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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file is to convert ScanQA to LLaVA-3D dataset format. Ref: https://github.com/ZCMax/LLaVA-3D/issues/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load json and show\n",
    "import json\n",
    "import os\n",
    "\n",
    "data = json.load(open('ScanQA_v1.0_train.json'))\n",
    "# print(json.dumps(data, indent=4))\n",
    "\n",
    "# {\"answers\": [\"brown cabinet with tv sitting in it\"], \n",
    "# \"object_ids\": [8], \n",
    "# \"object_names\": [\"cabinet\"], \n",
    "# \"question\": \"What is in the right corner of room by curtains?\", \n",
    "# \"question_id\": \"train-scene0000-0\", \n",
    "# \"scene_id\": \"scene0000_00\"},\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# Read the input JSON file\n",
    "with open('ScanQA_v1.0_train.json', 'r') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "output = []\n",
    "\n",
    "for entry in data:\n",
    "    conversation = {\n",
    "        \"id\": entry['object_ids'][0],\n",
    "        \"video\": f\"scannet/{entry['scene_id']}\",\n",
    "        \"conversations\": [\n",
    "            {\n",
    "                \"from\": \"human\",\n",
    "                \"value\": f\"<video>\\n{entry['question']}\"\n",
    "            },\n",
    "            {\n",
    "                \"from\": \"gpt\",\n",
    "                \"value\": entry['answers'][0].capitalize() + '.'\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "    output.append(conversation)\n",
    "\n",
    "# Write the output to a JSON file\n",
    "with open('LLaVA_canQA_v1.0_train.json', 'w') as f:\n",
    "    json.dump(output, f, indent=4)"
   ]
  }
 ],
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