{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'table_rows' from 'benchmark_nerfstudio_paper' (/home/ethanweber/nerfactory/scripts/benchmarking/benchmark_nerfstudio_paper.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [4], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mcopy\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpprint\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mbenchmark_nerfstudio_paper\u001b[39;00m \u001b[39mimport\u001b[39;00m table_rows\n\u001b[1;32m      6\u001b[0m \u001b[39m# methods sorted in the same order we want in the paper tables\u001b[39;00m\n\u001b[1;32m      7\u001b[0m methods \u001b[39m=\u001b[39m [row[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m row \u001b[39min\u001b[39;00m table_rows]\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'table_rows' from 'benchmark_nerfstudio_paper' (/home/ethanweber/nerfactory/scripts/benchmarking/benchmark_nerfstudio_paper.py)"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import copy\n",
    "import pprint\n",
    "from benchmark_nerfstudio_paper import table_rows\n",
    "\n",
    "# methods sorted in the same order we want in the paper tables\n",
    "methods = [row[0] for row in table_rows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def csv_filename_to_results(csv_filename, num_decimals=2, num_rows_to_consider=None):\n",
    "    # code to read the csv file\n",
    "    # and return a dictionary\n",
    "\n",
    "    df = pd.read_csv(csv_filename)\n",
    "\n",
    "    column_titles = list(df.columns.values)\n",
    "\n",
    "    # iterate as long as the value is not None\n",
    "    if num_rows_to_consider is None:\n",
    "        rows = list(df.values)\n",
    "    else:\n",
    "        rows = list(df.values)[:num_rows_to_consider]\n",
    "    valid_row = list(df.values[-1])\n",
    "    for row in rows:\n",
    "        for i in range(len(row)):\n",
    "            value = row[i]\n",
    "            if not pd.isnull(value):\n",
    "                valid_row[i] = value\n",
    "    last_row = valid_row\n",
    "    # last_row will be the row that's valid\n",
    "\n",
    "    from collections import defaultdict\n",
    "    results = defaultdict(dict)\n",
    "    for column_title, last_row_value in zip(column_titles, last_row):\n",
    "        if column_title == \"Step\" or \"__MAX\" in column_title or \"__MIN\" in column_title:\n",
    "            continue\n",
    "        name = column_title.split(\" - \")[0]\n",
    "        capture_name, method = name.split(\"_\")\n",
    "        metric = float(last_row_value)\n",
    "        # format to have 2 decimal places\n",
    "        if num_decimals == 2:\n",
    "            metric = f\"{metric:.2f}\"\n",
    "        elif num_decimals == 3:\n",
    "            metric = f\"{metric:.3f}\"\n",
    "        results[capture_name][method] = metric\n",
    "\n",
    "    # import pprint\n",
    "    # pprint.pprint(results)\n",
    "    # print(len(results))\n",
    "\n",
    "    results = dict(results)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the csv filenames\n",
    "# these are generated from exporting CSVs from wandb graphs\n",
    "# csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-psnr.csv\"\n",
    "# csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-ssim.csv\"\n",
    "# csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-lpips.csv\"\n",
    "csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-jan9-psnr.csv\"\n",
    "csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-jan9-ssim.csv\"\n",
    "csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-jan9-lpips.csv\"\n",
    "\n",
    "results_psrn = csv_filename_to_results(csv_filename_psrn)\n",
    "results_ssim = csv_filename_to_results(csv_filename_ssim, num_decimals=3)\n",
    "results_lpips = csv_filename_to_results(csv_filename_lpips, num_decimals=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_row(method, capture_names):\n",
    "    # remove spaces from method\n",
    "    method_text = method.replace(\"-\", \" \")\n",
    "    string = f\"{method_text}\"\n",
    "    for idx, capture_name in enumerate(capture_names):\n",
    "        psnr = results_psrn[capture_name][method]\n",
    "        ssim = results_ssim[capture_name][method]\n",
    "        lpips = results_lpips[capture_name][method]\n",
    "        if idx == len(capture_names) - 1:\n",
    "            string += \" & \\\\multicolumn{1}{r|}{\" + f\"{psnr} / {ssim} / {lpips}\" + \"}\"\n",
    "        else:\n",
    "            string += \" & \\\\multicolumn{1}{r}{\" + f\"{psnr} / {ssim} / {lpips}\" + \"}\"\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Egypt', 'person', 'kitchen', 'plane', 'dozer']\n",
      "no contraction & \\multicolumn{1}{r}{18.06 / 0.520 / 0.151} & \\multicolumn{1}{r}{21.96 / 0.511 / 0.125} & \\multicolumn{1}{r}{19.35 / 0.764 / 0.078} & \\multicolumn{1}{r}{18.45 / 0.519 / 0.161} & \\multicolumn{1}{r|}{19.53 / 0.786 / 0.081} \\\\ \n",
      "synthetic on real & \\multicolumn{1}{r}{18.51 / 0.546 / 0.119} & \\multicolumn{1}{r}{24.68 / 0.667 / 0.099} & \\multicolumn{1}{r}{20.98 / 0.745 / 0.074} & \\multicolumn{1}{r}{19.32 / 0.578 / 0.102} & \\multicolumn{1}{r|}{21.41 / 0.758 / 0.080} \\\\ \\hline\n",
      "\n",
      "['floating-tree', 'aspen', 'stump', 'sculpture', 'Giannini-Hall']\n",
      "no contraction & \\multicolumn{1}{r}{18.98 / 0.679 / 0.088} & \\multicolumn{1}{r}{15.80 / 0.330 / 0.342} & \\multicolumn{1}{r}{20.69 / 0.704 / 0.075} & \\multicolumn{1}{r}{21.43 / 0.720 / 0.073} & \\multicolumn{1}{r|}{17.75 / 0.596 / 0.134} \\\\ \n",
      "synthetic on real & \\multicolumn{1}{r}{21.13 / 0.726 / 0.072} & \\multicolumn{1}{r}{15.53 / 0.301 / 0.332} & \\multicolumn{1}{r}{23.86 / 0.786 / 0.053} & \\multicolumn{1}{r}{22.62 / 0.706 / 0.076} & \\multicolumn{1}{r|}{18.41 / 0.576 / 0.133} \\\\ \\hline\n"
     ]
    }
   ],
   "source": [
    "capture_names_row1 = [\n",
    "    \"Egypt\",\n",
    "    \"person\",\n",
    "    \"kitchen\",\n",
    "    \"plane\",\n",
    "    \"dozer\"\n",
    "]\n",
    "capture_names_row2 = [\n",
    "    \"floating-tree\",\n",
    "    \"aspen\",\n",
    "    \"stump\",\n",
    "    \"sculpture\",\n",
    "    \"Giannini-Hall\"\n",
    "]\n",
    "\n",
    "print(capture_names_row1)\n",
    "for method in methods[:-1]:\n",
    "    # print(generate_row(method, capture_names_row1) + ' \\\\\\ \\cline{1-1}')\n",
    "    print(generate_row(method, capture_names_row1) + ' \\\\\\ ')\n",
    "print(generate_row(methods[-1], capture_names_row1) + ' \\\\\\ \\hline')\n",
    "\n",
    "print()\n",
    "\n",
    "print(capture_names_row2)\n",
    "for method in methods[:-1]:\n",
    "    # print(generate_row(method, capture_names_row2) + ' \\\\\\ \\cline{1-1}')\n",
    "    print(generate_row(method, capture_names_row2) + ' \\\\\\ ')\n",
    "print(generate_row(methods[-1], capture_names_row2) + ' \\\\\\ \\hline')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_filename_psrn = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-psnr.csv\"\n",
    "csv_filename_ssim = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-ssim.csv\"\n",
    "csv_filename_lpips = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-lpips.csv\"\n",
    "\n",
    "results_psrn = csv_filename_to_results(csv_filename_psrn, num_rows_to_consider=1)\n",
    "results_ssim = csv_filename_to_results(csv_filename_ssim, num_decimals=3, num_rows_to_consider=1)\n",
    "results_lpips = csv_filename_to_results(csv_filename_lpips, num_decimals=3, num_rows_to_consider=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "psnr\n",
      "[('bicycle', {'instant-ngp-w/o-pose-app': '19.92'}),\n",
      " ('garden', {'instant-ngp-w/o-pose-app': '22.68'}),\n",
      " ('stump', {'instant-ngp-w/o-pose-app': '20.25'}),\n",
      " ('room', {'instant-ngp-w/o-pose-app': '28.04'}),\n",
      " ('counter', {'instant-ngp-w/o-pose-app': '23.59'}),\n",
      " ('kitchen', {'instant-ngp-w/o-pose-app': '24.61'}),\n",
      " ('bonsai', {'instant-ngp-w/o-pose-app': '25.67'})]\n",
      "\n",
      "ssim\n",
      "[('bicycle', {'instant-ngp-w/o-pose-app': '0.448'}),\n",
      " ('garden', {'instant-ngp-w/o-pose-app': '0.542'}),\n",
      " ('stump', {'instant-ngp-w/o-pose-app': '0.465'}),\n",
      " ('room', {'instant-ngp-w/o-pose-app': '0.828'}),\n",
      " ('counter', {'instant-ngp-w/o-pose-app': '0.701'}),\n",
      " ('kitchen', {'instant-ngp-w/o-pose-app': '0.642'}),\n",
      " ('bonsai', {'instant-ngp-w/o-pose-app': '0.792'})]\n",
      "\n",
      "lpips\n",
      "[('bicycle', {'instant-ngp-w/o-pose-app': '0.449'}),\n",
      " ('garden', {'instant-ngp-w/o-pose-app': '0.402'}),\n",
      " ('stump', {'instant-ngp-w/o-pose-app': '0.440'}),\n",
      " ('room', {'instant-ngp-w/o-pose-app': '0.170'}),\n",
      " ('counter', {'instant-ngp-w/o-pose-app': '0.271'}),\n",
      " ('kitchen', {'instant-ngp-w/o-pose-app': '0.281'}),\n",
      " ('bonsai', {'instant-ngp-w/o-pose-app': '0.186'})]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mipnerf360_nerfacto_capture_names = [\"bicycle\", \"garden\", \"stump\", \"room\", \"counter\", \"kitchen\", \"bonsai\"]\n",
    "\n",
    "print(\"psnr\")\n",
    "s = [(key, results_psrn[key]) for key in mipnerf360_nerfacto_capture_names]\n",
    "pprint.pprint(s)\n",
    "print()\n",
    "\n",
    "print(\"ssim\")\n",
    "s = [(key, results_ssim[key]) for key in mipnerf360_nerfacto_capture_names]\n",
    "pprint.pprint(s)\n",
    "print()\n",
    "\n",
    "print(\"lpips\")\n",
    "s = [(key, results_lpips[key]) for key in mipnerf360_nerfacto_capture_names]\n",
    "pprint.pprint(s)\n",
    "print()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "psnr\n",
      "[('bicycle', {'instant-ngp-w/o-pose-app': '18.78'}),\n",
      " ('garden', {'instant-ngp-w/o-pose-app': '20.82'}),\n",
      " ('stump', {'instant-ngp-w/o-pose-app': '19.73'}),\n",
      " ('room', {'instant-ngp-w/o-pose-app': '25.26'}),\n",
      " ('counter', {'instant-ngp-w/o-pose-app': '22.09'}),\n",
      " ('kitchen', {'instant-ngp-w/o-pose-app': '22.82'}),\n",
      " ('bonsai', {'instant-ngp-w/o-pose-app': '23.86'})]\n",
      "\n",
      "ssim\n",
      "[('bicycle', {'instant-ngp-w/o-pose-app': '0.389'}),\n",
      " ('garden', {'instant-ngp-w/o-pose-app': '0.390'}),\n",
      " ('stump', {'instant-ngp-w/o-pose-app': '0.408'}),\n",
      " ('room', {'instant-ngp-w/o-pose-app': '0.749'}),\n",
      " ('counter', {'instant-ngp-w/o-pose-app': '0.621'}),\n",
      " ('kitchen', {'instant-ngp-w/o-pose-app': '0.530'}),\n",
      " ('bonsai', {'instant-ngp-w/o-pose-app': '0.665'})]\n",
      "\n",
      "lpips\n",
      "[('bicycle', {'instant-ngp-w/o-pose-app': '0.620'}),\n",
      " ('garden', {'instant-ngp-w/o-pose-app': '0.619'}),\n",
      " ('stump', {'instant-ngp-w/o-pose-app': '0.607'}),\n",
      " ('room', {'instant-ngp-w/o-pose-app': '0.311'}),\n",
      " ('counter', {'instant-ngp-w/o-pose-app': '0.417'}),\n",
      " ('kitchen', {'instant-ngp-w/o-pose-app': '0.427'}),\n",
      " ('bonsai', {'instant-ngp-w/o-pose-app': '0.382'})]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mipnerf360_nerfacto_capture_names = [\"bicycle\", \"garden\", \"stump\", \"room\", \"counter\", \"kitchen\", \"bonsai\"]\n",
    "\n",
    "print(\"psnr\")\n",
    "s = [(key, results_psrn[key]) for key in mipnerf360_nerfacto_capture_names]\n",
    "pprint.pprint(s)\n",
    "print()\n",
    "\n",
    "print(\"ssim\")\n",
    "s = [(key, results_ssim[key]) for key in mipnerf360_nerfacto_capture_names]\n",
    "pprint.pprint(s)\n",
    "print()\n",
    "\n",
    "print(\"lpips\")\n",
    "s = [(key, results_lpips[key]) for key in mipnerf360_nerfacto_capture_names]\n",
    "pprint.pprint(s)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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