{
 "cells": [
  {
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   "source": [
    "import os\n",
    "import csv\n",
    "import random\n",
    "\n",
    "def generate_dataset_csv(images_dir, masks_dir, output_file, train_ratio=0.7):\n",
    "    # Get list of images and sort them\n",
    "    images = sorted([f for f in os.listdir(images_dir) if f.endswith('.png')])\n",
    "    \n",
    "    # Create pairs of image paths and corresponding mask paths\n",
    "    data = []\n",
    "    for img in images:\n",
    "        # Get corresponding mask name by adding '_mask' before .png\n",
    "        mask = img.replace('.png', '_mask.png')\n",
    "        \n",
    "        # Create full paths\n",
    "        img_path = os.path.join(images_dir, img)\n",
    "        mask_path = os.path.join(masks_dir, mask)\n",
    "        \n",
    "        # Verify both files exist before adding\n",
    "        if os.path.exists(img_path) and os.path.exists(mask_path):\n",
    "            data.append([img_path, mask_path])\n",
    "    \n",
    "    # Randomly split into training and validation sets\n",
    "    random.seed(42)  # for reproducibility\n",
    "    random.shuffle(data)\n",
    "    split_idx = int(len(data) * train_ratio)\n",
    "    \n",
    "    # Assign splits\n",
    "    for i in range(len(data)):\n",
    "        split = 'training' if i < split_idx else 'val'\n",
    "        data[i].extend([split, 0])  # add split and fold (0 for all)\n",
    "    \n",
    "    # Write to CSV\n",
    "    with open(output_file, 'w', newline='') as f:\n",
    "        writer = csv.writer(f)\n",
    "        writer.writerow(['imgs', 'labels', 'split', 'fold'])  # header\n",
    "        writer.writerows(data)\n",
    "\n",
    "# Example usage\n",
    "images_dir = '/l/users/sarim.hashmi/for_the_little_interns/the_experiments/drive_dataset/dataset_drive/images'\n",
    "masks_dir = '/l/users/sarim.hashmi/for_the_little_interns/the_experiments/drive_dataset/dataset_drive/masks'\n",
    "output_file = 'data_split.csv'\n",
    "\n",
    "generate_dataset_csv(images_dir, masks_dir, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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