characteristics/raw_characteristics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/charlie/projects/entropy/input/binary/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_otsu/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_otsu/treatment/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_otsu/treatment/active/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_otsu/treatment/inactive/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_otsu/reactive/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_otsu/reactive/active/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_otsu/reactive/inactive/\n",
      "/home/charlie/projects/entropy/input/binary/otsu/\n",
      "/home/charlie/projects/entropy/input/binary/otsu/treatment/\n",
      "/home/charlie/projects/entropy/input/binary/otsu/treatment/active/\n",
      "/home/charlie/projects/entropy/input/binary/otsu/treatment/inactive/\n",
      "/home/charlie/projects/entropy/input/binary/otsu/reactive/\n",
      "/home/charlie/projects/entropy/input/binary/otsu/reactive/active/\n",
      "/home/charlie/projects/entropy/input/binary/otsu/reactive/inactive/\n",
      "/home/charlie/projects/entropy/input/binary/mean/\n",
      "/home/charlie/projects/entropy/input/binary/mean/treatment/\n",
      "/home/charlie/projects/entropy/input/binary/mean/treatment/active/\n",
      "/home/charlie/projects/entropy/input/binary/mean/treatment/inactive/\n",
      "/home/charlie/projects/entropy/input/binary/mean/reactive/\n",
      "/home/charlie/projects/entropy/input/binary/mean/reactive/active/\n",
      "/home/charlie/projects/entropy/input/binary/mean/reactive/inactive/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_mean/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_mean/treatment/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_mean/treatment/active/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_mean/treatment/inactive/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_mean/reactive/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_mean/reactive/active/\n",
      "/home/charlie/projects/entropy/input/binary/gabor_mean/reactive/inactive/\n",
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0.2321733951821316], ('966', 'gabor_otsu_treatment_active', '968', 'gabor_otsu_treatment_inactive'): [0.21909335781059625, 0.22125333056507146], ('966', 'mean_treatment_active', '968', 'mean_treatment_inactive'): [0.2580202192390558, 0.24969904258205985], ('966', 'otsu_treatment_active', '968', 'otsu_treatment_inactive'): [0.23075466096987512, 0.23339179465324789], ('981', 'gabor_mean_treatment_active', '982', 'gabor_mean_treatment_inactive'): [0.1691735827682983, 0.14159841626281003], ('981', 'gabor_otsu_treatment_active', '982', 'gabor_otsu_treatment_inactive'): [0.08532620870144841, 0.03153086703054741], ('981', 'mean_treatment_active', '982', 'mean_treatment_inactive'): [0.17304914939690136, 0.14167308648450774], ('981', 'otsu_treatment_active', '982', 'otsu_treatment_inactive'): [0.1005480065782644, 0.04618444273249302], ('983', 'gabor_mean_treatment_active', '985', 'gabor_mean_treatment_inactive'): [0.1545200070663527, 0.1839983536126728], ('983', 'gabor_otsu_treatment_active', '985', 'gabor_otsu_treatment_inactive'): [0.032048094907673005, 0.13406218754609975], ('983', 'mean_treatment_active', '985', 'mean_treatment_inactive'): [0.15074825025815863, 0.19372915835732799], ('983', 'otsu_treatment_active', '985', 'otsu_treatment_inactive'): [0.04825881791575378, 0.14155106441490417], ('986', 'gabor_mean_treatment_active', '988', 'gabor_mean_treatment_inactive'): [0.1879522329128125, 0.21857612993347064], ('986', 'gabor_otsu_treatment_active', '988', 'gabor_otsu_treatment_inactive'): [0.15729919629344305, 0.18013007188374758], ('986', 'mean_treatment_active', '988', 'mean_treatment_inactive'): [0.19566512044671006, 0.23959488672891613], ('986', 'otsu_treatment_active', '988', 'otsu_treatment_inactive'): [0.15425410822811206, 0.18426425245091343], ('1003', 'gabor_mean_treatment_active', '1004', 'gabor_mean_treatment_inactive'): [0.15912224243781883, 0.1455650441373859], ('1003', 'gabor_otsu_treatment_active', '1004', 'gabor_otsu_treatment_inactive'): [0.04375857113977719, 0.06039910322884966], ('1003', 'mean_treatment_active', '1004', 'mean_treatment_inactive'): [0.1535602215337992, 0.14414084625037107], ('1003', 'otsu_treatment_active', '1004', 'otsu_treatment_inactive'): [0.06096186172896167, 0.07024100269359165], ('1015', 'gabor_mean_treatment_active', '1016', 'gabor_mean_treatment_inactive'): [0.1539481424416434, 0.14912007518016468], ('1015', 'gabor_otsu_treatment_active', '1016', 'gabor_otsu_treatment_inactive'): [0.05337645993942606, 0.03481271433540771], ('1015', 'mean_treatment_active', '1016', 'mean_treatment_inactive'): [0.15407198573616643, 0.14414084625037107], ('1015', 'otsu_treatment_active', '1016', 'otsu_treatment_inactive'): [0.0699514279313981, 0.06333673902393272], ('1044', 'gabor_mean_treatment_active', '1045', 'gabor_mean_treatment_inactive'): [0.1758338022987501, 0.1624095534174375], ('1044', 'gabor_otsu_treatment_active', '1045', 'gabor_otsu_treatment_inactive'): [0.10479874554027548, 0.09049120257302656], ('1044', 'mean_treatment_active', '1045', 'mean_treatment_inactive'): [0.1806837242592623, 0.16834856787978458], ('1044', 'otsu_treatment_active', '1045', 'otsu_treatment_inactive'): [0.11736883993436305, 0.0991183450164912], ('1049', 'gabor_mean_treatment_active', '1052', 'gabor_mean_treatment_inactive'): [0.15696409090826308, 0.21083228157594236], ('1049', 'gabor_otsu_treatment_active', '1052', 'gabor_otsu_treatment_inactive'): [0.03126132574246787, 0.14861741710239473], ('1049', 'mean_treatment_active', '1052', 'mean_treatment_inactive'): [0.15106332216922458, 0.23248846709319754], ('1049', 'otsu_treatment_active', '1052', 'otsu_treatment_inactive'): [0.04221235118315877, 0.15464385036087572], ('1055', 'gabor_mean_treatment_active', '1056', 'gabor_mean_treatment_inactive'): [0.14677615870882438, 0.17666063841218327], ('1055', 'gabor_otsu_treatment_active', '1056', 'gabor_otsu_treatment_inactive'): [0.04231616100356778, 0.1373713532247519], ('1055', 'mean_treatment_active', '1056', 'mean_treatment_inactive'): [0.14515891098034717, 0.18527685350613116], ('1055', 'otsu_treatment_active', '1056', 'otsu_treatment_inactive'): [0.05199232900063925, 0.14828231171721476], ('1063', 'gabor_mean_treatment_active', '1064', 'gabor_mean_treatment_inactive'): [0.2204793099743025, 0.2330439406936317], ('1063', 'gabor_otsu_treatment_active', '1064', 'gabor_otsu_treatment_inactive'): [0.20517373575119155, 0.2336504085918107], ('1063', 'mean_treatment_active', '1064', 'mean_treatment_inactive'): [0.24438288704216682, 0.257657795480084], ('1063', 'otsu_treatment_active', '1064', 'otsu_treatment_inactive'): [0.22000214904540497, 0.24815100140052196], ('1076', 'gabor_mean_treatment_active', '1077', 'gabor_mean_treatment_inactive'): [0.18544258497380167, 0.1945159275225331], ('1076', 'gabor_otsu_treatment_active', '1077', 'gabor_otsu_treatment_inactive'): [0.09245266181128103, 0.1318384719194436], ('1076', 'mean_treatment_active', '1077', 'mean_treatment_inactive'): [0.20047679668391366, 0.21931918970060885], ('1076', 'otsu_treatment_active', '1077', 'otsu_treatment_inactive'): [0.11059388323398552, 0.14836973051334867], ('1094', 'gabor_mean_treatment_active', '1095', 'gabor_mean_treatment_inactive'): [0.1682975735820398, 0.19490749088021622], ('1094', 'gabor_otsu_treatment_active', '1095', 'gabor_otsu_treatment_inactive'): [0.10684033867498602, 0.1914963366060745], ('1094', 'mean_treatment_active', '1095', 'mean_treatment_inactive'): [0.17581559004955552, 0.20719893786162696], ('1094', 'otsu_treatment_active', '1095', 'otsu_treatment_inactive'): [0.12917584108719843, 0.20106140988305915], ('1100', 'gabor_mean_treatment_active', '1101', 'gabor_mean_treatment_inactive'): [0.1632108923819983, 0.18931815160240476], ('1100', 'gabor_otsu_treatment_active', '1101', 'gabor_otsu_treatment_inactive'): [0.08584890025333239, 0.20179172107576113], ('1100', 'mean_treatment_active', '1101', 'mean_treatment_inactive'): [0.16944676650621676, 0.19985940143621797], ('1100', 'otsu_treatment_active', '1101', 'otsu_treatment_inactive'): [0.09610239654987152, 0.21156259276864434], ('1104', 'gabor_mean_treatment_active', '1105', 'gabor_mean_treatment_inactive'): [0.16250243588832977, 0.15362942808073854], ('1104', 'gabor_otsu_treatment_active', '1105', 'gabor_otsu_treatment_inactive'): [0.07638945802167622, 0.07583398442124203], ('1104', 'mean_treatment_active', '1105', 'mean_treatment_inactive'): [0.15831543979849969, 0.1505952673649243], ('1104', 'otsu_treatment_active', '1105', 'otsu_treatment_inactive'): [0.09766318630584558, 0.09758123118447005], ('1128', 'gabor_mean_treatment_active', '1129', 'gabor_mean_treatment_inactive'): [0.15469120220878158, 0.14324844603983747], ('1128', 'gabor_otsu_treatment_active', '1129', 'gabor_otsu_treatment_inactive'): [0.07927974196885341, 0.05137857620278247], ('1128', 'mean_treatment_active', '1129', 'mean_treatment_inactive'): [0.15031115627748912, 0.13901409810210152], ('1128', 'otsu_treatment_active', '1129', 'otsu_treatment_inactive'): [0.10496265578302655, 0.06947426700250055], ('1145', 'gabor_mean_treatment_active', '1146', 'gabor_mean_treatment_inactive'): [0.16026050801247904, 0.14553590453867463], ('1145', 'gabor_otsu_treatment_active', '1146', 'gabor_otsu_treatment_inactive'): [0.05699159140454687, 0.06364088358548192], ('1145', 'mean_treatment_active', '1146', 'mean_treatment_inactive'): [0.16040984845587444, 0.1501417823599797], ('1145', 'otsu_treatment_active', '1146', 'otsu_treatment_inactive'): [0.06654573733201477, 0.0798097184204152], ('1184', 'gabor_mean_treatment_active', '1185', 'gabor_mean_treatment_inactive'): [0.15316319450135774, 0.1858632879301961], ('1184', 'gabor_otsu_treatment_active', '1185', 'gabor_otsu_treatment_inactive'): [0.054068525408819464, 0.1683686013538986], ('1184', 'mean_treatment_active', '1185', 'mean_treatment_inactive'): [0.15607351192264893, 0.19440301157752682], ('1184', 'otsu_treatment_active', '1185', 'otsu_treatment_inactive'): [0.06457881441900193, 0.17638199099950644], ('1193', 'gabor_mean_treatment_active', '1194', 'gabor_mean_treatment_inactive'): [0.15737568774006022, 0.15977970463374255], ('1193', 'gabor_otsu_treatment_active', '1194', 'gabor_otsu_treatment_inactive'): [0.07103869920831353, 0.07509274587902331], ('1193', 'mean_treatment_active', '1194', 'mean_treatment_inactive'): [0.1582680879505938, 0.1596030458165553], ('1193', 'otsu_treatment_active', '1194', 'otsu_treatment_inactive'): [0.07970408737508673, 0.08683964660951662], ('1206', 'gabor_mean_treatment_active', '1207', 'gabor_mean_treatment_inactive'): [0.17096931053888223, 0.2531411576798323], ('1206', 'gabor_otsu_treatment_active', '1207', 'gabor_otsu_treatment_inactive'): [0.0808605651989415, 0.26986546611519974], ('1206', 'mean_treatment_active', '1207', 'mean_treatment_inactive'): [0.16873466756270933, 0.27140075872230146], ('1206', 'otsu_treatment_active', '1207', 'otsu_treatment_inactive'): [0.09215944459924856, 0.2843587740242332], ('1212', 'gabor_mean_treatment_active', '1213', 'gabor_mean_treatment_inactive'): [0.1471713645163464, 0.1517207843651483], ('1212', 'gabor_otsu_treatment_active', '1213', 'gabor_otsu_treatment_inactive'): [0.03032339490894786, 0.09450700352042776], ('1212', 'mean_treatment_active', '1213', 'mean_treatment_inactive'): [0.14018878817515085, 0.15881263420151126], ('1212', 'otsu_treatment_active', '1213', 'otsu_treatment_inactive'): [0.05113817451341423, 0.10677113212804668], ('1219', 'gabor_mean_treatment_active', '1220', 'gabor_mean_treatment_inactive'): [0.17711594464204736, 0.23512377955165084], ('1219', 'gabor_otsu_treatment_active', '1220', 'gabor_otsu_treatment_inactive'): [0.10688404807305298, 0.2941387518417137], ('1219', 'mean_treatment_active', '1220', 'mean_treatment_inactive'): [0.18529506575532573, 0.24394943551133622], ('1219', 'otsu_treatment_active', '1220', 'otsu_treatment_inactive'): [0.11299790012766786, 0.28245013030864297], ('1266', 'gabor_mean_treatment_active', '1267', 'gabor_mean_treatment_inactive'): [0.18654624727499222, 0.2218015192658278], ('1266', 'gabor_otsu_treatment_active', '1267', 'gabor_otsu_treatment_inactive'): [0.078671452845755, 0.1365008077132518], ('1266', 'mean_treatment_active', '1267', 'mean_treatment_inactive'): [0.20749761874841782, 0.23938726708809813], ('1266', 'otsu_treatment_active', '1267', 'otsu_treatment_inactive'): [0.091119525170239, 0.14905815353290316], ('1268', 'gabor_mean_treatment_active', '1269', 'gabor_mean_treatment_inactive'): [0.2098542837941943, 0.18296936153318], ('1268', 'gabor_otsu_treatment_active', '1269', 'gabor_otsu_treatment_inactive'): [0.15959940336671638, 0.09655952400465505], ('1268', 'mean_treatment_active', '1269', 'mean_treatment_inactive'): [0.2348505958137324, 0.20468018379801886], ('1268', 'otsu_treatment_active', '1269', 'otsu_treatment_inactive'): [0.16923368319064036, 0.12008428628927244], ('1285', 'gabor_mean_treatment_active', '1286', 'gabor_mean_treatment_inactive'): [0.25382229579970894, 0.1968434529695983], ('1285', 'gabor_otsu_treatment_active', '1286', 'gabor_otsu_treatment_inactive'): [0.2338161400594812, 0.11234954405634141], ('1285', 'mean_treatment_active', '1286', 'mean_treatment_inactive'): [0.27534917434768275, 0.2229907791382328], ('1285', 'otsu_treatment_active', '1286', 'otsu_treatment_inactive'): [0.23726371883201203, 0.12182355608735324], ('1321', 'gabor_mean_treatment_active', '1323', 'gabor_mean_treatment_inactive'): [0.17826149511638537, 0.15747767633554977], ('1321', 'gabor_otsu_treatment_active', '1323', 'gabor_otsu_treatment_inactive'): [0.11281942008556115, 0.047930997430251636], ('1321', 'mean_treatment_active', '1323', 'mean_treatment_inactive'): [0.18414769405606823, 0.15495527982210275], ('1321', 'otsu_treatment_active', '1323', 'otsu_treatment_inactive'): [0.12109324489465124, 0.06079613026129114], ('1327', 'gabor_mean_treatment_active', '1329', 'gabor_mean_treatment_inactive'): [0.20891453173575483, 0.21290665675920312], ('1327', 'gabor_otsu_treatment_active', '1329', 'gabor_otsu_treatment_inactive'): [0.24095898419358894, 0.23878626286467752], ('1327', 'mean_treatment_active', '1329', 'mean_treatment_inactive'): [0.21780210934270172, 0.22186161968816986], ('1327', 'otsu_treatment_active', '1329', 'otsu_treatment_inactive'): [0.22703754090926476, 0.23647877089172636], ('1330', 'gabor_mean_treatment_active', '1333', 'gabor_mean_treatment_inactive'): [0.2088908558118019, 0.2156257455639514], ('1330', 'gabor_otsu_treatment_active', '1333', 'gabor_otsu_treatment_inactive'): [0.20367851009231788, 0.1882144893012142], ('1330', 'mean_treatment_active', '1333', 'mean_treatment_inactive'): [0.2179860530595668, 0.23727646740644823], ('1330', 'otsu_treatment_active', '1333', 'otsu_treatment_inactive'): [0.21172468178647594, 0.20221242403215556], ('1335', 'gabor_mean_treatment_active', '1336', 'gabor_mean_treatment_inactive'): [0.14959905733398168, 0.14004673263143325], ('1335', 'gabor_otsu_treatment_active', '1336', 'gabor_otsu_treatment_inactive'): [0.05639058718112628, 0.047772550862258935], ('1335', 'mean_treatment_active', '1336', 'mean_treatment_inactive'): [0.14687814730431392, 0.14113582513326814], ('1335', 'otsu_treatment_active', '1336', 'otsu_treatment_inactive'): [0.07067445422442226, 0.058639799956654845], ('1341', 'gabor_mean_treatment_active', '1342', 'gabor_mean_treatment_inactive'): [0.20245828939628216, 0.1902542612110053], ('1341', 'gabor_otsu_treatment_active', '1342', 'gabor_otsu_treatment_inactive'): [0.19957346912386334, 0.12997171637700083], ('1341', 'mean_treatment_active', '1342', 'mean_treatment_inactive'): [0.2157932982565414, 0.21636516288125068], ('1341', 'otsu_treatment_active', '1342', 'otsu_treatment_inactive'): [0.2067891622547493, 0.14618243938508163], ('1371', 'gabor_mean_treatment_active', '1372', 'gabor_mean_treatment_inactive'): [0.22386678832449128, 0.22016970173799494], ('1371', 'gabor_otsu_treatment_active', '1372', 'gabor_otsu_treatment_inactive'): [0.17311653471892124, 0.22780063415051696], ('1371', 'mean_treatment_active', '1372', 'mean_treatment_inactive'): [0.2461804360376702, 0.24450308788685093], ('1371', 'otsu_treatment_active', '1372', 'otsu_treatment_inactive'): [0.18470134643158295, 0.24200072484751795], ('1443', 'gabor_mean_treatment_active', '1444', 'gabor_mean_treatment_inactive'): [0.29583777263161604, 0.28529792460970865], ('1443', 'gabor_otsu_treatment_active', '1444', 'gabor_otsu_treatment_inactive'): [0.11765534608916166, 0.1012160008453011], ('1443', 'mean_treatment_active', '1444', 'mean_treatment_inactive'): [0.27301464308041806, 0.2516619852248413], ('1443', 'otsu_treatment_active', '1444', 'otsu_treatment_inactive'): [0.12942792487386523, 0.11426533649147215], ('1456', 'gabor_mean_treatment_active', '1457', 'gabor_mean_treatment_inactive'): [0.27776946173691763, 0.32109994804920355], ('1456', 'gabor_otsu_treatment_active', '1457', 'gabor_otsu_treatment_inactive'): [0.07913250975178085, 0.09517562010759978], ('1456', 'mean_treatment_active', '1457', 'mean_treatment_inactive'): [0.242522167140681, 0.27724995377259637], ('1456', 'otsu_treatment_active', '1457', 'otsu_treatment_inactive'): [0.08490873389745442, 0.1042890225325573], ('1469', 'gabor_mean_treatment_active', '1470', 'gabor_mean_treatment_inactive'): [0.344178428972695, 0.3565409574795939], ('1469', 'gabor_otsu_treatment_active', '1470', 'gabor_otsu_treatment_inactive'): [0.14569997094277487, 0.20736292474178694], ('1469', 'mean_treatment_active', '1470', 'mean_treatment_inactive'): [0.2977837261928871, 0.2345710537206456], ('1469', 'otsu_treatment_active', '1470', 'otsu_treatment_inactive'): [0.13898158828553567, 0.1042890225325573], ('1482', 'gabor_mean_treatment_active', '1483', 'gabor_mean_treatment_inactive'): [0.3055587352182374, 0.2820840194067043], ('1482', 'gabor_otsu_treatment_active', '1483', 'gabor_otsu_treatment_inactive'): [0.07826079299808927, 0.10946649173630128], ('1482', 'mean_treatment_active', '1483', 'mean_treatment_inactive'): [0.24930218633606002, 0.23152444769259217], ('1482', 'otsu_treatment_active', '1483', 'otsu_treatment_inactive'): [0.09226109237556024, 0.11585908126337292]}\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skimage import io\n",
    "import util2\n",
    "import cv2\n",
    "import tortuosity\n",
    "\n",
    "\n",
    "parent_dir = r\"/home/charlie/projects/entropy/input/\"\n",
    "\n",
    "\n",
    "# Create dictionaries to store the results\n",
    "#active_stats: list[tuple[tuple, float]]\n",
    "#inactive_stats: list[tuple[tuple, float]]\n",
    "\n",
    "faz_area = {}\n",
    "faz_circularity = {}\n",
    "vessel_density = {}\n",
    "vessel_length_density = {}\n",
    "\n",
    "\n",
    "#第一層 列出前處理方式種類\n",
    "for dirname in os.listdir(parent_dir):\n",
    "    if (dirname.endswith(\".DS_Store\")):\n",
    "        pass\n",
    "    \n",
    "    if(dirname == 'binary' or 'binary2'):\n",
    "        pass\n",
    "    #each image has up to two parameters, use 'parameter' list to store them\n",
    "    parameter = []\n",
    "    \n",
    "    #each image has up to two parameters, use four lists to store the metrics\n",
    "    reactive_active1 = []\n",
    "    reactive_inactive1 = []\n",
    "    treatment_active1 = []\n",
    "    treatment_inactive1 = []\n",
    "    \n",
    "    reactive_active2 = []\n",
    "    reactive_inactive2 = []\n",
    "    treatment_active2 = []\n",
    "    treatment_inactive2 = []\n",
    "       \n",
    "    type_dir = parent_dir + dirname+'/'\n",
    "    print(type_dir)\n",
    "    \n",
    "    #第二層 列出前處理方式\n",
    "    for dirname1 in os.listdir(type_dir):\n",
    "        if (dirname1.endswith(\".DS_Store\")):\n",
    "            continue\n",
    "        \n",
    "        pre_dir = type_dir + dirname1+'/'\n",
    "        print(pre_dir)\n",
    "        \n",
    "        #第三層 列出reactive/treatment\n",
    "        for dirname2 in os.listdir(pre_dir):\n",
    "            if (dirname2.endswith(\".DS_Store\")):\n",
    "                continue\n",
    "            \n",
    "            cond_dir = pre_dir + dirname2+'/'\n",
    "            print(cond_dir)\n",
    "            \n",
    "            #第四層 列出active/inactive\n",
    "            for dirname3 in os.listdir(cond_dir):\n",
    "                if (dirname3.endswith(\".DS_Store\")):\n",
    "                    continue\n",
    "                    \n",
    "                amd_dir = cond_dir + dirname3+'/'\n",
    "                print(amd_dir)\n",
    "\n",
    "                #列出目錄中的所有圖像\n",
    "                for filename in os.listdir(amd_dir):\n",
    "                    if not (filename.endswith(\".bmp\") or filename.endswith(\".jpg\")):\n",
    "                        continue\n",
    "                        \n",
    "                    # 組合文件路徑\n",
    "                    filepath = os.path.join(amd_dir, filename)\n",
    "                    image = cv2.imread(filepath)\n",
    "                    #print(filepath)\n",
    "                    \n",
    "                    #column名稱\n",
    "                    column_name = dirname1+ '_'+ dirname2 + '_' + dirname3\n",
    "                        \n",
    "                    #initialize the parameters\n",
    "                    img_parameter_1 = 0\n",
    "                    img_parameter_2 = 0\n",
    "                    image_name = filename.split('_')\n",
    "                    \n",
    "                    '''done'''\n",
    "                    # if(dirname == 'faz'):\n",
    "                    #     image = cv2.imread(filepath, cv2.COLOR_BGR2GRAY)\n",
    "                    #     # calculate the blood vessel tortuosity\n",
    "                    #     area, circularity = util2.calculate_faz_ci(image)\n",
    "                    #     img_parameter_1 = area\n",
    "                    #     # calculate the blood vessel caliber\n",
    "                    #     img_parameter_2 = circularity\n",
    "                    #     if(len(parameter)<3):\n",
    "                    #         parameter.append(\"fazarea\")\n",
    "                    #         parameter.append('fazcirc') \n",
    "                    \n",
    "                    \n",
    "                    #分類：binary，parameter為entropy和VD\n",
    "                    if(dirname == 'binary'):\n",
    "                        # Read the image and calculate the entropy\n",
    "                        img_parameter_1 = util2.calculate_image_entropy(filepath)\n",
    "                        # Calculate the blood vessel density\n",
    "                        img_parameter_2 = util2.calculate_bvd(filepath)\n",
    "                        if(len(parameter)<3):\n",
    "                            parameter.append(\"entropy\")\n",
    "                            parameter.append('vd')\n",
    "                    \n",
    "                    # if(dirname == 'skeletonize'):\n",
    "                    #     print(image.shape)\n",
    "                    #     # calculate the blood vessel tortuosity\n",
    "                    #     img_parameter_1 = tortuosity.calculate_bvt_from_image(image, 128)\n",
    "                    #     # calculate the fractal dimension\n",
    "                    #     img_parameter_2 = util2.calculate_fd(image)\n",
    "                    #     if(len(parameter)<3):\n",
    "                    #         parameter.append(\"tortuosity\")\n",
    "                    #         parameter.append('fd')\n",
    "                    \n",
    "                    \"\"\"not done\"\"\"\n",
    "                    # #fd img_parameter_2 = util2.calculate_vpi(image)\n",
    "                    # if(dirname == 'binary3'):\n",
    "                    #     # calculate the blood vessel tortuosity\n",
    "                    #     img_parameter_1 = util2.calculate_bvc(image)\n",
    "                    #     # calculate the blood vessel fractal dimension\n",
    "                    #     img_parameter_2 = util2.calculate_fd(image)\n",
    "                        \n",
    "                    #     if(len(parameter)<3):\n",
    "                    #         parameter.append(\"caliber\")\n",
    "                    #         parameter.append('fd')\n",
    "                    \n",
    "                    # if(dirname == 'canny' or 'gabor' or 'raw'):\n",
    "                    #     # Read the image and calculate the entropy\n",
    "                    #     img_parameter_1 = util2.calculate_image_entropy(filepath)\n",
    "                    #     img_parameter_2 = util2.calculate_image_entropy(filepath)\n",
    "                    #     if(len(parameter)<3):                         \n",
    "                    #         parameter.append(\"entropy\")\n",
    "                    #         parameter.append('entropy')   \n",
    "                            \n",
    "                    # Add the results to the dictionary\n",
    "                    #entropy[(column_name, image_name[0], image.shape[0], image.shape[1])] = img_entropy\n",
    "                    if dirname2 == 'reactive':\n",
    "                        if dirname3 == 'active':\n",
    "                            reactive_active1.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                            reactive_active2.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_2))\n",
    "                        if dirname3 == 'inactive':\n",
    "                            reactive_inactive1.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1)) \n",
    "                            reactive_inactive2.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_2))\n",
    "                            \n",
    "                    if dirname2 == 'treatment':\n",
    "                        if dirname3 == 'active':\n",
    "                            treatment_active1.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                            treatment_active2.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_2))\n",
    "                        if dirname3 == 'inactive':\n",
    "                            treatment_inactive1.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                            treatment_inactive2.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_2))\n",
    "                            \n",
    "    # After reading all data, sort them by patient_number\n",
    "    #先用方法分再用數字分\n",
    "    n=0\n",
    "    for a, b, c, d in ((reactive_active1, reactive_inactive1, treatment_active1, treatment_inactive1),(reactive_active2, reactive_inactive2, treatment_active2, treatment_inactive2)):\n",
    "        for k in a,b,c,d:\n",
    "            k.sort(key = lambda x: x[0][3])\n",
    "            k.sort(key = lambda x: int(x[0][0]))\n",
    "\n",
    "        dict_name_reactive = {}\n",
    "        dict_name_treatment = {}\n",
    "        \n",
    "        #pair the active and inactive keys\n",
    "        for (active_key, active_value), (inactive_key, inactive_value) in zip(a, b):\n",
    "            pass\n",
    "            dict_name_reactive[(inactive_key[0], inactive_key[3], active_key[0], active_key[3])] = [inactive_value, active_value]\n",
    "        \n",
    "        for (active_key, active_value), (inactive_key, inactive_value) in zip(c, d):\n",
    "            pass\n",
    "            dict_name_treatment[(active_key[0], active_key[3], inactive_key[0], inactive_key[3])] = [active_value, inactive_value]\n",
    "            \n",
    "        #print(dict_name_reactive)\n",
    "        #print(dict_name_treatment)\n",
    "            \n",
    "        # Convert the results dictionary to a DataFrame with a multi-level index\n",
    "        df_reactive = pd.DataFrame.from_dict(dict_name_reactive, orient='index')\n",
    "        df_reactive.index = pd.MultiIndex.from_tuples(df_reactive.index, names=['inactive_number', 'inactive_process', 'active_number', 'active_process'])\n",
    "        df_treatment = pd.DataFrame.from_dict(dict_name_treatment, orient='index')\n",
    "        df_treatment.index = pd.MultiIndex.from_tuples(df_treatment.index, names=['active_number', 'active_process', 'inactive_number', 'inactive_process'])\n",
    "        \n",
    "        # Pivot the DataFrame to have directory names as columns\n",
    "        df_reactive_inactive = df_reactive.pivot_table(values=0, index=['active_number','inactive_number'], columns=['inactive_process'])\n",
    "        df_reactive_active = df_reactive.pivot_table(values=1, index=['active_number','inactive_number'], columns=['active_process'])\n",
    "        df_treatment_active = df_treatment.pivot_table(values=0, index=['active_number','inactive_number'], columns=['active_process'])\n",
    "        df_treatment_inactive = df_treatment.pivot_table(values=1, index=['active_number','inactive_number'], columns=['inactive_process'])\n",
    "        \n",
    "        # combine the two data frames using concat\n",
    "        combined_df1 = pd.concat([df_reactive_inactive, df_reactive_active], axis = 1, ignore_index=False)\n",
    "        combined_df2 = pd.concat([df_treatment_inactive, df_treatment_active], axis = 1, ignore_index=False)\n",
    "        \n",
    "        # Save the results to an Excel file\n",
    "        excel_name1 = dirname + '_' + parameter[n] + '_reactive'\n",
    "        excel_name2 = dirname + '_' + parameter[n] + '_treatment'\n",
    "        combined_df1.to_excel('/home/charlie/projects/entropy/excel/'+ excel_name1+ '.xlsx')\n",
    "        combined_df2.to_excel('/home/charlie/projects/entropy/excel/'+ excel_name2+ '.xlsx')\n",
    "        \n",
    "        #計次   \n",
    "        n+=1  \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(treatment_active2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39460 4.0334067952621675\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from skimage.measure import regionprops, label\n",
    "#from scipy.spatial import distance\n",
    "\n",
    "path ='/home/charlie/projects/entropy/input/faz/faz/reactive/active/6_1054482-8_20170926_OCT_00000256.bmp_superficial.jpg'\n",
    "\n",
    "faz_image = cv2.imread('/home/charlie/projects/entropy/input/faz/faz/reactive/active/6_1054482-8_20170926_OCT_00000256.bmp_superficial.jpg', cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "#faz_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "# Label the binary FAZ image and extract the properties of each connected component\n",
    "labeled_image = label(faz_image)\n",
    "regions = regionprops(labeled_image)\n",
    "# Find the largest connected component (i.e., the FAZ)\n",
    "faz_region = max(regions, key=lambda r: r.area)\n",
    "# Calculate the perimeter of the FAZ\n",
    "faz_perimeter = faz_region.perimeter\n",
    "# Calculate the radius of a reference circle with area identical to the FAZ\n",
    "faz_area = faz_region.filled_area\n",
    "faz_radius = np.sqrt(faz_area / np.pi)\n",
    "# Calculate the perimeter of the reference circle\n",
    "reference_perimeter = 2 * np.pi * faz_radius\n",
    "# Calculate the FAZ-CI as the ratio of the FAZ perimeter to the reference circle perimeter\n",
    "faz_ci = faz_perimeter / reference_perimeter\n",
    "print(faz_area, faz_ci)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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