{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1bba141c-5345-4833-960e-59a5e65f08b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ee26a6-ba9b-4228-847a-8fdb9dca42bd",
   "metadata": {
    "tags": []
   },
   "source": [
    "https://harminder.dev/projects/ai-powered-property-surveillance/training/#create-a-data-configuration-file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4ee3ad6-2555-403f-a68b-c2a102649fe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset successfully split into train, val, and test sets.\n"
     ]
    }
   ],
   "source": [
    "# Set the seed for reproducibility\n",
    "\n",
    "random.seed(42)\n",
    "\n",
    "# Paths\n",
    "\n",
    "base_path = 'yolo'\n",
    "images_path = os.path.join(base_path, 'images')\n",
    "labels_path = os.path.join(base_path, 'labels')\n",
    "\n",
    "# Split Ratios\n",
    "test_ratio = 0.20\n",
    "\n",
    "# Create directories for train, val, and test sets\n",
    "\n",
    "for set_type in ['train', 'test']:\n",
    "    for content_type in ['images', 'labels']:\n",
    "        os.makedirs(os.path.join(base_path, set_type, content_type), exist_ok=True)\n",
    "\n",
    "# Get all image filenames\n",
    "\n",
    "all_files = [f for f in os.listdir(images_path) if os.path.isfile(os.path.join(images_path, f))]\n",
    "random.shuffle(all_files)\n",
    "\n",
    "# Calculate split indices\n",
    "\n",
    "total_files = len(all_files)\n",
    "train_end = int(total_files*test_ratio)\n",
    "\n",
    "# Split files\n",
    "\n",
    "test_files = all_files[:train_end]\n",
    "train_files = all_files[train_end:]\n",
    "\n",
    "# Function to copy files\n",
    "\n",
    "def copy_files(files, set_type):\n",
    "    for file in files: # Copy image\n",
    "        shutil.copy(os.path.join(images_path, file), os.path.join(base_path, set_type, 'images')) # Copy corresponding label\n",
    "        label_file = file.rsplit('.', 1)[0] + '.txt'\n",
    "        shutil.copy(os.path.join(labels_path, label_file), os.path.join(base_path, set_type, 'labels'))\n",
    "\n",
    "# Copy files to respective directories\n",
    "\n",
    "copy_files(train_files, 'train')\n",
    "copy_files(test_files, 'test')\n",
    "\n",
    "print(\"Dataset successfully split into train, val, and test sets.\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "language": "python",
   "name": "torch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}