Load_imgs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please make sure you have installed scikit-image,matplotlib,pandas \n",
    "from skimage.io import imread\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from util import labeledImage, normalize_coordinates, convert_to_yolo_format\n",
    "import os, shutil, yaml\n",
    "from sklearn.model_selection import train_test_split\n",
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the Sample1.png\n",
    "# Make sure use the correct path of the image\n",
    "img1 = imread('./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_and_measurement_set/images/Sample1.png')\n",
    "plt.imshow(img1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Xstart</th>\n",
       "      <th>Ystart</th>\n",
       "      <th>Wpixel</th>\n",
       "      <th>Hpixel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215</td>\n",
       "      <td>200</td>\n",
       "      <td>13</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROI</th>\n",
       "      <td>92</td>\n",
       "      <td>55</td>\n",
       "      <td>225</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Xstart  Ystart  Wpixel  Hpixel\n",
       "Row                                \n",
       "1       215     200      13      61\n",
       "ROI      92      55     225     332"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the labels for Sample1.png\n",
    "# Make sure use the correct path of the label file\n",
    "label1 = pd.read_table('./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_and_measurement_set/labels/Sample1.txt',sep='\\s+',index_col=0)\n",
    "label1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150)\n",
    "plt.imshow(img1)\n",
    "plt.title('The Region of Interest(out) and Defect Region(inside)')\n",
    "plt.vlines((label1['Xstart']['ROI'],label1['Xstart']['ROI']+label1['Wpixel']['ROI']),label1['Ystart']['ROI'],label1['Ystart']['ROI']+label1['Hpixel']['ROI'])\n",
    "plt.hlines((label1['Ystart']['ROI'],label1['Ystart']['ROI']+label1['Hpixel']['ROI']),label1['Xstart']['ROI'],label1['Xstart']['ROI']+label1['Wpixel']['ROI'])\n",
    "\n",
    "plt.vlines((label1['Xstart']['1'],label1['Xstart']['1']+label1['Wpixel']['1']),label1['Ystart']['1'],label1['Ystart']['1']+label1['Hpixel']['1'])\n",
    "plt.hlines((label1['Ystart']['1'],label1['Ystart']['1']+label1['Hpixel']['1']),label1['Xstart']['1'],label1['Xstart']['1']+label1['Wpixel']['1']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "path='./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_and_measurement_set'\n",
    "path2='./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_trainset'\n",
    "path_val='./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_testset'\n",
    "path_test='./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_speedtest'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_images_dir = [f'{path}/images/',f'{path2}/images/',f'{path_val}/images/',f'{path_test}/images/']\n",
    "source_labels_dir = [f'{path}/labels/',f'{path2}/labels/',f'{path_val}/labels/',f'{path_test}/labels/']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#only ROI\n",
    "labeled_images_train = []\n",
    "tag_train = 'ROI' \n",
    "for file in os.listdir(source_images_dir[0]):\n",
    "    # find all png file and it's label\n",
    "    if file.endswith(\".png\"):\n",
    "        image_path = os.path.join(source_images_dir[0], file)\n",
    "        label_path = os.path.join(source_labels_dir[0], file.split('.')[0] + '.txt')\n",
    "        labeled_images_train.append(labeledImage(image_path))\n",
    "        labeled_images_train[-1].add_labels_from_file(tag_train,1,label_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#only Defect\n",
    "labeled_images_train = []\n",
    "tag_train = 'Defect' \n",
    "for file in os.listdir(source_images_dir[0]):\n",
    "    # find all png file and it's label\n",
    "    if file.endswith(\".png\"):\n",
    "        image_path = os.path.join(source_images_dir[0], file)\n",
    "        label_path = os.path.join(source_labels_dir[0], file.split('.')[0] + '.txt')\n",
    "        labeled_images_train.append(labeledImage(image_path))\n",
    "        labeled_images_train[-1].add_labels_from_file(tag_train,0,label_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#only ROI\n",
    "labeled_images_train_wt = []\n",
    "tag_train_wt = 'ROI' \n",
    "\n",
    "for file in os.listdir(source_images_dir[1]):\n",
    "    if file.endswith(\".png\"):\n",
    "        image_path = os.path.join(source_images_dir[1], file)\n",
    "        label_path = os.path.join(source_labels_dir[1], file.split('.')[0] + '.txt')\n",
    "        labeled_images_train_wt.append(labeledImage(image_path))\n",
    "        labeled_images_train_wt[-1].add_labels_from_file(tag_train_wt,0, label_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled_images_val = []\n",
    "tag_val = 'ROI' \n",
    "\n",
    "for file in os.listdir(source_images_dir[2]):\n",
    "    if file.endswith(\".png\"):\n",
    "        image_path = os.path.join(source_images_dir[2], file)\n",
    "        label_path = os.path.join(source_labels_dir[2], file.split('.')[0] + '.txt')\n",
    "        labeled_images_val.append(labeledImage(image_path))\n",
    "        labeled_images_val[-1].add_labels_from_file(tag_val,0, label_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled_images_test = []\n",
    "tag_test = 'ROI' \n",
    "\n",
    "for file in os.listdir(source_images_dir[3]):\n",
    "    if file.endswith(\".png\"):\n",
    "        image_path = os.path.join(source_images_dir[3], file)\n",
    "        label_path = os.path.join(source_labels_dir[3], file.split('.')[0] + '.txt')\n",
    "        labeled_images_test.append(labeledImage(image_path))\n",
    "        labeled_images_test[-1].add_labels_from_file(tag_test,0, label_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "284\n",
      "800\n",
      "20\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "print(len(labeled_images_train))\n",
    "print(len(labeled_images_train_wt))\n",
    "print(len(labeled_images_test))\n",
    "print(len(labeled_images_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled_images_tot=labeled_images_train+labeled_images_train_wt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labeled image Sample1.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_and_measurement_set/images/Sample1.png\n",
      "    shape: (398, 744)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [92, 55, 225, 332]\n",
      "\n",
      "Labeled image Sample10.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_and_measurement_set/images/Sample10.png\n",
      "    shape: (391, 1128)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [174, 5, 241, 244]\n",
      "\n",
      "Labeled image Sample100.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_and_measurement_set/images/Sample100.png\n",
      "    shape: (398, 744)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [99, 50, 190, 244]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(labeled_images_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labeled image Sample1016.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_trainset/images/Sample1016.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [0, 387, 0, 0]\n",
      "\n",
      "Labeled image Sample1018.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_trainset/images/Sample1018.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [0, 387, 0, 0]\n",
      "\n",
      "Labeled image Sample1025.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_trainset/images/Sample1025.png\n",
      "    shape: (393, 850)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [0, 387, 0, 0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(labeled_images_train_wt[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labeled image Sample1192.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_speedtest/images/Sample1192.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [0, 387, 0, 0]\n",
      "\n",
      "Labeled image Sample1268.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_speedtest/images/Sample1268.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [0, 387, 0, 0]\n",
      "\n",
      "Labeled image Sample1301.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_speedtest/images/Sample1301.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [151, 171, 273, 195]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(labeled_images_test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labeled image Sample1020.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_testset/images/Sample1020.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [0, 387, 0, 0]\n",
      "\n",
      "Labeled image Sample1062.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_testset/images/Sample1062.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [166, 181, 198, 130]\n",
      "\n",
      "Labeled image Sample1068.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_testset/images/Sample1068.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [149, 179, 277, 77]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(labeled_images_val[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir = os.path.join(os.getcwd(),'data_train_images')\n",
    "if not os.path.exists(output_dir): os.mkdir(output_dir)\n",
    "\n",
    "train_dir = os.path.join(output_dir, 'train') \n",
    "val_dir   = os.path.join(output_dir, 'val') \n",
    "test_dir  = os.path.join(output_dir, 'test') \n",
    "\n",
    "# Create the sub-directories\n",
    "for d in [train_dir, val_dir, test_dir]:\n",
    "    if not os.path.exists(d): os.mkdir(d)\n",
    "    \n",
    "    images_sub_dir = os.path.join(d, 'images')\n",
    "    labels_sub_dir = os.path.join(d, 'labels')\n",
    "    \n",
    "    for sub_dir in [images_sub_dir, labels_sub_dir]:\n",
    "        if not os.path.exists(sub_dir): os.mkdir(sub_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1084"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labeled_images_tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labeled image Sample1020.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_testset/images/Sample1020.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [0, 387, 0, 0]\n",
      "\n",
      "Labeled image Sample1062.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_testset/images/Sample1062.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [166, 181, 198, 130]\n",
      "\n",
      "Labeled image Sample1068.png\n",
      "    location: ./UW_Boeing_data/UW_Boeing_Hackweek_data/boeing_data_roi_testset/images/Sample1068.png\n",
      "    shape: (391, 849)\n",
      "    labels:\n",
      "    - ROI: \n",
      "      [149, 179, 277, 77]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(labeled_images_val[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully generated labels for image  Sample1.png\n",
      "successfully generated labels for image  Sample10.png\n",
      "successfully generated labels for image  Sample100.png\n",
      "successfully generated labels for image  Sample101.png\n",
      "successfully generated labels for image  Sample102.png\n",
      "successfully generated labels for image  Sample103.png\n",
      "successfully generated labels for image  Sample104.png\n",
      "successfully generated labels for image  Sample105.png\n",
      "successfully generated labels for image  Sample106.png\n",
      "successfully generated labels for image  Sample107.png\n",
      "successfully generated labels for image  Sample108.png\n",
      "successfully generated labels for image  Sample109.png\n",
      "successfully generated labels for image  Sample11.png\n",
      "successfully generated labels for image  Sample110.png\n",
      "successfully generated labels for image  Sample111.png\n",
      "successfully generated labels for image  Sample112.png\n",
      "successfully generated labels for image  Sample113.png\n",
      "successfully generated labels for image  Sample114.png\n",
      "successfully generated labels for image  Sample115.png\n",
      "successfully generated labels for image  Sample116.png\n",
      "successfully generated labels for image  Sample117.png\n",
      "successfully generated labels for image  Sample118.png\n",
      "successfully generated labels for image  Sample119.png\n",
      "successfully generated labels for image  Sample12.png\n",
      "successfully generated labels for image  Sample120.png\n",
      "successfully generated labels for image  Sample121.png\n",
      "successfully generated labels for image  Sample122.png\n",
      "successfully generated labels for image  Sample123.png\n",
      "successfully generated labels for image  Sample124.png\n",
      "successfully generated labels for image  Sample125.png\n",
      "successfully generated labels for image  Sample126.png\n",
      "successfully generated labels for image  Sample127.png\n",
      "successfully generated labels for image  Sample128.png\n",
      "successfully generated labels for image  Sample129.png\n",
      "successfully generated labels for image  Sample13.png\n",
      "successfully generated labels for image  Sample130.png\n",
      "successfully generated labels for image  Sample131.png\n",
      "successfully generated labels for image  Sample132.png\n",
      "successfully generated labels for image  Sample133.png\n",
      "successfully generated labels for image  Sample134.png\n",
      "successfully generated labels for image  Sample135.png\n",
      "successfully generated labels for image  Sample136.png\n",
      "successfully generated labels for image  Sample137.png\n",
      "successfully generated labels for image  Sample138.png\n",
      "successfully generated labels for image  Sample139.png\n",
      "successfully generated labels for image  Sample14.png\n",
      "successfully generated labels for image  Sample140.png\n",
      "successfully generated labels for image  Sample141.png\n",
      "successfully generated labels for image  Sample142.png\n",
      "successfully generated labels for image  Sample143.png\n",
      "successfully generated labels for image  Sample144.png\n",
      "successfully generated labels for image  Sample145.png\n",
      "successfully generated labels for image  Sample146.png\n",
      "successfully generated labels for image  Sample147.png\n",
      "successfully generated labels for image  Sample148.png\n",
      "successfully generated labels for image  Sample149.png\n",
      "successfully generated labels for image  Sample15.png\n",
      "successfully generated labels for image  Sample150.png\n",
      "successfully generated labels for image  Sample151.png\n",
      "successfully generated labels for image  Sample152.png\n",
      "successfully generated labels for image  Sample153.png\n",
      "successfully generated labels for image  Sample154.png\n",
      "successfully generated labels for image  Sample155.png\n",
      "successfully generated labels for image  Sample156.png\n",
      "successfully generated labels for image  Sample157.png\n",
      "successfully generated labels for image  Sample158.png\n",
      "successfully generated labels for image  Sample159.png\n",
      "successfully generated labels for image  Sample16.png\n",
      "successfully generated labels for image  Sample160.png\n",
      "successfully generated labels for image  Sample161.png\n",
      "successfully generated labels for image  Sample162.png\n",
      "successfully generated labels for image  Sample163.png\n",
      "successfully generated labels for image  Sample164.png\n",
      "successfully generated labels for image  Sample165.png\n",
      "successfully generated labels for image  Sample166.png\n",
      "successfully generated labels for image  Sample167.png\n",
      "successfully generated labels for image  Sample168.png\n",
      "successfully generated labels for image  Sample169.png\n",
      "successfully generated labels for image  Sample17.png\n",
      "successfully generated labels for image  Sample170.png\n",
      "successfully generated labels for image  Sample171.png\n",
      "successfully generated labels for image  Sample172.png\n",
      "successfully generated labels for image  Sample173.png\n",
      "successfully generated labels for image  Sample174.png\n",
      "successfully generated labels for image  Sample175.png\n",
      "successfully generated labels for image  Sample176.png\n",
      "successfully generated labels for image  Sample177.png\n",
      "successfully generated labels for image  Sample178.png\n",
      "successfully generated labels for image  Sample179.png\n",
      "successfully generated labels for image  Sample18.png\n",
      "successfully generated labels for image  Sample180.png\n",
      "successfully generated labels for image  Sample181.png\n",
      "successfully generated labels for image  Sample182.png\n",
      "successfully generated labels for image  Sample183.png\n",
      "successfully generated labels for image  Sample184.png\n",
      "successfully generated labels for image  Sample185.png\n",
      "successfully generated labels for image  Sample186.png\n",
      "successfully generated labels for image  Sample187.png\n",
      "successfully generated labels for image  Sample188.png\n",
      "successfully generated labels for image  Sample189.png\n",
      "successfully generated labels for image  Sample19.png\n",
      "successfully generated labels for image  Sample190.png\n",
      "successfully generated labels for image  Sample191.png\n",
      "successfully generated labels for image  Sample192.png\n",
      "successfully generated labels for image  Sample193.png\n",
      "successfully generated labels for image  Sample194.png\n",
      "successfully generated labels for image  Sample195.png\n",
      "successfully generated labels for image  Sample196.png\n",
      "successfully generated labels for image  Sample197.png\n",
      "successfully generated labels for image  Sample198.png\n",
      "successfully generated labels for image  Sample199.png\n",
      "successfully generated labels for image  Sample2.png\n",
      "successfully generated labels for image  Sample20.png\n",
      "successfully generated labels for image  Sample200.png\n",
      "successfully generated labels for image  Sample201.png\n",
      "successfully generated labels for image  Sample202.png\n",
      "successfully generated labels for image  Sample203.png\n",
      "successfully generated labels for image  Sample204.png\n",
      "successfully generated labels for image  Sample205.png\n",
      "successfully generated labels for image  Sample206.png\n",
      "successfully generated labels for image  Sample207.png\n",
      "successfully generated labels for image  Sample208.png\n",
      "successfully generated labels for image  Sample209.png\n",
      "successfully generated labels for image  Sample21.png\n",
      "successfully generated labels for image  Sample210.png\n",
      "successfully generated labels for image  Sample211.png\n",
      "successfully generated labels for image  Sample212.png\n",
      "successfully generated labels for image  Sample213.png\n",
      "successfully generated labels for image  Sample214.png\n",
      "successfully generated labels for image  Sample215.png\n",
      "successfully generated labels for image  Sample216.png\n",
      "successfully generated labels for image  Sample217.png\n",
      "successfully generated labels for image  Sample218.png\n",
      "successfully generated labels for image  Sample219.png\n",
      "successfully generated labels for image  Sample22.png\n",
      "successfully generated labels for image  Sample220.png\n",
      "successfully generated labels for image  Sample221.png\n",
      "successfully generated labels for image  Sample222.png\n",
      "successfully generated labels for image  Sample223.png\n",
      "successfully generated labels for image  Sample224.png\n",
      "successfully generated labels for image  Sample225.png\n",
      "successfully generated labels for image  Sample226.png\n",
      "successfully generated labels for image  Sample227.png\n",
      "successfully generated labels for image  Sample228.png\n",
      "successfully generated labels for image  Sample229.png\n",
      "successfully generated labels for image  Sample23.png\n",
      "successfully generated labels for image  Sample230.png\n",
      "successfully generated labels for image  Sample231.png\n",
      "successfully generated labels for image  Sample232.png\n",
      "successfully generated labels for image  Sample233.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully generated labels for image  Sample234.png\n",
      "successfully generated labels for image  Sample235.png\n",
      "successfully generated labels for image  Sample236.png\n",
      "successfully generated labels for image  Sample237.png\n",
      "successfully generated labels for image  Sample238.png\n",
      "successfully generated labels for image  Sample239.png\n",
      "successfully generated labels for image  Sample24.png\n",
      "successfully generated labels for image  Sample240.png\n",
      "successfully generated labels for image  Sample241.png\n",
      "successfully generated labels for image  Sample242.png\n",
      "successfully generated labels for image  Sample243.png\n",
      "successfully generated labels for image  Sample244.png\n",
      "successfully generated labels for image  Sample245.png\n",
      "successfully generated labels for image  Sample246.png\n",
      "successfully generated labels for image  Sample247.png\n",
      "successfully generated labels for image  Sample248.png\n",
      "successfully generated labels for image  Sample249.png\n",
      "successfully generated labels for image  Sample25.png\n",
      "successfully generated labels for image  Sample250.png\n",
      "successfully generated labels for image  Sample251.png\n",
      "successfully generated labels for image  Sample252.png\n",
      "successfully generated labels for image  Sample253.png\n",
      "successfully generated labels for image  Sample254.png\n",
      "successfully generated labels for image  Sample255.png\n",
      "successfully generated labels for image  Sample256.png\n",
      "successfully generated labels for image  Sample257.png\n",
      "successfully generated labels for image  Sample258.png\n",
      "successfully generated labels for image  Sample259.png\n",
      "successfully generated labels for image  Sample26.png\n",
      "successfully generated labels for image  Sample260.png\n",
      "successfully generated labels for image  Sample261.png\n",
      "successfully generated labels for image  Sample262.png\n",
      "successfully generated labels for image  Sample263.png\n",
      "successfully generated labels for image  Sample264.png\n",
      "successfully generated labels for image  Sample265.png\n",
      "successfully generated labels for image  Sample266.png\n",
      "successfully generated labels for image  Sample267.png\n",
      "successfully generated labels for image  Sample268.png\n",
      "successfully generated labels for image  Sample269.png\n",
      "successfully generated labels for image  Sample27.png\n",
      "successfully generated labels for image  Sample270.png\n",
      "successfully generated labels for image  Sample271.png\n",
      "successfully generated labels for image  Sample272.png\n",
      "successfully generated labels for image  Sample273.png\n",
      "successfully generated labels for image  Sample274.png\n",
      "successfully generated labels for image  Sample275.png\n",
      "successfully generated labels for image  Sample276.png\n",
      "successfully generated labels for image  Sample277.png\n",
      "successfully generated labels for image  Sample278.png\n",
      "successfully generated labels for image  Sample279.png\n",
      "successfully generated labels for image  Sample28.png\n",
      "successfully generated labels for image  Sample280.png\n",
      "successfully generated labels for image  Sample281.png\n",
      "successfully generated labels for image  Sample282.png\n",
      "successfully generated labels for image  Sample283.png\n",
      "successfully generated labels for image  Sample284.png\n",
      "successfully generated labels for image  Sample29.png\n",
      "successfully generated labels for image  Sample3.png\n",
      "successfully generated labels for image  Sample30.png\n",
      "successfully generated labels for image  Sample31.png\n",
      "successfully generated labels for image  Sample32.png\n",
      "successfully generated labels for image  Sample33.png\n",
      "successfully generated labels for image  Sample34.png\n",
      "successfully generated labels for image  Sample35.png\n",
      "successfully generated labels for image  Sample36.png\n",
      "successfully generated labels for image  Sample37.png\n",
      "successfully generated labels for image  Sample38.png\n",
      "successfully generated labels for image  Sample39.png\n",
      "successfully generated labels for image  Sample4.png\n",
      "successfully generated labels for image  Sample40.png\n",
      "successfully generated labels for image  Sample41.png\n",
      "successfully generated labels for image  Sample42.png\n",
      "successfully generated labels for image  Sample43.png\n",
      "successfully generated labels for image  Sample44.png\n",
      "successfully generated labels for image  Sample45.png\n",
      "successfully generated labels for image  Sample46.png\n",
      "successfully generated labels for image  Sample47.png\n",
      "successfully generated labels for image  Sample48.png\n",
      "successfully generated labels for image  Sample49.png\n",
      "successfully generated labels for image  Sample5.png\n",
      "successfully generated labels for image  Sample50.png\n",
      "successfully generated labels for image  Sample51.png\n",
      "successfully generated labels for image  Sample52.png\n",
      "successfully generated labels for image  Sample53.png\n",
      "successfully generated labels for image  Sample54.png\n",
      "successfully generated labels for image  Sample55.png\n",
      "successfully generated labels for image  Sample56.png\n",
      "successfully generated labels for image  Sample57.png\n",
      "successfully generated labels for image  Sample58.png\n",
      "successfully generated labels for image  Sample59.png\n",
      "successfully generated labels for image  Sample6.png\n",
      "successfully generated labels for image  Sample60.png\n",
      "successfully generated labels for image  Sample61.png\n",
      "successfully generated labels for image  Sample62.png\n",
      "successfully generated labels for image  Sample63.png\n",
      "successfully generated labels for image  Sample64.png\n",
      "successfully generated labels for image  Sample65.png\n",
      "successfully generated labels for image  Sample66.png\n",
      "successfully generated labels for image  Sample67.png\n",
      "successfully generated labels for image  Sample68.png\n",
      "successfully generated labels for image  Sample69.png\n",
      "successfully generated labels for image  Sample7.png\n",
      "successfully generated labels for image  Sample70.png\n",
      "successfully generated labels for image  Sample71.png\n",
      "successfully generated labels for image  Sample72.png\n",
      "successfully generated labels for image  Sample73.png\n",
      "successfully generated labels for image  Sample74.png\n",
      "successfully generated labels for image  Sample75.png\n",
      "successfully generated labels for image  Sample76.png\n",
      "successfully generated labels for image  Sample77.png\n",
      "successfully generated labels for image  Sample78.png\n",
      "successfully generated labels for image  Sample79.png\n",
      "successfully generated labels for image  Sample8.png\n",
      "successfully generated labels for image  Sample80.png\n",
      "successfully generated labels for image  Sample81.png\n",
      "successfully generated labels for image  Sample82.png\n",
      "successfully generated labels for image  Sample83.png\n",
      "successfully generated labels for image  Sample84.png\n",
      "successfully generated labels for image  Sample85.png\n",
      "successfully generated labels for image  Sample86.png\n",
      "successfully generated labels for image  Sample87.png\n",
      "successfully generated labels for image  Sample88.png\n",
      "successfully generated labels for image  Sample89.png\n",
      "successfully generated labels for image  Sample9.png\n",
      "successfully generated labels for image  Sample90.png\n",
      "successfully generated labels for image  Sample91.png\n",
      "successfully generated labels for image  Sample92.png\n",
      "successfully generated labels for image  Sample93.png\n",
      "successfully generated labels for image  Sample94.png\n",
      "successfully generated labels for image  Sample95.png\n",
      "successfully generated labels for image  Sample96.png\n",
      "successfully generated labels for image  Sample97.png\n",
      "successfully generated labels for image  Sample98.png\n",
      "successfully generated labels for image  Sample99.png\n",
      "successfully generated labels for image  Sample1016.png\n",
      "successfully generated labels for image  Sample1018.png\n",
      "successfully generated labels for image  Sample1025.png\n",
      "successfully generated labels for image  Sample1026.png\n",
      "successfully generated labels for image  Sample1030.png\n",
      "successfully generated labels for image  Sample1037.png\n",
      "successfully generated labels for image  Sample1045.png\n",
      "successfully generated labels for image  Sample1051.png\n",
      "successfully generated labels for image  Sample1053.png\n",
      "successfully generated labels for image  Sample1054.png\n",
      "successfully generated labels for image  Sample1055.png\n",
      "successfully generated labels for image  Sample1069.png\n",
      "successfully generated labels for image  Sample1087.png\n",
      "successfully generated labels for image  Sample1101.png\n",
      "successfully generated labels for image  Sample1123.png\n",
      "successfully generated labels for image  Sample1124.png\n",
      "successfully generated labels for image  Sample1126.png\n",
      "successfully generated labels for image  Sample1127.png\n",
      "successfully generated labels for image  Sample1130.png\n",
      "successfully generated labels for image  Sample1133.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully generated labels for image  Sample1139.png\n",
      "successfully generated labels for image  Sample1142.png\n",
      "successfully generated labels for image  Sample1159.png\n",
      "successfully generated labels for image  Sample1165.png\n",
      "successfully generated labels for image  Sample1167.png\n",
      "successfully generated labels for image  Sample1173.png\n",
      "successfully generated labels for image  Sample1183.png\n",
      "successfully generated labels for image  Sample1189.png\n",
      "successfully generated labels for image  Sample1203.png\n",
      "successfully generated labels for image  Sample1204.png\n",
      "successfully generated labels for image  Sample1216.png\n",
      "successfully generated labels for image  Sample1217.png\n",
      "successfully generated labels for image  Sample1221.png\n",
      "successfully generated labels for image  Sample1225.png\n",
      "successfully generated labels for image  Sample1235.png\n",
      "successfully generated labels for image  Sample1238.png\n",
      "successfully generated labels for image  Sample1239.png\n",
      "successfully generated labels for image  Sample1240.png\n",
      "successfully generated labels for image  Sample1244.png\n",
      "successfully generated labels for image  Sample1252.png\n",
      "successfully generated labels for image  Sample1253.png\n",
      "successfully generated labels for image  Sample1262.png\n",
      "successfully generated labels for image  Sample1265.png\n",
      "successfully generated labels for image  Sample1269.png\n",
      "successfully generated labels for image  Sample1287.png\n",
      "successfully generated labels for image  Sample1299.png\n",
      "successfully generated labels for image  Sample1300.png\n",
      "successfully generated labels for image  Sample1308.png\n",
      "successfully generated labels for image  Sample1309.png\n",
      "successfully generated labels for image  Sample1311.png\n",
      "successfully generated labels for image  Sample1315.png\n",
      "successfully generated labels for image  Sample1317.png\n",
      "successfully generated labels for image  Sample1341.png\n",
      "successfully generated labels for image  Sample1344.png\n",
      "successfully generated labels for image  Sample1351.png\n",
      "successfully generated labels for image  Sample1352.png\n",
      "successfully generated labels for image  Sample1356.png\n",
      "successfully generated labels for image  Sample1374.png\n",
      "successfully generated labels for image  Sample1385.png\n",
      "successfully generated labels for image  Sample1387.png\n",
      "successfully generated labels for image  Sample1390.png\n",
      "successfully generated labels for image  Sample1398.png\n",
      "successfully generated labels for image  Sample1405.png\n",
      "successfully generated labels for image  Sample1406.png\n",
      "successfully generated labels for image  Sample1434.png\n",
      "successfully generated labels for image  Sample1441.png\n",
      "successfully generated labels for image  Sample1443.png\n",
      "successfully generated labels for image  Sample1470.png\n",
      "successfully generated labels for image  Sample1475.png\n",
      "successfully generated labels for image  Sample1477.png\n",
      "successfully generated labels for image  Sample1481.png\n",
      "successfully generated labels for image  Sample1485.png\n",
      "successfully generated labels for image  Sample1488.png\n",
      "successfully generated labels for image  Sample1489.png\n",
      "successfully generated labels for image  Sample1493.png\n",
      "successfully generated labels for image  Sample1494.png\n",
      "successfully generated labels for image  Sample1504.png\n",
      "successfully generated labels for image  Sample1509.png\n",
      "successfully generated labels for image  Sample1520.png\n",
      "successfully generated labels for image  Sample1526.png\n",
      "successfully generated labels for image  Sample1531.png\n",
      "successfully generated labels for image  Sample1535.png\n",
      "successfully generated labels for image  Sample1544.png\n",
      "successfully generated labels for image  Sample1545.png\n",
      "successfully generated labels for image  Sample1553.png\n",
      "successfully generated labels for image  Sample1555.png\n",
      "successfully generated labels for image  Sample1558.png\n",
      "successfully generated labels for image  Sample1561.png\n",
      "successfully generated labels for image  Sample1563.png\n",
      "successfully generated labels for image  Sample1581.png\n",
      "successfully generated labels for image  Sample1583.png\n",
      "successfully generated labels for image  Sample1601.png\n",
      "successfully generated labels for image  Sample1612.png\n",
      "successfully generated labels for image  Sample1632.png\n",
      "successfully generated labels for image  Sample1638.png\n",
      "successfully generated labels for image  Sample1645.png\n",
      "successfully generated labels for image  Sample1651.png\n",
      "successfully generated labels for image  Sample1656.png\n",
      "successfully generated labels for image  Sample1666.png\n",
      "successfully generated labels for image  Sample1667.png\n",
      "successfully generated labels for image  Sample1673.png\n",
      "successfully generated labels for image  Sample1674.png\n",
      "successfully generated labels for image  Sample1676.png\n",
      "successfully generated labels for image  Sample1690.png\n",
      "successfully generated labels for image  Sample1691.png\n",
      "successfully generated labels for image  Sample1695.png\n",
      "successfully generated labels for image  Sample1706.png\n",
      "successfully generated labels for image  Sample1708.png\n",
      "successfully generated labels for image  Sample1729.png\n",
      "successfully generated labels for image  Sample1736.png\n",
      "successfully generated labels for image  Sample1737.png\n",
      "successfully generated labels for image  Sample1738.png\n",
      "successfully generated labels for image  Sample1739.png\n",
      "successfully generated labels for image  Sample1746.png\n",
      "successfully generated labels for image  Sample1751.png\n",
      "successfully generated labels for image  Sample1753.png\n",
      "successfully generated labels for image  Sample1767.png\n",
      "successfully generated labels for image  Sample1768.png\n",
      "successfully generated labels for image  Sample1769.png\n",
      "successfully generated labels for image  Sample1770.png\n",
      "successfully generated labels for image  Sample1777.png\n",
      "successfully generated labels for image  Sample1779.png\n",
      "successfully generated labels for image  Sample1790.png\n",
      "successfully generated labels for image  Sample1792.png\n",
      "successfully generated labels for image  Sample1794.png\n",
      "successfully generated labels for image  Sample1805.png\n",
      "successfully generated labels for image  Sample1806.png\n",
      "successfully generated labels for image  Sample1807.png\n",
      "successfully generated labels for image  Sample1813.png\n",
      "successfully generated labels for image  Sample1815.png\n",
      "successfully generated labels for image  Sample1819.png\n",
      "successfully generated labels for image  Sample1821.png\n",
      "successfully generated labels for image  Sample1828.png\n",
      "successfully generated labels for image  Sample1830.png\n",
      "successfully generated labels for image  Sample1840.png\n",
      "successfully generated labels for image  Sample1842.png\n",
      "successfully generated labels for image  Sample1854.png\n",
      "successfully generated labels for image  Sample1856.png\n",
      "successfully generated labels for image  Sample1862.png\n",
      "successfully generated labels for image  Sample1871.png\n",
      "successfully generated labels for image  Sample1876.png\n",
      "successfully generated labels for image  Sample1879.png\n",
      "successfully generated labels for image  Sample1892.png\n",
      "successfully generated labels for image  Sample1896.png\n",
      "successfully generated labels for image  Sample1910.png\n",
      "successfully generated labels for image  Sample1911.png\n",
      "successfully generated labels for image  Sample1922.png\n",
      "successfully generated labels for image  Sample1926.png\n",
      "successfully generated labels for image  Sample1933.png\n",
      "successfully generated labels for image  Sample1936.png\n",
      "successfully generated labels for image  Sample1937.png\n",
      "successfully generated labels for image  Sample1940.png\n",
      "successfully generated labels for image  Sample1944.png\n",
      "successfully generated labels for image  Sample1954.png\n",
      "successfully generated labels for image  Sample1964.png\n",
      "successfully generated labels for image  Sample1970.png\n",
      "successfully generated labels for image  Sample1975.png\n",
      "successfully generated labels for image  Sample1979.png\n",
      "successfully generated labels for image  Sample1988.png\n",
      "successfully generated labels for image  Sample1997.png\n",
      "successfully generated labels for image  Sample2003.png\n",
      "successfully generated labels for image  Sample2021.png\n",
      "successfully generated labels for image  Sample2023.png\n",
      "successfully generated labels for image  Sample2025.png\n",
      "successfully generated labels for image  Sample2027.png\n",
      "successfully generated labels for image  Sample2030.png\n",
      "successfully generated labels for image  Sample2042.png\n",
      "successfully generated labels for image  Sample2054.png\n",
      "successfully generated labels for image  Sample2055.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully generated labels for image  Sample2059.png\n",
      "successfully generated labels for image  Sample2063.png\n",
      "successfully generated labels for image  Sample2065.png\n",
      "successfully generated labels for image  Sample2067.png\n",
      "successfully generated labels for image  Sample2072.png\n",
      "successfully generated labels for image  Sample2073.png\n",
      "successfully generated labels for image  Sample2080.png\n",
      "successfully generated labels for image  Sample2092.png\n",
      "successfully generated labels for image  Sample2094.png\n",
      "successfully generated labels for image  Sample2097.png\n",
      "successfully generated labels for image  Sample2098.png\n",
      "successfully generated labels for image  Sample2111.png\n",
      "successfully generated labels for image  Sample2117.png\n",
      "successfully generated labels for image  Sample2128.png\n",
      "successfully generated labels for image  Sample2137.png\n",
      "successfully generated labels for image  Sample2140.png\n",
      "successfully generated labels for image  Sample2142.png\n",
      "successfully generated labels for image  Sample2151.png\n",
      "successfully generated labels for image  Sample2154.png\n",
      "successfully generated labels for image  Sample2161.png\n",
      "successfully generated labels for image  Sample2162.png\n",
      "successfully generated labels for image  Sample2164.png\n",
      "successfully generated labels for image  Sample2168.png\n",
      "successfully generated labels for image  Sample2170.png\n",
      "successfully generated labels for image  Sample2172.png\n",
      "successfully generated labels for image  Sample2176.png\n",
      "successfully generated labels for image  Sample2180.png\n",
      "successfully generated labels for image  Sample2193.png\n",
      "successfully generated labels for image  Sample2211.png\n",
      "successfully generated labels for image  Sample2218.png\n",
      "successfully generated labels for image  Sample2223.png\n",
      "successfully generated labels for image  Sample2227.png\n",
      "successfully generated labels for image  Sample2230.png\n",
      "successfully generated labels for image  Sample2235.png\n",
      "successfully generated labels for image  Sample2249.png\n",
      "successfully generated labels for image  Sample2250.png\n",
      "successfully generated labels for image  Sample2251.png\n",
      "successfully generated labels for image  Sample2254.png\n",
      "successfully generated labels for image  Sample2292.png\n",
      "successfully generated labels for image  Sample2303.png\n",
      "successfully generated labels for image  Sample2304.png\n",
      "successfully generated labels for image  Sample2309.png\n",
      "successfully generated labels for image  Sample2325.png\n",
      "successfully generated labels for image  Sample2328.png\n",
      "successfully generated labels for image  Sample2330.png\n",
      "successfully generated labels for image  Sample2355.png\n",
      "successfully generated labels for image  Sample2359.png\n",
      "successfully generated labels for image  Sample2365.png\n",
      "successfully generated labels for image  Sample2367.png\n",
      "successfully generated labels for image  Sample2389.png\n",
      "successfully generated labels for image  Sample2392.png\n",
      "successfully generated labels for image  Sample2393.png\n",
      "successfully generated labels for image  Sample2405.png\n",
      "successfully generated labels for image  Sample2415.png\n",
      "successfully generated labels for image  Sample2422.png\n",
      "successfully generated labels for image  Sample2425.png\n",
      "successfully generated labels for image  Sample2436.png\n",
      "successfully generated labels for image  Sample2438.png\n",
      "successfully generated labels for image  Sample2441.png\n",
      "successfully generated labels for image  Sample2452.png\n",
      "successfully generated labels for image  Sample2460.png\n",
      "successfully generated labels for image  Sample2475.png\n",
      "successfully generated labels for image  Sample2477.png\n",
      "successfully generated labels for image  Sample2485.png\n",
      "successfully generated labels for image  Sample2491.png\n",
      "successfully generated labels for image  Sample2504.png\n",
      "successfully generated labels for image  Sample2510.png\n",
      "successfully generated labels for image  Sample2515.png\n",
      "successfully generated labels for image  Sample2516.png\n",
      "successfully generated labels for image  Sample2530.png\n",
      "successfully generated labels for image  Sample2531.png\n",
      "successfully generated labels for image  Sample2537.png\n",
      "successfully generated labels for image  Sample2546.png\n",
      "successfully generated labels for image  Sample2547.png\n",
      "successfully generated labels for image  Sample2548.png\n",
      "successfully generated labels for image  Sample2554.png\n",
      "successfully generated labels for image  Sample2558.png\n",
      "successfully generated labels for image  Sample2572.png\n",
      "successfully generated labels for image  Sample2579.png\n",
      "successfully generated labels for image  Sample2586.png\n",
      "successfully generated labels for image  Sample2592.png\n",
      "successfully generated labels for image  Sample2598.png\n",
      "successfully generated labels for image  Sample2604.png\n",
      "successfully generated labels for image  Sample2616.png\n",
      "successfully generated labels for image  Sample2618.png\n",
      "successfully generated labels for image  Sample2629.png\n",
      "successfully generated labels for image  Sample2631.png\n",
      "successfully generated labels for image  Sample2637.png\n",
      "successfully generated labels for image  Sample2642.png\n",
      "successfully generated labels for image  Sample2653.png\n",
      "successfully generated labels for image  Sample2655.png\n",
      "successfully generated labels for image  Sample2671.png\n",
      "successfully generated labels for image  Sample2674.png\n",
      "successfully generated labels for image  Sample2681.png\n",
      "successfully generated labels for image  Sample2696.png\n",
      "successfully generated labels for image  Sample2698.png\n",
      "successfully generated labels for image  Sample2715.png\n",
      "successfully generated labels for image  Sample2725.png\n",
      "successfully generated labels for image  Sample2733.png\n",
      "successfully generated labels for image  Sample2736.png\n",
      "successfully generated labels for image  Sample2738.png\n",
      "successfully generated labels for image  Sample2745.png\n",
      "successfully generated labels for image  Sample2746.png\n",
      "successfully generated labels for image  Sample2762.png\n",
      "successfully generated labels for image  Sample2763.png\n",
      "successfully generated labels for image  Sample2765.png\n",
      "successfully generated labels for image  Sample2776.png\n",
      "successfully generated labels for image  Sample2780.png\n",
      "successfully generated labels for image  Sample2788.png\n",
      "successfully generated labels for image  Sample2789.png\n",
      "successfully generated labels for image  Sample2791.png\n",
      "successfully generated labels for image  Sample2795.png\n",
      "successfully generated labels for image  Sample2796.png\n",
      "successfully generated labels for image  Sample2799.png\n",
      "successfully generated labels for image  Sample2800.png\n",
      "successfully generated labels for image  Sample2810.png\n",
      "successfully generated labels for image  Sample2849.png\n",
      "successfully generated labels for image  Sample2868.png\n",
      "successfully generated labels for image  Sample2870.png\n",
      "successfully generated labels for image  Sample2874.png\n",
      "successfully generated labels for image  Sample2877.png\n",
      "successfully generated labels for image  Sample2893.png\n",
      "successfully generated labels for image  Sample2894.png\n",
      "successfully generated labels for image  Sample2895.png\n",
      "successfully generated labels for image  Sample2898.png\n",
      "successfully generated labels for image  Sample2906.png\n",
      "successfully generated labels for image  Sample2913.png\n",
      "successfully generated labels for image  Sample2925.png\n",
      "successfully generated labels for image  Sample2928.png\n",
      "successfully generated labels for image  Sample2934.png\n",
      "successfully generated labels for image  Sample2947.png\n",
      "successfully generated labels for image  Sample2949.png\n",
      "successfully generated labels for image  Sample2950.png\n",
      "successfully generated labels for image  Sample2952.png\n",
      "successfully generated labels for image  Sample2977.png\n",
      "successfully generated labels for image  Sample2981.png\n",
      "successfully generated labels for image  Sample2990.png\n",
      "successfully generated labels for image  Sample2993.png\n",
      "successfully generated labels for image  Sample2999.png\n",
      "successfully generated labels for image  Sample3008.png\n",
      "successfully generated labels for image  Sample3011.png\n",
      "successfully generated labels for image  Sample3017.png\n",
      "successfully generated labels for image  Sample3020.png\n",
      "successfully generated labels for image  Sample3024.png\n",
      "successfully generated labels for image  Sample3030.png\n",
      "successfully generated labels for image  Sample3035.png\n",
      "successfully generated labels for image  Sample3043.png\n",
      "successfully generated labels for image  Sample3051.png\n",
      "successfully generated labels for image  Sample3057.png\n",
      "successfully generated labels for image  Sample3060.png\n",
      "successfully generated labels for image  Sample3064.png\n",
      "successfully generated labels for image  Sample3067.png\n",
      "successfully generated labels for image  Sample3070.png\n",
      "successfully generated labels for image  Sample3071.png\n",
      "successfully generated labels for image  Sample3072.png\n",
      "successfully generated labels for image  Sample3074.png\n",
      "successfully generated labels for image  Sample3077.png\n",
      "successfully generated labels for image  Sample3085.png\n",
      "successfully generated labels for image  Sample3086.png\n",
      "successfully generated labels for image  Sample3104.png\n",
      "successfully generated labels for image  Sample3106.png\n",
      "successfully generated labels for image  Sample3114.png\n",
      "successfully generated labels for image  Sample3116.png\n",
      "successfully generated labels for image  Sample3119.png\n",
      "successfully generated labels for image  Sample3125.png\n",
      "successfully generated labels for image  Sample3127.png\n",
      "successfully generated labels for image  Sample3133.png\n",
      "successfully generated labels for image  Sample3135.png\n",
      "successfully generated labels for image  Sample3146.png\n",
      "successfully generated labels for image  Sample3163.png\n",
      "successfully generated labels for image  Sample3167.png\n",
      "successfully generated labels for image  Sample3170.png\n",
      "successfully generated labels for image  Sample3186.png\n",
      "successfully generated labels for image  Sample3189.png\n",
      "successfully generated labels for image  Sample3199.png\n",
      "successfully generated labels for image  Sample3201.png\n",
      "successfully generated labels for image  Sample3206.png\n",
      "successfully generated labels for image  Sample3208.png\n",
      "successfully generated labels for image  Sample3212.png\n",
      "successfully generated labels for image  Sample3215.png\n",
      "successfully generated labels for image  Sample3223.png\n",
      "successfully generated labels for image  Sample3224.png\n",
      "successfully generated labels for image  Sample3238.png\n",
      "successfully generated labels for image  Sample3242.png\n",
      "successfully generated labels for image  Sample3251.png\n",
      "successfully generated labels for image  Sample3267.png\n",
      "successfully generated labels for image  Sample3273.png\n",
      "successfully generated labels for image  Sample3283.png\n",
      "successfully generated labels for image  Sample3290.png\n",
      "successfully generated labels for image  Sample3292.png\n",
      "successfully generated labels for image  Sample3294.png\n",
      "successfully generated labels for image  Sample3302.png\n",
      "successfully generated labels for image  Sample3307.png\n",
      "successfully generated labels for image  Sample3315.png\n",
      "successfully generated labels for image  Sample3317.png\n",
      "successfully generated labels for image  Sample3329.png\n",
      "successfully generated labels for image  Sample3332.png\n",
      "successfully generated labels for image  Sample3333.png\n",
      "successfully generated labels for image  Sample3342.png\n",
      "successfully generated labels for image  Sample3351.png\n",
      "successfully generated labels for image  Sample3362.png\n",
      "successfully generated labels for image  Sample3377.png\n",
      "successfully generated labels for image  Sample3379.png\n",
      "successfully generated labels for image  Sample3382.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully generated labels for image  Sample3385.png\n",
      "successfully generated labels for image  Sample3389.png\n",
      "successfully generated labels for image  Sample3393.png\n",
      "successfully generated labels for image  Sample3407.png\n",
      "successfully generated labels for image  Sample3411.png\n",
      "successfully generated labels for image  Sample3425.png\n",
      "successfully generated labels for image  Sample3434.png\n",
      "successfully generated labels for image  Sample3435.png\n",
      "successfully generated labels for image  Sample3436.png\n",
      "successfully generated labels for image  Sample3439.png\n",
      "successfully generated labels for image  Sample3453.png\n",
      "successfully generated labels for image  Sample3459.png\n",
      "successfully generated labels for image  Sample3467.png\n",
      "successfully generated labels for image  Sample3479.png\n",
      "successfully generated labels for image  Sample3480.png\n",
      "successfully generated labels for image  Sample3484.png\n",
      "successfully generated labels for image  Sample3486.png\n",
      "successfully generated labels for image  Sample3487.png\n",
      "successfully generated labels for image  Sample3489.png\n",
      "successfully generated labels for image  Sample3491.png\n",
      "successfully generated labels for image  Sample3492.png\n",
      "successfully generated labels for image  Sample3496.png\n",
      "successfully generated labels for image  Sample3500.png\n",
      "successfully generated labels for image  Sample3502.png\n",
      "successfully generated labels for image  Sample3505.png\n",
      "successfully generated labels for image  Sample3506.png\n",
      "successfully generated labels for image  Sample3517.png\n",
      "successfully generated labels for image  Sample3530.png\n",
      "successfully generated labels for image  Sample3531.png\n",
      "successfully generated labels for image  Sample3537.png\n",
      "successfully generated labels for image  Sample3542.png\n",
      "successfully generated labels for image  Sample3550.png\n",
      "successfully generated labels for image  Sample3552.png\n",
      "successfully generated labels for image  Sample3561.png\n",
      "successfully generated labels for image  Sample3589.png\n",
      "successfully generated labels for image  Sample3591.png\n",
      "successfully generated labels for image  Sample3592.png\n",
      "successfully generated labels for image  Sample3595.png\n",
      "successfully generated labels for image  Sample3596.png\n",
      "successfully generated labels for image  Sample3597.png\n",
      "successfully generated labels for image  Sample3604.png\n",
      "successfully generated labels for image  Sample3606.png\n",
      "successfully generated labels for image  Sample3637.png\n",
      "successfully generated labels for image  Sample3645.png\n",
      "successfully generated labels for image  Sample3646.png\n",
      "successfully generated labels for image  Sample3648.png\n",
      "successfully generated labels for image  Sample3649.png\n",
      "successfully generated labels for image  Sample3676.png\n",
      "successfully generated labels for image  Sample3677.png\n",
      "successfully generated labels for image  Sample3678.png\n",
      "successfully generated labels for image  Sample3680.png\n",
      "successfully generated labels for image  Sample3681.png\n",
      "successfully generated labels for image  Sample3688.png\n",
      "successfully generated labels for image  Sample3692.png\n",
      "successfully generated labels for image  Sample3694.png\n",
      "successfully generated labels for image  Sample3695.png\n",
      "successfully generated labels for image  Sample3698.png\n",
      "successfully generated labels for image  Sample3700.png\n",
      "successfully generated labels for image  Sample3706.png\n",
      "successfully generated labels for image  Sample3707.png\n",
      "successfully generated labels for image  Sample3711.png\n",
      "successfully generated labels for image  Sample3716.png\n",
      "successfully generated labels for image  Sample3723.png\n",
      "successfully generated labels for image  Sample3726.png\n",
      "successfully generated labels for image  Sample3732.png\n",
      "successfully generated labels for image  Sample3733.png\n",
      "successfully generated labels for image  Sample3735.png\n",
      "successfully generated labels for image  Sample3743.png\n",
      "successfully generated labels for image  Sample3748.png\n",
      "successfully generated labels for image  Sample3754.png\n",
      "successfully generated labels for image  Sample3762.png\n",
      "successfully generated labels for image  Sample3774.png\n",
      "successfully generated labels for image  Sample3775.png\n",
      "successfully generated labels for image  Sample3782.png\n",
      "successfully generated labels for image  Sample3786.png\n",
      "successfully generated labels for image  Sample3790.png\n",
      "successfully generated labels for image  Sample3791.png\n",
      "successfully generated labels for image  Sample3799.png\n",
      "successfully generated labels for image  Sample3815.png\n",
      "successfully generated labels for image  Sample3816.png\n",
      "successfully generated labels for image  Sample3818.png\n",
      "successfully generated labels for image  Sample3822.png\n",
      "successfully generated labels for image  Sample3829.png\n",
      "successfully generated labels for image  Sample3830.png\n",
      "successfully generated labels for image  Sample3835.png\n",
      "successfully generated labels for image  Sample3836.png\n",
      "successfully generated labels for image  Sample3847.png\n",
      "successfully generated labels for image  Sample3855.png\n",
      "successfully generated labels for image  Sample3864.png\n",
      "successfully generated labels for image  Sample3876.png\n",
      "successfully generated labels for image  Sample3877.png\n",
      "successfully generated labels for image  Sample3879.png\n",
      "successfully generated labels for image  Sample3903.png\n",
      "successfully generated labels for image  Sample3907.png\n",
      "successfully generated labels for image  Sample3911.png\n",
      "successfully generated labels for image  Sample3922.png\n",
      "successfully generated labels for image  Sample3924.png\n",
      "successfully generated labels for image  Sample3930.png\n",
      "successfully generated labels for image  Sample3934.png\n",
      "successfully generated labels for image  Sample3939.png\n",
      "successfully generated labels for image  Sample3949.png\n",
      "successfully generated labels for image  Sample3951.png\n",
      "successfully generated labels for image  Sample3956.png\n",
      "successfully generated labels for image  Sample3959.png\n",
      "successfully generated labels for image  Sample3967.png\n",
      "successfully generated labels for image  Sample3969.png\n",
      "successfully generated labels for image  Sample3978.png\n",
      "successfully generated labels for image  Sample3992.png\n",
      "successfully generated labels for image  Sample3993.png\n",
      "successfully generated labels for image  Sample4001.png\n",
      "successfully generated labels for image  Sample4022.png\n",
      "successfully generated labels for image  Sample4029.png\n",
      "successfully generated labels for image  Sample4034.png\n",
      "successfully generated labels for image  Sample4049.png\n",
      "successfully generated labels for image  Sample4052.png\n",
      "successfully generated labels for image  Sample4063.png\n",
      "successfully generated labels for image  Sample4066.png\n",
      "successfully generated labels for image  Sample4068.png\n",
      "successfully generated labels for image  Sample4072.png\n",
      "successfully generated labels for image  Sample4108.png\n",
      "successfully generated labels for image  Sample4111.png\n",
      "successfully generated labels for image  Sample4115.png\n",
      "successfully generated labels for image  Sample4120.png\n",
      "successfully generated labels for image  Sample4123.png\n",
      "successfully generated labels for image  Sample4126.png\n",
      "successfully generated labels for image  Sample4146.png\n",
      "successfully generated labels for image  Sample4149.png\n",
      "successfully generated labels for image  Sample4152.png\n",
      "successfully generated labels for image  Sample4156.png\n",
      "successfully generated labels for image  Sample4162.png\n",
      "successfully generated labels for image  Sample4164.png\n",
      "successfully generated labels for image  Sample4180.png\n",
      "successfully generated labels for image  Sample4182.png\n",
      "successfully generated labels for image  Sample4183.png\n",
      "successfully generated labels for image  Sample4221.png\n",
      "successfully generated labels for image  Sample4228.png\n",
      "successfully generated labels for image  Sample4233.png\n",
      "successfully generated labels for image  Sample4250.png\n",
      "successfully generated labels for image  Sample4259.png\n",
      "successfully generated labels for image  Sample4266.png\n",
      "successfully generated labels for image  Sample4270.png\n",
      "successfully generated labels for image  Sample4328.png\n",
      "successfully generated labels for image  Sample4330.png\n",
      "successfully generated labels for image  Sample4339.png\n",
      "successfully generated labels for image  Sample4344.png\n",
      "successfully generated labels for image  Sample4361.png\n",
      "successfully generated labels for image  Sample4369.png\n",
      "successfully generated labels for image  Sample4379.png\n",
      "successfully generated labels for image  Sample4382.png\n",
      "successfully generated labels for image  Sample4389.png\n",
      "successfully generated labels for image  Sample4391.png\n",
      "successfully generated labels for image  Sample4399.png\n",
      "successfully generated labels for image  Sample4400.png\n",
      "successfully generated labels for image  Sample4402.png\n",
      "successfully generated labels for image  Sample4408.png\n",
      "successfully generated labels for image  Sample4410.png\n",
      "successfully generated labels for image  Sample4423.png\n",
      "successfully generated labels for image  Sample4424.png\n",
      "successfully generated labels for image  Sample4426.png\n",
      "successfully generated labels for image  Sample4437.png\n",
      "successfully generated labels for image  Sample4443.png\n",
      "successfully generated labels for image  Sample4445.png\n",
      "successfully generated labels for image  Sample4446.png\n",
      "successfully generated labels for image  Sample4459.png\n",
      "successfully generated labels for image  Sample4461.png\n",
      "successfully generated labels for image  Sample4464.png\n",
      "successfully generated labels for image  Sample4468.png\n",
      "successfully generated labels for image  Sample4487.png\n",
      "successfully generated labels for image  Sample4489.png\n",
      "successfully generated labels for image  Sample4490.png\n",
      "successfully generated labels for image  Sample4497.png\n",
      "successfully generated labels for image  Sample4514.png\n",
      "successfully generated labels for image  Sample4520.png\n",
      "successfully generated labels for image  Sample4521.png\n",
      "successfully generated labels for image  Sample4522.png\n",
      "successfully generated labels for image  Sample4536.png\n",
      "successfully generated labels for image  Sample4540.png\n",
      "successfully generated labels for image  Sample4544.png\n",
      "successfully generated labels for image  Sample4552.png\n",
      "successfully generated labels for image  Sample4562.png\n",
      "successfully generated labels for image  Sample4566.png\n",
      "successfully generated labels for image  Sample4567.png\n",
      "successfully generated labels for image  Sample4575.png\n",
      "successfully generated labels for image  Sample4578.png\n",
      "successfully generated labels for image  Sample4586.png\n",
      "successfully generated labels for image  Sample4591.png\n",
      "successfully generated labels for image  Sample4593.png\n",
      "successfully generated labels for image  Sample4596.png\n",
      "successfully generated labels for image  Sample4598.png\n",
      "successfully generated labels for image  Sample4608.png\n",
      "successfully generated labels for image  Sample4611.png\n",
      "successfully generated labels for image  Sample4615.png\n",
      "successfully generated labels for image  Sample4639.png\n",
      "successfully generated labels for image  Sample4640.png\n",
      "successfully generated labels for image  Sample4643.png\n",
      "successfully generated labels for image  Sample4647.png\n",
      "successfully generated labels for image  Sample4650.png\n",
      "successfully generated labels for image  Sample4672.png\n",
      "successfully generated labels for image  Sample4676.png\n",
      "successfully generated labels for image  Sample4678.png\n",
      "successfully generated labels for image  Sample4681.png\n",
      "successfully generated labels for image  Sample4709.png\n",
      "successfully generated labels for image  Sample4714.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully generated labels for image  Sample4715.png\n",
      "successfully generated labels for image  Sample4724.png\n",
      "successfully generated labels for image  Sample4726.png\n",
      "successfully generated labels for image  Sample4727.png\n",
      "successfully generated labels for image  Sample4728.png\n",
      "successfully generated labels for image  Sample4731.png\n",
      "successfully generated labels for image  Sample4747.png\n",
      "successfully generated labels for image  Sample4756.png\n",
      "successfully generated labels for image  Sample4758.png\n",
      "successfully generated labels for image  Sample4761.png\n",
      "successfully generated labels for image  Sample4789.png\n",
      "successfully generated labels for image  Sample4794.png\n",
      "successfully generated labels for image  Sample4796.png\n",
      "successfully generated labels for image  Sample4801.png\n",
      "successfully generated labels for image  Sample4807.png\n",
      "successfully generated labels for image  Sample4812.png\n",
      "successfully generated labels for image  Sample4813.png\n",
      "successfully generated labels for image  Sample4821.png\n",
      "successfully generated labels for image  Sample4823.png\n",
      "successfully generated labels for image  Sample4831.png\n",
      "successfully generated labels for image  Sample4840.png\n",
      "successfully generated labels for image  Sample4845.png\n",
      "successfully generated labels for image  Sample4866.png\n",
      "successfully generated labels for image  Sample4868.png\n",
      "successfully generated labels for image  Sample4883.png\n",
      "successfully generated labels for image  Sample4884.png\n",
      "successfully generated labels for image  Sample4886.png\n",
      "successfully generated labels for image  Sample4889.png\n",
      "successfully generated labels for image  Sample4892.png\n",
      "successfully generated labels for image  Sample4893.png\n",
      "successfully generated labels for image  Sample4908.png\n",
      "successfully generated labels for image  Sample4909.png\n",
      "successfully generated labels for image  Sample4914.png\n",
      "successfully generated labels for image  Sample4915.png\n",
      "successfully generated labels for image  Sample4916.png\n",
      "successfully generated labels for image  Sample4922.png\n",
      "successfully generated labels for image  Sample4925.png\n",
      "successfully generated labels for image  Sample4935.png\n",
      "successfully generated labels for image  Sample4938.png\n",
      "successfully generated labels for image  Sample4940.png\n",
      "successfully generated labels for image  Sample4942.png\n",
      "successfully generated labels for image  Sample4944.png\n",
      "successfully generated labels for image  Sample4949.png\n",
      "successfully generated labels for image  Sample4964.png\n",
      "successfully generated labels for image  Sample4967.png\n",
      "successfully generated labels for image  Sample4971.png\n",
      "successfully generated labels for image  Sample4976.png\n",
      "successfully generated labels for image  Sample4980.png\n",
      "successfully generated labels for image  Sample4981.png\n",
      "successfully generated labels for image  Sample4983.png\n",
      "successfully generated labels for image  Sample4984.png\n",
      "successfully generated labels for image  Sample4998.png\n",
      "successfully generated labels for image  Sample5002.png\n",
      "successfully generated labels for image  Sample5003.png\n",
      "successfully generated labels for image  Sample5014.png\n",
      "successfully generated labels for image  Sample5020.png\n",
      "successfully generated labels for image  Sample5030.png\n",
      "successfully generated labels for image  Sample5041.png\n",
      "successfully generated labels for image  Sample5043.png\n",
      "successfully generated labels for image  Sample5055.png\n",
      "successfully generated labels for image  Sample5068.png\n",
      "successfully generated labels for image  Sample5077.png\n",
      "successfully generated labels for image  Sample5120.png\n",
      "successfully generated labels for image  Sample5135.png\n",
      "successfully generated labels for image  Sample5137.png\n",
      "successfully generated labels for image  Sample5142.png\n",
      "successfully generated labels for image  Sample5147.png\n",
      "successfully generated labels for image  Sample5165.png\n",
      "successfully generated labels for image  Sample5177.png\n",
      "successfully generated labels for image  Sample5181.png\n",
      "successfully generated labels for image  Sample5183.png\n",
      "successfully generated labels for image  Sample5196.png\n",
      "successfully generated labels for image  Sample5206.png\n",
      "successfully generated labels for image  Sample5215.png\n",
      "successfully generated labels for image  Sample5220.png\n",
      "successfully generated labels for image  Sample5221.png\n",
      "successfully generated labels for image  Sample5224.png\n",
      "successfully generated labels for image  Sample5227.png\n",
      "successfully generated labels for image  Sample5231.png\n",
      "successfully generated labels for image  Sample5232.png\n",
      "successfully generated labels for image  Sample5233.png\n",
      "successfully generated labels for image  Sample5248.png\n",
      "successfully generated labels for image  Sample5250.png\n",
      "successfully generated labels for image  Sample5251.png\n",
      "successfully generated labels for image  Sample5258.png\n",
      "successfully generated labels for image  Sample5260.png\n",
      "successfully generated labels for image  Sample5265.png\n",
      "successfully generated labels for image  Sample5266.png\n",
      "successfully generated labels for image  Sample5284.png\n",
      "successfully generated labels for image  Sample5286.png\n",
      "successfully generated labels for image  Sample5288.png\n",
      "successfully generated labels for image  Sample5299.png\n",
      "successfully generated labels for image  Sample5303.png\n",
      "successfully generated labels for image  Sample5304.png\n",
      "successfully generated labels for image  Sample5306.png\n",
      "successfully generated labels for image  Sample5313.png\n",
      "successfully generated labels for image  Sample5319.png\n",
      "successfully generated labels for image  Sample5321.png\n",
      "successfully generated labels for image  Sample5335.png\n",
      "successfully generated labels for image  Sample5342.png\n",
      "successfully generated labels for image  Sample5353.png\n",
      "successfully generated labels for image  Sample5355.png\n",
      "successfully generated labels for image  Sample5362.png\n",
      "successfully generated labels for image  Sample5363.png\n",
      "successfully generated labels for image  Sample5370.png\n",
      "successfully generated labels for image  Sample5386.png\n",
      "successfully generated labels for image  Sample5391.png\n",
      "successfully generated labels for image  Sample5395.png\n",
      "successfully generated labels for image  Sample5411.png\n",
      "successfully generated labels for image  Sample5417.png\n",
      "successfully generated labels for image  Sample5424.png\n",
      "successfully generated labels for image  Sample5436.png\n",
      "successfully generated labels for image  Sample5437.png\n",
      "successfully generated labels for image  Sample5444.png\n",
      "successfully generated labels for image  Sample5448.png\n",
      "successfully generated labels for image  Sample5449.png\n",
      "successfully generated labels for image  Sample5452.png\n",
      "successfully generated labels for image  Sample5455.png\n",
      "successfully generated labels for image  Sample5456.png\n",
      "successfully generated labels for image  Sample5457.png\n",
      "successfully generated labels for image  Sample5459.png\n",
      "successfully generated labels for image  Sample5465.png\n",
      "successfully generated labels for image  Sample5479.png\n",
      "successfully generated labels for image  Sample5499.png\n",
      "successfully generated labels for image  Sample5517.png\n",
      "successfully generated labels for image  Sample5518.png\n",
      "successfully generated labels for image  Sample5522.png\n",
      "successfully generated labels for image  Sample5524.png\n",
      "successfully generated labels for image  Sample5532.png\n",
      "successfully generated labels for image  Sample5534.png\n",
      "successfully generated labels for image  Sample5544.png\n",
      "successfully generated labels for image  Sample5551.png\n",
      "successfully generated labels for image  Sample5564.png\n",
      "successfully generated labels for image  Sample5569.png\n",
      "successfully generated labels for image  Sample5571.png\n",
      "successfully generated labels for image  Sample5590.png\n",
      "successfully generated labels for image  Sample5594.png\n",
      "successfully generated labels for image  Sample5600.png\n",
      "successfully generated labels for image  Sample5602.png\n",
      "successfully generated labels for image  Sample5607.png\n",
      "successfully generated labels for image  Sample5611.png\n",
      "successfully generated labels for image  Sample5616.png\n",
      "successfully generated labels for image  Sample5617.png\n",
      "successfully generated labels for image  Sample5618.png\n",
      "successfully generated labels for image  Sample5621.png\n",
      "successfully generated labels for image  Sample5637.png\n",
      "successfully generated labels for image  Sample5649.png\n",
      "successfully generated labels for image  Sample5667.png\n",
      "successfully generated labels for image  Sample5670.png\n",
      "successfully generated labels for image  Sample5672.png\n",
      "successfully generated labels for image  Sample5673.png\n",
      "successfully generated labels for image  Sample5679.png\n",
      "successfully generated labels for image  Sample5697.png\n",
      "successfully generated labels for image  Sample5699.png\n",
      "successfully generated labels for image  Sample5701.png\n",
      "successfully generated labels for image  Sample5706.png\n",
      "successfully generated labels for image  Sample5711.png\n",
      "successfully generated labels for image  Sample5717.png\n",
      "successfully generated labels for image  Sample5719.png\n",
      "successfully generated labels for image  Sample5744.png\n",
      "successfully generated labels for image  Sample5746.png\n",
      "successfully generated labels for image  Sample5751.png\n",
      "successfully generated labels for image  Sample5754.png\n",
      "successfully generated labels for image  Sample5755.png\n",
      "successfully generated labels for image  Sample5772.png\n",
      "successfully generated labels for image  Sample5774.png\n",
      "successfully generated labels for image  Sample5775.png\n",
      "successfully generated labels for image  Sample5781.png\n",
      "successfully generated labels for image  Sample5790.png\n",
      "successfully generated labels for image  Sample5795.png\n",
      "successfully generated labels for image  Sample5802.png\n",
      "successfully generated labels for image  Sample5804.png\n",
      "successfully generated labels for image  Sample5810.png\n",
      "successfully generated labels for image  Sample5820.png\n",
      "successfully generated labels for image  Sample5825.png\n",
      "successfully generated labels for image  Sample5833.png\n",
      "successfully generated labels for image  Sample5843.png\n",
      "successfully generated labels for image  Sample5853.png\n",
      "successfully generated labels for image  Sample5866.png\n",
      "successfully generated labels for image  Sample5874.png\n",
      "successfully generated labels for image  Sample5877.png\n",
      "successfully generated labels for image  Sample5886.png\n",
      "successfully generated labels for image  Sample5897.png\n",
      "successfully generated labels for image  Sample5902.png\n",
      "successfully generated labels for image  Sample5908.png\n",
      "successfully generated labels for image  Sample5912.png\n",
      "successfully generated labels for image  Sample5939.png\n",
      "successfully generated labels for image  Sample5948.png\n",
      "successfully generated labels for image  Sample5949.png\n",
      "successfully generated labels for image  Sample5952.png\n",
      "successfully generated labels for image  Sample5955.png\n",
      "successfully generated labels for image  Sample5959.png\n",
      "successfully generated labels for image  Sample5963.png\n",
      "successfully generated labels for image  Sample6006.png\n",
      "successfully generated labels for image  Sample6010.png\n",
      "successfully generated labels for image  Sample6021.png\n",
      "successfully generated labels for image  Sample6029.png\n",
      "successfully generated labels for image  Sample6030.png\n",
      "successfully generated labels for image  Sample6043.png\n",
      "successfully generated labels for image  Sample6045.png\n",
      "successfully generated labels for image  Sample6064.png\n",
      "successfully generated labels for image  Sample6069.png\n",
      "successfully generated labels for image  Sample6079.png\n",
      "successfully generated labels for image  Sample6085.png\n",
      "successfully generated labels for image  Sample6091.png\n",
      "successfully generated labels for image  Sample6092.png\n",
      "successfully generated labels for image  Sample6100.png\n",
      "successfully generated labels for image  Sample6102.png\n",
      "successfully generated labels for image  Sample6106.png\n",
      "successfully generated labels for image  Sample6117.png\n",
      "successfully generated labels for image  Sample6120.png\n",
      "successfully generated labels for image  Sample6131.png\n",
      "successfully generated labels for image  Sample6144.png\n",
      "successfully generated labels for image  Sample6145.png\n",
      "successfully generated labels for image  Sample6148.png\n",
      "successfully generated labels for image  Sample6152.png\n",
      "successfully generated labels for image  Sample6154.png\n",
      "successfully generated labels for image  Sample6158.png\n",
      "successfully generated labels for image  Sample6160.png\n",
      "successfully generated labels for image  Sample6168.png\n",
      "successfully generated labels for image  Sample6170.png\n",
      "successfully generated labels for image  Sample6177.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully generated labels for image  Sample6179.png\n",
      "successfully generated labels for image  Sample6182.png\n",
      "successfully generated labels for image  Sample1020.png\n",
      "successfully generated labels for image  Sample1062.png\n",
      "successfully generated labels for image  Sample1068.png\n",
      "successfully generated labels for image  Sample1079.png\n",
      "successfully generated labels for image  Sample1094.png\n",
      "successfully generated labels for image  Sample1118.png\n",
      "successfully generated labels for image  Sample1193.png\n",
      "successfully generated labels for image  Sample1233.png\n",
      "successfully generated labels for image  Sample1242.png\n",
      "successfully generated labels for image  Sample1303.png\n",
      "successfully generated labels for image  Sample1318.png\n",
      "successfully generated labels for image  Sample1362.png\n",
      "successfully generated labels for image  Sample1365.png\n",
      "successfully generated labels for image  Sample1368.png\n",
      "successfully generated labels for image  Sample1371.png\n",
      "successfully generated labels for image  Sample1381.png\n",
      "successfully generated labels for image  Sample1386.png\n",
      "successfully generated labels for image  Sample1414.png\n",
      "successfully generated labels for image  Sample1433.png\n",
      "successfully generated labels for image  Sample1437.png\n",
      "successfully generated labels for image  Sample1510.png\n",
      "successfully generated labels for image  Sample1602.png\n",
      "successfully generated labels for image  Sample1642.png\n",
      "successfully generated labels for image  Sample1662.png\n",
      "successfully generated labels for image  Sample1668.png\n",
      "successfully generated labels for image  Sample1694.png\n",
      "successfully generated labels for image  Sample1755.png\n",
      "successfully generated labels for image  Sample1764.png\n",
      "successfully generated labels for image  Sample1782.png\n",
      "successfully generated labels for image  Sample1825.png\n",
      "successfully generated labels for image  Sample1886.png\n",
      "successfully generated labels for image  Sample1889.png\n",
      "successfully generated labels for image  Sample1914.png\n",
      "successfully generated labels for image  Sample1958.png\n",
      "successfully generated labels for image  Sample1959.png\n",
      "successfully generated labels for image  Sample1969.png\n",
      "successfully generated labels for image  Sample1992.png\n",
      "successfully generated labels for image  Sample2004.png\n",
      "successfully generated labels for image  Sample2005.png\n",
      "successfully generated labels for image  Sample2011.png\n",
      "successfully generated labels for image  Sample2017.png\n",
      "successfully generated labels for image  Sample2020.png\n",
      "successfully generated labels for image  Sample2032.png\n",
      "successfully generated labels for image  Sample2035.png\n",
      "successfully generated labels for image  Sample2068.png\n",
      "successfully generated labels for image  Sample2075.png\n",
      "successfully generated labels for image  Sample2100.png\n",
      "successfully generated labels for image  Sample2124.png\n",
      "successfully generated labels for image  Sample2126.png\n",
      "successfully generated labels for image  Sample2204.png\n",
      "successfully generated labels for image  Sample2222.png\n",
      "successfully generated labels for image  Sample2228.png\n",
      "successfully generated labels for image  Sample2233.png\n",
      "successfully generated labels for image  Sample2241.png\n",
      "successfully generated labels for image  Sample2255.png\n",
      "successfully generated labels for image  Sample2259.png\n",
      "successfully generated labels for image  Sample2274.png\n",
      "successfully generated labels for image  Sample2307.png\n",
      "successfully generated labels for image  Sample2324.png\n",
      "successfully generated labels for image  Sample2326.png\n",
      "successfully generated labels for image  Sample2354.png\n",
      "successfully generated labels for image  Sample2370.png\n",
      "successfully generated labels for image  Sample2372.png\n",
      "successfully generated labels for image  Sample2457.png\n",
      "successfully generated labels for image  Sample2480.png\n",
      "successfully generated labels for image  Sample2495.png\n",
      "successfully generated labels for image  Sample2544.png\n",
      "successfully generated labels for image  Sample2551.png\n",
      "successfully generated labels for image  Sample2658.png\n",
      "successfully generated labels for image  Sample2660.png\n",
      "successfully generated labels for image  Sample2667.png\n",
      "successfully generated labels for image  Sample2700.png\n",
      "successfully generated labels for image  Sample2756.png\n",
      "successfully generated labels for image  Sample2764.png\n",
      "successfully generated labels for image  Sample2803.png\n",
      "successfully generated labels for image  Sample2808.png\n",
      "successfully generated labels for image  Sample2835.png\n",
      "successfully generated labels for image  Sample2912.png\n",
      "successfully generated labels for image  Sample2924.png\n",
      "successfully generated labels for image  Sample2967.png\n",
      "successfully generated labels for image  Sample3006.png\n",
      "successfully generated labels for image  Sample3032.png\n",
      "successfully generated labels for image  Sample3046.png\n",
      "successfully generated labels for image  Sample3056.png\n",
      "successfully generated labels for image  Sample3122.png\n",
      "successfully generated labels for image  Sample3160.png\n",
      "successfully generated labels for image  Sample3266.png\n",
      "successfully generated labels for image  Sample3279.png\n",
      "successfully generated labels for image  Sample3282.png\n",
      "successfully generated labels for image  Sample3306.png\n",
      "successfully generated labels for image  Sample3337.png\n",
      "successfully generated labels for image  Sample3341.png\n",
      "successfully generated labels for image  Sample3345.png\n",
      "successfully generated labels for image  Sample3394.png\n",
      "successfully generated labels for image  Sample3400.png\n",
      "successfully generated labels for image  Sample3402.png\n",
      "successfully generated labels for image  Sample3423.png\n",
      "successfully generated labels for image  Sample3446.png\n",
      "successfully generated labels for image  Sample3461.png\n",
      "successfully generated labels for image  Sample3471.png\n",
      "successfully generated labels for image  Sample3472.png\n",
      "successfully generated labels for image  Sample3494.png\n",
      "successfully generated labels for image  Sample3495.png\n",
      "successfully generated labels for image  Sample3608.png\n",
      "successfully generated labels for image  Sample3625.png\n",
      "successfully generated labels for image  Sample3653.png\n",
      "successfully generated labels for image  Sample3655.png\n",
      "successfully generated labels for image  Sample3687.png\n",
      "successfully generated labels for image  Sample3737.png\n",
      "successfully generated labels for image  Sample3758.png\n",
      "successfully generated labels for image  Sample3761.png\n",
      "successfully generated labels for image  Sample3765.png\n",
      "successfully generated labels for image  Sample3767.png\n",
      "successfully generated labels for image  Sample3803.png\n",
      "successfully generated labels for image  Sample3812.png\n",
      "successfully generated labels for image  Sample3820.png\n",
      "successfully generated labels for image  Sample3874.png\n",
      "successfully generated labels for image  Sample3898.png\n",
      "successfully generated labels for image  Sample3904.png\n",
      "successfully generated labels for image  Sample3912.png\n",
      "successfully generated labels for image  Sample3915.png\n",
      "successfully generated labels for image  Sample3928.png\n",
      "successfully generated labels for image  Sample3935.png\n",
      "successfully generated labels for image  Sample3955.png\n",
      "successfully generated labels for image  Sample3979.png\n",
      "successfully generated labels for image  Sample4088.png\n",
      "successfully generated labels for image  Sample4136.png\n",
      "successfully generated labels for image  Sample4206.png\n",
      "successfully generated labels for image  Sample4293.png\n",
      "successfully generated labels for image  Sample4306.png\n",
      "successfully generated labels for image  Sample4315.png\n",
      "successfully generated labels for image  Sample4349.png\n",
      "successfully generated labels for image  Sample4373.png\n",
      "successfully generated labels for image  Sample4387.png\n",
      "successfully generated labels for image  Sample4388.png\n",
      "successfully generated labels for image  Sample4409.png\n",
      "successfully generated labels for image  Sample4432.png\n",
      "successfully generated labels for image  Sample4452.png\n",
      "successfully generated labels for image  Sample4463.png\n",
      "successfully generated labels for image  Sample4510.png\n",
      "successfully generated labels for image  Sample4512.png\n",
      "successfully generated labels for image  Sample4517.png\n",
      "successfully generated labels for image  Sample4530.png\n",
      "successfully generated labels for image  Sample4548.png\n",
      "successfully generated labels for image  Sample4554.png\n",
      "successfully generated labels for image  Sample4555.png\n",
      "successfully generated labels for image  Sample4577.png\n",
      "successfully generated labels for image  Sample4590.png\n",
      "successfully generated labels for image  Sample4592.png\n",
      "successfully generated labels for image  Sample4613.png\n",
      "successfully generated labels for image  Sample4625.png\n",
      "successfully generated labels for image  Sample4673.png\n",
      "successfully generated labels for image  Sample4680.png\n",
      "successfully generated labels for image  Sample4815.png\n",
      "successfully generated labels for image  Sample4819.png\n",
      "successfully generated labels for image  Sample4875.png\n",
      "successfully generated labels for image  Sample4923.png\n",
      "successfully generated labels for image  Sample4947.png\n",
      "successfully generated labels for image  Sample4960.png\n",
      "successfully generated labels for image  Sample4977.png\n",
      "successfully generated labels for image  Sample4986.png\n",
      "successfully generated labels for image  Sample4990.png\n",
      "successfully generated labels for image  Sample5062.png\n",
      "successfully generated labels for image  Sample5067.png\n",
      "successfully generated labels for image  Sample5102.png\n",
      "successfully generated labels for image  Sample5105.png\n",
      "successfully generated labels for image  Sample5144.png\n",
      "successfully generated labels for image  Sample5166.png\n",
      "successfully generated labels for image  Sample5168.png\n",
      "successfully generated labels for image  Sample5264.png\n",
      "successfully generated labels for image  Sample5315.png\n",
      "successfully generated labels for image  Sample5343.png\n",
      "successfully generated labels for image  Sample5351.png\n",
      "successfully generated labels for image  Sample5359.png\n",
      "successfully generated labels for image  Sample5442.png\n",
      "successfully generated labels for image  Sample5470.png\n",
      "successfully generated labels for image  Sample5609.png\n",
      "successfully generated labels for image  Sample5625.png\n",
      "successfully generated labels for image  Sample5638.png\n",
      "successfully generated labels for image  Sample5645.png\n",
      "successfully generated labels for image  Sample5666.png\n",
      "successfully generated labels for image  Sample5676.png\n",
      "successfully generated labels for image  Sample5688.png\n",
      "successfully generated labels for image  Sample5709.png\n",
      "successfully generated labels for image  Sample5723.png\n",
      "successfully generated labels for image  Sample5759.png\n",
      "successfully generated labels for image  Sample5769.png\n",
      "successfully generated labels for image  Sample5806.png\n",
      "successfully generated labels for image  Sample5831.png\n",
      "successfully generated labels for image  Sample5837.png\n",
      "successfully generated labels for image  Sample5850.png\n",
      "successfully generated labels for image  Sample5928.png\n",
      "successfully generated labels for image  Sample5947.png\n",
      "successfully generated labels for image  Sample5988.png\n",
      "successfully generated labels for image  Sample6012.png\n",
      "successfully generated labels for image  Sample6055.png\n",
      "successfully generated labels for image  Sample6067.png\n",
      "successfully generated labels for image  Sample6114.png\n",
      "successfully generated labels for image  Sample6121.png\n",
      "successfully generated labels for image  Sample6180.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully generated labels for image  Sample1192.png\n",
      "successfully generated labels for image  Sample1268.png\n",
      "successfully generated labels for image  Sample1301.png\n",
      "successfully generated labels for image  Sample1801.png\n",
      "successfully generated labels for image  Sample1999.png\n",
      "successfully generated labels for image  Sample2013.png\n",
      "successfully generated labels for image  Sample2114.png\n",
      "successfully generated labels for image  Sample2444.png\n",
      "successfully generated labels for image  Sample2490.png\n",
      "successfully generated labels for image  Sample3001.png\n",
      "successfully generated labels for image  Sample3239.png\n",
      "successfully generated labels for image  Sample3449.png\n",
      "successfully generated labels for image  Sample3860.png\n",
      "successfully generated labels for image  Sample4457.png\n",
      "successfully generated labels for image  Sample4713.png\n",
      "successfully generated labels for image  Sample4805.png\n",
      "successfully generated labels for image  Sample4858.png\n",
      "successfully generated labels for image  Sample5112.png\n",
      "successfully generated labels for image  Sample5984.png\n",
      "successfully generated labels for image  Sample6034.png\n"
     ]
    }
   ],
   "source": [
    "tags = 'ROI'\n",
    "\n",
    "# zip the dataset\n",
    "dataset = [(train_dir,labeled_images_tot),(val_dir, labeled_images_val),(test_dir, labeled_images_test)]\n",
    "\n",
    "for d, s in dataset:\n",
    "    images_sub_dir = os.path.join(d, 'images')\n",
    "    labels_sub_dir = os.path.join(d, 'labels')\n",
    "\n",
    "    # copy over the images\n",
    "    for img in s:\n",
    "        shutil.copyfile(img.path, os.path.join(images_sub_dir, img.name))\n",
    "    \n",
    "    # covert labels to yolo format and save it to labels_sub_dir\n",
    "    convert_to_yolo_format(s, labels_sub_dir, tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "yolo_yaml = os.path.join(output_dir, 'boeing_yolov5.yaml')\n",
    "\n",
    "with open(yolo_yaml, 'w') as yamlout:\n",
    "    yaml.dump(\n",
    "        {'train': train_dir,\n",
    "         'val': val_dir,\n",
    "         'nc': len(tags),\n",
    "         'names': tags},\n",
    "        yamlout,\n",
    "        default_flow_style=None,\n",
    "        sort_keys=False\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/\n",
      "./data_train_images/boeing_yolov5.yaml\n",
      "./data_train_images/test/\n",
      "./data_train_images/test/images/\n",
      "./data_train_images/test/images/Sample1192.png\n",
      "./data_train_images/test/images/Sample1268.png\n",
      "./data_train_images/test/images/Sample1301.png\n",
      "./data_train_images/test/images/Sample1801.png\n",
      "./data_train_images/test/images/Sample1999.png\n",
      "./data_train_images/test/images/Sample2013.png\n",
      "./data_train_images/test/images/Sample2114.png\n",
      "./data_train_images/test/images/Sample2444.png\n",
      "./data_train_images/test/images/Sample2490.png\n",
      "./data_train_images/test/images/Sample3001.png\n",
      "./data_train_images/test/images/Sample3239.png\n",
      "./data_train_images/test/images/Sample3449.png\n",
      "./data_train_images/test/images/Sample3860.png\n",
      "./data_train_images/test/images/Sample4457.png\n",
      "./data_train_images/test/images/Sample4713.png\n",
      "./data_train_images/test/images/Sample4805.png\n",
      "./data_train_images/test/images/Sample4858.png\n",
      "./data_train_images/test/images/Sample5112.png\n",
      "./data_train_images/test/images/Sample5984.png\n",
      "./data_train_images/test/images/Sample6034.png\n",
      "./data_train_images/test/labels/\n",
      "./data_train_images/test/labels/Sample1192.txt\n",
      "./data_train_images/test/labels/Sample1268.txt\n",
      "./data_train_images/test/labels/Sample1301.txt\n",
      "./data_train_images/test/labels/Sample1801.txt\n",
      "./data_train_images/test/labels/Sample1999.txt\n",
      "./data_train_images/test/labels/Sample2013.txt\n",
      "./data_train_images/test/labels/Sample2114.txt\n",
      "./data_train_images/test/labels/Sample2444.txt\n",
      "./data_train_images/test/labels/Sample2490.txt\n",
      "./data_train_images/test/labels/Sample3001.txt\n",
      "./data_train_images/test/labels/Sample3239.txt\n",
      "./data_train_images/test/labels/Sample3449.txt\n",
      "./data_train_images/test/labels/Sample3860.txt\n",
      "./data_train_images/test/labels/Sample4457.txt\n",
      "./data_train_images/test/labels/Sample4713.txt\n",
      "./data_train_images/test/labels/Sample4805.txt\n",
      "./data_train_images/test/labels/Sample4858.txt\n",
      "./data_train_images/test/labels/Sample5112.txt\n",
      "./data_train_images/test/labels/Sample5984.txt\n",
      "./data_train_images/test/labels/Sample6034.txt\n",
      "./data_train_images/train/\n",
      "./data_train_images/train/images/\n",
      "./data_train_images/train/images/Sample1.png\n",
      "./data_train_images/train/images/Sample10.png\n",
      "./data_train_images/train/images/Sample100.png\n",
      "./data_train_images/train/images/Sample101.png\n",
      "./data_train_images/train/images/Sample1016.png\n",
      "./data_train_images/train/images/Sample1018.png\n",
      "./data_train_images/train/images/Sample102.png\n",
      "./data_train_images/train/images/Sample1025.png\n",
      "./data_train_images/train/images/Sample1026.png\n",
      "./data_train_images/train/images/Sample103.png\n",
      "./data_train_images/train/images/Sample1030.png\n",
      "./data_train_images/train/images/Sample1037.png\n",
      "./data_train_images/train/images/Sample104.png\n",
      "./data_train_images/train/images/Sample1045.png\n",
      "./data_train_images/train/images/Sample105.png\n",
      "./data_train_images/train/images/Sample1051.png\n",
      "./data_train_images/train/images/Sample1053.png\n",
      "./data_train_images/train/images/Sample1054.png\n",
      "./data_train_images/train/images/Sample1055.png\n",
      "./data_train_images/train/images/Sample106.png\n",
      "./data_train_images/train/images/Sample1069.png\n",
      "./data_train_images/train/images/Sample107.png\n",
      "./data_train_images/train/images/Sample108.png\n",
      "./data_train_images/train/images/Sample1087.png\n",
      "./data_train_images/train/images/Sample109.png\n",
      "./data_train_images/train/images/Sample11.png\n",
      "./data_train_images/train/images/Sample110.png\n",
      "./data_train_images/train/images/Sample1101.png\n",
      "./data_train_images/train/images/Sample111.png\n",
      "./data_train_images/train/images/Sample112.png\n",
      "./data_train_images/train/images/Sample1123.png\n",
      "./data_train_images/train/images/Sample1124.png\n",
      "./data_train_images/train/images/Sample1126.png\n",
      "./data_train_images/train/images/Sample1127.png\n",
      "./data_train_images/train/images/Sample113.png\n",
      "./data_train_images/train/images/Sample1130.png\n",
      "./data_train_images/train/images/Sample1133.png\n",
      "./data_train_images/train/images/Sample1139.png\n",
      "./data_train_images/train/images/Sample114.png\n",
      "./data_train_images/train/images/Sample1142.png\n",
      "./data_train_images/train/images/Sample115.png\n",
      "./data_train_images/train/images/Sample1159.png\n",
      "./data_train_images/train/images/Sample116.png\n",
      "./data_train_images/train/images/Sample1165.png\n",
      "./data_train_images/train/images/Sample1167.png\n",
      "./data_train_images/train/images/Sample117.png\n",
      "./data_train_images/train/images/Sample1173.png\n",
      "./data_train_images/train/images/Sample118.png\n",
      "./data_train_images/train/images/Sample1183.png\n",
      "./data_train_images/train/images/Sample1189.png\n",
      "./data_train_images/train/images/Sample119.png\n",
      "./data_train_images/train/images/Sample12.png\n",
      "./data_train_images/train/images/Sample120.png\n",
      "./data_train_images/train/images/Sample1203.png\n",
      "./data_train_images/train/images/Sample1204.png\n",
      "./data_train_images/train/images/Sample121.png\n",
      "./data_train_images/train/images/Sample1216.png\n",
      "./data_train_images/train/images/Sample1217.png\n",
      "./data_train_images/train/images/Sample122.png\n",
      "./data_train_images/train/images/Sample1221.png\n",
      "./data_train_images/train/images/Sample1225.png\n",
      "./data_train_images/train/images/Sample123.png\n",
      "./data_train_images/train/images/Sample1235.png\n",
      "./data_train_images/train/images/Sample1238.png\n",
      "./data_train_images/train/images/Sample1239.png\n",
      "./data_train_images/train/images/Sample124.png\n",
      "./data_train_images/train/images/Sample1240.png\n",
      "./data_train_images/train/images/Sample1244.png\n",
      "./data_train_images/train/images/Sample125.png\n",
      "./data_train_images/train/images/Sample1252.png\n",
      "./data_train_images/train/images/Sample1253.png\n",
      "./data_train_images/train/images/Sample126.png\n",
      "./data_train_images/train/images/Sample1262.png\n",
      "./data_train_images/train/images/Sample1265.png\n",
      "./data_train_images/train/images/Sample1269.png\n",
      "./data_train_images/train/images/Sample127.png\n",
      "./data_train_images/train/images/Sample128.png\n",
      "./data_train_images/train/images/Sample1287.png\n",
      "./data_train_images/train/images/Sample129.png\n",
      "./data_train_images/train/images/Sample1299.png\n",
      "./data_train_images/train/images/Sample13.png\n",
      "./data_train_images/train/images/Sample130.png\n",
      "./data_train_images/train/images/Sample1300.png\n",
      "./data_train_images/train/images/Sample1308.png\n",
      "./data_train_images/train/images/Sample1309.png\n",
      "./data_train_images/train/images/Sample131.png\n",
      "./data_train_images/train/images/Sample1311.png\n",
      "./data_train_images/train/images/Sample1315.png\n",
      "./data_train_images/train/images/Sample1317.png\n",
      "./data_train_images/train/images/Sample132.png\n",
      "./data_train_images/train/images/Sample133.png\n",
      "./data_train_images/train/images/Sample134.png\n",
      "./data_train_images/train/images/Sample1341.png\n",
      "./data_train_images/train/images/Sample1344.png\n",
      "./data_train_images/train/images/Sample135.png\n",
      "./data_train_images/train/images/Sample1351.png\n",
      "./data_train_images/train/images/Sample1352.png\n",
      "./data_train_images/train/images/Sample1356.png\n",
      "./data_train_images/train/images/Sample136.png\n",
      "./data_train_images/train/images/Sample137.png\n",
      "./data_train_images/train/images/Sample1374.png\n",
      "./data_train_images/train/images/Sample138.png\n",
      "./data_train_images/train/images/Sample1385.png\n",
      "./data_train_images/train/images/Sample1387.png\n",
      "./data_train_images/train/images/Sample139.png\n",
      "./data_train_images/train/images/Sample1390.png\n",
      "./data_train_images/train/images/Sample1398.png\n",
      "./data_train_images/train/images/Sample14.png\n",
      "./data_train_images/train/images/Sample140.png\n",
      "./data_train_images/train/images/Sample1405.png\n",
      "./data_train_images/train/images/Sample1406.png\n",
      "./data_train_images/train/images/Sample141.png\n",
      "./data_train_images/train/images/Sample142.png\n",
      "./data_train_images/train/images/Sample143.png\n",
      "./data_train_images/train/images/Sample1434.png\n",
      "./data_train_images/train/images/Sample144.png\n",
      "./data_train_images/train/images/Sample1441.png\n",
      "./data_train_images/train/images/Sample1443.png\n",
      "./data_train_images/train/images/Sample145.png\n",
      "./data_train_images/train/images/Sample146.png\n",
      "./data_train_images/train/images/Sample147.png\n",
      "./data_train_images/train/images/Sample1470.png\n",
      "./data_train_images/train/images/Sample1475.png\n",
      "./data_train_images/train/images/Sample1477.png\n",
      "./data_train_images/train/images/Sample148.png\n",
      "./data_train_images/train/images/Sample1481.png\n",
      "./data_train_images/train/images/Sample1485.png\n",
      "./data_train_images/train/images/Sample1488.png\n",
      "./data_train_images/train/images/Sample1489.png\n",
      "./data_train_images/train/images/Sample149.png\n",
      "./data_train_images/train/images/Sample1493.png\n",
      "./data_train_images/train/images/Sample1494.png\n",
      "./data_train_images/train/images/Sample15.png\n",
      "./data_train_images/train/images/Sample150.png\n",
      "./data_train_images/train/images/Sample1504.png\n",
      "./data_train_images/train/images/Sample1509.png\n",
      "./data_train_images/train/images/Sample151.png\n",
      "./data_train_images/train/images/Sample152.png\n",
      "./data_train_images/train/images/Sample1520.png\n",
      "./data_train_images/train/images/Sample1526.png\n",
      "./data_train_images/train/images/Sample153.png\n",
      "./data_train_images/train/images/Sample1531.png\n",
      "./data_train_images/train/images/Sample1535.png\n",
      "./data_train_images/train/images/Sample154.png\n",
      "./data_train_images/train/images/Sample1544.png\n",
      "./data_train_images/train/images/Sample1545.png\n",
      "./data_train_images/train/images/Sample155.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/images/Sample1553.png\n",
      "./data_train_images/train/images/Sample1555.png\n",
      "./data_train_images/train/images/Sample1558.png\n",
      "./data_train_images/train/images/Sample156.png\n",
      "./data_train_images/train/images/Sample1561.png\n",
      "./data_train_images/train/images/Sample1563.png\n",
      "./data_train_images/train/images/Sample157.png\n",
      "./data_train_images/train/images/Sample158.png\n",
      "./data_train_images/train/images/Sample1581.png\n",
      "./data_train_images/train/images/Sample1583.png\n",
      "./data_train_images/train/images/Sample159.png\n",
      "./data_train_images/train/images/Sample16.png\n",
      "./data_train_images/train/images/Sample160.png\n",
      "./data_train_images/train/images/Sample1601.png\n",
      "./data_train_images/train/images/Sample161.png\n",
      "./data_train_images/train/images/Sample1612.png\n",
      "./data_train_images/train/images/Sample162.png\n",
      "./data_train_images/train/images/Sample163.png\n",
      "./data_train_images/train/images/Sample1632.png\n",
      "./data_train_images/train/images/Sample1638.png\n",
      "./data_train_images/train/images/Sample164.png\n",
      "./data_train_images/train/images/Sample1645.png\n",
      "./data_train_images/train/images/Sample165.png\n",
      "./data_train_images/train/images/Sample1651.png\n",
      "./data_train_images/train/images/Sample1656.png\n",
      "./data_train_images/train/images/Sample166.png\n",
      "./data_train_images/train/images/Sample1666.png\n",
      "./data_train_images/train/images/Sample1667.png\n",
      "./data_train_images/train/images/Sample167.png\n",
      "./data_train_images/train/images/Sample1673.png\n",
      "./data_train_images/train/images/Sample1674.png\n",
      "./data_train_images/train/images/Sample1676.png\n",
      "./data_train_images/train/images/Sample168.png\n",
      "./data_train_images/train/images/Sample169.png\n",
      "./data_train_images/train/images/Sample1690.png\n",
      "./data_train_images/train/images/Sample1691.png\n",
      "./data_train_images/train/images/Sample1695.png\n",
      "./data_train_images/train/images/Sample17.png\n",
      "./data_train_images/train/images/Sample170.png\n",
      "./data_train_images/train/images/Sample1706.png\n",
      "./data_train_images/train/images/Sample1708.png\n",
      "./data_train_images/train/images/Sample171.png\n",
      "./data_train_images/train/images/Sample172.png\n",
      "./data_train_images/train/images/Sample1729.png\n",
      "./data_train_images/train/images/Sample173.png\n",
      "./data_train_images/train/images/Sample1736.png\n",
      "./data_train_images/train/images/Sample1737.png\n",
      "./data_train_images/train/images/Sample1738.png\n",
      "./data_train_images/train/images/Sample1739.png\n",
      "./data_train_images/train/images/Sample174.png\n",
      "./data_train_images/train/images/Sample1746.png\n",
      "./data_train_images/train/images/Sample175.png\n",
      "./data_train_images/train/images/Sample1751.png\n",
      "./data_train_images/train/images/Sample1753.png\n",
      "./data_train_images/train/images/Sample176.png\n",
      "./data_train_images/train/images/Sample1767.png\n",
      "./data_train_images/train/images/Sample1768.png\n",
      "./data_train_images/train/images/Sample1769.png\n",
      "./data_train_images/train/images/Sample177.png\n",
      "./data_train_images/train/images/Sample1770.png\n",
      "./data_train_images/train/images/Sample1777.png\n",
      "./data_train_images/train/images/Sample1779.png\n",
      "./data_train_images/train/images/Sample178.png\n",
      "./data_train_images/train/images/Sample179.png\n",
      "./data_train_images/train/images/Sample1790.png\n",
      "./data_train_images/train/images/Sample1792.png\n",
      "./data_train_images/train/images/Sample1794.png\n",
      "./data_train_images/train/images/Sample18.png\n",
      "./data_train_images/train/images/Sample180.png\n",
      "./data_train_images/train/images/Sample1805.png\n",
      "./data_train_images/train/images/Sample1806.png\n",
      "./data_train_images/train/images/Sample1807.png\n",
      "./data_train_images/train/images/Sample181.png\n",
      "./data_train_images/train/images/Sample1813.png\n",
      "./data_train_images/train/images/Sample1815.png\n",
      "./data_train_images/train/images/Sample1819.png\n",
      "./data_train_images/train/images/Sample182.png\n",
      "./data_train_images/train/images/Sample1821.png\n",
      "./data_train_images/train/images/Sample1828.png\n",
      "./data_train_images/train/images/Sample183.png\n",
      "./data_train_images/train/images/Sample1830.png\n",
      "./data_train_images/train/images/Sample184.png\n",
      "./data_train_images/train/images/Sample1840.png\n",
      "./data_train_images/train/images/Sample1842.png\n",
      "./data_train_images/train/images/Sample185.png\n",
      "./data_train_images/train/images/Sample1854.png\n",
      "./data_train_images/train/images/Sample1856.png\n",
      "./data_train_images/train/images/Sample186.png\n",
      "./data_train_images/train/images/Sample1862.png\n",
      "./data_train_images/train/images/Sample187.png\n",
      "./data_train_images/train/images/Sample1871.png\n",
      "./data_train_images/train/images/Sample1876.png\n",
      "./data_train_images/train/images/Sample1879.png\n",
      "./data_train_images/train/images/Sample188.png\n",
      "./data_train_images/train/images/Sample189.png\n",
      "./data_train_images/train/images/Sample1892.png\n",
      "./data_train_images/train/images/Sample1896.png\n",
      "./data_train_images/train/images/Sample19.png\n",
      "./data_train_images/train/images/Sample190.png\n",
      "./data_train_images/train/images/Sample191.png\n",
      "./data_train_images/train/images/Sample1910.png\n",
      "./data_train_images/train/images/Sample1911.png\n",
      "./data_train_images/train/images/Sample192.png\n",
      "./data_train_images/train/images/Sample1922.png\n",
      "./data_train_images/train/images/Sample1926.png\n",
      "./data_train_images/train/images/Sample193.png\n",
      "./data_train_images/train/images/Sample1933.png\n",
      "./data_train_images/train/images/Sample1936.png\n",
      "./data_train_images/train/images/Sample1937.png\n",
      "./data_train_images/train/images/Sample194.png\n",
      "./data_train_images/train/images/Sample1940.png\n",
      "./data_train_images/train/images/Sample1944.png\n",
      "./data_train_images/train/images/Sample195.png\n",
      "./data_train_images/train/images/Sample1954.png\n",
      "./data_train_images/train/images/Sample196.png\n",
      "./data_train_images/train/images/Sample1964.png\n",
      "./data_train_images/train/images/Sample197.png\n",
      "./data_train_images/train/images/Sample1970.png\n",
      "./data_train_images/train/images/Sample1975.png\n",
      "./data_train_images/train/images/Sample1979.png\n",
      "./data_train_images/train/images/Sample198.png\n",
      "./data_train_images/train/images/Sample1988.png\n",
      "./data_train_images/train/images/Sample199.png\n",
      "./data_train_images/train/images/Sample1997.png\n",
      "./data_train_images/train/images/Sample2.png\n",
      "./data_train_images/train/images/Sample20.png\n",
      "./data_train_images/train/images/Sample200.png\n",
      "./data_train_images/train/images/Sample2003.png\n",
      "./data_train_images/train/images/Sample201.png\n",
      "./data_train_images/train/images/Sample202.png\n",
      "./data_train_images/train/images/Sample2021.png\n",
      "./data_train_images/train/images/Sample2023.png\n",
      "./data_train_images/train/images/Sample2025.png\n",
      "./data_train_images/train/images/Sample2027.png\n",
      "./data_train_images/train/images/Sample203.png\n",
      "./data_train_images/train/images/Sample2030.png\n",
      "./data_train_images/train/images/Sample204.png\n",
      "./data_train_images/train/images/Sample2042.png\n",
      "./data_train_images/train/images/Sample205.png\n",
      "./data_train_images/train/images/Sample2054.png\n",
      "./data_train_images/train/images/Sample2055.png\n",
      "./data_train_images/train/images/Sample2059.png\n",
      "./data_train_images/train/images/Sample206.png\n",
      "./data_train_images/train/images/Sample2063.png\n",
      "./data_train_images/train/images/Sample2065.png\n",
      "./data_train_images/train/images/Sample2067.png\n",
      "./data_train_images/train/images/Sample207.png\n",
      "./data_train_images/train/images/Sample2072.png\n",
      "./data_train_images/train/images/Sample2073.png\n",
      "./data_train_images/train/images/Sample208.png\n",
      "./data_train_images/train/images/Sample2080.png\n",
      "./data_train_images/train/images/Sample209.png\n",
      "./data_train_images/train/images/Sample2092.png\n",
      "./data_train_images/train/images/Sample2094.png\n",
      "./data_train_images/train/images/Sample2097.png\n",
      "./data_train_images/train/images/Sample2098.png\n",
      "./data_train_images/train/images/Sample21.png\n",
      "./data_train_images/train/images/Sample210.png\n",
      "./data_train_images/train/images/Sample211.png\n",
      "./data_train_images/train/images/Sample2111.png\n",
      "./data_train_images/train/images/Sample2117.png\n",
      "./data_train_images/train/images/Sample212.png\n",
      "./data_train_images/train/images/Sample2128.png\n",
      "./data_train_images/train/images/Sample213.png\n",
      "./data_train_images/train/images/Sample2137.png\n",
      "./data_train_images/train/images/Sample214.png\n",
      "./data_train_images/train/images/Sample2140.png\n",
      "./data_train_images/train/images/Sample2142.png\n",
      "./data_train_images/train/images/Sample215.png\n",
      "./data_train_images/train/images/Sample2151.png\n",
      "./data_train_images/train/images/Sample2154.png\n",
      "./data_train_images/train/images/Sample216.png\n",
      "./data_train_images/train/images/Sample2161.png\n",
      "./data_train_images/train/images/Sample2162.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/images/Sample2164.png\n",
      "./data_train_images/train/images/Sample2168.png\n",
      "./data_train_images/train/images/Sample217.png\n",
      "./data_train_images/train/images/Sample2170.png\n",
      "./data_train_images/train/images/Sample2172.png\n",
      "./data_train_images/train/images/Sample2176.png\n",
      "./data_train_images/train/images/Sample218.png\n",
      "./data_train_images/train/images/Sample2180.png\n",
      "./data_train_images/train/images/Sample219.png\n",
      "./data_train_images/train/images/Sample2193.png\n",
      "./data_train_images/train/images/Sample22.png\n",
      "./data_train_images/train/images/Sample220.png\n",
      "./data_train_images/train/images/Sample221.png\n",
      "./data_train_images/train/images/Sample2211.png\n",
      "./data_train_images/train/images/Sample2218.png\n",
      "./data_train_images/train/images/Sample222.png\n",
      "./data_train_images/train/images/Sample2223.png\n",
      "./data_train_images/train/images/Sample2227.png\n",
      "./data_train_images/train/images/Sample223.png\n",
      "./data_train_images/train/images/Sample2230.png\n",
      "./data_train_images/train/images/Sample2235.png\n",
      "./data_train_images/train/images/Sample224.png\n",
      "./data_train_images/train/images/Sample2249.png\n",
      "./data_train_images/train/images/Sample225.png\n",
      "./data_train_images/train/images/Sample2250.png\n",
      "./data_train_images/train/images/Sample2251.png\n",
      "./data_train_images/train/images/Sample2254.png\n",
      "./data_train_images/train/images/Sample226.png\n",
      "./data_train_images/train/images/Sample227.png\n",
      "./data_train_images/train/images/Sample228.png\n",
      "./data_train_images/train/images/Sample229.png\n",
      "./data_train_images/train/images/Sample2292.png\n",
      "./data_train_images/train/images/Sample23.png\n",
      "./data_train_images/train/images/Sample230.png\n",
      "./data_train_images/train/images/Sample2303.png\n",
      "./data_train_images/train/images/Sample2304.png\n",
      "./data_train_images/train/images/Sample2309.png\n",
      "./data_train_images/train/images/Sample231.png\n",
      "./data_train_images/train/images/Sample232.png\n",
      "./data_train_images/train/images/Sample2325.png\n",
      "./data_train_images/train/images/Sample2328.png\n",
      "./data_train_images/train/images/Sample233.png\n",
      "./data_train_images/train/images/Sample2330.png\n",
      "./data_train_images/train/images/Sample234.png\n",
      "./data_train_images/train/images/Sample235.png\n",
      "./data_train_images/train/images/Sample2355.png\n",
      "./data_train_images/train/images/Sample2359.png\n",
      "./data_train_images/train/images/Sample236.png\n",
      "./data_train_images/train/images/Sample2365.png\n",
      "./data_train_images/train/images/Sample2367.png\n",
      "./data_train_images/train/images/Sample237.png\n",
      "./data_train_images/train/images/Sample238.png\n",
      "./data_train_images/train/images/Sample2389.png\n",
      "./data_train_images/train/images/Sample239.png\n",
      "./data_train_images/train/images/Sample2392.png\n",
      "./data_train_images/train/images/Sample2393.png\n",
      "./data_train_images/train/images/Sample24.png\n",
      "./data_train_images/train/images/Sample240.png\n",
      "./data_train_images/train/images/Sample2405.png\n",
      "./data_train_images/train/images/Sample241.png\n",
      "./data_train_images/train/images/Sample2415.png\n",
      "./data_train_images/train/images/Sample242.png\n",
      "./data_train_images/train/images/Sample2422.png\n",
      "./data_train_images/train/images/Sample2425.png\n",
      "./data_train_images/train/images/Sample243.png\n",
      "./data_train_images/train/images/Sample2436.png\n",
      "./data_train_images/train/images/Sample2438.png\n",
      "./data_train_images/train/images/Sample244.png\n",
      "./data_train_images/train/images/Sample2441.png\n",
      "./data_train_images/train/images/Sample245.png\n",
      "./data_train_images/train/images/Sample2452.png\n",
      "./data_train_images/train/images/Sample246.png\n",
      "./data_train_images/train/images/Sample2460.png\n",
      "./data_train_images/train/images/Sample247.png\n",
      "./data_train_images/train/images/Sample2475.png\n",
      "./data_train_images/train/images/Sample2477.png\n",
      "./data_train_images/train/images/Sample248.png\n",
      "./data_train_images/train/images/Sample2485.png\n",
      "./data_train_images/train/images/Sample249.png\n",
      "./data_train_images/train/images/Sample2491.png\n",
      "./data_train_images/train/images/Sample25.png\n",
      "./data_train_images/train/images/Sample250.png\n",
      "./data_train_images/train/images/Sample2504.png\n",
      "./data_train_images/train/images/Sample251.png\n",
      "./data_train_images/train/images/Sample2510.png\n",
      "./data_train_images/train/images/Sample2515.png\n",
      "./data_train_images/train/images/Sample2516.png\n",
      "./data_train_images/train/images/Sample252.png\n",
      "./data_train_images/train/images/Sample253.png\n",
      "./data_train_images/train/images/Sample2530.png\n",
      "./data_train_images/train/images/Sample2531.png\n",
      "./data_train_images/train/images/Sample2537.png\n",
      "./data_train_images/train/images/Sample254.png\n",
      "./data_train_images/train/images/Sample2546.png\n",
      "./data_train_images/train/images/Sample2547.png\n",
      "./data_train_images/train/images/Sample2548.png\n",
      "./data_train_images/train/images/Sample255.png\n",
      "./data_train_images/train/images/Sample2554.png\n",
      "./data_train_images/train/images/Sample2558.png\n",
      "./data_train_images/train/images/Sample256.png\n",
      "./data_train_images/train/images/Sample257.png\n",
      "./data_train_images/train/images/Sample2572.png\n",
      "./data_train_images/train/images/Sample2579.png\n",
      "./data_train_images/train/images/Sample258.png\n",
      "./data_train_images/train/images/Sample2586.png\n",
      "./data_train_images/train/images/Sample259.png\n",
      "./data_train_images/train/images/Sample2592.png\n",
      "./data_train_images/train/images/Sample2598.png\n",
      "./data_train_images/train/images/Sample26.png\n",
      "./data_train_images/train/images/Sample260.png\n",
      "./data_train_images/train/images/Sample2604.png\n",
      "./data_train_images/train/images/Sample261.png\n",
      "./data_train_images/train/images/Sample2616.png\n",
      "./data_train_images/train/images/Sample2618.png\n",
      "./data_train_images/train/images/Sample262.png\n",
      "./data_train_images/train/images/Sample2629.png\n",
      "./data_train_images/train/images/Sample263.png\n",
      "./data_train_images/train/images/Sample2631.png\n",
      "./data_train_images/train/images/Sample2637.png\n",
      "./data_train_images/train/images/Sample264.png\n",
      "./data_train_images/train/images/Sample2642.png\n",
      "./data_train_images/train/images/Sample265.png\n",
      "./data_train_images/train/images/Sample2653.png\n",
      "./data_train_images/train/images/Sample2655.png\n",
      "./data_train_images/train/images/Sample266.png\n",
      "./data_train_images/train/images/Sample267.png\n",
      "./data_train_images/train/images/Sample2671.png\n",
      "./data_train_images/train/images/Sample2674.png\n",
      "./data_train_images/train/images/Sample268.png\n",
      "./data_train_images/train/images/Sample2681.png\n",
      "./data_train_images/train/images/Sample269.png\n",
      "./data_train_images/train/images/Sample2696.png\n",
      "./data_train_images/train/images/Sample2698.png\n",
      "./data_train_images/train/images/Sample27.png\n",
      "./data_train_images/train/images/Sample270.png\n",
      "./data_train_images/train/images/Sample271.png\n",
      "./data_train_images/train/images/Sample2715.png\n",
      "./data_train_images/train/images/Sample272.png\n",
      "./data_train_images/train/images/Sample2725.png\n",
      "./data_train_images/train/images/Sample273.png\n",
      "./data_train_images/train/images/Sample2733.png\n",
      "./data_train_images/train/images/Sample2736.png\n",
      "./data_train_images/train/images/Sample2738.png\n",
      "./data_train_images/train/images/Sample274.png\n",
      "./data_train_images/train/images/Sample2745.png\n",
      "./data_train_images/train/images/Sample2746.png\n",
      "./data_train_images/train/images/Sample275.png\n",
      "./data_train_images/train/images/Sample276.png\n",
      "./data_train_images/train/images/Sample2762.png\n",
      "./data_train_images/train/images/Sample2763.png\n",
      "./data_train_images/train/images/Sample2765.png\n",
      "./data_train_images/train/images/Sample277.png\n",
      "./data_train_images/train/images/Sample2776.png\n",
      "./data_train_images/train/images/Sample278.png\n",
      "./data_train_images/train/images/Sample2780.png\n",
      "./data_train_images/train/images/Sample2788.png\n",
      "./data_train_images/train/images/Sample2789.png\n",
      "./data_train_images/train/images/Sample279.png\n",
      "./data_train_images/train/images/Sample2791.png\n",
      "./data_train_images/train/images/Sample2795.png\n",
      "./data_train_images/train/images/Sample2796.png\n",
      "./data_train_images/train/images/Sample2799.png\n",
      "./data_train_images/train/images/Sample28.png\n",
      "./data_train_images/train/images/Sample280.png\n",
      "./data_train_images/train/images/Sample2800.png\n",
      "./data_train_images/train/images/Sample281.png\n",
      "./data_train_images/train/images/Sample2810.png\n",
      "./data_train_images/train/images/Sample282.png\n",
      "./data_train_images/train/images/Sample283.png\n",
      "./data_train_images/train/images/Sample284.png\n",
      "./data_train_images/train/images/Sample2849.png\n",
      "./data_train_images/train/images/Sample2868.png\n",
      "./data_train_images/train/images/Sample2870.png\n",
      "./data_train_images/train/images/Sample2874.png\n",
      "./data_train_images/train/images/Sample2877.png\n",
      "./data_train_images/train/images/Sample2893.png\n",
      "./data_train_images/train/images/Sample2894.png\n",
      "./data_train_images/train/images/Sample2895.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/images/Sample2898.png\n",
      "./data_train_images/train/images/Sample29.png\n",
      "./data_train_images/train/images/Sample2906.png\n",
      "./data_train_images/train/images/Sample2913.png\n",
      "./data_train_images/train/images/Sample2925.png\n",
      "./data_train_images/train/images/Sample2928.png\n",
      "./data_train_images/train/images/Sample2934.png\n",
      "./data_train_images/train/images/Sample2947.png\n",
      "./data_train_images/train/images/Sample2949.png\n",
      "./data_train_images/train/images/Sample2950.png\n",
      "./data_train_images/train/images/Sample2952.png\n",
      "./data_train_images/train/images/Sample2977.png\n",
      "./data_train_images/train/images/Sample2981.png\n",
      "./data_train_images/train/images/Sample2990.png\n",
      "./data_train_images/train/images/Sample2993.png\n",
      "./data_train_images/train/images/Sample2999.png\n",
      "./data_train_images/train/images/Sample3.png\n",
      "./data_train_images/train/images/Sample30.png\n",
      "./data_train_images/train/images/Sample3008.png\n",
      "./data_train_images/train/images/Sample3011.png\n",
      "./data_train_images/train/images/Sample3017.png\n",
      "./data_train_images/train/images/Sample3020.png\n",
      "./data_train_images/train/images/Sample3024.png\n",
      "./data_train_images/train/images/Sample3030.png\n",
      "./data_train_images/train/images/Sample3035.png\n",
      "./data_train_images/train/images/Sample3043.png\n",
      "./data_train_images/train/images/Sample3051.png\n",
      "./data_train_images/train/images/Sample3057.png\n",
      "./data_train_images/train/images/Sample3060.png\n",
      "./data_train_images/train/images/Sample3064.png\n",
      "./data_train_images/train/images/Sample3067.png\n",
      "./data_train_images/train/images/Sample3070.png\n",
      "./data_train_images/train/images/Sample3071.png\n",
      "./data_train_images/train/images/Sample3072.png\n",
      "./data_train_images/train/images/Sample3074.png\n",
      "./data_train_images/train/images/Sample3077.png\n",
      "./data_train_images/train/images/Sample3085.png\n",
      "./data_train_images/train/images/Sample3086.png\n",
      "./data_train_images/train/images/Sample31.png\n",
      "./data_train_images/train/images/Sample3104.png\n",
      "./data_train_images/train/images/Sample3106.png\n",
      "./data_train_images/train/images/Sample3114.png\n",
      "./data_train_images/train/images/Sample3116.png\n",
      "./data_train_images/train/images/Sample3119.png\n",
      "./data_train_images/train/images/Sample3125.png\n",
      "./data_train_images/train/images/Sample3127.png\n",
      "./data_train_images/train/images/Sample3133.png\n",
      "./data_train_images/train/images/Sample3135.png\n",
      "./data_train_images/train/images/Sample3146.png\n",
      "./data_train_images/train/images/Sample3163.png\n",
      "./data_train_images/train/images/Sample3167.png\n",
      "./data_train_images/train/images/Sample3170.png\n",
      "./data_train_images/train/images/Sample3186.png\n",
      "./data_train_images/train/images/Sample3189.png\n",
      "./data_train_images/train/images/Sample3199.png\n",
      "./data_train_images/train/images/Sample32.png\n",
      "./data_train_images/train/images/Sample3201.png\n",
      "./data_train_images/train/images/Sample3206.png\n",
      "./data_train_images/train/images/Sample3208.png\n",
      "./data_train_images/train/images/Sample3212.png\n",
      "./data_train_images/train/images/Sample3215.png\n",
      "./data_train_images/train/images/Sample3223.png\n",
      "./data_train_images/train/images/Sample3224.png\n",
      "./data_train_images/train/images/Sample3238.png\n",
      "./data_train_images/train/images/Sample3242.png\n",
      "./data_train_images/train/images/Sample3251.png\n",
      "./data_train_images/train/images/Sample3267.png\n",
      "./data_train_images/train/images/Sample3273.png\n",
      "./data_train_images/train/images/Sample3283.png\n",
      "./data_train_images/train/images/Sample3290.png\n",
      "./data_train_images/train/images/Sample3292.png\n",
      "./data_train_images/train/images/Sample3294.png\n",
      "./data_train_images/train/images/Sample33.png\n",
      "./data_train_images/train/images/Sample3302.png\n",
      "./data_train_images/train/images/Sample3307.png\n",
      "./data_train_images/train/images/Sample3315.png\n",
      "./data_train_images/train/images/Sample3317.png\n",
      "./data_train_images/train/images/Sample3329.png\n",
      "./data_train_images/train/images/Sample3332.png\n",
      "./data_train_images/train/images/Sample3333.png\n",
      "./data_train_images/train/images/Sample3342.png\n",
      "./data_train_images/train/images/Sample3351.png\n",
      "./data_train_images/train/images/Sample3362.png\n",
      "./data_train_images/train/images/Sample3377.png\n",
      "./data_train_images/train/images/Sample3379.png\n",
      "./data_train_images/train/images/Sample3382.png\n",
      "./data_train_images/train/images/Sample3385.png\n",
      "./data_train_images/train/images/Sample3389.png\n",
      "./data_train_images/train/images/Sample3393.png\n",
      "./data_train_images/train/images/Sample34.png\n",
      "./data_train_images/train/images/Sample3407.png\n",
      "./data_train_images/train/images/Sample3411.png\n",
      "./data_train_images/train/images/Sample3425.png\n",
      "./data_train_images/train/images/Sample3434.png\n",
      "./data_train_images/train/images/Sample3435.png\n",
      "./data_train_images/train/images/Sample3436.png\n",
      "./data_train_images/train/images/Sample3439.png\n",
      "./data_train_images/train/images/Sample3453.png\n",
      "./data_train_images/train/images/Sample3459.png\n",
      "./data_train_images/train/images/Sample3467.png\n",
      "./data_train_images/train/images/Sample3479.png\n",
      "./data_train_images/train/images/Sample3480.png\n",
      "./data_train_images/train/images/Sample3484.png\n",
      "./data_train_images/train/images/Sample3486.png\n",
      "./data_train_images/train/images/Sample3487.png\n",
      "./data_train_images/train/images/Sample3489.png\n",
      "./data_train_images/train/images/Sample3491.png\n",
      "./data_train_images/train/images/Sample3492.png\n",
      "./data_train_images/train/images/Sample3496.png\n",
      "./data_train_images/train/images/Sample35.png\n",
      "./data_train_images/train/images/Sample3500.png\n",
      "./data_train_images/train/images/Sample3502.png\n",
      "./data_train_images/train/images/Sample3505.png\n",
      "./data_train_images/train/images/Sample3506.png\n",
      "./data_train_images/train/images/Sample3517.png\n",
      "./data_train_images/train/images/Sample3530.png\n",
      "./data_train_images/train/images/Sample3531.png\n",
      "./data_train_images/train/images/Sample3537.png\n",
      "./data_train_images/train/images/Sample3542.png\n",
      "./data_train_images/train/images/Sample3550.png\n",
      "./data_train_images/train/images/Sample3552.png\n",
      "./data_train_images/train/images/Sample3561.png\n",
      "./data_train_images/train/images/Sample3589.png\n",
      "./data_train_images/train/images/Sample3591.png\n",
      "./data_train_images/train/images/Sample3592.png\n",
      "./data_train_images/train/images/Sample3595.png\n",
      "./data_train_images/train/images/Sample3596.png\n",
      "./data_train_images/train/images/Sample3597.png\n",
      "./data_train_images/train/images/Sample36.png\n",
      "./data_train_images/train/images/Sample3604.png\n",
      "./data_train_images/train/images/Sample3606.png\n",
      "./data_train_images/train/images/Sample3637.png\n",
      "./data_train_images/train/images/Sample3645.png\n",
      "./data_train_images/train/images/Sample3646.png\n",
      "./data_train_images/train/images/Sample3648.png\n",
      "./data_train_images/train/images/Sample3649.png\n",
      "./data_train_images/train/images/Sample3676.png\n",
      "./data_train_images/train/images/Sample3677.png\n",
      "./data_train_images/train/images/Sample3678.png\n",
      "./data_train_images/train/images/Sample3680.png\n",
      "./data_train_images/train/images/Sample3681.png\n",
      "./data_train_images/train/images/Sample3688.png\n",
      "./data_train_images/train/images/Sample3692.png\n",
      "./data_train_images/train/images/Sample3694.png\n",
      "./data_train_images/train/images/Sample3695.png\n",
      "./data_train_images/train/images/Sample3698.png\n",
      "./data_train_images/train/images/Sample37.png\n",
      "./data_train_images/train/images/Sample3700.png\n",
      "./data_train_images/train/images/Sample3706.png\n",
      "./data_train_images/train/images/Sample3707.png\n",
      "./data_train_images/train/images/Sample3711.png\n",
      "./data_train_images/train/images/Sample3716.png\n",
      "./data_train_images/train/images/Sample3723.png\n",
      "./data_train_images/train/images/Sample3726.png\n",
      "./data_train_images/train/images/Sample3732.png\n",
      "./data_train_images/train/images/Sample3733.png\n",
      "./data_train_images/train/images/Sample3735.png\n",
      "./data_train_images/train/images/Sample3743.png\n",
      "./data_train_images/train/images/Sample3748.png\n",
      "./data_train_images/train/images/Sample3754.png\n",
      "./data_train_images/train/images/Sample3762.png\n",
      "./data_train_images/train/images/Sample3774.png\n",
      "./data_train_images/train/images/Sample3775.png\n",
      "./data_train_images/train/images/Sample3782.png\n",
      "./data_train_images/train/images/Sample3786.png\n",
      "./data_train_images/train/images/Sample3790.png\n",
      "./data_train_images/train/images/Sample3791.png\n",
      "./data_train_images/train/images/Sample3799.png\n",
      "./data_train_images/train/images/Sample38.png\n",
      "./data_train_images/train/images/Sample3815.png\n",
      "./data_train_images/train/images/Sample3816.png\n",
      "./data_train_images/train/images/Sample3818.png\n",
      "./data_train_images/train/images/Sample3822.png\n",
      "./data_train_images/train/images/Sample3829.png\n",
      "./data_train_images/train/images/Sample3830.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/images/Sample3835.png\n",
      "./data_train_images/train/images/Sample3836.png\n",
      "./data_train_images/train/images/Sample3847.png\n",
      "./data_train_images/train/images/Sample3855.png\n",
      "./data_train_images/train/images/Sample3864.png\n",
      "./data_train_images/train/images/Sample3876.png\n",
      "./data_train_images/train/images/Sample3877.png\n",
      "./data_train_images/train/images/Sample3879.png\n",
      "./data_train_images/train/images/Sample39.png\n",
      "./data_train_images/train/images/Sample3903.png\n",
      "./data_train_images/train/images/Sample3907.png\n",
      "./data_train_images/train/images/Sample3911.png\n",
      "./data_train_images/train/images/Sample3922.png\n",
      "./data_train_images/train/images/Sample3924.png\n",
      "./data_train_images/train/images/Sample3930.png\n",
      "./data_train_images/train/images/Sample3934.png\n",
      "./data_train_images/train/images/Sample3939.png\n",
      "./data_train_images/train/images/Sample3949.png\n",
      "./data_train_images/train/images/Sample3951.png\n",
      "./data_train_images/train/images/Sample3956.png\n",
      "./data_train_images/train/images/Sample3959.png\n",
      "./data_train_images/train/images/Sample3967.png\n",
      "./data_train_images/train/images/Sample3969.png\n",
      "./data_train_images/train/images/Sample3978.png\n",
      "./data_train_images/train/images/Sample3992.png\n",
      "./data_train_images/train/images/Sample3993.png\n",
      "./data_train_images/train/images/Sample4.png\n",
      "./data_train_images/train/images/Sample40.png\n",
      "./data_train_images/train/images/Sample4001.png\n",
      "./data_train_images/train/images/Sample4022.png\n",
      "./data_train_images/train/images/Sample4029.png\n",
      "./data_train_images/train/images/Sample4034.png\n",
      "./data_train_images/train/images/Sample4049.png\n",
      "./data_train_images/train/images/Sample4052.png\n",
      "./data_train_images/train/images/Sample4063.png\n",
      "./data_train_images/train/images/Sample4066.png\n",
      "./data_train_images/train/images/Sample4068.png\n",
      "./data_train_images/train/images/Sample4072.png\n",
      "./data_train_images/train/images/Sample41.png\n",
      "./data_train_images/train/images/Sample4108.png\n",
      "./data_train_images/train/images/Sample4111.png\n",
      "./data_train_images/train/images/Sample4115.png\n",
      "./data_train_images/train/images/Sample4120.png\n",
      "./data_train_images/train/images/Sample4123.png\n",
      "./data_train_images/train/images/Sample4126.png\n",
      "./data_train_images/train/images/Sample4146.png\n",
      "./data_train_images/train/images/Sample4149.png\n",
      "./data_train_images/train/images/Sample4152.png\n",
      "./data_train_images/train/images/Sample4156.png\n",
      "./data_train_images/train/images/Sample4162.png\n",
      "./data_train_images/train/images/Sample4164.png\n",
      "./data_train_images/train/images/Sample4180.png\n",
      "./data_train_images/train/images/Sample4182.png\n",
      "./data_train_images/train/images/Sample4183.png\n",
      "./data_train_images/train/images/Sample42.png\n",
      "./data_train_images/train/images/Sample4221.png\n",
      "./data_train_images/train/images/Sample4228.png\n",
      "./data_train_images/train/images/Sample4233.png\n",
      "./data_train_images/train/images/Sample4250.png\n",
      "./data_train_images/train/images/Sample4259.png\n",
      "./data_train_images/train/images/Sample4266.png\n",
      "./data_train_images/train/images/Sample4270.png\n",
      "./data_train_images/train/images/Sample43.png\n",
      "./data_train_images/train/images/Sample4328.png\n",
      "./data_train_images/train/images/Sample4330.png\n",
      "./data_train_images/train/images/Sample4339.png\n",
      "./data_train_images/train/images/Sample4344.png\n",
      "./data_train_images/train/images/Sample4361.png\n",
      "./data_train_images/train/images/Sample4369.png\n",
      "./data_train_images/train/images/Sample4379.png\n",
      "./data_train_images/train/images/Sample4382.png\n",
      "./data_train_images/train/images/Sample4389.png\n",
      "./data_train_images/train/images/Sample4391.png\n",
      "./data_train_images/train/images/Sample4399.png\n",
      "./data_train_images/train/images/Sample44.png\n",
      "./data_train_images/train/images/Sample4400.png\n",
      "./data_train_images/train/images/Sample4402.png\n",
      "./data_train_images/train/images/Sample4408.png\n",
      "./data_train_images/train/images/Sample4410.png\n",
      "./data_train_images/train/images/Sample4423.png\n",
      "./data_train_images/train/images/Sample4424.png\n",
      "./data_train_images/train/images/Sample4426.png\n",
      "./data_train_images/train/images/Sample4437.png\n",
      "./data_train_images/train/images/Sample4443.png\n",
      "./data_train_images/train/images/Sample4445.png\n",
      "./data_train_images/train/images/Sample4446.png\n",
      "./data_train_images/train/images/Sample4459.png\n",
      "./data_train_images/train/images/Sample4461.png\n",
      "./data_train_images/train/images/Sample4464.png\n",
      "./data_train_images/train/images/Sample4468.png\n",
      "./data_train_images/train/images/Sample4487.png\n",
      "./data_train_images/train/images/Sample4489.png\n",
      "./data_train_images/train/images/Sample4490.png\n",
      "./data_train_images/train/images/Sample4497.png\n",
      "./data_train_images/train/images/Sample45.png\n",
      "./data_train_images/train/images/Sample4514.png\n",
      "./data_train_images/train/images/Sample4520.png\n",
      "./data_train_images/train/images/Sample4521.png\n",
      "./data_train_images/train/images/Sample4522.png\n",
      "./data_train_images/train/images/Sample4536.png\n",
      "./data_train_images/train/images/Sample4540.png\n",
      "./data_train_images/train/images/Sample4544.png\n",
      "./data_train_images/train/images/Sample4552.png\n",
      "./data_train_images/train/images/Sample4562.png\n",
      "./data_train_images/train/images/Sample4566.png\n",
      "./data_train_images/train/images/Sample4567.png\n",
      "./data_train_images/train/images/Sample4575.png\n",
      "./data_train_images/train/images/Sample4578.png\n",
      "./data_train_images/train/images/Sample4586.png\n",
      "./data_train_images/train/images/Sample4591.png\n",
      "./data_train_images/train/images/Sample4593.png\n",
      "./data_train_images/train/images/Sample4596.png\n",
      "./data_train_images/train/images/Sample4598.png\n",
      "./data_train_images/train/images/Sample46.png\n",
      "./data_train_images/train/images/Sample4608.png\n",
      "./data_train_images/train/images/Sample4611.png\n",
      "./data_train_images/train/images/Sample4615.png\n",
      "./data_train_images/train/images/Sample4639.png\n",
      "./data_train_images/train/images/Sample4640.png\n",
      "./data_train_images/train/images/Sample4643.png\n",
      "./data_train_images/train/images/Sample4647.png\n",
      "./data_train_images/train/images/Sample4650.png\n",
      "./data_train_images/train/images/Sample4672.png\n",
      "./data_train_images/train/images/Sample4676.png\n",
      "./data_train_images/train/images/Sample4678.png\n",
      "./data_train_images/train/images/Sample4681.png\n",
      "./data_train_images/train/images/Sample47.png\n",
      "./data_train_images/train/images/Sample4709.png\n",
      "./data_train_images/train/images/Sample4714.png\n",
      "./data_train_images/train/images/Sample4715.png\n",
      "./data_train_images/train/images/Sample4724.png\n",
      "./data_train_images/train/images/Sample4726.png\n",
      "./data_train_images/train/images/Sample4727.png\n",
      "./data_train_images/train/images/Sample4728.png\n",
      "./data_train_images/train/images/Sample4731.png\n",
      "./data_train_images/train/images/Sample4747.png\n",
      "./data_train_images/train/images/Sample4756.png\n",
      "./data_train_images/train/images/Sample4758.png\n",
      "./data_train_images/train/images/Sample4761.png\n",
      "./data_train_images/train/images/Sample4789.png\n",
      "./data_train_images/train/images/Sample4794.png\n",
      "./data_train_images/train/images/Sample4796.png\n",
      "./data_train_images/train/images/Sample48.png\n",
      "./data_train_images/train/images/Sample4801.png\n",
      "./data_train_images/train/images/Sample4807.png\n",
      "./data_train_images/train/images/Sample4812.png\n",
      "./data_train_images/train/images/Sample4813.png\n",
      "./data_train_images/train/images/Sample4821.png\n",
      "./data_train_images/train/images/Sample4823.png\n",
      "./data_train_images/train/images/Sample4831.png\n",
      "./data_train_images/train/images/Sample4840.png\n",
      "./data_train_images/train/images/Sample4845.png\n",
      "./data_train_images/train/images/Sample4866.png\n",
      "./data_train_images/train/images/Sample4868.png\n",
      "./data_train_images/train/images/Sample4883.png\n",
      "./data_train_images/train/images/Sample4884.png\n",
      "./data_train_images/train/images/Sample4886.png\n",
      "./data_train_images/train/images/Sample4889.png\n",
      "./data_train_images/train/images/Sample4892.png\n",
      "./data_train_images/train/images/Sample4893.png\n",
      "./data_train_images/train/images/Sample49.png\n",
      "./data_train_images/train/images/Sample4908.png\n",
      "./data_train_images/train/images/Sample4909.png\n",
      "./data_train_images/train/images/Sample4914.png\n",
      "./data_train_images/train/images/Sample4915.png\n",
      "./data_train_images/train/images/Sample4916.png\n",
      "./data_train_images/train/images/Sample4922.png\n",
      "./data_train_images/train/images/Sample4925.png\n",
      "./data_train_images/train/images/Sample4935.png\n",
      "./data_train_images/train/images/Sample4938.png\n",
      "./data_train_images/train/images/Sample4940.png\n",
      "./data_train_images/train/images/Sample4942.png\n",
      "./data_train_images/train/images/Sample4944.png\n",
      "./data_train_images/train/images/Sample4949.png\n",
      "./data_train_images/train/images/Sample4964.png\n",
      "./data_train_images/train/images/Sample4967.png\n",
      "./data_train_images/train/images/Sample4971.png\n",
      "./data_train_images/train/images/Sample4976.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/images/Sample4980.png\n",
      "./data_train_images/train/images/Sample4981.png\n",
      "./data_train_images/train/images/Sample4983.png\n",
      "./data_train_images/train/images/Sample4984.png\n",
      "./data_train_images/train/images/Sample4998.png\n",
      "./data_train_images/train/images/Sample5.png\n",
      "./data_train_images/train/images/Sample50.png\n",
      "./data_train_images/train/images/Sample5002.png\n",
      "./data_train_images/train/images/Sample5003.png\n",
      "./data_train_images/train/images/Sample5014.png\n",
      "./data_train_images/train/images/Sample5020.png\n",
      "./data_train_images/train/images/Sample5030.png\n",
      "./data_train_images/train/images/Sample5041.png\n",
      "./data_train_images/train/images/Sample5043.png\n",
      "./data_train_images/train/images/Sample5055.png\n",
      "./data_train_images/train/images/Sample5068.png\n",
      "./data_train_images/train/images/Sample5077.png\n",
      "./data_train_images/train/images/Sample51.png\n",
      "./data_train_images/train/images/Sample5120.png\n",
      "./data_train_images/train/images/Sample5135.png\n",
      "./data_train_images/train/images/Sample5137.png\n",
      "./data_train_images/train/images/Sample5142.png\n",
      "./data_train_images/train/images/Sample5147.png\n",
      "./data_train_images/train/images/Sample5165.png\n",
      "./data_train_images/train/images/Sample5177.png\n",
      "./data_train_images/train/images/Sample5181.png\n",
      "./data_train_images/train/images/Sample5183.png\n",
      "./data_train_images/train/images/Sample5196.png\n",
      "./data_train_images/train/images/Sample52.png\n",
      "./data_train_images/train/images/Sample5206.png\n",
      "./data_train_images/train/images/Sample5215.png\n",
      "./data_train_images/train/images/Sample5220.png\n",
      "./data_train_images/train/images/Sample5221.png\n",
      "./data_train_images/train/images/Sample5224.png\n",
      "./data_train_images/train/images/Sample5227.png\n",
      "./data_train_images/train/images/Sample5231.png\n",
      "./data_train_images/train/images/Sample5232.png\n",
      "./data_train_images/train/images/Sample5233.png\n",
      "./data_train_images/train/images/Sample5248.png\n",
      "./data_train_images/train/images/Sample5250.png\n",
      "./data_train_images/train/images/Sample5251.png\n",
      "./data_train_images/train/images/Sample5258.png\n",
      "./data_train_images/train/images/Sample5260.png\n",
      "./data_train_images/train/images/Sample5265.png\n",
      "./data_train_images/train/images/Sample5266.png\n",
      "./data_train_images/train/images/Sample5284.png\n",
      "./data_train_images/train/images/Sample5286.png\n",
      "./data_train_images/train/images/Sample5288.png\n",
      "./data_train_images/train/images/Sample5299.png\n",
      "./data_train_images/train/images/Sample53.png\n",
      "./data_train_images/train/images/Sample5303.png\n",
      "./data_train_images/train/images/Sample5304.png\n",
      "./data_train_images/train/images/Sample5306.png\n",
      "./data_train_images/train/images/Sample5313.png\n",
      "./data_train_images/train/images/Sample5319.png\n",
      "./data_train_images/train/images/Sample5321.png\n",
      "./data_train_images/train/images/Sample5335.png\n",
      "./data_train_images/train/images/Sample5342.png\n",
      "./data_train_images/train/images/Sample5353.png\n",
      "./data_train_images/train/images/Sample5355.png\n",
      "./data_train_images/train/images/Sample5362.png\n",
      "./data_train_images/train/images/Sample5363.png\n",
      "./data_train_images/train/images/Sample5370.png\n",
      "./data_train_images/train/images/Sample5386.png\n",
      "./data_train_images/train/images/Sample5391.png\n",
      "./data_train_images/train/images/Sample5395.png\n",
      "./data_train_images/train/images/Sample54.png\n",
      "./data_train_images/train/images/Sample5411.png\n",
      "./data_train_images/train/images/Sample5417.png\n",
      "./data_train_images/train/images/Sample5424.png\n",
      "./data_train_images/train/images/Sample5436.png\n",
      "./data_train_images/train/images/Sample5437.png\n",
      "./data_train_images/train/images/Sample5444.png\n",
      "./data_train_images/train/images/Sample5448.png\n",
      "./data_train_images/train/images/Sample5449.png\n",
      "./data_train_images/train/images/Sample5452.png\n",
      "./data_train_images/train/images/Sample5455.png\n",
      "./data_train_images/train/images/Sample5456.png\n",
      "./data_train_images/train/images/Sample5457.png\n",
      "./data_train_images/train/images/Sample5459.png\n",
      "./data_train_images/train/images/Sample5465.png\n",
      "./data_train_images/train/images/Sample5479.png\n",
      "./data_train_images/train/images/Sample5499.png\n",
      "./data_train_images/train/images/Sample55.png\n",
      "./data_train_images/train/images/Sample5517.png\n",
      "./data_train_images/train/images/Sample5518.png\n",
      "./data_train_images/train/images/Sample5522.png\n",
      "./data_train_images/train/images/Sample5524.png\n",
      "./data_train_images/train/images/Sample5532.png\n",
      "./data_train_images/train/images/Sample5534.png\n",
      "./data_train_images/train/images/Sample5544.png\n",
      "./data_train_images/train/images/Sample5551.png\n",
      "./data_train_images/train/images/Sample5564.png\n",
      "./data_train_images/train/images/Sample5569.png\n",
      "./data_train_images/train/images/Sample5571.png\n",
      "./data_train_images/train/images/Sample5590.png\n",
      "./data_train_images/train/images/Sample5594.png\n",
      "./data_train_images/train/images/Sample56.png\n",
      "./data_train_images/train/images/Sample5600.png\n",
      "./data_train_images/train/images/Sample5602.png\n",
      "./data_train_images/train/images/Sample5607.png\n",
      "./data_train_images/train/images/Sample5611.png\n",
      "./data_train_images/train/images/Sample5616.png\n",
      "./data_train_images/train/images/Sample5617.png\n",
      "./data_train_images/train/images/Sample5618.png\n",
      "./data_train_images/train/images/Sample5621.png\n",
      "./data_train_images/train/images/Sample5637.png\n",
      "./data_train_images/train/images/Sample5649.png\n",
      "./data_train_images/train/images/Sample5667.png\n",
      "./data_train_images/train/images/Sample5670.png\n",
      "./data_train_images/train/images/Sample5672.png\n",
      "./data_train_images/train/images/Sample5673.png\n",
      "./data_train_images/train/images/Sample5679.png\n",
      "./data_train_images/train/images/Sample5697.png\n",
      "./data_train_images/train/images/Sample5699.png\n",
      "./data_train_images/train/images/Sample57.png\n",
      "./data_train_images/train/images/Sample5701.png\n",
      "./data_train_images/train/images/Sample5706.png\n",
      "./data_train_images/train/images/Sample5711.png\n",
      "./data_train_images/train/images/Sample5717.png\n",
      "./data_train_images/train/images/Sample5719.png\n",
      "./data_train_images/train/images/Sample5744.png\n",
      "./data_train_images/train/images/Sample5746.png\n",
      "./data_train_images/train/images/Sample5751.png\n",
      "./data_train_images/train/images/Sample5754.png\n",
      "./data_train_images/train/images/Sample5755.png\n",
      "./data_train_images/train/images/Sample5772.png\n",
      "./data_train_images/train/images/Sample5774.png\n",
      "./data_train_images/train/images/Sample5775.png\n",
      "./data_train_images/train/images/Sample5781.png\n",
      "./data_train_images/train/images/Sample5790.png\n",
      "./data_train_images/train/images/Sample5795.png\n",
      "./data_train_images/train/images/Sample58.png\n",
      "./data_train_images/train/images/Sample5802.png\n",
      "./data_train_images/train/images/Sample5804.png\n",
      "./data_train_images/train/images/Sample5810.png\n",
      "./data_train_images/train/images/Sample5820.png\n",
      "./data_train_images/train/images/Sample5825.png\n",
      "./data_train_images/train/images/Sample5833.png\n",
      "./data_train_images/train/images/Sample5843.png\n",
      "./data_train_images/train/images/Sample5853.png\n",
      "./data_train_images/train/images/Sample5866.png\n",
      "./data_train_images/train/images/Sample5874.png\n",
      "./data_train_images/train/images/Sample5877.png\n",
      "./data_train_images/train/images/Sample5886.png\n",
      "./data_train_images/train/images/Sample5897.png\n",
      "./data_train_images/train/images/Sample59.png\n",
      "./data_train_images/train/images/Sample5902.png\n",
      "./data_train_images/train/images/Sample5908.png\n",
      "./data_train_images/train/images/Sample5912.png\n",
      "./data_train_images/train/images/Sample5939.png\n",
      "./data_train_images/train/images/Sample5948.png\n",
      "./data_train_images/train/images/Sample5949.png\n",
      "./data_train_images/train/images/Sample5952.png\n",
      "./data_train_images/train/images/Sample5955.png\n",
      "./data_train_images/train/images/Sample5959.png\n",
      "./data_train_images/train/images/Sample5963.png\n",
      "./data_train_images/train/images/Sample6.png\n",
      "./data_train_images/train/images/Sample60.png\n",
      "./data_train_images/train/images/Sample6006.png\n",
      "./data_train_images/train/images/Sample6010.png\n",
      "./data_train_images/train/images/Sample6021.png\n",
      "./data_train_images/train/images/Sample6029.png\n",
      "./data_train_images/train/images/Sample6030.png\n",
      "./data_train_images/train/images/Sample6043.png\n",
      "./data_train_images/train/images/Sample6045.png\n",
      "./data_train_images/train/images/Sample6064.png\n",
      "./data_train_images/train/images/Sample6069.png\n",
      "./data_train_images/train/images/Sample6079.png\n",
      "./data_train_images/train/images/Sample6085.png\n",
      "./data_train_images/train/images/Sample6091.png\n",
      "./data_train_images/train/images/Sample6092.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/images/Sample61.png\n",
      "./data_train_images/train/images/Sample6100.png\n",
      "./data_train_images/train/images/Sample6102.png\n",
      "./data_train_images/train/images/Sample6106.png\n",
      "./data_train_images/train/images/Sample6117.png\n",
      "./data_train_images/train/images/Sample6120.png\n",
      "./data_train_images/train/images/Sample6131.png\n",
      "./data_train_images/train/images/Sample6144.png\n",
      "./data_train_images/train/images/Sample6145.png\n",
      "./data_train_images/train/images/Sample6148.png\n",
      "./data_train_images/train/images/Sample6152.png\n",
      "./data_train_images/train/images/Sample6154.png\n",
      "./data_train_images/train/images/Sample6158.png\n",
      "./data_train_images/train/images/Sample6160.png\n",
      "./data_train_images/train/images/Sample6168.png\n",
      "./data_train_images/train/images/Sample6170.png\n",
      "./data_train_images/train/images/Sample6177.png\n",
      "./data_train_images/train/images/Sample6179.png\n",
      "./data_train_images/train/images/Sample6182.png\n",
      "./data_train_images/train/images/Sample62.png\n",
      "./data_train_images/train/images/Sample63.png\n",
      "./data_train_images/train/images/Sample64.png\n",
      "./data_train_images/train/images/Sample65.png\n",
      "./data_train_images/train/images/Sample66.png\n",
      "./data_train_images/train/images/Sample67.png\n",
      "./data_train_images/train/images/Sample68.png\n",
      "./data_train_images/train/images/Sample69.png\n",
      "./data_train_images/train/images/Sample7.png\n",
      "./data_train_images/train/images/Sample70.png\n",
      "./data_train_images/train/images/Sample71.png\n",
      "./data_train_images/train/images/Sample72.png\n",
      "./data_train_images/train/images/Sample73.png\n",
      "./data_train_images/train/images/Sample74.png\n",
      "./data_train_images/train/images/Sample75.png\n",
      "./data_train_images/train/images/Sample76.png\n",
      "./data_train_images/train/images/Sample77.png\n",
      "./data_train_images/train/images/Sample78.png\n",
      "./data_train_images/train/images/Sample79.png\n",
      "./data_train_images/train/images/Sample8.png\n",
      "./data_train_images/train/images/Sample80.png\n",
      "./data_train_images/train/images/Sample81.png\n",
      "./data_train_images/train/images/Sample82.png\n",
      "./data_train_images/train/images/Sample83.png\n",
      "./data_train_images/train/images/Sample84.png\n",
      "./data_train_images/train/images/Sample85.png\n",
      "./data_train_images/train/images/Sample86.png\n",
      "./data_train_images/train/images/Sample87.png\n",
      "./data_train_images/train/images/Sample88.png\n",
      "./data_train_images/train/images/Sample89.png\n",
      "./data_train_images/train/images/Sample9.png\n",
      "./data_train_images/train/images/Sample90.png\n",
      "./data_train_images/train/images/Sample91.png\n",
      "./data_train_images/train/images/Sample92.png\n",
      "./data_train_images/train/images/Sample93.png\n",
      "./data_train_images/train/images/Sample94.png\n",
      "./data_train_images/train/images/Sample95.png\n",
      "./data_train_images/train/images/Sample96.png\n",
      "./data_train_images/train/images/Sample97.png\n",
      "./data_train_images/train/images/Sample98.png\n",
      "./data_train_images/train/images/Sample99.png\n",
      "./data_train_images/train/labels/\n",
      "./data_train_images/train/labels/Sample1.txt\n",
      "./data_train_images/train/labels/Sample10.txt\n",
      "./data_train_images/train/labels/Sample100.txt\n",
      "./data_train_images/train/labels/Sample101.txt\n",
      "./data_train_images/train/labels/Sample1016.txt\n",
      "./data_train_images/train/labels/Sample1018.txt\n",
      "./data_train_images/train/labels/Sample102.txt\n",
      "./data_train_images/train/labels/Sample1025.txt\n",
      "./data_train_images/train/labels/Sample1026.txt\n",
      "./data_train_images/train/labels/Sample103.txt\n",
      "./data_train_images/train/labels/Sample1030.txt\n",
      "./data_train_images/train/labels/Sample1037.txt\n",
      "./data_train_images/train/labels/Sample104.txt\n",
      "./data_train_images/train/labels/Sample1045.txt\n",
      "./data_train_images/train/labels/Sample105.txt\n",
      "./data_train_images/train/labels/Sample1051.txt\n",
      "./data_train_images/train/labels/Sample1053.txt\n",
      "./data_train_images/train/labels/Sample1054.txt\n",
      "./data_train_images/train/labels/Sample1055.txt\n",
      "./data_train_images/train/labels/Sample106.txt\n",
      "./data_train_images/train/labels/Sample1069.txt\n",
      "./data_train_images/train/labels/Sample107.txt\n",
      "./data_train_images/train/labels/Sample108.txt\n",
      "./data_train_images/train/labels/Sample1087.txt\n",
      "./data_train_images/train/labels/Sample109.txt\n",
      "./data_train_images/train/labels/Sample11.txt\n",
      "./data_train_images/train/labels/Sample110.txt\n",
      "./data_train_images/train/labels/Sample1101.txt\n",
      "./data_train_images/train/labels/Sample111.txt\n",
      "./data_train_images/train/labels/Sample112.txt\n",
      "./data_train_images/train/labels/Sample1123.txt\n",
      "./data_train_images/train/labels/Sample1124.txt\n",
      "./data_train_images/train/labels/Sample1126.txt\n",
      "./data_train_images/train/labels/Sample1127.txt\n",
      "./data_train_images/train/labels/Sample113.txt\n",
      "./data_train_images/train/labels/Sample1130.txt\n",
      "./data_train_images/train/labels/Sample1133.txt\n",
      "./data_train_images/train/labels/Sample1139.txt\n",
      "./data_train_images/train/labels/Sample114.txt\n",
      "./data_train_images/train/labels/Sample1142.txt\n",
      "./data_train_images/train/labels/Sample115.txt\n",
      "./data_train_images/train/labels/Sample1159.txt\n",
      "./data_train_images/train/labels/Sample116.txt\n",
      "./data_train_images/train/labels/Sample1165.txt\n",
      "./data_train_images/train/labels/Sample1167.txt\n",
      "./data_train_images/train/labels/Sample117.txt\n",
      "./data_train_images/train/labels/Sample1173.txt\n",
      "./data_train_images/train/labels/Sample118.txt\n",
      "./data_train_images/train/labels/Sample1183.txt\n",
      "./data_train_images/train/labels/Sample1189.txt\n",
      "./data_train_images/train/labels/Sample119.txt\n",
      "./data_train_images/train/labels/Sample12.txt\n",
      "./data_train_images/train/labels/Sample120.txt\n",
      "./data_train_images/train/labels/Sample1203.txt\n",
      "./data_train_images/train/labels/Sample1204.txt\n",
      "./data_train_images/train/labels/Sample121.txt\n",
      "./data_train_images/train/labels/Sample1216.txt\n",
      "./data_train_images/train/labels/Sample1217.txt\n",
      "./data_train_images/train/labels/Sample122.txt\n",
      "./data_train_images/train/labels/Sample1221.txt\n",
      "./data_train_images/train/labels/Sample1225.txt\n",
      "./data_train_images/train/labels/Sample123.txt\n",
      "./data_train_images/train/labels/Sample1235.txt\n",
      "./data_train_images/train/labels/Sample1238.txt\n",
      "./data_train_images/train/labels/Sample1239.txt\n",
      "./data_train_images/train/labels/Sample124.txt\n",
      "./data_train_images/train/labels/Sample1240.txt\n",
      "./data_train_images/train/labels/Sample1244.txt\n",
      "./data_train_images/train/labels/Sample125.txt\n",
      "./data_train_images/train/labels/Sample1252.txt\n",
      "./data_train_images/train/labels/Sample1253.txt\n",
      "./data_train_images/train/labels/Sample126.txt\n",
      "./data_train_images/train/labels/Sample1262.txt\n",
      "./data_train_images/train/labels/Sample1265.txt\n",
      "./data_train_images/train/labels/Sample1269.txt\n",
      "./data_train_images/train/labels/Sample127.txt\n",
      "./data_train_images/train/labels/Sample128.txt\n",
      "./data_train_images/train/labels/Sample1287.txt\n",
      "./data_train_images/train/labels/Sample129.txt\n",
      "./data_train_images/train/labels/Sample1299.txt\n",
      "./data_train_images/train/labels/Sample13.txt\n",
      "./data_train_images/train/labels/Sample130.txt\n",
      "./data_train_images/train/labels/Sample1300.txt\n",
      "./data_train_images/train/labels/Sample1308.txt\n",
      "./data_train_images/train/labels/Sample1309.txt\n",
      "./data_train_images/train/labels/Sample131.txt\n",
      "./data_train_images/train/labels/Sample1311.txt\n",
      "./data_train_images/train/labels/Sample1315.txt\n",
      "./data_train_images/train/labels/Sample1317.txt\n",
      "./data_train_images/train/labels/Sample132.txt\n",
      "./data_train_images/train/labels/Sample133.txt\n",
      "./data_train_images/train/labels/Sample134.txt\n",
      "./data_train_images/train/labels/Sample1341.txt\n",
      "./data_train_images/train/labels/Sample1344.txt\n",
      "./data_train_images/train/labels/Sample135.txt\n",
      "./data_train_images/train/labels/Sample1351.txt\n",
      "./data_train_images/train/labels/Sample1352.txt\n",
      "./data_train_images/train/labels/Sample1356.txt\n",
      "./data_train_images/train/labels/Sample136.txt\n",
      "./data_train_images/train/labels/Sample137.txt\n",
      "./data_train_images/train/labels/Sample1374.txt\n",
      "./data_train_images/train/labels/Sample138.txt\n",
      "./data_train_images/train/labels/Sample1385.txt\n",
      "./data_train_images/train/labels/Sample1387.txt\n",
      "./data_train_images/train/labels/Sample139.txt\n",
      "./data_train_images/train/labels/Sample1390.txt\n",
      "./data_train_images/train/labels/Sample1398.txt\n",
      "./data_train_images/train/labels/Sample14.txt\n",
      "./data_train_images/train/labels/Sample140.txt\n",
      "./data_train_images/train/labels/Sample1405.txt\n",
      "./data_train_images/train/labels/Sample1406.txt\n",
      "./data_train_images/train/labels/Sample141.txt\n",
      "./data_train_images/train/labels/Sample142.txt\n",
      "./data_train_images/train/labels/Sample143.txt\n",
      "./data_train_images/train/labels/Sample1434.txt\n",
      "./data_train_images/train/labels/Sample144.txt\n",
      "./data_train_images/train/labels/Sample1441.txt\n",
      "./data_train_images/train/labels/Sample1443.txt\n",
      "./data_train_images/train/labels/Sample145.txt\n",
      "./data_train_images/train/labels/Sample146.txt\n",
      "./data_train_images/train/labels/Sample147.txt\n",
      "./data_train_images/train/labels/Sample1470.txt\n",
      "./data_train_images/train/labels/Sample1475.txt\n",
      "./data_train_images/train/labels/Sample1477.txt\n",
      "./data_train_images/train/labels/Sample148.txt\n",
      "./data_train_images/train/labels/Sample1481.txt\n",
      "./data_train_images/train/labels/Sample1485.txt\n",
      "./data_train_images/train/labels/Sample1488.txt\n",
      "./data_train_images/train/labels/Sample1489.txt\n",
      "./data_train_images/train/labels/Sample149.txt\n",
      "./data_train_images/train/labels/Sample1493.txt\n",
      "./data_train_images/train/labels/Sample1494.txt\n",
      "./data_train_images/train/labels/Sample15.txt\n",
      "./data_train_images/train/labels/Sample150.txt\n",
      "./data_train_images/train/labels/Sample1504.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/labels/Sample1509.txt\n",
      "./data_train_images/train/labels/Sample151.txt\n",
      "./data_train_images/train/labels/Sample152.txt\n",
      "./data_train_images/train/labels/Sample1520.txt\n",
      "./data_train_images/train/labels/Sample1526.txt\n",
      "./data_train_images/train/labels/Sample153.txt\n",
      "./data_train_images/train/labels/Sample1531.txt\n",
      "./data_train_images/train/labels/Sample1535.txt\n",
      "./data_train_images/train/labels/Sample154.txt\n",
      "./data_train_images/train/labels/Sample1544.txt\n",
      "./data_train_images/train/labels/Sample1545.txt\n",
      "./data_train_images/train/labels/Sample155.txt\n",
      "./data_train_images/train/labels/Sample1553.txt\n",
      "./data_train_images/train/labels/Sample1555.txt\n",
      "./data_train_images/train/labels/Sample1558.txt\n",
      "./data_train_images/train/labels/Sample156.txt\n",
      "./data_train_images/train/labels/Sample1561.txt\n",
      "./data_train_images/train/labels/Sample1563.txt\n",
      "./data_train_images/train/labels/Sample157.txt\n",
      "./data_train_images/train/labels/Sample158.txt\n",
      "./data_train_images/train/labels/Sample1581.txt\n",
      "./data_train_images/train/labels/Sample1583.txt\n",
      "./data_train_images/train/labels/Sample159.txt\n",
      "./data_train_images/train/labels/Sample16.txt\n",
      "./data_train_images/train/labels/Sample160.txt\n",
      "./data_train_images/train/labels/Sample1601.txt\n",
      "./data_train_images/train/labels/Sample161.txt\n",
      "./data_train_images/train/labels/Sample1612.txt\n",
      "./data_train_images/train/labels/Sample162.txt\n",
      "./data_train_images/train/labels/Sample163.txt\n",
      "./data_train_images/train/labels/Sample1632.txt\n",
      "./data_train_images/train/labels/Sample1638.txt\n",
      "./data_train_images/train/labels/Sample164.txt\n",
      "./data_train_images/train/labels/Sample1645.txt\n",
      "./data_train_images/train/labels/Sample165.txt\n",
      "./data_train_images/train/labels/Sample1651.txt\n",
      "./data_train_images/train/labels/Sample1656.txt\n",
      "./data_train_images/train/labels/Sample166.txt\n",
      "./data_train_images/train/labels/Sample1666.txt\n",
      "./data_train_images/train/labels/Sample1667.txt\n",
      "./data_train_images/train/labels/Sample167.txt\n",
      "./data_train_images/train/labels/Sample1673.txt\n",
      "./data_train_images/train/labels/Sample1674.txt\n",
      "./data_train_images/train/labels/Sample1676.txt\n",
      "./data_train_images/train/labels/Sample168.txt\n",
      "./data_train_images/train/labels/Sample169.txt\n",
      "./data_train_images/train/labels/Sample1690.txt\n",
      "./data_train_images/train/labels/Sample1691.txt\n",
      "./data_train_images/train/labels/Sample1695.txt\n",
      "./data_train_images/train/labels/Sample17.txt\n",
      "./data_train_images/train/labels/Sample170.txt\n",
      "./data_train_images/train/labels/Sample1706.txt\n",
      "./data_train_images/train/labels/Sample1708.txt\n",
      "./data_train_images/train/labels/Sample171.txt\n",
      "./data_train_images/train/labels/Sample172.txt\n",
      "./data_train_images/train/labels/Sample1729.txt\n",
      "./data_train_images/train/labels/Sample173.txt\n",
      "./data_train_images/train/labels/Sample1736.txt\n",
      "./data_train_images/train/labels/Sample1737.txt\n",
      "./data_train_images/train/labels/Sample1738.txt\n",
      "./data_train_images/train/labels/Sample1739.txt\n",
      "./data_train_images/train/labels/Sample174.txt\n",
      "./data_train_images/train/labels/Sample1746.txt\n",
      "./data_train_images/train/labels/Sample175.txt\n",
      "./data_train_images/train/labels/Sample1751.txt\n",
      "./data_train_images/train/labels/Sample1753.txt\n",
      "./data_train_images/train/labels/Sample176.txt\n",
      "./data_train_images/train/labels/Sample1767.txt\n",
      "./data_train_images/train/labels/Sample1768.txt\n",
      "./data_train_images/train/labels/Sample1769.txt\n",
      "./data_train_images/train/labels/Sample177.txt\n",
      "./data_train_images/train/labels/Sample1770.txt\n",
      "./data_train_images/train/labels/Sample1777.txt\n",
      "./data_train_images/train/labels/Sample1779.txt\n",
      "./data_train_images/train/labels/Sample178.txt\n",
      "./data_train_images/train/labels/Sample179.txt\n",
      "./data_train_images/train/labels/Sample1790.txt\n",
      "./data_train_images/train/labels/Sample1792.txt\n",
      "./data_train_images/train/labels/Sample1794.txt\n",
      "./data_train_images/train/labels/Sample18.txt\n",
      "./data_train_images/train/labels/Sample180.txt\n",
      "./data_train_images/train/labels/Sample1805.txt\n",
      "./data_train_images/train/labels/Sample1806.txt\n",
      "./data_train_images/train/labels/Sample1807.txt\n",
      "./data_train_images/train/labels/Sample181.txt\n",
      "./data_train_images/train/labels/Sample1813.txt\n",
      "./data_train_images/train/labels/Sample1815.txt\n",
      "./data_train_images/train/labels/Sample1819.txt\n",
      "./data_train_images/train/labels/Sample182.txt\n",
      "./data_train_images/train/labels/Sample1821.txt\n",
      "./data_train_images/train/labels/Sample1828.txt\n",
      "./data_train_images/train/labels/Sample183.txt\n",
      "./data_train_images/train/labels/Sample1830.txt\n",
      "./data_train_images/train/labels/Sample184.txt\n",
      "./data_train_images/train/labels/Sample1840.txt\n",
      "./data_train_images/train/labels/Sample1842.txt\n",
      "./data_train_images/train/labels/Sample185.txt\n",
      "./data_train_images/train/labels/Sample1854.txt\n",
      "./data_train_images/train/labels/Sample1856.txt\n",
      "./data_train_images/train/labels/Sample186.txt\n",
      "./data_train_images/train/labels/Sample1862.txt\n",
      "./data_train_images/train/labels/Sample187.txt\n",
      "./data_train_images/train/labels/Sample1871.txt\n",
      "./data_train_images/train/labels/Sample1876.txt\n",
      "./data_train_images/train/labels/Sample1879.txt\n",
      "./data_train_images/train/labels/Sample188.txt\n",
      "./data_train_images/train/labels/Sample189.txt\n",
      "./data_train_images/train/labels/Sample1892.txt\n",
      "./data_train_images/train/labels/Sample1896.txt\n",
      "./data_train_images/train/labels/Sample19.txt\n",
      "./data_train_images/train/labels/Sample190.txt\n",
      "./data_train_images/train/labels/Sample191.txt\n",
      "./data_train_images/train/labels/Sample1910.txt\n",
      "./data_train_images/train/labels/Sample1911.txt\n",
      "./data_train_images/train/labels/Sample192.txt\n",
      "./data_train_images/train/labels/Sample1922.txt\n",
      "./data_train_images/train/labels/Sample1926.txt\n",
      "./data_train_images/train/labels/Sample193.txt\n",
      "./data_train_images/train/labels/Sample1933.txt\n",
      "./data_train_images/train/labels/Sample1936.txt\n",
      "./data_train_images/train/labels/Sample1937.txt\n",
      "./data_train_images/train/labels/Sample194.txt\n",
      "./data_train_images/train/labels/Sample1940.txt\n",
      "./data_train_images/train/labels/Sample1944.txt\n",
      "./data_train_images/train/labels/Sample195.txt\n",
      "./data_train_images/train/labels/Sample1954.txt\n",
      "./data_train_images/train/labels/Sample196.txt\n",
      "./data_train_images/train/labels/Sample1964.txt\n",
      "./data_train_images/train/labels/Sample197.txt\n",
      "./data_train_images/train/labels/Sample1970.txt\n",
      "./data_train_images/train/labels/Sample1975.txt\n",
      "./data_train_images/train/labels/Sample1979.txt\n",
      "./data_train_images/train/labels/Sample198.txt\n",
      "./data_train_images/train/labels/Sample1988.txt\n",
      "./data_train_images/train/labels/Sample199.txt\n",
      "./data_train_images/train/labels/Sample1997.txt\n",
      "./data_train_images/train/labels/Sample2.txt\n",
      "./data_train_images/train/labels/Sample20.txt\n",
      "./data_train_images/train/labels/Sample200.txt\n",
      "./data_train_images/train/labels/Sample2003.txt\n",
      "./data_train_images/train/labels/Sample201.txt\n",
      "./data_train_images/train/labels/Sample202.txt\n",
      "./data_train_images/train/labels/Sample2021.txt\n",
      "./data_train_images/train/labels/Sample2023.txt\n",
      "./data_train_images/train/labels/Sample2025.txt\n",
      "./data_train_images/train/labels/Sample2027.txt\n",
      "./data_train_images/train/labels/Sample203.txt\n",
      "./data_train_images/train/labels/Sample2030.txt\n",
      "./data_train_images/train/labels/Sample204.txt\n",
      "./data_train_images/train/labels/Sample2042.txt\n",
      "./data_train_images/train/labels/Sample205.txt\n",
      "./data_train_images/train/labels/Sample2054.txt\n",
      "./data_train_images/train/labels/Sample2055.txt\n",
      "./data_train_images/train/labels/Sample2059.txt\n",
      "./data_train_images/train/labels/Sample206.txt\n",
      "./data_train_images/train/labels/Sample2063.txt\n",
      "./data_train_images/train/labels/Sample2065.txt\n",
      "./data_train_images/train/labels/Sample2067.txt\n",
      "./data_train_images/train/labels/Sample207.txt\n",
      "./data_train_images/train/labels/Sample2072.txt\n",
      "./data_train_images/train/labels/Sample2073.txt\n",
      "./data_train_images/train/labels/Sample208.txt\n",
      "./data_train_images/train/labels/Sample2080.txt\n",
      "./data_train_images/train/labels/Sample209.txt\n",
      "./data_train_images/train/labels/Sample2092.txt\n",
      "./data_train_images/train/labels/Sample2094.txt\n",
      "./data_train_images/train/labels/Sample2097.txt\n",
      "./data_train_images/train/labels/Sample2098.txt\n",
      "./data_train_images/train/labels/Sample21.txt\n",
      "./data_train_images/train/labels/Sample210.txt\n",
      "./data_train_images/train/labels/Sample211.txt\n",
      "./data_train_images/train/labels/Sample2111.txt\n",
      "./data_train_images/train/labels/Sample2117.txt\n",
      "./data_train_images/train/labels/Sample212.txt\n",
      "./data_train_images/train/labels/Sample2128.txt\n",
      "./data_train_images/train/labels/Sample213.txt\n",
      "./data_train_images/train/labels/Sample2137.txt\n",
      "./data_train_images/train/labels/Sample214.txt\n",
      "./data_train_images/train/labels/Sample2140.txt\n",
      "./data_train_images/train/labels/Sample2142.txt\n",
      "./data_train_images/train/labels/Sample215.txt\n",
      "./data_train_images/train/labels/Sample2151.txt\n",
      "./data_train_images/train/labels/Sample2154.txt\n",
      "./data_train_images/train/labels/Sample216.txt\n",
      "./data_train_images/train/labels/Sample2161.txt\n",
      "./data_train_images/train/labels/Sample2162.txt\n",
      "./data_train_images/train/labels/Sample2164.txt\n",
      "./data_train_images/train/labels/Sample2168.txt\n",
      "./data_train_images/train/labels/Sample217.txt\n",
      "./data_train_images/train/labels/Sample2170.txt\n",
      "./data_train_images/train/labels/Sample2172.txt\n",
      "./data_train_images/train/labels/Sample2176.txt\n",
      "./data_train_images/train/labels/Sample218.txt\n",
      "./data_train_images/train/labels/Sample2180.txt\n",
      "./data_train_images/train/labels/Sample219.txt\n",
      "./data_train_images/train/labels/Sample2193.txt\n",
      "./data_train_images/train/labels/Sample22.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/labels/Sample220.txt\n",
      "./data_train_images/train/labels/Sample221.txt\n",
      "./data_train_images/train/labels/Sample2211.txt\n",
      "./data_train_images/train/labels/Sample2218.txt\n",
      "./data_train_images/train/labels/Sample222.txt\n",
      "./data_train_images/train/labels/Sample2223.txt\n",
      "./data_train_images/train/labels/Sample2227.txt\n",
      "./data_train_images/train/labels/Sample223.txt\n",
      "./data_train_images/train/labels/Sample2230.txt\n",
      "./data_train_images/train/labels/Sample2235.txt\n",
      "./data_train_images/train/labels/Sample224.txt\n",
      "./data_train_images/train/labels/Sample2249.txt\n",
      "./data_train_images/train/labels/Sample225.txt\n",
      "./data_train_images/train/labels/Sample2250.txt\n",
      "./data_train_images/train/labels/Sample2251.txt\n",
      "./data_train_images/train/labels/Sample2254.txt\n",
      "./data_train_images/train/labels/Sample226.txt\n",
      "./data_train_images/train/labels/Sample227.txt\n",
      "./data_train_images/train/labels/Sample228.txt\n",
      "./data_train_images/train/labels/Sample229.txt\n",
      "./data_train_images/train/labels/Sample2292.txt\n",
      "./data_train_images/train/labels/Sample23.txt\n",
      "./data_train_images/train/labels/Sample230.txt\n",
      "./data_train_images/train/labels/Sample2303.txt\n",
      "./data_train_images/train/labels/Sample2304.txt\n",
      "./data_train_images/train/labels/Sample2309.txt\n",
      "./data_train_images/train/labels/Sample231.txt\n",
      "./data_train_images/train/labels/Sample232.txt\n",
      "./data_train_images/train/labels/Sample2325.txt\n",
      "./data_train_images/train/labels/Sample2328.txt\n",
      "./data_train_images/train/labels/Sample233.txt\n",
      "./data_train_images/train/labels/Sample2330.txt\n",
      "./data_train_images/train/labels/Sample234.txt\n",
      "./data_train_images/train/labels/Sample235.txt\n",
      "./data_train_images/train/labels/Sample2355.txt\n",
      "./data_train_images/train/labels/Sample2359.txt\n",
      "./data_train_images/train/labels/Sample236.txt\n",
      "./data_train_images/train/labels/Sample2365.txt\n",
      "./data_train_images/train/labels/Sample2367.txt\n",
      "./data_train_images/train/labels/Sample237.txt\n",
      "./data_train_images/train/labels/Sample238.txt\n",
      "./data_train_images/train/labels/Sample2389.txt\n",
      "./data_train_images/train/labels/Sample239.txt\n",
      "./data_train_images/train/labels/Sample2392.txt\n",
      "./data_train_images/train/labels/Sample2393.txt\n",
      "./data_train_images/train/labels/Sample24.txt\n",
      "./data_train_images/train/labels/Sample240.txt\n",
      "./data_train_images/train/labels/Sample2405.txt\n",
      "./data_train_images/train/labels/Sample241.txt\n",
      "./data_train_images/train/labels/Sample2415.txt\n",
      "./data_train_images/train/labels/Sample242.txt\n",
      "./data_train_images/train/labels/Sample2422.txt\n",
      "./data_train_images/train/labels/Sample2425.txt\n",
      "./data_train_images/train/labels/Sample243.txt\n",
      "./data_train_images/train/labels/Sample2436.txt\n",
      "./data_train_images/train/labels/Sample2438.txt\n",
      "./data_train_images/train/labels/Sample244.txt\n",
      "./data_train_images/train/labels/Sample2441.txt\n",
      "./data_train_images/train/labels/Sample245.txt\n",
      "./data_train_images/train/labels/Sample2452.txt\n",
      "./data_train_images/train/labels/Sample246.txt\n",
      "./data_train_images/train/labels/Sample2460.txt\n",
      "./data_train_images/train/labels/Sample247.txt\n",
      "./data_train_images/train/labels/Sample2475.txt\n",
      "./data_train_images/train/labels/Sample2477.txt\n",
      "./data_train_images/train/labels/Sample248.txt\n",
      "./data_train_images/train/labels/Sample2485.txt\n",
      "./data_train_images/train/labels/Sample249.txt\n",
      "./data_train_images/train/labels/Sample2491.txt\n",
      "./data_train_images/train/labels/Sample25.txt\n",
      "./data_train_images/train/labels/Sample250.txt\n",
      "./data_train_images/train/labels/Sample2504.txt\n",
      "./data_train_images/train/labels/Sample251.txt\n",
      "./data_train_images/train/labels/Sample2510.txt\n",
      "./data_train_images/train/labels/Sample2515.txt\n",
      "./data_train_images/train/labels/Sample2516.txt\n",
      "./data_train_images/train/labels/Sample252.txt\n",
      "./data_train_images/train/labels/Sample253.txt\n",
      "./data_train_images/train/labels/Sample2530.txt\n",
      "./data_train_images/train/labels/Sample2531.txt\n",
      "./data_train_images/train/labels/Sample2537.txt\n",
      "./data_train_images/train/labels/Sample254.txt\n",
      "./data_train_images/train/labels/Sample2546.txt\n",
      "./data_train_images/train/labels/Sample2547.txt\n",
      "./data_train_images/train/labels/Sample2548.txt\n",
      "./data_train_images/train/labels/Sample255.txt\n",
      "./data_train_images/train/labels/Sample2554.txt\n",
      "./data_train_images/train/labels/Sample2558.txt\n",
      "./data_train_images/train/labels/Sample256.txt\n",
      "./data_train_images/train/labels/Sample257.txt\n",
      "./data_train_images/train/labels/Sample2572.txt\n",
      "./data_train_images/train/labels/Sample2579.txt\n",
      "./data_train_images/train/labels/Sample258.txt\n",
      "./data_train_images/train/labels/Sample2586.txt\n",
      "./data_train_images/train/labels/Sample259.txt\n",
      "./data_train_images/train/labels/Sample2592.txt\n",
      "./data_train_images/train/labels/Sample2598.txt\n",
      "./data_train_images/train/labels/Sample26.txt\n",
      "./data_train_images/train/labels/Sample260.txt\n",
      "./data_train_images/train/labels/Sample2604.txt\n",
      "./data_train_images/train/labels/Sample261.txt\n",
      "./data_train_images/train/labels/Sample2616.txt\n",
      "./data_train_images/train/labels/Sample2618.txt\n",
      "./data_train_images/train/labels/Sample262.txt\n",
      "./data_train_images/train/labels/Sample2629.txt\n",
      "./data_train_images/train/labels/Sample263.txt\n",
      "./data_train_images/train/labels/Sample2631.txt\n",
      "./data_train_images/train/labels/Sample2637.txt\n",
      "./data_train_images/train/labels/Sample264.txt\n",
      "./data_train_images/train/labels/Sample2642.txt\n",
      "./data_train_images/train/labels/Sample265.txt\n",
      "./data_train_images/train/labels/Sample2653.txt\n",
      "./data_train_images/train/labels/Sample2655.txt\n",
      "./data_train_images/train/labels/Sample266.txt\n",
      "./data_train_images/train/labels/Sample267.txt\n",
      "./data_train_images/train/labels/Sample2671.txt\n",
      "./data_train_images/train/labels/Sample2674.txt\n",
      "./data_train_images/train/labels/Sample268.txt\n",
      "./data_train_images/train/labels/Sample2681.txt\n",
      "./data_train_images/train/labels/Sample269.txt\n",
      "./data_train_images/train/labels/Sample2696.txt\n",
      "./data_train_images/train/labels/Sample2698.txt\n",
      "./data_train_images/train/labels/Sample27.txt\n",
      "./data_train_images/train/labels/Sample270.txt\n",
      "./data_train_images/train/labels/Sample271.txt\n",
      "./data_train_images/train/labels/Sample2715.txt\n",
      "./data_train_images/train/labels/Sample272.txt\n",
      "./data_train_images/train/labels/Sample2725.txt\n",
      "./data_train_images/train/labels/Sample273.txt\n",
      "./data_train_images/train/labels/Sample2733.txt\n",
      "./data_train_images/train/labels/Sample2736.txt\n",
      "./data_train_images/train/labels/Sample2738.txt\n",
      "./data_train_images/train/labels/Sample274.txt\n",
      "./data_train_images/train/labels/Sample2745.txt\n",
      "./data_train_images/train/labels/Sample2746.txt\n",
      "./data_train_images/train/labels/Sample275.txt\n",
      "./data_train_images/train/labels/Sample276.txt\n",
      "./data_train_images/train/labels/Sample2762.txt\n",
      "./data_train_images/train/labels/Sample2763.txt\n",
      "./data_train_images/train/labels/Sample2765.txt\n",
      "./data_train_images/train/labels/Sample277.txt\n",
      "./data_train_images/train/labels/Sample2776.txt\n",
      "./data_train_images/train/labels/Sample278.txt\n",
      "./data_train_images/train/labels/Sample2780.txt\n",
      "./data_train_images/train/labels/Sample2788.txt\n",
      "./data_train_images/train/labels/Sample2789.txt\n",
      "./data_train_images/train/labels/Sample279.txt\n",
      "./data_train_images/train/labels/Sample2791.txt\n",
      "./data_train_images/train/labels/Sample2795.txt\n",
      "./data_train_images/train/labels/Sample2796.txt\n",
      "./data_train_images/train/labels/Sample2799.txt\n",
      "./data_train_images/train/labels/Sample28.txt\n",
      "./data_train_images/train/labels/Sample280.txt\n",
      "./data_train_images/train/labels/Sample2800.txt\n",
      "./data_train_images/train/labels/Sample281.txt\n",
      "./data_train_images/train/labels/Sample2810.txt\n",
      "./data_train_images/train/labels/Sample282.txt\n",
      "./data_train_images/train/labels/Sample283.txt\n",
      "./data_train_images/train/labels/Sample284.txt\n",
      "./data_train_images/train/labels/Sample2849.txt\n",
      "./data_train_images/train/labels/Sample2868.txt\n",
      "./data_train_images/train/labels/Sample2870.txt\n",
      "./data_train_images/train/labels/Sample2874.txt\n",
      "./data_train_images/train/labels/Sample2877.txt\n",
      "./data_train_images/train/labels/Sample2893.txt\n",
      "./data_train_images/train/labels/Sample2894.txt\n",
      "./data_train_images/train/labels/Sample2895.txt\n",
      "./data_train_images/train/labels/Sample2898.txt\n",
      "./data_train_images/train/labels/Sample29.txt\n",
      "./data_train_images/train/labels/Sample2906.txt\n",
      "./data_train_images/train/labels/Sample2913.txt\n",
      "./data_train_images/train/labels/Sample2925.txt\n",
      "./data_train_images/train/labels/Sample2928.txt\n",
      "./data_train_images/train/labels/Sample2934.txt\n",
      "./data_train_images/train/labels/Sample2947.txt\n",
      "./data_train_images/train/labels/Sample2949.txt\n",
      "./data_train_images/train/labels/Sample2950.txt\n",
      "./data_train_images/train/labels/Sample2952.txt\n",
      "./data_train_images/train/labels/Sample2977.txt\n",
      "./data_train_images/train/labels/Sample2981.txt\n",
      "./data_train_images/train/labels/Sample2990.txt\n",
      "./data_train_images/train/labels/Sample2993.txt\n",
      "./data_train_images/train/labels/Sample2999.txt\n",
      "./data_train_images/train/labels/Sample3.txt\n",
      "./data_train_images/train/labels/Sample30.txt\n",
      "./data_train_images/train/labels/Sample3008.txt\n",
      "./data_train_images/train/labels/Sample3011.txt\n",
      "./data_train_images/train/labels/Sample3017.txt\n",
      "./data_train_images/train/labels/Sample3020.txt\n",
      "./data_train_images/train/labels/Sample3024.txt\n",
      "./data_train_images/train/labels/Sample3030.txt\n",
      "./data_train_images/train/labels/Sample3035.txt\n",
      "./data_train_images/train/labels/Sample3043.txt\n",
      "./data_train_images/train/labels/Sample3051.txt\n",
      "./data_train_images/train/labels/Sample3057.txt\n",
      "./data_train_images/train/labels/Sample3060.txt\n",
      "./data_train_images/train/labels/Sample3064.txt\n",
      "./data_train_images/train/labels/Sample3067.txt\n",
      "./data_train_images/train/labels/Sample3070.txt\n",
      "./data_train_images/train/labels/Sample3071.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/labels/Sample3072.txt\n",
      "./data_train_images/train/labels/Sample3074.txt\n",
      "./data_train_images/train/labels/Sample3077.txt\n",
      "./data_train_images/train/labels/Sample3085.txt\n",
      "./data_train_images/train/labels/Sample3086.txt\n",
      "./data_train_images/train/labels/Sample31.txt\n",
      "./data_train_images/train/labels/Sample3104.txt\n",
      "./data_train_images/train/labels/Sample3106.txt\n",
      "./data_train_images/train/labels/Sample3114.txt\n",
      "./data_train_images/train/labels/Sample3116.txt\n",
      "./data_train_images/train/labels/Sample3119.txt\n",
      "./data_train_images/train/labels/Sample3125.txt\n",
      "./data_train_images/train/labels/Sample3127.txt\n",
      "./data_train_images/train/labels/Sample3133.txt\n",
      "./data_train_images/train/labels/Sample3135.txt\n",
      "./data_train_images/train/labels/Sample3146.txt\n",
      "./data_train_images/train/labels/Sample3163.txt\n",
      "./data_train_images/train/labels/Sample3167.txt\n",
      "./data_train_images/train/labels/Sample3170.txt\n",
      "./data_train_images/train/labels/Sample3186.txt\n",
      "./data_train_images/train/labels/Sample3189.txt\n",
      "./data_train_images/train/labels/Sample3199.txt\n",
      "./data_train_images/train/labels/Sample32.txt\n",
      "./data_train_images/train/labels/Sample3201.txt\n",
      "./data_train_images/train/labels/Sample3206.txt\n",
      "./data_train_images/train/labels/Sample3208.txt\n",
      "./data_train_images/train/labels/Sample3212.txt\n",
      "./data_train_images/train/labels/Sample3215.txt\n",
      "./data_train_images/train/labels/Sample3223.txt\n",
      "./data_train_images/train/labels/Sample3224.txt\n",
      "./data_train_images/train/labels/Sample3238.txt\n",
      "./data_train_images/train/labels/Sample3242.txt\n",
      "./data_train_images/train/labels/Sample3251.txt\n",
      "./data_train_images/train/labels/Sample3267.txt\n",
      "./data_train_images/train/labels/Sample3273.txt\n",
      "./data_train_images/train/labels/Sample3283.txt\n",
      "./data_train_images/train/labels/Sample3290.txt\n",
      "./data_train_images/train/labels/Sample3292.txt\n",
      "./data_train_images/train/labels/Sample3294.txt\n",
      "./data_train_images/train/labels/Sample33.txt\n",
      "./data_train_images/train/labels/Sample3302.txt\n",
      "./data_train_images/train/labels/Sample3307.txt\n",
      "./data_train_images/train/labels/Sample3315.txt\n",
      "./data_train_images/train/labels/Sample3317.txt\n",
      "./data_train_images/train/labels/Sample3329.txt\n",
      "./data_train_images/train/labels/Sample3332.txt\n",
      "./data_train_images/train/labels/Sample3333.txt\n",
      "./data_train_images/train/labels/Sample3342.txt\n",
      "./data_train_images/train/labels/Sample3351.txt\n",
      "./data_train_images/train/labels/Sample3362.txt\n",
      "./data_train_images/train/labels/Sample3377.txt\n",
      "./data_train_images/train/labels/Sample3379.txt\n",
      "./data_train_images/train/labels/Sample3382.txt\n",
      "./data_train_images/train/labels/Sample3385.txt\n",
      "./data_train_images/train/labels/Sample3389.txt\n",
      "./data_train_images/train/labels/Sample3393.txt\n",
      "./data_train_images/train/labels/Sample34.txt\n",
      "./data_train_images/train/labels/Sample3407.txt\n",
      "./data_train_images/train/labels/Sample3411.txt\n",
      "./data_train_images/train/labels/Sample3425.txt\n",
      "./data_train_images/train/labels/Sample3434.txt\n",
      "./data_train_images/train/labels/Sample3435.txt\n",
      "./data_train_images/train/labels/Sample3436.txt\n",
      "./data_train_images/train/labels/Sample3439.txt\n",
      "./data_train_images/train/labels/Sample3453.txt\n",
      "./data_train_images/train/labels/Sample3459.txt\n",
      "./data_train_images/train/labels/Sample3467.txt\n",
      "./data_train_images/train/labels/Sample3479.txt\n",
      "./data_train_images/train/labels/Sample3480.txt\n",
      "./data_train_images/train/labels/Sample3484.txt\n",
      "./data_train_images/train/labels/Sample3486.txt\n",
      "./data_train_images/train/labels/Sample3487.txt\n",
      "./data_train_images/train/labels/Sample3489.txt\n",
      "./data_train_images/train/labels/Sample3491.txt\n",
      "./data_train_images/train/labels/Sample3492.txt\n",
      "./data_train_images/train/labels/Sample3496.txt\n",
      "./data_train_images/train/labels/Sample35.txt\n",
      "./data_train_images/train/labels/Sample3500.txt\n",
      "./data_train_images/train/labels/Sample3502.txt\n",
      "./data_train_images/train/labels/Sample3505.txt\n",
      "./data_train_images/train/labels/Sample3506.txt\n",
      "./data_train_images/train/labels/Sample3517.txt\n",
      "./data_train_images/train/labels/Sample3530.txt\n",
      "./data_train_images/train/labels/Sample3531.txt\n",
      "./data_train_images/train/labels/Sample3537.txt\n",
      "./data_train_images/train/labels/Sample3542.txt\n",
      "./data_train_images/train/labels/Sample3550.txt\n",
      "./data_train_images/train/labels/Sample3552.txt\n",
      "./data_train_images/train/labels/Sample3561.txt\n",
      "./data_train_images/train/labels/Sample3589.txt\n",
      "./data_train_images/train/labels/Sample3591.txt\n",
      "./data_train_images/train/labels/Sample3592.txt\n",
      "./data_train_images/train/labels/Sample3595.txt\n",
      "./data_train_images/train/labels/Sample3596.txt\n",
      "./data_train_images/train/labels/Sample3597.txt\n",
      "./data_train_images/train/labels/Sample36.txt\n",
      "./data_train_images/train/labels/Sample3604.txt\n",
      "./data_train_images/train/labels/Sample3606.txt\n",
      "./data_train_images/train/labels/Sample3637.txt\n",
      "./data_train_images/train/labels/Sample3645.txt\n",
      "./data_train_images/train/labels/Sample3646.txt\n",
      "./data_train_images/train/labels/Sample3648.txt\n",
      "./data_train_images/train/labels/Sample3649.txt\n",
      "./data_train_images/train/labels/Sample3676.txt\n",
      "./data_train_images/train/labels/Sample3677.txt\n",
      "./data_train_images/train/labels/Sample3678.txt\n",
      "./data_train_images/train/labels/Sample3680.txt\n",
      "./data_train_images/train/labels/Sample3681.txt\n",
      "./data_train_images/train/labels/Sample3688.txt\n",
      "./data_train_images/train/labels/Sample3692.txt\n",
      "./data_train_images/train/labels/Sample3694.txt\n",
      "./data_train_images/train/labels/Sample3695.txt\n",
      "./data_train_images/train/labels/Sample3698.txt\n",
      "./data_train_images/train/labels/Sample37.txt\n",
      "./data_train_images/train/labels/Sample3700.txt\n",
      "./data_train_images/train/labels/Sample3706.txt\n",
      "./data_train_images/train/labels/Sample3707.txt\n",
      "./data_train_images/train/labels/Sample3711.txt\n",
      "./data_train_images/train/labels/Sample3716.txt\n",
      "./data_train_images/train/labels/Sample3723.txt\n",
      "./data_train_images/train/labels/Sample3726.txt\n",
      "./data_train_images/train/labels/Sample3732.txt\n",
      "./data_train_images/train/labels/Sample3733.txt\n",
      "./data_train_images/train/labels/Sample3735.txt\n",
      "./data_train_images/train/labels/Sample3743.txt\n",
      "./data_train_images/train/labels/Sample3748.txt\n",
      "./data_train_images/train/labels/Sample3754.txt\n",
      "./data_train_images/train/labels/Sample3762.txt\n",
      "./data_train_images/train/labels/Sample3774.txt\n",
      "./data_train_images/train/labels/Sample3775.txt\n",
      "./data_train_images/train/labels/Sample3782.txt\n",
      "./data_train_images/train/labels/Sample3786.txt\n",
      "./data_train_images/train/labels/Sample3790.txt\n",
      "./data_train_images/train/labels/Sample3791.txt\n",
      "./data_train_images/train/labels/Sample3799.txt\n",
      "./data_train_images/train/labels/Sample38.txt\n",
      "./data_train_images/train/labels/Sample3815.txt\n",
      "./data_train_images/train/labels/Sample3816.txt\n",
      "./data_train_images/train/labels/Sample3818.txt\n",
      "./data_train_images/train/labels/Sample3822.txt\n",
      "./data_train_images/train/labels/Sample3829.txt\n",
      "./data_train_images/train/labels/Sample3830.txt\n",
      "./data_train_images/train/labels/Sample3835.txt\n",
      "./data_train_images/train/labels/Sample3836.txt\n",
      "./data_train_images/train/labels/Sample3847.txt\n",
      "./data_train_images/train/labels/Sample3855.txt\n",
      "./data_train_images/train/labels/Sample3864.txt\n",
      "./data_train_images/train/labels/Sample3876.txt\n",
      "./data_train_images/train/labels/Sample3877.txt\n",
      "./data_train_images/train/labels/Sample3879.txt\n",
      "./data_train_images/train/labels/Sample39.txt\n",
      "./data_train_images/train/labels/Sample3903.txt\n",
      "./data_train_images/train/labels/Sample3907.txt\n",
      "./data_train_images/train/labels/Sample3911.txt\n",
      "./data_train_images/train/labels/Sample3922.txt\n",
      "./data_train_images/train/labels/Sample3924.txt\n",
      "./data_train_images/train/labels/Sample3930.txt\n",
      "./data_train_images/train/labels/Sample3934.txt\n",
      "./data_train_images/train/labels/Sample3939.txt\n",
      "./data_train_images/train/labels/Sample3949.txt\n",
      "./data_train_images/train/labels/Sample3951.txt\n",
      "./data_train_images/train/labels/Sample3956.txt\n",
      "./data_train_images/train/labels/Sample3959.txt\n",
      "./data_train_images/train/labels/Sample3967.txt\n",
      "./data_train_images/train/labels/Sample3969.txt\n",
      "./data_train_images/train/labels/Sample3978.txt\n",
      "./data_train_images/train/labels/Sample3992.txt\n",
      "./data_train_images/train/labels/Sample3993.txt\n",
      "./data_train_images/train/labels/Sample4.txt\n",
      "./data_train_images/train/labels/Sample40.txt\n",
      "./data_train_images/train/labels/Sample4001.txt\n",
      "./data_train_images/train/labels/Sample4022.txt\n",
      "./data_train_images/train/labels/Sample4029.txt\n",
      "./data_train_images/train/labels/Sample4034.txt\n",
      "./data_train_images/train/labels/Sample4049.txt\n",
      "./data_train_images/train/labels/Sample4052.txt\n",
      "./data_train_images/train/labels/Sample4063.txt\n",
      "./data_train_images/train/labels/Sample4066.txt\n",
      "./data_train_images/train/labels/Sample4068.txt\n",
      "./data_train_images/train/labels/Sample4072.txt\n",
      "./data_train_images/train/labels/Sample41.txt\n",
      "./data_train_images/train/labels/Sample4108.txt\n",
      "./data_train_images/train/labels/Sample4111.txt\n",
      "./data_train_images/train/labels/Sample4115.txt\n",
      "./data_train_images/train/labels/Sample4120.txt\n",
      "./data_train_images/train/labels/Sample4123.txt\n",
      "./data_train_images/train/labels/Sample4126.txt\n",
      "./data_train_images/train/labels/Sample4146.txt\n",
      "./data_train_images/train/labels/Sample4149.txt\n",
      "./data_train_images/train/labels/Sample4152.txt\n",
      "./data_train_images/train/labels/Sample4156.txt\n",
      "./data_train_images/train/labels/Sample4162.txt\n",
      "./data_train_images/train/labels/Sample4164.txt\n",
      "./data_train_images/train/labels/Sample4180.txt\n",
      "./data_train_images/train/labels/Sample4182.txt\n",
      "./data_train_images/train/labels/Sample4183.txt\n",
      "./data_train_images/train/labels/Sample42.txt\n",
      "./data_train_images/train/labels/Sample4221.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/labels/Sample4228.txt\n",
      "./data_train_images/train/labels/Sample4233.txt\n",
      "./data_train_images/train/labels/Sample4250.txt\n",
      "./data_train_images/train/labels/Sample4259.txt\n",
      "./data_train_images/train/labels/Sample4266.txt\n",
      "./data_train_images/train/labels/Sample4270.txt\n",
      "./data_train_images/train/labels/Sample43.txt\n",
      "./data_train_images/train/labels/Sample4328.txt\n",
      "./data_train_images/train/labels/Sample4330.txt\n",
      "./data_train_images/train/labels/Sample4339.txt\n",
      "./data_train_images/train/labels/Sample4344.txt\n",
      "./data_train_images/train/labels/Sample4361.txt\n",
      "./data_train_images/train/labels/Sample4369.txt\n",
      "./data_train_images/train/labels/Sample4379.txt\n",
      "./data_train_images/train/labels/Sample4382.txt\n",
      "./data_train_images/train/labels/Sample4389.txt\n",
      "./data_train_images/train/labels/Sample4391.txt\n",
      "./data_train_images/train/labels/Sample4399.txt\n",
      "./data_train_images/train/labels/Sample44.txt\n",
      "./data_train_images/train/labels/Sample4400.txt\n",
      "./data_train_images/train/labels/Sample4402.txt\n",
      "./data_train_images/train/labels/Sample4408.txt\n",
      "./data_train_images/train/labels/Sample4410.txt\n",
      "./data_train_images/train/labels/Sample4423.txt\n",
      "./data_train_images/train/labels/Sample4424.txt\n",
      "./data_train_images/train/labels/Sample4426.txt\n",
      "./data_train_images/train/labels/Sample4437.txt\n",
      "./data_train_images/train/labels/Sample4443.txt\n",
      "./data_train_images/train/labels/Sample4445.txt\n",
      "./data_train_images/train/labels/Sample4446.txt\n",
      "./data_train_images/train/labels/Sample4459.txt\n",
      "./data_train_images/train/labels/Sample4461.txt\n",
      "./data_train_images/train/labels/Sample4464.txt\n",
      "./data_train_images/train/labels/Sample4468.txt\n",
      "./data_train_images/train/labels/Sample4487.txt\n",
      "./data_train_images/train/labels/Sample4489.txt\n",
      "./data_train_images/train/labels/Sample4490.txt\n",
      "./data_train_images/train/labels/Sample4497.txt\n",
      "./data_train_images/train/labels/Sample45.txt\n",
      "./data_train_images/train/labels/Sample4514.txt\n",
      "./data_train_images/train/labels/Sample4520.txt\n",
      "./data_train_images/train/labels/Sample4521.txt\n",
      "./data_train_images/train/labels/Sample4522.txt\n",
      "./data_train_images/train/labels/Sample4536.txt\n",
      "./data_train_images/train/labels/Sample4540.txt\n",
      "./data_train_images/train/labels/Sample4544.txt\n",
      "./data_train_images/train/labels/Sample4552.txt\n",
      "./data_train_images/train/labels/Sample4562.txt\n",
      "./data_train_images/train/labels/Sample4566.txt\n",
      "./data_train_images/train/labels/Sample4567.txt\n",
      "./data_train_images/train/labels/Sample4575.txt\n",
      "./data_train_images/train/labels/Sample4578.txt\n",
      "./data_train_images/train/labels/Sample4586.txt\n",
      "./data_train_images/train/labels/Sample4591.txt\n",
      "./data_train_images/train/labels/Sample4593.txt\n",
      "./data_train_images/train/labels/Sample4596.txt\n",
      "./data_train_images/train/labels/Sample4598.txt\n",
      "./data_train_images/train/labels/Sample46.txt\n",
      "./data_train_images/train/labels/Sample4608.txt\n",
      "./data_train_images/train/labels/Sample4611.txt\n",
      "./data_train_images/train/labels/Sample4615.txt\n",
      "./data_train_images/train/labels/Sample4639.txt\n",
      "./data_train_images/train/labels/Sample4640.txt\n",
      "./data_train_images/train/labels/Sample4643.txt\n",
      "./data_train_images/train/labels/Sample4647.txt\n",
      "./data_train_images/train/labels/Sample4650.txt\n",
      "./data_train_images/train/labels/Sample4672.txt\n",
      "./data_train_images/train/labels/Sample4676.txt\n",
      "./data_train_images/train/labels/Sample4678.txt\n",
      "./data_train_images/train/labels/Sample4681.txt\n",
      "./data_train_images/train/labels/Sample47.txt\n",
      "./data_train_images/train/labels/Sample4709.txt\n",
      "./data_train_images/train/labels/Sample4714.txt\n",
      "./data_train_images/train/labels/Sample4715.txt\n",
      "./data_train_images/train/labels/Sample4724.txt\n",
      "./data_train_images/train/labels/Sample4726.txt\n",
      "./data_train_images/train/labels/Sample4727.txt\n",
      "./data_train_images/train/labels/Sample4728.txt\n",
      "./data_train_images/train/labels/Sample4731.txt\n",
      "./data_train_images/train/labels/Sample4747.txt\n",
      "./data_train_images/train/labels/Sample4756.txt\n",
      "./data_train_images/train/labels/Sample4758.txt\n",
      "./data_train_images/train/labels/Sample4761.txt\n",
      "./data_train_images/train/labels/Sample4789.txt\n",
      "./data_train_images/train/labels/Sample4794.txt\n",
      "./data_train_images/train/labels/Sample4796.txt\n",
      "./data_train_images/train/labels/Sample48.txt\n",
      "./data_train_images/train/labels/Sample4801.txt\n",
      "./data_train_images/train/labels/Sample4807.txt\n",
      "./data_train_images/train/labels/Sample4812.txt\n",
      "./data_train_images/train/labels/Sample4813.txt\n",
      "./data_train_images/train/labels/Sample4821.txt\n",
      "./data_train_images/train/labels/Sample4823.txt\n",
      "./data_train_images/train/labels/Sample4831.txt\n",
      "./data_train_images/train/labels/Sample4840.txt\n",
      "./data_train_images/train/labels/Sample4845.txt\n",
      "./data_train_images/train/labels/Sample4866.txt\n",
      "./data_train_images/train/labels/Sample4868.txt\n",
      "./data_train_images/train/labels/Sample4883.txt\n",
      "./data_train_images/train/labels/Sample4884.txt\n",
      "./data_train_images/train/labels/Sample4886.txt\n",
      "./data_train_images/train/labels/Sample4889.txt\n",
      "./data_train_images/train/labels/Sample4892.txt\n",
      "./data_train_images/train/labels/Sample4893.txt\n",
      "./data_train_images/train/labels/Sample49.txt\n",
      "./data_train_images/train/labels/Sample4908.txt\n",
      "./data_train_images/train/labels/Sample4909.txt\n",
      "./data_train_images/train/labels/Sample4914.txt\n",
      "./data_train_images/train/labels/Sample4915.txt\n",
      "./data_train_images/train/labels/Sample4916.txt\n",
      "./data_train_images/train/labels/Sample4922.txt\n",
      "./data_train_images/train/labels/Sample4925.txt\n",
      "./data_train_images/train/labels/Sample4935.txt\n",
      "./data_train_images/train/labels/Sample4938.txt\n",
      "./data_train_images/train/labels/Sample4940.txt\n",
      "./data_train_images/train/labels/Sample4942.txt\n",
      "./data_train_images/train/labels/Sample4944.txt\n",
      "./data_train_images/train/labels/Sample4949.txt\n",
      "./data_train_images/train/labels/Sample4964.txt\n",
      "./data_train_images/train/labels/Sample4967.txt\n",
      "./data_train_images/train/labels/Sample4971.txt\n",
      "./data_train_images/train/labels/Sample4976.txt\n",
      "./data_train_images/train/labels/Sample4980.txt\n",
      "./data_train_images/train/labels/Sample4981.txt\n",
      "./data_train_images/train/labels/Sample4983.txt\n",
      "./data_train_images/train/labels/Sample4984.txt\n",
      "./data_train_images/train/labels/Sample4998.txt\n",
      "./data_train_images/train/labels/Sample5.txt\n",
      "./data_train_images/train/labels/Sample50.txt\n",
      "./data_train_images/train/labels/Sample5002.txt\n",
      "./data_train_images/train/labels/Sample5003.txt\n",
      "./data_train_images/train/labels/Sample5014.txt\n",
      "./data_train_images/train/labels/Sample5020.txt\n",
      "./data_train_images/train/labels/Sample5030.txt\n",
      "./data_train_images/train/labels/Sample5041.txt\n",
      "./data_train_images/train/labels/Sample5043.txt\n",
      "./data_train_images/train/labels/Sample5055.txt\n",
      "./data_train_images/train/labels/Sample5068.txt\n",
      "./data_train_images/train/labels/Sample5077.txt\n",
      "./data_train_images/train/labels/Sample51.txt\n",
      "./data_train_images/train/labels/Sample5120.txt\n",
      "./data_train_images/train/labels/Sample5135.txt\n",
      "./data_train_images/train/labels/Sample5137.txt\n",
      "./data_train_images/train/labels/Sample5142.txt\n",
      "./data_train_images/train/labels/Sample5147.txt\n",
      "./data_train_images/train/labels/Sample5165.txt\n",
      "./data_train_images/train/labels/Sample5177.txt\n",
      "./data_train_images/train/labels/Sample5181.txt\n",
      "./data_train_images/train/labels/Sample5183.txt\n",
      "./data_train_images/train/labels/Sample5196.txt\n",
      "./data_train_images/train/labels/Sample52.txt\n",
      "./data_train_images/train/labels/Sample5206.txt\n",
      "./data_train_images/train/labels/Sample5215.txt\n",
      "./data_train_images/train/labels/Sample5220.txt\n",
      "./data_train_images/train/labels/Sample5221.txt\n",
      "./data_train_images/train/labels/Sample5224.txt\n",
      "./data_train_images/train/labels/Sample5227.txt\n",
      "./data_train_images/train/labels/Sample5231.txt\n",
      "./data_train_images/train/labels/Sample5232.txt\n",
      "./data_train_images/train/labels/Sample5233.txt\n",
      "./data_train_images/train/labels/Sample5248.txt\n",
      "./data_train_images/train/labels/Sample5250.txt\n",
      "./data_train_images/train/labels/Sample5251.txt\n",
      "./data_train_images/train/labels/Sample5258.txt\n",
      "./data_train_images/train/labels/Sample5260.txt\n",
      "./data_train_images/train/labels/Sample5265.txt\n",
      "./data_train_images/train/labels/Sample5266.txt\n",
      "./data_train_images/train/labels/Sample5284.txt\n",
      "./data_train_images/train/labels/Sample5286.txt\n",
      "./data_train_images/train/labels/Sample5288.txt\n",
      "./data_train_images/train/labels/Sample5299.txt\n",
      "./data_train_images/train/labels/Sample53.txt\n",
      "./data_train_images/train/labels/Sample5303.txt\n",
      "./data_train_images/train/labels/Sample5304.txt\n",
      "./data_train_images/train/labels/Sample5306.txt\n",
      "./data_train_images/train/labels/Sample5313.txt\n",
      "./data_train_images/train/labels/Sample5319.txt\n",
      "./data_train_images/train/labels/Sample5321.txt\n",
      "./data_train_images/train/labels/Sample5335.txt\n",
      "./data_train_images/train/labels/Sample5342.txt\n",
      "./data_train_images/train/labels/Sample5353.txt\n",
      "./data_train_images/train/labels/Sample5355.txt\n",
      "./data_train_images/train/labels/Sample5362.txt\n",
      "./data_train_images/train/labels/Sample5363.txt\n",
      "./data_train_images/train/labels/Sample5370.txt\n",
      "./data_train_images/train/labels/Sample5386.txt\n",
      "./data_train_images/train/labels/Sample5391.txt\n",
      "./data_train_images/train/labels/Sample5395.txt\n",
      "./data_train_images/train/labels/Sample54.txt\n",
      "./data_train_images/train/labels/Sample5411.txt\n",
      "./data_train_images/train/labels/Sample5417.txt\n",
      "./data_train_images/train/labels/Sample5424.txt\n",
      "./data_train_images/train/labels/Sample5436.txt\n",
      "./data_train_images/train/labels/Sample5437.txt\n",
      "./data_train_images/train/labels/Sample5444.txt\n",
      "./data_train_images/train/labels/Sample5448.txt\n",
      "./data_train_images/train/labels/Sample5449.txt\n",
      "./data_train_images/train/labels/Sample5452.txt\n",
      "./data_train_images/train/labels/Sample5455.txt\n",
      "./data_train_images/train/labels/Sample5456.txt\n",
      "./data_train_images/train/labels/Sample5457.txt\n",
      "./data_train_images/train/labels/Sample5459.txt\n",
      "./data_train_images/train/labels/Sample5465.txt\n",
      "./data_train_images/train/labels/Sample5479.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/train/labels/Sample5499.txt\n",
      "./data_train_images/train/labels/Sample55.txt\n",
      "./data_train_images/train/labels/Sample5517.txt\n",
      "./data_train_images/train/labels/Sample5518.txt\n",
      "./data_train_images/train/labels/Sample5522.txt\n",
      "./data_train_images/train/labels/Sample5524.txt\n",
      "./data_train_images/train/labels/Sample5532.txt\n",
      "./data_train_images/train/labels/Sample5534.txt\n",
      "./data_train_images/train/labels/Sample5544.txt\n",
      "./data_train_images/train/labels/Sample5551.txt\n",
      "./data_train_images/train/labels/Sample5564.txt\n",
      "./data_train_images/train/labels/Sample5569.txt\n",
      "./data_train_images/train/labels/Sample5571.txt\n",
      "./data_train_images/train/labels/Sample5590.txt\n",
      "./data_train_images/train/labels/Sample5594.txt\n",
      "./data_train_images/train/labels/Sample56.txt\n",
      "./data_train_images/train/labels/Sample5600.txt\n",
      "./data_train_images/train/labels/Sample5602.txt\n",
      "./data_train_images/train/labels/Sample5607.txt\n",
      "./data_train_images/train/labels/Sample5611.txt\n",
      "./data_train_images/train/labels/Sample5616.txt\n",
      "./data_train_images/train/labels/Sample5617.txt\n",
      "./data_train_images/train/labels/Sample5618.txt\n",
      "./data_train_images/train/labels/Sample5621.txt\n",
      "./data_train_images/train/labels/Sample5637.txt\n",
      "./data_train_images/train/labels/Sample5649.txt\n",
      "./data_train_images/train/labels/Sample5667.txt\n",
      "./data_train_images/train/labels/Sample5670.txt\n",
      "./data_train_images/train/labels/Sample5672.txt\n",
      "./data_train_images/train/labels/Sample5673.txt\n",
      "./data_train_images/train/labels/Sample5679.txt\n",
      "./data_train_images/train/labels/Sample5697.txt\n",
      "./data_train_images/train/labels/Sample5699.txt\n",
      "./data_train_images/train/labels/Sample57.txt\n",
      "./data_train_images/train/labels/Sample5701.txt\n",
      "./data_train_images/train/labels/Sample5706.txt\n",
      "./data_train_images/train/labels/Sample5711.txt\n",
      "./data_train_images/train/labels/Sample5717.txt\n",
      "./data_train_images/train/labels/Sample5719.txt\n",
      "./data_train_images/train/labels/Sample5744.txt\n",
      "./data_train_images/train/labels/Sample5746.txt\n",
      "./data_train_images/train/labels/Sample5751.txt\n",
      "./data_train_images/train/labels/Sample5754.txt\n",
      "./data_train_images/train/labels/Sample5755.txt\n",
      "./data_train_images/train/labels/Sample5772.txt\n",
      "./data_train_images/train/labels/Sample5774.txt\n",
      "./data_train_images/train/labels/Sample5775.txt\n",
      "./data_train_images/train/labels/Sample5781.txt\n",
      "./data_train_images/train/labels/Sample5790.txt\n",
      "./data_train_images/train/labels/Sample5795.txt\n",
      "./data_train_images/train/labels/Sample58.txt\n",
      "./data_train_images/train/labels/Sample5802.txt\n",
      "./data_train_images/train/labels/Sample5804.txt\n",
      "./data_train_images/train/labels/Sample5810.txt\n",
      "./data_train_images/train/labels/Sample5820.txt\n",
      "./data_train_images/train/labels/Sample5825.txt\n",
      "./data_train_images/train/labels/Sample5833.txt\n",
      "./data_train_images/train/labels/Sample5843.txt\n",
      "./data_train_images/train/labels/Sample5853.txt\n",
      "./data_train_images/train/labels/Sample5866.txt\n",
      "./data_train_images/train/labels/Sample5874.txt\n",
      "./data_train_images/train/labels/Sample5877.txt\n",
      "./data_train_images/train/labels/Sample5886.txt\n",
      "./data_train_images/train/labels/Sample5897.txt\n",
      "./data_train_images/train/labels/Sample59.txt\n",
      "./data_train_images/train/labels/Sample5902.txt\n",
      "./data_train_images/train/labels/Sample5908.txt\n",
      "./data_train_images/train/labels/Sample5912.txt\n",
      "./data_train_images/train/labels/Sample5939.txt\n",
      "./data_train_images/train/labels/Sample5948.txt\n",
      "./data_train_images/train/labels/Sample5949.txt\n",
      "./data_train_images/train/labels/Sample5952.txt\n",
      "./data_train_images/train/labels/Sample5955.txt\n",
      "./data_train_images/train/labels/Sample5959.txt\n",
      "./data_train_images/train/labels/Sample5963.txt\n",
      "./data_train_images/train/labels/Sample6.txt\n",
      "./data_train_images/train/labels/Sample60.txt\n",
      "./data_train_images/train/labels/Sample6006.txt\n",
      "./data_train_images/train/labels/Sample6010.txt\n",
      "./data_train_images/train/labels/Sample6021.txt\n",
      "./data_train_images/train/labels/Sample6029.txt\n",
      "./data_train_images/train/labels/Sample6030.txt\n",
      "./data_train_images/train/labels/Sample6043.txt\n",
      "./data_train_images/train/labels/Sample6045.txt\n",
      "./data_train_images/train/labels/Sample6064.txt\n",
      "./data_train_images/train/labels/Sample6069.txt\n",
      "./data_train_images/train/labels/Sample6079.txt\n",
      "./data_train_images/train/labels/Sample6085.txt\n",
      "./data_train_images/train/labels/Sample6091.txt\n",
      "./data_train_images/train/labels/Sample6092.txt\n",
      "./data_train_images/train/labels/Sample61.txt\n",
      "./data_train_images/train/labels/Sample6100.txt\n",
      "./data_train_images/train/labels/Sample6102.txt\n",
      "./data_train_images/train/labels/Sample6106.txt\n",
      "./data_train_images/train/labels/Sample6117.txt\n",
      "./data_train_images/train/labels/Sample6120.txt\n",
      "./data_train_images/train/labels/Sample6131.txt\n",
      "./data_train_images/train/labels/Sample6144.txt\n",
      "./data_train_images/train/labels/Sample6145.txt\n",
      "./data_train_images/train/labels/Sample6148.txt\n",
      "./data_train_images/train/labels/Sample6152.txt\n",
      "./data_train_images/train/labels/Sample6154.txt\n",
      "./data_train_images/train/labels/Sample6158.txt\n",
      "./data_train_images/train/labels/Sample6160.txt\n",
      "./data_train_images/train/labels/Sample6168.txt\n",
      "./data_train_images/train/labels/Sample6170.txt\n",
      "./data_train_images/train/labels/Sample6177.txt\n",
      "./data_train_images/train/labels/Sample6179.txt\n",
      "./data_train_images/train/labels/Sample6182.txt\n",
      "./data_train_images/train/labels/Sample62.txt\n",
      "./data_train_images/train/labels/Sample63.txt\n",
      "./data_train_images/train/labels/Sample64.txt\n",
      "./data_train_images/train/labels/Sample65.txt\n",
      "./data_train_images/train/labels/Sample66.txt\n",
      "./data_train_images/train/labels/Sample67.txt\n",
      "./data_train_images/train/labels/Sample68.txt\n",
      "./data_train_images/train/labels/Sample69.txt\n",
      "./data_train_images/train/labels/Sample7.txt\n",
      "./data_train_images/train/labels/Sample70.txt\n",
      "./data_train_images/train/labels/Sample71.txt\n",
      "./data_train_images/train/labels/Sample72.txt\n",
      "./data_train_images/train/labels/Sample73.txt\n",
      "./data_train_images/train/labels/Sample74.txt\n",
      "./data_train_images/train/labels/Sample75.txt\n",
      "./data_train_images/train/labels/Sample76.txt\n",
      "./data_train_images/train/labels/Sample77.txt\n",
      "./data_train_images/train/labels/Sample78.txt\n",
      "./data_train_images/train/labels/Sample79.txt\n",
      "./data_train_images/train/labels/Sample8.txt\n",
      "./data_train_images/train/labels/Sample80.txt\n",
      "./data_train_images/train/labels/Sample81.txt\n",
      "./data_train_images/train/labels/Sample82.txt\n",
      "./data_train_images/train/labels/Sample83.txt\n",
      "./data_train_images/train/labels/Sample84.txt\n",
      "./data_train_images/train/labels/Sample85.txt\n",
      "./data_train_images/train/labels/Sample86.txt\n",
      "./data_train_images/train/labels/Sample87.txt\n",
      "./data_train_images/train/labels/Sample88.txt\n",
      "./data_train_images/train/labels/Sample89.txt\n",
      "./data_train_images/train/labels/Sample9.txt\n",
      "./data_train_images/train/labels/Sample90.txt\n",
      "./data_train_images/train/labels/Sample91.txt\n",
      "./data_train_images/train/labels/Sample92.txt\n",
      "./data_train_images/train/labels/Sample93.txt\n",
      "./data_train_images/train/labels/Sample94.txt\n",
      "./data_train_images/train/labels/Sample95.txt\n",
      "./data_train_images/train/labels/Sample96.txt\n",
      "./data_train_images/train/labels/Sample97.txt\n",
      "./data_train_images/train/labels/Sample98.txt\n",
      "./data_train_images/train/labels/Sample99.txt\n",
      "./data_train_images/val/\n",
      "./data_train_images/val/images/\n",
      "./data_train_images/val/images/Sample1020.png\n",
      "./data_train_images/val/images/Sample1062.png\n",
      "./data_train_images/val/images/Sample1068.png\n",
      "./data_train_images/val/images/Sample1079.png\n",
      "./data_train_images/val/images/Sample1094.png\n",
      "./data_train_images/val/images/Sample1118.png\n",
      "./data_train_images/val/images/Sample1193.png\n",
      "./data_train_images/val/images/Sample1233.png\n",
      "./data_train_images/val/images/Sample1242.png\n",
      "./data_train_images/val/images/Sample1303.png\n",
      "./data_train_images/val/images/Sample1318.png\n",
      "./data_train_images/val/images/Sample1362.png\n",
      "./data_train_images/val/images/Sample1365.png\n",
      "./data_train_images/val/images/Sample1368.png\n",
      "./data_train_images/val/images/Sample1371.png\n",
      "./data_train_images/val/images/Sample1381.png\n",
      "./data_train_images/val/images/Sample1386.png\n",
      "./data_train_images/val/images/Sample1414.png\n",
      "./data_train_images/val/images/Sample1433.png\n",
      "./data_train_images/val/images/Sample1437.png\n",
      "./data_train_images/val/images/Sample1510.png\n",
      "./data_train_images/val/images/Sample1602.png\n",
      "./data_train_images/val/images/Sample1642.png\n",
      "./data_train_images/val/images/Sample1662.png\n",
      "./data_train_images/val/images/Sample1668.png\n",
      "./data_train_images/val/images/Sample1694.png\n",
      "./data_train_images/val/images/Sample1755.png\n",
      "./data_train_images/val/images/Sample1764.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/val/images/Sample1782.png\n",
      "./data_train_images/val/images/Sample1825.png\n",
      "./data_train_images/val/images/Sample1886.png\n",
      "./data_train_images/val/images/Sample1889.png\n",
      "./data_train_images/val/images/Sample1914.png\n",
      "./data_train_images/val/images/Sample1958.png\n",
      "./data_train_images/val/images/Sample1959.png\n",
      "./data_train_images/val/images/Sample1969.png\n",
      "./data_train_images/val/images/Sample1992.png\n",
      "./data_train_images/val/images/Sample2004.png\n",
      "./data_train_images/val/images/Sample2005.png\n",
      "./data_train_images/val/images/Sample2011.png\n",
      "./data_train_images/val/images/Sample2017.png\n",
      "./data_train_images/val/images/Sample2020.png\n",
      "./data_train_images/val/images/Sample2032.png\n",
      "./data_train_images/val/images/Sample2035.png\n",
      "./data_train_images/val/images/Sample2068.png\n",
      "./data_train_images/val/images/Sample2075.png\n",
      "./data_train_images/val/images/Sample2100.png\n",
      "./data_train_images/val/images/Sample2124.png\n",
      "./data_train_images/val/images/Sample2126.png\n",
      "./data_train_images/val/images/Sample2204.png\n",
      "./data_train_images/val/images/Sample2222.png\n",
      "./data_train_images/val/images/Sample2228.png\n",
      "./data_train_images/val/images/Sample2233.png\n",
      "./data_train_images/val/images/Sample2241.png\n",
      "./data_train_images/val/images/Sample2255.png\n",
      "./data_train_images/val/images/Sample2259.png\n",
      "./data_train_images/val/images/Sample2274.png\n",
      "./data_train_images/val/images/Sample2307.png\n",
      "./data_train_images/val/images/Sample2324.png\n",
      "./data_train_images/val/images/Sample2326.png\n",
      "./data_train_images/val/images/Sample2354.png\n",
      "./data_train_images/val/images/Sample2370.png\n",
      "./data_train_images/val/images/Sample2372.png\n",
      "./data_train_images/val/images/Sample2457.png\n",
      "./data_train_images/val/images/Sample2480.png\n",
      "./data_train_images/val/images/Sample2495.png\n",
      "./data_train_images/val/images/Sample2544.png\n",
      "./data_train_images/val/images/Sample2551.png\n",
      "./data_train_images/val/images/Sample2658.png\n",
      "./data_train_images/val/images/Sample2660.png\n",
      "./data_train_images/val/images/Sample2667.png\n",
      "./data_train_images/val/images/Sample2700.png\n",
      "./data_train_images/val/images/Sample2756.png\n",
      "./data_train_images/val/images/Sample2764.png\n",
      "./data_train_images/val/images/Sample2803.png\n",
      "./data_train_images/val/images/Sample2808.png\n",
      "./data_train_images/val/images/Sample2835.png\n",
      "./data_train_images/val/images/Sample2912.png\n",
      "./data_train_images/val/images/Sample2924.png\n",
      "./data_train_images/val/images/Sample2967.png\n",
      "./data_train_images/val/images/Sample3006.png\n",
      "./data_train_images/val/images/Sample3032.png\n",
      "./data_train_images/val/images/Sample3046.png\n",
      "./data_train_images/val/images/Sample3056.png\n",
      "./data_train_images/val/images/Sample3122.png\n",
      "./data_train_images/val/images/Sample3160.png\n",
      "./data_train_images/val/images/Sample3266.png\n",
      "./data_train_images/val/images/Sample3279.png\n",
      "./data_train_images/val/images/Sample3282.png\n",
      "./data_train_images/val/images/Sample3306.png\n",
      "./data_train_images/val/images/Sample3337.png\n",
      "./data_train_images/val/images/Sample3341.png\n",
      "./data_train_images/val/images/Sample3345.png\n",
      "./data_train_images/val/images/Sample3394.png\n",
      "./data_train_images/val/images/Sample3400.png\n",
      "./data_train_images/val/images/Sample3402.png\n",
      "./data_train_images/val/images/Sample3423.png\n",
      "./data_train_images/val/images/Sample3446.png\n",
      "./data_train_images/val/images/Sample3461.png\n",
      "./data_train_images/val/images/Sample3471.png\n",
      "./data_train_images/val/images/Sample3472.png\n",
      "./data_train_images/val/images/Sample3494.png\n",
      "./data_train_images/val/images/Sample3495.png\n",
      "./data_train_images/val/images/Sample3608.png\n",
      "./data_train_images/val/images/Sample3625.png\n",
      "./data_train_images/val/images/Sample3653.png\n",
      "./data_train_images/val/images/Sample3655.png\n",
      "./data_train_images/val/images/Sample3687.png\n",
      "./data_train_images/val/images/Sample3737.png\n",
      "./data_train_images/val/images/Sample3758.png\n",
      "./data_train_images/val/images/Sample3761.png\n",
      "./data_train_images/val/images/Sample3765.png\n",
      "./data_train_images/val/images/Sample3767.png\n",
      "./data_train_images/val/images/Sample3803.png\n",
      "./data_train_images/val/images/Sample3812.png\n",
      "./data_train_images/val/images/Sample3820.png\n",
      "./data_train_images/val/images/Sample3874.png\n",
      "./data_train_images/val/images/Sample3898.png\n",
      "./data_train_images/val/images/Sample3904.png\n",
      "./data_train_images/val/images/Sample3912.png\n",
      "./data_train_images/val/images/Sample3915.png\n",
      "./data_train_images/val/images/Sample3928.png\n",
      "./data_train_images/val/images/Sample3935.png\n",
      "./data_train_images/val/images/Sample3955.png\n",
      "./data_train_images/val/images/Sample3979.png\n",
      "./data_train_images/val/images/Sample4088.png\n",
      "./data_train_images/val/images/Sample4136.png\n",
      "./data_train_images/val/images/Sample4206.png\n",
      "./data_train_images/val/images/Sample4293.png\n",
      "./data_train_images/val/images/Sample4306.png\n",
      "./data_train_images/val/images/Sample4315.png\n",
      "./data_train_images/val/images/Sample4349.png\n",
      "./data_train_images/val/images/Sample4373.png\n",
      "./data_train_images/val/images/Sample4387.png\n",
      "./data_train_images/val/images/Sample4388.png\n",
      "./data_train_images/val/images/Sample4409.png\n",
      "./data_train_images/val/images/Sample4432.png\n",
      "./data_train_images/val/images/Sample4452.png\n",
      "./data_train_images/val/images/Sample4463.png\n",
      "./data_train_images/val/images/Sample4510.png\n",
      "./data_train_images/val/images/Sample4512.png\n",
      "./data_train_images/val/images/Sample4517.png\n",
      "./data_train_images/val/images/Sample4530.png\n",
      "./data_train_images/val/images/Sample4548.png\n",
      "./data_train_images/val/images/Sample4554.png\n",
      "./data_train_images/val/images/Sample4555.png\n",
      "./data_train_images/val/images/Sample4577.png\n",
      "./data_train_images/val/images/Sample4590.png\n",
      "./data_train_images/val/images/Sample4592.png\n",
      "./data_train_images/val/images/Sample4613.png\n",
      "./data_train_images/val/images/Sample4625.png\n",
      "./data_train_images/val/images/Sample4673.png\n",
      "./data_train_images/val/images/Sample4680.png\n",
      "./data_train_images/val/images/Sample4815.png\n",
      "./data_train_images/val/images/Sample4819.png\n",
      "./data_train_images/val/images/Sample4875.png\n",
      "./data_train_images/val/images/Sample4923.png\n",
      "./data_train_images/val/images/Sample4947.png\n",
      "./data_train_images/val/images/Sample4960.png\n",
      "./data_train_images/val/images/Sample4977.png\n",
      "./data_train_images/val/images/Sample4986.png\n",
      "./data_train_images/val/images/Sample4990.png\n",
      "./data_train_images/val/images/Sample5062.png\n",
      "./data_train_images/val/images/Sample5067.png\n",
      "./data_train_images/val/images/Sample5102.png\n",
      "./data_train_images/val/images/Sample5105.png\n",
      "./data_train_images/val/images/Sample5144.png\n",
      "./data_train_images/val/images/Sample5166.png\n",
      "./data_train_images/val/images/Sample5168.png\n",
      "./data_train_images/val/images/Sample5264.png\n",
      "./data_train_images/val/images/Sample5315.png\n",
      "./data_train_images/val/images/Sample5343.png\n",
      "./data_train_images/val/images/Sample5351.png\n",
      "./data_train_images/val/images/Sample5359.png\n",
      "./data_train_images/val/images/Sample5442.png\n",
      "./data_train_images/val/images/Sample5470.png\n",
      "./data_train_images/val/images/Sample5609.png\n",
      "./data_train_images/val/images/Sample5625.png\n",
      "./data_train_images/val/images/Sample5638.png\n",
      "./data_train_images/val/images/Sample5645.png\n",
      "./data_train_images/val/images/Sample5666.png\n",
      "./data_train_images/val/images/Sample5676.png\n",
      "./data_train_images/val/images/Sample5688.png\n",
      "./data_train_images/val/images/Sample5709.png\n",
      "./data_train_images/val/images/Sample5723.png\n",
      "./data_train_images/val/images/Sample5759.png\n",
      "./data_train_images/val/images/Sample5769.png\n",
      "./data_train_images/val/images/Sample5806.png\n",
      "./data_train_images/val/images/Sample5831.png\n",
      "./data_train_images/val/images/Sample5837.png\n",
      "./data_train_images/val/images/Sample5850.png\n",
      "./data_train_images/val/images/Sample5928.png\n",
      "./data_train_images/val/images/Sample5947.png\n",
      "./data_train_images/val/images/Sample5988.png\n",
      "./data_train_images/val/images/Sample6012.png\n",
      "./data_train_images/val/images/Sample6055.png\n",
      "./data_train_images/val/images/Sample6067.png\n",
      "./data_train_images/val/images/Sample6114.png\n",
      "./data_train_images/val/images/Sample6121.png\n",
      "./data_train_images/val/images/Sample6180.png\n",
      "./data_train_images/val/labels/\n",
      "./data_train_images/val/labels/Sample1020.txt\n",
      "./data_train_images/val/labels/Sample1062.txt\n",
      "./data_train_images/val/labels/Sample1068.txt\n",
      "./data_train_images/val/labels/Sample1079.txt\n",
      "./data_train_images/val/labels/Sample1094.txt\n",
      "./data_train_images/val/labels/Sample1118.txt\n",
      "./data_train_images/val/labels/Sample1193.txt\n",
      "./data_train_images/val/labels/Sample1233.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_train_images/val/labels/Sample1242.txt\n",
      "./data_train_images/val/labels/Sample1303.txt\n",
      "./data_train_images/val/labels/Sample1318.txt\n",
      "./data_train_images/val/labels/Sample1362.txt\n",
      "./data_train_images/val/labels/Sample1365.txt\n",
      "./data_train_images/val/labels/Sample1368.txt\n",
      "./data_train_images/val/labels/Sample1371.txt\n",
      "./data_train_images/val/labels/Sample1381.txt\n",
      "./data_train_images/val/labels/Sample1386.txt\n",
      "./data_train_images/val/labels/Sample1414.txt\n",
      "./data_train_images/val/labels/Sample1433.txt\n",
      "./data_train_images/val/labels/Sample1437.txt\n",
      "./data_train_images/val/labels/Sample1510.txt\n",
      "./data_train_images/val/labels/Sample1602.txt\n",
      "./data_train_images/val/labels/Sample1642.txt\n",
      "./data_train_images/val/labels/Sample1662.txt\n",
      "./data_train_images/val/labels/Sample1668.txt\n",
      "./data_train_images/val/labels/Sample1694.txt\n",
      "./data_train_images/val/labels/Sample1755.txt\n",
      "./data_train_images/val/labels/Sample1764.txt\n",
      "./data_train_images/val/labels/Sample1782.txt\n",
      "./data_train_images/val/labels/Sample1825.txt\n",
      "./data_train_images/val/labels/Sample1886.txt\n",
      "./data_train_images/val/labels/Sample1889.txt\n",
      "./data_train_images/val/labels/Sample1914.txt\n",
      "./data_train_images/val/labels/Sample1958.txt\n",
      "./data_train_images/val/labels/Sample1959.txt\n",
      "./data_train_images/val/labels/Sample1969.txt\n",
      "./data_train_images/val/labels/Sample1992.txt\n",
      "./data_train_images/val/labels/Sample2004.txt\n",
      "./data_train_images/val/labels/Sample2005.txt\n",
      "./data_train_images/val/labels/Sample2011.txt\n",
      "./data_train_images/val/labels/Sample2017.txt\n",
      "./data_train_images/val/labels/Sample2020.txt\n",
      "./data_train_images/val/labels/Sample2032.txt\n",
      "./data_train_images/val/labels/Sample2035.txt\n",
      "./data_train_images/val/labels/Sample2068.txt\n",
      "./data_train_images/val/labels/Sample2075.txt\n",
      "./data_train_images/val/labels/Sample2100.txt\n",
      "./data_train_images/val/labels/Sample2124.txt\n",
      "./data_train_images/val/labels/Sample2126.txt\n",
      "./data_train_images/val/labels/Sample2204.txt\n",
      "./data_train_images/val/labels/Sample2222.txt\n",
      "./data_train_images/val/labels/Sample2228.txt\n",
      "./data_train_images/val/labels/Sample2233.txt\n",
      "./data_train_images/val/labels/Sample2241.txt\n",
      "./data_train_images/val/labels/Sample2255.txt\n",
      "./data_train_images/val/labels/Sample2259.txt\n",
      "./data_train_images/val/labels/Sample2274.txt\n",
      "./data_train_images/val/labels/Sample2307.txt\n",
      "./data_train_images/val/labels/Sample2324.txt\n",
      "./data_train_images/val/labels/Sample2326.txt\n",
      "./data_train_images/val/labels/Sample2354.txt\n",
      "./data_train_images/val/labels/Sample2370.txt\n",
      "./data_train_images/val/labels/Sample2372.txt\n",
      "./data_train_images/val/labels/Sample2457.txt\n",
      "./data_train_images/val/labels/Sample2480.txt\n",
      "./data_train_images/val/labels/Sample2495.txt\n",
      "./data_train_images/val/labels/Sample2544.txt\n",
      "./data_train_images/val/labels/Sample2551.txt\n",
      "./data_train_images/val/labels/Sample2658.txt\n",
      "./data_train_images/val/labels/Sample2660.txt\n",
      "./data_train_images/val/labels/Sample2667.txt\n",
      "./data_train_images/val/labels/Sample2700.txt\n",
      "./data_train_images/val/labels/Sample2756.txt\n",
      "./data_train_images/val/labels/Sample2764.txt\n",
      "./data_train_images/val/labels/Sample2803.txt\n",
      "./data_train_images/val/labels/Sample2808.txt\n",
      "./data_train_images/val/labels/Sample2835.txt\n",
      "./data_train_images/val/labels/Sample2912.txt\n",
      "./data_train_images/val/labels/Sample2924.txt\n",
      "./data_train_images/val/labels/Sample2967.txt\n",
      "./data_train_images/val/labels/Sample3006.txt\n",
      "./data_train_images/val/labels/Sample3032.txt\n",
      "./data_train_images/val/labels/Sample3046.txt\n",
      "./data_train_images/val/labels/Sample3056.txt\n",
      "./data_train_images/val/labels/Sample3122.txt\n",
      "./data_train_images/val/labels/Sample3160.txt\n",
      "./data_train_images/val/labels/Sample3266.txt\n",
      "./data_train_images/val/labels/Sample3279.txt\n",
      "./data_train_images/val/labels/Sample3282.txt\n",
      "./data_train_images/val/labels/Sample3306.txt\n",
      "./data_train_images/val/labels/Sample3337.txt\n",
      "./data_train_images/val/labels/Sample3341.txt\n",
      "./data_train_images/val/labels/Sample3345.txt\n",
      "./data_train_images/val/labels/Sample3394.txt\n",
      "./data_train_images/val/labels/Sample3400.txt\n",
      "./data_train_images/val/labels/Sample3402.txt\n",
      "./data_train_images/val/labels/Sample3423.txt\n",
      "./data_train_images/val/labels/Sample3446.txt\n",
      "./data_train_images/val/labels/Sample3461.txt\n",
      "./data_train_images/val/labels/Sample3471.txt\n",
      "./data_train_images/val/labels/Sample3472.txt\n",
      "./data_train_images/val/labels/Sample3494.txt\n",
      "./data_train_images/val/labels/Sample3495.txt\n",
      "./data_train_images/val/labels/Sample3608.txt\n",
      "./data_train_images/val/labels/Sample3625.txt\n",
      "./data_train_images/val/labels/Sample3653.txt\n",
      "./data_train_images/val/labels/Sample3655.txt\n",
      "./data_train_images/val/labels/Sample3687.txt\n",
      "./data_train_images/val/labels/Sample3737.txt\n",
      "./data_train_images/val/labels/Sample3758.txt\n",
      "./data_train_images/val/labels/Sample3761.txt\n",
      "./data_train_images/val/labels/Sample3765.txt\n",
      "./data_train_images/val/labels/Sample3767.txt\n",
      "./data_train_images/val/labels/Sample3803.txt\n",
      "./data_train_images/val/labels/Sample3812.txt\n",
      "./data_train_images/val/labels/Sample3820.txt\n",
      "./data_train_images/val/labels/Sample3874.txt\n",
      "./data_train_images/val/labels/Sample3898.txt\n",
      "./data_train_images/val/labels/Sample3904.txt\n",
      "./data_train_images/val/labels/Sample3912.txt\n",
      "./data_train_images/val/labels/Sample3915.txt\n",
      "./data_train_images/val/labels/Sample3928.txt\n",
      "./data_train_images/val/labels/Sample3935.txt\n",
      "./data_train_images/val/labels/Sample3955.txt\n",
      "./data_train_images/val/labels/Sample3979.txt\n",
      "./data_train_images/val/labels/Sample4088.txt\n",
      "./data_train_images/val/labels/Sample4136.txt\n",
      "./data_train_images/val/labels/Sample4206.txt\n",
      "./data_train_images/val/labels/Sample4293.txt\n",
      "./data_train_images/val/labels/Sample4306.txt\n",
      "./data_train_images/val/labels/Sample4315.txt\n",
      "./data_train_images/val/labels/Sample4349.txt\n",
      "./data_train_images/val/labels/Sample4373.txt\n",
      "./data_train_images/val/labels/Sample4387.txt\n",
      "./data_train_images/val/labels/Sample4388.txt\n",
      "./data_train_images/val/labels/Sample4409.txt\n",
      "./data_train_images/val/labels/Sample4432.txt\n",
      "./data_train_images/val/labels/Sample4452.txt\n",
      "./data_train_images/val/labels/Sample4463.txt\n",
      "./data_train_images/val/labels/Sample4510.txt\n",
      "./data_train_images/val/labels/Sample4512.txt\n",
      "./data_train_images/val/labels/Sample4517.txt\n",
      "./data_train_images/val/labels/Sample4530.txt\n",
      "./data_train_images/val/labels/Sample4548.txt\n",
      "./data_train_images/val/labels/Sample4554.txt\n",
      "./data_train_images/val/labels/Sample4555.txt\n",
      "./data_train_images/val/labels/Sample4577.txt\n",
      "./data_train_images/val/labels/Sample4590.txt\n",
      "./data_train_images/val/labels/Sample4592.txt\n",
      "./data_train_images/val/labels/Sample4613.txt\n",
      "./data_train_images/val/labels/Sample4625.txt\n",
      "./data_train_images/val/labels/Sample4673.txt\n",
      "./data_train_images/val/labels/Sample4680.txt\n",
      "./data_train_images/val/labels/Sample4815.txt\n",
      "./data_train_images/val/labels/Sample4819.txt\n",
      "./data_train_images/val/labels/Sample4875.txt\n",
      "./data_train_images/val/labels/Sample4923.txt\n",
      "./data_train_images/val/labels/Sample4947.txt\n",
      "./data_train_images/val/labels/Sample4960.txt\n",
      "./data_train_images/val/labels/Sample4977.txt\n",
      "./data_train_images/val/labels/Sample4986.txt\n",
      "./data_train_images/val/labels/Sample4990.txt\n",
      "./data_train_images/val/labels/Sample5062.txt\n",
      "./data_train_images/val/labels/Sample5067.txt\n",
      "./data_train_images/val/labels/Sample5102.txt\n",
      "./data_train_images/val/labels/Sample5105.txt\n",
      "./data_train_images/val/labels/Sample5144.txt\n",
      "./data_train_images/val/labels/Sample5166.txt\n",
      "./data_train_images/val/labels/Sample5168.txt\n",
      "./data_train_images/val/labels/Sample5264.txt\n",
      "./data_train_images/val/labels/Sample5315.txt\n",
      "./data_train_images/val/labels/Sample5343.txt\n",
      "./data_train_images/val/labels/Sample5351.txt\n",
      "./data_train_images/val/labels/Sample5359.txt\n",
      "./data_train_images/val/labels/Sample5442.txt\n",
      "./data_train_images/val/labels/Sample5470.txt\n",
      "./data_train_images/val/labels/Sample5609.txt\n",
      "./data_train_images/val/labels/Sample5625.txt\n",
      "./data_train_images/val/labels/Sample5638.txt\n",
      "./data_train_images/val/labels/Sample5645.txt\n",
      "./data_train_images/val/labels/Sample5666.txt\n",
      "./data_train_images/val/labels/Sample5676.txt\n",
      "./data_train_images/val/labels/Sample5688.txt\n",
      "./data_train_images/val/labels/Sample5709.txt\n",
      "./data_train_images/val/labels/Sample5723.txt\n",
      "./data_train_images/val/labels/Sample5759.txt\n",
      "./data_train_images/val/labels/Sample5769.txt\n",
      "./data_train_images/val/labels/Sample5806.txt\n",
      "./data_train_images/val/labels/Sample5831.txt\n",
      "./data_train_images/val/labels/Sample5837.txt\n",
      "./data_train_images/val/labels/Sample5850.txt\n",
      "./data_train_images/val/labels/Sample5928.txt\n",
      "./data_train_images/val/labels/Sample5947.txt\n",
      "./data_train_images/val/labels/Sample5988.txt\n",
      "./data_train_images/val/labels/Sample6012.txt\n",
      "./data_train_images/val/labels/Sample6055.txt\n",
      "./data_train_images/val/labels/Sample6067.txt\n",
      "./data_train_images/val/labels/Sample6114.txt\n",
      "./data_train_images/val/labels/Sample6121.txt\n",
      "./data_train_images/val/labels/Sample6180.txt\n"
     ]
    }
   ],
   "source": [
    "!tar -cvf data_train_images.tar ./data_train_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "0b48ab3240dc41ffa029ce879fa5e087e0a83cbe7a72ef65a19cb71535771faa"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}