{ "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": [ { "data": { "text/plain": [ "'/Users/ziyingye/Desktop/stats/24spring/STA663/kidney_CT_abnormality'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 17, "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": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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