{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fae21126-1f50-4be3-9aa7-1e2e5ab682cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import os.path as osp\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b9888b7-fab6-4c90-9dc5-4fdd8f577486",
   "metadata": {},
   "outputs": [],
   "source": [
    "SFM_DATASETS_PATH = r\"D:\\Deep_Learning\\Endo-SfMLearner\\dataset_w_dpt\\datasets\"\n",
    "ESO_DATASETS_PATH = r\"D:\\Deep_Learning\\WasteNet\\dataset\"\n",
    "TARGET_IMGS_NUM = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf2acaf1-e06a-49c1-b63c-2117e9f5cc5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sfm_imgs_path = glob(osp.join(SFM_DATASETS_PATH, \"*\", \"*.*\"))\n",
    "eso_imgs_path = glob(osp.join(ESO_DATASETS_PATH, \"*\", \"*.*\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a478848-283f-4613-aa69-72140bf032ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D:', 'Deep_Learning', 'WasteNet', 'dataset', 'sequence0', '00000000.png']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eso_imgs_path[0].split(\"\\\\\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a8e0dce0-b210-426c-8d7d-eefdfa4eede9",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_list = []\n",
    "for i in range(len(eso_imgs_path) - 2):\n",
    "    img_path1, img_path2, img_path3 = eso_imgs_path[i], eso_imgs_path[i + 1], eso_imgs_path[i + 2]\n",
    "    exists_list = []\n",
    "    for img_path in [img_path1, img_path2, img_path3]:\n",
    "        path_elements = img_path.split(\"\\\\\")\n",
    "        searched_path = osp.join(SFM_DATASETS_PATH, path_elements[-2], path_elements[-1])\n",
    "        exists_list.append(osp.exists(searched_path))\n",
    "    is_exists = all(exists_list)\n",
    "    out_list.append([img_path1, img_path2, img_path3, is_exists])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2eff971c-2b02-4536-8336-655e2faba252",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"train.csv\", \"w\", newline=\"\") as f:\n",
    "    csv_writer = csv.writer(f)\n",
    "    csv_writer.writerows(out_list)"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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