Upload ini_pro.ipynb

ini_pro.ipynb

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+{
+ "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": []
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "5dd334af8dc9350ae3498c72a940dd1e233d0cdb387a7b49f9cbba56097a7a15"
+ },
+ "kernelspec": {
+ "display_name": "Python 3.9.7",
+ "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": "3.9.7"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}