segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segmentation (SegNet by Keras) for gyoza image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare training data\n",
    "**TODO：学習するとき、fit_generatorを使うように改変（現状読み込み遅い）**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from io import StringIO, BytesIO\n",
    "import os\n",
    "import pickle\n",
    "import google.datalab.storage as storage\n",
    "from google.datalab import Context\n",
    "import random, string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# settings\n",
    "cols = 224\n",
    "rows = 224\n",
    "channels = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'kagglebu'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project = Context.default().project_id\n",
    "project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "kagglebu_bucket = storage.Bucket('kagglebu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kagglebu_bucket.exists()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example of reading gyoza image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/gyoza.png"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_object = kagglebu_bucket.object('user/akihisa/gyoza/gyoza.png')\n",
    "sample_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "img = Image.open(BytesIO(sample_object.read_stream()), 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RgS+3Gf0nX2/o/s79pqen3fXoB/G42K/Z8v7wd7aj0chtMf5O+eJzj8eV9rF4/fdrvL9c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xyYN99U/Zuzlltva8v0vGZNL8x0eyRwCCICEslcGSfwiEE8fBKyldoZbhp/DbkDMxIGx8GXL+qH\nRA8y3TUgGsOgxxOzpuNxxFx3EeGi3gEIeYxrgMfhG4LrbG0pN2owCB4vz7/tPVzqC8dnZO0yM8MD\n8J5gznEZHoS1A4hByglnCvAvvKAchPsP1kVEpD9QT+vtt3X/4NDrr6V+E/TCQChSDtIG50TPGxiC\n0Wl55Cj2MzpKeu9BuEb+OTa5K+ORqpLuW0QU4xY9MRCniGzheTaVmz2SG5GtHE3rc95lDqBFA8Lh\nMMBo3saNowFJ57776Jr1fQ639N6CuMXXPiB6CaEL7zntEDMn0P+Jns7PwXPd8OTX1vR6t27hsdMO\nxr0y4I/xUpKOETkV4XwZd3XGjw+seCT9Q/HjCNyuXF+qae3WJfrOdPjSOMDW6w7GaNe4ze9lqX/Q\n3zwHNSOsPGeP3GTE2XNBBwP/fYRThP4WUXSduVl3/mDo3695+zvcY5CpKXvePM9DuJbGqdojZ6rp\n9M0kpJ/3jnZn3PP9LkaVE4XYnWIc03rBZaQ8mbtn/WXE+Ltt7afXeeXll7Vchtx+6EMaZfjuu++J\niMis/X716jURETmw9woOJZxAkCW43ehpgVTxPqNrBieM82KO17wi4VdqhkOPLEeE66jpJ8bvWSnz\nRpnjqVYRrmrVqlWrVq1atWdsXWZ6MSoRpWWynxOleO8eudeC3lJD2dyvpWeka3w0WrdL9m4Ue7tu\nnxkoyBaeE1E6eGzM8Ps9XF3jDqC3AmXBXEyiMCBjoCj++uuqHP7Zz35Gf7cokrm5VVfvTL3x0W9x\nBTc7tKkAYw/InnqEtgKXZjT++OwZeo8x5yi0Gf9wPHJR4lDhMdBPoscQPQOuw3lYjl7F00Ifxkd9\nJGXwWTgecD989CLlSX83JHMw8O2Fp5c9cFNk3wc5EquX95CxhMwRhZcQIPNozGM8fkI9NBBRcnLi\nqa6Z4vjtOzfsPiCPnqOVEK2AcI0CRyW9PyA6LY/4gOjm/vNkH2sSshXf54hwRb2ayJmJmRYiIlFC\nuPJ44/tP3Mfg3LCFG8p1QbqWZhfc7+WMCCD8HpHjdQT56LU8ssrrmKJMG1vq1XXXT+9JTl2h9UHB\n3hCTPisRHd4nO9zOm18wxM6Q1719Hb8HpljfHnhkH2X9iHxn3TVDiokuTAgXCA0cM/qJ9/SJzkUp\nfX/XI3qlaMG4UhKR2FLOu5hbMUf7tV19GTeSLFmK6vbR7oOQuQGOVr6+XjdnwPBc21Qe+zPjZuxX\ncLSOWe5IxlEU0Xfse8SKU8opaBdO34HIKeT5pffMEMcpELmW3UcRo7gSxT7BfUsWZTg/r4jnd7/3\nmoiIXLyo5f6+7RONy/cbfbFW4prqlqjED33oJRERuXv3nl1fzwfRuhD0OeNKXcpFSm5Q+47B7YrR\n7/G7xXVALNmPCvexH2JVab5atWrVqlWrVu0HZN0c9eNnsCl3IDNa9KoMUcDzS56JRE/X72eOx/i1\nTaI1NkzXa3VFkYJ793SNfG1FoxVAvIiOwYjyiZylpOTc9wrA6BO1UTg2D+2+3R9kD8846+l4jkHk\nHsWowAan67EjHy9gOQrMIxRR6AtuRq4v56E83HHHRz0qcvZlTopHTlgbx7OCW1DKdVaKTo1cr6js\nG5G6GF0U9b2yPpJFScl4ZDV7xJFLZAjXLNE35rGQq7AV+5FxpNq+fJkz1HLlgZsxZxkc1uz5kaNz\ndxe9HZS84c7hsXqEKyGV4g0dOaIa4UYRTdQNUVVJnyogIkRzRh29iBhEbkJEGEp6fPG4zFkcj3TF\nbRnR8s8p5jYkqov3OHnCKXenjXc9EF3zwG18a9vf0fEbJOQgcIhARAxZbYeMAA2kK9RvyspJuWZM\nofwABHPKv28pN6b9DgKyneSG9MLkVATh2tlVhADl+XlDGtpkVEgIsyHWXbhOXv8tITkzIDkhyjZk\nGEiIUOrf1t6m/5SRWHHHl6JUu0kQzx8PQp0zjYB8hGhCMp7MeN2lkmI472EPjqvpbbESFDmE3Ddy\nqnKuWUOk7PmNOD/oV6YcqawgBK7XodVLIreaesM1ChzC/F766M3+FAiZXxnhuafvoLUj34c9e44n\nLNqXnIxnz2quxTfefFvPN67YrVsarX36zBkRyUgVHK5vf/s77vy339bziVrkuxJz+TI/AQmL35Go\nEB/7F88764TNu/14H+YjpZyf0SrCVa1atWrVqlWr9oytC5LFTI0ZIzoYFy8+JyIit++oXsZxW1Nm\nxr65qS5VUwF4PAckIw0WhZZyGB66+99/oMjWEZvRgjgtLr7o7pOjfA7d75n74aOfQCrYPzRPZdai\nlFirRn+H8s/ZGnWvh+Ks3o+oxSZrK3IPAhcmcDmSvx4QrSbnZvzacMxVmKKkOv45ZCRK/75tulEx\negMPAO4aejD8jqcQEUyspAidc2patI4hQDHrPbm6ssfgy589bRAyQ1YKulsR2YPDtrw0beUVqwdI\nk+l5RZ2fkS9P8owFZE/7B54vucKOHll17Ua0VuaEjNerizkIW9QrIZ5t9zvWDtwIoqjgetEv4KiA\nhHUNAQPxGBA1BsJj7weAR0Io0bcDiWp13TYrx/tt7M8l7kOO+qN+4XkGhIK/J27RFP0aRB6uoleQ\nRg6vS/Sj7ZPrDsXsyF1MiE6qlderKyWU2A/Rh4wvUzN+pSFxK+092dvTfkPU4IO9Df1935A7q++R\nY6wMmCK39WM4uUsLqvA96HkkM3EeQWACcs/fW2l89YhWQmTT++uR717Q0WoXkPIS4h0R9sgljFzP\niFxlzk9cgRmvGxjHsVaLKH50zfiO7rr2gSOcES5x7dMP3ChUAdAl47u8f+C5Rnwnd+x+ILNkBhgO\nPec1R+XxIlp7gnSlhjEuoiHCszP2HU3vtfVrQ/wWFsjAoOW6e0/7GRkrbt9GP4voTF0BOH7c3k97\njpcuXnTlRZ2A78GHXlJO16P1dSuftseGIVlJF7Pjo5JjpgeOA7Hi+bByw3OIXGa+g0RBct35oNtX\nzNlsVhGuatWqVatWrVq1Z2zdrFejcy9m1MeOqf7E9o4iSwsW7bK1pZ5UVoxnRs4MHI/SlGWJ/tsl\nWk1nkOgXoagLlyLnAPRroHGGChLR7ZAbTOsBB6xtHghcmqVFoiLIbUU0jXF5zKOYshnvwGaoh3af\ndk/3F6zcB/uHVh7LrQiXp+09gViPLO+SXHXdJoqW56oQxZG5IOaJpagUPR5OAZysGMU3P48ysVi5\niQ7tunYih2XmLmn50EdjjZ7nkKOZPDJHVEqOPoM7Ycrrc+gdeUQr6hCVsrbnaExx5UgcH/TIBh4x\niZyKhsK0cftmuujPgDRouYgOopx4mhmB8UjPXIqytNx39rxOn1RF5Y31R1aetrsuDUeUJnphPLes\n4O1zeYHcxdyZrdTfKCfPyThwQQF935AWODd4zpSjZfV4+HDDXQ/uFB5vjr5tuXaIen5EcYJ8JCQP\nZICo25Z/D/ZtvEq6QomzCWJquULtuc4bok6U3JxxpbLunnJGZg1h2tzQ5wOSsRSU1dFFa4doUJBD\nnuMQ7k/S86H+2u+JLjtyVBGprW2QEsuVaEgpXJnpaUWotncVeSb6dXffcioe6PkvvfSCiIi89hrj\ntvWjvs80MNXx+oiZ42PVKXAX6a8JSe14zi/IYHyfQTbISRuRzkiBiQgm70GKzmtwlOBmecQYS8e1\n7f0hZQiZSYYeyeW9njbkcHqK3LueO8pKjQREECS/3eH6Vh/rz33GoYE/D72ybuJSe0QOZBLECiSG\nce7kceNU2QrF5rp+z1eMEz2yaNd9e46Hhriiz5fekznt/zv2HZ82xHzXvhtT0xbtb8+R69+xKMN+\nQkAN6bTvLd8dogiZfxCFuLGh/fb4cR0vN23/ued05e3OnTtWPr3/jiFX9CaQ08UFxjl9TiBo20Z6\nBDHju8rxW4agwYnl/D1r5/i+RN2yaBXhqlatWrVq1apVe8bWbSqi+mgU1ujRU8nRUnGtkn3dA7lg\nrhn1eLKSvc7gl5YUWWHGSzZwohSPHjnqziPXogRF8cgBiXopwxE5EC0XlHmswxjdmJAQv/bdQ+eJ\nKJSUFV5c/UeBW4LFnIopqiZwrzi/E6KDYo45Ztzcv5uiivgFrsChu/4wKawzI9ffYyaAEydOiEhe\nq15Z0XYHsWxyG3yUGshAiqpK3Au9H2vp/ZSTzHMoAM5ANAbJM1fPFSQtRqtFpe/M9RArN4ragVzT\n4BZ5xBEuFL/jcSYkzsqbczbacxuBEFEP+qHn8EnilhhCkKL7KDdRlaZMbkhrbm+vf5W346MGiebh\nBhEpIMopIagglf3xSssDdK5GhpCBZDWiFH3UU0bsfPnoH7y/U9N4lHBZlt3xkSwVuXHoB/XtejeM\nq3rHuCZrK6t6XRuP5mbQJ1LbMQ4XHjk6PSkVKhkdQpQ2nJjElUq5Pm18sXFp3RDPnV1DfA99VDSI\nUreLfhicFEUujh7Vct+8+a7u6zAqm5uKDJw8qchB5LoOW3CYPFKY2nXo93MUNsiQPd80XHkuH4hf\nKynk+/erlLMTK+lyxWjGGFUbo9BKUbF53OI7NT7aOXK7os4gLzLI8E6IRo7j7+LSqt4nRFUyfjPu\ndFsR6afcHjOZI2eh1RuO1Sx6cmRYgTPId2DaZ04AgUzRnSHqNn13QsaQmKkmrpTwvZ+fhyut90EP\njXZ9/fXXRSS/X4wD5E4ktyIrDEkvLEQfMq7E6PlGBgh0KpPQIu3g9ejYZ4Vsb3fPXSfOc6JVhKta\ntWrVqlWrVu0ZWzdyY3LUj1+bxhKSYfsHhx7pyDNbFOE9UoThIaJzw1otUQO3b+vaLAgJUYvnzqpu\nB54F2ebxeBOXJ+UU9GusEjyezEHzUR1RT4NcjL3DgFw1PDLP5YBq1E4eYUBO8PxBtkL0ZeQ2RB2i\nvb0dq7fpSCVdNarrOQ95pm8eYZ+/xygbckuBeOHxDN1xrJ03ounwEIhyCp5iRh5BQDzCEtfGBwFp\nidEg0bOlGbvBQ4nK7q0Q7YZFXbCI2LFPP07HBY5URP6yJ67P49Gjh66+tEeKsgpK3Skn5sgr+WcO\nW9udH9/v2A6dFG1mHl7w1Frhejn6TM9fWSRKifKN19+jfdshhxucF96Dbrj/VHfVrus5o9QXfbjM\nNRJXXtonRjHnjA2e8wPCvr6+4cofMyRMpdxzQ3c+v+d25r60DwiB7sNZmZsHSbN2NeRuc0v/fvrk\nSfd3OIW86HMLOg6SKePI2qqIiDy4p3+HQ8R5q8uWc5bn1bJxD+SpFdqJ2keEK3HsPBI3Ss+b4zwS\nmjMq+HEjvi8lJCsi6zHKsKQLF433sLQyEqMdS/pyjDMY9Y+IS0Q+yCGcc9165AjdPK4fc9EyfFGe\nzBnVcsNtbtsKxaG9D3ABDw8YB73eIc8nZXqZYtzxKzJd42o3n68f39lGZHvGEK+ow8XKyln73t+8\necudD9cYpCvm8C3lcI3K/EnfLIz3pQwFJYX5eJ+SVYSrWrVq1apVq1btGVsD4cpRW2L7eAx4Ivp7\nOyASWNZj8UgSM0OuC+KysKhrtMy48cS2LXpxJc1kfU4lPG1mrDnq0MoXdJ9AagbDwD0wA+HCE8aT\npNy9XtDHEma4HpFKOGHiUHluw6jAIRgGJJF2lMBpygrK/r4pi33fc+2yR+bPxzOHA4UHFXOosQae\nn994pKqpg+Rn/NHzSR56yx8fOURYibsRo91iO0WkKp6P0nI+f7wPEnWAKCdRMhmRidnoiV7S+sZs\n9yCn6ETRn3jfsu6br+/+vt7v/Pnzdr7ed9Y4R3Al4vuZkVndkLOt8dysvskjNN2dAFTnKMBAYhz0\nvWcbPXz627z1PxTcaRf6L7o3tH/SzSLzBYrwMarRyjE3Y1HHAQHFcnSjRziI6oqId9R/AgHASlzV\nxCELOmsgFLPomVkDLxs3BUT9wvlzIiKyuY0Hr/dZsPESZGPPohaJIksIqiGZKQrNOJ3dwElsrnC4\n6jXaKfbzUnQWxnXj96F53Pj3PSJZIBTRSkhUvA79M+u6qcVcsqV+MBPenzze2334ng799wREdNOQ\n1NhsUT9v0PWINeMUSGzMCENUMbVi5WjeuFIH+4wrFu1JtGNaGfII39Q0CLRlNjClfOrDeXHc5nze\nE6Io6Wdb21p+MiLQnnC2WOmCy8V4S85FkDAsIqSpfGYxV2/ixja+q/56pZWUSSst0SrCVa1atWrV\nqlWr9owtTf/i2nycsQ1ClAUeAfpcGAhLv8Ft8FnM2TIDJkrh0SON0kF3Y92UZddWdQaMbsbSknq+\nB+aBkForcZUS8uO5BcOkc+K5MHjqBwlR8NwZ6gFSlKNxbIYbOEAAVMlxYuuDIDInKSBcKZolRF9F\nLkPXEJXEQTOPjfpP2xr77IznbIyGXuGaGX/WS/LchbgGH5GTkocbka7I7YkeUcziHnM74rlFJeCI\n0NDCTe4D/RwkB92u8ZyVRpRpeE9AmEoK2dFTjDpyO9vLVk4tB/39wQNFJg4sE0DW3+G563ZpSTlU\neMIzMyHaL/hUrQxR63FTvCfUl/4BF4d+CULVctukQ9X2SCr6c1Nt328j92d3e9OVe9RHp8oQjI5v\nT6L9QDamAiIauaLoQjWQWHufdvdQkF5015mf93pbeOboZ4FYUg6Uvvt97+HSPyMSS/stWzTkrEUL\n71oU5JZxWtFpW1rWKO1OC26O1guEa8tWBJYMEbxx4z0REblw/nkREXn7Tb3eypKGLd6/p9yXC+e0\n/+ZuPwpbb/F96PWe7NGXkOXEoen6XKql+5W2mVvo398Sp6bMMRyPqJfKEZHreFyJazZMSBdIsOeQ\nZX2wltu2QB5B+Czq+dD67+DQI430w30QVOtfC/Me4RrZFOCB6ekNAtITOXAgsGRaSRy8kMEkjn8Z\n4d9z9cxRx+OR+MidIyqRnIpwiLGISGIZySRa0r+3pVyajXGr0L/i32suxWrVqlWrVq1atR+QdSMn\nYTTyCsPkHIv6XEmYtxBFEj0bPL3EgUrRcj6aCE9nyzxfZorbFs0Rjwc5a0YPWPms2IPk2VA+iyJJ\nOiOea8FMGcSMHH2UD12V5AkGZKAl3jNJ+kx4AOkHd5UGAhMFvjIHwvbtMKIN+z2vdwUCNjMDV6/j\nzo+eOhP8nGMOD9J7MEQfxtyGmavjkRAsekDTQZk39qNSv4rRiyVkKkaZRY4Y9Y25P0seT7ToSU3S\nCYqcSKLReL+2rd/D3TrseZ2teUOC4N7twtmZ9uUA8crRN54jlxDg8H6kqGT6Gfp7cBVNT4nrjpIO\n3cBdF46ntHk/Qk7KNKwMrR2O2P3VY+X9m5kGwTUkzTh3a2vq6T56tG7XIwODRzyaXFSQbcqlGxAt\nPGE4cM1MCuLKx3sWkWDGu8j9gROJkr0wviY9JPO87b1ELwxOEBysw4MwrjLO2nlrpldErk6Usw8M\nYej19qxe8sTtpPegHxCaEsLbiB608zPy578Xk96jJqJNucdHG5aQp8y5CwhTiJbOnFA//kSEvaQH\nFvW/6HfpfUz6eoac8P3IHViv21jh0PMj0gdHcH9fEbDeob5X9L/paVvcamt/u3lLEU9yNrLig4oA\nUelty10LJ6vTNQ6lATp5BSQijP558f2YN8SNKEUyPcDdPHpUx4V3370iInkl5vRpzXxy/foNbafA\nOcW6SU/MZ1ah3YmSjBb7Gxb7R2z3Sd+LinBVq1atWrVq1ao9Y+tGBCDO4DpDj4xEhCtyU5jBZ0Vu\ncX+HY8FMm+iDBw+Vq7C6qp7r/fsahXD+7AURyTNZ1m4DFSXrUHVA7DwHKyp1Y9ETYe0bpeddi55c\nMJ0bEIG9pPAN2SrdwG3h0LTN00d5vMV5SadG3PVyMX3UC+VLSJyVM+lK2VndbiSP2R63Tboi5vnM\ngXzp39ud8R4rhmeFjlLH6tnyDkHiRiVPD2CvUyrfeKQqKwLzu24TAlnQSYmeZ4lThu8R9dIywDh0\n9YlRsKW1/xiVlY4zxHB3V3Wkkl6O3ZCcbe229js4TjMB4QKJjAr+WVneI9gJ8bLXeZ+cdAHZYttL\nuS6JDjSFfJTgtxSRA1kCmevZ8YchRxoIy4EhA2fP6/tNjtPoKRKFRLDRoXHa9na1AkuLPtoIBDsi\nXAko4O9wFW1/yjxnOKQpOisg6HBGiJaOuj6xH0ZkNiMqWp5909EDEZwzpG13B26q7/c8F3KP9uz9\nQwEbXbIzp5UD++ih6hcuG9fv8FCf13zK0OBtEmcqIjbUu6SwXUQKrB/m3IMe4YpW0umKUdWxHiVE\nLCPeHoEpK97H33XL+1jS7coIetCFssv0Q1RubOc8PgWE2LZTtvLCygYIWavrETjeO7hcRK2CYB0c\nkMs2ZiQZuL9Pz/ho4Mj5i98X2he9P9QCeI/IgABne3lZ5wMPHyqHlWjFyKW8cuU9d5/Yv1g5yTl7\nPRKbojp74+c/pSj4rE85PbZ8Jb01rCJc1apVq1atWrVqz9i66M1kZW9meBa9lxTYPWdp2GGG75WV\nOwHBih4mUWPkQkzZ6s3Dv3tXdTYWFnSme/PmDbuPeqAo0q+ZkjIcI6g0aSYr/G4z2D5RVXicYvWE\nm2KIR+I2mQ6LbaknUSP7+3r9GdbCE4cLhMCaS/A8aDfzBEwPrHdg5bX27Ha8BwbSAoJwmBAE9JjU\n88BjmQlcrKzfJK7e+e+0k1+Tzs/FR3nFaI/IAcSaHpq462DRg4jHNbgb6XSPGETORdTfKilQRw5Y\nkqlKXBauMwr7MrZ+2TznroF08X5Z/edm9bktWm5PFJcT5zBwzng/t7a33XExSg+EK+rNtEAAzeUa\nWj9tBU4imSTgiuEpw8kCEt03jgeI1/aWvqfblquRnI1b9vc9u86//je/KSIiJ42Tgef43HMXRUTk\ni1/4KREROXfunN2X/ueRhKQv14+IiudExufM+DATop2wGLUbOVoRwWKcwrOmv2X9NMvFaO8x40eO\nwtXrwmnZt/Fhbl6Rf3TWZk13i/6PQv6CIWQocK+taTnu3FHk7sKFk1be8YgiVuJQlf6ORc8+riBE\npCcq+JeiDUu56kqIGu0Z3++klzcFhy6+pyWky/ebhDDZdXj/Yj1y9K7XA4PiON3lfN+/SjplGeGT\nsVuQ7cQ17hNd7rm1IE1DmfbndzzXEARMWuQ81X43bfp20ppyx+Xx2CNhbKN+2Up72eqvz+nq1Wsi\nInLhgkbP3rhxU0RETp06ZfXRdvriF78gIiK/+Zv/o4g0keZo8f1jn3ZKORIDMp2552qxH8M1i/0m\n6n9hFeGqVq1atWrVqlV7xtbFo8yKvSA8ntVPNEFSeDcOAZZyGRknpLdJlKHPBcXMEa5Emgmn4/zM\nkJkjM/J2QpjgOOj5S5Z1PXE4hn7NNits6/FEAQ2GPhceHkrPPOUbN26LiMixYx+1dtmz++vMFz2c\ne3eVKwHHDH0guCso5jNDf2gK0GtH9fiHxmEjSuPGdZ3pM/P+R//oH4mIyM2bN61+fm39pRdfFBGR\nn/npvygiIl/60pdERGRxUZ/b+vq2a7e5OXJY3bF2MASNNX+ipEIUXo5+xOOZdeVmTRuEBk/i/n1t\nHzwVkFU8P9rtxg1FNOHI8FzYf/RwXetvj3MxZSrYc8eDeBB9Rjljbr2FBc9lAfHI/SFys8xj78fo\nIzs6IVmRc+E9rxgVRVQcysroQnH3a9eui4jI8rIiHXiy5BTLOlD6vOgntNvamj6PO4Z8dK3fHpru\n1dyCV6JO77m1F/0tRS+Sg87Kd890neBuQY0B0bp508pvnM0XX1R9qB/78R8REZHtXe03KEm/9db3\nRUTkj//4j/R6Nj7A6Tp6VPWk/tJf+jkREXlwX9+nl176kLaDPV/62cmT2u9ArhcXtd1mBowHplxv\n/QRPOeVsnff9hPEyR1drS8BJBdHL45ceD0IPojk9DaKg+yiHc91Ll/S93t330eJwFzHKB8dlaqpt\n11sXkdxvMiLnyxWRoRJnK0b7cj7vH8gF7U490E+KivuZO+M5NBGBjkha5G7BXaMdyFiyubnlzqN8\nORfsrLXbgfs7CCX1SjkFAxKSddfE2sOi9lqeW5QRNc99hLvH+BczUWS9KD++sc9KEfpwEWFhfEOv\nD4RqYLmOtzZ3XLvdusX3Tt+vGzd1f25+0bUD0YmL1t8fPtL6gNSB/DRzuWorgJjD5eW6fB/4HmI8\np1df/ZaI5P7DPIL7UY/Yn3kvaa+Uyznq9tlzov+uWrQv1+P68TkxbuTn4+dHWEW4qlWrVq1atWrV\nnrF1mfFFXazBwGbwA4945LVm/X3NogqT52Pciv7Ar0FnpMQjAsxcO1Nd9/fIvWlGC5gOUyMqpKDj\nYtLvKEz34XoYJ4RcU1ev3rDr6X3+4A/Vw8YDQT9ndVURg75xMZZX1uw8vf69dY2ynDZPYz9Fgejx\nq6YjdOXKFRER+fJv/obd/6q2i82Y/9Z/+p+KiMh/8bf/tl1fPap33nlHRETefPMNERHZNM/5W9/6\nlqv3pz71QyIicvLkSaunj6oBiUqyX8WoxO7YLc/93XffFRGRX/3VXxURke9+97si8ljU1BnN+v7L\nv/zLIiLyoiFyeJKcjydM+SkfnJSYAwsPJ+YSA/nC409cNzsOTyUiWtnDjlwOWsLvN3WefJQK/XRo\n0b70YzICzJvnuL2jHt6HP/IxERF5++23RURkY0M9x0uXLotIVqDHg6a+p0+fFpHM/aFePPf33tOo\nniNHjlo7WL+093jXouVuPFAk8jAggZsb6yIicu3qFRERuWVI5HBALlOtL8/1I6+8rOV+7jmt3zbR\nmMbhXNZ6b27r85udV0+eDBLPPaccDjx3nuP21o7ta3v91//1P9b2ME/1p37qS649GN9GQk43Lcee\n9YflVW0fkHbahecEgsy4R/8DcV9b035048Y9115w8Xjf6Q9EYRFdNrTxlfrcMoST/rtlmTVm50Ay\nDRmw6GKiueeN83r27Dk7Tuu5sICO0Wt632k9rt3yUc+Ri1nKpQpCBxILIhGjho8dO27XEWs/P44z\nLkwn7rAfTyJ3q5QLkd9PnFjV9rul7XHnjvYX+j9IBOPFqVP6+7op+fOcGSdABrkP0Yj0M5T9Vy0D\nyr5lCCATxvQ0UYJaXxAbEPczZ3Scu3d32+5/6K5Lv+X7GHNH8neQIcbRUq5LOMhwuogC/O73dZzZ\nNU4y4+2K1evAyr9tUbNwlFtwNw1p20Enc8iKErphPqqe729GKLWcfF9B5jHG6bfe0nKCcNN/IrJF\nP42ZTCI3K3ECOz4TCe83723UEeU8+nF8P5qqDd4qwlWtWrVq1apVq/aMrctMPq/t2x+STodX3u50\nfE4mFOAxoupGAamK2c/xKA+MC9YZMgPV65DNPnOLvKeYoudCVEKOqgHZgkPjo/yI6jl5Sj3CI2vq\n+X/qU39BRES+9idfFZHM4frv//tfExGRn/riT4iIyOzsJRER2TKOwPqjB3Z/Lc/qinqSRB/+3u//\nroiIfOc73xYRkZdfVgTgiz/1PxMRkZ/8yZ8UEZHr1xThApH4+te/LiIi588pR+qjH/uIiGQP7fOf\n/zEREbln0Z1bxh2BwxBn/DnqyjgCM14vJUYBRY8pIzvarjuGzJw9qwjWf/Qf/YcikvsTHhiclqNH\ntZ0fGJKCZ/eVr3xFRET+6I/+yN33H/7DfygimVPw7jtXtB0+qkgQHj7969o15b6dOHHS7u/X0rku\nHkwzytJH1UQkK1qOZvGIEOfTT7O+i+d2zVi0T9feqxvXtb8ZoJJyYUKhOn1K2/nWrVsi0swlFjkH\n140L+Hf+zv9GREQePND2+vjHP67bT+o2KSdb+eHiUd4jR9QDPXtGkaOXX3rJGkbvAycF7iJcJLgy\nINGJA2HtdfS4lrdjyE3OQKCe5vIyUcC6yciTnv+SlQOPfcr0y27fvuXqBYKBLS6tWXsoMjU7B8cF\nvTPjuIVciAcHICxEk4EceF2+TuKmeK4iCt9Egy5ZOTYMQTxvumQIpbVMCfzhOkiXPu9OV9vxaEKS\n9Hh7/PLeVS3fseM6Dm3Y+3fqtCIYcGdydJ0fHxgvQFwYV+nPx4+fsPbxKwQxRx3vPQbnEB25Yd+v\nTERuTMxIEXPzwfmEc8N7FhEgkAvKzzhBx5q25z1nSBbIXylaezNF3+r4v7bqo4q3DImlvmv2faE8\nN2/qeV17DiCZS8a14/3b2YWrpUjOTeNYgdxN8Z50/DgP4hR1wagH7bNsKwrLq/r76dM6vrTa9t5Z\nv3rnXf0eJQSL3Kr0o8SZ1uuAqKWcq0P0rtCz03YGMQWpgmvFuIai/Guvve6O4/sIMlbKRFDiJvL7\n+va6lhOVAmu/yAmOXM2YQaIUxRytIlzVqlWrVq1atWrP2LrMGPEsQC4SMjL0+hXMVNGP2bS11KnE\n6fHZv5FJQfk863CYZzggV5i58GGmyfGZk+X1MeBcJGTGzk9+2wCP1KKWLBpqc1O3G5u6Nnz6tNbv\noSEvf+GznxMRka9+RRGXY8d0Jv3Vryri9Id/+IciIjJt0UCbm+siInL7lkZj7e/p9X/sx35UREQ+\nbVwqPFy4Wl//xtdEJK+d/8W/+CVrH/Vc0lq1zfiPHjtq9Td9JEPqnn9eo75mpr0nlnVJQCjhRpAD\ni99LyM54blzWHdLnSPuDhESPkP6T+5uWn6iYX/mVXxERkZ/5mZ8RkeyxgojAWfv1X/sXVl6iJLVd\nWNvP0WL6O5wn7p84CubZRU+mkcsyWdQd8p4Sng1cI55PVjbuum3HPEiihM6dO21/t+d8VOsD9wmP\nfG1Ny3XmjCKeIDtEsYKI8px/7uc0iu9nf/ZnXDt8zvoluRxTva12vEczsyDS+j7vWH98+FCRsqRz\nZ/WZSlHPcBt8VBdIBe890U1wopYW11x5QECTUv0BUW70W8sxaVy4qD9HjUA6c7SY2qbphR0cgqR7\nLgzjTFSS5zooZfO+8rzwiCOHJuqGvfnmW+64aRAWa58r772p190hmpT+q/2DaGra59oN3a5vGHfy\n7BG7LrpJtkJAey9Z9GDPR2nOzbWtPtqv4VAS/b1sUeH0e/TxFhf0ug8eWO490286atHY9+5pvyFq\nGe4f7cvvvO9ErUalcNqR/n/79u1Q/jn3d9qX3x9ZlPjVq4qU8D584Qtf0PaylRieD0gWyDlRsry/\nly8/b/cBadLywWVLiIqtKDEO8D2F23fSMJC9kEEE5Hom6bBNWf20/yWkHu5bikLW57Nj/YMP8oJF\nLX7qU5/S+m2BBOr1HyTOmUcuh1a+nr1/tDfWNYR5do6oPbH28JkbeB4RiQQx4rlFZDVGwUfkKepw\nRQ4gxnWTXp69j1EnjPJxf+rLuMCKAcfH8SFaRbiqVatWrVq1atWesXVjNAgcqlLuqrg/bzPE/Dtz\nOK9/hYeS2f96I9Zs4ViMmEGbxzk3aznLDv3MFSThiCnOZx0Xco/5fXJBktsQj4wccq+/rp7Mxz6q\nelsPH6jn/SM/+nkREXn7LY0G/NCHXhERkSvv6vFEozz/vEaRHfvSF0VE5I3XvyciItvb6oG0TGn+\nC1/8ca2PeYZENxI9k7lWWj4840XzSPAU4MZQkZQJYOQ5QjE7O9dNa87kxgsK9yVF6OihxGiOqORc\n6jdsP/1p5aS9/bZyMeB4sTYPVwkEC30x6kf/ATEEeYQjgod5/rwiQnDOiIokqivX15c3IlsRac31\n7rjtwYH+jt4Mx8MVme6qp3TmtEbx3bqpzx9PCUTr+HH1pHuHev7f/bv/OxHJ/YBovs99Tj30v/E3\n/oaIiHzjG4p0EQX7i7/0CyIicvSoeobkXJu1+6GrxXvFFh2u1rTPKEF0Gcg2ytkgXlvbhnBYe66s\nmM6XRdMlnSsyJyTdLLuvvR8gZMvL2h8uXHhBRPJzx/OGS0L/AxnGI8YjxZPePzCFf/PEiSJ++Oi+\nlUOfG/0MTxikYbGj7+PC4pwrT0KCiLI9qsdfv67I9/e//32rn7b3ieOKmKwZYvLwrnLK4M689oYi\nXGfPXbZ6GgfHkKldQ0Lg3GwYcr9gOmP7xjljXL12XaPAjhtif+yYIWU9Mgqsi0huV/rjcStn5sR4\nXaL33rtm19Hn+lu/9VsikhFXuEz0q4985MMiIvK1P/ljLa/pRfHe8fzoByAa58+ft3Jre4E4UF44\nXSAPr7zyirsv1/uxH9MVDDKW9PpeTwnF/suXtd3RwXv33SsiIvJHf6Sc01Ubv8ntR4YUdM8ODjRq\n/Dvf0ahtxsUvfvELIiLy0oeUg8jzuWLtePKkPhcQ2xsWFUr/Wjdu3ZJxu9o9xnHGIfqJb3eQqylD\noBeXdLzlu0DUIdyqQ3uejDftrs9RiCVdtW5UJ/A6anHFKiKPvK/0K/YzN1D7O8hTjDqcNF/BOH7J\n3pOIsMXMBim6PMxjeO9jDsaoX4lVhKtatWrVqlWrVu0Z28QoRZCZiGDhucY1XKKJSFGYo778DJQZ\nLkhNVrDVmSweKTNQKF7MdBN3oKC7lRW8yS0IV4BoI52B3nugM+iZaRRkjbNmHvC/+le/Ze2h9UC3\n5cJ55dx02tpu98wzPX1KPa+//Jf/AxER6Q90Jry4QPTTtNuCgOD5oAgM1wnPBARwb88jWMfN02P+\nPgq6JzFqAyQs5VAc+OzwJSQqzvhjdF9UhI45F2POMzznO3fUk8JzuXBBkaj797WdyaHHdT/0IVUS\nBwmBu4FH9MlPflJEskcNJwQEkd/xlNFno9/GXIkxh1ozK7x/D+Lz3dw07tMuejuG6BwYErSp9Thm\nyN7Nm+rB/st/qbpsRHN2U65Mvc8nPqHRhSAsRPF8+9saBfsf/8d/U0RE9vZ3XX3ob1vGPepblNlx\n4wamHKhWvweBSxHf3/v37rvz0GUCgYhRmjxndInu3ddyEJW4uqqIZFaUh4OEsvu+1UPrNRK4o94D\nPjzE8yU6DYRW24voLKLADtN44hXd0WPj90OLqt7ZGVk9iLbV54oOHuWgv8FFwjP/oR/6tIiI7Nl4\nA0LSM0Tga1//ptb/mCJLVw1h+ejHdNx5+EjbbW4eHSL0xmzlwe7zaH3T7mPRp4bcXbhgel17Wu4T\nJ5bkcbtr+lBEAeecjTuuPn//7/99V/9f/MVfFJH8fqM7RTQjyB76VT//8z+v9Zjzeka0HwgH/Qek\niy3fjcjJOX5c+xFIGVGFUZ8Jzt3QEOisf6XXIaqe8n72sz/q6vUvfv3/IyI5MwRRx/Rn6j1nXFO4\nXeitXbdcgUesn//ar2s0/P/h7/09ERE5f16fE7qG6CqCQHVSZhZD1PfInKK/zpmagNg41WcJy57D\nrVs3rdzocfrxutdj5cmvVM0vmB5iI3dkRJQ814vvWuZW6vMjGpHndc6i8tEhjLpbvP9R4b2EaJWi\nF5ln0N9iJgT+zjYidHONFb5WqKe3inBVq1atWrVq1ao9Y+vGnHUgIHiShyE6Eb2ZviFFx2xmnmeQ\nemEQrczpIZrNK8xubqmny0zXHIG0D7cDTyHqcOGxxJkpXCY81Lw263M7rq2qZ7+zq/e5cEE5NW+9\npdyJV15RjtHOjs60d3fUs0Bn6JWXlVPyiY8rJ+H551VH5/BAPcFeXysK9wOuBDPq/LuPBkVHCI+L\nKK5pQ+JSbjZDKLuGQM0mvRmvy5JzlxF9ZtEZoxlrJ2u2gBRG5CruR30TylWK6sJylJd6LujK7O15\nrg36XkQbHlh/yHotPndWzCpPeWLUZFIAb8VcX6UoxZaMs8xp88hvjJ4l2g+OxMD6BZyXB4a0wiU8\nMOXndUMouC6cNBCRv/1f/GeunW7fVo8Vj+ztt/V6REGdOUM0pCnuk/vOOCE3rysHhqg+uHQXL10S\nkYws0I54mLv2vBJSLkT5+fd23hTUl5b1Opdf0PdmMPD9i+MtSFH6A5Bda+eWKULvowTOuKL7i4ur\nItLMYcj23j1T7LdUauhiLRvCDDemhKCjV3bUonJ5Ty9fuigiud+jnP297ymnE0/9D//wD/T45xWx\nfbSuv7esQHBtnn/xw/Z3dMr8+9szxGTXkKr79p50p+iHcLjEtnjg+hwAPK5e1fPgmP3O7/yOKzf1\ngxN14YKOcz/8w6pbSIYD+usnPvEJERH5kR/5nN3PK+8nJHEEJ4bvjOfMpehYQ9ZAqiPnBsQDzhVI\nd4x2PnJEy7G5uW/tOGXXtfHlgKh5xje+I4wH3E/v/9M//TNWXp+5AgSe8z73o5+z9tH+d9t0E0Fa\n/9W//lciIvL5zyvH9xvffFVERF57XbnDIFv/zf/1vxURkb/yV35JRES+/Z3viIjICy88b+Xw+pgH\nQReQftBGd69PtD9RlI+s/CjIk5tT22l3D32pGPXuuU+R+5qR/xnXfuyz0sZzfPvtd6zcXueKfhE5\nZKUoeiwiXFiOIvdRiJlD6jMgRL2wUvRjjJbEKsJVrVq1atWqVav2jK2LXlCK9kk6TH7mB/cBzwSP\nJOlejby+TDO6C12ovttnLRxPgKjEnS2d6fYNAcj6RjZjT3osFl05CNEBI3RbUITVnwd9og30Ondu\nK0dhxaJNQKbm53Rm+847yp348CsaTbK1qTPyv/ilL1i7EbWFbpBx0awdz51V5GbfOBToToFsofsF\nkrBiOeYO7ToLpoNCvWk3ospOn1auy+bGtt1Hz8vPja1HvDJS5aMZY3Reard0PPte5yj3FzhzUZrd\nc73abfTeOq7ceJpwrPB88CgMgEg6XiIHdpxxMcyT2dhQT42cdLOznvsHdxDuHo4ZCAkObWoOyz0X\nXZSOIaXJs0nNQs5GounwnAwhNh2b3oFe8NeNuwGSuX+g5Vu1XKU/+7M/LSI5muvkqePu+OvXr1q7\naDvevnPL7j/ntm++oR7zMeNs9ex9PW/Rmq+8oohKL3AY0G067KG8blGuRBsaMoDHOmeIwtQMnp5x\nLOACTpmy+B7RgiC3PuNEQgQHHlGbW9VxY2f3wMpnnDlDkLDZuQW7n+nk2PMapnro/SOyyXuBpVyx\njeglbQc4aVjOuajbn/zJL4hI5iT93/7b/7uIiHznu8rVO3vWkHVDJF82ZP1b3/pTERH58EcUMfr2\nt3X/8z+uGSpWV4my8nphPDeQvwfGxbp5U6NzD0wB/83X3rB9G88T91XLfemiIka8hw8NQVszTtNf\n+SVFWk4bchp1yUDs90yXEG4VyCtK6yjtc/+zZ5XDc8kQQ75PWdfM57wDwYoK9WfP6v12TMeMnJe8\nDxsbIDjT9rv2F747PF+uj84kyPz8vJ6/abpn9Fe+L3wP24bIELU6M6fl79r4/Lf/8/9cRES+9jXV\nZTxqOU/fM52w3/7t/0lE8nj2deP4ffk3viwiIn/zbypn8zOf+Yze1zjLm1ueu5YRfuMcdbRcm9ta\n3tfffM/Vm885nEB0OUGkO10/ICYO76GP5uP7PLDrgqjzXN++qYgWGSyuWlTvKYvWnDV9sLdMn23N\nvp8RSWJe0Anfs8b3qAX3zH//YvQ9844N69cY49BVywzD9+mEZWBgxSZaRbiqVatWrVq1atWesbU+\n+6X/40jkcY6LeaxBKRtPOuvloPiqng4zQY5HyRsEA88OZCIiaHA94FgxF5yZ1hk4Obc+/jHNoTeX\noqm0vOR2PHniqP19ysq5btelynr8DYsOOTggV9a0q8/sDNFVOjO+eEkRl/PnlCvTOzQkyzy06Zmu\nO68bcucdwolLytU602dtezrdz+t5dNpeqZq/D0Iup5iDrMlJItpQj0/K/B2v3MwWi9EX0VMoZWWP\nuihxjZ122TTPFo8CY608cjlSuVq+3pzPfvLQQn0yp1D7S6elW547VK29A/PAZqxehrz0kr6KHWhK\n2quWi8yCAOXeXfWInr+s78HNm+bp2wH/p//qvxIRkRlDEq6ZjhhcrJ/8wk+ISI7eSTo4IRMA7yPR\niCB3WZ/GlKXNQ0ffB6RhNISD4PsJNmk/9hv+DkcSy1wOotdmrV76935AqFMUaIiSzcrRYvXdE28+\nCqnE8UwZAgb+95hzNOYWjQgx79t0Utj3mRfi+8DxBxadOBzo77/+L/6liIjcN8R7YNAC+kwXLipX\n9IwhYefOK/L01a++auWxceq5iyIiMmvj0dmzGi369tvKzXr1Vc0Ra6+9fOHHf1JERL5hiAnIEzl0\nT57Q8198Ue//yiuaA/aE/T4c+dx9W4ao0C9H4hEI+jHtDycLbiAcTPT46C9YiWM6yWIUW0LcaedC\nFHJILPH4FUXksWjZxGnV8mRkD4TWc1p5P+gHOSODz8zA/l7IzEFORfTjQKJQwmc8vGQK+H/6p3/q\nyj01DffVFORTFL+9B2Hc7HQ9F5dyEVUL8kc07Inj+hy//5pyoR9ZRgkyUsAJ3LbobeYJIIK0X3wf\nI3eY8TLrrPXHHh+j5lMUqr2vDOf8nahW+h/cT6KnuS7IJ7pvUUXhf/+//oQ8bhXhqlatWrVq1apV\ne8bWRWEXzsok/SX244w+eghEhyQ9L7vequVuY2a4bjobzGzJej8wjssoeKppazNccnH1www2r81S\nD4uaMo+AXJB3jetyytZcT59UpOPYMVPkNsDtyJoiFVOmpLu7g4I4nrchhMbl4kQ88RwdOXK/U/5u\nWHPGv0jRbyHXHRY9vchJiR56Uw9tfPRGvH7p96iz1EQixkdxoIuGpwHihUcIpwPdKDwHkC/6D7m7\nRmJcKvSTAvcsITBtr6sEcgtnQ9rkBrTzjbNwiKI32e5N92h2Vg+8dRvdOC3n0aPaX668p57mb/2b\nfy0iItvb2t+Pmyd645pyJv7CZ1WX6eMfVwQ3K3NvWz08wnXY80rHIAwgXCAFL76gyASK2ni+29va\nrnCcnhbhir8nhMtdJWdwwNJ4AeLbDznPjAvSnfI5BxMiZc+L8SAhqtNeD67U3yLiBaekFXK38v5E\n/R04KTFKt2vvfYp2JYNFn2jN4djfQaT2jTv1+R/TjBZ/YJkSaKe7lntwzbh8t0036YXnX3bXb8OR\nA3gVuDTUw7huhkzMWIaM3/3d39XfrR4/+iM/IiIiH/mocvnSOGgvBBzVu/fuWDtoOTNHVNtva3td\nRHLU3KmknK7lvW+IwGc+88MiknNIgihEpD8iHWlb4uhEo78G5D9nIvGHt9M44bcY+wuLPko6RUcb\nwji/4KPekmJ5j35oSCflY5vGU93y/i6ZcvzZc7rigv7X628qknTrtur4oct3+65y7u6ZXh5RzlOH\nNj7beDYcgQjRXKxY2Hhu/aaTVkb09+W5Ras347fneMK9pJ/T74mC7g38ikzkEsd24zvA+I9OXNSD\njLk343cq6SEap3vJorjR/Tp1Ssfn26aXRsYPkCy+byBhIGB8n0r9sSJc1apVq1atWrVqz9i6KYot\n6WP5tc/o+cUZPzM9dH6iEiueVfQYd2zNlZxT2dMwD37kZ6glDzbqdGRkZeTKQ87DHcttSJTK6dMa\nXXP2jK4FgwwsLMzZdb0OGdGZ6JVNRw87RGeWuCQgFnOzPkddKRdU6XpRX6ofuF0RmcyIFxwGj0S8\nX4tIVkmpvmlwA9HtmnXlj9ysblJUFnefHF3rPSPKRb+NyARIyUgsN+W+cQmm0Dma4kZWXNN/M07h\nnCFbBMW1W5YhwahoV64ocgqCetRy5e1ZdBj9CA4CuThB/PCk6CeLSRfKKyTDXSCqk+gYPC36KZ4b\nXMATdhx6OyWECyv9znOJ7VtSdo77MfcmSAmePbpbPEcsXi8+/0nvkwnUJ8+8lBGhdB8sjoOl8ar0\nPvCc0MUiV+jv/cHvi4jIxYsXRSTrd50+fd7ag/HR+mXQfQOZiM+FaGgyX7z0o4qAElUNsgqCQNTd\ncAi3h+hVEAs4WZ4DTBQX9ztmnEGQepBMEJeIVEV9Pyy+37F+2ETEK1yvpLNX0mEr9evYnzHqw/u6\nb9HV8wsgk4RJ+yjxxD2y+iZdwZkVu68ezThCf4GTumXRgGSWAKG8Z5k81tbO2N08NzIhW51Z93tp\nBQwONogWzyVH64Jci2uv85ZJZJfcwML9tR1B4uH6cd7s7Iz9vm77Ws74vaSclINxhfIyjoKU0e8f\nWoYNMr/sJQX/lpVL2/VYyNDBfbhutIpwVatWrVq1atWqPWPrljzDJsuf6Cw/w49rl3Nz/vw04++i\nr2NITNTPSGv24q4fo8ywGBUl0YMVHxUSo4zgFrAmftrWbOeT7pV6IOjHHBzYTNkQLTw9onHg/PR6\neErmegwp78D/PqE+0ZOOXKRG/bn6BI8wP297vuKfe2zvEtJW8igbnKkiwuXPx0Ogv8DlggsI0sWa\nOQjYGUMmSwhDfO4pWu0QzzzoMLVNR4wHZ8XMufr0OvcfWA4+c2RefVVzGD4wzg2IVbvtkTkQKnIE\nPn9Jo85WVtBTQkdNzyOXGvpb9Dd0sejHeFYgWDGXWUZGdQtiMjVFFNgkRHK8lRCHUn+NCG2M3pvE\nASwhZfF5TxrPeK7REy1FtcZop1L0VCxHOfeoIWZwVm2ffvDFL35RRES+/Bv/o4iIPP+C9nOi93ZD\ndGZGsGNUs/fwyVG3uqz96fwFQzjCeAwSu7m1YdfX+oAscJ9uF8TA55o7fkKRK7hHRLXRLqdO6coC\n73e7M76dMufURyknXbe0MhLHpfHjFFYCwEocxtL3qISwxX6XMjsYgjJt7x1cQk7P0br+uvsWhYyC\nPMfBHQYZO3pUka2LzykSCgdpZ+cUJRMRkYODO1Z+45QN4vcdxJn25T3g/bXy9tHb8lwsyh9XWODM\nctwty4VLO3J8yukachbmKEnL4JGQKz9elL53+Xno+SBYO/Y+0V/JbBI5Wazkxahnniu6iVVpvlq1\natWqVatW7Qdk3cjmR/cpIlQ52s6v9UaPmrVOuCPMLNHNSjo1NlPdT7ka8RA8J4ucbDIa72ngccKF\nykrz3B/Oi+cEZI4Za7NwNGgHPxMeDX2OyRSdg4eXdDzM8wqIQZyBp+sW9GQm6R2VOAWTokujknw3\nII8lD28SwoWVkIdmvXx50UGLSFfkdCTl36CLknMaoucyXik8IXl4YOYg4qlPTfkowJ5FlXUs08Kt\n2+r5fOMb37XrqYfV6+lxd+6si4jIhQsgKZ57RO6969ev2Pli9dbrwAkg+hA9OxBXEK41u05GBM0D\n3ibnHtf1SC7vJe/dJA7LJCvpvcV+E6Ob2aZoocARzXpd824fi7o65WjY0fi/23gTdXPi8bRv5BbC\n4cADbnIkPdIW+3O3a5zQfaKw9ProAkW9tNh+e7teby0ih5GrOWvj9LHj2r+WF7VfrK9r9BrRjSAJ\nvBdzcx4pS9Heh0NrP8+9m1/A018VkfzcMteL6GBynNr3I/ST2I9iPSNn94NaCakv6Q5Gi9zSOP7F\n7yvtsbCg5x3se26z0A+m+O6y8mNIFzlg7TjGjU3jat27e9uuR65efe8/sqA5Ow+tn/O5PTy0chz6\n7yn9IX4P09ejsPIU3xf6I+P27CxR6ZY5gqjUhGz795n6gfzGdp0K8wjeN1YEsk6l1/9MmQZsxYQB\n8+ZN5dzSP7/7Xc0lCkfu7l3NVHDCENw7dzQqNI5j+Xv+kmufinBVq1atWrVq1ao9Y+vO2YyzGJ2U\nFLXHz/TxiLJOkNcPSVFVNvM7CEroeDh5Td57OslTa/koDWaKeIYnzHNLiuUjFHxBkDxHBARjwcqf\n1paJwun56EQU7SOylZXQrdwtj9BFBCvrlWm7wAmTlH3de7IlbhXtBOcnelJlZEvccXgwJSspzceo\nrRLHoYQwZCTOR6WVEAY8meThBwVn/l5C5GK79pDatlyb9O+2cSJaCXA05KtrejqHety1q+pJfvKT\nnxURkatXb4iIyO6u9rOzZy5Ye6hn981v/ns9v6eeKDnuVswDRecFRCv3m75rB5oRDhaeHdFlGREj\ninFHHjeiHRMXaBfEVp5oJU8/6r7F4yLnAotK7fTfeL2cw84jHzHDQQmpKEav2QZksxR9HREU2jsi\nqQ2dr6T0Pz7jwmMlEpHMxZsGsTDP+4d/WPXZ3nr7PRHJ7UMOWS6H4nfO2KAIaV6Z0PvDQZm1HLDL\nphuHjtvB4a67Dkgezw9ED/272TkfjZijYw3RGjDOzbpao5DO8cMwTpa4UXE8+qBRipMQ0UmcrLiS\ngMWVl1J0f0K8bGUGTjALNCmYNulYolPnkVJWlMhw0korSoyXVj+7DwjZhfOK2Ozs6AFb2x655XlM\n23PmOwVnDDWB+F2J42wcl9lPSOyUR2L7oR/H70xEmMm5GfvBpPeOcpy0FZJDQ/4ZP+EWMi9AGZ/y\nMS5lpfs9O27BlT9aRbiqVatWrVq1atWesXVZUy7lGhsR5RR1jAyhWV9XfQxmksz80hqqORJxxgdS\ntb2jHlVGOvzMNHmabc+NkICkkF19e0e5PdFlx5Mmp9yW6XElz9Jm8geHKJ1bvVp4JLYV9IOM64Dn\nbe3UJ+qjh8dua9mpXX0UTW/G69dQ3xh9ET2juHYdPf6SrlD20OTPxaIiNPZ+PcxSLsfMASFn247b\nL3GFYns0dZkoBxw/U+JOelBE/9DeFqU1o57O/Lx6QtNT2p/m5hRhWlpUT//733tDjzOdnZWVVbuP\n7t+5rf3vk5/8hF4/cH8yt1HLRzQNgDO6NCAEGFwF3s+5uXkrv+fqocuVcx6OR6gmcfUmIZiRwxTP\nj9ws2p3fIzcvIp+lKOZJXEOsybnw5ee+cOFAEilXRDJyrlTf/5pRl1Y+i6ZeXVOE8+q1ayIicumy\n6mN95at/IiJZZ+3RunG3RhEx0Kv2g4J/jgaGO4Y+nSEFhqTt7Xoki36RdQbRvSPaTvvd4uKSbRfd\n+XDkhgJC4ccxViyItosIYRnZilHV4/W6ojVXcFph6xH6SchajH7GIsKCxfrxfvP9SfpXU4zbnO85\nS5S320HxXX9dtucB4rWzrd8xkJd+4PadPaNR+Rsb5BJWxDxxlU2naoocil3GRXIv2nek7XMg5vfR\nOFr23rASBMdvz+qf9eL8Chkcq/j9Yl4B1zVxwUJUYylani3vK+PM5iY6dxo9C5cLPbOE/KXxc8OV\ni+fJOJtVHbxVhKtatWrVqlWrVu0ZWxcuDDmFSkrlTT0bvcCxY+qZxaghZoI7Fj1FlAIzXaKwHjx8\nZPfBs/Az0Da5zgyJSGu76GkFRePdPZ2ptgQOmPdsQAS2d9SzA6E7smZK+YZ0DW3G3bF9SVw0i3aw\nXFublvNPgq5UmukHxwvPjuzrGbHxUTc8hxjdFLkjnJ+jOmbd/iTl8JhL8f1G6ZSUl6On2NQXI/qU\n8vhyRaSD8/As4tp+REZLiEtEGGYs59nWlvbPvnH9uC/RO/v71E/Pnp3VfvSxjytX65uvajTLwYHl\n5lpCsVuPf+fdqyIiMjen1/lrf+2vunLsGMIAQjxHFI3Vd936Gc8Xo94lZevHWkBEHtc18vo/saNO\nQrqisnYp9108PkadlpBc6pMzCfDcxj/vSRwcLD1/uE+98e9Z5E7G+s4l7qeWL42jAfGK28hpof5X\nrrwrIiLLhoSi8M54t7ev0bGXLmsOxa989VUREZmd0XKASGDtjkey09YqfmDjGDkq4RB1BITFrpNe\nI881Q0duxnIgEh0LsgUCxvi7s+OVxGk/nlIzKn48Ah6j396vlRD/p+3vpSjcyNmLufv4O88zZoqg\nJXLUJxk4LEfhwLiWKecwz4OoRxBGvf+SIY6sHMEJvHtXo+oG9vwXLaMK5bDTpdcCGbLvTtvXtwW3\n1fTEov4W9YETy0pZL0SbE+0HghS/eyD2kUsIFxGLKyHogpUQcBCtmKnktumCER1JVCK5Fe/de0dE\nchQu70HMfNJEmtUqwlWtWrVq1apVq/aMrZtyE5mn0pn3CMphD04CM147MSiCRw8xRs9FT3bLZqzR\n48ADAImiXDvb6HhYziQ7Hm7U17/xdREROXJEuTX7hnQNBqyV68zzxg2NJpudA0ExTkOfaKmhld8U\nkNuKOBw9smr1t5m8tQse4ihws1pJhyus/ScPOyI0Xs+spGdFe+IhlTgiUUG7qXhtf2iNRwRKivPR\n84yIUsytFz3SzL2yC4xYg/dcnqhcjecAV6Skz1Rasy9FjxGdhWeZc/j5nHC9vl6HKB88x1u3FFlY\nNA9zYP3o1VdfFRGRL35RoxhPn1al50ePboqIyJr1p2EfPZ3IQSCH4qIrN1vaI3LWSrn/Gnp7Ha+X\nU3re8blFjmGJyxKjZGO/ofzZw/fG86dckVsXOYwlvTuspHgfEWWsFH1LvYlmAjnA4nta0onKekXa\nbnjQj9aNG7Kl90eHa3dPr7dtiDb139rU+m9uWUYGQ8aOH9fyweFbWtZ+tLWt5e33LHp1eGjH+f63\nZ8rmIFXkPISzRXvQ/jPT9AdfXxDEdtJ3pD/aONAZj7RP0hvEYvuWkPZo9H+QxzhORaQ93ieOb3EF\nInIWY39j/II7yniT32PT27J+iN4l7cb3iv64bytJaVjnM2PI5ZxxhY/ZuLNj0dS05pE107kyDvLG\nJuOiIerhu9c1ZIv7pHmA/T4Y+vZDZ40cseR8pAQZUdp07cN8ACQJpBSuGtxWciqiFE+/YzwHKUPP\na93es8gFJfck0YfPPfeciIhcuXJFRJqI5a1bt1354MbyXkSrCFe1atWqVatWrdoztm70CEseQvZg\n+Tse7/icZaUouWh5rdvnFCuVJ3ncIBl2nZKyuMiTPd6FBfPYBnAg9PdlmzmvrVk0miEgHDc7YzmW\nhHK0XHlye/g173aGdrQeI8vdN3oyojRJkT5yY+J+k+Ok58NZK1kzum98uUrRkSWFcdofjkhEyEr3\nI0qMqK1J5Z7EZcPTjf005mTj9KmEuGl/OHJEtyBWItrea6uKaBG1mjkc6vmgDN9NHlMsb+SijUcW\nG1y5qBhv1Rj0C1y9tucqYZO4f5M5cqEYBU5V7B+xPiUuWAnZLfXPcn/wSF5JwT4h/oa8JWRnZsYd\nF/XisGZ/9v0tR/+Zrl/Xc/JAAocjzxkD8QBxO31KdYXIMToawaHV8sB9WV3R44Z9Q2ytONQfRIDM\nI3CuQCpkQH8Z3z9z9f14l41253mP75+l62UEdnx0dCnnLMV/2ijWxngVkM9SVGVETCOSXI5eFbff\n1Avz5SNacdgaugMyB9v6kSFIIElLS6aDZ7l/9yzjAYgY/S2LAoCs24qC9d+YA5XncdjjvfXfg5xB\nQbnfr7/+ursO4z/3532CIxiRRpCtrH8GB8zr6E1P+/eX8mxt60oYCO9D45QTpTgc6ooY+lqR8xUR\nssj1jFYRrmrVqlWrVq1atWdsaRrW4DgUEJc4s2cCHqMbS5yg6LHGmX5Sdp+oFP3kKJKIcGFpjXyA\nJ6C/7+xsufosndAZOB7n4YF6lCjQz8xaFEjLRx11I5IkXj+mERWWPK7x+i9YKUpnEqIQuT0R4eoP\nfHTTJEXliHgdJE6T5y5EZCCWm/qi84LnEe8bz+M+RMeW6h/r04xe5D4xKmr8c8rcRdPjmoXbIVY+\nLT96XRcvafRir7fhzoObgOc2TJ4v70X0dCNy6PtX6gcS6xXf1/HRo4mzFt736EmWoj8nWSmnZoxG\nLHGs4nVKuemwSUh7Glc6PqqqlEkhIq5RET+Oe0+7YiACB4iMGHDjyDWr9cLzFtm063ftOP19w6JX\n5+Z1PILzt7Cg7bqzw3uEgr/Y37Uf9g7s/U7jl3ED27byYPebniGjBu+LrTQE5DTrDD55XErPc9B/\n0uGP2XikJw6Xzd/9e1E6Lx1d6KclTm3sv08bHd4Yh0LGj3bov7k/spLScsclPJ7y0s+G6EYah9fe\nFwNUSd34WP9WBPT69ev6d8vJSLT2svVLkPrpae1HKxaVvZdygypCBRLMd4LoPhDavMKl9YC7DdLE\n95eoQvZpb/YzJ5ToXp/BgvqDgNH/HzzU6N9jhrjRfiDOcL1A5OBoUd4t405SHqIpQYSjVYSrWrVq\n1apVq1btGVs3IQ5BaRfLM3kfhQhCUXJ4s4c8PsqkiZQFHR/zAIcdX56SsnlD5ykhZp6jkbOMo4hs\nitCtkfudbbvl6wsSwRbkImVVDznYWmlOGz0s8zymu26/xDmJnKwYJfRBrRTFM4mrFXOGRX2wiHA1\nPXzq6aOFMDyWqMwco3yilXKgRcvlCYieIaKjwD3hMubgpxydVGd9/Z6VT9uHqFgQ1r09PR6F5NQ/\neO52ncEwetLjuUCxfhHhysrP9F+O9zpLpX6HRY7VpFx2k3LRRYucp1K0ZaO+tsVTLnEIixxSu+xh\nyO1aynRRUri/d++eq0fkeJQQu/R+jODSWFRax3v8IHjN89H9IVPIwLUHFCKQM7aMWylDRdA7TP0l\nIfM+ZyMrA/TX9HvQh4rjfh7/AocwC4SNtUn9qhQNWDqvhLiWOVPjOVzx/pPeg5I+YeIiB85mK+SW\nTXqFSWk+rChYeQbp+/JkjlkaD9CrtGjRxQVFTqeM29uzaMbDfYsaXjQu1AFcKO3v9PvhyKICTY+w\nt08UJhwq3b9mGRViBhrKFTOo8H0AuYrfw7U1fW/gfsXvD9GMXI/o362C/lfK8Rm4m9wXBIv3DkQM\nI2oxWkW4qlWrVq1atWrVnrF1oydWmvm323Hmj8fndYCyJ9gpnOc9yZIiaylrfPQYYg665IGMoo6O\nvw8IFhyJ5aU1tz8742fuyMvM9ALXgvu38BSod8f9njy8xJFDqdcjh9FKCBPtEvW2StdpRn3Zc+iO\nn3PH/tBEJMc/10kK440oybS2Pl4ZOOqHla5byuVY4ubgqfeh0rVAMvwWpGtEFJDAOdGtyRTJoeka\nLVr0an+gntvcvCIQDx5q/Y4e1X4GErFkXAJylYp5cs3HGD18/x6NApLV9Oh9VA3vJ1yMrCNn7VHg\nPpUQ10ZpC0hGCTmNx5W4oLHfxf4/iUOTCyhW/vE5SLHI0YpW0hsrIbuxfDnHpZUj6aPRD7SgZJSY\nmtbnlaMkDVmw5wdiAGeP8nFcQu6tPlMzIMWGWPaDXp755L1DxmH/dxTRI3KfrcS9svJZeVLiiQJC\nVIpiLY0LpdyMEZmNyv8l7l20OA6X9OziSkDj+zcYH7WYjktcYM/ZYgDl+9cBoewEhK/QD0HSyZjS\nNk4jyvMnjisSf/PWfREROTz039vpqZGr1+6eIV4dvzKR1QAWXP3ovxFZemicqqyTqP2eTDLxuUeE\nm5UPVhoOD327o7PVbt8RkazILwFRfvDgobsu3K2oywai1ut5XcCIeGEV4apWrVq1atWqVXvG1o2c\nhcZas0RP+cncIix7On4mjMXot8yN0L8P+uM9moikgETF6/RHXv8oKkUzYyZq4tSJNff3fh9FXb3u\nbAsdLhSVfZRKO+Sqi/pbj7lwj7WKSKcLl2M8RwErtQOeQykqtLwdjyyWokJLCEfJo590PdqPfhM9\nxPg8YyaDSVGsk9pxkDxLz7VqEbgb6immtyYtEBE73wDMVhtEAM/O9NpM9waPB72k6KkN0ntmXJtU\nD4rDexf3+QGP+8kIQ9TnScjiKNbXWwlhiIhnCblKpQnHlTkmT8cBSzn5JnBmGuPaEKTBZzKIyv2T\nysf7F3+fdF7SNepMu32idnmMMTqScqKndffuht1PTwDhwtNut3W7u2s5W4faHxP3Z2jjPuQf8e9n\nLrd/z2M3aT5vvzJSQl6lwZUcv8JSeq5RdzAiTvHvkzh+k/pPCYlqZtKYhODacz4k6tZHR7MlCjRd\nL4wDvUO+u40HMnZ/lPq/RwAltYfunrQch7duKUex26Efes7uvOWU3djUHI1Lc4oYZT03j8RiIJso\nw3O9Bw/8dxWEi/4OooVxPu8F7Ryj3vmdjB4JgbN5ytLCsrvO/fsPrNwzVh6PpBJFCTeM625tjddV\nwyrCVa1atWrVqlWr9oyt21TijdwnPBrPCWCNVAIC1siVJ94DSDdOek3e08gzQ+8RxOi/QTg+eh6D\nvp/Z8vvioq1Rn1DF22XLMYanjEfWMkRrapoZq7jrpiiiqbhW66PFWiO/n5ATcbtjFOqfHCVY4t7F\nmXV8Lv2g+zQYjufgRGtEgZpx3dj+WBnp0G3m4Ph+FKMSS55liWsWn3sTAdHtVNc8fnJZtuFuxSg5\nkDfzwKYtGmcfJMuQKstNh04X9cITmpoej9jFdulE7lSIgi1xq3J9xdUz1nsSFy8ilSUPPpf7ydyp\n0ns6KUq2tI9FhKuUIaOBjA5BGKds6/XYIrck/h6jqyZxhho6YCM/rtJPQEL3D+x9NZ08uCKUB2R+\nZ0f/vm25ZnmfKN/0tL3vI48IgUC0Rray0B//3EsK+XAAS/2Mx1UATDOCM0lPMDVPa+z28MCvnEQE\nOyN3dhr/Cc8zWuzvWBxPYr+ICH3p+wX3rdcn2n1q7HmjpJ/HyohH2njOOVpZXPkkIrsJMrfjWOkY\nWVTiyOcchONJM6EzhQ7l7KzpVFq/WzI9LpFbrnxzxq3as3EdBKukZweyRXnR4YrfBzhbd+8aEhe4\nXSBUjBMXLlxw97t5S3Pb0u7Ug/pHZBldrsihptyUq5QjtiJc1apVq1atWrVqz9i6hz2Ujole8ZBL\n04NG/4XoDj0ure3CbUqeoV0nREuRXZ6cXaMkIOI95RRNYwreU4n97zlmZDNHB4boi5ZFlwHIzS/o\nfVdXdU0YxIvzkodi3KqUQxEF/JQF3RAKytGIEqNYnsOVc6jZ/ULUWao3HlFSFjdEoAOXguiJDddO\n0ZMKQKQI5zc4Zt5KSt0l5C17blNj/x6jVjAU/KNnlxC5AR6g6aNRr+hCB47CYxURu7Hb8jwz4mT1\npD8PPCLU7njuBf17aB7oiuV43NzUaBYUkHd3Nlz593bNM5ubseN07T9xJYlGbHlPlPtTHYDGfh8u\nGeWPyCfVHtnxBc4Hv05AuCIiMBUQ3lL/KCFcJYTtaa3JHfVIS0khvxXex8hFxGIUYmyfiGxErk+M\naopR0zFaF+Rj2zJf4Nmj93X6jPan/kCvd+niRRERuX7jpjWA1xlcXqZ/KXdwdtaiyIxjuDCrCEG/\nNf75RmQ0Pp9SlGBcqUgWVkzS/dq+36T+w2lcLXCZGpzRcJyk52/vBwhxB84jHCpf/lJ/j/WPCA0W\n+02z3+txfF9iTtfc7hLqGRD6jm9/GiBFJw48spXeQ75DIJVJv09/Xlzw+m5E1fcMsZqd0+8nSA7j\nHkhQRkCNmwgny7iEIE5379y1ehtX2rhQXBeuNe1LTsP43QFJQ0E+ItBEJ1I+ELGzZ8+KiMi+6YPd\nunVbRHIuUnI1Zo6Wvo/k8iWqkswPs7OmWxaip7GKcFWrVq1atWrVqj1j607PopjK2j7RVp47kj1/\ni2qytXP8GBTZO+ZZPXqkLH64UsyUZy0nV4oSM6XxjQ2dOTJT3NnRGe7ysnpmzCQTkmVUoeUlnfGu\nriza78axONTrHT2myMOtm1dFROTjH/sRuz9rwEQ3WK6+gOQNh0S5wGHzkFHk1mzbTHphQcuDh8r+\nrq1NE13ZghuEh2ctemhrwSUuCx7PiWPqAXzjG98QEZFPfOITWg7W2m0N+84DjbrAY2hbrrSdbdag\nUURXxAmPmufIfcnVxto5M/9bt3TNHg+CLc8fD+j06dMiInLcdF7WN7zHyP0jItLv+f52YO3D9dfX\n11258HCGgWO2bR6KOWBysK+IwSPzqI4c0eickUUtTpmu1tA4Dg/v6/GzM9oOo5GP+js0xJb257l2\nWiBjPlcd0YXtKT2ua/UmepAKo5dGFBu2YO/vo4dafzzlbpd+7aNzUSbf3NR67O9rvS88p8/lzTff\nFRGR55+/JCIiOzt7trV+a887cTPm/PgxSb8tbkvRpyUrcRS5DuWkXCDARBPieVIPXPrFRXITar8C\nuSCqNHM2zMO36NPdXZ8Dk/cI29/fc9fjvovG6eN6c3Navt0900UzpH9tVd9X3kOQguWVVRER+dNv\nfV9ERI4e1fdpmJA93T58pO/xMRsHt3d0/8gRvf/ewY61gyErEZFKze25b5m7aX9NgKyNi5SDywSy\nakJeo75coh5FLt74qMQcDR4QVHsfp8LzyGbfq6SjSD0ip3I8Aps4cIGjhUXEPyL/tN/MjM+0wHco\n3d+eYyctOflywLF6bElKRB7jrpU4nvZkZgzhPOyR8UDfj4ePTCdwaVVERB483LTr6Hnpe2b6W0QL\nMp4krpkVa/2Rfn9AGBnfTpw8Yfv6XOfs/WAlK34PHtk4v2wrCglRtu/1Q3tPEkfUxj/qddO+U3TA\nO3cVYaN/HT+m5SE6lBW32ZBLlJUKjjswJf61VVWYL41jFeGqVq1atWrVqlV7xtbN2d/x1ImyMK7W\nFHpIrEHjSegFQELw7PEs4bDgETEjhAPFjHya6Iw5U961Gevqip/Brq6qp9nrB6TDcpB1pogqIGpF\n78uMe2DI3N6uIg/Hjq1YvcXuQ9QeHhXcHUo7cvfN0Sl2HPsD1vTRAfHcMrhhYtyy7S31HLLHrOWN\nukB45vfvq/IvHtM3vvF1Ecke7t6env/ggR73+uuvu+tiffMoFs3zT8r4IQoxIhZbm5azzdbiY46p\nG9c1RxZIA8gC0SHU99Vv6nN44YUX9D7WvkRfRd2UWH+ud/OmcleicjQW9bxyNAntbJy8aTxU7xHC\nhSFYaWZ63v6u5ZqzaMRZU+xeXFRuw4ohs8OhIV4D2gkkgSgj7S8zdp0pQ6K6cBVHvv2zB6u/7+xo\nOw6s/87NgTxq/+n3xutJ8VyWDCG+c0c90I98RJGtd9+9Y+2j1zl27JiI5PYHqQRZbCIP4xGuyL2Y\nlEEAKyG9MXchSB4G4hlzNsbooivvXXf1WrR2gWNK+Xft/eK6czbOwe0A2cQiVw1PG8Q363iFaEbx\nOlLReL/u3VMPHZ2ii5fO6f4KukV6v0ePdIWg0/XcsaSTZdeFS9to/5QL0XMDOTMqoLcSJ8hziIap\nH+jvPbixKQej7zckQIi6SXCYpqd5r/34lbh7EqNDPfKTc+a66jaiaEs6b/SrUkaBUtR2zsnrkT+J\nuwzH1Cc9FutPBd29yEmL9+dnou5WDJk5OND+9vWvf1NERO4Yx6rT1fGOaGv6L8rycLbI1MIWzlV3\nykcDU+p9Q4a2trU/7xtyzvvJ+833EcX3jcAZ6zH+2HVBLqdtPGA+k7l05FLW43l/ed94v/nOsM97\nB3LOc+a9B5HL7X5ZHreKcFWrVq1atWrVqj1j6zZ1anwU18g4TD1SyKXgE7/WzJo2M05m/nA9moq/\neqGZWbhMpr9hM0S4E4d2YxCcvGaPHodeP3Jopqd1ht071Blpp6MzXTgply9plAEOMQrfA0PIIlI1\nsC1rzyBpSUkXpdvkcMD5sKzpfc/tGcCVswZdFeOa2RpzzD1Fu129etW1DzPuU6dOiYjI1772NauP\nFoSZ+Llz6vmCdO3u2tq4eYYgFcz0sYgM8XxBMo8cUc8Ijh31g1v1wLhjb7zxhrveCy++KCIib7/7\nrtVDz8OT+OhHP+rKTzt87GMviYjI97//joiInDx5UkQy0kr5aT/6N+XDM8MDw4Om/+yl/moISFev\ng9zPgT3PlnGwLHm97Fh78neiK2nnza0da0fzTFGIhgMD4guwFfSZMsfEc1ngPE51PUJgwVfJk+X4\n7IHhiePp642vXdPnlT1Lz9nCvvOd74hI5vRhXIf+AeKUFe677riYgeJpOVwlBXI8Y/pdrAfloT68\nR3AbaR/KR/+hnHBH+DvvDf2Tv0dko4R8xJx8SQcuIHEYz2193TiyJ5VzuGeILQhkuw23ZsbO0/NB\nQBk3Z6b0+cVMFyUl+FwOf1yn7ZE8Schm0IFia/2u2/FRh4+RxpwNOwGpsuLxfGPGhSb3Sny9iIaX\n8Huw2M+wGIVaiu4tR+GCAEJ+88gwCGFGSA2hCe2TEMOhv38rIYQe0UvtYNGsRGlOhXHn5s0bej8Q\nLFMJSCsxe2RysZWF8J3g/UpR9CmDg15/N3GULfrRON6MJ/G9oJ0YVxi/94zzGLmgkSsX9d46Hbvf\nwpK7H+/59euKePM9ikg67zn7jBt8j0vIdEW4qlWrVq1atWrVnrF1o05N4jINmAHbDNpOSL8njpL3\nOGN2bqIPmIEyU2zmKvPRgMyQ8fxBIKJCMQgcUV9wZJYWdQa6u6MnLC3p8RubzED1OsiYEE2WctuZ\nTg33yQqyisQc7OMJ2/Ed75EcHKjHCVeqj4fCIrxxueDY3La18j24cPOKrHz/u98Tkcx1OnVa9UFA\nctj/91/5qoiIfPnLXxYRkZ/4iZ+weusM/nf+7b8TkYwEvWQI054hMHCh7txR7k7Mbp+jatTDYYZ/\n+fJl12545ESLgHjhubC9dk25XkRFoUC8sqLt9d/96j8TkYxUfepTnxKRjCS9/PLLep55FgvGnUq5\nF6e850t7rq555IhcmXiqRN2MZMr9vWenwbFCl8aC/qTT7bi/p+ifw323D1di1RCVjHzY+3Lo9crQ\n34JLmbgqcHBa6P3oeQcGuR3sE0Wl5cRTI1p41ziVc6YH1ut7/RiQHziAL1p/oT985jOKQK6vH/jy\nBiQLw+MrKbFPspKSfUSAUzRq6HfxfiCdIJ+REwhnjfeHch87tmDHaf+O0Y9seX+iXlesP/dh3Gu3\nApITyg2HidyInA+id/u26ghNT8OB1Pd0bU3rMT1lyJlxdSxIbgzC5Vc8UruDyJgl5MD2E9Jg+0lR\nv5ARo2vcycThipk5GjYeMcoAFPfzZ8ExS5lEDGuYtf6fOVWhvly1oMAfEchSppBoKVPIyCNuiYOV\nuLQBYUwcL7hI1l/afBg9skjUY36+9h0C8Zzn/dfjNjfXRSRzO1GU39vX/pJWIhZ0fLj/QBGsng/W\nTUZ/mLFoaroPuVu3d1mB8vODUq7WOI+I843IuYs6afE6bYsej9+1uKLD9zau3KHHFVdiapRitWrV\nqlWrVq3aD8i6zNwy58BmloYctRMS4JV5e4dwXXQGieJt9PCYaS8v6YwYpAhkYmBRPnAKpmdQdLXo\nG9Ob4bzBYMnub7o202QhJwpBy0HU2MA4V6xp37VorDffUg7QhQvH7XivNE70BMhLP3BN8CyIzojR\njpsb6649chQdXC89YWFeZ9Q3buiaOVwTkKhf+7VfE5GM8MCN+v3f/313HIgPa9xvvvmmiIh8/vOf\nFxGRX/iFXxCRPJPfQM/Ecl/F3FUgIhjcLzwIyonHQHl4TiALICVf+cpX3HWox9a29r/Tpux76dIl\nd/4/+2eKdP3zf/7PRUTkH/yDfyAiIndNP4V+CxIBsvHee++58sEBJAry4UPtB3v7QUnZok7nTAEe\nvbdeUlzW/XXzaI4d13rANUz6P/RL649tEB/b397RfjFlx+MYDpJ+jth1zBPuoGwPkoJunkXNkQu0\nBULso2SJ0oVThqd6754imlvbivh885sanfTLv/zLIiLy8Y9/XERElpa0HKdOgfBs2e8eAcJKUV4x\n40CMii1FJ5a29Dc80dVV7TcoRVO++B7+f9v70zDbs+ysD1xx4sQccSPizkMON+esHKoqa5RUKqlK\nKjSgARBIj8VgW2CDMZYFsqHVxt3ttgHbjdsYP7TFAw/Q2BZGsgGbRkgWApVUGqhRqsoac7yZeecb\ncWOeI070h7V+e+d+z9l5Uli3qz/s9eGee078h733f+/93+vd73oX533yk44Mv/zyy3H+QnE9jXpd\nWQEZ9PaemUG/y8tN/2P+Y7wxDjT3XuaURP8YKRFC5ZBg/J1xwHicn3cE9ckn3uHlXb0W9wGR8fKj\nX5iQrKRoPjhKMSFt8j19ikJ6iroc7RTn5UwaJeKUELF8RxtkGTGiHdnpKJEtLT/tmzhRnbJcuuNS\nyxnbn7O0RCBrmQ1q9VAkri8nrJx3lJ5DqT91LNH1vYRsCaIc1zlO+o/++15k/Lh1xxHSySkQJLib\nfubKmiO7k3CfQr2gK5w2Pjso6PMZ10Fn69KiK72jBM+4UL28iYkylyGIEn+v5YTGFBnjvNzvQY7L\nnKQcpxlcQMjhTLKe4TrMD2oN4WrWrFmzZs2aNbvH1u2PEkKZNVa2sULu7PvK8SB+R4eJ40+fdASh\n5gnkqLn14veDA195srKdmh4tvqu+Us4FVeYeYy93dDSii2Llik4WOefu3HZkY2/Xy3/q5Df5eR2i\nvShXREEGp6eXOBb+d6IgV1edA0LUBB41K2k81Lxy9nKurTpC8H/6c/+RmZn98A//QTPLSBf1/+Ef\n/mEzM3v8cY/Og6PxV/7KXzGzjEzRTqy48WgeeeQRM8tRFxw/HdwTclvhEVBejcbSfqLROX/kj/yR\n4ncQpjNnHEEEMeG5v3rFoxPJ5QkCBoeGerJHTvl+6qd+yszMvud7vsfMzN7//vebWX905RNPeHvh\naC4t+XPaTdnqI+oED20soumCWzcz64hEBMHYWmQM2I/nQo6tpWX3+NCRSVy26D/8nqNXQEB9HDzz\ntHOhQGqnIgooAWLhEqWcpcdRzhGJJk76RVbU8+5dLx+cuaUl7/8gtK+95s/hdiBdf/Ev/sW4b8kR\nicvZiy86Mvz4487dW1srkdFaDkGNTuymcTY4mgcbhnBxX65PO+k4THo90d/olz/2Y3/UzMx++qf/\nqZmZvfvd7zazPJ5+9md/1swyxwvOVMqNCYcwxhOIlnIadSeBz/Gx0uMGSZ0IfbiFhVLnCKSdcbqx\nQZT0RFGOEye8nW/dgsNCtKjF/YnqsuIzRcOJMJVyuI4VmjGuU0ah039MMmmMpH6dbuyXF0Q0X5f/\nlPdjBybplyWhqhJpUn0uoJ5eH5e4RKwUmcU0Ck6jUnO53zrqdjTprpU2ItwtvQzc1/y+AjkuOdhJ\ntxLOZ8rJGtdN7QXyGwh4vBdWY3wvnvRocMZvel8HkgPHK11Noob3ReZgNxD4iej3/VxyVAiYr8uo\nYazWX4bpojEfrK44UsZ44n6Umx0k7su8QVSz6u6Nid6YWkO4mjVr1qxZs2bN7rF11RNkhby7C2cE\n3SFWkLFyTXvYvnLEQ9sVxXk8qYP9ErEiSu7atatx/9IjVm4I3+EYELWIq7K1GbmfZqeK73weR72I\n3nojdGy+9EWPwrrvPl/BnzlzOuodtT3yNenhUbl3TvTH2XPOFQHxW11xT/aN11+L4/w8spCzYn71\n1StmZvaTP/nXi/qALOEpE0XFyvqRRzxakdxqp055eTOXxc+D2wGiRk5KvqeoQ3LKofuTcmndjXKV\ne98pylSiejqH5Oby+z/yqCNrmCoHP/Pss2aWc00++ODlot4nT3p5P/3pz5mZ2S/9kkdZ4ql8/vNf\nMDOzn/7pnzGz7KFcvuzXgbPGd3JzggDMh4eyFcgjyALIKDntrl33dtzY9O8nT3k/WTjp/Xya6Ev0\nl0Bg0HUhJxfK8jEObgfSem3+VtTXuTcnF718AIpEufaO/YfuaIkcrAeX4dVXrpiZ2Upw0/C0QGCe\nf/55M8sI6KVLzp148MEHzczs/AWv1/y8n7eyshfXGY3vjsgRlYpDOT9PecsoISznaC2j9oZF82A1\nbky/0rwfv7lZRk1qLkWuA1L1/PNXzMzsu7/7u83MbGLCr3v79qqZmf2BP/AHzCz3v5/4iZ8oyv/j\nP/7jZpbHJ5/Mh5PiwWO9pE8FUgEyWnrwM7Pknj2M+oXO3Kz3l8ce8/lgNZ4P8+ydOxElTb+Ofjhz\nYjru69fPiAf9qoJspSSHPOeS66QcraxkX/496UXF/JmCE5lPRLerH8EoOVwZYSo5Wtpf0FfcDz3E\no0Do4AyjVJ6inEXXiflVo974XRFcRby03+bywR2Wdk7ndYrf+56HcMUOox0PQLji/Q2SNjYSO0WJ\ngxy5jNcj92tCzModDep7IjLAcF9tB87juqwD4HDxvkEJ/to139GgvdD5wogG5L1A+zKea5kA+vVF\ny3al/VeDa824hcPJvKgcraRiENeHG6zPvUUpNmvWrFmzZs2afZ0sRSnmqDP/w36K7oucUcI94JMs\n96xk4WhxvfHgxqwd+O8gEe98pyMcL7/8kpnlqLSDxFHw73eCe8MKF0QEzkL3CKVXX2HPzZZKykmJ\nPhyOsSjPekRb/NIvfcLMzJ5+2qP8nn3mKTMzm49cZMfH5Fxibzx0dWKF+8aXXEH9zm1HKvBA4ew8\n99xzZmb2bd/u0V5wkpJC7mi5Mqb+eFznI7cbHLCN2DOmvUFo8DBAyC6Qs6pXlnczynU6VuZwQ1Sp\nOyESx6UnxXXIecVeNit8yoengIcyF+Wiv8Hdoh78neNv3HQk8Kmnn/Z2jChNkD0MTwMP5BOf8Of5\n3/8P/2NxPxCkp556R1GvyxEVuRkZCOBeoST/0suOVI5PeD//hm/ydqO/zs35cfRr2o8o161NclgG\nMpuieb3+H/9lL++Fc54p4MxpLyc6cJubjtDA2SIKbz7lyvPrf/KTv2FmZm9EJoLHH3cEmf6mSugP\nP+z1hmtzftYRLgTl8eQ2QiGf8UzQ3u3bK0W9a7kSa9wJ5TjVbBjCxfnK8ZiZ8XKpp04/YT559NHL\nZma2GQjm0pL3l/vvPxP19HmLaN9f/mVHun78x/9DMzP7m3/zbxbXpRzveIf3s4997GNmZva+973P\nzHJ/Xw0OYOa8evlANI8CCZqMeStzwbw8IFz0a7im9Auuy/wMx2dykujj6OcGIhQ/i+5WQuaSTFZZ\n3oQEJWSz5FSlz8RBDK5PlG8v6pW5R4M/+/W2/ILMF3AylUNHsfK4DCQm5tetHRCv4+J8xrcipczD\ntA/Rpxr9pohHTZ9OyVspk0riuJX9vYZ0weFkXjuU9kuctHyjuI+Xb3n5bnH/vOPi/ZX3xtTMQnEc\nOmZbO6qHFdGPRAdHTtm9GI+0EzlA2YHgk+fEeCEnI1HWPAd+x+gf2u58Z15gHjx31uc9nh86lCnT\nTZQfRI360e80ah9jPjB7sPi9IVzNmjVr1qxZs2b32EY++vv+m2OzN+tfxIo/dLgsRUHhaaB34t9P\nnnSEo5OipNhDDWQlPCu4BD1RKsbzSQjLAdyAUqH22WcdISKqCf2imYgOvP8SXCr3UNdWfe914YSv\nTN943aOxJkNa+e5ycJz20IVyTsf3fo9zOf6X/+VnooXwBEMR/iZ6WaH3Fbkg/9gf82indz77hJmZ\nra66R4BngeeU97hRpCenlBXH95KuSsmV0r1h5Q5geADkekM3hPvjHxHNxwofTwAPg/O4nt73WDxD\n1Q2q5bzDyAXYEY+kVn/1vLNeUifKuVfU8+/9vb83sF1SC8R13rjqXIKd4PjBzbt12z2/c+ed8/Q9\n3/d7o/5+3wsXHfH6lV/5tJ8fns373/teMzO78uorUY/giARH5oVQcB8f8/793uccwZua9Of4k3/9\n/2VmZn/oD3l05z/8B/+zmZmtrjlH69FHLpuZ2aXo96dPwwFbMLM39zf30FR5/YEHHPk6d851xHop\nh2qNOxOtNiRKEFM9I0Wg8BCTHk5FKVrvq0iX6lnp35XDoddXJEbHnx6vCD9RtXC7VCkbj/nv/t2/\na2YZkVpfC6Q++j/zZZd2inlyNTJj/P2/73p8C4vn4vrBtVv1/vz7fu/vNTOz11/3aFSmg17P//7w\nwxejfHGfiM6dJOdsjBd2CDY3/DxyNJ6KKHS4qmTYUH0n+nne8Vg1M7P5QBSuXLliZmZf/pJzCj//\n+d80M7MbN7wdv/d7v9fMzJ4Njic7FJRPuasgW1yX+S7rmBG16YgF89lXvurj76sv+A7LR77t28zM\n7IMf/KCZ9WcooB+Q6/all/y8v/23/7aZZURfOURPB0L/Xd/1XWaWM4bQ/4l6P3fO36M3b96N8np7\nkdOX/sm8zDw7NRXvs+Dw8f7meTDf6TxNxorFk2Qq8HYjCvlrL/jnWMxP07MLcf/IuHDGd15eveLv\nw4lJb+fHQ//tC89/ya8bSC76hLQL45Yoep7b1avX4nfnitLe9IePf/yXzSyPMzjX1I/5hOuDTNIf\naTd2ymh/zaXI+KYfUG76D9fnPOWqUp7/23/oUfRYQ7iaNWvWrFmzZs3usXU1q3XSu4qfiXrAkq7O\nuHsQrLhBuLJn6cfnnE6DPVb2cPEgiOJiLTgWXC1W5uwdo2yPbsjy3aX4vYySWV/3FS57vSjrUp6p\n4IrduuWcoU996rNmZnbqlHv+S0vuoRK19sADzn35oz/yrxfXf/wxX6lHcWx7S/Z+e5EtnZVx8tBL\nTkQ/IlRyF9Ief/Lo0Tkq9T/4nfMSkpQQhzKqh71q9eSJwoCrxfPGQ1OOhSruZo9qsHIzXK7RXnnf\nbDxP5fCgnO3nb2+T9R6lY/eA/uAf/ENmlp//889/0cxydOOHPvxhMzO7eSuiUUJPhnqAIM1Ebk64\nepcfKqMwE5dCuCg5iifa5QjPaz2+e//5zd/8XJzvf7///vvMLCvsX37Qvz/7rHMNP/7xf25mZj/4\ng7/fzMw2Qyl+Jzxaon4fesg5BJoZYDpyqME10xxow3LCaT/Vvw9T3FYdo9rfa9fTnGhqtfmmhsQN\nuz+mCC3R03/pL/0lMzP7a3/tr5lZRhZAKjSTw9RkyfljnpyKzBtroe9G1DFcxIVFn5fGQ3n7h37o\nO8zM7FOf/ELUz9vp9/we13f7mZ9xRGCs6/f98Ie9/9y+7eUBUTh3zpGzl1+6YmZm5887p/BUQkB8\nfPQEwUo7Iglp9HYBsfr1X/81MzO7HFFfIM7//o/+e2Zm9sEPOgLw/PNefsb/L/zCLxTlI6qUdmWn\n43f/7t9tZpmr+KUvfak47t/8N3/ECxQd/Bd/8ReL+sLh4vogGjknbhklB0JFFNsP/MD3m5nZ9Yhm\nZrzDASLKjYwGP//zPx/tFBy0GH/ovM1F5o93v/tdZpbns55w63gvLN/18+nX6E3m3Jv+eTKeIwYX\naie4f53grJKpYG7OkfQkk3fM+IuvcV2QWD4Pox8fpPk9MnDE3+HOHcV77/XgnOZoT2/3F17wTCmM\nmzt3vB01gwPcM31vMa45Hs70Zugi8vvJRX+/6Y5M1jG8W1wPAyljXlXOYTWH5sBfmzVr1qxZs2bN\nmv2OWZcVGSvkrEAcHIaIAkR3KUcb+XF4ADaiXIgyOmB0tEQmQHhUnycrTweXJxxVVYDtBaKzEFE5\nIAzjE+wVx952KGufmHOPjByHh4dErfj9zsZeMIrcs8FleOIJ9wh/7+9xbsHmVijlx1L18uWH4n4W\n1w/kIhzhrITv99lYd89rbDwQKDhiI6VHrqaIVtL7iL/nLOy0Rnj0cf+pThklhqI5CB2eK/fBs1Id\nIY0+w1SHBavp2qB7lTIEBPAwDOni8zjl5PL67ATXYyF0rIg2o74owr/zXe75kwNxLbg0v/AL/8LM\nzC4/4J5rL3lwflcyCSwsuOd6GNyokBPKnkvitJT1IIqL8bIfSsvb4cFtBUJ1+UH31J9+2rkQn/uc\ne8Y7O/6cfuzf/1N+3GVHvNY3VuM+nSife8hwhebmZu3NlqNQ4WZS3ppO0GCEC1PPr6Z7U4s60/5S\nVQYXyx5tKHXT/4VzVdM/4rKKWNWQNeUS8nnmjLc37YpS/5/9s3/WzMx+7Md+zMz69ZpAuJTj2Us6\nV1EP4cLxCeL88suOEIyk+gTiIMjEVkThfuF5n9/gsH76U96/yGF6sEfUoHOrbgei+8f/+L/t9fsL\n/5mZmT355BNRb+/PXwyEit+/7ds+amZmzz7jHCYQsT/6R38k6u3Fy1w/uKMg515/9OLe9a53RYX8\nRM1ZCdL4Hd/xHfF3kIwSaX73uz1q/NJ9Pn6W7q6amdlCjBeeg0bP0p80A8pK6C7qe5T580JEmX/0\no94eH/nIR8wsI2DMB7/+ax5l/M//uSPXv/Vbv2VmWZfq/Hm/zoMPXTYzs8XFk8X1eQ4oy3P8waHP\neyur3l5El58+7VG4zGvoY/WCG3jrjs83y8s+LxG1zTxNktkRks0SJZn6b9leKXoy6cKFrteJcvyA\n/PP99GlHoEDkmH/JvcjzwpRrnHQjidKPfsN7TXd2NNezcvL0PcjzU65fQ7iaNWvWrFmzZs2+TtbV\nldiw6CBMPT3NvaXnswLOuiyljkhGbrh/qVui3AuiDdGdGQ9F7E7oxBzucz9WqL7yv3vXuVoXLzpH\nYT88qq2tzTjfj//e73VuzMJi7MUv+3nL8fnUO9yTYw8aT4Fa4eGgK8Ie8s52RJmMhOLuGOSswVyY\nmo6RIkBphR5Rj8kTg4OHDk64vjt7sSIPJHN0TJSS4zuKxQaQEc95cppMAqPF78fIj4Sh6MxnUjjm\n+kSv8vdAltKnIixEtXJ8dEt043Z2e8V3stLTLiQoeOABR7h2dvzzz/zpP21vvsG//KRHHU7PBHcr\nELKV4ApORfTsTkRFgnihaG2qXweyG9cHYZwIpPPiBe+Pn/iEc27e+x5HFP7Mn/lRMzObmfH7ffVr\nzlEBAUP37ewZP//s2TNxPNF/XhyQPPoF+jndEXIQKrJlxafm3FMki+vWEC7lbGkUqiI4NaXm/ihJ\nG3i8zlf989zg69bOVw+Wz+VlnzfgAJED9L/6r/4rM8tRVlwfzxoPGl0sPHX0jpjvNsOzn5lFP8uN\n+Qbu0+/+bkfgyWDx8ssgVV7uF1962czM/sk/+V/NLOsrPRrRYL/wv/8zv2/0VxTsQQT+xJ/4E2Zm\nthbRZN/xHb/LzMzOn3cu1Pvf95747v0QziJRaMyv/A6Hig5GbtfFQJrQx8s5af3vq6uOvBC9Dsf0\nTuxkcN37H7hsZma3bt6O+vo4fjQyYNwOfUcQInS4FNlShF/7b0ZiTkd1RqKc3k70GxAt/k703FYg\ncR/4gHPZvvVbvzXay98bcL6+8IXno55EMfr1QLIejnYG8b+ztFJch+O6Y+hHeX1WViPzSOQc3tzy\neXJ3B+5pcPZiOBCdDbI1EmoClqL93lphX5YD6f2p3DmiD0EqNRdp2qmZKqOcNWoZ3S3ev/yd8QZC\nxSfjkvNVjw1OGc+vFs3alOabNWvWrFmzZs2+TtbtqiJ58lRF92ikXOHjyaMQPCKeJlytnFSeFW7p\n0XYTJ6gsWM2jTDpgx0Q1+sp1Zto9GHRiDo/c85id8xXu4UHkQhrzG73//c/F8e4BPPP0U1Euvz+c\ns8MDOFf++4MPuqewsekexEgnEKJuIFFR7709v99u6Niw8p8O5IGV+dFxKPp3BnNOFOGqIX6s0JUT\nl6MWS30rPLOskFveX/eia9yenAOrVNqlHCkaURTBU/lTrrCyA2TkdDDSgKGnknNaleViTx7Pm6Dc\n27f8+c2Hp3gU5T1zxj3kdz7jXK+vveh6O8tL7inD6Xv5JY+iecc7vN8wHkC6DvGUExcSXTsv/2x4\nXnPz/gly9md+/EfjesHxCgXn23dWzczsW77lQ2Zm9tJLnuHgve91ZGF8rMzxtRPIyHjSRys9wakp\nMht4sRTRwoYhW5iOU2yYrpWerzYMedKooLeP0JX3qfU3va5yw+DI3bzpXCeikul/Z886gvq5z3kU\n6rvf/W4zM1u6sxzHlTlh4Xzu7JJRwp8b0VsTEZ2Ih/3e0Htj+DHvzsyUyND1GyBqjgw9GNHW/+6f\n/CEzM5ud8Xr8D//9f29mZi+88LW4jvfP//K//C/MzGw/kGKinNHF+sZvcITmU59yZBgdpddeu2Jm\nWQ+O9kj7GKkdT0R5/X5wEPM4jowVwdWanfVPOEFnQzGc48ASQMrg+pEzkPYGARqbKJXqed7Mq8rB\noxwpc4bMb3DsVC8uRXEHR+2+4KhxnZWUw9avA+frG77hm6Ld/HovvuiI5Usvu17WrcjNuh6ZIU6e\nLKPvxsa/XNR3YcHLNzfj8992IFvMx8t316Lcx1GvQKDiwW1vgwSWkJVyiLE0juI5jEaGg63gYvF+\n5L2RuL2jZSaUnnBhlRus+lg1pB2rzR+aGaBPsV90/PR7Lfq5IVzNmjVr1qxZs2b32BJMUkOSOmlF\nZ/EZyFOKyoA7pVFFJeem5iHnlSMITIlsoe+xl7hJKKX738kldzCLsjbRcuTYCk8lFMTvu+RIx+OP\nPxx/D+QoVt4bG76yR/fj5MkFMzPbDe5V78g9BrgzKAyPj8NNixUvOcpibxtPE8+FqJIU9dcpV8hq\nNcX2nCGgzIGo0RSqT6Lcme1ABvEc8Izw5DSqI3NQSn0nVbzXqJ5cnjIqVjk9mHoOapw3NVV6Louh\nuH79+s3idzyW06fdw7sV+jkL8+5hr4aHuRjRMN/1Hd9uZmbTv+bRXHAH3/ns01F+/z4bHCuQrVHa\nP4XDeT1AdOfjfmfPeTmfeeaxKKE/v6Xlm3F9r9djj3l/Jcfic+/xqK2p0NNigO1G5oSjg1IXKXP+\nGHdW/B0DaMqfb0+BXvtVis6sZBgYpnOln9qPh+mE9SOjfA4eP2qai63GZQVhSdGHEY13586d4n5P\nPFFyPi+cd303ouj2Ijrw/ge8361vwEGZKe4PcnL9hs9PjzzyjjjfywMSs7q6U5RnYX4xPv16D132\naNjHHvcckfT3/+nv/ZSZmd26dTPa6ai4DtG3vB+eCST4X/yLXzKznDNyMhCjc8HxeuRhjwr8UuSe\nRYH+gdCzmguOk0avJr2/uN/4BFBeKK/HTgJcLJ5nei4x/69HdDh6ihcuBEdoK5DpXhnNqPORKolr\npgTt/5pLEQPBA1E8YL7guKg/3Ln5eG6LoRd18tREfDpS+N73uTL+ldc8+vQzn/lsfL9qZhnJytHd\nmD+fw31vn9u3l6I+ET0b3Cw4YZ3ROP4onk+8D4975fpA399ppyRlsjgqPolCBSGm3ZaXHbEj6pXr\nMG/euuXzNs9BdfZ0/kGXUHeItrfK9xjGc9YMLfl94+OBqMhhuoNYQ7iaNWvWrFmzZs3usXVVByfv\nSfp3lK+Vu5C5QSXHSxGunpxnovczPuYrdpCOQ+WCkJV8Ep0adGw0V5wjTl3hYIF4we157jlHBqaD\n40D43Whwsc6fd+7AVIRlTEwQbRi6P1Hf+0+6R8UKt9cLxd8xkKHpaA9W1KEYjBLvYSgYdxP5oqgv\nlpBAPIdALjpHJRcreRJEiaBzFp4en8mDO+LTnz+ese6Bp9yLopuWc0IOjnLDU6lxvlI/i8d8lJSI\nS52mzF0bjDCQiWBpOZCdUXygQwAAx2JJREFU8DDPn3dk7vJDjmhubvrveE4WUZBnQo/rjUAeiH5C\n72Vzk1xyi1FeL/C1q+5RPv2M5/jC8zrcL3Nl5tKGpxXcl5ngTqD3dnSE4rOfdzkQiMsPuQI1+nEa\nlbsd0V9wbcjcwAAe7aBAXXro9EM81ppnWkO2dD7QfqjPWZFLRVxr99N+o581vbBhCvmY6vBo+WpI\nBddBSfyRR0Kp/WX31ImKY3zgKV+8SM5XH28TE0T7gnz79UGYydnHd+VkfvGLnjnhW7/lG80sI2Do\nCR4flzlsQaaIovv5n/s5M8uIE8jBO97hXEUQBP4O4gY3i/n/ySefjPJ7Ba5fc4Tlne9yBO6rX3u5\naLcLF7wdXr3ympnl3IgazUr7E3Wb59HjOM/b7/btO0X7UE70pxhXM8H9uhkcTvSndiJ6MuU47HSK\n64BMZeVyPx4kk+etOwFaD/oZOnzTwdFj/uB+d+96+eh3RLvu7QcHLkVxeznf+15v//kFR8L+/t//\naf97p8wpCfdtM7heFy/6PLO5GTsuobc1EUjXccrwUXKtea/RrpoJpA/ZPiqRQz6J6oWzdSGitXkO\nN27435m3aU+iPmln7gvnMec49OMV4ULvs8YV1x0gTLnUyv3U96FaQ7iaNWvWrFmzZs3usXX7o8dQ\nxvYDDsKTOD4iGo+VnH+mqAtRlk97xslxhMxVIhUbW+hrdIvjj5NuVxm9YLIi3Q9kxoLbcurkbFGP\n/b1QIg4k4d3POSJBTsWMu4GolIgaK+OZ0J1aWnKPhhU5K23aQT1wfj86co9Co/QSV461r3jax4r4\nUV48/2jHcUGUNDoQj0GjUJNe1FjpCWUEAq7cbnFfcjNqtFde6YMQlDmqVDH/IHKJHYOwCOLB8zkG\nyRBEA8XihYXZqJ8ff/PGepTD68XzPBPKxZAZ0Gc7dSqQzfBkNyOah/7+8MMe7fTCi68W7ZS4c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0ioh6JCoMpG8qosXYaeD3ycSlseK6ePjkbtzfL8cVf98KZXmzweO+Np60v2l/warcxJh2Moey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FouM4360vLwyXl6PTxVRfI0CgqDI4EnyfnUj3Ip4jYb+m97Fa4IHofm7sx753A6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\nlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=604x402 at 0x7FAA8476DB50>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read train images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_objects = kagglebu_bucket.objects('user/akihisa/gyoza/segmentation/input_imgs/')\n",
    "label_objects = kagglebu_bucket.objects('user/akihisa/gyoza/segmentation/labels/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " idx 3668 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "np_inputs = np.array([])\n",
    "for idx, obj in enumerate(list(input_objects)[1:]): # list(input_objects)[0] is ignored because it's a directory, not an image path\n",
    "  np_inputs_tmp = np.asarray(Image.open(BytesIO(obj.read_stream()), 'r'))\n",
    "  if idx == 0:\n",
    "    np_inputs = np_inputs_tmp[np.newaxis,:,:,:]\n",
    "  else:\n",
    "    np_inputs = np.concatenate((np_inputs, np_inputs_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "  sys.stdout.write(\"\\r idx %s proceeds\" % str(idx))\n",
    "  sys.stdout.flush"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " idx 3668 proceeds"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'from PIL import Image \\nnp_labels = np.array([np.array(Image.fromarray(np.asarray(Image.open(BytesIO(obj.read_stream()), \\'r\\'))).convert(\"RGB\"))[np.newaxis,:,:,:] \\n                   if np.asarray(Image.open(BytesIO(obj.read_stream()), \\'r\\')).shape[2] == 4 \\n                   else np.asarray(Image.open(BytesIO(obj.read_stream()), \\'r\\'))[np.newaxis,:,:,:] \\n                   for idx, obj in enumerate(list(label_objects)[1:])])'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "np_labels = np.array([])\n",
    "for idx, obj in enumerate(list(label_objects)[1:]):\n",
    "  np_labels_tmp = np.asarray(Image.open(BytesIO(obj.read_stream()), 'r'))\n",
    "  if idx == 0:\n",
    "    np_labels = np_labels_tmp[np.newaxis,:,:,:]\n",
    "  elif np_labels_tmp.shape[2] == 4: # with alpha channel\n",
    "    np_labels = np.concatenate((np_labels, np.array(Image.fromarray(np_labels_tmp).convert(\"RGB\"))[np.newaxis,:,:,:]), axis=0)\n",
    "  else:\n",
    "    np_labels = np.concatenate((np_labels, np_labels_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "  sys.stdout.write(\"\\r idx %s proceeds\" % str(idx))\n",
    "  sys.stdout.flush"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3669\n",
      "3669\n"
     ]
    }
   ],
   "source": [
    "print(len(np_inputs))\n",
    "print(len(np_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data for training and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 省略"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CNN configuration\n",
    "batch_size = 32 # batch_size = 64 provokes OOM error, so alternatively batch_size=32 is used  \n",
    "epochs = 50 # 200\n",
    "\n",
    "img_rows, img_cols, img_channels = rows, cols, channels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Branch processing whether Therano or TensorFlow is used as a backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "# from tensorflow.contrib.keras.python.keras import backend as K\n",
    "from tensorflow.contrib.keras import backend as K\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    np_inputs = np_inputs.reshape(np_inputs.shape[0], img_channels, img_rows, img_cols)\n",
    "    #x_eval_reshaped = x_eval_reshaped.reshape(x_eval.shape[0], img_channels, img_rows, img_cols)\n",
    "    \n",
    "    input_shape = (img_channels, img_rows, img_cols)\n",
    "else:\n",
    "    np_inputs = np_inputs.reshape(np_inputs.shape[0], img_rows, img_cols, img_channels)\n",
    "    #x_eval_reshaped = x_eval_reshaped.reshape(x_eval.shape[0], img_rows, img_cols, img_channels)\n",
    "    \n",
    "    input_shape = (img_rows, img_cols, img_channels)\n",
    "\n",
    "print(input_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# cast img_array for keras default setting(np.float32)\n",
    "np_inputs = np_inputs.astype('float32') \n",
    "#x_eval_reshaped = x_eval_reshaped.astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Keras SegNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pixel value experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = np_labels[0][:,:,0] - np_labels[0][:,:,1] # borderと内部領域の画素値を確かめる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   0,   0, 255, 255, 255, 255,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46, 255,\n",
       "       255, 255,   0,   0,   0,   0,   0,   0,   0,   0, 255, 255, 255,\n",
       "       255,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46, 255,\n",
       "       255, 255, 255, 255,   0,   0,   0,   0,   0,   0, 255, 255, 255,\n",
       "       255,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,  46,\n",
       "        46,  46,  46], dtype=uint8)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[101,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# (old) Methods of label creation (as 1 class)\n",
    "\"\"\"def create_label(img):\n",
    "  np_label_img = img[:,:,0]\n",
    "  np_label_img.flags.writeable = True\n",
    "  #print(np_label_img.flags)\n",
    "  \n",
    "  is_color = np_label_img >= 209\n",
    "  isnt_color = np_label_img < 209 \n",
    "  np_label_img[is_color] = 0\n",
    "  np_label_img[isnt_color] = 1\n",
    "  #print(np_label_img)\n",
    "  y = np.zeros((1,224,224))\n",
    "  \n",
    "  for i in range(224):\n",
    "    for j in range(224):\n",
    "      if np_label_img[i,j] == 0:\n",
    "        y[0,i,j] = 0\n",
    "      else:\n",
    "        y[0,i,j] = 1\n",
    "\n",
    "  return y.astype('float32')\"\"\"\n",
    "\n",
    "# As two class\n",
    "def create_label(np_label):\n",
    "  \"\"\"label image array -> binarized label array (rows x cols x classes)\"\"\"\n",
    "  classes = 2\n",
    "  border = 255\n",
    "  inner = 46\n",
    "  y = np.zeros((224,224, classes))\n",
    "  y_labeled_tmp = labeling(y, np_label, 0, border)\n",
    "  y_labeled = labeling(y_labeled_tmp, np_label, 1, inner)\n",
    "  return y_labeled.astype('float32')\n",
    "\n",
    "def labeling(y, np_label, class_idx, thresh):\n",
    "  \"\"\"label image array, class index, pixel value of border or inner -> binarized label array\"\"\"\n",
    "  np_label_2d = np_label[:,:,0] - np_label[:,:,1]\n",
    "  np_label_2d.flags.writeable = True\n",
    "  \n",
    "  is_color = np_label_2d == thresh\n",
    "  isnt_color = np_label_2d != thresh\n",
    "  np_label_2d[is_color] = 1\n",
    "  np_label_2d[isnt_color] = 0\n",
    "  #print(np_label_2d)\n",
    "  \n",
    "  for i in range(224):\n",
    "    for j in range(224):\n",
    "      if np_label_2d[i,j] == 0:\n",
    "        y[i,j,class_idx] = 0\n",
    "      else:\n",
    "        y[i,j,class_idx] = 1  \n",
    "  return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 3669 of 3669 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "for idx, label_img in enumerate(np_labels):\n",
    "  y_tmp = create_label(label_img) # use above method \n",
    "  if idx == 0:\n",
    "    y = y_tmp[np.newaxis,:,:,:]\n",
    "  else:\n",
    "    y = np.concatenate((y, y_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "    \n",
    "  sys.stdout.write(\"\\r %s of %s proceeds\" % (str(idx+1), str(len(np_labels))))\n",
    "  sys.stdout.flush"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Using API r1.5\n",
    "from tensorflow.python.keras.models import Sequential, Model\n",
    "from tensorflow.python.keras._impl.keras.layers import merge\n",
    "from tensorflow.python.keras.layers import Flatten, Dense, Dropout, Input, Activation, Reshape\n",
    "from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, Conv2D, UpSampling2D\n",
    "from tensorflow.python.keras.layers import BatchNormalization\n",
    "from tensorflow.python.keras.optimizers import SGD\n",
    "#from tensorflow.python.keras.utils import np_utils\n",
    "from tensorflow.python.keras import backend as K\n",
    "from tensorflow.python import keras\n",
    "\n",
    "#K.set_image_dim_ordering('th')\n",
    "\n",
    "def SegNet(input_shape=(224, 224, 3), classes=1):\n",
    "    ### @ https://github.com/alexgkendall/SegNet-Tutorial/blob/master/Example_Models/bayesian_segnet_camvid.prototxt\n",
    "    img_input = Input(shape=input_shape)\n",
    "    x = img_input\n",
    "    # Encoder\n",
    "    x = Conv2D(64, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "    x = Conv2D(128, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "    x = Conv2D(256, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "    x = Conv2D(512, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "\n",
    "    # Decoder\n",
    "    x = Conv2D(512, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "\n",
    "    x = UpSampling2D(size=(2, 2))(x)\n",
    "    x = Conv2D(256, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "\n",
    "    x = UpSampling2D(size=(2, 2))(x)\n",
    "    x = Conv2D(128, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "\n",
    "    x = UpSampling2D(size=(2, 2))(x)\n",
    "    x = Conv2D(64, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "\n",
    "    x = Conv2D(classes, (1, 1), padding=\"valid\")(x)\n",
    "    x = Reshape((input_shape[0] * input_shape[1], classes))(x)\n",
    "    x = Activation(\"sigmoid\")(x)\n",
    "    model = Model(img_input, x)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reshape_labels(y):\n",
    "  return np.reshape(y, (len(y), 224*224, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "batch_normalization_17 (Batc (None, 224, 224, 64)      256       \n",
      "_________________________________________________________________\n",
      "activation_19 (Activation)   (None, 224, 224, 64)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_18 (Batc (None, 112, 112, 128)     512       \n",
      "_________________________________________________________________\n",
      "activation_20 (Activation)   (None, 112, 112, 128)     0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_19 (Batc (None, 56, 56, 256)       1024      \n",
      "_________________________________________________________________\n",
      "activation_21 (Activation)   (None, 56, 56, 256)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_22 (Conv2D)           (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "batch_normalization_20 (Batc (None, 28, 28, 512)       2048      \n",
      "_________________________________________________________________\n",
      "activation_22 (Activation)   (None, 28, 28, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "batch_normalization_21 (Batc (None, 28, 28, 512)       2048      \n",
      "_________________________________________________________________\n",
      "activation_23 (Activation)   (None, 28, 28, 512)       0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_7 (UpSampling2 (None, 56, 56, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_24 (Conv2D)           (None, 56, 56, 256)       1179904   \n",
      "_________________________________________________________________\n",
      "batch_normalization_22 (Batc (None, 56, 56, 256)       1024      \n",
      "_________________________________________________________________\n",
      "activation_24 (Activation)   (None, 56, 56, 256)       0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_8 (UpSampling2 (None, 112, 112, 256)     0         \n",
      "_________________________________________________________________\n",
      "conv2d_25 (Conv2D)           (None, 112, 112, 128)     295040    \n",
      "_________________________________________________________________\n",
      "batch_normalization_23 (Batc (None, 112, 112, 128)     512       \n",
      "_________________________________________________________________\n",
      "activation_25 (Activation)   (None, 112, 112, 128)     0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_9 (UpSampling2 (None, 224, 224, 128)     0         \n",
      "_________________________________________________________________\n",
      "conv2d_26 (Conv2D)           (None, 224, 224, 64)      73792     \n",
      "_________________________________________________________________\n",
      "batch_normalization_24 (Batc (None, 224, 224, 64)      256       \n",
      "_________________________________________________________________\n",
      "activation_26 (Activation)   (None, 224, 224, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_27 (Conv2D)           (None, 224, 224, 2)       130       \n",
      "_________________________________________________________________\n",
      "reshape_3 (Reshape)          (None, 50176, 2)          0         \n",
      "_________________________________________________________________\n",
      "activation_27 (Activation)   (None, 50176, 2)          0         \n",
      "=================================================================\n",
      "Total params: 5,467,330\n",
      "Trainable params: 5,463,490\n",
      "Non-trainable params: 3,840\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = SegNet(input_shape=(224, 224, 3), classes=2)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"binary_crossentropy\", optimizer='adadelta', metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#log_filepath='./logs/'\n",
    "#tb_cb = keras.callbacks.TensorBoard(log_dir=log_filepath, histogram_freq=1, write_graph=True, write_images=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del input_imgs\n",
    "del label_imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "3669/3669 [==============================] - 49s - loss: 0.2909 - acc: 0.8927    \n",
      "Epoch 2/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.1568 - acc: 0.9468    \n",
      "Epoch 3/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.1160 - acc: 0.9576    \n",
      "Epoch 4/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0944 - acc: 0.9634    \n",
      "Epoch 5/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0786 - acc: 0.9686    \n",
      "Epoch 6/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0692 - acc: 0.9721    \n",
      "Epoch 7/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0623 - acc: 0.9749    \n",
      "Epoch 8/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0552 - acc: 0.9778    \n",
      "Epoch 9/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0504 - acc: 0.9798    \n",
      "Epoch 10/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0460 - acc: 0.9818    \n",
      "Epoch 11/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0434 - acc: 0.9829    \n",
      "Epoch 12/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0389 - acc: 0.9847    \n",
      "Epoch 13/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0365 - acc: 0.9858    \n",
      "Epoch 14/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0327 - acc: 0.9874    \n",
      "Epoch 15/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0310 - acc: 0.9881    \n",
      "Epoch 16/50\n",
      "3669/3669 [==============================] - 49s - loss: 0.0287 - acc: 0.9890    \n",
      "Epoch 17/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0258 - acc: 0.9902    \n",
      "Epoch 18/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0243 - acc: 0.9907    \n",
      "Epoch 19/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0228 - acc: 0.9914    \n",
      "Epoch 20/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0209 - acc: 0.9921    \n",
      "Epoch 21/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0190 - acc: 0.9928    \n",
      "Epoch 22/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0190 - acc: 0.9928    \n",
      "Epoch 23/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0166 - acc: 0.9938    \n",
      "Epoch 24/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0160 - acc: 0.9940    \n",
      "Epoch 25/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0149 - acc: 0.9944    \n",
      "Epoch 26/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0166 - acc: 0.9939    \n",
      "Epoch 27/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0135 - acc: 0.9950    \n",
      "Epoch 28/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0128 - acc: 0.9953    \n",
      "Epoch 29/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0120 - acc: 0.9955    \n",
      "Epoch 30/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0114 - acc: 0.9958    \n",
      "Epoch 31/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0108 - acc: 0.9960    \n",
      "Epoch 32/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0096 - acc: 0.9964    \n",
      "Epoch 33/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0093 - acc: 0.9965    \n",
      "Epoch 34/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0086 - acc: 0.9968    \n",
      "Epoch 35/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0083 - acc: 0.9969    \n",
      "Epoch 36/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0083 - acc: 0.9969    \n",
      "Epoch 37/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0074 - acc: 0.9973    \n",
      "Epoch 38/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0069 - acc: 0.9975    \n",
      "Epoch 39/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0069 - acc: 0.9974    \n",
      "Epoch 40/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0066 - acc: 0.9976    \n",
      "Epoch 41/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0060 - acc: 0.9978    \n",
      "Epoch 42/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0098 - acc: 0.9966    \n",
      "Epoch 43/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0055 - acc: 0.9980    \n",
      "Epoch 44/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0048 - acc: 0.9982    \n",
      "Epoch 45/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0053 - acc: 0.9981    \n",
      "Epoch 46/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0051 - acc: 0.9982    \n",
      "Epoch 47/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0066 - acc: 0.9976    \n",
      "Epoch 48/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0078 - acc: 0.9977    \n",
      "Epoch 49/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0049 - acc: 0.9982    \n",
      "Epoch 50/50\n",
      "3669/3669 [==============================] - 48s - loss: 0.0039 - acc: 0.9986    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7f8ce3cd6810>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(np_inputs, reshape_labels(y.astype(bool)), batch_size=batch_size, epochs=epochs,\n",
    "          verbose=1, shuffle=True) # , class_weight=class_weighting , validation_data=(test_X, test_Y), callbacks=[tb_cb]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save(\"seg_class2_50.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model.h5 to GCS\n"
     ]
    }
   ],
   "source": [
    "\"\"\" TensorFlowのioモジュールを使えばgcsへの保存は難しくない\n",
    "ローカルに一度保存 -> gcsへエクスポート　という流れ\n",
    "\"\"\"\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.lib.io import file_io\n",
    "\n",
    "with file_io.FileIO('seg_class2_50.h5', mode='r') as input_f:\n",
    "  with file_io.FileIO('gs://kagglebu/user/akihisa/gyoza/segmentation/seg_class2_50.h5', mode='w+') as output_f:\n",
    "    output_f.write(input_f.read())\n",
    "    print(\"Saved model.h5 to GCS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model.h5 in local\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.lib.io import file_io\n",
    "\n",
    "file_stream = file_io.FileIO('gs://kagglebu/user/akihisa/gyoza/segmentation/seg_class2_50.h5', mode='r+') # gs://経由は 'r+'\n",
    "\n",
    "with file_io.FileIO('./seg_class2_50.h5', mode='w') as output_f:\n",
    "  output_f.write(file_stream.read())\n",
    "  print(\"Saved model.h5 in local\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.keras.models import load_model\n",
    "model = load_model('seg_class2_50.h5') #from local directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from io import StringIO, BytesIO\n",
    "import os\n",
    "import pickle\n",
    "import google.datalab.storage as storage\n",
    "from google.datalab import Context\n",
    "import random, string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "project = Context.default().project_id\n",
    "kagglebu_bucket = storage.Bucket('kagglebu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#test_input_objects = kagglebu_bucket.objects('user/akihisa/gyoza/segmentation/test_data/')\n",
    "test_input_objects = kagglebu_bucket.objects('user/akihisa/gyoza/segmentation/test_input_only_ok/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "hiddenCell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000029.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000029_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000029_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000036.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000036_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000036_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000037.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000037_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085454-0000.avi.000037_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000002.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000002_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000002_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000006.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000006_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000006_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000007.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000007_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000007_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000010.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000010_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000010_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000014.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000014_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000014_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000020.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000020_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000020_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000022.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000022_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000022_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000025.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000025_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000025_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000026.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000026_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000026_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000031.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000031_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000031_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000032.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000032_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000032_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000034.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000034_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000034_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000036.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000036_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000036_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000039.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000039_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000039_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000045.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000045_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000045_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000047.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000047_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000047_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000048.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000048_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000048_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000049.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000049_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000049_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000053.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000053_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000053_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000054.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000054_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000054_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000055.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000055_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000055_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000058.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000058_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000058_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000067.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000067_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000067_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000070.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000070_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000070_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000071.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000071_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000071_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000075.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000075_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000075_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000079.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0000.avi.000079_flip.jpg,\n",
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       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000225_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000230.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000230_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000230_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000231.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000231_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000231_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000232.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000232_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000232_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000234.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000234_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000234_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000237.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000237_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000237_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000252.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000252_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000252_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000254.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000254_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000254_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000255.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000255_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000255_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000256.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000256_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000256_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000257.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000257_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000257_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000258.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000258_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000258_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000260.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000260_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000260_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000267.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000267_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000267_mirror.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000269.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000269_flip.jpg,\n",
       " Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/OK_2017-11-06-085858-0001.avi.000269_mirror.jpg]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(test_input_objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " idx 531 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "def get_np_tests(test_input_objects):\n",
    "  np_tests = np.array([])\n",
    "  for idx, obj in enumerate(list(test_input_objects)[1:]):\n",
    "    np_tests_tmp = np.asarray(Image.open(BytesIO(obj.read_stream()), 'r'))\n",
    "    if idx == 0:\n",
    "      np_tests = np_tests_tmp[np.newaxis,:,:,:]\n",
    "    else:\n",
    "      np_tests = np.concatenate((np_tests, np_tests_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "    sys.stdout.write(\"\\r idx %s proceeds\" % str(idx+1))\n",
    "    sys.stdout.flush\n",
    "  return np_tests\n",
    "  \n",
    "np_tests = get_np_tests(test_input_objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(531, 224, 224, 3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_tests.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "# from tensorflow.contrib.keras.python.keras import backend as K\n",
    "from tensorflow.contrib.keras import backend as K\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    np_tests = np_tests.reshape(np_tests.shape[0], np_tests.shape[3], np_tests.shape[1], np_tests.shape[2])\n",
    "    input_shape = (np_labels.shape[3], np_labels.shape[1], np_labels.shape[2])\n",
    "else:\n",
    "    np_tests = np_tests.reshape(np_tests.shape[0], np_tests.shape[1], np_tests.shape[2], np_tests.shape[3])\n",
    "    input_shape = (np_tests.shape[1], np_tests.shape[2], np_tests.shape[3])\n",
    "\n",
    "print(input_shape)\n",
    "\n",
    "# cast dtype of img_array for keras default setting(np.float32)\n",
    "np_tests = np_tests.astype('float32') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict output labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(np_tests) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example <br>\n",
    "(order: original -> flip -> mirror)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred_eg = np.uint8(y_pred[2].reshape(224,224,2)*255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7faa844541d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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STMtMdW1FLjus1qElnW1Nych1u/Tt2zen+RcC5HYWRTGjjygXrHIoBBM6EQqd/kyQy5hM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\nPHSRJ4c88shDF/8fWxvHj4WV/rUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa8c25e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(y_pred_eg[:,:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save pred_imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create filename list \n",
    "\"\"\"filenames = [str(test_input_object).replace('Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_data/', '')\n",
    "             .replace('.png.jpg', '_pred_2class') for test_input_object in list(test_input_objects)[1:]]\"\"\"\n",
    "\n",
    "filenames = [str(test_input_object).replace('Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/test_input_only_ok/', '')\n",
    "             .replace('.jpg', '_pred_2class') for test_input_object in list(test_input_objects)[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# to local (temporary)\n",
    "for idx, y_pred_elem in enumerate(y_pred):\n",
    "  tmp_img_all = np.uint8(y_pred_elem.reshape(224,224,2)*255)\n",
    "  tmp_img_border = Image.fromarray(tmp_img_all[:,:,0])\n",
    "  tmp_img_inner = Image.fromarray(tmp_img_all[:,:,1])\n",
    "  # tmp_img_border.save(filenames[idx]+\"_border_50.png\") \n",
    "  # tmp_img_inner.save(filenames[idx]+\"_inner_50.png\") \n",
    "  tmp_img_border.save(\"./test_pred_only_ok/\"+filenames[idx]+\"_border_50.png\") \n",
    "  tmp_img_inner.save(\"./test_pred_only_ok/\"+filenames[idx]+\"_inner_50.png\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " image idx 139 saved"
     ]
    }
   ],
   "source": [
    "\"\"\"# local to gcs\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.lib.io import file_io\n",
    "\n",
    "# save _border.png\n",
    "for idx in range(len(y_pred)):\n",
    "    with file_io.FileIO(filenames[idx]+\"_border_50.png\", mode='r') as input_f:\n",
    "      with file_io.FileIO('gs://kagglebu/user/akihisa/gyoza/segmentation/pred_imgs/iter50/'+filenames[idx]+\"_border.png\", mode='w+') as output_f:\n",
    "        output_f.write(input_f.read())\n",
    "        sys.stdout.write(\"\\r image idx %s saved\" % str(idx+1))\n",
    "        sys.stdout.flush\n",
    "        \n",
    "# save _inner.png\n",
    "for idx in range(len(y_pred)):\n",
    "    with file_io.FileIO(filenames[idx]+\"_inner_50.png\", mode='r') as input_f:\n",
    "      with file_io.FileIO('gs://kagglebu/user/akihisa/gyoza/segmentation/pred_imgs/iter50/'+filenames[idx]+\"_inner.png\", mode='w+') as output_f:\n",
    "        output_f.write(input_f.read())\n",
    "        sys.stdout.write(\"\\r image idx %s saved\" % str(idx+1))\n",
    "        sys.stdout.flush\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " image idx 531 saved"
     ]
    }
   ],
   "source": [
    "# local to gcs\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.lib.io import file_io\n",
    "\n",
    "# save _border.png\n",
    "for idx in range(len(y_pred)):\n",
    "    with file_io.FileIO(\"./test_pred_only_ok/\"+filenames[idx]+\"_border_50.png\", mode='r') as input_f:\n",
    "      with file_io.FileIO('gs://kagglebu/user/akihisa/gyoza/segmentation/pred_imgs/test_pred_only_ok/'+filenames[idx]+\"_border.png\", mode='w+') as output_f:\n",
    "        output_f.write(input_f.read())\n",
    "        sys.stdout.write(\"\\r image idx %s saved\" % str(idx+1))\n",
    "        sys.stdout.flush\n",
    "        \n",
    "# save _inner.png\n",
    "for idx in range(len(y_pred)):\n",
    "    with file_io.FileIO(\"./test_pred_only_ok/\"+filenames[idx]+\"_inner_50.png\", mode='r') as input_f:\n",
    "      with file_io.FileIO('gs://kagglebu/user/akihisa/gyoza/segmentation/pred_imgs/test_pred_only_ok/'+filenames[idx]+\"_inner.png\", mode='w+') as output_f:\n",
    "        output_f.write(input_f.read())\n",
    "        sys.stdout.write(\"\\r image idx %s saved\" % str(idx+1))\n",
    "        sys.stdout.flush"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save before-after compared imgs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "余力があるとき作る"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation of border accuracy in occuluded area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load overlapping gyoza images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from io import StringIO, BytesIO\n",
    "import os\n",
    "import pickle\n",
    "import google.datalab.storage as storage\n",
    "from google.datalab import Context\n",
    "import random, string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "project = Context.default().project_id\n",
    "kagglebu_bucket = storage.Bucket('kagglebu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_input_objects = kagglebu_bucket.objects('user/akihisa/gyoza/segmentation/input_for_occulu/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " idx 6 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "def get_np_tests(test_input_objects):\n",
    "  np_tests = np.array([])\n",
    "  for idx, obj in enumerate(list(test_input_objects)[1:]): # \"list(test_input_objects)[0]\" is a directory, so it's not included.\n",
    "    np_tests_tmp = np.asarray(Image.open(BytesIO(obj.read_stream()), 'r'))\n",
    "    if idx == 0:\n",
    "      np_tests = np_tests_tmp[np.newaxis,:,:,:]\n",
    "    else:\n",
    "      np_tests = np.concatenate((np_tests, np_tests_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "    sys.stdout.write(\"\\r idx %s proceeds\" % str(idx))\n",
    "    sys.stdout.flush\n",
    "  return np_tests\n",
    "  \n",
    "np_tests = get_np_tests(test_input_objects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "# from tensorflow.contrib.keras.python.keras import backend as K\n",
    "from tensorflow.contrib.keras import backend as K\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    np_tests = np_tests.reshape(np_tests.shape[0], np_tests.shape[3], np_tests.shape[1], np_tests.shape[2])\n",
    "    input_shape = (np_labels.shape[3], np_labels.shape[1], np_labels.shape[2])\n",
    "else:\n",
    "    np_tests = np_tests.reshape(np_tests.shape[0], np_tests.shape[1], np_tests.shape[2], np_tests.shape[3])\n",
    "    input_shape = (np_tests.shape[1], np_tests.shape[2], np_tests.shape[3])\n",
    "\n",
    "print(input_shape)\n",
    "\n",
    "# cast img_array for keras default setting(np.float32)\n",
    "np_tests = np_tests.astype('float32') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load pretrained model\n",
    "from tensorflow.python.keras.models import load_model\n",
    "model = load_model('seg_class2_50.h5') #from local directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(np_tests) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred_for_eval = y_pred.reshape(7, 224, 224, 2) # labelデータの形式に合わせる"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load true labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "true_label_objects = kagglebu_bucket.objects('user/akihisa/gyoza/segmentation/labals_for_occulu/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " idx 6 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "def get_np_tests(true_label_objects):\n",
    "  np_labels = np.array([])\n",
    "  for idx, obj in enumerate(list(true_label_objects)[1:]): # list(test_input_objects)[0] is directory\n",
    "    np_labels_tmp = np.asarray(Image.open(BytesIO(obj.read_stream()), 'r'))\n",
    "    if idx == 0:\n",
    "      np_labels = np_labels_tmp[np.newaxis,:,:,:]\n",
    "    else:\n",
    "      np_labels = np.concatenate((np_labels, np_labels_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "    sys.stdout.write(\"\\r idx %s proceeds\" % str(idx))\n",
    "    sys.stdout.flush\n",
    "  return np_labels\n",
    "  \n",
    "np_labels = get_np_tests(true_label_objects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make True outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method for making outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_label(np_label):\n",
    "  classes = 2\n",
    "  border = 255\n",
    "  inner = 46\n",
    "  y = np.zeros((224,224, classes))\n",
    "  y_labeled_tmp = labeling(y, np_label, 0, border) \n",
    "  y_labeled = labeling(y_labeled_tmp, np_label, 1, inner) \n",
    "  return y_labeled.astype('float32')\n",
    "\n",
    "def labeling(y, np_label, class_idx, thresh):\n",
    "  np_label_2d = np_label[:,:,0] - np_label[:,:,1]\n",
    "  np_label_2d.flags.writeable = True\n",
    "  \n",
    "  is_color = np_label_2d == thresh\n",
    "  isnt_color = np_label_2d != thresh\n",
    "  np_label_2d[is_color] = 1\n",
    "  np_label_2d[isnt_color] = 0\n",
    "  #print(np_label_2d)\n",
    "  \n",
    "  for i in range(224):\n",
    "    for j in range(224):\n",
    "      if np_label_2d[i,j] == 0:\n",
    "        y[i,j,class_idx] = 0\n",
    "      else:\n",
    "        y[i,j,class_idx] = 1  \n",
    "  return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 7 of 7 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "#y = np.array([])\n",
    "for idx, label_img in enumerate(np_labels):\n",
    "  y_tmp = create_label(label_img)\n",
    "  if idx == 0:\n",
    "    y_true = y_tmp[np.newaxis,:,:,:]\n",
    "  else:\n",
    "    y_true = np.concatenate((y_true, y_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "    \n",
    "  sys.stdout.write(\"\\r %s of %s proceeds\" % (str(idx+1), str(len(np_labels))))\n",
    "  sys.stdout.flush"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation(calculate loss functions) by using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12119727066514056"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "log_loss(y_true.reshape(702464,-1), y_pred_for_eval.reshape(702464,-1), eps=1e-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03744573"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mean_squared_error(y_true.reshape(y_true.shape[0], -1), y_pred_for_eval.reshape(y_pred_for_eval.shape[0], -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rsmle(y_true, y_pred):\n",
    "  \"\"\"y_true,y_pred (2darray) -> remle score\n",
    "  \"\"\"\n",
    "  return np.sqrt(np.mean(np.power(np.log(y_true+1)-np.log(y_pred+1), 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13541287"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsmle(y_true.reshape(y_true.shape[0], -1), y_pred_for_eval.reshape(y_pred_for_eval.shape[0], -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_for_eval_eg = np.uint8(y_pred_for_eval[6].reshape(224,224,2)*255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f16e5f78210>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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69GiYTCZ4PB48/PDD0Ov1DbYQFosFubm5OHToEKKjo1FWVga32w2bzYbIyEjo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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16e5ff9190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(y_pred_for_eval_eg[:,:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For evaluation data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Processings of input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from io import StringIO, BytesIO\n",
    "import os\n",
    "import pickle\n",
    "import google.datalab.storage as storage\n",
    "from google.datalab import Context\n",
    "import random, string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "project = Context.default().project_id\n",
    "kagglebu_bucket = storage.Bucket('kagglebu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eval_input_objects = kagglebu_bucket.objects('user/akihisa/gyoza/segmentation/occulu_eval_input/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " idx 19 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "def get_np_tests(test_input_objects):\n",
    "  np_tests = np.array([])\n",
    "  for idx, obj in enumerate(list(test_input_objects)):\n",
    "    np_tests_tmp = np.asarray(Image.open(BytesIO(obj.read_stream()), 'r'))\n",
    "    if idx == 0:\n",
    "      np_tests = np_tests_tmp[np.newaxis,:,:,:]\n",
    "    else:\n",
    "      np_tests = np.concatenate((np_tests, np_tests_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "    sys.stdout.write(\"\\r idx %s proceeds\" % str(idx))\n",
    "    sys.stdout.flush\n",
    "  return np_tests\n",
    "  \n",
    "np_eval_inputs = get_np_tests(eval_input_objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "# from tensorflow.contrib.keras.python.keras import backend as K\n",
    "from tensorflow.contrib.keras import backend as K\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    np_eval_inputs = np_eval_inputs.reshape(np_eval_inputs.shape[0], np_eval_inputs.shape[3], np_eval_inputs.shape[1], np_eval_inputs.shape[2])\n",
    "    input_shape = (np_eval_inputs.shape[3], np_eval_inputs.shape[1], np_eval_inputs.shape[2])\n",
    "else:\n",
    "    np_eval_inputs = np_eval_inputs.reshape(np_eval_inputs.shape[0], np_eval_inputs.shape[1], np_eval_inputs.shape[2], np_eval_inputs.shape[3])\n",
    "    input_shape = (np_eval_inputs.shape[1], np_eval_inputs.shape[2], np_eval_inputs.shape[3])\n",
    "\n",
    "print(input_shape)\n",
    "\n",
    "# cast img_array for keras default setting(np.float32)\n",
    "np_eval_inputs = np_eval_inputs.astype('float32') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.keras.models import load_model\n",
    "model = load_model('seg_class2_50.h5') #from local directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(np_eval_inputs) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred_for_eval = y_pred.reshape(-1, 224, 224, 2) # reshape predicted output array for label data shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Processings of output data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eval_label_objects = kagglebu_bucket.objects('user/akihisa/gyoza/segmentation/occulu_eval_output/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " idx 19 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "def get_np_tests(true_label_objects):\n",
    "  np_labels = np.array([])\n",
    "  for idx, obj in enumerate(list(true_label_objects)):\n",
    "    np_labels_tmp = np.asarray(Image.open(BytesIO(obj.read_stream()), 'r'))\n",
    "    if idx == 0:\n",
    "      np_labels = np_labels_tmp[np.newaxis,:,:,:]\n",
    "    else:\n",
    "      np_labels = np.concatenate((np_labels, np_labels_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "    sys.stdout.write(\"\\r idx %s proceeds\" % str(idx))\n",
    "    sys.stdout.flush\n",
    "  return np_labels\n",
    "  \n",
    "np_eval_labels = get_np_tests(eval_label_objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_label(np_label):\n",
    "  classes = 2\n",
    "  border = 255\n",
    "  inner = 46\n",
    "  y = np.zeros((224,224, classes))\n",
    "  y_labeled_tmp = labeling(y, np_label, 0, border)\n",
    "  y_labeled = labeling(y_labeled_tmp, np_label, 1, inner)\n",
    "  return y_labeled.astype('float32')\n",
    "\n",
    "def labeling(y, np_label, class_idx, thresh):\n",
    "  np_label_2d = np_label[:,:,0] - np_label[:,:,1] # RGBのうちRの画素値で条件分岐 46:餃子内側領域, 255:\n",
    "  np_label_2d.flags.writeable = True\n",
    "  \n",
    "  is_color = np_label_2d == thresh\n",
    "  isnt_color = np_label_2d != thresh\n",
    "  np_label_2d[is_color] = 1\n",
    "  np_label_2d[isnt_color] = 0\n",
    "  #print(np_label_2d)\n",
    "  \n",
    "  for i in range(224):\n",
    "    for j in range(224):\n",
    "      if np_label_2d[i,j] == 0:\n",
    "        y[i,j,class_idx] = 0\n",
    "      else:\n",
    "        y[i,j,class_idx] = 1  \n",
    "  return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 20 of 20 proceeds"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "#y = np.array([])\n",
    "for idx, label_img in enumerate(np_eval_labels):\n",
    "  y_tmp = create_label(label_img)\n",
    "  if idx == 0:\n",
    "    y_true_eval = y_tmp[np.newaxis,:,:,:]\n",
    "  else:\n",
    "    y_true_eval = np.concatenate((y_true_eval, y_tmp[np.newaxis,:,:,:]), axis=0)\n",
    "    \n",
    "  sys.stdout.write(\"\\r %s of %s proceeds\" % (str(idx+1), str(len(np_eval_labels))))\n",
    "  sys.stdout.flush"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.706902224657388"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "log_loss(y_true_eval.reshape(2007040,-1), y_pred_for_eval.reshape(2007040,-1), eps=1e-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1218683"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mean_squared_error(y_true_eval.reshape(y_true_eval.shape[0], -1), y_pred_for_eval.reshape(y_pred_for_eval.shape[0], -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rsmle(y_true, y_pred):\n",
    "  \"\"\"y_true,y_pred (2darray) -> remle score\n",
    "  \"\"\"\n",
    "  return np.sqrt(np.mean(np.power(np.log(y_true+1)-np.log(y_pred+1), 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24131869"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsmle(y_true_eval.reshape(y_true_eval.shape[0], -1), y_pred_for_eval.reshape(y_pred_for_eval.shape[0], -1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "visualization e.g.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2a8a0d9a50>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DwzBEo9GQ22+/nXzyySekra2NOJ1OYrfbSSAQIIFAQFiMdNzEP/HsI0oKtA30x+PxEKvV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\nt7FcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2a902a8850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_for_eval_eg = np.uint8(y_pred_for_eval[9].reshape(224,224,2)*255)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(y_pred_for_eval_eg[:,:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[重複あり餃子に関する定量的評価]\n",
    "\n",
    "考察：\n",
    "- loglossが0.7　-> 正解率は約50%　(train data は 約88%)\n",
    "    -当たらずも遠からず　という感じ -> 特徴を捉えているものも、いないものもある\n",
    "- 画像を観察しても、境界を捉えていそうなものもあれば、そうでないものもあり\n",
    "    - 重複（or 霜）がない領域は、境界がはっきりしている。\n",
    "    - 重複領域の近傍は、境界がかなりぼやけている \n",
    "\n",
    "気になる点：\n",
    "- 重複餃子のデータをあまり用意できなかったことが原因？ （重複のデータが多ければもう少し確率が上がる？）\n",
    "- 入力ラベルを所々雑に作ったこともlossを増やしていそう\n",
    "    \n",
    "結論：\n",
    "- 餃子とプレートの境界は高精度で分離可能\n",
    "- 霜、餃子同士の重複がない場合は餃子の領域抽出は高精度で可能\n",
    "- 霜、餃子同士の重複は、特徴を捉えられているものもありそう。改良次第では、これらの餃子の抽出の精度は上がる可能性あり。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save predicted images for occuluded gyoza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create filename list \n",
    "filenames = [str(eval_input_object).replace('Google Cloud Storage Object gs://kagglebu/user/akihisa/gyoza/segmentation/occulu_eval_input/', '')\n",
    "             .replace('.png.jpg', '_pred_2class') for eval_input_object in list(eval_input_objects)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to local (temporary)\n",
    "for idx, y_pred_elem in enumerate(y_pred):\n",
    "  tmp_img_all = np.uint8(y_pred_elem.reshape(224,224,2)*255)\n",
    "  tmp_img_border = Image.fromarray(tmp_img_all[:,:,0])\n",
    "  tmp_img_inner = Image.fromarray(tmp_img_all[:,:,1])\n",
    "  tmp_img_border.save(filenames[idx]+\"_occulu_border_50.png\") \n",
    "  tmp_img_inner.save(filenames[idx]+\"_occulu_inner_50.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " image idx 20 saved"
     ]
    }
   ],
   "source": [
    "# local to gcs\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.lib.io import file_io\n",
    "\n",
    "# save _border.png\n",
    "for idx in range(len(y_pred)):\n",
    "    with file_io.FileIO(filenames[idx]+\"_occulu_border_50.png\", mode='r') as input_f:\n",
    "      with file_io.FileIO('gs://kagglebu/user/akihisa/gyoza/segmentation/pred_occulu_imgs/'+filenames[idx]+\"_border.png\", mode='w+') as output_f:\n",
    "        output_f.write(input_f.read())\n",
    "        sys.stdout.write(\"\\r image idx %s saved\" % str(idx+1))\n",
    "        sys.stdout.flush\n",
    "        \n",
    "# save _inner.png\n",
    "for idx in range(len(y_pred)):\n",
    "    with file_io.FileIO(filenames[idx]+\"_occulu_inner_50.png\", mode='r') as input_f:\n",
    "      with file_io.FileIO('gs://kagglebu/user/akihisa/gyoza/segmentation/pred_occulu_imgs/'+filenames[idx]+\"_inner.png\", mode='w+') as output_f:\n",
    "        output_f.write(input_f.read())\n",
    "        sys.stdout.write(\"\\r image idx %s saved\" % str(idx+1))\n",
    "        sys.stdout.flush"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
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