resize_img.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import os, sys\n",
    "from IPython.display import display\n",
    "from IPython.display import Image as _Imgdis\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from time import time\n",
    "from time import sleep\n",
    "from scipy import ndimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "(300, 300)\n",
      "(300, 300)\n",
      "(300, 300)\n"
     ]
    }
   ],
   "source": [
    "folder = \"/home/sharanya/Assignment2/Train_Data/augment/pneumonia\"\n",
    "onlyfiles = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]\n",
    "\n",
    "train_files = []\n",
    "y_train = []\n",
    "i=0\n",
    "for _file in onlyfiles:\n",
    "    train_files.append(_file)\n",
    "    \n",
    "for _file in train_files:\n",
    "    img = Image.open(folder + \"/\" + _file)\n",
    "    new_image = img.resize((300, 300))\n",
    "    new_image.save( \"/home/sharanya/Assignment2/Train_Data/see/\" + _file)\n",
    "    print(new_image.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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