{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. load skybox image of the path[-3:]\n",
    "#2. display instruction, adversarial instruction, (navgpt captions and objects)\n",
    "\n",
    "from PIL import Image\n",
    "import json, os\n",
    "\n",
    "def load_json(fn):\n",
    "    with open(fn) as f:\n",
    "        ret = json.load(f)\n",
    "    return ret\n",
    "\n",
    "def dump_json(data, fn, force=False):\n",
    "    if not force:\n",
    "        assert not os.path.exists(fn)\n",
    "    with open(fn, 'w') as f:\n",
    "        json.dump(data, f)\n",
    "        \n",
    "def concat_images(images):\n",
    "    #images = [Image.open(x) for x in ['Test1.jpg', 'Test2.jpg', 'Test3.jpg']]\n",
    "    widths, heights = zip(*(i.size for i in images))\n",
    "\n",
    "    total_width = sum(widths)+(len(images)-1)*10\n",
    "    max_height = max(heights)\n",
    "\n",
    "    new_im = Image.new('RGB', (total_width, max_height))\n",
    "\n",
    "    x_offset = 0\n",
    "    for im in images:\n",
    "        new_im.paste(im, (x_offset,0))\n",
    "        x_offset += im.size[0]+10\n",
    "    return new_im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "reverie_val_unseen = load_json('../REVERIE/tasks/REVERIE/data/REVERIE_val_unseen_first_ins.json')\n",
    "reverie_val_unseen_fnf = load_json(\"..//fnf/reverie_val_unseen_fnf.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnf_pairs = []\n",
    "for idx, r in enumerate(reverie_val_unseen_fnf):\n",
    "    if not r['found']:\n",
    "        fnf_pairs.append((reverie_val_unseen_fnf[idx-1], r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 1\n",
    "fnf_pair = fnf_pairs[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scan_id, path = fnf_pair[0]['scan'], fnf_pair[0]['path']\n",
    "fn_template = '/work/ganymede9487/mp3d/unzipped/{}/matterport_skybox_images/{}_skybox{}_sami.jpg'\n",
    "skybox_image_files = [fn_template.format(scan_id, path[-1], skybox_idx) for skybox_idx in range(6)]\n",
    "skybox_images = []\n",
    "for skybox_image_file in skybox_image_files:\n",
    "    skybox_images.append(Image.open(skybox_image_file))\n",
    "\n",
    "concat_images(skybox_images)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to the lounge on level 1 with the fire extinguisher and push the rope around the table closer to the walls\n",
      "Go to the lounge on level 1 with the fire extinguisher and push the towel around the table closer to the walls\n"
     ]
    }
   ],
   "source": [
    "print(fnf_pair[0]['instruction'])\n",
    "print(fnf_pair[1]['instruction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#0 ok\n",
    "#1 error 2 (not reasonable)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "minigpt4",
   "language": "python",
   "name": "minigpt4"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}