{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5cf20b5b",
   "metadata": {},
   "source": [
    "## Get Transformation file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe5a09ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import json\n",
    "import os\n",
    "\n",
    "categories = ['bottle2', 'lightbulb', 'lighter', 'eyeglasses', 'magnifying_glass', 'spray']\n",
    "fill_rate = ['100', '75', '50', '25', '0']\n",
    "result_path = './Fast-Robust-ICP/Result/'\n",
    "\n",
    "# assign your folder \n",
    "\n",
    "category =  categories[0]\n",
    "\n",
    "\n",
    "result_path =result_path +  category\n",
    "\n",
    "json_path = result_path + \"ply_files.json\"\n",
    "\n",
    "\n",
    "### Generating T matrix list.\n",
    "\n",
    "# bring the filename json file.\n",
    "try: \n",
    "    with open(json_path, \"r\", encoding=\"utf-8\") as f:\n",
    "        categorized_files = json.load(f)\n",
    "\n",
    "except FileNotFoundError:\n",
    "    print(f\"오류: '{json_path}' 파일을 찾을 수 없습니다. 먼저 파일 분류 코드를 실행해 주세요.\")\n",
    "    exit() # 파일이 없으면 프로그램 종료\n",
    "\n",
    "\n",
    "## get GT\n",
    "gt = []\n",
    "\n",
    "gt_T =[]\n",
    "\n",
    "for fill in fill_rate:\n",
    "    filenames = categorized_files.get(fill, [])\n",
    "    T_array = []\n",
    "\n",
    "    for file in filenames:\n",
    "        gt_name = f\"gt_{file}.txt\"\n",
    "        matrix = np.loadtxt(gt_name)\n",
    "        T_array.append(matrix)\n",
    "    gt_T.append(T_array)\n",
    "\n",
    "\n",
    "\n",
    "print(np.gt_T)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# get T matrix array\n",
    "overall_T =[]\n",
    "\n",
    "for fill in fill_rate:\n",
    "    filenames = categorized_files.get(fill, [])\n",
    "    T_array = []\n",
    "\n",
    "    for file in filenames:\n",
    "        matrix_path = result_path + file+\".txt\"\n",
    "        matrix = np.loadtxt(matrix_path)\n",
    "        T_array.append(matrix)\n",
    "    overall_T.append(T_array)\n",
    "\n",
    "\n",
    "\n",
    "print(np.overall_T.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7cf4b9",
   "metadata": {},
   "source": [
    "# compute RMSE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "758cc248",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE(T_star, T):\n",
    "    diff = T_star - T\n",
    "    sq_norms = np.sum(diff**2, axis =1)\n",
    "\n",
    "    r = np.sqrt(np.mean(sq_norms))\n",
    "\n",
    "    return r\n",
    "\n",
    "def mean(array):\n",
    "    return np.mean(array)\n",
    "\n",
    "\n",
    "RMSE_mean = []\n",
    "for gt, overall in zip(gt_T, overall_T):\n",
    "    rmse = []\n",
    "    for T_star, T in zip(gt, overall):\n",
    "        r= RMSE(T_star, T)\n",
    "        rmse.append(r)\n",
    "    RMSE_mean.append(mean(rmse))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b859fdc3",
   "metadata": {},
   "source": [
    "## Save in json\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c0faa07",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RMSE_mean' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cat \u001b[38;5;129;01min\u001b[39;00m categories:\n\u001b[1;32m      7\u001b[0m     rmse_dict[cat] \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m----> 8\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m mean, fr \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(RMSE_mean,fill_rate):\n\u001b[1;32m      9\u001b[0m         rmse_dict[cat][fr] \u001b[38;5;241m=\u001b[39m mean\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrmse_Results.json\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
      "\u001b[0;31mNameError\u001b[0m: name 'RMSE_mean' is not defined"
     ]
    }
   ],
   "source": [
    "categories = ['bottle2', 'lightbulb', 'lighter', 'eyeglasses', 'magnifying_glass', 'spray']\n",
    "fill_rate = ['100', '75', '50', '25', '0']\n",
    "\n",
    "rmse_dict = {}\n",
    "\n",
    "for cat in categories:\n",
    "    rmse_dict[cat] = {}\n",
    "    for mean, fr in zip(RMSE_mean,fill_rate):\n",
    "        rmse_dict[cat][fr] = mean\n",
    "\n",
    "with open('rmse_Results.json', 'w') as f:\n",
    "    json.dump(rmse_dict, f, indent=4)\n",
    "        \n",
    "\n"
   ]
  }
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