",
       " 'E://Users//User//sign//test//문틈',\n",
       " 'E://Users//User//sign//test//무릎',\n",
       " 'E://Users//User//sign//test//목요일',\n",
       " 'E://Users//User//sign//test//목',\n",
       " 'E://Users//User//sign//test//멧돼지',\n",
       " 'E://Users//User//sign//test//머리',\n",
       " 'E://Users//User//sign//test//맹견',\n",
       " 'E://Users//User//sign//test//배',\n",
       " 'E://Users//User//sign//test//배고프다',\n",
       " 'E://Users//User//sign//test//뱀',\n",
       " 'E://Users//User//sign//test//부엌',\n",
       " 'E://Users//User//sign//test//뼈',\n",
       " 'E://Users//User//sign//test//빌라',\n",
       " 'E://Users//User//sign//test//비상약',\n",
       " 'E://Users//User//sign//test//비닐하우스',\n",
       " 'E://Users//User//sign//test//붕대',\n",
       " 'E://Users//User//sign//test//불나다',\n",
       " 'E://Users//User//sign//test//불',\n",
       " 'E://Users//User//sign//test//볼',\n",
       " 'E://Users//User//sign//test//벌',\n",
       " 'E://Users//User//sign//test//복통',\n",
       " 'E://Users//User//sign//test//보내주세요(구급차)',\n",
       " 'E://Users//User//sign//test//보내주세요(경찰)',\n",
       " 'E://Users//User//sign//test//보건소',\n",
       " 'E://Users//User//sign//test//병원',\n",
       " 'E://Users//User//sign//test//벼락',\n",
       " 'E://Users//User//sign//test//범람',\n",
       " 'E://Users//User//sign//test//낯선사람',\n",
       " 'E://Users//User//sign//test//말벌',\n",
       " 'E://Users//User//sign//test//마포구',\n",
       " 'E://Users//User//sign//test//독극물',\n",
       " 'E://Users//User//sign//test//도둑',\n",
       " 'E://Users//User//sign//test//대문앞',\n",
       " 'E://Users//User//sign//test//달(월)',\n",
       " 'E://Users//User//sign//test//다음',\n",
       " 'E://Users//User//sign//test//다리',\n",
       " 'E://Users//User//sign//test//눈',\n",
       " 'E://Users//User//sign//test//누출',\n",
       " 'E://Users//User//sign//test//누전',\n",
       " 'E://Users//User//sign//test//누수',\n",
       " 'E://Users//User//sign//test//누나',\n",
       " 'E://Users//User//sign//test//농약',\n",
       " 'E://Users//User//sign//test//논',\n",
       " 'E://Users//User//sign//test//노원구',\n",
       " 'E://Users//User//sign//test//냄새나다',\n",
       " 'E://Users//User//sign//test//내일',\n",
       " 'E://Users//User//sign//test//내년',\n",
       " 'E://Users//User//sign//test//낯선여자',\n",
       " 'E://Users//User//sign//test//도로',\n",
       " 'E://Users//User//sign//test//도봉구',\n",
       " 'E://Users//User//sign//test//동작구',\n",
       " 'E://Users//User//sign//test//등',\n",
       " 'E://Users//User//sign//test//뒤통수',\n",
       " 'E://Users//User//sign//test//뒤',\n",
       " 'E://Users//User//sign//test//두드러기생기다',\n",
       " 'E://Users//User//sign//test//독버섯',\n",
       " 'E://Users//User//sign//test//떨어지다',\n",
       " 'E://Users//User//sign//test//동전',\n",
       " 'E://Users//User//sign//test//딸',\n",
       " 'E://Users//User//sign//test//동생',\n",
       " 'E://Users//User//sign//test//동대문구',\n",
       " 'E://Users//User//sign//test//독사',\n",
       " 'E://Users//User//sign//test//뜨거운물',\n",
       " 'E://Users//User//sign//test//마당',\n",
       " 'E://Users//User//sign//test//피나다',\n",
       " 'E://Users//User//sign//test//쓰러지다',\n",
       " 'E://Users//User//sign//test//공장',\n",
       " 'E://Users//User//sign//test//부러지다',\n",
       " 'E://Users//User//sign//test//붕괴',\n",
       " 'E://Users//User//sign//test//아래',\n",
       " 'E://Users//User//sign//test//손가락',\n",
       " 'E://Users//User//sign//test//절도',\n",
       " 'E://Users//User//sign//test//복부',\n",
       " 'E://Users//User//sign//test//거실',\n",
       " 'E://Users//User//sign//test//산']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path=[]\n",
    "\n",
    "for i in range(len(list_a)):\n",
    "\n",
    "    videos2 = \"E://Users//User//sign//test//\" + str(list_a['한국어'][i])\n",
    "    train_path.append(videos2)\n",
    "    \n",
    "# len(train_path)   #419\n",
    "\n",
    "train_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11512/3770497986.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtrain_path_count\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mfile_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_path\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'train_path' is not defined"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "train_path_count=[]\n",
    "\n",
    "for k in range(len(train_path)):\n",
    "\n",
    "    file_list = os.listdir(train_path[k])\n",
    "    \n",
    "    for p in range(len(file_list)):\n",
    "        train_path_count.append(train_path[k]+ '//' + file_list[p] )\n",
    "    \n",
    "    \n",
    "path = train_path_count\n",
    "\n",
    "len_file_list=[]\n",
    "path_last=[]\n",
    "\n",
    "for j in range(len(path)):\n",
    "    file_list = os.listdir(path[j])\n",
    "    print(path[j], len(file_list))\n",
    "    len_file_list.append(len(file_list))\n",
    "    path_last.append(path[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "432"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(len_file_list)\n",
    "\n",
    "max(len_file_list)   #옆쪽//6     0~431"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#디렉토리를 복사하는 법\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "#~0을 ~0_2 이름으로 복사함\n",
    "shutil.copytree(\"C://Users//User//sign//test//0\", \"C://Users//User//sign//test//0_2\")\n",
    "\n",
    "for f in os.listdir(\"C://Users//User//sign//test//0_2\"):\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train 폴더로 했다가 잘못하면 위험하기 때문에\n",
    "#test 폴더로 시도해볼 것..!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '93', '수요일']\n",
      "93\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "file_list_1=os.listdir(\"C://Users//User//sign//test\")\n",
    "\n",
    "print(file_list_1)\n",
    "print(file_list_1[1])\n",
    "print(len(file_list_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '1', '2', '3', '4', '5', '6', '7']\n",
      "['0', '1', '2', '3', '4', '5', '6']\n",
      "['0', '1', '2', '3', '4', '5', '6']\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(file_list_1)):\n",
    "    path=\"C://Users//User//sign//test//\" + str(file_list_1[i])\n",
    "    file_list_2=os.listdir(path)\n",
    "    print(file_list_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C://Users//User//sign//test//0//0\n",
      "C://Users//User//sign//test//0//1\n",
      "C://Users//User//sign//test//0//2\n",
      "C://Users//User//sign//test//0//3\n",
      "C://Users//User//sign//test//0//4\n",
      "C://Users//User//sign//test//0//5\n",
      "C://Users//User//sign//test//0//6\n",
      "C://Users//User//sign//test//0//7\n",
      "C://Users//User//sign//test//93//0\n",
      "C://Users//User//sign//test//93//1\n",
      "C://Users//User//sign//test//93//2\n",
      "C://Users//User//sign//test//93//3\n",
      "C://Users//User//sign//test//93//4\n",
      "C://Users//User//sign//test//93//5\n",
      "C://Users//User//sign//test//93//6\n",
      "C://Users//User//sign//test//수요일//0\n",
      "C://Users//User//sign//test//수요일//1\n",
      "C://Users//User//sign//test//수요일//2\n",
      "C://Users//User//sign//test//수요일//3\n",
      "C://Users//User//sign//test//수요일//4\n",
      "C://Users//User//sign//test//수요일//5\n",
      "C://Users//User//sign//test//수요일//6\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(file_list_1)):\n",
    "    path=\"C://Users//User//sign//test//\" + str(file_list_1[i])\n",
    "    file_list_2=os.listdir(path)\n",
    "    for p in range(len(file_list_2)):\n",
    "        path2=path+'//'+str(file_list_2[p])\n",
    "        print(path2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E://Users//User//sign//test//0//0 141\n",
      "E://Users//User//sign//test//0//1 145\n",
      "E://Users//User//sign//test//0//2 85\n",
      "E://Users//User//sign//test//0//3 120\n",
      "E://Users//User//sign//test//0//4 191\n",
      "E://Users//User//sign//test//0//5 87\n",
      "E://Users//User//sign//test//0//6 197\n",
      "E://Users//User//sign//test//0//7 203\n",
      "E://Users//User//sign//test//93//0 144\n",
      "E://Users//User//sign//test//93//1 76\n",
      "E://Users//User//sign//test//93//2 166\n",
      "E://Users//User//sign//test//93//3 120\n",
      "E://Users//User//sign//test//93//4 191\n",
      "E://Users//User//sign//test//93//5 94\n",
      "E://Users//User//sign//test//93//6 61\n",
      "E://Users//User//sign//test//수요일//0 116\n",
      "E://Users//User//sign//test//수요일//1 50\n",
      "E://Users//User//sign//test//수요일//2 121\n",
      "E://Users//User//sign//test//수요일//3 87\n",
      "E://Users//User//sign//test//수요일//4 126\n",
      "E://Users//User//sign//test//수요일//5 189\n",
      "E://Users//User//sign//test//수요일//6 58\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(file_list_1)):\n",
    "    path=\"E://Users//User//sign//test//\" + str(file_list_1[i])\n",
    "    file_list_2=os.listdir(path)\n",
    "    for p in range(len(file_list_2)):\n",
    "        path2=path+'//'+str(file_list_2[p])\n",
    "        file_list_3=os.listdir(path2)\n",
    "        print(path2, len(file_list_3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E://Users//User//sign//test//0//0 62\n",
      "E://Users//User//sign//test//0//1 58\n",
      "E://Users//User//sign//test//0//2 118\n",
      "E://Users//User//sign//test//0//3 83\n",
      "E://Users//User//sign//test//0//4 12\n",
      "E://Users//User//sign//test//0//5 116\n",
      "E://Users//User//sign//test//0//6 6\n",
      "E://Users//User//sign//test//0//7 0\n",
      "E://Users//User//sign//test//93//0 59\n",
      "E://Users//User//sign//test//93//1 127\n",
      "E://Users//User//sign//test//93//2 37\n",
      "E://Users//User//sign//test//93//3 83\n",
      "E://Users//User//sign//test//93//4 12\n",
      "E://Users//User//sign//test//93//5 109\n",
      "E://Users//User//sign//test//93//6 142\n",
      "E://Users//User//sign//test//수요일//0 87\n",
      "E://Users//User//sign//test//수요일//1 153\n",
      "E://Users//User//sign//test//수요일//2 82\n",
      "E://Users//User//sign//test//수요일//3 116\n",
      "E://Users//User//sign//test//수요일//4 77\n",
      "E://Users//User//sign//test//수요일//5 14\n",
      "E://Users//User//sign//test//수요일//6 145\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(file_list_1)):\n",
    "    path=\"E://Users//User//sign//test//\" + str(file_list_1[i])\n",
    "    file_list_2=os.listdir(path)\n",
    "    for p in range(len(file_list_2)):\n",
    "        path2=path+'//'+str(file_list_2[p])\n",
    "        file_list_3=os.listdir(path2)\n",
    "        print(path2, 203-len(file_list_3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    " \n",
    "def customized_copy(input_path, output_path,file_name):\n",
    "    subdir_list = os.listdir(input_path)\n",
    "    for subdir in subdir_list:\n",
    "        print(subdir)\n",
    "        raw_file_path = os.path.join(input_path, subdir, file_name)#type：file_name_str\n",
    "        target_file_path = os.path.join(output_path, subdir, file_name)#type：file_name_str\n",
    "        shutil.copyfile(raw_file_path,target_file_path)\n",
    " \n",
    " if __name__ == '__main__':\n",
    "    input_path = \"/data/dataset/xxx/06_results_rai_30_0415/\"\n",
    "    output_path = \"/data/dataset/xxx/06_results/\"\n",
    "    file_name = \"seg_kidney_tumor.nii.gz\"\n",
    "    customized_copy(input_path, output_path,file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'input_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11512/222450869.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m             \u001b[0minput_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpath2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_list_3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcustomized_copy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'input_path' is not defined"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "def customized_copy(input_path):\n",
    "    sbbdir_list=os.listdir(input_path) \n",
    "    for i in range(len(file_list_1)):\n",
    "        path=\"E://Users//User//sign//test//\" + str(file_list_1[i])\n",
    "        file_list_2=os.listdir(path)\n",
    "        for p in range(len(file_list_2)):\n",
    "            path2=path+'//'+str(file_list_2[p])\n",
    "            file_list_3=os.listdir(path2)\n",
    "            input_path=path2[len(file_list_3)]\n",
    "    \n",
    "print(customized_copy(input_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import shutil\n",
    " \n",
    "data_path = ''\n",
    " \n",
    "for root, dirs, files in os.walk(data_path):\n",
    "    for file in files:\n",
    "        if file.endswith(\"npy\"): #확장자 \n",
    "            old_file_path = os.path.join(root, file)\n",
    "            #print(old_file_path)\n",
    "            new_path = ''\n",
    "            if not os.path.exists(new_path):  # make new dir\n",
    "                os.makedirs(new_path)\n",
    "            new_file_path = new_path + '/' + file\n",
    "            print(new_file_path)\n",
    "            shutil.copyfile(old_file_path, new_file_path) # copy \n",
    "print('finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[WinError 183] 파일이 이미 있으므로 만들 수 없습니다: 'E://Users//User//sign//test//0//0//140'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11512/3976303852.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m             \u001b[1;31m#print(path3)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_list_3\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m203\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m             \u001b[0mshutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopytree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath2\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'//'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcount_list3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath2\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'//140'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;31m#print(max(count_list3))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;31m#len(file_list_3)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\envs\\snowdeer_env1\\lib\\shutil.py\u001b[0m in \u001b[0;36mcopytree\u001b[1;34m(src, dst, symlinks, ignore, copy_function, ignore_dangling_symlinks, dirs_exist_ok)\u001b[0m\n\u001b[0;32m    552\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscandir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mitr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    553\u001b[0m         \u001b[0mentries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 554\u001b[1;33m     return _copytree(entries=entries, src=src, dst=dst, symlinks=symlinks,\n\u001b[0m\u001b[0;32m    555\u001b[0m                      \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy_function\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy_function\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    556\u001b[0m                      \u001b[0mignore_dangling_symlinks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore_dangling_symlinks\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\envs\\snowdeer_env1\\lib\\shutil.py\u001b[0m in \u001b[0;36m_copytree\u001b[1;34m(entries, src, dst, symlinks, ignore, copy_function, ignore_dangling_symlinks, dirs_exist_ok)\u001b[0m\n\u001b[0;32m    453\u001b[0m         \u001b[0mignored_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 455\u001b[1;33m     \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdirs_exist_ok\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    456\u001b[0m     \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m     \u001b[0muse_srcentry\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcopy_function\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mcopy2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mcopy_function\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\envs\\snowdeer_env1\\lib\\os.py\u001b[0m in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m    221\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    222\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m         \u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    224\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m         \u001b[1;31m# Cannot rely on checking for EEXIST, since the operating system\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileExistsError\u001b[0m: [WinError 183] 파일이 이미 있으므로 만들 수 없습니다: 'E://Users//User//sign//test//0//0//140'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "count_list3=[]\n",
    "for i in range(len(file_list_1)):\n",
    "    path=\"E://Users//User//sign//test//\" + str(file_list_1[i])\n",
    "    file_list_2=os.listdir(path)\n",
    "    for p in range(len(file_list_2)):\n",
    "        path2=path+'//'+str(file_list_2[p])\n",
    "        file_list_3=os.listdir(path2)\n",
    "        count_list3.append(len(file_list_3))\n",
    "        for j in range(len(file_list_3)):\n",
    "            path3=path2+'//'+str(file_list_3[j])\n",
    "            #print(path3)        \n",
    "        while len(file_list_3) < 203:\n",
    "            shutil.copytree(path2+'//'+str(max(count_list3)-1), path2+'//')             \n",
    "#print(max(count_list3))\n",
    "#len(file_list_3)\n",
    "           # print(path3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "count_list3=[]\n",
    "cost=[]\n",
    "file_list_1=os.listdir(\"E://Users//User//sign//test\")\n",
    "for i in range(len(file_list_1)):\n",
    "    path=\"E:///Users//User//sign//test//\"+str(file_list_1[i])\n",
    "    file_list_2=os.listdir(path)\n",
    "    for j in range(len(file_list_2)):\n",
    "        path2=path+'//'+str(file_list_2[j])\n",
    "        file_list_3=os.listdir(path2)\n",
    "        for k in path2:\n",
    "            if len(file_list_3) <203:\n",
    "                cost.append(203-len(file_list_3))\n",
    "print(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'141'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#맨 끝 폴더(디렉토리)명만 얻는 법\n",
    "\n",
    "os.path.basename('C://Users//User//sign//test//'+str(len(file_list_3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import  os,time,shutil,datetime\n",
    "def rmdir(deldir,N):\n",
    "    dellist=os.listdir(deldir)\n",
    "    deldate= datetime.timedelta(days=N)\n",
    "    now = datetime.datetime.now()\n",
    "    for  i  in  dellist:\n",
    "        os.chdir(deldir)\n",
    "        ctime = datetime.datetime.fromtimestamp(os.path.getctime(i))\n",
    "        if ctime < (now - deldate):\n",
    "            #print (\"%s rm  dir :%s\" %(now,i))\n",
    "            if os.path.isfile(i):\n",
    "                os.remove(i)\n",
    "            else:\n",
    "                shutil.rmtree(i)\n",
    " \n",
    "#Timed C--logs deletion\n",
    "#Delete the directory at midnight every day and keep the last few days\n",
    "rmdir('C:\\logs\\Adf.NumberServer',7)\n",
    "rmdir('E:\\',7)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    " \n",
    "    # 删除文件夹下的文件&&保留但清空子文件夹\n",
    "def del_file(filepath):\n",
    "    print(\"hello\")\n",
    "    listdir = os.listdir(filepath)  # 获取文件和子文件夹\n",
    "    print(listdir)\n",
    "    for dirname in listdir:\n",
    "        dirname = filepath + \"//\" + dirname\n",
    "        if os.path.isfile(dirname): # 是文件\n",
    "            print(dirname)\n",
    "            os.remove(dirname)      # 删除文件\n",
    "        elif os.path.isdir(dirname):        # 是子文件夹\n",
    "            print(dirname)\n",
    "            dellist = os.listdir(dirname)\n",
    "            for f in dellist:               # 遍历该子文件夹\n",
    "                file_path = os.path.join(dirname, f)\n",
    "                if os.path.isfile(file_path):       # 删除子文件夹下文件\n",
    "                    os.remove(file_path)\n",
    "                elif os.path.isdir(file_path):      # 强制删除子文件夹下的子文件夹\n",
    "                    shutil.rmtree(file_path)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "stat: path should be string, bytes, os.PathLike or integer, not list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11512/3002092585.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlistdir2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mlistdir3\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'E://Users//User//sign//test'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'//'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'//'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlistdir3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m             \u001b[0mdellist\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlistdir3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdellist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\envs\\snowdeer_env1\\lib\\genericpath.py\u001b[0m in \u001b[0;36misdir\u001b[1;34m(s)\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[1;34m\"\"\"Return true if the pathname refers to an existing directory.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: stat: path should be string, bytes, os.PathLike or integer, not list"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "#filename=[]\n",
    "listdir=os.listdir('E://Users//User//sign//test')\n",
    "for i in listdir:\n",
    "    listdir2=os.listdir('E://Users//User//sign//test'+'//'+str(i))\n",
    "    for j in range(len(listdir2)):\n",
    "        listdir3=os.listdir('E://Users//User//sign//test'+'//'+str(i)+'//'+str(j))\n",
    "        if os.path.isdir(listdir3):\n",
    "            dellist=os.listdir(listdir3)\n",
    "            print(dellist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 데이터 라벨링하기 \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 동영상 데이터 라벨링하기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다른 액션에 대한 레이블 만들기 \n",
    "# 필요한 데이터 임포트\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 정답데이터 라벨링 하기 \n",
    "label_map = {label:num for num, label in enumerate(dt1['한국어'])} # enumerate-> 0 부터 순서대로 나타내 주는 것 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### 프레임 개수만 맞추면 됩니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파일 불러와서 해당 영상에 대한 정답 데이터 labels에 담기 \n",
    "# labeling actions to labels \n",
    "\n",
    "sequences, labels = [], []\n",
    "DATA_PATH  =  'C://Users//User//sign//train//'\n",
    "for action in dt1['한국어']:\n",
    "    for sequence in np.array(os.listdir(os.path.join(DATA_PATH, action))).astype(int): #단어 안에 있는 폴더의 이름을 리스트로 출력하고 int형으로 변경 \n",
    "        window = []\n",
    "        for currentframe in range(****sequence_length(프레임 개수****)):\n",
    "            # 데이터 파이썬 파일로 불러오기 \n",
    "            res = np.load(os.path.join(DATA_PATH, action, str(sequence), \"{}.npy\".format(currentfrmae)))\n",
    "            # 리스트에 append 하기 \n",
    "            window.append(res)\n",
    "            #sequence라는 리스트안에 window가 리스트 형터로 존재 \n",
    "            #리스트 안의 리스트 \n",
    "        sequences.append(window)\n",
    "        labels.append(label_map[action])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(sequences).shape\n",
    "# 총 90개의 비디오, 1편 당 30시퀀스 /  # (1662,)키포인트 -> 468*3 + 33*4 + 21*3 + 21*3 각(xyz 즉 세개씩 곱한다.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(labels).shape  #(90,) 라벨링한 정답 데이터 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape\n",
    "#90 channels, 30 frames, 1662 keypoints (68*3 + 33*4 + 21*3 + 21*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 원핫인코딩 하기 \n",
    "# one hot encoding \n",
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 나누기\n",
    "# split data \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05) # 5%가 테스트 되겠다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Neural Network 만들기 \n",
    "\n",
    "Build and Train LSTM Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성 -> cnn 넣었을 때, 넣지 않았을 때 모두 해보기 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 임포트 \n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense  #LSTM layer 를  쓰겠다.  \n",
    "from tensorflow.keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set call back ( prevent over fitting )\n",
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir=log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 이 타입 쓰는 이유 CNN 과 비교 시 장점 \n",
    "# 미디아 type + LSTM 레이어\n",
    "# 1. Less DaTa Required \n",
    "# 2. Faster To train\n",
    "# 3. Faster Detections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()  # 시작 \n",
    "# 30, 1662) 각각의 비디오 30 시퀀스 1662 키포인트\n",
    "\n",
    "\n",
    "#######\n",
    "# cnn 모델 추가 \n",
    "\n",
    "########\n",
    "\n",
    "\n",
    "model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,1662))) #64 유닛 -> LSTM은 다음 레이어에 sequences가 필요해서 다음 레이어가 있으면 True를 해준다\n",
    "model.add(LSTM(128, return_sequences=True, activation='relu'))\n",
    "model.add(LSTM(64, return_sequences=False, activation='relu'))# 다음은 dense슬라이드라 sequences 필요없어\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(actions.shape[0], activation='softmax')) # ctions.shape[0] -> 3네트워크 유닛 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 아담 그레이드 , 비아너리 -> 바이너리 크로스 / 이건 분류하는 거-> multi class categoty  예측하는 확률을 보여준다. \n",
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " -  결과보기 \n",
    " - visualize the result of model\n",
    "\n",
    "\n",
    "\n",
    " cmd 창을 킨다 <br>\n",
    "open cmd  <br>\n",
    "<br>\n",
    "C:\\Users\\User>cd Logs <br>\n",
    "<br>\n",
    "C:\\Users\\User\\Logs>cd Train<br>\n",
    "<br>\n",
    "C:\\Users\\User\\Logs\\train>dir<br>\n",
    "\n",
    "- 파일을 텐서플로우로 실행시킨다. (run the file )\n",
    "C:\\Users\\User\\Logs\\train>tensorboard --logdir=. \n",
    "\n",
    "- 링크를 복사하여 크롬창에 띄운다 ( copy the like and open it at Chrome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 예측하기 \n",
    "8. Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "actions[np.argmax(res[4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actions[np.argmax(y_test[4])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 모델 저장 \n",
    "9. Save Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('action.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('action.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 모델 평가 \n",
    "10. Evaluation using Confusion Matrix and Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_train, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)\n",
    "# 이원교차표 출력 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 실시간 예측하기 \n",
    "11. Test in Real Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 동영상에 점 찍기 \n",
    "\n",
    "def draw_landmarks(image, results):  # 동영상의 frame과 동영상에서 받은 result를 받는다. \n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS) # 얼굴의 점을 연결하여 그린다.\n",
    "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) # POSE를 연결햐여 그린다.\n",
    "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # 왼손 그리기\n",
    "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # 오른손 그리기 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 더 정확한 판단을 위해 각각의 노드에 두개의 점을 추가한다. \n",
    "#add aditional 2 spots to the image\n",
    "\n",
    "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [(245,117,16), (117,245,16), (16,117,245)]\n",
    "def prob_viz(res, actions, input_frame, colors):\n",
    "    output_frame = input_frame.copy()\n",
    "    for num, prob in enumerate(res):\n",
    "        cv2.rectangle(output_frame, (0,60+num*40), (int(prob*100), 90+num*40), colors[num], -1)\n",
    "        cv2.putText(output_frame, actions[num], (0, 85+num*40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "    return output_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(18,18))\n",
    "plt.imshow(prob_viz(res, actions, image, colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 1. New detection variables #30개 차면 detect 할 수 있게\n",
    "sequence = []\n",
    "sentence = []\n",
    "predictions = []\n",
    "threshold = 0.5\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        \n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks(image, results)\n",
    "        \n",
    "        # 2. Prediction logic\n",
    "        keypoints = extract_keypoints(results)\n",
    "        sequence.append(keypoints)\n",
    "        sequence = sequence[-30:] #마지막 30프레임\n",
    "        \n",
    "        if len(sequence) == 30: #30개차면 predict한다 \n",
    "            res = model.predict(np.expand_dims(sequence, axis=0))[0] # 2차원을 3차원으로 늘려준다. [0]\n",
    "            print(actions[np.argmax(res)])\n",
    "            predictions.append(np.argmax(res))\n",
    "            \n",
    "            \n",
    "        #3. Viz logic\n",
    "            if np.unique(predictions[-10:])[0]==np.argmax(res): \n",
    "                if res[np.argmax(res)] > threshold: \n",
    "                    \n",
    "                    if len(sentence) > 0: \n",
    "                        if actions[np.argmax(res)] != sentence[-1]:\n",
    "                            sentence.append(actions[np.argmax(res)])\n",
    "                    else:\n",
    "                        sentence.append(actions[np.argmax(res)])\n",
    "\n",
    "            if len(sentence) > 5: \n",
    "                sentence = sentence[-5:]\n",
    "\n",
    "            # Viz probabilities\n",
    "            image = prob_viz(res, actions, image, colors)\n",
    "            \n",
    "        cv2.rectangle(image, (0,0), (640, 40), (245, 117, 16), -1)\n",
    "        cv2.putText(image, ' '.join(sentence), (3,30), \n",
    "                       cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        # Show to screen\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  }
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