post process-stage2-gen prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in /usr/local/lib/python3.5/dist-packages\n",
      "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.5/dist-packages (from keras)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.5/dist-packages (from keras)\n",
      "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.5/dist-packages (from keras)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.5/dist-packages (from keras)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 9.0.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: cython in /usr/local/lib/python3.5/dist-packages\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 9.0.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('Mask_RCNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imageio in /usr/local/lib/python3.5/dist-packages\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.5/dist-packages (from imageio)\n",
      "Requirement already satisfied: pillow in /usr/local/lib/python3.5/dist-packages (from imageio)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 9.0.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install imageio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-image in /usr/local/lib/python3.5/dist-packages\n",
      "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.5/dist-packages (from scikit-image)\n",
      "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.5/dist-packages (from scikit-image)\n",
      "Requirement already satisfied: six>=1.7.3 in /usr/local/lib/python3.5/dist-packages (from scikit-image)\n",
      "Requirement already satisfied: matplotlib>=1.3.1 in /usr/local/lib/python3.5/dist-packages (from scikit-image)\n",
      "Requirement already satisfied: networkx>=1.8 in /usr/local/lib/python3.5/dist-packages (from scikit-image)\n",
      "Requirement already satisfied: pillow>=2.1.0 in /usr/local/lib/python3.5/dist-packages (from scikit-image)\n",
      "Requirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.5/dist-packages (from scipy>=0.17.0->scikit-image)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.5/dist-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.5/dist-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: pytz in /usr/local/lib/python3.5/dist-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.5/dist-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: decorator>=4.1.0 in /usr/local/lib/python3.5/dist-packages (from networkx>=1.8->scikit-image)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 9.0.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch in /usr/local/lib/python3.5/dist-packages\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.5/dist-packages\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.5/dist-packages (from torch)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.5/dist-packages (from torch)\n",
      "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.5/dist-packages (from torchvision)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.5/dist-packages (from torchvision)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 9.0.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install torch torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "import math\n",
    "import re\n",
    "import time\n",
    "import gc; gc.enable()\n",
    "\n",
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = tf.InteractiveSession(config=config)\n",
    "#sess = tf.Session(config=config)\n",
    "\n",
    "from keras import backend as K\n",
    "K.set_session(sess)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import imageio\n",
    "import skimage\n",
    "from config import Config\n",
    "from skimage.morphology import label\n",
    "from skimage.feature import canny\n",
    "from skimage import exposure\n",
    "from keras.callbacks import Callback\n",
    "from skimage.morphology import binary_closing, binary_opening, disk, binary_dilation\n",
    "from scipy.ndimage.morphology import binary_fill_holes\n",
    "from sklearn.externals import joblib\n",
    "from skimage.transform import PiecewiseAffineTransform, warp\n",
    "from skimage.morphology import watershed\n",
    "from skimage.filters import sobel\n",
    "from preparation import vgg_feature_extractor\n",
    "from random import *\n",
    "\n",
    "import gc; gc.enable()\n",
    "import utils\n",
    "import model as modellib\n",
    "import visualize\n",
    "from model import log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download the mask_rcnn trained on COCO dataset to initialize the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows=100\n",
    "\n",
    "# Root directory of the project\n",
    "ROOT_DIR = os.getcwd()\n",
    "\n",
    "# Directory to save logs and trained model\n",
    "MODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n",
    "\n",
    "# Local path to trained weights file\n",
    "COCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n",
    "# Download COCO trained weights from Releases if needed\n",
    "if not os.path.exists(COCO_MODEL_PATH):\n",
    "    utils.download_trained_weights(COCO_MODEL_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now define the monitor that will help keep track on the training process.\n",
    "With this you will be able to see your charts in the `charts` tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will load the metadata and divide them in train and valid splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_PATH = '../stage1_train'\n",
    "TEST_PATH = '../stage2_test_final'\n",
    "META_DATA_PATH = '../stage2_metadata.csv'\n",
    "BG_DATA_PATH = '../stage2_foreground_background_classes.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>20</th>\n",
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       "      <td>1</td>\n",
       "      <td>../stage1_train/d7d12a2acc47a94961aeb56fd56e8a...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
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       "      <td>1</td>\n",
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       "      <td>9</td>\n",
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       "      <th>22</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>25</th>\n",
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       "      <td>1</td>\n",
       "      <td>../stage1_train/4948e3dfe0483c2198b9616612071e...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
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       "      <td>1</td>\n",
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       "      <td>6</td>\n",
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       "      <td>1</td>\n",
       "      <td>../stage1_train/88d5a03f8ecd459f076a06e0d50351...</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2dd3356f2dcf470aec4003800744dfec6490e75d88011e...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/2dd3356f2dcf470aec4003800744df...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>abbfff07379bceb69dba41dad8b0db5eb80cc8baf3d4af...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/abbfff07379bceb69dba41dad8b0db...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>6b72b61b80060a9e79a4747f9c5d5af135af9db466681c...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/6b72b61b80060a9e79a4747f9c5d5a...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>a1777737270c5f96c4523dff76e4097756f8f7d4c9d59b...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/a1777737270c5f96c4523dff76e409...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>f81ca7ee25e733ff37240c34c8e3044d9937bb0166e315...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/f81ca7ee25e733ff37240c34c8e304...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df9a4212ecb67bb4e58eba62f293b91f9d6f1dde73e38f...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/df9a4212ecb67bb4e58eba62f293b9...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>547ef286ee5f4e5dce533e982e6992ada67b7d727fdd3c...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/547ef286ee5f4e5dce533e982e6992...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>72b18a405555ad491721e29454e5cd325055ce81a9e785...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/72b18a405555ad491721e29454e5cd...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>751f421d322940d6efe3bd570a66ecda16d08a1b90bc32...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/751f421d322940d6efe3bd570a66ec...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>f487cc82271cf84b4414552aa8b0a9d82d902451ebe8e8...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/f487cc82271cf84b4414552aa8b0a9...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>TCGA-RD-A8N9-01A-01-TS1</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/TCGA-RD-A8N9-01A-01-TS1/images...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>06350c7cc618be442c15706db7a68e91f313758d224de4...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/06350c7cc618be442c15706db7a68e...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>TCGA-50-5931-01Z-00-DX1</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/TCGA-50-5931-01Z-00-DX1/images...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7aae06bc4558829473071defec0b7ab3bfa9c5005548a1...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/7aae06bc4558829473071defec0b7a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>f9ac03b0344ce8c48bc058448541f9211a1e5f4c94fdaf...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/f9ac03b0344ce8c48bc058448541f9...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5ba4facefc949c920d7054813a3e846b000969da2ed860...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/5ba4facefc949c920d7054813a3e84...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>a6e81120d1cb9f71f8a25f90a5d56c4b714a642fc496a7...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/a6e81120d1cb9f71f8a25f90a5d56c...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6fc83b33896f58a4a067d8fdcf51f15d4ae9be05d8c381...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/6fc83b33896f58a4a067d8fdcf51f1...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>bf4a61bb81589c9a67e3343408befd3e135af5e88b50c1...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/bf4a61bb81589c9a67e3343408befd...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>40bcdad218ac5f0885fc247d88fcad9f729f55c81c79d2...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/40bcdad218ac5f0885fc247d88fcad...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>b4de1e3eec159d8af1bd5447696f8996c31709edaf33e2...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/b4de1e3eec159d8af1bd5447696f89...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>573a657d5e5fcb9574a758b0ab34b09c79d7ba374ceb71...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/573a657d5e5fcb9574a758b0ab34b0...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1e8408fbb1619e7a0bcdd0bcd21fae57e7cb1f297d4c79...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/1e8408fbb1619e7a0bcdd0bcd21fae...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>b6edad733399c83c8eb7a59c0d37b54e10cc0d59894e39...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/b6edad733399c83c8eb7a59c0d37b5...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>8f94a80b95a881d0efdec36affc915dca9609f4cba8134...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/8f94a80b95a881d0efdec36affc915...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>bb8ebf465c968a5f6f715de5d9e2e664afd1bcaa533e0e...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/bb8ebf465c968a5f6f715de5d9e2e6...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>8de0b1a2e8f614af29fe5fafeaa5bdf55e6b3e65edf363...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/8de0b1a2e8f614af29fe5fafeaa5bd...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>337b6eed0726f07531cd467cd62b6676c31a8c9e716bdb...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/337b6eed0726f07531cd467cd62b66...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>bde3727f3a9e8b2b58f383ebc762b2157eb50cdbff23e6...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/bde3727f3a9e8b2b58f383ebc762b2...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>a3a65623e079af7988b0c1cf1e54041003c6d730c91ecf...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/a3a65623e079af7988b0c1cf1e5404...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>62057502c387145ed4f8f7f0d5e5bedcb72d3bcec15fa7...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/62057502c387145ed4f8f7f0d5e5be...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>309ba76b12ecb5ce28b99f3445b2b5dc54c0564c3c0e24...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/309ba76b12ecb5ce28b99f3445b2b5...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>449fe932622db3b49366a260ddd20077219f96fb2dc0f9...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/449fe932622db3b49366a260ddd200...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>b24ea5c268469a95ed155eeaf809e36030b78a2eb530a0...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/b24ea5c268469a95ed155eeaf809e3...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>c96109cbebcf206f20035cbde414e43872074eee8d839b...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/c96109cbebcf206f20035cbde414e4...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>e856511ac1c34d24320eb7c56c05a4a3340d06667b4f5b...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/e856511ac1c34d24320eb7c56c05a4...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>7b5987a24dd57325e82812371b3f4df7edc528e0526754...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/7b5987a24dd57325e82812371b3f4d...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1f9e429c12f4477221b5b855a5f494fda2ef6d064ff75b...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/1f9e429c12f4477221b5b855a5f494...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>648636ee314d7bdba3ab2fc0fe49a863de35c3e2caf619...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/648636ee314d7bdba3ab2fc0fe49a8...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>f73e37957c74f554be132986f38b6f1d75339f636dfe2b...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/f73e37957c74f554be132986f38b6f...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>623cf6987b3fac8f384c09f40d98c5e739c097aa9a9627...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/623cf6987b3fac8f384c09f40d98c5...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>8bef203fce625e4d8c89dca728158be4662dfdfdcd4dc7...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/8bef203fce625e4d8c89dca728158b...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>245b995878370ef4ea977568b2b67f93d4ecaa9308761b...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/245b995878370ef4ea977568b2b67f...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>57bd029b19c1b382bef9db3ac14f13ea85e36a6053b92e...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/57bd029b19c1b382bef9db3ac14f13...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>93cfd412c7de5210bbd262ec3a602cfea65072e9272e9f...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/93cfd412c7de5210bbd262ec3a602c...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>da5f98f2b8a64eee735a398de48ed42cd31bf17a6063db...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/da5f98f2b8a64eee735a398de48ed4...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>ce37f6dd0615d45e66e41a8f2ed6fbc0bbe3103a290394...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/ce37f6dd0615d45e66e41a8f2ed6fb...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>449a9c32e53a37c8a86e01c199155c8da3958b631088e1...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/449a9c32e53a37c8a86e01c199155c...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>20468e8779c43e089dc0ff30f25e6cf3872d5aa6a0fdad...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/20468e8779c43e089dc0ff30f25e6c...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2bf594e9d06f78b4b79d7ffb395497a0a91126b6b0d710...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/2bf594e9d06f78b4b79d7ffb395497...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>TCGA-G9-6362-01Z-00-DX1</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/TCGA-G9-6362-01Z-00-DX1/images...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>f43169e3d8b4f71e687945b9e72cbfdfe2e40e68842568...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/f43169e3d8b4f71e687945b9e72cbf...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>866a8cba7bfe1ea73e383d6cf492e53752579140c8b833...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/866a8cba7bfe1ea73e383d6cf492e5...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>62570c4ff1c5ab6d9d383aba9f25e604768520b4266afd...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/62570c4ff1c5ab6d9d383aba9f25e6...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>12f89395ad5d21491ab9cec137e247652451d283064773...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/12f89395ad5d21491ab9cec137e247...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>f728de04267283f0b4daab9a840e7433b2c6034baf195f...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/f728de04267283f0b4daab9a840e74...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>08ae2741df2f5ac815c0f272a8c532b5167ee853be9b93...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/08ae2741df2f5ac815c0f272a8c532...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>58406ed8ef944831c413c3424dc2b07e59aef13eb1ff16...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/58406ed8ef944831c413c3424dc2b0...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>516a0e20327d6dfcedcf57e3056115e4fb29cdf4cb3490...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/516a0e20327d6dfcedcf57e3056115...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>TCGA-B0-5698-01Z-00-DX1</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/TCGA-B0-5698-01Z-00-DX1/images...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>a246bcaa64af48ee5ca181cd594c0fc43466e7614406eb...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/a246bcaa64af48ee5ca181cd594c0f...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89be66f88612aae541f5843abcd9c015832b5d6c54a281...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/89be66f88612aae541f5843abcd9c0...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>8f6597cd978c060378177df76e554d0578b97eab471e23...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/8f6597cd978c060378177df76e554d...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>aa58ba4512955771b4f9b459cb4e6a8adb71d11cd6cae6...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/aa58ba4512955771b4f9b459cb4e6a...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>ff3407842ada5bc18be79ae453e5bdaa1b68afc842fc22...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/ff3407842ada5bc18be79ae453e5bd...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>edd36ed822e7ed760ff73e0524df22aa5bf5c565efcdc6...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/edd36ed822e7ed760ff73e0524df22...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>3ebd2ab34ba86e515feb79ffdeb7fc303a074a98ba3994...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/3ebd2ab34ba86e515feb79ffdeb7fc...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>f26f4c2c70c38fe12e00d5a814d5116691f2ca54890812...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/f26f4c2c70c38fe12e00d5a814d511...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1b6044e4858a9b7cee9b0028d8e54fbc8fb72e6c4424ab...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/1b6044e4858a9b7cee9b0028d8e54f...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>e66f25e175abab08ecb4e5f6859db64a211e0ddffb262d...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/e66f25e175abab08ecb4e5f6859db6...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>d7db360fabfce9828559a21f6bffff589ae868e0dc6101...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/d7db360fabfce9828559a21f6bffff...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>c04fa1a74a980d790ba6f3e595fd9851f14370bb71c7cb...</td>\n",
       "      <td>1</td>\n",
       "      <td>../stage1_train/c04fa1a74a980d790ba6f3e595fd98...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              ImageId  is_train  \\\n",
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       "38                            TCGA-RD-A8N9-01A-01-TS1         1   \n",
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       "78                            TCGA-G9-6362-01Z-00-DX1         1   \n",
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       "99  c04fa1a74a980d790ba6f3e595fd9851f14370bb71c7cb...         1   \n",
       "\n",
       "                                      file_path_image  vgg_features_clusters  \n",
       "0   ../stage1_train/ef6634efb46567d87b811be786b18c...                      1  \n",
       "1   ../stage1_train/351771edfc5db5665ded8aa4940257...                      6  \n",
       "2   ../stage1_train/785555c0cbb49dad83563521708528...                      4  \n",
       "3   ../stage1_train/7ba20aa731cc21af74a8d940254176...                      1  \n",
       "4   ../stage1_train/43cf6b2ec0b0745ac2b87b4d8780f6...                      3  \n",
       "5   ../stage1_train/0b2e702f90aee4fff2bc6e4326308d...                      4  \n",
       "6   ../stage1_train/61dc249314d7b965eb4561ec739eab...                      6  \n",
       "7   ../stage1_train/28d33efef218392e79e385906deb88...                      0  \n",
       "8   ../stage1_train/4596961c789d3b4191649291879772...                      6  \n",
       "9   ../stage1_train/1ec74a26e772966df764e063f13911...                      9  \n",
       "10  ../stage1_train/5c235b945b25b9905b9b0429ce59f1...                      0  \n",
       "11  ../stage1_train/TCGA-21-5784-01Z-00-DX1/images...                      7  \n",
       "12  ../stage1_train/f952cc65376009cfad8249e53b9b2c...                      2  \n",
       "13  ../stage1_train/3b0709483b1e86449cc355bb797e84...                      9  \n",
       "14  ../stage1_train/3582166ee20755856adf4882a8bfac...                      5  \n",
       "15  ../stage1_train/e5384c905e9879cb6e8ff5250fb031...                      0  \n",
       "16  ../stage1_train/a6593632dcbbe4c9e9429a9cec573d...                      5  \n",
       "17  ../stage1_train/TCGA-HE-7130-01Z-00-DX1/images...                      7  \n",
       "18  ../stage1_train/3bfd6bb152310f93daa6f4e1867c10...                      1  \n",
       "19  ../stage1_train/305a8baaf726d7c9e695bff31d3a6a...                      5  \n",
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       "21  ../stage1_train/a90401357d50e1376354ae6e5f56a2...                      9  \n",
       "22  ../stage1_train/8fdc34509a0c3721f7b5e235c8a93e...                      1  \n",
       "23  ../stage1_train/4d4ebfcae4374165ea6ae7c7e18fd0...                      6  \n",
       "24  ../stage1_train/bbce7ebc40323a0eff6574d0c3842f...                      0  \n",
       "25  ../stage1_train/4948e3dfe0483c2198b9616612071e...                      0  \n",
       "26  ../stage1_train/cc88627344305b9a9b07f8bd042cb0...                      6  \n",
       "27  ../stage1_train/88d5a03f8ecd459f076a06e0d50351...                      5  \n",
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       "37  ../stage1_train/f487cc82271cf84b4414552aa8b0a9...                      6  \n",
       "38  ../stage1_train/TCGA-RD-A8N9-01A-01-TS1/images...                      7  \n",
       "39  ../stage1_train/06350c7cc618be442c15706db7a68e...                      4  \n",
       "40  ../stage1_train/TCGA-50-5931-01Z-00-DX1/images...                      7  \n",
       "41  ../stage1_train/7aae06bc4558829473071defec0b7a...                      1  \n",
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       "53  ../stage1_train/bb8ebf465c968a5f6f715de5d9e2e6...                      6  \n",
       "54  ../stage1_train/8de0b1a2e8f614af29fe5fafeaa5bd...                      4  \n",
       "55  ../stage1_train/337b6eed0726f07531cd467cd62b66...                      2  \n",
       "56  ../stage1_train/bde3727f3a9e8b2b58f383ebc762b2...                      6  \n",
       "57  ../stage1_train/a3a65623e079af7988b0c1cf1e5404...                      4  \n",
       "58  ../stage1_train/62057502c387145ed4f8f7f0d5e5be...                      6  \n",
       "59  ../stage1_train/309ba76b12ecb5ce28b99f3445b2b5...                      4  \n",
       "60  ../stage1_train/449fe932622db3b49366a260ddd200...                      6  \n",
       "61  ../stage1_train/b24ea5c268469a95ed155eeaf809e3...                      3  \n",
       "62  ../stage1_train/c96109cbebcf206f20035cbde414e4...                      0  \n",
       "63  ../stage1_train/e856511ac1c34d24320eb7c56c05a4...                      5  \n",
       "64  ../stage1_train/7b5987a24dd57325e82812371b3f4d...                      6  \n",
       "65  ../stage1_train/1f9e429c12f4477221b5b855a5f494...                      1  \n",
       "66  ../stage1_train/648636ee314d7bdba3ab2fc0fe49a8...                      1  \n",
       "67  ../stage1_train/f73e37957c74f554be132986f38b6f...                      1  \n",
       "68  ../stage1_train/623cf6987b3fac8f384c09f40d98c5...                      1  \n",
       "69  ../stage1_train/8bef203fce625e4d8c89dca728158b...                      5  \n",
       "70  ../stage1_train/245b995878370ef4ea977568b2b67f...                      6  \n",
       "71  ../stage1_train/57bd029b19c1b382bef9db3ac14f13...                      2  \n",
       "72  ../stage1_train/93cfd412c7de5210bbd262ec3a602c...                      4  \n",
       "73  ../stage1_train/da5f98f2b8a64eee735a398de48ed4...                      6  \n",
       "74  ../stage1_train/ce37f6dd0615d45e66e41a8f2ed6fb...                      4  \n",
       "75  ../stage1_train/449a9c32e53a37c8a86e01c199155c...                      3  \n",
       "76  ../stage1_train/20468e8779c43e089dc0ff30f25e6c...                      5  \n",
       "77  ../stage1_train/2bf594e9d06f78b4b79d7ffb395497...                      4  \n",
       "78  ../stage1_train/TCGA-G9-6362-01Z-00-DX1/images...                      7  \n",
       "79  ../stage1_train/f43169e3d8b4f71e687945b9e72cbf...                      1  \n",
       "80  ../stage1_train/866a8cba7bfe1ea73e383d6cf492e5...                      4  \n",
       "81  ../stage1_train/62570c4ff1c5ab6d9d383aba9f25e6...                      4  \n",
       "82  ../stage1_train/12f89395ad5d21491ab9cec137e247...                      1  \n",
       "83  ../stage1_train/f728de04267283f0b4daab9a840e74...                      1  \n",
       "84  ../stage1_train/08ae2741df2f5ac815c0f272a8c532...                      4  \n",
       "85  ../stage1_train/58406ed8ef944831c413c3424dc2b0...                      0  \n",
       "86  ../stage1_train/516a0e20327d6dfcedcf57e3056115...                      0  \n",
       "87  ../stage1_train/TCGA-B0-5698-01Z-00-DX1/images...                      7  \n",
       "88  ../stage1_train/a246bcaa64af48ee5ca181cd594c0f...                      6  \n",
       "89  ../stage1_train/89be66f88612aae541f5843abcd9c0...                      0  \n",
       "90  ../stage1_train/8f6597cd978c060378177df76e554d...                      4  \n",
       "91  ../stage1_train/aa58ba4512955771b4f9b459cb4e6a...                      2  \n",
       "92  ../stage1_train/ff3407842ada5bc18be79ae453e5bd...                      3  \n",
       "93  ../stage1_train/edd36ed822e7ed760ff73e0524df22...                      6  \n",
       "94  ../stage1_train/3ebd2ab34ba86e515feb79ffdeb7fc...                      6  \n",
       "95  ../stage1_train/f26f4c2c70c38fe12e00d5a814d511...                      6  \n",
       "96  ../stage1_train/1b6044e4858a9b7cee9b0028d8e54f...                      1  \n",
       "97  ../stage1_train/e66f25e175abab08ecb4e5f6859db6...                      4  \n",
       "98  ../stage1_train/d7db360fabfce9828559a21f6bffff...                      0  \n",
       "99  ../stage1_train/c04fa1a74a980d790ba6f3e595fd98...                      1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists(META_DATA_PATH):\n",
    "    print('Generating stage2_metadata.csv')\n",
    "    \n",
    "    stage1_metadata = pd.DataFrame()\n",
    "    \n",
    "    train_image_ids = os.listdir(TRAIN_PATH) # ImageId\n",
    "    train_is_train = [1] * len(train_image_ids)\n",
    "    \n",
    "    train_file_path = []\n",
    "    for p in train_image_ids:\n",
    "        if p[:4] == 'TCGA':\n",
    "            train_file_path.append(TRAIN_PATH + '/' + p + '/images/' + p + '.tif')\n",
    "        else:\n",
    "            train_file_path.append(TRAIN_PATH + '/' + p + '/images/' + p + '.png')\n",
    "    \n",
    "    test_image_ids = os.listdir(TEST_PATH) # ImageId\n",
    "    test_is_train = [0] * len(test_image_ids)\n",
    "    test_file_path = [TEST_PATH + '/' + p + '/images/' + p + '.png' for p in test_image_ids]\n",
    "    \n",
    "    stage1_metadata['ImageId'] = np.concatenate([train_image_ids, test_image_ids], axis=0)\n",
    "    stage1_metadata['is_train'] = np.concatenate([train_is_train, test_is_train], axis=0)\n",
    "    stage1_metadata['file_path_image'] = np.concatenate([train_file_path, test_file_path], axis=0)\n",
    "    \n",
    "    \n",
    "    ve = vgg_feature_extractor(n_clusters=10)\n",
    "    train_vgg_features_clusters = ve.get_vgg_clusters(stage1_metadata[stage1_metadata.is_train == 1].reset_index(drop=True), is_train=True)\n",
    "    test_vgg_features_clusters =ve.get_vgg_clusters(stage1_metadata[stage1_metadata.is_train == 0].reset_index(drop=True), is_train=False)\n",
    "    \n",
    "    stage1_metadata['vgg_features_clusters'] = np.concatenate([train_vgg_features_clusters, test_vgg_features_clusters], axis=0)\n",
    "    \n",
    "    \n",
    "    stage1_metadata.to_csv(META_DATA_PATH, index=False)\n",
    "    meta = stage1_metadata\n",
    "    \n",
    "    #del vgg_feature_extractor; gc.collect()\n",
    "else:\n",
    "    meta = pd.read_csv(META_DATA_PATH)\n",
    "    \n",
    "\n",
    "meta.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ImageId</th>\n",
       "      <th>is_train</th>\n",
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       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>a2c12dec525c5e619783dad0637cd7355542c50a168af2...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/a2c12dec525c5e619783dad06...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>5662665ab4b6341818f74b029f5f997f81320312ea34bb...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/5662665ab4b6341818f74b029...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>5e6d5cf7ab4fa2f9279210f9faf36ac8877fccdc2607ce...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/5e6d5cf7ab4fa2f9279210f9f...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3655</th>\n",
       "      <td>45ab35f6951ccedf48f346ae7a9bc117a7479a46636b4d...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/45ab35f6951ccedf48f346ae7...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3656</th>\n",
       "      <td>86f28c2c2bed586ed75ff6267b24af1de4c1347787753f...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/86f28c2c2bed586ed75ff6267...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3657</th>\n",
       "      <td>3d96585c1070108bda4c15ea5eedb81c40eb18a3efb557...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/3d96585c1070108bda4c15ea5...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3658</th>\n",
       "      <td>522ce42064ddfc4a0e6469ba6384b3b052da41ba26ce59...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/522ce42064ddfc4a0e6469ba6...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3659</th>\n",
       "      <td>9c45c366823c80fb2e16280dbcf2e17e834d0bb9991d7f...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/9c45c366823c80fb2e16280db...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3660</th>\n",
       "      <td>4c68676a9c53c685790835bf4a3e85d4b2ea3a90d7a930...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/4c68676a9c53c685790835bf4...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3661</th>\n",
       "      <td>b2b9b85cf0e5dd9f6803e26e71c7681db346d82c839501...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/b2b9b85cf0e5dd9f6803e26e7...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3662</th>\n",
       "      <td>a53c0335a658b1e6fd683a36b2d44d4068dd0e8aca0091...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/a53c0335a658b1e6fd683a36b...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3663</th>\n",
       "      <td>81537757f724d396ad6c803d8db775b2bd2814c711a839...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/81537757f724d396ad6c803d8...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3664</th>\n",
       "      <td>61f15758840c4068c0633f53795f33949dbfca53fe846c...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/61f15758840c4068c0633f537...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3665</th>\n",
       "      <td>b435afe8d2fc55eb5c898b068cb53a5039cc259b971f5b...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/b435afe8d2fc55eb5c898b068...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3666</th>\n",
       "      <td>bec3dd21227544e1012867f361e3cff766e3db79256c28...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/bec3dd21227544e1012867f36...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3667</th>\n",
       "      <td>0db9a7f318c3fab55e90a7b11e43f0545a592b6cd69e7d...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/0db9a7f318c3fab55e90a7b11...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3668</th>\n",
       "      <td>a19b065f27f139135084a25e17fe4054416947202d63d5...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/a19b065f27f139135084a25e1...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3669</th>\n",
       "      <td>54913a8172590e7268be62281e348c49fb1e66f9ce271e...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/54913a8172590e7268be62281...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3670</th>\n",
       "      <td>1c69629d540046c19795f48d6ecc7150ea213c810b92a6...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/1c69629d540046c19795f48d6...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3671</th>\n",
       "      <td>ea2ec7d4b1c58de4029faf58074f012fdf212e631fb115...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/ea2ec7d4b1c58de4029faf580...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3672</th>\n",
       "      <td>e40fb2e18142f0e7ae7f970fee866f89e1b7b414161515...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/e40fb2e18142f0e7ae7f970fe...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3673</th>\n",
       "      <td>59062eacbf265af92719fea96f85a8487804db18f47b83...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/59062eacbf265af92719fea96...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3674</th>\n",
       "      <td>a332606fd5bfc159c2688b752b6a64664645fc0b018efb...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/a332606fd5bfc159c2688b752...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3675</th>\n",
       "      <td>b615c5b5a72a30225b6772c7258c67e40f47be3873b809...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/b615c5b5a72a30225b6772c72...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3676</th>\n",
       "      <td>a0bf5958f91dcfeb45a431944f8664261230b461a5aa99...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/a0bf5958f91dcfeb45a431944...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3677</th>\n",
       "      <td>6cac54fefe515d19363e6da44f5800535fc9bd6f215765...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/6cac54fefe515d19363e6da44...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3678</th>\n",
       "      <td>bd5384e61cd26fc4e6c0833996d1a3fcfd7555bef1cf8c...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/bd5384e61cd26fc4e6c083399...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3679</th>\n",
       "      <td>b5d585ce67095b353a8596274af0cf18cb95d9e03a4b48...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/b5d585ce67095b353a8596274...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3680</th>\n",
       "      <td>5694ac60bf95b3a8e655ff8ad43e84f8bb8170c23e41f3...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/5694ac60bf95b3a8e655ff8ad...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3681</th>\n",
       "      <td>13e4eef807af99a8537235fbcbacf0f96f6362f1f3f978...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/13e4eef807af99a8537235fbc...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3682</th>\n",
       "      <td>87d4534ea8555b2ee490a4e6d408f9dad61a330b24f8f0...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/87d4534ea8555b2ee490a4e6d...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>218eadb4336ce74de6ff2d9661839ac739cbb2850c682b...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/218eadb4336ce74de6ff2d966...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3684</th>\n",
       "      <td>6fba8c28f3113451dc9e57dae1ce0d934ce85f8a56c105...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/6fba8c28f3113451dc9e57dae...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3685</th>\n",
       "      <td>970a6a7c4a4e0a67f23378bfac45b6a98537fe8dc7ee56...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/970a6a7c4a4e0a67f23378bfa...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3686</th>\n",
       "      <td>e94e718dfcd212e4af16f095fced0c37273adfeca56748...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/e94e718dfcd212e4af16f095f...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3687</th>\n",
       "      <td>754893e0818d8292fae48fe2cf8f70bef003992813f620...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/754893e0818d8292fae48fe2c...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3688</th>\n",
       "      <td>912a1d0d563de4f5e2c8e6ca8c8721994492e64702e18d...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/912a1d0d563de4f5e2c8e6ca8...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3689</th>\n",
       "      <td>6d9facf21108870819ebf0d649194337b109f4fae58d75...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/6d9facf21108870819ebf0d64...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3690</th>\n",
       "      <td>c7fabd67abacd9e825637d22ea79223304d6969f94283b...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/c7fabd67abacd9e825637d22e...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3691</th>\n",
       "      <td>c0b7829acc12f87ab54f1ce7779749cfedea7695d99741...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/c0b7829acc12f87ab54f1ce77...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3692</th>\n",
       "      <td>5a4d8a5919cfaa10cf43a7404827154340d41f5df2d328...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/5a4d8a5919cfaa10cf43a7404...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3693</th>\n",
       "      <td>68717e395e763c1e00f3aa5f6358ac1bb4c71b42fa30d4...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/68717e395e763c1e00f3aa5f6...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3694</th>\n",
       "      <td>0a9b684358ddd521550545ffc44a4f2ab335f7e2cfa412...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/0a9b684358ddd521550545ffc...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3695</th>\n",
       "      <td>23d5887339759baab135fc252655a609d98bdd6d981e0f...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/23d5887339759baab135fc252...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3696</th>\n",
       "      <td>cab1048d120984536932df523f8b06eb33d3b5f4704b8a...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/cab1048d120984536932df523...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3697</th>\n",
       "      <td>50664b1805096e83717f86b4713c4d8f77b2a32bedaee6...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/50664b1805096e83717f86b47...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3698</th>\n",
       "      <td>58ed3013e2e776f7b52ad20f79ea1c4a88d7f67805b035...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/58ed3013e2e776f7b52ad20f7...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3699</th>\n",
       "      <td>b5de7304dabacb5d36ff92a82580f49d84004fa06b4581...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/b5de7304dabacb5d36ff92a82...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3700</th>\n",
       "      <td>e5d0fee7b49a212832118a5f55b1135e998a866a0542bb...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/e5d0fee7b49a212832118a5f5...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3701</th>\n",
       "      <td>6b6070c899829c3d29968474d495a7483079f75053c4b8...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/6b6070c899829c3d29968474d...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3702</th>\n",
       "      <td>b57c8330eb99bf32c4eca0fcb3dcee30161d4be007c286...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/b57c8330eb99bf32c4eca0fcb...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3703</th>\n",
       "      <td>2406640f7869d4c3c021ffa01fb5cad0059c46837baa3a...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/2406640f7869d4c3c021ffa01...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3704</th>\n",
       "      <td>4edad1bb8932fc0d1b1d7af10d31918c075e26af50c2c6...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/4edad1bb8932fc0d1b1d7af10...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3705</th>\n",
       "      <td>7e23ad482f74d9410eb280c7d8f99f9855651f4253dd61...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/7e23ad482f74d9410eb280c7d...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3706</th>\n",
       "      <td>0a8dfb6763ec049b57454e6b5605f872cdf41cd13951fb...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/0a8dfb6763ec049b57454e6b5...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3707</th>\n",
       "      <td>330b9666c7962207e4e9d054fe6c135f59aab62395d61b...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/330b9666c7962207e4e9d054f...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3708</th>\n",
       "      <td>7d467ae316bf2a0b824c9f02b062cd2fc0a26ec009e944...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/7d467ae316bf2a0b824c9f02b...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3709</th>\n",
       "      <td>63ca8df5136ecd722e10263950a490f22f41702ab7dcea...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/63ca8df5136ecd722e1026395...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3710</th>\n",
       "      <td>0801c13e30c0e1c6918699ba8b28fb5381513de55902de...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/0801c13e30c0e1c6918699ba8...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3711</th>\n",
       "      <td>4d13b19f4d9b74ba6e69d4bd47c7f534b035716473edba...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/4d13b19f4d9b74ba6e69d4bd4...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3712</th>\n",
       "      <td>7f1377ba86c59405c8b02f8cb933e8f965ae5a29d51885...</td>\n",
       "      <td>0</td>\n",
       "      <td>../stage2_test_final/7f1377ba86c59405c8b02f8cb...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
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       "\n",
       "                                        file_path_image  vgg_features_clusters  \n",
       "3613  ../stage2_test_final/5d71876e8bba86a57c3e92947...                      6  \n",
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       "3705  ../stage2_test_final/7e23ad482f74d9410eb280c7d...                      4  \n",
       "3706  ../stage2_test_final/0a8dfb6763ec049b57454e6b5...                      4  \n",
       "3707  ../stage2_test_final/330b9666c7962207e4e9d054f...                      4  \n",
       "3708  ../stage2_test_final/7d467ae316bf2a0b824c9f02b...                      0  \n",
       "3709  ../stage2_test_final/63ca8df5136ecd722e1026395...                      6  \n",
       "3710  ../stage2_test_final/0801c13e30c0e1c6918699ba8...                      0  \n",
       "3711  ../stage2_test_final/4d13b19f4d9b74ba6e69d4bd4...                      4  \n",
       "3712  ../stage2_test_final/7f1377ba86c59405c8b02f8cb...                      0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2919</th>\n",
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       "      <th>2933</th>\n",
       "      <td>f87e3ce60c112ce476f2889241195f507da956345e1593...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>f8b7784c82db3c77d15b45a8108ce6f4ac859f27d4a672...</td>\n",
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       "      <th>2935</th>\n",
       "      <td>f8b83bbb07b06ff8cd433253f9342a5f62388323813a33...</td>\n",
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       "      <th>2936</th>\n",
       "      <td>f8bfc24b997300d53af5bccd9a703f7ea96acac5383d8f...</td>\n",
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       "      <th>2937</th>\n",
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       "      <th>2938</th>\n",
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       "      <th>2939</th>\n",
       "      <td>f920dc5b27c57acc4bd451baa709c414898e50e326314d...</td>\n",
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       "    <tr>\n",
       "      <th>2940</th>\n",
       "      <td>f93683c4be070215b6a0fdf5d2a8579c02cefc3c6e7578...</td>\n",
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       "    <tr>\n",
       "      <th>2942</th>\n",
       "      <td>f94eeb9ab8d3c14ba7c56b5fe255654c760ab204b6c3d1...</td>\n",
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       "      <th>2943</th>\n",
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       "      <th>2944</th>\n",
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       "    <tr>\n",
       "      <th>2945</th>\n",
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       "    <tr>\n",
       "      <th>2946</th>\n",
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       "    <tr>\n",
       "      <th>2948</th>\n",
       "      <td>f9f27d4797e4d6eeca25d42b09c8ba8063394a7512eb95...</td>\n",
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       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>f9fe33e39911e2260a0d9543f45b4c535fe39d60222f31...</td>\n",
       "      <td>bw</td>\n",
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       "    <tr>\n",
       "      <th>2950</th>\n",
       "      <td>fa093da63bccd776ff425e7b429c45979a594cde5f4f7f...</td>\n",
       "      <td>bw</td>\n",
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       "    <tr>\n",
       "      <th>2951</th>\n",
       "      <td>fa1425b9d2e99aece351b53a6a3cc172405324754239c2...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2952</th>\n",
       "      <td>fa16fecc502a349053cb9eb5bb99abe7479c01d5ee0845...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>fa19815dd4d3b8749f1ada5b22d14449ed5882f88ecf2b...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2954</th>\n",
       "      <td>fa35e04551705189a4fbeed752a353291a4af42e95e44d...</td>\n",
       "      <td>bw</td>\n",
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       "    <tr>\n",
       "      <th>2955</th>\n",
       "      <td>fa3b20c9d7e20465d16cf6083f396bc062daf964419eae...</td>\n",
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       "    <tr>\n",
       "      <th>2956</th>\n",
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       "      <td>bw</td>\n",
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       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>fa572e719ef3247cdbd8ec3c174d79a54322d0c8c9c683...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>fa5bed46d74f3b5bd58ef1bd5ff1dfebc30c3a9ce82fee...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>fa601d1c11cb2308b1afd3d74a46b0547522a8ec3ccf16...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>fa9479a7fcbc4915f250de4a20f5fb9cc372f99f2ea890...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>fa9ceb03eb18aeeaa33d7cffb4c588d42378c31eb5c84e...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>faa5e6c3397f97d07763f5071e15ed8b8eb4699f3430a5...</td>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2963</th>\n",
       "      <td>fabc53b72fbf0e697bea3d175ef3fff20c7bd5d34a7c0d...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2964</th>\n",
       "      <td>fac5068b5f17c8ee88ecf97b94baa6330fb4cd4034225f...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2965</th>\n",
       "      <td>facc44c79862d8a7e7d2877309ba005eb4200b3e8118fe...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2966</th>\n",
       "      <td>fae7766ca49d917e00d7967686ac86bdbf2d92b3439145...</td>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>fafd3d92a42b2fcbc19a6f1be750d0c67d7fb943a22784...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>fb10eec85881d6e88ca45085699c64c227672d7bae4855...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>fb11432e1637804c44c66fa7a02c28886ebcfc4ae2bb9c...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2970</th>\n",
       "      <td>fb31cfa38bfcb472996c8d074315c3ab627160b2b4e554...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>fb378bc29e67c4a47c194554162ccdd8a51c1f42b1b734...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>fb4812a5cd8fde7fb7f0fd1c799cd83213f27d6f3aa324...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2973</th>\n",
       "      <td>fb4f4b3dfb7be2ca930c69ad7fad5a26a3743fad57441f...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>fb5adda589173e51944174c68d39b5dadddac553967cb8...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>fb8b51e0962520fbee072a722a0ecea80df565656ea206...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>fb9b0b2daa50af5e0eb219c08f1f2b8926efbb48277063...</td>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>fb9cec77e92d7f7c4949d0d9e24919f16da98c315f5a40...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>fbe652a46a627fd3bcab420b38f85f85f50ee7d1baf529...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>fc0aa411669765298e51994f270758a4fd91e668610b73...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>fc40ddb37a7c3b9dd58f78ff59533792adf746387f8291...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2981</th>\n",
       "      <td>fc42ec97c6a0a90cb7bc4935592d8889040dd472f14967...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>fc672a05003a5f85e37ececabee7e79bd5470566f772e0...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>fc8b408c87454baad88c2ca1a4b02f726aa542e8622bcf...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>fc95f46ecb966a59b2086020d45fc645f2ec2562edfe48...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2985</th>\n",
       "      <td>fcd5cf4810bcc01fdb863941c8a2991e16810cfc18e6e1...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>fd11b29f504e0abe7fe5684f69aa9cdc8a4c20458c603d...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>fd21e389750fd86f6127208b89b12b6f25c6abc44dd782...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>fd2ecc4d69378f10d401992f83dd7a256701fefd45878f...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>fd450e91e5104ee3d3e9dbcb7118ddf0dcb061e1aefc09...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>fd4df12b775dd8eece392ec47cd7565c1b52b7d622bff3...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>fd849405326b95ff997939d18b8560212d602d6c2fb574...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>fd8538c83832e683d0ed1a35f4c878765c604d92cf96ed...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>fd959b91f1fd8971f6e0cfc0848e80e302ca748bb26daf...</td>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>fda5c593adf95f91aac8aae34f4ce0c5ad38ff49e74085...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>fda7e9a21f40ce89f7368c5a2fb47957ab4de41bfb52d6...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>fdb3f0ce1ce05bec83681ae64d549820d3c2f5e543ba8c...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>fdbc93d21ee6545e8d48a6545a348c464df0378e9ee1e5...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>fdbdd162d5c1454d1f74a4581158aa2ccf6a7e65a5860f...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>fdd370a664bcbff938b3ff394ffb3c431a4beaa3a0123e...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>fdee330bfce2c945e54b88b97da54de37dd4774e90ca6e...</td>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>fe2f4d33f45b77d045540e97957591b407cc19bc1e79c9...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3002</th>\n",
       "      <td>fe3536ba88d6daef98a9060232b43326fb1c634c923201...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3003</th>\n",
       "      <td>fe5d3329f6827b46e59eed1c2c10237319648aded6646e...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3004</th>\n",
       "      <td>fe79efc09c442a1df17f7ea319d43f197f27a5477fdc16...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3005</th>\n",
       "      <td>fe98836971a07c50394146b8b3ad9ca8b359558840d881...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>fe98e8841da36e4ee2c5540e5a568d1f20a55a0565af5f...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3007</th>\n",
       "      <td>fe9d16de910249cd379426f02c5068dd49a2556b07428b...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3008</th>\n",
       "      <td>fecf2003df7dc97f4bf0d7dba917477ced19e955ee7f6f...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3009</th>\n",
       "      <td>fed8bfbbd40e1ff1fb0e29b457e8b41486e7d9ead28779...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3010</th>\n",
       "      <td>fee623d0c937c25da543383a223df2795312f76564336d...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3011</th>\n",
       "      <td>feea4190b5bbcd274b76a93dd986b992040eb9cde3c865...</td>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3012</th>\n",
       "      <td>ff4571dd5d496ab22d4388881688e1b6d0bf97ec00bf34...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3013</th>\n",
       "      <td>ff4dafefa29d4ea375b36321bc55926e743fab64dcaa5a...</td>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>ff6bd41cbb8c7666eb6c3c375b97c69d7d09ba121fe1c1...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>ff8e3a989a4ea67b1ed8fd8d7091bb1089869cdda06e49...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>ffa053af65884b5fbe0626d350b5c3fc2e6279a5dd6fbd...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>ffb383db5ee04d316f2e40f74649aab78648cf9b9d7fc6...</td>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3018</th>\n",
       "      <td>ffbc47f9b8387b12dee3dbb453a39f7e0c3348cd055896...</td>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                ImageId bg_type\n",
       "2919  f712caa5d4ec696c143dee607100664f983820cee20b5a...      bw\n",
       "2920  f749bd3d0801a25581ebdecb696f4883c13e2f44ae071a...      bw\n",
       "2921  f7525279fa7d5896baf5dfb8b7535e0f4b5f19f2deed19...      bw\n",
       "2922  f76ae9f189bdb18a7ce90ddc760388cef13785866c1610...      bw\n",
       "2923  f77e0a7408802b56ad0d44915a882003dc048a8c04baf5...      bw\n",
       "2924  f7a5c69c3908e4a7443722bc0a59f998afff7735ae63a8...      bw\n",
       "2925  f7a6b7d515d1dc27915128663085998f978bdc5e9917f9...      bw\n",
       "2926  f7ad1329e948e9a540af770c2af37c235144d014aae3a0...      bw\n",
       "2927  f7c60564c8da0a484eb81b011f8fcc46c5749d20b012d0...      bw\n",
       "2928  f7c7666bd0e4a6a3abc0be51479133df8f77d649203aa5...      bw\n",
       "2929  f7f8276acd9c0b9ef4c4fbb02c8773219b0c6677114b88...      bw\n",
       "2930  f85455514a6c9d18f833071b99b5788daa6369405139fe...      bw\n",
       "2931  f8771dede575152dc5d196ce644b26237dbebab42dc2f7...      bw\n",
       "2932  f87c64c8301dbc35f85ed31bcd56ed95115511768080c8...      bw\n",
       "2933  f87e3ce60c112ce476f2889241195f507da956345e1593...      bw\n",
       "2934  f8b7784c82db3c77d15b45a8108ce6f4ac859f27d4a672...      bw\n",
       "2935  f8b83bbb07b06ff8cd433253f9342a5f62388323813a33...      bw\n",
       "2936  f8bfc24b997300d53af5bccd9a703f7ea96acac5383d8f...      bw\n",
       "2937  f8ea9320da06c050431bc818db1d1d4c87ed26f11b59f6...      bw\n",
       "2938  f90794930bbcbcc80e43f76b9a99122f46f7eff3c64054...      bw\n",
       "2939  f920dc5b27c57acc4bd451baa709c414898e50e326314d...      bw\n",
       "2940  f93683c4be070215b6a0fdf5d2a8579c02cefc3c6e7578...      bw\n",
       "2941  f94d0bb208576e2cd46b63e7ba191fdeed4a7b46522c53...      bw\n",
       "2942  f94eeb9ab8d3c14ba7c56b5fe255654c760ab204b6c3d1...      bw\n",
       "2943  f94f5e38e1bd467538bb5ce686afb3ca85fd8f15d978cc...      bw\n",
       "2944  f9544bb41d7f422747e9bc9101e368f094662a5fe8d827...      bw\n",
       "2945  f95b1128aea15019e4aafee22a07407f5b56c51ea483a5...      bw\n",
       "2946  f9ca7abf4fdaf6082b40fe3be2a01a8d5367a5fe711a5d...      bw\n",
       "2947  f9da8312544a4a431b72cd71ce240ec2f060e0be9642e6...      he\n",
       "2948  f9f27d4797e4d6eeca25d42b09c8ba8063394a7512eb95...      bw\n",
       "2949  f9fe33e39911e2260a0d9543f45b4c535fe39d60222f31...      bw\n",
       "2950  fa093da63bccd776ff425e7b429c45979a594cde5f4f7f...      bw\n",
       "2951  fa1425b9d2e99aece351b53a6a3cc172405324754239c2...      bw\n",
       "2952  fa16fecc502a349053cb9eb5bb99abe7479c01d5ee0845...      bw\n",
       "2953  fa19815dd4d3b8749f1ada5b22d14449ed5882f88ecf2b...      bw\n",
       "2954  fa35e04551705189a4fbeed752a353291a4af42e95e44d...      bw\n",
       "2955  fa3b20c9d7e20465d16cf6083f396bc062daf964419eae...      bw\n",
       "2956  fa46454ed30dd1e1681d7e4926baee039c3cd15f9c2713...      bw\n",
       "2957  fa572e719ef3247cdbd8ec3c174d79a54322d0c8c9c683...      bw\n",
       "2958  fa5bed46d74f3b5bd58ef1bd5ff1dfebc30c3a9ce82fee...      bw\n",
       "2959  fa601d1c11cb2308b1afd3d74a46b0547522a8ec3ccf16...      bw\n",
       "2960  fa9479a7fcbc4915f250de4a20f5fb9cc372f99f2ea890...      bw\n",
       "2961  fa9ceb03eb18aeeaa33d7cffb4c588d42378c31eb5c84e...      bw\n",
       "2962  faa5e6c3397f97d07763f5071e15ed8b8eb4699f3430a5...      he\n",
       "2963  fabc53b72fbf0e697bea3d175ef3fff20c7bd5d34a7c0d...      bw\n",
       "2964  fac5068b5f17c8ee88ecf97b94baa6330fb4cd4034225f...      bw\n",
       "2965  facc44c79862d8a7e7d2877309ba005eb4200b3e8118fe...      bw\n",
       "2966  fae7766ca49d917e00d7967686ac86bdbf2d92b3439145...      he\n",
       "2967  fafd3d92a42b2fcbc19a6f1be750d0c67d7fb943a22784...      bw\n",
       "2968  fb10eec85881d6e88ca45085699c64c227672d7bae4855...      bw\n",
       "2969  fb11432e1637804c44c66fa7a02c28886ebcfc4ae2bb9c...      bw\n",
       "2970  fb31cfa38bfcb472996c8d074315c3ab627160b2b4e554...      bw\n",
       "2971  fb378bc29e67c4a47c194554162ccdd8a51c1f42b1b734...      bw\n",
       "2972  fb4812a5cd8fde7fb7f0fd1c799cd83213f27d6f3aa324...      bw\n",
       "2973  fb4f4b3dfb7be2ca930c69ad7fad5a26a3743fad57441f...      bw\n",
       "2974  fb5adda589173e51944174c68d39b5dadddac553967cb8...      bw\n",
       "2975  fb8b51e0962520fbee072a722a0ecea80df565656ea206...      bw\n",
       "2976  fb9b0b2daa50af5e0eb219c08f1f2b8926efbb48277063...      he\n",
       "2977  fb9cec77e92d7f7c4949d0d9e24919f16da98c315f5a40...      bw\n",
       "2978  fbe652a46a627fd3bcab420b38f85f85f50ee7d1baf529...      bw\n",
       "2979  fc0aa411669765298e51994f270758a4fd91e668610b73...      bw\n",
       "2980  fc40ddb37a7c3b9dd58f78ff59533792adf746387f8291...      bw\n",
       "2981  fc42ec97c6a0a90cb7bc4935592d8889040dd472f14967...      bw\n",
       "2982  fc672a05003a5f85e37ececabee7e79bd5470566f772e0...      bw\n",
       "2983  fc8b408c87454baad88c2ca1a4b02f726aa542e8622bcf...      bw\n",
       "2984  fc95f46ecb966a59b2086020d45fc645f2ec2562edfe48...      bw\n",
       "2985  fcd5cf4810bcc01fdb863941c8a2991e16810cfc18e6e1...      bw\n",
       "2986  fd11b29f504e0abe7fe5684f69aa9cdc8a4c20458c603d...      bw\n",
       "2987  fd21e389750fd86f6127208b89b12b6f25c6abc44dd782...      bw\n",
       "2988  fd2ecc4d69378f10d401992f83dd7a256701fefd45878f...      bw\n",
       "2989  fd450e91e5104ee3d3e9dbcb7118ddf0dcb061e1aefc09...      bw\n",
       "2990  fd4df12b775dd8eece392ec47cd7565c1b52b7d622bff3...      bw\n",
       "2991  fd849405326b95ff997939d18b8560212d602d6c2fb574...      bw\n",
       "2992  fd8538c83832e683d0ed1a35f4c878765c604d92cf96ed...      bw\n",
       "2993  fd959b91f1fd8971f6e0cfc0848e80e302ca748bb26daf...      he\n",
       "2994  fda5c593adf95f91aac8aae34f4ce0c5ad38ff49e74085...      bw\n",
       "2995  fda7e9a21f40ce89f7368c5a2fb47957ab4de41bfb52d6...      bw\n",
       "2996  fdb3f0ce1ce05bec83681ae64d549820d3c2f5e543ba8c...      bw\n",
       "2997  fdbc93d21ee6545e8d48a6545a348c464df0378e9ee1e5...      bw\n",
       "2998  fdbdd162d5c1454d1f74a4581158aa2ccf6a7e65a5860f...      bw\n",
       "2999  fdd370a664bcbff938b3ff394ffb3c431a4beaa3a0123e...      bw\n",
       "3000  fdee330bfce2c945e54b88b97da54de37dd4774e90ca6e...      he\n",
       "3001  fe2f4d33f45b77d045540e97957591b407cc19bc1e79c9...      bw\n",
       "3002  fe3536ba88d6daef98a9060232b43326fb1c634c923201...      bw\n",
       "3003  fe5d3329f6827b46e59eed1c2c10237319648aded6646e...      bw\n",
       "3004  fe79efc09c442a1df17f7ea319d43f197f27a5477fdc16...      bw\n",
       "3005  fe98836971a07c50394146b8b3ad9ca8b359558840d881...      bw\n",
       "3006  fe98e8841da36e4ee2c5540e5a568d1f20a55a0565af5f...      bw\n",
       "3007  fe9d16de910249cd379426f02c5068dd49a2556b07428b...      bw\n",
       "3008  fecf2003df7dc97f4bf0d7dba917477ced19e955ee7f6f...      bw\n",
       "3009  fed8bfbbd40e1ff1fb0e29b457e8b41486e7d9ead28779...      bw\n",
       "3010  fee623d0c937c25da543383a223df2795312f76564336d...      bw\n",
       "3011  feea4190b5bbcd274b76a93dd986b992040eb9cde3c865...      he\n",
       "3012  ff4571dd5d496ab22d4388881688e1b6d0bf97ec00bf34...      bw\n",
       "3013  ff4dafefa29d4ea375b36321bc55926e743fab64dcaa5a...      he\n",
       "3014  ff6bd41cbb8c7666eb6c3c375b97c69d7d09ba121fe1c1...      bw\n",
       "3015  ff8e3a989a4ea67b1ed8fd8d7091bb1089869cdda06e49...      bw\n",
       "3016  ffa053af65884b5fbe0626d350b5c3fc2e6279a5dd6fbd...      bw\n",
       "3017  ffb383db5ee04d316f2e40f74649aab78648cf9b9d7fc6...      he\n",
       "3018  ffbc47f9b8387b12dee3dbb453a39f7e0c3348cd055896...      bw"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "back_ground_df = pd.read_csv(BG_DATA_PATH)\n",
    "back_ground_df.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bg_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ImageId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0019c086029dd3be01f72131edb74e21ee995574e6d5c136ea868630b0d73523</th>\n",
       "      <td>he</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>004a078bb44ee55ee7d6f1c19f96b3a0d3b5037746a3a75197dbb0be06da05cf</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>005463e6d4a0a0b21161f1d97392f22556fbddba970d9440ae774229308a91ed</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>005af293e8e53218ae96746ecf9bb88b511154d4a0b35e4ec6296b4623e15836</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>005d47447abac7f7fa0ac56ba82f13edbf485105baf0672504d84b58d562f38b</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00ac87390253a22f6eb67c5771a73050e06200d1aa682fcf1b1e3783b2944166</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00b4b9c026cfc91af21691503bad67081fc02d5c77b3cd27200d02dca6966b83</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00da4fe5346bbb36ccf178964a22fdf7e33888989624176a6af3b4a7cfd7da63</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00e36f2470960e84cfc9f0d30c1f76abd7b35e4516380bf74985cbe641086806</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00e67980783eaae30d41f6479beb9c04eb91de4608537673898039094800ddb0</th>\n",
       "      <td>bw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   bg_type\n",
       "ImageId                                                   \n",
       "0019c086029dd3be01f72131edb74e21ee995574e6d5c13...      he\n",
       "004a078bb44ee55ee7d6f1c19f96b3a0d3b5037746a3a75...      bw\n",
       "005463e6d4a0a0b21161f1d97392f22556fbddba970d944...      bw\n",
       "005af293e8e53218ae96746ecf9bb88b511154d4a0b35e4...      bw\n",
       "005d47447abac7f7fa0ac56ba82f13edbf485105baf0672...      bw\n",
       "00ac87390253a22f6eb67c5771a73050e06200d1aa682fc...      bw\n",
       "00b4b9c026cfc91af21691503bad67081fc02d5c77b3cd2...      bw\n",
       "00da4fe5346bbb36ccf178964a22fdf7e33888989624176...      bw\n",
       "00e36f2470960e84cfc9f0d30c1f76abd7b35e4516380bf...      bw\n",
       "00e67980783eaae30d41f6479beb9c04eb91de460853767...      bw"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "back_ground_df = back_ground_df.set_index('ImageId')\n",
    "back_ground_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta = meta[meta['is_train'] == 0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta['bg_type'] = meta.ImageId.map(back_ground_df.bg_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3016\n",
       "1       2\n",
       "3       1\n",
       "Name: process_type, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta['process_type'] = meta['bg_type'].map({\n",
    "    'bw': 2, # do nothing\n",
    "    'wb': 3, # pure reverse\n",
    "    'he': 2, # do nothing\n",
    "    'c_bw': 0, # turns into gray \n",
    "    'c_wb': 1, # turns into gray and reverse\n",
    "})\n",
    "meta['process_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2803\n",
       "1     216\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta['group'] = meta['bg_type'].map({\n",
    "    'bw': 0, # do nothing\n",
    "    'wb': 0, # pure reverse\n",
    "    'he': 1, # do nothing\n",
    "    'c_bw': 0, # turns into gray \n",
    "    'c_wb': 0, # turns into gray and reverse\n",
    "})\n",
    "meta['group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_test = meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Mask-RCNN configuration\n",
    "We will now define parameters for our mask-rcnn. \n",
    "Tweak them to get better results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DsbConfig(Config):\n",
    "\n",
    "    # Give the configuration a recognizable name\n",
    "    NAME = \"dsb\"\n",
    "      \n",
    "    LEARNING_RATE = 1e-2\n",
    "    \n",
    "    # If enabled, resizes instance masks to a smaller size to reduce\n",
    "    # memory load. Recommended when using high-resolution image\n",
    "    USE_MINI_MASK = True\n",
    "    MINI_MASK_SHAPE = (56, 56)  # (height, width) of the mini-mask\n",
    "    \n",
    "    # Train on 1 GPU and 8 images per GPU. Batch size is GPUs * images/GPU.\n",
    "    GPU_COUNT = 1\n",
    "    IMAGES_PER_GPU = 2\n",
    "    # Total number of steps (batches of samples) to yield from generator before declaring one epoch finished and starting the next epoch.\n",
    "    # typically be equal to the number of samples of your dataset divided by the batch size\n",
    "    STEPS_PER_EPOCH = 300\n",
    "    VALIDATION_STEPS = 70\n",
    "\n",
    "    # Number of classes (including background)\n",
    "    NUM_CLASSES = 1 + 1  # background + nucleis\n",
    "    IMAGE_MIN_DIM = 512\n",
    "    IMAGE_MAX_DIM = 512\n",
    "    IMAGE_PADDING = True  # currently, the False option is not supported\n",
    "    RPN_ANCHOR_SCALES = (8, 16, 32, 64, 128)  # anchor side in pixels, maybe add a 256?\n",
    "    # The strides of each layer of the FPN Pyramid. These values\n",
    "    # are based on a Resnet101 backbone.\n",
    "    BACKBONE_STRIDES = [4, 8, 16, 32, 64]\n",
    "    # How many anchors per image to use for RPN training\n",
    "    RPN_TRAIN_ANCHORS_PER_IMAGE = 320 #300\n",
    "    \n",
    "    # ROIs kept after non-maximum supression (training and inference)\n",
    "    POST_NMS_ROIS_TRAINING = 2000\n",
    "    POST_NMS_ROIS_INFERENCE = 2000\n",
    "    POOL_SIZE = 7\n",
    "    MASK_POOL_SIZE = 14 # 14\n",
    "    MASK_SHAPE = [28, 28] # 28\n",
    "    TRAIN_ROIS_PER_IMAGE = 512\n",
    "    RPN_DETECTION_MIN_CONFIDENCE = .6\n",
    "    RPN_NMS_THRESHOLD = 0.6 #0.7\n",
    "    MAX_GT_INSTANCES = 256\n",
    "    DETECTION_MAX_INSTANCES = 450 \n",
    "    # Minimum probability value to accept a detected instance\n",
    "    # ROIs below this threshold are skipped\n",
    "    DETECTION_MIN_CONFIDENCE = 0.67 # may be smaller?\n",
    "    # Non-maximum suppression threshold for detection\n",
    "    DETECTION_NMS_THRESHOLD = 0.3 # 0.3\n",
    "    \n",
    "    # DETECTION_MAX_INSTANCES=400, DETECTION_MIN_CONFIDENCE=.7 => Val loss: .21xx, LB: .424\n",
    "    # DETECTION_MAX_INSTANCES=450, DETECTION_MIN_CONFIDENCE=.65 => Val loss: .3, LB: .438\n",
    "    # DETECTION_MAX_INSTANCES=, DETECTION_MIN_CONFIDENCE= => Val loss: ., LB: .\n",
    "    \n",
    "    '''\n",
    "    0324 note: \n",
    "        RPN_NMS_THRESHOLD .7 -> .6 to generate more proposals at training,\n",
    "        DETECTION_MAX_INSTANCES 400 -> 450\n",
    "        DETECTION_MIN_CONFIDENCE .7\n",
    "        BACKBONE_STRIDES add `2` ..?\n",
    "    '''\n",
    "    \n",
    "    MEAN_PIXEL = np.array([0.,0.,0.])\n",
    "    \n",
    "    # Weight decay regularization\n",
    "    WEIGHT_DECAY = 0.0001\n",
    "    \n",
    "    # Min Box Size\n",
    "    MIN_BOX_SIZE = 36./(512.**2.) # (36/512**2) (delete objects smaller than 6*6 pixels)\n",
    "    BORDER_RATIO = 4. / 512. # smaller than 4 pixels width\\height at the border\n",
    "    BORDER_MIN_BOX_SIZE = 16./(512.**2.)\n",
    "  \n",
    "    LR_SHRINK_PERIOD = 10\n",
    "    \n",
    "config = DsbConfig()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset definition\n",
    "\n",
    "We need to define our datasets for the data science bowl competition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from augment import Augment\n",
    "\n",
    "class DsbDataset(utils.Dataset):\n",
    "    '''\n",
    "    process_info is a image-id <=> process type mapping pd.series\n",
    "    '''\n",
    "    def load_dataset(self, process_info, train_mode=True, augment=False):\n",
    "        self.add_class(\"dsb\", 1, \"nuclei\")\n",
    "        self.process_info = process_info\n",
    "        self.augmenter = Augment()\n",
    "        self.augment = augment\n",
    "        \n",
    "        if train_mode:\n",
    "            directory = dsb_dir\n",
    "        else:\n",
    "            directory = test_dir\n",
    "            \n",
    "        ids = []\n",
    "        for i, id in enumerate(process_info.ImageId.values):\n",
    "            image_dir = os.path.join(directory, id)\n",
    "            self.add_image(\"dsb\", image_id=i, path=image_dir)\n",
    "            ids.append(i)\n",
    "        \n",
    "        self.process_info.reset_index(drop=True, inplace=True)\n",
    "        #print(self.process_info)\n",
    "        self.process_info['id'] = ids\n",
    "        self.process_info.set_index('id')\n",
    "        self.process_info = self.process_info['process_type']\n",
    "        #print(self.process_info)\n",
    "        \n",
    "    def load_image(self, image_id, non_zero=None):\n",
    "        info = self.image_info[image_id]\n",
    "        path = info['path']\n",
    "        image_name = os.listdir(os.path.join(path, 'images'))\n",
    "        image_path = os.path.join(path, 'images', image_name[0])\n",
    "        image = imageio.imread(image_path)\n",
    "        \n",
    "        #print(self.process_info[image_id])\n",
    "        if image_path[-4:] == '.tif':\n",
    "            self.offset = (randrange(488), randrange(488))\n",
    "            image = image[self.offset[0]:self.offset[0]+512, self.offset[1]:self.offset[1]+512, :]\n",
    "            #image = skimage.transform.resize(image, (512,512),  preserve_range=True)\n",
    "        \n",
    "        try:\n",
    "            if image.shape[2] != 3:\n",
    "                image = image[:,:,:3]\n",
    "        except:\n",
    "            print(image)\n",
    "            image = image / 255.\n",
    "            image = skimage.color.gray2rgb(image.astype('uint8'))\n",
    "            print('new\\n', image)\n",
    "            \n",
    "        image = self.preprocess(image, process_type=self.process_info[image_id])\n",
    "        image = image.astype('float32')\n",
    "        \n",
    "        if self.augment:\n",
    "            self.augment_func = self.augmenter.get_random_augement()\n",
    "            image = self.augment_func(image)\n",
    "            \n",
    "        return image\n",
    "\n",
    "    def image_reference(self, image_id):\n",
    "        info = self.image_info[image_id]\n",
    "        if info[\"source\"] == \"shapes\":\n",
    "            return info[\"shapes\"]\n",
    "        else:\n",
    "            super(self.__class__).image_reference(self, image_id)\n",
    "\n",
    "    def load_mask(self, image_id):\n",
    "        info = self.image_info[image_id]\n",
    "        path = info['path']\n",
    "        mask_dir = os.path.join(path, 'masks')\n",
    "        mask_names = os.listdir(mask_dir)\n",
    "        count = len(mask_names)\n",
    "        mask = []\n",
    "        \n",
    "        for i, el in enumerate(mask_names):\n",
    "            msk_path = os.path.join(mask_dir, el)\n",
    "            msk = imageio.imread(msk_path)\n",
    "            if np.sum(msk) == 0:\n",
    "                print('invalid mask')\n",
    "                continue\n",
    "                \n",
    "            if 'TCGA' in mask_dir:\n",
    "                #msk = skimage.transform.resize(msk, (512,512), preserve_range=True)\n",
    "                msk = msk[self.offset[0]:self.offset[0]+512, self.offset[1]:self.offset[1]+512]\n",
    "                if np.sum(msk) == 0:\n",
    "                    continue\n",
    "                \n",
    "            msk = msk.astype('float32')/255.\n",
    "            \n",
    "            if len(msk.shape) > 2:\n",
    "                msk = np.sum(msk, axis=2)\n",
    "                \n",
    "            mask.append(msk)\n",
    "            \n",
    "        try:\n",
    "            mask = np.array(mask)\n",
    "        except:\n",
    "            for i, m in enumerate(mask):\n",
    "                if len(m.shape) > 2:\n",
    "                    print(mask_names[i])\n",
    "                    \n",
    "        mask[mask > 0.] = 1.\n",
    "        mask = np.transpose(mask, (1,2,0))\n",
    "        if self.augment:\n",
    "            mask = self.augment_func(mask) # self.augment_func was decided when loading image before loading masks\n",
    "            \n",
    "        occlusion = np.logical_not(mask[:, :, -1]).astype(np.uint8)\n",
    "        count = mask.shape[2]\n",
    "        for i in range(count-2, -1, -1):\n",
    "            mask[:, :, i] = mask[:, :, i] * occlusion\n",
    "            occlusion = np.logical_and(occlusion, np.logical_not(mask[:, :, i]))\n",
    "        class_ids = [self.class_names.index('nuclei') for s in range(count)]\n",
    "        class_ids = np.asarray(class_ids)\n",
    "        \n",
    "        return mask, class_ids.astype(np.int32)\n",
    "    \n",
    "    def preprocess(self, img, process_type=0):\n",
    "        if process_type == 0: # grey-rgb-grey\n",
    "            gray = skimage.color.rgb2gray(img.astype('uint8'))\n",
    "            img = skimage.color.gray2rgb(gray)\n",
    "            img *= 255.\n",
    "        elif process_type == 1:  # grey-rgb-grey-reverse\n",
    "            gray = skimage.color.rgb2gray(img.astype('uint8'))\n",
    "            img = skimage.color.gray2rgb(gray)\n",
    "            img *= 255.\n",
    "            img = 255. - img\n",
    "        elif process_type == 2:\n",
    "            img = img.astype(float) # do nothing\n",
    "        elif process_type == 3:\n",
    "            img = 255. - img\n",
    "        \n",
    "        return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsb_dir = TRAIN_PATH\n",
    "test_dir = TEST_PATH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate\n",
    "I think we are ready to see how our model does on the validation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InferenceConfig(DsbConfig):\n",
    "    GPU_COUNT = 1\n",
    "    IMAGES_PER_GPU = 1\n",
    "    # MEAN_PIXEL = np.array([56.02288505, 54.02376286, 54.26675248])\n",
    "\n",
    "    USE_FLEXIBLE_NMS = True\n",
    "    DETECTION_MIN_CONFIDENCE = 0.88\n",
    "    DETECTION_NMS_THRESHOLD = 0.3\n",
    "    SNMS_METHOD = 2 # 0: normal greedy nms, 1: linear method 2: Gaussian method\n",
    "    SNMS_SIGMA = 0.5\n",
    "\n",
    "inference_config = InferenceConfig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = ['logs/Final Models/partial_bw_50.h5',\n",
    "         'logs/Final Models/partial_he_70.h5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ax(rows=1, cols=1, size=8):\n",
    "    \"\"\"Return a Matplotlib Axes array to be used in\n",
    "    all visualizations in the notebook. Provide a\n",
    "    central point to control graph sizes.\n",
    "    \n",
    "    Change the default size attribute to control the size\n",
    "    of rendered images\n",
    "    \"\"\"\n",
    "    _, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ids = os.listdir(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_split(meta_test):\n",
    "    meta_test_groups = [meta_test[meta_test['group'] == group].reset_index(drop=True) for group in range(2)]\n",
    "    return meta_test_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_test_groups = test_split(meta_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "test_datasets = []\n",
    "for i in range(2):\n",
    "    \n",
    "    te = meta_test_groups[i]\n",
    "\n",
    "    # Testing dataset\n",
    "    dataset_test = DsbDataset()\n",
    "    dataset_test.load_dataset(te[['ImageId', 'process_type']], train_mode=False)\n",
    "    dataset_test.prepare()\n",
    "    \n",
    "    test_datasets.append(dataset_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2803\n"
     ]
    }
   ],
   "source": [
    "print(len(test_datasets[0].image_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "216\n"
     ]
    }
   ],
   "source": [
    "print(len(test_datasets[1].image_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prediction_saver import prediction_saver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "start_time = time()\n",
    "PRED_SAVER_DIR = os.path.join(ROOT_DIR, \"prediction_savers_stage2_5070\")\n",
    "ps = prediction_saver(PRED_SAVER_DIR, meta_test.ImageId.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACCEPT_RATIO = 1.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TTA + Flexble Avg takes 0.014 secs\n"
     ]
    }
   ],
   "source": [
    "print('TTA + Flexble Avg takes {:.3f} secs'.format(time()-start_time))\n",
    "start_time = time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rle_encoding(x):\n",
    "    '''\n",
    "    x: numpy array of shape (height, width), 1 - mask, 0 - background\n",
    "    Returns run length as list\n",
    "    '''\n",
    "    dots = np.where(x.T.flatten()==1)[0] # .T sets Fortran order down-then-right\n",
    "    run_lengths = []\n",
    "    prev = -2\n",
    "    for b in dots:\n",
    "        if (b>prev+1): run_lengths.extend((b+1, 0))\n",
    "        run_lengths[-1] += 1\n",
    "        prev = b\n",
    "    return run_lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy2encoding_no_overlap(predicts, img_name, scores):\n",
    "    if len(scores) == 0:\n",
    "        return [], []\n",
    "    \n",
    "    sum_predicts = np.sum(predicts, axis=2)\n",
    "    rows, cols = np.where(sum_predicts>=2)\n",
    "    \n",
    "    for i in zip(rows, cols):\n",
    "        instance_indicies = np.where(np.any(predicts[i[0],i[1],:]))[0]\n",
    "        highest = instance_indicies[0]\n",
    "        predicts[i[0],i[1],:] = predicts[i[0],i[1],:]*0\n",
    "        predicts[i[0],i[1],highest] = 1\n",
    "    \n",
    "    ImageId = []\n",
    "    EncodedPixels = []\n",
    "    #print(predicts.shape)\n",
    "    for i in range(predicts.shape[2]): \n",
    "        rle = rle_encoding(predicts[:,:,i])\n",
    "        if len(rle)>0:\n",
    "            ImageId.append(img_name)\n",
    "            EncodedPixels.append(rle)    \n",
    "    return ImageId, EncodedPixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new_test_ids = []\n",
    "rles = []\n",
    "\n",
    "def update_final_predictions(test_ids, predictions, new_test_ids, rles):\n",
    "    \n",
    "    for id, raw_pred in zip(test_ids, predictions):\n",
    "        ids, rle = numpy2encoding_no_overlap(raw_pred[0], id, raw_pred[1])\n",
    "        new_test_ids += ids\n",
    "        rles += rle\n",
    "    \n",
    "    return new_test_ids, rles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processed_count = 0\n",
      "image  0  processed, bg type = he \n",
      "id = fdee330bfce2c945e54b88b97da54de37dd4774e90ca6ec4033dd4e76d18a757\n",
      "image  1  processed, bg type = he \n",
      "id = 4f9a3e99f1ae71d6af95c22d815492aa57037b1cf5aa9c2c64d88f18db7c3dca\n",
      "image  2  processed, bg type = he \n",
      "id = a236bdb20d8232642f64a09e41625c170bd825c4f92d766792499925a5ef9d58\n",
      "image  3  processed, bg type = he \n",
      "id = 59976c22f60f691d9daaa812e7c3ed8e7482dad89dbf6f20758828a141caa77e\n",
      "image  4  processed, bg type = he \n",
      "id = 62376362668799384fffe37e3d59b702bda79ab97578d3866620022820394507\n",
      "image  5  processed, bg type = he \n",
      "id = 3c20f44a4d4f215ff42b752e31444d674a20de481fa44137aeb3893edbffb08d\n",
      "image  6  processed, bg type = he \n",
      "id = 6c51b3db308c819131ffb1c470276497f4916ded38702056169f47a57b6dd78d\n",
      "image  7  processed, bg type = he \n",
      "id = e2ae4a70f70fa7cc1efe688b7e35bc2307c30ca532cd85b7ecaa48475df420b7\n",
      "image  8  processed, bg type = he \n",
      "id = 5164fda32d6a57286d2f6063abda877ca81a4348bb4445b787533454b9a9c1be\n",
      "image  9  processed, bg type = he \n",
      "id = fb9b0b2daa50af5e0eb219c08f1f2b8926efbb4827706311435b6c3d2aff8a20\n",
      "image  10  processed, bg type = he \n",
      "id = 2e0f1c047c4674fcd361edc271ff7cf66f7712fe91435b88f7848a8b5f06da83\n",
      "image  11  processed, bg type = he \n",
      "id = 0c05a71fc7981ba2c0c6b5dde6d8b3eea6ed084c929eb5445b302886bc9eb57c\n",
      "image  12  processed, bg type = he \n",
      "id = 16b73858cc19211efbe8d3950800b33d66f9e430e4558826eec60004b19eb2fe\n",
      "image  13  processed, bg type = he \n",
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      "image  78  processed, bg type = he \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "id = be4e502a6bd28ffed69cc3b3841c84967585bd1e6806f39b8215005cd4cfe038\n",
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      "image  94  processed, bg type = he \n",
      "id = 17e1b2324ba299fbddf2643520d2a5e860a44f4af19f09797fb20eb684e1c47e\n",
      "image  95  processed, bg type = he \n",
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      "image  96  processed, bg type = he \n",
      "id = ef20c80c5795f0cb058f018ad1e43a87005f2cf2727cf963d786574d50800f1d\n",
      "image  97  processed, bg type = he \n",
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      "image  98  processed, bg type = he \n",
      "id = d8d003b17919e765b801c66b8bbe982282b67b2fbd402bf0482ce0a34f793686\n",
      "image  99  processed, bg type = he \n",
      "id = 285a1b852717703a725c2ca471405311a662e3a33766379cf0768b5f766e4964\n",
      "image  100  processed, bg type = he \n",
      "id = 8b1693c65643b7081cc84bf55aa7323940ca89ace609eeb99bf3aa2524986197\n",
      "image  101  processed, bg type = he \n",
      "id = 3a829587e31df7853846d9b8df7eb42acc28145ecb5de6dbb2c5d19ef98954c3\n",
      "image  102  processed, bg type = he \n",
      "id = c9e2d6b09f6f55999dad8daabcc04f73f65d35d6afefacb57db32df35546a017\n",
      "image  103  processed, bg type = he \n",
      "id = 4bbf235ec8c0a339626a749274affa9bb2a9e51ee8d4725a2044bc1a2342e710\n",
      "image  104  processed, bg type = he \n",
      "id = 04c7531bd6e7db41341892f85f26163885bace79ea4501c8388e044d7da8e35f\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-fc523cc54115>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scores'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m                 \u001b[0mb_masks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'masks'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m                     \u001b[0;31m# binarize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m                     \u001b[0mb_masks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m>=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    233\u001b[0m                 return format.read_array(bytes,\n\u001b[1;32m    234\u001b[0m                                          \u001b[0mallow_pickle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallow_pickle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m                                          pickle_kwargs=self.pickle_kwargs)\n\u001b[0m\u001b[1;32m    236\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/numpy/lib/format.py\u001b[0m in \u001b[0;36mread_array\u001b[0;34m(fp, allow_pickle, pickle_kwargs)\u001b[0m\n\u001b[1;32m    678\u001b[0m                     \u001b[0mread_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_read_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m                     \u001b[0mread_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mread_count\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitemsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"array data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m                     array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,\n\u001b[1;32m    682\u001b[0m                                                              count=read_count)\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/numpy/lib/format.py\u001b[0m in \u001b[0;36m_read_bytes\u001b[0;34m(fp, size, error_template)\u001b[0m\n\u001b[1;32m    817\u001b[0m         \u001b[0;31m# done about that.  note that regular files can't be non-blocking\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    820\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/zipfile.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m    842\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_eof\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 844\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    845\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    846\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_readbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/zipfile.py\u001b[0m in \u001b[0;36m_read1\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m    918\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compress_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mZIP_DEFLATED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMIN_READ_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 920\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decompressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecompress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    921\u001b[0m             self._eof = (self._decompressor.eof or\n\u001b[1;32m    922\u001b[0m                          \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compress_left\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "TESTING_BATCH_SIZE = 1000\n",
    "TO_VISUALIZE =False\n",
    "gen_index = 1\n",
    "count = [0, 216]\n",
    "\n",
    "for i in range(2):\n",
    "    test_index = i\n",
    "    dataset_test = test_datasets[test_index]\n",
    "    \n",
    "    processed_count = 0\n",
    "    \n",
    "    if test_index == gen_index:\n",
    "        processed_count = count[0]\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "    while processed_count < len(dataset_test.image_ids) and processed_count < count[1]:\n",
    "        print('processed_count =', processed_count)\n",
    "        selected_test_ids = dataset_test.image_ids[processed_count:processed_count+TESTING_BATCH_SIZE]\n",
    "           \n",
    "        #print(meta_test_groups[test_index].ImageId.values[processed_count:processed_count+TESTING_BATCH_SIZE])\n",
    "        '''\n",
    "        ps.finalize_predictions(meta_test_groups[test_index].ImageId.values[processed_count:processed_count+TESTING_BATCH_SIZE], \n",
    "                                      inference_config.DETECTION_MIN_CONFIDENCE, 4, .5, \n",
    "                                      inference_config.SNMS_SIGMA)\n",
    "        '''\n",
    "        \n",
    "        # def finalize_predictions(self, min_confidence, total_vote_count, ratio, sigma)\n",
    "        \n",
    "        '''\n",
    "        data = {\n",
    "                'masks': np.array([]),\n",
    "                'boxes': np.array([]),\n",
    "                'scores': np.array([]),\n",
    "                'sample_counts': np.array([])\n",
    "                }\n",
    "        '''\n",
    "        raw_predictions = []\n",
    "        image_ids = []\n",
    "        for x, test_id in enumerate(selected_test_ids):\n",
    "            image_id = meta_test_groups[test_index].ImageId.values[x+processed_count]\n",
    "            pred = ps.load(image_id, is_final=True) \n",
    "        \n",
    "            if len(pred['scores']) > 0:\n",
    "                b_masks = []\n",
    "                for mask in pred['masks']:\n",
    "                    # binarize\n",
    "                    b_masks.append(np.where(mask>=0.5, 1, 0).astype(np.uint8))\n",
    "\n",
    "                new_masks = np.stack(b_masks, axis=2)\n",
    "                boxes = np.array(pred['boxes'])\n",
    "                scores = np.array(pred['scores'])\n",
    "                class_ids = np.ones((len(pred['scores'],))).astype(np.uint8)\n",
    "            \n",
    "                #res[x]\n",
    "                '''\n",
    "                data = {\n",
    "                   'boxes': res[i][0],\n",
    "                   'masks': res[i][1],\n",
    "                   'scores': res[i][2],\n",
    "                   'class_ids': res[i][3],\n",
    "                }\n",
    "                filtered_boxes, filtered_masks, filtered_scores, filtered_class_ids, nb_filter_masks\n",
    "                '''\n",
    "\n",
    "                image_ids.append(image_id)\n",
    "                raw_predictions.append((np.array(new_masks), np.array(pred['scores'])))\n",
    "                \n",
    "            print('image ', x+processed_count, \n",
    "                  ' processed, bg type =', meta_test_groups[test_index].bg_type.values[x+processed_count],\n",
    "                  '\\nid =', image_id)\n",
    "            \n",
    "            del pred; gc.collect()\n",
    "        \n",
    "        \n",
    "        new_test_ids, rles = update_final_predictions(image_ids, raw_predictions, new_test_ids, rles)\n",
    "        old_processed_count = processed_count\n",
    "        processed_count += len(selected_test_ids)\n",
    "        \n",
    "        df = pd.DataFrame({ 'ImageId' : new_test_ids , 'EncodedPixels' : rles})\n",
    "        df.to_csv('output/submission_ci88_stage2_5070_{}_{}.csv'.format(old_processed_count, processed_count), \n",
    "                  index=False, columns=['ImageId', 'EncodedPixels'])\n",
    "\n",
    "        \n",
    "        new_test_ids, rles = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(new_test_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(rles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Summarize TTA Results takes {:.3f} secs'.format(time()-start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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