Organ-aware 3D lesion segmentation dataset and pipeline for abdominal CT analysis (ACM Multimedia 2025 candidate)
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

monai_wholeBody_ct_segmentation.ipynb 213KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714715716717718719720721722723724725726727728729730731732733734735736737738739740741742743744745746747748749750751752753754755756757758759760761762763764765766767768769770771772773774775776777778779780781782783784785786787788789790791792793794795796797798799800801802803804805806807808809810811812813814815816817818819820821822823824825826827828829830831832833834835836837838839840841842843844845846847848849850851852853854855856857858859860861862863864865866867868869870871872873874875876877878879880881882883884885886887888889890891892893894895896897898899900901902903904905906907908909910911912913914915916917918919920921922923924925926927928929930931932933934935936937938939940941942943944945946947948949950951952953954955956957958959960961962963964965966967
  1. {
  2. "nbformat": 4,
  3. "nbformat_minor": 0,
  4. "metadata": {
  5. "colab": {
  6. "provenance": [],
  7. "gpuType": "T4",
  8. "authorship_tag": "ABX9TyOsNL7zeUDPTLklO8UfddKR",
  9. "include_colab_link": true
  10. },
  11. "kernelspec": {
  12. "name": "python3",
  13. "display_name": "Python 3"
  14. },
  15. "language_info": {
  16. "name": "python"
  17. },
  18. "accelerator": "GPU"
  19. },
  20. "cells": [
  21. {
  22. "cell_type": "markdown",
  23. "metadata": {
  24. "id": "view-in-github",
  25. "colab_type": "text"
  26. },
  27. "source": [
  28. "<a href=\"https://colab.research.google.com/github/ytl0623/monai_wholeBody_ct_segmentation/blob/master/monai_wholeBody_ct_segmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  29. ]
  30. },
  31. {
  32. "cell_type": "markdown",
  33. "source": [
  34. "Change Python version 3.10 -> 3.9"
  35. ],
  36. "metadata": {
  37. "id": "_r2WxIAWn6T8"
  38. }
  39. },
  40. {
  41. "cell_type": "code",
  42. "source": [
  43. "!sudo apt-get install python3.9\n",
  44. "!sudo ln -sf /usr/bin/python3.9 /usr/bin/python3\n",
  45. "!sudo apt-get install python3-pip\n",
  46. "!sudo apt-get install python3.9-distutils"
  47. ],
  48. "metadata": {
  49. "id": "YmredzKthlTy",
  50. "colab": {
  51. "base_uri": "https://localhost:8080/"
  52. },
  53. "outputId": "47d284e1-9318-4888-fb9e-9298301496af"
  54. },
  55. "execution_count": 1,
  56. "outputs": [
  57. {
  58. "output_type": "stream",
  59. "name": "stdout",
  60. "text": [
  61. "Reading package lists... Done\n",
  62. "Building dependency tree... Done\n",
  63. "Reading state information... Done\n",
  64. "The following additional packages will be installed:\n",
  65. " libpython3.9-minimal libpython3.9-stdlib mailcap mime-support\n",
  66. " python3.9-minimal\n",
  67. "Suggested packages:\n",
  68. " python3.9-venv binfmt-support\n",
  69. "The following NEW packages will be installed:\n",
  70. " libpython3.9-minimal libpython3.9-stdlib mailcap mime-support python3.9\n",
  71. " python3.9-minimal\n",
  72. "0 upgraded, 6 newly installed, 0 to remove and 16 not upgraded.\n",
  73. "Need to get 5,278 kB of archives.\n",
  74. "After this operation, 19.4 MB of additional disk space will be used.\n",
  75. "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 mailcap all 3.70+nmu1ubuntu1 [23.8 kB]\n",
  76. "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 mime-support all 3.66 [3,696 B]\n",
  77. "Get:3 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 libpython3.9-minimal amd64 3.9.17-1+jammy1 [835 kB]\n",
  78. "Get:4 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 python3.9-minimal amd64 3.9.17-1+jammy1 [2,078 kB]\n",
  79. "Get:5 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 libpython3.9-stdlib amd64 3.9.17-1+jammy1 [1,842 kB]\n",
  80. "Get:6 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 python3.9 amd64 3.9.17-1+jammy1 [496 kB]\n",
  81. "Fetched 5,278 kB in 6s (910 kB/s)\n",
  82. "debconf: unable to initialize frontend: Dialog\n",
  83. "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 6.)\n",
  84. "debconf: falling back to frontend: Readline\n",
  85. "debconf: unable to initialize frontend: Readline\n",
  86. "debconf: (This frontend requires a controlling tty.)\n",
  87. "debconf: falling back to frontend: Teletype\n",
  88. "dpkg-preconfigure: unable to re-open stdin: \n",
  89. "Selecting previously unselected package libpython3.9-minimal:amd64.\n",
  90. "(Reading database ... 120511 files and directories currently installed.)\n",
  91. "Preparing to unpack .../0-libpython3.9-minimal_3.9.17-1+jammy1_amd64.deb ...\n",
  92. "Unpacking libpython3.9-minimal:amd64 (3.9.17-1+jammy1) ...\n",
  93. "Selecting previously unselected package python3.9-minimal.\n",
  94. "Preparing to unpack .../1-python3.9-minimal_3.9.17-1+jammy1_amd64.deb ...\n",
  95. "Unpacking python3.9-minimal (3.9.17-1+jammy1) ...\n",
  96. "Selecting previously unselected package mailcap.\n",
  97. "Preparing to unpack .../2-mailcap_3.70+nmu1ubuntu1_all.deb ...\n",
  98. "Unpacking mailcap (3.70+nmu1ubuntu1) ...\n",
  99. "Selecting previously unselected package mime-support.\n",
  100. "Preparing to unpack .../3-mime-support_3.66_all.deb ...\n",
  101. "Unpacking mime-support (3.66) ...\n",
  102. "Selecting previously unselected package libpython3.9-stdlib:amd64.\n",
  103. "Preparing to unpack .../4-libpython3.9-stdlib_3.9.17-1+jammy1_amd64.deb ...\n",
  104. "Unpacking libpython3.9-stdlib:amd64 (3.9.17-1+jammy1) ...\n",
  105. "Selecting previously unselected package python3.9.\n",
  106. "Preparing to unpack .../5-python3.9_3.9.17-1+jammy1_amd64.deb ...\n",
  107. "Unpacking python3.9 (3.9.17-1+jammy1) ...\n",
  108. "Setting up libpython3.9-minimal:amd64 (3.9.17-1+jammy1) ...\n",
  109. "Setting up python3.9-minimal (3.9.17-1+jammy1) ...\n",
  110. "Setting up mailcap (3.70+nmu1ubuntu1) ...\n",
  111. "Setting up mime-support (3.66) ...\n",
  112. "Setting up libpython3.9-stdlib:amd64 (3.9.17-1+jammy1) ...\n",
  113. "Setting up python3.9 (3.9.17-1+jammy1) ...\n",
  114. "Processing triggers for man-db (2.10.2-1) ...\n",
  115. "Reading package lists... Done\n",
  116. "Building dependency tree... Done\n",
  117. "Reading state information... Done\n",
  118. "The following additional packages will be installed:\n",
  119. " python3-setuptools python3-wheel\n",
  120. "Suggested packages:\n",
  121. " python-setuptools-doc\n",
  122. "The following NEW packages will be installed:\n",
  123. " python3-pip python3-setuptools python3-wheel\n",
  124. "0 upgraded, 3 newly installed, 0 to remove and 16 not upgraded.\n",
  125. "Need to get 1,677 kB of archives.\n",
  126. "After this operation, 8,965 kB of additional disk space will be used.\n",
  127. "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-setuptools all 59.6.0-1.2ubuntu0.22.04.1 [339 kB]\n",
  128. "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 python3-wheel all 0.37.1-2ubuntu0.22.04.1 [32.0 kB]\n",
  129. "Get:3 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 python3-pip all 22.0.2+dfsg-1ubuntu0.3 [1,305 kB]\n",
  130. "Fetched 1,677 kB in 1s (2,398 kB/s)\n",
  131. "debconf: unable to initialize frontend: Dialog\n",
  132. "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 3.)\n",
  133. "debconf: falling back to frontend: Readline\n",
  134. "debconf: unable to initialize frontend: Readline\n",
  135. "debconf: (This frontend requires a controlling tty.)\n",
  136. "debconf: falling back to frontend: Teletype\n",
  137. "dpkg-preconfigure: unable to re-open stdin: \n",
  138. "Selecting previously unselected package python3-setuptools.\n",
  139. "(Reading database ... 121176 files and directories currently installed.)\n",
  140. "Preparing to unpack .../python3-setuptools_59.6.0-1.2ubuntu0.22.04.1_all.deb ...\n",
  141. "Unpacking python3-setuptools (59.6.0-1.2ubuntu0.22.04.1) ...\n",
  142. "Selecting previously unselected package python3-wheel.\n",
  143. "Preparing to unpack .../python3-wheel_0.37.1-2ubuntu0.22.04.1_all.deb ...\n",
  144. "Unpacking python3-wheel (0.37.1-2ubuntu0.22.04.1) ...\n",
  145. "Selecting previously unselected package python3-pip.\n",
  146. "Preparing to unpack .../python3-pip_22.0.2+dfsg-1ubuntu0.3_all.deb ...\n",
  147. "Unpacking python3-pip (22.0.2+dfsg-1ubuntu0.3) ...\n",
  148. "Setting up python3-setuptools (59.6.0-1.2ubuntu0.22.04.1) ...\n",
  149. "Setting up python3-wheel (0.37.1-2ubuntu0.22.04.1) ...\n",
  150. "Setting up python3-pip (22.0.2+dfsg-1ubuntu0.3) ...\n",
  151. "Processing triggers for man-db (2.10.2-1) ...\n",
  152. "Reading package lists... Done\n",
  153. "Building dependency tree... Done\n",
  154. "Reading state information... Done\n",
  155. "The following additional packages will be installed:\n",
  156. " python3.9-lib2to3\n",
  157. "The following NEW packages will be installed:\n",
  158. " python3.9-distutils python3.9-lib2to3\n",
  159. "0 upgraded, 2 newly installed, 0 to remove and 16 not upgraded.\n",
  160. "Need to get 319 kB of archives.\n",
  161. "After this operation, 1,234 kB of additional disk space will be used.\n",
  162. "Get:1 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 python3.9-lib2to3 all 3.9.17-1+jammy1 [127 kB]\n",
  163. "Get:2 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 python3.9-distutils all 3.9.17-1+jammy1 [192 kB]\n",
  164. "Fetched 319 kB in 2s (153 kB/s)\n",
  165. "debconf: unable to initialize frontend: Dialog\n",
  166. "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 2.)\n",
  167. "debconf: falling back to frontend: Readline\n",
  168. "debconf: unable to initialize frontend: Readline\n",
  169. "debconf: (This frontend requires a controlling tty.)\n",
  170. "debconf: falling back to frontend: Teletype\n",
  171. "dpkg-preconfigure: unable to re-open stdin: \n",
  172. "Selecting previously unselected package python3.9-lib2to3.\n",
  173. "(Reading database ... 122038 files and directories currently installed.)\n",
  174. "Preparing to unpack .../python3.9-lib2to3_3.9.17-1+jammy1_all.deb ...\n",
  175. "Unpacking python3.9-lib2to3 (3.9.17-1+jammy1) ...\n",
  176. "Selecting previously unselected package python3.9-distutils.\n",
  177. "Preparing to unpack .../python3.9-distutils_3.9.17-1+jammy1_all.deb ...\n",
  178. "Unpacking python3.9-distutils (3.9.17-1+jammy1) ...\n",
  179. "Setting up python3.9-lib2to3 (3.9.17-1+jammy1) ...\n",
  180. "Setting up python3.9-distutils (3.9.17-1+jammy1) ...\n"
  181. ]
  182. }
  183. ]
  184. },
  185. {
  186. "cell_type": "markdown",
  187. "source": [
  188. "Clone Repository"
  189. ],
  190. "metadata": {
  191. "id": "KaymznnwM9BN"
  192. }
  193. },
  194. {
  195. "cell_type": "code",
  196. "execution_count": 2,
  197. "metadata": {
  198. "colab": {
  199. "base_uri": "https://localhost:8080/"
  200. },
  201. "id": "SccRcqOXMFKT",
  202. "outputId": "ac301d5c-c128-4447-d841-9bdefdde376e"
  203. },
  204. "outputs": [
  205. {
  206. "output_type": "stream",
  207. "name": "stdout",
  208. "text": [
  209. "Cloning into 'monai_wholeBody_ct_segmentation'...\n",
  210. "remote: Enumerating objects: 417, done.\u001b[K\n",
  211. "remote: Counting objects: 100% (219/219), done.\u001b[K\n",
  212. "remote: Compressing objects: 100% (212/212), done.\u001b[K\n",
  213. "remote: Total 417 (delta 22), reused 106 (delta 6), pack-reused 198\u001b[K\n",
  214. "Receiving objects: 100% (417/417), 223.31 MiB | 21.87 MiB/s, done.\n",
  215. "Resolving deltas: 100% (28/28), done.\n",
  216. "Updating files: 100% (199/199), done.\n"
  217. ]
  218. }
  219. ],
  220. "source": [
  221. "!git clone https://github.com/ytl0623/monai_wholeBody_ct_segmentation.git"
  222. ]
  223. },
  224. {
  225. "cell_type": "markdown",
  226. "source": [
  227. "Go to the cloned folder"
  228. ],
  229. "metadata": {
  230. "id": "3EAZt1jRNFX0"
  231. }
  232. },
  233. {
  234. "cell_type": "code",
  235. "source": [
  236. "%cd monai_wholeBody_ct_segmentation"
  237. ],
  238. "metadata": {
  239. "colab": {
  240. "base_uri": "https://localhost:8080/"
  241. },
  242. "id": "kOE7W2EoMdvV",
  243. "outputId": "f773a61d-6446-4b65-b7ed-80e4b0e7dbee"
  244. },
  245. "execution_count": 3,
  246. "outputs": [
  247. {
  248. "output_type": "stream",
  249. "name": "stdout",
  250. "text": [
  251. "/content/monai_wholeBody_ct_segmentation\n"
  252. ]
  253. }
  254. ]
  255. },
  256. {
  257. "cell_type": "markdown",
  258. "source": [
  259. "Install the dependencies"
  260. ],
  261. "metadata": {
  262. "id": "H2-H_2rcNGlT"
  263. }
  264. },
  265. {
  266. "cell_type": "code",
  267. "source": [
  268. "!pip install -r requirements.txt"
  269. ],
  270. "metadata": {
  271. "colab": {
  272. "base_uri": "https://localhost:8080/",
  273. "height": 1000
  274. },
  275. "id": "jn25fz1CMfMs",
  276. "outputId": "175e368f-993d-44ee-8c30-87bb67a3532e"
  277. },
  278. "execution_count": 4,
  279. "outputs": [
  280. {
  281. "output_type": "stream",
  282. "name": "stdout",
  283. "text": [
  284. "Collecting flake8>=3.8.1\n",
  285. " Downloading flake8-6.1.0-py2.py3-none-any.whl (58 kB)\n",
  286. "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/58.3 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 KB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  287. "\u001b[?25hCollecting flake8-bugbear\n",
  288. " Downloading flake8_bugbear-23.7.10-py3-none-any.whl (30 kB)\n",
  289. "Collecting flake8-comprehensions\n",
  290. " Downloading flake8_comprehensions-3.14.0-py3-none-any.whl (8.1 kB)\n",
  291. "Collecting flake8-executable\n",
  292. " Downloading flake8_executable-2.1.3-py3-none-any.whl (35 kB)\n",
  293. "Collecting pylint!=2.13\n",
  294. " Downloading pylint-2.17.5-py3-none-any.whl (536 kB)\n",
  295. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.7/536.7 KB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  296. "\u001b[?25hCollecting mccabe\n",
  297. " Downloading mccabe-0.7.0-py2.py3-none-any.whl (7.3 kB)\n",
  298. "Collecting pep8-naming\n",
  299. " Downloading pep8_naming-0.13.3-py3-none-any.whl (8.5 kB)\n",
  300. "Collecting pycodestyle\n",
  301. " Downloading pycodestyle-2.11.0-py2.py3-none-any.whl (31 kB)\n",
  302. "Collecting pyflakes\n",
  303. " Downloading pyflakes-3.1.0-py2.py3-none-any.whl (62 kB)\n",
  304. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.6/62.6 KB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  305. "\u001b[?25hCollecting black\n",
  306. " Downloading black-23.7.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
  307. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m75.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  308. "\u001b[?25hCollecting isort\n",
  309. " Downloading isort-5.12.0-py3-none-any.whl (91 kB)\n",
  310. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 KB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  311. "\u001b[?25hCollecting pytype>=2020.6.1\n",
  312. " Downloading pytype-2023.7.28-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.9 MB)\n",
  313. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.9/3.9 MB\u001b[0m \u001b[31m94.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  314. "\u001b[?25hCollecting types-pkg_resources\n",
  315. " Downloading types_pkg_resources-0.1.3-py2.py3-none-any.whl (4.8 kB)\n",
  316. "Collecting mypy>=0.790\n",
  317. " Downloading mypy-1.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.0 MB)\n",
  318. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.0/12.0 MB\u001b[0m \u001b[31m50.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  319. "\u001b[?25hCollecting pre-commit\n",
  320. " Downloading pre_commit-3.3.3-py2.py3-none-any.whl (202 kB)\n",
  321. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202.8/202.8 KB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  322. "\u001b[?25hCollecting fire\n",
  323. " Downloading fire-0.5.0.tar.gz (88 kB)\n",
  324. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.3/88.3 KB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  325. "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
  326. "Collecting pytorch-ignite>=0.4.9\n",
  327. " Downloading pytorch_ignite-0.4.12-py3-none-any.whl (266 kB)\n",
  328. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m266.8/266.8 KB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  329. "\u001b[?25hCollecting einops\n",
  330. " Downloading einops-0.6.1-py3-none-any.whl (42 kB)\n",
  331. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 KB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  332. "\u001b[?25hCollecting nibabel\n",
  333. " Downloading nibabel-5.1.0-py3-none-any.whl (3.3 MB)\n",
  334. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m93.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  335. "\u001b[?25hCollecting pyyaml\n",
  336. " Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB)\n",
  337. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m738.9/738.9 KB\u001b[0m \u001b[31m69.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  338. "\u001b[?25hCollecting jsonschema\n",
  339. " Downloading jsonschema-4.19.0-py3-none-any.whl (83 kB)\n",
  340. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.4/83.4 KB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  341. "\u001b[?25hCollecting gdown>=4.5.4\n",
  342. " Downloading gdown-4.7.1-py3-none-any.whl (15 kB)\n",
  343. "Collecting tensorboard\n",
  344. " Downloading tensorboard-2.13.0-py3-none-any.whl (5.6 MB)\n",
  345. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m115.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  346. "\u001b[?25hCollecting parameterized\n",
  347. " Downloading parameterized-0.9.0-py2.py3-none-any.whl (20 kB)\n",
  348. "Collecting monai>=1.2.0\n",
  349. " Downloading monai-1.2.0-202306081546-py3-none-any.whl (1.3 MB)\n",
  350. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m85.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  351. "\u001b[?25hCollecting pillow!=8.3.0\n",
  352. " Downloading Pillow-10.0.0-cp39-cp39-manylinux_2_28_x86_64.whl (3.4 MB)\n",
  353. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m107.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  354. "\u001b[?25hCollecting itk>=5.2\n",
  355. " Downloading itk-5.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (8.3 kB)\n",
  356. "Collecting scikit-learn\n",
  357. " Downloading scikit_learn-1.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.9 MB)\n",
  358. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.9/10.9 MB\u001b[0m \u001b[31m76.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  359. "\u001b[?25hCollecting pandas\n",
  360. " Downloading pandas-2.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.4 MB)\n",
  361. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m35.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  362. "\u001b[?25hCollecting cucim==22.8.1\n",
  363. " Downloading cucim-22.8.1-py3-none-manylinux2014_x86_64.whl (8.6 MB)\n",
  364. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m38.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  365. "\u001b[?25hCollecting scikit-image>=0.19.0\n",
  366. " Downloading scikit_image-0.21.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.8 MB)\n",
  367. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m69.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  368. "\u001b[?25hCollecting PyGithub\n",
  369. " Downloading PyGithub-1.59.1-py3-none-any.whl (342 kB)\n",
  370. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m342.2/342.2 KB\u001b[0m \u001b[31m33.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  371. "\u001b[?25hCollecting opencv-python\n",
  372. " Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.7 MB)\n",
  373. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.7/61.7 MB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  374. "\u001b[?25hCollecting natsort\n",
  375. " Downloading natsort-8.4.0-py3-none-any.whl (38 kB)\n",
  376. "Collecting pydicom\n",
  377. " Downloading pydicom-2.4.2-py3-none-any.whl (1.8 MB)\n",
  378. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m85.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  379. "\u001b[?25hCollecting matplotlib\n",
  380. " Downloading matplotlib-3.7.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)\n",
  381. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m116.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  382. "\u001b[?25hCollecting click\n",
  383. " Downloading click-8.1.6-py3-none-any.whl (97 kB)\n",
  384. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.9/97.9 KB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  385. "\u001b[?25hCollecting numpy\n",
  386. " Downloading numpy-1.25.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n",
  387. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.3/18.3 MB\u001b[0m \u001b[31m97.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  388. "\u001b[?25hCollecting attrs>=19.2.0\n",
  389. " Downloading attrs-23.1.0-py3-none-any.whl (61 kB)\n",
  390. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.2/61.2 KB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  391. "\u001b[?25hCollecting astroid<=2.17.0-dev0,>=2.15.6\n",
  392. " Downloading astroid-2.15.6-py3-none-any.whl (278 kB)\n",
  393. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m278.3/278.3 KB\u001b[0m \u001b[31m32.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  394. "\u001b[?25hCollecting tomli>=1.1.0\n",
  395. " Downloading tomli-2.0.1-py3-none-any.whl (12 kB)\n",
  396. "Collecting tomlkit>=0.10.1\n",
  397. " Downloading tomlkit-0.12.1-py3-none-any.whl (37 kB)\n",
  398. "Collecting dill>=0.2\n",
  399. " Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
  400. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 KB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  401. "\u001b[?25hCollecting platformdirs>=2.2.0\n",
  402. " Downloading platformdirs-3.10.0-py3-none-any.whl (17 kB)\n",
  403. "Collecting typing-extensions>=3.10.0\n",
  404. " Downloading typing_extensions-4.7.1-py3-none-any.whl (33 kB)\n",
  405. "Collecting pathspec>=0.9.0\n",
  406. " Downloading pathspec-0.11.2-py3-none-any.whl (29 kB)\n",
  407. "Collecting packaging>=22.0\n",
  408. " Downloading packaging-23.1-py3-none-any.whl (48 kB)\n",
  409. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.9/48.9 KB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  410. "\u001b[?25hCollecting mypy-extensions>=0.4.3\n",
  411. " Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
  412. "Collecting libcst>=1.0.1\n",
  413. " Downloading libcst-1.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.9 MB)\n",
  414. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m106.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  415. "\u001b[?25hCollecting ninja>=1.10.0.post2\n",
  416. " Downloading ninja-1.11.1-py2.py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (145 kB)\n",
  417. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m146.0/146.0 KB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  418. "\u001b[?25hCollecting pydot>=1.4.2\n",
  419. " Downloading pydot-1.4.2-py2.py3-none-any.whl (21 kB)\n",
  420. "Collecting tabulate>=0.8.10\n",
  421. " Downloading tabulate-0.9.0-py3-none-any.whl (35 kB)\n",
  422. "Collecting toml>=0.10.2\n",
  423. " Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB)\n",
  424. "Collecting networkx<3.2\n",
  425. " Downloading networkx-3.1-py3-none-any.whl (2.1 MB)\n",
  426. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m95.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  427. "\u001b[?25hCollecting importlab>=0.8\n",
  428. " Downloading importlab-0.8-py2.py3-none-any.whl (21 kB)\n",
  429. "Collecting jinja2>=3.1.2\n",
  430. " Downloading Jinja2-3.1.2-py3-none-any.whl (133 kB)\n",
  431. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.1/133.1 KB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  432. "\u001b[?25hCollecting cfgv>=2.0.0\n",
  433. " Downloading cfgv-3.3.1-py2.py3-none-any.whl (7.3 kB)\n",
  434. "Collecting nodeenv>=0.11.1\n",
  435. " Downloading nodeenv-1.8.0-py2.py3-none-any.whl (22 kB)\n",
  436. "Collecting virtualenv>=20.10.0\n",
  437. " Downloading virtualenv-20.24.2-py3-none-any.whl (3.0 MB)\n",
  438. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m113.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  439. "\u001b[?25hCollecting identify>=1.0.0\n",
  440. " Downloading identify-2.5.26-py2.py3-none-any.whl (98 kB)\n",
  441. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.8/98.8 KB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  442. "\u001b[?25hRequirement already satisfied: six in /usr/lib/python3/dist-packages (from fire->-r requirements.txt (line 17)) (1.16.0)\n",
  443. "Collecting termcolor\n",
  444. " Downloading termcolor-2.3.0-py3-none-any.whl (6.9 kB)\n",
  445. "Collecting torch<3,>=1.3\n",
  446. " Downloading torch-2.0.1-cp39-cp39-manylinux1_x86_64.whl (619.9 MB)\n",
  447. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m619.9/619.9 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  448. "\u001b[?25hCollecting referencing>=0.28.4\n",
  449. " Downloading referencing-0.30.2-py3-none-any.whl (25 kB)\n",
  450. "Collecting jsonschema-specifications>=2023.03.6\n",
  451. " Downloading jsonschema_specifications-2023.7.1-py3-none-any.whl (17 kB)\n",
  452. "Collecting rpds-py>=0.7.1\n",
  453. " Downloading rpds_py-0.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
  454. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m73.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  455. "\u001b[?25hCollecting filelock\n",
  456. " Downloading filelock-3.12.2-py3-none-any.whl (10 kB)\n",
  457. "Collecting beautifulsoup4\n",
  458. " Downloading beautifulsoup4-4.12.2-py3-none-any.whl (142 kB)\n",
  459. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.0/143.0 KB\u001b[0m \u001b[31m19.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  460. "\u001b[?25hCollecting tqdm\n",
  461. " Downloading tqdm-4.65.0-py3-none-any.whl (77 kB)\n",
  462. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.1/77.1 KB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  463. "\u001b[?25hCollecting requests[socks]\n",
  464. " Downloading requests-2.31.0-py3-none-any.whl (62 kB)\n",
  465. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.6/62.6 KB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  466. "\u001b[?25hRequirement already satisfied: setuptools>=41.0.0 in /usr/lib/python3/dist-packages (from tensorboard->-r requirements.txt (line 24)) (59.6.0)\n",
  467. "Collecting grpcio>=1.48.2\n",
  468. " Downloading grpcio-1.56.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.2 MB)\n",
  469. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m98.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  470. "\u001b[?25hCollecting protobuf>=3.19.6\n",
  471. " Downloading protobuf-4.23.4-cp37-abi3-manylinux2014_x86_64.whl (304 kB)\n",
  472. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m304.5/304.5 KB\u001b[0m \u001b[31m35.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  473. "\u001b[?25hCollecting google-auth<3,>=1.6.3\n",
  474. " Downloading google_auth-2.22.0-py2.py3-none-any.whl (181 kB)\n",
  475. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m181.8/181.8 KB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  476. "\u001b[?25hCollecting werkzeug>=1.0.1\n",
  477. " Downloading Werkzeug-2.3.6-py3-none-any.whl (242 kB)\n",
  478. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m242.5/242.5 KB\u001b[0m \u001b[31m29.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  479. "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/lib/python3/dist-packages (from tensorboard->-r requirements.txt (line 24)) (0.37.1)\n",
  480. "Collecting tensorboard-data-server<0.8.0,>=0.7.0\n",
  481. " Downloading tensorboard_data_server-0.7.1-py3-none-manylinux2014_x86_64.whl (6.6 MB)\n",
  482. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.6/6.6 MB\u001b[0m \u001b[31m107.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  483. "\u001b[?25hCollecting markdown>=2.6.8\n",
  484. " Downloading Markdown-3.4.4-py3-none-any.whl (94 kB)\n",
  485. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.2/94.2 KB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  486. "\u001b[?25hCollecting google-auth-oauthlib<1.1,>=0.5\n",
  487. " Downloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)\n",
  488. "Collecting absl-py>=0.4\n",
  489. " Downloading absl_py-1.4.0-py3-none-any.whl (126 kB)\n",
  490. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.5/126.5 KB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  491. "\u001b[?25hCollecting itk-core==5.3.0\n",
  492. " Downloading itk_core-5.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (81.2 MB)\n",
  493. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.2/81.2 MB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  494. "\u001b[?25hCollecting itk-segmentation==5.3.0\n",
  495. " Downloading itk_segmentation-5.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (16.5 MB)\n",
  496. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.5/16.5 MB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  497. "\u001b[?25hCollecting itk-io==5.3.0\n",
  498. " Downloading itk_io-5.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (25.6 MB)\n",
  499. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m25.6/25.6 MB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  500. "\u001b[?25hCollecting itk-numerics==5.3.0\n",
  501. " Downloading itk_numerics-5.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (58.8 MB)\n",
  502. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 MB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  503. "\u001b[?25hCollecting itk-filtering==5.3.0\n",
  504. " Downloading itk_filtering-5.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (73.5 MB)\n",
  505. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.5/73.5 MB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  506. "\u001b[?25hCollecting itk-registration==5.3.0\n",
  507. " Downloading itk_registration-5.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (26.6 MB)\n",
  508. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m26.6/26.6 MB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  509. "\u001b[?25hCollecting scipy>=1.5.0\n",
  510. " Downloading scipy-1.11.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.5 MB)\n",
  511. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m36.5/36.5 MB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  512. "\u001b[?25hCollecting threadpoolctl>=2.0.0\n",
  513. " Downloading threadpoolctl-3.2.0-py3-none-any.whl (15 kB)\n",
  514. "Collecting joblib>=1.1.1\n",
  515. " Downloading joblib-1.3.1-py3-none-any.whl (301 kB)\n",
  516. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 KB\u001b[0m \u001b[31m31.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  517. "\u001b[?25hCollecting tzdata>=2022.1\n",
  518. " Downloading tzdata-2023.3-py2.py3-none-any.whl (341 kB)\n",
  519. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.8/341.8 KB\u001b[0m \u001b[31m30.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  520. "\u001b[?25hCollecting python-dateutil>=2.8.2\n",
  521. " Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)\n",
  522. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m247.7/247.7 KB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  523. "\u001b[?25hCollecting pytz>=2020.1\n",
  524. " Downloading pytz-2023.3-py2.py3-none-any.whl (502 kB)\n",
  525. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m502.3/502.3 KB\u001b[0m \u001b[31m54.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  526. "\u001b[?25hCollecting imageio>=2.27\n",
  527. " Downloading imageio-2.31.1-py3-none-any.whl (313 kB)\n",
  528. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m313.2/313.2 KB\u001b[0m \u001b[31m34.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  529. "\u001b[?25hCollecting PyWavelets>=1.1.1\n",
  530. " Downloading PyWavelets-1.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.9 MB)\n",
  531. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.9/6.9 MB\u001b[0m \u001b[31m119.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  532. "\u001b[?25hCollecting tifffile>=2022.8.12\n",
  533. " Downloading tifffile-2023.7.18-py3-none-any.whl (221 kB)\n",
  534. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m221.4/221.4 KB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  535. "\u001b[?25hCollecting lazy_loader>=0.2\n",
  536. " Downloading lazy_loader-0.3-py3-none-any.whl (9.1 kB)\n",
  537. "Collecting deprecated\n",
  538. " Downloading Deprecated-1.2.14-py2.py3-none-any.whl (9.6 kB)\n",
  539. "Collecting pynacl>=1.4.0\n",
  540. " Downloading PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (856 kB)\n",
  541. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m856.7/856.7 KB\u001b[0m \u001b[31m71.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  542. "\u001b[?25hCollecting pyjwt[crypto]>=2.4.0\n",
  543. " Downloading PyJWT-2.8.0-py3-none-any.whl (22 kB)\n",
  544. "Collecting importlib-resources>=3.2.0\n",
  545. " Downloading importlib_resources-6.0.1-py3-none-any.whl (34 kB)\n",
  546. "Collecting contourpy>=1.0.1\n",
  547. " Downloading contourpy-1.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (300 kB)\n",
  548. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m300.4/300.4 KB\u001b[0m \u001b[31m37.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  549. "\u001b[?25hCollecting pyparsing<3.1,>=2.3.1\n",
  550. " Downloading pyparsing-3.0.9-py3-none-any.whl (98 kB)\n",
  551. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.3/98.3 KB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  552. "\u001b[?25hCollecting cycler>=0.10\n",
  553. " Downloading cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
  554. "Collecting kiwisolver>=1.0.1\n",
  555. " Downloading kiwisolver-1.4.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n",
  556. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m70.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  557. "\u001b[?25hCollecting fonttools>=4.22.0\n",
  558. " Downloading fonttools-4.42.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB)\n",
  559. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m111.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  560. "\u001b[?25hCollecting lazy-object-proxy>=1.4.0\n",
  561. " Downloading lazy_object_proxy-1.9.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62 kB)\n",
  562. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.1/62.1 KB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  563. "\u001b[?25hCollecting wrapt<2,>=1.11\n",
  564. " Downloading wrapt-1.15.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (78 kB)\n",
  565. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.3/78.3 KB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  566. "\u001b[?25hCollecting urllib3<2.0\n",
  567. " Downloading urllib3-1.26.16-py2.py3-none-any.whl (143 kB)\n",
  568. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.1/143.1 KB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  569. "\u001b[?25hCollecting cachetools<6.0,>=2.0.0\n",
  570. " Downloading cachetools-5.3.1-py3-none-any.whl (9.3 kB)\n",
  571. "Collecting rsa<5,>=3.1.4\n",
  572. " Downloading rsa-4.9-py3-none-any.whl (34 kB)\n",
  573. "Collecting pyasn1-modules>=0.2.1\n",
  574. " Downloading pyasn1_modules-0.3.0-py2.py3-none-any.whl (181 kB)\n",
  575. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m181.3/181.3 KB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  576. "\u001b[?25hCollecting requests-oauthlib>=0.7.0\n",
  577. " Downloading requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)\n",
  578. "Collecting zipp>=3.1.0\n",
  579. " Downloading zipp-3.16.2-py3-none-any.whl (7.2 kB)\n",
  580. "Collecting MarkupSafe>=2.0\n",
  581. " Downloading MarkupSafe-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)\n",
  582. "Collecting typing-inspect>=0.4.0\n",
  583. " Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
  584. "Requirement already satisfied: importlib-metadata>=4.4 in /usr/lib/python3/dist-packages (from markdown>=2.6.8->tensorboard->-r requirements.txt (line 24)) (4.6.4)\n",
  585. "Requirement already satisfied: cryptography>=3.4.0 in /usr/lib/python3/dist-packages (from pyjwt[crypto]>=2.4.0->PyGithub->-r requirements.txt (line 33)) (3.4.8)\n",
  586. "Collecting cffi>=1.4.1\n",
  587. " Downloading cffi-1.15.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (441 kB)\n",
  588. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m441.2/441.2 KB\u001b[0m \u001b[31m50.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  589. "\u001b[?25hCollecting certifi>=2017.4.17\n",
  590. " Downloading certifi-2023.7.22-py3-none-any.whl (158 kB)\n",
  591. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.3/158.3 KB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  592. "\u001b[?25hCollecting idna<4,>=2.5\n",
  593. " Downloading idna-3.4-py3-none-any.whl (61 kB)\n",
  594. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.5/61.5 KB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  595. "\u001b[?25hCollecting charset-normalizer<4,>=2\n",
  596. " Downloading charset_normalizer-3.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (202 kB)\n",
  597. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202.1/202.1 KB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  598. "\u001b[?25hCollecting nvidia-cuda-nvrtc-cu11==11.7.99\n",
  599. " Downloading nvidia_cuda_nvrtc_cu11-11.7.99-2-py3-none-manylinux1_x86_64.whl (21.0 MB)\n",
  600. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.0/21.0 MB\u001b[0m \u001b[31m81.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  601. "\u001b[?25hCollecting nvidia-nccl-cu11==2.14.3\n",
  602. " Downloading nvidia_nccl_cu11-2.14.3-py3-none-manylinux1_x86_64.whl (177.1 MB)\n",
  603. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.1/177.1 MB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  604. "\u001b[?25hCollecting nvidia-cudnn-cu11==8.5.0.96\n",
  605. " Downloading nvidia_cudnn_cu11-8.5.0.96-2-py3-none-manylinux1_x86_64.whl (557.1 MB)\n",
  606. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m557.1/557.1 MB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  607. "\u001b[?25hCollecting nvidia-cusparse-cu11==11.7.4.91\n",
  608. " Downloading nvidia_cusparse_cu11-11.7.4.91-py3-none-manylinux1_x86_64.whl (173.2 MB)\n",
  609. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.2/173.2 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  610. "\u001b[?25hCollecting sympy\n",
  611. " Downloading sympy-1.12-py3-none-any.whl (5.7 MB)\n",
  612. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m74.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  613. "\u001b[?25hCollecting nvidia-curand-cu11==10.2.10.91\n",
  614. " Downloading nvidia_curand_cu11-10.2.10.91-py3-none-manylinux1_x86_64.whl (54.6 MB)\n",
  615. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.6/54.6 MB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  616. "\u001b[?25hCollecting nvidia-cuda-cupti-cu11==11.7.101\n",
  617. " Downloading nvidia_cuda_cupti_cu11-11.7.101-py3-none-manylinux1_x86_64.whl (11.8 MB)\n",
  618. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.8/11.8 MB\u001b[0m \u001b[31m109.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  619. "\u001b[?25hCollecting nvidia-cuda-runtime-cu11==11.7.99\n",
  620. " Downloading nvidia_cuda_runtime_cu11-11.7.99-py3-none-manylinux1_x86_64.whl (849 kB)\n",
  621. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m849.3/849.3 KB\u001b[0m \u001b[31m65.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  622. "\u001b[?25hCollecting nvidia-cufft-cu11==10.9.0.58\n",
  623. " Downloading nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux1_x86_64.whl (168.4 MB)\n",
  624. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.4/168.4 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  625. "\u001b[?25hCollecting nvidia-nvtx-cu11==11.7.91\n",
  626. " Downloading nvidia_nvtx_cu11-11.7.91-py3-none-manylinux1_x86_64.whl (98 kB)\n",
  627. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.6/98.6 KB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  628. "\u001b[?25hCollecting nvidia-cublas-cu11==11.10.3.66\n",
  629. " Downloading nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl (317.1 MB)\n",
  630. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.1/317.1 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  631. "\u001b[?25hCollecting triton==2.0.0\n",
  632. " Downloading triton-2.0.0-1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (63.3 MB)\n",
  633. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.3/63.3 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  634. "\u001b[?25hCollecting nvidia-cusolver-cu11==11.4.0.1\n",
  635. " Downloading nvidia_cusolver_cu11-11.4.0.1-2-py3-none-manylinux1_x86_64.whl (102.6 MB)\n",
  636. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.6/102.6 MB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  637. "\u001b[?25hCollecting cmake\n",
  638. " Downloading cmake-3.27.1-py2.py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (26.0 MB)\n",
  639. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m26.0/26.0 MB\u001b[0m \u001b[31m54.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  640. "\u001b[?25hCollecting lit\n",
  641. " Downloading lit-16.0.6.tar.gz (153 kB)\n",
  642. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.7/153.7 KB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  643. "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
  644. " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
  645. " Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
  646. " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
  647. "Collecting distlib<1,>=0.3.7\n",
  648. " Downloading distlib-0.3.7-py2.py3-none-any.whl (468 kB)\n",
  649. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.9/468.9 KB\u001b[0m \u001b[31m38.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  650. "\u001b[?25hCollecting soupsieve>1.2\n",
  651. " Downloading soupsieve-2.4.1-py3-none-any.whl (36 kB)\n",
  652. "Collecting PySocks!=1.5.7,>=1.5.6\n",
  653. " Downloading PySocks-1.7.1-py3-none-any.whl (16 kB)\n",
  654. "Collecting pycparser\n",
  655. " Downloading pycparser-2.21-py2.py3-none-any.whl (118 kB)\n",
  656. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.7/118.7 KB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  657. "\u001b[?25hCollecting pyasn1<0.6.0,>=0.4.6\n",
  658. " Downloading pyasn1-0.5.0-py2.py3-none-any.whl (83 kB)\n",
  659. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.9/83.9 KB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  660. "\u001b[?25hCollecting oauthlib>=3.0.0\n",
  661. " Downloading oauthlib-3.2.2-py3-none-any.whl (151 kB)\n",
  662. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m151.7/151.7 KB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  663. "\u001b[?25hCollecting mpmath>=0.19\n",
  664. " Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
  665. "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.2/536.2 KB\u001b[0m \u001b[31m52.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
  666. "\u001b[?25hBuilding wheels for collected packages: fire, lit\n",
  667. " Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
  668. " Created wheel for fire: filename=fire-0.5.0-py2.py3-none-any.whl size=116951 sha256=532122dd1bd434615ee929ed20dad80c3a272be109739d6422d626f266f93f50\n",
  669. " Stored in directory: /root/.cache/pip/wheels/f7/f1/89/b9ea2bf8f80ec027a88fef1d354b3816b4d3d29530988972f6\n",
  670. " Building wheel for lit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
  671. " Created wheel for lit: filename=lit-16.0.6-py3-none-any.whl size=93583 sha256=15df868c627dda7845b33112eb0dff5ff5455bcb8a9e34ec161261a3a7ca093c\n",
  672. " Stored in directory: /root/.cache/pip/wheels/a5/36/d6/cac2e6fb891889b33a548f2fddb8b4b7726399aaa2ed32b188\n",
  673. "Successfully built fire lit\n",
  674. "Installing collected packages: types-pkg_resources, pytz, ninja, mpmath, lit, distlib, cmake, zipp, wrapt, urllib3, tzdata, typing-extensions, tqdm, tomlkit, tomli, toml, threadpoolctl, termcolor, tensorboard-data-server, tabulate, sympy, soupsieve, rpds-py, pyyaml, python-dateutil, PySocks, pyparsing, pyjwt, pyflakes, pydicom, pycparser, pycodestyle, pyasn1, protobuf, platformdirs, pillow, pathspec, parameterized, packaging, oauthlib, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, numpy, nodeenv, networkx, natsort, mypy-extensions, mccabe, MarkupSafe, markdown, lazy-object-proxy, lazy_loader, kiwisolver, joblib, isort, idna, identify, grpcio, fonttools, filelock, einops, dill, cycler, click, charset-normalizer, cfgv, certifi, cachetools, attrs, absl-py, werkzeug, virtualenv, typing-inspect, tifffile, scipy, rsa, requests, referencing, PyWavelets, pydot, pyasn1-modules, pandas, opencv-python, nvidia-cusolver-cu11, nvidia-cudnn-cu11, nibabel, mypy, jinja2, itk-core, importlib-resources, importlab, imageio, flake8, fire, deprecated, cucim, contourpy, cffi, black, beautifulsoup4, astroid, scikit-learn, scikit-image, requests-oauthlib, pynacl, pylint, pre-commit, pep8-naming, matplotlib, libcst, jsonschema-specifications, itk-numerics, itk-io, google-auth, flake8-executable, flake8-comprehensions, flake8-bugbear, pytype, PyGithub, jsonschema, itk-filtering, google-auth-oauthlib, gdown, tensorboard, itk-segmentation, itk-registration, itk, triton, torch, pytorch-ignite, monai\n",
  675. " Attempting uninstall: pyjwt\n",
  676. " Found existing installation: PyJWT 2.3.0\n",
  677. " Not uninstalling pyjwt at /usr/lib/python3/dist-packages, outside environment /usr\n",
  678. " Can't uninstall 'PyJWT'. No files were found to uninstall.\n",
  679. "Successfully installed MarkupSafe-2.1.3 PyGithub-1.59.1 PySocks-1.7.1 PyWavelets-1.4.1 absl-py-1.4.0 astroid-2.15.6 attrs-23.1.0 beautifulsoup4-4.12.2 black-23.7.0 cachetools-5.3.1 certifi-2023.7.22 cffi-1.15.1 cfgv-3.3.1 charset-normalizer-3.2.0 click-8.1.6 cmake-3.27.1 contourpy-1.1.0 cucim-22.8.1 cycler-0.11.0 deprecated-1.2.14 dill-0.3.7 distlib-0.3.7 einops-0.6.1 filelock-3.12.2 fire-0.5.0 flake8-6.1.0 flake8-bugbear-23.7.10 flake8-comprehensions-3.14.0 flake8-executable-2.1.3 fonttools-4.42.0 gdown-4.7.1 google-auth-2.22.0 google-auth-oauthlib-1.0.0 grpcio-1.56.2 identify-2.5.26 idna-3.4 imageio-2.31.1 importlab-0.8 importlib-resources-6.0.1 isort-5.12.0 itk-5.3.0 itk-core-5.3.0 itk-filtering-5.3.0 itk-io-5.3.0 itk-numerics-5.3.0 itk-registration-5.3.0 itk-segmentation-5.3.0 jinja2-3.1.2 joblib-1.3.1 jsonschema-4.19.0 jsonschema-specifications-2023.7.1 kiwisolver-1.4.4 lazy-object-proxy-1.9.0 lazy_loader-0.3 libcst-1.0.1 lit-16.0.6 markdown-3.4.4 matplotlib-3.7.2 mccabe-0.7.0 monai-1.2.0 mpmath-1.3.0 mypy-1.4.1 mypy-extensions-1.0.0 natsort-8.4.0 networkx-3.1 nibabel-5.1.0 ninja-1.11.1 nodeenv-1.8.0 numpy-1.25.2 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 oauthlib-3.2.2 opencv-python-4.8.0.74 packaging-23.1 pandas-2.0.3 parameterized-0.9.0 pathspec-0.11.2 pep8-naming-0.13.3 pillow-10.0.0 platformdirs-3.10.0 pre-commit-3.3.3 protobuf-4.23.4 pyasn1-0.5.0 pyasn1-modules-0.3.0 pycodestyle-2.11.0 pycparser-2.21 pydicom-2.4.2 pydot-1.4.2 pyflakes-3.1.0 pyjwt-2.8.0 pylint-2.17.5 pynacl-1.5.0 pyparsing-3.0.9 python-dateutil-2.8.2 pytorch-ignite-0.4.12 pytype-2023.7.28 pytz-2023.3 pyyaml-6.0.1 referencing-0.30.2 requests-2.31.0 requests-oauthlib-1.3.1 rpds-py-0.9.2 rsa-4.9 scikit-image-0.21.0 scikit-learn-1.3.0 scipy-1.11.1 soupsieve-2.4.1 sympy-1.12 tabulate-0.9.0 tensorboard-2.13.0 tensorboard-data-server-0.7.1 termcolor-2.3.0 threadpoolctl-3.2.0 tifffile-2023.7.18 toml-0.10.2 tomli-2.0.1 tomlkit-0.12.1 torch-2.0.1 tqdm-4.65.0 triton-2.0.0 types-pkg_resources-0.1.3 typing-extensions-4.7.1 typing-inspect-0.9.0 tzdata-2023.3 urllib3-1.26.16 virtualenv-20.24.2 werkzeug-2.3.6 wrapt-1.15.0 zipp-3.16.2\n",
  680. "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
  681. "\u001b[0m"
  682. ]
  683. },
  684. {
  685. "output_type": "display_data",
  686. "data": {
  687. "application/vnd.colab-display-data+json": {
  688. "pip_warning": {
  689. "packages": [
  690. "certifi",
  691. "cffi",
  692. "cycler",
  693. "dateutil",
  694. "kiwisolver",
  695. "socks"
  696. ]
  697. }
  698. }
  699. },
  700. "metadata": {}
  701. }
  702. ]
  703. },
  704. {
  705. "cell_type": "markdown",
  706. "source": [
  707. "Execute inference"
  708. ],
  709. "metadata": {
  710. "id": "5MUQJU2WNn40"
  711. }
  712. },
  713. {
  714. "cell_type": "markdown",
  715. "source": [
  716. "\n",
  717. "It will cost about three minutes. Check NIFTI directory after run done."
  718. ],
  719. "metadata": {
  720. "id": "9x7qRnttNuPu"
  721. }
  722. },
  723. {
  724. "cell_type": "code",
  725. "source": [
  726. "!python -m monai.bundle run --config_file configs/inference.json"
  727. ],
  728. "metadata": {
  729. "colab": {
  730. "base_uri": "https://localhost:8080/"
  731. },
  732. "id": "HbDBe8jANMpY",
  733. "outputId": "d26f8fa1-7dfc-473b-8817-48d871fc1413"
  734. },
  735. "execution_count": 5,
  736. "outputs": [
  737. {
  738. "output_type": "stream",
  739. "name": "stdout",
  740. "text": [
  741. "2023-08-08 01:02:00,317 - INFO - --- input summary of monai.bundle.scripts.run ---\n",
  742. "2023-08-08 01:02:00,317 - INFO - > config_file: 'configs/inference.json'\n",
  743. "2023-08-08 01:02:00,317 - INFO - ---\n",
  744. "\n",
  745. "\n",
  746. "2023-08-08 01:02:00,317 - INFO - Setting logging properties based on config: configs/logging.conf.\n",
  747. "monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n",
  748. "monai.transforms.io.dictionary SaveImaged.__init__:resample: Current default value of argument `resample=True` has been deprecated since version 1.1. It will be changed to `resample=False` in version 1.3.\n",
  749. "monai.handlers.stats_handler StatsHandler.__init__:name: Current default value of argument `name=None` has been deprecated since version 1.1. It will be changed to `name=StatsHandler` in version 1.3.\n",
  750. "2023-08-08 01:02:00,562 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 0 until 1 epochs\n",
  751. "2023-08-08 01:02:02,723 - ignite.engine.engine.SupervisedEvaluator - INFO - Restored all variables from ./models/model_lowres.pt\n",
  752. "^C\n"
  753. ]
  754. }
  755. ]
  756. },
  757. {
  758. "cell_type": "markdown",
  759. "source": [
  760. "Unzip inference file"
  761. ],
  762. "metadata": {
  763. "id": "zlHCcB4rQMS5"
  764. }
  765. },
  766. {
  767. "cell_type": "code",
  768. "source": [
  769. "!gzip -d NIFTI/DLCSI033/DLCSI033_trans.nii.gz"
  770. ],
  771. "metadata": {
  772. "colab": {
  773. "base_uri": "https://localhost:8080/"
  774. },
  775. "id": "jd_5gJdRVlZF",
  776. "outputId": "1d0e159d-a86c-4410-92fd-cf6572d204ff"
  777. },
  778. "execution_count": 6,
  779. "outputs": [
  780. {
  781. "output_type": "stream",
  782. "name": "stdout",
  783. "text": [
  784. "gzip: NIFTI/DLCSI033/DLCSI033_trans.nii.gz: No such file or directory\n"
  785. ]
  786. }
  787. ]
  788. },
  789. {
  790. "cell_type": "markdown",
  791. "source": [
  792. "Convert NIFTI file to mask file"
  793. ],
  794. "metadata": {
  795. "id": "KokHjc9zaUXm"
  796. }
  797. },
  798. {
  799. "cell_type": "markdown",
  800. "source": [
  801. "It will cost about three minutes. Check MONAI directory after run done."
  802. ],
  803. "metadata": {
  804. "id": "rRb-NTzoaXTm"
  805. }
  806. },
  807. {
  808. "cell_type": "code",
  809. "source": [
  810. "!python nii2png.py"
  811. ],
  812. "metadata": {
  813. "colab": {
  814. "base_uri": "https://localhost:8080/"
  815. },
  816. "id": "VP2O7ECYaVxu",
  817. "outputId": "a0b263bd-4faa-42a6-f672-70f90e2e79fe"
  818. },
  819. "execution_count": 7,
  820. "outputs": [
  821. {
  822. "output_type": "stream",
  823. "name": "stdout",
  824. "text": [
  825. ".gitignore ==================================================\n",
  826. "Label's shape:\n",
  827. " (105, 0)\n"
  828. ]
  829. }
  830. ]
  831. },
  832. {
  833. "cell_type": "markdown",
  834. "source": [
  835. "Generate DICOM-RT file"
  836. ],
  837. "metadata": {
  838. "id": "MzjEOxCCabPF"
  839. }
  840. },
  841. {
  842. "cell_type": "markdown",
  843. "source": [
  844. "It will cost about two minutes. Check DICOM directory after run done."
  845. ],
  846. "metadata": {
  847. "id": "F7tWolcQad7u"
  848. }
  849. },
  850. {
  851. "cell_type": "code",
  852. "source": [
  853. "!python main.py"
  854. ],
  855. "metadata": {
  856. "colab": {
  857. "base_uri": "https://localhost:8080/"
  858. },
  859. "id": "--GWn5vOacXk",
  860. "outputId": "9be6e087-b3ca-4d2a-d9c1-c0c70bca5847"
  861. },
  862. "execution_count": 8,
  863. "outputs": [
  864. {
  865. "output_type": "stream",
  866. "name": "stdout",
  867. "text": [
  868. ".gitignore ==================================================\n",
  869. "DICOM/.gitignore/ MONAI/.gitignore/\n",
  870. "get Contour Sequence from Images.Traceback (most recent call last):\n",
  871. " File \"/content/monai_wholeBody_ct_segmentation/main.py\", line 22, in <module>\n",
  872. " spacingDatabase, DICOMInformation = SegmentiontoImageData(dcm_file_path, label_path)()\n",
  873. " File \"/content/monai_wholeBody_ct_segmentation/SegmentiontoImageData.py\", line 29, in __call__\n",
  874. " self.getDICOMinformation()\n",
  875. " File \"/content/monai_wholeBody_ct_segmentation/SegmentiontoImageData.py\", line 43, in getDICOMinformation\n",
  876. " DICOMdir = os.listdir(self.DICOM_File_PATH)\n",
  877. "FileNotFoundError: [Errno 2] No such file or directory: 'DICOM/.gitignore/'\n"
  878. ]
  879. }
  880. ]
  881. },
  882. {
  883. "cell_type": "markdown",
  884. "source": [
  885. "Download DICOM directory"
  886. ],
  887. "metadata": {
  888. "id": "5FC-8olzagBm"
  889. }
  890. },
  891. {
  892. "cell_type": "markdown",
  893. "source": [
  894. "There are two DICOM-RT files. (Original and MONAI)"
  895. ],
  896. "metadata": {
  897. "id": "38eTIn_Cai3W"
  898. }
  899. },
  900. {
  901. "cell_type": "markdown",
  902. "source": [
  903. "![螢幕擷取畫面 2023-08-07 163256.jpg](data:image/jpeg;base64,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)"
  904. ],
  905. "metadata": {
  906. "id": "vuKqblW9bCH-"
  907. }
  908. },
  909. {
  910. "cell_type": "markdown",
  911. "source": [
  912. "Download [Dicompyler](https://github.com/bastula/dicompyler/releases/download/release-0.4.2/dicompyler_setup-0.4.2.win32.exe)"
  913. ],
  914. "metadata": {
  915. "id": "eBhwQg3cam_e"
  916. }
  917. },
  918. {
  919. "cell_type": "markdown",
  920. "source": [
  921. "![螢幕擷取畫面 2023-08-07 162717.jpg](data:image/jpeg;base64,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)"
  922. ],
  923. "metadata": {
  924. "id": "K2hKyLSabEeW"
  925. }
  926. },
  927. {
  928. "cell_type": "markdown",
  929. "source": [
  930. "Show results with Dicompyler"
  931. ],
  932. "metadata": {
  933. "id": "RwDs61JhbGpO"
  934. }
  935. },
  936. {
  937. "cell_type": "markdown",
  938. "source": [
  939. "Pay attention to the Chinese path."
  940. ],
  941. "metadata": {
  942. "id": "VBDUHhTnbK1e"
  943. }
  944. },
  945. {
  946. "cell_type": "markdown",
  947. "source": [
  948. "![螢幕擷取畫面 2023-08-07 162941.jpg](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCAF7Al0DASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD8qqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiil42nruzx6f56UAJRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFW9JuLO1vkkv7Rr22UHdAspjLccfMAe9AFSnRsquNy7l5B68ZHXqOR1/CkcqzsVXaueFznA9KSgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAop8MMlw22NGkbGSFGcD1q9DpSbS084GASY4RvYc9zkLj3yfpQBnVbstJvdRVntbSaeNSA0kaEquf7x6D8a6PT/D+50kgtd8T/cMi+azeoIxtB/Cvefhn+z/46+Ldjb3WjaNqN1pdvJ9na8FtJJEjYztGBgHA6UAfO9v4Kv5lbzJLe3bsrybi3uNgYfnWkvw7ZoVcXUrHOCot8A+uDu/pX3XoX/BP3XPOR9SurqxJGWVrcR5/76OP0rvLD9iWx0qSOSLy2vFIKia6UqSO+AuPw/yAD837nwHbWsalxdgtxlmUc+mNtVJfCVsrNtFyV/hw4b/2UV+nt1+yXo8Mjz3tjYPIzb2/fkDPfA6A8D8q5LxD8K/DfhPOzSLOR/UbSx9etAH5zf8ACGkqxNyY27K0eePrnn8qhuvBOp26xsoin3jP7t8Y+u7FfqZ8Ofhb4Z1RLcXekWM8bvgrJGpGM9M9q958efssfBb4zNcSXPhmDwONHtWM8+ixrFksVWNtqLtYABs/LklhzxQB+E1xpN7axebNZzxRZx5jxkLn64xVWv1c1j/gn34AuJpx4X+MmlRmJts0etAWoB643EnOB6VR0T/gmZbapdKknxh8K+UcMw0+5EzkHvj5aAPy9j0XUJVylhcuPVYWP9Ks2/hbVLn7toVPpK6xn/x4iv101T/gmD8NPA7WZ1vxTr2srdqYoZNPWGH97j5Nynd8p7EHsfrXgepfspaRoGrXWnTPeBoZGQt5w3AhuhOMHigD4K/4RLVOf3EZI6gXEZ/9moHhHVSu7yIwOvzXEY/9mr7wh/ZP0PUFLQrq7SKDzG6PntVbVv2J5ZI8xy6lAVPy+fAG5/D/ADzQB8KzeF9St1DPAhBGRsnjYn6ANzVK4027s41kntZoI2OA0kZUE+mSK+w9c/Y416OEC2ubSYKDlJImjJ55IxXI3f7NPjbSP9Tp5uVzlmtrhT+Izg9qAPmKivV/E3g+60VpIdX07ZMxz/pkBjlZs9RIMEj8a43UPDMMOWJktBkn5hvX2x3A+pNAHNUVcbSbja7xAXMa9WhOe2c7fvAe5FU6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoopVVpGCqCzMcBQMkmgARWkZVVSzMcBVGST6V1/hvwOl40bX8ux3+5bDOc9g5A4z6DnnqCMVreA/Bc91cQrawm91GYYwo4jzxgHvx1P4DjJP3F8CP2A/E2v6fD4ivkezt4wzKzJ8zNt6Jx+vNAHyvoPwPvNQt4/7Qmt9FtGxiNh8xGMcr64xyeTXrngT4C+DYdzzRyal5ADSM+VXP0q/8RPiJ4D+FeuahZXckd5d2Mhi+zRp5k0rjqTyQOR1JFcPB4u+Lfxq0C71D4f6Np2h+G/tL2X2u41aztZ3kRVZgDPKmSFkjJ2KcbvvZp6vYzqVIUlzVJJLz0PrT4aeCfhTpqNceIPsVlZxLu8m38qMnA6tLKflHrhT+Hb366/au+EXg74Htb+GvHHg3w3cWcpC6XZ69bSzGMlhuVUfdI3TPHUHpxX5y6T+y74V0Xxxp2uat481PxkNPvormeyvPDyeRqSxSBjE0j3jMI3C7clCcN93tXo/2TRf+hN8F/wDhJaZ/8j1fs31PHqZtST/dLmXzX5o7zUv+Ch3gVplsoJte8YardyrbQ2elWzM8kjEABN+ASSRgLkk4ArO1/wDat+KEFusegfs7+OpnBwJNS0u6QY7nasTc/jXM2v8AZ2n3MV1ZeGPCun3kLiSG7svDWnwTwuDlXjkSAMjA4IZSCCMg1FVezOWebVG/cil66/5GbfftKftH3kkuPgf4hhhk/wCWa6Fe8cY6+VXJ2Pjb4yap4iN34l+CfjK6spI2Qi00W8WWMnoy5jAJ9jXfUU/Zoj+1a3Zfj/mUdG+KXxC8J+N7K/svg18RJfDaujXcFx4fuIptoPzlWAYZxnkivVR/wU/8DaLZ+K5fDXgm91Oz1EQR2MetTJGse0ZkEuwk43E4AI6CvPbW6msrmK4t5GhnhcSRyIcMrA5BB7EGuB/aB8J6dqnwjsZ7Xw/oej3knimwtUvdL0a1sXaE2140yloY03gFYmIbODt6Z5lwsro6MPmc51FCpFa6aHpOqft9eGNfkRtY+DHh6/tWZTJ9g1G5ti3v8p4OAMZzXaeA/wBv74NafqUT3XwXv9Ft0IDXVnrj3ciLnqEcJ9cbq+I7XwHcSXH2G1jkaPOUbqD7/jXpHhv9nmW6Ed1f3vkj7wVVLZ/4D19qyPoT7y+Kn7cHwf8AFNxo02meKZrW0iRbmRZrKVZIZFU7V27eSMAZGRk9SK+Zm+Odr468T3V7arJcLLOXSS5ZVVhuABPJOeRxx0NclD8C/B9/aSAG7uHaMlV8iQCRuSAG2YUH1Jxz6Uq/s3aXDg2kH2aYfMH+2kjP08oV58sfho/bv6a/lc9ynkeY1Ff2TX+K0f8A0po+iIPjdonw5sLb+2JVtGmZgGiAZTtPOAPf/Jok/bK+HduN8uq3E5C5YwwN+JOeB/Livm7WvhWjwWZNzLfmIAtDPbTMUOORuxtYg9wcH6VWj+HlvNbkJpP2aUY3sbd2BXPKjA4z71osZhnr7RfejB5TmCdvq8//AAF/5H1foX7Wnwr1/ZB/wkSW8knX7XEY/wAMsMZ/xr0u0bwx4ysxPYvY6hA43eZbyD8xjPNfCOvfDHRL7Wrh4tGSytBZDaptZdrS4x2Ckd/asTw78K/Evh+6/tXwteatpNxbyAiSxlO0uMMAUODjkHv+NaQxFGo+WE035NGFbAYvDx561GUV3cWl+KP0I+LP7Lvh/Xvh/o12LJDqN3K0T2WoSKBPnJUxluVbj1wcjpnNfD/xa/Y9s9EuZYoLi98N34+Y6fdxE7QeQecEA+vPat/WP2vfjhp/k2vjF9J8WWEHyRm/02NJEXp95AjZ46nNcL8RP+CgWq+IryPQtf0ZZdOtMeXc2r75osgZCF+q+xYV0HCeUat+yv4mhHn2j2t2w5WWFiC3uPeuR8QfDLVNPhzruntBLn/j4UHd/wB9AYP/AAIcV9e+BPGnhb4oWcbeFPE6w6lgGezkBiuBwMkxN97GQNygj34rqdN+FvizxbfPo+lz2up3sg3fYriML5nHueh/KgD82dY8N3OlB5B/pFspwZUGNvOMMOx6eo54JrJr7G+O37Pnib4e6osWreFbrw/M6b2iKb7d15B2t0I6jHI618weMvCbaHcPLDGVt8gPHyTET39dp7H8D2yAcxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXVeDfDk2pTRSRwtNNK22JU577Tx6k5A+hPoawdI019W1CK2XOGOWI7KBkn8ga+nv2ffCsTancavOojg09B5SsMqrkDnP8AsgAdaAPsj9hT9mXSoXm1nX4PtFppcST3eBy0jf6uED6gk49MZGa7r9pD9rvV/ANw3w50K2/sH4leKJoNH8PW8yKtvpVrdo8cN0hSQOJPMZCXKMFKkAHG2vmXUv2o/FfwU8D+ILnR5ltln8t4fM5VpMFY/lPXGc/TNfL37P3j6TxV+03oOteMYJ/FfiLXdWt4rbVLu8dHs9RkuIhBeEL/AKwRMB+6OFI46ACqjuceL51RlKDs1rp/wT2L4L/A3/hTfiq48X6n4ntfEHiiMSJpkmiTXAihM0M0U8s5nt43ZwJFMew8NlmPADem6trmo69Os+p6hdajMq7Fku5mlYL1wCxPFUahl8TeDNP1680fUfGdpbanp9y9pf2UOmajcS28qMVkjLJbGMuCCOH25H3u9dGkVY+NnKpiKnPJOUvJX0+SJqKxfCfxFTxVbzTR/DTV7dE4US+KYg8h/wB0WBA/E/nTNe+IV7oG9pPg/r9xGvO6HxREePX/AJB9Lmj3Oj6ji/8An2/vj/mbtFef6H+0V4N1bVZ9O8QaRqPw1McJlivtYvJNRhlcMo8opBZK6EqWYNhh8uCBnNegQzWeoabYanpl/Dquk6hE09nf26yJHOiyPGxCyKrjDxuuGUHKn2ppp7HPUpVKLtUi1/Xfb8QooqvrWs6b4T8N33iDWZJI9NtCIljhVjJdXLJI8Nsm1W2tJ5Tjew2qASegBDNJt2S1JNS1jSPCulnWfEFw0GmpIsaW9u0f2u9csimK3R2XewDqzHoi/MewPkd7fa38Xtf0vU9T0mw0ptPtBYw22m+b5UcfmyS7m8x3dmLSMSc+nAAFct418W+Lvin4i0wXsH2bT7S4nfS9LtbWIJYJMUzukSNWmO2OIGR8s2wHgcV7lovhuDwb4ftoYlMtzcFTNMzDexPpnqaxlK+x9XgcD7Be0qfH+X9dTZ8M6anh+P7PY28f23rcXFxGHJJAKhcMMDB+g4xXVL4d+0zfar+OC4uCirt8n5AASehJ557egqPwtpLNfX1xcx+SY3UeUT/0yQjj6EV0r5J5X3r4nE1J1qk4zd0m9OmjtsfueX4elhaNKdGNpOMW3195JvXff8CrDCkK7sAA8BR/SptnnDcflTsPWmbTI2WwB6dKnaQKuBz+GP0rmO8oTKsbYHygdv6VTuNSZd0CxSysOWEMLSED3Cg47/lS3Uk11OIbSJrmZlLLDGVGQMZJJIGAWHr1711Xh7QBoscjs3mXcwHmy4IyASVAGTgDJqoQlVlyw+bPj+IeJMPkdGyanWltC/S+spfyq23WT2VlJx56z8N6pqTxSTbLO1kUEsHzOowcDYUwD69e9WobPRPCOsCe+1xEungKBL2WKPKlgc4Cgnlf50mueLnvJHsdGkaN1dknvfLI8oowBVNyYcnDDjhcc9gcmK1mkuHuru7kvLllVDLOFDbVLEDCqBgFm7Z5+lfT4PI/aJVKz089/u2t66/Kx8DFZ7xFTc8ZU9lQmmuVKzave/LbZ95Su0r2s03o+JLrwZ4osXt7/V9MlX+HFygZfoc8V8q/Gj9my5tWvbzQ7KbVYZIjcWz2UTTFhyQpKjGcfnkevH09cKI0woGAKyF1C4s4rPTWUTRQsWtJywDwrHEXEZUL8w/dnknPzDrjJ9WrgZ4am50XdLdPtuebickxWSYZ18DPnjBNyi9Elq21r01btr2TZ8TWWv3l9p1peSTyxm6icukMrKEO90ynJ2kYBHoa9Y+B/wC0d4j+EPjrw7rU8uoeL9F064BvdKvpvPu5LdmAfyJAoJYAsdrkjgdOo4D4i6daeH/ih4i0DTbdLTR7O0t7m1tl+bymljgkcBjliu6ZzgkgZrna+rjKnmGHhUmr3XXe/X8T1aFaOKowrRWkknrvqrn69eHZNH+IHh8LpsOj69pt1Ym2lu9oYxTkKTuKkEOGXAfn7zAhgSK+D/2qPg3beFddubnSYMWsUjQyRvg7Mk/K4BIweeBkD1rm/gD8ftc+FN4dOgnkudAvF8ufTRIkKrMWyLjeQS2AAhj9GyOmK9v8Ra8PGS6na6im57mJRIsnO0kdPc85B718DRyvEZXXlSf8K2j7vT8V1+R72Y4qjioRqJe/+nr112+ex+d2vaQ2j3xiG4wsN8bNjJHocdwcj8M96zq9T+IHhvyY9QspAPtljIzo3ABAyT/30ozx1KivLK9A8EKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDu/hxYPGkt0A4a4byAVJHyAgt9QTt+hWvuP4D/A7WdV+H91d29vN9kR1Ny0YIeR2GRGM+wGT718w/BPwn/bGoW9hwiRqhmEgGVIILD3+ZmxX7gfs76BaeGPgr4OjhWKG2fzNQvZZDtXaA+GJPAIPkj6L9aAPwl/aU+IkfiTxGnh6wXZp2kMUkYEHzZwMEnH93lfqWre/YrttP/4TjxhqV8LGOXR/DUup217fxLILGSK8tC9xHlWKukRlIZBvHO3nFeBahfTapf3N5cNvuLiVppGxjLMSSfzNffPxb+H2nXPhjTfh7pdqth4a0nx1Z2VnZLI8ixxbNSeQ5dmYs7DLEn0AwFUDjrYpYerThy3cn923+aMMRR9vSlTva53cmnm3Oh6hp/jK/wBH0KaPz7OTQDfW8+pExQmV7gIA5RHJEasqAK+SpZiaxk0FmuLmeNLJkknkdXureV5ZAXOJGJkB3MMMcgHJ55rrNbiaHxNeWJ/49tK2WtpCqhREjwQO/IGSScdT2GMc1Xkxgk/l3rzcRiqlSo0nZJnx1StKnN0qPuxi7X2bto2311vbYxY7Gb9yrlbVYQwVdNWS1JJxyzBznGOOR1NWZrO4lgZV1TUxnp/xMJmAPrgvyfqKttGRngrgfUVHuK/TuVArhk3J3bOSUpTfNJ3Zznibwnpfjy+W38Tabpzx3S+VHPaWjIwmLKqKSWbhsnngAgfSuBg8C/ED4X+H9W8N+FvEFxbaXNIjWshnl+16XiQu32KRWHkGXJD4GW9jzXq90q3EbxlRIjDBVuVPsc1n6Rf/ANk38dkyF7SVtsEagBY2+dnGR0B4I69COOK9LC4uUZKE+vU9fC4xTkqGJXMm9G9bPpf+vLbbwyx8S/Gfw94shubjxTrHijSFDLJpPirU7u8s5gUKnfH5oJIJ3KylSGUEHisrxxqXjX4hasV1i6MGmJIkttoljLMNOtGWPZvhgd3wxGSWJLEsxJ5r6w1DQbeSzeVVjG/5htwfz4wfr/KvKfEGipHeS+VnfJ8rAZHvk88/pXt3ex9HHDUYz9pGCTLPwM8E+WqeaPtN9JGxG7A28dz0GBnvXtU/gW5/tCC58mHEYAEe5SDzyOTzml8C6bBp2n20Vlbrbm4s7eaZlZizsYg2Tknux4Ar0K2hF1aKsibmXqfp3r4XEZ7iHUf1eKUf7ybfrpJW9Ne99bL5ytm9ZzfsUlHzV3+DX9fcvKLO3W3v9Rt455bryZlUTztvkfMUbfMe+M7R7KKtOrdSWz0Pet3xV4Zuo7yTVNNiWYsjNeRb23zbVUIUGCCwVSMcZyOa59ZkuIyQzZP8OCMYOCCDyCCCMHpXFQxMa97Kz107a/ivM/fMnzWjm2EhWpv3kkpLqpJWfybTcX1XRO6SeuWyPc1T1G8EMbPI6xRADMkjAY/E1akuBt+XAPTJ5NV9EsZtW163nTH2e1lLySuDhjsddq9mIJ59Mfl1a3SW7OnMsfSyzB1cZV2gm0r25nbSKeusnotHvexo+C7Bliub6WH57hwYJnA3GLYv5AsCce9XvFHiiLw7ZsqHzNTmika1iETMHZQMbiB8oyVGTgc9a6BlEcbMRjAJx2ry19Rk8T6idVcsls6/6FDIFBjiZUJzt6lmXPJPGOnNfVZZgVUlyP4Vq/n/AMH8Ez+esFRq8UZrUxGIVofFKz2W0Ypu+9vL3VK1nYLG2MMbktukkkeV3AIUs7FmAXnAyT3zjuau+YwwCOwJJGM1CzFQM/KenJqGSbClRjP+1x+Vfb7aI/ZYxUIqMVZIlkkznnLd6pQKr6/oyOPka4kDf+A8uaJJMjPXHt096r26yy3Av/3aWunyujsWIdpGgIAUY6YlByT2PBrlxUlHD1OZ7pr70eLnlaFHLMTzu3NCaXm3FpL5s8c+NOnWjeHtbvmsrT7fHdWUK3q20a3BjKS5RpANzD90nDE42jGK8Cr6D+MkkUng/wAQeUdwW+sQSPXbcV8+qpdgqgsxOAB3rbJ/90j8/wAz4Ph//dGu0n+SH28xt7iKVQC0bBwD04Oa+kvDfi6PxL4RsZPMNxqltGsU/PzFgqsN2BySrKeABk9B0Hznf6ZdaXIqXUDQsw3KG7ivsv8A4Je6Toeq/FzxPd67pqahb2Hh+fUyr/ON9u9uikIxwTslYdBnAzXVmFP2mHb6xafyen+X3Hv1JxjTdRvRa38j55+IekyR65DqON6OoxzggjJU8d84r518Saeul65eW0a7IVfdEu7JEbDcgJ9dpGa/T79tL4faPp/iDUdS0WwOl2Dus32ORUHl71+cYUkY3cgA9Melfm/8SbFrbUoHYqcb4Dx8xKtuyfXh1H4V8q007M56NaFeCqU3dM4+iiikbBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABV7QrOPUNasLabd5M06JIUHIQsNxH0Gao11fwzsZbzxQrxqrJbwSySbjj5SpTP5uKAPp79njSS19qNwFUmONSWxzuZs/wCfrX2541/aq8OeB/2MfF/h24uJl8ZW+gX+lWscZAIM6lY5QewXzFP/AGzPTNfLX7Nekp/wj+qXjAfvrgAb8ZO0DjnHA9q4b9rTTfsujarf2zMrYigm68qWXgnp/FQB4d+y7pdjrHxq0m31GxtdStlsdTn+z3sCTws8en3MkZZHBVsOitggjIFfVmrabqGsePvDvjCXxZqlzpaatZWT+GLhGNpb3D2spa6jbzSGeRreV2JjU5mPLd/lz9k3/kuWl/8AYM1j/wBNd1X054gnNr4PsZh1j8Vaa3fta6hXzuKk45hBrsvxbuV0PWPFJx448SAFnH2qL5iP+nWCs4vtXqMf7JzUGoX5vPEGsTBg/mSwk4P/AE6w00SbuWPPcrXPU/iS9X+bPzyt/Fqf4pfmyWRjweFH0qBmLAHG4fXNLIvRmH09ahYd/mz2yc1BmOYrIME8fpWdNZzSagksEYZLPMs7EjCK0cir16knOMelWZGKjJ2j6f1NZWteJoPDnhTWr+6mWBL5EggXcd37p5VYnjjlj69K3w8PaVor5/d/wbHXg6ftsTCPZ3+7X87L5nTR68s/h9AhC3WCQvTdjrx/jXllr4iS+8SS2saE3HZZc8kc4HbjB5/nXAaX8ZYLi3uLE4CR/OuW+XP1z0+la3g/URresHVHjljnWP72cIR0yWPJwB7Gvpj7o+xPDNnstrJmUlxaQKTzx+6QV21p8sKjaGA6HHQ+1eafCTxI/iXS/KIiMtmIomaHPzLtIGc98IBkV6jGBwFHtg9f1zX5NVpyo1ZUp7x3+5P8mfndSnKlUlTluv8AhyJlO7c4AHYk8/5+lcjrXw+lv9WkvdOurXTxMg8yGS13h5AWJcEOvzNuwTg9BXaSFypYYLA8c5/X/ClVhhSwGfXOawcW2nF2a6o7MDmGKy2r7bCT5ZWtsnp6STX4HhLJd/b5dMe3MWqIwEtuzBhCDg7zzyMEEY6+3OOx8M6T/YulLBJMspEjyNIF2LlnLHGScAE+prS8eX66bpdxcyB3j3qQE6g4bPB/zzXBeKtWh1fw/YaYIZGkvWS4JkVdixRyRlw4zzkMBgZ6+nNfXZPSni3GUt2+W627t2/G3ke1muc5hxRVw+ClBU6bmttVzqL5pa2fuxc2oX2vdyeqd4q1xvEV8llYyTxWNrKfOuYZnhaWQb0MYxgkA4O4HHGOecUktUtbdY4lxGg2qoHAxwAKW1hSCMJGixqowFUYVR6DFOmkz8vmBsdvSv1CjRhh4ckP+HP0XLsuoZZQVCivV9W+7/y6FWT5VIU8dKqbS/HTH4fjVyRd2doOe9RSKMbcg464OK3PUKM25pIYIYWnuJW2Rwx4BdsEkAk4HAJ6jpUHirz9D8N6VpkcKpqEyxRyRxlQZbhgAcnuSRya2fDMMN54gnnmlOzS40mQLjBkdZFIbjsvb/a+lec/E3xFqNxearrGjxRyvoqxvaNeBvsz3XnRqQxXGSqszBQeqema+bzKpOtWjhaau9PnJ7L8UfkPF2OqV8ZHL6Vvd5fnOWy3tazj03b1PDPFGtXHirxZe3P9t3N34Jks4Fs0hmPlvOqRLK8cDkFcypNlyozzzyKim8QSofKtFiitVUIqNBGTgAAnkHqcnGTjNZ0+mxaGselW/mG2sQ0MTSkFnUuz7iQAOSxIwOmOvWoqzhiKsKSoxlaK+R5sJONKNKD9yO3+b8318y/Drt7bqQsqtkkkyRo55JJ5YHuSfxNe8/sY/EyTwj8dLKS5+zC21zRdW8PXk7BUaMTRRS221VGWLT26JkA4DknA5r53rp/hjqjaH4+0XUVKhrOf7QN4yuUBbn24qY1Zqy5nbsZVef2clT3s0u2qa/U+5f2vLgRXd1pJYt/oKsXJ6YjABP8A3zX5nfFK1haF59+6SO5RU2n5SrI24j8Y1r7w8XeIrn4lafJrl4VeZ7HyV8pTsaNQ+Dkkk9fWvh/4l6bH/YksokRjuE6rHjgeYUwff566Ju8ro+ny2hPDYSFKotVf8W2eT0UUVB6YUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV3/wft3/ALXvJwwWLy0gf1+Zw3/tM1wFel/Bd0W4vw4XLTQbS3TpLQB92fBTQX0nwkIJHSUm6dge20gEDjv6muU/4KAfBvW/hj8H9E16e1kn07xFNDuvEO6O3bG9Im9GZV3DPXB9CK3v2ffEQ1PwXdlsNJHfyBscYBCkcfiK+2v2yvAMHxR/YB8XWe+GF7LwzDrsE8sYkKGzRLohfRnSJ49w7SHtmgD8kf2Vfh3bWdjF8SZ9VmE8VxqWi2+kx2gZZC1isbSPMZAUwL0EKEbOwjIzmvZNa0keJPB+o2H9oPpcmnyHxEk6wiUSfY7S7ZoW+YbQyyN84zgqBtOcjif2Z7Oe5/Z7E0MEksNt4mvzPIiErFvtbAJuI+7kq2M9cH0rvre4e1kLoEYMjRuksayI6MpVkZGBVlZSVKkEEEggg18Xias3iZTvrF2XyehRPoOreYtvM8gl+3J5hwOhRUT15BGO3Y9a6iGbzAMDn3/wFch4svLGw0WDXotPW1sLdturpZNbwRWK/wCjxQSww7QVWR3YMqBlDDJ2hgKu2XiKw8mKeyvzc6dIikTXDJlXO7KnAGBgDHHUnnpXXL96nXgvdb18n1v89fRo+NzDCTo1J1Uvcbv6X3v8/lqdKFXqSM/57Ux2Crk59Mn/AD/SslfEunleb2D/AL+L/kVBfeIrRbSd4Ly3aRUYogkBLkA4H5//AK65/aRWtzxXVglfmRc1C+WNoVkJWMyR+dtPzLFvUSHPVRtzz2yPavlf42eOr74oeMI/CHgmxvr+1id44rGwjknmuXG5nwq7mcD5ievQntXs/ia+/wCEw8cJ4E8PTySWKyx3PiDWhNBHPZaeJY45mRmwCRvG1ACWJHysa1/Duh6D4J0u10/w5otpZpbxyRjUru1tp9UkEhcvvu1hRzlXKYXaNgCkHknudZYG6teb/D1/yPtMvwTw6c6nxP8ABev5nyJqPw3+JHgDTX1bVPB/iTQ9NgKiS81HSZ4YE3EKAXdABkkAc9SK6rwB4+vYtN1ee0aSW7WJX8vdvPB5GCORjNfTsM/lxzRPBbXdvOnlzW17bR3EEq7gwDxyKyMAyqwyDgqD1Arlta+D/hHxFqWq6zpdte+HPFFxYyC1j0u6tLTSzdJbkQqbc24EaySIm4+YqguzZUcC6Oa9K0fmj17Hd/sR+L7fxHZ6skkg+2KeI93KjcCeM9OvWvqz5VYgsAM/h9K/L/SfBvxZ+Ax1Pxn9gs7XTIdr6h/ZevWcrLG8ioPlgndwN7qM7SBkZ4r60+Fn7WWn+N5JH1G9torARsDNBZTrtlBTCnls5Vieg6d64sxy+rmE3isHaokrNR1ejb0S332Wqt5nymbUKntVWSurJfc3/mfRc0qKmGyE6dQRXP6x4lh0GxluJ5UWJeib+X5xgA/54rzXxp+0BZQTadF4dlt9SjYs12ZbeUGNcqBjO3HV/XpXinxM+Imr+JJH82No7ENuG3kAE9M9hgkD615+EyavWcZ11yx6p6S0drW0cb93Z21Ry4PL6mIalUTjHz0b/X5/cfUN1DB448Pz3G3MIYhNzEKF4wcHueema86vIR/ZdndQRPLc6bJNBNDHGZGaOR4skAc/KVLY25IJHpil8FfiHaNCthdTZZkWNlJIbuPpzz+dbfjzWLjwBqUN/Y+TNp9wdzzqhLAZ+6SP0yDX295YdR+rxXu2stLW6r7tPmfe4L2NCtCcoq0NtNtHHTto2vTQpRXKTQCROV5AyCCCDggjqCDkc8g1WabdJ/EfZhz+VX7rR5Nagt9d8OWrXUNyw86GCSJFbIY7xuIG/JUHnkHpwKypLHWmx/xIrrn1ktzn/wAi171HF0qlNTm1Fvo2l+Z9LWx+CoWdWvCN9uaUV+bWxM025cdAeoo07T5fEGqLZRs0dui7551ViFAK/u8g8MwJ56gDOOQadpnhO/1RrkamLvRbaNF2bXi3SMd28cF8AALjGDya2b7U7Dw/p7QWVvHZ2xbJWGPaXY8cADkn9a4sXmEIJwou77rZf5nxOdcUUqUJYfAS5pfzppxWiej1TetvJ+asYHj/AF2z8I+G5rLT5YbaJQT5t1chUUluXkkkYYGW6sRywHUivjXxxrLa34q1G9Zo7mG0vbuy0m4UbVFgHPlsm3AcMJHPmMCWz14Fdn+0H4jm8WXdnob3dxaLayTTaxYQSNGUjaOFrRJcjDNuVzgBtu7nB6eVsV2xqgISONIlDHJ2qoUZPrgCtsmwj/3yp1va/r8Wv5nz+RYNyX1+q7uV7X1er+K71blvfqne7ubln4slSOKC7t4LmBflZmjBl2+zHkf5+tXI9T0GaRY1h1BGYhQzyxhRnufl6VytSw2s1znyYZJcddik4/Kvop4WhXledNN/Nfla/wAz6OpgsPVlzShr5XX5WOl1HT5NPnKMQ8ZJ2SKQQw9eOhxjjqM1seDo92sWS/IEkhvGk3IrE7FhCjJ5H+tbOMZrI0mKe18P3i3gKIxUW0cnBDhgW46j5T+vvz6X+z38GvEPxt8c2Gi+HrdpJY7a9mnm/hhjD2fJPbPT6mvkMVh1h6/s07/11PnoUY08XGk3zJP9Gz2vSVj/AOEVMGFTy7WTaqj/AGD2/KvjHxvCf+EU1N2LK4iXK9AczxmvuLWvC9x4T8N61Z3w/fWVrOu5BghghHpxyK+KPHdx53gO/HyswRASRhgBOnasz6Y8UooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAop8MMlxII4kaR26KoyTXpvw9+Aev+OmDwQgwg4Z92EU8cFu/fgfnQB5fU8en3U3+rtpn/3Yya+0/B37I+i6aqnUrjz7jGf3A2hfxLHP4132k/szeELe4aa4t5r1mYEedJlfyHWgD8+tW8MaxoMoj1LS7ywcqHC3EDJlT0PI6Gsyv1T1jwPo+qOJb+xtZ51iWFWkXJCgYAx7V5f8RP2b/CnijSZ1is4bK925S4gUKVPv/wDXNAH5+UV03xA8A6l8O9fk03UU9TFKvSRfUVzNABXefDOdoLTVjGAZN8IXg5BKyjI/OuDrsPh63mnU7fdgGNJSMcYVtv8A7PQB9ffs6tNpFzq+lM+7Jgu4sHJZWQH8+3Nfevjr4opff8E+/iESr3k9n4d1TQrv1i3WkqxN9Arwj8x2r4b+H9v/AGb4k8L3Nt5P2bUdKELMpwDJGMDJ+mPfivSPEHjifwdb674f1JPtPhTxrpcujajDtJWKYDfbyjrkg7157P7DAB8Mfsmk/wDC8NNXPytpmr5HY40y6I/UA/gK+na+JPDniTW/hb4zXUdLnS01nTpJrfe0SSp8yvFKpVwVZWVnU5HQ195eMNPg0nxdrdjbLstrW+nhiUnOFWRgBn6AV8tmkXGvGfRr8n/wUUihp+pXek3SXNjdTWdymds1vIY3GRg4IOax/Gnh/WdQ1OPxV4U05r+aTnV9K06O4muXunknke6WMKwERXywdpAVyRtUMudCrGm6hPpOoWt9bP5dzbSrNE2M4ZSCDj6gV5tKrOhP2lPf8/UDkfDfxqguIVlmvFjwuRvIwB6nJ/kKLz4q698Wrqfwz4GdY7eONnv/ABBMsy2VgoR2HmvGjbS5RlUAZZiAMk1bb4a/DqRSr/DzSmU9QdS1TB/D7ZWpo+i6B4V0+7sfDfh6z8O217LFNdJaT3M3nvEJBGSZ5ZCNolk+7jO7nOBj2qmaqUGqUWpedrfmKxpPHpGh2t5o/hewOj+HXvpLxbYTzStPIwVRLK0sjkvsRBwdo5wBk5qUUV4Hq7lBRRRQBY0/UrvSbpLmxuprO5TO2a3kMbjIwcEHNdxofwrT9qTWvDHw48W6/qjrfXkHiGOaZ3mQ28P2uCSBXLFldzvJwAAI0+bLfLzfg3SYNV12A3zLHpVqyT38jsQFgEiq3TnJLBRjuw6da+6/2b/hnL8MfBukeNfEGmW994w1WJbK1zGlsLKzIkmVAsceFLEux+Xcd+Cetejl+G9tXVS2kf6/4f8A4JhXrQw9N1Kjsl/XQ8T/AGgv2OIvg7rFh4k8D6Rdan4VYMNR06KEzvaqB8z55OzGTntjBPQ15RqN/v1zw/ZxLFe+FNQiNo7AMwjmBJXcc/Lj5OvJAfuK/RNfFHiS3X5b9LssBnzUSPaec42xnIPHXpjqa+HvGfwz1rwn468TXkhjsNC1i+mvoLSOLZDFM3zFA4X5eCpwBjPQYIr9JwOJhhrxqbS8vz7ry19DjyvO8Piqjo8ri/NLX7m7+Zwl38PdMXUoZrXFhO3KeWCTkDoO3bIyc10PiiZbrw+bK+tkmt3Xad4JOex4x+nFcJ4J+LMvijxc/hr+yZxeRzmIn7WsoBU4PSIf5Ne3XuitDNf6bJbxOY2jjNwSCUyqucIVIOQ+3JP4Usb9R5JVKUve7JOz181p18j0sdicLhaftZPV7JJ6ve22nXsj5m8D/EvU/APii48P2wEVm8hEalgRu/HpXq9n42aOOR7qQqpwV8uLoO4OTz/9auM+NHwXnutON/4fhEOoQLuCRvuWRu55UbM8nGcDkZFfLNx8VPiF4yj/ALEt9RGk6dYyrHe3URjSUb9w3FiQzYUPhUOeD7Y+flGFVang1vqOZ0XKb+HrtKN/Xa9rdntqfVvjz4/eFPBULSaxd3RlVdyWccatJN7Ku/8ADJ4FfNmsfFz4h+OtauYtQibQvCrgrLBcaakTy25IVo0lMe7zGRiPlYYGeRjNcW3g+1utSS+1bWb/AMQuqkeTexMu/ggZkExYAEg4HXGO9bVxM9zPJNId0kjF2PqScmsPZwjsr+p4jw2DpNqlHnv1kr2/w3Ss+79Caxvv7NtUsreCNNORSq2JLtFyxY9WyTknnPt04qb+0rb/AKA2m/8Afpv/AIqs+iuuniK1JctObS8m0aRnOF+WTV+za+fqaH9pW3/QG03/AL9N/wDFUHWpYk22cUemgnLfY9ybvryf8/QVn0U54mvUi4zqNrs22EpzmuWcm15tsluLue8cPPNJO4GA0jFjj8a+jf2S/GPjbwDeS/8ACN61c+F7bWEug2o2tjaTySyW/wBlbyQ9xDKFAW6V2VQCd0RPG2vm2vq7wD4+s7j4X+FRHokFgPB9ve2ktzHNul1W9v2sXjZk2gJhYoIQSzZ77Aoz6mSUVWxi5opxim3ezW1lo/Nr8zswEFLER8k3+n6mt4n8YX/ir4VNrOpXKnVNY0uG6uZ1UIjTSxK7naoAXLFjgDA7Cvj34lWc2h+Hdc0yZhJJGke5tv8A03Q8Gvobxk1za/DvwX4ckHlahItlaSKjg4KRqrjcpPTB5zivBPjxcH+1fFcbAIcxqu09f3kZ/pXLiOX20+Ta7t959OeC0UUVzgFFPhhe4mSKJC8jsFVVGSSTgAV95ar+wz4K8E6n4Ou9O8S/8Jba3Og2t1qjpKk0A1F9xlWEqi4iA2YD5YZOTngAHwtY6LqGp4+yWVxcj+9HGWH54rttW/Z9+ImhfDWy+IF/4VvLbwfe3ZsbbVHZNssw3fKq7tx+4/OMZVhng1+h2h+CdE0u1ijt7SKNUwCvl4JA6dK6rV5f+Ev8HWvhDVWF54Thl82HSZAoihf5jvUdQcsx/wCBH1NAH5DyRvC5SRGRx1Vhgim190fEr9lOKPUJJdIhS/0+bKi2kG54zjqp4/nkV8x+N/gzf6FqE8VpbXEUiH/j0ul2sef4W6H6HnjqTQB5nRTpI3hkaORWR1JVlYYII6gim0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFWdN0+fVr6CztYzJPMwRVAJ/H6DqfYVWr1X4BfC3WviB4hM+mqkUFudr3MoG1OAT+OCPzoA6/4bfs661qmr2dqbdkWc4abyydw/Lhf8K+3vC/gWTR9Di0e2sxbS2SBGVFB3AYG/A6ZOeor0j9l34O3fjpbmJdWIi0kRRXF2wDfNghVCg9cAnsPXtX2P4T+Dvhnwn5ksdit/eygCW7vgJXb2APCj6CgD87Vt5Y7lo5o3gC/wAUx259xz/IVbXxNYWVmUFwJrkDBGCwB9//AK9fbfx0/Z98K/ELw5c37WCafq+nxNcQXVmPL37VJKOq4DBhxnGRxzxg/Bzap4e0G8Yz2e6YZxuOc++CKALMerSXM0j5i5PGzAxntzVS6YTJzHM7gZDBVwPwFW7fxRpF9KvlxIm5cAcDFXPMtGX5MJz6MM/rzQB8s/tXeEItR8HrqvypcWcmQzIQdpHTP4V8eV93ftbaqlj8M7i3aX57qQIq7gc4BJx6V8I0AFdv8KbVbrUdaBRnKacXUKueRPD1HpXEV13wxvDba9dQhipurKaPhQ2doEmP/IdAH3N4VtBH4H8NaiqN5ulyxzlnXCmN8q5HPYEZ+ldb4306PxBaqCu9kkWRCuQTj0/A/rWd8F5F1f4V6IBIkqiNoZoQCN6nIYEdD1B6Vs2tjeWobTbuZDc2aiMTKcErg7GHfkD8waAPz6+P3hc+FPilq0CpsguSt3F15Dj5ic/7Yevtn4hf8j94m/7Cdz/6NavMf22vgXqNh4P0f4hRWcjWe9baW5Vfk2OCQCfUPnj/AGz6Gun074i6F8UNOh8Wrrfh7QrnWHmubrSdS8Q2cM1nN50gZCJXjYqQodTt+7IBkkE189m0ZXpztorr77f5MaJ6KPtWj/8AQ3eD/wDwqtN/+P0fatH/AOhu8H/+FVpv/wAfrwCgoo+1aP8A9Dd4P/8ACq03/wCP1JPA9vJsfacqrqyMHV1YAqysCQykEEMCQQQRwaAI6Kkgge4k2JtGFZ2Z2CKiqCWZmJAVQASWJAABJ4FEsPlrE6TW91BMu+K4s7hLiGVckbkkjJVxkEZUnkEdQaAI6KXjaTnn0pKAPoz9j34a6J4+8VC11tYbizvLe7861kkZWmFvJZSIi4IyC0u5u+I1xgbs/ZGp6xF4gure4tYprbTY7eNbe2kIVVI3fOEUlR8rKB3wCOK/MVfHOrfDhfh3r+jXUtnd2+vX674S2WVorJSmAOQwJBz61+kuhyiTSbL5XQiBOHUqSMYyM9QcHB6cV9blMUsMpLdt/m1+SR8hxBUmvZU18Lu36q1vzf8ASL1c5498L23ijwvqFlcvMkTI0uI3Iw4UgOBnAcDGGxngdRxXRMwUZOeuAAMkk8AAdz7UnmBgAqSSMxKiNI2ZyRnK7QM5GDxjPBr2D49yad4uzWz7eZ+fOjfCu78CeONfl1e2SHUrNbqITQ9GkRSA/wBcgflXqar5e9meSRmOXkmZndjgDJZsk8AD6Ctf46+H9X8K69qeoz6dOdKvrbzYoXiK7ZMugBHBVSqL24yOleV+BvibpviTT2MbiK5VyjWaOZpIyoAYE8n7wY89iK46q1ufWZhz16dHFNWUktOzav2+V/wOwvFNwjL95SMFfUHrXxz8cPh/p3hHWtV1OyVopdWvo/PhUr5YMcRwygAYJ3knJOTz3r6o8ReNdM8N2Et5qF1HYQR9ZbwGFB7FmAA/Gvjj4lfFLT/G+uaxp8d7YBbW7iuUum1CLy5/MhwyoSQuE2qpwTkknpWOvQ8eEajkuTbr6X/zscTRR5lp/wBBTS//AAZW/wD8XR5lp/0FNL/8GVv/APF1R6YUUeZaf9BTS/8AwZW//wAXR5lp/wBBTS//AAZW/wD8XQAUUeZaf9BTS/8AwZW//wAXR5lp/wBBTS//AAZW/wD8XQBY063W81C1t2JCyyqhI6gEgV9Drpcf/Cv/AIW22km1tNYu/MvdfjjU/PBFcWpt+cZJWS3ugm7nax55rw7wjp8mpalbDTZrS9nW6j3i2miuCIyshP3S235gnJweDg19ReDbezsbSJpNom2JD87FgqIMce2Sx47k13YOvUwtT2tPfb7/AOvvPay2k05VX6L9f0+4qXWjx6r4us7iWLYmm2jzK65K+ZJ8qnB74DV8ffHb7Suta9IzM6SXiIcjGAd5H/oAr7ljs/tTSXIljKXjea8eDlowu1V9R618NfHBmuLjWp1VvKbVI8EjttmwKg9s8eooooA6L4eaQmveNNI0+R9iXFwqHkgkE8gfhX6UeCNNXT9Pj0+0P7mBAqxeXjaoGK/Of4RTQW/j7RXnbywLuMq2M4OSB/Ov0l0nTUmjt7qKRnVvlYFgdpx2oA1dskcyxSJ0xgMv8vTt+dXInjjkTyRHuDDcu3n+dJPpM1vYpcuqmJxiN26N07isuL7Ss0cnl+UfMGdz54z05oA68Q+bqMMEYK+YVx261S+JvgHT7yUW+sWkdwLhPkyqll46g5yDXvvgn9n7WfEWqWGsXyppmmJGjRq8mZZeOuBnAPuRXn37a9k3w/uNC1C6cwaezqi3AGE46jPqKAPzG/aI+At34X1qe4ska5DJ50TjG6WMdVIHVx+ZHHPFfO9frP8AEbQbTXvha/iZbNpdP08ect4X4wR0GK/KjxDfQ6nr2oXdvEsEE87yJGgwACxPA7fSgDPooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr7X/Y/sNYh+HLPbWjC2uJ3JkC8sATXxRX3l+w38RxJ4AudCDM11ZTHaox90nIIz9aAP0G/Yu8SaX4d/tbwxJHJaXl/KLyJpFO2Rwu1l3HvgAj15r0D44fs96z8XvFWmX9v41vNF0qCJY5tPjDFchmJZQGAJbcAd390cnoPlzR/EWrWV7FdC5Nk0RDLIY03D0OSK9UuP2vvEejaeIzb2F8yrj7VNA4Y+52sB+lAHu3x48fWnwx+FepXM16sV5cQ/YbMzNl3kcbd3uVXc5J4+X3r8+7618OXyNLcXsN7NjcQNvX04/xrqfGXxU1D4nX/wBr8QvHqLpkRRsoVIlx0UZwPwHPeuVvtH8P6hblRZeXIRwY1BP0GeKAMrw7J4e8WXU1ro8hje1UCT7w2k5GM4PpVTVStg0zQ3TII8qzbs5I65OOav8AhnRfDfgX7Re3cM0kk7tJlkG0EnCqDwK8j+Ll1A3iC7stT1WXw3pdxG481SXbJGQDjIGf60AfNX7QXxMm8aeK5rOGZpdMsswxlv4myCzD8hXk9WNQjjhvZ4oZhcRRuypKB98ZPNV6ACup+GM8dv40tHl+55Nyp+Xd1t5AOPqa5atHw3dR2PiDTZ532QR3EZlb/Y3Dd+maAP0C/Zv1iKX4W3aQv/pMczgecpOAeg/TtXol/r0GoaXFqlvHm8sxtubUoA2zqy8HqOo47e9fNH7Nviu30nVNS0K8ikgklP7sFWXcwzxz7Z6+leya5a39xrFne2EixPCGSSOXOJskcEAemeaAPsz9nXWvB37QXwh174V+IGi1mwmt3lW3ZcFrSRhllOOGjkbOe29eOtfk1+2h+xp4l/ZJ8efZrlZNS8HajIx0fW1X5JVHPlSf3JVHVT1HIyK9m1bVvFXwN1zTviH4CbyG0u5+0XFiyfIhGRJ/EC0TjKuoxwx6V+k/gH4i/Cz/AIKH/s93tveacl9p10iwazoU8ga70q5wSrKwGcggtHKoG4A8Ah0AB/PPRX1N+2h+wT4u/ZS15r61juPEfw+unIstfjQExnGfKuAv3HA5BwFYAkdGA+WaACu20v44fEfRNOttP07x/wCKLCwto1igtbXWbmOKJAMBVVXAUAdAK4mpX8j7LFt8z7RubzCcbNvG0Dvn72fqPTmJU4VNJq4HrN9+0t4n1j4Vz+FNUvNS1TVbjU/tNx4hu9Ule5nsTCUbT3J+ZoS3z/fx1GO9fQ/wdsLrVPgv8M7aztpru5k0y7CQwIXdv+JnfHgDk18NV9DaT+0VoOifs723hK1g1NfFtvY/YI5HtIXsgP7VN95m8ybs7CU2+X1Od1eNjcJeEIUI2vJN+V01f5DPep4JLWZ4Zo2iljYq8cilWUjqCD0NOhtzLFdTu6wWlpBJdXVzID5cEKKWeR8AnAA7Ak8AAkgVX8NanoviLQPB/wBn8V+GVkk0PSbaSO4160SdZls4I5FaEy+ZvEisCu3cSOAciuR8U+K7TxnZ3vhyHRZJLG3uLe+giaJ1v3nRGQG4xK0aDMsrCNRlV2biXGF8Shh6mInyRXq+wzrvDs2t+NPEepXtrd3uu6BqmvTroGn3zttitzMxjjiic5iVUKDaAv3RxnNfdngf4nN4V8Kf2T40u5LDX/DtlHZyr9m88SQKpkhJbzFIYLJtIIP3QcnJr46+B/jjRP2ebuPxP4mlttW1qGF4tL8OybCkLH+OR+QvJ+p6cdR+nnwH8K6lpXw5gvfE8St4m8QbtT1eORT8ss2W8kqwyAiFUKdAQ2K+0pwVGChDZHJiMNSxcPZ1ldb7tflY+Ym/aE1/xBDHcaXYRWKLtmt5hMhJPDRMUdDyCFO3Pscitbwn+2F/wh+sWreOfDmmulxdLbnXrBEhuIy+755Ex827gZBUDnOa7Tx9+yTeSaqZvBuoW9pp8z5ayvXfEPJ4Qj+EZ4zyB69a3vhn+zLpnhW7t7jxhNF4j1HJe3tRE8ltFtIG45HJ+bowxz04yNLsww+X4bDfw4eerb29W7Hzt+1F8Ztf+LHjSOy8HRvc+GNNslGFKlpp5GbfIducqAqKFOeQT3r4M8WaFq/hm+TUba6+yuuZRljlM9QeMY9z61+nv7YnhDWvh7r3h74i+D9LtTp8KNYa3BHCVVUYjy5G2g8dV3EcEJ618P8AjmbTfGviK4vNIu4PJOftWksqlSpILsvAOPXHSkegfDnjL4hax41kRb+7llt4zmOJ2z+J965mug8aeG20DXtSS3hlGmJdOlvKwONm47QT64H44Nc/QAUVLa3BtbiOYJHIUYNslUMrexB6ioqACiiigAqzpmm3Ws6hb2NlA1zd3DiOKJOrMajs7OfULuC1tYJLm5ndYooYULvI7HCqqjkkkgACv0c/Yy/Zr+Gfwg8WeH7/AOM2u2MvijVDPG+kQXsBtvDZiAYR6tL5g+zSyn5FjIyWGwkEMp2pUalZtU4ttdgPNfhj8Jb/AOHXh2GwWFZL7UXybvy2KZIAd1JA+UAYHrjNerR6L9nvLHRYBv8AOBMshJBjhAGSeerdPzruv2rv2gNF8R/HB/Dfge2sdU07R7ZbLGn7fKkmBJk27RtCqSFLdPkOM5rgpLPUNQhljl1A2t1McTzWYUE4GdgJHCisQOouJLcrc3UUimG3Qqph5GFUZXHpXwL8VbpbnR9RlR8CbVI3KquFxtnx+NfU/wASvFUfgnwHei0DQnZ5C72G5iRjOR3618VeJNYmvtGAn8zdNdbwXJ6Ipz195KAOVooooAt6VPJbX8MsLmOZGVo2DYwwII/lX6Yfss6P4p/aOZtG06zbTZ4bJbi/1B1/0aI52qfNGRuY5woyTgnGAcfmRbxrNcRRu4jRmCl26KCetfqZ+wB+138P/gv8EZ/h7pMF7qfj1pp9RMskO6ymJKBYzKMMoC54AIBycnJFAH0/8fNG0Zfh2nhOxEenX3hU2b36wSBo47cIwR2fAbdI0kfLdT75z8++AfDsfjX4meGNLbUvOs7i/hSeKNvn8veN2O3SvQtY8dLqXiD4iaxYeGYbgeP4LSPxBb3weQx/Z4PJhEPzAAKvOcctzgVU+HemzeEbe61ew0y2tbiOJlt5QxODj+YoA+4vHvjGHwB4Vu9bns7i9gtdu6O3A3AEgZOTwMkVwEv/AAgf7Ynwl1fQ9UsJ7jR7r9xc28w8ue2mAyskTjIDKeVYemCMEiqX7O/xQ1DxV4ONj4yvLOfU4SyC4ZgvnxHoJAeNwHGe4xnnJM2qftIfDDwF4ps/BmkXFvd6rPJmSz0KFDDa56vM64RfoCW46UAeafHhvDv7I/7Js+gTaQ3jGze1ksZY58RPOmw5kJwQGXKke4zxX4FNjccDA7Zr9hP+CuHxAhuvhxZWFjqiNAybTCoPzl2GfpwBX490AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFewfsz/ABQ/4Vr40ad3YRTJgqCQGx1B5Hb+VeP06GZ7eVJY2KSIwZWHUEcg0AfrJpPjGx8ZaZFfWFy01pIASoPI9QcA4qvqwa6hChiIlxwxXBx9T/IV+eHhf4+eIvDEglsrx7SXPzLHkxNjoSpPX8K9b0X9sqa+2prmnK5AwZYZyAf+AnFAH1UsS28ahtpA6BWzt+vaprOFJQAUUk5y27n+VeL6L8fvB+tRq9xejTyx+VbhQPyYk1s3Pxv8LaPbPNHqkFy6ggeV8x/OgBP2gJbjT9Ck2fJF8ojTPJYHOT09q+NPiN461TXLjyr6eaVyM5lbPt6/Wu9+NHxyPjRWQTsgQ/urVQOfdiOn55rwe4ne5maSQ7nY5NAEdFFFABRRRQB7povi0QatpevwRslxdolwZELAeavD4GDxvVx+NfYXhvxBpvinSrLUYC4My5dI/lKt3ByBnBr4I+HGsRX1u+gXJXzzJ52ns3OZDgNEP97AK/7Qx1avffhR8SE8H3jwXZb7HJgyRmIEqem4A8fWgD6Tj0O2tI7jzTNLFK7OQ4HKnscmuF8D+C/EvwR+JUvjr4W6+ml6jgiTTLhC1tdRnloZVUYZCR0yCCAylWVSPR9L13SdX09brTr83sLgHZAFBz3ye1WHaSS33WnmKd2P3kZJ/Lv+FAH158Df2jvCn7Q2iyeFfFOlQ6R4puImTUPDOqQmS2u8AFntncbZUIy2z764YkYG9vlD9q7/AIJV6HjUPE3w8sZmtWLSSaPaZNxbgnJMWT+9XOeGBcA9W6jnbrSNS02ZGS5a/hkfdKoiHmIO20nHQ19HfC/9p/xL4Ot7LT/EOzxRpgZEeee6C6hbocDO48TYAPDYYljl8ACgD8mdW/ZVv4LjyrPVlEmSGhvYDGyc9yCefwHSkT9ly5t5sXWsF49uc29vnJ46Et069q/afx5pnwY+O2nsb29tdI1eQbvtixLDcKxzw5KlX556n618hfEb9mrw/ps9x/Znjax1mCEMyi3ma3m2+6jKn8DzigD4o/4Z58NaerG5v9RuGZMAYVNp456f41Utfhz4K0ySK2S0udX1DJ+V5JHY+2xEA4r6h/4VHpawKbm+vha4yFDN84+oArZ0WHw54NYHT9OVXx/rCUB/Xc1AHjngH4B614pmiurHSLHwrE33bmOFfte3GCQesYx7ivWbnwv4V+EPhufT9Cm87W7kgXmsSyFiDnoCCBnPXH51pat8Rjet5MtxDZ2xXmNYtufY4Jz+IrldQ1rTVRmCrOmMbUV1H0xg8c/rQBzvw58J/Z/ito3ifXbObXtF0u7junEwAhlZXDAOSDkfL0PXNfTuv/tLfETXPHU2q6b4on0eJciPT42R7ZB2/dtkE4/vAmvnW48SXEmlNaRoLeHf5gWOIMN4HXJJOevbvVBtbvJLdfNvngRMsbiSXaqcc5BA4oA/TP8AZz/aOb4mQ6lpOtzWtxrWlWv2ua60+NgjxLhSZF52yZ544OThVxz5P8V/2qvGGqeJL+y8F65pui2NnM0CqtvHPcuwO0iTfuGcgnCquMkFmxmvGf2Of2htA+DV/wDEWbW3W/1C70qG7tCJkTzWt/N/dZOdu/zl5/2Oh4FfPV5q2o+KPEN5rMmoNdzXl1JdzyssnmOXYu5UMmCST17ZoA+6/hL+0FrniJrvwb8Sruz1zR9Zia1W7uEW2ZGcFdjOo27T2YjKkjn0+VfG/wAJ1+HvxC1Rbe5ktEtbpolaQ+ajpn5eVx2yM5xXDaffawsZMn7uEPnazKhPpnDD9RWvqnia/wDEE0D6peNcG3j8qJpZxJsXP3QSe3uaAF8afCfwx44tbm70me1stWmQpPZSKTbzg85HJ2njpn6V8i/Ez4Iav4JnlnhtZJ7IHloT5yJ/wIdvrzxX1napGz4+2uBnAAZSPxAbikuLRfO3TXEDwtxuaQo2P9rOfpQB8AFWHUEfhSV91XHgPwdcyfapdNtYpWOfMh25J99oGec/56WLf4Z6JdSxxwWNtO7ttjHlbG5BxhywoA+FbfTru8kMdvazTyAZKRxliPwAr1H4Hfsu/EP9oLxZDoXhPQpppi3+kXE6mOG0TvJKx4UD35J4AJOK/Tv4W/sE6Lq0WnalrfirR4LRsPLb2kwll7HZlgB+IJ696+uf+Ey+GH7Pfg9NP0lrO1s4VZ00/R0WaadwBkttON7cfM5APrxQB8PfsU/Bfwd8IdH8VeK/DmlrqtpaaBdXUPxxvpEmgspEjZZ2tNLeLzY1iYTDL5ebyTyI5MD5g+K3xU8SePrq58IeEXub201C+uJ9W8WSweQuoied5HnVMDy0mDmUx4BXzPLwAp3fXHxj+N2q+N/DkfhLQfDdp4Q8E7nEXh/TdsSXKvkkT+WFXaSzMUA2ksc7yA1eJyeE/sl0lzKtv+7UL9jiULEoXptHsAK9uOYLDwnDCx5b7PdpLrf+Z9ei+ykMw/Anw/i8DWZt7SSSeeRf9JvJQQ8jfj0Ge1dhaW/l7/nOMcPIeT0yT1/nVuTxNZ6bZvLf3FnZW6jO/eoOMehHXivG/iL8XodftZbDQZWtrVgVkvHUBn7bUXHevEEcN+0B4wXxDqI0u1umfTrQlMDO15MHccgDIHTrivBfGCrZy2enoTmCLzJQ3USSfNjPf5dn616Rq1rBpuny61eyBbS1KrHbsxBmkwSsQIHfqT2ANeN3l3Lf3c1zO2+aZzI7erE5NAEVFFFABXt37L/jq18G/EC0kuwiWky+WWIztY9+vHNeI1NZ3TWlwkq/wsCcYzQB+uVh4oGoWYu7W4xPByGU5V1PYgVpSa0upQrBDJJbxsMvtztLH26Yr86fh/8AtOan4Nuo1yZbRV27Zo1b+hr2zwp+2ZosLMuoW6YdQ4KyIv4fdH5UAfXNnrk3hXR54wpe8nQxxtHn09uleSRWUfgGa41YHN5NL5ksjN8wPpnrXKeH/wBsbw5rGtQGWykuI1JCAsGx7cV5V8Zv2p9NkvtUs9O064ErONscr7UH0OKAKv7bXxIk8WWej2n2lpVMnmFC2einnr618l10HjLxtf8AjbUEuLw7UjXZHCrEhR+PU1z9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAafhu10q91aGLWb6XTtPOd9xDCZWH/Aak8VW+i2utTRaBd3N9pq4CTXUYRmOOTj0z6gf1r7M/Y/8A2S/hz8UvDunXvjJ55rm8Ksqx3EkWATwMIfTvXueofsV/AC3+MF9pE2gyQaVCkcMdvHqt3tdgPmfduLZP1xQB+VNFfrj8Rf8Agnb8CdP0m1vdFtLiJZJAWUatOx29wd2f0r3X4V/8E6f2abrwvb3sXgCDVppkAmkvNTu5irdwAZQF/ACgD8GaK/oAvP8Agl9+zNeyb2+GqxH0h1rUEH5C4xXK+KP+Caf7MPhz97J4MmhnlQi3tjrl5tZh/vSk9+9AH4XQXk9rnyZpIs9fLcrn8qmk1a9lTY93Mwznlzn86/bNf+Cc37NWpW8PleA9VmuQo3i21mZQWxz/AMthxXlX7RX7Mf7Kv7MPgSHxR4t+E3iqWwurxdOtvseszOzzsjuBg3IAG2NzkntQB+S1FeqfHLxP8JvEF3aD4X+DdV8L26ZM7apftOz+gCl3x9d3bpXmFq0CzobhHeHPzCNgDj8RQBFRXoehah8MVjQavpevM2fmNrJGePxcV3ukal+zZMoXUNL8cW7cZdBCw9/+W39KAPn+ivrXTbf9jW42/bb74h2vPOy0jf8A9qV0Vr4V/YXvmCyeN/iBp4Y43y6aSF9/lDUAfFAJUgjg16r4R+JOm6lbi08TXNxZXqn93q0UZkVxg/65R82e25Qc8ZHVj+rVn/wRn+Bs1vG7674wnDKGWW31C3VWBHUZgbint/wRf+BbZxrnjhfpqVrx/wCStAH5pSa19lX7XYuLyNTsN3Ysj89eSuccevNTWvxq1TRYSW17VoYzkKhQvt9CDivHfiVoFv4C+KHivQ9Jurl7TR9Yu7G1uZiFmaOKZ40ZtuAGKqCccZJrDm1vUbhsy391KfV5mP8AM0Ae9Wv7SWtaTMkq+LNSmO4kia1DKfwyK6zT/wBsy+jh23e2+QfxeTJFn8Ru5r5YXWdQT7t9cr9JmH9alXxJq8eNuqXq46YuHH9aAPriH9sHS7iL94by2bswidx9MjHH4VU1j9pjSb62eIazJFI643vauzD81I/OvluPxt4hiGE13UlGc4+1yY/nUi+PfEadNbvvxnY0Ae9Xfx202VX+zX9wzEYG20RgD6gEj8qig+O0ccYd31OZ+7Roykn/AHRwK8THxO8VKoH9t3WB6kH+lOb4peKpFAbWZ2A6ZVc/yoA980v44RXkx81NQtl/h86ORiT9M4qa8+LsS5SzjvLplYZ8obOO5OTg189f8LK8S5ydTZj0y0MZ/mtSx/FPxRFjy9UMZHQpBEp/MLQB9Gaf8Vkul3x2Eh5C5uLcsQev8Lf5xWhN8QJNSt3hn06RraQbXVraRUdcc5GOelfMi/FbxWmMaxJ+MUZP/oNSw/F/xfD9zWG65+aCI/zWgD2rx5q+n6t4fuoLexniulUiJ7cTReWQOCW28KD25FZnwz+IUsN4mk+JtNlvjbqGttQKhjHxyNwA49/bFfb3gfwD4V8Tf8EqNS+Jmr6JDP41k0PVXOrQyNDJ5sd7PBE4AO0EBEyABnB6E5r8zm8SeCF0Dy4/Bt+uu+VtF42uk2okwPnEAhD46kDzfxNAH1T/AMJppptUkOn3rwuASUhlIK9sEDFQ3XxE0S3ChdO1WORSeVt9pPQdx83WvoT9o7wr4a+Ef/BNv4a+OdA0aK38Z6zpegI+rySNM4kmtUmmcpIWRiwRxjHG7Ixivzdm+OHjadiX1vk8cWsA/klAHtXiL4vXlndBNN8PS6jY+txF5Tj2Aw2arxfHCVo1Fx4feyGME4UAe/TpXirfGHxg2c6y/XOfIiz/AOg1C3xV8VMc/wBrup/2Yo1/ktAHuE3xksix+0anfWaL2hgGOvr1/wA/lq6T8e9At2xHq19cPnq9tmvnKT4jeJJvvatN6/Kqj+Qqs3jbX5ODrN7+EzD+tAH2LF+1Bo9jbrGNc1GID5WDIxBz7bqytU/as0NYnEa6hfzsc48kYbr1OT79q+S28Ya8xyda1DPtdOP61G3ijWXJLatfMT1zcvz+tAH0HfftX37bo7C2ksFbncLYSMT9SR/KuNvfi/q2oeY82ra4zvncpdlH0AHQe1eUya9qc339Ru3/AN6dj/WoJL65m/1lxLJ/vOTQB6W2qS6tCGeOecqS7S3ZwF+pLAYwD19aWbWNA0eMy6nfyahc4+Sy0uYMehIDS42KuQM7dx56V5ZRQBseJvFN34oukefbDbQgrb2sf+rhU9cepOBljycD0AGPXa/BHwTY/Er40eAfCOpy3EGm+IPEGn6VdS2jKsyRT3McTshZWAYK5wSCM4yD0r9cpP8Agin8E9v7vxX4+Df7d/ZEfpaCgD8WKK/Ynxx/wSH+Bnw/8Ha54m1fxj4sstH0aym1C8uZJIZTHDEhd2CpECx2qcADJPABr401DR/2L9Pz9l8Q+P8AVMDgtYKmf/HloA+QaK+ndQ1L9k634tdL8fXfodsSD8czVVg8V/stQk7/AAZ45uPQPcRr/K4oA+bKK+oY/iF+ynFtDfC7xdcAdd2plCfyuDVi3+Jf7Jm8+d8IPFhXHG3WX/8Aj9AHysrFWBBwRyCKdJI8zl3Znc9WY5Jr0n4ya58K9cv7Sb4ceG9c8OwDi4ttTvFmVh6qSWYH6nHtXQ+J9W+FFxL4Xl8I/DzWLjTkMcWryajqE7vNMQNyIY8KGJJIx7cUAeJ0V+jP7Ov7C/g74qeIGn1nwJ4lstDE38b3SjYeQNwUY/OvuLw//wAEv/2bNDkEo+Hp1CQoVzf6peyL9dpl25/CgD8BKK/ocj/4J2/s5RrgfCrST/vTXDfzkrJ1z/gmZ+zZrUJV/hrDaN2ksdSvIWH4LNg/iKAP5+aK/YL4zf8ABOj4CeCdFuZtP0HVo7xASq/b7plP5k1+YnxP8FaJ4V8dtYRi80vSy+0tJE8mweoLYLfhQB5xRXo3j/4V2fg3QLXVLTVLnVre6wI5lsWiiVsjhmJ44zivOaACiiigAooooAKKKKACiiigAooooAKKKKACiiigArqvhXoemeJfiN4d0rWXaPS7y9jhuGXOdpPsQQPfIxXK1peHfEuq+EtUj1LRtQuNMv41ZVuLWQo4DAgjI9QaAP2u+E/7Mf7N9vrmk22lLo11eY3unmKrEquSCd/P5VheLvgD+z7J461SW7GjhvPOC1xnbj6E4r8ifDvxa8Z+E/EEet6T4n1Sz1WPdtuVuWZvmBB4JIPB7il1D4veN9UunuLrxXq80znczm8cZPrwaAP2D1b4U/s4yaTBazHQjEpGJGnyD+Yr3H9nv4d/BPwtZlfCA8O3N8x58l42cDthT/QV+A0nxU8ZyR+W3izWtn90X8oH/oVafhj49fEjwbfJd6L468QafcL0aPUZcfkWI/SgD+la+8I6HqkEkN5o1hdRSAq6TWyMGB65yK/Nz9sr/gmjqHxe+O/g+1+FeieHfBXhCPQXj1O+jVYUgnSeaQNJEp3yNJ5qqGCk/KdzYCgfDMf/AAUN/aLjgWEfFbWCi8gskDN+LGPJ/E1Wuf2/P2g7yeOaX4pa00kasqkCIcHqCAnP40AfTlz/AMEVPG0UZKfE/wAKlvSSGdB+eDXif7RP/BOP4gfs5/D3UfGmreKPCOuaHYSQxyx6TfTNcjzJFjU+W8SjG5hnDd68qvP2tfjDqDOZ/iFrTljk4mC/yHFZX/DRnxNLEnxvrDZ67rkkH6g0Aec0V3f/AAvTx4ZC58S3ZY9yEP8A7LS/8L28f4x/wlF8BnOAVH9KAODort2+N3jx858Uahz1xIB/So2+NHjtv+Zt1Yc5+W6YfyNAHGUV2P8AwuTx4DkeMdcU/wCzfyj/ANmpy/Gr4gKuF8beIFHX5dTmH/s1AH9LXgi1ex8F6BbP9+HT7eNseojUH+VbdfzT2v7U3xnsYVit/i144gjUYVY/EV4oAHYYkqrpP7SXxc0AXg0z4peNNNF5cveXP2TxDdxefO+N8r7ZBudsDLHk4GTQBjfF+K6g+LXjaK+Qx3qa3fLOhxlZBO4Yce+a5KpLq6mvrmW4uJZLi4mcySTSsWd2JyWJPJJPOTUdABRRRQAUUUUAFFFFABRRRQAUUUUAfr38L7WST/gibqITLFtH1aTAGcKuqzk9vRSa/ISuxs/jN8QNP8Et4NtfHPiW28ING8R8Pw6vcJYFHYu6/Zw/l4ZmZiNvJYk8muOoA/Wf9trTbmb/AIJO/BJ4UkljtrPw3POyrkIjacygtxwNzoM+pHrX5MV2WrfGr4ha94Ph8Jan488Taj4Vhiigi0O71i4lsY44seUiwM5QKm1doAwu0YxiuNoAKKKKACiiigAooooAKK0/Eegnw7qEVodQsNS8yztbzztNnE0S+fbxzeUWA4kj8zy5F/hkR1/hrMoAKKK1dR8M3ml6DpGrzTae9pqnneRHb6lbz3CeUwVvOgSQywZJ+XzVTeOV3DmgD0D9k7/k6b4N/wDY56N/6XQ1/SkWCkAkAscD3r+WLStVvdB1Sz1LTby407UbOZLi2vLWVopoJUYMkiOpBVlYAhgcggEV2uq/tB/FLXdY0nVtS+JXi/UNU0h5JNOvrrXrqWeyZ12u0LtIWjLKACVIyBg0Af0B/tnQtcfsl/F9VOCPC2oP+CwOT+gr+b2vSL79pf4v6pbyQXnxV8bXcEi7Xin8RXjqw9CDJgiuYm+Iviu4bdL4n1mVuuX1CUn9WoA56itk+NvETdde1M/9vkn/AMVR/wAJnr/fWr88Y5uHP9aAMaitRPFOsxyb11W8Devnt/jXQ6P8aPG+gsDY+JLyHb0UlXH5MCKAOKr9R/8Agkr8CdK+Inw08WanrtpZywf2oFgW4hEkrqsaZZQT8oDZGcda+ObP9vr4+6foT6PbfEW8g0512NFHZWinH+8Itw/OvFn8Za/JdSXB1rUPPkcyPILpwSxOSevXNAH9OXh+w0PQb260vTHtIrpUSWW0iceai4wrMucgHHXArkvjZ408D+FbHSYfGM8ltcXE7XGlzQ2E1xIk8G2TcjojCN+mNxXcCRyM1/Ozpfxx+Imi63HrFl458Q2+qRxiJbxdTm8zYOili3I9jXsGl/8ABSb9pLR9HGm2/wAULxrYAgNcafZTzf8Af2SFn/8AHqAP388K+JLXxf4d0/WrJJY7S9iE0azqFcKf7wBOD+NX7uW3ht3e6eNIB95piAv45r+cPRf2zPjj4dWZNO+KXiW1jlcyNGt8xXcTkkA8D8Kn1T9tj4663am3vvih4guYiMFXuBn88ZoA/cP43Q/D650Oa8uJNGueMMsMcEh+pORX4t/tn6Polr40gvdAOnJZyllaO0RFfPUE7e3XvivGNS+J3jDWXke/8V63eNJnf5+oTPnP1aufuLue6IM00kxHQyMW/nQALeTpbPbrNItu7bmiDnYSOhI6ZqKiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiip7RLZ3IuZpYU7GGISH8iy0AQUVNdrbpNi1llmix96aMRtn6Bm/nUNABRRShQVJ3AEducmgBKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK2tFtfDs6/8AE31TVLE+llpsdz/6FcR0ahaeHYw32LVdUuD/AA/aNMjiz9cXDYoAxaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK7LwnY+BdU8PXNvruqaxoviM3BNteR26XGn+TtHyyIMSh9wb5l3DBHHBzxtFAG9p/gjVte1BbLQbdvEVy7KkcOlI00sjMcALFjzCT/u1lappd5oepXenajaT2GoWcz29zaXUTRywyoxV0dGAKspBBBGQQRTLO8uNNvILu0nktbqB1linhco8bg5DKw5BBGQRWzq/jzXvEmqXeo65qU2vXl3JJLPLqrG5Z3kIMjbnyVZiBllIb3oAwKKvO1vq2rDCWuiwTSKuF81oIASAScl5No5J+8fQGtDUvBeo2NnLfW5g1bTIxva+02UTxxoW2q0qj54Nx+6JlRj6UAYNFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRW9p3gvULy1hvbswaNpkoDJfam/kxum7aXjXBeYKfvCJXI7igDBoALEADJNXo3t9L1TJS21iCGRhtfzVgnAJAIwUk2kYI+6fUCoLy8a8lDtHFEFUIqwxqgAAwOg5P+0ck9SSaAOy/4U3r9jJpR1trHw7banaR31vcaldoqmCT7jkKWZM9gwFavxH8L/DDwt4P0638NeL9T8XeNGuib6aGy+z6VDbhW+VDIBI8u7ZyMoQG56V5jRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVPp+oXWlXsN5ZXM1ndwsHiuLeQpJGw6FWHIPuKgooA6FPFVvqLAa9pcOp52hru2YWt5gFif3iqUdmLfM8scjHHUdajn0PTbq1M+lazHI6puez1CP7NNkIWco2WjZRjaMuruSAI+awqKAHTQyW8jRyo0cinDI4IIPoRTakW4kWMx7sx8/KwBAzjJGeh4HI54pztDJuIRoW5IVfmXrwBnkADPJJoAhoqQ27ZOwiUZYApzkKMk46gY5yR6+hqOgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACirH2GSNsTlbb5gp844YZGQSv3sY74xzTN0MewhWlb5SQ/wAq98rgHJHTnI70AMjieaQJGjSOeiqMk1t2+i6Xa2/n6rq6hym5LHTk8+YkqrJvfIjRWBwSGd0IwY8gisZriRo9m7CfLlVAAOAQCQOp5PPXk1HQB0UniyHTpMaBpkWlBdwW6uGF1eYLBlPmsoVHUj5XhSNsdSetYd5e3GpXc11dzyXV1M5klmmcu7sTkszHkknuahooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAs2unyXvEDRvJyfKLBXOMYwD94knhVyeDxUV1azWNxLb3EMlvcRMUkilUqyMDggg8gg9qjrXsPFV7Z20VpMIdS0+PAWzvoxKirv3ssZ+/EGP3jEyMcnmgDIorqIYvCuufKZrrwxetjBlBu7EszncSVAmhjVcYG24Zu5HUsvPh3rcNjNf2UEet6bDG08l5pEq3SQwg4EkyoS9uD1AmVG68cHABzVSrcvtIbEgICneATgdAD1H4YqKigC9HJp9xIPPilsw82Wktj5ixx45VY2IJOe5k9vepE0mK68oWmoW0kk0zRpBcMYHVR0d2fEag/75xWbRQBdm0S/t7WS7a0lazjlMLXca74N47CQZU/geapUscjRSK6MUdTlWU4II7itH/hItQa6mup51vriZNjy38SXLY9jIGwfcc0AZtFXPt0P9m/Zf7OtvP3Z+27pfNxnpjfsx/wABzS3k2nSW8K2lrdQTj/WyTXKyK3+6ojUr+JNAFKitC8h0qO1VrS8vJrn+KOa0SNB9GErE/lUljo9pdW/mS65YWT/88Z0uC3/jkTD9aAMuipbqFLeYpHcR3K/89IgwX/x4A/pTrexkuhlGhX/rpMif+hEUAQUVoNoVyq7jLZ49r6En/wBDqtcWclr99oj/ANc5kf8A9BJoAgoqW1hS4mCPPHbL/wA9JQxUf98gn9Kt32l21rDvi1ezvG/55wJOG/8AH41H60AZ9FaNnBpMlm7Xd7ew3X8McNmkiH6sZVI/75NNs5tMjs5lurO7muiD5UsN0saL6bkMbFvwYUAUKKuG/g/s0Ww061E4bP23dL5p56Y37Mf8Bq23i3VVvI7u2uV025jjMSy6ZDHZttPUHylXP40AVo9C1CS0gu/scsdlPL5Md5KvlwM/93zGwoPXvxinf2XDbq5udQt43jnETwQZmkK93QqPLYD/AHxn6c1Qd2kdndizMclmOST60lAF7zrC3b93byXZSYkNcNsSSLsGjXlW7nEh9PeoFv50iWNH8pQjRkxqELKTkhiBlh9c1BRQAUUV0Gm+Atb1LTY9SNoLHSpVdotQ1GVLW3m2HDiKSQqJWH9yPc3HANAHP06GGS4mSKJGllkYKiICWYk4AA7munmsfCehiVZtQu/Et2u5ANNQ2lpkqCkgllUyuAchozDGfR+5q33jS9mjlg06C10GykV42ttLQx7o327o3lYtLKhKg7ZHYA9AKAKN1oc+mErqDLZSqzK1s5zOrK+xlaMcow5OJNuQOO1Z1FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVLa3U9jdQ3NtNJb3MLrJFNExV0YHIZSOQQRkEVFRQB1jfEnUNSkD+IbLT/ABWdzyNLq0LfaZZGGN0l1E0dxJgdFeQqOOOBhhXwZqittbWfD8yxAIsnlajFNKepZh5LQxjjoszfXHPLUUAdZJ8Obm6WeTRtY0XxBDDtXNnfCCaV26RxW9wIp5W5A/dxsCTgE1i+IPDOseE9QNhrmk32jXwUObXULZ4Jdp6Ha4BwcHms2t3Q/HfiPwzp9zp+la9qOn6ddNuuLG3unW3uDwP3kQO1xgAYYEEcUAYVFdl/wsr7dc+drXhXwzre2Py4o/7O/s5I/wDaxYNb726cvu6fWov7Q8EXllHHLouuaZfu2Zby21OK4t4wevl2zQK5A7BrjPqxoA5Kiuxm8OeDb68hg0nxlcWyEZluPEejPaxr7L9lkumb8VFNX4ctqGpfZNG8S+HNZGMtcHURp0Q9s3wt8/gDQByFFdTcfDHxHHqC2NpZw63dt0h0G9g1Rvx+zPJiptQ+Dvj7SYDPfeB/ElnCBkyXGk3Ea/mUoA5CinSRvDI0ciMjqcMrDBB9CKbQAUUUUAFFABYgAZNdPpPwt8aa/GJNM8Ia9qMZ6PaaZNKPzVTQBzFFdG/w68RWupCw1DTH0O6IyE1yRNOB/G4ZB+tS3Hw/m0y+hg1XW9B02KXpdR6nFfxr/vCzMzD/AL5oA5eius/4RvwtpupCHUfGP260ZMi68O6ZLc7W9GW6NqR9RmoxceCrOC7h+wa9q0+f9GvvtsNivtvt/KmP5S/lQBy9XdH0TUfEWpQ6fpVhdanfzHEVrZwtNK564VVBJ/AVvr4/jsZrafSfC3h3SbmFSjyG0e/WbPdo7ySeMH3VVqjqnj3xHrWlnS7vWr2TSPM81dLSYpZxtnOUgXEaewVQBQBeX4a6jZsh1u903wzH5xgmGq3YFzbOM/620j33Kjjr5WOQenNNW28GaSU+0XereI5RvjlhsUSwgDfwSRzyCV3X/ZaCM89RiuVooA6lfiBPp6bdF0nStAbYgae0tzLcb1ORKk07SSQvnvCyD2rA1TVb3XNQnv8AUby41C+nbfNdXUrSyyN6szEkn61VooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA6DTPiJ4q0WNU0/wATaxYIvAW1v5YwP++WFav/AAu74i4A/wCE+8UY9P7Zuf8A4uuKooA7T/hdnxE3bv8AhPfE+7+9/bFxn/0Og/G74itwfH3igj/sM3P/AMXXF0UAdHqXxK8XaxGyX/inWr5G4K3OozSA/gWNc5RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH//Z)"
  949. ],
  950. "metadata": {
  951. "id": "D15tjOn_bNP2"
  952. }
  953. },
  954. {
  955. "cell_type": "markdown",
  956. "source": [
  957. "Reference\n",
  958. "- https://github.com/Kiragroh/Kira_DICOM-RT-Anonymizer-MG\n",
  959. "- https://github.com/Project-MONAI/model-zoo\n",
  960. "- https://monai.io/model-zoo.html"
  961. ],
  962. "metadata": {
  963. "id": "cFLRp9zDbRE-"
  964. }
  965. }
  966. ]
  967. }