Upload ini_pro.ipynb

ini_pro.ipynb

@@ -0,0 +1,165 @@
+{
+ "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
+}