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{
"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"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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",
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"nbformat": 4,
"nbformat_minor": 5
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