{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25747a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab382eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find all glb file under the directory\n",
    "mesh_dir = './ours_meshes'\n",
    "mesh_paths = glob.glob(os.path.join(mesh_dir, '*.glb'))\n",
    "img_names = [path.split('/')[-1].replace('.glb', '') for path in mesh_paths]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21426d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "ours_mesh_path_img = './ours_meshes'\n",
    "trellis_mesh_path_img = './trellis_meshes'\n",
    "ours_voxel_path_img = './ours_voxels'\n",
    "trellis_voxel_path_img = './trellis_meshes'\n",
    "ours_mesh_path_text = './ours_meshes_text'\n",
    "trellis_mesh_path_text = './trellis_meshes_text'\n",
    "ours_voxel_path_text = './ours_voxels_text_f'\n",
    "trellis_voxel_path_text = './trellis_meshes_text'\n",
    "img_prompt_dir = './user_study_pic'\n",
    "text_prompt_dir = json.load(open('./captions.json', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eba1b950",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create task json file\n",
    "\n",
    "trials = []\n",
    "for name in img_names:\n",
    "    trial = {\n",
    "        \"trial_id\": f\"img_{name}\",\n",
    "        \"task\": \"image\",\n",
    "        \"prompt_image\": f\"{img_prompt_dir}/{name}.png\",\n",
    "\n",
    "        \"ours_voxel\": f\"{ours_voxel_path_img}/{name}_cubify64.obj\",\n",
    "        \"cont_voxel\": f\"{trellis_voxel_path_img}/{name}_cubify64.obj\",\n",
    "\n",
    "        \"ours_mesh\": f\"{ours_mesh_path_img}/{name}.glb\",\n",
    "        \"cont_mesh\": f\"{trellis_mesh_path_img}/{name}.glb\",\n",
    "\n",
    "        \"ours_video\": f\"{ours_mesh_path_img}/{name}_gs.mp4\",\n",
    "        \"cont_video\": f\"{trellis_mesh_path_img}/{name}_gs.mp4\"  \n",
    "    }\n",
    "    trials.append(trial)\n",
    "\n",
    "for name,caption in text_prompt_dir.items():\n",
    "    trial = {\n",
    "        \"trial_id\": f\"text_{name}\",\n",
    "        \"task\": \"text\",\n",
    "        \"prompt_text\": caption,\n",
    "\n",
    "        \"ours_voxel\": f\"{ours_voxel_path_text}/{name}_cubify64.obj\",\n",
    "        \"cont_voxel\": f\"{trellis_voxel_path_text}/{name}_cubified64.obj\",\n",
    "\n",
    "        \"ours_mesh\": f\"{ours_mesh_path_text}/{name}.glb\",\n",
    "        \"cont_mesh\": f\"{trellis_mesh_path_text}/{name}.glb\",\n",
    "\n",
    "        \"ours_video\": f\"{ours_mesh_path_text}/{name}_gs.mp4\",\n",
    "        \"cont_video\": f\"{trellis_mesh_path_text}/{name}_gs.mp4\"  \n",
    "    }\n",
    "    trials.append(trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a43be243",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('trials.json', 'w') as f:\n",
    "    json.dump(trials, f, indent=4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "voxel",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}