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"cells": [
{
"cell_type": "markdown",
"id": "781eee9c",
"metadata": {},
"source": [
"## using pandas\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "70fc5658",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"## column : file no 1~25\n",
"\n",
"# array 4X4\n",
"# for i in range(rows):\n",
"# for j in range(cols):\n",
"# object_array[i,j] = np.zeros((4,4))\n",
"\n",
"\n",
"data = np.zeros((20,25))\n",
"\n",
"\n",
"\n",
"## row : bottle_0, bottle_25 ... gt 0 25 --> 10 rows. \n",
"\n",
"categories = ['bottle2', 'lightbulb', 'lighter', 'eyeglasses', 'magnifying_glass', 'spray']\n",
"\n",
"category = categories[3]\n",
"fill_rate = ['100', '75', '50', '25', '0']\n",
"\n",
"columns = [f'file_{i}' for i in range(1,26)]\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "22195309",
"metadata": {},
"source": [
"## Get transformation file "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3dcc164",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"id": "86c0ea73",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method DataFrame.info of file_1 file_2 file_3 file_4 file_5 file_6 file_7 \\\n",
"eyeglasses_100_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_75_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_50_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_25_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_0_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_100_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_75_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_50_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_25_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_0_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_100_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_75_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_50_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_25_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_0_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_100_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_75_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_50_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_25_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_0_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" file_8 file_9 file_10 ... file_16 file_17 file_18 \\\n",
"eyeglasses_100_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_75_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_50_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_25_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_0_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_100_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_75_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_50_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_25_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_0_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_100_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_75_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_50_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_25_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_0_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_100_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_75_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_50_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_25_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"eyeglasses_0_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"\n",
" file_19 file_20 file_21 file_22 file_23 file_24 \\\n",
"eyeglasses_100_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_75_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_50_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_25_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_0_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_100_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_75_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_50_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_25_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_0_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_100_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_75_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_50_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_25_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_0_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_100_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_75_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_50_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_25_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"eyeglasses_0_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" file_25 \n",
"eyeglasses_100_ICP 0.0 \n",
"eyeglasses_75_ICP 0.0 \n",
"eyeglasses_50_ICP 0.0 \n",
"eyeglasses_25_ICP 0.0 \n",
"eyeglasses_0_ICP 0.0 \n",
"eyeglasses_100_FAST ICP 0.0 \n",
"eyeglasses_75_FAST ICP 0.0 \n",
"eyeglasses_50_FAST ICP 0.0 \n",
"eyeglasses_25_FAST ICP 0.0 \n",
"eyeglasses_0_FAST ICP 0.0 \n",
"eyeglasses_100_Robust ICP 0.0 \n",
"eyeglasses_75_Robust ICP 0.0 \n",
"eyeglasses_50_Robust ICP 0.0 \n",
"eyeglasses_25_Robust ICP 0.0 \n",
"eyeglasses_0_Robust ICP 0.0 \n",
"eyeglasses_100_Sparse ICP 0.0 \n",
"eyeglasses_75_Sparse ICP 0.0 \n",
"eyeglasses_50_Sparse ICP 0.0 \n",
"eyeglasses_25_Sparse ICP 0.0 \n",
"eyeglasses_0_Sparse ICP 0.0 \n",
"\n",
"[20 rows x 25 columns]>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Tmatrix FOlder access -> save in pandas\n",
"robust_no = ['0','2','3','6']\n",
"new_row_names = []\n",
"# 결과를 저장할 딕셔너리를 카테고리별로 초기화합니다.\n",
"grouped_files = {fill: [] for fill in fill_rate}\n",
"\n",
"for no in robust_no:\n",
" \n",
" ## get txt file\n",
"\n",
" ######################## We got the txt file list#################\n",
" for fills in fill_rate:\n",
" \n",
" if no =='0':\n",
" name = \"ICP\"\n",
" elif no == '2':\n",
" name = \"FAST ICP\"\n",
" elif no =='3':\n",
" name = \"Robust ICP\"\n",
" else:\n",
" name = \"Sparse ICP\"\n",
"\n",
" new_row_names.append(f\"{category}_{fills}_{name}\")\n",
"\n",
"df = pd.DataFrame(data, index=new_row_names, columns=columns, dtype=object)\n",
"# 2. df.index에 새로운 이름 리스트를 바로 할당 object for array 4x4\n",
"\n",
"df.info"
]
},
{
"cell_type": "markdown",
"id": "173149df",
"metadata": {},
"source": [
"## RMSE function"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5334ae14",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"⚠️ 경고: './result3/result_3_100_1.txt' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n"
]
}
],
"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",
"## get T from Result Txt file\n",
"def get_T(file_path):\n",
"\n",
" try:\n",
" with open(file_path, 'r') as f:\n",
" T_matrix = np.loadtxt(file_path)\n",
" return T_matrix\n",
" except FileNotFoundError:\n",
" # try 블록에서 FileNotFoundError가 발생했을 때만 이 코드가 실행됩니다.\n",
" print(f\"⚠️ 경고: '{file_path}' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\")\n",
" return None # 파일이 없으므로 None을 반환\n",
"\n",
"\n",
"\n",
"\n",
"def get_GT_T(file_path,data_name):\n",
"\n",
" try:\n",
" with open(file_path, 'r') as f:\n",
" loaded_data = json.load(f)\n",
" noisy_data = loaded_data[data_name]\n",
" T_matrix = noisy_data['matrix_world']\n",
" np.array(T_matrix)\n",
" return T_matrix\n",
"\n",
" except FileNotFoundError:\n",
" # try 블록에서 FileNotFoundError가 발생했을 때만 이 코드가 실행됩니다.\n",
" print(f\"⚠️ 경고: '{file_path}' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\")\n",
" return None # 파일이 없으므로 None을 반환\n",
"\n",
" except KeyError as e:\n",
" # try 블록에서 KeyError가 발생했을 때 실행됩니다. (e.g., 'matrix_world' 키가 없음)\n",
" print(f\"⚠️ 경고: 파일 '{os.path.basename(file_path)}' 안에 필요한 키({e})가 없습니다.\")\n",
" return None\n",
" \n",
" \n",
"\n",
"def compute_RMSE(gt_files):\n",
" \n",
" robust_no = ['0','2','3','6']\n",
" \n",
" for no in robust_no:\n",
" if no =='0':\n",
" name = \"ICP\"\n",
" elif no == '2':\n",
" name = \"FAST ICP\"\n",
" elif no =='3':\n",
" name = \"Robust ICP\"\n",
" else:\n",
" name = \"Sparse ICP\"\n",
"\n",
" for key, value_list in gt_files.items():\n",
" rmse = []\n",
" np.array(rmse)\n",
" # get gt_T and noisy_T\n",
" for value in value_list:\n",
" profix = value.split('_')[1]\n",
" gt_path = f\"./gt_raw/noisy_filtered_{key}_{profix}.json\"\n",
" gt_name = f\"noisy_filtered_{key}_{profix}\"\n",
"\n",
" #### RESULT FOLDER PATH.\n",
" result_path = f'./result{no}/result_{key}_{profix}.txt'\n",
" icp_T = get_T(result_path)\n",
" gt_T = get_GT_T(gt_path,gt_name)\n",
" \n",
" \n",
"\n",
" if (gt_T is None or icp_T is None):\n",
" df.loc[f'{category}_{key}_{name}',f'file_{profix}'] = 0.0\n",
"\n",
" else:\n",
" ## conpute rmse\n",
" r = RMSE(gt_T, icp_T)\n",
" \n",
" df.loc[f'{category}_{key}_{name}',f'file_{profix}'] = r\n",
"\n",
"\n",
"noisy_T = get_T(\"./result3/result_3_100_1.txt\")\n",
"gt_T = get_GT_T(\"./gt/noisy_filtered_100_1.json\",\"noisy_filtered_100_1\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "587f5b2d",
"metadata": {},
"source": [
"## Bring GT"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c4883f09",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"⚠️ 경고: './gt_raw/noisy_filtered_100_3.json' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n",
"⚠️ 경고: './gt_raw/noisy_filtered_75_21.json' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n",
"⚠️ 경고: './gt_raw/noisy_filtered_100_3.json' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n",
"⚠️ 경고: './gt_raw/noisy_filtered_75_21.json' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n",
"⚠️ 경고: './gt_raw/noisy_filtered_100_3.json' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n",
"⚠️ 경고: './gt_raw/noisy_filtered_75_21.json' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n",
"⚠️ 경고: './gt_raw/noisy_filtered_100_3.json' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n",
"⚠️ 경고: './gt_raw/noisy_filtered_75_21.json' 경로에 파일이 없습니다. 해당 처리를 건너뜁니다.\n",
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
"eyeglasses_100_ICP 49.177524 49.806584 0.0 138.441225 87.915898 120.186261 120.15116 123.894466 89.380514 73.315877 48.166215 6.039374 115.531124 77.997241 88.023412 43.083893 96.244094 117.313122 122.726607 32.79982 0.0 0.0 0.0 0.0 0.0 84.220758\n",
"eyeglasses_75_ICP 87.588102 87.952244 86.888912 44.465704 43.706854 46.803776 83.053832 86.934602 119.085669 118.664201 127.226752 89.041529 25.653662 76.212343 116.570636 110.974039 121.662971 92.39682 92.948404 45.527907 0.0 0.0 0.0 0.0 0.0 85.167948\n",
"eyeglasses_50_ICP 86.077398 85.515931 56.203467 39.658613 55.964432 85.659654 81.994906 86.296592 125.03123 120.92935 120.172806 93.555076 53.094512 52.153707 95.846049 82.616041 85.503566 120.062881 3.460667 90.995474 0.0 0.0 0.0 0.0 0.0 81.039618\n",
"eyeglasses_25_ICP 88.437185 91.31789 47.286129 42.121124 43.6699 44.493015 50.610979 88.285632 121.91528 121.430682 117.920522 89.293436 77.573422 45.97554 43.442207 84.104947 94.560476 119.785534 121.267815 94.969581 0.0 0.0 0.0 0.0 0.0 81.423065\n",
"eyeglasses_0_ICP 115.42656 129.844337 44.737188 42.579934 43.417908 83.214014 29.491695 123.077669 118.621548 117.292488 123.34339 114.669807 47.984773 94.707256 41.521857 42.982327 84.91653 120.417353 133.505992 81.56058 117.780668 115.444352 91.911517 62.998183 0.0 88.393664\n",
"eyeglasses_100_FAST ICP 83.259331 83.645555 0.0 138.441165 87.915654 120.192528 120.16229 123.927474 89.380702 73.341829 48.162544 6.053365 115.531124 77.998005 88.023421 43.043867 96.244094 117.31153 122.726611 32.816015 0.0 0.0 0.0 0.0 0.0 87.798795\n",
"eyeglasses_75_FAST ICP 87.593486 87.954906 86.888759 44.470837 43.710619 46.793999 83.054473 86.934602 119.080369 118.665765 127.226687 89.043718 25.65575 76.255087 116.570636 78.510193 121.658091 92.397359 92.949963 45.527629 0.0 0.0 0.0 0.0 0.0 83.547146\n",
"eyeglasses_50_FAST ICP 86.076355 49.681895 56.206844 39.659207 55.970009 85.6618 81.995082 86.297193 125.030481 120.940702 119.333142 93.555076 53.094512 52.15224 95.846028 82.647037 85.484721 120.062273 3.460217 90.995474 0.0 0.0 0.0 0.0 0.0 79.207514\n",
"eyeglasses_25_FAST ICP 88.436958 91.355311 47.275427 42.120321 43.676167 44.498545 50.610979 88.284004 121.9152 121.430384 117.920431 89.292972 77.581779 45.975742 43.446218 84.110273 94.560495 119.779661 121.248484 94.965692 0.0 0.0 0.0 0.0 0.0 81.424252\n",
"eyeglasses_0_FAST ICP 115.42656 129.844985 44.736931 42.580005 43.418869 83.216159 29.491718 123.058863 118.619352 117.288364 123.345308 114.66969 47.983268 136.483019 41.523866 43.033534 84.913399 120.413866 133.497748 81.560175 117.777147 115.442671 91.834041 63.008174 0.0 90.131988\n",
"eyeglasses_100_Robust ICP 86.706648 87.550122 0.0 163.059025 88.657162 122.168079 122.876288 124.316046 2.024247 46.971363 48.601167 16.502839 3.776909 86.079746 71.630269 163.053315 89.672166 85.70942 124.58407 39.207876 0.0 0.0 0.0 0.0 0.0 82.797198\n",
"eyeglasses_75_Robust ICP 151.41524 150.31776 1.81071 51.252233 46.856081 90.632477 87.766717 88.139124 120.170606 121.48972 121.421837 3.040087 0.633951 92.450141 85.345478 84.340541 124.259725 3.88856 47.404646 50.859014 0.0 0.0 0.0 0.0 0.0 76.174732\n",
"eyeglasses_50_Robust ICP 1.56464 0.751704 52.268807 42.884698 50.529565 88.666486 85.292645 85.090955 123.343195 123.688799 123.172204 11.937183 58.305113 46.758012 85.425053 82.88777 83.149764 121.562022 5.581632 108.902694 0.0 0.0 0.0 0.0 0.0 69.088147\n",
"eyeglasses_25_Robust ICP 45.422566 60.675765 44.991866 48.96448 47.907548 49.815408 86.961364 88.40623 123.518214 123.869926 120.11876 2.261041 4.281067 51.381574 49.784994 88.151235 109.011523 120.460096 123.827282 92.882345 0.0 0.0 0.0 0.0 0.0 74.134664\n",
"eyeglasses_0_Robust ICP 123.234351 121.445912 53.025448 43.83628 52.887759 87.003592 51.652271 136.030958 120.702919 119.634498 122.65242 112.316815 57.562732 134.230881 49.144408 47.889125 83.13567 121.126821 134.682975 88.377431 119.872811 117.8035 84.55052 26.765552 0.0 92.065235\n",
"eyeglasses_100_Sparse ICP 78.881009 79.132542 0.0 161.702818 88.473492 122.745418 119.394692 80.692248 18.86516 36.50056 45.538846 8.924245 107.029653 43.537395 88.652852 69.197221 92.27113 84.338137 95.198425 21.550318 0.0 0.0 0.0 0.0 0.0 75.927693\n",
"eyeglasses_75_Sparse ICP 2.760445 2.500606 5.701879 48.877519 42.806559 52.169592 87.087468 88.496797 118.09202 117.066411 4.664192 4.772974 5.393414 85.533386 88.041404 81.56911 115.74126 3.103941 47.332934 43.208846 0.0 0.0 0.0 0.0 0.0 52.246038\n",
"eyeglasses_50_Sparse ICP 2.662489 8.362011 57.043531 42.963002 49.176657 83.142893 86.212548 84.757268 91.590711 117.766474 118.944513 14.286125 57.324034 122.92677 83.14378 80.079108 95.31367 115.935388 4.063046 103.015609 0.0 0.0 0.0 0.0 0.0 70.935481\n",
"eyeglasses_25_Sparse ICP 129.711864 110.97634 49.67125 41.613502 42.037786 48.471344 87.542938 88.77044 123.064149 118.953458 115.809029 5.083772 3.368678 45.859338 79.97394 83.047998 111.944448 90.690014 111.13601 1.443622 0.0 0.0 0.0 0.0 0.0 74.458496\n",
"eyeglasses_0_Sparse ICP 115.189712 119.835916 60.675744 43.585937 47.503253 47.556993 93.675667 123.237753 120.54543 116.717029 115.945411 109.501177 44.063482 94.247467 41.225771 46.006457 79.146285 124.16092 136.315118 85.654087 117.709611 115.315982 87.317356 37.100023 0.0 88.426357\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_285739/3042233176.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" df['mean_Val'] = df.replace(0, np.nan).mean(axis=1)\n"
]
}
],
"source": [
"json_path = \"ply_files.json\"\n",
"try: \n",
" with open(json_path, \"r\", encoding=\"utf-8\") as f:\n",
" gt_files = json.load(f)\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",
"\n",
"### get \n",
"\n",
"\n",
"\n",
"compute_RMSE(gt_files)\n",
"\n",
"##get mean value\n",
"df['mean_Val'] = df.replace(0, np.nan).mean(axis=1)\n",
"\n",
"\n",
"\n",
"# 모든 행/열을 전부 보여줌\n",
"pd.set_option('display.max_rows', None) # 행 전체 출력\n",
"pd.set_option('display.max_columns', None) # 열 전체 출력\n",
"\n",
"# 각 열의 너비 제한 해제 (긴 문자열도 잘리지 않음)\n",
"pd.set_option('display.max_colwidth', None)\n",
"\n",
"# 화면 너비에 따라 줄바꿈을 할지 말지\n",
"pd.set_option('display.width', None) # None이면 자동으로 콘솔 너비를 사용\n",
"pd.set_option('display.expand_frame_repr', False) # True면 줄바꿈 허용, False면 한 줄로 출력 시도\n",
"\n",
"# 예: DataFrame 출력\n",
"print(df)\n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "7493fb27",
"metadata": {},
"source": [
"## GET RMSE MEAN by ICP Methods\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e49285b9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3]\n",
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
"ICP 85.341354 88.887397 47.023139 61.45332 54.934998 76.071344 73.060515 101.697793 114.806848 110.32652 107.365937 78.519844 63.967499 69.409217 77.080832 72.752249 96.577527 113.995142 94.781897 69.170672 23.556134 23.08887 18.382303 12.599637 0.0 84.049010\n",
"FAST ICP 92.158538 88.49653 47.021592 61.454307 54.938263 76.072606 73.062909 101.700427 114.805221 110.333409 107.197623 78.522964 63.969287 77.772819 77.082034 66.268981 96.57216 113.992938 94.776605 69.172997 23.555429 23.088534 18.366808 12.601635 0.0 84.421939\n",
"FAST AND ROBUST ICP 81.668689 84.148253 30.419366 69.999343 57.367623 87.657208 86.909857 104.396662 97.951836 107.130861 107.193278 29.211593 24.911955 82.180071 68.26604 93.264397 97.84577 90.549384 87.216121 76.045872 23.974562 23.5607 16.910104 5.35311 0.0 78.851995\n",
"SPARSE ICP 65.841104 64.161483 34.618481 67.748556 53.99955 70.817248 94.782662 93.190901 94.431494 101.400787 80.180398 28.513659 43.435852 78.420871 76.207549 71.979979 98.883359 83.64568 78.809106 50.974496 23.541922 23.063196 17.463471 7.420005 0.0 72.398813\n",
"<class 'pandas.core.frame.DataFrame'>\n"
]
}
],
"source": [
"df_mean = np.zeros((5,5))\n",
"\n",
"## make 25 lengths array\n",
"\n",
"grouping = []\n",
"\n",
"for i in range(0,len(df)):\n",
" grouping.append(i)\n",
"\n",
"grouping = np.arange(len(df)) //5\n",
"\n",
"print(grouping)\n",
"block_avg_df = df.groupby(grouping).mean()\n",
"\n",
"\n",
"ICP_Method = ['ICP', 'FAST ICP', 'FAST AND ROBUST ICP', 'SPARSE ICP']\n",
"\n",
"\n",
"\n",
"block_avg_df.index = ICP_Method\n",
"\n",
"\n",
"print(block_avg_df)\n",
"\n",
"print(type(block_avg_df))\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14ebb074",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "d03a908e",
"metadata": {},
"source": [
"## merge in Pandas"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "92386801",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
"eyeglasses_100_ICP 49.177524 49.806584 0.0 138.441225 87.915898 120.186261 120.15116 123.894466 89.380514 73.315877 48.166215 6.039374 115.531124 77.997241 88.023412 43.083893 96.244094 117.313122 122.726607 32.79982 0.0 0.0 0.0 0.0 0.0 84.220758\n",
"eyeglasses_75_ICP 87.588102 87.952244 86.888912 44.465704 43.706854 46.803776 83.053832 86.934602 119.085669 118.664201 127.226752 89.041529 25.653662 76.212343 116.570636 110.974039 121.662971 92.39682 92.948404 45.527907 0.0 0.0 0.0 0.0 0.0 85.167948\n",
"eyeglasses_50_ICP 86.077398 85.515931 56.203467 39.658613 55.964432 85.659654 81.994906 86.296592 125.03123 120.92935 120.172806 93.555076 53.094512 52.153707 95.846049 82.616041 85.503566 120.062881 3.460667 90.995474 0.0 0.0 0.0 0.0 0.0 81.039618\n",
"eyeglasses_25_ICP 88.437185 91.31789 47.286129 42.121124 43.6699 44.493015 50.610979 88.285632 121.91528 121.430682 117.920522 89.293436 77.573422 45.97554 43.442207 84.104947 94.560476 119.785534 121.267815 94.969581 0.0 0.0 0.0 0.0 0.0 81.423065\n",
"eyeglasses_0_ICP 115.42656 129.844337 44.737188 42.579934 43.417908 83.214014 29.491695 123.077669 118.621548 117.292488 123.34339 114.669807 47.984773 94.707256 41.521857 42.982327 84.91653 120.417353 133.505992 81.56058 117.780668 115.444352 91.911517 62.998183 0.0 88.393664\n",
"eyeglasses_100_FAST ICP 83.259331 83.645555 0.0 138.441165 87.915654 120.192528 120.16229 123.927474 89.380702 73.341829 48.162544 6.053365 115.531124 77.998005 88.023421 43.043867 96.244094 117.31153 122.726611 32.816015 0.0 0.0 0.0 0.0 0.0 87.798795\n",
"eyeglasses_75_FAST ICP 87.593486 87.954906 86.888759 44.470837 43.710619 46.793999 83.054473 86.934602 119.080369 118.665765 127.226687 89.043718 25.65575 76.255087 116.570636 78.510193 121.658091 92.397359 92.949963 45.527629 0.0 0.0 0.0 0.0 0.0 83.547146\n",
"eyeglasses_50_FAST ICP 86.076355 49.681895 56.206844 39.659207 55.970009 85.6618 81.995082 86.297193 125.030481 120.940702 119.333142 93.555076 53.094512 52.15224 95.846028 82.647037 85.484721 120.062273 3.460217 90.995474 0.0 0.0 0.0 0.0 0.0 79.207514\n",
"eyeglasses_25_FAST ICP 88.436958 91.355311 47.275427 42.120321 43.676167 44.498545 50.610979 88.284004 121.9152 121.430384 117.920431 89.292972 77.581779 45.975742 43.446218 84.110273 94.560495 119.779661 121.248484 94.965692 0.0 0.0 0.0 0.0 0.0 81.424252\n",
"eyeglasses_0_FAST ICP 115.42656 129.844985 44.736931 42.580005 43.418869 83.216159 29.491718 123.058863 118.619352 117.288364 123.345308 114.66969 47.983268 136.483019 41.523866 43.033534 84.913399 120.413866 133.497748 81.560175 117.777147 115.442671 91.834041 63.008174 0.0 90.131988\n",
"eyeglasses_100_Robust ICP 86.706648 87.550122 0.0 163.059025 88.657162 122.168079 122.876288 124.316046 2.024247 46.971363 48.601167 16.502839 3.776909 86.079746 71.630269 163.053315 89.672166 85.70942 124.58407 39.207876 0.0 0.0 0.0 0.0 0.0 82.797198\n",
"eyeglasses_75_Robust ICP 151.41524 150.31776 1.81071 51.252233 46.856081 90.632477 87.766717 88.139124 120.170606 121.48972 121.421837 3.040087 0.633951 92.450141 85.345478 84.340541 124.259725 3.88856 47.404646 50.859014 0.0 0.0 0.0 0.0 0.0 76.174732\n",
"eyeglasses_50_Robust ICP 1.56464 0.751704 52.268807 42.884698 50.529565 88.666486 85.292645 85.090955 123.343195 123.688799 123.172204 11.937183 58.305113 46.758012 85.425053 82.88777 83.149764 121.562022 5.581632 108.902694 0.0 0.0 0.0 0.0 0.0 69.088147\n",
"eyeglasses_25_Robust ICP 45.422566 60.675765 44.991866 48.96448 47.907548 49.815408 86.961364 88.40623 123.518214 123.869926 120.11876 2.261041 4.281067 51.381574 49.784994 88.151235 109.011523 120.460096 123.827282 92.882345 0.0 0.0 0.0 0.0 0.0 74.134664\n",
"eyeglasses_0_Robust ICP 123.234351 121.445912 53.025448 43.83628 52.887759 87.003592 51.652271 136.030958 120.702919 119.634498 122.65242 112.316815 57.562732 134.230881 49.144408 47.889125 83.13567 121.126821 134.682975 88.377431 119.872811 117.8035 84.55052 26.765552 0.0 92.065235\n",
"eyeglasses_100_Sparse ICP 78.881009 79.132542 0.0 161.702818 88.473492 122.745418 119.394692 80.692248 18.86516 36.50056 45.538846 8.924245 107.029653 43.537395 88.652852 69.197221 92.27113 84.338137 95.198425 21.550318 0.0 0.0 0.0 0.0 0.0 75.927693\n",
"eyeglasses_75_Sparse ICP 2.760445 2.500606 5.701879 48.877519 42.806559 52.169592 87.087468 88.496797 118.09202 117.066411 4.664192 4.772974 5.393414 85.533386 88.041404 81.56911 115.74126 3.103941 47.332934 43.208846 0.0 0.0 0.0 0.0 0.0 52.246038\n",
"eyeglasses_50_Sparse ICP 2.662489 8.362011 57.043531 42.963002 49.176657 83.142893 86.212548 84.757268 91.590711 117.766474 118.944513 14.286125 57.324034 122.92677 83.14378 80.079108 95.31367 115.935388 4.063046 103.015609 0.0 0.0 0.0 0.0 0.0 70.935481\n",
"eyeglasses_25_Sparse ICP 129.711864 110.97634 49.67125 41.613502 42.037786 48.471344 87.542938 88.77044 123.064149 118.953458 115.809029 5.083772 3.368678 45.859338 79.97394 83.047998 111.944448 90.690014 111.13601 1.443622 0.0 0.0 0.0 0.0 0.0 74.458496\n",
"eyeglasses_0_Sparse ICP 115.189712 119.835916 60.675744 43.585937 47.503253 47.556993 93.675667 123.237753 120.54543 116.717029 115.945411 109.501177 44.063482 94.247467 41.225771 46.006457 79.146285 124.16092 136.315118 85.654087 117.709611 115.315982 87.317356 37.100023 0.0 88.426357\n",
"ICP 85.341354 88.887397 47.023139 61.45332 54.934998 76.071344 73.060515 101.697793 114.806848 110.32652 107.365937 78.519844 63.967499 69.409217 77.080832 72.752249 96.577527 113.995142 94.781897 69.170672 23.556134 23.08887 18.382303 12.599637 0.0 84.049010\n",
"FAST ICP 92.158538 88.49653 47.021592 61.454307 54.938263 76.072606 73.062909 101.700427 114.805221 110.333409 107.197623 78.522964 63.969287 77.772819 77.082034 66.268981 96.57216 113.992938 94.776605 69.172997 23.555429 23.088534 18.366808 12.601635 0.0 84.421939\n",
"FAST AND ROBUST ICP 81.668689 84.148253 30.419366 69.999343 57.367623 87.657208 86.909857 104.396662 97.951836 107.130861 107.193278 29.211593 24.911955 82.180071 68.26604 93.264397 97.84577 90.549384 87.216121 76.045872 23.974562 23.5607 16.910104 5.35311 0.0 78.851995\n",
"SPARSE ICP 65.841104 64.161483 34.618481 67.748556 53.99955 70.817248 94.782662 93.190901 94.431494 101.400787 80.180398 28.513659 43.435852 78.420871 76.207549 71.979979 98.883359 83.64568 78.809106 50.974496 23.541922 23.063196 17.463471 7.420005 0.0 72.398813\n"
]
}
],
"source": [
"combined_df = pd.concat([df, block_avg_df], ignore_index=False)\n",
"\n",
"# 모든 행/열을 전부 보여줌\n",
"pd.set_option('display.max_rows', None) # 행 전체 출력\n",
"pd.set_option('display.max_columns', None) # 열 전체 출력\n",
"\n",
"# 각 열의 너비 제한 해제 (긴 문자열도 잘리지 않음)\n",
"pd.set_option('display.max_colwidth', None)\n",
"\n",
"# 화면 너비에 따라 줄바꿈을 할지 말지\n",
"pd.set_option('display.width', None) # None이면 자동으로 콘솔 너비를 사용\n",
"pd.set_option('display.expand_frame_repr', False) # True면 줄바꿈 허용, False면 한 줄로 출력 시도\n",
"\n",
"print(combined_df)"
]
},
{
"cell_type": "markdown",
"id": "a9b19689",
"metadata": {},
"source": [
"## Save bottle csv"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9e8dcfae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ICP 84.049010\n",
"FAST ICP 84.421939\n",
"FAST AND ROBUST ICP 78.851995\n",
"SPARSE ICP 72.398813\n",
"Name: mean_Val, dtype: float64\n"
]
}
],
"source": [
"sliced_data = combined_df.loc['ICP':'SPARSE ICP', 'mean_Val']\n",
"print(sliced_data)\n",
"combined_df.to_csv(f'{category}.csv', index=True)"
]
},
{
"cell_type": "markdown",
"id": "1c228eca",
"metadata": {},
"source": [
"## Load num of dataset in each category. + save array"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e81b4de4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val Counts\n",
"eyeglasses_100_ICP 49.177524 49.806584 0.0 138.441225 87.915898 120.186261 120.15116 123.894466 89.380514 73.315877 48.166215 6.039374 115.531124 77.997241 88.023412 43.083893 96.244094 117.313122 122.726607 32.79982 0.0 0.0 0.0 0.0 0.0 84.220758 19\n",
"eyeglasses_75_ICP 87.588102 87.952244 86.888912 44.465704 43.706854 46.803776 83.053832 86.934602 119.085669 118.664201 127.226752 89.041529 25.653662 76.212343 116.570636 110.974039 121.662971 92.39682 92.948404 45.527907 0.0 0.0 0.0 0.0 0.0 85.167948 20\n",
"eyeglasses_50_ICP 86.077398 85.515931 56.203467 39.658613 55.964432 85.659654 81.994906 86.296592 125.03123 120.92935 120.172806 93.555076 53.094512 52.153707 95.846049 82.616041 85.503566 120.062881 3.460667 90.995474 0.0 0.0 0.0 0.0 0.0 81.039618 20\n",
"eyeglasses_25_ICP 88.437185 91.31789 47.286129 42.121124 43.6699 44.493015 50.610979 88.285632 121.91528 121.430682 117.920522 89.293436 77.573422 45.97554 43.442207 84.104947 94.560476 119.785534 121.267815 94.969581 0.0 0.0 0.0 0.0 0.0 81.423065 20\n",
"eyeglasses_0_ICP 115.42656 129.844337 44.737188 42.579934 43.417908 83.214014 29.491695 123.077669 118.621548 117.292488 123.34339 114.669807 47.984773 94.707256 41.521857 42.982327 84.91653 120.417353 133.505992 81.56058 117.780668 115.444352 91.911517 62.998183 0.0 88.393664 24\n",
"eyeglasses_100_FAST ICP 83.259331 83.645555 0.0 138.441165 87.915654 120.192528 120.16229 123.927474 89.380702 73.341829 48.162544 6.053365 115.531124 77.998005 88.023421 43.043867 96.244094 117.31153 122.726611 32.816015 0.0 0.0 0.0 0.0 0.0 87.798795 19\n",
"eyeglasses_75_FAST ICP 87.593486 87.954906 86.888759 44.470837 43.710619 46.793999 83.054473 86.934602 119.080369 118.665765 127.226687 89.043718 25.65575 76.255087 116.570636 78.510193 121.658091 92.397359 92.949963 45.527629 0.0 0.0 0.0 0.0 0.0 83.547146 20\n",
"eyeglasses_50_FAST ICP 86.076355 49.681895 56.206844 39.659207 55.970009 85.6618 81.995082 86.297193 125.030481 120.940702 119.333142 93.555076 53.094512 52.15224 95.846028 82.647037 85.484721 120.062273 3.460217 90.995474 0.0 0.0 0.0 0.0 0.0 79.207514 20\n",
"eyeglasses_25_FAST ICP 88.436958 91.355311 47.275427 42.120321 43.676167 44.498545 50.610979 88.284004 121.9152 121.430384 117.920431 89.292972 77.581779 45.975742 43.446218 84.110273 94.560495 119.779661 121.248484 94.965692 0.0 0.0 0.0 0.0 0.0 81.424252 20\n",
"eyeglasses_0_FAST ICP 115.42656 129.844985 44.736931 42.580005 43.418869 83.216159 29.491718 123.058863 118.619352 117.288364 123.345308 114.66969 47.983268 136.483019 41.523866 43.033534 84.913399 120.413866 133.497748 81.560175 117.777147 115.442671 91.834041 63.008174 0.0 90.131988 24\n",
"eyeglasses_100_Robust ICP 86.706648 87.550122 0.0 163.059025 88.657162 122.168079 122.876288 124.316046 2.024247 46.971363 48.601167 16.502839 3.776909 86.079746 71.630269 163.053315 89.672166 85.70942 124.58407 39.207876 0.0 0.0 0.0 0.0 0.0 82.797198 19\n",
"eyeglasses_75_Robust ICP 151.41524 150.31776 1.81071 51.252233 46.856081 90.632477 87.766717 88.139124 120.170606 121.48972 121.421837 3.040087 0.633951 92.450141 85.345478 84.340541 124.259725 3.88856 47.404646 50.859014 0.0 0.0 0.0 0.0 0.0 76.174732 20\n",
"eyeglasses_50_Robust ICP 1.56464 0.751704 52.268807 42.884698 50.529565 88.666486 85.292645 85.090955 123.343195 123.688799 123.172204 11.937183 58.305113 46.758012 85.425053 82.88777 83.149764 121.562022 5.581632 108.902694 0.0 0.0 0.0 0.0 0.0 69.088147 20\n",
"eyeglasses_25_Robust ICP 45.422566 60.675765 44.991866 48.96448 47.907548 49.815408 86.961364 88.40623 123.518214 123.869926 120.11876 2.261041 4.281067 51.381574 49.784994 88.151235 109.011523 120.460096 123.827282 92.882345 0.0 0.0 0.0 0.0 0.0 74.134664 20\n",
"eyeglasses_0_Robust ICP 123.234351 121.445912 53.025448 43.83628 52.887759 87.003592 51.652271 136.030958 120.702919 119.634498 122.65242 112.316815 57.562732 134.230881 49.144408 47.889125 83.13567 121.126821 134.682975 88.377431 119.872811 117.8035 84.55052 26.765552 0.0 92.065235 24\n",
"eyeglasses_100_Sparse ICP 78.881009 79.132542 0.0 161.702818 88.473492 122.745418 119.394692 80.692248 18.86516 36.50056 45.538846 8.924245 107.029653 43.537395 88.652852 69.197221 92.27113 84.338137 95.198425 21.550318 0.0 0.0 0.0 0.0 0.0 75.927693 19\n",
"eyeglasses_75_Sparse ICP 2.760445 2.500606 5.701879 48.877519 42.806559 52.169592 87.087468 88.496797 118.09202 117.066411 4.664192 4.772974 5.393414 85.533386 88.041404 81.56911 115.74126 3.103941 47.332934 43.208846 0.0 0.0 0.0 0.0 0.0 52.246038 20\n",
"eyeglasses_50_Sparse ICP 2.662489 8.362011 57.043531 42.963002 49.176657 83.142893 86.212548 84.757268 91.590711 117.766474 118.944513 14.286125 57.324034 122.92677 83.14378 80.079108 95.31367 115.935388 4.063046 103.015609 0.0 0.0 0.0 0.0 0.0 70.935481 20\n",
"eyeglasses_25_Sparse ICP 129.711864 110.97634 49.67125 41.613502 42.037786 48.471344 87.542938 88.77044 123.064149 118.953458 115.809029 5.083772 3.368678 45.859338 79.97394 83.047998 111.944448 90.690014 111.13601 1.443622 0.0 0.0 0.0 0.0 0.0 74.458496 20\n",
"eyeglasses_0_Sparse ICP 115.189712 119.835916 60.675744 43.585937 47.503253 47.556993 93.675667 123.237753 120.54543 116.717029 115.945411 109.501177 44.063482 94.247467 41.225771 46.006457 79.146285 124.16092 136.315118 85.654087 117.709611 115.315982 87.317356 37.100023 0.0 88.426357 24\n",
"###################\n",
"eyeglasses_100_ICP 19\n",
"eyeglasses_75_ICP 20\n",
"eyeglasses_50_ICP 20\n",
"eyeglasses_25_ICP 20\n",
"eyeglasses_0_ICP 24\n",
"Name: Counts, dtype: int64\n"
]
}
],
"source": [
"\n",
"\n",
"df['Counts'] = (df != 0).sum(axis=1)-1\n",
"\n",
"# 모든 행/열을 전부 보여줌\n",
"pd.set_option('display.max_rows', None) # 행 전체 출력\n",
"pd.set_option('display.max_columns', None) # 열 전체 출력\n",
"\n",
"# 각 열의 너비 제한 해제 (긴 문자열도 잘리지 않음)\n",
"pd.set_option('display.max_colwidth', None)\n",
"\n",
"# 화면 너비에 따라 줄바꿈을 할지 말지\n",
"pd.set_option('display.width', None) # None이면 자동으로 콘솔 너비를 사용\n",
"pd.set_option('display.expand_frame_repr', False) # True면 줄바꿈 허용, False면 한 줄로 출력 시도\n",
"\n",
"print(df)\n",
"\n",
"\n",
"\n",
"sliced_data = df.loc['eyeglasses_100_ICP':'eyeglasses_0_ICP', 'Counts']\n",
"print(f\"###################\\n{sliced_data}\")\n",
"sliced_data.to_csv(f'{category}_data_num.csv', index=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "icp",
"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": "3.10.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|