{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import shutil\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import json "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create train-test folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = 'kidney-ct-abnormality'\n",
    "images_path = os.path.join(base_path, 'imagesTr')\n",
    "all_images = [f for f in os.listdir(images_path) if f.endswith('.mha')]\n",
    "train_files, test_files = train_test_split(all_images, test_size=0.2, random_state=219)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = os.path.join(base_path, 'train')\n",
    "test_dir = os.path.join(base_path, 'test')\n",
    "os.makedirs(train_dir, exist_ok=True)\n",
    "os.makedirs(test_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def move_files(files, destination):\n",
    "    for f in files:\n",
    "        shutil.move(os.path.join(images_path, f), os.path.join(destination, f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "move_files(train_files, train_dir)\n",
    "move_files(test_files, test_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modify the json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_file_path = 'dataset.json' \n",
    "with open(json_file_path, 'r') as file:\n",
    "    img_label = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = img_label['training']\n",
    "df = pd.DataFrame(data)\n",
    "df.rename(columns={'Abnormality': 'abnormality'}, inplace=True)\n",
    "df['image'] = df['image'].apply(lambda x: x[11:-4]+'_0000'+x[-4:])\n",
    "test_images = [f for f in os.listdir('test') if f.endswith('.mha')]\n",
    "df['split'] = df['image'].apply(lambda x: 'test' if x in test_images else 'train')\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_copy = df.copy()\n",
    "# df_copy['count']=1\n",
    "# df_copy.groupby(['split', 'abnormality']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_dicts = df.to_dict(orient='records')\n",
    "# list_of_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_json('dataset_m.json', orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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