1.6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building an image retrieval system with deep features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Graphlab Create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import graphlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the CIFAR-10 dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(The data has been reduced to just 4 categories = {'cat','bird','automobile','dog'}.)\n",
    "\n",
    "This dataset is already split into a training set and test set. In this simple retrieval example, there is no notion of \"testing\", so we will only use the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to soubhik.dd2015@cs.iiests.ac.in and will expire on August 22, 2017.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: C:\\Users\\soubhik\\AppData\\Local\\Temp\\graphlab_server_1487047375.log.0\n"
     ]
    }
   ],
   "source": [
    "image_train = graphlab.SFrame('image_train_data/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing deep features for our images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two lines below allow us to compute deep features. This computation takes a little while, so we have already computed them and saved the results as a column in the data you loaded.\n",
    "\n",
    "(Note that if you would like to compute such deep features and have a GPU on your machine, you should use the GPU enabled GraphLab Create, which will be significantly faster for this task.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#deep_learning_model = graphlab.load_model('http://s3.amazonaws.com/GraphLab-Datasets/deeplearning/imagenet_model_iter45')\n",
    "#image_train['deep_features'] = deep_learning_model.extract_features(image_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">automobile</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
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      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\timage\tImage\n",
       "\tlabel\tstr\n",
       "\tdeep_features\tarray\n",
       "\timage_array\tarray\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "|  id |        image         |   label    |         deep_features         |\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "|  24 | Height: 32 Width: 32 |    bird    | [0.242871761322, 1.0954537... |\n",
       "|  33 | Height: 32 Width: 32 |    cat     | [0.525087952614, 0.0, 0.0,... |\n",
       "|  36 | Height: 32 Width: 32 |    cat     | [0.566015958786, 0.0, 0.0,... |\n",
       "|  70 | Height: 32 Width: 32 |    dog     | [1.12979578972, 0.0, 0.0, ... |\n",
       "|  90 | Height: 32 Width: 32 |    bird    | [1.71786928177, 0.0, 0.0, ... |\n",
       "|  97 | Height: 32 Width: 32 | automobile | [1.57818555832, 0.0, 0.0, ... |\n",
       "| 107 | Height: 32 Width: 32 |    dog     | [0.0, 0.0, 0.220677852631,... |\n",
       "| 121 | Height: 32 Width: 32 |    bird    | [0.0, 0.23753464222, 0.0, ... |\n",
       "| 136 | Height: 32 Width: 32 | automobile | [0.0, 0.0, 0.0, 0.0, 0.0, ... |\n",
       "| 138 | Height: 32 Width: 32 |    bird    | [0.658935725689, 0.0, 0.0,... |\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "+-------------------------------+\n",
       "|          image_array          |\n",
       "+-------------------------------+\n",
       "| [73.0, 77.0, 58.0, 71.0, 6... |\n",
       "| [7.0, 5.0, 8.0, 7.0, 5.0, ... |\n",
       "| [169.0, 122.0, 65.0, 131.0... |\n",
       "| [154.0, 179.0, 152.0, 159.... |\n",
       "| [216.0, 195.0, 180.0, 201.... |\n",
       "| [33.0, 44.0, 27.0, 29.0, 4... |\n",
       "| [97.0, 51.0, 31.0, 104.0, ... |\n",
       "| [93.0, 96.0, 88.0, 102.0, ... |\n",
       "| [35.0, 59.0, 53.0, 36.0, 5... |\n",
       "| [205.0, 193.0, 195.0, 200.... |\n",
       "+-------------------------------+\n",
       "[10 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a nearest-neighbors model for retrieving images using deep features\n",
    "We will now build a simple image retrieval system that finds the nearest neighbors for any image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_model = graphlab.nearest_neighbors.create(image_train,features=['deep_features'], label='id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Use image retrieval model with deep features to find similar images.\n",
    "\n",
    "Let's find similar images to this cat picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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93, 66, 46, 93, 64, 46, 103, 75, 55, 113, 88, 68, 113, 92, 75, 122, 102, 85, 138, 119, 99, 154, 137, 115, 179, 163, 141, 187, 171, 151, 177, 162, 145, 164, 148, 133, 174, 158, 142, 190, 176, 160, 190, 177, 158, 194, 180, 158, 179, 163, 136, 174, 156, 125, 169, 148, 117, 177, 154, 126, 178, 155, 127, 182, 157, 129, 189, 163, 135, 194, 167, 137, 190, 162, 132, 180, 151, 120, 191, 158, 124, 202, 166, 130, 196, 159, 122, 50, 45, 41, 77, 64, 46, 87, 65, 42, 97, 67, 47, 107, 75, 57, 113, 84, 62, 115, 87, 63, 120, 92, 70, 137, 110, 87, 159, 135, 110, 177, 157, 131, 193, 176, 152, 202, 187, 167, 196, 181, 165, 185, 172, 154, 184, 170, 151, 188, 175, 156, 208, 195, 175, 193, 179, 156, 196, 177, 150, 199, 175, 144, 198, 171, 137, 192, 162, 129, 186, 155, 122, 181, 149, 116, 177, 144, 110, 174, 140, 105, 180, 145, 110, 192, 157, 121, 202, 163, 126, 201, 160, 122, 196, 154, 116, 47, 40, 35, 87, 73, 54, 104, 80, 56, 113, 82, 61, 119, 86, 66, 122, 91, 67, 129, 97, 70, 138, 103, 75, 142, 108, 80, 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68, 55, 95, 77, 64, 87, 69, 59, 87, 69, 58], \"height\": 32, \"channels\": 3, \"width\": 32, \"type\": \"image\", \"id\": 715737072}], \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"Image\", \"view_component\": \"Images\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 1}, \"type\": \"SArray\", \"view_components\": [\"Images\"]}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphlab.canvas.set_target('ipynb')\n",
    "cat = image_train[18:19]\n",
    "cat['image'].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.0498753   | 28.057ms     |</pre>"
      ],
      "text/plain": [
       "| 0            | 1       | 0.0498753   | 28.057ms     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 371.92ms     |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 371.92ms     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">query_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reference_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">distance</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">384</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6910</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36.9403137951</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">39777</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">38.4634888975</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36870</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">39.7559623119</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">41734</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">39.7866014148</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[5 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tquery_label\tint\n",
       "\treference_label\tint\n",
       "\tdistance\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 5\n",
       "\n",
       "Data:\n",
       "+-------------+-----------------+---------------+------+\n",
       "| query_label | reference_label |    distance   | rank |\n",
       "+-------------+-----------------+---------------+------+\n",
       "|      0      |       384       |      0.0      |  1   |\n",
       "|      0      |       6910      | 36.9403137951 |  2   |\n",
       "|      0      |      39777      | 38.4634888975 |  3   |\n",
       "|      0      |      36870      | 39.7559623119 |  4   |\n",
       "|      0      |      41734      | 39.7866014148 |  5   |\n",
       "+-------------+-----------------+---------------+------+\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model.query(cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have created a simple function to view the nearest neighbors to save typing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_images_from_ids(query_result):\n",
    "    return image_train.filter_by(query_result['reference_label'],'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
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       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.0498753   | 32.064ms     |</pre>"
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       "| 0            | 1       | 0.0498753   | 32.064ms     |"
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    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 347.35ms     |</pre>"
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      "text/plain": [
       "| Done         |         | 100         | 347.35ms     |"
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       "+--------------+---------+-------------+--------------+"
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   "source": [
    "cat_neighbors = get_images_from_ids(knn_model.query(cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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       "                });\n",
       "            })();\n",
       "        "
      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "cat_neighbors['image'].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding similar images to a car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_images_from_ids(knn_model.query(car))['image'].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Just for fun, let's create a lambda to find and show nearest neighbor images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "show_neighbors = lambda i: get_images_from_ids(knn_model.query(image_train[i:i+1]))['image'].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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    "collapsed": false
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   "outputs": [
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     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
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     },
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     "output_type": "display_data"
    },
    {
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      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
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      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.0498753   | 37.055ms     |</pre>"
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      "text/plain": [
       "| 0            | 1       | 0.0498753   | 37.055ms     |"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 374.407ms    |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 374.407ms    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Images', {\"ipython\": true, \"rows\": [{\"format\": \"raw\", \"value\": [231, 222, 227, 232, 217, 221, 224, 206, 200, 194, 174, 171, 209, 189, 189, 198, 181, 178, 187, 172, 175, 176, 162, 170, 167, 153, 158, 157, 141, 144, 148, 129, 130, 144, 123, 121, 151, 132, 122, 129, 111, 105, 110, 94, 93, 101, 87, 89, 100, 89, 88, 116, 106, 110, 147, 140, 147, 174, 170, 173, 198, 196, 197, 216, 215, 217, 229, 227, 230, 235, 234, 236, 234, 231, 234, 227, 223, 227, 211, 210, 214, 181, 182, 188, 193, 197, 204, 204, 211, 215, 209, 216, 223, 215, 222, 231, 223, 213, 215, 221, 206, 205, 193, 174, 168, 148, 128, 124, 203, 182, 179, 211, 192, 185, 185, 166, 164, 191, 171, 171, 197, 178, 174, 203, 183, 178, 214, 192, 187, 216, 193, 187, 200, 179, 169, 193, 174, 166, 189, 172, 167, 187, 172, 168, 186, 175, 169, 211, 202, 203, 234, 228, 234, 237, 232, 236, 234, 231, 233, 224, 223, 225, 212, 210, 212, 203, 201, 204, 204, 201, 203, 211, 207, 209, 219, 218, 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       "                });\n",
       "            })();\n",
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